Explaining the spatial variation in American life expectancy

Perspectives From Public Health Practitioners and Advocates on Community Development for Active Living: What are the Lasting Impacts?

PURPOSE

Evidence suggests differential impacts of community development, including gentrification and displacement. Public health practitioners and advocates are key stakeholders involved in the community development process related to active living, yet little is known about their perceptions of its impacts. We explored the perspectives of relevant leaders of public health departments and key community and advocacy organizations on community development, gentrification, and displacement.

APPROACH

Purposive key informant interviews.

SETTING

CDC State Physical Activity and Nutrition (SPAN) funding recipients.

PARTICIPANTS

CDC SPAN recipient leadership (n = 10 of 16) and advocacy organizations they partnered with (n = 7 of 16).

METHOD

Interviews were recorded, transcribed, coded, and thematically analyzed with direct quotes representing key themes.

RESULTS

Both groups felt community development held important benefits, specifically by creating healthy living opportunities, but also potentially leading to the displacement of long-time residents. Practitioners reported the benefits were for all community members, whereas advocates noted the benefits were seen in those with privilege, and the consequences were disproportionately seen in disadvantaged communities. Both mentioned the importance and difficulty of getting diverse representation for community engagement.

CONCLUSIONS

Learning how key stakeholders perceive and navigate the community development process can help inform recommendations for better equity in active living community improvements. More work is needed to further elucidate best practices for health and social equity in the community development process.

Spatiotemporal patterns of healthy life expectancy and the effects of health financing in West African countries, 1995-2019: A Spatial Panel Modelling Study

Background

Health financing produce a broad range of healthy life expectancy (HLE) disparities. In West Africa, limited research exists on the association between health financing and HLE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between health financing and HLE.

Methods

A Dynamic Spatial Durbin model was used to explain the association between HLE and health financing level and structure during 1995-2019 in West Africa. Spatial spillover effects were introduced to interpret the direct and indirect effects caused by health financing level and structure on HLE during the long and short terms.

Results

Spatial dependence and clustering on HLE were observed in West Africa. Although the overall level of total health spending, government health spending, out-of-pocket health spending, and development assistance for health (DAH) increased from 1995 to 2019, government health spending per person experienced a declining trend. Out-of-pocket health spending per total health spending was the highest among other sources of health financing, decreasing from 57% during 1995-1999 to 42% during 2015-2019. Total health spending and out-of-pocket health spending affected HLE positively and negatively in the long term, respectively. Government health spending and prepaid private health spending per person had positive effects on local and adjacent country HLE in the short-term, while DAH had negative effects on the same. The short-term spatial spillover effects of government health spending, DAH, and prepaid private health spending per person were more pronounced than the long-term effects.

Conclusions

Spatial variations of HLE existed at country-level in West Africa. Health financing regarding government, non-government, as well as external assistance not only affected HLE disparities at local scale but also among nearby countries. Policymakers should optimise supportive health financing transition policies and narrow the national gap to reduce health disparities and increase HLE. Externalities of policy of those health financing proxies should be took into consideration to promote health equity to improve global health governance.

Factors Associated with Severe COVID-19 Among Patients with Rheumatoid Arthritis: A Large, Nationwide Electronic Health Record Cohort Study in the United States

INTRODUCTION

To evaluate factors associated with severe coronavirus disease 2019 (COVID-19) among patients with rheumatoid arthritis (RA) in the US.

METHODS

Adults with RA who had a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, based on molecular or antigen test or clinical diagnosis, were identified from the Optum® COVID-19 Electronic Health Record dataset (March 1, 2020-April 28, 2021). The primary outcome was the occurrence of severe COVID-19 (hospitalization or death) within 30 days from SARS-CoV-2 infection. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated using multivariable logistic regression models to assess the association between severe COVID-19 and patient characteristics, including demographics, baseline comorbidities, and recent RA treatments.

