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. 2024 Mar 20;14(1):6664.
doi: 10.1038/s41598-024-57187-8.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Shikha Dixit  1 , Manoja Kumar Das  2 , Durga Chitra Ramadugu  1 , Narendra Kumar Arora  1 ; INCLEN Intussusception Surveillance Network Study Group
Collaborators, Affiliations

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Shikha Dixit et al. Sci Rep. .

Abstract

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran's I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km-max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

Keywords: Children; GIS; Geospatial; Hospital proximity; India; Intussusception; Moran’s I; Spatial clustering.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Study processes.
Figure 2
Figure 2
Distribution of intussusception cases. (a) Country-level distribution of patients. (b) Spatial pattern of intussusception cases and sentinel site hospital location in the northern region. (c) Spatial pattern of intussusception cases and sentinel site hospital location in the western region. (d) Spatial pattern of intussusception cases and sentinel site hospital location in the eastern region. (e) Spatial pattern of intussusception cases and sentinel site hospital location in the southern region. Software used: Arc GIS 10.8.2 https://desktop.arcgis.com/en/arcmap.
Figure 3
Figure 3
Hotspot analysis of the residence location of children under two years old with intussusception in India. Software used: Arc GIS 10.8.2 https://desktop.arcgis.com/en/arcmap.
Figure 4
Figure 4
Interpolation of hotspots (z scores) and inverse distance weights (IDWs) for the residence locations of children under two years old with intussusception in India. Software used: Arc GIS 10.8.2 https://desktop.arcgis.com/en/arcmap.
See this image and copyright information in PMC

References

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