A practical guide for assessing priorities in health research and interventions on risk factors

To develop a practical guide for health professionals who want to design appropriate interventions in health programs by means of organized research? Specific objectives included finding the most practical and accurate methods for decomposition of outcomes, measuring the impact of each determinant in applied settings, and prioritizing research for achieving this goal.

In this paper all causal models were reviewed (i.e. graphical, structural, sufficient-component cause and counterfactual models), and judged as to whether they can provide an appropriate conceptual framework to prioritize research and interventions.

The Attributable Fraction is the classic epidemiologic measure for causal attribution of a risk factor. Since Attributable Fraction depends on measuring zero levels, which is neither feasible nor desirable, "Generalized Impact Fraction" was substituted for this purpose. The appropriate conceptual framework in practical settings could be Counterfactual Analysis for comparative risk assessment.

As it is not possible to perform every theoretical approach, choosing the most practical methods is essential in selecting epiderniologic methods for health programs. It seems that despite its limitations, Counterfactual Analysis and subsequent "General Impact Fraction" estimation could be most practical and accurate way in this situation. Further development of the method can be accompanied by selection of a sequence of different interventions based on "Average Attributable Fraction", provided the required data are available.