RESULTS

During the study period, 6769 SARS-CoV-2 infections were identified in patients with RA, among whom 1460 (22%) developed severe COVID-19. Multivariable logistic regression analysis showed that being older, male, and non-White and having diabetes and cardiovascular conditions are associated with greater odds of severe COVID-19. In addition, compared with no use, the adjusted odds of severe COVID-19 were lower with recent use of tumor necrosis factor inhibitors (aOR 0.60, 95% CI 0.41-0.86) and higher with recent use of corticosteroids (aOR 1.38, 95% CI 1.13-1.69) or rituximab (aOR 2.87, 95% CI 1.60-5.14), respectively.

CONCLUSION

Nearly one in five patients with RA developed severe COVID-19 disease within 30 days after SARS-CoV-2 infection. In patients with RA, recent use of corticosteroids and rituximab were two factors associated with a greater risk of severe COVID-19 in addition to the risk factors among demographics and comorbidities previously identified in the general population.

Current Programs and Incentives to Overcome Rural Physician Shortages in the United States: A Narrative Review

Access to healthcare continues to be a top priority and prominent challenge in rural communities, with 20% of the total U.S. population living in rural areas while only 10% of physicians practice in rural areas. In response to physician shortages, a variety of programs and incentives have been implemented to recruit and retain physicians in rural areas; however, less is known about the types and structures of incentives that are offered in rural areas and how that compares to physician shortages. The purpose of our study is to conduct a narrative review of the literature to identify and compare current incentives that are offered by rural physician shortage areas to better understand how resources are being allocated to vulnerable areas. We reviewed published peer-reviewed articles from 2015-2022 to identify incentives and programs designed to address physician shortages in rural areas. We augment that review by examining the gray literature, including reports and white papers on the topic. Identified incentive programs were aggregated for comparison and translated into a map that depicts high, medium, and low levels of geographically designated Health Professional Shortage Areas (HPSAs) and the number of incentives offered by state. Surveying current literature regarding different types of incentivization strategies while comparing to primary care HPSAs provides general insights on the potential influence of incentive programs on shortages, allows easy visual review, and may provide greater awareness of available support for potential recruits. Providing a broad overview of the incentives offered in rural areas will help illuminate whether diverse and appealing incentives are offered in the most vulnerable areas and guide future efforts to address these issues.

Live and let live: understanding the temporal drivers and spillovers of life expectancy in Europe for public planning

The European continent has one of the longest life expectancies in the world, but still faces a significant challenge to meet the health targets set by the Sustainable Development Goals of the United Nations for 2030. To improve the understanding of the rationale that guides health outcomes in Europe, this study assesses the direction and magnitude effects of the drivers that contribute to explain life expectancy at birth across 30 European countries for the period 2008-2018 at macro-level. For this purpose, an aggregated health production function is used allowing for spatial effects. The results indicate that an increase in the income level, health expenditure, trade openness, education attainment, or urbanisation might lead to an increase in life expectancy at birth, whereas calories intake or quantity of air pollutants have a negative impact on this health indicator. This implies that health policies should look beyond economic factors and focus also on social and environmental drivers. The results also indicate the existence of significant spillover effects, highlighting the need for coordinated European policies that account for the synergies between countries. Finally, a foresight analysis is conducted to obtain projections for 2030 under different socioeconomic pathways. Results reveal significant differences on longevity projections depending on the adoption, or not, of a more sustainable model of human development and provides valuable insight on the need for anticipatory planning measures to make longer life-spans compatible with the maintenance of the welfare state.

Urban-Rural Disparities in Deaths of Despair: A County-Level Analysis 2004-2016 in the U.S

Introduction

The purpose of this study is to examine nationwide disparities in drug, alcohol, and suicide mortality; evaluate the association between county-level characteristics and these mortality rates; and illustrate spatial patterns of mortality risk to identify areas with elevated risk.

Methods

The authors applied a Bayesian spatial regression technique to investigate the association between U.S. county-level characteristics and drug, alcohol, and suicide mortality rates for 2004-2016, accounting for spatial correlation that occurs among counties.

