Socioeconomic Disparities Associated with Access to Oculofacial Plastic Surgeons: A Cross-Sectional Analysis of US County Demographics.

PURPOSE: To compare population demographics with the geographic distribution of oculofacial plastic surgeons (OPSs) in the United States. DESIGN: A cross-sectional study design was used to investigate demographic differences between counties with 1 or more OPSs and counties with zero OPSs. PARTICIPANTS: The number of OPSs were identified in each US county using online public databases: American Society of Ophthalmic Plastic and Reconstructive Surgeons and American Academy of Ophthalmology. Counties were categorized into 2 groups: 1 or more OPSs and zero OPSs. Demographic characteristics at the county level were obtained from the 2021 US Census Bureau Population Estimates and the American Community Survey. Cost of living was collected from the 2022 Economic Policy Institute Family Budget Calculator. MAIN OUTCOME MEASURES: Socioeconomic demographics of the US population as related to geographic OPS distribution. RESULTS: A total of 1238 OPSs were identified. States with the most OPSs per million were Hawaii (6.2), D.C. (6.0), Connecticut (5.8), Utah (5.1), and Maryland (5.0). Among 3143 counties, 2725 (86.7%) had zero OPSs and 418 (13.3%) had 1 or more OPSs. Counties with 1 or more OPSs had a higher median (standard deviation) household income versus counties with zero OPSs ($72 471 [$19 152] vs. $56 152 [$13 675]; difference $16 319; 95% confidence interval [CI], $14 300-$18 338; P < 0.001). The annual cost of living per person (standard deviation) was higher in counties with 1 or more OPSs versus counties with zero OPSs ($39 238 [$6992] vs. $36 227 [$3516]; difference $3011; 95% CI, $2328-$3694; P < 0.001). Counties with zero OPSs versus counties with 1 or more OPSs had higher proportions of persons with only Medicaid (15.6% vs. 13.6%; difference 2.0%; 95% CI, 1.4%-2.5%; P < 0.001), no health insurance (9.9% vs. 8.0%; difference 1.9%; 95% CI, 1.5%-2.4%; P < 0.001), no household internet access (17.2% vs. 9.6%; difference 7.6%; 95% CI, 7.1%-8.0%; P < 0.001), and higher proportions of persons aged 65 years or older (20.0% vs. 17.0%; difference 3.0%; 95% CI, 2.5%-3.5%; P < 0.001). CONCLUSIONS: This cross-sectional analysis of all US counties revealed socioeconomic disparities associated with access to OPSs. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Availability and Quality of Dialysis Care in Rural versus Urban US Counties.

INTRODUCTION: Rural areas face significant disparities in dialysis care compared to urban areas due to limited access to dialysis facilities, longer travel distances, and a shortage of healthcare professionals. The objective of this study was to conduct a national examination of rural-urban differences in quality of dialysis care offered across counties in the USA. METHODS: Data were gathered from Medicare-certified dialysis facilities in 2020 from the Centers for Medicare and Medicaid Services website. To identify high-need counties, county-level estimated crude prevalence of diabetes in adults was obtained from the 2022 CDC PLACES data portal. Our analysis reviewed 3,141 counties in the USA. The primary outcome measured was whether the county had a dialysis facility. Among those counties that had a dialysis facility, additional outcomes were the average star rating, whether peritoneal dialysis was offered, and whether home dialysis was offered. RESULTS: The type of services offered by dialysis facilities varied significantly, with peritoneal dialysis being the most commonly offered service (50.8%), followed by home hemodialysis (28.5%) and late-shift services (16.0%). These service availabilities are more prevalent in urban facilities than in rural facilities. The Centers for Medicare and Medicaid Services Five Star Quality ratings were quite different between urban and rural facilities, with 40.4% of rural facilities having a ranking of five, compared to 27.1% in urban. CONCLUSION: The majority of rural counties lack a single dialysis facility. Counties with high rates of chronic kidney disease, diabetes, and blood pressure, deemed high need, were less likely to have a highly rated dialysis facility. The findings can be used to further inform targeted efforts to increase diabetes educational programming and design appropriate interventions to those residing in rural communities and high-need counties who may need it the most.

County-level industrial composition of the labor force and drug overdose mortality rates in the United States in 2018-2021.

