County-level Data Research Articles

Study on the spatio-temporal evolution characteristics and driving mechanism of China’s carbon emissions

The present study aims to explore the spatial and temporal changes and drivers of carbon emission patterns in China, with the aim of encouraging county-level carbon reduction policies in pursuit of sustainable development. To this end, we have studied the spatial disparities, spatio-temporal patterns, and evolution characteristics of carbon emissions using county-level carbon emissions data from China between 2002 and 2017. Additionally, we have comprehensively considered the dynamic impacts of both county-level and city-level environmental factors on carbon emissions based on an optimized hierarchical random forest model. The results show that the carbon emissions of China’s counties have generally followed an upward trend before stabilizing. Notable characteristics include elevated carbon emissions in the northern regions and reduced carbon emissions in the southern areas. Additionally, there are higher carbon emissions in the eastern regions compared to lower emissions in the western and inland areas, with discernible local clustering patterns. These findings underscore the importance of tailoring the government’s emission reduction strategy to address the phased variations in carbon emissions across different districts and counties. It is essential to emphasize the key role of major urban agglomerations and metropolitan areas in carbon emission reduction, while also addressing potential emission sources in the resource-rich, yet technologically disadvantaged, northwest region. Furthermore, improving energy efficiency through technological innovation should be the primary means of carbon emission reduction at the county level.

The impact of extreme temperature on labor wage: Evidence from Chinese manufacturing firms

Labor wage is a common focus of welfare economics and public policy, but little is known about its response to climate factors. With random year-to-year variation in temperature based on county-level data while using comprehensive and nationwide manufacturing data in China, this paper identifies the effect of extreme temperatures on firm-level labor wage. The result shows an inverted U-shape relationship between temperature and firm-level labor wage. We find that extremely high and low temperatures affect labor wage through different channels. Specifically, extremely cold temperatures lower labor wage by reducing the average wage from the intensive margin, whereas the negative impact of high temperatures on labor wage is mainly driven by the decrease in the number of employees from the extensive margin aspect. The temperature effect is more pronounced for labor-intensive firms, firms with employees with lower education levels, and low-tech firms. Areas with a hot climate are more sensitive to extremely cold temperatures but are not affected by extremely high temperatures because of the adaptation to climate change. We also find that the high-temperature subsidy policy has not achieved the expected goal of protecting workers’ welfare during this period. Our results suggest that China should raise public awareness of the danger of extreme temperatures and take various adaptive measures to deal with extreme temperatures.

Soil health explains the yield-stabilizing effects of soil organic matter under drought

Soil organic matter (SOM) serves as an important indicator of soil health. Soils with high SOM are associated with high crop yield under drought conditions. However, a critical question remains unanswered: is the yield-stabilizing effect of SOM attributable to inherent soil properties, such as soil texture and taxonomy? Or is it driven by dynamic soil properties that reflect the overall health of the soil? Following the Soil Health Assessment Protocol and Evaluation, we derived a soil health score (SHS; range: 0–1) from the SOM concentration by accounting for site-specific variables, including climate, texture, and soil suborder. Using county-level data of rainfed corn across the U.S. from 2000 to 2016, we found that higher SHS were associated with higher yields. During the most severe drought events, an increase of 0.5 in SHS was associated with a 1.15 ​± ​0.18 ​Mg ​ha−1 increment in corn yield, suggesting that high SHS helps to stabilize yield in drought. Interestingly, smaller but statistically significant effects of SHS on yield were found during less intensive droughts. The SOM concentration was a slightly better predictor of corn yield than the SHS. We also found similar effects of SHS on corn yield across different soil types, i.e., different textures or soil suborders, under severe drought conditions. Our results suggest that soil health is a main factor in explaining the yield benefits of SOM, while the effects of soil health were not driven by differences in soil texture or suborder. We argue that the resilience of corn yield against drought can be potentially increased by adopting agronomic practices aimed at augmenting SOM and improving overall soil health across a broad spectrum of geographical locations and site characteristics.

