Michelle A Lopez, MD, MPH

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Staphylococcal scalded skin syndrome (SSSS) is an exfoliative toxin-mediated dermatitis that predominantly occurs in young children. Multiple recent reports indicate a rising incidence of this disease.^1-4 Recommended treatment for SSSS includes antistaphylococcal antibiotics and supportive care measures.^5,6 Elimination or reduction of the toxin-producing Staphylococcus aureus is thought to help limit disease progression and promote recovery. Experts advocate for the use of antibiotics even when there is no apparent focal source of infection, such as an abscess.^6,7

Several factors may affect antibiotic selection, including the desire to inhibit toxin production and to target the causative pathogen in a bactericidal fashion. Because SSSS is toxin mediated, clindamycin is often recommended because of its inhibition of toxin synthesis.^5,8 The clinical utility of adding other antibiotics to clindamycin for coverage of methicillin-sensitive S aureus (MSSA) or methicillin-resistant S aureus (MRSA) is uncertain. Several studies report MSSA to be the predominant pathogen identified by culture^2,9; however, SSSS caused by MRSA has been reported.^9-11 Additionally, bactericidal antibiotics (eg, nafcillin) have been considered to hold potential clinical advantage as compared with bacteriostatic antibiotics (eg, clindamycin), even though clinical studies have not clearly demonstrated this advantage in the general population.^12,13 Some experts recommend additional MRSA or MSSA coverage (such as vancomycin or nafcillin) in patients with high illness severity or nonresponse to therapy, or in areas where there is high prevalence of staphylococcal resistance to clindamycin.^5,7,9,14 Alternatively, for areas with low MRSA prevalence, monotherapy with an anti-MSSA antibiotic is another potential option. No recent studies have compared patient outcomes among antibiotic regimens in children with SSSS.

Knowledge of the outcomes associated with different antibiotic regimens for children hospitalized with SSSS is needed and could be used to improve patient outcomes and potentially promote antibiotic stewardship. In this study, our objectives were to (1) describe antibiotic regimens given to children hospitalized with SSSS, and (2) examine the association of three antibiotic regimens commonly used for SSSS (clindamycin monotherapy, clindamycin plus additional MSSA coverage, and clindamycin plus additional MRSA coverage) with patient outcomes of length of stay (LOS), treatment failure, and cost in a large cohort of children at US children’s hospitals.

We conducted a multicenter, retrospective cohort study utilizing data within the Pediatric Health Information System (PHIS) database from July 1, 2011, to June 30, 2016. Thirty-five free-standing tertiary care US children’s hospitals within 24 states were included. The Children’s Hospital Association (Lenexa, Kansas) maintains the PHIS database, which contains de-identified patient information, including diagnoses (with International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification [ICD-9-CM, ICD-10-CM]), demographics, procedures, and daily billing records. Data quality and reliability are confirmed by participating institutions and the Children’s Hospital Association.¹⁵ The local institutional review board (IRB) deemed the study exempt from formal IRB review, as patient information was de-identified.

Study Population

We included hospitalized children aged newborn to 18 years with a primary or secondary diagnosis of SSSS (ICD-9, 695.81; ICD-10, L00). Children whose primary presentation and admission were to a PHIS hospital were included; children transferred from another hospital were excluded. The following exclusion criteria were based on previously published methodology.¹⁶ Children with complex chronic medical conditions as classified by Feudtner et al¹⁷ were excluded, since these children may require a different treatment approach than the general pediatric population. In order to decrease diagnostic ambiguity, we excluded children if an alternative dermatologic diagnosis was recorded as a principal or secondary diagnosis (eg, Stevens-Johnson syndrome or scarlet fever).¹⁶ Finally, hospitals with fewer than 10 children with SSSS during the study period were excluded.

