Development and validation of the Michigan Chronic Disease Simulation Model (MICROSIM).

Strategies to prevent or delay Alzheimer's disease and related dementias (AD/ADRD) are urgently needed, and blood pressure (BP) management is a promising strategy. Yet the effects of different BP control strategies across the life course on AD/ADRD are unknown. Randomized trials may be infeasible due to prolonged follow-up and large sample sizes. Simulation analysis is a practical approach to estimating these effects using the best available existing data. However, existing simulation frameworks cannot estimate the effects of BP control on both dementia and cardiovascular disease. This manuscript describes the design principles, implementation details, and population-level validation of a novel population-health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP-COG) study of the effect of BP levels over the life course on dementia and cardiovascular disease. MICROSIM is an agent-based Monte Carlo simulation designed using computer programming best practices. MICROSIM estimates annual vascular risk factor levels and transition probabilities in all-cause dementia, stroke, myocardial infarction, and mortality in a nationally representative sample of US adults 18+ using the National Health and Nutrition Examination Survey (NHANES). MICROSIM models changes in risk factors over time, cognition and dementia using changes from a pooled dataset of individual participant data from 6 US prospective cardiovascular cohort studies. Cardiovascular risks were estimated using a widely used risk model and BP treatment effects were derived from meta-analyses of randomized trials. MICROSIM is an extensible, open-source framework designed to estimate the population-level impact of different BP management strategies and reproduces US population-level estimates of BP and other vascular risk factors levels, their change over time, and incident all-cause dementia, stroke, myocardial infarction, and mortality.

An Analysis of Social Determinants of Health and Their Implications for Hepatitis C Virus Treatment in People Who Inject Drugs: The Case of Baltimore.

Background: Sixty-eight percent of the nearly 3.5 million people living with hepatitis C virus (HCV) in the United States are people who inject drugs (PWID). Despite effective treatments, uptake remains low in PWID. We examined the social determinants of health (SDoH) that affect the HCV care cascade. Methods: We conducted a secondary analysis of data from 720 PWID in a cluster-randomized trial. We recruited PWID from 12 drug-affected areas in Baltimore. Inclusion criteria were injection in the prior month or needle sharing in the past 6 months. Intake data consisted of a survey and HCV testing. Focusing on SDoH, we analyzed self-report of (1) awareness of HCV infection (in those with active or previously cured HCV) and (2) prior HCV treatment (in the aware subgroup). We used descriptive statistics and logistic regression for statistical analyses. Results: The 342 participants were majority male and Black with a median age of 52 years. Women were more likely to be aware of their status but less likely to be treated. Having a primary care provider and HIV-positive status were associated with increased awareness and treatment. Unhoused people had 51% lower odds of HCV treatment. People who reported that other PWID had shared their HCV status with them had 2.3-fold higher odds of awareness of their own status. Conclusions: Further study of gender disparities in HCV treatment access is needed. Increased social support was associated with higher odds of HCV treatment, suggesting an area for future interventions. Strategies to identify and address SDoH are needed to end HCV.

Health Care Expenditures for Black and White US Adults Living Under Similar Conditions.

Importance: Evidence suggests that racial disparities in health outcomes disappear or diminish when Black and White adults in the US live under comparable living conditions; however, whether racial disparities in health care expenditures concomitantly disappear or diminish is unknown. Objective: To examine whether disparities in health care expenditures are minimized when Black and White US adults live in similar areas of racial composition and economic condition. Design, Setting, and Participants: This cross-sectional study used a nationally representative sample of 7062 non-Hispanic Black or White adults who live in 2238 of 2275 US census tracts with a 5% or greater Black population and who participated in the Medical Expenditure Panel Study (MEPS) in 2016. Differences in total health care expenditures and 6 specific categories of health care expenditures were assessed. Two-part regression models compared expenditures between Black and White adults living in the same Index of Concentration at the Extremes (ICE) quintile, a measure of racialized economic segregation. Estimated dollar amount differences in expenditures were calculated. All analyses were weighted to account for the complex sampling design of the MEPS. Data analysis was performed from December 1, 2019, to August 7, 2023. Exposure: Self-reported non-Hispanic Black or non-Hispanic White race. Main Outcomes and Measures: Presence and amount of patient out-of-pocket and insurance payments for annual total health care expenditures; office-based, outpatient, emergency department, inpatient hospital, or dental visits; and prescription medicines. ICE quintile 5 (Q5) reflected tracts that were mostly high income with mostly White individuals, whereas Q1 reflected tracts that were mostly low income with mostly Black individuals. Results: A total of 7062 MEPS respondents (mean [SD] age, 49 [18] years; 33.1% Black and 66.9% White; 56.1% female and 43.9% male) who lived in census tracts with a 5% or greater Black population in 2016 were studied. In Q5, Black adults had 56% reduced odds of having any health care expenditures (odds ratio, 0.44; 95% CI, 0.27-0.71) compared with White adults, at an estimated $2145 less per year, despite similar health status. Among those in Q5 with any expenditures, Black adults spent 30% less on care (cost ratio, 0.70; 95% CI, 0.56-0.86). In Q3 (most racially and economically integrated), differences in total annual health care spending were minimal ($79 annually; 95% CI, -$1187 to $1345). Conclusions and Relevance: In this cross-sectional study of Black and White adults in the US, health care expenditure disparities diminished or disappeared under conditions of both racial and economic equity and equitable health care access; in areas that were mostly high income and had mostly White residents, Black adults spent substantially less. Results underscore the continuing need to recognize place as a contributor to race-based differences in health care spending.

