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OBJECTIVE: To determine if surgery and anesthesia in the elderly may promote Alzheimer’s Disease and Related Dementias (ADRD). BACKGROUND: There is a substantial conflicting literature concerning the hypothesis that surgery and anesthesia promotes ADRD. Much of the literature is confounded by indications for surgery or has small sample size. This study examines elderly patients with appendicitis, a common condition that strikes mostly at random after controlling for some known associations. METHODS: A matched natural experiment of patients undergoing appendectomy for appendicitis versus control patients without appendicitis using Medicare data from 2002 to 2017, examining 54,996 patients without previous diagnoses of ADRD, cognitive impairment, or neurological degeneration, who developed appendicitis between ages 68 through 77 years and underwent an appendectomy (the “Appendectomy” treated group), matching them 5:1 to 274,980 controls, examining the subsequent hazard for developing ADRD. RESULTS: The hazard ratio (HR) for developing ADRD or death was lower in the Appendectomy group than controls: HR = 0.96 (95% CI 0.94, 0.98), P < 0.0001, (28.2% in Appendectomy versus 29.1% in controls, at 7.5 years). The HR for death was 0.97 (0.95, 0.99), P = 0.002, (22.7% versus 23.1% at 7.5 years). The HR for developing ADRD alone was 0.89 (0.86, 0.92), P < 0.0001, (7.6% in Appendectomy versus 8.6% in controls, at 7.5 years). No subgroup analyses found significantly elevated rates of ADRD in the Appendectomy group. CONCLUSIONS: In this natural experiment involving 329,976 elderly patients, exposure to appendectomy surgery and anesthesia did not increase the subsequent rate of ADRD.

The term "multimorbidity" identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers.Develop new medical condition-specific multimorbidity definitions for patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia patients. We developed three medical condition-specific multimorbidity definitions as the presence of single, double, or triple combinations of comorbidities - called Qualifying Comorbidity Sets (QCSs) - associated with at least doubling the risk of 30-day mortality for AMI and pneumonia, or one-and-a-half times for HF patients, compared to typical patients with these conditions.Cohort-based matching study PARTICIPANTS: One hundred percent Medicare Fee-for-Service beneficiaries with inpatient admissions between 2016 and 2019 for AMI, HF, and pneumonia.Thirty-day all-location mortality KEY RESULTS: We defined multimorbidity as the presence of ≥1 QCS. The new definitions labeled fewer patients as multimorbid with a much higher risk of death compared to the conventional definition (≥2 comorbidities). The proportions of patients labeled as multimorbid using the new definition versus the conventional definition were: for AMI 47% versus 87% (p value0.0001), HF 53% versus 98% (p value0.0001), and pneumonia 57% versus 91% (p value0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p0.0001), HF 9.9% versus 7.0% (p0.0001), and pneumonia 18.4% versus 13.2% (p0.0001).The presence of ≥2 comorbidities identified almost all patients as multimorbid. In contrast, our new QCS-based definitions selected more specific combinations of comorbidities associated with substantial excess risk in older patients admitted for AMI, HF, and pneumonia. Thus, our new definitions offer a better approach to identifying multimorbid patients, allowing physicians, hospitals, and policymakers to more effectively use such information to consider focused interventions for these vulnerable patients.

Multivariate matching has two goals (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneou...

ObjectivesEvaluate whether hospital factors, including nurse resources, explain racial differences in Medicare black and white patient surgical outcomes and whether disparities changed over time.DesignRetrospective tapered-match.Setting571 hospitals at two time points (Early Era 2003–2005; Recent Era 2013–2015).Participants6752 black patients and three sets of 6752 white controls selected from 107 001 potential controls (Early Era). 4964 black patients and three sets of 4964 white controls selected from 74 108 potential controls (Recent Era).InterventionsBlack patients were matched to white controls on demographics (age, sex, state and year of procedure), procedure (demographics variables plus 136 International Classification of Diseases (ICD)-9 principal procedure codes) and presentation (demographics and procedure variables plus 34 comorbidities, a mortality risk score, a propensity score for being black, emergency admission, transfer status, predicted procedure time).Outcomes30-day and 1-year mortality.ResultsBefore matching, black patients had more comorbidities, higher risk of mortality despite being younger and underwent procedures at different percentages than white patients. Whites in the demographics match had lower mortality at 30 days (5.6% vs 6.7% Early Era; 5.4% vs 5.7% Recent Era) and 1-year (15.5% vs 21.5% Early Era; 12.3% vs 15.9% Recent Era). Black–white 1-year mortality differences were equivalent after matching patients with respect to presentation, procedure and demographic factors. Black–white 30-day mortality differences were equivalent after matching on procedure and demographic factors. Racial disparities in outcomes remained unchanged between the two time periods spanning 10 years. All patients in hospitals with better nurse resources had lower odds of 30-day (OR 0.60, 95% CI 0.46 to 0.78, pConclusionsSurvival disparities among black and white patients are largely explained by differences in demographic, procedure and presentation factors. Better nurse resources (eg, staffing, work environment) were associated with lower mortality for all patients.

Older adults experience a higher prevalence of multiple chronic conditions (MCCs). Establishing the presence and pattern of MCCs in individuals or populations is important for healthcare delivery, research, and policy. This report describes four emerging approaches and discusses their potential applications for enhancing assessment, treatment, and policy for the aging population. The National Institutes of Health convened a 2-day panel workshop of experts in 2018. Four emerging models were identified by the panel, including classification and regression tree (CART), qualifying comorbidity sets (QCS), the multimorbidity index (MMI), and the application of omics to network medicine. Future research into models of multiple chronic condition assessment may improve understanding of the epidemiology, diagnosis, and treatment of older persons.

