Estimating the US Baseline Distribution of Health Inequalities Across Race, Ethnicity, and Geography for Equity-Informative Cost-Effectiveness Analysis

The impacts of household factors and proxies of human social determinants of health on dog behavior

Social determinants of health (SDOH) have an enormous impact on human health and behavior, although their possible effects on canine behavior have received limited scientific attention. The goals of this observational cohort study were to identify associations between canine behavior, household environments, and zip code-level proxies for human SDOH, and to explore longitudinal impacts on behavior. We used an existing dataset of C-BARQ behavioral assessments from 3044 golden retrievers in the United States, including up to eight years of data per dog collected between 2012 and 2023. The data were analyzed using linear mixed effect models and generalized estimating equations. We found dogs from single-dog homes had increased odds of dog-directed fear (OR 1.44, 95 % CI 1.30-1.61) and poorer trainability, particularly during early adulthood (F=14.32, p < 0.001). Sleeping in the owners' bed was associated with increased aggression towards strangers, and a greater reduction in trainability (F=20.71, p < 0.001) and energy with age (F=8.20, p = 0.004). Dogs in the most densely populated neighborhoods had greater odds of showing aggression to strangers compared with dogs in sparsely (OR 0.78, 95 % CI 0.63-0.95) or moderately populated neighborhoods (OR 0.73, 95 % CI 0.60-0.90). Together, our findings illustrate how conspecific relationships, human interactions, home and neighborhood environments affect dog behavior, and show, for the first time, that household characteristics and ownership behaviors have differential impacts on behavior across the lifespan. Future studies including more diverse human and canine populations are needed to provide further insights about the impacts of SDOH on dog health and welfare.

The Health Inequality Impact of Darolutamide for Non-Metastatic Castration-Resistant Prostate Cancer in the United States: A Distributional Cost-Effectiveness Analysis

OBJECTIVES

Non-Hispanic (NH) Black patients are disproportionally affected by nonmetastatic castration-resistant prostate cancer (nmCRPC). The objective was to quantify the health inequality impact of darolutamide + androgen deprivation therapy (ADT) relative to ADT for nmCRPC in the United States using a distributional cost-effectiveness analysis.

METHODS

With a health economic model, quality-adjusted life years (QALYs) and costs were estimated for NH-White, NH-Black, NH-Asian, and Hispanic patients. Given the lifetime risk of nmCRPC and assuming equally distributed opportunity costs, the incremental net health benefits of darolutamide were calculated, which were used to estimate general population quality-adjusted life expectancy at birth (QALE) by race and ethnicity with and without darolutamide. The extent of QALYs and QALE differences between race and ethnicity subgroups with each strategy was quantified with an inequality index, and their difference defined as the inequality impact of darolutamide.

RESULTS

Darolutamide + ADT resulted in an additional 1.04 (95% uncertainty interval 0.56-1.51) QALYs per treated patient relative to ADT, with the greatest gain observed among NH-Black patients (1.48 [0.48-2.71]). The relative inequality in QALYs among patients reduced by 66%, from an inequality score of 0.033 (0.004-0.082) with ADT to 0.011 (0.000-0.051) with darolutamide + ADT. Factoring in disease risk and health opportunity costs, nmCRPC treatment with darolutamide resulted in the largest net gain in QALYs among the NH-Black population, thereby having a favorable impact on inequalities in QALE.

CONCLUSIONS

Darolutamide + ADT results in greater and a more even distribution of QALYs than ADT for nmCRPC. The greatest gains among NH-Black individuals implies a favorable health inequality impact with darolutamide.

Barriers and unmet needs related to healthcare for American Indian and Alaska Native communities: improving access to specialty care and clinical trials

