Objectives: Heterogeneity of effect measures in intervention studies undermines the use of evidence to inform policy. Our objective was to develop a comprehensive algorithm to convert all types of effect measures to one standard metric, relative risk reduction (RRR).
Study design and setting: This work was conducted to facilitate synthesis of published intervention effects for our epidemic modeling of the health impact of human immunodeficiency virus [HIV testing and counseling (HTC)]. We designed and implemented an algorithm to transform varied effect measures to RRR, representing the proportionate reduction in undesirable outcomes.
Results: Our extraction of 55 HTC studies identified 473 effect measures representing unique combinations of intervention-outcome-population characteristics, using five outcome metrics: pre-post proportion (70.6%), odds ratio (14.0%), mean difference (10.2%), risk ratio (4.4%), and RRR (0.9%). Outcomes were expressed as both desirable (29.5%, eg, consistent condom use) and undesirable (70.5%, eg, inconsistent condom use). Using four examples, we demonstrate our algorithm for converting varied effect measures to RRR and provide the conceptual basis for advantages of RRR over other metrics.
Conclusion: Our review of the literature suggests that RRR, an easily understood and useful metric to convey risk reduction associated with an intervention, is underused by original and review studies.
Keywords: Decision making; Effect measures; Hazard ratio; Odds ratio; Relative risk reduction; Risk factor; Risk ratio.
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