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One important concern around the spread of respiratory infectious diseases has been the contribution of public transportation, a space where people are in close contact with one another and with high-use surfaces. While disease clearly spreads along transportation routes, there is limited evidence about whether public transportation use itself is associated with the overall prevalence of contagious respiratory illnesses at the local level. We examine the extent of the association between public transportation and influenza mortality, a proxy for disease prevalence, using city-level data on influenza and pneumonia mortality and public transit use from 121 large cities in the United States (US) between 2006 and 2015. We find no evidence of a positive relationship between city-level transit ridership and influenza/pneumonia mortality rates, suggesting that population level rates of transit use are not a singularly important factor in the transmission of influenza.

Improving the collection and quality of race and ethnicity reported in hospital data is a key step in identifying disparities in health service utilization and outcomes and opportunities for quality improvement. The objective of this study was to assess the quality of race/ethnicity reported in hospital discharge data and examine the impact on the identification of disparities in select health outcomes in New York City. Using the birth certificate as a gold standard, we examined the quality of hospital discharge race/ethnicity and estimated the impact of misclassification on racial/ethnic disparities in severe maternal morbidity and preventable hospitalizations. Delivery hospitalizations from the New York State hospital discharge data (Statewide Planning and Research Cooperative System) linked with 2015 New York City birth certificates. Sensitivity and positive predictive value (PPV). The non-Hispanic white and black race had relatively high sensitivity and PPV. Hispanic ethnicity and Asian race had moderate sensitivity and high PPV, but were often misclassified as \"Other.\" As a result, health disparities may be underestimated for those of Hispanic ethnicity and Asian race, particularly for indicators that use population denominators drawn from another source. The quality of hospital discharge data varies by race/ethnicity and may underestimate disparities in some groups. Future research should validate findings with other data sources, identify driving factors, and evaluate progress over time.

This study assessed the impact of individual social risk factor variables and social determinants of health (SDOH) measures on hospital readmission rates and penalties used in the centers for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). Using 2012-16 hospital discharge data from New York City, we projected HRRP penalties by augmenting CMS's readmission model for heart attack, heart failure, and pneumonia with SDOH scores constructed at each of four geographic levels and a measure of individual-level social risk. Including additional SDOH scores in the model, especially those constructed with the most granular geographic data, along with social risk factor variables substantially affects projected penalties for hospitals treating the highest proportion of patients with high SDOH scores. Improved performance occurred even after we included peer-group stratification in the HRRP model pursuant to the 21st Century Cures Act. Small improvements in model accuracy were associated with substantial shifts in projected performance. Our results suggest that CMS's continued omission of relevant patient and geographic data from the HRRP readmission model misallocates penalties attributable to SDOH and social risk factor effects to hospitals with the largest share of high-risk patients.

Objectives Severe maternal morbidity (SMM) is an important indicator for identifying and monitoring efforts to improve maternal health. Studies have identified independent risk factors, including race/ethnicity; however, there has been limited investigation of the modifying effect of socioeconomic factors. Study aims were to quantify SMM risk factors and to determine if socioeconomic status modifies the effect of race/ethnicity on SMM risk. Methods We used 2008–2012 NYC birth certificates matched with hospital discharge records for maternal deliveries. SMM was defined using an algorithm developed by the Centers for Disease Control and Prevention. Mixed-effects logistic regression models estimated SMM risk by demographic, socioeconomic, and health characteristics. The final model was stratified by Medicaid status (as a proxy for income), education, and neighborhood poverty. Results Of 588,232 matched hospital deliveries, 13,505 (229.6 per 10,000) had SMM. SMM rates varied by maternal age, birthplace, education, income, pre-existing chronic conditions, pre-pregnancy weight status, trimester of prenatal care entry, plurality, and parity. Race/ethnicity was consistently and significantly associated with SMM. While racial differences in SMM risk persisted across all socioeconomic groupings, the risk was exacerbated among Latinas and Asian-Pacific Islanders with lower income when compared to white non-Latinas. Similarly, living in the poorest neighborhoods exacerbated SMM risk among both black non-Latinas and Latinas. Conclusions for Practice SMM determinants in NYC mirror national trends, including racial/ethnic disparities. However, these disparities persisted even in the highest income and educational groups suggesting other pathways are needed to explain racial/ethnic differences.

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.

Abstract While SARS-CoV-2 is a novel virus, contagious respiratory illnesses are not a new problem. Limited research has examined the extent to which place- and race-based disparities in severe illness are similar across waves of the COVID-19 pandemic and historic influenza seasons. In this study, we focused on these disparities within a low-income population, those enrolled in Medicaid in New York City. We used 2015–2020 New York State Medicaid claims to compare the characteristics of patients hospitalized with COVID-19 during three separate waves of 2020 (first wave: January 1–April 30, 2020; second wave: May 1–August 31, 2020; third wave: September 1–December 31, 2020) and with influenza during the 2016 (July 1, 2016–June 30, 2017) and 2017 influenza seasons (July 1, 2017–June 30, 2018). We found that patterns of hospitalization by race/ethnicity and ZIP code across the two influenza seasons and the first wave of COVID-19 were similar (increased risk among non-Hispanic Black (aOR = 1.17, 95% CI: 1.10–1.25) compared with non-Hispanic white Medicaid recipients). Black/white disparities in hospitalization dissipated in the second COVID wave and reversed in the third wave. The commonality of disparities across influenza seasons and the first wave of COVID-19 suggests there are community factors that increase hospitalization risk across novel respiratory illness incidents that emerge in the period before aggressive public health intervention. By contrast, convergence in hospitalization patterns in later pandemic waves may reflect, in part, the distinctive public health response to COVID-19.

