Faculty Bibliography

INTRODUCTION/BACKGROUND:Identifying strategies to mitigate the effects of secondhand smoke exposure is crucial for public health. Thus, we estimated the effect of a 2018 federal smoke-free housing (SFH) policy on adverse birth outcomes among New York City (NYC) public-housing residents. METHODS:We obtained data on all live births to NYC residents in NYC from 2013 to 2022, using the borough-block-lot of the birthing person's address to identify births to public-housing residents. We then estimated the effect of the SFH policy on risk of preterm birth or low birth weight among births to NYC public-housing residents using a linear-probability difference-in-differences estimator, weighted by inverse probability weights to increase the plausibility of the parallel-trends assumption. RESULTS:Our sample included 44 455 births to public-housing residents and 803 648 births to non-public-housing residents. Difference-in-difference analyses suggested the SFH policy did not affect risk of preterm birth (risk difference (RD) per 100: 0.1; 95% CI -0.6 to 0.9) or low birth weight (RD per 100: 0.3, 95% CI -0.4 to 1.0). Event-study analyses supported these findings and lent credibility to the parallel-trends assumption. CONCLUSIONS:We estimated no initial effects of a federal SFH policy on risk of preterm birth or low birth weight among births to NYC public-housing residents.

BACKGROUND/UNASSIGNED:Environmental epidemiology studies increasingly integrate "big" geospatial and health datasets to examine associations between environmental factors and health outcomes. Using such datasets - and linking between them - presents a number of complexities with regard to study design and analytic approaches. These complexities are often magnified with the integration of additional contextual data representing other neighborhood characteristics, including socioeconomic factors. Guidance regarding the design of environmental health studies that leverage "big" geospatial and health outcome data is limited and fragmented. OBJECTIVE/UNASSIGNED:Drawing on methodological literature and case studies, this commentary outlines common challenges related to geospatial and health data linkages, posing a series of guiding questions and considerations for investigators conducting environmental health studies, particularly analyses with an etiological focus. DISCUSSION/UNASSIGNED:Recommendations include: 1) using a target trial approach to guide causal analysis, 2) aligning measures with hypothesized causal mechanisms, 3) exploring opportunities to "groundtruth" and validate data, and 4) prioritizing interdisciplinary science. The goal of the commentary is to consolidate insights from multiple disciplines - including exposure science, epidemiology, and sociology - to provide a foundation for etiologic research focused on advancing environmental health for all populations. https://doi.org/10.1289/EHP15756.

BACKGROUND:Disruption of ambulatory healthcare in New York City (NYC) during the COVID-19 pandemic was common, but the impact on the cardiometabolic health of vulnerable patient groups is unknown. Therefore, we estimated the effect of total care disruption (TCD) on blood pressure (BP) control among older NYC residents with hypertension and at least one other chronic condition, and examined whether neighborhood poverty moderated this impact. METHODS:From the INSIGHT Clinical Research Network, we identified NYC residents ≥50 years of age with hypertension and at least one other chronic condition. TCD was defined as no ambulatory or telehealth visit during the pandemic. We contrasted the change in prevalence of controlled BP (BP <140/90) before and after the pandemic among those with and without TCD via an inverse probability weighted (IPW) difference-in-difference regression model. RESULTS:Among 212,673 eligible individuals, mean age was 69.5 years (SD: 10.2 years) and 15.1% experienced TCD. BP control declined from 52.4% to 45.9% among those with TCD and from 53.6% to 48.9% among those without TCD. After IPW adjustment, a larger decline in BP control was noted among those with TCD (adjusted difference-in-difference = 1.13 percentage points (95% CI 0.32-1.94, p-value=0.0058)). There was no consistent difference in the relationship between TCD and post-pandemic BP control across neighborhood poverty levels. CONCLUSION/CONCLUSIONS:COVID-19-related TCD was associated with a modest decline in BP control among older adults with hypertension in NYC; this was not moderated by neighborhood poverty level.