BACKGROUND: Drug mortality risks vary among industries, creating distinctive geographic patterns across US counties. However, less is known about how local labor market structure relates to drug overdose mortality amid the synthetic opioid era in the United States. This study investigates the relationship between industry-specific job composition and drug overdose mortality at the county level while exploring how fentanyl's presence in illicit drug supplies may moderate the relationship. METHODS: Data were derived from the National Center for Health Statistics' Multiple Cause of Death files for the rates of drug overdose mortality of any intent, linked with four other sources on industry-specific job shares, drug supply, and county-level sociodemographic characteristics and opioid prescribing rates from the US Census Bureau, the CDC, and the Drug Enforcement Administration. Negative binomial regression models were employed to examine associations between county industry-specific job composition and drug overdose mortality, with tests for moderating effects of state-level fentanyl seizure rates. RESULTS: Our models indicate negative associations between job shares of manufacturing, retail trade, and educational services industries and drug overdose mortality. Positive associations were found for arts/entertainment/recreation and public administration. State-level fentanyl seizure rates had moderating effects on administrative/support/waste management/remediation (A/S/WM/R) and educational services. CONCLUSION: Counties with a higher concentration of arts/entertainment/recreation and public administration jobs need targeted efforts to mitigate drug-related overdose risks. Additionally, areas with higher concentrations of A/S/WM/R service jobs, particularly where fentanyl seizure rates are higher, may require proactive harm reduction strategies for reducing overdose risks.

Filling the data gap on CGRP mAb therapy in low- to middle-income countries in Southeast Asia: insights from a real-world study in Thailand.

BACKGROUND: Most real-world data on CGRP mAbs have been published from high-income countries such as the USA, Western countries, Japan, Korea, and Singapore. However, data from low- and middle-income countries in Southeast Asia is lacking. This is the first real-world study from Thailand to describe the efficacy of CGRP mAbs therapy in migraine patients and to analyze the response trends between episodic migraine and chronic migraine. METHODS: We conducted a single-center, real-world retrospective chart review study with an observation period of 6 months after CGRP mAbs initiation. We aim to compare treatment responses to CGRP mAbs between EM and CM patients. RESULTS: A total of 47 Thai patients were enrolled (median [IQR] age 37.2 [28.6-50.4] years; 85.1%F, 44.7% EM; 70.2% galcanezumab). There was no difference in baseline characteristics and migraine disability assessment (MIDAS) between EM and CM. The overall ≥ 30%, ≥ 50%, and ≥ 70% monthly migraine day reduction rates at 6 months were 89.0%, 71.6%, and 58.5% with higher responders in EM. There was a significant decrease in monthly headache days (MHDs) over time (adjusted ß = -0.42, p < 0.001) and a significant decrease in MIDAS score over time after the initiation of CGRP mAbs (adjusted ß = -1.12, p = 0.003). However, there were no differences between the two diagnoses. There was no significant decrease in the number of abortive medication pills used over time after the initiation of CGRP mAbs. CM had a significantly steeper trend compared to those with EM. CONCLUSION: The first real-world study in Thailand demonstrated that CGRP mAbs therapy had efficacy for migraine treatment, as evidenced by a reduction in MHDs, decreased disability, and reduced use of abortive medications. Additionally, the response pattern to CGRP mAbs therapy was similar between EM and CM in terms of MHDs reduction and MIDAS score improvement.

Geographic disparities of cardiovascular and cancer mortality in the USA: 1981-2019.

BACKGROUND: Cardiovascular and cancer mortality are the two leading causes of death in the developed world including the USA. However, mortality trends for these diseases are highly dynamic, and the geographic landscape is in transition. We analyze patterns of mortality improvement at county level during recent decades focusing on mortality decline and geographic diversity. METHODS: We grouped age-adjusted mortality rates of cardiovascular and cancer diseases from CDC WONDER for 2959 US counties into 3-year time periods to improve reliability. We calculated percent mortality decrease between 1981-83 and 2016-19 for both causes to quantify mortality improvements for counties. RESULTS: Using standard deviation as an index of disparities, place-based cancer mortalities were 68% larger than cardiovascular disparities. Significantly, 566 US counties had same or higher rates of cancer mortality in 2019 as in 1981. The geographic distribution of mortality improvement in either cause tends to favor largely populated areas along coasts. Less-populated, rural places in the interior and southeastern regions were experiencing less improvement. CONCLUSIONS: At the county level, large place-based disparities exist for both causes of death with the magnitude of disparities being substantially larger for the reduction in cancer deaths. Put differently, place matters, more for cancer than cardiovascular mortality.

