Rural-urban residence and life expectancies with and without pain.

This paper analyzes rural-urban disparities in life expectancy with and without pain among upper-middle age and older adults. Data are from the nationally representative Health and Retirement Study, 2000-2018, N = 18,160, age 53+. Interpolated Markov Chain software, based on the multistate life tables, is used to calculate absolute and relative pain expectancies by age, sex, rural-suburban-urban residence and U.S. regions. Results show significant rural disadvantages versus those in urban and often suburban areas. Example: males at 55 in rural areas can expect to live 15.1 years, or 65.2 percent pain-free life, while those in suburban areas expect to live 1.7 more years, or 2.6 percentage points more, pain-free life and urban residents expect to live 2.4 more year, or 4.7 percentage points more. The rural disadvantage persists for females, with differences being a little less prominent. At very old age (85+), rural-urban differences diminish or reverse. Rural-urban pain disparities are most pronounced in the Northeast and South regions, and least in the Midwest and West. The findings highlight that rural-urban is an important dimension shaping the geography of pain. More research is needed to disentangle the mechanisms through which residential environments impact people's pain experiences.

Are We Adding Pain-Free Years to Life? A Test of Compression Versus Expansion of Morbidity.

BACKGROUND: There has been debate regarding whether increases in longevity result in longer and healthier lives or more disease and suffering. To address the issue, this paper uses health expectancy methods and tests an expansion versus compression of morbidity with respect to pain. METHODS: Data are from 1993 to 2018 Health and Retirement Study. Pain is categorized as no pain, non-limiting and limiting pain. Multistate life tables examine 77,996 wave-to-wave transitions across pain states or death using the Stochastic Population Analysis for Complex Events program. Results are presented as expected absolute and relative years of life for 70-, 80- and 90-year-old males and females. Confidence intervals assess significance of differences over time. Population- and status-based results are presented. RESULTS: For those 70 and 80 years old, relative and absolute life with non-limiting and limiting pain increased substantially for males and females, and despite variability on a wave-to-wave basis, results generally confirm an expanding pain morbidity trend. Results do not vary by baseline status, indicating those already in pain are just as likely to experience expansion of morbidity as those pain-free at baseline. Results are different for 90-year-olds who have not experienced expanding pain morbidity and do not show an increase in life expectancy. CONCLUSIONS: Findings are consistent with extant literature indicating increasing pain prevalence among older Americans and portend a need for attention on pain-coping resources, therapies, and prevention strategies.

The geography of arthritis-attributable pain outcomes: a county-level spatial analysis.

ABSTRACT: Research on the geographic distribution of pain and arthritis outcomes, especially at the county level, is limited. This is a high-priority topic, however, given the heterogeneity of subnational and substate regions and the importance of county-level governments in shaping population health. Our study provides the most fine-grained picture to date of the geography of pain in the United States. Combining 2011 Behavioral Risk Factor Surveillance System data with county-level data from the Census and other sources, we examined arthritis and arthritis-attributable joint pain, severe joint pain, and activity limitations in US counties. We used small area estimation to estimate county-level prevalences and spatial analyses to visualize and model these outcomes. Models considering spatial structures show superiority over nonspatial models. Counties with higher prevalences of arthritis and arthritis-related outcomes are mostly clustered in the Deep South and Appalachia, while severe consequences of arthritis are particularly common in counties in the Southwest, Pacific Northwest, Georgia, Florida, and Maine. Net of arthritis, county-level percentages of racial/ethnic minority groups are negatively associated with joint pain prevalence, but positively associated with severe joint pain prevalence. Severe joint pain is also more common in counties with more female individuals, separated or divorced residents, more high school noncompleters, fewer chiropractors, and higher opioid prescribing rates. Activity limitations are more common in counties with higher percentages of uninsured people. Our findings show that different spatial processes shape the distribution of different arthritis-related pain outcomes, which may inform local policies and programs to reduce the risk of arthritis and its consequences.

Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning.

Background Existing studies on cardiovascular diseases (CVDs) often focus on individual-level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county-level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

Pain and Disability Transitions Among Older Americans: The Role of Education.

Previous literature has rarely examined the role of pain in the process of disablement. We investigate how pain associates with disability transitions among older adults, using educational attainment as a moderator. Data are from the National Health and Aging Trends Study, N = 6,357; 33,201 1 year transitions between 2010 to 2020. We estimate multinomial logistic models predicting incidence or onset of and recovery from functional limitation and disability. Results show pain significantly predicts functional limitation and disability onset 1 year after a baseline observation, and decreases odds of recovery from functional limitation or disability. Contrary to expectations, higher education does not buffer the association of pain in onset of disability, but supporting expectations, it facilitates recovery from functional limitation or disability among those with pain. The analysis implicates pain as having a key role in the disablement process and suggests that education may moderate this with respect to coping with and subsequently recovering from disability. PERSPECTIVE: This article is among the first examining how pain is placed in the disablement process by affecting onset of and recovery from disability. Both paths are affected by pain, but education moderates the association only with respect to the recovery process.