OBJECTIVE The aim of the study was to address the controversy surrounding the effects of duty hour reform on new surgeon performance, we analyzed patients treated by new surgeons following the transition to independent practice. SUMMARY BACKGROUND DATA In 2003, duty hour reform affected all US surgical training programs. Its impact on the performance of new surgeons remains unstudied. METHODS We studied 30-day mortality among 1,483,074 Medicare beneficiaries undergoing general and orthopedic operations between 1999 and 2003 ("traditional" era) and 2009 and 2013 ("modern" era). The operations were performed by 2762 new surgeons trained before the reform, 2119 new surgeons trained following reform and 15,041 experienced surgeons. We used a difference-in-differences analysis comparing outcomes in matched patients treated by new versus experienced surgeons within each era, controlling for the hospital, operation, and patient risk factors. RESULTS Traditional era odds of 30-day mortality among matched patients treated by new versus experienced surgeons were significantly elevated [odds ratio (OR) 1.13; 95% confidence interval (CI) (1.05, 1.22), P < 0.001). The modern era elevated odds of mortality were not significant [OR 1.06; 95% CI (0.97-1.16), P = 0.239]. Relative performance of new and experienced surgeons with respect to 30-day mortality did not appear to change from the traditional era to the modern era [OR 0.93; 95% CI (0.83-1.05), P = 0.233]. There were statistically significant adverse changes over time in relative performance to experienced surgeons in prolonged length of stay [OR 1.08; 95% CI (1.02-1.15), P = 0.015], anesthesia time [9 min; 95% CI (8-10), P < 0.001], and costs [255USD; 95% CI (2-508), P = 0.049]. CONCLUSIONS Duty hour reform showed no significant effect on 30-day mortality achieved by new surgeons compared to their more experienced colleagues. Patients of new surgeons, however, trained after duty hour reform displayed some increases in the resources needed for their care.

To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics.Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear.A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery.In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%,0.0001), and overall paired cost difference = $915 (P0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P0.0001), and paired cost difference = $3773 (P0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals.Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.

PURPOSE: The 2010 Dependent Coverage Provision (DCP) of the Affordable Care Act (ACA) allowed enrollees to remain on their parents' health insurance until 26 years of age. We compared rates of insurance disenrollment among patients with cancer who were DCP-eligible at age 19 to those who were not eligible at age 19. METHODS: Using OptumLabs Data Warehouse, which contains longitudinal, real-world, de-identified administrative claims for commercial enrollees, we examined patients born between 1982 and 1993 and diagnosed with cancer between 2000 and 2015. In the recent cohort, patients who turned 19 in 2010-2012 (DCP-eligible to stay on parents’ insurance) were matched to patients who turned 19 in 2007-2009 (not DCP-eligible when turning 19). In an earlier control cohort, patients who turned 19 between 2004 and 2006 (not DCP-eligible) were matched to patients who turned 19 between 2001 and 2003 (not DCP-eligible). Patients were matched on cancer type, diagnosis date, demographics, and treatment characteristics. The time to loss of coverage was estimated using Cox models. Difference-in-difference between the recent and earlier cohorts was also evaluated. RESULTS: A total of 2,829 patients who turned 19 years of age in 2010-2012 were matched to patients who turned 19 in 2007-2009. Median time to disenrollment was 26 months for younger patients versus 22 months for older patients (hazard ratio [HR], 0.85; 95% CI, 0.80 to 0.90; P = .001). In 8,978 patients who turned 19 between 2001 and 2006, median time to disenrollment was 20 months among both younger and older patients (HR, 0.99; 95% CI, 0.94 to 1.03; P = .59). The difference between the recent cohort and the earlier control cohort was a 15% greater reduction in coverage loss ( P < .0001), favoring those turning 19 after the DCP went into effect. CONCLUSION: In the vulnerable population of adolescent and young adult cancer survivors, the ACA may have lowered the insurance dropout rate.

BACKGROUND: Concern persists that extended shifts in medical residency programs may adversely affect patient safety. METHODS: We conducted a cluster-randomized noninferiority trial in 63 internal-medicine residency programs during the 2015–2016 academic year. Programs underwent randomization to a group with standard duty hours, as adopted by the Accreditation Council for Graduate Medical Education (ACGME) in July 2011, or to a group with more flexible duty-hour rules that did not specify limits on shift length or mandatory time off between shifts. The primary outcome for each program was the change in unadjusted 30-day mortality from the pretrial year to the trial year, as ascertained from Medicare claims. We hypothesized that the change in 30-day mortality in the flexible programs would not be worse than the change in the standard programs (difference-in-difference analysis) by more than 1 percentage point (noninferiority margin). Secondary outcomes were changes in five other patient safety measures and risk-adjusted outcomes for all measures. RESULTS: The change in 30-day mortality (primary outcome) among the patients in the flexible programs (12.5% in the trial year vs. 12.6% in the pretrial year) was noninferior to that in the standard programs (12.2% in the trial year vs. 12.7% in the pretrial year). The test for noninferiority was significant (P = 0.03), with an estimate of the upper limit of the one-sided 95% confidence interval (0.93%) for a between-group difference in the change in mortality that was less than the prespecified noninferiority margin of 1 percentage point. Differences in changes between the flexible programs and the standard programs in the unadjusted rate of readmission at 7 days, patient safety indicators, and Medicare payments were also below 1 percentage point; the noninferiority criterion was not met for 30-day readmissions or prolonged length of hospital stay. Risk-adjusted measures generally showed similar findings. CONCLUSIONS: Allowing program directors flexibility in adjusting duty-hour schedules for trainees did not adversely affect 30-day mortality or several other measured outcomes of patient safety. (Funded by the National Heart, Lung, and Blood Institute and Accreditation Council for Graduate Medical Education; iCOMPARE ClinicalTrials.gov number, NCT02274818.)

BACKGROUND: A purpose of duty-hour regulations is to reduce sleep deprivation in medical trainees, but their effects on sleep, sleepiness, and alertness are largely unknown. METHODS: We randomly assigned 63 internal-medicine residency programs in the United States to follow either standard 2011 duty-hour policies or flexible policies that maintained an 80-hour workweek without limits on shift length or mandatory time off between shifts. Sleep duration and morning sleepiness and alertness were compared between the two groups by means of a noninferiority design, with outcome measures including sleep duration measured with actigraphy, the Karolinska Sleepiness Scale (with scores ranging from 1 [extremely alert] to 9 [extremely sleepy, fighting sleep]), and a brief computerized Psychomotor Vigilance Test (PVT-B), with long response times (lapses) indicating reduced alertness. RESULTS: Data were obtained over a period of 14 days for 205 interns at six flexible programs and 193 interns at six standard programs. The average sleep time per 24 hours was 6.85 hours (95% confidence interval [CI], 6.61 to 7.10) among those in flexible programs and 7.03 hours (95% CI, 6.78 to 7.27) among those in standard programs. Sleep duration in flexible programs was noninferior to that in standard programs (between-group difference, −0.17 hours per 24 hours; one-sided lower limit of the 95% confidence interval, −0.45 hours; noninferiority margin, −0.5 hours; P = 0.02 for noninferiority), as was the score on the Karolinska Sleepiness Scale (between-group difference, 0.12 points; one-sided upper limit of the 95% confidence interval, 0.31 points; non-inferiority margin, 1 point; P

