County-level Indicators Research Articles

Contribution of county-level socioeconomic indicators to racial or ethnic differences in neonatal anthropometry in the USA: a prospective cohort study

IntroductionRacial and ethnic differences in fetal growth and birth size in the USA have not been adequately explained by individual-level socioeconomic status (SES) factors. We explored whether differences may be...

Demographic Characteristics and County-level Indicators of Social Vulnerability in Salmonellosis Outbreaks Linked to Ground Beef—United States, 2012–2018

Ground beef is a common source of US Salmonella illnesses and outbreaks. However, the demographic and socioeconomic factors that are related to risk in ground beef-associated outbreaks of Salmonella infections are poorly understood. We describe the individual-level demographic characteristics and county-level indicators of social vulnerability for people infected with Salmonella linked to outbreaks associated with ground beef in the United States during 2012–2018. Non-Hispanic (NH) White and NH American Indian/Alaska Native persons, and people living in nonmetropolitan areas, were overrepresented among people in salmonellosis outbreaks linked to ground beef. Case patients disproportionately resided in counties with high social vulnerability, suggesting that one or more community social risk factors may contribute to or be associated with some food safety risks. Collecting and analyzing socioeconomic and demographic characteristics of people in outbreaks can help identify disparities in foodborne disease, which can be further characterized and inform equity-focused interventions.

Abstract 4137880: Access to exercise opportunities, physical inactivity and cardiovascular mortality: A nationwide study in the U.S.

Aims: Despite efforts to promote physical activity, the prevalence of physical inactivity continues to rise. Exercise access influences population activity levels, but county-level variation in physical activity and its link to cardiovascular disease (CVD) mortality is unclear. We explored the associations between access to exercise opportunities, levels of physical inactivity, and CVD mortality across U.S. counties Methods: The County Health Rankings and Roadmaps data and CDC WONDER mortality data were integrated. We compared age-adjusted CVD mortality across county-level quartiles of access to exercise opportunities and physical inactivity. Stratification was performed based on age, sex, race, and urbanization. The rate ratio (RR) for CVD mortality was calculated using generalized linear models. Results: CVD mortality decreased from high to low values of the county-level of physical activity across all demographic subgroups (P<0.001) (Table 1&Figure 1). Access to exercise opportunities was significantly associated with a reduced risk of CVD mortality (RR, 0.93 [0.91–0.95]), which was most pronounced in relation to acute myocardial infarction (AMI) mortality (RR, 0.80 [0.76–0.85]). The county-level physical inactivity was associated with an increased risk of CVD mortality (RR, 1.16 [1.14–1.17]), especially for ischemic heart disease (IHD) mortality (RR, 1.35 [1.31–1.38]) and AMI mortality (RR, 1.32 [1.25–1.38]) (Figure 2). All demographic subgroups demonstrated similar benefits by improving the county-level indicators of physical activity. Conclusion: Counties have the potential to make significant strides in preventing CVD and improving the cardiovascular health of their populations by enhancing access to exercise opportunities and reducing physical inactivity.

Abstract A054 Liver cancer mortality by structural disadvantage and race: An analysis of U.S. area-level factors

Abstract Introduction: Racial/ethnic and sex disparities in liver cancer (LC) mortality are well established; yet little is known about the contributions of county-level social risks on these disparities. Methods: We used U.S. national death certificate data from the National Center for Health Statistics (2018-2022) for LC mortality. County-level estimates of income inequality (ratio of household income at the 80th and 20th percentile), and median household income were obtained using the American Community Survey. Severe housing problems (percentage of households with 1 of 4 housing problems: overcrowding, high housing costs, lack of kitchen facilities, or lack of plumbing facilities) were abstracted from the U.S. Department of Housing and Urban Development (HUD) and U.S. Census Bureau. We estimated age-adjusted mortality rates (MR) and MR rate ratios (aRR) across metrics of county-level social risk grouped in quartiles overall and by race/ethnicity. Results: There were 101,088 liver cancer (LC) deaths from 2018-2022 (MR=6.16 per 100,000). Areas of highest county-level income inequality had 31% higher LC mortality compared to areas within the lowest quartile (aRR=1.31). LC MRs were 45% higher in counties with the lowest vs. highest median household income (aRR=1.45). Men and women in the highest quartile of severe housing problems had 29% and 22% higher LC MR (aRR=1.29 and 1.22, respectively), than those in the lowest quartiles. Men and women in the lowest quartile of median household income had 43% and 49% higher LC mortality than those in the highest median income quartiles (aRR=1.43 and 1.49, respectively). Within the highest quartile of income inequality, American Indian/Alaskan Native (AI/AN) persons had the highest LC mortality rates (MR=7.07), followed by Hispanic (MR=6.75), Black (MR=6.48), and Asian (MR=5.83) individuals. Similarly, in the highest quartile of severe housing problems, AI/AN persons had the highest LC mortality rate (MR 7.74), while Hispanic persons had 58% higher MR (MR 7.01), followed by Black (MR=6.19), Asian (MR=5.85) and White (MR=4.07) individuals. In the lowest median household income quartile, compared to White persons (MR=5.91), AI/AN persons had the highest MR (MR=8.86), and Hispanic persons had a 70% increase in mortality (MR=8.48). Among counties with the highest income inequality, compared to White persons (aRR=1.15), Black persons had an 80% increase in mortality (aRR=1.43). For those in the most concentrated quartile of severe housing problems, compared to White persons (aRR=1.07), AI/AN persons had an 80% increase in mortality (MR=1.34).Conclusion: All county-level indicators of highest social risk were associated with greater liver cancer mortality, but do not appear to fully account for racial and ethnic disparities in mortality. Therefore, place- based interventions are needed to address the role of county-level economic attributes in liver cancer mortality disparities. Citation Format: Jennifer K. McGee-Avila, Wayne R. Lawrence, Natalie Joe, Cameron B. Haas, Meredith S Shiels. Liver cancer mortality by structural disadvantage and race: An analysis of U.S. area-level factors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A054.

Investigating the impact of structural racism on black birthing people - associations between racialized economic segregation, incarceration inequality, and severe maternal morbidity

Black birthing people are twice as likely to experience severe maternal morbidity (SMM) as their white counterparts. Structural racism provides a framework for understanding root causes of perinatal health disparities. Our objective was to investigate associations between measures of structural racism and severe maternal morbidity (SMM) among Black birthing people in the US. We linked delivery hospitalizations for Black birthing people in the National Inpatient Sample (2008–2011) with data from the American Community Survey 5-year estimates and the Vera Institute of Justice Incarceration Trends datasets (2008–2011). Structural racism measures included the Index of Concentration at the Extremes for race and income (i.e., racialized economic segregation) and Black-white incarceration inequality, assessed as quintiles by hospital county. Multilevel logistic regression assessed the relationship between these county-level indicators of structural racism and SMM. Black birthing people delivering in quintiles 5 (concentrated deprivation; OR = 1.45, 95% CI = 1.16–1.81) and 3 (OR = 1.27, 95% CI = 1.04–1.56) experienced increased odds of SMM compared to those in quintile 1 (concentrated privilege). After adjusting for individual characteristics, obstetric comorbidities, and hospital characteristics the odds of SMM remained elevated for Black birthing people delivering in quintiles 5 (aOR = 1.32, 95% CI = 1.02–1.71) and 3 (aOR = 1.24, 95% CI = 1.02–1.51). Delivering in the quintile with the highest incarceration inequality (Q5) was not significantly associated with SMM (aOR = 0.95, 95% CI = 0.72–1.25) compared to those delivering in counties with the lowest incarceration inequality (Q1). In this national-level study, racialized economic segregation was associated with SMM among Black birthing people. Our findings highlight the need to promote maternal and perinatal health equity through actionable policies that prioritize investment in communities experiencing deprivation.