Political environment and overall survival after cancer diagnosis in the United States (US).

184 Background: Political environment in the US is closely tied to state and national-level budgetary spending as well as laws and regulations that influence health. It is unclear if survival gains in cancer care differ by county political voting status. We investigated survival trends based on county level presidential voting patterns. Methods: A 5% sample of individuals diagnosed with cancer from 2001 to 2017 in the SEER-Medicare Program was linked to county level data on US presidential elections over the same period. Using the total number of votes in each party, county political environment was classified as either Democratic or Republican for the four years that followed a presidential election. Cox proportional hazards models estimated changes in survival over time, and differences by political party were assessed with interaction analysis. To account for the association between follow-up time and year of diagnosis, overall survival was censored at 3 years for individuals diagnosed in 2015 or earlier. Results: Of 100,755 individuals in our cohort from 2001-2017, 66439 (65.9%) resided in Democratic and 34316 (34.1%) in Republican counties with similar median age at diagnosis and distribution of regional/distant disease. Democratic counties had more urban-located residents (93% vs 70%). 1-year survival in both Democratic and Republican counties increased from 2001 (77% for both) to 2017 (81% Democratic, 80% Republican). 3-year survival increased in Democratic (62% in 2001 to 66% in 2015) but not in Republican counties (65% in 2001 and 2015). Compared to the 2000 election cycle, patients residing in Democratic counties had an 11% decreased risk of death (Hazard Ratio (HR) = 0.89) after the 2008 election cycle while Republican counties did not see these improvements during the same time period (Table; interaction P = 0.02). Among White patients, survival improved (HR = 0.89 for 2016 election cycle compared to 2000) in Democratic counties, but not in Republican counties (HR = 0.99 for same years, P = 0.02). However, among Black patients, survival improved in both Democratic (HR = 0.89) and Republican counties (HR = 0.89) for the same years (P = 0.87). Conclusions: Survival after cancer diagnosis in Republican voting counties has concerningly lagged behind that of Democratic voting counties following the 2008 elections coinciding with the passage of the Affordable Care Act. Our study sheds light on the sharp contrast between political policy and legislation, and rural-urban divide driving outcomes in these areas.[Table: see text]

Impact of the COVID-19 Pandemic on Social Service Organizations in a Clinical-Community Collaboration

Abstract: Increasingly, interventions are being developed to promote collaboration across health care and social service (such as food, housing, and transportation) sectors. During the COVID-19 pandemic, demand for social services grew while social service organizations' capacity declined due to constraints on staffing, funding, and operations. We used an organizational survey fielded from July through November 2020 and publicly available, county-level data to assess the pandemic's impact on 253 social service organizations in the Accountable Health Communities Model evaluation. Over half of surveyed organizations reported being severely impacted by the pandemic, and 92% reported being at least moderately impacted. Social service organizations without federal funding and those in counties with lower poverty (smaller proportion of residents in poverty) and higher COVID-19 case rates were most impacted by the pandemic. Understanding the pandemic's burden on social service organizations can inform planning for future collaborations across health care and social service sectors.

Impact of Social Disparities on 5-Year Survival Rates in Pediatric Hematologic Malignancies.

