Contribution of involuntary job loss to the burden of depressive symptoms over two decades in a national study of aging adults.

OBJECTIVES: In recent decades, risk of job loss in America after age 50 has been high, potentially causing significant stress during the period preceding retirement. Yet no study has quantified the burden of clinically relevant depressive symptoms attributable to job loss in this age group over this period or identified the most vulnerable populations. METHODS: Participants aged 50+ in the Health and Retirement Study (recruited 1992-2016) who were employed and scored <5 on the Center for Epidemiologic Studies Depression 8-item scale (CESD-8) at baseline (N=18,571) were followed for depressive symptoms until they first had CESD-8≥5 or died, or through the 2018 survey. Parametric g-formula analyses examined the difference in cumulative risk of having CESD-8≥5 if there had been no involuntary job loss compared to the observed scenario, adjusting for sex, race/ethnicity, age, and dynamic measures of recent marriage end (divorce or widowhood), having a working spouse, assets/debt, and health changes. RESULTS: We estimated that risk of CESD-8≥5 would have been 1.1% (95%CI[0.55,1.37]) lower if no involuntary job loss had occurred; job loss accounted for 11% of the total burden among those who lost a job. Stronger associations were observed for women (1.2% [0.7,1.8] vs. men 0.5% [0.2,1.1]), white respondents (1.0% [0.6,1.5] vs. Black respondents 0.5% [-0.1,1.4]), and those in the lowest quartile of baseline assets (1.1% [0.4,1.9] vs. wealthiest quartile 0.5% [-0.4,0.9]). DISCUSSION: Involuntary job loss is associated with high depressive symptom burden in older persons, suggesting that screening and intervention soon after job loss may help mitigate depression.

Diesel exhaust and respiratory dust exposure in miners and chronic obstructive pulmonary disease (COPD) mortality in DEMS II.

BACKGROUND: Diesel exhaust and respirable dust exposures in the mining industry have not been studied in depth with respect to non-malignant respiratory disease including chronic obstructive pulmonary disease (COPD), with most available evidence coming from other settings. OBJECTIVES: To assess the relationship between occupational diesel exhaust and respirable dust exposures and COPD mortality, while addressing issues of survivor bias in exposed miners. METHODS: The study population consisted of 11,817 male workers from the Diesel Exhaust in Miners Study II, followed from 1947 to 2015, with 279 observed COPD deaths. We fit Cox proportional hazards models for the relationship between respirable elemental carbon (REC) and respirable dust (RD) exposure and COPD mortality. To address healthy worker survivor bias, we leveraged the parametric g-formula to assess effects of hypothetical interventions on both exposures. RESULTS: Cox models yielded elevated estimates for the associations between average intensity of REC and RD and COPD mortality, with hazard ratios (HR) corresponding to an interquartile range width increase in exposure of 1.46 (95 % confidence interval (CI): 1.12, 1.91) and 1.20 (95 % CI: 0.96, 1.49), respectively for each exposure. HRs for cumulative exposures were negative for both REC and RD. Based on results from the parametric g-formula, the risk ratio (RR) for COPD mortality comparing risk under an intervention eliminating REC to the observed risk was 0.85 (95 % CI: 0.55, 1.06), equivalent to an attributable risk of 15 %. The corresponding RR comparing risk under an intervention eliminating RD to the observed risk was 0.93 (95 % CI: 0.56, 1.31). CONCLUSIONS: Our findings, based on data from a cohort of nonmetal miners, are suggestive of an increased risk of COPD mortality associated with REC and RD, as well as evidence of survivor bias in this population leading to negative associations between cumulative exposures and COPD mortality in traditional regression analysis.

Measuring long-term exposure to wildfire PM2.5 in California: Time-varying inequities in environmental burden.

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 < 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups.