Descriptive study of causes of death and COVID-19-associated morbidities from the New York City electronic death record: first wave of the pandemic March-July 2020.

OBJECTIVE: Assessing excess deaths from benchmarks across causes of death during the first wave of the COVID-19 pandemic and identifying morbidities most frequently mentioned alongside COVID-19 deaths in the death record. METHODS: Descriptive study of death records between 11 March 2020 and 27 July 2020, from the New York City Bureau of Vital Statistics. Mortality counts and percentages were compared with the average for the same calendar period of the previous 2 years. Distributions of morbidities from among forty categories of conditions were generated citywide and by sex, race/ethnicity and four age groups. Causes of death were assumed to follow Poisson processes for Z-score construction. RESULTS: Within the study period, 46 563 all-cause deaths were reported; 132.9% higher than the average for the same period of the previous 2 years (19 989). Of those 46 563 records, 19 789 (42.5%) report COVID-19 as underlying cause of death. COVID-19 was the most prevalent cause across all demographics, with respiratory conditions (prominently pneumonia), hypertension and diabetes frequently mentioned morbidities. Black non-Hispanics had greater proportions of mentions of pneumonia, hypertension, and diabetes. Hispanics had the largest proportion of COVID-19 deaths (52.9%). Non-COVID-19 excess deaths relative to the previous 2-year averages were widely reported. CONCLUSION: Mortality directly due to COVID-19 was accompanied by significant increases across most other causes from their reference averages, potentially suggesting a sizable COVID-19 death undercount. Indirect effects due to COVID-19 may partially account for some increases, but findings are hardly dispositive. Unavailability of vaccines for the time period precludes any impact over excess deaths. Respiratory and cardiometabolic-related conditions were most frequently reported among COVID-19 deaths across demographic characteristics.

Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

Measles - United States, January 1, 2020-March 28, 2024.

Measles is a highly infectious febrile rash illness and was declared eliminated in the United States in 2000. However, measles importations continue to occur, and U.S. measles elimination status was threatened in 2019 as the result of two prolonged outbreaks among undervaccinated communities in New York and New York City. To assess U.S. measles elimination status after the 2019 outbreaks and to provide context to understand more recent increases in measles cases, CDC analyzed epidemiologic and laboratory surveillance data and the performance of the U.S. measles surveillance system after these outbreaks. During January 1, 2020-March 28, 2024, CDC was notified of 338 confirmed measles cases; 97 (29%) of these cases occurred during the first quarter of 2024, representing a more than seventeenfold increase over the mean number of cases reported during the first quarter of 2020-2023. Among the 338 reported cases, the median patient age was 3 years (range = 0-64 years); 309 (91%) patients were unvaccinated or had unknown vaccination status, and 336 case investigations included information on ≥80% of critical surveillance indicators. During 2020-2023, the longest transmission chain lasted 63 days. As of the end of 2023, because of the absence of sustained measles virus transmission for 12 consecutive months in the presence of a well-performing surveillance system, U.S. measles elimination status was maintained. Risk for widespread U.S. measles transmission remains low because of high population immunity. However, because of the increase in cases during the first quarter of 2024, additional activities are needed to increase U.S. routine measles, mumps, and rubella vaccination coverage, especially among close-knit and undervaccinated communities. These activities include encouraging vaccination before international travel and rapidly investigating suspected measles cases.

Hospital segregation, critical care strain, and inpatient mortality during the COVID-19 pandemic in New York City.

