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. 2021 Oct 14;11(1):20422.
doi: 10.1038/s41598-021-99137-8.

Spatio-temporal patterns of childhood pneumonia in Bhutan: a Bayesian analysis

Kinley Wangdi #  1 , Kinley Penjor #  2 , Tsheten Tsheten  3   4 , Chachu Tshering  5 , Peter Gething  6   7 , Darren J Gray  3 , Archie C A Clements  6   7
Affiliations

Spatio-temporal patterns of childhood pneumonia in Bhutan: a Bayesian analysis

Kinley Wangdi et al. Sci Rep. .

Erratum in

Abstract

Pneumonia is one of the top 10 diseases by morbidity in Bhutan. This study aimed to investigate the spatial and temporal trends and risk factors of childhood pneumonia in Bhutan. A multivariable Zero-inflated Poisson regression model using a Bayesian Markov chain Monte Carlo simulation was undertaken to quantify associations of age, sex, altitude, rainfall, maximum temperature and relative humidity with monthly pneumonia incidence and to identify the underlying spatial structure of the data. Overall childhood pneumonia incidence was 143.57 and 10.01 per 1000 persons over 108 months of observation in children aged < 5 years and 5-14 years, respectively. Children < 5 years or male sex were more likely to develop pneumonia than those 5-14 years and females. Each 1 °C increase in maximum temperature was associated with a 1.3% (95% (credible interval [CrI] 1.27%, 1.4%) increase in pneumonia cases. Each 10% increase in relative humidity was associated with a 1.2% (95% CrI 1.1%, 1.4%) reduction in the incidence of pneumonia. Pneumonia decreased by 0.3% (CrI 0.26%, 0.34%) every month. There was no statistical spatial clustering after accounting for the covariates. Seasonality and spatial heterogeneity can partly be explained by the association of pneumonia risk to climatic factors including maximum temperature and relative humidity.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Map of Bhutan with districts and sub-districts with altitude. Map was created using ArcMap 10.5 software (ESRI, Redlands, CA).
Figure 2
Figure 2
Decomposed monthly cases of pneumonia: (a) under 5 years and (b) 5–14 years during the study period, 2010–2018. (Maps were created using ArcMap 10.5 software (ESRI, Redlands, CA).
Figure 3
Figure 3
Crude standardized morbidity ratios (SMR) of pneumonia by sub-district during the study period, 2010–2018. (Maps were created using ArcMap 10.5 software (ESRI, Redlands, CA).
Figure 4
Figure 4
(a) Spatial distribution (b) significance map of the posterior means of unstructured random effects of pneumonia in Bhutan, 2010–2018. (Maps were created using ArcMap 10.5 software (ESRI, Redlands, CA).
Figure 5
Figure 5
Trend of pneumonia burden by sub-district in Bhutan during the study period, 2010–2018. (Maps were created using ArcMap 10.5 software (ESRI, Redlands, CA).
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