Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.

Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties.

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.

Percentage of Black Population and Primary Care Shortage Areas Associated with Higher COVID-19 Case and Death Rates in Georgia Counties.

OBJECTIVES: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional shortage areas (PCHPSAs) would be associated with higher COVID-19 death rates, and the lack of primary care access would exacerbate racial disparities in death rates. We undertook this study to test these hypotheses and discern the independent effects of racial composition, socioecologic characteristics, and healthcare system factors on COVID-19 cases and deaths in Georgia counties. METHODS: We used county-level COVID-19 cases and deaths on April 23, 2020 from the Johns Hopkins Coronavirus Resource Center and estimates of 2019 county-level populations from the US Census Bureau to calculate the cumulative event rates for the state of Georgia. We used multiple regression models to examine crude and adjusted associations of socioecologic and health system variables with county-level COVID-19 case and mortality rates. RESULTS: After adjustment, a 1% increase in the proportion of Black people in the county resulted in a 2.3% increase in the county COVID-19 confirmed case rate and a 3.0% increase in the death rate (relative risk 1.03, 95% confidence interval 1.01-1.05, P < 0.001). Primary care shortage areas had a 74% higher death rate (relative risk 1.74, 95% confidence interval 1.00-3.00, P = 0.049). CONCLUSIONS: These results highlight the impact of racial disparities on the spatial patterns of COVID-19 disease burden in Georgia, which can guide interventions to mitigate racial disparities. The results also support the need for robust primary care infrastructure throughout the state.

Identification of Resilient and At-Risk Neighborhoods for Cardiovascular Disease Among Black Residents: the Morehouse-Emory Cardiovascular (MECA) Center for Health Equity Study.

INTRODUCTION: Despite the growing interest in place as a determinant of health, areas that promote rather than reduce cardiovascular disease (CVD) in blacks are understudied. We performed an ecologic analysis to identify areas with high levels of CVD resilience and risk among blacks from a large southern, US metropolitan area. METHODS: We obtained census tract-level rates of cardiovascular deaths, emergency department (ED) visits, and hospitalizations for black adults aged 35 to 64 from 2010 through 2014 for the Atlanta, Georgia, metropolitan area. Census tracts with substantially lower rates of cardiovascular events on the basis of neighborhood socioeconomic status were identified as resilient and those with higher rates were identified as at risk. Logistic regression was used to estimate the odds ratios (OR) and 95% confidence intervals (CIs) of being classified as an at-risk versus resilient tract for differences in census-derived measures. RESULTS: We identified 106 resilient and 121 at-risk census tracts, which differed in the rates per 5,000 person years of cardiovascular outcomes (mortality, 8.13 vs 13.81; ED visits, 32.25 vs 146.3; hospitalizations, 26.69 vs 130.0), despite similarities in their median black income ($46,123 vs $45,306). Tracts with a higher percentage of residents aged 65 or older (odds ratio [OR], 2.29; 95% CI, 1.41-3.85 per 5% increment) and those with incomes less than 200% of the federal poverty level (OR, 1.19; 95% CI, 1.02-1.39 per 5% increment) and greater Gini index (OR, 1.56; 95% CI, 1.19- 2.07 per 0.05 increment) were more likely to be classified as at risk than resilient neighborhoods. DISCUSSION: Despite matching on median income level, at-risk neighborhoods for CVD among black populations were associated with a higher prevalence of socioeconomic indicators of inequality than resilient neighborhoods.

The shape of health to come: prospective study of the determinants of 30-year health trajectories in the Alameda County Study.

BACKGROUND: Considerable evidence now exists indicating that incidence and progression of disease and disability are associated with socio-economic, behavioural, demographic and psychosocial factors. The emerging life course perspective suggests that these factors might be associated with not just the incidence of disease and death, but also trajectories of health over decades. METHODS: Prospective study of a representative, population-based cohort studied on four occasions over 30 years. Trajectories of health over this period, combining self-rated health and date of death, were related to behavioural, psychosocial and socio-economic risk factors. RESULTS: Trajectories of health were associated with behavioural, socioeconomic, and psychosocial risk factors, the strongest predictors being household income and physical activity. Those with an income 1 SD above the mean were approximately 25% more likely to die having previously consistently reported excellent health (Men: relative risk (RR) = 1.27, 95% CI = 1.10-1.46; Women: RR = 1.25, 95% CI = 1.06-1.47), were more likely to remain 'Alive in Excellent Health' (Men: RR = 1.35, 95% CI = 1.21-1.51; Women: RR = 1.30, 95% CI = 1.19-1.43) and were less likely to have shown an 'Unremitting Decline' (Men: RR = 0.72, 95% CI = 0.49-1.05; Women: RR = 0.71, 95% CI = 0.48-1.04). Those with low physical activity were approximately 50% less likely to die having consistently reported excellent health (Men: RR = 0.54, 95% CI = 0.39-0.76; Women: RR = 0.48, 95% CI = 0.33-0.71), and were five-times more likely to show an 'Unremitting Decline' (Men: RR = 5.05, 95% CI = 1.75-14.56; Women: RR = 5.00, 95% CI = 1.48-16.92). They were also less likely to be 'Alive in Excellent Health' (Men: RR = 0.41, 95% CI = 0.29-0.57; Women: RR = 0.44, 95% CI = 0.33-0.57). CONCLUSIONS: The burden of illness associated with behavioural, socio-economic and psychosocial risk factors extends beyond shortening of life to poorer trajectories of health over decades.

Race/ethnicity, life-course socioeconomic position, and body weight trajectories over 34 years: the Alameda County Study.

OBJECTIVES: We investigated whether race differences in weight gain over 34 years were because of socioeconomic position (SEP) and psychosocial and behavioral factors (physical activity, cigarette smoking, alcohol consumption, depression, marital status, number of children). We used a life-course approach to SEP with 4 measures of SEP (childhood SEP, education, occupation, income) and a cumulative measure of SEP. METHODS: We used mixed models and data collected from the Alameda County Study to examine the association between race and weight change slopes and baseline weight in men (n=1186) and women (n=1375) aged 17 to 40 years at baseline (in 1965). RESULTS: All subjects gained weight over time. African American women weighed 4.96 kg (P < .001) more at baseline and gained 0.10 kg/year (P = .043) more weight than White women. Black men weighed 2.41 kg (P = .006) more at baseline but did not gain more weight than White men. The association of race with weight gain in women was largely because of cumulative SEP score. CONCLUSIONS: Interventions to prevent overweight and obesity should begin early in life and target the socioeconomically disadvantaged.