18 Health-related participation bias

assessment Table 2 shows that there are several small done yet often statistically significant differences in the prevalence of selected disorders. The most notable differences (with no overlap between the 95% CIs of the prevalence rates) were seen for migraine and hypertension, which were more prevalent, and for diabetes and COPD (in particular among older women), which were less prevalent in the cohort compared with the source population. Table 2 Prevalence rates (per 1000) of selected disorders among cohort members compared with the SP Strengths and limitations In the AMIGO, participants will be prospectively followed through linkages to registries and follow-up measurements, such as questionnaires. A major strength of the prospective AMIGO for this field of research is

its focus on environmental and occupational health from the outset, including a broad range of determinants and health outcomes. For example, baseline or current residential address or job is usually taken to model exposures. In AMIGO, we aim to extend this to health effects of exposures across the life course based on full residential and full occupational histories up to baseline supplemented with updates during prospective follow-up. While there is no reason to suspect differential recall bias by disease status, we will evaluate the potential cohort effects in future analyses, for example, related to differential recall or to incomplete job history because some participants were still working at the time of the questionnaires. Another major asset of AMIGO is the availability of medical information from the EMRs of the general practitioners of the cohort members, not only at baseline but also for longitudinal follow-up because of the recruitment within an established information

and surveillance network. This offers several rather unique opportunities. First, as shown here, unlike many other epidemiological studies, we were able to assess potential participation bias at baseline using aggregate data from the EMRs of the source population. The EMR data of the cohort members also enable us to assess future attrition GSK-3 bias in the active follow-up by means of questionnaires. Second, besides the more usual registry linkages to obtain causes of death and cancer incidence, the additional medical data from general practitioners (diagnoses, prescriptions and referrals) enable us to study other recorded health outcomes, for which other cohort studies mostly rely on self-reported questionnaire data that are prone to reporting and recall bias and selective loss to follow-up. In particular, the main focus of prospective epidemiological studies has traditionally been on cancer, respiratory and cardiovascular health.