The Lives Lost to Inequities: Avertable Deaths From Neurologic Diseases in the Past Decade.

BACKGROUND AND OBJECTIVES: Mortality rates for neurologic diseases are increasing in the United States, with large disparities across geographical areas and populations. Racial and ethnic populations, notably the non-Hispanic (NH) Black population, experience higher mortality rates for many causes of death, but the magnitude of the disparities for neurologic diseases is unclear. The objectives of this study were to calculate mortality rates for neurologic diseases by race and ethnicity and-to place this disparity in perspective-to estimate how many US deaths would have been averted in the past decade if the NH Black population experienced the same mortality rates as other groups. METHODS: Mortality rates for deaths attributed to neurologic diseases, as defined by the International Classification of Diseases, were calculated for 2010 to 2019 using death and population data obtained from the Centers for Disease Control and Prevention and the US Census Bureau. Avertable deaths were calculated by indirect standardization: For each calendar year of the decade, age-specific death rates of NH White persons in 10 age groups were multiplied by the NH Black population in each age group. A secondary analysis used Hispanic and NH Asian populations as the reference groups. RESULTS: In 2013, overall age-adjusted mortality rates for neurologic diseases began increasing, with the NH Black population experiencing higher rates than NH White, NH American Indian and Alaska Native, Hispanic, and NH Asian populations (in decreasing order). Other populations with higher mortality rates for neurologic diseases included older adults, the male population, and adults older than 25 years without a high school diploma. The gap in mortality rates for neurologic diseases between the NH Black and NH White populations widened from 4.2 individuals per 100,000 in 2011 to 7.0 per 100,000 in 2019. Over 2010 to 2019, had the NH Black population experienced the neurologic mortality rates of NH White, Hispanic, or NH Asian populations, 29,986, 88,407, or 117,519 deaths, respectively, would have been averted. DISCUSSION: Death rates for neurologic diseases are increasing. Disproportionately higher neurologic mortality rates in the NH Black population are responsible for a large number of excess deaths, making research and policy efforts to address the systemic causes increasingly urgent.

The Economic Burden of Racial, Ethnic, and Educational Health Inequities in the US.

Importance: Health inequities exist for racial and ethnic minorities and persons with lower educational attainment due to differential exposure to economic, social, structural, and environmental health risks and limited access to health care. Objective: To estimate the economic burden of health inequities for racial and ethnic minority populations (American Indian and Alaska Native, Asian, Black, Latino, and Native Hawaiian and Other Pacific Islander) and adults 25 years and older with less than a 4-year college degree in the US. Outcomes include the sum of excess medical care expenditures, lost labor market productivity, and the value of excess premature death (younger than 78 years) by race and ethnicity and the highest level of educational attainment compared with health equity goals. Evidence Review: Analysis of 2016-2019 data from the Medical Expenditure Panel Survey (MEPS) and state-level Behavioral Risk Factor Surveillance System (BRFSS) and 2016-2018 mortality data from the National Vital Statistics System and 2018 IPUMS American Community Survey. There were 87 855 survey respondents to MEPS, 1 792 023 survey respondents to the BRFSS, and 8 416 203 death records from the National Vital Statistics System. Findings: In 2018, the estimated economic burden of racial and ethnic health inequities was $421 billion (using MEPS) or $451 billion (using BRFSS data) and the estimated burden of education-related health inequities was $940 billion (using MEPS) or $978 billion (using BRFSS). Most of the economic burden was attributable to the poor health of the Black population; however, the burden attributable to American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander populations was disproportionately greater than their share of the population. Most of the education-related economic burden was incurred by adults with a high school diploma or General Educational Development equivalency credential. However, adults with less than a high school diploma accounted for a disproportionate share of the burden. Although they make up only 9% of the population, they bore 26% of the costs. Conclusions and Relevance: The economic burden of racial and ethnic and educational health inequities is unacceptably high. Federal, state, and local policy makers should continue to invest resources to develop research, policies, and practices to eliminate health inequities in the US.

How Income Inequality and Race Concentrate Depression in Low-Income Women in the US; 2005-2016.

Aim: To estimate the association between income and depressive symptoms in adult women, ages 20 years and older. Methods: Data for this study came from the 2005-2016 National Health and Nutrition Examination Survey (NHANES). We measured the presence of depressive symptoms by using a 9-item PHQ (Public Health Questionnaire, PHQ-9) and the Poverty to Income Ratio (PIR) as a proxy for income. We employed Negative Binomial Regression (NBRG) and logistic regression models in a sample of 11,420 women. We adjusted models by age, racial/ethnic groups, marital status, education, health insurance, comorbidity, and utilization of mental health professionals. We calculated the Gini Coefficient (GC) as a measure of income inequality, using PIR. Results: Between 2005 and 2016, 20.1% of low-PIR women suffered from depression (PHQ ≥10) compared with 12.0% of women in medium-PIR and 5.0% in high-PIR. The highest probabilities of being depressed were in Black Non-Hispanics (BNH) and Hispanics (12.0%), and then in White NH (WNH; 9.1%). The results of NBRG have shown that women in medium-PIR (0.90 [CI: 0.84-0.97]) and high-PIR 0.76 (CI: 0.70-0.82) had a lower incidence-rate ratio than women in low-PIR. The logistic regression results showed that income is protective in High-PIR groups (OR = 0.56, CI [0.43-0.73]). Conclusion: Policies to treat depression should prioritize the needs of low-income women of all racial groups and women.

How place and race drive the numbers of fatal police shootings in the US: 2015-2020.

Place and race are two important predictors of fatal police shootings. We used Mapping Police Violence Data and the Washington Post Fatal Force Data to determine whether a county's deprivation status within communities influences the association between the number of fatal police shootings, and how the number of fatal police shootings differs by race and ethnicity. We categorized counties based on the Social Vulnerability Index (SVI) to three categories: low-, medium-, and high-SVI. The analytical sample included 3136 US counties between 2015 and 2020; during this time, 5525 individuals were fatally shot by police. Our findings show that place strongly impacts the number of fatal police shootings. Among all fatal shootings, 713 occurred in low-SVI counties, 1660 in middle-SVI, and 3152 in high-SVI counties. Race played a significant role; fatal shooting deaths increased by 2.3 times among White individuals, 9.6 times among Black individuals, and 15 times among Hispanic individuals between low- and high-SVI counties. The results of negative binomial regressions show a strong association between fatal police shootings and the counties' characteristics. In comparison with low-SVI counties, residents in counties with moderate and high-SVI are more likely to be fatally shot by police by 4.9 and 5.8 percentage points. In addressing violence and fatal police shootings, the vulnerability of counties and the population's racial composition play significant roles and need specific attention in addressing systemic racial disparities in the criminal justice system.

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models.

OBJECTIVE: Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias. MATERIALS AND METHODS: Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist. RESULTS: We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern. DISCUSSION: Our approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed. CONCLUSION: The potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.

Value of Triage Treatment Strategies to Distribute Hepatitis C Direct-Acting Antiviral Agents in an Integrated Healthcare System: A Cost-Effectiveness Analysis.