Antibiotic Regimen Groups

We used PHIS daily billing codes to determine the antibiotics received by the study population. Children were classified into antibiotic regimen groups based on whether they received specific antibiotic combinations. Antibiotics received on any day during the hospitalization, including in the emergency department (ED), were used to assign patients to regimen groups. Antibiotics were classified into regimen groups based on consensus among study investigators, which included two board-certified pediatric infectious diseases specialists (A.C., R.M.). Antibiotic group definitions are listed in Table 1. Oral and intravenous (IV) therapies were grouped together for clindamycin, cephalexin/cefazolin, and linezolid because of good oral bioavailability in most situations.¹⁸ The three most common antistaphylococcal groups were chosen for further analysis: clindamycin alone, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage. The clindamycin group was defined as children with receipt of oral or IV clindamycin. Children who received clindamycin with additional MSSA coverage, such as cefazolin or nafcillin, were categorized as the clindamycin plus MSSA group. Children who received clindamycin with additional MRSA coverage, such as vancomycin or linezolid, were categorized as the clindamycin plus MRSA group. We chose not to include children who received the above regimens plus other antibiotics with partial antistaphylococcal activity, such as ampicillin, gentamicin, or ceftriaxone, in the clindamycin plus MSSA and clindamycin plus MRSA groups. We excluded these antibiotics to decrease the heterogeneity in the definition of regimen groups and allow a more direct comparison for effectiveness.

Covariates

Covariates included age, sex, ethnicity and/or race, payer type, level of care, illness severity, and region. The variable definitions below are in keeping with a prior study of SSSS.¹⁶ Age was categorized as: birth to 59 days, 2 to 11 months, 1 to 4 years (preschool age), 5 to 10 years (school age), and 11 to 18 years (adolescent). We examined infants younger than 60 days separately from older infants because this population may warrant additional treatment considerations. Race and ethnicity were categorized as White (non-Hispanic), African American (non-Hispanic), Hispanic, or other. Payer types included government, private, or other. Level of care was assigned as either intensive care or acute care. Illness severity was assigned using the All Patient Refined Diagnosis Related Group (APR-DRG; 3M Corporation, St. Paul, Minnesota) severity levels.¹⁹ In line with a prior study,¹⁶ we defined “low illness severity” as the APR-DRG minor (1) classification. The moderate (2), major (3), and extreme (4) classifications were defined as “moderate to high illness severity,” since there were very few classifications of major or extreme (<5%) illness severity. We categorized hospitals into the following US regions: Northeast, Midwest, South, and West.

Outcome Measures

The primary outcome was hospital LOS in days, and secondary outcomes were treatment failure and hospital costs. Hospital LOS was chosen as the primary outcome to represent the time needed for the child to show clinical improvement. Treatment failure was defined as a same-cause 14-day ED revisit or hospital readmission, and these were determined to be same-cause if a diagnosis for SSSS (ICD-9, 695.81; ICD-10, L00) was documented for the return encounter. The 14-day interval for readmission and ED revisit was chosen to measure any relapse of symptoms after completion of antibiotic therapy, similar to a prior study of treatment failure in skin and soft tissue infections.²⁰ Total costs of the hospitalization were estimated from charges using hospital- and year-specific cost-to-charge ratios. Subcategories of cost, including clinical, pharmacy, imaging, laboratory, supply, and other, were also compared among the three groups.

Statistical Analysis

Demographic and clinical characteristics of children were summarized using frequencies and percentages for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. These were compared across antibiotic groups using chi-square and Kruskal–Wallis tests, respectively. In unadjusted analyses, outcomes were compared across antibiotic regimen groups using these same statistical tests. In order to account for patient clustering within hospitals, generalized linear mixed-effects models were used to model outcomes with a random intercept for each hospital. Models were adjusted for SSSS being listed as a principal or secondary diagnosis, race, illness severity, and level of care. We log-transformed LOS and cost data prior to modeling because of the nonnormal distributions for these data. Owing to the inability to measure the number of antibiotic doses, and to reduce the possibility of including children who received few regimen-defined combination antibiotics, a post hoc sensitivity analysis was performed. This analysis used an alternative definition for antibiotic regimen groups, for which children admitted for 2 or more calendar days must have received regimen-specified antibiotics on at least 2 days of the admission. Additionally, outcomes were stratified by low and moderate/high illness severity and compared across the three antibiotic regimen groups. All analyses were performed with SAS (SAS 9.4; SAS Institute, Cary, North Carolina), and P values of less than .05 were considered statistically significant.