Associations Between Hospital Readmissions and Proximity to Brownfields in North Carolina

Background: Contaminated sites known as brownfields contribute to air, water, and land pollution and also prevent the beneficial use of lands by the community. The health effects surrounding brownfields have been understudied which can impair efforts to quantify the benefits of their remediation. Methods: Our study cohort was composed of 21,776 heart failure patients diagnosed between 2004 and 2016 at a University of North Carolina Healthcare System affiliated hospital or clinic. We examined the associations between brownfields and hospital readmissions. We used proximity to the nearest brownfield as our exposure based on the primary residence at the time of heart failure diagnosis. Zero-inflated Poisson models were used to associate distance to the nearest brownfield with readmissions while adjusting for age, race, sex, and socioeconomic status indicators based on the 2010 US Census. In sensitivity analyses we examined log transformed distance, restricted to only those brownfields within 2 km of an individual, and further adjusted for county-level indicators of access to healthcare. Results: A 1 km increase in distance to the nearest brownfield was associated with a 1.29% (95% confidence interval = 0.43-2.14%) decrease in 7-day readmissions as well as a 0.55% decrease (95% confidence interval = 0.20-0.90%) in 30-day readmissions. Associations with 7-day readmissions remained when restricting to brownfields within 2km, in the log-transformed distance models, and after adjusting for county-level indicators of access to healthcare. Associations with 30-day readmissions were substantially attenuated under all sensitivity analyses. Conclusion: Proximity to brownfields is associated with increases in hospital visits and readmissions among heart failure patients. These associations may reflect exposure to chemicals present in the brownfields as well as land disinvestment. Understanding the health risks surrounding brownfields may help communities to better protect their environmental health. This abstract does not necessarily represent the views or policies of the US EPA.

Assessment of Tennessee's county-level vulnerability to hepatitis C virus and HIV outbreaks using socioeconomic, healthcare, and substance use indicators.

BackgroundHuman immunodeficiency virus (HIV), hepatitis C virus (HCV), and injection drug use are syndemic in the central Appalachian states. In Tennessee (TN), declines in HIV among persons who inject drugs (PWID) stalled, and HCV infection rates increased significantly from 2013–2017. To better target strategies to address the syndemic, county-level socioeconomic, opioid use, access to healthcare, and health factors were modeled to identify indicators predictive of vulnerability to an HIV/HCV outbreak among PWID in TN.MethodsNewly reported chronic HCV cases among persons aged 13–39 years in 2016–2017 were used as a proxy for county-level HIV/HCV vulnerability among TN’s 95 counties. Seventy-five publicly available county-level measures from 2016–2017 were collected and reduced through multiple dimension reduction techniques. Negative binomial regression identified indicators associated with HCV which were used to calculate county-level vulnerability to a local HIV/HCV outbreak.ResultsThirteen county-level indicators were identified as strongly predictive of HIV/HCV vulnerability with the statistically significant indicators being percentage of the population aged 20–44 years, per capita income, teen birth rate, percentage of clients in TDMHSAS-funded opioid treatment and recovery, syphilis case rate, and percentage of homes with at least one vehicle. Based on the 13 indicators, we identified the distribution of vulnerability to an HIV/HCV outbreak among TN’s counties. Eleven high vulnerability counties were identified, with the preponderance located in east and middle TN.ConclusionThis analysis identified the county-level factors most associated with vulnerability to an HIV/HCV outbreak among PWID in TN. These results, alongside routine surveillance, will guide targeted prevention and linkage to care efforts for the most vulnerable communities.

A longitudinal descriptive assessment of US college COVID-19 dashboards

Background The purposes of this study were to (1) longitudinally examine US college and university COVID-19 dashboard content and (2) explore county-level population health and COVID-19 community indicators where colleges with large undergraduate enrollments were located. Methods We identified 10 colleges with the largest undergraduate enrollments. Next, we located all public and private two- and four-year nonprofit colleges (n = 58) in each respective county. We assessed active COVID-19 dashboards using established criteria from We Rate COVID Dashboards in March 2021 and February 2022. Results In 2021 and 2022, two-thirds (n = 38/58) of colleges had an active COVID-19 dashboard. While most COVID-19 dashboards were easy to read, they were less likely to include updated daily infection rates and testing frequency details. Conclusion Overall quality, content, and features of COVID-19 dashboards varied greatly across colleges. Future COVID-19 dashboard designs should focus on providing evidence-based information with the goal of promoting and supporting informed decision making.