Does a Rising Median Income Lift All Birth Weights? County Median Income Changes and Low Birth Weight Rates Among Births to Black and White Mothers.

Policy Points Policies that increase county income levels, particularly for middle-income households, may reduce low birth weight rates and shrink disparities between Black and White infants. Given the role of aggregate maternal characteristics in predicting low birth weight rates, policies that increase human capital investments (e.g., funding for higher education, job training) could lead to higher income levels while improving population birth outcomes. The association between county income levels and racial disparities in low birth weight is independent of disparities in maternal risks, and thus a broad set of policies aimed at increasing income levels (e.g., income supplements, labor protections) may be warranted. CONTEXT: Low birth weight (LBW; <2,500 grams) and infant mortality rates vary among place and racial group in the United States, with economic resources being a likely fundamental contributor to these disparities. The goals of this study were to examine time-varying county median income as a predictor of LBW rates and Black-White LBW disparities and to test county prevalence and racial disparities in maternal sociodemographic and health risk factors as mediators. METHODS: Using national birth records for 1992-2014 from the National Center for Health Statistics, a total of approximately 27.4 million singleton births to non-Hispanic Black and White mothers were included. Data were aggregated in three-year county-period observations for 868 US counties meeting eligibility requirements (n = 3,723 observations). Sociodemographic factors included rates of low maternal education, nonmarital childbearing, teenage pregnancy, and advanced-age pregnancy; and health factors included rates of smoking during pregnancy and inadequate prenatal care. Among other covariates, linear models included county and period fixed effects and unemployment, poverty, and income inequality. FINDINGS: An increase of $10,000 in county median income was associated with 0.34 fewer LBW cases per 100 live births and smaller Black-White LBW disparities of 0.58 per 100 births. Time-varying county rates of maternal sociodemographic and health risks mediated the association between median income and LBW, accounting for 65% and 25% of this estimate, respectively, but racial disparities in risk factors did not mediate the income association with Black-White LBW disparities. Similarly, county median income was associated with very low birth weight rates and related Black-White disparities. CONCLUSIONS: Efforts to increase income levels-for example, through investing in human capital, enacting labor union protections, or attracting well-paying employment-have broad potential to influence population reproductive health. Higher income levels may reduce LBW rates and lead to more equitable outcomes between Black and White mothers.

Contextualizing Educational Disparities in Health: Variations by Race/Ethnicity, Nativity, and County-Level Characteristics.

Educational disparities in health are well documented, yet the education-health relationship is inconsistent across racial/ethnic and nativity groups. These inconsistencies may arise from characteristics of the early life environments in which individuals attain their education. We evaluate this possibility by investigating (1) whether educational disparities in cardiometabolic risk vary by race/ethnicity and nativity among Black, Hispanic, and White young adults; (2) the extent to which racial/ethnic-nativity differences in the education-health relationship are contingent on economic, policy, and social characteristics of counties of early life residence; and (3) the county characteristics associated with the best health at higher levels of education for each racial/ethnic-nativity group. Using data from the National Longitudinal Study of Adolescent to Adult Health, we find that Black young adults who achieve high levels of education exhibit worse health across a majority of contexts relative to their White and Hispanic counterparts. Additionally, we observe more favorable health at higher levels of education across almost all contexts for White individuals. For all other racial/ethnic-nativity groups, the relationship between education and health depends on the characteristics of the early life counties of residence. Findings highlight place-based factors that may contribute to the development of racial/ethnic and nativity differences in the education-health relationship among U.S. young adults.

College reopening and community spread of COVID-19 in the United States.

OBJECTIVE: After months of lockdown due to the COVID-19 outbreak, the US postsecondary institutions implemented different instruction approaches to bring their students back for the Fall 2020 semester. Given public health concerns with reopening campuses, the study evaluated the impact of Fall 2020 college reopenings on COVID-19 transmission within the 632 US university counties. STUDY DESIGN: This was a retrospective and observational study. METHODS: Bayesian Structural Time Series (BSTS) models were conducted to investigate the county-level COVID-19 case increases during the first 21 days of Fall 2020. The case increase for each county was estimated by comparing the observed time series (actual daily cases after school reopening) to the BSTS counterfactual time series (predictive daily cases if not reopening during the same time frame). We then used multilevel models to examine the associations between opening approaches (in-person, online, and hybrid) and county-level COVID-19 case increases within 21 and 42 days after classes began. The multigroup comparison between mask and non-mask-required states for these associations were also performed, given that the statewide guidelines might moderate the effects of college opening approaches. RESULTS: More than 80% of our university county sample did not experience a significant case increase in Fall 2020. There were no significant relationships between opening approaches and community transmission in both mask-required and non-mask-required states. Only small metropolitan counties and counties with a non-community college or a higher percentage of student population showed significantly positive associations with the case number increase within the first 21-day period of Fall 2020. For the longer 42-day period, the counties with a higher percentage of the student population showed a significant case increase. CONCLUSION: The overall findings underscored the outcomes of US higher education reopening efforts when the vaccines were still under development in Fall 2020. For individual county results, we invite the college- and county-level decision-makers to interpret their results using our web application.