Importance Medicaid patients with mental illness comprise one of the most high-need and complex patient populations. Value-based reforms aim to improve care, but their efficacy in the Medicaid program is unclear. Objective To investigate if New York state’s Medicaid value-based payment reform was associated with improved utilization patterns for patients with mental illness. Design, Setting, and Participants This retrospective cohort study used a difference-in-differences analysis to compare changes in utilization between Medicaid beneficiaries whose outpatient practices participated in value-based payment reform and beneficiaries whose practices did not participate from before (July 1, 2013-June 30, 2015) to after reform (July 1, 2015-June 30, 2019). Participants were Medicaid beneficiaries in New York state aged 18 to 64 years with major depression disorder, bipolar disorder, and/or schizophrenia. Data analysis was performed from April 2021 to July 2023. Exposure Beneficiaries were exposed to value-based payment reforms if their attributed outpatient practice participated in value-based payment reform at baseline (July 1, 2015). Main Outcomes and Measures Primary outcomes were the number of outpatient primary care visits and the number of behavioral health visits per year. Secondary outcomes were the number of mental health emergency department visits and hospitalizations per year. Results The analytic population comprised 306 290 individuals with depression (67.4% female; mean [SD] age, 38.6 [11.9] years), 85 105 patients with bipolar disorder (59.6% female; mean [SD] age, 38.0 [11.6] years), and 71 299 patients with schizophrenia (45.1% female; mean [SD] age, 40.3 [12.2] years). After adjustment, analyses estimated a statistically significant, positive association between value-based payments and behavioral health visits for patients with depression (0.91 visits; 95% CI, 0.51-1.30) and bipolar disorder (1.01 visits; 95% CI, 0.22-1.79). There was no statistically significant changes to primary care visits for patients with depression and bipolar disorder, but value-based payments were associated with reductions in primary care visits for patients with schizophrenia (−1.31 visits; 95% CI, −2.51 to −0.12). In every diagnostic population, value-based payment was associated with significant reductions in mental health emergency department visits (population with depression: −0.01 visits [95% CI, −0.02 to −0.002]; population with bipolar disorder: −0.02 visits [95% CI, −0.05 to −0.001]; population with schizophrenia: −0.04 visits [95% CI, −0.07 to −0.01]). Conclusions and Relevance In this cohort study, Medicaid value-based payment reform was statistically significantly associated with an increase in behavioral health visits and a reduction in mental health emergency department visits for patients with mental illness. Medicaid value-based payment may be effective at altering health care utilization in patients with mental illness.

Maternal smoking is captured on the 2003 US Standard Birth Certificate based on self-reported tobacco use before and during pregnancy collected on post-delivery maternal worksheets. Study objectives were to compare smoking reported on the birth certificate to maternal worksheets and prenatal and hospital medical records. The authors analyzed a sample of New York City (NYC) and Vermont women (n = 1,037) with a live birth from January to August 2009 whose responses to the Pregnancy Risk Assessment Monitoring System survey were linked with birth certificates and abstracted medical records and maternal worksheets. We calculated smoking prevalence and agreement (kappa) between sources overall and by maternal and hospital characteristics. Smoking before and during pregnancy was 13.7 and 10.4 % using birth certificates, 15.2 and 10.7 % using maternal worksheets, 18.1 and 14.1 % using medical records, and 20.5 and 15.0 % using either maternal worksheets or medical records. Birth certificates had “almost perfect” agreement with maternal worksheets for smoking before and during pregnancy (κ = 0.92 and 0.89) and “substantial” agreement with medical records (κ = 0.70 and 0.74), with variation by education, insurance, and parity. Smoking information on NYC and Vermont birth certificates closely agreed with maternal worksheets but was underestimated compared with medical records, with variation by select maternal characteristics. Opportunities exist to improve birth certificate smoking data, such as reducing the stigma of smoking, and improving the collection, transcription, and source of information.

Timing of breast development (or thelarche) and its endogenous and exogenous determinants may underlie global variation in breast cancer incidence. The study objectives were to characterize endogenous estrogen levels and bisphenol A (BPA) exposure using a migrant study of adolescent girls and test whether concentrations explained differences in thelarche by birthplace and growth environment. Estrogen metabolites (EM) and BPA-glucuronide (BPA-G) were quantified in urine spot samples using liquid chromatography tandem mass spectrometry (LC-MS/MS) from a cross-sectional study of Bangladeshi, first- and second-generation Bangladeshi migrants to the UK, and white British girls aged 5–16 years (n = 348). Thelarche status at the time of interview was self-reported and defined equivalent to Tanner Stage ≥2. We compared geometric means (and 95% confidence interval (CIs)) of EM and BPA-G using linear regression and assessed whether EM and BPA-G explained any of the association between exposure to the UK and the age at thelarche using hazard ratios and 95% confidence intervals. Average EM decreased with exposure to the UK, whereas BPA-G increased and was significantly higher among white British (0.007 ng/mL, 95% CI: 0.0024–0.0217) and second-generation British-Bangladeshi girls (0.009 ng/mL, 95% CI: 0.0040–0.0187) compared to Bangladeshi girls (0.002 ng/mL, 95% CI: 0.0018–0.0034). Two of four EM ratios (16-pathway/parent and parent/all pathways) were significantly associated with thelarche. The relationship between exposure to the UK and thelarche did not change appreciably after adding EM and BPA-G to the models. While BPA-G is often considered a ubiquitous exposure, our findings suggest it can vary based on birthplace and growth environment, with increasing levels for girls who were born in or moved to the UK. Our study did not provide statistically significant evidence that BPA-G or EM concentrations explained earlier thelarche among girls who were born or raised in the UK.