Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

Introduction Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease...

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Bibliographic Details
Published in:BMC public health 2023-10, Vol.23 (1), p.1-2014, Article 2014
Main Authors: Verma, Madhur, Panwar, Shweta, Sahoo, Soumya Swaroop, Grover, Gagandeep Singh, Aggarwal, Seema, Tripathy, Jaya Prasad, Shah, Jitendra, Kakkar, Rakesh
Format: Article
Language:English
Subjects:
Chikungunya virus
Cluster analysis
Clustering
Dengue fever
Digital mapping
Disease control
Disease hot spots
Dispersion
Distribution
Ecological effects
Ecological studies
Epidemics
Epidemiology
Fever
Forecasts and trends
Geospatial analysis
Health surveillance
Hospitals
Hyperthermia
Illnesses
Infectious disease epidemiology
Infectious diseases
Malaria
Missing data
Morbidity
Mortality
Plasmodium falciparum
Plasmodium vivax
Public health
Regression analysis
Scientific visualization
Signs and symptoms
Spatial analysis
Trends
Tropical diseases
Typhoid
Vector-borne disease
Vector-borne diseases
Vectors (Biology)
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Summary:Introduction Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention. Methods A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data. Results PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis. Conclusions The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points. Keywords: Vector-borne disease, Infectious disease epidemiology, Geospatial analysis
ISSN:1471-2458
1471-2458
DOI:10.1186/s12889-023-16930-y