The County Health Rankings: rationale and methods

The Impact of Funding Inpatient Treatments for COVID-19 on Health Equity in the United States: A Distributional Cost-Effectiveness Analysis

OBJECTIVES

We conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States.

METHODS

A DCEA, based on an existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups.

RESULTS

More socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses.

CONCLUSIONS

To the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.

Association between EMS Workforce Density and Population Health Outcomes in the U.S

BACKGROUND

The prehospital care provided by emergency medical services (EMS) personnel is a critical component of the public health, public safety, and health care systems in the U.S.; however, the population-level value of EMS care is often overlooked. No studies have examined how the density of EMS personnel relates to population-level health outcomes. Our objectives were to examine the geographic distribution and density of EMS personnel in the U.S.; and quantify the association between EMS personnel density and population-level health outcomes.

METHODS

We conducted a cross-sectional evaluation of county-level EMS personnel density using estimates from the National Registry of Emergency Medical Technicians in nine states that require continuous national certification (Alabama, Louisiana, Massachusetts, Minnesota, New Hampshire, North Dakota, South Carolina, Vermont, and Washington, D.C.). Outcomes of interest included life expectancy, all-cause mortality, and cardiac arrest mortality. We used quantile regression models to examine the association between a 10-person increase in EMS personnel density and each outcome at the 10th, 50th (median), and 90th percentiles, controlling for population characteristics and area health resources.

RESULTS

There were 356 counties included, with a mean EMS density of 223 EMS personnel per 100,000 population. Density was higher in rural compared to urban counties (247 versus 186 per 100,000 population; p = 0.001). In unadjusted models, there was a significant association between increase in EMS personnel density and an increase in life expectancy at each examined percentile (e.g., 50th percentile, increase of 52.9 days; 95% CI 40.2, 65.5; p < 0.001), decrease in all-cause mortality at each examined percentile, and decrease in cardiac arrest mortality at the 50th and 90th percentiles. These associations were not statistically significant in the adjusted models.

CONCLUSIONS

EMS personnel density differs between urban and rural areas, with higher density per population in rural areas. There were no statistically significant associations between EMS density and population-level health outcomes after controlling for population characteristics and other health resources. The best approach to quantifying the community-level value that EMS care may or may not provide remains unclear.

Associations between long-term fine particulate matter exposure and hospital procedures in heart failure patients

BACKGROUND

Ambient fine particulate matter (PM2.5) contributes to global morbidity and mortality. One way to understand the health effects of PM2.5 is by examining its impact on performed hospital procedures, particularly among those with existing chronic disease. However, such studies are rare. Here, we investigated the associations between annual average PM2.5 and hospital procedures among individuals with heart failure.

METHODS

Using electronic health records from the University of North Carolina Healthcare System, we created a retrospective cohort of 15,979 heart failure patients who had at least one of 53 common (frequency > 10%) procedures. We used daily modeled PM2.5 at 1x1 km resolution to estimate the annual average PM2.5 at the time of heart failure diagnosis. We used quasi-Poisson models to estimate associations between PM2.5 and the number of performed hospital procedures over the follow-up period (12/31/2016 or date of death) while adjusting for age at heart failure diagnosis, race, sex, year of visit, and socioeconomic status.

RESULTS

A 1 μg/m3 increase in annual average PM2.5 was associated with increased glycosylated hemoglobin tests (10.8%; 95% confidence interval = 6.56%, 15.1%), prothrombin time tests (15.8%; 95% confidence interval = 9.07%, 22.9%), and stress tests (6.84%; 95% confidence interval = 3.65%, 10.1%). Results were stable under multiple sensitivity analyses.

CONCLUSIONS

These results suggest that long-term PM2.5 exposure is associated with an increased need for diagnostic testing on heart failure patients. Overall, these associations give a unique lens into patient morbidity and potential drivers of healthcare costs linked to PM2.5 exposure.

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.

Understanding health inequalities in Wales using the Blinder-Oaxaca decomposition method

Background

Throughout Wales and the world, health inequality remains a problem that is interconnected with a wider and complex social, economic and environmental dynamic. Subsequently, action to tackle inequality in health needs to take place at a structural level, acknowledging the constraints affecting an individual's (or community's) capability and opportunity to enable change. While the 'social determinants of health' is an established concept, fully understanding the composition of the health gap is dependent on capturing the relative contributions of a myriad of social, economic and environmental factors within a quantitative analysis.

Method

The decomposition analysis sought to explain the differences in the prevalence of these outcomes in groups stratified by their ability to save at least £10 a month, whether they were in material deprivation, and the presence of a limiting long-standing illness, disability of infirmity. Responses to over 4,200 questions within the National Survey for Wales (n = 46,189; 2016-17 to 2019-20) were considered for analysis. Variables were included based on (1) their alignment to a World Health Organization (WHO) health equity framework ("Health Equity Status Report initiative") and (2) their ability to allow for stratification of the survey sample into distinct groups where considerable gaps in health outcomes existed. A pooled Blinder-Oaxaca model was used to analyse inequalities in self-reported health (fair/poor health, low mental well-being and low life satisfaction) and were stratified by the variables relating to financial security, material deprivation and disability status.

Results

The prevalence of fair/poor health was 75% higher in those who were financially insecure and 95% higher in those who are materially deprived. Decomposition of the outcome revealed that just under half of the health gap was "explained" i.e., 45.5% when stratifying by the respondent's ability to save and 46% when stratifying by material deprivation status. Further analysis of the explained component showed that "Social/Human Capital" and "Income Security/Social Protection" determinants accounted the most for disparities observed; it also showed that "Health Services" determinants accounted the least. These findings were consistent across the majority of scenarios modeled.

Conclusion

The analysis not only quantified the significant health gaps that existed in the years leading up to the COVID-19 pandemic but it has also shown what determinants of health were most influential. Understanding the factors most closely associated with disparities in health is key in identifying policy levers to reduce health inequalities and improve the health and well-being across populations.

Context Matters: Assessing the Association Between Area Deprivation and the Severity of Injury and Types of Domestic Violence Victimization Among Women

OBJECTIVES

To analyze the association between social determinants of health (SDOH), as measured by the Area Deprivation Index (ADI), and the severity of injury and types of domestic violence (DV) victimization among women (≥18 years of age) in Milwaukee, Wisconsin.

METHODS

Neighborhood ADI data from the American Community Survey (2014-2018) were merged with Milwaukee Police Department DV data (2013-2017). ADI included multiple SDOH domains (education, employment, income/poverty, and housing quality). Types of DV were classified using an adaptation of the FBI-Uniform Crime Reporting-Hierarchy Rule, including Crimes Against Persons (homicide/negligent manslaughter, sexual assault/rape, and aggravated battery/assault). Chi-square, Anova tests, and logistic regression analyses were performed using Stata v.14.2; p-values ≤ .05 were considered statistically significant.

