James C. Gay, MD, MMHC

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Hospitals are increasingly assessed comparatively in terms of costs and quality for benchmarking purposes. These comparisons can be used by patients and families to determine where to seek care, to report compliance and grant certifications by oversight organizations (eg, Leapfrog, Magnet, Joint Commission), and by payers, to determine reimbursement models and/or to assess financial penalty or bonuses for underperforming or overperforming hospitals. As these efforts can cause substantial reputational and financial consequences for hospitals, these metrics must be contextualized within the population of patients that each hospital serves.

In adult Medicare patient populations, methods have been developed to assess the relative severity of a hospital’s full complement of patients.^1,2 These methods assume a relationship between severity and hospital resource intensity (ie, cost) and typically assume the form of relative weights (RWs), which are developed for clinically similar groups of patients (eg, Medicare Diagnosis Related Groups; MS-DRG) from a reference population. A RW for each MS-DRG is calculated as the average cost of patients within the group divided by the average cost for all patients in the reference population. These weights are then applied to a hospital’s discharges over a specific time period and averaged to obtain a hospital-level case-mix index (CMI). A value of 1 indicates that a hospital serves a mix of patients with similar severity (or resource intensity) to that of an “average” hospital discharge in the reference population, whereas a value of 1.2 indicates that a hospital serves a population of patients with 20% more severity than that of an “average” hospital discharge. Since 1983, the Centers for Medicare and Medicaid Services (CMS) has used RWs in their inpatient prospective payment system.³

Similar pediatric methods are less developed and necessitate special consideration as the use of existing weights may be inappropriate for a pediatric population. First, MS-DRGs were developed primarily for the Medicare population and lack sufficient granularity for pediatric populations, specifically newborns. Second, a severity stratification which incorporates important patient characteristics, such as age in pediatrics, does not exist in the MS-DRG system . Finally, although the reference populations that are used to develop MS-DRG weights do not explicitly exclude children, children typically account for approximately 15% of hospitalizations (6% excluding neonatal/maternal) and possibly feature different utilization patterns than adults with similar conditions. Thus, weights developed from a combined pediatric/adult reference population primarily reflect an adult population.

With valid pediatric RWs, stakeholders can assess a hospital’s severity mix of patients in a comparable fashion and contextualize outcome metrics. Additionally, these same weights can be used to estimate expected costs for hospitalizations or for risk adjusting various outcomes at the discharge- or hospital-level. Thus, we sought to develop hospitalization resource intensity scores for kids (H-RISK) using pediatric-specific weights and compare hospital-level CMIs across various hospital types and locations as an example of the application of this novel methodology.

METHODS

Dataset

Data for this analysis were obtained from the 2012 Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID).⁴ KID is the largest publicly available all-payer inpatient administrative database in the United States and is sponsored by the Agency for Healthcare Research and Quality as part of the HCUP. The 2012 KID included a sample of approximately 3.2 million discharge records of children <21 years old from 44 states and 4,179 community, nonrehabilitation hospitals weighted for national estimates.

Hospital discharge costs were estimated from charges using cost-to-charge ratios (CCR) provided by HCUP as a supplement to the 2012 KID.⁵ Cost estimates associated with a specific discharge were estimated by multiplying the total charges reported in the data by the appropriate hospital-specific CCR and then adjusted for price factors beyond a hospital’s control using the area wage index also provided by HCUP as a supplement.

H-RISK and Case-Mix Index Calculations

We calculated H-RISK as pediatric-specific RWs based on version 30 of 3M’s All Patient Refined DRG (APR-DRG; 3M Health Information Systems, Salt Lake City, Utah) system as a measure of resource intensity. The APR-DRG system classifies hospital discharges into over 300 base DRGs based on demographic, diagnostic, and therapeutic characteristics. Each APR-DRG is further sub-divided into 4 subclasses of severity of illness (SOI; eg, minor, moderate, major, and extreme) to indicate the intensity of resource utilization during hospitalization. However, SOI levels for differing APR-DRGs are not comparable.

 

For every APR-DRG SOI combinations available in the 2012 KID, calculation of RW was based on the ratio of the mean cost for patients assigned to a particular APR-DRG SOI compared with the mean cost for all patients in the database. Inpatient costs less than $0.50 were set to missing and removed from analysis. Mortalities and discharges with missing CCR and wage index values were also excluded from analysis. We required that estimates for RWs be based on a reasonable set of data (ie, 10 or more discharges) for each APR-DRG SOI, and that estimates across the 4 SOI levels within an APR-DRG be monotonically nondecreasing (ie, as SOI level increases, weights must either be the same or increasing). Winsorized means were used as point estimates for mean cost in both the numerator and denominator of RW computation. Winsorizing refers to an analytic transformation by which the influence of outliers (eg, values beyond a certain threshold) is mitigated by replacing the value of outliers with the value of the threshold. We used the 5^th and 95^th percentiles as thresholds for Winsorizing our point estimates.

Winsorized point estimates failing to meet the minimum sample size of 10 or nondecreasing monotonicity requirement were modified by one of the two following methods:

• Cost data were modeled using a generalized linear model assuming an exponential distribution. Covariates in the model included APR-DRG and SOI within APR-DRG as a continuous variable. Where applicable, Winsorized estimates of the mean were replaced with modeled estimates.
• Data from an APR-DRG SOI in question were combined with other SOIs within the same APR-DRG with the closest Winsorized mean value. Once data were combined, a common Winsorized value was re-computed and values across SOIs were checked to ensure that nondecreasing monotonicity was maintained. In some APR-DRGs with sparse data, this involved combining pairs of severity levels; in others, it involved combining three or four severity levels together.

For APR-DRGs in which no discharges at any SOI were recorded in the 2012 KID, we used the Winsorized mean of all encounters with a common major diagnostic category (MDC) as the missing APR-DRG as point estimate for all 4 SOI levels.

To calculate the CMI for a set of discharges (eg, discharges at a hospital in a year), RWs were assigned to each discharge based on APR-DRG SOI designation. Consequently, all discharges from a specific APR-DRG SOI were assigned the same RW. Once RWs were assigned, CMI was calculated as the mean RW across all discharges. To compare hospital types based on acute-care hospital stays which are usually considered with the realm of pediatric care, we excluded RWs for normal newborns, defined as APR-DRG 626 (neonate birthweight of 2000–2499 g, normal newborn or neonate with other problems) and 640 (neonate birthweight >2499 g, normal newborn or neonate with other problems), and maternal hospitalizations, defined as APR-DRG 540 (cesarean delivery) and 560 (vaginal delivery), from our CMI calculations.

Statistical Methodology

Categorical variables were summarized using frequencies and percentages; continuous variables were summarized using medians and interquartile ranges. Differences between hospital

types (eg, rural, urban nonteaching, urban teaching, and

free-standing) were assessed using a Chi-square test for association for categorical variables. Differences in continuous variables including comparisons of neonatal (MDC 15) and nonneonatal discharges, and medical versus procedural discharges as defined by the APR-DRG grouper were assessed using a Kruskal–Wallis test. All analyses were performed using SAS, Version 9.4 (SAS Institute, Cary, North Carolina); P values <.05 were considered statistically significant.

This study was considered nonhuman subjects research by the Institutional Review Board of Vanderbilt University Medical Center.

RESULTS

Patient Population

Table 1 summarizes the patient characteristics for all 4 hospital types. All comparisons of patient characteristics across the four hospital types are significant (P < .001). Of the 6,675,222 weighted discharges in HCUP KID 2012, almost two-thirds were less than one year old (4,269,984). Three-quarters of those infant discharges (3,733,760) were in-hospital births. The South was the Census region with the most number of discharges (38.8%), and over half of discharges (53.2%) included patients who lived in metro areas with more than 1 million residents. Patients disproportionately originated from lower-income areas with 30.9% living in zip codes with median incomes in the first quartile.

More than 80% of discharges were classified by a medical APR-DRG. The most common medical APR-DRG SOI was APR-DRG 640 SOI 1, “Neonate birthweight >2499 g, normal newborn or neonate with other problem,” which accounted for almost half of medical APR-DRG discharges (44.5%, Table 2). Of the 10 most common medical APR-DRG SOIs, the only nonneonate, nonvaginal delivery APR-DRG SOIs included Asthma SOI 1, Bronchiolitis & RSV pneumonia SOI 1, and Pneumonia NEC SOI 1. Caesarian delivery and appendectomy represented half of the 10 most common procedural APR-DRG SOIs.

 

H-RISK Generation

Of the 1,258 APR-DRG SOI cost-based RWs (H-RISK), 1,119 (89.0%) met the minimum sample size and adhered to the monotonicity requirement. Thus, the Winsorized mean within the APR-DRG SOI was used. Modeling was used for 112 (8.9%) APR-DRG SOIs, and 23 (1.8%) were grouped with others to ensure that results were monotonically nondecreasing. For one APR-DRG, 482–Transurethral Prostatectomy, the dataset contained no discharges. Thus, Winsorized mean of all encounters within MDC 12, Diseases and Disorders of Male Reproductive System, was used.

The weighted Winsorized mean cost of all discharges was $6,135 per discharge. The majority of cost-based H-RISK were higher than 1, with 1,038 (82.5%) of APR-DRG SOIs incurring an estimated cost higher than $6,135. Solid organ and bone marrow transplantations represented 4 of the 10 highest cost-based RWs for procedural APR-DRG SOIs (Table 3). Neonatal APR-DRG SOIs accounted for 8 of the 10 highest medical RWs. A list of all APR-DRG SOIs and H-RISK can be found in Appendix A.

Hospital-Level Case-Mix Index for Acute Hospitalizations

After excluding normal newborn and maternal hospitalizations, median CMI of the 3117 hospitals with at least 20 unweighted discharges was 1.0 (interquartile range [IQR]: 0.8, 1.7). CMI varied significantly across hospital types (P < .001). Free-standing children’s hospitals exhibited the highest cost-based CMI (median: 2.7, IQR: 2.2–3.1), followed by urban teaching hospitals (median: 1.8, IQR: 1.3–2.6), urban nonteaching hospitals (median: 1.1, IQR: 0.9–1.5), and rural hospitals (median: 0.9, IQR: 0.7–0.9).

These differences in CMI persist when analyzing specific subpopulations. Significant differences in CMI were observed across the 4 hospital types for both procedural (P < .001) and medical APR-DRGs (P < .001), with free-standing children’s hospitals demonstrating the highest CMI of all hospital types (Figure). Similarly, within both neonatal and nonneonatal populations, significant variation in CMI was noted across hospital types (P < .001) with free-standing children’s hospitals incurring the highest CMIs (Figure).

DISCUSSION

Currently, no widely available measures can compare the relative intensity of hospital care specific for inpatient pediatric populations. To meet this important need, we have developed a methodology to determine valid pediatric RWs (H-RISK) which can be used to estimate the intensity of care for applications across entire hospital patient populations and specific subpopulations. H-RISK allow calculation of CMIs for risk adjustment of various outcomes at the discharge- or hospital-level and for comparisons among hospitals and populations. Using this methodology, we demonstrated that the CMI for free-standing children’s hospitals was significantly higher than those of rural, urban, nonteaching and urban teaching hospitals for all discharges and medical or procedural subgroups.