Results

Mortality risks from drug, alcohol, and suicide were positively associated with the degree of rurality, the proportion of vacant housing units, the population with a disability, the unemployed population, the population with low access to grocery stores, and the population with no health insurance. Conversely, risks were negatively associated with Hispanic population, non-Hispanic Black population, and population with a bachelor's degree or higher.

Conclusions

Spatial disparities in drug, alcohol, and suicide mortality exist at the county level across the U.S. social determinants of health; educational attainment, degree of rurality, ethnicity, disability, unemployment, and health insurance status are important factors associated with these mortality rates. A comprehensive strategy that includes downstream interventions providing equitable access to healthcare services and upstream efforts in addressing socioeconomic conditions is warranted to effectively reduce these mortality burdens.

County-Level Life Expectancy Change: A Novel Metric for Monitoring Public Health

Life expectancy (LE) is a core measure of population health. Studies have confirmed the predictive importance of modifiable determinants on LE, but less is known about their association with LE change over time at the US county level. In addition, we explore the predictive association of LE change with COVID-19 mortality. We used a linear regression model to calculate county-level annual LE change from 2011 to 2016, and categorized LE change (≤-0.1 years change per year as decreasing, ≥0.1 years as increasing, otherwise no change). A multinomial regression model was used to determine the association between modifiable determinants of health indicators from the County Health Rankings and LE change. A Poisson regression model was used to evaluate the relationship between change in life expectancy and COVID-19 mortality through September 2021. Among 2943 counties, several modifiable determinants of health were significantly associated with odds of being in increasing LE or decreasing LE counties, including adult smoking, obesity, unemployment, and proportion of children in poverty. The presence of an increasing LE in 2011-2016, as compared to no change, was significantly associated with a 5% decrease in COVID-19 mortality between 2019 and 2021 (β = 0.953, 95% CI: 0.943, 0.963). We demonstrated that change in LE at the county level is a useful metric for tracking public health progress, measuring the impact of public health initiatives, and gauging preparedness and vulnerability for future public health emergencies.

Spatial variations and social determinants of life expectancy in China, 2005-2020: A population-based spatial panel modelling study

BACKGROUND

Social determinants of health (SDOH) produce a broad range of life expectancy (LE) disparities. In China, limited literatures were found to report association between SDOH and LE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between SDOH and LE in China.

METHODS

Provincial-level LE were estimated from mortality records during 2005-2020 from National Mortality Surveillance System in China. A spatial panel Durbin model was used to investigate LE associated SDOH proxies. Spatial spillover effects were introduced to interpret direct and indirect effects caused by SDOH during long-term and short-term period on LE disparities.

FINDINGS

Nationwide, LE increased from 73.1 (95% confidence interval (CI): 71.3, 74.4) years to 77.7 (95%CI: 76.5, 78.7) years from 2005 to 2020. Unequally spatial distribution of LE with High-High clustering in coastal areas and Low-Low clustering in western regions were observed. Locally, it was estimated that SDOH proxies statistically significant related to an increase of LE, including GDP (coefficient: 0.02, 95%CI: 0.00, 0.03), Gini index (coefficient: 2.35, 95%CI: 1.82, 2.88), number of beds in health care institutions (coefficient: 0.02, 95%CI: 0.00, 0.05) and natural growth rate of resident population (coefficient: 0.02, 95%CI: 0.01, 0.02). Direct and indirect effects decomposition during long-term and short-term of LE associated SDOH proxies demonstrated that GDP, urbanization rate, unemployment rate, education attainment, Gini index, number of beds in health care institutions, sex ratio, gross dependence ratio and natural growth rate of resident population not only affected local LE, but also exerted spatial spillover effects towards geographical neighbors.

