Studying place effects on health by synthesising individual and area-level outcomes.

It is well established that there exist substantial area-level socio-demographic variations in population health. However, area-level associations between deprivation and health cannot necessarily be interpreted as place effects on individual health. We demonstrate how recently developed statistical models for combining individual and aggregate data can help to separate the effects of place of residence and personal circumstances. We apply these to two health outcomes: risk of hospitalisation for cardiovascular disease (CVD) and risk of self-reported limiting long-term illness (LLTI). A combination of small-area data from UK hospital episode statistics and the UK census and individual data from the Health Survey for England are analysed, using a new multilevel modelling method termed hierarchical related regression (HRR). The standard multilevel model for place and health explains outcomes from individual data in terms of individual and area-level characteristics. HRR models increase precision by also explaining population aggregate outcomes, in terms of the same predictors. Aggregate outcomes are modelled by averaging the individual-level exposure-outcome relationship over the area, which can alleviate the ecological bias associated with interpreting the relationship between aggregate quantities as an individual-level relationship. We find that there are associations between area-level deprivation indicators and both area-level rates of hospital admission for CVD and area-level rates of LLTI. Multilevel models fitted to the individual data alone had insufficient power to determine whether these associations were due to compositional or contextual effects. Using HRR models which incorporate area-level outcomes in addition to individual outcomes, we found that for CVD, the area-level differences were mostly explained by individual-level effects, in particular the increased risk for individuals from non-white ethnic backgrounds. In contrast, there remained a significant association between LLTI and area-level deprivation even after adjusting for the significant increased risk associated with individual-level ethnicity and income. Our study illustrates that extending multilevel models to incorporate both individual and area-level outcomes increases power to distinguish between contextual and compositional effects.

Lessons from ecological and spatial studies in relation to occupational lung disease.

PURPOSE OF REVIEW: The majority of epidemiological research into occupational lung disease has been advanced by the study of individuals, typically in a workplace setting. This review examines how recent advances in ecological and spatial study methodology and in the information held in routine databases could be used to enhance occupational health studies. RECENT FINDINGS: Ecological studies often use routinely collected data, and this is becoming much more extensive and better validated with potential for increasing use in occupational health research. Improvements in computing power and statistical and geographical information systems methodology have led to more sophisticated mapping techniques and greater use of spatial information when investigating lung diseases usually related to occupational exposures. Ecological study methodology is experiencing a radical overhaul with supplementation of group-level data with information from small-scale individual-level studies. This hybrid design can be used to reduce bias and increase power and is directly applicable to the enhancement of aggregate information from job exposure matrices. SUMMARY: Studies of occupational lung disease can be enhanced by incorporating methodological innovations from ecological and spatial studies.