CMS has used RWs based on DRGs since the inception of the prospective payment system in 1983. The sequence of DRGs used by CMS has purposely focused on older adult Medicare population, and CMS itself recommends applying Medicare-focused DRGs (MS-DRGs being the current iteration) only for the >65 year population.⁶ Nevertheless, many payers, both government and commercial, utilize MS-DRGs and their RWs for payment purposes when reimbursing children’s hospitals. The validity of using weights developed using this grouper in hospitals treating large numbers of pediatric patients and childhood illnesses has been called into question, particularly when such weights are used in reimbursement of children’s hospitals.⁷

Several factors contribute to the validity of a model for developing RWs. First, the system used to describe patient hospitalizations and illnesses should be appropriate to the population in question. As described above, the original DRG system and its subsequent iterations were designed to describe hospitalizations for adults >65 years of age.^{8, 9} Over the years, CMS DRGs incorporated rudimentary categories for neonatal and obstetrical hospitalizations. Still, the current MS-DRGs lack sufficient focus on common inpatient pediatric conditions to adequately describe pediatric hospitalizations, particularly those in free-standing children’s hospitals delivering tertiary and quaternary care. Thus, a more appropriate classification schema for developing RWs specific for pediatric hospitalization should include patients across the entire age spectrum. APR-DRGs represent one such classification system.

Once an appropriate patient classification system is selected, then the population of hospitalized patients to be used as the reference group becomes important. For a system targeting a pediatric inpatient population, a hospital discharge database representing a broad sample of pediatric hospitalizations offers the best basis for developing a system of weights applicable to different types of hospitals providing care for children. For this purpose, we selected the 2012 KID database, a nationally representative dataset containing data on newborn and pediatric discharges from the majority of states within the US. This choice assured that the RWs developed were based on and applicable to pediatric hospitalizations across the entire spectrum of SOI and resource intensity.

A number of measures of hospital performance and quality have been developed and are used by various entities, including individual hospitals, CMS, Leapfrog, Magnet, Joint Commission, and payers, for purposes ranging from benchmarking for improvement to payment models to reimbursement penalties. However, SOI of a hospital’s patient population influences not only the intensity of care that a hospital provides but also presents a potential impact on process and outcome measures. Thus, fair and appropriate measures must consider differences in SOI when comparing hospital performances. Using the weights derived in this paper, these adjustments can be possibly made at either the discharge- or hospital-level, depending on the application, and may include comparisons by hospital location, ownership, payer mix, or socioeconomic strata.

It is also common for hospitals to quantitatively express the uniqueness of services that they deliver to payers or the general public. A hospital-level CMI (derived as the average discharge weight for patients within a hospital) is one way that hospitals may differentiate themselves. This can be accomplished by considering the ratio of one hospital’s CMI to another hospital’s (or an average of a group of hospitals) as an expression of the relative intensity of services. For example, if hospital x has a CMI of 2.3, and hospital y has a CMI of 1.4, the population of children hospitalized at hospital x was 64.3% (1–2.3/1.4) more resource intensive than the children seen at hospital y.

This study should be considered in terms of several limitations. We used costs as the basis for determining intensity of service. Thus, the difference in cost structure among children’s hospitals and between children’s hospitals and other hospital types in the KID could have affected the final calculated weights. Also, the RWs calculated in this study rely on hospital discharge data. Thus, complications which were not “present on admission” and occurred during a hospitalization could have reflected poor quality of care yet still increase resource intensity as measured by total costs. Future studies should examine the potential impact of using present-on-admission diagnoses only for the APR-DRG grouping on the values of RWs. Significant variation may have existed among hospitals in resource utilization, and some hospitals may have exhibited significant overutilization of resources for the same conditions. However, as we used Winsorized means, the impact of potential outliers should have been reduced. Some APR-DRG-SOI combinations were seen mainly at children’s hospitals. Thus, cost structure and resource utilization practices of this subset of hospitals would have been the only contributors to weights for these patients. Given that the 2012 KID contained a broad representation of pediatric hospitalizations, with age 0–20 years, newborns accounted for the majority of total cases in the database. While providing a full range of pediatric weights, inclusion of these patients lowered the overall average RW. For this reason, we excluded normal newborn categories and maternal categories from analysis of CMI across hospital types and focused on acute-care hospitalizations. Lastly, as with any study relying on administrative data, there is always the possibility of coding errors or data entry errors in the reference dataset.

 

CONCLUSIONS

H-RISK can be used to risk adjust measures to account for severity differences across populations. These weights can also be averaged across hospitals’ patient populations to compare relative resource intensities of the patients served.

Disclosures

The authors have nothing to disclose.

Hospitals are increasingly assessed comparatively in terms of costs and quality for benchmarking purposes. These comparisons can be used by patients and families to determine where to seek care, to report compliance and grant certifications by oversight organizations (eg, Leapfrog, Magnet, Joint Commission), and by payers, to determine reimbursement models and/or to assess financial penalty or bonuses for underperforming or overperforming hospitals. As these efforts can cause substantial reputational and financial consequences for hospitals, these metrics must be contextualized within the population of patients that each hospital serves.

In adult Medicare patient populations, methods have been developed to assess the relative severity of a hospital’s full complement of patients.^1,2 These methods assume a relationship between severity and hospital resource intensity (ie, cost) and typically assume the form of relative weights (RWs), which are developed for clinically similar groups of patients (eg, Medicare Diagnosis Related Groups; MS-DRG) from a reference population. A RW for each MS-DRG is calculated as the average cost of patients within the group divided by the average cost for all patients in the reference population. These weights are then applied to a hospital’s discharges over a specific time period and averaged to obtain a hospital-level case-mix index (CMI). A value of 1 indicates that a hospital serves a mix of patients with similar severity (or resource intensity) to that of an “average” hospital discharge in the reference population, whereas a value of 1.2 indicates that a hospital serves a population of patients with 20% more severity than that of an “average” hospital discharge. Since 1983, the Centers for Medicare and Medicaid Services (CMS) has used RWs in their inpatient prospective payment system.³

Similar pediatric methods are less developed and necessitate special consideration as the use of existing weights may be inappropriate for a pediatric population. First, MS-DRGs were developed primarily for the Medicare population and lack sufficient granularity for pediatric populations, specifically newborns. Second, a severity stratification which incorporates important patient characteristics, such as age in pediatrics, does not exist in the MS-DRG system . Finally, although the reference populations that are used to develop MS-DRG weights do not explicitly exclude children, children typically account for approximately 15% of hospitalizations (6% excluding neonatal/maternal) and possibly feature different utilization patterns than adults with similar conditions. Thus, weights developed from a combined pediatric/adult reference population primarily reflect an adult population.

With valid pediatric RWs, stakeholders can assess a hospital’s severity mix of patients in a comparable fashion and contextualize outcome metrics. Additionally, these same weights can be used to estimate expected costs for hospitalizations or for risk adjusting various outcomes at the discharge- or hospital-level. Thus, we sought to develop hospitalization resource intensity scores for kids (H-RISK) using pediatric-specific weights and compare hospital-level CMIs across various hospital types and locations as an example of the application of this novel methodology.

METHODS

Dataset

Data for this analysis were obtained from the 2012 Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID).⁴ KID is the largest publicly available all-payer inpatient administrative database in the United States and is sponsored by the Agency for Healthcare Research and Quality as part of the HCUP. The 2012 KID included a sample of approximately 3.2 million discharge records of children <21 years old from 44 states and 4,179 community, nonrehabilitation hospitals weighted for national estimates.

Hospital discharge costs were estimated from charges using cost-to-charge ratios (CCR) provided by HCUP as a supplement to the 2012 KID.⁵ Cost estimates associated with a specific discharge were estimated by multiplying the total charges reported in the data by the appropriate hospital-specific CCR and then adjusted for price factors beyond a hospital’s control using the area wage index also provided by HCUP as a supplement.

H-RISK and Case-Mix Index Calculations

We calculated H-RISK as pediatric-specific RWs based on version 30 of 3M’s All Patient Refined DRG (APR-DRG; 3M Health Information Systems, Salt Lake City, Utah) system as a measure of resource intensity. The APR-DRG system classifies hospital discharges into over 300 base DRGs based on demographic, diagnostic, and therapeutic characteristics. Each APR-DRG is further sub-divided into 4 subclasses of severity of illness (SOI; eg, minor, moderate, major, and extreme) to indicate the intensity of resource utilization during hospitalization. However, SOI levels for differing APR-DRGs are not comparable.

 

For every APR-DRG SOI combinations available in the 2012 KID, calculation of RW was based on the ratio of the mean cost for patients assigned to a particular APR-DRG SOI compared with the mean cost for all patients in the database. Inpatient costs less than $0.50 were set to missing and removed from analysis. Mortalities and discharges with missing CCR and wage index values were also excluded from analysis. We required that estimates for RWs be based on a reasonable set of data (ie, 10 or more discharges) for each APR-DRG SOI, and that estimates across the 4 SOI levels within an APR-DRG be monotonically nondecreasing (ie, as SOI level increases, weights must either be the same or increasing). Winsorized means were used as point estimates for mean cost in both the numerator and denominator of RW computation. Winsorizing refers to an analytic transformation by which the influence of outliers (eg, values beyond a certain threshold) is mitigated by replacing the value of outliers with the value of the threshold. We used the 5^th and 95^th percentiles as thresholds for Winsorizing our point estimates.

Winsorized point estimates failing to meet the minimum sample size of 10 or nondecreasing monotonicity requirement were modified by one of the two following methods:

• Cost data were modeled using a generalized linear model assuming an exponential distribution. Covariates in the model included APR-DRG and SOI within APR-DRG as a continuous variable. Where applicable, Winsorized estimates of the mean were replaced with modeled estimates.
• Data from an APR-DRG SOI in question were combined with other SOIs within the same APR-DRG with the closest Winsorized mean value. Once data were combined, a common Winsorized value was re-computed and values across SOIs were checked to ensure that nondecreasing monotonicity was maintained. In some APR-DRGs with sparse data, this involved combining pairs of severity levels; in others, it involved combining three or four severity levels together.

For APR-DRGs in which no discharges at any SOI were recorded in the 2012 KID, we used the Winsorized mean of all encounters with a common major diagnostic category (MDC) as the missing APR-DRG as point estimate for all 4 SOI levels.

To calculate the CMI for a set of discharges (eg, discharges at a hospital in a year), RWs were assigned to each discharge based on APR-DRG SOI designation. Consequently, all discharges from a specific APR-DRG SOI were assigned the same RW. Once RWs were assigned, CMI was calculated as the mean RW across all discharges. To compare hospital types based on acute-care hospital stays which are usually considered with the realm of pediatric care, we excluded RWs for normal newborns, defined as APR-DRG 626 (neonate birthweight of 2000–2499 g, normal newborn or neonate with other problems) and 640 (neonate birthweight >2499 g, normal newborn or neonate with other problems), and maternal hospitalizations, defined as APR-DRG 540 (cesarean delivery) and 560 (vaginal delivery), from our CMI calculations.

