Screening for Financial Hardship: Comparing Patient Survey Responses Using Two Different Screening Tools.

BACKGROUND: Healthcare delivery organizations are increasingly screening patients for social risks using tools that vary in content and length. OBJECTIVES: To compare two screening tools both containing questions related to financial hardship. DESIGN: Cross-sectional survey. PARTICIPANTS: Convenience sample of adult patients (n = 471) in three primary care clinics. MAIN MEASURES: Participants randomly assigned to self-complete either: (1) a screening tool developed by the Centers for Medicare & Medicaid Services (CMS) consisting of six questions on financial hardship (housing stability, housing quality, food security, transportation security, utilities security); or (2) social and behavioral risk measures recommended by the National Academy of Medicine (NAM), including one question on financial hardship (financial strain). We compared patient acceptability of screening, positive screening rates for financial hardship, patient interest in assistance, and self-rated health. RESULTS: Ninety-one percent of eligible/interested patients completed the relevant survey questions to be included in the study (N = 471/516). Patient acceptability was high for both tools, though more participants reported screening was appropriate when answering the CMS versus NAM questions (87% vs. 79%, p = 0.02). Of respondents completing the CMS tool, 57% (132/232) reported at least one type of financial hardship; on the NAM survey, 52% (125/239) reported financial hardship (p = 0.36). Nearly twice as many respondents indicated interest in assistance related to financial hardship after completing items on the CMS tool than on the NAM question (39% vs. 21%, p < 0.01). CONCLUSIONS: Patients reported high acceptability of both social risk assessment tools. While rates of positive screens for financial hardship were similar across the two measures, more patients indicated interest in assistance after answering questions about financial hardship on the CMS tool. This might be because the screening questions on the CMS tool help patients to appreciate the types of assistance related to financial hardship that may be available after screening. Future research should assess the validity and comparative validity of individual measures and measure sets. Tool selection should be based on setting and population served, screening goals, and resources available.

Predicting Self-Rated Health Across the Life Course: Health Equity Insights from Machine Learning Models.

BACKGROUND: Self-rated health is a strong predictor of mortality and morbidity. Machine learning techniques may provide insights into which of the multifaceted contributors to self-rated health are key drivers in diverse groups. OBJECTIVE: We used machine learning algorithms to predict self-rated health in diverse groups in the Behavioral Risk Factor Surveillance System (BRFSS), to understand how machine learning algorithms might be used explicitly to examine drivers of self-rated health in diverse populations. DESIGN: We applied three common machine learning algorithms to predict self-rated health in the 2017 BRFSS survey, stratified by age, race/ethnicity, and sex. We replicated our process in the 2016 BRFSS survey. PARTICIPANTS: We analyzed data from 449,492 adult participants of the 2017 BRFSS survey. MAIN MEASURES: We examined area under the curve (AUC) statistics to examine model fit within each group. We used traditional logistic regression to predict self-rated health associated with features identified by machine learning models. KEY RESULTS: Each algorithm, regularized logistic regression (AUC: 0.81), random forest (AUC: 0.80), and support vector machine (AUC: 0.81), provided good model fit in the BRFSS. Predictors of self-rated health were similar by sex and race/ethnicity but differed by age. Socioeconomic features were prominent predictors of self-rated health in mid-life age groups. Income [OR: 1.70 (95% CI: 1.62-1.80)], education [OR: 2.02 (95% CI: 1.89, 2.16)], physical activity [OR: 1.52 (95% CI: 1.46-1.58)], depression [OR: 0.66 (95% CI: 0.63-0.68)], difficulty concentrating [OR: 0.62 (95% CI: 0.58-0.66)], and hypertension [OR: 0.59 (95% CI: 0.57-0.61)] all predicted the odds of excellent or very good self-rated health. CONCLUSIONS: Our analysis of BRFSS data show social determinants of health are prominent predictors of self-rated health in mid-life. Our work may demonstrate promising practices for using machine learning to advance health equity.

Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care.

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

Structural inequities in seasonal influenza vaccination rates.