BACKGROUND: Hospital segregation by race, ethnicity, and health insurance coverage is prevalent, with some hospitals providing a disproportionate share of undercompensated care. We assessed whether New York City (NYC) hospitals serving a higher proportion of Medicaid and uninsured patients pre-pandemic experienced greater critical care strain during the first wave of the COVID-19 pandemic, and whether this greater strain was associated with higher rates of in-hospital mortality. METHODS: In a retrospective analysis of all-payer NYC hospital discharge data, we examined changes in admissions, stratified by use of intensive care unit (ICU), from the baseline period in early 2020 to the first COVID-19 wave across hospital quartiles (265,329 admissions), and crude and risk-adjusted inpatient mortality rates, also stratified by ICU use, in the first COVID wave across hospital quartiles (23,032 inpatient deaths), based on the proportion of Medicaid or uninsured admissions from 2017-2019 (quartile 1 lowest to 4 highest). Logistic regressions were used to assess the cross-sectional association between ICU strain, defined as ICU volume in excess of the baseline average, and patient-level mortality. RESULTS: ICU admissions in the first COVID-19 wave were 84%, 97%, 108%, and 123% of the baseline levels by hospital quartile 1-4, respectively. The risk-adjusted mortality rates for ICU admissions were 36.4 (CI = 34.7,38.2), 43.6 (CI = 41.5,45.8), 45.9 (CI = 43.8,48.1), and 45.7 (CI = 43.6,48.0) per 100 admissions, and those for non-ICU admissions were 8.6 (CI = 8.3,9.0), 10.9 (CI = 10.6,11.3), 12.6 (CI = 12.1,13.0), and 12.1 (CI = 11.6,12.7) per 100 admissions by hospital quartile 1-4, respectively. Compared with the reference group of 100% or less of the baseline weekly average, ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.17 (95% CI = 1.10, 1.26), 2.63 (95% CI = 2.31, 3.00), and 3.26 (95% CI = 2.82, 3.78) for inpatient mortality, and non-ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.28 (95% CI = 1.22, 1.34), 2.60 (95% CI = 2.40, 2.82), and 3.44 (95% CI = 3.11, 3.63) for inpatient mortality. CONCLUSIONS: Our findings are consistent with hospital segregation as a potential driver of COVID-related mortality inequities and highlight the need to desegregate health care to address structural racism, advance health equity, and improve pandemic resiliency.

Immediate Injury Deaths Related to the Remnants From Hurricane Ida in New York City, September 1-2, 2021.

The remnants from Hurricane Ida in September 2021 caused unprecedented rainfall and inland flooding in New York City (NYC) and resulted in many immediate deaths. We reviewed death records (electronic death certificates and medical examiner reports) to systematically document the circumstances of death and demographics of decedents to inform injury prevention and climate adaptation actions for future extreme precipitation events. There were 14 Ida-related injury deaths in NYC, of which 13 (93%) were directly caused by Ida, and 1 (7%) was indirectly related. Most decedents were Asian (71%) and foreign-born (71%). The most common circumstance of death was drowning in unregulated basement apartments (71%). Themes that emerged from the death records review included the suddenness of flooding, inadequate exits, nighttime risks, and multiple household members were sometimes affected. These deaths reflect interacting housing and climate crises, and their disproportionate impact on disadvantaged populations needing safe and affordable housing. Climate adaptation actions, such as improving stormwater management infrastructure, informing residents about flood risk, implementing Federal Emergency Management Agency recommendations to make basements safer, and expanding emergency notification measures can mitigate risk. As climate change increases extreme precipitation events, multi-layered efforts are needed to keep residents safe.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

"One of the Grand Works of the World": Walt Whitman's Advocacy for the Brooklyn Waterworks, 1856-59.

When the Brooklyn Waterworks opened in 1859, it was one of America's most advanced water and sewer systems. Yet after Brooklyn was annexed by New York City, the waterworks' history slipped into obscurity, despite having a now-famous champion: the "poet of America," Walt Whitman, whose brother worked on the project. This article shows the Brooklyn poet's fierce, multiyear lobbying effort for the waterworks in various newspapers and introduces a wealth of newly recovered Whitman writings on the issue. As a journalist, Whitman exemplifies the nineteenth-century press as an intermediary between expert engineers and popular readers. The poet brought precise expertise, translated engineers' technical arguments into everyday language for his readers, and fought the resulting day-to-day political battles over construction in print. Whitman, then, is an underappreciated case study of the confluence of technology, public health, and local journalism.

Association of Park Renovation With Park Use in New York City.