Statistical Methodology

Categorical variables were summarized using frequencies and percentages; continuous variables were summarized using medians and interquartile ranges. Differences between hospital

types (eg, rural, urban nonteaching, urban teaching, and

free-standing) were assessed using a Chi-square test for association for categorical variables. Differences in continuous variables including comparisons of neonatal (MDC 15) and nonneonatal discharges, and medical versus procedural discharges as defined by the APR-DRG grouper were assessed using a Kruskal–Wallis test. All analyses were performed using SAS, Version 9.4 (SAS Institute, Cary, North Carolina); P values <.05 were considered statistically significant.

This study was considered nonhuman subjects research by the Institutional Review Board of Vanderbilt University Medical Center.

RESULTS

Patient Population

Table 1 summarizes the patient characteristics for all 4 hospital types. All comparisons of patient characteristics across the four hospital types are significant (P < .001). Of the 6,675,222 weighted discharges in HCUP KID 2012, almost two-thirds were less than one year old (4,269,984). Three-quarters of those infant discharges (3,733,760) were in-hospital births. The South was the Census region with the most number of discharges (38.8%), and over half of discharges (53.2%) included patients who lived in metro areas with more than 1 million residents. Patients disproportionately originated from lower-income areas with 30.9% living in zip codes with median incomes in the first quartile.

More than 80% of discharges were classified by a medical APR-DRG. The most common medical APR-DRG SOI was APR-DRG 640 SOI 1, “Neonate birthweight >2499 g, normal newborn or neonate with other problem,” which accounted for almost half of medical APR-DRG discharges (44.5%, Table 2). Of the 10 most common medical APR-DRG SOIs, the only nonneonate, nonvaginal delivery APR-DRG SOIs included Asthma SOI 1, Bronchiolitis & RSV pneumonia SOI 1, and Pneumonia NEC SOI 1. Caesarian delivery and appendectomy represented half of the 10 most common procedural APR-DRG SOIs.

 

H-RISK Generation

Of the 1,258 APR-DRG SOI cost-based RWs (H-RISK), 1,119 (89.0%) met the minimum sample size and adhered to the monotonicity requirement. Thus, the Winsorized mean within the APR-DRG SOI was used. Modeling was used for 112 (8.9%) APR-DRG SOIs, and 23 (1.8%) were grouped with others to ensure that results were monotonically nondecreasing. For one APR-DRG, 482–Transurethral Prostatectomy, the dataset contained no discharges. Thus, Winsorized mean of all encounters within MDC 12, Diseases and Disorders of Male Reproductive System, was used.

The weighted Winsorized mean cost of all discharges was $6,135 per discharge. The majority of cost-based H-RISK were higher than 1, with 1,038 (82.5%) of APR-DRG SOIs incurring an estimated cost higher than $6,135. Solid organ and bone marrow transplantations represented 4 of the 10 highest cost-based RWs for procedural APR-DRG SOIs (Table 3). Neonatal APR-DRG SOIs accounted for 8 of the 10 highest medical RWs. A list of all APR-DRG SOIs and H-RISK can be found in Appendix A.

Hospital-Level Case-Mix Index for Acute Hospitalizations

After excluding normal newborn and maternal hospitalizations, median CMI of the 3117 hospitals with at least 20 unweighted discharges was 1.0 (interquartile range [IQR]: 0.8, 1.7). CMI varied significantly across hospital types (P < .001). Free-standing children’s hospitals exhibited the highest cost-based CMI (median: 2.7, IQR: 2.2–3.1), followed by urban teaching hospitals (median: 1.8, IQR: 1.3–2.6), urban nonteaching hospitals (median: 1.1, IQR: 0.9–1.5), and rural hospitals (median: 0.9, IQR: 0.7–0.9).

These differences in CMI persist when analyzing specific subpopulations. Significant differences in CMI were observed across the 4 hospital types for both procedural (P < .001) and medical APR-DRGs (P < .001), with free-standing children’s hospitals demonstrating the highest CMI of all hospital types (Figure). Similarly, within both neonatal and nonneonatal populations, significant variation in CMI was noted across hospital types (P < .001) with free-standing children’s hospitals incurring the highest CMIs (Figure).

DISCUSSION

Currently, no widely available measures can compare the relative intensity of hospital care specific for inpatient pediatric populations. To meet this important need, we have developed a methodology to determine valid pediatric RWs (H-RISK) which can be used to estimate the intensity of care for applications across entire hospital patient populations and specific subpopulations. H-RISK allow calculation of CMIs for risk adjustment of various outcomes at the discharge- or hospital-level and for comparisons among hospitals and populations. Using this methodology, we demonstrated that the CMI for free-standing children’s hospitals was significantly higher than those of rural, urban, nonteaching and urban teaching hospitals for all discharges and medical or procedural subgroups.

CMS has used RWs based on DRGs since the inception of the prospective payment system in 1983. The sequence of DRGs used by CMS has purposely focused on older adult Medicare population, and CMS itself recommends applying Medicare-focused DRGs (MS-DRGs being the current iteration) only for the >65 year population.⁶ Nevertheless, many payers, both government and commercial, utilize MS-DRGs and their RWs for payment purposes when reimbursing children’s hospitals. The validity of using weights developed using this grouper in hospitals treating large numbers of pediatric patients and childhood illnesses has been called into question, particularly when such weights are used in reimbursement of children’s hospitals.⁷

Several factors contribute to the validity of a model for developing RWs. First, the system used to describe patient hospitalizations and illnesses should be appropriate to the population in question. As described above, the original DRG system and its subsequent iterations were designed to describe hospitalizations for adults >65 years of age.^{8, 9} Over the years, CMS DRGs incorporated rudimentary categories for neonatal and obstetrical hospitalizations. Still, the current MS-DRGs lack sufficient focus on common inpatient pediatric conditions to adequately describe pediatric hospitalizations, particularly those in free-standing children’s hospitals delivering tertiary and quaternary care. Thus, a more appropriate classification schema for developing RWs specific for pediatric hospitalization should include patients across the entire age spectrum. APR-DRGs represent one such classification system.

Once an appropriate patient classification system is selected, then the population of hospitalized patients to be used as the reference group becomes important. For a system targeting a pediatric inpatient population, a hospital discharge database representing a broad sample of pediatric hospitalizations offers the best basis for developing a system of weights applicable to different types of hospitals providing care for children. For this purpose, we selected the 2012 KID database, a nationally representative dataset containing data on newborn and pediatric discharges from the majority of states within the US. This choice assured that the RWs developed were based on and applicable to pediatric hospitalizations across the entire spectrum of SOI and resource intensity.

A number of measures of hospital performance and quality have been developed and are used by various entities, including individual hospitals, CMS, Leapfrog, Magnet, Joint Commission, and payers, for purposes ranging from benchmarking for improvement to payment models to reimbursement penalties. However, SOI of a hospital’s patient population influences not only the intensity of care that a hospital provides but also presents a potential impact on process and outcome measures. Thus, fair and appropriate measures must consider differences in SOI when comparing hospital performances. Using the weights derived in this paper, these adjustments can be possibly made at either the discharge- or hospital-level, depending on the application, and may include comparisons by hospital location, ownership, payer mix, or socioeconomic strata.

It is also common for hospitals to quantitatively express the uniqueness of services that they deliver to payers or the general public. A hospital-level CMI (derived as the average discharge weight for patients within a hospital) is one way that hospitals may differentiate themselves. This can be accomplished by considering the ratio of one hospital’s CMI to another hospital’s (or an average of a group of hospitals) as an expression of the relative intensity of services. For example, if hospital x has a CMI of 2.3, and hospital y has a CMI of 1.4, the population of children hospitalized at hospital x was 64.3% (1–2.3/1.4) more resource intensive than the children seen at hospital y.

This study should be considered in terms of several limitations. We used costs as the basis for determining intensity of service. Thus, the difference in cost structure among children’s hospitals and between children’s hospitals and other hospital types in the KID could have affected the final calculated weights. Also, the RWs calculated in this study rely on hospital discharge data. Thus, complications which were not “present on admission” and occurred during a hospitalization could have reflected poor quality of care yet still increase resource intensity as measured by total costs. Future studies should examine the potential impact of using present-on-admission diagnoses only for the APR-DRG grouping on the values of RWs. Significant variation may have existed among hospitals in resource utilization, and some hospitals may have exhibited significant overutilization of resources for the same conditions. However, as we used Winsorized means, the impact of potential outliers should have been reduced. Some APR-DRG-SOI combinations were seen mainly at children’s hospitals. Thus, cost structure and resource utilization practices of this subset of hospitals would have been the only contributors to weights for these patients. Given that the 2012 KID contained a broad representation of pediatric hospitalizations, with age 0–20 years, newborns accounted for the majority of total cases in the database. While providing a full range of pediatric weights, inclusion of these patients lowered the overall average RW. For this reason, we excluded normal newborn categories and maternal categories from analysis of CMI across hospital types and focused on acute-care hospitalizations. Lastly, as with any study relying on administrative data, there is always the possibility of coding errors or data entry errors in the reference dataset.

 

CONCLUSIONS

H-RISK can be used to risk adjust measures to account for severity differences across populations. These weights can also be averaged across hospitals’ patient populations to compare relative resource intensities of the patients served.

Disclosures

The authors have nothing to disclose.

Hospitals are increasingly assessed comparatively in terms of costs and quality for benchmarking purposes. These comparisons can be used by patients and families to determine where to seek care, to report compliance and grant certifications by oversight organizations (eg, Leapfrog, Magnet, Joint Commission), and by payers, to determine reimbursement models and/or to assess financial penalty or bonuses for underperforming or overperforming hospitals. As these efforts can cause substantial reputational and financial consequences for hospitals, these metrics must be contextualized within the population of patients that each hospital serves.

In adult Medicare patient populations, methods have been developed to assess the relative severity of a hospital’s full complement of patients.^1,2 These methods assume a relationship between severity and hospital resource intensity (ie, cost) and typically assume the form of relative weights (RWs), which are developed for clinically similar groups of patients (eg, Medicare Diagnosis Related Groups; MS-DRG) from a reference population. A RW for each MS-DRG is calculated as the average cost of patients within the group divided by the average cost for all patients in the reference population. These weights are then applied to a hospital’s discharges over a specific time period and averaged to obtain a hospital-level case-mix index (CMI). A value of 1 indicates that a hospital serves a mix of patients with similar severity (or resource intensity) to that of an “average” hospital discharge in the reference population, whereas a value of 1.2 indicates that a hospital serves a population of patients with 20% more severity than that of an “average” hospital discharge. Since 1983, the Centers for Medicare and Medicaid Services (CMS) has used RWs in their inpatient prospective payment system.³

Similar pediatric methods are less developed and necessitate special consideration as the use of existing weights may be inappropriate for a pediatric population. First, MS-DRGs were developed primarily for the Medicare population and lack sufficient granularity for pediatric populations, specifically newborns. Second, a severity stratification which incorporates important patient characteristics, such as age in pediatrics, does not exist in the MS-DRG system . Finally, although the reference populations that are used to develop MS-DRG weights do not explicitly exclude children, children typically account for approximately 15% of hospitalizations (6% excluding neonatal/maternal) and possibly feature different utilization patterns than adults with similar conditions. Thus, weights developed from a combined pediatric/adult reference population primarily reflect an adult population.

With valid pediatric RWs, stakeholders can assess a hospital’s severity mix of patients in a comparable fashion and contextualize outcome metrics. Additionally, these same weights can be used to estimate expected costs for hospitalizations or for risk adjusting various outcomes at the discharge- or hospital-level. Thus, we sought to develop hospitalization resource intensity scores for kids (H-RISK) using pediatric-specific weights and compare hospital-level CMIs across various hospital types and locations as an example of the application of this novel methodology.

