US Counties Research Articles

The Role of County-Level Persistent Poverty in Stroke Mortality in the USA.

Stroke is a major health concern in the USA, disproportionately affecting socioeconomically disadvantaged groups. This study investigates the link between persistent poverty and stroke mortality rates in residents aged 65 and above, positing that sustained economic challenges at the county level correlate with an increase in stroke-related deaths. Persistent poverty refers to a long-term state where a significant portion of a population lives below the poverty threshold for an extended period, typically measured over several decades. It captures the chronic nature of economic hardship faced by a community across multiple generations. Utilizing data from the CDC Wonder database and the American Community Survey, we conducted a comprehensive analysis across US counties, differentiating them by persistent poverty status. Our results indicate a statistically significant link between persistent poverty and increased mortality from ischemic and hemorrhagic strokes; counties afflicted by long-standing poverty were associated with an additional 33.49 ischemic and 8.16 hemorrhagic stroke deaths per 100,000 residents annually compared to their wealthier counterparts. These disparities persisted when controlling for known stroke risk factors and other socioeconomic variables. These results highlight the need for targeted public health strategies and interventions to address the disparities in stroke mortality rates and the broader implications for healthcare equity. The study underscores the vital role of socioeconomic context in health outcomes and the urgency of addressing long-term poverty as a key determinant of public health.

Abstract P389: Prevalence, Awareness, Treatment, and Control of Major Cardiovascular Risk Factors in Rural Alabama

Background: CVD rates are high across the rural southern United States, but less is known about risk factor prevalence and control. Using data from two rural Alabama counties, we measured the prevalence, awareness, treatment, and control of hypertension, high cholesterol, and diabetes. Methods: The Risk Underlying Areas Longitudinal (RURAL) Heart and Lung Study began in 2021 to assess cardiopulmonary risk across 10 rural southern US counties. This analysis used data from two Alabama counties (N=942), 70% women, 79% non-Hispanic Black individuals, mean age 48.7 years. Hypertension was defined as SBP≥130 mmHg, DBP≥80 mmHg, and/or self-reporting hypertension medication use. High cholesterol was defined as TC≥200 mg/dL and/or cholesterol medication use. Diabetes was defined as fasting glucose≥126 mg/dL, non-fasting glucose>200 mg/dL, and/or diabetes medication use. Awareness, treatment, and control were assessed via self-reported physician diagnoses and medication use. All analyses applied sampling weights to produce representative estimates. Results: In the RURAL Alabama sample, the prevalence of hypertension was 73.7%, high cholesterol was 59.5%, and diabetes was 25.4%. The majority of individuals were aware of and taking medication for these risk factor conditions, but one-quarter of hypertension and less than half of diabetes was controlled (Figure). There were no significant sex differences observed. As compared with white individuals, Black individuals had a significantly higher prevalence of hypertension (79.2% vs 61.9%, p<0.001) and diabetes (29.9% vs 17.0%, p=0.005); however, there were no significant racial differences in risk factor control. Conclusion: There was a high prevalence of CVD risk factors in rural Alabama, with Black-white racial disparities for hypertension and diabetes, and control was low. This underscores the need for primary prevention of risk factors in the rural south and elucidating factors contributing to ‘treatment-control’ gaps for managing hypertension and diabetes.

Best linear and quadratic moments for spatial econometric models with an application to spatial interdependence patterns of employment growth in US counties

SummaryWe provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic. Two normality tests of disturbances are developed. We apply the model to employment data in US counties, which demonstrates spatial interdependence patterns of regional employment growth.

Business dynamism and regional growth across the business cycle: Implications for recovery from the COVID‐19 crisis

This paper sheds light on regional recovery prospects from the coronavirus disease 2019 (COVID‐19) crisis by examining the link between gross rates of establishment openings and closures and local economic growth spanning the 2001 recession and the 2007–2009 global financial crisis (GFC). Consistent with existing literature, our results show that US counties with historically higher rates of dynamism – measured here as rates of establishment openings and closures, or churn – experience faster medium‐run employment growth rates. Our novel finding relates to the time‐varying effects of churn. Using a fixed‐effects panel model of US counties from 1998 to 2018, we find higher pre‐recession churn to be associated with larger employment losses during the GFC, and slightly faster employment gains during the subsequent recovery. High‐churn counties experienced slower gains in median household incomes during both the 2001 recession and the GFC, but more rapid decreases in poverty rates during macroeconomic expansions. We conclude by identifying regions with historically low establishment churn that have suffered high employment losses during the COVID‐19 crisis and may be in need of support to aid recovery.