FINDINGS

Except for aggravated battery/assault (OR: 1.003, 95% CI: 1.001-1.010), there was no statistically significant relationship between neighborhood disadvantage and DV victimization in 21,095 DV incidents between 2013 and 2017. Adjusted model results indicate that with each increase in neighborhood disadvantage (by ADI), there was a 1.003 increase in the likelihood for aggravated battery/assault (OR: 1.003, 95% CI: 1.001-1.005). Severity of DV injury was not significantly associated with ADI (OR: 1.002, 95% CI: 0.999-1.004). However, non-Hispanic Black women were 1.3 times more likely than non-Hispanic Whites to be victims of aggravated battery/assault (OR: 1.321, 95% CI: 1.189-1.469). Hispanic women were more likely than non-Hispanic Whites to sustain a more severe injury (OR: 0.841, 95% CI: 0.732-0.970]).

CONCLUSION

The likelihood of DV-aggravated battery/assault increased with neighborhood deprivation, and significant associations (and highly lopsided prevalence) were found in types of DV victimization by race/ethnicity, with non-Hispanic Black women experiencing higher prevalence than others. This study adds to the body of knowledge by looking at how macro-level neighborhood-SDOH characteristics influence women's exposure to various forms of DV victimization and demonstrated the feasibility of linking law enforcement DV data to SDOH metrics, providing context for law enforcement DV victimizations.

Place-based heterogeneity in lung transplant recipient outcomes

Place is defined as a social or environmental area of residence with meaning to a patient. We hypothesize there is an association between place and the clinical outcomes of lung transplant recipients in the United States. In a retrospective cohort study of transplants between January 1, 2010, and December 31, 2019, in the Scientific Registry of Transplant Recipients, multivariable Cox regression models were used to test the association between place (through social and environmental factors) with readmission, lung rejection, and survival. Among 18,465 recipients, only 20% resided in the same county as the transplant center. Recipients from the most socially vulnerable counties when compared to the least vulnerable were more likely to have COPD as a native disease, Black or African American race, and travel long distances to reach a transplant center. Higher local life expectancy was associated with lower likelihood for readmission (odds ratio [OR] = 0.90, 95% confidence interval [CI]: 0.84, 0.98, p = .01). Higher social vulnerability was associated with a higher likelihood of lung rejection (OR = 1.37, [CI]: 1.07, 1.76, p = .01). There was no association of residence with posttransplant survival. Recipient place-based factors were associated with complications and processes of care after transplant and warrant further investigation.

Socioeconomic vulnerability and public water arsenic concentrations across the US

Inorganic arsenic is a known human carcinogen and is routinely detected in US community water systems (CWSs). Inequalities in CWS arsenic exist across broad sociodemographic subgroups. Our objective was to evaluate the county-level association between socioeconomic vulnerability and CWS arsenic concentrations across the US. We evaluated previously developed, population-weighted CWS arsenic concentrations (2006-2011) and three socioeconomic domains (the proportion of adults with a high school diploma, median household income, and the Centers for Disease Control and Prevention's overall socioeconomic vulnerability score) for 2,604 conterminous US counties. We used spatial lag models and evaluated the adjusted geometric mean ratio (GMR) of CWS arsenic concentrations per higher socioeconomic domain score corresponding to the interquartile range, and also evaluated flexible quadratic spline models. We also stratified by region and by United States Department of Agriculture Rural-Urban Continuum Codes to assess potential effect measure modification by region and rurality. Associations between socioeconomic vulnerability and CWS arsenic were modified by region and rurality and specific to socioeconomic domain. The fully adjusted GMR (95% CIs) of CWS arsenic per interquartile range higher proportion of adults with a high school education was 0.83 (0.71, 0.98) in the Southwest (corresponding to 17% lower arsenic with higher education), 0.82 (0.71, 0.94) in the Eastern Midwest (18% lower), and 0.65 (0.31, 1.36) in New England (35% lower). Associations between median household income and CWS arsenic were largely null. Higher overall socioeconomic vulnerability was significantly associated with lower CWS arsenic, but only in counties in the Central Midwest and those with total populations less than 20,000. Findings may reflect regional/local differences in both socioeconomic/socio-cultural context and public drinking water regulatory efforts. Across the US, individual domains of socioeconomic vulnerability (especially educational attainment) are more strongly associated with inequalities in CWS arsenic than the complex overall socioeconomic vulnerability index.

Community determinants of COPD exacerbations in elderly patients in Lodz province, Poland: a retrospective observational Big Data cohort study

OBJECTIVES

To evaluate the prevalence and identify demographic, economic and environmental local community determinants of chronic obstructive pulmonary disease (COPD) exacerbations in elderly in primary care using Big Data approach.

DESIGN

Retrospective observational case-control study based on Big Data from the National Health Found, Tax Office and National Statistics Center databases in 2016.

SETTING

Primary care clinics in the Lodz province in Poland.

PARTICIPANTS

472 314 patients aged 65 and older in primary care, including 17 240 patients with COPD and 1784 with exacerbations (including deaths).

OUTCOME MEASURES

Exacerbations with demographic, economic and environmental local community determinants were retrieved. Conditional logistic regression for matched pairs was used to evaluate the local community determinants of COPD exacerbations among patients with COPD.

RESULTS

The overall prevalence of COPD in the population of elderly patients registered in primary healthcare clinic clinics in Lodz province in 2016 was 3.65%, 95% CI (3.60% to 3.70%) and the prevalence of exacerbations was 10.35%, 95% CI (9.89% to 10.80%). The high number of consultations in primary care clinics was associated with higher risk of COPD exacerbations (p=0.0687).High-income patients were less likely to have exacerbations than low-income patients (high vs low OR 0.601, 95% CI (0.385 to 0.939)). The specialisation of the primary care physician did not have an effect on exacerbations (OR 1.076, 95% CI (0.920 to 1.257)). Neither the forest cover per gmina (high vs low OR 0.897, 95% CI (0.605 to 1.331); medium vs low OR 0.925, 95% CI (0.648 to 1.322)), nor location of gmina (urban vs urban-rural OR 1.044; 95% CI (0.673 to 1.620)), (rural vs urban-rural OR 0.897, 95% CI (0.630 to 1.277)) appears to influence COPD exacerbations.

CONCLUSIONS

Big Data statistical analysis facilitated the evaluation of the prevalence and determinants of COPD exacerbation in the elderly residents of Lodz province, Poland.Modification of identified local community determinants may potentially decrease the number of exacerbations in elderly patients with COPD.

Differences in overdose deaths by intent: Unintentional & suicide drug poisonings in North Carolina, 2015-2019

Comprehensive fatal overdose prevention requires an understanding of the fundamental causes and context surrounding drug overdose. Using a social determinants of health (SDOH) framework, this descriptive study examined unintentional and self-inflicted (i.e., suicide) overdose deaths in North Carolina (NC), focusing on specific drug involvement and contextual factors. Unintentional and suicide overdose deaths were identified using 2015-2019 NC death certificate data. Specific drug involvement was assessed by searching literal text fields for drug mentions. County-level contextual factors were obtained from NC Institute of Medicine and County Health Rankings, encompassing five SDOH domains (economic stability, social/community context, health care access/quality, education access/quality, neighborhood/built environment). Descriptive statistics were calculated by intent for drug involvement and a variety of contextual factors. During 2015-2019, 9% of NC drug overdose deaths were self-inflicted and 89% were unintentional (2% other/undetermined). Unintentional overdoses largely involved illicit drugs [fentanyl (47%), cocaine (33%), heroin (29%)]. Suicide overdoses frequently involved prescription opioids [oxycodone (18%), hydrocodone (10%)] and antidepressants (32%). Overall, overdose deaths tended to occur in under-resourced counties across all SDOH domains, though unintentional overdoses occurred more often among residents of under-resourced counties than suicide overdoses, with differences most pronounced for economic stability-related factors. There are notable distinctions between unintentional and suicide overdose deaths in demographics and drug involvement, though the assessment of SDOH demonstrated that overdose mortality is broadly associated with marginalization across all domains. These findings highlight the value of allocating resources to prevention and intervention approaches that target upstream causes of overdose (e.g., housing first, violence prevention programs).

