Larval source management in Ethiopia : modelling to assess its effectiveness in curbing malaria surge in dire Dawa and Batu Towns

Ayana, Galana Mamo and Jalilian, Abdollah and Ashine, Temesgen and Molla, Eshetu and Hailemeskel, Elifaged and Yemane, Dagmawi Hailu and Yirgu, Hailegiorgis and Negash, Nigatu and Teferi, Natnael and Teshome, Daniel and Reynolds, Alison M. and Weetman, David and Wilson, Anne L. and Kenate, Birhanu and Donnelly, Martin J. and Sedda, Luigi and Gadisa, Endalamaw (2024) Larval source management in Ethiopia : modelling to assess its effectiveness in curbing malaria surge in dire Dawa and Batu Towns. Malaria Journal, 23 (1): 366. ISSN 1475-2875

Abstract

Background: Ethiopia faces several severe challenges in terms of malaria elimination, including drug resistance and diagnostic evasion in the Plasmodium falciparum parasite, insecticide resistance in the primary Anopheles malaria vector, and, most recently, the invasion of the Asian malaria vector Anopheles stephensi. Novel malaria control methods are therefore needed, and in this paper, we describe the evaluation of a larval source management (LSM) strategy implemented in response to An. stephensi. The primary outcome was the malaria incidence rate compared between intervention and non-intervention sites in the presence of An. stephensi. Methods: Intervention (Batu and Dire Dawa) and control (Metehara) towns were selected, and weekly malaria passive case detection data collected between 2014 and 2023 were obtained from the Oromia regional state and Dire Dawa City Administration Health Bureau. In addition, data regarding intervention were obtained from the President’s Malaria Initiative (PMI) reports. Weekly malaria passive case data were used to evaluate the change in the estimated malaria incidence rate and trends of temporal patterns of the estimated malaria incidence rate before and after interventions. An interrupted time series model with a cyclic second-order random walk structure periodic seasonal term was used to assess the impact of LSM on malaria incidence rate in the intervention and control settings. Results: An upsurge in malaria cases occurred after 2020 at both the intervention and control sites. The temporal patterns of malaria incidence rate showed an increasing trend after the intervention. The ITS model depicted that the LSM has no impact in reducing the malaria incidence rate at both intervention site Dire Dawa [immediate impact = 1.462 (0.891, 2.035)], [Lasting impact = 0.003 (− 0.012, 0.018)], and Batu [Immediate impact 0.007 (− 0.235, 0.249), [Lasting impact = 0.008 (− 0.003, 0.013)]. Conclusions: An overall increasing trend in the malaria incidence rate was observed irrespective of the implementation of LSM in the urban settings of Ethiopia, where An. stephensi has been found. Further investigations and validations of the incorporation of LSM into control activities are warranted.