Mental Health and Resilience in Aging Populations: Insurance Systems, Risk Factors, and Public Health Strategies

Population aging is one of the defining public health challenges of this century: by 2050, the global population aged 60 and older will nearly double, and mental health conditions—particularly depression, anxiety, and social isolation—remain among the most under-recognized and under-treated health burdens in this group. The World Health Organization estimates that a significant share of older adults experience a mental or neurological disorder, yet mental health services for this population remain fragmented, underfunded, and inconsistently integrated with physical health care. A structural factor underlying much of this fragmentation has received comparatively little research attention: the design of health and long-term care insurance systems, which shapes who can access mental health services, at what cost, and with what downstream effect on care utilization and caregiver wellbeing. These effects are not distributed evenly—disparities in insurance coverage carry direct implications for mental health equity across rural, low-income, and minority populations, compounding existing gaps in access to community-based and digital health interventions.

This Research Topic aims to consolidate evidence on how insurance system design, caregiving structures, and social determinants shape mental health outcomes in older adults, and to translate that evidence into actionable public health and policy strategies. It further aims to highlight emerging computational approaches—machine learning and predictive analytics applied to insurance claims data, service utilization records, and risk stratification—as tools to inform more responsive and equitable policy evaluation.

We welcome Topics that include, but not limited to:

• Health insurance benefit design and its relationship to older adults’ access to mental health services, out-of-pocket burden, and care utilization patterns

• The role of long-term care insurance in alleviating caregiver strain and improving mental health outcomes for both caregivers and care recipients

• Disparities in insurance coverage and their implications for mental health equity across rural, low-income, and minority populations

• Machine learning and predictive analytics applied to insurance claims data, service utilization records, and risk stratification to inform policy evaluation

• Prevalence, incidence, and risk factors for depression, anxiety, and other common mental health conditions in older adults

• Social isolation, loneliness, and their measurable public health impact

• Community-based and home-based care models and their mental health outcomes

• Digital health, telehealth, and technology-enabled interventions for late-life mental health

• Resilience, protective factors, and positive aging frameworks

• Integration of mental health into primary care and elder-care policy