Nurse practitioners' workforce outcomes under implementation of full practice authority.

BACKGROUND: Full practice authority laws that permit nurse practitioners (NPs) to practice independently and prescribe medications may influence NPs' workforce outcomes. PURPOSE: To examine whether implementation of full practice authority laws affect NP self-employment, average earnings, and likelihood of residing in a primary care health professional shortage area (HPSA). METHODS: A nationally representative U.S. sample of 9,782 NPs employed in health care during 2010 to 2018 was drawn from the American Community Survey. Difference-in-differences regression was used to estimate covariate-adjusted mean differences in NPs' workforce outcomes after full practice authority implementation. FINDINGS: Among full-time employed NPs, full practice authority was associated with an increased probability of residing in a HPSA (adjusted odds ratio [aOR]:2.34, 95%CI 1.14, 4.83) and with a higher mean probability of self-employment (aOR:4.97, 95%CI 1.00, 24.86). DISCUSSION: Full practice authority implementation improves access to primary care providers in health professional shortage areas and may increase practice ownership among NPs.

Measuring Residential Segregation With the ACS: How the Margin of Error Affects the Dissimilarity Index.

The American Community Survey (ACS) provides valuable, timely population estimates but with increased levels of sampling error. Although the margin of error is included with aggregate estimates, it has not been incorporated into segregation indexes. With the increasing levels of diversity in small and large places throughout the United States comes a need to track accurately and study changes in racial and ethnic segregation between censuses. The 2005-2009 ACS is used to calculate three dissimilarity indexes (D) for all core-based statistical areas (CBSAs) in the United States. We introduce a simulation method for computing segregation indexes and examine them with particular regard to the size of the CBSAs. Additionally, a subset of CBSAs is used to explore how ACS indexes differ from those computed using the 2000 and 2010 censuses. Findings suggest that the precision and accuracy of D from the ACS is influenced by a number of factors, including the number of tracts and minority population size. For smaller areas, point estimates systematically overstate actual levels of segregation, and large confidence intervals lead to limited statistical power.

Spatial Variation in the Quality of American Community Survey Estimates.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system.

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

A latent class model to identify city/town chronic disease patterns.

OBJECTIVE: Most departments of health grapple with how to most effectively allocate resources to address chronic diseases. We adapted a model created by Massachusetts to create customized city/town profiles in order to identify the patterns of chronic disease among 39 cities/towns in Rhode Island. METHODS: We used four data sources to identify 20 indicators of four domains: demographics and socioeconomic status; health behaviors and chronic diseases prevalence; no regular provider and non-emergent emergency department visits; and chronic disease-related hospitalizations. A latent class model was used to group cities/towns into distinct latent class memberships based on similar patterns of indicators. Data were analyzed in 2014. RESULTS: The latent class model differentiated three distinct classes of city/town, reflecting three levels of economic and health indicators. CONCLUSIONS: Our model was a simplified version of one constructed by Massachusetts that larger states can also use to understand chronic disease patterns among cities/towns. Chronic disease programs and policies can use the findings to direct resources toward targets not always identified by more traditional analyses.

Patterns and causes of uncertainty in the American Community Survey.

In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.

Disparities in Health Insurance Among Middle Eastern and North African American Children in the US.

OBJECTIVE: To estimate and compare the proportion of foreign-born Middle Eastern/North African (MENA) children without health insurance, public, or private insurance to foreign- and US-born White and US-born MENA children. METHODS: Using 2000 to 2018 National Health Interview Survey data (N = 311 961 children) and 2015 to 2019 American Community Survey data (n = 1 892 255 children), we ran multivariable logistic regression to test the association between region of birth among non-Hispanic White children (independent variable) and health insurance coverage types (dependent variables). RESULTS: In the NHIS and ACS, foreign-born MENA children had higher odds of being uninsured (NHIS OR = 1.50, 95%CI = 1.10-2.05; ACS OR = 2.11, 95%CI = 1.88-2.37) compared to US-born White children. In the ACS, foreign-born MENA children had 2.11 times higher odds (95%CI = 1.83-2.45) of being uninsured compared to US-born MENA children. CONCLUSION: Our findings have implications for the health status of foreign-born MENA children, who are currently more likely to be uninsured. Strategies such as interventions to increase health insurance enrollment, updating enrollment forms to capture race, ethnicity, and nativity can aid in identifying and monitoring key disparities among MENA children.