INTERPRETATION

Spatial variations of LE existed at provincial-level in China. SDOH regarding socioeconomic development and equity, healthcare resources, as well as population characteristics not only affected LE disparities at local scale but also among nearby provinces. Externalities of policy of those SDOH proxies should be took into consideration to promote health equity nationally. Comprehensive approaches on the basis of population strategy should be consolidated to optimize supportive socioeconomic environment and narrow the regional gap to reduce health disparities and increase LE.

FUNDING

National Key Research & Development Program of China (Grant No.2018YFC1315301); Ministry of Education of China Humanities and Social Science General Program (Grant No.18YJC790138).

Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017

Studies have documented significant geographic divergence in U.S. mortality in recent decades. However, few studies have examined the extent to which county-level trends in mortality can be explained by national, state, and metropolitan-level trends, and which county-specific factors contribute to remaining variation. Combining vital statistics data on deaths and Census data with time-varying county-level contextual characteristics, we use a spatially explicit Bayesian hierarchical model to analyze the associations between working-age mortality, state, metropolitan status and county-level socioeconomic conditions, family characteristics, labor market conditions, health behaviors, and population characteristics between 2000 and 2017. Additionally, we employ a Shapley decomposition to illustrate the additive contributions of each changing county-level characteristic to the observed mortality change in U.S. counties between 1999-2001 and 2015-2017 over and above national, state, and metropolitan-nonmetropolitan mortality trends. Mortality trends varied by state and metropolitan status as did the contribution of county-level characteristics. Metropolitan status predicted more of the county-level variance in mortality than state of residence. Of the county-level characteristics, changes in percent college-graduates, smoking prevalence and the percent of foreign-born population contributed to a decline in all-cause mortality over this period, whereas increasing levels of poverty, unemployment, and single-parent families and declines manufacturing employment slowed down these improvements, and in many nonmetropolitan areas were large enough to overpower the positive contributions of the protective factors.

U.S. regional differences in physical distancing: Evaluating racial and socioeconomic divides during the COVID-19 pandemic

Health varies by U.S. region of residence. Despite regional heterogeneity in the outbreak of COVID-19, regional differences in physical distancing behaviors over time are relatively unknown. This study examines regional variation in physical distancing trends during the COVID-19 pandemic and investigates variation by race and socioeconomic status (SES) within regions. Data from the 2015-2019 five-year American Community Survey were matched with anonymized location pings data from over 20 million mobile devices (SafeGraph, Inc.) at the Census block group level. We visually present trends in the stay-at-home proportion by Census region, race, and SES throughout 2020 and conduct regression analyses to examine these patterns. From March to December, the stay-at-home proportion was highest in the Northeast (0.25 in March to 0.35 in December) and lowest in the South (0.24 to 0.30). Across all regions, the stay-at-home proportion was higher in block groups with a higher percentage of Blacks, as Blacks disproportionately live in urban areas where stay-at-home rates were higher (0.009 [CI: 0.008, 0.009]). In the South, West, and Midwest, higher-SES block groups stayed home at the lowest rates pre-pandemic; however, this trend reversed throughout March before converging in the months following. In the Northeast, lower-SES block groups stayed home at comparable rates to higher-SES block groups during the height of the pandemic but diverged in the months following. Differences in physical distancing behaviors exist across U.S. regions, with a pronounced Southern and rural disadvantage. Results can be used to guide reopening and COVID-19 mitigation plans.

Rural Winery Resiliency and Sustainability through the COVID-19 Pandemic

The COVID-19 pandemic has adversely affected the tourism industry worldwide, including the wine industry in the western U.S. due to mandated winery and tasting room closures, followed by restrictions on capacity and food- and drink-handling once wineries reopened. In California, tasting rooms were fully closed from mid-March to mid-May 2020 and could not have visitors indoors through to October 2020. Hence, this study examines the resiliency of wineries in minor California wine regions, including the challenges faced during the pandemic, strategies used to sustain their business, and the attributes of their operation which contributed to success. Data were collected through structured in-person interviews with five wineries in minor California wine regions, specifically Russian River Valley and Sierra Foothills. The four themes which emerged include: lifestyle business; market differentiation; direct marketing; and the effects of COVID-19. These wineries are primarily family-owned, which gives them the ability to control costs and make decisions rapidly. They did not have a large workforce or multiple layers of management, allowing them to pivot quickly to adjust to the regulatory environment. This study on rural winery resilience during the COVID-19 pandemic will assist rural tourism operations in dealing with social and economic shocks in the future.