Socioeconomic disparities exist in pediatric patients with hematologic malignancies, leading to suboptimal survival rates. Social determinants of health impact health outcomes, and in children, they may not only lead to worse survival outcomes but carry over into late effects in adult life. The social deprivation index (SDI) is a composite score using geographic county data to measure social determinants of health. Using the SDI, the purpose of the present study is to stratify survival outcomes in pediatric patients with hematologic malignancies based on area deprivation. A retrospective cohort study was performed using the national Surveillance, Epidemiology, and End Results oncology registry in the USA from 1975 to 2016 based on county-level data. Pediatric patients (≤18y old) with a diagnosis of leukemia or lymphoma based on the International Classification for Oncology, third edition (ICD-O-3) were used for inclusion criteria. Patients were grouped by cancer subtype for leukemia into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia while for lymphoma into non-Hodgkin's lymphoma and Hodgkin's lymphoma. SDI scores were calculated for each patient and divided into quartiles, with Q1 being the lowest area of deprivation and Q4 being the highest, respectively. A total of 38,318 leukemia and lymphoma patients were included. Quartile data demonstrated stratification in survival based on area deprivation for ALL, with no survival differences in the other cancer subtypes. Patients with ALL from the most deprived area had a roughly 3% difference in both overall and cancer-specific morality at 5 years compared with the least deprived area. Disparities in pediatric patients with ALL represent a significant area for quality improvement. Social programs may have value in improving survival outcomes and could rely on metrics such as SDI.

Policies to reduce child poverty and child maltreatment: A scoping review and preliminary estimates of indirect effects

This study includes a scoping review of prior studies investigating the effects of policy changes on child poverty rates. It further conducts an empirical analysis to estimate the relationship between child poverty rates and child maltreatment report (CMR) rates, utilizing national county-level data. The study then calculates the indirect effects of policy changes on CMR rates, mediated through child poverty rates, by integrating information from previous studies with its own empirical findings. Among the policy changes explored in prior studies, those related to a child allowance and a fully refundable Child Tax Credit demonstrate the largest indirect effects but also the highest costs. The expansion of in-kinds and near-cash benefits, such as the Supplemental Nutrition Assistance Program benefits and housing vouchers, shows moderate effects with moderate costs. Tax credits like the Earned Income Tax Credit exhibit lower effects and costs when targeted at the lowest earners, and moderate effects and costs for broader expansion. Focused tax credits, such as the Child and Dependent Care Tax Credit, had lower effects and costs, even if made fully refundable. Despite certain limitations, the study's approach yields consistent estimates with a recent simulation study, indicating its potential validity. While some proposed policy changes may seem expensive, implementing them is anticipated to substantially reduce CMR rates, with the benefits outweighing the associated costs. Overall, the findings suggest that addressing child poverty to reduce CMRs is an attractive strategy with numerous potential benefits.

Geospatial and Socioeconomic Disparities in Access to IR Care in the United States

PurposeTo determine and analyze the geographic distribution of interventional radiologists in the United States to identify populations with decreased accessibility to interventional radiology (IR) care. Materials and MethodsThe Society of Interventional Radiology public database was reviewed for board-certified interventional radiologists in all cities within the United States. U.S. Census data were used to acquire county-level data sets on poverty, population, unemployment, median household income, education, and racial diversity. Odds ratios were calculated for access to IR care between the 75th and 25th percentiles for each population variable. Counties with interventional radiologists were compared with those without for each variable using t-tests. Quantum Geographic Information System was used to map the distribution of interventional radiologists. ResultsA total of 2,989 board-certified U.S. interventional radiologists were found to be operating in 15.5% (n = 487) of all counties. Almost one-third (31.2%) of the country’s population did not have access to an interventional radiologist within their county. The mean interventional radiologist-to-population ratio was 0.305 per 100,000 people. The mean median income among counties with interventional radiologists was greater at $67,649 compared with $53,528 among counties without (P < .05). In addition, a mean of 31.3% of the population had a college degree in counties with interventional radiologists versus 20.3% in other counties (P < .001). Higher mean percentages of African Americans, Pacific Islanders, and Hispanics were observed in counties with interventional radiologists, at 13.1%, 0.232%, and 13.7%, respectively (P < .05). ConclusionsInterventional radiologists are disproportionately distributed, with higher densities of radiologists practicing near urban areas. Access is also limited to counties with higher incomes and a greater percentage of citizens with a college education.

