Assessing the spatial heterogeneity in overall health across the United States using spatial regression methods: The contribution of health factors and county-level demographics

Abstract Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

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Association between county level cannabis dispensary counts and opioid related mortality rates in the United States: panel data study

BMJ ◽

10.1136/bmj.m4957 ◽

2021 ◽

pp. m4957 ◽

Cited By ~ 1

Author(s):

Greta Hsu ◽

Balázs Kovács

Keyword(s):

United States ◽

Confidence Interval ◽

The United States ◽

Mortality Rates ◽

Mortality Data ◽

Panel Regression ◽

County Level ◽

Regression Methods ◽

Synthetic Opioids ◽

Related Mortality

Abstract Objective To examine county level associations between the prevalence of medical and recreational cannabis stores (referred to as dispensaries) and opioid related mortality rates. Design Panel regression methods. Setting 812 counties in the United States in the 23 states that allowed legal forms of cannabis dispensaries to operate by the end of 2017. Participants The study used US mortality data from the Centers for Disease Control and Prevention combined with US census data and data from Weedmaps.com on storefront dispensary operations. Data were analyzed at the county level by using panel regression methods. Main outcome measure The main outcome measures were the log transformed, age adjusted mortality rates associated with all opioid types combined, and with subcategories of prescription opioids, heroin, and synthetic opioids other than methadone. The associations of medical dispensary and recreational dispensary counts with age adjusted mortality rates were also analyzed. Results County level dispensary count (natural logarithm) is negatively related to the log transformed, age adjusted mortality rate associated with all opioid types (β=−0.17, 95% confidence interval −0.23 to −0.11). According to this estimate, an increase from one to two storefront dispensaries in a county is associated with an estimated 17% reduction in all opioid related mortality rates. Dispensary count has a particularly strong negative association with deaths caused by synthetic opioids other than methadone (β=−0.21, 95% confidence interval −0.27 to −0.14), with an estimated 21% reduction in mortality rates associated with an increase from one to two dispensaries. Similar associations were found for medical versus recreational storefront dispensary counts on synthetic (non-methadone) opioid related mortality rates. Conclusions Higher medical and recreational storefront dispensary counts are associated with reduced opioid related death rates, particularly deaths associated with synthetic opioids such as fentanyl. While the associations documented cannot be assumed to be causal, they suggest a potential association between increased prevalence of medical and recreational cannabis dispensaries and reduced opioid related mortality rates. This study highlights the importance of considering the complex supply side of related drug markets and how this shapes opioid use and misuse.

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Reduced Lung Cancer Mortality With Lower Atmospheric Pressure

Dose-Response ◽

10.1177/1559325818769484 ◽

2018 ◽

Vol 16 (2) ◽

pp. 155932581876948 ◽

Cited By ~ 1

Author(s):

Ray M. Merrill ◽

Aaron Frutos

Keyword(s):

United States ◽

Lung Cancer ◽

Cancer Mortality ◽

Atmospheric Pressure ◽

Regression Models ◽

Spatial Regression ◽

Lung Cancer Mortality ◽

The United States ◽

Mortality Rates ◽

County Level

Background: Research has shown that higher altitude is associated with lower risk of lung cancer and improved survival among patients. The current study assessed the influence of county-level atmospheric pressure (a measure reflecting both altitude and temperature) on age-adjusted lung cancer mortality rates in the contiguous United States, with 2 forms of spatial regression. Methods: Ordinary least squares regression and geographically weighted regression models were used to evaluate the impact of climate and other selected variables on lung cancer mortality, based on 2974 counties. Results: Atmospheric pressure was significantly positively associated with lung cancer mortality, after controlling for sunlight, precipitation, PM2.5 (µg/m3), current smoker, and other selected variables. Positive county-level β coefficient estimates ( P < .05) for atmospheric pressure were observed throughout the United States, higher in the eastern half of the country. Conclusion: The spatial regression models showed that atmospheric pressure is positively associated with age-adjusted lung cancer mortality rates, after controlling for other selected variables.

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Supplemental Material for Future-Oriented Tweets Predict Lower County-Level HIV Prevalence in the United States

Health Psychology ◽

10.1037/hea0000279.supp ◽

2015 ◽

Keyword(s):

United States ◽

The United States ◽

Hiv Prevalence ◽

County Level

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Introducing the United States Police-Shooting Database: A Multi-Level Bayesian Analysis of Racial Bias in Police Shootings at the County-Level in the United States, 2011-2014

SSRN Electronic Journal ◽

10.2139/ssrn.2534673 ◽

2014 ◽

Cited By ~ 1

Author(s):

Cody Ross

Keyword(s):

United States ◽

Bayesian Analysis ◽

Racial Bias ◽

The United States ◽

County Level ◽

Police Shootings ◽

Multi Level ◽

Police Shooting

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Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

Nature Communications ◽

10.1038/s41467-021-23865-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Bingyi Yang ◽

Angkana T. Huang ◽

Bernardo Garcia-Carreras ◽

William E. Hart ◽

Andrea Staid ◽

...

Keyword(s):

United States ◽

Home Visiting ◽

Leisure Activities ◽

The United States ◽

Causal Effects ◽

Basic Reproductive Number ◽

Reproductive Number ◽

County Level ◽

Metapopulation Model ◽

Pharmaceutical Interventions

AbstractNon-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58–1.39) or face masks (median Reff 0.97, 95% CI 0.58–1.39). While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

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