The Link between Food Environment and Colorectal Cancer: A Systematic Review

Food and diet are critical risk factors for colorectal cancer (CRC). Food environments (FEs) can contribute to disease risk, including CRC. This review investigated the link between FEs and CRC incidence and mortality risk. The systematic search of studies utilised three primary journal databases: PubMed, Scopus, and Web of Science. Retrieved citations were screened and the data were extracted from articles related to the FE-exposed populations who were at risk for CRC and death. We evaluated ecological studies and cohort studies with quality assessment and the Newcastle-Ottawa Quality Assessment Form for Cohort Studies, respectively. A descriptive synthesis of the included studies was performed. Out of 89 articles identified, eight were eligible for the final review. The included studies comprised six ecological studies and two cohort studies published from 2013 to 2021. Six articles were from the US, one was from Africa, and one was from Switzerland. All eight studies were of good quality. The significant finding was that CRC incidence was associated with the availability of specific foods such as red meat, meat, animal fats, energy from animal sources, and an unhealthy FE. Increased CRC mortality was linked with the availability of animal fat, red meat, alcoholic beverages, and calorie food availability, residence in food deserts, and lower FE index. There were a variety of associations between CRC and the FE. The availability of specific foods, unhealthy FE, and food desserts impact CRC incidence and mortality. Creating a healthy FE in the future will require focus and thorough planning.

Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort

Per and polyfluoroalkyl substances (PFAS) are associated with health outcomes ranging from cancer to high cholesterol. However, there has been little examination of how PFAS exposure might impact the development of multiple chronic diseases, known as multimorbidity. Here, we associated the presence of one or more PFAS in water systems serving the zip code of residence with chronic disease and multimorbidity.

Emergency departments in the United States treating high proportions of patients with ambulatory care sensitive conditions: a retrospective cross-sectional analysis

Background

One in nine emergency department (ED) visits by Medicare beneficiaries are for ambulatory care sensitive conditions (ACSCs). This study aimed to examine the association between ACSC ED visits to hospitals with the highest proportion of ACSC visits (“high ACSC hospitals) and safety-net status.

Methods

This was a cross-sectional study of ED visits by Medicare fee-for-service beneficiaries ≥ 65 years using 2013–14 claims data, Area Health Resources File data, and County Health Rankings. Logistic regression estimated the association between an ACSC ED visit to high ACSC hospitals, accounting for individual, hospital, and community factors, including whether the visit was to a safety-net hospital. Safety net status was measured by Disproportionate Share Hospital (DSH) index patient percentage; public hospital status; and proportion of dual-eligible beneficiaries. Hospital-level correlation was calculated between ACSC visits, DSH index, and dual-eligible patients. We stratified by type of ACSC visit: acute or chronic.

Results

Among 5,192,729 ACSC ED visits, the odds of visiting a high ACSC hospital were higher for patients who were Black (1.37), dual-eligible (1.18), and with the highest comorbidity burden (1.26, p < 0.001 for all). ACSC visits had increased odds of being to high ACSC hospitals if the hospitals were high DSH (1.43), served the highest proportion of dual-eligible beneficiaries (2.23), and were for-profit (relative to non-profit) (1.38), and lower odds were associated with public hospitals (0.64) (p < 0.001 for all). This relationship was similar for visits to high chronic ACSC hospitals (high DSH: 1.59, high dual-eligibility: 2.60, for-profit: 1.41, public: 0.63, all p < 0.001) and to a lesser extent, high acute ACSC hospitals (high DSH: 1.02; high dual-eligibility: 1.48, for-profit: 1.17, public: 0.94, p < 0.001). The proportion of ACSC visits at all hospitals was weakly correlated with DSH proportion (0.2) and the proportion of dual-eligible patients (0.29), and this relationship was also seen for both chronic and acute ACSC visits, though stronger for the chronic ACSC visits.

Conclusion

Visits to hospitals with a high proportion of acute ACSC ED visits may be less likely to be to hospitals classified as safety net hospitals than those with a high proportion of chronic ACSC visits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08240-7.

What drives variations in public health and social services expenditures? the association between political fragmentation and local expenditure patterns

The US spends two times more than the OECD average in health expenditure but has a much smaller portion of public health spending to total health expenditure than other OECD countries. While it has been suggested that public health and social services spending is crucial to promoting health outcomes, less is known about what drives variations in public health expenditure across regions. This study aims to examine whether political fragmentation in local governance is associated with variations in public health and social services expenditures. Using the US Census of Governments, we constructed a panel dataset of political fragmentation and local government spending patterns (1997-2012) for 792 US counties (population > 60,882, top 25%) and employed Least Squares Dummy Variable (LSDV) and Generalized Estimating Equations (GEE) models. We found that per capita public health spending tended to be smaller in areas where the degree of political fragmentation was higher (Coef:  - 0.034; p < 0.01), particularly when general-purpose governments were more fragmented (Coef:  - 0.087; p < 0.001). The proportion of public health spending also decreased when local governments were more fragmented (Coef:  - 0.012; p < 0.001). Social services expenditures and their proportions to total government expenditure fell with an increase in the degree of political fragmentation. Our findings suggest that fragmented governance settings, in which localities are more likely to face competition with others, may lead to a reduction in public spending essential for population health and that political fragmentation can also have a deterrent effect on broader categories of health-related social services spending.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

The relationship between voting restrictions and COVID-19 case and mortality rates between US counties

Background

Since the 2010 election, the number of laws in the U.S. that create barriers to voting has increased dramatically. These laws may have spillover effects on population health by creating a disconnect between voter preferences and political representation, thereby limiting protective public health policies and funding. We examine whether voting restrictions are associated with county-level COVID-19 case and mortality rates.

Methods

To obtain information on restricted access to voting, we used the Cost of Voting Index (COVI), a state-level measure of barriers to voting during a U.S. election from 1996 to 2016. COVID-19 case and mortality rates were obtained from the New York Times’ GitHub database (a compilation from multiple academic sources). Multilevel modeling was used to determine whether restrictive voting laws were associated with county-level COVID-19 case and mortality rates after controlling for county-level characteristics from the County Health Rankings. We tested whether associations were heterogeneous across racial and socioeconomic groups.

Results

A significant association was observed between increasing voting restrictions and COVID-19 case (ß = 580.5, 95% CI = 3.9, 1157.2) and mortality rates (ß = 16.5, 95% CI = 0.33,32.6) when confounders were included.

Conclusions

Restrictive voting laws were associated with higher COVID-19 case and mortality rates.