Background Adults have a higher prevalence of multimorbidity-or having multiple chronic health conditions-than having a single condition in isolation. Researchers, health care providers, and health policymakers find it challenging to decide upon the most appropriate assessment tool from the many available multimorbidity measures. Objective The objective of this study was to describe a broad range of instruments and data sources available to assess multimorbidity and offer guidance about selecting appropriate measures. Design Instruments were reviewed and guidance developed during a special expert workshop sponsored by the National Institutes of Health on September 25-26, 2018. Results Workshop participants identified 4 common purposes for multimorbidity measurement as well as the advantages and disadvantages of 5 major data sources: medical records/clinical assessments, administrative claims, public health surveys, patient reports, and electronic health records. Participants surveyed 15 instruments and 2 public health data systems and described characteristics of the measures, validity, and other features that inform tool selection. Guidance on instrument selection includes recommendations to match the purpose of multimorbidity measurement to the measurement approach and instrument, review available data sources, and consider contextual and other related constructs to enhance the overall measurement of multimorbidity. Conclusions The accuracy of multimorbidity measurement can be enhanced with appropriate measurement selection, combining data sources and special considerations for fully capturing multimorbidity burden in underrepresented racial/ethnic populations, children, individuals with multiple Adverse Childhood Events and older adults experiencing functional limitations, and other geriatric syndromes. The increased availability of comprehensive electronic health record systems offers new opportunities not available through other data sources.

We propose new optimal matching techniques for large administrative data sets. In current practice, very large matched samples are constructed by subdividing the population and solving a series of smaller problems, for instance, matching men to men and separately matching women to women. Without simplification of some kind, the time required to optimally match $T$ treated individuals to $T$ controls selected from $C\geq T$ potential controls grows much faster than linearly with the number of people to be matched—the required time is of order $O\{(T+C)^{3}\}$—so splitting one large problem into many small problems greatly accelerates the computations. This common practice has several disadvantages that we describe. In its place, we propose a single match, using everyone, that accelerates the computations in a different way. In particular, we use an iterative form of Glover’s algorithm for a doubly convex bipartite graph to determine an optimal caliper for the propensity score, radically reducing the number of candidate matches; then we optimally match in a large but much sparser graph. In this graph, a modified form of near-fine balance can be used on a much larger scale, improving its effectiveness. We illustrate the method using data from US Medicaid, matching children receiving surgery at a children’s hospital to similar children receiving surgery at a hospital that mostly treats adults. In the example, we form 38,841 matched pairs from 159,527 potential controls, controlling for 29 covariates plus 463 Principal Surgical Procedures, plus 973 Principal Diagnoses. The method is implemented in an $\mathtt{R}$ package $\mathtt{bigmatch}$ available from $\mathtt{CRAN}$.

Background Nursing resources, such as staffing ratios and skill mix, vary across hospitals. Better nursing resources have been linked to better patient outcomes but are assumed to increase costs. The value of investments in nursing resources, in terms of clinical benefits relative to costs, is unclear. Objective To determine whether there are differential clinical outcomes, costs, and value among medical patients at hospitals characterized by better or worse nursing resources. Design Matched cohort study of patients in 306 acute care hospitals. Patients A total of 74,045 matched pairs of fee-for-service Medicare beneficiaries admitted for common medical conditions (25,446 sepsis pairs; 16,332 congestive heart failure pairs; 12,811 pneumonia pairs; 10,598 stroke pairs; 8858 acute myocardial infarction pairs). Patients were also matched on hospital size, technology, and teaching status. Main Measures Better (n = 76) and worse (n = 230) nursing resourced hospitals were defined by patient-to-nurse ratios, skill mix, proportions of bachelors-degree nurses, and nurse work environments. Outcomes included 30-day mortality, readmission, and resource utilization-based costs. Key Results Patients in hospitals with better nursing resources had significantly lower 30-day mortality (16.1% vs 17.1%, p

BackgroundThere are known clinical benefits associated with investments in nursing. Less is known about their value.AimsTo compare surgical patient outcomes and costs in hospitals with better versus worse nursing resources and to determine if value differs across these hospitals for patients with different mortality risks.MethodsRetrospective matched-cohort design of patient outcomes at hospitals with better versus worse nursing resources, defined by patient-to-nurse ratios, skill mix, proportions of bachelors-degree nurses and nurse work environments. The sample included 62 715 pairs of surgical patients in 76 better nursing resourced hospitals and 230 worse nursing resourced hospitals from 2013 to 2015. Patients were exactly matched on principal procedures and their hospital’s size category, teaching and technology status, and were closely matched on comorbidities and other risk factors.ResultsPatients in hospitals with better nursing resources had lower 30-day mortality: 2.7% vs 3.1% (pConclusionHospitals with better nursing resources provided better clinical outcomes for surgical patients at a small additional cost. Generally, the sicker the patient, the greater the value at better nursing resourced hospitals.

INTRODUCTION: This study develops a measure of Alzheimer’s disease and Related Dementias (ADRD) using Medicare claims. METHODS: Validation resembles the approach of the American Psychological Association, including (i) content validity, (ii) construct validity, and (iii) predictive validity. RESULTS: We found that four items -- a Medicare claim recording ADRD 1-year ago, 2-years ago, 3-years ago, and a total stay of 6-months in a nursing home -- exhibit a pattern of association consistent with a single underlying ADRD construct, and presence of any two of these four items predict a direct measure of cognitive function and also future claims for ADRD. DISCUSSION: Our four items are internally consistent with the measurement of a single quantity. The presence of any two items do a better job than a single claim when predicting both a direct measure of cognitive function and future ADRD claims.

