Health Care Access and Utilization and the Latino Health Paradox.

BACKGROUND: The Latino health paradox is the phenomenon whereby recent Latino immigrants have, on average, better health outcomes on some indicators than Latino immigrants who have lived in the United States longer and US-born Latinos and non-Latino Whites. This study examined whether the paradox holds after accounting for health care access and utilization. METHODS: The 2019-2020 National Health Interview Survey data were used. The main predictors included population groups of foreign-born and US-born Latinos (Mexican or non-Mexican) versus US-born non-Latino Whites. Predicted probabilities of health outcomes (self-reported poor/fair health, overweight/obesity, hypertension, coronary heart disease, diabetes, cancer, and depression) were calculated and stratified by length of residence in the United States (<15 or ≥15 years) among foreign-born Latinos and sex (female or male). Multivariable analyses adjusted for having a usual source of care other than the emergency department, health insurance, a doctor visit in the past 12 months, predisposing and enabling factors, and survey year. RESULTS: After adjusting for health care access, utilization, and predisposing and enabling factors, foreign-born Latinos, including those living in the United States ≥15 years, had lower predicted probabilities for most health outcomes than US-born non-Latino Whites, except overweight/obesity and diabetes. US-born Latinos had higher predicted probabilities of overweight/obesity and diabetes and a lower predicted probability of depression than US-born non-Latino Whites. CONCLUSIONS: In this national survey, the Latino health paradox was observed after adjusting for health care access and utilization and predisposing and enabling factors, suggesting that, although these are important factors for good health, they do not necessarily explain the paradox.

Democratizing Native Hawaiian and Pacific Islander Data: Examining Community Accessibility of Data for Health and the Social Drivers of Health.

Making data accessible to communities is essential for developing community-driven solutions to address health inequities. In this analytic essay, we highlight the importance of democratizing data for Native Hawaiians and Pacific Islanders (NHPIs)-diverse populations that historically have had little access to their data-in the context of achieving equity in health and the social drivers of health. We provide a framework for evaluating community accessibility of data, which includes concepts of data availability, salience, cost, and report back. We apply the framework to evaluate community accessibility of NHPI data from 29 federal data sources. In addition, we provide results from a survey of NHPI-serving community organizations in California conducted from December 2021 to February 2022 to assess community data needs. Findings reveal federal gaps in data accessibility, as well as NHPI community organizational needs for increased data accessibility, data saliency, and technical capacity. Furthermore, organization leads expressed concerns about data privacy, security, and misuse. We provide recommendations for data custodians to improve accessibility of timely, accurate, and robust data to support NHPI communities. (Am J Public Health. 2024;114(S1):S103-S111. https://doi.org/10.2105/AJPH.2023.307503).

Making Communities More Visible: Equity-Centered Data to Achieve Health Equity.

Policy Points Despite decades of research exposing health disparities between populations and communities in the US, health equity goals remain largely unfulfilled. We argue these failures call for applying an equity lens in the way we approach data systems, from collection and analysis to interpretation and distribution. Hence, health equity requires data equity. There is notable federal interest in policy changes and federal investments to improve health equity. With this, we outline the opportunities to align these health equity goals with data equity by improving the way communities are engaged and how population data are collected, analyzed, interpreted, made accessible, and distributed. Policy priority areas for data equity include increasing the use of disaggregated data, increasing the use of currently underused federal data, building capacity for equity assessments, developing partnerships between government and community, and increasing data accountability to the public.

After the Crisis: Racial/Ethnic Disparities and Predictors of Care Use Following Youth Psychiatric Emergencies.

OBJECTIVE: Youth psychiatric emergencies have increased at alarming rates, and disproportionately so for youth of color. Outpatient follow-up care is critical for positive youth outcomes, but rates of follow-up remain low, especially for racial/ethnic minoritized youth. Mobile crisis response can initiate care connection. The current study (1) describes the population who received mobile crisis response (MCR) within the nation's largest county public mental health system, (2) assesses rates of follow-up outpatient services after MCR, and (3) examines racial/ethnic disparities in outpatient services and correlates of receipt of therapy dose (≥8 sessions). METHOD: Administrative claims for MCR and outpatient services for youth ages 0 to 18 were abstracted from the Los Angeles County Department of Mental Health. RESULTS: From October 2016-2019, 20,782 youth received a MCR, 52.5% of youth were female, and youth mean age was 13.41 years. The majority of youth (91.8%) received some outpatient services after their first MCR. However, only 56.7% of youth received ≥1 therapy session. In a logistic regression, youth age, gender, race/ethnicity, primary language, primary diagnosis, insurance status, MCR call location, and MCR disposition significantly predicted receipt of ≥8 therapy sessions. CONCLUSIONS: Findings highlight disparities in therapy receipt for Asian American Pacific Islander, Black, and White youth (relative to Latinx youth), older youth, youth whose MCR was initiated from a police station call, and youth whose MCR did not result in hospitalization. We discuss priorities for quality improvement for MCR processes and strategies to promote linkage to care to achieve mental health equity.

