Spatiotemporal variation of malaria incidence in parasite clearance interventions and non-intervention areas in the Amhara Regional State, Ethiopia

Abstract

Background: In Ethiopia, malaria remains a major public health problem. To eliminate malaria, parasite clearance interventions were implemented in six kebeles (the lowest administrative unit) in the Amhara region. Understanding the spatiotemporal distribution of malaria is essential for targeting appropriate parasite clearance interventions to achieve the elimination goal. However, little is known about the spatiotemporal distribution of malaria incidence in the intervention and non-intervention areas. This study aimed to investigate the spatiotemporal distribution of community-based malaria in the intervention and non-intervention kebeles between 2013 and 2018 in the Amhara Regional State, Ethiopia.

Methods: Malaria data from 212 kebeles in eight districts were downloaded from the District Health Information System2 (DHIS2) database. We used Autoregressive integrated moving average (ARIMA) model to investigate seasonal variations; Anselin Local Moran's I statistical analysis to detect hotspot and cold spot clusters of malaria cases; and a discrete Poisson model using Kulldorff scan statistics to identify statistically significant clusters of malaria cases.

Results: The result showed that the reduction in the trend of malaria incidence was higher in the intervention areas compared to the non-intervention areas during the study period with a slope of -0.044 (-0.064, -0.023) and -0.038 (-0.051, -0.024), respectively. However, the difference was not statistically significant. The Global Moran's I statistics detected the presence of malaria clusters (z-score = 12.05; p<0.001); the Anselin Local Moran's I statistics identified hotspot malaria clusters at 21 locations in Gendawuha and Metema districts. A statistically significant spatial, temporal, and space-time cluster of malaria cases were detected. Most likely type of spatial clusters of malaria cases (LLR = 195501.5; p <0.001) were detected in all kebeles of Gendawuha and Metema districts. The temporal scan statistic identified three peak periods between September 2013 and November 2015 (LLR = 8727.5; p<0.001). Statistically significant most-likely type of space-time clusters of malaria cases (LLR = 97494.3; p<0.001) were detected at 22 locations from June 2014 to November 2016 in Metema district.

Conclusion: There was a significant decline in malaria incidence in the intervention areas. There were statistically significant spatiotemporal variations of malaria in the study areas. Applying appropriate parasite clearance interventions is highly recommended for the better achievement of the elimination goal. A more rigorous evaluation of the impact of parasite clearance interventions is recommended.

Fig 1. Map of study districts in the Amhara Regional State, Ethiopia.

Copyright: © 2022 Zeleke et al. The data on the map is derived from CSA, Ethiopia and APHI, Bahir Dar.

Fig 2. Seasonal decomposition of malaria incidence per 1000 population at risk between September 2013 and 2018 in the intervention kebeles.

Fig 3. Seasonal decomposition of malaria incidence per 1000 population at risk between September 2013 and 2018 in the non-intervention kebeles.

Fig 4. Global Moran’s I spatial autocorrelation report.

Fig 5. Hotspot clusters of malaria cases in Metema and Gendawuha districts during the study period.

Copyright: © 2022 Zeleke et al. The data on the map is derived from CSA, Ethiopia and APHI, Bahir Dar.

Fig 6. Purely spatial clusters of malaria cases were detected using SaTScan^TM in the Amhara Regional State, Ethiopia between 2013 and 2018.

Copyright: © 2022 Zeleke et al. The data on the map is derived from CSA, Ethiopia and APHI, Bahir Dar.

Fig 7. Purely temporal clusters of malaria cases in the study areas between 2013/09 and 2018/09.