BACKGROUND Approximately 50% of children with cancer in the United States who are aged

BACKGROUND: There are numerous definitions of multimorbidity. None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets. OBJECTIVES: Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual Qualifying Comorbidity Set (QCS) at least doubling the odds of mortality versus its reference population. Patients with at least one QCS were defined as having multimorbidity. RESEARCH DESIGN: Cohort-based study with a matching validation study. SUBJECTS: All fee-for-service Medicare patients between age 65 and 85 without dementia or metastatic solid tumors undergoing general surgery in 2009–2010, and an additional 2011–2013 dataset. MEASURES: 30-day all-location mortality RESULTS: There were 576 qualifying comorbidity sets (2 singles, 63 doubles and 511 triples), each set at least doubling the odds of dying. In 2011, 36% of eligible patients had multimorbidity. As a group, multimorbid patients (mortality rate = 7.0%) had a mortality Mantel-Haenszel OR = 1.90 (1.77, 2.04) versus a reference that included both multimorbid and non-multimorbid patients (mortality rate = 3.3%), and Mantel-Haenszel OR = 3.72 (3.51, 3.94) versus only non-multimorbid patients (mortality rate = 1.6%). When matching 3,151 pairs of multimorbid patients from low-volume hospitals to similar patients in high-volume hospitals, the mortality rates were 6.7% versus 5.2%, respectively (P = 0.006). CONCLUSIONS: A list of QCSs identified a third of older patients undergoing general surgery that had greatly elevated mortality. These sets can be used to identify vulnerable patients and the specific combinations of comorbidities that make them susceptible to poor outcomes.

BACKGROUND Failure-to-rescue (FTR), originally developed to study quality of care in surgery, measures an institution's ability to prevent death after a patient becomes complicated. OBJECTIVES Develop an FTR metric modified to analyze acute myocardial infarction (AMI) outcomes. RESEARCH DESIGN Split-sample design: a random 20% of hospitals to develop FTR definitions, a second 20% to validate test characteristics, and an out-of-sample 60% to validate results. SUBJECTS Older Medicare beneficiaries admitted to short-term acute-care hospitals for AMI between 2009 and 2011. MEASURES Thirty-day mortality and FTR rates, and in-hospital complication rates. RESULTS The 60% out-of-sample validation included 234,277 patients across 1142 hospitals that admitted at least 50 patients over 2.5 years. In total, 72.1% of patients were defined as Medically Complicated (complex on admission or subsequently developed a complication or died without a recorded complication) of whom 19.3% died. Spearman r between hospital risk-adjusted 30-day mortality and FTR was 0.89 (P

Background Concern persists that inflexible duty-hour rules in medical residency programs may adversely affect the training of physicians. Methods We randomly assigned 63 internal medicine residency programs in the United States to be governed by standard duty-hour policies of the 2011 Accreditation Council for Graduate Medical Education (ACGME) or by more flexible policies that did not specify limits on shift length or mandatory time off between shifts. Measures of educational experience included observations of the activities of interns (first-year residents), surveys of trainees (both interns and residents) and faculty, and intern examination scores. Results There were no significant between-group differences in the mean percentages of time that interns spent in direct patient care and education nor in trainees’ perceptions of an appropriate balance between clinical demands and education (primary outcome for trainee satisfaction with education; response rate, 91%) or in the assessments by prog...

To quantify the contribution of acute versus postacute care factors to survival and functional outcomes after hip fracture.Retrospective cohort study using Medicare data; subjects included previously ambulatory nursing home residents hospitalized for hip fracture between 2005 and 2009.We used logistic regression to measure the associations of hospital and nursing home factors with functional and survival outcomes at 30 and 180 days among patients discharged to a nursing facility; we quantified the contribution of hospital versus nursing home factors to outcomes by the ω statistic.Among 45,996 hospitalized patients, 1814 (3.9%) died during hospitalization. A total of 42,781 (93%) were discharged alive to a nursing home. Of these, 12,126 (28%) died within 180 days and 20,479 (48%) died or were newly unable to walk within 180 days. Hospital characteristics were not consistently associated with outcomes. Multiple nursing home characteristics predicted 30- and 180-day outcomes, including bed count, chain membership, and performance on selected quality measures. Nursing home factors explained 3 times more variation in the odds of 30-day mortality than did hospital factors [ω, hospital vs. nursing home: 0.32; 95% confidence interval (CI), 0.11, 0.96], 7 times more variation in the odds of 180-day mortality (ω: 0.15; 95% CI, 0.04, 0.61), and 8 times more variation in the odds of 180-day death or new dependence in locomotion (ω: 0.12; 95% CI, 0.05, 0.31).Nursing home factors explain a larger proportion of the variation in clinical outcomes following hip fracture than do hospital factors.

Introduction Duty hour regulations affect resident sleep, education, and patient care in complex ways. We performed a national cluster-randomized trial (iCOMPARE) in 63 internal medicine residency programs comparing the effects of the 2011 duty-hour standards to a more flexible set of duty hour rules characterized by maintaining an 80-hour workweek but without limits on shift length or mandatory time off between shifts, relative to patient mortality, intern educational outcomes, and intern sleep and alertness. Methods In the sleep and alertness sub-study, sleep duration and morning sleepiness and alertness were assessed with actigraphy, the Karolinska Sleepiness Scale, and a 3-minute Psychomotor Vigilance Test (PVT-B) for 14 days in 193 interns from 6 standard programs and 205 interns from 6 flexible programs. Results During the 14-day study periods, interns in standard and flexible programs averaged 7.03h sleep/24h (95% confidence interval [CI] 6.78h, 7.27h) and 6.85h sleep/24h (95% CI 6.61h, 7.10h), respectively. Sleep duration (difference between arms of -0.17h/24h; 1-sided lower 95% confidence limit -0.45h; NIM -0.5h; P=0.02 for noninferiority) and KSS sleepiness (difference 0.12 points; 1-sided upper 95% confidence limit 0.31 points; NIM 1 point; P Conclusion There were no signs of relevant chronic sleep loss across shifts in interns in flexible programs relative to their standard program counterparts. Interns were able to compensate for the sleep lost during extended overnight shifts by increasing sleep duration on nights prior to day shifts, night shifts, and days off. Increased sleepiness and reduced alertness of interns following extended overnight shifts need to be mitigated and suggest a role for fatigue-risk management programs. Support Supported by NHLBI grants U01HL125388 and U01HL126088 and grants from the ACGME.