Predictors of County-Level Home Modification Use Across the US

BACKGROUND

Geospatial analyses illustrating where the Home Improvements and Structural Alterations program (HISA) have been prescribed suggest that home modification (HM) services under US Department of Veterans Affairs (VA) is not prescribed and used uniformly across the US.

METHODS

The objective of this study was to identify county characteristics associated with HISA use rates, such as county-level measures of clinical care and quality of care, variables related to physical environment, and sociodemographic characteristics. Multiple regression analysis was used to predict county-level utilization rate from county-level variables.

RESULTS

County-level HISA use was highly skewed and ranged from 0.09 to 59.7%, with a mean of 6.6% and median of 5%. Percent uninsured adults and rate of preventable hospital stays emerged as significant predictors of county-level HISA utilization rate. Specifically, county percentage of uninsured adults was negatively related to county-level HISA utilization rate (b = -8.99, P = .005). The higher the proportion of uninsured adults the lower the HISA utilization rate. The county rate of preventable hospital stays was positively related to county-level HISA utilization rate (b = .0004, P = .009). County-level predictors of housing quality were not significantly associated with county-level HISA utilization rate.

CONCLUSIONS

Our research fills a gap in the literature about the impact of county-level variables and the geographic distribution and use of HISA. More research is needed to understand and account for geographical variation in HISA use. This work serves as a first step at quantifying and predicting HISA utilization rate at a broad level, with the goal of increasing access to HMs for veterans with disabilities.

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, "out-of-the-box" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R ²) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.

Demographic and Geospatial Analysis of Buprenorphine and Methadone Prescription Rates

Background

The medical community continues to seek to understand both the causes and consequences of opioid use disorder (OUD). The recent 2019 public release of the Automation of Reports and Consolidated Orders System (ARCOS) database from the years 2006 to 2012 provides a unique opportunity to analyze a critical period of the opioid epidemic with unprecedented data granularity.

Objectives

This study aims to use the ARCOS dataset to (1) determine significant contributory variables to opioid overdose death rates, (2) determine significant contributory variables to the relative prescription of buprenorphine and methadone, and (3) evaluate the existence of statistically significant geospatial clusters in buprenorphine and methadone prescription rates.

Methods

This study utilizes multiple databases, including the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER), the Drug Enforcement Administration (DEA) prescription drug data, and the United States (US) Census demographics, to examine the relationship between the different treatments of OUD. Linear regressions are used to determine significant contributory factors in overdose rate and the buprenorphine-to-methadone ratio. Geospatial analysis is used to identify geographic clusters in opioid overdoses and treatment patterns.

Results

Methadone prescriptions, racial demographics, and poverty were found to significantly correspond to opioid overdose death rates (p < 0.05). Buprenorphine prescriptions were not found to be significant (p = 0.20). Opioid overdoses, metro character, racial categorization, and education were found to significantly correspond to the ratio of buprenorphine to methadone prescribed (p < 0.05). Cluster analysis demonstrated different geospatial distributions in the prescriptions of buprenorphine and methadone (p < 0.05).

Conclusion

Historically, methadone prescriptions have been higher in areas with high overdose rates. Buprenorphine and methadone prescribing patterns have historically demonstrated different geographic trends.

Patterns of Colorectal Cancer in Texas Counties From 2000 to 2017

PURPOSE

Residents living in Texas counties along the United States-Mexico border make up a unique demographic. These counties consist of a large proportion of Hispanic-Latinx people who experience a high rate of health uninsurance and underinsurance, low household income averages, and, as a whole, exhibiting relatively poor health outcomes compared to the US general population. Limited information exists regarding the effects of these characteristics on the incidence of colorectal cancer (CRC).

METHODS

Using data from the Texas Department of State Health Service, we calculated that the overall age-adjusted incidence rate (AAIR) of CRC was lower and decreased at a slower rate over time in Texas border counties compared with nonborder counties in Texas.

RESULTS

The AAIR of CRC was lower and decreased at a slower rate over time in Texas border counties compared with nonborder counties in Texas. Conversely to other groups analyzed, the AAIR of CRC in individuals age 50-64 years in border counties increased.

CONCLUSION

These findings are likely a reflection of less utilization of cancer screening in border counties than in nonborder counties. The increase in AAIR of CRC among individuals age 50-64 years in border counties warrants further investigation.

The Relative Contribution of Social Determinants of Health Among Health Resources and Services Administration-Funded Health Centers

Frameworks for identifying and assessing social determinants of health (SDOH) are effective for developing long-term societal policies to promote health and well-being, but may be less applicable in clinical settings. The authors compared the relative contribution of a specific set of SDOH indicators with several measures of health status among patients served by health centers (HCs). The 2014 Health Center Patient Survey was used to identify a sample of HC patient adults 18 years and older that reported the HC as their usual source of care (n = 5024). The authors examined the relationship between SDOH indicators organized in categories (health behaviors, access and utilization, social factors, economic factors, quality of care, physical environment) with health status measures (fair or poor health, diabetes, hypertension, cardiovascular disease, depression, or anxiety) using logistic regressions and predicted probabilities. Findings indicated that access to care and utilization indicators had the greatest relative contribution to all health status measures, but the relative contribution of other SDOH indicators varied. For example, access indicators had the highest predicted probability in the model with fair or poor health as the dependent variable (72.4%) and the model with hypertension as the dependent variable (47.4%). However, the second highest predicted probability was for social indicators (54.1%) in the former model and physical environment (44.7%) indicators in the latter model. These findings have implications for HCs that serve as the primary point of access to medical care in underserved communities and to mitigate SDOH particularly for patients with diabetes, depression, or anxiety.

Emergency Medical Services Demand: An Analysis of County-Level Social Determinants

OBJECTIVES

Variations in the demand for Emergency Medical Services (EMS) exist when observed at a local level. This unspecified heterogeneity leads to an investigation of social factors contributing to EMS demand.

METHODS

Data for this study were collected from publicly available EMS reports from Florida and Oklahoma for 2009 - 2015. Health and social data were gathered from County health rankings and roadmap reports. Data were combined into a single dataset, and pooled ordinary-least-squares models with time-fixed effects were utilized for tests of inference. EMS call volume was log-transformed to derive a semi-elasticity function.

RESULTS

A total of 874 county-year observations were analyzed. Increases in poor/fair health (95% CI: 0.6% - 3.9%), binge drinking (95% CI: 1.6% - 3.5%), teen birth rate (95% CI: 1.1% - 5.2%), unemployment rate (95% CI: 0.5% - 3.9%), and violent crime rate (95% CI: 1.0% - 3.0%) were associated with an increase in the EMS demand rate.

CONCLUSION

The data supports the notion that some community measures have an effect on EMS demand as counties with higher levels of poor health, binge drinking, teen births, unemployment, and violent crime saw higher EMS demand. These factors may have been treated as spurious, or overlooked by policy makers and EMS leadership.

Closing the gap: Collaborative care addresses social determinants of health

ABSTRACT

Social determinants of health have a significant impact on individual and community health outcomes. Using an integrated behavioral health model at a primary care clinic-a Federally Qualified Health Center-NPs led an interdisciplinary team to address outcome measures that are influenced by social determinants of health.

