Active detection of asymptomatic malaria cases and resolution of associated factors are essential for malaria elimination. There are no nationwide estimates for asymptomatic malaria and associated factors in Ethiopia. Therefore, this study aims to generate comprehensive and conclusive evidence from various studies conducted in Ethiopia. Published articles from various electronic databases such as PubMed, Google Scholar, CINAHL, Scopes, Cochrane Library, the Web of Science, and African Journals Online were accessed. Also, unpublished studies from Addis Ababa digital library were identified. All observational study designs were included in the search. Data were extracted on the Microsoft Excel spreadsheet and analyzed using STATA version 14.1. A random-effects model was fitted to estimate the pooled prevalence of asymptomatic malaria. A meta-regression and subgroup analysis was computed to see heterogeneity. The publication bias was assessed by the funnel plots and Egger’s statistical tests. The analysis found that the pooled burden of asymptomatic malaria was 6.7 (95% confidence interval = 4.60, 8.79). The pooled prevalence of Plasmodium falciparum was 3.75 (95% confidence interval = 2.25, 5.18), and that of Plasmodium vivax was 2.22 (95% confidence interval = 1.46, 2.99). Factors such indoor residual spray service (odds ratio = 0.46; 95% confidence interval = 0.26, 0.81), never used insecticide-treated nets (odds ratio = 6.36; 95% confidence interval = 4.01, 10.09), and presence of stagnant water in the vicinity (odds ratio = 3.24; 95% confidence interval = 1.20, 8.71) were found to have a significant association with asymptomatic malaria. This study highlighted that pooled prevalence of asymptomatic malaria is high and varied by population groups. Prevalence of asymptomatic malaria was increased among those who never used insecticide-treated nets and were living near stagnant water by six and three times, respectively. The use of more sensitive diagnostic methods could yield a higher burden of the disease. Furthermore, active case detection is recommended for effective intervention toward elimination.
Background: Since its occurrence in late December, 2019, in Wuhan, China; COVID-19 is rapidly spreading across the world nations. Case detection and contact identification remains the key surveillance objectives for effective containment of the pandemic. This study was aimed at evaluating the performance of COVID-19 surveillance in Western Oromia towns, Ethiopia.Methods: CDC-update guideline for surveillance system evaluation and surveillance documents prepared by Ethiopian Public Health Institute were used as a benchmark. Qualitative interview of health workers and quantitative review of surveillance data were conducted. Semi structured questionnaire was used to interview 436 systematically selected local community to assess their awareness, perceived risk, health system utilization experience and current practices. We analyzed the data using descriptive approach by aligning the data from community, health facility and health authority along with suspect identification, case detection and reporting process of the surveillance system.Results: One hundred seventy-nine (41%) of the participants believe they have high risk of contracting COVID-19 and 127 (29%) of them reported they have been visited by health extension worker. One hundred ninety-seven (45.2%) reported that they are not using health facilities for routine services during this pandemic. Except one hospital, all health facilities (92%) were using updated case definition. From March to July 30, 2020, there were 150 contacts, 116 suspects and 634 risk group tested for COVID-19 of which cases were found only from risk group testing, 10/521 (2%) in Nekemte and none from Shambu. Surveillance data was not being analyzed at all level. Conclusion: In this study it is reasonable to conclude that community/risk group testing was more effective than suspect or contact testing. Surveillance data was not being used to identify group and/or area most exposed for guiding response strategy. Therefore, targeting risk group for testing can improve the effectiveness of COVID-19 surveillance in settings where mass testing is not feasible. Surveillance data analysis should be done to identify areas and groups at higher risk and investigate to avoid further crisis.
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