Rotational complexity across US counties is currently insufficient to observe yield gains in major crops

Landscape complexity promotes ecosystem services and agricultural productivity, and often encompasses aspects of compositional or configurational land cover diversity across space. However, a key agricultural diversification practice, crop rotation, extends crop land cover complexity concurrently across space and time. Long-term experiments suggest that complex crop rotations can facilitate yield increases in major crops. Using a compiled county-annual panel dataset, we examined whether yield benefits of crop rotational complexity were apparent on a landscape scale in the conterminous US for four major crops between 2008 and 2020. We found that the benefit of rotational complexity was only apparent for cotton and winter wheat, and that the benefit for wheat was driven by one region. Corn exhibited the opposite pattern, wherein higher yields were consistently obtained with lower rotational complexity, while soybean yield appeared relatively insensitive to rotational complexity. Effects of rotational complexity were sometimes influenced by agrochemical usage. Positive effects of rotational complexity were only apparent with high fertilizer for soybean and wheat, and with low fertilizer for cotton. Corn yield in high-complexity, low-yielding counties appeared to improve with high fertilizer inputs. For the overwhelming majority of acres growing these major crops, crop rotation patterns were quite simple, which when combined with the short time span of available data, may explain the apparent discrepancy between long-term experiments and nationwide data. Current demand and incentives that promote highly intensified and specialized agriculture likely hinder realization of the benefits of rotational complexity for production of key crops in the US. Increasing rotational complexity where major crops are grown thus remains an underutilized approach to mitigate landscape simplification and to promote ecosystem services and crop yields.

Distance lending & social connectedness

Using Facebook’s social network data for the US counties, we examine whether social connectedness reduces the informational disadvantage in lending to small businesses at a distance. We find that for a given distance, there is a pecking order of lending. Banks first lend to more socially connected counties, and later, banks expand credit to socially less connected areas. The probability of loan charge-off decreases in social connectedness and more so for the loans originated by small banks. In the cross-section, the positive effect of social connectedness on loan performance is higher for the loans originated by out of state banks. These findings suggest that loan officers get valuable information through their social networks.

Disparities in Social Vulnerability and Premature Mortality among Decedents with Hepatitis B, United States, 2010-2019.

Current US hepatitis B mortality rates remain three times higher than the national target. Mortality reduction will depend on addressing hepatitis B disparities influenced by social determinants of health. This study aims to describe characteristics of hepatitis B-listed decedents, which included US birthplace status and county social vulnerability attributes and quantify premature mortality. We conducted a cross-sectional analysis of 17,483 hepatitis B-listed decedents using the 2010-2019 US Multiple-Cause-of-Death data merged with the county-level Social Vulnerability Index (SVI). Outcomes included the distribution of decedents according to US birthplace status and residence in higher versus lower death burden counties by sociodemographic characteristics, years of potential life lost (YPLL), and SVI quartiles. Most hepatitis B-listed decedents were US-born, male, and born during 1945-1965. Median YPLL was 17.2; 90.0% died prematurely. US-born decedents were more frequently White, non-college graduates, unmarried, and had resided in a county with < 500,000 people; non-US-born decedents were more frequently Asian/Pacific Islander, college graduates, married, and had resided in a county with ≥ 1 million people. Higher death burden (≥ 20) counties were principally located in coastal states. US-born decedents more frequently resided in counties in the highest SVI quartile for "Household Characteristics" and "Uninsured," whereas non-US-born decedents more frequently resided in counties in the highest SVI quartile for "Racial/Ethnic Minority Status" and "Housing Type/Transportation." This analysis found substantial premature hepatitis B mortality and residence in counties ranked high in social vulnerability. Successful interventions should be tailored to disproportionately affected populations and the social vulnerability features of their geographic areas.

Integrating Healthy Nutrition Standards and Practices Into Food Service Contracting in a Large US County Government.