Overall, 1,815 hospitalized children with SSSS were identified in the PHIS database, and after application of the exclusion criteria, 1,259 children remained, with 1,247 (99%) receiving antibiotics (Figure). The antibiotic regimens received by these children are described in Table 1. Of these, 828 children (66%) received one of the three most common antistaphylococcal regimens (clindamycin, clindamycin + MSSA, and clindamycin + MRSA) and were included for further analysis.

Characteristics of the 828 children are presented in Table 2. Most children (82%) were aged 4 years or younger, and distributions of age, sex, and insurance payer were similar among children receiving the three regimens. Thirty-two percent had moderate to high illness severity, and 3.5% required management in the intensive care setting. Of the three antibiotic regimens, clindamycin monotherapy was most common (47%), followed by clindamycin plus MSSA coverage (33%), and clindamycin plus MRSA coverage (20%). A higher proportion of children in the clindamycin plus MRSA group were African American and were hospitalized in the South. Children receiving clindamycin plus MRSA coverage had higher illness severity (44%) as compared with clindamycin monotherapy (28%) and clindamycin plus MSSA coverage (32%) (P = .001). Additionally, a larger proportion of children treated with clindamycin plus MRSA coverage were managed in the intensive care setting as compared with the clindamycin plus MSSA or clindamycin monotherapy groups.

Among the 828 children with SSSS, the median LOS was 2 days (IQR, 2-3), and treatment failure was 1.1% (95% CI, 0.4-1.8). After adjustment for illness severity, race, payer, and region (Table 3), the three antibiotic regimens were not associated with significant differences in LOS or treatment failure. Costs were significantly different among the three antibiotic regimens. Clindamycin plus MRSA coverage was associated with the greatest costs, whereas clindamycin monotherapy was associated with the lowest costs (mean, $5,348 vs $4,839, respectively; P < .001) (Table 3). In a sensitivity analysis using an alternative antibiotic regimen definition, we found results in line with the primary analysis, with no statistically significant differences in LOS (P = .44) or treatment failure (P = .54), but significant differences in cost (P < .001). Additionally, the same findings were present for LOS, treatment failure, and cost when outcomes were stratified by illness severity (Appendix Table). However, significant contributors to the higher cost in the clindamycin plus MRSA group did vary by illness severity stratification. Laboratory, supply, and pharmacy cost categories differed significantly among antibiotic groups for the low illness severity strata, whereas pharmacy was the only significant cost category difference in moderate/high illness severity.

Clindamycin monotherapy, clindamycin plus MSSA coverage, and clindamycin plus MRSA coverage are the most commonly administered antistaphylococcal antibiotic regimens for children hospitalized with SSSS at US children’s hospitals. Our multicenter study found that, across these antistaphylococcal antibiotic regimens, there were no associated differences in hospital LOS or treatment failure. However, the antibiotic regimens were associated with significant differences in overall hospital costs. These findings suggest that the use of clindamycin with additional MSSA or MRSA antibiotic coverage for children with SSSS may not be associated with additional clinical benefit, as compared with clindamycin monotherapy, and could potentially be more costly.

Prior literature describing LOS in relation to antibiotic use for children with SSSS is limited. Authors of a recent case series of 21 children in Philadelphia reported approximately 50% of children received clindamycin monotherapy or combination therapy, but patient outcomes such as LOS were not described.⁹ Clindamycin use and outcomes have been described in smaller studies and case reports of SSSS, which reported positive outcomes such as patient recovery and lack of disease recurrence.^2,9,21 A small retrospective, comparative effectiveness study of 30 neonates with SSSS examined beta-lactamase–resistant penicillin use with and without cephalosporins. They found no effect on LOS, but findings were limited by a small sample size.²² Our study cohort included relatively few neonates, and thus our findings may not be applicable to this population subgroup. We chose not to include regimens with third-generation cephalosporins or ampicillin, which may have limited the number of included neonates, because these antibiotics are frequently administered during evaluation for invasive bacterial infections.²³ We found a very low occurrence of treatment failure in our study cohort across all three groups, which is consistent with other studies of SSSS that report an overall good prognosis and low recurrence and/or readmission rates.^6,16,24 The low prevalence of treatment failure, however, precluded our ability to detect small differences among antibiotic regimen groups that may exist.

