The increase in life expectancy followed by the growing proportion of old individuals living with chronic diseases contributes to the burden of disability worldwide. The estimation of how much each chronic condition contributes to the disability prevalence can be useful to develop public health strategies to reduce the burden. In this presentation, we will introduce the
R package
addhaz, which is based on the attribution method (Nusselder and Looman 2004) to partition the total disability prevalence into the additive contributions of chronic diseases using cross-sectional data. The
R package includes tools to fit the binomial and multinomial additive hazard models, the core of the attribution method. The models are fitted by maximizing the binomial and multinomial log-likelihood functions using constrained optimization (constrOptim). The 95% Wald and bootstrap percentile confidence intervals can be obtained for the parameter estimates. Also, the absolute and relative contribution of each chronic condition to the disability prevalence and their bootstrap confidence intervals can be estimated. An additional feature of
addhaz is the possibility to use parallel computing to obtain the bootstrap confidence intervals, reducing computation time. In this presentation, we will illustrate the use of
addhaz with examples for the binomial and multinomial models, using the data from the Brazilian National Health Survey, 2013.
Keywords: Disability, Binomial outcome, Multinomial outcome, Additive hazard model, Cross-sectional data
Webpage:
https://cran.r-project.org/web/packages/addhaz/index.html References Nusselder, Wilma J, and Caspar WN Looman. 2004. “Decomposition of Differences in Health Expectancy by Cause.”
Demography 41 (2). Springer: 315–34.
