Impact of Social Determinants of Health on Predictive Models for Outcomes After Congenital Heart Surgery.

BACKGROUND: Despite documented associations between social determinants of health and outcomes post-congenital heart surgery, clinical risk models typically exclude these factors. OBJECTIVES: The study sought to characterize associations between social determinants and operative and longitudinal mortality as well as assess impacts on risk model performance. METHODS: Demographic and clinical data were obtained for all congenital heart surgeries (2006-2021) from locally held Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources Society of Thoracic Surgeons Congenital Heart Surgery Database data. Neighborhood-level American Community Survey and composite sociodemographic measures were linked by zip code. Model prediction, discrimination, and impact on quality assessment were assessed before and after inclusion of social determinants in models based on the 2020 Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model. RESULTS: Of 14,173 total index operations across New York State, 12,321 cases, representing 10,271 patients at 8 centers, had zip codes for linkage. A total of 327 (2.7%) patients died in the hospital or before 30 days, and 314 children died by December 31, 2021 (total n = 641; 6.2%). Multiple measures of social determinants of health explained as much or more variability in operative and longitudinal mortality than clinical comorbidities or prior cardiac surgery. Inclusion of social determinants minimally improved models' predictive performance (operative: 0.834-0.844; longitudinal 0.808-0.811), but significantly improved model discrimination; 10.0% more survivors and 4.8% more mortalities were appropriately risk classified with inclusion. Wide variation in reclassification was observed by site, resulting in changes in the center performance classification category for 2 of 8 centers. CONCLUSIONS: Although indiscriminate inclusion of social determinants in clinical risk modeling can conceal inequities, thoughtful consideration can help centers understand their performance across populations and guide efforts to improve health equity.

Major Traffic Safety Reform and Road Traffic Injuries Among Low-Income New York Residents, 2009-2021.

Objectives. To evaluate the effects of a comprehensive traffic safety policy-New York City's (NYC's) 2014 Vision Zero-on the health of Medicaid enrollees. Methods. We conducted difference-in-differences analyses using individual-level New York Medicaid data to measure traffic injuries and expenditures from 2009 to 2021, comparing NYC to surrounding counties without traffic reforms (n = 65 585 568 person-years). Results. After Vision Zero, injury rates among NYC Medicaid enrollees diverged from those of surrounding counties, with a net impact of 77.5 fewer injuries per 100 000 person-years annually (95% confidence interval = -97.4, -57.6). We observed marked reductions in severe injuries (brain injury, hospitalizations) and savings of $90.8 million in Medicaid expenditures over the first 5 years. Effects were largest among Black residents. Impacts were reversed during the COVID-19 period. Conclusions. Vision Zero resulted in substantial protection for socioeconomically disadvantaged populations known to face heightened risk of injury, but the policy's effectiveness decreased during the pandemic period. Public Health Implications. Many cities have recently launched Vision Zero policies and others plan to do so. This research adds to the evidence on how and in what circumstances comprehensive traffic policies protect public health. (Am J Public Health. 2024;114(6):633-641. https://doi.org/10.2105/AJPH.2024.307617).

Housing-Sensitive Health Conditions Can Predict Poor-Quality Housing.

Improving housing quality may improve residents' health, but identifying buildings in poor repair is challenging. We developed a method to improve health-related building inspection targeting. Linking New York City Medicaid claims data to Landlord Watchlist data, we used machine learning to identify housing-sensitive health conditions correlated with a building's presence on the Watchlist. We identified twenty-three specific housing-sensitive health conditions in five broad categories consistent with the existing literature on housing and health. We used these results to generate a housing health index from building-level claims data that can be used to rank buildings by the likelihood that their poor quality is affecting residents' health. We found that buildings in the highest decile of the housing health index (controlling for building size, community district, and subsidization status) scored worse across a variety of housing quality indicators, validating our approach. We discuss how the housing health index could be used by local governments to target building inspections with a focus on improving health.

Variation in Preterm Birth Rates Across Prenatal Care Sites in New York.