Although considered a promising model of practice, integrating healthy nutrition standards and practices into a large county government's contracting process with food vendors has not been widely described in empirical literature. We conducted an implementation evaluation project to address this gap. County of Los Angeles food vendors provide food or meals annually to more than 100,000 employees and millions of clients and visitors. In 2011, the County of Los Angeles Board of Supervisors adopted a policy to integrate healthy nutrition standards and practices into its requests for proposals (RFPs) and contracting process with food vendors. The policy required all contracts awarded to adhere to these new standards. In 2011, the Los Angeles County Department of Public Health (DPH) began reviewing RFPs for food services for county departments that procured, served, or sold food. From 2011 through 2021, DPH applied a 4-pronged formative-evaluative approach to help county departments implement the Board of Supervisors policy and ensure that nutritional requirements were appropriately integrated into all RFPs for new and renewing contracts with food vendors. We focused our evaluation on understanding the process and tracking the progress of this policy intervention. Our evaluation included 13 key informant interviews, a 2-part survey, reviews of contract data, and synthesis of lessons learned. Based on reviews and subsequent actions taken on more than 20 RFPs, DPH successfully assisted 7 county departments to incorporate healthy nutrition standards and practices into their food vendor contracts. Implementation of the food policy encountered several challenges, including staffing and training constraints and a limited infrastructure. An iterative approach to program improvement facilitated the process. Although the model for integrating healthy nutrition standards and practices into a government contracting process is promising, more work is needed to make it less resource-intensive and to increase user buy-in.

Associations of Medical Debt With Health Status, Premature Death, and Mortality in the US

Medical debt is increasingly common in the US. Little is known regarding its association with population health. To examine the associations of medical debt with health status, premature death, and mortality at the county level in the US. This cross-sectional study was conducted at the US county level using 2018 medical debt data from the Urban Institute Debt in America project linked with 2018 data on self-reported health status and premature death from the County Health Rankings & Roadmaps and with 2015 to 2019 mortality data from the National Center for Health Statistics. Data analysis was performed from August 2022 to May 2023. Share of population with any medical debt in collections and median amount of medical debt. Health status was measured as (1) the mean number of physically and mentally unhealthy days in the past 30 days per 1000 people, (2) the mean number of premature deaths measured as years of life lost before age 75 years per 1000 people, and (3) age-adjusted all-cause and 18 cause-specific mortality rates (eg, malignant cancers, heart disease, and suicide) per 100 000 person-years. Multivariable linear models were fitted to estimate the associations between medical debt and health outcomes. A total of 2943 counties were included in this analysis. The median percentage of the county population aged 65 years or older was 18.3% (IQR, 15.8%-20.9%). Across counties, a median 3.0% (IQR, 1.2%-11.9%) of the population were Black residents, 4.3% (IQR, 2.3%-9.7%) were Hispanic residents, and 84.5% (IQR, 65.7%-93.3%) were White residents. On average, 19.8% (range, 0%-53.6%) of the population had medical debt. After adjusting for county-level sociodemographic characteristics, a 1-percentage point increase in the population with medical debt was associated with 18.3 (95% CI, 16.3-20.2) more physically unhealthy days and 17.9 (95% CI, 16.1-19.8) more mentally unhealthy days per 1000 people during the past month, 1.12 (95% CI, 1.03-1.21) years of life lost per 1000 people, and an increase of 7.51 (95% CI, 6.99-8.04) per 100 000 person-years in age-adjusted all-cause mortality rate. Associations of medical debt and elevated mortality rates were consistent for all leading causes of death, including cancer (1.12 [95% CI, 1.02-1.22]), heart disease (1.39 [95% CI, 1.21-1.57]), and suicide (0.09 [95% CI, 0.06-0.11]) per 100 000 person-years. Similar patterns were observed for associations between the median amount of medical debt and the aforementioned health outcomes. These findings suggest that medical debt is associated with worse health status, more premature deaths, and higher mortality rates at the county level in the US. Therefore, policies increasing access to affordable health care, such as expanding health insurance coverage, may improve population health.

