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.

中文翻译：

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使用横断面研究评估疾病对残疾的贡献的方法：归因分数和归因方法的不同版本的比较

背景

这项研究旨在说明在多因果情况下将残疾分配给不同疾病的方法之间的差异，即未调整的归因分数（AF），调整后的AF，平均AF和归因方法（AM）。此信息有助于更好地解释从横截面数据获得的结果，并帮助决策者决定公共卫生策略。

方法

纳入了29 931名法国家庭人口代表的数据，他们参加了2008-09年横断面残疾与健康调查。残疾定义为全球活动限制指标报告的任何限制。使用Levin公式计算未调整的AF。在假定个体未暴露于特定疾病的情况下（逻辑模​​型），通过计算数据集中每个个体的预期残疾概率来估计每种疾病的调整后房颤。平均房颤基于相同的方法，但具有其他优势，即不同疾病的平均房颤加总为与消除所有疾病相关的总房颤。

结果

除了未调整的房颤外，所有方法在疾病对残疾患病率和疾病等级的贡献估计方面都获得了相似的结果，因为该方法忽略了多发病。除疾病外，年龄和性别等混杂因素都应予以准确考虑。

结论

讨论了不同方法的概念差异，优势和局限性。