Importance: Equity-driven citywide park redesign and renovation, such as the Community Parks Initiative (CPI), has the potential to increase park use and opportunities for physical activity in underserved communities. Objective: To evaluate changes in patterns of park use following park redesign and renovation in low-income New York City (NYC) neighborhoods. Design, Setting, and Participants: The Physical Activity and Redesigned Community Spaces study was a prospective quality improvement preintervention-postintervention study design with matched control parks. Thirty-three intervention and 21 control neighborhood parks were selected based on specific criteria related to poverty rates, population growth, and population density in park neighborhoods and not having received more than $250 000 in investment in the past 2 decades. Data were collected at baseline (prerenovation) and 2 follow-up points (3 months and 1 year post renovation) between June 5 and December 4 from 2016 to 2022. Participants were individuals observed as users of study parks. Intervention: The CPI, which involved the redesign and renovation of neighborhood parks by the municipal government of New York City. Main Outcomes and Measures: Main outcomes encompassed park use and physical activity levels assessed using the well-validated System for Observing Play and Recreation in Communities. Park use was quantified by total number of park users, categorized by age group (≤20 years vs ≥21 years), sex, and physical activity level (sitting or standing vs walking or vigorous activity). Changes in outcomes between groups were compared via the generalized estimation equation. Results: A total of 28 322 park users were observed across 1458 scans. At baseline, 6343 of 10 633 users (59.7%) were 20 years or younger, 4927 of 10 632 (46.3%) were female and 5705 (53.7%) were male, and 4641 of 10 605 (43.8%) were sitting or standing. Intervention parks showed more net park users compared with control parks from baseline to the final follow-up (difference-in-difference relative rate ratio, 1.69 [95% CI, 1.22-2.35] users/scan; P = .002). The association was driven by a significant increase in adult users at intervention parks and overall decrease in all users at control parks. Park users engaging in sitting or standing at intervention parks increased (difference, 4.68 [95% CI, 1.71-7.62] users/scan; P = .002) and park users engaging in walking or vigorous physical activity at control parks decreased (difference, -7.30 [95% CI, -10.80 to -4.26] users/scan; P < .001) over time. Conclusions and Relevance: In this quality improvement study, park redesign and renovation were positively associated with park use in low-income neighborhoods. However, park renovations may need to be accompanied by other programmatic strategies to increase physical activity.

Development of a community severance index for urban areas in the United States: A case study in New York City.

BACKGROUND AND AIMS: Traffic-related exposures, such as air pollution and noise, have a detrimental impact on human health, especially in urban areas. However, there remains a critical research and knowledge gap in understanding the impact of community severance, a measure of the physical separation imposed by road infrastructure and motorized road traffic, limiting access to goods, services, or social connections, breaking down the social fabric and potentially also adversely impacting health. We aimed to robustly quantify a community severance metric in urban settings exemplified by its characterization in New York City (NYC). METHODS: We used geospatial location data and dimensionality reduction techniques to capture NYC community severance variation. We employed principal component pursuit, a pattern recognition algorithm, combined with factor analysis as a novel method to estimate the Community Severance Index. We used public data for the year 2019 at census block group (CBG) level on road infrastructure, road traffic activity, and pedestrian infrastructure. As a demonstrative application of the Community Severance Index, we investigated the association between community severance and traffic collisions, as a proxy for road safety, in 2019 in NYC at CBG level. RESULTS: Our data revealed one multidimensional factor related to community severance explaining 74% of the data variation. In adjusted analyses, traffic collisions in general, and specifically those involving pedestrians or cyclists, were nonlinearly associated with an increasing level of Community Severance Index in NYC. CONCLUSION: We developed a high spatial-resolution Community Severance Index for NYC using data available nationwide, making it feasible for replication in other cities across the United States. Our findings suggest that increases in the Community Severance Index across CBG may be linked to increases in traffic collisions in NYC. The Community Severance Index, which provides a novel traffic-related exposure, may be used to inform equitable urban policies that mitigate health risks and enhance well-being.

Inverse associations of cord blood mitochondrial DNA copy number with childhood adiposity.