Opposing Patterns of Spatial Synchrony in Lyme Disease Incidence

Incidence of Lyme disease, a tick-borne illness prevalent in the US, is increasing in endemic regions and regions with no previous history of the disease, significantly impacting public health. We examined space–time patterns of Lyme disease incidence and the influence of ecological and social factors on spatial synchrony, i.e., correlated incidence fluctuations across US counties. Specifically, we addressed these questions: Does Lyme disease incidence exhibit spatial synchrony? If so, what geographic patterns does Lyme disease synchrony exhibit? Are geographic patterns of disease synchrony related to weather, land cover, access to health care, or tick-borne disease awareness? How do effects of these variables on Lyme disease synchrony differ geographically? We used network analysis and matrix regression to examine geographical patterns of Lyme disease synchrony and their potential mechanisms in 399 counties in the eastern and Midwestern US. We found two distinct regions of synchrony in Northeast and upper Midwest regions exhibiting opposing temporal fluctuations in incidence. Spatial patterns of Lyme disease synchrony were partly explained by land cover, weather, poverty, and awareness of tick-borne illness, with significant predictive variables changing regionally. However, the two regions may have become more synchronous over time, potentially leading to higher-amplitude nation-wide fluctuations in disease incidence.

Beyond the annual averages: Impact of seasonal temperature on employment growth in US counties

Using quarterly temperature and employment data between 1990 and 2021, this paper uncovers nuanced evidence on the impact of seasonal temperature within US counties: higher winter temperature increases private sector employment growth while higher summer temperature decreases it. The impacts of higher temperature in milder seasons, fall and spring, are statistically insignificant. Moreover, the negative impact of higher summer temperature persists while the positive impact of higher temperature in the winter is more short-lived. The negative effects of a hotter summer are pervasive and persistent in many sectors: most significantly in “Construction” and “Leisure and Hospitality” but also in “Trade, Transport, and Utilities” and “Financial Activities”. In contrast, the positive effects of a warmer winter are less pervasive. The employment effect of a hotter summer has been more severe in recent decades.

Climate risk in mortgage markets: Evidence from Hurricanes Harvey and Irma

AbstractUsing the Credit Risk Transfers (CRTs) issued by Fannie Mae and Freddie Mac, we study how, absent government intervention, mortgage markets would price hurricane risk. Currently, such risk is priced equally across locations even if it is location‐specific. We hand collect a novel and detailed database to exploit CRTs' heterogeneous exposure to Hurricanes Harvey and Irma. Using a diff‐in‐diff specification, we estimate the reaction of private investors to hurricane risk. We use the previous results to calibrate a model of mortgage lending. We simulate hurricane frequencies and mortgage default probabilities in each US county to derive the market price of mortgage credit risk, that is, the implied guarantee fees (g‐fees). Market‐implied g‐fees in counties most exposed to hurricanes would be 70% higher than inland counties.

Race, local context, and traffic stops: an examination of racial profiling in traffic stops in rural and non-rural places

PurposeThe current study examines the effects of race, agency and environment on traffic stops in rural and non-rural spaces.Design/methodology/approachUsing traffic stop data collected in a Midwest US County from January 1, 2020 to December 31, 2021, the current study uses logistic regression to examine racial disparities in traffic stops.FindingsThe results indicate that police decision-making in traffic stops may be influenced by other factors besides a driver’s race or ethnicity. In other words, the police officer’s decision making in a traffic stop varies between small and large agencies as well as rural and non-rural places.Originality/valueThis study provides one of the few examinations of racial disparities in traffic stops in rural places.

Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of these tools by using deidentified public health datasets for notifiable sexually transmitted infections (STIs) from a southern US county known for high HIV incidence. The goal is to assess the feasibility and accuracy of ML in predicting HIV incidence, which could inform and enhance public health interventions. We analyzed 2 deidentified public health datasets from January 2010 to December 2021, focusing on notifiable STIs. Our process involved data processing and feature extraction, including sociodemographic factors, STI cases, and social vulnerability index (SVI) metrics. Various ML models were trained and evaluated for predicting HIV incidence using metrics such as accuracy, precision, recall, and F1 score. We included 85 224 individuals; 2027 (2.37%) were newly diagnosed with HIV during the study period. The ML models demonstrated high performance in predicting HIV incidence among males and females. Influential features for males included age at STI diagnosis, previous STI information, provider type, and SVI. For females, predictive features included age, ethnicity, previous STI information, overall SVI, and race. The high accuracy of our ML models in predicting HIV incidence highlights the potential of using public health datasets for public health interventions such as tailored HIV testing and prevention. While these findings are promising, further research is needed to translate these models into practical public health applications.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study

ObjectiveTo estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate...

An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.

This article introduces bayesian spatial smoothing models for disease mapping-a specific application of small area estimation where the full universe of data is known-to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space-time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers.

