Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand
Issued Date
2024-07-30
Resource Type
eISSN
14752875
Scopus ID
2-s2.0-85200185463
Pubmed ID
39080748
Journal Title
Malaria journal
Volume
23
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Malaria journal Vol.23 No.1 (2024) , 224
Suggested Citation
Walshe R., Pongsoipetch K., Mukem S., Kamsri T., Singkham N., Sudathip P., Kitchakarn S., Maude R.R., Maude R.J. Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand. Malaria journal Vol.23 No.1 (2024) , 224. doi:10.1186/s12936-024-05044-4 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/100375
Title
Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand
Corresponding Author(s)
Other Contributor(s)
Abstract
BACKGROUND: Thailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data. Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours. METHODS: Geolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated Rc at village level. Forest cover was calculated using raster maps of tree crown cover density and year of forest loss from the publicly available Hansen dataset. Incidence and forest cover were compared graphically and using Spearman's rho. The current foci classification system was applied to data from the last 5 years (2017-2021) and forest cover for 2021 compared between the classifications. A simple risk score was developed to identify villages with high receptivity. RESULTS: There was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated Rc were higher in villages in highly forested subdistricts, and with higher forest cover within 5 km. Forest cover was also higher in malaria foci which consistently reported malaria cases each year than those which did not. An Rc > 1 was only reported in villages in subdistricts with > 25% forest cover. When applying a simple risk score using forest cover and recent case history, the classifications were comparable to those of the risk stratification system currently used by the DVBD. CONCLUSIONS: There was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.