OBJECTIVE: To investigate variation in preterm birth rates by the site at which prenatal care was received. DESIGN: Cross-sectional cohort study. SETTING: New York State. PARTICIPANTS: Claims and encounter data on singleton live births that were covered by New York Medicaid (N = 154,377). METHODS: We analyzed data from New York Medicaid and the American Community Survey. We established sites of prenatal care using geocoded billing addresses for prenatal visits. We calculated descriptive statistics and conducted logistic regression analyses to determine variation in crude and risk-adjusted preterm birth rates by prenatal care site. RESULTS: The crude preterm birth rates averaged 7.8% (range = 2.0%-18.7%) by prenatal care site. The adjusted preterm birth rate was 8.0% (range = 2.8%-18.5%) by prenatal care site. Risk-adjusted preterm birth site-level rates at the 90th percentile were 2.7 times higher than those in the 10th percentile. The variation in risk-adjusted preterm birth site-level rates was not fully explained by birth volume, rural site location, or racial and ethnic composition of the patients who received prenatal care at the site. CONCLUSION: Wide variation in risk-adjusted preterm birth rates across prenatal care sites exists, and factors beyond known individual demographics and medical factors contribute to the variation. Further research is warranted to identify why receiving care at some prenatal sites is associated with higher risk of preterm birth than receiving care at others.

Chronic Medication Burden After Cardiac Surgery for Pediatric Medicaid Beneficiaries.

BACKGROUND: Congenital heart defects are the most common and resource-intensive birth defects. As children with congenital heart defects increasingly survive beyond early childhood, it is imperative to understand longitudinal disease burden. OBJECTIVES: The purpose of this study was to examine chronic outpatient prescription medication use and expenditures for New York State pediatric Medicaid enrollees, comparing children who undergo cardiac surgery (cardiac enrollees) and the general pediatric population. METHODS: This was a retrospective cohort study of all Medicaid enrollees age <18 years using the New York State Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources database (2006-2019). Primary outcomes were total chronic medications per person-year, enrollees per 100 person-years using ≥1 and ≥3 medications, and medication expenditures per person-year. We described and compared outcomes between cardiac enrollees and the general pediatric population. Among cardiac enrollees, multivariable regression examined associations between outcomes and clinical characteristics. RESULTS: We included 5,459 unique children (32,131 person-years) who underwent cardiac surgery and 4.5 million children (22 million person-years) who did not. More than 4 in 10 children who underwent cardiac surgery used ≥1 chronic medication compared with approximately 1 in 10 children who did not have cardiac surgery. Medication expenditures were 10 times higher per person-year for cardiac compared with noncardiac enrollees. Among cardiac enrollees, disease severity was associated with chronic medication use; use was highest among infants; however, nearly one-half of adolescents used ≥1 chronic medication. CONCLUSIONS: Children who undergo cardiac surgery experience high medication burden that persists throughout childhood. Understanding chronic medication use can inform clinicians (both pediatricians and subspecialists) and policymakers, and ultimately the value of care for this medically complex population.

Long-Term Health Care Utilization After Cardiac Surgery in Children Covered Under Medicaid.

BACKGROUND: Understanding the longitudinal burden of health care expenditures and utilization after pediatric cardiac surgery is needed to counsel families, improve care, and reduce outcome inequities. OBJECTIVES: The purpose of this study was to describe and identify predictors of health care expenditures and utilization for Medicaid-insured pediatric cardiac surgical patients. METHODS: All Medicaid enrolled children age <18 years undergoing cardiac surgery in the New York State CHS-COLOUR database, from 2006 to 2019, were followed in Medicaid claims data through 2019. A matched cohort of children without cardiac surgical disease was identified as comparators. Expenditures and inpatient, primary care, subspecialist, and emergency department utilization were modeled using log-linear and Poisson regression models to assess associations between patient characteristics and outcomes. RESULTS: In 5,241 New York Medicaid-enrolled children, longitudinal health care expenditures and utilization for cardiac surgical patients exceeded noncardiac surgical comparators (cardiac surgical children: $15,500 ± $62,000 per month in year 1 and $1,600 ± $9,100 per month in year 5 vs noncardiac surgical children: $700 ± $6,600 per month in year 1 and $300 ± $2,200 per month in year 5). Children after cardiac surgery spent 52.9 days in hospitals and doctors' offices in the first postoperative year and 90.5 days over 5 years. Being Hispanic, compared with non-Hispanic White, was associated with having more emergency department visits, inpatient admissions, and subspecialist visits in years 2 to 5, but fewer primary care visits and greater 5-year mortality. CONCLUSIONS: Children after cardiac surgery have significant longitudinal health care needs, even among those with less severe cardiac disease. Health care utilization differed by race/ethnicity, although mechanisms driving disparities should be investigated further.

