Effects of residential socioeconomic polarization on high blood pressure among nursing home residents.

OBJECTIVE: Neighborhood concentration of racial, income, education, and housing deprivation is known to be associated with higher rates of hypertension. The objective of this study is to examine the association between tract-level spatial social polarization and hypertension in a cohort with relatively equal access to health care, a Veterans Affairs nursing home. METHODS: 41,973 long-term care residents aged ≥65 years were matched with tract-level Indices of Concentration at the Extremes across four socioeconomic domains. We modeled high blood pressure against these indices controlling for individual-level cardiovascular confounders. RESULTS: We found participants who had resided in the most disadvantaged quintile had a 1.10 (95% 1.01, 1.19) relative risk of high blood pressure compared to those in the other quintiles for the joint measuring race/ethnicity and income domain. CONCLUSIONS: We achieved our objective by demonstrating that concentrated deprivation is associated with worse cardiovascular outcomes even in a population with equal access to care. Measures that jointly consider economic and racial/ethnic polarization elucidate larger disparities than single domain measures.

Telemedicine Utilization Among Patients With Multiple Sclerosis in the US Veterans Health Administration, 2010-2020.

Background and Objectives: Telemedicine may help to bridge the specialty care access gap for patients with MS (PwMS) restricted by distance or disability. The objective of this study is to assess the frequency and longitudinal patterns of telemedicine utilization among PwMS and controls. Methods: We conducted a population-based nested case-control study within the Veterans Health Administration (VHA) from January 1, 2010, to December 31, 2020. MS cases and controls were individually matched on sex, age, and Veterans Affair (VA) service region (Veterans Integrated Service Network). MS case and control participants sample sizes by year included 2010: 19,387/86,379; 2016: 19,752/88,535; and 2020: 16,451/78,315. Telemedicine encounter codes were used to identify telemedicine utilization among MS cases and controls in the VHA for 2010, 2016, and 2020. Telemedicine encounters were categorized according to mode (video, phone, and supplementary remote) and type of care provided. Results: Patients in the VHA have had increasing utilization of telemedicine over the past decade. Among PwMS, mean telemedicine encounters increased steadily from 2010 to 2020 (5.6-10.5 encounters/patient, respectively). Across all years, MS cases were significantly more likely than controls to use telemedicine. The odds ratios (ORs) (95% confidence interval [CI]) of any telemedicine utilization comparing MS cases with controls in 2010, 2016, and 2020, respectively, were 1.5 (CI: 1.3-1.5), 1.9 (CI: 1.8-2.0), and 1.7 (CI: 1.6-1.8). Compared with non-Hispanic White veterans, non-Hispanic Black veterans were more likely to use telemedicine (adjusted OR = 1.5; [CI: 1.40-1.60]). The most common and least common modes of telemedicine among cases and controls were telephone and supplementary remote, respectively. Comparing 2010 with 2020, the largest increases in telemedicine utilization exhibited among MS cases were among primary care, specialty care, specialty neurology care, and other types of health care. States with the highest increases in telemedicine utilization were AL, CA, CO, FL, GA, KS, IL, NY, and SC. PwMS tended to live in counties with more adverse social determinants of health compared with controls. Discussion: PwMS were significantly more likely to use telemedicine than their matched controls. There were significant increases in telemedicine utilization between 2010 and 2020. The VHA has a robust telemedicine system of care that has grown to supplement in person care more so than other US health care systems. Future work is needed to assess the determinants of telemedicine utilization.

Cardiovascular Diseases, Medications, and ALS: A Population-Based Case-Control Study.

