Impact of long-term temporal trends in fine particulate matter (PM: An analysis of over 20 million Medicare beneficiaries2.5: An analysis of over 20 million Medicare beneficiaries) on associations of annual PM: An analysis of over 20 million Medicare beneficiaries2.5: An analysis of over 20 million Medicare beneficiaries exposure and mortality: An analysis of over 20 million Medicare beneficiaries

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Decreasing ambient fine particulate matter (PM2.5) concentrations over time together with increasing life expectancy raise concerns about temporal confounding of associations between PM2.5 and mortality. To address this issue, we examined PM2.5-associated mortality risk ratios (MRRs) estimated for approximately 20,000,000 US Medicare beneficiaries, who lived within six miles of an Environmental Protection Agency air quality monitoring site, between December 2000 and December 2012. We assessed temporal confounding by examining whether PM2.5-associated MRRs vary by study period length. We then evaluated three approaches to control for temporal confounding: (1) assessing exposures using the residual of PM2.5 regressed on time; (2) adding a penalized spline term for time to the health model; and (3) including a term that describes temporal variability in PM2.5 into the health model, with this term estimated using decomposition approaches. We found a 10 μg/m3 increase in PM2.5 exposure to be associated with a 1.20 times (95% confidence interval [CI] = 1.20, 1.21) higher risk of mortality across the 13-year study period, with the magnitude of the association decreasing with shorter study periods. MRRs remained statistically significant but were attenuated when models adjusted for long-term time trends in PM2.5. The residual-based, time-adjusted MRR equaled 1.12 (95% CI = 1.11, 1.12) per 10 μg/m3 for the 13-year study period and did not change when shorter study periods were examined. Spline- and decomposition-based approaches produced similar but less-stable MRRs. Our findings suggest that epidemiological studies of long-term PM2.5 can be confounded by long-term time trends, and this confounding can be controlled using the residuals of PM2.5 regressed on time.

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