US local public health department spending between 2008 and 2016 did not increase for communities in need

BackgroundGreater US local public health department (LPHD) spending has been associated with decreases in population-wide mortality. We examined the association between changes in LPHD spending between 2008 and 2016 and county-level sociodemographic indicators of public health need.MethodsMultivariable linear regression was used to estimate the association between changes in county-level per-capita LPHD spending and 2008 sociodemographic indicators of interest: percent of population that was over 65 years old, Black, Hispanic, in poverty, unemployed, and uninsured. A second model assessed the relationship between changes in LPHD spending and sociodemographic shifts between 2008 and 2016.ResultsLPHD spending increases were associated with higher percentage points of 2008 adults over 65 years of age (+$0.53 per higher percentage point; 95% CI: +$0.01 to +$1.06) and unemployment (−$1.31; 95% CI: −$2.34 to −$0.27). Spending did not increase for communities with a higher proportion of people who identified as Black or Hispanic, or those with a greater proportion of people in poverty or uninsured, using either baseline or sociodemographic shifts between 2008 and 2016.ConclusionFuture LPHD funding decisions should consider increasing investments in counties serving disadvantaged communities to counteract the social, political, and structural barriers which have historically prevented these communities from achieving better health.

A county-level analysis of unemployment and mental health in the Southeastern U.S.: applying a social determinants of health framework

ABSTRACT Informed by a Social Determinants of Health framework, this study examines associations between county-level unemployment rates and two county-level indicators of mental health challenges. With 2017 data from County Health Rankings, the study focuses on 418 counties in the southeastern U.S., a region with longstanding unmet service needs and ongoing mental health care challenges. Linear regression models show positive associations between unemployment and two county-level indicators of mental health challenges, while accounting for multiple social determinants of health. The study illustrates how county-level analysis can help social workers target mental health needs and increase access to care.

Association of Area-Level Socioeconomic Deprivation With Hypoglycemic and Hyperglycemic Crises in US Adults With Diabetes

Social determinants of health play a role in diabetes management and outcomes, including potentially life-threatening complications of severe hypoglycemia and diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS). Although several person-level socioeconomic factors have been associated with these complications, the implications of area-level socioeconomic deprivation are unknown. To examine the association between area-level deprivation and the risks of experiencing emergency department visits or hospitalizations for hypoglycemic and hyperglycemic crises (ie, DKA or HHS). This cohort study used deidentified administrative claims data for privately insured individuals and Medicare Advantage beneficiaries across the US. The analysis included adults with diabetes who met the claims criteria for diabetes between January 1, 2016, and December 31, 2017. Data analyses were performed from November 17, 2020, to November 11, 2021. Area deprivation index (ADI) was derived for each county for 2016 and 2017 using 17 county-level indicators from the American Community Survey. ADI values were applied to patients who were living in each county based on their index dates and were categorized according to county-level ADI quintile (with quintile 1 having the least deprivation and quintile 5 having the most deprivation). The numbers of emergency department visits or hospitalizations related to the primary diagnoses of hypoglycemia and DKA or HHS (ascertained using validated diagnosis codes in the first or primary position of emergency department or hospital claims) between 2016 and 2019 were calculated for each ADI quintile using negative binomial regression models and adjusted for patient age, sex, health plan type, comorbidities, glucose-lowering medication type, and percentage of White residents in the county. The study population included 1 116 361 individuals (563 943 women [50.5%]), with a mean (SD) age of 64.9 (13.2) years. Of these patients, 343 726 (30.8%) resided in counties with the least deprivation (quintile 1) and 121 810 (10.9%) lived in counties with the most deprivation (quintile 5). Adjusted rates of severe hypoglycemia increased from 13.54 (95% CI, 12.91-14.17) per 1000 person-years in quintile 1 counties to 19.13 (95% CI, 17.62-20.63) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.41 (95% CI, 1.29-1.54; P < .001). Adjusted rates of DKA or HHS increased from 7.49 (95% CI, 6.96-8.02) per 1000 person-years in quintile 1 counties to 8.37 (95% CI, 7.50-9.23) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.12 (95% CI, 1.00-1.25; P = .049). This study found that living in counties with a high area-level deprivation was associated with an increased risk of severe hypoglycemia and DKA or HHS. The concentration of these preventable events in areas of high deprivation signals the need for interventions that target the structural barriers to optimal diabetes management and health.