Educational Differences in Life Expectancies With and Without Pain.

OBJECTIVES: This study computes years and proportion of life that older adults living in the United States can expect to live pain-free and in different pain states, by age, sex, and level of education. The analysis addresses challenges related to dynamics and mortality selection when studying associations between education and pain in older populations. METHODS: Data are from National Health and Aging Trends Study, 2011-2020. The sample contains 10,180 respondents who are age 65 and older. Pain expectancy estimates are computed using the Interpolated Markov Chain software that applies probability transitions to multistate life tables. RESULTS: Those with higher educational levels expect not only a longer life but also a higher proportion of life without pain. For example, a 65-year-old female with less than high school education expects 18.1 years in total and 5.8 years, or 32% of life, without pain compared with 23.7 years in total with 10.7 years, or 45% of life without pain if she completed college. The education gradient in pain expectancies is more salient for females than males and narrows at the oldest ages. There is no educational disparity in the percent of life with nonlimiting pain. DISCUSSION: Education promotes longer life and more pain-free years, but the specific degree of improvement by education varies across demographic groups. More research is needed to explain associations between education and more and less severe and limiting aspects of pain.

Multiscale dimensions of county-level disparities in opioid use disorder rates among older Medicare beneficiaries.

Background: Opioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates. Methods: Data are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016-2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely "level of influence" (the percentage of older Medicare beneficiaries affected by a certain determinant), "scalability" (the spatial process of a determinant as global, regional, or local), and "specificity" (the determinant that has the strongest association with the OUD rate). Results: The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others. Conclusions: Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties.

INTRODUCTION: This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate. METHODS: This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022. RESULTS: The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local. CONCLUSIONS: The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties.

PURPOSE: Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks. METHODS: Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors. RESULTS: The key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana. CONCLUSIONS: The COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Rurality and opioid prescribing rates in U.S. counties from 2006 to 2018: A spatiotemporal investigation.

Previous literature on the uneven development of the opioid crisis across U.S. counties fails to account for the temporal and spatial dependency simultaneously. Assembling a spatiotemporal dataset from 2006 to 2018 based on the U.S. Opioid Dispensing Rate Maps, the American Community Survey, and other national data sources, this study examines how rurality impacts the county-level opioid prescribing rates. The results show significant spatial clustering patterns of opioid prescribing rates over the years. Taking the spatial structures into account, it is found that counties with a higher degree of rurality have higher opioid prescribing rates and this association could be explained by higher percentages of whites, higher unemployment rates, less nurse practitioners and physician assistants, and more specialized opioid prescribers such as surgeons and oncologists. Higher level of social capital is related to higher opioid prescribing rates, but it cannot explain the association between rurality and opioid prescribing. The findings highlight the role of healthcare services play in shaping the spatial inequality of opioid prescribing.

Parental migration and anemia status of children in China.

Previous literature on parental migration and children's health outcomes mainly focuses on subjective measures and often omits the selectivity issue. Taking advantage of a unique nationally representative longitudinal dataset from the China Health and Nutrition Survey, this paper uses anemia status as an objective measure of children's health outcomes and examines the different effects of parents' current migration status, migration history, and migration duration. The results show that father's migration does not harm children's physical health, especially for children in rural areas, for whom father's migration decreases the likelihood of being anemic; while mother's migration increases the likelihood of being anemic. Importantly, children with return migrant mothers are less likely to experience anemia. We also find that the longer the father migrated, the better the child's health, but mother's longer migration duration is more detrimental. Our findings highlight the gender dimension in the migration story and indicate that policymakers should encourage the return migration of migrant mothers.

COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation.

OBJECTIVE: To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections. DESIGN: We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure. RESULTS: Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county. CONCLUSIONS: The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE: This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States. METHODS: We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence. RESULTS: The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties. CONCLUSIONS: Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress.

INTRODUCTION: Levels of mental distress in the United States are a health policy concern. The association between social capital and mental distress is well documented, but evidence comes primarily from individual-level studies. Our objective was to examine this association at the county level with advanced spatial econometric methods and to explore the importance of between-county effects. METHODS: We used County Health Rankings and Roadmaps data for 3,106 counties of the contiguous United States. We used spatial Durbin modeling to assess the direct (within a county) and indirect (between neighboring counties) effects of social capital on mental distress. We also examined the spatial spillover effects from neighboring counties based on higher-order spatial weights matrices. RESULTS: Counties with the highest prevalence of mental distress were found in regional clusters where levels of social capital were low, including the Black Belt, central/southern Appalachia, on the Mississippi River, and around some Indian Reservations. Most of the association between social capital and mental distress was indirect, from the neighboring counties, although significant direct effects showed the within-county association. Models also confirmed the importance of county-level socioeconomic status. CONCLUSION: We found that county social capital is negatively related to mental distress. Counties are not isolated places and are often part of wider labor and housing markets, so understanding spatial dependencies is important in addressing population-level mental distress.