ObjectiveTo determine whether outcomes achieved by new surgeons are attributable to inexperience or to differences in the context in which care is delivered and patient complexity.BackgroundAlthough prior studies suggest that new surgeon outcomes are worse than those of experienced surgeons, factors that underlie these phenomena are poorly understood.MethodsA nationwide observational tapered matching study of outcomes of Medicare patients treated by new and experienced surgeons in 1221 US hospitals (2009-2013). The primary outcome studied is 30-day mortality. Secondary outcomes were examined.ResultsIn total, 694,165 patients treated by 8503 experienced surgeons were matched to 68,036 patients treated by 2119 new surgeons working in the same hospitals. New surgeons' patients were older (25.8% aged ≥85 vs 16.3%,P

BACKGROUND: Teaching hospitals typically pioneer investment in new technology and cultivate workforce characteristics generally associated with better quality, but the value of this extra investment is unclear. OBJECTIVE: Compare outcomes and costs between major teaching and non-teaching hospitals by closely matching on patient characteristics. DESIGN: Medicare patients at 339 major teaching hospitals (resident-to-bed (RTB) ratios ≥ 0.25); matched patient controls from 2439 non-teaching hospitals (RTB ratios

POLICY POINTS: Patients with low socioeconomic status (SES) experience poorer survival rates after diagnosis of breast cancer, even when enrolled in Medicare and Medicaid. Most of the difference in survival is due to more advanced cancer on presentation and the general poor health of lower SES patients, while only a very small fraction of the SES disparity is due to differences in cancer treatment. Even when comparing only low‐ versus not‐low‐SES whites (without confounding by race) the survival disparity between disparate white SES populations is very large and is associated with lower use of preventive care, despite having insurance. CONTEXT: Disparities in breast cancer survival by socioeconomic status (SES) exist despite the “safety net” programs Medicare and Medicaid. What is less clear is the extent to which SES disparities affect various racial and ethnic groups and whether causes differ across populations. METHODS: We conducted a tapered matching study comparing 1,890 low‐SES (LSES) non‐Hispanic white, 1,824 black, and 723 Hispanic white women to 60,307 not‐low‐SES (NLSES) non‐Hispanic white women, all in Medicare and diagnosed with invasive breast cancer between 1992 and 2010 in 17 US Surveillance, Epidemiology, and End Results (SEER) regions. LSES Medicare patients were Medicaid dual‐eligible and resided in neighborhoods with both high poverty and low education. NLSES Medicare patients had none of these factors. Measurements: 5‐year and median survival. FINDINGS: LSES non‐Hispanic white patients were diagnosed with more stage IV disease (6.6% vs 3.6%; p < 0.0001), larger tumors (24.6 mm vs 20.2 mm; p < 0.0001), and more chronic diseases such as diabetes (37.8% vs 19.0%; p < 0.0001) than NLSES non‐Hispanic white patients. Disparity in 5‐year survival (NLSES − LSES) was 13.7% (p < 0.0001) when matched for age, year, and SEER site (a 42‐month difference in median survival). Additionally, matching 55 presentation factors, including stage, reduced the disparity to 4.9% (p = 0.0012), but further matching on treatments yielded little further change in disparity: 4.6% (p = 0.0014). Survival disparities among LSES blacks and Hispanics, also versus NLSES whites, were significantly associated with presentation factors, though black patients also displayed disparities related to initial treatment. Before being diagnosed, all LSES populations used significantly less preventive care services than matched NLSES controls. CONCLUSIONS: In Medicare, SES disparities in breast cancer survival were large (even among non‐Hispanic whites) and predominantly related to differences of presentation characteristics at diagnosis rather than differences in treatment. Preventive care was less frequent in LSES patients, which may help explain disparities at presentation.

IntroductionMedical trainees’ duty hours have received attention globally; restrictions in Europe, New Zealand and some Canadian provinces are much lower than the 80 hours per week enforced in USA. In USA, resident duty hours have been implemented without evidence simultaneously reflecting competing concerns about patient safety and physician education. The objective is to prospectively evaluate the implications of alternative resident duty hour rules for patient safety, trainee education and intern sleep and alertness.Methods and analysis63 US internal medicine training programmes were randomly assigned 1:1 to the 2011 Accreditation Council for Graduate Medical Education resident duty hour rules or to rules more flexible in intern shift length and number of hours off between shifts for academic year 2015–2016. The primary outcome is calculated for each programme as the difference in 30-day mortality rate among Medicare beneficiaries with any of several prespecified principal diagnoses in the intervention year minus 30-day mortality in the preintervention year among Medicare beneficiaries with any of several prespecified principal diagnoses. Additional safety outcomes include readmission rates, prolonged length of stay and costs. Measures derived from trainees’ and faculty responses to surveys and from time-motion studies of interns compare the educational experiences of residents. Measures derived from wrist actigraphy, subjective ratings and psychomotor vigilance testing compare the sleep and alertness of interns. Differences between duty hour groups in outcomes will be assessed by intention-to-treat analyses.Ethics and disseminationThe University of Pennsylvania Institutional Review Board (IRB) approved the protocol and served as the IRB of record for 40 programmes that agreed to sign an Institutional Affiliation Agreement. Twenty-three programmes opted for a local review process.Trial registration numberNCT02274818; Pre-results.

BACKGROUND Racial disparities in general surgical outcomes are known to exist but not well understood. OBJECTIVES To determine if black-white disparities in general surgery mortality for Medicare patients are attributable to poorer health status among blacks on admission or differences in the quality of care provided by the admitting hospitals. RESEARCH DESIGN Matched cohort study using Tapered Multivariate Matching. SUBJECTS All black elderly Medicare general surgical patients (N=18,861) and white-matched controls within the same 6 states or within the same 838 hospitals. MEASURES Thirty-day mortality (primary); others include in-hospital mortality, failure-to-rescue, complications, length of stay, and readmissions. RESULTS Matching on age, sex, year, state, and the exact same procedure, blacks had higher 30-day mortality (4.0% vs. 3.5%, P

Objective Though stable insurance is important to support optimal child health, the reproducibility of metrics to assess child health insurance retention at the state and county level has not been examined. We sought to determine reproducibility of public insurance retention rates for children using 3 different metrics at the state and county level. Methods Public health insurance retention for children was assessed using 3 different metrics calculated from 2006–2009 Medicaid Analytic Extract data from 12 selected states. The metrics were: 1) Duration: a prospective metric that quantifies the number of newly enrolled children continuously enrolled in public insurance 6, 12, and 18 months after initial enrollment during a selected period; (2) Infant Duration: assesses Duration only among infants born during a selected period; (3) Coverage: a prospective metric that quantifies the average percentage of time a selected population is enrolled over an 18-month interval. Reproducibility of the metrics was assessed using a range of sample sizes with resampling and determining changes in relative rankings of states/counties by retention rate. Results All 3 metrics demonstrated reproducible estimates at the state level with sample sizes of 2000, 5000, and 10,000. Reproducibility of relative rankings for child health insurance retention of counties within states were sensitive to county child population size and the amount of variability in retention rates within the county and at the state level. Conclusions As health care reform unfolds, the complete set of these 3 reproducible metrics can be used to evaluate multipronged and multilevel strategies to retain eligible children in public health insurance.