Activos de salud, calidad de vida y morbimortalidad de la población en Asturias

INTRODUCCIÓN

n promoción de la salud se ha detectado en los últimos años un incremento de investigaciones con enfoques teóricos basados en activos de salud. Pese a los estudios identificados, no se dispone de suficiente evidencia sobre los efectos que diferentes tipos de activos pueden tener en la calidad de vida y en la morbimortalidad de la población.

OBJETIVO

analizar la relación entre los activos de salud disponibles con indicadores de morbilidad, mortalidad y calidad de vida de la población asturiana en el año 2018.

METODOLOGÍA

diseño ecológico a partir de datos agregados municipales procedentes de los 78 municipios de Asturias (1.034.960 habitantes). Tras aplicar la definición de activos de salud como aquellas variables que pudieran redundar en una mejora de la salud y del bienestar de los individuos y de las comunidades, se seleccionaron 19 variables de activos agrupados en cuatro categorías: individuales, socioeconómicas, comunitarias e infraestructura. Una vez controladas las variables relacionadas con las características demográficas de la población, se analizó la asociación de los activos con las tasas de morbimortalidad y de calidad de vida. Se desarrollaron 5 modelos predictivos a partir de modelos de regresión lineal múltiple para las variables dependientes: calidad de vida, enfermedades crónicas, mortalidad por todas las causas, mortalidad por enfermedades cardiovasculares (ECV) y por cáncer.

RESULTADOS

la disponibilidad de recursos sanitarios (beta = 0.474), coberturas sociales (beta = 0.305) y redes de apoyo social (beta = 0.225) constituyen los activos de salud con mayor peso explicativo en los resultados de salud de la población asturiana. Las variables incluidas en los modelos predictivos de calidad de vida (R² = 0.650) y de mortalidad por ECV (R² = 0.544) son las que mostraron una mayor capacidad explicativa.

CONCLUSIONES

la inversión en recursos sociosanitarios y la mejora de redes de apoyo social impulsados desde el ámbito de la salud pública pueden producir importantes mejoras en la salud de la población asturiana.

Population distribution by ethnicities and the disparities in health risk and coping in the United States during the pandemic: the spatial and time dynamics

Background

As a multi-ethnic country, the US is increasingly concerned about ethnic minorities facing disproportionate health risks of the coronavirus disease 2019 (COVID-19) pandemic. This study attempted to provide a macro picture of the associations between population distribution by ethnicity and the vulnerability to COVID-19 in terms of infection risk and vaccination coverage in the US.

Methods

This study used multi-source data from New York Times, County Health Rankings & Roadmap Program (2020), and the Center for Disease Control and Prevention. Multiple linear regressions were performed at equidistant time points (May 2020-Jan 2021, with one-month interval between each time point) to reveal the association between population distribution by ethnicities and the infection risk and the dynamics over time. Besides, multiple linear regressions were also conducted at equidistant time points (Jan 2021-Aug 2021) to reveal whether health disparities between ethnicities would hold true for the COVID-19 vaccination coverage (in total population, and among those > 12, > 18, and > 65 years of age).

Results

Both the COVID-19 confirmed cases (population standardized) and the vaccination coverage (in total population, and among those > 12, > 18, and > 65 years of age) were significantly associated with the population distribution by ethnicity (e.g., population percentage of ethnic minorities). Above associations were statistically significant for non-Hispanic blacks and Hispanics, but not for Asian Americans.

Conclusions

A proportion of socioeconomically-disadvantageous population could be a key intuitive reflection of the risk level of this public health crisis. The policy focusing on the vulnerable population is important in this pandemic.

Beyond patient care: a qualitative study of rural hospitals' role in improving community health

OBJECTIVES

Rural population face more health disadvantages than those living in urban and suburban areas. In rural communities, hospitals are frequently the primary organisation with the resources and capabilities to address health issues. This characteristic highlights their potential to be a partner and leader for community health initiatives. This study aims to understand rural hospitals' motivations to engage in community health improvement efforts and examine their strategies to address community health issues.

DESIGN

Eleven semistructured interviews were conducted with key leaders from four rural hospitals in a US Midwestern state. On-site and telephone interviews were audio-recorded and transcribed. The combination of inductive and deductive qualitative analysis was applied to identify common themes and categories.

SETTINGS

Participating hospitals are located in US rural counties that have demonstrated progress in creating healthier communities.

RESULTS

Three types of motivation drive rural hospitals' community health improvement efforts: internal values, economic conditions and social responsibilities. Three categories of strategies to address community health issues were identified: building capacity, building relationships and building programmes.

CONCLUSIONS

Despite the challenges, rural hospitals can successfully conduct community-oriented programmes. The finds extend the literature on how rural hospitals may strategise to improve rural health by engaging their communities and conduct activities beyond patient care.

Postpartum Help-Seeking: The Role of Stigma and Mental Health Literacy

BACKGROUND

Postpartum depression is the most common complication associated with child-bearing. The current study investigated attitudes toward professional psychological help-seeking and the effects of stigma and mental health literacy on postpartum women recruited from social media (N = 326).

METHOD

Hierarchical linear regression was used to analyze the data and the interaction effect of stigma and mental health literacy.

RESULTS

Stigma was negatively associated with attitudes toward professional psychological help-seeking, while mental health literacy was positively associated with attitudes toward professional psychological help-seeking. The interaction effect was not statistically significant. The results yield implications for screening practices and reducing stigma for mental health care in the postpartum period.

Social Determinants of Health, Race, and Diabetes Population Health Improvement: Black/African Americans as a Population Exemplar

PURPOSE OF REVIEW

To summarize evidence of impact of social determinants of health (SDOH) on diabetes risk, morbidity, and mortality and to illustrate this impact in a population context.

RECENT FINDINGS

Key findings from the American Diabetes Association's scientific review of five SDOH domains (socioeconomic status, neighborhood and physical environment, food environment, health care, social context) are highlighted. Population-based data on Black/African American adults illustrate persisting diabetes disparities and inequities in the SDOH conditions in which this population is born, grows, lives, and ages, with historical contributors. SDOH recommendations from US national committees largely address a health sector response, including health professional education, SDOH measurement, and patient referral to services for social needs. Fewer recommendations address solutions for systemic racism and socioeconomic discrimination as root causes. SDOH are systemic, population-based, cyclical, and intergenerational, requiring extension beyond health care solutions to multi-sector and multi-policy approaches to achieve future population health improvement.

Comparative Study on the Evaluation of Healthy City Construction in Typical Chinese Cities Based on Statistical Data and Land Use Data

The 17 Sustainable Development Goals of the United Nations propose to “ensure healthy lives and promote well-being for all at all ages”, and to achieve this goal requires that countries strengthen their capacity to manage health risks. As a concept to describe urban construction, advocated by the World Health Organization, healthy cities can effectively solve the contradictions existing along the current urban development path at a macro level. A healthy city is a sustainable city that interacts with its environment, economy, population, services, and space, and realizes the well-being of its population from all perspectives. The construction of a healthy city is an important part of the transformation of Chinese urbanization. This article refers to the index systems of domestic and foreign government agencies, along with a literature research, to construct a healthy city evaluation index that takes into account the five aspects of environment, economy, population, service, and space, and selects Beijing (a policy-oriented city), Shanghai (an economy-oriented city), Nanchang (an industry-oriented city), Guiyang (a tourism-oriented city), Datong (resource-oriented city) as five cities according to type of urban development, using the entire-array-polygon method to analyze the construction level of these cities in terms of environment, economy, population, service, space and overall state of health from 2014 to 2018 based on statistical and land use data. The results of the study found that, in general, the construction of healthy cities in China currently experiences large year-to-year fluctuations and significant differences between cities. The construction and development of healthy cities are also closely related to factors such as urban economic strength, social welfare, and policy support.