Projecting high resolution population distribution using Local Climate Zones and multi-source big data

High precision and easily updatable data on population estimates are critical for urban planning, disaster risk assessment, and public health campaigns. However, traditional administrative census data often faces limitations in spatial resolution, making it difficult for further applications. In this study, we construct a fast and efficient population estimation model using Local Climate Zones (LCZ) products at a fine spatial scale. The population totals are estimated by LCZ units, based on a robust linkage between built LCZ types and population density. Validated by city- and county-level census data in 21 cities in China, the new model exhibits a very good fit with R2 values of 0.77 and 0.73 respectively, which confirms the effectiveness of LCZ-based population estimates. To our knowledge, this is the first study that directly estimates the population via LCZ maps. In addition, it was proven that population density can be used as a new property for LCZ type definition. As LCZ products are convenient to obtain, this work provides a simple, economical, and reliable population estimation method that can complement the traditional census.

NeuralFlood: an AI-driven flood susceptibility index

Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.

Impact of Social Vulnerability on Diabetes-Related Cardiovascular Mortality in the United States.

Background Social vulnerability impacts the natural history of diabetes as well as cardiovascular disease (CVD). However, there are little data regarding the social vulnerability association with diabetes-related CVD mortality. Methods and Results County-level mortality data (where CVD was the underlying cause of death with diabetes among the multiple causes) extracted from the Centers for Disease Control multiple cause of death (2015-2019) and the 2018 Social Vulnerability Index databases were aggregated into quartiles based on their Social Vulnerability Index ranking from the least (first quartile) to the most vulnerable (fourth quartile). Stratified by demographic groups, the data were analyzed for overall CVD, as well as for ischemic heart disease, hypertensive disease, heart failure, and cerebrovascular disease. In the 5-year study period, 387 139 crude diabetes-related cardiovascular mortality records were identified. The age-adjusted mortality rate for CVD was higher in the fourth quartile compared with the first quartile (relative risk [RR], 1.66 [95% CI, 1.64-1.67]) with an estimated 39 328 excess deaths. Among the youngest age group (<55 years), those with the highest social vulnerability had 2 to 4 times the rate of cardiovascular mortality compared with the first quartile: ischemic heart disease (RR, 2.07 [95% CI, 1.97-2.17]; heart failure (RR, 3.03 [95% CI, 2.62-3.52]); hypertensive disease (RR, 3.79 [95% CI, 3.45-4.17]; and cerebrovascular disease (RR, 4.39 [95% CI, 3.75-5.13]). Conclusions Counties with greater social vulnerability had higher diabetes-related CVD mortality, especially among younger adults. Targeted health policies that are designed to reduce these disparities are warranted.

Ambient particulate matter air pollution exposure and ovarian cancer incidence in the USA: An ecological study.

To investigate associations between air particulate matter of ≤2.5 μm in diameter (PM2.5 ) and ovarian cancer. County-level ecological study. Surveillance, epidemiology, and end results from a collection of state-level cancer registries across 744 counties. Data from the Environmental Protection Agency's network for PM2.5 monitoring was used to calculate trailing 5- and 10-year PM2.5 county-level values. County-level data on demographic characteristics were obtained from the American Community Survey. A total of 98 751 patients with histologically confirmed ovarian cancer as a primary malignancy from 2000 to 2016. Generalised linear regression models were developed to estimate the association between PM2.5 and PM10 levels, over 5- and 10-year periods of exposure, and ovarian cancer risk, after accounting for county-level covariates. Risk ratios for associations between ovarian cancer (both overall and specifically epithelial ovarian cancer) and PM2.5 levels. For the 744 counties included, the average PM2.5 level from 1990 through 2018 was 11.75 μg/m3 (SD = 3.7) and the average PM10 level was 22.7 μg/m3 (SD = 5.7). After adjusting for county-level covariates, the overall annualised ovarian cancer incidence was significantly associated with increases in 5-year PM2.5 (RR = 1.11 per 10 units (μg/m3 ) increase, 95% CI 1.06-1.16). Similarly, when the analysis was limited to epithelial cell tumours and adjusted for county-level covariates there was a significant association with trailing 5-year PM2.5 exposure models (RR = 1.12 per 10 units increase, 95% CI 1.08-1.17). Likewise, 10-year PM2.5 exposure was associated with ovarian cancer overall and with epithelial ovarian cancer. Higher county-level ambient PM2.5 levels are associated with 5- and 10-year incidences of ovarian cancer, as measurable in an ecological study.

