Abstract
BackgroundPopulation attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation.MethodsTaylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables.ResultsThe methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke.ConclusionsThe described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.
Highlights
Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated
PAF from ‘etiologic fractions’ that truly represent the proportion of disease prevalence that is caused by a particular risk factor [4], a quantity that can only be estimated under certain conditions Despite this misinterpretation, the attention garnered by PAF calculations signify their importance in both informing public policy regarding appropriate disease interventions and their power to influence public perception about what might and might not be healthy behaviour, or healthy levels of physiologic measures such as blood pressure
Conditional Odds Ratios were estimated via a logistic regression model that adjusted for age, sex and country as well as the other 9 risk factors
Summary
Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. PAF from ‘etiologic fractions’ that truly represent the proportion of disease prevalence that is caused by a particular risk factor [4], a quantity that can only be estimated under certain conditions Despite this misinterpretation, the attention garnered by PAF calculations signify their importance in both informing public policy regarding appropriate disease interventions and their power to influence public perception about what might and might not be healthy behaviour, or healthy levels of physiologic measures such as blood pressure. We used attributable fractions to quantify and compare disease burden due to major stroke risk factors [7]; the analysis indicating that high blood pressure, physical inactivity and apolipoprotein levels were the most important risk factors contributing
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