Association of Insurance Mix and Diagnostic Coding Practices in New York State Hospitals.

Importance: Given higher reimbursement rates, hospitals primarily serving privately insured patients may invest more in intensive coding than hospitals serving publicly insured patients. This may lead these hospitals to code more diagnoses for all patients. Objective: To estimate whether, for the same Medicaid enrollee with multiple hospitalizations, a hospital's share of privately insured patients is associated with the number of diagnoses on claims. Design, Setting, and Participants: This cross-sectional study used patient-level fixed effects regression models on inpatient Medicaid claims from Medicaid enrollees with at least 2 admissions in at least 2 different hospitals in New York State between 2010 and 2017. Analyses were conducted from 2019 to 2021. Exposures: The annual share of privately insured patients at the admitting hospital. Main Outcomes and Measures: Number of diagnostic codes per admission. Probability of diagnoses being from a list of conditions shown to be intensely coded in response to payment incentives. Results: This analysis included 1 614 630 hospitalizations for Medicaid-insured patients (mean [SD] age, 48.2 [20.1] years; 829 684 [51.4%] women and 784 946 [48.6%] men). Overall, 74 998 were Asian (4.6%), 462 259 Black (28.6%), 375 591 Hispanic (23.3%), 486 313 White (30.1%), 128 896 unknown (8.0%), and 86 573 other (5.4%). When the same patient was seen in a hospital with a higher share of privately insured patients, more diagnoses were recorded (0.03 diagnoses per percentage point [pp] increase in share of privately insured; 95% CI, 0.02-0.05; P < .001). Patients discharged from hospitals in the bottom quartile of privately insured patient share received 1.37 more diagnoses when they were subsequently discharged from hospitals in the top quartile, relative to patients whose admissions were both in the bottom quartile (95% CI, 1.21-1.53; P < .001). Those going from hospitals in the top quartile to the bottom had 1.67 fewer diagnoses (95% CI, -1.84 to -1.50; P < .001). Diagnoses in hospitals with a higher private payer share were more likely to be for conditions sensitive to payment incentives (0.08 pp increase for each pp increase in private share; 95% CI, 0.06-0.10; P < .001). These findings were replicated in 2016 to 2017 data. Conclusions and Relevance: In this cross-sectional study of Medicaid enrollees, admission to a hospital with a higher private payer share was associated with more diagnoses on Medicaid claims. This suggests payment policy may drive differential investments in infrastructure to document diagnoses. This may create a feedback loop that exacerbates resource inequity.

Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2).

