A self-controlled approach to survival analysis, with application to air pollution and mortality

Short-term air pollution exposure and mortality in Brazil: Investigating the susceptible population groups

This is the first study to examine the association between ambient air pollution (PM2.5, O3, and NO2) and mortality (in different population groups by sex and age) based on a nationwide death record across Brazil over a 15-year period (2003-2017). We used a time-series analytic approach with a distributed lag model. Our study population includes 2,872,084 records of deaths in Brazil between 2003 and 2017. Men accounted for a higher proportion of deaths, with 58% for all-cause mortality, 54% for respiratory mortality, and 52% for circulatory mortality. Most individuals were over 65 years of age. Our results suggest an association between air pollution and mortality in Brazil. The direction, statistical significance, and effect size of these associations varied considerably by type of air pollutant, region, and population group (sex and age group). In particular, the older population group (>65 years) was most affected. The national meta-analysis for the entire data set (without stratification by sex and age) showed that for every 10 μg/m3 increase in PM2.5 concentration, the risk of death from respiratory diseases increased by 2.93% (95%CI: 1.42; 4.43). For every 10 ppb increase in O3, there is a 2.21% (95%CI: 0.59; 3.83) increase in the risk of all-cause mortality for the group of all people between 46 and 65 years old, and a 3.53% (95%CI: 0.34; 6.72) increase in the risk of circulatory mortality for the group of women, all ages. For every 10 ppb increase in NO2, the risk of respiratory mortality increases by 17.56% (95%CI: 4.44; 30.64) and the risk of all-cause mortality by 5.63% (95%CI: 1.83; 9.44). The results of our study provide epidemiological evidence that air pollution is associated with a higher risk of cardiorespiratory mortality in Brazil. Given the lack of nationwide studies on air pollution in Brazil, our research is an important contribution to the local and international literature that can provide better support to policymakers to improve air quality and public health.

NYS Nonprofit Hospital Assessment and Response to Environmental Pollution as Community Health Need: Prevalence in Community Benefit Practices

CONTEXT

Given the impact of environmental pollution on health and health inequity, there may be substantial value in integrating assessment and response to pollution into nonprofit hospital community benefit processes. Such hospital engagement has not yet been studied.

OBJECTIVES

We take a preliminary step of inquiry in investigating if nonprofit hospitals in New York State (NYS) assess, identify, or respond to environmental pollution as part of community benefit processes.

DESIGN

This study is of retrospective, observational design. Data were abstracted from community health needs reports (2015-2017), associated implementation plans, and related IRS (Internal Revenue Service) filings from a randomly geographically stratified selection of NYS nonprofit hospitals.

PARTICIPANTS

The sample includes 53 hospitals from 23 counties. The sampling frame consists of NYS nonspecialty private nonprofit hospitals.

MAIN OUTCOME MEASURES

Dichotomous findings for the following: (1) engagement of environmental pollution in the process of assessment of community health needs; (2) environmental pollution concern identified as a priority community health need; (3) strategic planning present to address pollution identified as community health need; and (4) action taken on same.

RESULTS

We found that 60.5% (95% confidence interval [CI], 0.46-0.74) of hospitals evidenced some form of assessment of environmental pollution and 18.9% (95% CI, 0.09-0.32) identified pollution as a priority community health need. However, no hospital went on to take independent or collaborative planning or action to address pollution. In additional analysis, we found that social justice in hospital mission was a positive predictor of assessment of environmental pollution.

CONCLUSIONS

For NYS hospitals, we found a substantial presence of assessment and identification of pollution as a community health concern. Our finding of the absence of response to environmental pollution represents a gap in community benefit implementation. This indicates a yet untaken opportunity to address racial and economic environmental health injustices and to improve population health.

Short-term air pollution exposure and hospital admissions for cardiorespiratory diseases in Brazil: A nationwide time-series study between 2008 and 2018

The established evidence associating air pollution with health is limited to populations from specific regions. Further large-scale studies in several regions worldwide are needed to support the literature to date and encourage national governments to act. Brazil is an example of these regions where little research has been performed on a large scale. To address this gap, we conducted a study looking at the relationship between daily PM_2.5, NO₂, and O₃, and hospital admissions for circulatory and respiratory diseases across Brazil between 2008 and 2018. A time-series analytic approach was applied with a distributed lag modeling framework. We used a generalized conditional quasi-Poisson regression model to estimate relative risks (RRs) of the association of each air pollutant with the hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. Our study population includes 23, 791, 093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. Our findings suggest significant associations of ambient air pollution (PM_2.5, NO₂, and O₃) with respiratory and circulatory hospital admissions in Brazil. The national meta-analysis for the whole population showed that for every increase of PM_2.5 by 10 μg/m³, there is a 3.28% (95%CI: 2.61; 3.94) increase in the risk of hospital admission for respiratory diseases. For O₃, we found positive associations only for some sub-group analyses by age and sex. For NO₂, our findings suggest that a 10 ppb increase in this pollutant, there was a 35.26% (95%CI: 24.07; 46.44) increase in the risk of hospital admission for respiratory diseases. This study may better support policymakers to improve the air quality and public health in Brazil.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements.