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.

Detección de violencia contra la mujer en la consulta del médico de familia.

Objectives. To find out the magnitude of violence against female partners among patients who visit their family doctor. To study frequency and acceptance of its investigation by the family doctor and to assess the effectiveness of a screening question on abuse. Design. Descriptive, cross-sectional study. Setting. Primary care, 4 samples from 2 urban health centres in Jaén, Spain. Participants. Who participated 170 women randomly selected from the female consulting population. Measurements. Interviews by means of the Bradley modified test and the anxiety and depression Goldberg scales. Perceived health, frequency of detection of domestic violence, by the family doctor, and female opinions were also studied. Results. During the last year, abuse against women was detected in 22.9% of the female population consulting their family doctor (95% confidence interval [95% CI], 16.6-29.2). Abused women had a worse perception of health (odds ratio [OR] =4.2; 95% CI, 1.02-17.5) and a higher probability of depression (OR=4.7; 95% CI, 1.8-12.5) independently from the rest of variables. The question "How are the things going with your partner?" as a screening of abuse does obtain a positive probability quotient of 6.23 (95% CI, 3.6-10.9), a specificity of 89% and a negative predictive value of 90%. Of those interviewed, 96.5% would not mind if their family doctor approached the couple's relationships, a situation that occurs in 24.7% of cases. Conclusions. Some degree of abuse was detected in almost a quarter of women who consult their family doctor. Family doctors do not usually ask about family and partner relationships and environment, although for almost all women it is well appreciated and the item has an increased likelihood ratio and high negative predictive value in detecting abuse.