Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study”

• Main Authors: Ahmad Khosravi, Mohammad Ali Mansournia
• Format: Article
• Language: English
• Published: BMC 2019-08-01
• Series: Infectious Diseases of Poverty
• Subjects: Population attributable fraction; Confounding; Sample-weighted parametric g-formula
• Online Access: http://link.springer.com/article/10.1186/s40249-019-0587-8

Abstract Population attributable fraction (PAF) refers to the proportion of all cases with a particular outcome in a population that could be prevented by eliminating a specific exposure. The authors of a recent paper evaluated the prevalence and estimated the PAFs for risk factors of TB among elderly people in China [Inf Dis Poverty. 2019;8:7]. Confounding is inevitable in observational studies and Levin’s formula is of limited use in practice for unbiasedly estimating PAF. In a complex survey design, an unbiased estimation of the PAF can be calculated using a sample-weighted version of the Miettinen formula or a sample weighed parametric g-formula. With respect to causal interpretation of PAF in public health setting, computation of PAF is logical and practical when the exposure is amenable to intervention.