Segregation Predicts COVID-19 Fatalities in Less Densely Populated Counties

Aim

It is well known that social determinants of health (SDoH) have affected COVID-19 outcomes, but these determinants are broad and complex. Identifying essential determinants is a prerequisite to address widening health disparities during the evolving COVID-19 pandemic.

Methods

County-specific COVID-19 fatality data from California, Illinois, and New York, three US states with the highest county-cevel COVID-19 fatalities as of June 15, 2020, were analyzed. Twenty-three county-level SDoH, collected from County Health Rankings & Roadmaps (CHRR), were considered. A median split on the population-adjusted COVID-19 fatality rate created an indicator for high or low fatality. The decision tree method, which employs machine learning techniques, analyzed and visualized associations between SDoH and high COVID-19 fatality rate at the county level.

Results

Of the 23 county-level SDoH considered, population density, residential segregation (between white and non-white populations), and preventable hospitalization rates were key predictors of COVID-19 fatalities. Segregation was an important predictor of COVID-19 fatalities in counties of low population density. The model area under the curve (AUC) was 0.79, with a sensitivity of 74% and specificity of 76%.

Conclusion

Our findings, using a novel analytical lens, suggest that COVID-19 fatality is high in areas of high population density. While population density correlates to COVID-19 fatality, our study also finds that segregation predicts COVID-19 fatality in less densely populated counties. These findings have implications for COVID-19 resource planning and require appropriate attention.

County-level societal predictors of COVID-19 cases and deaths changed through time in the United States: A longitudinal ecological study

People of different racial/ethnic backgrounds, demographics, health, and socioeconomic characteristics have experienced disproportionate rates of infection and death due to COVID-19. This study tests if and how county-level rates of infection and death have changed in relation to societal county characteristics through time as the pandemic progressed. This longitudinal study sampled monthly county-level COVID-19 case and death data per 100,000 residents from April 2020 to March 2022, and studied the relationships of these variables with racial/ethnic, demographic, health, and socioeconomic characteristics for 3125 or 97.0% of U.S. counties, accounting for 96.4% of the U.S. population. The association of all county-level characteristics with COVID-19 case and death rates changed significantly through time, and showed different patterns. For example, counties with higher population proportions of Black, Native American, foreign-born non-citizen, elderly residents, households in poverty, or higher income inequality suffered disproportionately higher COVID-19 case and death rates at the beginning of the pandemic, followed by reversed, attenuated or fluctuating patterns, depending on the variable. Patterns for counties with higher White versus Black population proportions showed somewhat inverse patterns. Counties with higher female population proportions initially had lower case rates but higher death rates, and case and death rates become more coupled and fluctuated later in the pandemic. Counties with higher population densities had fluctuating case and death rates, with peaks coinciding with new variants of COVID-19. Counties with a greater proportion of university-educated residents had lower case and death rates throughout the pandemic, although the strength of this relationship fluctuated through time. This research clearly shows that how different segments of society are affected by a pandemic changes through time. Therefore, targeted policies and interventions that change as a pandemic unfolds are necessary to mitigate its disproportionate effects on vulnerable populations, particularly during the first six months of a pandemic.

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010-2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985-0.999), have Medicare insurance (1.013, 1.005-1.020), and have hypertension (1.014, 1.005-1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Secondary organic aerosol association with cardiorespiratory disease mortality in the United States

Fine particle pollution, PM2.5, is associated with increased risk of death from cardiorespiratory diseases. A multidecadal shift in the United States (U.S.) PM2.5 composition towards organic aerosol as well as advances in predictive algorithms for secondary organic aerosol (SOA) allows for novel examinations of the role of PM2.5 components on mortality. Here we show SOA is strongly associated with county-level cardiorespiratory death rates in the U.S. independent of the total PM2.5 mass association with the largest associations located in the southeastern U.S. Compared to PM2.5, county-level variability in SOA across the U.S. is associated with 3.5× greater per capita county-level cardiorespiratory mortality. On a per mass basis, SOA is associated with a 6.5× higher rate of mortality than PM2.5, and biogenic and anthropogenic carbon sources both play a role in the overall SOA association with mortality. Our results suggest reducing the health impacts of PM2.5 requires consideration of SOA.

Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)-the ratio of the prevalence among BAAs to that among nHWs-as an index of BAA-nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains-health behaviors, clinical care, social and economic and physical environment-as contributors to BAA-nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (β = -0.21, p < 0.001) and social and economic (β = -0.11, p < 0.01) domains were significantly inversely associated with the observed BAA-nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA-nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke.

Basic behavioral science research priorities in minority health and health disparities

Achieving health equity among disparity populations has been a national, regional, and local priority for several years. Health promotion and disease prevention behaviors play an important role in achieving health equity; the first generation of behavioral science studies in minority health and health disparities have provided important insights about the nature and distribution of risk exposure behaviors in disparity populations. Interventions have also been developed to enhance health promotion and disease prevention behaviors using behavioral counseling, tailored health communications, and interventions that are developed collaboratively with community stakeholders. Although intervention development and evaluation are components of transdisciplinary translational behavior research, discovery science is a critical first step in translational research. Consistent with this, conceptual models and frameworks of minority health and health disparities have evolved to include multilevel determinants that include basic behavioral mechanisms such as stress responses and stress reactivity that have physiological, psychological, and behavioral components that are relevant to minority health and health disparities. This report describes priorities, opportunities, and barriers to conducting transdisciplinary translational behavioral research during the next generation of minority health and health disparities research.

Association of Chronic Lower Respiratory Disease With County Health Disparities in New York State

IMPORTANCE

Chronic lower respiratory disease (CLRD) is the fourth leading cause of death in the United States, which imposes a considerable burden on individuals, families, and societies. The association between county-level health disparity and CLRD outcomes in New York state needs investigation.

OBJECTIVE

To evaluate the associations of CLRD outcomes with county-level health disparities in New York state.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, CLRD age-adjusted hospitalization for 2016 and mortality rates from 2014 to 2016 were obtained from the New York state Community Health Indicator Reports provided by the New York state Department of Health. County Health Rankings were used to evaluate various health factors to provide a summary z score for each county representing the county health status and how that county ranks in the state. Data analysis was performed from November 2020 to March 2021.

MAIN OUTCOMES AND MEASURES

The main outcomes were age-adjusted hospitalization and mortality rates for CLRD. The z score was calculated from the County Health Rankings, which includes subindicators of health behaviors, clinical care, social and economic factors, and physical environment. Pearson r and linear regression models were estimated.

