Spatial Co-Morbidity of Childhood Acute Respiratory Infection, Diarrhoea and Stunting in Nigeria

Modeling repeated measurements data using the multilevel Bayesian network: A case of child morbidity

BACKGROUND AND OBJECTIVE

In epidemiological research, studying the long-term dependencies between multiple diseases is important. This study extends the multilevel Bayesian network (MBN) for repeated measures data that can estimate the rate of change in outcomes over time while quantifying the variabilities of these rates across higher-level units through various variance-covariance structures.

METHOD

The performance and reliability of a model are examined through a simulation study, and its practical application is demonstrated using child morbidity data. This data has a hierarchical structure in which children were randomly selected from clusters (villages) and their conditions were assessed quarterly from March 2015 to May 2016. MBN was used to explore the relationship between outcomes weight-for-age (WAZ), height-for-age (HAZ), the number of days a child suffers from diarrhea (NOD), and flu (NOF), and estimate the rate of change of these outcomes over time. Since the outcomes considered were hybrid in nature, the connected three-parent set block Gibbs sampler with a multilevel generalized Poisson regression, multilevel zero inflated Poisson regression, and linear mixed-effects models were considered during the structure and parametric learning of the MBN.

RESULT

The simulation study confirmed that a MBN using the time metric t as a node performed well for repeated measures data. The result from the structure learning of MBN shows a causal relationship between WAZ, HAZ, NOD and NOF. Furthermore, exclusive breastfeeding months and usage of micronutrient powder appeared as a strong predictor for all outcomes considered in this study.

CONCLUSION

This study reveals that MBN is suitable in modeling repeated measures data to study the relationship between outcomes and estimate rate of change of an outcome over time while quantifying the variability due to higher-level clustering variables. Furthermore, the study highlights the importance of focusing on monitoring children with low WAZ and HAZ scores together with good feeding practices against the frequency of getting flu and diarrhea.

A predictive modelling approach to illustrate factors correlating with stunting among children aged 12-23 months: a cluster randomised pre-post study

OBJECTIVE

The aim of this study was to construct a predictive model in order to develop an intervention study to reduce the prevalence of stunting among children aged 12-23 months.

DESIGN

The study followed a cluster randomised pre-post design and measured the impacts on various indicators of livelihood, health and nutrition. The study was based on a large dataset collected from two cross-sectional studies (baseline and endline).

SETTING

The study was conducted in the north-eastern region of Bangladesh under the Sylhet division, which is vulnerable to both natural disasters and poverty. The study specifically targeted children between the ages of 12 and 23 months.

MAIN OUTCOME MEASURES

Childhood stunting, defined as a length-for-age z-score <-2, was the outcome variable in this study. Logistic and probit regression models and a decision tree were constructed to predict the factors associated with childhood stunting. The predictive performance of the models was evaluated by computing the area under the receiver operating characteristic (ROC) curve analysis.

RESULTS

The baseline survey showed a prevalence of 52.7% stunting, while 50.0% were stunted at endline. Several factors were found to be associated with childhood stunting. The model's sensitivity was 61% and specificity was 56%, with a correctly classified rate of 59% and an area under the ROC curve of 0.615.

CONCLUSION

The study found that childhood stunting in the study area was correlated with several factors, including maternal nutrition and education, food insecurity and hygiene practices. Despite efforts to address these factors, they remain largely unchanged. The study suggests that a more effective approach may be developed in future to target adolescent mothers, as maternal nutrition and education are age-dependent variables. Policy makers and programme planners need to consider incorporating both nutrition-sensitive and nutrition-specific activities and enhancing collaboration in their efforts to improve the health of vulnerable rural populations.

TRIAL REGISTRATION NUMBER

RIDIE-STUDY-ID-5d5678361809b.

Association between extreme rainfall and acute respiratory infection among children under-5 years in sub-Saharan Africa: an analysis of Demographic and Health Survey data, 2006-2020

OBJECTIVE

Despite an increase in the number of studies examining the association between extreme weather events and infectious diseases, evidence on respiratory infection remains scarce. This study examined the association between extreme rainfall and acute respiratory infection (ARI) in children aged <5 years in sub-Saharan Africa.

SETTING

Study data were taken from recent (2006-2020) Demographic and Health Survey data sets from 33 countries in sub-Saharan Africa.

PARTICIPANTS

280 157 children aged below 5 years were included.

OUTCOME MEASURES

The proportions of ARI according to individual, household and geographical characteristics were compared using the χ² test. The association between extreme rainfall (≥90th percentile) and ARI was examined using multivariate logistic regression for 10 of 33 countries with an adequate sample size of ARI and extreme rainfall events. The model was adjusted for temperature, comorbidity and sociodemographic factors as covariates. Stratification analyses by climate zone were also performed.

RESULTS

The prevalence of ARI in children aged <5 years ranged from 1.0% to 9.1% across sub-Saharan Africa. By country, no significant association was observed between extreme rainfall and ARI, except in Nigeria (OR: 2.14, 95% CI 1.06 to 4.31). Larger effect estimates were observed in the tropical zone (OR: 1.13, 95% CI 0.69 to 1.84) than in the arid zone (OR: 0.72, 95% CI 0.17 to 2.95), although the difference was not statistically significant.

CONCLUSION

We found no association between extreme rainfall and ARI in sub-Saharan Africa. Effect estimates tended to be larger in the tropical zone where intense rainfall events regularly occur. Comprehensive studies to investigate subsequent extreme climate events, such as flooding, are warranted in the future.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique

Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.