A BAYESIAN HIERARCHICAL SMALL AREA POPULATION MODEL ACCOUNTING FOR DATA SOURCE SPECIFIC METHODOLOGIES FROM AMERICAN COMMUNITY SURVEY, POPULATION ESTIMATES PROGRAM, AND DECENNIAL CENSUS DATA.

Small area population counts are necessary for many epidemiological studies, yet their quality and accuracy are often not assessed. In the United States, small area population counts are published by the United States Census Bureau (USCB) in the form of the decennial census counts, intercensal population projections (PEP), and American Community Survey (ACS) estimates. Although there are significant relationships between these three data sources, there are important contrasts in data collection, data availability, and processing methodologies such that each set of reported population counts may be subject to different sources and magnitudes of error. Additionally, these data sources do not report identical small area population counts due to post-survey adjustments specific to each data source. Consequently, in public health studies, small area disease/mortality rates may differ depending on which data source is used for denominator data. To accurately estimate annual small area population counts and their associated uncertainties, we present a Bayesian population (BPop) model, which fuses information from all three USCB sources, accounting for data source specific methodologies and associated errors. We produce comprehensive small area race-stratified estimates of the true population, and associated uncertainties, given the observed trends in all three USCB population estimates. The main features of our framework are: (1) a single model integrating multiple data sources, (2) accounting for data source specific data generating mechanisms and specifically accounting for data source specific errors, and (3) prediction of population counts for years without USCB reported data. We focus our study on the Black and White only populations for 159 counties of Georgia and produce estimates for years 2006-2023. We compare BPop population estimates to decennial census counts, PEP annual counts, and ACS multi-year estimates. Additionally, we illustrate and explain the different types of data source specific errors. Lastly, we compare model performance using simulations and validation exercises. Our Bayesian population model can be extended to other applications at smaller spatial granularity and for demographic subpopulations defined further by race, age, and sex, and/or for other geographical regions.

Monitoring health reform efforts: which state-level data to use?

This study compares estimates of health insurance coverage from the American Community Survey (ACS) to those in twelve state-specific surveys. Uninsurance estimates for the nonelderly are consistently higher in the ACS than in state surveys, as are direct purchase insurance estimates. Estimates for employer-sponsored insurance are similar, but public coverage rates are lower in the ACS. The ACS meets some but not all of the states' data needs; its large sample size and inclusion of all U.S. counties in the sample allow for comparison of insurance coverage within and across states. State-specific surveys provide the flexibility to add policy-relevant questions, including questions needed to examine how health insurance translates into access, use, and affordability of health services.

The Disproportionate Burden of COVID-19 Cases among Arab Americans.

Racial and ethnic disparities in COVID-19 cases are pervasive. Some minority, immigrant, and marginalized groups, such as Arab Americans, have been excluded from the research. This population confronts barriers to health care, discrimination, and other factors that may affect understanding, testing, and treatment as it relates to COVID-19. Arab Americans are unique compared to Hispanic, non-Hispanic black, and Asians because Arab Americans do not have a specific ethnic identifier and are classified as non-Hispanic white. Given these issues, this study will estimate COVID-19 cases and examine associations among Arab Americans compared to Hispanic, non-Hispanic black, non-Hispanic white, and Asian adults. Data from the Michigan Disease Surveillance System (March 2020-July 2021), the American Community Survey (2015-2019), and an Arab/Chaldean surname algorithm were used. Chi-square tests were used to determine statistically significant differences between groups. Logistic regression was used to estimate age-adjusted and sex-stratified proportions among Arab Americans compared to non-Hispanic whites before and after adjusting for age and sex. Approximately 17% of Arab Americans tested positive for COVID-19 compared to 11.32% of Hispanics, 9.80% of non-Hispanic blacks, 7.50% of non-Hispanic whites, and 4.24% of Asians. Arab Americans had 2.63 (95% CI: 2.59, 2.66) times greater odds of testing positive for COVID-19 compared to non-Hispanic whites. When Arab Americans were disaggregated from non-Hispanic whites, alarming patterns in COVID-19 cases were observed for Arab Americans. To accurately represent the burden of COVID-19 among Arab Americans, this population needs to have an ethnic identifier that informs appropriate health policy decisions and practice.

Neighborhood-level COVID vaccination and booster disparities: A population-level analysis across California.