INTRODUCTION/BACKGROUND:Effective July 2018, the U.S. Department of Housing and Urban Development issued a rule requiring all public housing authorities to implement smoke-free housing (SFH) policies in their developments. We examined the differential impacts of SFH policy on hospitalizations for myocardial infarction (MI) and stroke among adults aged ≥50 years old living in New York City (NYC) Housing Authority (NYCHA) versus a matched-comparison population in NYC. AIMS AND METHODS/OBJECTIVE:We identified census block groups (CBGs) comprised solely of 100% NYCHA units (N = 160) and compared NYCHA CBGs to a selected subset of CBGs from all CBGs with no NYCHA units (N = 5646). We employed propensity score matching on distributions of key CBG-level sociodemographic and housing covariates. We constructed incident rates per 1000 persons by aggregating 3-month "quarterly" counts of New York State all-payer hospitalization data from October 2015 to December 2022 and dividing by the population aged ≥50 in selected CBGs, ascertained from 2016 American Community Survey 5-year estimates. We selected a difference-in-differences (DID) analytic approach to examine pre- and post-policy differences in incident hospitalizations between the intervention and matched-comparison groups. RESULTS:Matching results indicated a balanced match for all covariates, with standardized mean differences <0.10. In DID analyses, we observed small declines in both MI (DID = -0.26, p = .02) and stroke (DID = -0.28, p = .06) hospitalization rates for NYCHA CBGs compared to non-NYCHA CBGs from pre-to post-54 months' policy. CONCLUSIONS:SFH policies in NYC were associated with small reductions in CVD-related hospitalizations among older adults living in housing subject to the policy. IMPLICATIONS/CONCLUSIONS:Housing remains a key focal setting for interventions to reduce SHS exposure and associated morbidities. Ongoing monitoring is warranted to understand the long-term impacts of SFH policies in public housing developments.

PURPOSE OF REVIEW/OBJECTIVE:Despite emerging studies on neighborhood-level risk factors for chronic kidney disease (CKD), our understanding of the causal links between neighborhood characteristics and CKD is limited. In particular, there is a gap in identifying modifiable neighborhood factors, such as the built environment, in preventing CKD, that could be targets for feasible place-based interventions. RECENT FINDINGS/RESULTS:Most published studies on neighborhood factors and CKD have focused on a single social attribute, such as neighborhood disadvantage, while research on the role of the built environment is more nascent. Early studies on this topic have yielded inconsistent results, particularly regarding whether food deserts are an environmental risk factor for CKD onset. International studies have shown that walkable neighborhoods - characterized by features such as urban design, park access, and green spaces - can be protective against both the onset and progression of CKD. However, these findings are inconclusive and understudied in the context of United States, where neighborhood environments differ from those in other countries. SUMMARY/CONCLUSIONS:Future research on modifiable neighborhood factors and CKD using advanced study designs and population-representative datasets can yield stronger evidence on potential causal associations and suggest feasible place-based interventions as strategies for preventing CKD. As an example, we demonstrated the potential of electronic health record-based studies to advance research in this area.

Prior studies suggest that diabetes is associated with severe outcomes following COVID-19. However, most research has focused on early phases of the COVID-19 pandemic, and less is known about changing diabetes-associated risks over time. We constructed a retrospective cohort of U.S. Veterans with documented COVID-19 between March 2020 and August 2023 (N = 426,170). We used Poisson regression models to estimate relative risks of 60-day mortality following COVID-19 among Veterans with and without diabetes, incorporating demographic and clinical covariates, as well as weights to address unequal probabilities of selection into the sample. We then incorporated interaction terms representing six-month time windows and plotted predicted mortality risks over time. To contextualize risk estimates, we repeated the analysis among a cohort of Veterans without documented COVID-19. Diabetes was associated with overall higher risk of 60-day mortality following COVID-19 (RR = 1.21, 95% CI = 1.17-1.26). Mortality risks attenuated over time and converged with risks observed among Veterans without COVID-19 by March-August 2022. Results suggest that post-COVID-19 mortality risks associated with diabetes may have attenuated over time. Mechanisms underlying the attenuation of mortality risks were beyond the scope of the paper, however, future studies can potentially shed light on the contributions of population immunity (driven by previous infection or vaccination status), changing treatment patterns, and other factors to time-varying mortality risks following COVID-19 among individuals with diabetes.

