Despite these strengths, a few limitations should be noted. Firstly, since we do not have historical medical records of the study population, we were not able to determine the incidence of the chronic diseases studied and we were limited to focusing on prevalence risks. Furthermore, since the information of chronic disease comorbidity was not collected by the CNBSS, we were unable to exclude baseline prevalence chronic diseases. Secondly, without incidence date of the chronic diseases, we were not able to determine the prevalence of multiple comorbidity of more than one chronic disease at any given time. Some women may have more than one chronic disease and therefore they Anastrozole may be included more than once in the prevalence analyses. Thirdly, cumulative exposure measures were based on data from 1998 to 2006 and extrapolated to other years. This extrapolation may under- or over-estimate exposures. PM2.5 was higher in earlier years, but lower in more recent years. Thus, PM2.5 levels may be underestimated from 1992 to 1997 and overestimated from 2007 to 2013. Lastly, as demographic and lifestyle covariates were only collected at baseline, we were not able to model potential time-dependent effects. For example, lack of BMI data over time limits our ability to truly understand the effects of changing BMI on disease prevalence. A study in Canada by Hopman et al. (2007) found thorax although men under the age of 45 and women under the age of 55 gained approximately 0.45 kg (1 lb) per year, which leveled off with increased age and reversed in the oldest age groups, most remained in the same BMI category. This suggests that the BMI categories in our ONBSS cohort likely remained relatively stable over time. The potential for reverse causality between obesity and disease prevalence should also be noted and caution should be taken when interpreting the BMI results.