OBJECTIVES: This study aimed to assess the cost-effectiveness of fibrosis-based direct-acting antiviral treatment policies for patients with chronic hepatitis C virus at the Kaiser Permanente Mid-Atlantic States health system. METHODS: We used a Markov model to compare the lifetime costs and effects of treating patients with chronic hepatitis C virus at different stages of disease severity, or all stages simultaneously, based on a fibrosis score from the US healthcare sector perspective and societal perspective. The initial distribution of patients across fibrosis scores, the effectiveness of direct-acting antiviral therapy, and follow-up and monitoring protocols were specific to the Kaiser Permanente Mid-Atlantic States health system. Direct and indirect costs, transition probabilities, and utilities were derived from the literature. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of our results. RESULTS: The "Treat All" option was dominant from both the societal and healthcare sector perspectives. The conclusion was robust in deterministic sensitivity analysis. The range of incremental costs between the less restrictive policies was small-the difference between the "Treat F1+" and the "Treat All" option was only $111 per person. Probabilistic sensitivity analyses showed, at both the $100 000/quality-adjusted life-year and $150 000/quality-adjusted life-year thresholds, there was a 70% chance that the "Treat All" option was more cost-effective than the "Treat F1+" option. CONCLUSIONS: We found that expanded treatment access is cost-effective and, in many cases, cost saving. Although our results are primarily applicable to a regional integrated healthcare system, it offers some direction to any healthcare setting faced with resource constraints in the face of highly priced drugs.

How Income Inequality and Race/Ethnicity Drive Obesity in U.S. Adults: 1999-2016.

Obesity is a major public health problem both globally and within the U.S. It varies by multiple factors, including but not limited to income and sex. After controlling for potential covariates, there is little evidence to determine the association between income and obesity and how obesity may be moderated by sex and family income. We examined the association between income and obesity in U.S. adults aged 20 years and older, and tested whether this relationship differs by race or ethnicity groups. For this analysis, we used data from the 1999-2016 National Health and Nutrition Examination Surveys (NHANES). Obesity was determined using Body Mass Index ≥ 30 kg/m2; the Gini coefficient (GC) was calculated to measure income inequality using the Poverty Income Ratio (PIR). We categorized the PIR into five quintiles to examine the relationship between income inequality and obesity. For the first set of analyses, we used a modified Poisson regression in a sample of 36,665 adults, with an almost equal number of men and women (women's ratio was 50.6%), including 17,303 white non-Hispanics (WNH), 7475 black non-Hispanics (BNHs), and 6281 Mexican Americans. The models included age, racial/ethnic groups, marital status, education, health behaviors (smoking and drinking status and physical activities), health insurance coverage, self-reported health, and household structure (live alone and size of household). Adjusting for potential confounders, our findings showed that the association between PIR and obesity was positive and significant more frequently among WNH and BNH in middle and top PIR quintiles than among lower-PIR quintiles; this association was not significant in Mexican Americans (MAs). Results of GC in obese women showed that in comparison with WNHs (GC: 0.34, S.E.: 0.002), BNHs (GC: 0.38, S.E.: 0.004) and MAs (GC: 0.41, S.E.: 0.006) experienced higher income inequality, and that BNH obese men experienced the highest income inequality (GC: 0.45, S.E.: 0.011). The association between PIR and obesity was significant among WNHs and BNHs men in the 3rd, 4th and 5th PIR quintiles. The same association was not found for women. In treating obesity, policymakers should consider not only race/ethnicity and sex, but also strategies to reduce inequality in income.

Internet Access, Social Risk Factors, and Web-Based Social Support Seeking Behavior: Assessing Correlates of the "Digital Divide" Across Neighborhoods in The State of Maryland.