Spatiotemporal Economic Analysis of Corn and Wheat Production in the Texas High Plains

The aim of this study is to visualize the historical changes in wheat and corn cropping patterns in the Texas High Plains from the perspective of geographical concentration and spatial autocorrelation. Historical county-level agricultural census data were collected from the United States Department of Agriculture and the National Agricultural Statistics Service from 1978 to 2017. Exploratory data analysis techniques were employed to examine the geographical concentration and spatial dependence of crop production among nearby locations. The results of temporal changes indicate that the harvested acres of corn and wheat tended to decrease throughout the study period. Total and irrigated harvested corn and wheat acreages were concentrated in a smaller number of counties over time while wheat production was mainly concentrated in the northern part of the region. The Moran’s I test statistic for total and irrigated areas of cropland suggest that there was spatial dependence among the neighboring counties in crop production in this region. In summary, there was a spatiotemporal change in cropping patterns in the Texas High Plains over the study period. Based on the results of the spatiotemporal changes in cropping patterns in the Texas High Plains, policy makers should promote and support non-irrigated varieties of crops in order to decrease the dependence on irrigation water from the Ogallala Aquifer.

Social Vulnerability and the Prevalence of Opioid Use Disorder Among Older Medicare Beneficiaries in U.S. Counties.

Recent research has investigated the factors associated with the prevalence of opioid use disorder (OUD) among older adults (65+), which has rapidly increased in the past decade. However, little is known about the relationship between social vulnerability and the prevalence of OUD, and even less is about whether the correlates of the prevalence of OUD vary across the social vulnerability spectrum. This study aims to fill these gaps. We assemble a county-level data set in the contiguous United States (U.S.) by merging 2021 Medicare claims with the CDC's social vulnerability index and other covariates. Using the total number of older beneficiaries with OUD as the dependent variable and the total number of older beneficiaries as the offset, we implement a series of nested negative binomial regression models and then analyze by social vulnerability quartiles. Higher social vulnerability is associated with higher prevalence of OUD in U.S. counties. This association cannot be fully explained by the differences in the characteristics of older Medicare beneficiaries (e.g., average age) and/or other social conditions (e.g., social capital) across counties. Moreover, the group comparison tests indicate correlates of the prevalence of OUD vary across social vulnerability quartiles in that the average number of mental disorders is positively related to OUD prevalence in the least and the most vulnerable counties and social capital benefits the less vulnerable counties. A perspective drawing upon contextual factors, especially social vulnerability, may be more effective in reducing OUD among older adults in U.S. counties than a one-size-fits-all approach.

The Structure of Pandemic Vulnerability: Housing Wealth, Residential Segregation, and COVID-19 Mortality

The COVID-19 pandemic has been particularly devastating for those with limited economic resources. Extensive research demonstrates the negative relationship between wealth and mortality at both the individual and area levels. In addition, residential segregation has been linked to poor health and greater mortality. Home equity is the largest asset that many Americans own, but residential segregation devalues homes located in Black neighborhoods. Despite the interlocking relationships between wealth, residential segregation, and mortality, it remains unclear how wealth and residential segregation work to predict COVID-19 deaths. Using U.S. Census data and county-level COVID-19 data from Johns Hopkins University (n = 1164), I deploy median home value as a wealth proxy and negative binomial regression models to interrogate two questions. (1) What is the relationship between home value and COVID-19 deaths? (2) How does the relationship vary by level of residential segregation? Results indicate that COVID-19 mortality is 64 percent greater in the lowest wealth counties than in the wealthiest counties. At average median home value, the most segregated counties with the largest Black populations suffer 28 percent more COVID-19 deaths than similarly situated counties with low levels of residential segregation and small Black populations. This study underscores the importance of accounting for residential segregation in examinations of the well-established relationship between socioeconomic status and health and mortality.