METHODS

Dataset

Data for this analysis were obtained from the 2012 Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID).⁴ KID is the largest publicly available all-payer inpatient administrative database in the United States and is sponsored by the Agency for Healthcare Research and Quality as part of the HCUP. The 2012 KID included a sample of approximately 3.2 million discharge records of children <21 years old from 44 states and 4,179 community, nonrehabilitation hospitals weighted for national estimates.

Hospital discharge costs were estimated from charges using cost-to-charge ratios (CCR) provided by HCUP as a supplement to the 2012 KID.⁵ Cost estimates associated with a specific discharge were estimated by multiplying the total charges reported in the data by the appropriate hospital-specific CCR and then adjusted for price factors beyond a hospital’s control using the area wage index also provided by HCUP as a supplement.

H-RISK and Case-Mix Index Calculations

We calculated H-RISK as pediatric-specific RWs based on version 30 of 3M’s All Patient Refined DRG (APR-DRG; 3M Health Information Systems, Salt Lake City, Utah) system as a measure of resource intensity. The APR-DRG system classifies hospital discharges into over 300 base DRGs based on demographic, diagnostic, and therapeutic characteristics. Each APR-DRG is further sub-divided into 4 subclasses of severity of illness (SOI; eg, minor, moderate, major, and extreme) to indicate the intensity of resource utilization during hospitalization. However, SOI levels for differing APR-DRGs are not comparable.

 

For every APR-DRG SOI combinations available in the 2012 KID, calculation of RW was based on the ratio of the mean cost for patients assigned to a particular APR-DRG SOI compared with the mean cost for all patients in the database. Inpatient costs less than $0.50 were set to missing and removed from analysis. Mortalities and discharges with missing CCR and wage index values were also excluded from analysis. We required that estimates for RWs be based on a reasonable set of data (ie, 10 or more discharges) for each APR-DRG SOI, and that estimates across the 4 SOI levels within an APR-DRG be monotonically nondecreasing (ie, as SOI level increases, weights must either be the same or increasing). Winsorized means were used as point estimates for mean cost in both the numerator and denominator of RW computation. Winsorizing refers to an analytic transformation by which the influence of outliers (eg, values beyond a certain threshold) is mitigated by replacing the value of outliers with the value of the threshold. We used the 5^th and 95^th percentiles as thresholds for Winsorizing our point estimates.

Winsorized point estimates failing to meet the minimum sample size of 10 or nondecreasing monotonicity requirement were modified by one of the two following methods:

• Cost data were modeled using a generalized linear model assuming an exponential distribution. Covariates in the model included APR-DRG and SOI within APR-DRG as a continuous variable. Where applicable, Winsorized estimates of the mean were replaced with modeled estimates.
• Data from an APR-DRG SOI in question were combined with other SOIs within the same APR-DRG with the closest Winsorized mean value. Once data were combined, a common Winsorized value was re-computed and values across SOIs were checked to ensure that nondecreasing monotonicity was maintained. In some APR-DRGs with sparse data, this involved combining pairs of severity levels; in others, it involved combining three or four severity levels together.

For APR-DRGs in which no discharges at any SOI were recorded in the 2012 KID, we used the Winsorized mean of all encounters with a common major diagnostic category (MDC) as the missing APR-DRG as point estimate for all 4 SOI levels.

To calculate the CMI for a set of discharges (eg, discharges at a hospital in a year), RWs were assigned to each discharge based on APR-DRG SOI designation. Consequently, all discharges from a specific APR-DRG SOI were assigned the same RW. Once RWs were assigned, CMI was calculated as the mean RW across all discharges. To compare hospital types based on acute-care hospital stays which are usually considered with the realm of pediatric care, we excluded RWs for normal newborns, defined as APR-DRG 626 (neonate birthweight of 2000–2499 g, normal newborn or neonate with other problems) and 640 (neonate birthweight >2499 g, normal newborn or neonate with other problems), and maternal hospitalizations, defined as APR-DRG 540 (cesarean delivery) and 560 (vaginal delivery), from our CMI calculations.

Statistical Methodology

Categorical variables were summarized using frequencies and percentages; continuous variables were summarized using medians and interquartile ranges. Differences between hospital

types (eg, rural, urban nonteaching, urban teaching, and

free-standing) were assessed using a Chi-square test for association for categorical variables. Differences in continuous variables including comparisons of neonatal (MDC 15) and nonneonatal discharges, and medical versus procedural discharges as defined by the APR-DRG grouper were assessed using a Kruskal–Wallis test. All analyses were performed using SAS, Version 9.4 (SAS Institute, Cary, North Carolina); P values <.05 were considered statistically significant.

This study was considered nonhuman subjects research by the Institutional Review Board of Vanderbilt University Medical Center.

RESULTS

Patient Population

Table 1 summarizes the patient characteristics for all 4 hospital types. All comparisons of patient characteristics across the four hospital types are significant (P < .001). Of the 6,675,222 weighted discharges in HCUP KID 2012, almost two-thirds were less than one year old (4,269,984). Three-quarters of those infant discharges (3,733,760) were in-hospital births. The South was the Census region with the most number of discharges (38.8%), and over half of discharges (53.2%) included patients who lived in metro areas with more than 1 million residents. Patients disproportionately originated from lower-income areas with 30.9% living in zip codes with median incomes in the first quartile.

More than 80% of discharges were classified by a medical APR-DRG. The most common medical APR-DRG SOI was APR-DRG 640 SOI 1, “Neonate birthweight >2499 g, normal newborn or neonate with other problem,” which accounted for almost half of medical APR-DRG discharges (44.5%, Table 2). Of the 10 most common medical APR-DRG SOIs, the only nonneonate, nonvaginal delivery APR-DRG SOIs included Asthma SOI 1, Bronchiolitis & RSV pneumonia SOI 1, and Pneumonia NEC SOI 1. Caesarian delivery and appendectomy represented half of the 10 most common procedural APR-DRG SOIs.

 

H-RISK Generation

Of the 1,258 APR-DRG SOI cost-based RWs (H-RISK), 1,119 (89.0%) met the minimum sample size and adhered to the monotonicity requirement. Thus, the Winsorized mean within the APR-DRG SOI was used. Modeling was used for 112 (8.9%) APR-DRG SOIs, and 23 (1.8%) were grouped with others to ensure that results were monotonically nondecreasing. For one APR-DRG, 482–Transurethral Prostatectomy, the dataset contained no discharges. Thus, Winsorized mean of all encounters within MDC 12, Diseases and Disorders of Male Reproductive System, was used.

The weighted Winsorized mean cost of all discharges was $6,135 per discharge. The majority of cost-based H-RISK were higher than 1, with 1,038 (82.5%) of APR-DRG SOIs incurring an estimated cost higher than $6,135. Solid organ and bone marrow transplantations represented 4 of the 10 highest cost-based RWs for procedural APR-DRG SOIs (Table 3). Neonatal APR-DRG SOIs accounted for 8 of the 10 highest medical RWs. A list of all APR-DRG SOIs and H-RISK can be found in Appendix A.

Hospital-Level Case-Mix Index for Acute Hospitalizations

After excluding normal newborn and maternal hospitalizations, median CMI of the 3117 hospitals with at least 20 unweighted discharges was 1.0 (interquartile range [IQR]: 0.8, 1.7). CMI varied significantly across hospital types (P < .001). Free-standing children’s hospitals exhibited the highest cost-based CMI (median: 2.7, IQR: 2.2–3.1), followed by urban teaching hospitals (median: 1.8, IQR: 1.3–2.6), urban nonteaching hospitals (median: 1.1, IQR: 0.9–1.5), and rural hospitals (median: 0.9, IQR: 0.7–0.9).

These differences in CMI persist when analyzing specific subpopulations. Significant differences in CMI were observed across the 4 hospital types for both procedural (P < .001) and medical APR-DRGs (P < .001), with free-standing children’s hospitals demonstrating the highest CMI of all hospital types (Figure). Similarly, within both neonatal and nonneonatal populations, significant variation in CMI was noted across hospital types (P < .001) with free-standing children’s hospitals incurring the highest CMIs (Figure).

DISCUSSION

Currently, no widely available measures can compare the relative intensity of hospital care specific for inpatient pediatric populations. To meet this important need, we have developed a methodology to determine valid pediatric RWs (H-RISK) which can be used to estimate the intensity of care for applications across entire hospital patient populations and specific subpopulations. H-RISK allow calculation of CMIs for risk adjustment of various outcomes at the discharge- or hospital-level and for comparisons among hospitals and populations. Using this methodology, we demonstrated that the CMI for free-standing children’s hospitals was significantly higher than those of rural, urban, nonteaching and urban teaching hospitals for all discharges and medical or procedural subgroups.

CMS has used RWs based on DRGs since the inception of the prospective payment system in 1983. The sequence of DRGs used by CMS has purposely focused on older adult Medicare population, and CMS itself recommends applying Medicare-focused DRGs (MS-DRGs being the current iteration) only for the >65 year population.⁶ Nevertheless, many payers, both government and commercial, utilize MS-DRGs and their RWs for payment purposes when reimbursing children’s hospitals. The validity of using weights developed using this grouper in hospitals treating large numbers of pediatric patients and childhood illnesses has been called into question, particularly when such weights are used in reimbursement of children’s hospitals.⁷

Several factors contribute to the validity of a model for developing RWs. First, the system used to describe patient hospitalizations and illnesses should be appropriate to the population in question. As described above, the original DRG system and its subsequent iterations were designed to describe hospitalizations for adults >65 years of age.^{8, 9} Over the years, CMS DRGs incorporated rudimentary categories for neonatal and obstetrical hospitalizations. Still, the current MS-DRGs lack sufficient focus on common inpatient pediatric conditions to adequately describe pediatric hospitalizations, particularly those in free-standing children’s hospitals delivering tertiary and quaternary care. Thus, a more appropriate classification schema for developing RWs specific for pediatric hospitalization should include patients across the entire age spectrum. APR-DRGs represent one such classification system.

Once an appropriate patient classification system is selected, then the population of hospitalized patients to be used as the reference group becomes important. For a system targeting a pediatric inpatient population, a hospital discharge database representing a broad sample of pediatric hospitalizations offers the best basis for developing a system of weights applicable to different types of hospitals providing care for children. For this purpose, we selected the 2012 KID database, a nationally representative dataset containing data on newborn and pediatric discharges from the majority of states within the US. This choice assured that the RWs developed were based on and applicable to pediatric hospitalizations across the entire spectrum of SOI and resource intensity.

A number of measures of hospital performance and quality have been developed and are used by various entities, including individual hospitals, CMS, Leapfrog, Magnet, Joint Commission, and payers, for purposes ranging from benchmarking for improvement to payment models to reimbursement penalties. However, SOI of a hospital’s patient population influences not only the intensity of care that a hospital provides but also presents a potential impact on process and outcome measures. Thus, fair and appropriate measures must consider differences in SOI when comparing hospital performances. Using the weights derived in this paper, these adjustments can be possibly made at either the discharge- or hospital-level, depending on the application, and may include comparisons by hospital location, ownership, payer mix, or socioeconomic strata.

