Counties In State Research Articles

Resilience to disaster: Evidence from American wellbeing data

As the severity and frequency of extreme weather events become more pronounced with climate change and the increased habitation of at-risk areas, it is important to understand people’s resilience to them. We quantify resilience by estimating how disasters in the US impacted individual wellbeing in the initial weeks and months post-disaster, and whether the effect sizes differed by individual- and county-level factors. The methodological approach contrasts changes in wellbeing in counties affected by disasters with that of residents in unaffected counties of the same state. We find that people’s hedonic wellbeing is reduced by approximately 6% of a standard deviation in the first two weeks following the event, with the effect diminishing rapidly thereafter. The negative effects are driven by White, older, and economically advantaged sub-populations, who exhibit less resilience. We find no evidence that existing indices of community resilience moderate impacts. Our conclusion is that most people in the US have a resilient response to extreme weather events.

Social vulnerability and gastrointestinal cancer mortality among United States counties.

499 Background: The impact of socioeconomic status on gastrointestinal (GI) cancer mortality in the United States (US) is not well-established. We hypothesized that socially vulnerable populations have disproportionately higher mortality rates. Hence, we assessed the association of the social vulnerability index (SVI) with GI cancer mortality across US counties. Methods: Social vulnerability indices were obtained from the agency for toxic substances and disease registry (ATSDR) from 2014-2018 to compute percentile ranking scores (PRS: ranging from 0-1) for each US county. PRS were further categorized into quartiles (Q: 1st: 0-0.25 [least vulnerable]; 4th:0.75-1.00 [most vulnerable]). The wide-ranging online data for epidemiological research (WONDER) database was queried to abstract county-level age-adjusted mortality rates (AAMR) per 100,000 person-years (PY) for populations diagnosed with GI cancers. AAMRs were then linked with quartile rankings. Rate ratios (RR) of AAMRs between 4th and 1st Q were subsequently estimated with 95% confidence intervals using population-weighted, Poisson regression. Results: A total of 3142 counties were included in this analysis. The AAMR for overall deaths (OD) and premature deaths (PD; defined as death at age <65) in the GI cancer population was 53.8 and 17.7 per 100,000 PY. A gradient increase in GI cancer-related mortality was observed from 1st Q to 4th Q (OD: 49.2 vs 59.3; PD: 14.1 vs 21.3). This stepwise increase in AAMR over the quartiles was consistent across gender, different racial/ethnic subgroups, and rural/urban categories of counties. The AAMRs for OD were significantly higher in the 4th Q as compared to the 1st Q for gastric (RR: 1.67 [95% CI, 1.51-1.86]), hepatocellular including intrahepatic biliary (1.52 [1.45-1.61]), colorectal (1.21 [1.16-1.26]) and biliary (1.17 [1.06-1.28]) cancer. Similarly, significantly higher AAMRs for PD were observed in 4th Q vs1st Q for gastric (1.81 [1.60-2.05]), hepatocellular including intrahepatic biliary (1.78 [1.58-2.00]), biliary (1.64 [1.35-1.99]), colorectal (1.29 [1.21-1.39]) and pancreatic (1.16 [1.07-1.25]) cancer. However, no significant differences were observed for esophageal and small intestinal cancers. Men with gastric (1.58 [1.41-1.78]) and hepatocellular cancer (1.54 [1.43-1.66]), non-Hispanic Black population (1.54 [1.31-1.80]) with hepatocellular cancer, and Hispanic population with colorectal cancer (1.50 [1.31-1.72]) were observed to have higher overall mortality in the 4th Q compared to the 1st Q. The findings were similar for premature mortality. Conclusions: Population-level data suggests that the US counties with higher socio-economic adversities may be at an increased risk of GI cancer-related mortality. Investigations using patient-level data are required to probe the impact of socioeconomic vulnerabilities on cancer-related mortality.

Explaining obesity disparities by urbanicity, 2006 to 2016: A decomposition analysis.