We observed that cost differed significantly across antibiotic regimen groups, with lowest cost associated with clindamycin monotherapy in adjusted analysis despite similar LOS. Even with our illness-severity adjustment, there may have been other unmeasured factors resulting in the higher cost associated with the combination groups. Hence, we also examined cost breakdown with a stratified analysis by illness severity. We found that pharmacy costs were significantly different among antibiotic groups in both illness severity strata, whereas those with low illness severity also differed by laboratory and supply costs. Thus, pharmacy cost differences may be the largest driver in the cost differential among groups. Lower cost in the clindamycin monotherapy group is likely due to administration of a single antibiotic. The reason for supply and laboratory cost differences is uncertain, but higher cost in the clindamycin plus MRSA group could possibly be from laboratory testing related to drug monitoring (eg, renal function testing or drug levels). While other studies have reported costs for hospitalized children with SSSS associated with different patient characteristics or diagnostic testing,^1,16 to our knowledge, no other studies have reported cost related to antibiotic regimens for SSSS. As healthcare reimbursements shift to value-based models, identifying treatment regimens with equal efficacy but lower cost will become increasingly important. Future studies should also examine other covariates and outcomes, such as oral vs parenteral antibiotic use, use of monitoring laboratories related to antibiotic choice, and adverse drug effects.

Several strengths and additional limitations apply to our study. Our study is one of the few to describe outcomes associated with antibiotic regimens for children with SSSS. With the PHIS database, we were able to include a large number of children with SSSS from children’s hospitals across the United States. Although the PHIS database affords these strengths, there are limitations inherent to administrative data. Children with SSSS were identified by documented ICD-9 and ICD-10 diagnostic codes, which might lead to misclassification. However, misclassification is less likely because only one ICD-9 and ICD-10 code exists for SSSS, and the characteristics of this condition are specific. Also, diagnostic codes for other dermatologic conditions (eg, scarlet fever) were excluded to further reduce the chance of misclassification. A limitation to our use of PHIS billing codes was the inability to confirm the dosage of antibiotics given, the number of doses, or whether antibiotics were prescribed upon discharge. Another limitation is that children whose antibiotic therapy was changed during hospitalization (eg, from clindamycin monotherapy to cefazolin monotherapy) were categorized into the combination groups. However, the sensitivity analysis performed based on a stricter antibiotic group definition (receipt of both antibiotics on at least 2 calendar days) did not alter the outcomes, which is reassuring. We were unable to assess the use of targeted antibiotic therapy because clinical data (eg, microbiology results) were not available. However, this may be less important because some literature suggests that cultures for S aureus are obtained infrequently² and may be difficult to interpret when obtained,²⁵ since culture growth can represent colonization rather than causative strains. An additional limitation is that administrative data do not include certain clinical outcomes, such as fever duration or degree of skin involvement, which could have differed among the groups. Last, the PHIS database only captures revisits or readmissions to PHIS hospitals, and so we are unable to exclude the possibility of a child being seen at or readmitted to another hospital.

Due to the observational design of this study and potential for incomplete measurement of illness severity, we recommend a future prospective trial with randomization to confirm these findings. One possible reason that LOS did not differ among groups is that the burden of clindamycin-resistant strains in our cohort could be low, and the addition of MSSA or MRSA coverage does not result in a clinically important increase in S aureus coverage. However, pooled pediatric hospital antibiogram data suggest the overall rate of clindamycin resistance is close to 20% in hospitals located in all US regions.²⁶ Limited studies also suggest that MSSA may be the predominant pathogen associated with SSSS.^2,9 To address this, future randomized trials could compare the effectiveness of clindamycin monotherapy to MSSA-specific agents like cefazolin or nafcillin. Unfortunately, anti-MSSA monotherapy was not evaluated in our study because very few children received this treatment. Using monotherapy as opposed to multiple antibiotics has the potential to promote antibiotic stewardship for antistaphylococcal antibiotics in the management of SSSS. Reducing unnecessary antibiotic use not only potentially affects antibiotic resistance, but could also benefit patients in reducing possible side effects, cost, and IV catheter complications.²⁷ However, acknowledging our study limitations, our findings should be applied cautiously in clinical settings, in the context of local antibiogram data, individual culture results, and specific patient factors. The local clindamycin resistance rate for both MSSA and MRSA should be considered. Many antibiotics chosen to treat MRSA—such as vancomycin and trimethoprim/sulfamethoxazole—will also have anti-MSSA activity and may have lower local resistance rates than clindamycin. Practitioners may also consider how each antibiotic kills bacteria; for example, beta-lactams rely on bacterial replication, but clindamycin does not. Each factor should influence how empiric treatment, whether monotherapy or combination, is chosen for children with SSSS.