Consumption responses to an unconditional child allowance in the United States

The COVID-19 pandemic put families in the United States under financial stress. The federal government’s largest response in 2021 was the American Rescue Plan Act, which temporarily expanded the Child Tax Credit (CTC) into a large, unconditional child allowance providing monthly payments to families with children. This study investigates consumption responses to the CTC expansion using anonymized mobile-location data and debit/credit card data that track visits and spending at 1.3 million establishments across US counties. For identification, we exploit variation in the size of households’ income gains due to the CTC across counties in a difference-in-differences framework spanning January 2021 to May 2022. Counties benefiting most from the CTC expansion experienced larger increases in visits to childcare centres and health- and personal-care establishments, and increased visits to and spending per transaction at grocery and general stores. These findings suggest that the CTC expansion increased household consumption and spending on children.

Availability and Quality of Dialysis Care in Rural versus Urban US Counties

Introduction: Rural areas face significant disparities in dialysis care compared to urban areas due to limited access to dialysis facilities, longer travel distances, and a shortage of healthcare professionals. The objective of this study was to conduct a national examination of rural-urban differences in quality of dialysis care offered across counties in the USA. Methods: Data were gathered from Medicare-certified dialysis facilities in 2020 from the Centers for Medicare and Medicaid Services website. To identify high-need counties, county-level estimated crude prevalence of diabetes in adults was obtained from the 2022 CDC PLACES data portal. Our analysis reviewed 3,141 counties in the USA. The primary outcome measured was whether the county had a dialysis facility. Among those counties that had a dialysis facility, additional outcomes were the average star rating, whether peritoneal dialysis was offered, and whether home dialysis was offered. Results: The type of services offered by dialysis facilities varied significantly, with peritoneal dialysis being the most commonly offered service (50.8%), followed by home hemodialysis (28.5%) and late-shift services (16.0%). These service availabilities are more prevalent in urban facilities than in rural facilities. The Centers for Medicare and Medicaid Services Five Star Quality ratings were quite different between urban and rural facilities, with 40.4% of rural facilities having a ranking of five, compared to 27.1% in urban. Conclusion: The majority of rural counties lack a single dialysis facility. Counties with high rates of chronic kidney disease, diabetes, and blood pressure, deemed high need, were less likely to have a highly rated dialysis facility. The findings can be used to further inform targeted efforts to increase diabetes educational programming and design appropriate interventions to those residing in rural communities and high-need counties who may need it the most.

Race, disadvantage, and violence: A spatial exploration of macrolevel covariates of police-involved homicides within and between US counties

Efforts to explore the macrolevel determinants of police-involved homicides have expanded in recent years due in part to increased scrutiny and media attention to such events, and increased data availability of these events through crowdsourced databases. However, little empirical research has examined the spatial determinants of such events. The present study extends the extant macrolevel research on police-involved homicides by employing an underutilized spatial econometric model, the spatial Durbin model (SDM), to assess the direct and indirect county effects of racial threat, economic threat, social disorganization, and community violence on police killings within and between US counties from 2013 through 2020. Results indicate a direct inverse relationship between racial threat and police-involved homicides, no support for economic threat, and a direct positive association with two measures of social disorganization. Additionally, we find firearm availability exhibits significant direct and indirect spatial dependence on focal county police-involved homicides, reflecting spatial spillover processes. In essence, as firearm availability in neighboring counties increases, police-involved homicides within a focal county increase. The implications of these findings for racial threat, economic threat, social disorganization, and community violence are discussed.

An Area-Level Hierarchical Bayes Bivariate Threefold Linear Model for US State and County Indicators of Adult Skills in Literacy and Numeracy

Significant enhancements have been made in small area estimation (SAE) methodology because there has been an increased demand for reliable estimates through SAE over the past decades. This article describes the advanced statistical methodology used to produce state and county model-based estimates of average scores and various proficiency levels of adults for all states and counties, using data from the first cycle of the US Program for the International Assessment of Adult Competencies and the American Community Survey. Challenges and issues are discussed especially in light of the small number of sample counties with survey data. Each major stage of the estimation process is discussed, including the approach for generating design-based survey estimates and modelled variances, identifying a set of predictor variables (available and measured consistently for all counties), the hierarchical Bayes linear threefold models, including bivariate models for proportions, and the diagnostics and evaluation.