INTRODUCTION: We investigated the associations between antecedent all-cause CVD diagnoses, cause-specific CVD diagnosis, and CVD medication prescriptions with the risk of developing amyotrophic lateral sclerosis (ALS). MATERIALS AND METHODS: We conducted a population-based case-control study of U.S. Medicare enrollees from 2006 to 2013. The final sample included 3,714 incident ALS cases and 18,570 controls (matched on age, sex, enrollment length, and county). Information was collected from Medicare Parts A, B, and D administrative claims data on hypertension, ischemic heart disease, heart failure, acute myocardial infarction, atrial fibrillation, prescriptions of angiotensin-converting enzyme inhibitors, angiotensin II receptors blockers, calcium channel blockers, beta blockers, and antiarrhythmics. Associations were evaluated using conditional logistic regression adjusting for age, sex, race/ethnicity, geographical location, alcohol and tobacco use, and socioeconomic status. RESULTS: The odds ratio (OR) for having one or more ICD-9 codes for any cardiovascular disease diagnosis at least 24 months prior to the date of ALS diagnosis was 0.85 (95% confidence interval [CI]: 0.78-0.92). Cardiovascular conditions that were inversely associated with ALS included heart failure (OR = 0.79; 95% CI 0.70-0.89), atrial fibrillation (OR = 0.81; 95% CI 0.77-0.92), and hypertension (OR = 0.91; 95% CI 0.84-0.98). Exposures to several classes of cardiovascular medications were inversely associated with ALS risk even after adjusting for confounding by indication, including ACE inhibitors (OR = 0.84, 95% CI 0.77-0.91), calcium channel blockers (OR = 0.64, 95% CI 0.59-0.70), and beta blockers (OR = 0.76, 95% CI 0.71-0.83). DISCUSSION/CONCLUSION: These findings merit additional research, including animal studies and pilot clinical trials, to further evaluate and evidence the effects of ACEIs, CCBs, and BBs on the risk of developing and clinical expression of ALS.

Increasing Representation of Epidemiologists From Around the World in the Society for Epidemiologic Research: The Case of Mexico.

Mexico has a population of 129 million and is considered one of the most unequal countries in the world, suffering from widespread health disparities. There is a pressing need to strengthen epidemiologic capacity in Mexico, to help solve the complex health problems the country faces and to reduce health inequities. However, the representation of Mexican epidemiologists in the largest epidemiologic society in North America is low, despite the short distance to the United States. In this commentary, we discuss the barriers to higher representation of Mexican epidemiologists within the Society for Epidemiologic Research (SER), including language barriers, costs, and regional necessities. We also discuss opportunities to expand Mexican SER representation and collaboration. Overall, we hope that this is a call towards expanding SER global participation and starting a conversation on a common agenda for epidemiologic research.

Creation and validation of a polysocial score for mortality among community-dwelling older adults in the USA: the health and retirement study.

BACKGROUND: the interrelatedness between social determinants of health impedes researchers to identify important social factors for health investment. A new approach is needed to quantify the aggregate effect of social factors and develop person- centred social interventions. METHODS: participants ([n = 7,383], 54.5% female) were aged 65 years or above who complete an additional psychosocial questionnaire in the health and retirement study in 2006 or 2008. Social determinants of health encompassed five social domains: economic stability, neighbourhood and physical environment, education, community and social context, and healthcare system. We used the forward stepwise logistic regression to derive a polysocial score model for 5-year mortality. Indices of goodness-of-fit, discrimination and reclassification were used to assess model performance. We used logistic regression to identify the association between polysocial score and mortality. Subgroup analyses were conducted to examine sex- and race-specific association. RESULTS: polysocial score was created using 14 social determinants of health. In the training cohort, the C-statistic was 0.71 for the reference model (only age, sex and race/ethnicity) and increased to 0.75 for the continuous and categorical polysocial score. Compared with the reference model, the integrated discrimination index for adding the continuous or categorical polysocial score was both 0.03 (P values < 0.001). Participants with an intermediate (odds ratio [OR] = 0.69; 95% confidence interval [CI], 0.51-0.82) or high (OR = 0.48; 95% CI, 0.38-0.60) polysocial score had lower odds of death than those in the low category in the fully adjusted model, respectively. CONCLUSIONS: the polysocial approach may offer possible solutions to monitor social environments and suggestions for older people to improve their social status for specific health outcomes.

Association Between Income Inequality and County-Level COVID-19 Cases and Deaths in the US.

