Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Urinary metals and heart rate variability: a cross-sectional study of urban adults in Wuhan, China

BACKGROUND

Epidemiological studies have suggested an association between external estimates of exposure to metals in air particles and altered heart rate variability (HRV). However, studies on the association between internal assessments of metals exposure and HRV are limited.

OBJECTIVES

The purpose of this study was to examine the potential association between urinary metals and HRV among residents of an urban community in Wuhan, China.

METHODS

We performed a cross-sectional analysis of 23 urinary metals and 5-min HRV indices (SDNN, standard deviation of normal-to-normal intervals; r-MSSD, root mean square of successive differences in adjacent normal-to-normal intervals; LF, low frequency; HF, high frequency; TP, total power) using baseline data on 2,004 adult residents of Wuhan.

RESULTS

After adjusting for other metals, creatinine, and other covariates, natural log-transformed urine titanium concentration was positively associated with all HRV indices (all p < 0.05). Moreover, we estimated negative associations between cadmium and r-MSSD, LF, HF, and TP; between lead and r-MSSD, HF, and TP; and between iron, copper, and arsenic and HF, SDNN, and LF, respectively, based on models adjusted for other metals, creatinine, and covariates (all p < 0.10). Several associations differed according to cardiovascular disease risk factors. For example, negative associations between cadmium and r-MSSD were stronger among participants ≤ 52 years of age (vs. > 52), current smokers (vs. nonsmokers), body mass index < 25 kg/m2 (vs. ≥ 25), and among those who were not hypertensive.

CONCLUSIONS

Urine concentrations of several metals were associated with HRV parameters in our cross-sectional study population. These findings need replication in other studies with adequate sample sizes.

Studying the impact of early life exposures to pesticides on the risk of testicular germ cell tumors during adulthood (TESTIS project): study protocol

BACKGROUND

The incidence of testicular germ cell tumors (TGCT), the most common cancer in men aged 15 to 45 years, has doubled over the last 30 years in developed countries. Reasons remain unclear but a role of environmental factors, especially during critical periods of development, is strongly suspected. Reliable data on environmental exposure during this critical time period are sparse. Little is known on whether it could be a combined effect of early and later-life exposures.

METHODS/DESIGN

Our research aims to study the association between TGCT risk and pesticide exposures (domestic, occupational and environmental) during critical time periods of development and combined early and later-life exposures. The study design, developed during a 2-year pilot study, is a multicenter case-control study of 500 cases (ascertained through histology) and 1000 fertile/fecund controls recruited through 21 French 'Centres d'Etude et de Conservation des Œufs et de Sperme humain' (CECOS). Trained professional interviewers interview the subjects and their mothers by phone. Using a geographic information system developed and tested for application in this study design, environmental pesticides exposure assessment is based on life-time residential history. Occupational pesticides exposures are assessed by an industrial hygienist based on parents' occupations and tasks. Exposures during the prenatal period, early childhood and puberty are focused. A blood sample is collected from each participant to assess genetic polymorphisms known to be associated with TGCT risk, as well as to explore gene-environment interactions.

DISCUSSION

The results of our study will contribute to better understanding the causes of TGCT and the rapid increase of its incidence. We explore the effect of combined early and later-life pesticides exposure from multiple sources, as well as potential gene-environment interactions that have until now been rarely studied for TGCT. Our design allows future pooled studies and the bio-bank allows additional genetic or toxicological analyses.

Using community level strategies to reduce asthma attacks triggered by outdoor air pollution: a case crossover analysis

BACKGROUND

Evidence indicates that asthma attacks can be triggered by exposure to ambient air pollutants, however, detailed pollution information is missing from asthma action plans. Asthma is commonly associated with four criteria pollutants with standards derived by the United States Environmental Protection Agency. Since multiple pollutants trigger attacks and risks depend upon city-specific mixtures of pollutants, there is lack of specific guidance to reduce exposure. Until multi-pollutant statistical modeling fully addresses this gap, some guidance on pollutant attack risk is required. This study examines the risks from exposure to the asthma-related pollutants in a large metropolitan city and defines the city-specific association between attacks and pollutant mixtures. Our goal is that city-specific pollution risks be incorporated into individual asthma action plans as additional guidance to prevent attacks.

METHODS

Case-crossover analysis and conditional logistic regression were used to measure the association between ozone, fine particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide pollution and 11,754 emergency medical service ambulance treated asthma attacks in Houston, Texas from 2004-2011. Both single and multi-pollutant models are presented.

RESULTS

In Houston, ozone and nitrogen dioxide are important triggers (RR = 1.05; 95% CI: 1.00, 1.09), (RR = 1.10; 95% CI: 1.05, 1.15) with 20 and 8 ppb increase in ozone and nitrogen dioxide, respectively, in a multi-pollutant model. Both pollutants are simultaneously high at certain times of the year. The risk attributed to these pollutants differs when they are considered together, especially as concentrations increase. Cumulative exposure for ozone (0-2 day lag) is of concern, whereas for nitrogen dioxide the concern is with single day exposure. Persons at highest risk are aged 46-66, African Americans, and males.

CONCLUSIONS

Accounting for cumulative and concomitant outdoor pollutant exposure is important to effectively attribute risk for triggering of an asthma attack, especially as concentrations increase. Improved asthma action plans for Houston individuals should warn of these pollutants, their trends, correlation and cumulative effects. Our Houston based study identifies nitrogen dioxide levels and the three-day exposure to ozone to be of concern whereas current single pollutant based national standards do not.

Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels

Objective

A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an “Environmental Risk Score (ERS)” as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.

Methods and Results

We examined 134 environmental pollutants in relation to serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides) using data from the National Health and Nutrition Examination Survey between 1999 and 2006. Using a two-stage approach, stage-1 for discovery (n = 10818) and stage-2 for validation (n = 4615), we identified 13 associated pollutants for total cholesterol, 9 for HDL, 5 for LDL and 27 for triglycerides with adjustment for sociodemographic factors, body mass index and serum nutrient levels. Using the regression coefficients (weights) from joint analyses of the combined data and exposure concentrations, ERS were computed as a weighted sum of the pollutant levels. We computed ERS for multiple lipid outcomes examined individually (single-phenotype approach) or together (multi-phenotype approach). Although the contributions of ERS to overall risk predictions for lipid outcomes were modest, we found relatively stronger associations between ERS and lipid outcomes than with individual pollutants. The magnitudes of the observed associations for ERS were comparable to or stronger than those for socio-demographic factors or BMI.

Conclusions

This study suggests ERS is a promising tool for characterizing disease risk from multi-pollutant exposures. This new approach supports the need for moving from a single-pollutant to a multi-pollutant framework.

Classification and regression trees for epidemiologic research: an air pollution example

BACKGROUND

Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures.

METHODS

We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model.

RESULTS

The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom).

CONCLUSIONS

Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.