We aimed to empirically measure the degree to which there is a "digital divide" in terms of access to the internet at the small-area community level within the State of Maryland and the City of Baltimore and to assess the relationship and association of this divide with community-level SDOH risk factors, community-based social service agency location, and web-mediated support service seeking behavior. To assess the socio-economic characteristics of the neighborhoods across the state, we calculated the Area Deprivation Index (ADI) using the U.S. Census, American Community Survey (5-year estimates) of 2017. To assess the digital divide, at the community level, we used the Federal Communications Commission (FCC) data on the number of residential fixed Internet access service connections. We assessed the availability of and web-based access to community-based social service agencies using data provided by the "Aunt Bertha" information platform. We performed community and regional level descriptive and special analyses for ADI social risk factors, connectivity, and both the availability of and web-based searches for community-based social services. To help assess potential neighborhood linked factors associated with the rates of web-based social services searches by individuals in need, we applied logistic regression using generalized estimating equation modeling. Baltimore City contained more disadvantaged neighborhoods compared to other areas in Maryland. In Baltimore City, 20.3% of neighborhoods (defined by census block groups) were disadvantaged with ADI at the 90th percentile while only 6.6% of block groups across Maryland were in this disadvantaged category. Across the State, more than half of all census tracts had 801-1000 households (per 1000 households) with internet subscription. In contrast, in Baltimore City about half of all census tracts had only 401-600 of the households (per 1000 households) with internet subscriptions. Most block groups in Maryland and Baltimore City lacked access to social services facilities (61% of block groups at the 90th percentile of disadvantage in Maryland and 61.3% of block groups at the 90th percentile of disadvantage in Baltimore City). After adjusting for other variables, a 1% increase in the ADI measure of social disadvantage, resulting in a 1.7% increase in the number of individuals seeking social services. While more work is needed, our findings support the premise that the digital divide is closely associated with other SDOH factors. The policymakers must propose policies to address the digital divide on a national level and also in disadvantaged communities experiencing the digital divide in addition to other SDOH challenges.

Supplemental nutrition assistance program 2009 expansion and cardiometabolic markers among low-income adults.

A 2009 Supplemental Nutrition Assistance Program (SNAP) policy change that expanded eligibility and increased benefit amounts has been associated with reduced food insecurity. This study tests the hypothesis that the SNAP policy change corresponds with improved stress- and nutrition-sensitive cardiometabolic markers. This study included non-pregnant participants aged 18-59 with annual family incomes ≤185% of the federal poverty guideline from the repeated cross-sectional NHANES study. Those living in SNAP eligible households (income ≤130% of the poverty guideline) were compared to those who were likely non-eligible (income 131%-≤185%). Difference-in-differences analyses compared hemoglobin A1c (%), CRP (mg/dL), total cholesterol (mg/dL), LDL (mg/dL) and waist circumference (cm) across groups before (2007-2008) and after (2009-2010) the SNAP policy change. Sampling weights were applied. Adjusting for demographic, socioeconomic, household and health factors, there were statistically significant difference-in-differences estimates for hemoglobin A1c (p = 0.003, n = 3723) and total cholesterol (p = 0.028, n = 3710). SNAP eligible adults had no difference in hemoglobin A1c after the policy change and, among those less than 40 years of age, 5 mg/dL lower total cholesterol levels whereas likely non-SNAP eligible adults had 0.14% higher hemoglobin A1c and no difference in total cholesterol after the policy change. The 2009 SNAP expansion was associated with improved nutrition-sensitive cardiometabolic markers in SNAP-eligible adults. This study found less of an upward trend in hemoglobin A1c levels for young and middle aged adults and decreased total cholesterol for young adults. These results highlight the potential role of SNAP to prevent costly chronic conditions among low-income U.S. adults.

Race, ethnicity, poverty and the social determinants of the coronavirus divide: U.S. county-level disparities and risk factors.

BACKGROUND: Communities with more Black or Hispanic residents have higher coronavirus rates than communities with more White residents, but relevant community characteristics are underexplored. The purpose of this study was to investigate poverty-, race- and ethnic-based disparities and associated economic, housing, transit, population health and health care characteristics. METHODS: Six-month cumulative coronavirus incidence and mortality were examined using adjusted negative binomial models among all U.S. counties (n = 3142). County-level independent variables included percentages in poverty and within racial/ethnic groups (Black, Hispanic, Native American, Asian), and rates of unemployment, lacking a high school diploma, housing cost burden, single parent households, limited English proficiency, diabetes, obesity, smoking, uninsured, preventable hospitalizations, primary care physicians, hospitals, ICU beds and households that were crowded, in multi-unit buildings or without a vehicle. RESULTS: Counties with higher percentages of Black (IRR = 1.03, 95% CI: 1.02-1.03) or Hispanic (IRR = 1.02, 95% CI: 1.01-1.03) residents had more coronavirus cases. Counties with higher percentages of Black (IRR = 1.02, 95% CI: 1.02-1.03) or Native American (IRR = 1.02, 95% CI: 1.01-1.04) residents had more deaths. Higher rates of lacking a high school diploma was associated with higher counts of cases (IRR = 1.03, 95% CI: 1.01-1.05) and deaths (IRR = 1.04, 95% CI: 1.01-1.07). Higher percentages of multi-unit households were associated with higher (IRR = 1.02, 95% CI: 1.01-1.04) and unemployment with lower (IRR = 0.96, 95% CI: 0.94-0.98) incidence. Higher percentages of individuals with limited English proficiency (IRR = 1.09, 95% CI: 1.04-1.14) and households without a vehicle (IRR = 1.04, 95% CI: 1.01-1.07) were associated with more deaths. CONCLUSIONS: These results document differential pandemic impact in counties with more residents who are Black, Hispanic or Native American, highlighting the roles of residential racial segregation and other forms of discrimination. Factors including economic opportunities, occupational risk, public transit and housing conditions should be addressed in pandemic-related public health strategies to mitigate disparities across counties for the current pandemic and future population health events.