BACKGROUND: As the cardiac community strives to improve outcomes, accurate methods of risk stratification are imperative. Since adoption of International Classification of Disease-10th Revision (ICD-10) in 2015, there is no published method for congenital heart surgery risk stratification for administrative data. OBJECTIVES: This study sought to develop an empirically derived, publicly available Risk Stratification for Congenital Heart Surgery (RACHS-2) tool for ICD-10 administrative data. METHODS: The RACHS-2 stratification system was iteratively and empirically refined in a training dataset of Pediatric Health Information Systems claims to optimize sensitivity and specificity compared with corresponding locally held Society of Thoracic Surgeons-Congenital Heart Surgery (STS-CHS) clinical registry data. The tool was validated in a second administrative data source: New York State Medicaid claims. Logistic regression was used to compare the ability of RACHS-2 in administrative data to predict operative mortality vs STAT Mortality Categories in registry data. RESULTS: The RACHS-2 system captured 99.6% of total congenital heart surgery registry cases, with 1.0% false positives. RACHS-2 predicted operative mortality in both training and validation administrative datasets similarly to STAT Mortality Categories in registry data. C-statistics for models for operative mortality in training and validation administrative datasets-adjusted for RACHS-2-were 0.76 and 0.84 (95% CI: 0.72-0.80 and 0.80-0.89); C-statistics for models for operative mortality-adjusted for STAT Mortality Categories-in corresponding clinical registry data were 0.75 and 0.84 (95% CI: 0.71-0.79 and 0.79-0.89). CONCLUSIONS: RACHS-2 is a risk stratification system for pediatric cardiac surgery for ICD-10 administrative data, validated in 2 administrative-registry-linked datasets. Statistical code is publicly available upon request.

Improving Longitudinal Outcomes, Efficiency, and Equity in the Care of Patients With Congenital Heart Disease.

BACKGROUND: Longitudinal follow-up, resource utilization, and health disparities are top congenital heart research and care priorities. Medicaid claims include longitudinal data on inpatient, outpatient, emergency, pharmacy, rehabilitation, home health utilization, and social determinants of health-including mother-infant pairs. OBJECTIVES: The New York Congenital Heart Surgeons Collaborative for Longitudinal Outcomes and Utilization of Resources linked robust clinical details from locally held state and national registries from 10 of 11 New York congenital heart centers to Medicaid claims, building a novel, statewide mechanism for longitudinal assessment of outcomes, expenditures, and health inequities. METHODS: The authors included all children <18 years of age undergoing cardiac surgery in The Society of Thoracic Surgeons Congenital Heart Surgery Database or the New York State Pediatric Congenital Cardiac Surgery Registry from 10 of 11 New York centers, 2006 to 2019. Data were linked via iterative, ranked deterministic matching on direct identifiers. Match rates were calculated and compared. Proportions of the linked cohort trackable over 3, 5, and 10 years were described. RESULTS: Of 14,097 registry cases, 59% (n = 8,322) reported Medicaid use. Of these, 7,414 were linked to New York claims, at an 89% match rate. Of matched cases, the authors tracked 79%, 74%, and 65% of children over 3, 5, and 10 years when requiring near-continuous Medicaid enrollment. Allowing more lenient enrollment criteria, the authors tracked 86%, 82%, and 76%, respectively. Mortality over this time was 7.7%, 8.4%, and 10.0%, respectively. Manual validation revealed ∼100% true matches. CONCLUSIONS: This establishes a novel statewide data resource for assessment of longitudinal outcome, health expenditure, and disparities for children with congenital heart disease.

Identifying New York City Neighborhoods at Risk of Being Overlooked for Interventions.

Public health agencies are often faced with difficult decisions about where and how to allocate funding and resources. This question of resource allocation is central to public health policy; however, decisions related to resource allocation are sometimes made via informal or subjective approaches. We walk readers through a process of identifying needs across different neighborhoods in New York City (NYC) by examining community district-level health outcomes using data from published Community Health Profile reports released by the NYC Department of Health and Mental Hygiene (DOHMH) in 2015. In NYC, community districts are represented by community boards that provide a forum for addressing the needs of the community, making them a useful geographic unit for examining health information and turning data into action. We examined prevalence estimates and 95% confidence intervals of health indicators in each community district to identify significant disparities and calculated relative disparities in rates or prevalence to understand the relative magnitude of each disparity. Lastly, we demonstrate an application of this approach by identifying a cluster of neighborhoods with a high chance of being overlooked for public health interventions by conventional methods because of the relative number of disparities that exist in these neighborhoods. We present information on the disparity profile (number of disparities and relative disparity) for each neighborhood within the cluster and discuss potential public health implications. This approach can be applied to other jurisdictions to inform public health planning and resource allocation.