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

Both economic development level and environmental factors have significant impacts on life expectancy at birth (LE). This paper takes LE as the research object and selects nine economic and environmental indicators with various impacts on LE. Based on a dataset of economic and environmental indicators of 20 countries from 2004 to 2016, our research uses the Pearson Correlation Coefficient to evaluate the correlation coefficients between the indicators, and we use multiple regression models to measure the impact of each indicator on LE. Based on the results from models and calculations, this study conducts a comparative analysis of the influencing mechanisms of different indicators on LE in both developed and developing countries, with conclusions as follow: (1) GDP per capita and the percentage of forest area to land area have a positive impact on LE in developed countries; however, they have a negative impact on LE in developing countries. Total public expenditure on education as a percentage of GDP and fertilizer consumption have a negative impact on LE in developed countries; however, they have a positive impact on LE in developing countries. Gini coefficient and average annual exposure to PM2.5 have no significant effect on LE in developed countries; however, they have a negative impact on LE in developing countries. Current healthcare expenditures per capita have a negative impact on LE in developed countries, and there is no significant impact on LE in developing countries. (2) The urbanization rate has a significant positive impact on LE in both developed countries and developing countries. Carbon dioxide emissions have a negative impact on LE in both developed and developing countries. (3) In developed countries, GDP per capita has the greatest positive impact on LE, while fertilizer consumption has the greatest negative impact on LE. In developing countries, the urbanization rate has the greatest positive impact on LE, while the Gini coefficient has the greatest negative impact on LE. To improve and prolong LE, it is suggested that countries should prioritize increasing GDP per capita and urbanization level. At the same time, countries should also work on reducing the Gini coefficient and formulating appropriate healthcare and education policies. On the other hand, countries should balance between economic development and environmental protection, putting the emphasis more on environmental protection, reducing environmental pollution, and improving the environment's ability of self-purification.

Spatial and temporal inequalities in mortality in the USA, 1968-2016

Previous UK and European research has highlighted important variations in mortality between populations after adjustment for key determinants such as poverty and deprivation. The aim here was to establish whether similar populations could be identified in the US, and to examine changes over time. We employed Poisson regression models to compare county-level mortality with national rates between 1968 and 2016, adjusting for poverty, education, race (a proxy for exposure to racism), population change and deindustrialisation. Results are presented by means of population-weighted cartograms, and highlight widening spatial inequalities in mortality over time, including an urban to rural, and south-westward, shift in areas with the highest levels of such unexplained 'excess' mortality. There is a need to understand the causes of the excess in affected communities, given that it persists after adjustment for such a broad range of important health determinants.

Modeling Trade Openness and Life Expectancy in China

Objective

This study investigates life expectancy and trade openness in China for the period 1960–2018.

Methods

We purposed a theoretical model that is tested for China by applying regime-switching regression.

Results

Our findings suggest that trade openness increases life expectancy in China; trade affects life expectancy from two aspects; firstly, trade expansion and industrialization lead to high economic activities and resulted in raise the income of the people in society leading to improve life expectancy. Secondly, industrial expansion increases the CO₂ emissions which leads to imposes a negative implication on human health and thus reduces life expectancy.

Conclusion

Thus, the net effect of trade liberalization depends on the value of income effect and volume of CO₂ emissions. Therefore, the government needs to support the trade policies which causes a low level of CO₂ emissions, the government may provide incentives to exports and industrialists to adopted green energy in the production process. Besides, the government may impose some regulations such as carbon tax to mitigate the CO₂ emissions in society.

COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave

Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.