A Bayesian approach to estimate the prevalence of low height-for-age from the prevalence of low weight-for-age

Abstract

Victora et al. (1998) proposed the use of low weight-for-age prevalence to estimate the prevalence of height-for-age deficit in Brazilian children. This procedure was justified by the need to simplify methods used in the context of community health programs. From the same perspective, the present article broadens this proposal by using a Bayesian approach (based on Markov Chain Monte Carlo (MCMC) methods) to deal with the imprecision resulting from Victora et al.'s model. In order to avoid invalid estimated prevalence values which can occur with the original linear model, truncation or a logit transformation of the prevalences are suggested. The Bayesian approach is illustrated using a community study as an example. Imprecision arising from methodological complexities in the community study design, such as multi-stage sampling and clustering, is easily handled within the Bayesian framework by introducing a hierarchical or multilevel model structure. Since growth deficit was also evaluated in the community study, the article may also serve to validate the procedure proposed by Victora et al.

Keywords

• Anthropometry
• Nutritional Surveillance
• Statistical Model
• Bayes Theorem
• Markov chain Monte Carlo Method