In this large, multicenter cohort of hospitalized children with SSSS, we found that the addition of MSSA or MRSA coverage to clindamycin monotherapy was not associated with differences in outcomes of hospital LOS and treatment failure. Furthermore, clindamycin monotherapy was associated with lower overall cost. Prospective randomized studies are needed to confirm these findings and assess whether clindamycin monotherapy, monotherapy with an anti-MSSA antibiotic, or alternative regimens are most effective for treatment of children with SSSS.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

Study Design and Population

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

Study Design and Population

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.

Examining disparities in health-related quality of life (HRQoL) outcomes in children provides a unique patient-centered perspective on pediatric health services equity.^1,2 Prior studies have demonstrated the relationship between minority race, low socioeconomic status, and lower maternal education with poorer HRQoL outcomes in children.^3-6 Some studies have also shown a dose-response relationship between social disadvantage markers and poorer child health status.^7,8 Furthermore, the associations between social disadvantage and poor access to care,^9-11 and between poor access to care and lower HRQoL, are also well established.^12-14

Examining HRQoL before and after hospitalization can further our understanding of how disparities in HRQoL may change once children engage with the medical system for an acute illness.¹⁵ Children requiring hospitalization constitute a useful population for examination of this question as they represent a group of children with variable social disadvantage markers and access to outpatient care.¹⁶ Although interventions to address social determinants of health for patients with social disadvantages have been associated with within-group improvements in HRQoL, none have assessed changes in disparities as an outcome.¹⁷ Furthermore, many of these studies were conducted in the outpatient setting,^18,19 whereas hospitalization provides an additional point of care to address the social determinants of health for vulnerable families.²⁰ Even for short hospitalizations, the 24/7 nature of hospital care provides the opportunity for frequent interactions with clinicians, nurses, and support staff to clarify illness-related questions, discuss other health concerns and unmet needs, and connect with social services or community resources. These opportunities may be particularly important for families with a higher number of social disadvantage markers and even more beneficial to those with difficulty accessing needed care from their primary medical home.

In this study, we focused on children with common respiratory illnesses (asthma, bronchiolitis, and pneumonia), which constitute the majority of childhood hospitalizations.²¹ Additionally, we only focused on the child’s physical functioning component of HRQoL because this component is most likely to improve after hospitalization for children with an acute respiratory illness.²² A prior study examining HRQoL before and after hospitalization demonstrated that most children return to and/or surpass their baseline physical functioning by 1 month after hospital discharge.²³

Our primary objective was to examine associations between several markers of social disadvantage, access to care, and child physical functioning before and after hospitalization for acute respiratory illness. Second, we aimed to understand if access to care (defined as perceived difficulty/delays getting care) acts as an independent predictor of improvement in physical functioning from baseline to follow-up and/or if it modifies the relationship between social disadvantage and improvement in physical functioning (Appendix Figure).

Study Design and Population

This study was nested within a multicenter, prospective cohort study of children who were hospitalized for asthma, bronchiolitis, or pneumonia between July 2014 and June 2016 at one of five children’s hospitals in the Pediatric Research in Inpatient Settings Network.²⁴

We approached families for study participation within 72 hours of admission to the hospital using a standard protocol. Patients and their caregivers were eligible to participate in the study if the patient was 2 weeks to 16 years old and if the primary caregiver’s preferred language for medical communication was either English or Spanish. Patients with chronic medical conditions (except asthma), with moderate to severe developmental delay, with a history of prematurity <32 weeks, or who received care in the intensive care unit were excluded. Patients could only participate in the study once.

The study team set out to enroll an even number of patients across all three conditions. If a patient’s discharge diagnosis differed from their admission diagnosis (eg, from bronchiolitis to pneumonia), discharge diagnosis was used for condition group assignment. If the discharge diagnosis was not one of these three respiratory conditions, we excluded the patient from further analysis.