Neighborhood-Level Predictors of Sexual Violence Across Intimate Partner and Non-Intimate Partner Relationships: A Case–Control Study

Ample research explores individual factors associated with sexual violence, yet individual, dyadic, and environmental influences on intimate partner sexual violence (IPSV) occurring in the larger context of non-intimate partner violence (NIPSV) remain relatively unexplored. The current study aimed to determine the extent to which county-level indicators in combination with individual and dyadic factors are associated with sexual violence across relationship types. Reported IPSV and NIPSV cases were obtained from the Tennessee Bureau of Investigation’s (TBI) online incident-based reporting system. County-level variables indicative of neighborhood physical disorder, violent crime, income inequality, firearm prevalence, and community alcohol use patterns were retrieved from the online resource County Health Rankings and Roadmaps. Using a nested case–control design, we determined significant sexual violence risk within younger cohorts and across relationship types, finding three significant county-level factors: 1) income inequality, 2) rate of firearm fatalities, and 3) percentage of female residents. Suggested prevention and intervention efforts include targeting younger age groups for IPSV and NIPSV education, developing resources for a range of relational partners, improving legal access and law enforcement training for reporting, and continued examination of the role of firearms.

Are both individual-level and county-level social capital associated with individual health? A serial cross-sectional analysis in China, 2010–2015

ObjectivesWe aimed to examine the associations of both individual-level and county-level social capital with individual health in China during a period of rapid economic growth.Design and settingA serial cross-sectional study...

US trends in mask wearing during the COVID-19 pandemic depend on rurality.

Face masks are widely recommended as a COVID-19 prevention strategy. State mask mandates have generally reduced the spread of the disease, but decisions to wear a mask depend on many factors. Recent increases in case rates in rural areas following initial outbreaks in more densely populated areas highlight the need to focus on prevention and education. Messaging about disease risk has faced challenges in rural areas in the past. While surges in cases within some communities are likely an impetus for behavior change, rising case rates likely explain only part of mask-wearing decisions. The current study examined the relationship between county-level indicators of rurality and mask wearing in the USA. National data from the New York Times' COVID-19 cross-sectional mask survey was used to identify the percentage of a county's residents who reported always/frequently wearing a mask (2-14 July 2020). The New York Times' COVID-19 data repository was used to calculate county-level daily case rates for the 2 weeks preceding the mask survey (15 June - 1 July 2020), and defined county rurality using the Index of Relative Rurality (n=3103 counties). Multivariate linear regression was used to predict mask wearing across levels of rurality. The model was adjusted for daily case rates and other relevant county-level confounders, including county-level indicators of age, race/ethnicity, gender, political partisanship, income inequality, and whether each county was subject to a statewide mask mandate. Large clusters of counties with high rurality and low mask wearing were observed in the Midwest, upper Midwest, and mountainous West. Holding daily case rates and other county characteristics constant, the predicted probability of wearing a mask decreased significantly as counties became more rural (&beta;=-0.560; p<0.0001). Upticks in COVID-19 cases and deaths in rural areas are expected to continue, and localized outbreaks will likely occur indefinitely. The present findings highlight the need to better understand the mechanisms underlying perceptions of COVID-19 risk in rural areas. Dissemination of scientifically correct and consistent information is critical during national emergencies.

Abstract PO-004: Association between county-level socioeconomic characteristics and health indicators with COVID-19 deaths in Florida