It is also common for hospitals to quantitatively express the uniqueness of services that they deliver to payers or the general public. A hospital-level CMI (derived as the average discharge weight for patients within a hospital) is one way that hospitals may differentiate themselves. This can be accomplished by considering the ratio of one hospital’s CMI to another hospital’s (or an average of a group of hospitals) as an expression of the relative intensity of services. For example, if hospital x has a CMI of 2.3, and hospital y has a CMI of 1.4, the population of children hospitalized at hospital x was 64.3% (1–2.3/1.4) more resource intensive than the children seen at hospital y.

This study should be considered in terms of several limitations. We used costs as the basis for determining intensity of service. Thus, the difference in cost structure among children’s hospitals and between children’s hospitals and other hospital types in the KID could have affected the final calculated weights. Also, the RWs calculated in this study rely on hospital discharge data. Thus, complications which were not “present on admission” and occurred during a hospitalization could have reflected poor quality of care yet still increase resource intensity as measured by total costs. Future studies should examine the potential impact of using present-on-admission diagnoses only for the APR-DRG grouping on the values of RWs. Significant variation may have existed among hospitals in resource utilization, and some hospitals may have exhibited significant overutilization of resources for the same conditions. However, as we used Winsorized means, the impact of potential outliers should have been reduced. Some APR-DRG-SOI combinations were seen mainly at children’s hospitals. Thus, cost structure and resource utilization practices of this subset of hospitals would have been the only contributors to weights for these patients. Given that the 2012 KID contained a broad representation of pediatric hospitalizations, with age 0–20 years, newborns accounted for the majority of total cases in the database. While providing a full range of pediatric weights, inclusion of these patients lowered the overall average RW. For this reason, we excluded normal newborn categories and maternal categories from analysis of CMI across hospital types and focused on acute-care hospitalizations. Lastly, as with any study relying on administrative data, there is always the possibility of coding errors or data entry errors in the reference dataset.

 

CONCLUSIONS

H-RISK can be used to risk adjust measures to account for severity differences across populations. These weights can also be averaged across hospitals’ patient populations to compare relative resource intensities of the patients served.

Disclosures

The authors have nothing to disclose.

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.

Many hospitals are considering or currently employing initiatives to contact patients after discharge. Whether conducted via telephone or other means, the purpose of the contact is to help patients adhere to discharge plans, fulfill discharge needs, and alleviate postdischarge issues (PDIs). The effectiveness of hospital-initiated postdischarge phone calls has been studied in adult patients after hospitalization, and though some studies report positive outcomes,^1-3 a 2006 Cochrane review found insufficient evidence to recommend for or against the practice.⁴

Little is known about follow-up contact after hospitalization for children.^5-11 Rates of PDI vary substantially across hospitals. For example, one single-center study of postdischarge telephone contact after hospitalization on a general pediatric ward identified PDIs in ~20% of patients.¹⁰ Another study identified PDIs in 84% of patients discharged from a pediatric rehabilitation facility.¹¹ Telephone follow-up has been associated with reduced health resource utilization and improved patient satisfaction for children discharged after an elective surgical procedure⁶ and for children discharged home from the emergency department.^7-9

More information is needed on the clinical experiences of postdischarge contact in hospitalized children to improve the understanding of how the contact is made, who makes it, and which patients are most likely to report a PDI. These experiences are crucial to understand given the expense and time commitment involved in postdischarge contact, as many hospitals may not be positioned to contact all discharged patients. Therefore, we conducted a pragmatic, retrospective, naturalistic study of differing approaches to postdischarge contact occurring in multiple hospitals. Our main objective was to describe the prevalence and types of PDIs identified by the different approaches for follow-up contact across 4 children’s hospitals. We also assessed the characteristics of children who have the highest likelihood of having a PDI identified from the contact within each hospital.

METHODS

Study Design, Setting, and Population

This is a retrospective analysis of hospital-initiated follow-up contact that occurred for 12,986 children discharged from 4 US children’s hospitals between January 2012 and July 2015. Postdischarge follow-up contact was a component of ongoing, natural clinical operations at each institution during the study period. Methods for contact varied across hospitals (Table 1). In all hospitals, initial contact was made within 14 days of inpatient discharge by hospital staff (eg, administrative, nursing, or physician) via telephone call, text message, or e-mail. During contact, each site asked a child’s caregiver a set of standardized questions about medications, appointments, and other discharge-related issues (Table 1). Additional characteristics about each hospital and their processes for follow-up contact (eg, personnel involved, timing, eligibility criteria, etc.) are reported in the supplementary Appendix.

Main Outcome Measures

The main outcome measure was identification of a PDI, defined as a medication, appointment, or other discharge-related issue, that was reported and recorded by the child’s caregiver during conversation from the standardized questions that were asked during follow-up contact as part of routine discharge care (Table 1). Medication PDIs included issues filling prescriptions and tolerating medications. Appointment PDIs included not having a follow-up appointment scheduled. Other PDIs included issues with the child’s health condition, discharge instructions, or any other concerns. All PDIs had been recorded prospectively by hospital contact personnel (hospitals A, B, and D) or through an automated texting system into a database (hospital C). Where available, free text comments that were recorded by contact personnel were reviewed by one of the authors (KB) and categorized via an existing framework of PDI designed by Heath et al.¹⁰ in order to further understand the problems that were reported.

Patient Characteristics

Patient hospitalization, demographic, and clinical characteristics were obtained from administrative health data at each institution and compared between children with versus without a PDI. Hospitalization characteristics included length of stay, season of admission, and reason for admission. Reason for admission was categorized by using 3M Health’s All Patient Refined Diagnosis Related Groups (APR-DRG) (3M, Maplewood, MN). Demographic characteristics included age at admission in years, insurance type (eg, public, private, and other), and race/ethnicity (Asian/Pacific Islander, Hispanic, non-Hispanic black, non-Hispanic white, and other).

 

Clinical characteristics included a count of the different classes of medications (eg, antibiotics, antiepileptic medications, digestive motility medications, etc.) administered to the child during admission, the type and number of chronic conditions, and assistance with medical technology (eg, gastrostomy, tracheostomy, etc.). Except for medications, these characteristics were assessed with International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.

We used the Agency for Healthcare Research and Quality Chronic Condition Indicator classification system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic versus nonchronic conditions to identify the presence and number of chronic conditions.¹² Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) by using the ICD-9-CM diagnosis classification scheme of Feudtner et al.¹³ CCCs represent defined diagnosis groupings of conditions expected to last longer than 12 months and involve either multiple organ systems or a single organ system severely enough to require specialty pediatric care and hospitalization.^13,14 Children requiring medical technology were identified by using ICD-9-CM codes indicating their use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg a tracheostomy tube for breathing).^15,16

Statistical Analysis

Given that the primary purpose for this study was to leverage the natural heterogeneity in the approach to follow-up contact across hospitals, we assessed and reported the prevalence and type of PDIs independently for each hospital. Relatedly, we assessed the relationship between patient characteristics and PDI likelihood independently within each hospital as well rather than pool the data and perform a central analysis across hospitals. Of note, APR-DRG and medication class were not assessed for hospital D, as this information was unavailable. We used χ² tests for univariable analysis and logistic regression with a backwards elimination derivation process (for variables with P ≥ .05) for multivariable analysis; all patient demographic, clinical, and hospitalization characteristics were entered initially into the models. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC), and P < .05 was considered statistically significant. This study was approved by the institutional review board at all hospitals.

RESULTS

Study Population

There were 12,986 (51.4%) of 25,259 patients reached by follow-up contact after discharge across the 4 hospitals. Median age at admission for contacted patients was 4.0 years (interquartile range [IQR] 0-11). Of those contacted, 45.2% were female, 59.9% were non-Hispanic white, 51.0% used Medicaid, and 95.4% were discharged to home. Seventy-one percent had a chronic condition (of any complexity) and 40.8% had a CCC. Eighty percent received a prescribed medication during the hospitalization. Median (IQR) length of stay was 2.0 days (IQR 1-4 days). The top 5 most common reasons for admission were bronchiolitis (6.3%), pneumonia (6.2%), asthma (5.2%), seizure (4.9%), and tonsil and adenoid procedures (4.1%).

PDIs

Across all hospitals, 25.1% (n = 3263) of families contacted reported a PDI for their child (Table 2). PDI rates varied significantly across hospitals (range: 16.0%-62.8%; P < .001). Most (76.3%) PDIs were related to appointments (range across hospitals: 48.8%-87.3%), followed by medications (20.8%; range across hospitals: 14.0%-30.9%) and other problems (12.7%; range across hospitals: 9.4%-32.5%) (Table 2). Available qualitative comments indicated that most medication PDIs involved problems filling a prescription (84.2%); few involved dosing problems (5.5%) or medication side effects (2.3%). “Other” PDIs (n = 416) involved problems such as understanding discharge instructions (25.4%) and concerns about a change in the child’s health status (20.2%).

Characteristics Associated with PDIs

PDI rates varied significantly by patients’ demographic, hospitalization, and clinical characteristics in 3 of the hospitals (ie, all aside from hospital C) (Table 3 and Figure). The findings associated with age, medications, length of stay, and CCCs are presented below.

Age

Older age was a consistent characteristic associated with PDIs in 3 hospitals. For example, PDI rates in children 10 to 18 years versus <1 year were 30.8% versus 21.4% (P < .001) in hospital A, 19.4% versus 13.7% (P = .002) in hospital B, and 70.3% versus 62.8% (P < .001) in hospital D. In multivariable analysis, age 10 to 18 years versus <1 year at admission was associated with an increased likelihood of PDI in hospital A (odds ratio [OR] 1.7; 95% CI, 1.4-2.0), hospital B (OR 1.4; 95% CI, 1.1-1.8), and hospital D (OR 1.7; 95% CI, 0.9-3.0) (Table 3 and Figure).

Medications

The number of medication classes administered was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 12.7% to 29.2% as the number of medication classes administered increased from 0 to ≥5 (Table 3). In multivariable analysis, ≥5 versus 0 medication classes was not associated with a significantly increased likelihood of PDI (P > .05, data not shown).

 

Length of Stay

Shorter length of stay was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 19.0% to 33.9% as length of stay decreased from ≥7 days to ≤1 day (Table 3). In multivariable analysis, length of stay to ≤1 day versus ≥7 days was associated with increased likelihood of PDI (OR 2.1; 95% CI, 1.7-2.5) in hospital A (Table 3 and Figure).

CCCs

A neuromuscular CCC was associated with PDI in 2 hospitals. In hospital B, the PDI rate was higher in children with a neuromuscular CCC compared with a malignancy CCC (21.3% vs 11.2%). In hospital D, the PDI rates were higher in children with a neuromuscular CCC compared with a respiratory CCC (68.9% vs 40.6%) (Table 3). In multivariable analysis, children with versus without a neuromuscular CCC had an increased likelihood of PDI (OR 1.3; 95% CI, 1.0-1.7) in hospital B (Table 3 and Figure).

DISCUSSION

In this retrospective, pragmatic, multicentered study of follow-up contact with a standardized set of questions asked after discharge for hospitalized children, we found that PDIs were identified often, regardless of who made the contact or how the contact was made. The PDI rates varied substantially across hospitals and were likely influenced by the different follow-up approaches that were used. Most PDIs were related to appointments; fewer PDIs were related to medications and other problems. Older age, shorter length of stay, and neuromuscular CCCs were among the identified risk factors for PDIs.