OBJECTIVE:Racial/ethnic disparities in glycemic control among non-Hispanic Black (NHB) and non-Hispanic White (NHW) veterans with type 2 diabetes (T2D) have been reported. This study examined trends in early glycemic control by race/ethnicity to understand how disparities soon after T2D diagnosis have changed between 2008 and 2019 among cohorts of U.S. veterans with newly diagnosed T2D. RESEARCH DESIGN AND METHODS/METHODS:We estimated the annual percentage of early glycemic control (average A1C <7%) in the first 5 years after diagnosis among 837,023 veterans (95% male) with newly diagnosed T2D in primary care. We compared early glycemic control by racial/ethnic group among cohorts defined by diagnosis year (2008-2010, 2011-2013, 2014-2016, and 2017-2018) using mixed-effects models with random intercepts. We estimated odds ratios of early glycemic control comparing racial/ethnic groups with NHW, adjusting for age, sex, and years since diagnosis. RESULTS:The average annual percentage of veterans who achieved early glycemic control during follow-up was 73%, 72%, 72%, and 76% across the four cohorts, respectively. All racial/ethnic groups were less likely to achieve early glycemic control compared with NHW veterans in the 2008-2010 cohort. In later cohorts, NHB and Hispanic veterans were more likely to achieve early glycemic control; however, Hispanic veterans were also more likely to have an A1C ≥9% within 5 years in all cohorts. Early glycemic control disparities for non-Hispanic Asian, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native veterans persisted in cohorts until the 2017-2018 cohort. CONCLUSIONS:Overall early glycemic control trends among veterans with newly diagnosed T2D have been stable since 2008, but trends differed by racial/ethnic groups and disparities in very poor glycemic control were still observed. Efforts should continue to minimize disparities among racial/ethnic groups.

BACKGROUND/UNASSIGNED:Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations. OBJECTIVE/UNASSIGNED:In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults. METHODS/UNASSIGNED:We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems. RESULTS/UNASSIGNED:Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51). CONCLUSIONS/UNASSIGNED:Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates.

During infectious disease epidemics, accurate diagnostic testing is key to rapidly identify and treat cases, and mitigate transmission. When a novel pathogen is involved, building testing capacity and scaling testing services at the local level can present major challenges to healthcare systems, public health agencies, and laboratories. This mixed methods study examined lessons learned from the scale-up of SARS-CoV-2 testing services in New York City (NYC), as a core part of NYC's Test & Trace program. Using quantitative and geospatial analyses, the authors assessed program success at maximizing reach, equity, and timeliness of SARS-CoV-2 diagnostic testing services across NYC neighborhoods. Qualitative analysis of key informant interviews elucidated key decisions, facilitators, and barriers involved in the scale-up of SARS-CoV-2 testing services. A major early facilitator was the ability to establish working relationships with private sector vendors and contractors to rapidly procure and manufacture necessary supplies locally. NYC residents were, on average, less than 25 min away from free SARS-CoV-2 diagnostic testing services by public transport, and services were successfully directed to most neighborhoods with the highest transmission rates, with only one notable exception. A key feature was to direct mobile testing vans and rapid antigen testing services to areas based on real-time neighborhood transmission data. Municipal leaders should prioritize fortifying supply chains, establish cross-sectoral partnerships to support and extend testing services, plan for continuous testing and validation of assays, ensure open communication feedback loops with CBO partners, and maintain infrastructure to support mobile services during infectious disease emergencies.