A large, and potentially growing, disparity in obesity prevalence exists between large central metros and less urban United States counties. This study examines its key predictors. Using a rich county-year data set spanning 2006 to 2016, the authors conducted a Gelbach decomposition to examine the relative importance of demographic, socioeconomic, environmental, and behavioral factors in shaping the baseline obesity gap and the growth rate over time between large central metros and other counties. Predictors included in this model explain almost the entire obesity gap between large central metros and other counties in the baseline year but can explain only ~32% of the growing gap. At baseline, demographic predictors explain more than half the obesity gap, and socioeconomic and behavioral predictors explain the other half. Behavioral and socioeconomic predictors explain more than half the growing gap over time whereas controlling for environmental and demographic predictors decreases the obesity gap by urbanicity over time. Results suggest policy makers should prioritize interventions targeting health behaviors of residents in non-large central metros to slow the growth of the obesity gap between large central metros and other counties. However, to fundamentally eliminate the obesity gap, in addition to improving health behaviors, policies addressing socioeconomic inequalities are needed.

Comparative Evaluation of 2 Work Experience Approaches for Predominantly Black Youth in a Rural Community: Implications for Public Health Workforce Development.

In order to create a more diverse workforce, there is a need to involve historically excluded youth in public health-related work. Youth involvement in asset-based work experience approaches is especially relevant for rural areas with workforce shortages. To explore the public health workforce development implications of community-based career exploration and asset mapping work experience from the perspective of Black youth. We used qualitative in-depth interviews with youth, aged 14 to 22 years, who participated in a work experience program anchored in several rural counties in southeastern United States. A phenomenological lens was applied for qualitative analyses with iterative, team-based data coding. Participants were also surveyed pre- and postprogram to supplement findings. A rural community-based organization's work experience program consisted of 2 tracks: (1) Youth Connect-a career exploration track that included work placement within community agencies; and (2) MAPSCorps-a track that employs youth to conduct asset mapping for their community. We interviewed 28 of 31 total participants in the 2 tracks. We uncovered 4 emergent profiles in how youth described shifts in their perceptions of community: (1) Skill Developers; (2) Community Questioners; (3) Community Observers; and (4) Community Enthusiasts. In comparing between tracks, youth who participated in work experience that involved asset mapping uniquely described increased observation and expanded view of community resources and had greater increases in research self-efficacy than youth who participated only in career exploration. Asset mapping work experience that is directly placed in rural communities can expose Black youth to and engage them in essential public health services (assessing and mobilizing community assets) that impact their community. This type of program, directly integrated into rural communities rather than placed near academic centers, could play a role in creating a more diverse public health workforce.

Do Capitals of States Spatially Matter for the Payroll? A Spatial Analysis for United States Counties

Do Capitals of States Spatially Matter for the Payroll? A Spatial Analysis for United States Counties

Increasing knowledge of and promoting positive attitudes towards the consumption of fruit and vegetables among pre-school children, parents, and teachers in three childcares in one city in a Midwestern United States county: A multi-level intervention of teachers, parents, and children

Increasing knowledge of and promoting positive attitudes towards the consumption of fruit and vegetables among pre-school children, parents, and teachers in three childcares in one city in a Midwestern United States county: A multi-level intervention of teachers, parents, and children

Nationwide assessment of ambient monthly fine particulate matter (PM2.5) and the associations with total, cardiovascular and respiratory mortality in the United States

High air pollution events spanning multiple months and caused by environmental perturbations such as droughts and wildfires are increasing in frequency, intensity and duration due to climate change. While both daily and annual exposure to fine particulate matter (PM2.5) have regulatory standards in the United States, mid-scale exposure at the monthly interval remains unregulated and the public health impacts of mid-duration ambient air pollution exposure are poorly understood. These present a new public health challenge in mitigating harmful effects of persistent ambient air pollution on community health. We executed an ecological study of the associations between monthly mean PM2.5 exposure with total, cardiovascular and respiratory mortality counts, stratified by age, sex and race, in 698 counties in the conterminous United States from 1999 to 2018. A two-stage model was used to estimate the risk and number of deaths associated with this exposure timescale reported as incidence rate ratios (IRRs) and absolute risk differences per million persons (ARDs). Increased mortality of all types was positively associated with a 10 μg m−3 monthly change in PM2.5 exposure (total mortality IRR: 1.011, 95% confidence interval (CI): (1.009, 1.013), ARD: 8.558, 95% CI: (6.869, 10.247); cardiovascular mortality IRR: 1.014, 95% CI: (1.011, 1.018), ARD: 3.716, 95% CI: (2.924, 4.509); respiratory mortality IRR: 1.016, 95% CI: (1.011, 1.023), ARD: 1.676, 95% CI: (1.261, 2.091)). Our results suggest elderly, non-black minorities and males to be the most impacted subgroups along with metropolitan and highly socially vulnerable communities. Heterogeneities in the magnitude and direction of association were also detected across climate regions of the United States. These results elucidate potential effects of monthly PM2.5 on mortality and demonstrate a need for future health policies for this currently unregulated interval of ambient air pollution exposure.