Data Collection

We collected data using two surveys. The first survey was administered within 72 hours of admission. This survey asked questions related to (1) caregiver-reported markers of social disadvantage, (2) caregiver perceptions of access to care, and (3) caregiver- and patient-reported assessments of physical functioning. The second survey was administered within 2 to 8 weeks after the patient’s discharge and included a second assessment of physical functioning.

Social Disadvantage

Patients were considered to have a marker of social disadvantage if their caregiver reported (1) being of non-White race and/or Hispanic ethnicity, (2) primarily speaking a language other than English at home and not speaking English very well (ie, limited English proficiency), (3) attaining at most a high school or equivalent degree, or (4) having a =/<$30,000 annual household income.

Access to Care

We used the following survey item from the 2009-2010 National Survey of Children with Special Health Care Needs²⁵ to measure caregiver perceptions of access to care: “In the last six months, did you have any difficulties or delays getting care for your child because there were waiting lists, backlogs, or other problems getting an appointment?” We narrowed the original assessment time frame from 12 months to 6 months to provide a more proximal assessment of access in relation to the hospitalization.

Child Physical Functioning

We assessed child physical functioning using the physical functioning domain of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales, which have been validated for use in the inpatient setting.²² Caregivers completed one of these scales based on their child’s age. Assenting patients 8 to 16 years old completed the self-report PedsQL 4.0 Generic Core Scales instrument. When completing the first PedsQL survey, caregivers and patients reflected on the previous month before their child (or they) became ill to obtain a baseline physical functioning assessment.²³ When completing the second PedsQL survey, caregivers and patients reflected on the past 7 days to obtain a follow-up assessment.

All study procedures were approved by the Western Institutional Review Board (IRB) or the participating hospitals’ IRB.

Statistical Analysis

Patients with no missing data for all four social disadvantage markers were categorized based on the number of markers they reported: none, one, two, or three or more markers. We combined patients with three and four social disadvantage markers into one group to maximize power for the analyses. We dichotomized the access to care variable and coded response options as “no difficulty/delays accessing care” if the caregiver chose “Never” and “any difficulty/delays accessing care” if they chose “Sometimes/Usually/Always.”

For each patient–caregiver dyad, PedsQL items were scored using a standard method in which higher scores reflected better functioning.²² A single set of PedsQL scores was used for each patient–caregiver dyad. We used self-reported patient scores if the patient completed the PedsQL instrument; otherwise, we used proxy-reported caregiver scores. Intraclass correlations between child self-report and parent proxy-report demonstrate moderate to good agreement above age 8 years.²⁶ We computed a change in the physical functioning score by subtracting the baseline score from the follow-up score. The minimal clinically important difference (MCID) for the PedsQL instrument is 4.5 points, which we used to identify clinically meaningful differences.¹³

Analysis of variance models were constructed to test for differences in mean baseline and follow-up PedsQL scores (dependent variable) between the following independent variables: (1) social disadvantage groups and (2) those who reported having any difficulty/delays accessing care compared with those who did not. Only patient–caregiver dyads with both baseline and follow-up assessments were included in these analyses. Mixed-effects linear regression models were constructed to identify clinically meaningful differences in PedsQL scores between groups (MCID =/> 4.5) with adjustment for patient age, gender, respiratory condition, days between surveys, and hospital site as fixed effects. Site-specific random effects were included to account for within-hospital clustering. A similarly adjusted mixed-effects linear regression model was constructed to examine whether having any difficulty/delays accessing care modified the association between social disadvantage and PedsQL change scores (eg, an improvement from baseline to follow-up).

Because 17% of respondents had missing data for at least one social disadvantage marker, sensitivity analyses were conducted using multiple imputation to account for missing social disadvantage markers using chained equations.²⁷ Sensitivity analyses were also conducted to adjust for severity of illness using vital sign data within the first 24 hours, which could only be validly captured on patients with asthma within our dataset. By restricting this latter analysis to patients with asthma, we were able to examine the relationships of interest in a population with chronic disease.