Abstract COVID-19 has exposed the long standing racial health disparities and social inequities in the United States. It has also brought into focus the need of the vulnerable communities including racial and ethnic minority groups. health care reforms. The purpose of this analysis is to investigate county-level COVID-19 death rates with key socioeconomic and health indicators using correlation analysis. Death rates are calculated using deaths that occurred in Florida (n = 7,022 deaths) between March 1, 2020 and July 31, 2020. We hypothesize that counties with higher uninsured and older people have higher COVID-19 death rates. Data on COVID-19 county statistics is obtained from Florida Department of Health. County-level health indicators are obtained from Florida Health Community Health Assessment Resource Tool Set (CHARTS) and the County Health Rankings and Roadmap program of the Robert Wood Johnson Foundation. Spearman rank correlation, a non-parametric method, is applied to measure associations. We find that higher share of uninsured adults in a county is significantly associated with higher death rates from COVID-19. In addition, percent older than 65 is correlated death rates. However, contrary to other previous studies, we do not find a correlation between share of African Americans and death rates in Florida counties. Further, we do not find that death rates are correlated with measures of diabetes, smoking, and obesity. The policy implication of this investigation points to the vulnerability of communities without health insurance to the COVID-19 pandemic and the urgent need for health care reform. This research was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under the Award Number U54 MD007582. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research was also made possible by Grant Numbers U54CA233396, U54CA233444, &amp; U54CA233465 from the National Institutes of Health (NIH), National Cancer Institute (NCI). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCI. Citation Format: Gebre-Egziabher Kiros, Kira Gaddis. Association between county-level socioeconomic characteristics and health indicators with COVID-19 deaths in Florida [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-004.

Assessing County-Level Vulnerability for Opioid Overdose and Rapid Spread of Human Immunodeficiency Virus and Hepatitis C Infection in South Dakota.

Key indicators of vulnerability for the syndemic of opioid overdose, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) due to injection drug use (IDU) in rural reservation and frontier counties are unknown. We examined county-level vulnerability for this syndemic in South Dakota. Informed by prior methodology from the Centers for Disease Control and Prevention, we used acute and chronic HCV infections among persons aged ≤40 years as a proxy measure of IDU. Twenty-nine county-level indicators potentially associated with HCV infection rates were identified. Using these indicators, we examined relationships through bivariate and multivariate analysis and calculated a composite index score to identify the most vulnerable counties (top 20%) to this syndemic. Of the most vulnerable counties, 69% are reservation counties and 62% are rural. The county-level HCV infection rate is 4 times higher in minority counties than nonminority counties, and almost all significant indicators of opioid-related vulnerability in our analysis are structural and potentially modifiable through public health interventions and policies. Our assessment gives context to the magnitude of this syndemic in rural reservation and frontier counties and should inform the strategic allocation of prevention and intervention services.

Racism Predicts Police Killings of Black Civilians

Contrary to public opinion and high profile cases, science has not established a clear general link between police officer-involved killings of black civilians and racism. Influential studies even reported an anti-white bias in the use of fatal force. Using the largest independent database on police killings across the U.S. between 2013 and 2019 I show that the likelihood of a black citizen dying at the hands of police is significantly, positively, and robustly associated with a range of county-level indicators for extreme racial hatred. Black lives are more at risk in areas where white supremacy hate groups are located or active and where black citizens are already victims of racist hate crimes. These conditional correlations are consistent with the systemic racism hypothesis.

Predicting spatial and temporal responses to non-pharmaceutical interventions on COVID-19 growth rates across 58 counties in New York State: A prospective event-based modeling study on county-level sociological predictors.

Non-pharmaceutical interventions (NPIs) have been implemented in the New York State since the COVID-19 outbreak on March 1, 2020 to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Socioeconomic heterogeneity across counties closely manifests differences in the post-NPIs growth rate of incidence, which is a crucial indicator to guide future infectious control policy making. Few studies, however, examined the geospatial and sociological variations in the epidemic growth across different time points of NPIs. To guide a more effective reopening plan while controlling the transmission, the current study aims at 1) identifying hotspots of the growth rate of COVID-19 incidence among the 57 counties and New York City in NYS over time, and 2) examining the association of COVID-19 growth rates after eight critical NPIs time points and most relevant county-level sociological predictors. County-level COVID-19 incidence rates were retrieved from the Social Explorer Website between March 7, 2020 to June 22, 2020. 5-day moving average growth rates of COVID-19 incidence were calculated for 16 selected time points, including the dates of eight NPIs and their respective 14-day-lag-behind time points. A total of 36 county-level indicators were extracted from multiple public datasets. Geospatial mapping and heatmap were used to analyze spatial and temporal heterogeneity of county-level COVID-19 outbreak over selected NPIs-related dates. Generalized mixed effect least absolute shrinkage and selection operator (LASSO) regression, controlling for the 5-day moving average growth rates of COVID-19 testing rates, was employed to identify significant county-level predictors related to the changes of county-level COVID-19 growth rates over time. COVID-19 infection increased and peaked by the end of March (η=22.50%). Growth rates of COVID-19 decreased by 50.48% after implementing NPIs such as closures of schools, non-essential businesses, parks, and subways. There was a geospatial shift in the region with the highest growth rates from New York metropolitan area towards Western and Northern regions over time. Proportions of population aged 45 years and above (β=3.25 [0.17-6.32]), living alone at residential houses (β=3.31 [0.39--6.22]), and proportion of crowd residential houses (β=6.15 [2.15-10.14]) were positively associated with the growth rate of COVID-19 infection. In contrast, living alone at rental houses (β=-2.47 [-4.83--0.12]) and rate of mental health providers (β=-1.11 [-1.95--0.28]) were negatively associated with COVID-19 growth rate across all 16 time points. There are geospatial differences in COVID-19 incidence after implementing different NPIs. Socioeconomic, racial/ethnic, and healthcare resource disparities at the structural and historical levels across counties need to be considered in infection control policymaking to narrow the unequal health impact on vulnerable populations effectively.

Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors

BackgroundSocial distancing—when people limit close contact with others outside their household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed.PurposeWe examined whether one type of social distancing behavior—reduced movement outside the home—was associated with conventional health behaviors.MethodWe examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors.ResultsChanges in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA.ConclusionsSocial distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

Abstract B098: Individual and contextual factors associated with prostate cancer stage at diagnosis: A multistate study

Abstract Background: The success of prostate cancer (PCa) prevention and control programs should be measurable as a decrease in the likelihood of PCa diagnosis at a late stage in vulnerable and underserved populations. Objective: To examine the relationship between individual socioeconomic status (SES), county-level measures of social determinants, and PCa stage at diagnosis. Methods: Men aged 40 and older diagnosed with stage 0 to III PCa from 2000 to 2012 were identified from the Surveillance, Epidemiology, and End Results (SEER) tumor registries in 12 states. These states were retained because they had complete information on Federal Information Processing Standard (FIPS) county codes and across the study period. These data were combined with data from the Area Health Resource Files (AHRF). Stage at diagnosis was dichotomized as early stage (in situ, localized) versus late stage (regional, distant). Individual-level factors included age at diagnosis, race (white, black, others), ethnicity (Hispanic/Latino, non-Hispanic/Latino), marital status (married, unmarried), insurance status (insured, uninsured, unknown), tumor stage, tumor grade (undifferentiated, poorly differentiated, moderately differentiated, well differentiated), and year of diagnosis. Contextual-level factors included county level SES status, residence in rural/urban county, and county-level availability of health care resource. Student's t test for continuous variables and chi-square test for categorical variables were used for statistical comparisons. Hierarchical logistic regression models were used to identify factors associated with late-stage diagnosis. Results: The sample consisted of N = 707,108 PCa cases of which 16.48 % (N = 116,511) are late stage and 16.48 % (N= 590,597) are early stage. The mean diagnosis age was 66 years old. The majority of these individuals were married (76%), White (80.76%), had unknown health insurance coverage (56.30%), were diagnosed in the year 2007 (8.56%), and lived in the state of California (38.48%). In the multivariate analysis, the odds of late-stage PCa diagnosis were higher in older men (odds ratio [OR] =1.013, 95% CI=1.012, 1.014), Blacks (OR=1.07, 95% CI=1.05, 1.09), and those with unknown insurance status (OR=1.12, 95% CI= 1.10, 1.15). County-level socioeconomic indicators including unemployment rates, educational attainment, poverty rates, rural/urban classification were not predictors of late-stage diagnosis. Furthermore, county-level contextual variables related to the availability and accessibility of health care providers and health services were not associated with late-stage diagnosis. Conclusions: Community-based interventions designed to increase utilization of PCa testing should continue to target vulnerable and underserved populations especially Black, and uninsured/underinsured men. It is hope that these efforts will improve early detection and contribute to improve survival. Citation Format: Georges Adunlin, Maisha K. Freeman, Hadiza I. Galadima, Matthew Asare. Individual and contextual factors associated with prostate cancer stage at diagnosis: A multistate study [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B098.