BACKGROUND: Influenza immunization is a highly effective method of reducing illness, hospitalization and mortality from this disease. However, influenza vaccination rates in the U.S. remain below public health targets and persistent structural inequities reduce the likelihood that Black, American Indian and Alaska Native, Latina/o, Asian groups, and populations of low socioeconomic status will receive the influenza vaccine. METHODS: We analyzed correlates of influenza vaccination rates using the 2019 Behavioral Risk Factor Surveillance System (BRFSS) in the year 2020. Our analysis compared influenza vaccination as the outcome of interest with the variables age, sex, race, education, income, geographic location, health insurance status, access to primary care, history of delaying care due to cost, and comorbidities such as: asthma, cardiovascular disease, hypertension, body mass index, cancer and diabetes. RESULTS: Non-Hispanic White (46.5%) and Asian (44.1%) participants are more likely to receive the influenza vaccine compared to Non-Hispanic Black (36.7%), Hispanic (33.9%), American Indian/Alaskan Native (36.6%), and Native Hawaiian/Other Pacific Islander (37.9%) participants. We found persistent structural inequities that predict influenza vaccination, within and across racial and ethnic groups, including not having health insurance [OR: 0.51 (0.47-0.55)], not having regular access to primary care [OR: 0.50 (0.48-0.52)], and the need to delay medical care due to cost [OR: 0.75 (0.71-0.79)]. CONCLUSION: As COVID-19 vaccination efforts evolve, it is important for physicians and policymakers to identify the structural impediments to equitable U.S. influenza vaccination so that future vaccination campaigns are not impeded by these barriers to immunization.

Geographic Variation in Obesity at the State Level in the All of Us Research Program.

INTRODUCTION: National obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence. METHODS: To estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants. RESULTS: Data on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants. CONCLUSION: All of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

Pervasive Discrimination and Allostatic Load in African American and White Adults.

OBJECTIVE: This study aimed to examine associations among race, the accumulation of multiple forms of discriminatory experiences (i.e., "pervasive discrimination"), and allostatic load (AL) in African Americans and whites in midlife. METHODS: Using data collected in 2004 to 2006 from 226 African American and 978 white adults (57% female; mean [SD] age = 54.7 [0.11] years) in the Midlife in the United States II Biomarker Project, a pervasive discrimination score was created by combining three discrimination scales, and an AL score was created based on 24 biomarkers representing seven physiological systems. Linear regression models were conducted to examine the association between pervasive discrimination and AL, adjusting for demographics and medical, behavioral, and personality covariates. A race by pervasive discrimination interaction was also examined to determine whether associations varied by race. RESULTS: African Americans had higher pervasive discrimination and AL scores than did whites. In models adjusted for demographics, socioeconomic status, medications, health behaviors, neuroticism, and negative affect, a pervasive discrimination score of 2 versus 0 was associated with a greater AL score (b = 0.30, SE = 0.07, p < .001). Although associations seemed to be stronger among African Americans as compared with whites, associations did not statistically differ by race. CONCLUSIONS: More pervasive discrimination was related to greater multisystemic physiological dysregulation in a cohort of African American and white adults. Measuring discrimination by combining multiple forms of discriminatory experiences may be important for studying the health effects of discrimination.

Income Inequities and Medicaid Expansion are Related to Racial and Ethnic Disparities in Delayed or Forgone Care Due to Cost.

BACKGROUND: Monitoring political and social determinants of delayed or forgone care due to cost is necessary to evaluate efforts to reduce racial and ethnic disparities in access to care. Our objective was to examine the extent to which state Medicaid expansion decisions and personal household income may be associated with individual-level racial and ethnic disparities in delayed or forgone care due to cost, at baseline, before the implementation of the Affordable Care Act. METHODS: We used 2012 Behavioral Risk Factor Surveillance System survey data to examine racial and ethnic differences in delayed or forgone care due to cost in states that do and do not plan Medicaid expansion. We examined personal household income as a social factor that could contribute to racial and ethnic disparities in delayed or forgone care. RESULTS: We found that personal income differences were strongly related to disparities in delayed or forgone care in places with and without plans to expand Medicaid. In addition, while delayed or forgone care disparities between non-Hispanic whites and non-Hispanic blacks were lowest in places with plans to expand Medicaid access, disparities between non-Hispanic whites and Hispanics did not differ by state Medicaid expansion plans. CONCLUSIONS: As access to insurance improves for diverse groups, health systems must develop innovative strategies to overcome social determinants of health, including income inequities, as barriers to accessing care for Hispanic and non-Hispanic blacks. Additional efforts may be needed to ensure Hispanic groups achieve the benefits of investments in health care access.