Factors associated with county-level mental health during the COVID-19 pandemic.

The objective of this study is to determine county-level factors associated with anxiety, depression, and isolation during the coronavirus disease 2019 (COVID-19) pandemic. This study used daily data from 23,592,355 respondents of a nationwide Facebook-based survey from April 2020 to July 2021, aggregated to the week-county level to yield 212,581 observations. Mental distress prevalences were modeled using weighted linear mixed-effects models with a county random effect. These models revealed that weekly percentages of mental distress were higher in counties with higher unemployment rates, populations, and education levels; higher percentages of females, young adults, individuals with a medical condition, and individuals very worried about their finances and COVID-19; and lower percentages of individuals who were working outside the home, living with children, without health insurance, and Black. Anxiety peaked in April 2020, depression in October 2020, and isolation in December 2020. Therefore, United States counties experienced the mental health effects of the pandemic differently dependent upon their characteristics, and mental distress prevalence varied across time.

Transitioning Economic Statuses of Appalachian Counties during COVID-19.

In the past decade, the Appalachian economy in the United States was scarcely discussed in the literature. No studies were devoted to local economic development after the outbreak of the Coronavirus Disease in 2019. This paper fills the literature gap by empirically examining how the Appalachian economy transitioned under the influence of the pandemic. Using county-level data from the Appalachian Regional Commission between 2019 and 2022, the study investigates how the Appalachian economy regressed during the pandemic. Transitioning economy indices were calculated for 420 local counties by comparing their composite index values before and after the outbreak of the pandemic. Regressions were run to estimate the influences of the unemployment rate, per capita income, and the poverty rate. During the pandemic, the unemployment rate consistently had the largest impact on the Appalachian counties' composite index value and the least effect on the poverty rate. The results suggest that the most effective strategy is for the government to reduce the local unemployment rate to improve the economic ranking. Supplementary Information: The online version contains supplementary material available at 10.1007/s11293-022-09749-2.

A spatial analysis of the effect of neighborhood contexts on cumulative number of confirmed cases of COVID-19 in U.S. Counties through October 20 2020.

COVID-19 has become a nationwide public health crisis in the United States and the number of COVID-19 cases is different by U.S. counties. Also, previous studies have reported that neighborhood contexts have an influence on health outcomes. Therefore, the objective of this study was to examine the association between neighborhood contexts and cumulative number of confirmed COVID-19 cases (per 100,000) in U.S. counties. Cumulative number of COVID-19 cases gained from USA FACTS and variables related to neighborhood contexts gained from the 2018 5-Year American Community Survey at the county level. Data were analyzed using spatial autoregressive models. According to the present results, firstly, larger population, high poverty rate, higher % of bachelor's degree, higher % of no health insurance, higher employment rate, higher % of manufacturing jobs, higher % of primary industry jobs, higher % of commute by drove alone, higher % of foreign born, higher % of Hispanic, and higher % of Black are positively associated with higher cumulative number of COVID-19 cases. Secondly, higher income, higher % of cash assistance recipient, higher % of SNAP recipient, higher unemployment rate, higher % of commute by walked, higher % of Asian, and higher % of senior citizen are negatively associated with higher cumulative number of COVID-19 cases. In conclusion, there exist geographical differences in cumulative number of COVID-19 cases in U.S. counties, which is influenced by various neighborhood contexts. Hence, these findings emphasize the need to take various neighborhood contexts into account when planning COVID-19 prevention.

Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study.

BACKGROUND: Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. METHODS: Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. RESULTS: Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. CONCLUSIONS: Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

Advanced baseline immunosuppression is associated with elevated levels of plasma markers of fungal translocation and inflammation in long-term treated HIV-infected Tanzanians.

