Statistics Department, Universitas Negeri Makassar, Makassar.
IPB University, Bogor.
Geospat Health. 2024 Oct 3;19(2). doi: 10.4081/gh.2024.1321.
Stunting continues to be a significant health issue, particularly in developing nations, with Indonesia ranking third in prevalence in Southeast Asia. This research examined the risk of stunting and influencing factors in Indonesia by implementing various Bayesian spatial conditional autoregressive (CAR) models that include covariates. A total of 750 models were run, including five different Bayesian spatial CAR models (Besag-York-Mollie (BYM), CAR Leroux and three forms of localised CAR), with 30 covariate combinations and five different hyperprior combinations for each model. The Poisson distribution was employed to model the counts of stunting cases. After a comprehensive evaluation of all model selection criteria utilized, the Bayesian localised CAR model with three covariates were preferred, either allowing up to 2 clusters with a variance hyperprior of inverse-gamma (1, 0.1) or allowing 3 clusters with a variance hyperprior of inverse-gamma (1, 0.01). Poverty and recent low birth weight (LBW) births are significantly associated with an increased risk of stunting, whereas child diet diversity is inversely related to the risk of stunting. Model results indicated that Sulawesi Barat Province has the highest risk of stunting, with DKI Jakarta Province the lowest. These areas with high stunting require interventions to reduce poverty, LBW births and increase child diet diversity.
发育迟缓仍然是一个重大的健康问题，特别是在发展中国家，印度尼西亚在东南亚的发育迟缓患病率中排名第三。本研究通过实施各种包含协变量的贝叶斯空间条件自回归（CAR）模型，来研究印度尼西亚发育迟缓的风险和影响因素。共运行了 750 个模型，包括 5 种不同的贝叶斯空间 CAR 模型（Besag-York-Mollie（BYM）、CAR Leroux 和 3 种局部化 CAR），每个模型有 30 种协变量组合和 5 种不同的超先验组合。泊松分布用于对发育迟缓病例数进行建模。在对所有使用的模型选择标准进行全面评估后，贝叶斯局部化 CAR 模型与 3 个协变量被优先选择，要么允许最多有 2 个具有逆伽马（1,0.1）方差超先验的聚类，要么允许有 3 个具有逆伽马（1,0.01）方差超先验的聚类。贫困和近期低出生体重（LBW）出生与发育迟缓风险增加显著相关，而儿童饮食多样性与发育迟缓风险呈负相关。模型结果表明，西爪哇省的发育迟缓风险最高，雅加达特区的风险最低。这些发育迟缓高发地区需要采取干预措施，以减少贫困、LBW 出生和增加儿童饮食多样性。
