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

Dados Bibliográficos

ID5226340
AutoresMichael E Reichenheim (Universidade do Estado do Rio de Janeiro), Michael Reichenheim (0000-0001-7232-6745, Universidade do Estado do Rio de Janeiro), Nicola G Best (Imperial College School of Medicine, UK), Nicola Best (0009-0001-6173-4206, Imperial College London)
Ano2000
Volume16
Fascículo2
Páginas517-531
Data de publicação2000-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCadernos de Saude Publica (JOURNAL)
Identificadores do periódicoISSN: 0102-311X • E-ISSN: 1678-4464
EditoraFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/s0102-311x2000000200022
PMID10883050
OpenAlexW2105076876
SCIELO_PIDS0102-311X2000000200022
IdiomaEN
Referências citadas15

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

Bayesian probability · Context (archaeology) · Econometrics · Geography · Logistic regression · Markov chain Monte Carlo · Statistics · Computer Science · Economic and Environmental Valuation · Food Security and Health in Diverse Populations · Mathematics · Obesity, Physical Activity, Diet