Importance: Socioeconomically marginalized communities have been disproportionately affected by the COVID-19 pandemic. Income inequality may be a risk factor for SARS-CoV-2 infection and death from COVID-19. Objective: To evaluate the association between county-level income inequality and COVID-19 cases and deaths from March 2020 through February 2021 in bimonthly time epochs. Design, Setting, and Participants: This ecological cohort study used longitudinal data on county-level COVID-19 cases and deaths from March 1, 2020, through February 28, 2021, in 3220 counties from all 50 states, Puerto Rico, and the District of Columbia. Main Outcomes and Measures: County-level daily COVID-19 case and death data from March 1, 2020, through February 28, 2021, were extracted from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University in Baltimore, Maryland. Exposure: The Gini coefficient, a measure of unequal income distribution (presented as a value between 0 and 1, where 0 represents a perfectly equal geographical region where all income is equally shared and 1 represents a perfectly unequal society where all income is earned by 1 individual), and other county-level data were obtained primarily from the 2014 to 2018 American Community Survey 5-year estimates. Covariates included median proportions of poverty, age, race/ethnicity, crowding given by occupancy per room, urbanicity and rurality, educational level, number of physicians per 100 000 individuals, state, and mask use at the county level. Results: As of February 28, 2021, on average, each county recorded a median of 8891 cases of COVID-19 per 100 000 individuals (interquartile range, 6935-10 666 cases per 100 000 individuals) and 156 deaths per 100 000 individuals (interquartile range, 94-228 deaths per 100 000 individuals). The median county-level Gini coefficient was 0.44 (interquartile range, 0.42-0.47). There was a positive correlation between Gini coefficients and county-level COVID-19 cases (Spearman ρ = 0.052; P < .001) and deaths (Spearman ρ = 0.134; P < .001) during the study period. This association varied over time; each 0.05-unit increase in Gini coefficient was associated with an adjusted relative risk of COVID-19 deaths: 1.25 (95% CI, 1.17-1.33) in March and April 2020, 1.20 (95% CI, 1.13-1.28) in May and June 2020, 1.46 (95% CI, 1.37-1.55) in July and August 2020, 1.04 (95% CI, 0.98-1.10) in September and October 2020, 0.76 (95% CI, 0.72-0.81) in November and December 2020, and 1.02 (95% CI, 0.96-1.07) in January and February 2021 (P < .001 for interaction). The adjusted association of the Gini coefficient with COVID-19 cases also reached a peak in July and August 2020 (relative risk, 1.28 [95% CI, 1.22-1.33]). Conclusions and Relevance: This study suggests that income inequality within US counties was associated with more cases and deaths due to COVID-19 in the summer months of 2020. The COVID-19 pandemic has highlighted the vast disparities that exist in health outcomes owing to income inequality in the US. Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality.

Disentangling individual, school, and neighborhood effects on screen time among adolescents and young adults in the United States.

OBJECTIVES: To examine the association between individual, neighborhood, and school-level influences on individual screen time among adolescents and young adults (AYAs) in the National Longitudinal Study of Adolescent to Adult Health. METHODS: We classified screen time continuously as self-reported total hours per week of television, videos, and video/computer games at baseline and categorical as extended screen time (≥14 h per week). We fit cross-classified multilevel models (CCMM) to examine to examine the individual-, school- and neighborhood-level demographic and socioeconomic factors associated with screen time. Models were fit using MLwiN with Bayesian estimation procedures. RESULTS: AYAs reported an average of 22.8 (SD = 19.4) and 21.9 (SD = 20.3) hours of screen time, respectively. At the individual level, younger age, male sex, Black/multiracial race, receipt of public assistance, and lower parental education were associated with higher screen time. At the school level, being out of session (i.e., school and national holidays including summer), having a higher proportion of non-White students, and having a lower proportion of parents with a college education were associated with higher individual screen time. CONCLUSIONS: We found that individual-level factors most influence youth screen time, with smaller contributions from school factors.