RESULTS

During the study, 60 335 discharges were documented as CLRD hospitalizations in 2016 and 20 612 people died from CLRD from 2014 to 2016 in New York state. After adjusting for age, the CLRD hospitalization rate was 27.6 per 10 000 population, and the mortality rate was 28.9 per 100 000 population. Among 62 counties, Bronx had the highest hospitalization rate (64.7 per 10 000 population) whereas Hamilton had the lowest hospitalization rate (6.6 per 10 000 population). Mortality rates ranged from 17.4 per 100 000 population in Kings to 62.9 per 100 000 population in Allegany. County Health Rankings indicated Nassau had the lowest z score (the healthiest), at -1.17, but Bronx had the highest z score (the least healthy), at 1.43, for overall health factors in 2018. An increase of 1 point in social and economic factors z score was associated with an increase of 17.6 hospitalizations per 10 000 population (β = 17.61 [95% CI, 10.36 to 24.87]; P < .001). A 1-point increase in health behaviors z score was associated with an increase of 41.4 deaths per 100 000 population (β = 41.42 [95% CI, 29.88 to 52.97]; P < .001).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, CLRD outcomes were significantly associated with county-level health disparities in New York state. These findings suggest that public health interventions and resources aimed at improving CLRD outcomes should be tailored and prioritized in health disadvantaged areas.

Performance evaluation and ranking of regional primary health care and public health Systems in Iran

BACKGROUND

The present study has been undertaken with the aim to evaluate performance and ranking of various universities of medical sciences that are responsible for providing public health services and primary health care in Iran.

METHODS

Four models; Weighted Factor Analysis (WFA), Equal Weighting (EW), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA) have been applied for evaluating the performance of universities of medical sciences. This study was commenced based on the statistical reports of the Ministry of Health and Medical Education (MOHME), census data from the Statistical Center of Iran, indicators of Vital Statistics, results of Multiple Indicator of Demographic and Health Survey 2010, and results of the National Survey of Risk Factors of non-communicable diseases.

RESULTS

The average performance scores in WFA, EW, SFA, and DEA methods for the universities were 0.611, 0.663, 0.736 and 0.838, respectively. In all 4 models, the performance scores of universities were different (range from 0.56-1, 0.53-1, 0.73-1 and 0.83-1 in WFA, EW, SFA and DEA models, respectively). Gilan and Rafsanjan universities with the average ranking score of 4.75 and 41 had the highest and lowest rank among universities, respectively. The universities of Gilan, Ardabil and Bojnourd in all four models had the highest performance among the top 15 universities, while the universities of Rafsanjan, Ahvaz, Kerman and Jiroft showed poor performance in all models.

CONCLUSIONS

The average performance scores have varied based on different measurement methods, so judging the performance of universities based solely on the results of a model can be misleading. In all models, the performance of universities has been different, which indicates the need for planning to balance the performance improvement of universities based on learning from the experiences of well-performing universities.

Spatiotemporal Analysis of Overall Health in the United States Between 2010 and 2018

Background

Although many previous studies have documented spatial heterogeneity in health outcomes across the United States at different geographic scales, spatiotemporal analyses to understand overall health are scant.

Methodology

We used the County Health Rankings (CHR) data to analyze the three types of health outcomes, viz., overall health, length of life, and quality of life for 2010-2018 in the contiguous United States employing hierarchal Bayesian methods. Composite scores were created to proxy these outcomes utilizing predefined weights of several variables as recommended by CHR. Our methods assumed a convolution of spatially structured and unstructured errors to model the overall spatial error. Spatial effects were modeled using conditional autoregressive distribution.

Results

The substantial disparity in these health outcomes was evident, with counties having poorer health outcomes mostly concentrated in the southeastern United States. Models that incorporated county-level demographic and socioeconomic characteristics partially explained the observed spatial heterogeneity in health outcomes. Interestingly, there was no time effect in any of the outcomes suggesting a perpetuation of health disparity over the years.

Conclusions

County-specific health policy interventions that take into account the contextual factors might be beneficial in improving population health and breaking the perpetuation of health disparity.

Access to Health Care in Appalachia: Perception and Reality

INTRODUCTION

Health disparities such as cancer and diabetes are well documented in Appalachia. These disparities contribute to health status, and by many indicators, Appalachian people are less healthy than those who live in other parts of the country. Access to health care is one factor that contributes to health disparities. Access to care is complex and involves both intrinsic and extrinsic aspects, including satisfaction with quality of care. This research sought to compare Appalachian to non-Appalachian communities in terms of perceptions of access to care.

METHODS

We implemented a statewide survey to quantify perceptions of multiple components of access to care, including satisfaction with quality of care. We compared survey results to quantitative data from the County Health Rankings to document consistency with perceptions of access to care. We used chi-square analysis to compare Appalachian with non-Appalachian respondents.

RESULTS

More than 600 people completed the survey. Results of the survey identify significant differences between Appalachian and non-Appalachian residents' perceptions of access to care and their satisfaction with health care. Specifically, Appalachian residents are less satisfied with convenience, information, quality, and courtesy of health care. They perceive providers relying on stereotypes when communicating with patients.

IMPLICATIONS

Examining and documenting perceptions of health care is important because it could lead to improving access by focusing on cultural competency in addition to more resource intensive strategies. Health disparities in Appalachia might be minimized by being more compassionate and understanding of people who live here.

Generating Subcounty Health Data Products: Methods and Recommendations From a Multistate Pilot Initiative

BACKGROUND

County Health Rankings & Roadmaps (CHR&R) makes data on health determinants and outcomes available at the county level, but health data at subcounty levels are needed. Three pilot projects in California, Missouri, and New York explored multiple approaches for defining measures and producing data at subcounty geographic and demographic levels based on the CHR&R model. This article summarizes the collective technical and implementation considerations from the projects, challenges inherent in analyzing subcounty health data, and lessons learned to inform future subcounty health data projects.

METHODS

The research teams used 12 data sources to produce 40 subcounty measures that replicate or approximate county-level measures from the CHR&R model. Using varying technical methods, the pilot projects followed similar stages: (1) conceptual development of data sources and measures; (2) analysis and presentation of small-area and subpopulation measures for public health, health care, and lay audiences; and (3) positioning the subcounty data initiatives for growth and sustainability. Unique technical considerations, such as degree of data suppression or data stability, arose during the project implementation. A compendium of technical resources, including samples of automated programs for analyzing and reporting subcounty data, was also developed.

RESULTS

The teams summarized the common themes shared by all projects as well as unique technical considerations arising during the project implementation. Furthermore, technical challenges and implementation challenges involved in subcounty data analyses are discussed. Lessons learned and proposed recommendations for prospective analysts of subcounty data are provided on the basis of project experiences, successes, and challenges.

CONCLUSIONS

This multistate pilot project offers 3 successful approaches for creating and disseminating subcounty data products to communities. Subcounty data often are more difficult to obtain than county-level data and require additional considerations such as estimate stability, validating accuracy, and protecting individual confidentiality. We encourage future projects to further refine techniques for addressing these critical considerations.

