Ecological eVects in multi-level studies

Multi-level research that attempts to describe ecological eVects in themselves (for example, the eVect on individual health from living in deprived communities), while also including individual level effects (for example, the eVect of personal socioeconomic disadvantage), is now prominent in research on the socioeconomic determinants of health and disease. Such research often involves the application of advanced statistical multi-level methods. It is hypothesised that such research is at risk of reaching beyond an epidemiological understanding of what constitutes an ecological eVect, and what sources of error may be influencing any observed ecological eVect. This paper aims to present such an epidemiological understanding. Three basic types of ecological eVect are described: a direct cross level eVect (for example, living in a deprived community directly aVects individual personal health), cross level eVect modification (for example, living in a deprived community modifies the eVect of individual socioeconomic status on individual health), and an indirect cross level eVect (for example, living in a deprived community increases the risk of smoking, which in turn aVects individual health). Sources of error and weaknesses in study design that may aVect estimates of ecological eVects include: a lack of variation in the ecological exposure (and health outcome) in the available data; not allowing for intraclass correlation; selection bias; confounding at both the ecological and individual level; misclassification of variables; misclassification of units of analysis and assignment of individuals to those units; model mis-specification; and multicollinearity. Identification of ecological eVects requires the minimisation of these sources of error, and a study design that captures suYcient variation in the ecological exposure of interest. (J Epidemiol Community Health 2000;54:367–374) The aim of this paper is to discuss epidemiological issues in the investigation of ecological determinants of health. There has been a resurgence of interest in ecological research, premised on the assumption that social contexts may shape health status as much as traditional individual risk factors. In particular, many researchers of the socioeconomic determinants of health are beginning to analyse ecological and individual level exposures simultaneously, often using advanced multi-level statistical methods. We are concerned that the application of multi-level statistical methods may have surged ahead of a theoretical framework in which to conduct meaningful and robust analyses. In this paper we describe briefly the historical background to multi-level analysis, review possible categorisations of ecological variables, and present three basic types of ecological eVect. Then we explore the possible sources of error in ascribing ecological eVects in multi-level analysis that we believe are currently being overlooked in multi-level analyses, and some general strategies to overcome these sources of error. Background Research that combines the ecological and individual level has a long history in sociology. Durkheim is credited with the first such attempt when he investigated suicide. During and after the second world war interest in the United States increased, with an ensuing debate about the validity of ecological eVects. That debate was about whether the eVect of an ecological exposure on health is causally valid, independent of explanatory and intervening individual level causes. For example, is it valid to consider ascribing causation to the eVect of the ecological exposure “living in a deprived community” on the outcome “individual health status”? Or should we always seek to reduce such observed associations to individual level causal mechanisms like individual socioeconomic status, smoking, and other risk factors? In public health it is accepted that disease causation operates via chains, or webs, of events, and most public health practitioners are comfortable with the notion of proximal and distal causes. For example, we have no difficulty attributing cases of whooping cough to both exposure to the bacteria B pertussis (a proximal cause), and the loss of herd immunity (a distal or population level cause). Indeed, disregarding distal causation may overlook important causal mechanisms; immunisation against pertussis will have less apparent benefit if individual level protection (vaccine eYcacy) only is considered, compared with also considering the impact of community level immunisation on the background incidence of whooping cough. Susser has proposed that links should be made between possible levels of analysis, and uses the analogy of chinese boxes. It is possible to posit an infinite number of levels of organisation, from the individual up (for example, families, neighbourhoods, counties, states), from the individual down (for example, body organs, cellular matrices, DNA), and for overlapping units (for example, area of residence and work environment). This paper considers J Epidemiol Community Health 2000;54:367–374 367 Department of Public Health, Wellington School of Medicine, University of Otago, PO Box 7343, Wellington, New Zealand