York City Zip Codes Research Articles

Modeling food-related business closure in select New York City communities using multi-scale and spatial features

This paper introduces an extensible framework to predict small-business closures to inform urban planners, lenders, and business owners as to factors to improve business resilience. This paper couples machine learning with two point of interest (POI) datasets and infrastructure data and uses New York State’s COVID-19 PAUSE as a stressor for investigating small-business resiliency. The study included 2537 food-related, non-chain, retail businesses across select New York City zip codes, of which 17.7% closed permanently. Macro-, meso-, and micro-levels of features included the neighborhood profile, street dynamics, and venue-specific, location-related characteristics. A Gaussian Mixture Neural Network model achieved 74.1% precision, 92.5% recall, and an 82.3% F1-score without use of financial data. High-end restaurants located further than average from public transit were most at risk for closure, while non-restaurant, food businesses in commercially diverse areas having higher-than-average social media ratings were least at risk. This paper introduces a model for timely prediction of pandemic-induced, food-related, small-business closures without reliance on private or protected financial data, and provides insights into urban design to promote small, food business survivability.

High-income ZIP codes in New York City demonstrate higher case rates during off-peak COVID-19 waves.

Our study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic. New York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022. COVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes. This study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

A population study of the NYS measles epidemic: Lessons learned.

In 2019 the United States experienced the largest outbreak of measles in 27 years, 19 years after the United States declared measles eliminated. The purpose of this paper was to present a population study of a measles outbreak within Orthodox Jewish communities in New York that led to the elimination of religious exemption for school mandated vaccines. Peer reviewed articles, news media, health department, and government resources were used to investigate environmental factors that led to this outbreak. State, county, and city immunization records were accessed to explore measles compliance rates before and after policy change. Rockland County had low compliance rates compared to the rest of the state, and the elimination of religious exemptions raised compliance rate almost to state level. In all but one affected New York City zip codes, compliance following policy change rose to 97.95%-99.15%. Overall, changes in measles compliance rates reflect policy goals, but localized differences imply a need for more customized interventions for each unique community. Public health planning to promote vaccination should be sensitive to the concerns and perceptions of each community in order for health interventions to have a positive effect.

The Association of Structural Inequities and Race With Out-of-Hospital Sudden Death During the COVID-19 Pandemic.

[Figure: see text].

Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes.

We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

IntroductionThis study aims to determine whether subway ridership and built environmental factors, such as population density and points of interests, are linked to the per capita COVID-19 infection rate in New York City ZIP codes, after controlling for racial and socioeconomic characteristics.MethodsSpatial lag models were employed to model the cumulative COVID-19 per capita infection rate in New York City ZIP codes (N=177) as of April 1 and May 25, 2020, accounting for the spatial relationships among observations. Both direct and total effects (through spatial relationships) were reported.ResultsThis study distinguished between density and crowding. Crowding (and not density) was associated with the higher infection rate on April 1. Average household size was another significant crowding-related variable in both models. There was no evidence that subway ridership was related to the COVID-19 infection rate. Racial and socioeconomic compositions were among the most significant predictors of spatial variation in COVID-19 per capita infection rates in New York City, even more so than variables such as point-of-interest rates, density, and nursing home bed rates.ConclusionsPoint-of-interest destinations not only could facilitate the spread of virus to other parts of the city (through indirect effects) but also were significantly associated with the higher infection rate in their immediate neighborhoods during the early stages of the pandemic. Policymakers should pay particularly close attention to neighborhoods with a high proportion of crowded households and these destinations during the early stages of pandemics.

Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses

To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States. We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes. US counties and zip codes in Illinois and New York City, as of May 5, 2020. Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures. As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (≥20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed. Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation.

Black/African American Communities are at highest risk of COVID-19: spatial modeling of New York City ZIP Code–level testing results

PurposeThe population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role. MethodsWe analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. ResultsSpatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons. ConclusionsAreas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.

Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods

New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighborhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighborhoods, as well as in neighborhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-randomness in testing across New York City neighborhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.

Demographic Determinants of Testing Incidence and COVID-19 Infections in New York City Neighborhoods

New York City is the hot spot of the COVID-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighborhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighborhoods, as well as in neighborhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of positive tests among those tested. The non-randomness in testing across New York City neighborhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.

A Comparison of the Nutritional Quality of Food Products Advertised in Grocery Store Circulars of High- versus Low-Income New York City Zip Codes

Grocery stores can be an important resource for health and nutrition with the variety and economic value of foods offered. Weekly circulars are a means of promoting foods at a sale price. To date, little is known about the extent that nutritious foods are advertised and prominently placed in circulars. This study’s aim was to compare the nutritional quality of products advertised on the front page of online circulars from grocery stores in high- versus low-income neighborhoods in New York City (NYC). Circulars from grocery stores in the five highest and five lowest median household income NYC zip codes were analyzed. Nutrition information for food products was collected over a two-month period with a total of 805 products coded. The study found no significant difference between the nutritional quality of products advertised on the front page of online circulars from grocery stores in high- versus low-income neighborhoods in New York City (NYC). In both groups, almost two-thirds of the products advertised were processed, one-quarter were high in carbohydrates, and few to no products were low-sodium, high-fiber, or reduced-, low- or zero fat. Through innovative partnerships with health professionals, grocery stores are increasingly implementing in-store and online health promotion strategies. Weekly circulars can be used as a means to regularly advertise and prominently place more healthful and seasonal foods at an affordable price, particularly for populations at higher risk for nutrition-related chronic disease.

Variation in Use of Buprenorphine and Methadone Treatment by Racial, Ethnic, and Income Characteristics of Residential Social Areas in New York City

National data indicate that patients treated with buprenorphine for opiate use disorders are more likely to be White, highly educated, and to have greater incomes than those receiving methadone, but patterns of buprenorphine dissemination across demographic areas have not been documented in major metropolitan areas where poverty, minority populations and injection heroin use are concentrated. Rates of buprenorphine and methadone treatment are compared among areas of New York City defined by their income and ethnic/racial composition. Residential social areas (hereinafter called social areas) were defined as aggregations of ZIP codes with similar race/ethnicity and income characteristics, and were formed based on clustering techniques. Treatment rates were obtained for each New York City ZIP code: buprenorphine treatment rates were based on the annual number of buprenorphine prescriptions written, and the methadone treatment rate on the number of methadone clinic visits for persons in each ZIP code. Treatment rates were correlated univariately with ethnicity and income characteristics of ZIP codes. Social area treatment rates were compared using individual ANOVA models for each rate. Buprenorphine and methadone treatment rates were significantly correlated with the ethnicity and income characteristics of ZIP codes, and treatment rates differed significantly across the social areas. Buprenorphine treatment rates were highest in the social area with the highest income and lowest percentage of Black and Hispanic residents. Conversely, the methadone treatment rate was highest in the social area with the highest percentage of low income and Hispanic residents. The uneven dissemination of 0pioid maintenance treatment in New York City may be reflective of the limited public health impact of buprenorphine in ethnic minority and low income areas. Specific policy and educational interventions to providers are needed to promote the use of buprenorphine for opiate use disorders in diverse populations.

Disparate Advertising of Sugary Drinks: An Analysis of Beverages Promoted in Circulars from Grocery Stores in High- and Low-Income New York City Zip Codes

Background: The consumption of sugar-sweetened beverages (SSBs) has been associated with an increased risk of chronic disease including obesity, type 2 diabetes, and coronary heart disease. A correlation between low socioeconomics (SES) status and higher SSB intake has also been established. Objective: The objective of this study was to analyze the calorie content and added sugar in beverages advertised in circulars of grocery stores in high- and low-income New York City zip codes. Methods: Across a two-month period, we analyzed various nutritional characteristics of beverage products advertised on the front page of online circulars from grocery stores in 5 low- and 5 high-income New York City zip codes. Results: Three-fourths of beverage products for sale in circulars from low-income zip codes were sugar-sweetened (74.4%) as compared with just over one-third advertised in those from high-income zip codes (35.7%). Conclusion: High-calorie, sugary beverages are being marketed and priced to sell by grocery stores serving low SES populations with high SSB consumption patterns. Health education and promotion efforts that grocery stores can implement to contribute to chronic disease prevention are discussed.

