Jay G. Berry, MD, MPH

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.

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.

Study Population

jhm013070445_t1.jpg

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

jhm013070445_f1.jpg

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

jhm013070445_f2.jpg

Index Procedure Admissions Combined

jhm013070445_f3.jpg

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.

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.

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.

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.

Study Population

jhm013070445_t1.jpg

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

jhm013070445_f1.jpg

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

jhm013070445_f2.jpg

Index Procedure Admissions Combined

jhm013070445_f3.jpg

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.

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.

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.

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.

Study Population

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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

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Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

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Index Procedure Admissions Combined

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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.

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.

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.

Study Design, Setting, and Population

berry0228e_t1.jpg

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.

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

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Characteristics Associated with PDIs

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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

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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).

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.

Study Design, Setting, and Population

berry0228e_t1.jpg

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.

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

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Characteristics Associated with PDIs

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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

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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).

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.

Study Design, Setting, and Population

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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.

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

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Characteristics Associated with PDIs

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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

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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).

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.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the 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).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

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Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

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Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

jhm012080626_f1.jpg

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

jhm012080626_f2.jpg

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the 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).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

jhm012080626_t1.jpg

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

jhm012080626_t2.jpg

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

jhm012080626_f1.jpg

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

jhm012080626_f2.jpg

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the 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).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

jhm012080626_t1.jpg

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

jhm012080626_t2.jpg

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

jhm012080626_f1.jpg

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

jhm012080626_f2.jpg

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.