Although this research draws on U.S.-based data, many of the underlying insights into mental health trends, behavioural insights, and mortality risk are broadly applicable and may inform perspectives on the Canadian population.
Understanding the complex relationship between mental health and mortality is vital for the life insurance industry’s ability to anticipate emerging risks and better serve diverse populations. The Society of Actuaries Research Institute’s study, “Quantifying the Effects of Mental Health on U.S. Suicide and Mortality Rates,” sponsored in part by Munich Re Life US, leverages advanced statistical modeling to reveal how mental health indicators and socio-economic factors shape mortality outcomes across American communities. By integrating county-level data on depression, suicidal ideation, and social determinants, the research offers actionable insights for insurers and policymakers aiming to improve risk assessment, product design, and targeted interventions.
In this Q&A, Murali Niverthi, Director and Actuary at Munich Re Life US, shares his perspective on the study’s key findings and implications for the industry.
Why is it critical for the life insurance industry to better understand the links between mental health and mortality, and how does this research help address emerging risks?
Mental health has become an increasingly important component of population risk because it can be connected, directly or indirectly, to both mortality outcomes and disability experience. The spotlight on mental health has increased since the COVID-19 pandemic. For insurers, rising mental health conditions within a community can translate into changes in claims patterns, longevity trends, and cause-of-death experience, particularly for certain age groups. This research aims to quantify how mental health indicators relate to mortality at the community level, while accounting for geographic and temporal differences. The research provides a foundation for actuaries and policymakers to identify emerging trends, refine assumptions, and support public health initiatives that may have the greatest impact in mitigating risk.
The study uses statistical modeling to analyze county-level data. Can you explain how this approach improves our understanding of suicide and mortality risk compared to traditional models?
The study recognizes that risk can cluster geographically and persist over time, reflecting shared economic, social, and environmental conditions. By incorporating both geographic relationships and year-to-year patterns within a single framework, the study can produce more robust estimates of mortality impact in places with sparse populations, reducing “statistical noise” while still preserving local variation.
From an actuarial standpoint, that matters because it helps reveal persistent patterns that can be masked in simpler approaches. One example that stood out to me was how the analysis uncovered a concentrated region of elevated baseline suicide risk across parts of the Rocky Mountain states and the northern Great Plains, often referred to as the “suicide belt.” It’s an example of how the model can surface geographic patterns that warrant closer attention.
What were some of the most significant findings regarding the impact of mental health indicators, such as severe depression and suicidal ideation, on both suicide and all-cause mortality rates across different age groups and regions?
At a high level, the study finds that counties with higher levels of mental health burden also tend to experience higher suicide rates and higher overall mortality, even after adjusting for geography and time trends. These associations were strongest for younger age groups, where changes in mental health signals appear to track more strongly with adverse mortality outcomes than they do later in life. For several age groups, suicide rates increased over the study period from 2000 to 2023, although there was a decrease in 2020 when the COVID pandemic started. The results also underscore the importance of place: neighboring counties often exhibit similar risk profiles, highlighting the role of local context.
What factors interact with mental health to influence suicide outcomes?
The study also demonstrates that suicide outcomes are not explained by a single factor. Rather, they reflect a blend of mental health burden and broader community conditions. Several county-level characteristics, including measures related to education, housing value environment, marital patterns, and racial composition, show meaningful association with suicide risk, and those relationships can vary considerably by age and by sex. An important caveat to keep in mind when interpreting the study results is that the associations described are at the county, age, and sex level and not at the individual level.
One especially notable pattern involves education: higher educational attainment at the county level generally aligns with lower suicide rates for men, while for women, the relationship is less uniform and can shift direction later in life. This is an important distinction because many other predictors show more similar directional effects across sexes.
Based on the study’s results, how can integrating mental health surveillance data into actuarial models help insurers better identify emerging risks and support targeted interventions for vulnerable populations?
Integrating mental health surveillance indicators can strengthen actuarial modeling by improving explanatory power and help identify changing conditions earlier, particularly among at-risk populations and in regions where risk clusters persist. Because these community-level measures are associated with both suicide and all-cause mortality, they can complement traditional demographic and economic variables used in mortality experience studies analysis. Beyond modeling, responsible use of these signals may inform risk-mitigation-focused initiatives, such as wellness programs, and contribute to more data-informed decisions about effective interventions.
Read the full report here.