Our assessment of PDIs was, by design, associated with variation in methods and approach for detection across sites. Further investigation is needed to understand how different approaches for follow-up contact after discharge may influence the identification of PDIs. For example, in the current study, the hospital with the highest PDI rate (hospital D) used hospitalists who provided inpatient care for the patient to make follow-up contact. Although not determined from the current study, this approach could have led the hospitalists to ask questions beyond the standardized ones when assessing for PDIs. Perhaps some of the hospitalists had a better understanding of how to probe for PDIs specific to each patient; this understanding may not have been forthcoming for staff in the other hospitals who were unfamiliar with the patients’ hospitalization course and medical history.

Similar to previous studies in adults, our study reported that appointment PDIs in children may be more common than other types of PDIs.¹⁷ Appointment PDIs could have been due to scheduling difficulties, inadequate discharge instructions, lack of adherence to recommended follow-up, or other reasons. Further investigation is needed to elucidate these reasons and to determine how to reduce PDIs related to postdischarge appointments. Some children’s hospitals schedule follow-up appointments prior to discharge to mitigate appointment PDIs that might arise.¹⁸ However, doing that for every hospitalized child is challenging, especially for very short admissions or for weekend discharges when many outpatient and community practices are not open to schedule appointments. Additional exploration is necessary to assess whether this might help explain why some children in the current study with a short versus long length of stay had a higher likelihood of PDI.

The rate of medication PDIs (5.2%) observed in the current study is lower than the rate that is reported in prior literature. Dudas et al.¹ found that medication PDIs occurred in 21% of hospitalized adult patients. One reason for the lower rate of medication PDIs in children may be that they require the use of postdischarge medications less often than adults. Most medication PDIs in the current study involved problems filling a prescription. There was not enough information in the notes taken from the follow-up contact to distinguish the medication PDI etiologies (eg, a prescription was not sent from the hospital team to the pharmacy, prior authorization from an insurance company for a prescription was not obtained, the pharmacy did not stock the medication). To help overcome medication access barriers, some hospitals fill and deliver discharge medications to the patients’ bedside. One study found that children discharged with medication in hand were less likely to have emergency department revisits within 30 days of discharge.¹⁹ Further investigation is needed to assess whether initiatives like these help mitigate medication PDIs in children.

Hospitals may benefit from considering how risk factors for PDIs can be used to prioritize which patients receive follow-up contact, especially in hospitals where contact for all hospitalized patients is not feasible. In the current study, there was variation across hospitals in the profile of risk factors that correlated with increased likelihood of PDI. Some of the risk factors are easier to explain than others. For example, as mentioned above, for some hospitalized children, short length of stay might not permit enough time for hospital staff to set up discharge plans that may sufficiently prevent PDIs. Other risk factors, including older age and neuromuscular CCCs, may require additional assessment (eg, through chart review or in-depth patient and provider interviews) to discover the reasons why they were associated with increased likelihood of PDI. There are additional risk factors that might influence the likelihood of PDI that the current study was not positioned to assess, including health literacy, transportation availability, and language spoken.^20-23

This study has several other limitations in addition to the ones already mentioned. Some children may have experienced PDIs that were not reported at contact (eg, the respondent was unaware that an issue was present), which may have led to an undercounting of PDIs. Alternatively, some caregivers may have been more likely to respond to the contact if their child was experiencing a PDI, which may have led to overcounting. PDIs of nonrespondents were not measured. PDIs identified by postdischarge outpatient and community providers or by families outside of contact were not measured. The current study was not positioned to assess the severity of the PDIs or what interventions (including additional health services) were needed to address them. Although we assessed medication use during admission, we were unable to assess the number and type of medications that were prescribed for use postdischarge. Information about the number and type of follow-up visits needed for each child was not assessed. Given the variety of approaches for follow-up contact, the findings may generalize best to individual hospitals by using an approach that best matches to one of them. The current study is not positioned to correlate quality of discharge care with the rate of PDI.

Despite these limitations, the findings from the current study reinforce that PDIs identified through follow-up contact in discharged patients appear to be common. Of PDIs identified, appointment problems were more prevalent than medication or other types of problems. Short length of stay, older age, and other patient and/or hospitalization attributes were associated with an increased likelihood of PDI. Hospitals caring for children may find this information useful as they strive to optimize their processes for follow-up contact after discharge. To help further evaluate the value and importance of contacting patients after discharge, additional study of PDI in children is warranted, including (1) actions taken to resolve PDIs, (2) the impact of identifying and addressing PDIs on hospital readmission, and (3) postdischarge experiences and health outcomes of children who responded versus those who did not respond to the follow-up contact. Moreover, future multisite, comparative effectiveness studies of PDI may wish to consider standardization of follow-up contact procedures with controlled manipulation of key processes (eg, contact by administrator vs nurse vs physician) to assess best practices.

 

Disclosure

Mr. Blaine, Ms. O’Neill, and Drs. Berry, Brittan, Rehm, and Steiner were supported by the Lucile Packard Foundation for Children’s Health. The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest to disclose.

Many hospitals are considering or currently employing initiatives to contact patients after discharge. Whether conducted via telephone or other means, the purpose of the contact is to help patients adhere to discharge plans, fulfill discharge needs, and alleviate postdischarge issues (PDIs). The effectiveness of hospital-initiated postdischarge phone calls has been studied in adult patients after hospitalization, and though some studies report positive outcomes,^1-3 a 2006 Cochrane review found insufficient evidence to recommend for or against the practice.⁴

Little is known about follow-up contact after hospitalization for children.^5-11 Rates of PDI vary substantially across hospitals. For example, one single-center study of postdischarge telephone contact after hospitalization on a general pediatric ward identified PDIs in ~20% of patients.¹⁰ Another study identified PDIs in 84% of patients discharged from a pediatric rehabilitation facility.¹¹ Telephone follow-up has been associated with reduced health resource utilization and improved patient satisfaction for children discharged after an elective surgical procedure⁶ and for children discharged home from the emergency department.^7-9

More information is needed on the clinical experiences of postdischarge contact in hospitalized children to improve the understanding of how the contact is made, who makes it, and which patients are most likely to report a PDI. These experiences are crucial to understand given the expense and time commitment involved in postdischarge contact, as many hospitals may not be positioned to contact all discharged patients. Therefore, we conducted a pragmatic, retrospective, naturalistic study of differing approaches to postdischarge contact occurring in multiple hospitals. Our main objective was to describe the prevalence and types of PDIs identified by the different approaches for follow-up contact across 4 children’s hospitals. We also assessed the characteristics of children who have the highest likelihood of having a PDI identified from the contact within each hospital.

METHODS

Study Design, Setting, and Population

This is a retrospective analysis of hospital-initiated follow-up contact that occurred for 12,986 children discharged from 4 US children’s hospitals between January 2012 and July 2015. Postdischarge follow-up contact was a component of ongoing, natural clinical operations at each institution during the study period. Methods for contact varied across hospitals (Table 1). In all hospitals, initial contact was made within 14 days of inpatient discharge by hospital staff (eg, administrative, nursing, or physician) via telephone call, text message, or e-mail. During contact, each site asked a child’s caregiver a set of standardized questions about medications, appointments, and other discharge-related issues (Table 1). Additional characteristics about each hospital and their processes for follow-up contact (eg, personnel involved, timing, eligibility criteria, etc.) are reported in the supplementary Appendix.

Main Outcome Measures

The main outcome measure was identification of a PDI, defined as a medication, appointment, or other discharge-related issue, that was reported and recorded by the child’s caregiver during conversation from the standardized questions that were asked during follow-up contact as part of routine discharge care (Table 1). Medication PDIs included issues filling prescriptions and tolerating medications. Appointment PDIs included not having a follow-up appointment scheduled. Other PDIs included issues with the child’s health condition, discharge instructions, or any other concerns. All PDIs had been recorded prospectively by hospital contact personnel (hospitals A, B, and D) or through an automated texting system into a database (hospital C). Where available, free text comments that were recorded by contact personnel were reviewed by one of the authors (KB) and categorized via an existing framework of PDI designed by Heath et al.¹⁰ in order to further understand the problems that were reported.

Patient Characteristics

Patient hospitalization, demographic, and clinical characteristics were obtained from administrative health data at each institution and compared between children with versus without a PDI. Hospitalization characteristics included length of stay, season of admission, and reason for admission. Reason for admission was categorized by using 3M Health’s All Patient Refined Diagnosis Related Groups (APR-DRG) (3M, Maplewood, MN). Demographic characteristics included age at admission in years, insurance type (eg, public, private, and other), and race/ethnicity (Asian/Pacific Islander, Hispanic, non-Hispanic black, non-Hispanic white, and other).

 

Clinical characteristics included a count of the different classes of medications (eg, antibiotics, antiepileptic medications, digestive motility medications, etc.) administered to the child during admission, the type and number of chronic conditions, and assistance with medical technology (eg, gastrostomy, tracheostomy, etc.). Except for medications, these characteristics were assessed with International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.

We used the Agency for Healthcare Research and Quality Chronic Condition Indicator classification system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic versus nonchronic conditions to identify the presence and number of chronic conditions.¹² Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) by using the ICD-9-CM diagnosis classification scheme of Feudtner et al.¹³ CCCs represent defined diagnosis groupings of conditions expected to last longer than 12 months and involve either multiple organ systems or a single organ system severely enough to require specialty pediatric care and hospitalization.^13,14 Children requiring medical technology were identified by using ICD-9-CM codes indicating their use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg a tracheostomy tube for breathing).^15,16

Statistical Analysis

Given that the primary purpose for this study was to leverage the natural heterogeneity in the approach to follow-up contact across hospitals, we assessed and reported the prevalence and type of PDIs independently for each hospital. Relatedly, we assessed the relationship between patient characteristics and PDI likelihood independently within each hospital as well rather than pool the data and perform a central analysis across hospitals. Of note, APR-DRG and medication class were not assessed for hospital D, as this information was unavailable. We used χ² tests for univariable analysis and logistic regression with a backwards elimination derivation process (for variables with P ≥ .05) for multivariable analysis; all patient demographic, clinical, and hospitalization characteristics were entered initially into the models. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC), and P < .05 was considered statistically significant. This study was approved by the institutional review board at all hospitals.

RESULTS

Study Population

There were 12,986 (51.4%) of 25,259 patients reached by follow-up contact after discharge across the 4 hospitals. Median age at admission for contacted patients was 4.0 years (interquartile range [IQR] 0-11). Of those contacted, 45.2% were female, 59.9% were non-Hispanic white, 51.0% used Medicaid, and 95.4% were discharged to home. Seventy-one percent had a chronic condition (of any complexity) and 40.8% had a CCC. Eighty percent received a prescribed medication during the hospitalization. Median (IQR) length of stay was 2.0 days (IQR 1-4 days). The top 5 most common reasons for admission were bronchiolitis (6.3%), pneumonia (6.2%), asthma (5.2%), seizure (4.9%), and tonsil and adenoid procedures (4.1%).