The study sample included 1,860 patients, of which 1,325 had both baseline and follow-up PedsQL data (71%). Descriptive statistics were similar between those who completed the baseline and follow-up surveys (Table 1).

Twenty-two percent of patients had >/=3 social disadvantages and 30% of caregivers reported having any difficulty/delays accessing care. The mean follow-up PedsQL score was higher than the baseline score (90.4 vs 82.5; Table 1).

Social Disadvantage Markers and PedsQL Scores

The number of social disadvantage markers was inversely related to mean baseline PedsQL scores, but there was no difference in mean follow-up PedsQL scores between social disadvantage groups (Table 2). In adjusted analyses, the mean baseline PedsQL score was −6.1 points (95% CI: −8.7, −3.5) lower for patients with >/= 3 social disadvantage markers compared with patients with no social disadvantage markers, which exceeded the scale’s MCID.

Difficulty/Delays Accessing Care and PedsQL Scores

Having any difficulty/delays accessing care was significantly associated with lower baseline and follow-up PedsQL scores (Table 2). In adjusted analyses, the difference in baseline scores was 5.2 points (95% CI: −7.2, −3.2), which exceedes the scale’s MCID.

Interaction Between Social Disadvantage Markers, Difficulty/Delays Accessing Care, and Change in PedsQL Scores from Baseline to Follow-Up

While having =/>2 social disadvantage markers and difficulty/delays accessing care were each positively associated with changes in PedsQL scores from baseline to follow-up (Table 3), only patients with =/> 3 social disadvantage markers exceeded the PedsQL MCID. In stratified analyses, patients with a combination of social disadvantage makers and difficulties/delays accessing care had lower baseline PedsQL scores and greater change in PedsQL scores from baseline to follow-up compared with those without difficulties/delays accessing care (Figure). However, having any difficulty/delays accessing care did not significantly modify the relationship between social disadvantage and change in PedsQL scores, as none of the interaction terms were significant (Table 3, Model 3).

Sensitivity Analysis

Baseline, follow-up, and change in PedsQL scores were similar to our main analysis after performing multiple imputation for missing social disadvantage markers (Supplemental Table 1). Findings were also similar for patients with a diagnosis of asthma only; however, changes in PedsQL scores were greater in magnitude (Appendix Table 2).

This study examined the relationship between social disadvantage and child physical functioning before and after hospitalization for acute respiratory illness. Study findings indicated that patients with higher numbers of social disadvantage markers reported lower PedsQL scores before hospitalization; however, differences in PedsQL scores were not apparent after hospitalization. Patients who experienced difficulty/delays accessing care also reported lower PedsQL scores at baseline. This difference was still significant but did not exceed the PedsQL MCID threshold after hospitalization. Difficulty/delays accessing care appeared to be an additional social disadvantage marker; however, it did not modify the relationship between social disadvantage and improvement in physical functioning.

The study findings at baseline are consistent with prior studies demonstrating a negative association between social disadvantage markers and HRQoL and a cumulative effect based on the number of social disadvantages.^3,4,7,8 This study adds to the existing literature by examining how this relationship changes after hospitalization. As evidenced by the lack of association between social disadvantage markers and follow-up PedsQL scores, our findings suggest that receipt of inpatient care improved perceptions of physical functioning to a greater extent for patients with more social disadvantage markers (especially patients with =/> 3 social disadvantage markers). There are several potential reasons for these findings.

Social disadvantage was associated with lower child physical functioning before hospitalization, but differences were not apparent after hospitalization for children with acute respiratory illness. Caregiver-perceived difficulty/delays accessing care was found to be an additional predictor of lower physical functioning at baseline but did not significantly alter the association between social disadvantage and improvement in physical functioning over time. Further studies are needed to understand how hospital care may differentially impact child physical functioning for patients with higher social disadvantage makers in order to sustain improvements in HRQoL disparities.

Acknowledgments

The authors thank the following individuals of the Pediatric Respiratory Illness Measurement System (PRIMES) study team for their contributions to this work: Karen M. Wilson, New York, New York; Ricardo A. Quinonez, Houston, Texas; Joyee G. Vachani, Houston, Texas; and Amy Tyler, Aurora, Colorado. We would also like to thank the Pediatric Research in Inpatient Settings Network for facilitating this work.