Prevalence and changes over time of ideal cardiovascular health metrics among African-Americans: the Jackson Heart Study.

OBJECTIVES: The aim of this study is to assess the prevalence and changes over time of ideal Life's Simple Seven (LSS) in African-Americans. METHODS: Prospective cohort of 5301 African-Americans from the Jackson Heart Study (JHS) from 2000 to 2013. Each of the LSS metrics was categorized as poor, intermediate, or ideal. RESULTS: Among men, the prevalence of having 0, 1, 2, 3, 4, 5, 6, and 7 ideal LSS was 3.3%, 23.0%, 33.5%, 24.7%, 11.6%, 3.6%, 0.3%, and 0%, respectively. Corresponding values for women were 1.7%, 26.3%, 33.1%, 22.8%, 11.9%, 3.7%, 0.6%, and 0%. Prevalence of ideal diet was 0.9%. The proportions of those meeting LSS ideal recommendations for cholesterol and fasting glucose declined from the first through third JHS visits across all age groups, whereas prevalence of ideal BMI declined only in participants <40 years at a given visit. Prevalence of ideal blood pressure did not change over time and being ideal on physical activity improved from the first [18.3% (95% CI: 17.3% to 19.3%)] to third visit [24.8% (95% CI: 23.3% to 26.3%)]. CONCLUSIONS: Our data show a low prevalence of ideal LSS (especially diet, physical activity, and obesity) in the JHS and a slight improvement in adherence to physical activity recommendations over time.

Association of food insecurity with changes in diet quality, weight, and glycemia over two years in adults with prediabetes and type 2 diabetes on medicaid.

Little is known about longitudinal associations between food insecurity (FI) and diet, weight, and glycemia in people with prediabetes and type 2 diabetes (T2D). In a secondary analysis of Medicaid-enrolled health center patients with prediabetes or T2D in Boston, Massachusetts (N = 188), we examined associations between food security (FS) and measures of diet quality, weight, and hyperglycemia. FS (10-item USDA FS module) was ascertained at baseline, 1-year, and 2-year follow-up and categorized as persistently secure, intermittently insecure, or persistently insecure. Associations between FS category and changes in Healthy Eating Index-2020 (HEI-20), body mass index (BMI), and hemoglobin A1c (A1c) from baseline to year 2 were assessed using multivariate generalized linear models. Participants had median (p25, p75) age of 52 (42, 57); 71.8% were female and 62.8% Hispanic. Over follow-up, 32.4% were persistently food secure, 33.0% intermittently insecure, and 34.5% persistently insecure. Baseline mean (SD) HEI-20, BMI, and A1c were 55.8 (14.5), 35.9 (8.7) kg/m2, 7.1% (1.6) and did not differ by FS category. FS category was not associated with changes in HEI-20, BMI, and A1c at 2 years (all p > 0.05). Results suggest that Medicaid-enrolled adults with prediabetes or T2D, regardless of FS status, would benefit from dietary and weight management interventions.

Clinician-perceived barriers to cervical cancer screening before and during the COVID-19 pandemic at three US healthcare systems.

Introduction: The COVID-19 pandemic posed serious challenges to cancer screening delivery, including cervical cancer. While the impact of the pandemic on deferred screening has been documented, less is known about how clinicians experienced barriers to screening delivery, and, in particular, the role of pre-pandemic barriers to changes reported during the pandemic. Methods: Survey of clinicians who performed ≥ 10 cervical cancer screening tests in 2019 from Mass General Brigham, Kaiser Permanente Washington, and Parkland Health, the healthcare systems participating in the Population-based Research to Optimize the Screening Process (PROSPR II) consortium (administered 10/2020-12/2020, response rate 53.7 %). Results: Prior to the pandemic, clinicians commonly noted barriers to the delivery of cervical cancer screening including lack of staff support (57.6%), interpreters (32.5%), resources to support patients with social barriers to care (61.3%), and discrimination or bias in interactions between staff and patients (31.2%). Clinicians who reported experiencing a given barrier to care before the pandemic were more likely than those who did not experience one to report worsening during the pandemic: lack of staff support (odds ratio 4.70, 95% confidence interval 2.94-7.52); lack of interpreters (8.23, 4.46-15.18); lack of resources to support patients in overcoming social barriers (7.65, 4.41-13.27); and discrimination or bias (6.73, 3.03-14.97). Conclusions: Clinicians from three health systems who deliver cervical cancer screening commonly reported barriers to care. Barriers prior to the pandemic were associated with worsening of barriers during the pandemic. Addressing barriers to cervical cancer screening may promote resilience of care delivery during the next public health emergency.

