At-a-glance - Bringing equity into the fold: a review of interventions to improve mental health

Advancing healthcare equity through dissemination and implementation science

OBJECTIVE

To provide guiding principles and recommendations for how approaches from the field of dissemination and implementation (D&I) science can advance healthcare equity.

DATA SOURCES AND STUDY SETTING

This article, part of a special issue sponsored by the Agency for Healthcare Research and Quality (AHRQ), is based on an outline drafted to support proceedings of the 2022 AHRQ Health Equity Summit and further revised to reflect input from Summit attendees.

STUDY DESIGN

This is a narrative review of the current and potential applications of D&I approaches for understanding and advancing healthcare equity, followed by discussion and feedback with Summit attendees.

DATA COLLECTION/EXTRACTION METHODS

We identified major themes in narrative and systematic reviews related to D&I science, healthcare equity, and their intersections. Based on our expertise, and supported by synthesis of published studies, we propose recommendations for how D&I science is relevant for advancing healthcare equity. We used iterative discussions internally and at the Summit to refine preliminary findings and recommendations.

PRINCIPAL FINDINGS

We identified four guiding principles and three D&I science domains with strong promise for accelerating progress toward healthcare equity. We present eight recommendations and more than 60 opportunities for action by practitioners, healthcare leaders, policy makers, and researchers.

CONCLUSIONS

Promising areas for D&I science to impact healthcare equity include the following: attention to equity in the development and delivery of evidence-based interventions; the science of adaptation; de-implementation of low-value care; monitoring equity markers; organizational policies for healthcare equity; improving the economic evaluation of implementation; policy and dissemination research; and capacity building.

Implementation science should give higher priority to health equity

Background

There is growing urgency to tackle issues of equity and justice in the USA and worldwide. Health equity, a framing that moves away from a deficit mindset of what society is doing poorly (disparities) to one that is positive about what society can achieve, is becoming more prominent in health research that uses implementation science approaches. Equity begins with justice—health differences often reflect societal injustices. Applying the perspectives and tools of implementation science has potential for immediate impact to improve health equity.

Main text

We propose a vision and set of action steps for making health equity a more prominent and central aim of implementation science, thus committing to conduct implementation science through equity-focused principles to achieve this vision in U.S. research and practice. We identify and discuss challenges in current health disparities approaches that do not fully consider social determinants. Implementation research challenges are outlined in three areas: limitations of the evidence base, underdeveloped measures and methods, and inadequate attention to context. To address these challenges, we offer recommendations that seek to (1) link social determinants with health outcomes, (2) build equity into all policies, (3) use equity-relevant metrics, (4) study what is already happening, (5) integrate equity into implementation models, (6) design and tailor implementation strategies, (7) connect to systems and sectors outside of health, (8) engage organizations in internal and external equity efforts, (9) build capacity for equity in implementation science, and (10) focus on equity in dissemination efforts.

Conclusions

Every project in implementation science should include an equity focus. For some studies, equity is the main goal of the project and a central feature of all aspects of the project. In other studies, equity is part of a project but not the singular focus. In these studies, we should, at a minimum, ensure that we “leave no one behind” and that existing disparities are not widened. With a stronger commitment to health equity from funders, researchers, practitioners, advocates, evaluators, and policy makers, we can harvest the rewards of the resources being invested in health-related research to eliminate disparities, resulting in health equity.

Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study

PURPOSE

To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia.

DESIGN

A population-based, cross-sectional national health survey on mental health and its risk factors across Australia.

SETTING

National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS).

PARTICIPANTS

Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons.

MEASURES

Patient-reported mental disorder outcomes.

ANALYSIS

Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders.

RESULTS

Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups.

CONCLUSION

The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.