PDIs

Across all hospitals, 25.1% (n = 3263) of families contacted reported a PDI for their child (Table 2). PDI rates varied significantly across hospitals (range: 16.0%-62.8%; P < .001). Most (76.3%) PDIs were related to appointments (range across hospitals: 48.8%-87.3%), followed by medications (20.8%; range across hospitals: 14.0%-30.9%) and other problems (12.7%; range across hospitals: 9.4%-32.5%) (Table 2). Available qualitative comments indicated that most medication PDIs involved problems filling a prescription (84.2%); few involved dosing problems (5.5%) or medication side effects (2.3%). “Other” PDIs (n = 416) involved problems such as understanding discharge instructions (25.4%) and concerns about a change in the child’s health status (20.2%).

Characteristics Associated with PDIs

PDI rates varied significantly by patients’ demographic, hospitalization, and clinical characteristics in 3 of the hospitals (ie, all aside from hospital C) (Table 3 and Figure). The findings associated with age, medications, length of stay, and CCCs are presented below.

Age

Older age was a consistent characteristic associated with PDIs in 3 hospitals. For example, PDI rates in children 10 to 18 years versus <1 year were 30.8% versus 21.4% (P < .001) in hospital A, 19.4% versus 13.7% (P = .002) in hospital B, and 70.3% versus 62.8% (P < .001) in hospital D. In multivariable analysis, age 10 to 18 years versus <1 year at admission was associated with an increased likelihood of PDI in hospital A (odds ratio [OR] 1.7; 95% CI, 1.4-2.0), hospital B (OR 1.4; 95% CI, 1.1-1.8), and hospital D (OR 1.7; 95% CI, 0.9-3.0) (Table 3 and Figure).

Medications

The number of medication classes administered was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 12.7% to 29.2% as the number of medication classes administered increased from 0 to ≥5 (Table 3). In multivariable analysis, ≥5 versus 0 medication classes was not associated with a significantly increased likelihood of PDI (P > .05, data not shown).

 

Length of Stay

Shorter length of stay was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 19.0% to 33.9% as length of stay decreased from ≥7 days to ≤1 day (Table 3). In multivariable analysis, length of stay to ≤1 day versus ≥7 days was associated with increased likelihood of PDI (OR 2.1; 95% CI, 1.7-2.5) in hospital A (Table 3 and Figure).

CCCs

A neuromuscular CCC was associated with PDI in 2 hospitals. In hospital B, the PDI rate was higher in children with a neuromuscular CCC compared with a malignancy CCC (21.3% vs 11.2%). In hospital D, the PDI rates were higher in children with a neuromuscular CCC compared with a respiratory CCC (68.9% vs 40.6%) (Table 3). In multivariable analysis, children with versus without a neuromuscular CCC had an increased likelihood of PDI (OR 1.3; 95% CI, 1.0-1.7) in hospital B (Table 3 and Figure).

DISCUSSION

In this retrospective, pragmatic, multicentered study of follow-up contact with a standardized set of questions asked after discharge for hospitalized children, we found that PDIs were identified often, regardless of who made the contact or how the contact was made. The PDI rates varied substantially across hospitals and were likely influenced by the different follow-up approaches that were used. Most PDIs were related to appointments; fewer PDIs were related to medications and other problems. Older age, shorter length of stay, and neuromuscular CCCs were among the identified risk factors for PDIs.

Our assessment of PDIs was, by design, associated with variation in methods and approach for detection across sites. Further investigation is needed to understand how different approaches for follow-up contact after discharge may influence the identification of PDIs. For example, in the current study, the hospital with the highest PDI rate (hospital D) used hospitalists who provided inpatient care for the patient to make follow-up contact. Although not determined from the current study, this approach could have led the hospitalists to ask questions beyond the standardized ones when assessing for PDIs. Perhaps some of the hospitalists had a better understanding of how to probe for PDIs specific to each patient; this understanding may not have been forthcoming for staff in the other hospitals who were unfamiliar with the patients’ hospitalization course and medical history.

Similar to previous studies in adults, our study reported that appointment PDIs in children may be more common than other types of PDIs.¹⁷ Appointment PDIs could have been due to scheduling difficulties, inadequate discharge instructions, lack of adherence to recommended follow-up, or other reasons. Further investigation is needed to elucidate these reasons and to determine how to reduce PDIs related to postdischarge appointments. Some children’s hospitals schedule follow-up appointments prior to discharge to mitigate appointment PDIs that might arise.¹⁸ However, doing that for every hospitalized child is challenging, especially for very short admissions or for weekend discharges when many outpatient and community practices are not open to schedule appointments. Additional exploration is necessary to assess whether this might help explain why some children in the current study with a short versus long length of stay had a higher likelihood of PDI.

The rate of medication PDIs (5.2%) observed in the current study is lower than the rate that is reported in prior literature. Dudas et al.¹ found that medication PDIs occurred in 21% of hospitalized adult patients. One reason for the lower rate of medication PDIs in children may be that they require the use of postdischarge medications less often than adults. Most medication PDIs in the current study involved problems filling a prescription. There was not enough information in the notes taken from the follow-up contact to distinguish the medication PDI etiologies (eg, a prescription was not sent from the hospital team to the pharmacy, prior authorization from an insurance company for a prescription was not obtained, the pharmacy did not stock the medication). To help overcome medication access barriers, some hospitals fill and deliver discharge medications to the patients’ bedside. One study found that children discharged with medication in hand were less likely to have emergency department revisits within 30 days of discharge.¹⁹ Further investigation is needed to assess whether initiatives like these help mitigate medication PDIs in children.

Hospitals may benefit from considering how risk factors for PDIs can be used to prioritize which patients receive follow-up contact, especially in hospitals where contact for all hospitalized patients is not feasible. In the current study, there was variation across hospitals in the profile of risk factors that correlated with increased likelihood of PDI. Some of the risk factors are easier to explain than others. For example, as mentioned above, for some hospitalized children, short length of stay might not permit enough time for hospital staff to set up discharge plans that may sufficiently prevent PDIs. Other risk factors, including older age and neuromuscular CCCs, may require additional assessment (eg, through chart review or in-depth patient and provider interviews) to discover the reasons why they were associated with increased likelihood of PDI. There are additional risk factors that might influence the likelihood of PDI that the current study was not positioned to assess, including health literacy, transportation availability, and language spoken.^20-23

This study has several other limitations in addition to the ones already mentioned. Some children may have experienced PDIs that were not reported at contact (eg, the respondent was unaware that an issue was present), which may have led to an undercounting of PDIs. Alternatively, some caregivers may have been more likely to respond to the contact if their child was experiencing a PDI, which may have led to overcounting. PDIs of nonrespondents were not measured. PDIs identified by postdischarge outpatient and community providers or by families outside of contact were not measured. The current study was not positioned to assess the severity of the PDIs or what interventions (including additional health services) were needed to address them. Although we assessed medication use during admission, we were unable to assess the number and type of medications that were prescribed for use postdischarge. Information about the number and type of follow-up visits needed for each child was not assessed. Given the variety of approaches for follow-up contact, the findings may generalize best to individual hospitals by using an approach that best matches to one of them. The current study is not positioned to correlate quality of discharge care with the rate of PDI.

Despite these limitations, the findings from the current study reinforce that PDIs identified through follow-up contact in discharged patients appear to be common. Of PDIs identified, appointment problems were more prevalent than medication or other types of problems. Short length of stay, older age, and other patient and/or hospitalization attributes were associated with an increased likelihood of PDI. Hospitals caring for children may find this information useful as they strive to optimize their processes for follow-up contact after discharge. To help further evaluate the value and importance of contacting patients after discharge, additional study of PDI in children is warranted, including (1) actions taken to resolve PDIs, (2) the impact of identifying and addressing PDIs on hospital readmission, and (3) postdischarge experiences and health outcomes of children who responded versus those who did not respond to the follow-up contact. Moreover, future multisite, comparative effectiveness studies of PDI may wish to consider standardization of follow-up contact procedures with controlled manipulation of key processes (eg, contact by administrator vs nurse vs physician) to assess best practices.

 

Disclosure

Mr. Blaine, Ms. O’Neill, and Drs. Berry, Brittan, Rehm, and Steiner were supported by the Lucile Packard Foundation for Children’s Health. The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest to disclose.

Many hospitals are considering or currently employing initiatives to contact patients after discharge. Whether conducted via telephone or other means, the purpose of the contact is to help patients adhere to discharge plans, fulfill discharge needs, and alleviate postdischarge issues (PDIs). The effectiveness of hospital-initiated postdischarge phone calls has been studied in adult patients after hospitalization, and though some studies report positive outcomes,^1-3 a 2006 Cochrane review found insufficient evidence to recommend for or against the practice.⁴

Little is known about follow-up contact after hospitalization for children.^5-11 Rates of PDI vary substantially across hospitals. For example, one single-center study of postdischarge telephone contact after hospitalization on a general pediatric ward identified PDIs in ~20% of patients.¹⁰ Another study identified PDIs in 84% of patients discharged from a pediatric rehabilitation facility.¹¹ Telephone follow-up has been associated with reduced health resource utilization and improved patient satisfaction for children discharged after an elective surgical procedure⁶ and for children discharged home from the emergency department.^7-9

More information is needed on the clinical experiences of postdischarge contact in hospitalized children to improve the understanding of how the contact is made, who makes it, and which patients are most likely to report a PDI. These experiences are crucial to understand given the expense and time commitment involved in postdischarge contact, as many hospitals may not be positioned to contact all discharged patients. Therefore, we conducted a pragmatic, retrospective, naturalistic study of differing approaches to postdischarge contact occurring in multiple hospitals. Our main objective was to describe the prevalence and types of PDIs identified by the different approaches for follow-up contact across 4 children’s hospitals. We also assessed the characteristics of children who have the highest likelihood of having a PDI identified from the contact within each hospital.

METHODS

Study Design, Setting, and Population

This is a retrospective analysis of hospital-initiated follow-up contact that occurred for 12,986 children discharged from 4 US children’s hospitals between January 2012 and July 2015. Postdischarge follow-up contact was a component of ongoing, natural clinical operations at each institution during the study period. Methods for contact varied across hospitals (Table 1). In all hospitals, initial contact was made within 14 days of inpatient discharge by hospital staff (eg, administrative, nursing, or physician) via telephone call, text message, or e-mail. During contact, each site asked a child’s caregiver a set of standardized questions about medications, appointments, and other discharge-related issues (Table 1). Additional characteristics about each hospital and their processes for follow-up contact (eg, personnel involved, timing, eligibility criteria, etc.) are reported in the supplementary Appendix.

Main Outcome Measures

The main outcome measure was identification of a PDI, defined as a medication, appointment, or other discharge-related issue, that was reported and recorded by the child’s caregiver during conversation from the standardized questions that were asked during follow-up contact as part of routine discharge care (Table 1). Medication PDIs included issues filling prescriptions and tolerating medications. Appointment PDIs included not having a follow-up appointment scheduled. Other PDIs included issues with the child’s health condition, discharge instructions, or any other concerns. All PDIs had been recorded prospectively by hospital contact personnel (hospitals A, B, and D) or through an automated texting system into a database (hospital C). Where available, free text comments that were recorded by contact personnel were reviewed by one of the authors (KB) and categorized via an existing framework of PDI designed by Heath et al.¹⁰ in order to further understand the problems that were reported.