Substantial healthcare barriers, especially to specialty and cancer care, exist for American Indian and Alaska Native (AI/AN) individuals and communities at all levels. The unique history of AI/AN Tribal Nations and resulting policies, treaties, and relationships with the US government and federal agencies have created specific barriers to healthcare and clinical trial access for AI/AN peoples. Commonly, AI/AN peoples harbor a long-standing mistrust of the healthcare system based on lived and historical experiences. The intersection of various barriers to care for AI/AN communities results in health inequities, lack of representation in clinical research, and other disparities faced by historically marginalized and underrepresented peoples. AI/AN patients face unique barriers in their healthcare journey due to a disproportionate burden of life-threatening and chronic diseases, including many cancers. Identifying barriers specific to AI/AN peoples and improving access to high-quality care, with a focus on building on the strengths and capacities in each AI/AN community are vital to improving health equity. In this review, we describe patient, provider, and institutional barriers to healthcare, particularly specialty care and clinical research, for AI/AN peoples, with a focus on the Northern Plains AI communities. Examples and best practices to improve AI/AN patient access to health services, including screening and specialty care, as well as to clinical research, are provided. We emphasize the importance of longitudinal community-based partnerships and strength- and trust-based approaches as essential components of promoting equitable access to high-quality specialty care and recruitment and participation of AI/AN individuals and communities in clinical research.

Validating the Social Vulnerability Index for alternative geographies in the United States to explore trends in social determinants of health over time and geographic location

Objective

To create county-, 5-digit ZIP code (ZIP-5)-, and 3-digit ZIP code (ZIP-3)-level datasets of the Social Vulnerability Index (SVI) and its components for 2016-2022 to validate the methodology beyond county level, explore trends in SVI over time and space, and demonstrate its usage in an enrichment exercise with health plan claims.

Materials and methods

The SVI consolidates 16 structural, economic, and demographic variables from the American Community Survey (ACS) into 4 themes: socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. ACS estimates of the 16 variables for 2016-2022 were extracted for counties and ZIP code tabulation areas and for ZIP code geographies, crosswalked to ZIP-5, and aggregated to ZIP-3. Areas received a percentile ranking (range, 0-1) for SVI and each variable and composite theme, with higher values indicating greater social vulnerability.

Results

SVI estimates were produced for up to 3,143 counties, 32,243 ZIP-5s, and 886 ZIP-3s. SDoH trends across the US were largely consistent from 2016 to 2022 despite slight local changes over time. SVI varied across regions, with generally higher vulnerability in the South and lower vulnerability in the North and Northeast. When linked with health plan claims data, higher SVI (i.e., higher vulnerability) was associated with greater comorbidity burden.

Conclusion

SVI can be estimated at the ZIP-3 and ZIP-5 levels to provide area-level context, allowing for more routine integration of socioeconomic and health equity-related concepts into health claims and other datasets.

The Association Between the Digital Divide and Health Inequalities Among Older Adults in China: Nationally Representative Cross-Sectional Survey

BACKGROUND

Health inequalities among older adults become increasingly pronounced as aging progresses. In the digital era, some researchers argue that access to and use of digital technologies may contribute to or exacerbate these existing health inequalities. Conversely, other researchers believe that digital technologies can help mitigate these disparities.

OBJECTIVE

This study aimed to investigate the relationship between the digital divide and health inequality among older adults and to offer recommendations for promoting health equity.

METHODS

Data were obtained from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study. Physical, mental, and subjective health were assessed using the Activities of Daily Living (ADL) scale, the Instrumental Activities of Daily Living scale, the Mini-Mental State Examination scale, and a 5-point self-rated health scale, respectively. The chi-square and rank sum tests were used to explore whether internet use and access were associated with health inequality status. After controlling for confounders, multiple linear regression models were used to further determine this association. Sensitivity analysis was conducted using propensity score matching, and heterogeneity was analyzed for different influencing factors.

RESULTS

The 2018 analysis highlighted widening health disparities among older adults due to internet access and use, with statistically significant increases in inequalities in self-rated health (3.9%), ADL score (5.8%), and cognition (7.5%). Similarly, internet use widened gaps in self-rated health (7.5%) and cognition (7.6%). Conversely, the 2020 analysis demonstrated that internet access improved health disparities among older adults, reducing gaps in self-rated health (3.8%), ADL score (2.1%), instrumental ADL score (3.5%), and cognition (7.5%), with significant results, except for ADL. Internet use also narrowed disparities, with significant effects on self-rated health (4.8%) and cognition (12.8%). The robustness of the results was confirmed through propensity score-matching paired tests. In addition, the study found heterogeneity in the effects of internet access and use on health inequalities among older adults, depending on sex, age, education, and region.

CONCLUSIONS

The impact of internet access and use on health inequalities among older adults showed different trends in 2018 and 2020. These findings underscore the importance of addressing the challenges and barriers to internet use among older adults, particularly during the early stages of digital adoption. It is recommended to promote equitable access to the health benefits of the internet through policy interventions, social support, and technological advancements.