Administrative data are often used to estimate state Medicaid/Children's Health Insurance Program duration of enrollment and insurance continuity, but they are generally not used to estimate participation (the fraction of eligible children enrolled) because administrative data do not include reasons for disenrollment and cannot observe eligible never-enrolled children, causing estimates of eligible unenrolled to be inaccurate. Analysts are therefore forced to either utilize survey information that is not generally linkable to administrative claims or rely on duration and continuity measures derived from administrative data and forgo estimating claims-based participation. We introduce appendectomy-based participation (ABP) to estimate statewide participation rates using claims by taking advantage of a natural experiment around statewide appendicitis admissions to improve the accuracy of participation rate estimates.We used Medicaid Analytic eXtract (MAX) for 2008-2010; and the American Community Survey for 2008-2010 from 43 states to calculate ABP, continuity ratio, duration, and participation based on the American Community Survey (ACS).In the validation study, median participation rate using ABP was 86% versus 87% for ACS-based participation estimates using logical edits and 84% without logical edits. Correlations between ABP and ACS with or without logical edits was 0.86 (P .0001). Using regression analysis, ABP alone was a significant predictor of ACS (P .0001) with or without logical edits, and adding duration and/or the continuity ratio did not significantly improve the model.Using the ABP rate derived from administrative claims (MAX) is a valid method to estimate statewide public insurance participation rates in children.

Kidney transplant recipients often receive antibody induction. Previous studies of induction therapy were often limited by short follow-up and/or absence of information about complications. After linking Organ Procurement and Transplantation Network data with Medicare claims, we compared outcomes between three induction therapies for kidney recipients. Using novel matching techniques developed on the basis of 15 clinical and demographic characteristics, we generated 1:1 pairs of alemtuzumab-rabbit antithymocyte globulin (rATG) (5330 pairs) and basiliximab-rATG (9378 pairs) recipients. We used paired Cox regression to analyze the primary outcomes of death and death or allograft failure. Secondary outcomes included death or sepsis, death or lymphoma, death or melanoma, and healthcare resource utilization within 1 year. Compared with rATG recipients, alemtuzumab recipients had higher risk of death (hazard ratio [HR], 1.14; 95% confidence interval [95% CI], 1.03 to 1.26; P

By far, the most common method to compare hospital outcomes is indirect standardization, often accomplished by dividing the observed number of patient events at a hospital by the expected number of patient events derived from the population (Fleiss, Levin, and Paik 2003; Iezzoni 2012) to get the traditional “O/E” metric. In some cases the observed number of events (O) is replaced by a predicted number of events (P), where P is a function of O, as is done for Medicare’s Hospital Compare random effects model, where the quantity “P/E” is reported (Krumholz et al. 2006a,b; Silber et al. 2010). At times, investigators prefer to report (O-E)/N rather than O/E, which has the advantage that the metric is clearly defined and stable even when E is near zero (Chassin et al. 1989; Silber, Rosenbaum, and Ross 1995). All approaches have one aspect in common, that being the reliance on each individual hospital’s own patient case mix to be used when comparing across hospitals. The fact that the indirect standardization approach allows two hospitals with very different distributions of patients to be compared represents both a strength and weakness of the technique. In a sense, indirect standardization tries to describe what has happened when the hospital sees the mix of patients it usually sees, while direct standardization tries to describe what would happen if the hospital saw a mix of patients based on another population of interest. Direct standardization will utilize this external reference population to weight the results of the index hospital (Fleiss, Levin, and Paik 2003). Speaking informally, a hospital administrator trying to improve the care of the current patient population is interested in indirect standardization (i.e., would other hospitals do better with my patients?), while a patient looking at a ranking of hospitals to select one would be interested in direct standardization—(i.e., where would it be best for me to go if I resembled the typical patients used for direct standardization, or how well has the hospital done with other patients who have the condition for which I am being admitted). On the one hand, judging a hospital based on its own patient population makes intuitive sense—the hospital is being judged on cases relevant to what it sees. While it may be convenient to compare hospitals even if they treat very different types of patients, a concern of indirect standardization is that two hospitals that see very different types of patients may be unfairly compared, as the lack of overlap in patient populations places a great deal of burden on the indirectly standardized model to appropriately account for these nonoverlapping patient factors. If the model fails to incorporate the important differences between each hospital’s patient populations, then the inferences concerning hospital comparison with O/E methods may be misleading. An alternative approach that we have introduced is to use a form of direct standardization (Silber et al. 2014, this issue), that uses a template of patients and compares a multivariate matched set of patients across hospitals, thereby achieving closely overlapping patient distributions for observable variables—far more close on measured characteristics than if patients had been randomly assigned to hospitals. In this previous work we described how multivariate template matching, as a method to compare hospitals using direct standardization, produced excellent matches across 217 hospitals performing general and orthopedic surgery across three states. It had the strength that each hospital was evaluated with the same template; therefore, there was less concern about nonoverlapping patients, when hospitals did have overlapping patients to compare. When a hospital’s Chief Medical Officer desires to know precisely how well his or her hospital performs on its own distribution of patients, and not on an external template that may not be representative of the type of patients seen at his or her specific hospital, then direct standardization may not be the method of choice. In other words, the Chief Medical Officer may want to know, “How well do we do with the patients we see?” In this article, we develop a new method to perform indirect standardization with multivariate template matching, and we introduce what we define as “hospital-specific template matching” a form of direct standardization with a hospital’s own patients (thereby resembling aspects of indirect standardization). This approach will allow a close look at how well a hospital performs on the patients it sees by constructing a template of patients representative of the hospital’s own patient distributions, finding other hospitals that also see similar patients to compare outcomes between the hospital of interest and other hospitals that can be matched to the index hospital’s template. In so doing, we hope to provide a new method to better implement indirect standardization analyses for improving a hospital’s quality of care specifically tailored to the index hospital’s most relevant patients—the patients they see.