Improved prediction of new COVID-19 cases using a simple vector autoregressive model: evidence from seven New York state counties.

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and severe acute respiratory syndrome. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback - they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Uneven Growth in Social Capital Organizations After Disasters by Pre-Disaster Conditions in the United States 2000-2014.

Community-level social capital organizations are critical pre-existing resources that can be leveraged in a disaster. The study aimed to test the hypothesis that communities with larger pre-disaster stocks of social capital organizations would maintain pre-disaster levels or experience growth. An annual panel dataset of counties in the contiguous United States from 2000 to 2014 totaling 46620 county-years, including longitudinal data on disasters and social capital institutions was used to evaluate the effect of disaster on growth of social capital. When a county experienced more months of disasters, social capital organizations increased a year later. These findings varied based on the baseline level of social capital organizations. For counties experiencing minor disaster impacts, growth in social capital organizations tends to occur in counties with more social capital organizations in 2000; this effect is a countervailing finding to that of major disasters, and effect sizes are larger. Given the growing frequency of smaller-scale disasters and the considerable number of communities that experienced these disasters, the findings suggest that small scale events create the most common and potentially broadest impact opportunity for intervention to lessen disparities in organizational growth.

Public health applications of historical smoke forecasts: An evaluation of archived BlueSky data for the coterminous United States, 2015–2018

BackgroundWildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance. MethodsWe evaluated four years (2015–2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman's correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure. ResultsAcross all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001). ConclusionsIn general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

Electoral Effect of Protracted Presidential Primaries

Divisive presidential primaries have become common in the United States following the McGovern—Fraser reforms. Both politicians and political scientists have argued that parties that experience divisive primaries may be disadvantaged in the general election, with several political scientists demonstrating a relationship between a nominee receiving a smaller share of the primary vote and a smaller share of the general election vote. However, demonstrating that divisive primaries cause worse general election results is difficult because candidate weakness or other factors may cause poor performance in both primary and general elections. I use a linear regression model to estimate the effect of divisive presidential primaries on general election outcomes. I compare near-border counties in states that did not experience contested primaries to near-border counties in states that did experience contested primaries. I find that there is generally a penalty for parties that experience divisive primaries, but the effect varies over election cycles.

Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior.

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.

COVID-19 Vaccination: Sociopolitical and Economic Impact in the United States.

Since the outbreak of COVID-19, vaccination against the virus has been implemented and has progressed among various groups across all ethnicities, genders, and almost all ages in the United States. This study examines the impacts of socioeconomic status and political preference on COVID-19 vaccination in over 443 counties in the southwestern United States. Regression analysis was used to examine the association between a county’s vaccination rate and one’s personal income, employment status, education, race and ethnicity, age, occupation, residential area, and political preference. The results were as follows: First, counties with higher average personal income tend to have a higher vaccination rate (p < 0.001). Second, county-level vaccination is significantly associated with the percentage of Democrat votes (β = 0.242, p < 0.001). Third, race and ethnicity are vaccine-influencing factors. Counties with more Black residents have lower vaccine acceptance (β = −0.419, p < 0.001), while those where more Hispanics or Native Americans reside are more likely to accept vaccines for health protection (β = 0.202, p < 0.001; β = 0.057, p = 0.008, respectively). Lastly, pertaining to the age difference, seniors aged 65 and older show substantial support for vaccination, followed by the median age group (all p < 0.001).