The Impact of Tocilizumab Coverage on Health Equity for Inpatients with COVID-19 in the USA: A Distributional Cost-Effectiveness Analysis

OBJECTIVES

We conducted a distributional cost-effectiveness analysis to evaluate how coverage of tocilizumab for inpatients with COVID-19 from 2021 to present impacted health equity in the USA.

METHODS

A published, payer-perspective, distributional cost-effectiveness analysis for inpatient COVID-19 treatments was adapted to include information on baseline health disparities across 25 equity-relevant subgroups based on race and ethnicity (5 census-based groups), and county-level social vulnerability (5 geographic quintiles). The underlying cost-effectiveness analysis was updated to reflect patient characteristics at admission, standard of care outcomes, tocilizumab efficacy, and contemporary unit costs. The distributional cost-effectiveness analysis inputs for COVID-19 hospitalization and subgroup risk adjustments based on social vulnerability were derived from published estimates. Opportunity costs were estimated by converting total tocilizumab spend into quality-adjusted life-years (QALYs), distributed equally across subgroups.

RESULTS

Tocilizumab treatment was cost effective across all subgroups. Treatment resulted in larger relative QALY gains in more socially vulnerable subgroups than less socially vulnerable subgroups, given higher hospitalization rates and inpatient mortality. Using an opportunity cost threshold of US$150,000/QALY and an Atkinson index of 11, tocilizumab was estimated to have improved social welfare by increasing population health (53,252 QALYs gained) and reducing existing overall US health inequalities by 0.003% since 2021.

CONCLUSIONS

Use of tocilizumab for COVID-19 since 2021 increased population health while improving health equity, as more patients with lower baseline health were eligible for treatment and received larger relative health gains. Future equitable access to tocilizumab for inpatients with COVID-19 is expected to lead to continued increases in population health and reductions in disparities.

Modeling the Population Equity of Alzheimer Disease Treatments in the US

Importance

The arrival of new medications for Alzheimer disease (AD) has prompted efforts to measure their value using conventional cost-effectiveness analyses; however, these analyses focus on how much health improvement new medications generate per dollar spent. As AD disproportionately affects older adults, women, racial and ethnic minority individuals, and individuals with lower socioeconomic and educational levels, it is critical to also examine the health equity outcomes of treatment.

Objective

To estimate the health equity impact of a hypothetical disease-modifying treatment for early AD in the US and to examine targeted policies to mitigate health care disparities.

Design, Setting, and Participants

This economic evaluation, which used a distributional cost-effectiveness analysis, was conducted from June 16, 2022, to January 11, 2024. The study included subgroups defined by race and ethnicity and by social vulnerability quintiles in the US.

Exposures

A hypothetical disease-modifying treatment compared with best supportive care.

Main Outcomes and Measures

The main outcomes were population-level quality-adjusted life-years (QALYs), lifetime costs, and net health benefits. The social welfare impact and change in health inequality were estimated using the Atkinson index.

Results

The distributional cost-effectiveness analysis simulated 316 037 100 individuals from the US population, including 25 subgroups defined by 5 categories of race and ethnicity and population quintiles of social vulnerability, with the fifth quintile representing the most socially vulnerable group. At an opportunity cost benchmark of $150 000 per QALY, treatment was associated with improved population health, adding 28 197 QALYs per year to the US population. Accounting for health inequality preferences (using an aversion level of 11, based on an Atkinson inequality aversion parameter that can range from 0 to infinity, with higher values assigning greater weight to health gains that accrue to the population with the lowest lifetime quality-adjusted life expectancy), treatment was associated with a 0.009% reduction in existing population health inequalities annually. Scenario analyses examining earlier and expanded treatment access suggested a population health improvement of up to 221 358 QALYs.

Conclusions and Relevance

The findings of this economic evaluation suggest that treatment for AD could improve population health and health equity. Policies to enable earlier diagnosis and treatment initiation, as well as expanded access to treatment, may further improve treatment and health equity impacts.