Measuring Structural Racism: A Guide for Epidemiologists and Other Health Researchers.

There have been over 100 years of literature discussing the deleterious influence of racism on health. Much of the literature describes racism as a driver of social determinants of health, such as housing, employment, income, and education. More recently, increased attention has been given to measuring the structural nature of a system that advantages one racialized group over others rather than solely relying on individual acknowledgement of racism. Despite these advances, there is still a need for methodological and analytical approaches to complement the aforementioned. This commentary calls on epidemiologists and other health researchers at large to engage the discourse on measuring structural racism. First, we address the conflation between race and racism in epidemiologic research. Next, we offer methodological recommendations (linking of interdisciplinary variables and data sets and leveraging mixed-method and life-course approaches) and analytical recommendations (integration of mixed data, use of multidimensional models) that epidemiologists and other health researchers may consider in health equity research. The goal of this commentary is to inspire the use of up-to-date and theoretically driven approaches to increase discourse among public health researchers on capturing racism as well as to improve evidence of its role as the fundamental cause of racial health inequities.

How Has Access to Care for Medi-Cal Enrollees Fared Relative to Employer-Sponsored Insurance 4 Years After the Affordable Care Act Expansion?

BACKGROUND: The number of Californians covered by Medi-Cal increased more than 50% between 2013 and 2018, largely due to expansion under the Affordable Care Act (ACA). This rapid expansion of Medicaid rolls prompted concerns that Medi-Cal enrollees would face greater difficulty accessing health care. OBJECTIVE: Examine whether gaps in access to care between Medi-Cal and employer-sponsored insurance (ESI) present in 2013 (prior to ACA implementation) had changed by 2018 (several years post implementation). DESIGN: Secondary analysis of data from the 2013 and 2018 California Health Interview Survey. The sample included adults of ages 18-64 insured all year and covered by ESI or Medi-Cal at time of interview. Logistic regressions were used to examine variation across years in the association between access to care and insurance type. MAIN MEASURES: Five access to care outcomes were assessed: no usual source of care, not accepted as new patient in past year, insurance not accepted in past year, delayed medical care in past year, and difficulty getting timely appointment. The main predictors of interest were type of insurance (Medi-Cal or ESI) and survey year (2013 or 2018). KEY RESULTS: The association between insurance type and access to care changed significantly over time for three outcomes: not accepted as new patient in past year (OR = 0.55, 95% CI = 0.32-0.97), delayed medical care in past year (OR = 1.55, 95% CI = 1.06-2.25), and difficulty getting timely appointment (OR = 0.41, 95% CI = 0.23-0.74). Predicted probabilities indicate gaps between Medi-Cal and ESI narrowed for not accepted as new patient in past year and difficulty getting timely appointment, but widened for delayed medical care. CONCLUSIONS: Despite the rapid expansion in the number of Californians covered by Medi-Cal, most gaps in access to care between Medi-Cal and ESI enrollees improved or did not significantly change between 2013 and 2018.

Surveying Hate and Its Effects During the COVID-19 Pandemic Among Asian Americans and Native Hawaiians and Pacific Islanders.

Objectives. To summarize data collection on anti-Asian American and Native Hawaiian/Pacific Islander (AANHPI) experiences during COVID-19 and measure the associations of anti-AANHPI hate incidents with mental health, health access, and public safety among AANHPI adults. Methods. We cataloged COVID-19 surveys conducted in 2020 and 2021 on anti-AANHPI experiences. We then analyzed the 2020 California Health Interview Survey (CHIS) AANHPI COVID-19 module by constructing a variable of experiencing or witnessing a hate incident and estimating its associations with serious psychological distress, forgone care, and perceived neighborhood safety. Results. Estimates of being a victim of a hate incident ranged from 6% to 30%. In the CHIS, 28% of respondents experienced or witnessed a hate incident. Experiencing or witnessing a hate incident was significantly associated with serious psychological distress (adjusted odds ratio [AOR] = 5.33), forgoing care (AOR = 2.27), and not feeling safe in one's neighborhood (AOR = 2.70). Conclusions. Evidence from a multitude of data sources corroborates the toll of hate incidents suffered by AANHPIs. Findings regarding the negative effects of anti-AANHPI hate on mental health, health access, and public safety compel public and private investment to end victimization of AANHPI communities. (Am J Public Health. 2022;112(10):1446-1453. https://doi.org/10.2105/AJPH.2022.306977).