Patient Characteristics

Patient hospitalization, demographic, and clinical characteristics were obtained from administrative health data at each institution and compared between children with versus without a PDI. Hospitalization characteristics included length of stay, season of admission, and reason for admission. Reason for admission was categorized by using 3M Health’s All Patient Refined Diagnosis Related Groups (APR-DRG) (3M, Maplewood, MN). Demographic characteristics included age at admission in years, insurance type (eg, public, private, and other), and race/ethnicity (Asian/Pacific Islander, Hispanic, non-Hispanic black, non-Hispanic white, and other).

 

Clinical characteristics included a count of the different classes of medications (eg, antibiotics, antiepileptic medications, digestive motility medications, etc.) administered to the child during admission, the type and number of chronic conditions, and assistance with medical technology (eg, gastrostomy, tracheostomy, etc.). Except for medications, these characteristics were assessed with International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.

We used the Agency for Healthcare Research and Quality Chronic Condition Indicator classification system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic versus nonchronic conditions to identify the presence and number of chronic conditions.¹² Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) by using the ICD-9-CM diagnosis classification scheme of Feudtner et al.¹³ CCCs represent defined diagnosis groupings of conditions expected to last longer than 12 months and involve either multiple organ systems or a single organ system severely enough to require specialty pediatric care and hospitalization.^13,14 Children requiring medical technology were identified by using ICD-9-CM codes indicating their use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg a tracheostomy tube for breathing).^15,16

Statistical Analysis

Given that the primary purpose for this study was to leverage the natural heterogeneity in the approach to follow-up contact across hospitals, we assessed and reported the prevalence and type of PDIs independently for each hospital. Relatedly, we assessed the relationship between patient characteristics and PDI likelihood independently within each hospital as well rather than pool the data and perform a central analysis across hospitals. Of note, APR-DRG and medication class were not assessed for hospital D, as this information was unavailable. We used χ² tests for univariable analysis and logistic regression with a backwards elimination derivation process (for variables with P ≥ .05) for multivariable analysis; all patient demographic, clinical, and hospitalization characteristics were entered initially into the models. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC), and P < .05 was considered statistically significant. This study was approved by the institutional review board at all hospitals.

RESULTS

Study Population

There were 12,986 (51.4%) of 25,259 patients reached by follow-up contact after discharge across the 4 hospitals. Median age at admission for contacted patients was 4.0 years (interquartile range [IQR] 0-11). Of those contacted, 45.2% were female, 59.9% were non-Hispanic white, 51.0% used Medicaid, and 95.4% were discharged to home. Seventy-one percent had a chronic condition (of any complexity) and 40.8% had a CCC. Eighty percent received a prescribed medication during the hospitalization. Median (IQR) length of stay was 2.0 days (IQR 1-4 days). The top 5 most common reasons for admission were bronchiolitis (6.3%), pneumonia (6.2%), asthma (5.2%), seizure (4.9%), and tonsil and adenoid procedures (4.1%).

PDIs

Across all hospitals, 25.1% (n = 3263) of families contacted reported a PDI for their child (Table 2). PDI rates varied significantly across hospitals (range: 16.0%-62.8%; P < .001). Most (76.3%) PDIs were related to appointments (range across hospitals: 48.8%-87.3%), followed by medications (20.8%; range across hospitals: 14.0%-30.9%) and other problems (12.7%; range across hospitals: 9.4%-32.5%) (Table 2). Available qualitative comments indicated that most medication PDIs involved problems filling a prescription (84.2%); few involved dosing problems (5.5%) or medication side effects (2.3%). “Other” PDIs (n = 416) involved problems such as understanding discharge instructions (25.4%) and concerns about a change in the child’s health status (20.2%).

Characteristics Associated with PDIs

PDI rates varied significantly by patients’ demographic, hospitalization, and clinical characteristics in 3 of the hospitals (ie, all aside from hospital C) (Table 3 and Figure). The findings associated with age, medications, length of stay, and CCCs are presented below.

Age

Older age was a consistent characteristic associated with PDIs in 3 hospitals. For example, PDI rates in children 10 to 18 years versus <1 year were 30.8% versus 21.4% (P < .001) in hospital A, 19.4% versus 13.7% (P = .002) in hospital B, and 70.3% versus 62.8% (P < .001) in hospital D. In multivariable analysis, age 10 to 18 years versus <1 year at admission was associated with an increased likelihood of PDI in hospital A (odds ratio [OR] 1.7; 95% CI, 1.4-2.0), hospital B (OR 1.4; 95% CI, 1.1-1.8), and hospital D (OR 1.7; 95% CI, 0.9-3.0) (Table 3 and Figure).

Medications

The number of medication classes administered was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 12.7% to 29.2% as the number of medication classes administered increased from 0 to ≥5 (Table 3). In multivariable analysis, ≥5 versus 0 medication classes was not associated with a significantly increased likelihood of PDI (P > .05, data not shown).

 

Length of Stay

Shorter length of stay was associated with PDI in 1 hospital. In hospital A, the PDI rate increased significantly (P < .001) from 19.0% to 33.9% as length of stay decreased from ≥7 days to ≤1 day (Table 3). In multivariable analysis, length of stay to ≤1 day versus ≥7 days was associated with increased likelihood of PDI (OR 2.1; 95% CI, 1.7-2.5) in hospital A (Table 3 and Figure).

CCCs

A neuromuscular CCC was associated with PDI in 2 hospitals. In hospital B, the PDI rate was higher in children with a neuromuscular CCC compared with a malignancy CCC (21.3% vs 11.2%). In hospital D, the PDI rates were higher in children with a neuromuscular CCC compared with a respiratory CCC (68.9% vs 40.6%) (Table 3). In multivariable analysis, children with versus without a neuromuscular CCC had an increased likelihood of PDI (OR 1.3; 95% CI, 1.0-1.7) in hospital B (Table 3 and Figure).

DISCUSSION

In this retrospective, pragmatic, multicentered study of follow-up contact with a standardized set of questions asked after discharge for hospitalized children, we found that PDIs were identified often, regardless of who made the contact or how the contact was made. The PDI rates varied substantially across hospitals and were likely influenced by the different follow-up approaches that were used. Most PDIs were related to appointments; fewer PDIs were related to medications and other problems. Older age, shorter length of stay, and neuromuscular CCCs were among the identified risk factors for PDIs.

Our assessment of PDIs was, by design, associated with variation in methods and approach for detection across sites. Further investigation is needed to understand how different approaches for follow-up contact after discharge may influence the identification of PDIs. For example, in the current study, the hospital with the highest PDI rate (hospital D) used hospitalists who provided inpatient care for the patient to make follow-up contact. Although not determined from the current study, this approach could have led the hospitalists to ask questions beyond the standardized ones when assessing for PDIs. Perhaps some of the hospitalists had a better understanding of how to probe for PDIs specific to each patient; this understanding may not have been forthcoming for staff in the other hospitals who were unfamiliar with the patients’ hospitalization course and medical history.

Similar to previous studies in adults, our study reported that appointment PDIs in children may be more common than other types of PDIs.¹⁷ Appointment PDIs could have been due to scheduling difficulties, inadequate discharge instructions, lack of adherence to recommended follow-up, or other reasons. Further investigation is needed to elucidate these reasons and to determine how to reduce PDIs related to postdischarge appointments. Some children’s hospitals schedule follow-up appointments prior to discharge to mitigate appointment PDIs that might arise.¹⁸ However, doing that for every hospitalized child is challenging, especially for very short admissions or for weekend discharges when many outpatient and community practices are not open to schedule appointments. Additional exploration is necessary to assess whether this might help explain why some children in the current study with a short versus long length of stay had a higher likelihood of PDI.

The rate of medication PDIs (5.2%) observed in the current study is lower than the rate that is reported in prior literature. Dudas et al.¹ found that medication PDIs occurred in 21% of hospitalized adult patients. One reason for the lower rate of medication PDIs in children may be that they require the use of postdischarge medications less often than adults. Most medication PDIs in the current study involved problems filling a prescription. There was not enough information in the notes taken from the follow-up contact to distinguish the medication PDI etiologies (eg, a prescription was not sent from the hospital team to the pharmacy, prior authorization from an insurance company for a prescription was not obtained, the pharmacy did not stock the medication). To help overcome medication access barriers, some hospitals fill and deliver discharge medications to the patients’ bedside. One study found that children discharged with medication in hand were less likely to have emergency department revisits within 30 days of discharge.¹⁹ Further investigation is needed to assess whether initiatives like these help mitigate medication PDIs in children.

Hospitals may benefit from considering how risk factors for PDIs can be used to prioritize which patients receive follow-up contact, especially in hospitals where contact for all hospitalized patients is not feasible. In the current study, there was variation across hospitals in the profile of risk factors that correlated with increased likelihood of PDI. Some of the risk factors are easier to explain than others. For example, as mentioned above, for some hospitalized children, short length of stay might not permit enough time for hospital staff to set up discharge plans that may sufficiently prevent PDIs. Other risk factors, including older age and neuromuscular CCCs, may require additional assessment (eg, through chart review or in-depth patient and provider interviews) to discover the reasons why they were associated with increased likelihood of PDI. There are additional risk factors that might influence the likelihood of PDI that the current study was not positioned to assess, including health literacy, transportation availability, and language spoken.^20-23

This study has several other limitations in addition to the ones already mentioned. Some children may have experienced PDIs that were not reported at contact (eg, the respondent was unaware that an issue was present), which may have led to an undercounting of PDIs. Alternatively, some caregivers may have been more likely to respond to the contact if their child was experiencing a PDI, which may have led to overcounting. PDIs of nonrespondents were not measured. PDIs identified by postdischarge outpatient and community providers or by families outside of contact were not measured. The current study was not positioned to assess the severity of the PDIs or what interventions (including additional health services) were needed to address them. Although we assessed medication use during admission, we were unable to assess the number and type of medications that were prescribed for use postdischarge. Information about the number and type of follow-up visits needed for each child was not assessed. Given the variety of approaches for follow-up contact, the findings may generalize best to individual hospitals by using an approach that best matches to one of them. The current study is not positioned to correlate quality of discharge care with the rate of PDI.

Despite these limitations, the findings from the current study reinforce that PDIs identified through follow-up contact in discharged patients appear to be common. Of PDIs identified, appointment problems were more prevalent than medication or other types of problems. Short length of stay, older age, and other patient and/or hospitalization attributes were associated with an increased likelihood of PDI. Hospitals caring for children may find this information useful as they strive to optimize their processes for follow-up contact after discharge. To help further evaluate the value and importance of contacting patients after discharge, additional study of PDI in children is warranted, including (1) actions taken to resolve PDIs, (2) the impact of identifying and addressing PDIs on hospital readmission, and (3) postdischarge experiences and health outcomes of children who responded versus those who did not respond to the follow-up contact. Moreover, future multisite, comparative effectiveness studies of PDI may wish to consider standardization of follow-up contact procedures with controlled manipulation of key processes (eg, contact by administrator vs nurse vs physician) to assess best practices.

 

Disclosure

Mr. Blaine, Ms. O’Neill, and Drs. Berry, Brittan, Rehm, and Steiner were supported by the Lucile Packard Foundation for Children’s Health. The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest to disclose.