Objectives: To describe vaccine and booster uptake by neighborhood-level factors in California. Methods: We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions. Results: In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region. Conclusions: Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.

Cognitive Disability Among Arab Americans by Nativity Status and Arrival Year: Lack of Evidence for the Healthy Migrant Effect.

Limited research exists on cognitive disabilities among Arab Americans, especially as it relates to arrival year among the foreign-born. The objectives of this study were to estimate the age- and sex-adjusted prevalence and associations of cognitive disability by (1) nativity status and (2) arrival year (pre-1991, 1991-2000, 2001-2013, and 2014-2018). We analyzed 11 years (2008-2018) of data from the American Community Survey (ACS) Public Use Microdata Samples (weighted n = 264,086; ages ≥ 45 years). Weighted means, percentages, age- and sex-adjusted prevalence estimates, and logistic regression results (crude and adjusted) were calculated. Among all Arab Americans, the age- and sex-adjusted prevalence of cognitive disability was 6.5%. The prevalence was lower for US-born (4.0%) compared to foreign-born (6.0%) (p-value < 0.0001). In logistic regression results, foreign-born Arab Americans were more likely to have a cognitive disability compared to US-born Arab Americans after adjusting for age and sex (OR = 1.41; 95% CI = 1.24, 1.61). Among foreign-born, Arab Americans arriving in 2014 or later had a lower prevalence of cognitive disability (3.4%) compared to all other arrival years at approximately 4.7%. With those arriving prior to 1991 as the reference category, those arriving between 1991 and 2000 were more likely to report a cognitive disability (OR = 1.05; 95% CI = 1.00, 1.08). However, those arriving between 2014 and 2018 were less likely to report a cognitive disability (OR = 0.81; 95% CI = 0.73, 0.88). These findings challenge the universality of the "healthy migrant effect" and highlight the relevance of socioeconomic disparities for Arab American cognitive health.

Temporal Trends over a Decade in Serious Vision Impairment in a Large, Nationally Representative Population-based Sample of Older Americans: Gender, Cohort and Racial/Ethnic Differences.

PURPOSE: The objectives of this study are:1)To identify temporal trends in the age-sex-race/ethnicity adjusted prevalence of vision impairment among Americans aged 65+ from 2008-2017; To determine if these temporal trends in vision impairment differ by 2)gender and age cohort, and 3)race/ethnicity, and; 4)To investigate if improvements in cohort educational attainment partially attenuate these trends. METHODS: Secondary analysis of 10 years of annual nationally-representative data from the American Community Survey with 5.4 million community-dwelling and institutionalized older adults aged 65+. The question on vision impairment was "Is this person blind or does he/she have serious difficulty seeing even when wearing glasses?". RESULTS: The prevalence of serious vision impairment in the US population aged 65+ declined from 8.3% to 6.6% between 2008 and 2017. There would have been an additional 848,000 older Americans with serious vision impairment in 2017 if rates had remained at the 2008 level. After age, sex and race/ethnicity were controlled, women had a 2.1% per year decline in the odds of vision impairment (OR = 0.979; CI = 0.977, 0.980), which represents a 21% decline over the decade, and men had a 9% decline over the decade (OR = 0.991; CI = 0.989, 0.993). Adjusting for education attenuated the decade decline among women, reducing it to 13%, and completely attenuated the decline among men. Most of the decline was among those aged 75+. Racial/ethnic disparities narrowed over the decade. CONCLUSION: Between 2008 and 2017, the prevalence of serious vision impairment among older Americans declined significantly, with steeper declines among African Americans and Hispanic Americans than among non-Hispanic White Americans.

Arts majors and the Great Recession: a cross-sectional analysis of educational choices and employment outcomes.

This study uses American Community Survey data to examine the impact of the Great Recession on college graduates majoring in the arts. Arts graduates play important roles in an economy, through both artistic creation and in careers outside of the arts. While the Great Recession took a significant toll on the US economy generally, arts majors faced additional vulnerabilities as industries that rely on discretionary spending, like the arts and entertainment, are especially hard hit in times of economic downturn. This paper assesses the impact of graduating during or shortly after the recession relative to graduating shortly before this period on educational choices, including choice of major, double majoring, and completing an advanced degree, and career outcomes, including employment status, type of employment, hours worked, and earnings, for college graduates majoring in the arts. Graduating before or after the recession is found to have a negative impact on the share of graduates majoring in traditional arts fields, but a positive impact on the share majoring in related creative fields. Using a difference-in-difference estimation strategy, relative to non-art college graduates, traditional arts majors graduating during or after the Great Recession are more likely to complete a double major, be self-employed, be unemployed, work longer hours, and earn less income than those graduating prior to the recession. These impacts are likely to have a negative effect on the pipeline of college-educated artists working in the arts into the future.