Can Farmers Punch Their Tickets to Wealth? The Spillover Effect of High-Speed Railway on Agriculture Development

This paper studied the impact of high-speed railway linesonagricultural outputin the regions along their routes. It also investigated the heterogeneity of this impactbased on the terrain characteristics. The results of empirical testsusing county-level data show that ahigh-speed railway line can bring higher agricultural “dividends” to counties along the line. Notably, low altitude areas and regionswith gentle terrain exhibit a more pronounced promotional effect on local agricultural output. High-speed railways mainly promote agricultural output by promoting the mechanization of agriculture in counties along the route, thus reinvigorating the agricultural population and the development of specialty agriculture. Ourfindingsprovide quasi-microscopic evidence of the potential benefits of transportation infrastructure construction to break down geographical barriers and facilitate production factor flow, and providesa theoretical basis for the economic spillover effects of high-speed railway construction.

A dataset of the planting areas of three staple crops with a spatial resolution of 1 km in China during 2009–2015

The planting area of three staple crops (rice, maize and wheat) in China ranks among the highest in the world. Developing the planting area maps of three staple crops consistent with the county-level statistical data for agricultural policy formulation and disaster risk prevention. However, there is a scarcity of available planting area datasets of for these three crops in China, and the existing public datasets limited data for only a few years, often exhibit significant disparities from the statistical data. This dataset was established using a crop mapping method based on multi-source data. Firstly, we determined the rough spatial distribution of crops by extracting key phenology of these three crops based on GLASS leaf area index (LAI) products. Then, we filtered the crop pixels gradually by the optimization rules referring to county-level statistical data. Finally, we obtained a dataset of the planting areas of three staple crops with a spatial resolution of 1 km in China during 2009–2015 (ChinaCropArea1kmV2). The R2 (consistency with county-level statistical data) values of maize, rice and wheat products are 0.98, 0.89 and 0.82 respectively, and the accuracies of mapping based on point verification reached 0.85, 0.86 and 0.93 respectively. The consistency between the second generation products and statistical data is higher than the accuracy of existing products. The presented method for the production of this dataset can rapidly enables rapid and accurate crop mapping over large areas while maintaining a high level of consistency with county-level statistical yearbooks. The production of this dataset is helpful to provide a key data base for agricultural production research and scientific decision making.

A dataset of GDP spatial distribution on the Qinghai-Tibet Plateau (2020)

Economic spatial distribution data serve as an indicator for disaster loss assessment. The economy of the Qinghai-Tibet Plateau is relatively backward and geological disasters occur frequently, so it is of great significance to study the impact of disasters on the economy in this area. Based on the method of zoning and industry simulation, this study establishes the corresponding relationship between the GDP1（GDP of Primary Industry）and land use types, DEM（Digital Elevation Model）, village points, roads and river buffers. Spatial models of night lighting, building site and points of interest data after the coupling of the GDP23（GDP of Secondary and Tertiary Industries）and multiple information are established in urban areas and counties. We used the random forest to determine the weights of various indicators in the secondary and tertiary industries, and calculated the grid value of GDP23. Finally, we superimposed the grid of GDP1 and GDP23 and created the spatial distribution map of GDP（Gross Domestic Product）of 100 m×100 m on the Qinghai-Tibet Plateau in 2020. According to the accuracy verification using county-level GDP statistical data, the Root Mean Square Error is 0.0385, which shows that the data can better reflect the spatial distribution of GDP on the Qinghai-Tibet Plateau. This dataset can provide GDP spatial data support for disaster risk assessment, disaster prevention and mitigation on the Qinghai-Tibet Plateau.