Defining the social determinants of health for nursing action to achieve health equity: A consensus paper from the American academy of nursing

BACKGROUND

The 2019-2020 American Academy of Nursing (Academy, 2019) policy priorities document states that "they have a clear and distinct focus on social determinants of health and uses this lens to advance policies and solutions within each of the three overarching priorities" PURPOSE: This consensus paper seeks to establish conceptual clarity and consensus for what social determinants of health mean for nursing, with emphasis on examples of health policies that advance planetary health equity and improve planetary health-related quality of life.

METHODS

Volunteers from five Expert Panels of the Academy met via videoconference to determine roles and refine the focus of the paper. After the initial discussion, the first draft of the conceptual framework was written by the first three authors of the paper and, after discussion via videoconference with all the co-authors, successive drafts were developed and circulated for feedback. Consensus was reached when all authors indicated acceptance of what became the final version of the conceptual framework.

DISCUSSION

A conceptual framework was developed that describes how the social determinants of health can be addressed through nursing roles and actions at the individual, family, and population levels with a particular focus on the role of health policy. The paper provides a specific health policy example for each of the six key areas of the social determinants of health to illustrate how nurses can act to improve population health.

CONCLUSION

Nursing actions can support timely health policy changes that focus on upstream factors in the six key areas of the social determinants of health and thus improve population health. The urgent need to eliminate systematic and structural racism must be central to such policy change if equity in planetary health-related quality of life is to be attained.

The influence of community health on hospitals attainment of Magnet designation: Implications for policy and practice

AIMS

To determine if there is an association between better County Health Rankings and the increased odds of a hospital gaining Magnet designation in subsequent years (2014-2019) compared with counties with lower rankings.

BACKGROUND

The Magnet hospital model is recognized to have a great effect on nurses, patients and organizational outcomes. Although Magnet hospital designation is a well-established structural marker for nursing excellence, the effect of County Health Rankings and subsequent hospital achievement of Magnet status is unknown.

DESIGN

A descriptive, cross-sectional quantitative approach was adopted for this study.

METHODS

Data were derived from 2010 to 2019 U.S. County Health Rankings, American Hospital Association, and American Nursing Credentialing Center databases. Logistic regression models were utilized to determine associations between county rankings for health behaviours, clinical care, social and economic factors, physical environment and counties with a new Magnet hospital after 2014.

RESULTS

Counties with the worst rankings for clinical care and socio-economic status had reduced odds of obtaining a Magnet hospital designation compared with best-ranking counties. While middle-ranking counties for the physical environment ranking had increased odds of having Magnet designation compared with best-ranking counties. Additionally, having an increased percent of government non-federal hospital or a higher percentage of critical access hospitals in the county reduced the odds of having a Magnet-designated facility after 2014.

CONCLUSION

The findings underscore the important associations between Magnet-designated facilities' location and the health of its surrounding counties. This study is the first to examine the relationship between County Health Rankings and a hospital's likelihood of obtaining Magnet status and points to the need for future research to explore outcomes of care previously identified as improved in Magnet-designated hospitals.

IMPLICATIONS

Recognizing the benefits of Magnet facilities, it is important for health care leaders and policy makers to seek opportunities to promote centres of excellence in higher need communities through policy and financial intervention.

Regional personality assessment through social media language

OBJECTIVE

We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied to large social media datasets now allow for large-scale personality assessment.

METHOD

We applied a language-based assessment of the five factor model of personality to 6,064,267 U.S. Twitter users. We aggregated the Twitter-based personality scores to 2,041 counties and compared to political, economic, social, and health outcomes measured through surveys and by government agencies.

RESULTS

There was significant personality variation across counties. Openness to experience was higher on the coasts, conscientiousness was uniformly spread, extraversion was higher in southern states, agreeableness was higher in western states, and emotional stability was highest in the south. Across 13 outcomes, language-based personality estimates replicated patterns that have been observed in individual-level and geographic studies. This includes higher Republican vote share in less agreeable counties and increased life satisfaction in more conscientious counties.

CONCLUSIONS

Results suggest that regions vary in their personality and that these differences can be studied through computational linguistic analysis of social media. Furthermore, these methods may be used to explore other psychological constructs across geographies.

Ambient fine particulate matter air pollution and the risk of hospitalization among COVID-19 positive individuals: Cohort study

BACKGROUND

Ecologic analyses suggest that living in areas with higher levels of ambient fine particulate matter air pollution (PM2.5) is associated with higher risk of adverse COVID-19 outcomes. Studies accounting for individual-level health characteristics are lacking.

METHODS

We leveraged the breadth and depth of the US Department of Veterans Affairs national healthcare databases and built a national cohort of 169,102 COVID-19 positive United States Veterans, enrolled between March 2, 2020 and January 31, 2021, and followed them through February 15, 2021. Annual average 2018 PM2.5 exposure, at an approximately 1 km2 resolution, was linked with residential street address at the year prior to COVID-19 positive test. COVID-19 hospitalization was defined as first hospital admission between 7 days prior to, and 15 days after, the first COVID-19 positive date. Adjusted Poisson regression assessed the association of PM2.5 with risk of hospitalization.

RESULTS

There were 25,422 (15.0%) hospitalizations; 5,448 (11.9%), 5,056 (13.0%), 7,159 (16.1%), and 7,759 (19.4%) were in the lowest to highest PM2.5 quartile, respectively. In models adjusted for State, demographic and behavioral factors, contextual characteristics, and characteristics of the pandemic a one interquartile range increase in PM2.5 (1.9 µg/m3) was associated with a 10% (95% CI: 8%-12%) increase in risk of hospitalization. The association of PM2.5 and risk of hospitalization among COVID-19 individuals was present in each wave of the pandemic. Models of non-linear exposure-response suggested increased risk at PM2.5 concentrations below the national standard 12 µg/m3. Formal effect modification analyses suggested higher risk of hospitalization associated with PM2.5 in Black people compared to White people (p = 0.045), and in those living in socioeconomically disadvantaged neighborhoods (p < 0.001).

CONCLUSIONS

Exposure to higher levels of PM2.5 was associated with increased risk of hospitalization among COVID-19 infected individuals. The risk was evident at PM2.5 levels below the regulatory standards. The analysis identified those of Black race and those living in disadvantaged neighborhoods as population groups that may be more susceptible to the untoward effect of PM2.5 on risk of hospitalization in the setting of COVID-19.

Value-based focused global population health management

In 2018, approximately 18 million people worldwide were diagnosed with cancer and are predicted to double by 2040. The global quality chasm in improving health care worldwide requires "systems thinking" as the key to success. Aligning the goal around person-centered care captures the total needs of care of a population and not just disease categories. The integration of the Institute of Medicine's (IOM) six aims of quality termed "value-based focused" and population health management (PHM) provides all health care leaders grappling with improving the health care of the populations a framework for the communities they serve. In this context, the question becomes finding solutions to providing high quality, compassionate and patient-centered health care delivery. Over the last two decades, three paradigms have emerged; the six aims of quality, outcome-focused population health, and the "Quadruple Aim". We have termed the intersection of these concepts as Value-based focused Population Health Management (VBPHM). This review applies VBPHM across the geographic county and community levels in the United States. Specifically, we examine VBPHM at the county or county-equivalents and community levels within the United States. Lastly, the potential role of Community-based Participatory Research and it is applicability to our framework is discussed. VBPHM can comparably be applied globally to improve population health, especially in preventing and treating cancer better.