Survival analysis with functions of mismeasured covariate histories: the case of chronic air pollution exposure in relation to mortality in the nurses' health study

Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes

Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long-term exposure toPM 2.5 $\text{PM}_{2.5}$ , in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2-year decline in the standard measure of cognition was 0.018 (95% CI, -0.034 to -0.001) units worse per 10μ g/m 3 $\mu \text{g/m}^3$ increase inPM 2.5 $\text{PM}_{2.5}$ exposure. After correction, the estimated impact ofPM 2.5 $\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, -0.059 to 0.005) units lower per 10μ g/m 3 $\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, -0.047 to -0.040) units per 1 year older after applying our correction method.

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations

Electronic health record (EHR)-derived data can be linked to geospatially distributed socioeconomic and environmental factors to conduct large-scale epidemiologic studies. Ambient NO2 is a known environmental risk factor for asthma. However, health exposure studies often rely on data from geographically sparse regulatory monitors that may not reflect true individual exposure. We contrasted use of interpolated NO2 regulatory monitor data with raw satellite measurements and satellite-derived ground estimates, building on previous work which has computed improved exposure estimates from remotely sensed data. Raw satellite and satellite-derived ground measurements captured spatial variation missed by interpolated ground monitor measurements. Multivariable analyses comparing these three NO2 measurement approaches (interpolated monitor, raw satellite, and satellite-derived) revealed a positive relationship between exposure and asthma exacerbations for both satellite measurements. Exposure-outcome relationships using the interpolated monitor NO2 were inconsistent with known relationships to asthma, suggesting that interpolated monitor data might yield misleading results in small region studies.

Systematic review of the association between long-term exposure to fine particulate matter and mortality

We used a transparent systematic review framework based on best practices for evaluating study quality and integrating evidence to conduct a review of the available epidemiology studies evaluating associations between long-term exposure to ambient concentrations of PM_2.5 and mortality (all-cause and non-accidental) conducted in North America. We found that while there is some consistency across studies for reporting positive associations, these associations are weak and several important methodological issues have led to uncertainties with regard to the evidence from these studies, including potential confounding by measured and unmeasured factors, exposue measurement error, and model misspecification. These uncertainties provide a plausible, alternative explanation to causality for the weakly positive findings across studies. Using a causality framework that incorporates best practices for making causal determinations, we concluded that the evidence for a causal relationship between long-term exposure to ambient PM_2.5 concentrations and mortality from these studies is inadequate.

Evaluating Public Health Interventions: 4. The Nurses' Health Study and Methods for Eliminating Bias Attributable to Measurement Error and Misclassification

The Nurses' Health Study and many other large longitudinal cohorts around the world use the food frequency questionnaire to assess dietary intake over time, and to relate diet to health. Controversies concerning this questionnaire's ability to adequately measure diet have led to a flurry of methods for evaluating the magnitude of measurement error and misclassification in exposure assessment, and for correcting the point and interval estimates of effect on the basis of these assessment methods for this error. Nurses' Health Study investigators have been in the forefront of these developments and their applications, although hundreds of other investigators have also used them. This commentary provides an overview of the methods and their uses, and concludes with remarks on their potential applications in the evaluation of public health interventions.