BACKGROUND: For over a decade, antiretroviral therapy (ART) in resource-limited countries was only recommended for patients with advanced HIV disease. We investigated this group of patients in order to determine any relationship between degree of immunosuppression during treatment initiation and the subsequent levels of inflammatory biomarkers, reservoir size and plasma marker of fungal translocation after achieving long-term virological control. METHODS: We analyzed 115 virally suppressed (female 83.5%) and 40 untreated (female 70%) subjects from Dar es Salaam, Tanzania. The size of HIV latent reservoir (proviral DNA copy) was determined using quantitative PCR. Inflammatory biomarkers; IL-6, IL-10, and soluble CD14 (sCD14), were measured using multiplex cytometric beads array. Antibody titers for Cytomegalovirus (CMV) and Epstein Barr virus (EBV), plasma level of 1-3-beta-D-Glucan (BDG) was measured using ELISA. High-sensitivity C-reactive protein (hsCRP) was measured using nephelometric method. RESULTS: The median age was 36 (IQR 32-44) and 47 (IQR 43-54) years in untreated and virally suppressed patients respectively. Median duration of treatment for virally suppressed patients was 9 years (IQR 7-12) and median baseline CD4 count was 147 cells/mm3 (IQR 65-217). Virally suppressed patients were associated with significantly lower plasma levels of IL-10, sCD14 and BDG (P < 0.05) when compared to untreated patients. However, plasma level of IL-6 was similar between the groups. Baseline advanced level of immunosuppression (CD4 < 100cells/cm3) was associated with significantly higher plasma level of IL-6 (P = 0.02), hsCRP (P = 0.036) and BDG (P = 0.0107). This relationship was not seen in plasma levels of other tested markers. Degree of baseline immunosuppression was not associated with the subsequent proviral DNA copy. In addition, plasma levels of inflammatory marker were not associated with sex, CMV or EBV antibody titers, treatment duration or regimen. CONCLUSIONS: Our data suggest that advanced immunosuppression at ART initiation is associated with severity of inflammation and elevated fungal translocation marker despite long term virological control. Further studies are needed to evaluate the potential increased burden of non-AIDS comorbidities that are linked to elevated inflammatory and fungal translocation markers as a result of the policy of HIV treatment at CD4 count < 200 cells/cm3 implemented for over a decade in Tanzania.

A spatial epidemiology case study of mentally unhealthy days (MUDs): air pollution, community resilience, and sunlight perspectives.

The main objective of this spatial epidemiologic research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Mentally unhealthy days (MUDs) are studied in entire cross counties for year of 2014. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems.

Racial differences in 5-year relative survival rates of cervical cancer by stage at diagnosis, between African American (black) and white women, living in the state of Alabama, USA.

BACKGROUND: Our objective was to assess racial differences in the 5-year relative survival rates (RSRs) of Cervical Cancer (CerCancer) by stage at diagnosis, between Black and White women, living in Alabama, USA. METHODS: Data for 3484 Blacks and 21,059 Whites diagnosed with CerCancer were extracted from the 2004 to 2013 Surveillance, Epidemiology, and End Results (SEER) database. We incorporated age groups, CerCancer stages, county, and year of diagnosis to compare the RSR between Blacks and Whites, using SEER*Stat software. RESULTS: In urban, Black Belt (BB) and other rural counties, Whites diagnosed with localized stage of CerCancer always had better chances of survival because their RSRs were always more than 77%, compared to Blacks. Only exception was in Blacks living in other rural counties, who had a significantly higher RSR of 83.8% (95% Cl, 74.2-90.1). Which was the same as in Whites (83.8% (95% CI 74.5-89.9) living in BBC. Although, in other rural counties, Whites had a slightly lower RSR of 83.7% (95% CI 79.9-86.8%), their RSR was better compared to Blacks and Whites living in BB and other rural counties who had slightly higher RSRs of 83.8%. This was due to statistical precision, which depended on their larger sample size and a lower variability therefore, more reliability resulting in a tighter confidence interval with a smaller margin of error. In all the three county groups, Whites 15-44 years old diagnosed with localized stage of CerCancer had a higher RSR of 93.6% (95% CI 91.4-95.2%) for those living in urban and BB counties, and 94.6% (95% CI 93.6-95.4) for those living in other rural counties. The only exception was in Blacks 65-74 years old living in other rural counties who had the highest RSR of 96.9% (95% Cl, 82.9-99.5). However, Whites were considered to have a better RSR. This was also due to the statistical precision as mentioned above. CONCLUSION: There were significant racial differences in the RSRs of CerCancer. Overall, Black women experienced the worst RSRs compared to their White counterparts.