PSUN303 Nationwide Geospatial Analysis of Hospitalization in Diabetes Mellitus Complications

Abstract Diabetes mellitus and complications associated with the disease represent a significant source of morbidity within the United States. Substantial public health efforts have been devoted to understanding the factors that underlie poor outcomes in diabetes. The purpose of this work is to (1) determine whether there exist statistically significant geospatial disparities in the relative rate of diabetic complications in Medicare patients throughout the United States, and (2) if these exist, assess the socioeconomic, cultural, and medical attributes that distinguish communities delineated by these variations. This work utilized multiple public databases, including National Plan and Provider Enumeration System (NPPES), US Department of Agriculture (USDA), US Census, and Center for Medicare Services (CMS) data. Datasets were combined on a county level to quantify 18 socioeconomic, 22 health, and 4 diabetic management attributes of 3061 total counties in the contiguous United States. CMS Prevention Quality Indicators (PQI) #1 (Diabetes Short-Term Complications Admission Rate), #3 (Diabetes Long-Term Complications Admission Rate), and #16 (Lower-Extremity Amputation Among Patients with Diabetes Rate) were used as the main indicators of county-level diabetic control, along with the total number of practicing endocrinologists. These variables were scaled to the number of Medicare members with diabetes in each county. All variables were for the latest available year, 2019, with the exception of the number of practicing endocrinologists (from December 2021). The synthesized database was then exported to GeoDa, a geospatial analysis program, where statistically significant (p &amp;lt; 0.05) diabetes complication spatial hotspots, coldspots, and outliers were identified using Moran's I on a national level. ANOVA was then conducted across all Moran's I groupings with a two-tailed t-test between hotspot and coldspot groupings to identify what socioeconomic, health, and diabetic management attributes were statistically significant between complication geostructures. Geospatial visualization revealed an expansive hotspot cluster in the normalized diabetes long-term complication admission rate that encompassed the East Coast and included portions of Florida and Texas (p &amp;lt; 0.05). Conversely, an expansive coldspot cluster encompassed much of the West and Great Plains region. Similar geospatial patterns were observed in the short-term complication admission rate, with the caveat of a new hotspot cluster that encompassed California and the West Coast (p &amp;lt; 0.05). ANOVA using the identified clusters as groupings revealed differences between hotspot and coldspot clusters: Hotspots tended to have greater Hispanic (6.7% to 1.8%) and Black (10.9% to 0.6%) representation compared to coldspots. They tended to have greater population density (2877 to 124 persons/sq. mile). Hotspots also tended to have more endocrinologists per capita (3.0 to 0.87) than coldspots. Variables of interest that did not appear to differ included the percent in poverty and the percent uninsured. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Differences in Rural Built Environment Perceptions Across Demographic and Social Environment Characteristics.

The benefits of physical activity to health and obesity prevention are well established. However, attributes of the built environment influence participation in physical activity. The purpose of this study is to assess differences in perceptions of neighborhood walkability across demographic characteristics and social environment factors among rural residents. In a telephone survey, adult respondents (N = 448) across nine rural counties in a southeastern state answered questions about perceived neighborhood walkability, demographic characteristics, and their neighborhood social environment. Study recruitment for a convenience sample occurred through collaborations with local community organizations. Prevalence of destinations and barriers were estimated according to demographic and neighborhood social environment characteristics. Multiple logistic regression models assessed the association of demographic and neighborhood social environment characteristics with neighborhood walkability and calculated adjusted prevalence. Relaxing places to walk were the most often reported destinations (62.0%), followed by retail destinations (45.7%), and communal destinations (42.6%). Traffic was the most reported barrier to safe walking (40.4%), followed by animals (37.5%), and crime (30.5%). Perceptions of retail and communal destinations varied by age and race. Perceptions of traffic and crime as barriers varied by race, weight status, and income. Community belonging and social cohesion were associated with lower perceptions of barriers. Study findings present demographic characteristics and social environment attributes as key factors that shape perceived neighborhood walkability. Findings can help inform programmatic efforts and environmental change strategies to improve walking in rural areas.

