BackgroundWe examined the associations of informal (eg, family members and friends) and formal (eg, physician and visiting nurses) social support with caregiver’s burden in long-term care and the relationship between the number of available sources of social support and caregiver burden.MethodsWe conducted a mail-in survey in 2003 and used data of 2998 main caregivers of frail older adults in Aichi, Japan. We used a validated scale to assess caregiver burden.ResultsMultiple linear regression demonstrated that, after controlling for caregivers’ sociodemographic and other characteristics, informal social support was significantly associated with lower caregiver burden (β = −1.59, P < 0.0001), while formal support was not (β = −0.30, P = 0.39). Evaluating the associations by specific sources of social support, informal social supports from the caregiver’s family living together (β = −0.71, P < 0.0001) and from relatives (β = −0.61, P = 0.001) were associated with lower caregiver burden, whereas formal social support was associated with lower caregiver burden only if it was from family physicians (β = −0.56, P = 0.001). Compared to caregivers without informal support, those who had one support (β = −1.62, P < 0.0001) and two or more supports (β = −1.55, P < 0.0001) had significantly lower burden. This association was not observed for formal support.ConclusionsSocial support from intimate social relationships may positively affect caregivers’ psychological wellbeing independent of the receipt of formal social support, resulting in less burden.

Accumulating evidence shows that a higher sense of purpose in life is associated with lower risk of chronic conditions and premature mortality. Health behaviors might partially explain these findings, however, the prospective association between sense of purpose and health behaviors is understudied. We tested whether a higher sense of purpose at baseline was associated with lower likelihood of developing unhealthy behaviors over time.

BackgroundEmpirical evidence investigating heterogeneous impact of retirement on mental health depending on social backgrounds is lacking, especially among older adults.MethodsWe examined the impact of changes in working status on changes in mental health using Japanese community-dwelling adults aged ≥65 years participating in the Japan Gerontological Evaluation Study between 2010 and 2013 (N = 62,438). Between-waves changes in working status (“Kept working”, “Retired”, “Started work”, or “Continuously retired”) were used to predict changes in depressive symptoms measured by the Geriatric Depression Scale. First-difference regression models were stratified by gender, controlling for changes in time-varying confounding actors including equivalised household income, marital status, instrumental activities of daily living, incidence of serious illnesses and family caregiving. We then examined the interactions between changes in working status and occupational class, changes in marital status, and post-retirement social participation.ResultsParticipants who transitioned to retirement reported significantly increased depressive symptoms (β = 0.33, 95% CI: 0.21–0.45 for men, and β = 0.29, 95% CI: 0.13–0.45 for women) compared to those who kept working. Men who were continuously retired reported increased depressive symptoms (β = 0.13, 95% CI: 0.05–0.20), whereas males who started work reported decreased depressive symptoms (β = −0.20, 95% CI: -0.38–-0.02). Men from lower occupational class (compared to men from higher class) reported more increase in depressive symptoms when continuously retired (β = −0.16, 95% CI: -0.25–-0.08). Those reporting recreational social participation after retirement appeared to be less influenced by transition to retirement.ConclusionsRetirement may increase depressive symptoms among Japanese older adults, particularly men from lower occupational class backgrounds. Encouraging recreational social participation may mitigate the adverse effects of retirement on mental health of Japanese older men.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-017-4427-0) contains supplementary material, which is available to authorized users.

Background 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). Methods 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. Results 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). Conclusions 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.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record.

AimLong-term care systems may alleviate caregiver burdens, particularly for those with fewer resources. However, it remains unclear whether socioeconomic disparity in caregiver burdens exists under a public, universal long-term care insurance (LTCI) system. This study examined income-based inequalities in caregiving time and depressive symptoms in Japanese older family caregivers. We further compared inequality in depressive symptoms with that of non-caregivers to evaluate whether family caregiving exacerbates this disparity.MethodsData were obtained from a cross-sectional, nationwide survey conducted by the Japan Gerontological Evaluation Study in 2013. Participants were functionally independent older adults aged ≥65 years (N = 21,584). Depressive symptoms were assessed using the Geriatrics Depression Scale (GDS); caregiving hours per week, household income, and other covariates were also assessed.ResultsFamily caregivers occupied 8.3% of the total. A Poisson regression model revealed that caregivers in lower income groups (compared to those in the highest) were 1.32 to 1.95 and 1.63 to 2.68 times more likely to engage in ≥36 and ≥72 hours/week of caregiving, respectively. As for the GDS (≥5), an excess risk was found in the caregivers in lower (compared to higher) income groups (adjusted prevalence ratio: 1.57–3.10). However, an interaction effect of income by caregiving role indicated no significant difference in inequality between caregivers and non-caregivers (p = .603). The excess risk for GDS (≥5) in the caregivers compared to non-caregivers was observed across income groups.ConclusionsOur findings revealed a possible disparity in family caregivers under the public LTCI system. Further studies should examine factors associated with longer caregiving hours in lower income households. Our findings also suggest the necessity for more efforts to alleviate depressive symptoms in family caregivers under the LTCI system regardless of income level, rather than exclusively supporting those with a low income.

No abstract

We investigated the association between disaster experience and the cardiometabolic risk of survivors 2.5 years after disaster onset, adjusting for health information predating the disaster, using natural experiment data stemming from the 2011 Great East Japan Earthquake and Tsunami. We used data from a cohort of adults aged 65 years or older in Iwanuma City, Japan, located 80 km (128 miles) west of the earthquake epicenter. The baseline survey was completed 7 months before the disaster, and the follow-up survey was performed among survivors approximately 2.5 years after the disaster. The survey data were linked to medical records with information on objectively measured cardiometabolic risk factors (n = 1,195). The exposure of interest was traumatic disaster experiences (i.e., housing damage and loss of loved ones). Fixed-effects regression showed that complete housing destruction was significantly associated with a 0.81-unit greater change in body mass index (weight (kg)/height (m)2; 95% confidence interval (CI): 0.24, 1.38), a 4.26-cm greater change in waist circumference (95% CI: 1.12, 7.41), and a 4.77-mg/dL lower change in high-density lipoprotein cholesterol level (95% CI: −7.96, −1.58) as compared with no housing damage. We also observed a significant association between major housing damage and decreased systolic blood pressure. Continued health checkups and supports for victims who lost homes should be considered to maintain their cardiometabolic health.