SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.
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ABSTRACT:
SUBMITTER: Warren JL
PROVIDER: S-EPMC8189329 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Short-term exposure to air pollution has been associated with ischemic stroke (IS) hospitalizations, but the evidence of its effects on IS in low- and middle-income countries is limited and inconsistent. We aimed to quantitatively estimate the association between air pollution and hospitalizations for IS in Chongqing, China. This time series study included 2,299 inpatients with IS from three hospitals in Chongqing from January 2015 to December 2016. Generalized linear regression models combined with a distributed lag nonlinear model (DLNM) were used to investigate the impact of air pollution on IS hospitalizations. Stratification analysis was further implemented by sex, age, and season. The maximum lag-specific and cumulative percentage changes of IS were 1.2% (95% CI: 0.4-2.1%, lag 3 day) and 3.6% (95% CI: 0.5-6.7%, lag 05 day) for each 10 μg/m3 increase in PM2.5; 1.0% (95% CI: 0.3-1.7%, lag 3 day) and 2.9% (95% CI: 0.6-5.2%, lag 05 day) for each 10 μg/m3 increase in PM10; 4.8% (95% CI: 0.1-9.7%, lag 4 day) for each 10 μg/m3 increase in SO2; 2.5% (95% CI: 0.3-4.7%, lag 3 day) and 8.2% (95% CI: 0.9-16.0%, lag 05 day) for each 10 μg/m3 increase in NO2; 0.7% (95% CI: 0.0-1.5%, lag 6 day) for each 10 μg/m3 increase in O3. No effect modifications were detected for sex, age, and season. Our findings suggest that short-term exposure to PM2.5, PM10, SO2, NO2, and O3 contributes to more IS hospitalizations, which warrant the government to take effective actions in addressing air pollution issues.
| S-EPMC8804166 | biostudies-literature
Project description:Traditional studies of short-term air pollution health effects use time series data, while cohort studies generally focus on long-term effects. There is increasing interest in exploiting individual level cohort data to assess short-term health effects in order to understand the mechanisms and time scales of action. We extend semiparametric regression methods used to adjust for unmeasured confounding in time series studies to the cohort setting. Time series methods are not directly applicable since cohort data are typically collected over a prespecified time period and include exposure measurements on days without health observations. Therefore, long-time asymptotics are not appropriate, and it is possible to improve efficiency by exploiting the additional exposure data. We show that flexibility of the semiparametric adjustment model should match the complexity of the trend in the health outcome, in contrast to the time series setting where it suffices to match temporal structure in the exposure. We also demonstrate that pre-adjusting exposures concurrent with the health endpoints using trends in the complete exposure time series results in unbiased health effect estimation and can improve efficiency without additional confounding adjustment. A recently published article found evidence of an association between short-term exposure to ambient fine particulate matter (PM2.5 ) and retinal arteriolar diameter as measured by retinal photography in the Multi-Ethnic Study of Atherosclerosis (MESA). We reanalyze the data from this article in order to compare the methods described here, and we evaluate our methods in a simulation study based on the MESA data.
| S-EPMC4080094 | biostudies-literature
Project description:Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.
| S-EPMC7319179 | biostudies-literature
Project description:BACKGROUND:Although ambient concentrations of particulate matter ?10 ?m (PM10) are often used as proxies for total personal exposure, correlation (r) between ambient and personal PM10 concentrations varies. Factors underlying this variation and its effect on health outcome-PM exposure relationships remain poorly understood. METHODS:We conducted a random-effects meta-analysis to estimate effects of study, participant, and environmental factors on r; used the estimates to impute personal exposure from ambient PM10 concentrations among 4,012 nonsmoking, participants with diabetes in the Women's Health Initiative clinical trial; and then estimated the associations of ambient and imputed personal PM10 concentrations with electrocardiographic measures, such as heart rate variability. RESULTS:We identified 15 studies (in years 1990-2009) of 342 participants in five countries. The median r was 0.46 (range = 0.13 to 0.72). There was little evidence of funnel plot asymmetry but substantial heterogeneity of r, which increased 0.05 (95% confidence interval = 0.01 to 0.09) per 10 µg/m increase in mean ambient PM10 concentration. Substituting imputed personal exposure for ambient PM10 concentrations shifted mean percent changes in electrocardiographic measures per 10 µg/m increase in exposure away from the null and decreased their precision, for example, -2.0% (-4.6% to 0.7%) versus -7.9% (-15.9% to 0.9%), for the standard deviation of normal-to-normal RR interval duration. CONCLUSIONS:Analogous distributions and heterogeneity of r in extant meta-analyses of ambient and personal PM2.5 concentrations suggest that observed shifts in mean percent change and decreases in precision may be generalizable across particle size.
| S-EPMC3973436 | biostudies-literature
Project description:BACKGROUND:Ambient air pollution is associated with systemic increases in oxidative stress, to which sperm are particularly sensitive. Although decrements in semen quality represent a key mechanism for impaired fecundability, prior research has not established a clear association between air pollution and semen quality. To address this, we evaluated the association between ambient air pollution and semen quality among men with moderate air pollution exposure. METHODS:Of 501 couples in the LIFE study, 467 male partners provided one or more semen samples. Average residential exposure to criteria air pollutants and fine particle constituents in the 72?days before ejaculation was estimated using modified Community Multiscale Air Quality models. Generalized estimating equation models estimated the association between air pollutants and semen quality parameters (volume, count, percent hypo-osmotic swollen, motility, sperm head, morphology and sperm chromatin parameters). Models adjusted for age, body mass index, smoking and season. RESULTS:Most associations between air pollutants and semen parameters were small. However, associations were observed for an interquartile increase in fine particulates ?2.5?µm and decreased sperm head size, including -0.22 (95% CI -0.34, -0.11) µm2 for area, -0.06 (95% CI -0.09, -0.03) µm for length and -0.09 (95% CI -0.19, -0.06) µm for perimeter. Fine particulates were also associated with 1.03 (95% CI 0.40, 1.66) greater percent sperm head with acrosome. CONCLUSIONS:Air pollution exposure was not associated with semen quality, except for sperm head parameters. Moderate levels of ambient air pollution may not be a major contributor to semen quality.
