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Introduction: The population attributable fraction is an important measure for assessing the impact of intervention on the disease risk in populations, but it is frequently misused in the research literature. Methods: In this study, we review the definition, calculation, interpretation and assumptions of PAF in 43 textbooks and highlight important shortcomings. Results: While the Levin formula was proposed as a method of calculation in 29 (67%) textbooks, only in 4 (9%) was the Miettinen formula or its generalization for multilevel exposure recommended to calculate a confounding-adjusted population attributable fraction. Other concepts such as generalized impact fraction and prevented and preventable fractions were briefly discussed in few textbooks. Discussion: We recommend the authors revise the textbooks in light of our proposed framework for teaching the population attributable fraction.

Purpose: An area of interest to health policymakers is the effect of interventions aimed at risk factors on decreasing the number of new cardiovascular disease (CVD) cases. The aim of this study was to estimate the generalized impact fraction (GIF) and population attributable fraction (PAF) of hypertension (HTN) for CVD in Tehran. Patients and Methods: In this population-based cohort study, 8071 participants aged ≥ 30 years were followed for a median of 16 years. A survival model was used to estimate the 10- and 18-year risk of CVD. JNC-IV and 2017 ACC/AHA guidelines were used to categorize blood pressure (BP). PAF and GIF were estimated in different scenarios using the parametric G-formula. Results: Of 7378 participants included in analyses, 22.7% and 52.3% were classified as hypertensive according to the JNC-IV and 2017 ACC/AHA guidelines, respectively. According to the 2017 ACC/AHA, the 10-year risk of CVD was 5.1% (4.3– 6.0%), 8.9% (6.7– 12.0%), and 7.1% (6.1– 8.4%) for normal BP, elevated BP, and stage 1 HTN, respectively, and 20.8% (18.8– 23.0%) for stage 2 of the 2017 ACC/AHA and JNC-IV. The PAF of stage 2 vs stage 1 and vs normal BP for CVD was 17.4% (11.5– 21.8%) and 20.4% (14.6– 26.4%), respectively. The GIF of 30% reduction in the prevalence of stage 2 HTN to stage 1 and to normal BP for CVD was 5.1% (3.4– 6.6%) and 6.1% (4.4– 8.0%), respectively. Based on JNC-IV, the PAF and GIF of 30% for CVD were 17.8% (12.7– 22.9%) and 5.4% (4.0– 6.9%), respectively. Conclusion: By reducing the prevalence of HTN by 30%, a remarkable number of new CVD cases would be prevented. In an Iranian population, the comparison of HTN cases with normal BP showed no association between stage 1 HTN and CVD, whereas elevated BP was a significant risk factor for the incidence of CVD. [ABSTRACT FROM AUTHOR]

Objective(s): 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. Methods: 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. Results: 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. Conclusion: 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.

Many epidemiologic studies identify contrasts between an “always-exposed” population and a “never-exposed” population. Such “exposure effects” are perhaps most valuable in discussing individual lifestyle changes, or in clinical care; they may be less valuable in estimating the potential effects of realistic public health interventions. Various methods, among them population attributable fractions and generalized impact fractions, attempt to obtain more policy-relevant estimates of “population intervention” effects, but such methods remain rare in the epidemiologic literature. Here, we describe the use of the parametric g-formula as a tool for the estimation of population intervention effects in longitudinal data. Our discussion is motivated by a previous study of the effect of incident pregnancy on time to virological failure among human immunodeficiency virus-positive women initiating antiretroviral therapy in South Africa between 2004 and 2011. We show that 1) interventional estimates of effect can be estimated in longitudinal data using the parametric g-formula and 2) exposure effects and population interventional effects can have dramatically different interpretations and magnitudes in real-world data. Epidemiologists should consider estimating interventional effects in addition to exposure effects; doing so would allow the results of epidemiologic studies to be more immediately relevant to policy-makers and to implementation science efforts. [ABSTRACT FROM AUTHOR]

The attributable fraction (AF) is commonly used in epidemiology to quantify the impact of an exposure to a disease. Recently, Sjölander and Vansteelandt (2011. Doubly robust estimation of attributable fractions. Biostatistics 12, 112–121) introduced the doubly robust (DR) estimator of the AF, which involves positing models for both the exposure and the outcome and is consistent if at least one of these models is correct. In this article, we derived a DR estimator of the generalized impact fraction (IF) with a polytomous exposure. The IF is a measure that generalizes the AF by allowing the possibility of incomplete removal of the exposure. We demonstrated the performance of the proposed estimator via a simulation study and by application to data from a large prospective cohort study conducted in Japan. [ABSTRACT FROM PUBLISHER]

Introduction: The population attributable fraction is an important measure for assessing the impact of intervention on the disease risk in populations, but it is frequently misused in the research literature., Methods: In this study, we review the definition, calculation, interpretation and assumptions of PAF in 43 textbooks and highlight important shortcomings., Results: While the Levin formula was proposed as a method of calculation in 29 (67%) textbooks, only in 4 (9%) was the Miettinen formula or its generalization for multilevel exposure recommended to calculate a confounding-adjusted population attributable fraction. Other concepts such as generalized impact fraction and prevented and preventable fractions were briefly discussed in few textbooks., Discussion: We recommend the authors revise the textbooks in light of our proposed framework for teaching the population attributable fraction., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier Inc.)