No Equity without Data Equity: Data Reporting Gaps for Native Hawaiians and Pacific Islanders as Structural Racism.

Data on the health and social determinants for Native Hawaiians and Pacific Islanders (NHPIs) in the United States are hidden, because data are often not collected or are reported in aggregate with other racial/ethnic groups despite decades of calls to disaggregate NHPI data. As a form of structural racism, data omissions contribute to systemic problems such as inability to advocate, lack of resources, and limitations on political power. The authors conducted a data audit to determine how US federal agencies are collecting and reporting disaggregated NHPI data. Using the COVID-19 pandemic as a case study, they reviewed how states are reporting NHPI cases and deaths. They then used California's neighborhood equity metric-the California Healthy Places Index (HPI)-to calculate the extent of NHPI underrepresentation in communities targeted for COVID-19 resources in that state. Their analysis shows that while collection and reporting of NHPI data nationally has improved, federal data gaps remain. States are vastly underreporting: more than half of states are not reporting NHPI COVID-19 case and death data. The HPI, used to inform political decisions about allocation of resources to combat COVID-19 in at-risk neighborhoods, underrepresents NHPIs. The authors make recommendations for improving NHPI data equity to achieve health equity and social justice.

Disparities in Native Hawaiian and Pacific Islander COVID-19 Mortality: A Community-Driven Data Response.

As of March 2021, Native Hawaiians and Pacific Islanders (NHPIs) in the United States have lost more than 800 lives to COVID-19-the highest per capita death rate in 18 of 20 US states reporting NHPI deaths. However, NHPI risks are overlooked in policy discussions. We discuss the NHPI COVID-19 Data Policy Lab and dashboard, featuring the disproportionate COVID-19 mortality burden for NHPIs. The Lab democratized NHPI data, developed community infrastructure and resources, and informed testing site and outreach policies related to health equity.

Projected costs of single-payer healthcare financing in the United States: A systematic review of economic analyses.

BACKGROUND: The United States is the only high-income nation without universal, government-funded or -mandated health insurance employing a unified payment system. The US multi-payer system leaves residents uninsured or underinsured, despite overall healthcare costs far above other nations. Single-payer (often referred to as Medicare for All), a proposed policy solution since 1990, is receiving renewed press attention and popular support. Our review seeks to assess the projected cost impact of a single-payer approach. METHODS AND FINDINGS: We conducted our literature search between June 1 and December 31, 2018, without start date restriction for included studies. We surveyed an expert panel and searched PubMed, Google, Google Scholar, and preexisting lists for formal economic studies of the projected costs of single-payer plans for the US or for individual states. Reviewer pairs extracted data on methods and findings using a template. We quantified changes in total costs standardized to percentage of contemporaneous healthcare spending. Additionally, we quantified cost changes by subtype, such as costs due to increased healthcare utilization and savings due to simplified payment administration, lower drug costs, and other factors. We further examined how modeling assumptions affected results. Our search yielded economic analyses of the cost of 22 single-payer plans over the past 30 years. Exclusions were due to inadequate technical data or assuming a substantial ongoing role for private insurers. We found that 19 (86%) of the analyses predicted net savings (median net result was a savings of 3.46% of total costs) in the first year of program operation and 20 (91%) predicted savings over several years; anticipated growth rates would result in long-term net savings for all plans. The largest source of savings was simplified payment administration (median 8.8%), and the best predictors of net savings were the magnitude of utilization increase, and savings on administration and drug costs (R2 of 0.035, 0.43, and 0.62, respectively). Only drug cost savings remained significant in multivariate analysis. Included studies were heterogeneous in methods, which precluded us from conducting a formal meta-analysis. CONCLUSIONS: In this systematic review, we found a high degree of analytic consensus for the fiscal feasibility of a single-payer approach in the US. Actual costs will depend on plan features and implementation. Future research should refine estimates of the effects of coverage expansion on utilization, evaluate provider administrative costs in varied existing single-payer systems, analyze implementation options, and evaluate US-based single-payer programs, as available.