Flood-Related Federally Declared Disaster Events and Community Functioning (COPEWELL)

Objective: Understanding long-term disaster effects is key to building theories of recovery and informing policymaking. Findings regarding long-term recovery are inconsistent, with some scholars finding that disasters have little long-term impact, and others asserting otherwise. To assist in resolving this discord, we apply a conceptual framework and computational model of community resilience (“COPEWELL”) that places community functioning (CF) at the center of evaluating the effects of disaster over time. Using flooding as a disaster type, we hypothesize a change in baseline CF trend when a flood-related federally declared disaster event occurs. Methods: We used county-level flood-related federally declared disaster events (2010–2014) and selected population demographics to study their effects on annual CF trends among United States counties (N = 3141). Results: In multivariate analysis of baseline CF, we found a significant negative relationship of prior five-year flood status, federal regions relative to the Northeast (Region I), lower total earnings, and greater population size. Annual CF trend was 0.09% (95%CI: 0.01%–0.16%). In multivariate analysis, significant predictors included baseline CF (β = −0.0178, −0.0047–−0.0309), any concurrent flood-related federally declared disaster events (−0.0024, −0.0040–−0.0008), ten-year prior flood events (−0.0017, −0.0034–−0.0000) and concurrent population change (−0.0186, −0.0338–−0.0035). Conclusions: Recent floods depress baseline CF, while concurrent and ten-year-ago floods depress trend in CF. Resilience may potentially be modified by raising baseline CF and maintaining population over time.

CORR Insights®: Do Americans Lacking a Local Orthopaedic Surgeon Have Adequate Broadband for Telehealth?

CORR Insights®: Do Americans Lacking a Local Orthopaedic Surgeon Have Adequate Broadband for Telehealth?

Epidemic time series similarity is related to geographic distance and age structure.

ObjectiveMore similar locations may have similar infectious disease dynamics. There is clear overlap in putative causes for epidemic similarity, such as geographic distance, age structure, and population size. We compare the effects of these potential drivers on epidemic similarity compared to a baseline assumption that differences in the basic reproductive number (R0) will translate to differences in epidemic trajectories.MethodsUsing COVID-19 case counts from United States counties, we explore the importance of geographic distance, population size differences, and age structure dissimilarity on resulting epidemic similarity.ResultsWe find clear effects of geographic space, age structure, population size, and R0 on epidemic similarity, but notably the effect of age structure was stronger than the baseline assumption that differences in R0 would be most related to epidemic similarity.ConclusionsTogether, this highlights the role of spatial and demographic processes on SARS-CoV2 epidemics in the United States.

Have license, will travel: Measuring the effects of universal licensing recognition on mobility

Universal licensing recognition (ULR) is a recent policy trend in which states recognize other states’ licensing credentials, lowering labor market barriers for interstate migrants. Using county-to-county migration files from the Internal Revenue Service and policy-enactment dates from the Knee Center for the Study of Occupational Regulation, we find a sizeable increase in average net migration (eleven tax filers, twenty-two dependents) from border counties in non-ULR states to contiguous border counties in ULR states each year, as well as an increase in adjusted gross income tax receipts in the ULR counties ($1.7 million). These effects dissipate at the state level. • This paper presents the first empirical estimation of the effect of state universal licensing recognition on migration. • There is an additional average net migration of 11 tax filers and $ 1 . 7 million in adjusted gross income after enactment.

Spatiotemporal pattern and driving forces of ecological carrying capacity during urbanization process in the Dongting Lake area, China

Ecological carrying capacity (ECC) is a key indicator for assessing the sustainable urbanization. The Dongting Lake area, as a national eco-economic zone, is urgently required to focus on the spatiotemporal pattern of ECC based on interactive mechanism of urbanization and ECC, which is revealed through quantitative research method in 2001–2019. The results show the ECC value appeared a fluctuating downward trend from 1.4645 to 0.8527, and the bearing state changed from under-loading to overloading. The bearing states of 17 counties showed spatially higher heterogeneous with Moran’s I value from 0.2635 to 0.3194. Yuanjiang City, Nanxian County and Taojiang County were in an under-loading state and the remaining 14 counties in an overloading state. The spatial distribution pattern displayed an “M”-type spatial differentiation pattern from southwest to northeast, the central region was evidently better than the surrounding regions. The bearing state had a positive global spatial autocorrelation. Spatial distribution of hot spots showed that the central area was a cold spot area, the edge area was a hot spot area, and the peripheral area was a sub-cold and sub-hot area. Factor detector results reveal that the urban population concentration, economic growth, land expansion, technology levels, and government policies were the main driving factors that formed the spatiotemporal pattern of ECC. The results could make up for the research shortcomings of spatiotemporal pattern of ECC, provide support to control ecological protection red line and urban exploitation boundaries, and help Chinese government formulate effective measurements to optimize the spatiotemporal pattern of ECC.