OBJECTIVE: The objective of this study was to examine associations between umbilical cord mitochondrial DNA copy number (mtDNAcn) and adiposity across childhood. METHODS: In a prospective birth cohort of Dominican and African American children from New York City, New York (1998-2006), mtDNAcn was measured in cord blood. Children (N = 336) were evaluated for their height, weight, and bioimpedance at age 5, 7, 9, and 11 years. We used linear mixed-effects models to assess associations of mtDNAcn tertiles in cord blood with child BMI, BMI z scores, fat mass index, and body fat percentage. Latent class growth models and interactions between mtDNAcn and child age or child age2 were used to assess associations between age and adiposity trajectories. RESULTS: BMI was, on average, 1.5 kg/m2 higher (95% CI: 0.58, 2.5) in individuals with mtDNAcn in the low- compared with the middle-mtDNAcn tertile. Results were similar for BMI z score, fat mass index, and body fat percentage. Moreover, children in the low-mtDNAcn group had increased odds of being in an "increasing" or "high-stable" adiposity class. CONCLUSIONS: Lower mtDNAcn at birth may predict greater childhood adiposity, highlighting the potential key role of perinatal mitochondrial function in adiposity during development.

The immediate effects of vision-zero corridor upgrades on pedestrian crashes in New York: A before-and-after spatial point process approach.

The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates. The experimental design included a treatment and two types of control conditions, and we controlled for well-known covariates including traffic exposure, land use, and risk-prone areas. We estimated a Geyer Saturation model and kernel density function for modeling the effect of Vision-Zero on crash intensity and dispersion two years before and after the implementation of Vision-Zero. The results reveal a significant global decrease of 6.1 % (p = 0.004) in pedestrian crash incidence in the treated sections compared with the control group two years after the treatment, and a greater dispersion of pedestrian injuries following the policy implementation.

Population-based surveillance for birth defects potentially related to Zika virus infection including 3-year mortality and developmental outcomes, and Early Intervention Program service use-New York City, 2016 birth cohort.

BACKGROUND: In response to the 2015-2017 Zika virus outbreak, New York City (NYC) identified and monitored infants with birth defects potentially related to congenital Zika virus. METHODS: Administrative data matches were used to describe the birth characteristics of children born in 2016 meeting screening criteria for birth defects potentially related to congenital Zika virus infection relative to other NYC births and to monitor mortality and Early Intervention Program use through age 2. RESULTS: Among 120,367 children born in NYC in 2016, 463 met screening criteria and 155 met the Centers for Disease Control and Prevention's case definition for birth defects potentially related to congenital Zika virus infection (1.3 per 1000; 95% confidence interval [CI], 1.1-1.5). Post-neonatal deaths occurred among 7.7% of cases (12) and 5.2% of non-cases (8). Odds of referral to the Early intervention Program among children who met screening criteria were lower among children of mothers who were married (OR, 0.60; 95% CI, 0.37-0.97) and among children not classified as cases whose mothers were born in Latin America and the Caribbean (OR, 0.59; 95% CI, 0.37-1.09). DISCUSSION: Prevalence of birth defects potentially related to congenital Zika virus infection was similar to that seen in other jurisdictions without local transmission. Birth defects attributable to congenital Zika virus infection may also have been present among screened children who did not meet the case definition.

Characterization of anthropogenic noise and oyster toadfish (Opsanus tau) calling behavior in urban and small-town coastal soundscapesa).

The oyster toadfish (Opsanus tau) is an ideal model to examine the effects of anthropogenic noise on behavior because they rely on acoustic signals for mate attraction and social interactions. We predict that oyster toadfish have acclimated to living in noise-rich environments because they are common in waterways of urban areas, like New York City (NYC). We used passive acoustic monitoring at two locations to see if calling behavior patterns are altered in areas of typically high boat traffic versus low boat traffic (Pier 40, NYC, NY, and Eel Pond, Woods Hole, MA, respectively). We hypothesized that toadfish in NYC would adjust their circadian calling behavior in response to daily anthropogenic noise patterns. We quantified toadfish calls and ship noise over three 24-h periods in the summer reproductive period at both locations. We observed an inverse relationship between the duration of noise and the number of toadfish calls at Pier 40 in comparison to Eel Pond. Additionally, toadfish at Pier 40 showed significant differences in peak calling behavior compared to Eel Pond. Therefore, oyster toadfish may have acclimated to living in an urban environment by potentially altering their communication behavior in the presence of boat noise.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City.

