Methods to assess the contribution of diseases to disability using cross-sectional studies: comparison of different versions of the attributable fraction and the attribution method

• Published in: International journal of epidemiology Vol. 48; no. 2; pp. 559 - 570
• Main Authors: Palazzo, Clémence, Yokota, Renata T C, Ferguson, John, Tafforeau, Jean, Ravaud, Jean-François, Van Oyen, Herman, Nusselder, Wilma J
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.04.2019; Oxford University Press (OUP)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Child; Child, Preschool; Chronic Disease - epidemiology; Cross-Sectional Studies; Disability Evaluation; Disabled Persons - statistics & numerical data; Female; France - epidemiology; Health Surveys; Humanities and Social Sciences; Humans; Infant; Infant, Newborn; Male; Middle Aged; Models, Statistical; Risk Assessment; Risk Factors; Young Adult; France; Disability; attribution; diseases; attributable fraction; contribution
• ISSN: 0300-5771; 1464-3685
• DOI: 10.1093/ije/dyy222

Abstract Background This study aims to illustrate the differences between approaches proposed for apportioning disability to different diseases in a multicausal situation, i.e. the unadjusted attributable fraction (AF), the adjusted AF, the average AF and the attribution method (AM). This information is useful to better interpret results obtained from cross-sectional data and help policy makers decide on public health strategies. Methods Data for 29 931 individuals, representative of the French household population, who participated in the 2008–09 cross-sectional Disability-Health Survey, were included. Disability was defined as any limitation reported with the Global Activity Limitation Indicator. Unadjusted AFs were calculated using Levin’s formula. Adjusted AFs were estimated for each disease by calculating predicted probabilities of disability for each individual in the dataset, under the assumption that the individual is unexposed to this specific disease (logistic model). Average AFs are based on the same methodology, but have the additional advantage that the average AFs for different diseases sum to the total AF associated with eliminating all diseases. AM accounts for competing risks and partitions total disability prevalence into additive contributions of different diseases and background disability (additive model). Results All methods obtained similar results with respect to the estimates of the disease contribution to disability prevalences and to ranking of the diseases, except unadjusted AFs, as the method ignores multimorbidity. Confounders other than diseases, such as age and gender, should be accurately taken into account. Conclusions Conceptual differences, strengths and limitations of the different approaches were discussed.