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OBJECTIVES: To determine whether social determinants of health (SDH) risk adjustment changes hospital-level performance on the 30-day Pediatric All-Condition Readmission (PACR) measure and improves fit and accuracy of discharge-level models. STUDY DESIGN: We performed a retrospective cohort study of all hospital discharges meeting criteria for the PACR from 47 hospitals in the Pediatric Health Information database from January to December 2014. We built four nested regression models by sequentially adding risk adjustment factors as follows: chronic condition indicators (CCIs); PACR patient factors (age and sex); electronic health record-derived SDH (race, ethnicity, payer), and zip code-linked SDH (families below poverty level, vacant housing units, adults without a high school diploma, single-parent households, median household income, unemployment rate). For each model, we measured the change in hospitals' readmission decile-rank and assessed model fit and accuracy. RESULTS: For the 458 686 discharges meeting PACR inclusion criteria, in multivariable models, factors associated with higher discharge-level PACR measure included age <1 year, female sex, 1 of 17 CCIs, higher CCI count, Medicaid insurance, higher median household income, and higher percentage of single-parent households. Adjustment for SDH made small but significant improvements in fit and accuracy of discharge-level PACR models, with larger effect at the hospital level, changing decile-rank for 17 of 47 hospitals. CONCLUSIONS: We found that risk adjustment for SDH changed hospitals' readmissions rate rank order. Hospital-level changes in relative readmissions performance can have considerable financial implications; thus, for pay for performance measures calculated at the hospital level, and for research associated therewith, our findings support the inclusion of SDH variables in risk adjustment.

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OBJECTIVE: To validate the accuracy of pre-encounter hospital designation as a novel way to identify unplanned pediatric readmissions and describe the most common diagnoses for unplanned readmissions among children. STUDY DESIGN: We examined all hospital discharges from 2 tertiary care children's hospitals excluding deaths, normal newborn discharges, transfers to other institutions, and discharges to hospice. We performed blinded medical record review on 641 randomly selected readmissions to validate the pre-encounter planned/unplanned hospital designation. We identified the most common discharge diagnoses associated with subsequent 30-day unplanned readmissions. RESULTS: Among 166,994 discharges (hospital A: n = 55,383; hospital B: n = 111,611), the 30-day unplanned readmission rate was 10.3% (hospital A) and 8.7% (hospital B). The hospital designation of "unplanned" was correct in 98% (hospital A) and 96% (hospital B) of readmissions; the designation of "planned" was correct in 86% (hospital A) and 85% (hospital B) of readmissions. The most common discharge diagnoses for which unplanned 30-day readmissions occurred were oncologic conditions (up to 38%) and nonhypertensive congestive heart failure (about 25%), across both institutions. CONCLUSIONS: Unplanned readmission rates for pediatrics, using a validated, accurate, pre-encounter designation of "unplanned," are higher than previously estimated. For some pediatric conditions, unplanned readmission rates are as high as readmission rates reported for adult conditions. Anticipating unplanned readmissions for high-frequency diagnostic groups may help focus efforts to reduce the burden of readmission for families and facilities. Using timing of hospital registration in administrative records is an accurate, widely available, real-time way to distinguish unplanned vs planned pediatric readmissions.

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OBJECTIVE: To assess readmission rates identified by 3M-Potentially Preventable Readmissions software (3M-PPRs) in a national cohort of children's hospitals. STUDY DESIGN: A total of 1 719 617 hospitalizations for 1 531 828 unique patients in 58 children's hospitals from 2009 to 2011 from the Children's Hospital Association Case-Mix Comparative database were examined. Main outcome measures included rates, diagnoses, and costs of potentially preventable readmissions (PPRs) and all-cause readmissions. RESULTS: The 7-, 15-, and 30-day rates by 3M-PPRs were 2.5%, 4.1%, and 6.2%, respectively. Corresponding all-cause readmission rates were 5.0%, 8.7%, and 13.3%. At 30 days, 60.6% of all-cause readmissions were considered nonpreventable by 3M-PPRs, more than one-half of which were related to malignancies. The percentage of readmissions rated as potentially preventable was similar at all 3 time intervals. Readmissions after chemotherapy, acute leukemia, and cystic fibrosis were all considered nonpreventable, and at least 80% of readmissions after index admissions for sickle cell crisis, bronchiolitis, ventricular shunt procedures, asthma, and appendectomy were designated potentially preventable. Total costs for all readmissions were $1.7 billion; PPRs accounted for 27.3% of these costs. The most costly readmissions were associated with ventricular shunt procedures ($26.5 million/year), seizures ($15.5 million/year), and sickle cell crisis ($15.0 million/year). CONCLUSIONS: Rates of PPRs were significantly lower than all-cause readmission rates more than one-half of which were caused by exclusion of malignancies. Annual costs of PPRs, although significant in the aggregate, appear to represent a much smaller cost-savings opportunity for children than for adults. Our study may help guide children's hospitals to focus readmission reduction strategies on areas where the financial vulnerability is greatest based on 3M-PPRs.

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OBJECTIVE: To characterize factors associated with readmission for acute asthma exacerbation, particularly around caregiver asthma knowledge, beliefs, and reported adherence to prescribed medication regimens. STUDY DESIGN: We enrolled 601 children (aged 1-16 years) who had been hospitalized for asthma. Caregivers completed a face-to-face survey regarding their asthma knowledge, beliefs, and medication adherence. Caregivers also reported demographic data, child's asthma severity, exposure to triggers, access to primary care, and financial strains. We prospectively identified asthma readmission events via billing data over a 1-year minimum follow-up period. We examined time to readmission with Cox proportional hazards. RESULTS: The study cohort's median age was 5 years, 53% were African American, and 57% were covered by Medicaid. At 1 year, 22% had been readmitted for asthma. In the multivariate analysis, a caregiver's demonstration of increased asthma knowledge was associated with increased readmission risk. In addition, children whose caregivers reported less-than-perfect adherence to daily medication regimens had increased readmission risk. Likewise, having previously been admitted for asthma, decreased medical home access, and black race were associated with increased readmission risk. CONCLUSION: In a multifactorial assessment of risk factors for asthma readmission, greater asthma knowledge and decreased medication adherence were associated with readmission. Inpatient efforts to prevent readmission might best target medication adherence rather than continuing to primarily provide asthma education.

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