Factors associated with accurate reporting of public and private health insurance type.

OBJECTIVE: To examine factors associated with accurate reporting of private and public health insurance coverage. DATA SOURCES: Minnesota health plan enrollment records provided the sample for the Comparing Health Insurance Measurement Error (CHIME) study, a survey conducted in 2015 that randomly assigned enrollees to treatments that included health insurance questions from the American Community Survey (ACS) or the redesigned Current Population Survey Annual Social and Economic Supplement (CPS). STUDY DESIGN: Reverse record check study that compared CHIME study survey responses to enrollment records of coverage type (direct purchase on and off the Marketplace, Medicaid, or MinnesotaCare), service use, subsidy receipt, and duration of coverage from a major insurer. DATA COLLECTION METHODS: Using matched enrollment and CHIME survey data and logistic regression, we examined correlates of accurate insurance type reporting, including characteristics of the insurance coverage, the covered individual, respondent, and family. PRINCIPAL FINDINGS: Reporting accuracy across treatment and coverage type is high (77%-84%). As with past research, accurate reporting of public insurance is higher for people with characteristics consistent with eligibility for public insurance for both survey treatments. For the ACS treatment, reports of direct purchase insurance are more accurate for enrollees who receive a premium subsidy. CONCLUSIONS: Given the complexity of health insurance measurement and frequently changing policy environment, differences in reporting accuracy across treatments or coverage types are not surprising. Several results have important implications for data editing and modeling routines. First, adding premium and subsidy questions in federal surveys should prove useful given the finding that subsidy receipt is associated with reporting accuracy. Second, across both survey treatments, people whose opportunity structures (race, ethnicity, and income) match public program eligibility are accurate reporters of this coverage. This evidence supports using these commonly collected demographic variables in simulation, imputation, and editing routines.

Uncertainty in geospatial health: challenges and opportunities ahead.

PURPOSE: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

County-Level Social Determinants of Health and COVID-19 in Nursing Homes, United States, June 1, 2020-January 31, 2021.

OBJECTIVES: Nursing homes are a primary setting of COVID-19 transmission and death, but research has primarily focused only on factors within nursing homes. We investigated the relationship between US nursing home-associated COVID-19 infection rates and county-level and nursing home attributes. METHODS: We constructed panel data from the Centers for Medicare & Medicaid Services (CMS) minimum dataset, CMS nursing home data, 2010 US Census data, 5-year (2012-2016) American Community Survey estimates, and county COVID-19 infection rates. We analyzed COVID-19 data from June 1, 2020, through January 31, 2021, during 7 five-week periods. We used a maximum likelihood estimator, including an autoregressive term, to estimate effects and changes over time. We performed 3 model forms (basic, partial, and full) for analysis. RESULTS: Nursing homes with nursing (0.005) and staff (0.002) shortages had high COVID-19 infection rates, and locally owned (-0.007) or state-owned (-0.025) and nonprofit (-0.011) agencies had lower COVID-19 infection rates than privately owned agencies. County-level COVID-19 infection rates corresponded with COVID-19 infection rates in nursing homes. Racial and ethnic minority groups had high nursing home-associated COVID-19 infection rates early in the study. High median annual personal income (-0.002) at the county level correlated with lower nursing home-associated COVID-19 infection rates. CONCLUSIONS: Communities with low rates of nursing home infections had access to more resources (eg, financial resources, staffing) and likely had better mitigation efforts in place earlier in the pandemic than nursing homes that had access to few resources and poor mitigation efforts. Future research should address the social and structural determinants of health that are leaving racial and ethnic minority populations and institutions such as nursing homes vulnerable during times of crises.

Disability and the household context: Findings for the United States from the public Use Microdata Sample of the American Community Survey.