Respiratory Symptoms Necessitating Spirometry Among Soldiers With Iraq/Afghanistan War Lung Injury

New-onset asthma rates are higher among US soldiers deployed to Iraq/Afghanistan than stateside, but overall respiratory symptom and spirometry rates among soldiers returning from Iraq/Afghanistan have not yet been addressed. We determined these rates in soldiers deployed to Iraq/Afghanistan versus troops stationed elsewhere. Retrospective review of active-duty soldiers (2004 to 2010) registered at Veterans Affairs Medical Center, Northport, New York, with Long Island/New York City zip codes. Subjects were examined by physicians or physicians' assistants. We counted number of spirometries, which required respiratory symptoms, and the provider was required to submit a diagnosis as part of the request process. Twenty-five percent of 7151 troops went to Iraq/Afghanistan (n = 1816) and 75% went elsewhere (n = 5335), with more smokers in the Iraq/Afghanistan group (16.1% vs 3.3%). Rates of symptoms and spirometry were 14.5% and 1.8%, for Iraq/Afghanistan, versus troops deployed elsewhere, respectively (P < 0.001). Both groups had similar forced expired volume in 1 second/forced vital capacity ratios (78%). New-onset Iraq/Afghanistan war lung injury is common and rates of symptoms leading to a diagnosis requiring spirometry are high.

Increased prevalence of primary biliary cirrhosis near superfund toxic waste sites

Primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC) are uncommon liver diseases of unknown etiology. Reported clustering of PBC cases may be due to environmental factors. Individuals with PBC have a high prevalence of thyroid disease and thyroid disease is reportedly more prevalent near Superfund toxic waste sites (SFS). The objective of this study was to examine the prevalence and potential clustering of individuals with PBC and PSC near SFS. De-identified clinical and demographic data were used to determine the observed prevalence for each New York City zip code (n = 174) and borough (n = 5) of patients with PBC (PBC-OLT) or PSC (PSC-OLT) who were listed for liver transplantation. The expected prevalence was calculated using Organ Procurement and Transfer Network (OPTN) and U.S. Census data. Both PBC-OLT patients and patients not listed for liver transplantation (PBC-MSSM) were included in the cluster analysis. Prevalence ratios of PBC-OLT and PSC-OLT cases were compared for each zip code and for each borough with regard to the proximity or density of SFS, respectively. SaTScan software was used to identify clusters of PBC-OLT cases and PBC-MSSM cases. Prevalence ratio of PBC-OLT, not PSC-OLT, was significantly higher in zip codes containing or adjacent to SFS (1.225 vs. 0.670, respectively, P = .025). The borough of Staten Island had the highest prevalence ratio of PBC-OLT cases and density of SFS. Significant clusters of both PBC-OLT and PBC-MSSM were identified surrounding SFS. In conclusion, toxin exposure may be a risk factor influencing the clustering of PBC cases.

The use of zip coded population data in social area studies of service utilization

Social area analysis (SAA) is a well-established methodology for geographically based analysis of service delivery and other health related outcome measures. However, SAA has been limited in application by (a) the complexities of computerized geocoding and (b) difficulties in obtaining appropriate measures of community characteristics. In this paper we demonstrate that these problems can be overcome by (a) employing zip code as the unit of analysis for SAA and (b) obtaining commerically available measures of community characteristics. Regression analyses, using New York City zip code populations as the unit of observation were used to illustrate the role of epidemiologic risk factors in predicting rates of inpatient discharges and ambulatory care visits.