Commentary: estimating and understanding area health effects.

Breeze et al. report that living in a deprived area is associated with poor quality of life in a large population-based sample of older adults living in the UK. 1 Their paper adds to a large body of work reporting associations between area socioeconomic characteristics or area deprivation and a variety of health outcomes. 2 The focus on the elderly population is especially interesting because, as Breeze et al. note, there are reasons to believe that area characteristics may be especially relevant to the health and well-being of elderly people who are likely to spend more time in their local areas and rely on their local areas for services and social interactions. Like other researchers, Breeze et al. analyse data from an observational study to estimate 'area effects' after controlling for individual-level social class. The need to control for differences in the socioeconomic position of people living in different areas has been a key challenge in the field, especially because the forces shaping residential location generate associations between individual-level social class and area deprivation. The most common approach in the literature is to use regression methods to adjust for individual-level characteristics in the estimation of area effects. An important assumption in the use of regression methods is that the overlap in the distribution of measured individual-level confounders across categories of area deprivation is sufficient for the regression adjustment to yield valid estimates of the 'independent' effect of area. In the absence of sufficient overlap, adjusted estimates necessarily imply extrapolations beyond the information available in the data, the validity of which cannot be tested. 3 There is no unique answer as to how much overlap is sufficient to allow meaningful estimates. A certain amount of extrapolation is inherent in all scientific inquiry. In fact, the process of adjustment always involves varying amounts of extrapolation from the data at hand to what would have been observed if certain features of the data were different (e.g. if the age distributions of two groups being compared were not as different as they are). Nevertheless, it is important to be explicit about how far we are straying from the data in our extrapolated conclusions, so that readers can judge for themselves whether the assumptions implicit in the extrapolation are likely to be valid. Breeze et al. address the issue of overlap between area deprivation and social class in their sample by reporting the distribution of …