Background : Body mass index (BMI) is an important measure of adiposity. While BMI derived from self‐reported data generally agrees well with that derived from measured values, evidence from Australia is limited, particularly for the elderly. Methods : We compared self‐reported with measured height and weight in a random sample of 608 individuals aged ≥45 from the 45 and Up Study, an Australian population‐based cohort study. We assessed degree of agreement and correlation between measures, and calculated sensitivity and specificity to quantify BMI category misclassification. Results : On average, in males and females respectively, height was overestimated by 1.24cm (95% CI: 0.75–1.72) and 0.59cm (0.26–0.92); weight was underestimated by 1.68kg (–1.99– ‐1.36) and 1.02kg (–1.24– ‐0.80); and BMI based on self‐reported measures was underestimated by 0.90kg/m2 (–1.09– ‐0.70) and 0.60 kg/m2 (–0.75– ‐0.45). Underestimation increased with increasing measured BMI. There were strong correlations between self‐reported and measured height, weight and BMI (r=0.95, 0.99 and 0.95, respectively, p<0.001). While there was excellent agreement between BMI categories from self‐reported and measured data (kappa=0.80), obesity prevalence was underestimated. Findings did not differ substantially between middle‐aged and elderly participants. Conclusions : Self‐reported data on height and weight quantify body size appropriately in middle‐aged and elderly individuals for relative measures, such as quantiles of BMI. However, caution is necessary when reporting on absolute BMI and standard BMI categories, based on self‐reported data, particularly since use of such data is likely to result in underestimation of the prevalence of obesity.

This paper examines the role farmers’ health plays as an element of adaptive capacity. The study examines which of twenty aspects of adaptation may be related to overall health outcomes, controlling for demographic and on-farm-factors in health problems. The analysis is based on 3,993 farmers’ responses to a national survey of climate risk and adaptation. Hierarchical linear regression modelling was used examine the extent to which, in a multivariate analysis, the use of adaptive practices was predictively associated with self-assessed health, taking into account the farmer’s rating of whether their health was a barrier to undertaking farm work. We present two models, one excluding pre-existing health (model 1) and one including pre-existing health (model 2). The first model accounted for 21% of the variance. In this model better health was most strongly predicted by an absence of on-farm risk, greater financial viability, greater debt pressures, younger age and a desire to continue farming. Social capital (trust and reciprocity) was moderately associated with health as was the intention to adopt more sustainable practices. The second model (including the farmers’ health as a barrier to undertaking farm work) accounted for 43% of the variance. Better health outcomes were most strongly explained, in order of magnitude, by the absence of pre-existing health problems, greater access to social support, greater financial viability, greater debt pressures, a desire to continue farming and the condition of on-farm resources. Model 2 was a more parsimonious model (only nine predictors, compared with 15 in model 1), and explained twice as much variance in health outcomes. These results suggest that (i) pre-existing health problems are a very important factor to consider when designing adaptation programs and policies and (ii) these problems may mediate or modify the relationship between adaptation and health.