Introduction: Based on questions about impairments and activity limitations, the American Community Survey shows that roughly 13% of the U.S. population is experiencing disability. As most people live in households with other persons, this study explores disability at the household level. Considering the literature on household decision-making, solidarity, and capabilities in disability, this analysis of the household context of disability takes into account residential settings, household composition, and urban-rural differences. Method: The 2015-2019 ACS Public Use Microdata Sample (PUMS), which shows persons with disability (PwD) and persons without disability (PwoD), also indicates household membership, used here to separately identify PwoD as those living in households with persons with disability (PwoD_HHwD) and those in households without any household member with disability (PwoD_HHwoD). Relationship variables reveal the composition of households with and without disabilities. An adaption of Beale's rural-urban continuum code for counties is used to approximate rural-urban differences with ACS PUMS data. Results: Solo living is two times as common among persons with disability than among persons without disability, and higher in rural than urban areas. In addition to 43 million PwD, there are another 42 million PwoD_HHwD. Two times as many persons are impacted by disability, either of their own or that of a household member, than shown by an analysis of individual-level disability. For family households, differences in the composition of households with and without disabilities are considerable with much greater complexities in the makeup of families with disability. The presence of multiple generations stands out. Adult sons or daughters without disability play an important role. Modest urban-rural differences exist in the composition of family households with disability, with a greater presence of multigenerational households in large cities. Discussion: This research reveals the much wider scope of household-level disability than indicated by disability of individuals alone. The greater complexity and multigenerational makeup of households with disability imply intergenerational solidarity, reciprocity, and resource sharing. Household members without disability may add to the capabilities of persons with disabilities. For the sizeable share of PwD living solo, there is concern about their needs being met.

Relationship of political ideology of US federal and state elected officials and key COVID pandemic outcomes following vaccine rollout to adults: April 2021-March 2022.

Background: Scant research, including in the United States, has quantified relationships between the political ideologies of elected representatives and COVID-19 outcomes among their constituents. Methods: We analyzed observational cross-sectional data on COVID-19 mortality rates (age-standardized) and stress on hospital intensive care unit (ICU) capacity for all 435 US Congressional Districts (CDs) in a period of adult vaccine availability (April 2021-March 2022). Political metrics comprised: (1) ideological scores based on each US Representative's and Senator's concurrent overall voting record and their specific COVID-19 votes, and (2) state trifectas (Governor, State House, and State Senate under the same political party control). Analyses controlled for CD social metrics, population density, vaccination rates, the prevalence of diabetes and obesity, and voter political lean. Findings: During the study period, the higher the exposure to conservatism across several political metrics, the higher the COVID-19 age-standardized mortality rates, even after taking into account the CD's social characteristics; similar patterns occurred for stress on hospital ICU capacity for Republican trifectas and US Senator political ideology scores. For example, in models mutually adjusting for CD political and social metrics and vaccination rates, Republican trifecta and conservative voter political lean independently remained significantly associated with an 11%-26% higher COVID-19 mortality rate. Interpretation: Associations between the political ideologies of US federal elected officials and state concentrations of political party power with population health warrant greater consideration in public health analyses and monitoring dashboards. Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Persistent disparities in SARS-CoV-2 test percent positivity by neighborhood in New York City, March 1-July 25, 2020.

PURPOSE: To examine neighborhood-level disparities in SARS-CoV-2 molecular test percent positivity in New York City (NYC) by demographics and socioeconomic status over time to better understand COVID-19 inequities. METHODS: Across 177 neighborhoods, we calculated the Spearman correlation of neighborhood characteristics with SARS-CoV-2 molecular test percent positivity during March 1-July 25, 2020 by five periods defined by trend in case counts: increasing, declining, and three plateau periods to account for differential testing capacity and reopening status. RESULTS: Percent positivity was positively correlated with neighborhood racial and ethnic characteristics and socioeconomic status, including the proportion of the population who were Latino and Black non-Latino, uninsured, Medicaid enrollees, transportation workers, or had low educational attainment. Correlations were generally consistent over time despite increasing testing rates. Neighborhoods with high proportions of these correlates had median percent positivity values of 62.6%, 28.7%, 6.4%, 2.8%, and 2.2% in the five periods, respectively, compared with 40.6%, 11.7%, 1.7%, 0.9%, and 1.0% in neighborhoods with low proportions of these correlates. CONCLUSIONS: Disparities in SARS-CoV-2 molecular test percent positivity persisted in disadvantaged neighborhoods during multiple phases of the first few months of the COVID-19 epidemic in NYC. Mitigation of the COVID-19 burden is still urgently needed in disproportionately affected communities.