DOI: http://dx.doi.org/10.18203/2394-6040.ijcmph20220855

Spatiotemporal distribution of malaria prevalence in the district Anuppur, central India

Manish Kumar Dwivedi, Sanjeev Bakshi, Shruti Sonter, Shringika Mishra, Prashant Kumar Singh

Abstract


Background: In the current study, 2013±16 medical data were examined to evaluate intervention effects on malaria prevalence in central India. Spatiotemporal variation in the distribution of malaria types (PV, PF, and PV-PF) was also investigated and geographical distribution of malaria prevalence in central India first time.

Methods: The data was collected from the primary health centers, sub health centers, community health centers, and district hospital. The data was used for the calculation of annual blood examination rate, annual parasite incidence, blood slide positive rate, blood slide falciparum rate, % PF, % PV, and % PV-PF types of malaria. The rate of malaria was transformed ( ) for carrying out statistical analyses. ANOVA models were considered for analysis of the data. The minimum deviation informatics statistic has been used to compare the spatiotemporal distribution of malaria types. The spatiotemporal distribution maps of malaria prevalence and mosquito breeding sites were generated using the geographic component software ArcGIS 10.3.

Results: The results display that space was an important significant factor for malaria prevalence. Kotma, one of the blocks of the study region displayed varying geographic patterns of dominance. The analysis for the presence of malaria types indicated that space was an important source of variation for the rate of malaria due to Plasmodium falciparum. The distributional patterns of the malaria types, as observed in the empirical data were tested using the MDIS statistic and findings indicate that the distributions of malaria types for the spatial points, namely Pushparajgarh, Kotma, and Anuppur were not the same over the selected time points. The geographical maps mainly displayed the active mosquito breeding sites in river areas and handpump areas namely Karpa, Laidara, Khamraudh, Rajendragram, Sivarichndas, Basaniha, Soniyamar, Kiragi. The maps also displayed 46% high risk, 34% moderate risk, and 20% low-risk area.

Conclusions: The prevalence of malaria in this tribal-dominated area shows that was governed by mosquitogenic factors and their transmission.


Keywords


ANOVA, Malaria incidence, Malaria prevalence, Minimum deviation informatics statistic, Spatiotemporal

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References


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