Evaluating Depressive Symptoms Among Low-Socioeconomic-Status African American Women Aged 40 to 75 Years With Uncontrolled Hypertension: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Depression is one of the leading causes of disability in the United States. African American women of low socioeconomic status with uncontrolled hypertension are at risk of having severe depressive symptoms, yet there is limited research about the mental health of this vulnerable population. Data from the Prime Time Sister Circles randomized clinical trial (PTSC-RCT) study can shed light on the prevalence of depressive symptoms among low-socioeconomic-status older African American women with hypertension. Objective: To determine the prevalence of depressive symptoms among low-socioeconomic-status African American women aged 40 to 75 years with uncontrolled hypertension who receive their care from a federally qualified health center (FQHC) and to identify risk factors associated with depressive symptoms. Design, Setting, and Participants: Cross-sectional analysis of data from the PTSC-RCT of depressive symptomology, measured using an adapted version of the 10-item Center for Epidemiological Studies Depression Scale Revised (CES-D-10). Descriptive statistics were used to characterize the study population. We used logistic regression models to investigate the factors associated with participants with or without symptoms of depression. We used baseline data from the PTSC-RCT study, including 316 African American English-speaking women between ages 40 and 75 years with hypertension (systolic blood pressure ≥140 mm Hg or diastolic ≥90 mm Hg), who received their primary care at a FQHC in Washington, DC, in 2017 and 2018 and were flagged by the FQHC as uncontrolled. Main Outcomes and Measures: We used the CES-D-10 from the Center for Epidemiologic Studies Depression Scale to measure presence of depressive symptoms. Results: A total of 57.0% of the women in the study (180 of 316) scored greater than or equal to 10 on the CES-D-10. Depressive symptoms had a negative association with a postsecondary education (adjusted odds ratio [aOR], 0.492; 95% CI, 0.249-0.968) and a positive association with the number of chronic conditions (aOR, 1.235; 95% CI, 1.046-1.460) and smoking (aOR, 1.731; 95% CI, 1.039-2.881). Conclusions and Relevance: In this study of low-income African American women with uncontrolled hypertension, more than half had symptoms of depression that was associated with less than high-school education, chronic conditions, and smoking. Low-income African American women with uncontrolled hypertension should be screened and adequately treated for depressive symptoms. Trial Registration: ClinicalTrials.gov Identifier: NCT04371614.

Decomposing Race and Ethnic Differences in CVD Risk Factors for Mid-life Women.

OBJECTIVE: This study decomposes race and ethnic differences in hypertension, waist circumference, obesity and allostatic load between black non-Hispanic (BNH), Mexican American (MA), and white non-Hispanic (WNH) women. DATA: This study uses 10,109 observations from The National Health and Nutrition Examination Survey from years 1999-2014 for BNH, MA women, and WNH between 40 and 75 years old. METHODOLOGY: We used the Oaxaca-Blinder decomposition to explore how demographic, socioeconomic, healthcare access, and health behavior factors are associated with race and ethnic differences in blood pressure, waist circumference, body mass index (BMI), and allostatic load score (ALS). RESULTS: We found that demographic factors, socioeconomic status, healthcare access, and health behaviors explained from 0 to 50% of the difference in CVD risk factors between BNH and WNH. However, these factors explain from 39 to 100% of the difference in CVD risk factors between MA and WNH. Differences in demographic, socioeconomic, access to care, and health behavior factor variables explained very little of the differences in CVD risk factors between NHB and MA women. CONCLUSION: The impact of the determinants on CVD risk factors varies by race and ethnicity. Efforts to address differences in CVD risk factors should promote health equity programs and acknowledge that even race and ethnic groups that have similar demographic, SES, access to care, and health behavior factors can have different outcomes.

The Supply of Hospital Care to Minority and Low-Income Communities and the Hospital Readmission Reduction Program.

To determine if the Centers for Medicare and Medicaid Services Hospital Readmission Reduction Program reduced hospital discharges for penalized conditions in minority and low-income communities, we used hospital discharge data for 2006 and 2013 from Arizona, California, Colorado, Florida, New Jersey, New York, North Carolina, and Wisconsin and readmission data from the Medicare Hospital Compare website. Negative binomial regression was used for 6,564 zip codes for each year to estimate the association between the expected penalty for an excess readmission in the hospital service area and the number of hospital discharges for penalized conditions (acute myocardial infarction, congestive heart failure, and pneumonia) for zip codes. The results showed that the expected penalty for excess readmissions had a negative association with the number of discharges for acute myocardial infarction, congestive heart failure, and pneumonia. The negative association increased with the percentage of minority residents but not with the poverty rate.

Geographic disparities in COVID-19 infections and deaths: The role of transportation.

The US government imposed two travel restriction policies to prevent the spread of the COVID-19 but may have funneled asymptomatic air travelers to selected major airports and transportation hubs. Using the most recent JHU COVID-19 database, American Community Survey, Airport and Amtrak data form Bureau of Transportation Statistics from 3132 US counties we ran negative binomial regressions and Cox regression models to explore the associations between COVID-19 cases and death rates and proximity to airports, train stations, and public transportation. Counties within 25 miles of an airport had 1.392 times the rate of COVID-19 cases and 1.545 times the rate of COVID-19 deaths in comparison to counties that are more than 50 miles from an airport. More effective policies to detect and isolate infected travelers are needed. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to protect travelers and transportation workers from COVID-19.

Potential Role of Cost and Quality of Life in Treatment Decisions for Arthritis-Related Knee Pain in African American and Latina Women.

OBJECTIVE: The present study was undertaken to investigate whether Latina and African American women with arthritis-related knee pain and primary care providers who treat them believe their treatment decisions would benefit from having more information about the impact of treatment on their quality of life, medical care costs, and work productivity. METHODS: We conducted 4 focus groups of Latina and African American women over age 45 years who had knee pain. We also conducted 2 focus groups with primary care providers who treated Latina and African American women for knee pain. The participants were recruited from the community. They were asked their opinions about a decision tool that presented information on a range of treatment options and their impacts on quality of life, medical care costs, and work productivity. They were asked whether providing this information would help them make better treatment decisions. We analyzed the focus group transcripts using ATLAS.ti. RESULTS: We found that minority women and primary care providers endorsed the use of a decision-making tool that provided information of the impact of treatment on quality of life, medical care costs, and work productivity. Providers felt that patients would benefit from having the additional information but were concerned about its complexity and some patients' ability to comprehend the information. CONCLUSION: Latina and African American women could make more informed treatment decisions for their knee pain using a decision-making tool that provides them with significant information about how various treatment options may impact their quality of life, medical care costs, and workforce productivity.

Health Care Disparities in Race-Ethnic Minority Communities and Populations: Does the Availability of Health Care Providers Play a Role?

OBJECTIVES: To examine disparities in use and access to different health care providers by community and individual race-ethnicity and to test provider supply as a potential mediator. DATA SOURCES: National secondary data from 2014 Medical Expenditure Panel Survey, 5-year estimates (2010-2014) from American Community Survey, and 2014 InfoUSA. STUDY DESIGN: Multiple logistic regression models examined the association of community and individual race-ethnicity with reported health care visits and access. Mediation analyses tested the role of provider supply. DATA EXTRACTION METHODS: Individual-level survey data were linked to race-ethnic composition and health business counts of the respondent's primary care service area (PCSA). PRINCIPAL FINDINGS: Minority PCSAs are significantly and independently associated with lower odds of having a visit to a physician assistant/nurse practitioner, dentist, or other health professionals and having a usual care provider (all p < 0.05). Few significant associations were observed for integrated PCSAs or for health provider supply. A modest mediation effect for provider supply was observed for travel time to usual care provider and visit to other health professionals. CONCLUSIONS: Use of a range of health services is lower in minority communities and individuals. However, provider supply was not an important explanatory factor of these disparities.