Measuring social determinants of health in the All of Us Research Program.

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

Racial and Ethnic Disparities in Opioid Access and Urine Drug Screening Among Older Patients With Poor-Prognosis Cancer Near the End of Life.

PURPOSE: To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms. METHODS: Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS. RESULTS: Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities. CONCLUSION: There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

Food Insecurity and Diet Quality Among Adults on Medicaid With and Without a Mental Illness Diagnosis.

BACKGROUND: Adults with mental illnesses are more likely to have low income and diet-related chronic diseases. OBJECTIVE: This study examined associations of mental illness diagnosis status with food insecurity and diet quality and whether the relationship between food security status and diet quality differed by mental illness diagnosis status in adult Medicaid beneficiaries. DESIGN: This was a secondary cross-sectional analysis of baseline (2019-2020) data collected as part of the LiveWell study, a longitudinal study evaluating a Medicaid food and housing program. PARTICIPANTS/SETTING: Participants were 846 adult Medicaid beneficiaries from an eastern Massachusetts health system. MAIN OUTCOME MEASURES: Food security was measured with the 10-item US Adult Food Security survey module (0 = high food security, 1-2 = marginal food security, 3-10 = low/very low food security). Mental illness diagnoses included health record-documented anxiety, depression, or serious mental illness (eg, schizophrenia, bipolar disorder). Healthy Eating Index (HEI-2015) scores were calculated from 24-hour dietary recalls. STATISTICAL ANALYSES: Multivariable regression analyses adjusted for demographics, income, and survey date. RESULTS: Participants' mean (standard deviation) age was 43.1 (11.3) years, and 75% were female, 54% Hispanic, 33% non-Hispanic White, and 9% non-Hispanic Black. Fewer than half (43%) of participants reported high food security, with almost one third (32%) reporting low or very low food security. The 341 (40%) participants with one or more mental illness diagnosis had greater odds of low/very low food security (adjusted odds ratio [OR], 1.94; 95% confidence interval [CI], 1.38-2.70) and had similar mean HEI-2015 scores (53.1 vs 56.0; P = 0.12) compared with participants with no mental illness diagnosis. Mean adjusted HEI-2015 scores did not significantly differ by high vs low/very low food security for those without a mental illness diagnosis (57.9 vs 54.9; P = 0.052) or those with a mental illness diagnosis (53.0 vs 52.9; P = 0.99). CONCLUSION: In a cohort of adults with Medicaid, those with mental illness diagnoses had higher odds of experiencing food insecurity. Overall, diet quality among adults in this sample was low but did not differ by mental illness diagnosis or food security status. These results highlight the importance of augmenting efforts to improve both food security and diet quality among all Medicaid participants.

Assessment of the Massachusetts Flexible Services Program to Address Food and Housing Insecurity in a Medicaid Accountable Care Organization.