The associations of income and Black-White racial segregation with HIV outcomes among adults aged ≥18 years-United States and Puerto Rico, 2019.

To examine associations between Index of Concentration at the Extremes (ICE) measures for economic and racial segregation and HIV outcomes in the United States (U.S.) and Puerto Rico. County-level HIV testing data from CDC's National HIV Prevention Program Monitoring and Evaluation and census tract-level HIV diagnoses, linkage to HIV medical care, and viral suppression data from the National HIV Surveillance System were used. Three ICE measures of spatial polarization were obtained from the U.S. Census Bureau's American Community Survey: ICEincome (income segregation), ICErace (Black-White racial segregation), and ICEincome+race (Black-White racialized economic segregation). Rate ratios (RRs) for HIV diagnoses and prevalence ratios (PRs) for HIV testing, linkage to care within 1 month of diagnosis, and viral suppression within 6 months of diagnosis were estimated with 95% confidence intervals (CIs) to examine changes across ICE quintiles using the most privileged communities (Quintile 5, Q5) as the reference group. PRs and RRs showed a higher likelihood of testing and adverse HIV outcomes among persons residing in Q1 (least privileged) communities compared with Q5 (most privileged) across ICE measures. For HIV testing percentages and diagnosis rates, across quintiles, PRs and RRs were consistently greatest for ICErace. For linkage to care and viral suppression, PRs were consistently lower for ICEincome+race. We found that poor HIV outcomes and disparities were associated with income, racial, and economic segregation as measured by ICE. These ICE measures contribute to poor HIV outcomes and disparities by unfairly concentrating certain groups (i.e., Black persons) in highly segregated and deprived communities that experience a lack of access to quality, affordable health care. Expanded efforts are needed to address the social/economic barriers that impede access to HIV care among Black persons. Increased partnerships between government agencies and the private sector are needed to change policies that promote and sustain racial and income segregation.

Racial/ethnic disparities in timely receipt of buprenorphine among Medicare disability beneficiaries

BackgroundMedicare disability beneficiaries (MDBs) have disproportionately high risk of opioid use disorder (OUD) and related harms given high rates of comorbidities and high-dose opioid prescribing. Despite this increased risk, little is known about timely receipt of medication for opioid use disorder (MOUD), including potential disparities by patient race/ethnicity or moderation by county-level characteristics. MethodsNational Medicare claims for a sample of MDBs with incident OUD diagnosis between March 2016 and June 2019 were linked with county-level data. Multivariable mixed effects Cox proportional hazards models estimated time (in days) to buprenorphine receipt within 180 days of incident OUD diagnosis. Primary exposures included individual-level race/ethnicity and county-level buprenorphine prescriber availability, percent non-Hispanic white (NHW) residents, and Social Deprivation Index (SDI) score. ResultsThe sample (n=233,079) was predominantly White (72.3%), ≥45 years old (76.3%), and male (54.8%). Black (adjusted hazard ratio [aHR]=0.50; 95% CI, 0.47–0.54), Asian/Pacific Islander (aHR=0.54; 95% CI, 0.41–0.72), Hispanic/Latinx (aHR=0.81; 95% CI, 0.76–0.87), and Other racial/ethnic groups (aHR=0.75; 95% CI, 0.58–0.97) had a lower likelihood of timely buprenorphine than non-Hispanic white beneficiaries after adjusting for individual and county-level confounders. Timely buprenorphine receipt was positively associated with county-level buprenorphine prescriber availability (aHR=1.05; 95% CI, 1.04–1.07), percent non-Hispanic white residents (aHR=1.01; 95% CI, 1.00–1.01), and SDI (aHR=1.06; 95% CI, 1.01–1.10). ConclusionsRacial/ethnic disparities highlight the need to improve access to care for underserved groups. Implementing equity-focused quality and performance measures and developing interventions to increase office-based buprenorphine prescribing in predominantly minority race/ethnicity counties may reduce disparities in timely access to medication for OUD.