Benjamin Sabath’s research while affiliated with Harvard University and other places

Background: Recent studies have identified the association of environmental stressors with reduced kidney function and the development of kidney disease. While residential greenness has been linked to many health benefits, the association between residential greenness and the development of kidney disease is not clear. We aimed to investigate the association between residential greenness and the development of kidney disease. Methods: We performed a longitudinal population-based cohort study including all fee-for-service Medicare Part A beneficiaries (aged 65 years or older) in Massachusetts (2000-2016). We assessed greenness with the annual average Enhanced Vegetation Index (EVI) based on residential ZIP codes of beneficiaries. We applied Cox-equivalent Poisson models to estimate the association between EVI and first hospital admission for total kidney disease, chronic kidney disease (CKD), and acute kidney injury (AKI), separately. Results: Data for 1,462,949 beneficiaries who resided in a total of 644 ZIP codes were analyzed. The total person-years of follow-up for total kidney disease, CKD, and AKI were 9.8, 10.9, and 10.8 million person-years, respectively. For a 0.1 increase in annual EVI, the hazard ratios (HRs) were 0.95 (95% CI: 0.93 to 0.97) for the first hospital admission for total kidney disease, and the association was more prominent for AKI (HR: 0.94 with 95% CI: 0.92 to 0.97) than CKD (HR: 0.98 with 95% CI: 0.95-1.01]). The estimated effects of EVI on kidney disease were generally more evident in White beneficiaries and those residing in metropolitan areas compared to the overall population. Conclusions: This study found that higher levels of annual residential greenness were associated with a lower risk of the first hospital admission for kidney diseases. Results are consistent with the hypothesis that higher residential greenness benefits kidney patients.

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project. Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses. Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 μg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure. For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 μg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]). Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged. We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 μg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards. There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty. The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb. institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred. A list of abbreviations and other terms appears at the end of this volume.

Background Heat warnings are issued in advance of forecast extreme heat events, yet little evidence is available regarding their effectiveness in reducing heat-related illness and death. We estimated the association of heat warnings and advisories (collectively, “alerts”) issued by the United States National Weather Service with all-cause mortality and cause-specific hospitalizations among Medicare beneficiaries aged 65 years and older in 2,817 counties, 2006–2016. Methods In each county, we compared days with heat alerts to days without heat alerts, matched on daily maximum heat index and month. We used conditional Poisson regression models stratified on county, adjusting for year, day of week, federal holidays, and lagged daily maximum heat index. Results We identified a matched non-heat alert day for 92,029 heat alert days in 2,817 counties, or 54.6% of all heat alert days during the study period. Contrary to expectations, heat alerts were not associated with lower risk of mortality (RR: 1.005 [95% CI: 0.997, 1.013]). However, heat alerts were associated with higher risk of hospitalization for fluid and electrolyte disorders (RR: 1.040 [95% CI: 1.015, 1.065]) and heat stroke (RR: 1.094 [95% CI: 1.038, 1.152]). Results were similar in sensitivity analyses additionally adjusting for same-day heat index, ozone, and PM2.5. Conclusions Our results suggest that heat alerts are not associated with lower risk of mortality but may be associated with higher rates of hospitalization for fluid and electrolyte disorders and heat stroke, potentially suggesting that heat alerts lead more individuals to seek or access care.

Although research indicates health and well-being benefits of greenspace, little is known regarding how greenspace may influence adaptation to health risks from heat, particularly how these risks change over time. Using daily hospitalization rates of Medicare beneficiaries ≥65 years for 2000-2016 in 40 U.S. Northeastern urban counties, we assessed how temperature-related hospitalizations from cardiovascular causes (CVD) and heat stroke (HS) changed over time. We analyzed effect modification of those temporal changes by Enhanced Vegetation Index (EVI), approximating greenspace. We used a two-stage analysis including a generalized additive model and meta-analysis. Results showed that relative risk (RR) (per 1 • C increase in lag0-3 temperature) for temperature-HS hospitalization was higher in counties with the lowest quartile EVI (RR = 2.7, 95% CI: 2.0, 3.4) compared to counties with the highest quartile EVI (RR = 0.40, 95% CI: 0.14, 1.13) in the early part of the study period (2000-2004). RR of HS decreased to 0.88 (95% CI: 0.31, 2.53) in 2013-2016 in counties with the lowest quartile EVI. RR for HS changed over time in counties in the highest quartile EVI, with RRs of 0.4 (95% CI: − 0.7, 1.4) in 2000-2004 and 2.4 (95% CI: 1.6, 3.2) in 2013-2016. Findings suggest that adaptation to heat-health associations vary by greenness. Greenspace may help lower risks from heat but such health risks warrant continuous local efforts such as heat-health plans.

Daily 1km PM2.5 data are available on the NASA SEDAC website (https://beta.sedac.ciesin.columbia.edu/data/set/aqdh-pm2-5-concentrations-contiguous-us-1-km-2000-2016). The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.