IMPORTANCE: Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. OBJECTIVE: To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. METHODS: We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. RESULTS: The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. CONCLUSION: These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.

The privilege of working from home and health disparities during the covid-19 pandemic in major American cities.

Working from home (WFH) has been adopted as a key mitigation strategy in the COVID-19 pandemic; yet few research has studied its impact on pandemic outcomes. Using multiple sources of data including cellphone data and online survey during the pandemic, this study investigates the effect of WFH on intra-city health disparities during the COVID-19 pandemic in American cities. Pandemic data for zip code tabulation areas and cellphone mobility data for census block groups in New York City (NYC), Chicago, and Philadelphia are converted to census tract level, which are then merged with 2019 census data. WFH is measured with the proportion of workers who potentially can telework based on employment composition in census tracts and percentages of jobs in each industry that actually WFH during the pandemic. Results show that while infection and death rates are higher in NYC, intra-city disparities in pandemic outcomes are more pronounced in Philadelphia. Poisson regressions show a negative association between WFH and COVID-19 infection and death rates in NYC and Chicago, which is weakened by increased time spent at home during the pandemic and in minority neighborhoods (in NYC). In Philadelphia, WFH is barely relevant for infection rates but has a marginally positive association with death rates, which is also moderated by the time spent at home. This study demonstrates the relative effectiveness of WFH in mitigating pandemic outcomes and underscores the intersectionality between WFH and race/ethnicity and resident behaviors. It provides important policy implications for future pandemic mitigation.

Vertical stratification and seasonality of fecal indicator bacteria in New York City playground sandboxes.

Sandboxes in public play spaces afford a crucial opportunity for urban children to engage in naturalistic play that fosters development of cognitive, social, and motor skills. As open pits, sandboxes in New York City public playgrounds are potentially exposed to fecal inputs from various sources, including wild and domestic animals. A longitudinal study of thirteen sandboxes located in public playgrounds on the east side of Manhattan reveals ubiquity of the fecal indicator bacteria enterococci and Escherichia coli through all seasons. The highest concentrations of bacteria occur in surface sand (n = 42; mean enterococci 230 MPN/g and E. coli 182 MPN/g dry weight), with significantly lower levels at depths below the surface (n = 35; mean enterococci 21 MPN/g and E. coli 12 MPN/g dry weight), a stratification consistent with fecal loading at the surface. Generalized linear mixed models indicate that sand depth (surface vs. underlayers) is the most influential variable affecting bacterial levels (P <0.001 for both enterococci and E. coli), followed by sampling season (P <0.001 for both). Bacterial concentrations do not vary significantly as a function of playground location or ZIP code within the study area. Children's exposure while playing in sandboxes likely reaches 105 enterococci and 104E. coli in a typical play period. Microbial source tracking to identify fecal hosts reveals dog, bird, and human biomarkers in low concentrations. Open sandbox microcosms installed at ground level in the urban environment of Manhattan are fouled by enterococci and E. coli within two weeks, while adjacent closed microcosms exhibit no fecal contamination over a 33-day sampling period. Collectively, our results indicate that increasing the frequency of sand refills and covering sandboxes during times of disuse would be straightforward management strategies to mitigate fecal contamination in playground sandboxes.

Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.

BACKGROUND: Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. METHODS: This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. RESULTS: A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). CONCLUSION: The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.

Longitudinal Associations of Food Security with Health and Dietary Factors among Food FARMacy Participants during COVID-19 in New York City.

In cross-sectional studies, food insecurity is associated with adverse health and dietary outcomes. Whether self-reported health and dietary outcomes change in response to improvements in food security has not been examined. We sought to examine how increases in food security are related to changes in health and dietary factors. In this longitudinal, observational study, we included adult participants in a clinical-community emergency food assistance program in New York City from July 2020 to November 2021. Program staff measured food security with a validated six-item measure at program enrollment and six-month re-enrollment. Participants self-reported health and dietary factors (vegetable, fruit, juice, and sugar-sweetened beverage (SSB) consumption frequency). We used multivariable regression to examine associations between change in food security with change in health and dietary factors over six months. Among 310 participants, the mean food security score improved by 1.7 ± 2.3 points over six months. In unadjusted models, each point improvement in food security was associated with increased vegetable (ß = 0.10 times; 95% CI: 0.05-0.15); fruit (ß = 0.08 times; 95% CI: 0.03-0.14); and juice (ß = 0.10 times; 95% CI: 0.05-0.15) consumption. In adjusted models, results remained significant for vegetable and fruit consumption, but not juice. Change in food security was not associated with change in health or SSB outcomes. In this cohort during COVID-19, improved food security was associated with improved vegetable and fruit consumption. Randomized trials that examine the effectiveness of clinical-community partnerships focused on improving food security and nutrition are warranted.

Home Care Clinicians' Perspectives on Advance Care Planning for Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

Advance care planning is important and timely for patients receiving home health services; however, opportunities to facilitate awareness and engagement in this setting are often missed. This qualitative descriptive study elicited perspectives of home health nurses and social workers regarding barriers and facilitators to creating advance care plans in home health settings, with particular attention to patients with few familial or social contacts who can serve as surrogate decision-makers. We interviewed 15 clinicians employed in a large New York City-based home care agency in 2021-2022. Participants reported a multitude of barriers to supporting patients with advance care planning at the provider level (eg, lack of time and professional education, deferment, discomfort), patient level (lack of knowledge, mistrust, inadequate support, deferment, language barriers), and system level (eg, discontinuity of care, variations in advance care planning documents, legal concerns, lack of institutional protocols and centralized information). Participants noted that greater socialization and connection to existing educational resources regarding the intended purpose, scope, and applicability of advance directives could benefit home care patients.

School Vaccine Coverage and Medical Exemption Uptake After the New York State Repeal of Nonmedical Vaccination Exemptions.

Importance: Although New York State (NYS) recently adopted legislation eliminating nonmedical vaccination exemption options from school-entry requirements, the implications of the law for school vaccine coverage and medical vaccine exemption uptake have not been examined. Objective: To evaluate the implications of the repeal of school-entry nonmedical vaccination exemptions for vaccine coverage and medical exemption uptake at NYS schools outside of New York City (NYC). Design, Setting, and Participants: This cohort study had an interrupted time-series design and used generalized estimating equation models to examine longitudinal school immunization compliance data from the 2012 to 2013 through 2021 to 2022 school years. The cohort comprised NYS public and nonpublic schools, excluding NYC schools, with any students enrolled in kindergarten to 12th grade. Eligible schools had enrollment and immunization data before and after the implementation of the Senate Bill 2994A legislation. Data analyses were conducted in July 2023. Exposure: Senate Bill 2994A was passed in June 2019, eliminating school-entry nonmedical vaccination exemptions. Since compliance with the law was evaluated for most students during the next school year, the 2019 to 2020 school year was considered to be the law's effective date. Main Outcomes and Measures: The primary outcomes were school vaccine coverage (defined as the percentage of students at each school who completed grade-appropriate requirements for all required vaccines) and medical exemption uptake (defined as the percentage of students at each school who received a medical exemption). Results: Among the 3821 eligible schools, 3632 (95.1%) were included in the analysis, representing 2794 (96.9% of eligible) public schools and 838 (89.2% of eligible) nonpublic schools. The implementation of Senate Bill 2994A was associated with absolute increases in mean vaccine coverage of 5.5% (95% CI, 4.5%-6.6%) among nonpublic schools and 0.9% (95% CI, 0.7%-1.1%) among public schools, with additional annual increases in vaccine coverage observed through the 2021 to 2022 school year. The law's implementation was also associated with a 0.1% (95% CI, 0.0%-0.1%) mean absolute decrease in medical vaccination exemption uptake at both public and nonpublic schools, and small but significant mean annual decreases in medical vaccination exemptions (0.02%; 95% CI, 0.01%-0.03%) through the end of the study period. Conclusions and Relevance: Results of this cohort study suggested that repeal of school-entry nonmedical vaccination exemptions was associated with increased vaccine coverage at NYS schools outside of NYC. Coverage gains were not replaced by increases in medical vaccination exemptions.