Epidemiology and Health

Original Articles

Geospatial analysis of neonatal mortality in north-eastern India: a multilevel Bayesian approach: Vidhi Jain, Kh. Jitenkumar Singh, Deboshree Das, Shefali Gupta, Gunjan Singh; Epidemiol Health. 2025;47:e2025021. Published online April 27, 2025; DOI: https://doi.org/10.4178/epih.e2025021

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OBJECTIVES
Neonatal mortality remains a significant public health issue in India. This study investigates spatial patterns and contributing factors to neonatal mortality in the north-eastern states, identifying hotspot regions and spatial variations.
METHODS
A sample of 34,222 mothers from India’s National Family Health Survey (NFHS-5, 2019-21) in the north-eastern states was analysed. Descriptive and bivariate analyses were conducted alongside Bayesian multilevel logistic regression using integrated nested Laplace approximation to model neonatal mortality. Spatial hotspot analysis using Getis-Ord Gi* statistics identified clusters of high neonatal mortality, while geographically weighted regression (GWR) was used to examine spatial variations in the relationships between neonatal mortality and contributing factors.
RESULTS
The neonatal mortality rate in the north-eastern states declined from 45 to 21 per 1,000 live births (NFHS-1 to NFHS-5) but remains higher than the national average. Assam reported the highest mortality (42.16%), whereas Sikkim had the lowest (0.87%). Higher mortality was observed among male infants, mothers with advanced age, low maternal education, and mothers who attended less than 5 antenatal care (ANC) visits. Spatial analysis identified hotspots in Assam, Meghalaya, and Tripura. GWR indicated that areas with less than 5 ANC visits had the strongest association with neonatal mortality. Bayesian multilevel analysis highlighted spatial variations of up to 51% across districts in northeast India.
CONCLUSIONS
This study underscores spatial disparities in neonatal mortality across north-eastern India. Addressing childcare practices and healthcare access in hotspot regions is essential for improving new-born health outcomes. The findings provide critical insights for policymakers to develop targeted interventions aimed at reducing neonatal mortality in these underserved areas.; Summary

Using Bayesian Networks to Model Hierarchical Relationships in Epidemiological Studies: Georges Nguefack-Tsague; Epidemiol Health. 2011;33:e2011006. Published online June 17, 2011; DOI: https://doi.org/10.4178/epih/e2011006

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Abstract

<sec><title>OBJECTIVES</title>To propose an alternative procedure, based on a Bayesian network (BN), for estimation and prediction, and to discuss its usefulness for taking into account the hierarchical relationships among covariates.</sec><sec><title>METHODS</title>The procedure is illustrated by modeling the risk of diarrhea infection for 2,740 children aged 0 to 59 months in Cameroon. We compare the procedure with a standard logistic regression and with a model based on multi-level logistic regression.</sec><sec><title>RESULTS</title>The standard logistic regression approach is inadequate, or at least incomplete, in that it does not attempt to account for potentially causal relationships between risk factors. The multi-level logistic regression does model the hierarchical structure, but does so in a piecewise manner; the resulting estimates and interpretations differ from those of the BN approach proposed here. An advantage of the BN approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in any specific state, given the states of the others. The currently available approaches can only predict the outcome (disease), given the states of the covariates.</sec><sec><title>CONCLUSION</title>A major advantage of BNs is that they can deal with more complex interrelationships between variables whereas competing approaches deal at best only with hierarchical ones. We propose that BN be considered as well as a worthwhile method for summarizing the data in epidemiological studies whose aim is understanding the determinants of diseases and quantifying their effects.</sec>

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Citations

Citations to this article as recorded by

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Methodologic Considerations on the Cohort Study of Risk Factors of Stomach Cancer: On the Incompleteness of Case Ascertainment.: Moo Song Lee, Wee Chang Kang, Dong Hyun Kim, Jong Myun Bae, Myung Hee Shin, Young Jo Lee, Yoon Ok Ahn; Korean J Epidemiol. 1997;19(2):152-160.

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Abstract PDF: Abstract
BACKGROUND
AND PURPOSE: The authors conducted the study to evaluate the incompleteness of follow-up as well as the validity of the diagnostic code in the medical insurance databases in a cohort study. They also suggested several useful regression models for the analysis of such incomplete data.
METHODS
The subjects of Seoul Cohort(n=14,533) were followed up for three and a half years. Based on the chart reviews of the subjects who had the diagnostic code of gastric cancer in the medical insurance databases, forty-four cases of gastric cancer were idenfified, using cancer registry databases and death certificates as the secondary source. Regression coefficients and the associated p-values were estimated using the following six methods and the results were compared with each other. Method 1: The subjects with the diagnostic code in the medical insurance databases were considered as the cases of gastric cancer.
Method
2: The confirmed cases were considered as the cases of gastric cancer. Method 3: The cases were the subjects with the diagnositc code whose diagnosis was confirmed by medical chart reriew. Method 4: Ordinal logistic regression.
Method
5: Weighted logistic regression. Method 6: Polytomous logistic regression RESULTS: A total of 12,541 subjects were followed up excluding censored cases. One hundred and nine subjects were diagnosed with gastric cancer in the medical utilization databases: forty-three were probable cases whose dianosis was not confrimed by chart review, twenty-six were ruled out and 26 were confirmed cases. Another 14 cases were confirmed using the cancer registry and death certificates. Using the secondary sources, four another cases were confirmed and 44 cases were confirmed during follow-up. In method 1, past history of gastritis and gastric ulcer was significant risk factor whereas intake frequency of fresh vegetable, ice cream and coffee was associated with significantly decreased risk. In the second and the sixth method, green tea was a significant protective factor, whereas in methods 3-5, no significant variables were found.
CONCLUSIONS
Polytomous logistic regression was the preferred method in the cohort study using secondary sources of information for the follow-up, and it provided additional information for the risk factor identification, especially for the specificity of the risk factors.; Summary

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