In the new health care marketplace, families will be making important decisions concerning choice of health plan, health provider, and even accountable care organizations. Ideally, they would make these decisions using information on health care value, which comprises the relationships between patient/family-centered outcomes (the outputs of health care services) and costs of providing care to achieve these outcomes. Providing information on pediatric value will require new investments in data collection systems that include outcomes that matter to children and families and costs measured at the level of the child. The analysis of these data must account for the perspective of the user of the information. In the case of families, direct standardization should be used to contrast care in one health care system with another according to the unique characteristics of each family and child.

e18125 Background: The 2010 Dependent Coverage Provision (DCP) of the Affordable Care Act (ACA) allowed young adults to remain on their parents' health insurance until age 26 years (y), whereas pre-DCP the cut-off varied, but was most often 18 y. Our objective was to compare rates of insurance disenrollment among patients with cancer who were DCP-eligible compared to those who were not. Methods: A retrospective cohort from the OptumLabs Data Warehouse, which includes claims data for privately insured enrollees in a large US health plan, was used. Patients born between 1982-93 diagnosed with cancer between 2000-15 were included. In the Recent Cohort, patients who turned 19 in 2010-12 (who were always eligible to stay on parents’ insurance) were matched to patients who turned 19 in 2007-09 (who were not protected by the DCP when they turned 19). Sensitivity analyses paired patients in an Earlier Cohort, who turned 19 (in 2001-03 and 2004-06), none of whom were eligible for the DCP when they turned 19. Patients were matched on cancer type, diagnosis date, and additional clinical characteristics. Using a time to loss of coverage analysis (defined as > 90 d interruption in insurance enrollment), hazard ratios (HR) were calculated using Cox proportional hazards models. Difference-in-difference between pairs from the Recent and Earlier cohorts was evaluated. Results: Of the 3,013 patients who turned 19 in 2010-12, 2,829 were matched. Median time to disenrollment was 26 months (m) compared to 22 m among patients who turned 19 in 2007-09 (HR 0.88, 95% CI 0.81-0.95, p = 0.0009). In the 4,489 pairs of patients who turned 19 between 2001-06, median time to disenrollment was 20 m among both the younger and older patients in the pair (p = 0.047). In grouped analyses, the difference-in-difference between the Recent and Earlier sets of pairs displayed a 14% reduction in the hazard for losing coverage (p < 0.0001), favoring those who turned 19 after DCP became available. Conclusions: In pediatric cancer patients and survivors, a vulnerable population that needs continuous insurance coverage, these data suggest that the DCP of the ACA lowers the insurance drop-out rate.

Publicly financed insurance programs are tasked with maintaining coverage for eligible children, but published measures to assess coverage have not been evaluated. Therefore, we sought to identify and categorize measures of health insurance continuity for children and adolescents. We conducted a systematic review of Medline and HealthStar databases, review of reference lists of eligible articles, and contact with experts. We categorized measures into 8 domains based on a conceptual framework. We identified 147 measures from 84 eligible articles. Most measures evaluated the following domains: always insured (41%), repeatedly uninsured (36%), and transition out of coverage (29%), while fewer assessed single gap in coverage, always uninsured, transition into coverage, change in coverage, and eligibility. Only 18% of measures assessed associations between continuity of coverage and child and adolescent health outcomes. These results suggest that a number of measures of continuity of coverage exist, but few measures have assessed impact on outcomes.

OBJECTIVE To investigate the association between obesity and perioperative acute kidney injury (AKI), controlling for preoperative kidney dysfunction. BACKGROUND More than 30% of patients older than 60 years are obese and, therefore, at risk for kidney disease. Postoperative AKI is a significant problem. METHODS We performed a matched case-control study of patients enrolled in the Obesity and Surgical Outcomes Study, using data of Medicare claims enriched with detailed chart review. Each AKI patient was matched with a non-AKI control similar in procedure type, age, sex, race, emergency status, transfer status, baseline estimated glomerular filtration rate, admission APACHE score, and the risk of death score with fine balance on hospitals. RESULTS We identified 514 AKI cases and 694 control patients. Of the cases, 180 (35%) followed orthopedic procedures and 334 (65%) followed colon or thoracic surgery. After matching, obese patients undergoing a surgical procedure demonstrated a 65% increase in odds of AKI within 30 days from admission (odds ratio = 1.65, P < 0.005) when compared with the nonobese patients. After adjustment for potential confounders, the odds of postoperative AKI remained elevated in the elderly obese (odds ratio = 1.68, P = 0.01.) CONCLUSIONS : Obesity is an independent risk factor for postoperative AKI in patients older than 65 years. Efforts to optimize kidney function preoperatively should be employed in this at-risk population along with keen monitoring and maintenance of intraoperative hemodynamics. When subtle reductions in urine output or a rising creatinine are observed postoperatively, timely clinical investigation is warranted to maximize renal recovery.

Background: Using Pennsylvania Medicare claims from 1995 to 1996, the authors previously reported that anesthesia procedure length appears longer in blacks than whites. In a new study using a different and larger data set, the authors now examine whether body mass index (BMI), not available in Medicare claims, explains this difference. The authors also examine the relative contributions of surgical and anesthesia times. Methods: The Obesity and Surgical Outcomes Study of 47 hospitals throughout Illinois, New York, and Texas abstracted chart information including BMI on elder Medicare patients (779 blacks and 14,596 whites) undergoing hip and knee replacement and repair, colectomy, and thoracotomy between 2002 and 2006. The authors matched all black Medicare patients to comparable whites and compared procedure lengths. Results: Mean BMI in the black and white populations was 30.24 and 28.96 kg/m2, respectively (P < 0.0001). After matching on age, sex, procedure, comorbidities, hospital, and BMI, mean white BMI in the comparison group was 30.1 kg/m2 (P = 0.94). The typical matched pair difference (black–white) in anesthesia (induction to recovery room) procedure time was 7.0 min (P = 0.0019), of which 6 min reflected the surgical (cut-to-close) time difference (P = 0.0032). Within matched pairs, where the difference in procedure times was greater than 30 min between patients, blacks more commonly had longer procedure times (Odds = 1.39; P = 0.0008). Conclusions: Controlling for patient characteristics, BMI, and hospital, elder black Medicare patients experienced slightly but significantly longer procedure length than their closely matched white controls. Procedure length difference was almost completely due to surgery, not anesthesia.

