One key evaluation challenge is selective exposure to the intervention, leading exposed individuals or groups to differ from unexposed individuals or groups in characteristics associated with better or worse outcomes. Our focus here is on public health and other policy interventions that seek to improve population health or which may have important health impacts as a by-product of other policy goals. NE studies use this naturally occurring variation in exposure to identify the impact of the event on some outcome of interest. We follow the UK Medical Research Council guidance in defining NEs broadly to include any event not under the control of a researcher that divides a population into exposed and unexposed groups ( 16). Recently, NEs and other alternatives to RCTs have attracted interest because they are seen as the key to evaluating large-scale population health interventions that are not amenable to experimental manipulation but are essential to reducing health inequalities and tackling emerging health problems such as the obesity epidemic ( 15, 27, 40, 68, 76). Since the 1950s, when the first clinical trials were conducted, investigators have emphasized randomized controlled trials (RCTs) as the preferred way to evaluate health interventions.
Natural experiments (NEs) have a long history in public health research, stretching back to John Snow's classic study of London's cholera epidemics in the mid-nineteenth century. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized. NE studies often rely on existing (including routinely collected) data. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation.
Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Studies should be based on a clear theoretical understanding of the processes that determine exposure. One key challenge in evaluating NEs is selective exposure to the intervention. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation.
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