Importance: Health systems are increasingly addressing health-related social needs. The Massachusetts Flexible Services program (Flex) is a 3-year pilot program to address food insecurity and housing insecurity by connecting Medicaid accountable care organization (ACO) enrollees to community resources. Objective: To understand barriers and facilitators of Flex implementation in 1 Medicaid ACO during the first 17 months of the program. Design, Setting, and Participants: This mixed-methods qualitative evaluation study from March 2020 to July 2021 used the Reach, Efficacy, Adoption, Implementation, Maintenance/Practical, Robust Implementation, and Sustainability Model (RE-AIM/PRISM) framework. Two Mass General Brigham (MGB) hospitals and affiliated community health centers were included in the analysis. Quantitative data included all MGB Medicaid ACO enrollees. Qualitative interviews were conducted with 15 members of ACO staff and 17 Flex enrollees. Main Outcomes and Measures: Reach was assessed by the proportion of ACO enrollees who completed annual social needs screening (eg, food insecurity and housing insecurity) and the proportion and demographics of Flex enrollees. Qualitative interviews examined other RE-AIM/PRISM constructs (eg, implementation challenges, facilitators, and perceived effectiveness). Results: Of 67 098 Medicaid ACO enrollees from March 2020 to July 2021 (mean [SD] age, 28.8 [18.7] years), 38 442 (57.3%) completed at least 1 social needs screening; 10 730 (16.0%) screened positive for food insecurity, and 7401 (11.0%) screened positive for housing insecurity. There were 658 (1.6%) adults (mean [SD] age, 46.6 [11.8] years) and 173 (0.7%) children (<21 years; mean [SD] age, 10.1 [5.5]) enrolled in Flex; of these 831 people, 613 (73.8%) were female, 444 (53.4%) were Hispanic/Latinx, and 172 (20.7%) were Black. Most Flex enrollees (584 [88.8%] adults; 143 [82.7%] children) received the intended nutrition or housing services. Implementation challenges identified by staff interviewed included administrative burden, coordination with community organizations, data-sharing and information-sharing, and COVID-19 factors (eg, reduced clinical visits). Implementation facilitators included administrative funding for enrollment staff, bidirectional communication with community partners, adaptive strategies to identify eligible patients, and raising clinician awareness of Flex. In Flex enrollee interviews, those receiving nutrition services reported increased healthy eating and food security; they also reported higher program satisfaction than Flex enrollees receiving housing services. Enrollees who received nutrition services that allowed for selecting food based on preferences reported higher satisfaction than those not able to select food. Conclusions and Relevance: This mixed-methods qualitative evaluation study found that to improve implementation, Medicaid and health system programs that address social needs may benefit from providing funding for administrative costs, developing bidirectional data-sharing platforms, and tailoring support to patient preferences.

Contributors to Early Mortality in African Americans, the Jackson Heart Study.

Introduction: In recent years, premature "deaths of despair" (ie, due to alcohol, drug use, and suicide) among middle-aged White Americans have received increased attention in the popular press, yet there has been less discussion on what explains premature deaths among young African Americans. In this study, we examined factors related to deaths of despair (alcohol use, drug use, smoking) and contextual factors (perceived discrimination, socioeconomic status, neighborhood conditions) as predictors of premature deaths before the age of 65 years among African Americans. Methods: The Jackson Heart Study (JHS) is a longitudinal cohort study of African Americans in the Jackson, Mississippi, metropolitan statistical area. We included participants younger than 65 years at baseline (n=4000). Participant enrollment began in 2000 and data for these analyses were collected through 2019. To examine predictors of mortality, we calculated multivariable adjusted hazard ratios (HRs; 95% CI), using Cox proportional hazard models adjusted for age, sex, ideal cardiovascular health metrics, drug use, alcohol intake, functional status, cancer, chronic kidney disease, asthma, waist circumference, depression, income, education, health insurance status, perceived neighborhood safety, and exposure to lifetime discrimination. Results: There were 230 deaths in our cohort, which spanned from 2001-2019. After adjusting for all covariates, males (HR, 1.50; 95% CI, 1.11-2.03), participants who used drugs (HR, 1.53; 95% CI, 1.13-2.08), had a heavy alcohol drinking episode (HR, 1.71; 95% CI, 1.22-2.41), reported 0-1 ideal cardiovascular health metrics (HR, 1.78; 95% CI, 1.06-3.02), had cancer (HR, 2.38; 95% CI, 1.41-4.01), had poor functional status (HR, 1.68; 95% CI, 1.19-2.37), or with annual family income less than $25,000 (HR, 1.63; 95% CI, 1.02-2.62) were more likely to die before 65 years of age. Conclusions: In our large cohort of African American men and women, clinical predictors of premature death included poor cardiovascular health and cancer, and social predictors included low income, drug use, heavy alcohol use, and being a current smoker. Clinical and social interventions are warranted to prevent premature mortality in African Americans.

Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program.