Area deprivation index and social vulnerability index in Milwaukee County: Impact on hospital outcomes after traumatic injuries

BACKGROUND

Predisposing factors for traumatic injuries are complex and variable. Neighborhood environments may influence injury mechanism or outcomes. The Social Vulnerability Index (SVI) identifies areas at risk for emergencies; Area Deprivation Index (ADI) measures socioeconomic disadvantage. The objective was to assess the impact of SVI or ADI on hospital length of stay (LOS) and mortality for injured patients to determine whether SVI or ADI indicated areas where injury prevention may be most impactful.

METHODS

Adult patients who resided in Milwaukee County and were treated for injuries from 2015 to 2022 at a level I trauma center were included. Patients' addresses were geocoded and merged with 2020 state-level SVI and ADI measures. SVI ranks census tracts 0-100 from least to most vulnerable. ADI ranks census block groups 1-10 from least to most disadvantaged. ADI and SVI rankings were converted to deciles. Statistical analyses included descriptive statistics, chi-square tests, and regression models for LOS and in-hospital mortality, adjusted for either SVI or ADI within separate models, age, sex, race or ethnicity, mechanism of injury (MOI), injury severity score (ISS).

RESULTS

14,542 patients were included; 63 % were male. Mean total hospital LOS was 6.4 ± 9.8 days, and in-hospital mortalities occurred in 5.2 % of patients. Based on SVI and ADI, 5,280 (36 %) patients resided in high vulnerability areas and 5,576 (39 %) lived in highly disadvantaged areas, respectively. After adjusting for patient factors, SVI deciles #6, 9, 10 were associated with increased hospital LOS, and SVI decile #5 was associated with in-hospital mortality (OR = 2.22, 95 %CI:1.06-4.63; p = 0.034). When adjusted for ADI, the 7th-10th deciles were associated with increased hospital LOS. Greater age and ISS were associated with increased hospital LOS and mortality when adjusted for SVI and ADI.

CONCLUSIONS

SVI and ADI identified a similar proportion of patients in high vulnerability or disadvantaged areas. Higher SVI and ADI deciles were associated with longer hospital LOS, and only the 5th SVI decile was associated with in-hospital mortality. Highly disadvantaged or vulnerable areas may have a longer LOS, but SVI and ADI have limited influence on trauma mortality. Continued research on neighborhood and community factors and trauma outcomes is needed.

Race and Ethnicity of Infants Enrolled in Neonatal Clinical Trials: A Systematic Review

Importance

Representativeness of populations within neonatal clinical trials is crucial to moving the field forward. Although racial and ethnic disparities in research inclusion are well documented in other fields, they are poorly described within neonatology.

Objective

To describe the race and ethnicity of infants included in a sample of recent US neonatal clinical trials and the variability in this reporting.

Evidence Review

A systematic search of US neonatal clinical trials entered into Cochrane CENTRAL 2017 to 2021 was conducted. Two individuals performed inclusion determination, data extraction, and quality assessment independently with discrepancies adjudicated by consensus.

Findings

Of 120 studies with 14 479 participants that met the inclusion criteria, 75 (62.5%) included any participant race or ethnicity data. In the studies that reported race and ethnicity, the median (IQR) percentage of participants of each background were 0% (0%-1%) Asian, 26% (9%-42%) Black, 3% (0%-12%) Hispanic, 0% (0%-0%) Indigenous (eg, Alaska Native, American Indian, and Native Hawaiian), 0% (0%-0%) multiple races, 57% (30%-68%) White, and 7% (1%-21%) other race or ethnicity. Asian, Black, Hispanic, and Indigenous participants were underrepresented, while White participants were overrepresented compared with a reference sample of the US clinical neonatal intensive care unit (NICU) population from the Vermont Oxford Network. Many participants were labeled as other race or ethnicity without adequate description. There was substantial variability in terms and methods of reporting race and ethnicity data. Geographic representation was heavily skewed toward the Northeast, with nearly one-quarter of states unrepresented.

Conclusions and Relevance

These findings suggest that neonatal research may perpetuate inequities by underrepresenting Asian, Black, Hispanic, and Indigenous neonates in clinical trials. Studies varied in documentation of race and ethnicity, and there was regional variation in the sites included. Based on these findings, funders and clinical trialists are advised to consider a 3-point targeted approach to address these issues: prioritize identifying ways to increase diversity in neonatal clinical trial participation, agree on a standardized method to report race and ethnicity among neonatal clinical trial participants, and prioritize the inclusion of participants from all regions of the US in neonatal clinical trials.