Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (i.e., matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated for.

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Having a purpose in life has been linked to improved health and wellbeing; however, it remains unknown whether having “Ikigai”—a related but broader concept in Japan—is also beneficial for various physical and psychosocial outcomes. Using data from a nationwide longitudinal study of Japanese older adults aged ≥65 years, we examined the associations between having Ikigai in 2013 and a wide range of subsequent outcomes assessed in 2016 across two databases (n = 6,441 and n = 8,041), including dimensions of physical health, health behavior, psychological distress, social wellbeing, subjective wellbeing, and pro-social/altruistic behaviors. We adjusted for sociodemographic characteristics and the outcome values (whenever data were available) in the prior wave (2010). Having Ikigai (vs. not having Ikigai) was associated with a 31% lower risk of developing functional disability [95% confidence interval (CI) for risk ratio: 0.58, 0.82] and 36% lower risk of developing dementia [95% CI for risk ratio: 0.48, 0.86] during the three-year follow-up. Having Ikigai was associated with decreased depressive symptoms and hopelessness as well as higher happiness, life satisfaction, instrumental activity of daily living, and certain social outcomes (e.g., more frequent participation in hobby clubs). Some of these associations were stronger for men than women, and among individuals with high socioeconomic status (p-values for effect measure modification < 001). Having Ikigai may promote health and wellbeing outcomes among Japanese older adults, but particularly men and individuals with high socioeconomic status. NIH, John Templeton Foundation, JSPS, AMED, MHLW, MEXT, and WPE Foundation.

Background: Social participation has been suggested as a means to prevent depressive symptoms. However, it remains unclear whether a one-time boost suffices or whether participation needs to be sustained over time for long-term prevention. We estimated the impacts of alternative hypothetical interventions in social participation on subsequent depressive symptoms among older adults. Methods: Data were from a nationwide prospective cohort study of Japanese older adults ≥65 years of age (n = 32,748). We analyzed social participation (1) as a baseline exposure from 2010 (approximating a one-time boost intervention) and (2) as a time-varying exposure from 2010 and 2013 (approximating a sustained intervention). We defined binary depressive symptoms in 2016 using the Geriatric Depression Scale. We used the doubly robust targeted maximum likelihood estimation to address time-dependent confounding. Results: The magnitude of the association between sustained participation and the lower prevalence of depressive symptoms was larger than the association observed for baseline participation only (e.g., prevalence ratio [PR] for participation in any activity = 0.83 [95% confidence interval = 0.79, 0.88] vs. 0.90 [0.87, 0.94]). For activities with a lower proportion of consistent participation over time (e.g., senior clubs), there was little evidence of an association between baseline participation and subsequent depressive symptoms, while an association for sustained participation was evident (e.g., PR for senior clubs = 0.96 [0.90, 1.02] vs. 0.88 [0.79, 0.97]). Participation at baseline but withholding participation in 2013 was not associated with subsequent depressive symptoms. Conclusions: Sustained social participation may be more strongly associated with fewer depressive symptoms among older adults.

Older adults with low socioeconomic status and health problems may experience greater postdisaster cognitive disability.