Background: Monitoring and empirical evaluation are essential components of evidence based public health policies and programmes. Consequently, there is a growing interest in monitoring of, and indicators for, major environmental health risks, particularly in the developing world. Current large scale data collection efforts are generally disconnected from micro-scale studies in health sciences, which in turn have insufficiently investigated the behavioural and socioeconomic factors that influence exposure.
Study design: A basic framework is proposed for development of indicators of exposure to environmental health risks that would facilitate the (a) assessment of the health effects of risk factors, (b) design and evaluation of interventions and programmes to deliver the interventions, and (c) appraisal and quantification of inequalities in health effects of risk factors, and benefits of intervention programmes and policies. Specific emphasis is put on the features of environmental risks that should guide the choice of indicators, in particular the interactions of technology, the environment, and human behaviour in determining exposure. The indicators are divided into four categories: (a) access and infrastructure, (b) technology, (c) agents and vectors, and (d) behaviour. The study used water and sanitation, indoor air pollution from solid fuels, urban ambient air pollution, and malaria as illustrative examples for this framework.
Conclusions: Organised and systematic indicator selection and monitoring can provide an evidence base for design and implementation of more effective and equitable technological interventions, delivery programmes, and policies for environmental health risks in resource poor settings.
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↵† Similar issues exist in the context of nutritional epidemiology, resulting in the use of indicators like blood pressure or lipids and body mass index to represent more complex exposures such as diet, energy intake, and physical activity. Some physiological indicators have been successfully related to more distal factors (for example, blood pressure and salt intake15).
Funding: this work was sponsored by the National Institute of Aging (Grant PO1-AG17625). Jürg Utzinger was supported by the Swiss National Science Foundation (Project PPOB.102883).
Conflicts of interest: none declared.
The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Health Effects Institute (HEI) or its sponsors.
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