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

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

The effects of Medicaid expansion on labor market outcomes: Evidence from border counties.

This paper provides new empirical evidence on the employment and earning effects of the recent Medicaid expansion. Unlike most existing studies that use a conventional state and year fixed effects approach, our main identification strategy is based on the comparison of employment and wages in contiguous county-pairs in neighboring states (i.e., border counties) with different Medicaid expansion status. Using the 2008-2016 Quarterly Census of Employment and Wages, we estimate a set of distributed lag models in order to examine the dynamic effects of Medicaid expansion. Results from our preferred specification suggest a statistically significant decrease in total employment of 1.2% 1 year after the expansion of Medicaid. This translates into a 37% decrease in employment among newly eligible Medicaid enrollees under the strong assumption that Medicaid coverage did not crowd out private insurance coverage. We also find that this disemployment effect is transitory: 2 years after the Medicaid expansion employment returns to preexpansion levels. We do not find any statistically significant effect of the Medicaid expansion on wages at any point.

Mental health and related influencing factors among rural elderly in 14 poverty state counties of Chongqing, Southwest China: a cross-sectional study.

BACKGROUND: China has the largest elderly population in the world; little attention has been paid to the mental health of elderly in areas of extreme poverty. This is the first study to investigate the mental health of the rural elderly in poverty state counties in Chongqing and was part of the Chongqing 2018 health literacy promotion project. METHODS: In 2019, a cross-sectional study was conducted to investigate the mental health status of the rural elderly in fourteen poverty state counties of Chongqing, in which a total of 1400 elderly aged ≥ 65 years were interviewed, where mental health status was measured by the ten-item Kessler10 (K10) scale. Ordered multivariate logistic regression was performed to evaluate the influencing factors related to mental health of the elderly in these areas. RESULTS: The average score of K10 in 14 poverty state counties was 17.40 ± 6.31, 47.6% was labeled as good, 30.2% was moderate, 17.0% was poor, and lastly 5.1% was bad, and the mental health status of the elderly in the northeastern wing of Chongqing was better than the one in the southeastern wing of Chongqing. A worse self-rated health was the risk factor for mental health both in the northeastern and southeastern wings of Chongqing (all P < 0.001). Lower education level (OR (95% CI) = 1.45 (1.12-1.87), P = 0.004) was a risk factor in the northeastern wing, whereas older age (OR (95% CI) = 1.33 (1.13-1.56), P = 0.001) was a risk factors in the southeastern wing. CONCLUSIONS: The results showed that mental health of the elderly in poverty state counties was poor, especially in the southeastern wing of Chongqing. Particular attention needs to be paid to the males who were less educated, older, and single; female with lower annual per capital income; and especially the elderly with poor self-rated health.

[Assessing in-situ conservation status of threatened medicinal vascular plants in central China].

It is of great significance to carry out scientific investigation of threatened species and assess their in-situ conservation status in order to guide the conservation and management of these species within a region. In this study, we explored and assessed the biodiversity and in-situ conservation status of the threatened medicinal vascular plants(TMVPs) in central China. The results showed that there were 276 TMVPs in central China, including 18 critically endangered(CR), 77 endangered(EN), and 181 vulnerable(VU) species. Of which, 222 TMVPs were distributed in 49 national nature reserves, with an in-situ conservation rate of 80.43%. And the in-situ conservation rate of CR, EN and NR species were 83.33%, 77.92% and 81.22%, respectively. The complementary algorithm was used to select the nine national nature reserves with the highest protection and complementary contribution to the TMVPs. The cumulative protection contribution rate of these nine nature reserves was 81.98%. Fifty-four TMVPs were distributed outside of national nature reserves, while mainly distributed within 10 hotspot counties including Badong county, Hubei province, Sangzhi county, Hunan province, Jianghua Yao Autonomous county, Hunan province and so on. In general, TMVPs are well protected by the national nature reserves in central China. However, there are still some conservation gaps. It is necessary to carry out further field investigation on the species with conservation gaps and identify the gap areas, so as to provide scientific guidance for the optimization of the conservation of TMVPs in this region.

Using geographically weighted regression for social inequality analysis: association between mentally unhealthy days (MUDs) and socioeconomic status (SES) in U.S. counties.

This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.