Peer-reviewed article
Estimating double burden of malnutrition among rural and urban children in Amazonia using Bayesian latent models.
Jesem Douglas Yamall Orellana, Luke Parry, Francine Silva Dos Santos, Laísa Rodrigues Moreira, Patricia Carignano Torres et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study used advanced statistical methods to measure the double burden of malnutrition (stunting and overweight in the same child) among young children aged 6-59 months in remote areas of the Brazilian Amazon. The research surveyed urban and rural communities across four river-dependent municipalities to understand how common this combined form of malnutrition is in vulnerable populations. The findings show that the double burden of malnutrition varies significantly between locations, with rural areas of Jutai showing the highest prevalence. The study demonstrates that specialized statistical approaches can better detect malnutrition in small, hard-to-reach populations where conventional methods may miss important health problems.
Key points
- Rural prevalence of double burden of malnutrition was significantly higher in Jutai (3.3%) compared to other municipalities studied
- The likelihood that double burden of malnutrition prevalence exceeded 1% was very high in Jutai (99.7%) and Ipixuna (63.2%), but very low elsewhere
- Exceedance probabilities varied widely among urban sub-populations, ranging from 6.7% in Maues to 41.2% in Caapiranga
- Bayesian latent models enabled more accurate estimation of low-prevalence outcomes in small populations compared to conventional statistical methods
- Double burden of malnutrition is associated with increased risks of non-communicable diseases and healthcare costs in adulthood
Limitations
This is a descriptive epidemiological study that estimates prevalence but does not evaluate any intervention or establish causal relationships. The study cannot identify the underlying causes of the double burden of malnutrition or test the effectiveness of potential solutions.
Abstract
BACKGROUND: The double burden of malnutrition (DBM) in the same individual is a neglected public health concern, especially in low- and middle-income countries (LMICs). The DBM is associated with increased risks of non-communicable diseases, childbirth complications, and healthcare costs related to obesity in adulthood. However, evaluating low prevalence outcomes in relatively small populations is challenging using conventional frequentist statistics. Our study used Bayesian latent models to estimate DBM prevalence at the individual-level in small populations located in remote towns and rural communities in the Brazilian Amazon. METHODS: We employed a cross-sectional survey of urban and rural children aged 6-59 months, considering DBM as the coexistence of stunting and overweight in the same individual. We evaluated four river-dependent municipalities, sampling children in randomly selected households in each town and a total of 60 riverine forest-proximate communities. Through Bayesian modeling we estimated the latent double burden of malnutrition (LDBM) and credible intervals (CI). RESULTS: The exceedance probability of LDBM was used to quantify this form of malnutrition at the population level. Rural prevalence of LDBM was significantly higher in Jutai (3.3%; CI: 1.5% to 6.7%) compared to Maues and Caapiranga. The likelihood that LDBM rural prevalence exceeded 1% was very high in Jutai (99.7%), and Ipixuna (63.2%), and very low (< 2%) in rural communities elsewhere. Exceedance probabilities (at 1%) also varied widely among urban sub-populations, from 6.7% in Maues to 41.2% in Caapiranga. The exceedance probability of LDBM prevalence being above 3.0% was high in rural Jutai (59.7%). DISCUSSION: Our results have important implications for assessing DBM in vulnerable and marginalized populations, where health and nutritional status are often poorest, and public health efforts remain focused on undernutrition. Our analytical approach could enable more accurate estimation of low prevalence health outcomes, and strengthen DBM monitoring of hard-to-reach populations.