Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

El proyecto EMECAS: protocolo del estudio multicéntrico en España de los efectos a corto plazo de la contaminación atmosférica sobre la salud.

The EMECAM Project demonstrated the short-term effect of air pollution on the death rate in 14 cities in Spain throughout the 1990-1995 period. The Spanish Multicentre Study on Health Effects of Air Pollution (EMECAS) is broadening these objectives by incorporating more recent data, information on hospital disease admissions and totaling 16 Spanish cities. This is an ecological time series study in which the response variables are the daily deaths and the emergency hospitalizations due to circulatory system diseases and respiratory diseases among the residents in each city. Pollutants analyses: suspended particles, SO2, NO2, CO and O3. Control variables: meteorological, calendar, seasonality and influenza trend and incidence. Statistical analysis: estimate of the association in each city by means of the construction of generalized additive Poisson regression models and metanalysis for obtaining combined estimators. The EMECAS Project began with the creation of three working groups (Exposure, Epidemiology and Analysis Methodology) which defined the protocol. The average levels of pollutants were below those established under the current regulations for sulfur dioxide, carbon monoxide and ozone. The NO2 and PM10 values were around those established under the regulations (40 mg/m3). This is the first study of the relationship between air pollution and disease rate among one group of Spanish cities. The pollution levels studied are moderate for some pollutants, although for others, especially NO2 and particles, these levels could entail a problem with regard to complying with the regulations in force.