Worse Mental Health Among More-Acculturated and Younger Immigrants Experiencing Discrimination: California Health Interview Survey, 2015-2016.

BACKGROUND: Experiences of discrimination harm mental and physical health, with the strongest penalty on mental health. Among immigrants, it remains unclear how acculturation-the process by which immigrants acquire the beliefs and practices of a host culture-influences the mental health burden of navigating discrimination. On the one hand, acculturation can be associated with upward social mobility. Conversely, the acculturative process may increase exposure to, and recognition of, discrimination. OBJECTIVES: We examined the relationship between discrimination and mental illness across racial/ethnic groups, and pathways by which acculturation and age relate to the discrimination-mental health relationship. DESIGN: A secondary data analysis using population data from the 2015-2016 California Health Interview Survey. MAIN MEASURES: The Kessler 6-item Psychological Distress Scale (K6) assessed symptoms of psychological distress, with K6 score ≥ 13 associated with severe mental illness. Discrimination was measured using a self-reported measure of lifetime experience of unfair treatment in getting medical care. We used a 5-point acculturation index (constructed by measures of nativity, years living in the USA, and home language use). A weighted logistic regression model predicted mental illness as a function of discrimination. We ran mediational analysis using the Karlson-Holm-Breen method and used predictive margins to present predicted probabilities of mental illness for people reporting discrimination at different acculturation and age levels. KEY RESULTS: There were independent effects on mental illness associated with increased discrimination (OR 3.85, 95% CI = 2.46, 6.03, p < 0.001) and increased acculturation (OR 1.72, 95% CI = 1.24, 2.38, p = 0.001), including when stratified across racial/ethnic groups. Higher levels of acculturation led to a significant increase in discrimination's association with mental illness. There was a higher probability of mental illness in younger age groups than in older age groups. CONCLUSIONS: While discrimination is associated with poor mental health, a stronger link between discrimination and mental illness exists among younger immigrants and immigrants with increased acculturation. Health practitioners should not overlook the mental health needs of younger immigrants and immigrants who may seem more integrated into US society.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND: To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown. OBJECTIVE: Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors. DESIGN: Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status. PATIENTS: 20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized. CONCLUSIONS: Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

Health Conditions, Outcomes, and Service Access Among Filipino, Vietnamese, Chinese, Japanese, and Korean Adults in California, 2011-2017.

Objectives. To determine the impact of data disaggregation on the ability to identify health disparities and needs for future research for Filipino, Vietnamese, Chinese, Japanese, and Korean adults in California.Methods. Using available data from the 2011-2017 California Health Interview Survey, we conducted bivariate and multivariable analyses to assess disparities in health conditions, outcomes, and service access compared with non-Hispanic Whites for Asians as an overall group and for each individual subgroup.Results. As an aggregate category, Asians appeared healthier than did non-Hispanic Whites on most indicators. However, every Asian subgroup had at least 1 disparity disguised by aggregation. Filipinos had the most disparities, with higher prevalence of fair or poor health, being obese or overweight, and having high blood pressure, diabetes, or asthma compared with non-Hispanic Whites (P < .05) in multivariable analyses.Conclusions. Failure to disaggregate health data for individual Asian subgroups disguises disparities and leads to inaccurate conclusions about needs for interventions and research.

The impact of rising food prices on obesity in women: a longitudinal analysis of 31 low-income and middle-income countries from 2000 to 2014.

OBJECTIVES: To examine whether changes in food prices are associated with changes in obesity prevalence among women in developing countries, and assess effect modification by individual socioeconomic status (SES). METHODS: Longitudinal study of country-level food price inflation temporally and geographically linked to anthropometric data on non-pregnant adult women (n = 295,984) in 31 low-income and middle-income countries over the 2000-2014 time period, using separate multivariable multilevel growth models of five SES indicators. Post-estimation analysis computed the relationship between food price inflation and predicted mean probabilities of being obese, by SES. RESULTS: Rising food price inflation was strongly associated with women's obesity prevalence, and SES consistently modified the relationship. Regardless of indicator used, higher food price inflation was positively associated with obesity among women in top SES categories, but was flat or negative among women in low SES categories, averaging over time. The SES differences were widest across educational strata and were most pronounced when food price inflation was highest. Overall, for every 1-unit increase in food price inflation, predicted mean obesity prevalence was between 0.02 and 0.06 percentage points greater in women of high SES compared to low SES women. CONCLUSION: There is a strong link between food price inflation and obesity in adult women in developing countries which is clearly modified by individuals' SES. Greater food price inflation was associated with greater obesity prevalence only among women in higher SES groups, who may be net food buyers most at risk of obesity in low-income and middle-income countries.

Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions.

BACKGROUND: High-cost patients are a frequent focus of improvement projects based on primary care and other settings. Efforts to characterize high-cost, high-need patients are needed to inform care planning, but such efforts often rely on a priori assumptions, masking underlying complexities of a heterogenous population. OBJECTIVE: To define recognizable subgroups of patients among high-cost adults based on clinical conditions, and describe their survival and future spending. DESIGN: Retrospective observational cohort study. PARTICIPANTS: Within a large integrated delivery system with 2.7 million adult members, we selected the top 1% of continuously enrolled adults with respect to total healthcare expenditures during 2010. MAIN MEASURES: We used latent class analysis to identify clusters of alike patients based on 53 hierarchical condition categories. Prognosis as measured by healthcare spending and survival was assessed through 2014 for the resulting classes of patients. RESULTS: Among 21,183 high-cost adults, seven clinically distinctive subgroups of patients emerged. Classes included end-stage renal disease (12% of high-cost population), cardiopulmonary conditions (17%), diabetes with multiple comorbidities (8%), acute illness superimposed on chronic conditions (11%), conditions requiring highly specialized care (14%), neurologic and catastrophic conditions (5%), and patients with few comorbidities (the largest class, 33%). Over 4 years of follow-up, 6566 (31%) patients died, and survival in the classes ranged from 43 to 88%. Spending regressed to the mean in all classes except the ESRD and diabetes with multiple comorbidities groups. CONCLUSIONS: Data-driven characterization of high-cost adults yielded clinically intuitive classes that were associated with survival and reflected markedly different healthcare needs. Relatively few high-cost patients remain persistently high cost over 4 years. Our results suggest that high-cost patients, while not a monolithic group, can be segmented into few subgroups. These subgroups may be the focus of future work to understand appropriateness of care and design interventions accordingly.

Economic policy and the double burden of malnutrition: cross-national longitudinal analysis of minimum wage and women's underweight and obesity.

OBJECTIVE: To examine changes in minimum wage associated with changes in women's weight status. DESIGN: Longitudinal study of legislated minimum wage levels (per month, purchasing power parity-adjusted, 2011 constant US dollar values) linked to anthropometric and sociodemographic data from multiple Demographic and Health Surveys (2000-2014). Separate multilevel models estimated associations of a $10 increase in monthly minimum wage with the rate of change in underweight and obesity, conditioning on individual and country confounders. Post-estimation analysis computed predicted mean probabilities of being underweight or obese associated with higher levels of minimum wage at study start and end. SETTING: Twenty-four low-income countries. SUBJECTS: Adult non-pregnant women (n 150 796). RESULTS: Higher minimum wages were associated (OR; 95 % CI) with reduced underweight in women (0·986; 0·977, 0·995); a decrease that accelerated over time (P-interaction=0·025). Increasing minimum wage was associated with higher obesity (1·019; 1·008, 1·030), but did not alter the rate of increase in obesity prevalence (P-interaction=0·8). A $10 rise in monthly minimum wage was associated (prevalence difference; 95 % CI) with an average decrease of about 0·14 percentage points (-0·14; -0·23, -0·05) for underweight and an increase of about 0·1 percentage points (0·12; 0·04, 0·20) for obesity. CONCLUSIONS: The present longitudinal multi-country study showed that a $10 rise in monthly minimum wage significantly accelerated the decline in women's underweight prevalence, but had no association with the pace of growth in obesity prevalence. Thus, modest rises in minimum wage may be beneficial for addressing the protracted underweight problem in poor countries, especially South Asia and parts of Africa.

Addressing discriminatory benefit design for people living with HIV: a California case study.

Concern is growing in the United States regarding the potential for health insurance benefit designs to discriminate against persons living with HIV as research demonstrates that such practices are occurring. A recent complaint filed against health insurers in seven states alleges that some health insurance companies have been using benefit designs that discourage enrollment of people living with HIV either by not covering essential HIV medications or by requiring cost-sharing for these prohibitively expensive medications. Legislators across the country have reacted by introducing legislation to address these growing problems. This paper describes Assembly Bill 339, legislation passed in California in 2015 and going into effect on 1 January 2017, which provides protection for people living with HIV by requiring coverage for single-tablet regimens to manage HIV while placing a cap on patient cost-sharing. Given California's size and influence, and the uncertainty of the future of the Affordable Care Act, this legislation has the potential to influence the national policy debate.