When comparing outcomes, such as survival, in two groups— say a focal group and a comparison group—a common question is whether an adjustment for certain baseline differences that separate these two groups actually matters for the difference in outcomes. Did the adjustment matter? If it did matter, to what quantitative extent did it matter? This question is quite distinct from whether the baseline variables predict the outcome: baseline variables may predict the outcome, yet explain no part of the difference in outcomes in two groups. The question is also distinct from whether a difference between the groups remains after adjustment: an adjustment may matter quite a bit, yet fail to explain a substantial part of the difference in outcomes, and, indeed, adjustment may increase the difference. Whether an adjustment for (x 1, x 2) matters over and above an adjustment for x 1 alone can be addressed by comparing outcomes in two control groups formed from the comparison group, one matched to the focal group for...

Purpose Previous studies have found that teaching hospitals produce better acute myocardial infarction (AMI) outcomes than nonteaching hospitals. However, these analyses generally excluded patients transferred out of nonteaching hospitals and did not study outcomes by patient risk level. The objective of this study was to determine whether admission to a teaching hospital was associated with greater survival after accounting for patient transfers and patient severity. Method This observational study used logistic models to examine the association between hospital teaching status and 30-day mortality of AMI patients, adjusting for patient comorbidities and common time trends. The sample included 1,309,554 Medicare patients admitted from 1996 to 2004 to 3,761 acute care hospitals for AMI. The primary outcome was 30-day all-cause, all-location mortality. Results Mortality was slightly lower in minor teaching hospitals compared with nonteaching hospitals (odds ratio [OR] 0.97; 95% confidence interval [CI] 0.95–0.99) but not different between major teaching and nonteaching hospitals (OR 1.01; 95% CI 0.96–1.03). The odds of mortality in minor teaching hospitals decreased 4.2% relative to nonteaching hospitals during the seven-year period (OR from 0.98 to 0.94). There was no consistent pattern of association between teaching status and patient severity. Conclusions After correctly accounting for the ability of nonteaching hospitals to appropriately transfer patients in need of different care, there was no survival benefit on average for initial admission to a teaching hospital for AMI. Further more, higher-risk patients did not benefit from initial admission to teaching hospitals.

OBJECTIVES With differential payment between Medicaid and Non-Medicaid services, we asked whether style-of-practice differs between similar Medicaid and Non-Medicaid children with complex chronic conditions (CCCs) undergoing surgery. SUMMARY OF BACKGROUND DATA Surgery in children with CCCs accounts for a disproportionately large percentage of resource utilization at major children's hospitals. METHODS A matched cohort design, studying 23,582 pairs of children with CCCs undergoing surgery (Medicaid matched to Non-Medicaid within the same hospital) from 2009 to 2013 in 41 Children's Hospitals. Patients were matched on age, sex, principal procedure, CCCs, and other characteristics. RESULTS Median cost in Medicaid patients was $21,547 versus $20,527 in Non-Medicaid patients (5.0% higher, P < 0.001). Median paired difference in cost (Medicaid minus Non-Medicaid) was $320 [95% confidence interval (CI): $208, $445], (1.6% higher, P < 0.001). 90th percentile costs were $133,640 versus $127,523, (4.8% higher, P < 0.001). Mean paired difference in length of stay (LOS) was 0.50 days (95% CI: 0.36, 0.65), (P < 0.001). ICU utilization was 2.8% higher (36.7% vs 35.7%, P < 0.001). Finally, in-hospital mortality pooled across all pairs was higher in Medicaid patients (0.38% vs 0.22%, P = 0.002). After adjusting for multiple testing, no individual hospital displayed significant differences in cost between groups, only 1 hospital displayed significant differences in LOS and 1 in ICU utilization. CONCLUSIONS Treatment style differences between Medicaid and Non-Medicaid children were small, suggesting little disparity with in-hospital surgical care for patients with CCCs operated on within Children's Hospitals. However, in-hospital mortality, although rare, was slightly higher in Medicaid patients and merits further investigation.

With increasing Medicaid coverage, it has become especially important to determine whether racial differences exist within the Medicaid system. We asked whether disparities exist in hospital practice and patient outcomes between matched black and white Medicaid children with chronic conditions undergoing surgery.We conducted a matched cohort study, matching 6,398 pairs within states on detailed patient characteristics using data from 25 states contributing adequate Medicaid Analytic eXtract claims for admissions of children with chronic conditions undergoing the same surgical procedures between January 1, 2009 and November 30, 2010 for ages 1 to 18 years.The black patient 30-day revisit rate was 19.3% vs 19.8% in matched white patients (p = 0.61), 30-day readmission rates were 7.0% vs 6.9% (p = 0.43), and 30-day mortality rates were 0.38% vs 0.19% (p = 0.06), respectively. A higher percentage of black patients exceeded their own state's individual median length of stay (44.0% vs 39.6%; p0.001) and median ICU length of stay (25.9% vs 23.8%; p0.001). Intensive care unit use was higher in black patients (25.9% vs 23.8%; p0.001). After adjusting for multiple testing, only 2 states were found to differ significantly by race (New York for length of stay and New Jersey for ICU use).We did not observe disparities in 30-day revisits and readmissions for chronically ill children in Medicaid undergoing surgery, and only slight differences in length of stay, ICU length of stay, and use of the ICU, where blacks displayed somewhat elevated rates compared with white controls.

BACKGROUND AND OBJECTIVES: Black children with asthma comprise one-third of all asthma patients in Medicaid. With increasing Medicaid coverage, it has become especially important to monitor Medicaid for differences in hospital practice and patient outcomes by race. METHODS: A multivariate matched cohort design, studying 11 079 matched pairs of children in Medicaid (black versus white matched pairs from inside the same state) admitted for asthma between January 1, 2009 and November 30, 2010 in 33 states contributing adequate Medicaid Analytic eXtract claims. RESULTS: Ten-day revisit rates were 3.8% in black patients versus 4.2% in white patients (P = .12); 30-day revisit and readmission rates were also not significantly different by race (10.5% in black patients versus 10.8% in white patients; P = .49). Length of stay (LOS) was also similar; both groups had a median stay of 2.0 days, with a slightly lower percentage of black patients exceeding their own state’s median LOS (30.2% in black patients versus 31.8% in white patients; P = .01). The mean paired difference in LOS was 0.00 days (95% confidence interval, –0.08 to 0.08). However, ICU use was higher in black patients than white patients (22.2% versus 17.5%; P < .001). After adjusting for multiple testing, only 4 states were found to differ significantly, but only in ICU use, where blacks had higher rates of use. CONCLUSIONS: For closely matched black and white patients, racial disparities concerning asthma admission outcomes and style of practice are small and generally nonsignificant, except for ICU use, where we observed higher rates in black patients.