| S-EPMC5878741 | biostudies-literature
Project description:The emergence of a new coronavirus (COVID-19) has become a major global concern that has damaged human health and disturbing environmental quality. Some researchers have identified a positive relationship between air pollution (fine particulate matter PM2.5) and COVID-19. Nonetheless, no inclusive investigation has comprehensively examined this relationship for a tropical climate such as India. This study aims to address this knowledge gap by investigating the nexus between air pollution and COVID-19 in the ten most affected Indian states using daily observations from 9th March to September 20, 2020. The study has used the newly developed Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag (NPARDL) model for asymmetric analysis. Empirical results illustrate an asymmetric relationship between PM2.5 and COVID-19 cases. More precisely, a 1% change in the positive shocks of PM2.5 increases the COVID-19 cases by 0.439%. Besides, the estimates of individual states expose the heterogeneous effects of PM2.5 on COVID-19. The asymmetric causality test of Hatemi-J's (2011) also suggests that the positive shocks on PM2.5 Granger-cause positive shocks on COVID19 cases. Research findings indicate that air pollution is the root cause of this outbreak; thus, the government should recognize this channel and implement robust policy guidelines to control the spread of environmental pollution.
| S-EPMC8800540 | biostudies-literature
Project description:We introduce a Bayesian spatial-temporal hierarchical multivariate probit regression model that identifies weeks during the first trimester of pregnancy which are impactful in terms of cardiac congenital anomaly development. The model is able to consider multiple pollutants and a multivariate cardiac anomaly grouping outcome jointly while allowing the critical windows to vary in a continuous manner across time and space. We utilize a dataset of numerical chemical model output which contains information regarding multiple species of PM2.5. Our introduction of an innovative spatial-temporal semiparametric prior distribution for the pollution risk effects allows for greater flexibility to identify critical weeks during pregnancy which are missed when more standard models are applied. The multivariate kernel stick-breaking prior is extended to include space and time simultaneously in both the locations and the masses in order to accommodate complex data settings. Simulation study results suggest that our prior distribution has the flexibility to outperform competitor models in a number of data settings. When applied to the geo-coded Texas birth data, weeks 3, 7 and 8 of the pregnancy are identified as being impactful in terms of cardiac defect development for multiple pollutants across the spatial domain.
| S-EPMC3589577 | biostudies-literature
Project description:Purpose of reviewDuring the past century, exposure to particulate matter (PM) air pollution < 2.5 μm in diameter (PM2.5) has emerged as an all-pervading element of modern-day society. This increased exposure has come at the cost of heightened risk for cardiovascular (CV) morbidity and mortality. Not only can short-term PM2.5 exposure trigger acute CV events in susceptible individuals, but longer-term exposure over years augments CV risk to a greater extent in comparison with short-term exposure. The purpose of this review is to examine the available evidence for how ambient air pollution exposure may precipitate events at various time frames.Recent findingsRecent epidemiological studies have demonstrated an association between ambient PM2.5 exposure and the presence and progression of atherosclerosis in humans. Multiple animal exposure experiments over two decades have provided strong corroborative evidence that chronic exposure in fact does enhance the progression and perhaps vulnerability characteristics of atherosclerotic lesions. Evidence from epidemiological studies including surrogates of atherosclerosis, human translational studies, and mechanistic investigations utilizing animal studies have improved our understanding of how ambient air pollution may potentiate atherosclerosis and precipitate cardiovascular events. Even so, future research is needed to fully understand the contribution of different constituents in ambient air pollution-mediated atherosclerosis as well as how other systems may modulate the impact of exposure including adaptive immunity and the gut microbiome. Nevertheless, due to the billions of people continually exposed to PM2.5, the long-term pro-atherosclerotic effects of this ubiquitous air pollutant are likely to be of enormous and growing global public health importance.
| S-EPMC8379601 | biostudies-literature
Project description:Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.
| S-EPMC6454578 | biostudies-literature
Project description:The paper describes a Bayesian spatial discrete time survival model to estimate the effect of air pollution on the risk of preterm birth. The standard approach treats prematurity as a binary outcome and cannot effectively examine time varying exposures during pregnancy. Time varying exposures can arise either in short-term lagged exposures due to seasonality in air pollution or long-term cumulative exposures due to changes in length of exposure. Our model addresses this challenge by viewing gestational age as time-to-event data where each pregnancy becomes at risk at a prespecified time (e.g. the 28th week). The pregnancy is then followed until either a birth occurs before the 37th week (preterm), or it reaches the 37th week, and a full-term birth is expected. The model also includes a flexible spatially varying baseline hazard function to control for unmeasured spatial confounders and to borrow information across areal units. The approach proposed is applied to geocoded birth records in Mecklenburg County, North Carolina, for the period 2001-2005.We examine the risk of preterm birth that is associated with total cumulative and 4-week lagged exposure to ambient fine particulate matter.
| S-EPMC3864864 | biostudies-literature