BACKGROUND: The All of Us Research Program enrolls diverse US participants which provide a unique opportunity to better understand the problem of opioid use. This study aims to estimate the prevalence of opioid use and its association with sociodemographic characteristics from survey data and electronic health record (EHR). METHODS: A total of 214,206 participants were included in this study who competed survey modules and shared EHR data. Adjusted logistic regressions were used to explore the associations between sociodemographic characteristics and opioid use. RESULTS: The lifetime prevalence of street opioids was 4%, and the nonmedical use of prescription opioids was 9%. Men had higher odds of lifetime opioid use (aOR: 1.4 to 3.1) but reduced odds of current nonmedical use of prescription opioids (aOR: 0.6). Participants from other racial and ethnic groups were at reduced odds of lifetime use (aOR: 0.2 to 0.9) but increased odds of current use (aOR: 1.9 to 9.9) compared with non-Hispanic White participants. Foreign-born participants were at reduced risks of opioid use and diagnosed with opioid use disorders (OUD) compared with US-born participants (aOR: 0.36 to 0.67). Men, Younger, White, and US-born participants are more likely to have OUD. CONCLUSIONS: All of Us research data can be used as an indicator of national trends for monitoring the prevalence of receiving prescription opioids, diagnosis of OUD, and non-medical use of opioids in the US. The program employs a longitudinal design for routinely collecting health-related data including EHR data, that will contribute to the literature by providing important clinical information related to opioids over time. Additionally, this data will enhance the estimates of the prevalence of OUD among diverse populations, including groups that are underrepresented in the national survey data.

Achieving a Representative Sample of Asian Americans in Biomedical Research Through Community-Based Approaches: Comparing Demographic Data in the All of Us Research Program With the American Community Survey.

BACKGROUND: Underrepresented persons are often not included in biomedical research. It is unknown if the general Asian American population is being represented in All of Us. The purpose of this study was to compare the Asian demographic data in the All of Us cohort with the Asian nationally representative data from the American Community Survey. METHOD: Demographic characteristics and health literacy of Asians in All of Us were examined. Findings were qualitatively compared with the Asian data in the 2019 American Community Survey 1-year estimate. RESULTS: Compared with the national composition of Asians, less All of Us participants were born outside the United States (64% vs 79%), were younger, and had higher levels of education (76% vs 52%). Over 60% of All of Us participants reported high levels of health literacy. CONCLUSION: This study had implications for the development of strategies that ensure diverse populations are represented in biomedical research.

Cardiovascular inflammation in healthy women: multilevel associations with state-level prosperity, productivity and income inequality.

BACKGROUND: Cardiovascular inflammation is a key contributor to the development of atherosclerosis and the prediction of cardiovascular events among healthy women. An emerging literature suggests biomarkers of inflammation vary by geography of residence at the state-level, and are associated with individual-level socioeconomic status. Associations between cardiovascular inflammation and state-level socioeconomic conditions have not been evaluated. The study objective is to estimate whether there are independent associations between state-level socioeconomic conditions and individual-level biomarkers of inflammation, in excess of individual-level income and clinical covariates among healthy women. METHODS: The authors examined cross-sectional multilevel associations among state-level socioeconomic conditions, individual-level income, and biomarkers of inflammation among women (n = 26,029) in the Women's Health Study, a nation-wide cohort of healthy women free of cardiovascular diseases at enrollment. High sensitivity C-reactive protein (hsCRP), soluble intercellular adhesion molecule-1 (sICAM-1) and fibrinogen were measured between 1993 and 1996. Biomarker levels were examined among women within quartiles of state-level socioeconomic conditions and within categories of individual-level income. RESULTS: The authors found that favorable state-level socioeconomic conditions were correlated with lower hsCRP, in excess of individual-level income (e.g. state-level real per capital gross domestic product fixed effect standardized Βeta coefficient [Std B] -0.03, 95% CI -0.05, -0.004). Individual-level income was more closely associated with sICAM-1 (Std B -0.04, 95% CI -0.06, -0.03) and fibrinogen (Std B -0.05, 95% CI -0.06, -0.03) than state-level conditions. CONCLUSIONS: We found associations between state-level socioeconomic conditions and hsCRP among healthy women. Personal household income was more closely associated with sICAM-1 and fibrinogen than state-level socioeconomic conditions. Additional research should examine these associations in other cohorts, and investigate what more-advantaged states do differently than less-advantaged states that may influence levels of cardiovascular inflammation among healthy women.