Background: Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area.
South Africa, having met the World Health Organisation's pre-elimination criteria, has set a goal to achieve malaria elimination by 2018. Mpumalanga, one of three provinces where malaria transmission still occurs, has a malaria season subject to unstable transmission that is prone to sporadic outbreaks. As South Africa prepares to intensify efforts towards malaria elimination, there is a need to understand patterns in malaria transmission so that efforts may be targeted appropriately. This paper describes the seasonality of transmission by exploring the relationship between malaria cases and three potential drivers: rainfall, geography (physical location) and the source of infection (local/imported). Seasonal decomposition of the time series by Locally estimated scatterplot smoothing is applied to the case data for the geographical and source of infection sub-groups. The relationship between cases and rainfall is assessed using a cross-correlation analysis. The malaria season was found to have a short period of no/low level of reported cases and a triple peak in reported cases between September and May; the three peaks occurring in October, January and May. The seasonal pattern of locally-sourced infection mimics the triple-peak characteristic of the total series while imported infections contribute mostly to the second and third peak of the season (Christmas and Easter respectively). Geographically, Bushbuckridge municipality, which exhibits a different pattern of cases, contributed mostly to the first and second peaks in cases while Maputo province (Mozambique) experienced a similar pattern in transmission to the imported cases. Though rainfall lagged at 4 weeks was significantly correlated with malaria cases, this effect was dampened due to the growing proportion of imported cases since 2006. These findings may be useful as they enhance the understanding of the current incidence pattern and may inform mathematical models that enable one to predict the impact changes in these drivers will have on malaria transmission.
BackgroundSurveillance with timely follow-up of diagnosed cases is a key component of the malaria elimination strategy in South Africa. The strategy requires each malaria case to be reported within 24 hours, and a case should be followed up within 48 hours. However, reporting delays are common in rural parts of the country.MethodsA technical framework was implemented and for eight months a nurse was hired to use a smartphone to report malaria cases to the provincial malaria control programme, from selected primary health care clinics in a rural, malaria-endemic area in South Africa. In addition, a short text message (SMS) notification was sent to the local malaria case investigator for each positive case. The objective was to assess whether reporting over the smartphone led to timelier notification and follow-up of the cases. An evaluation on the simplicity, flexibility, stability, acceptability, and usability of the framework was conducted.ResultsUsing mobile reporting, 18 of 23 cases had basic information entered into the provincial malaria information system within 24 hours. For the study period, the complete case information was entered two to three weeks earlier with the mobile reporting than from other clinics. A major improvement was seen in the number of positive cases being followed up within 48 hours. In 2011/2012, only one case out of 22 reported from the same study clinics was followed up within this timeframe. During the study period in 2012/2013, 15 cases out of 23 were followed up within two days. For the other clinics in the area, only a small improvement was seen between the two periods, in the proportion of cases that was followed up within 48 hours.ConclusionsSMS notification for each diagnosed malaria case improved the timeliness of data transmission, was acceptable to users and was technically feasible in this rural area. For the malaria case investigations, time to follow-up improved compared to other clinics. Although malaria case numbers in the study were small, the results of the qualitative and quantitative evaluations are convincing and consideration should be given to larger-scale use within the national malaria control programme.
Background and objective: To evaluate the performance of a novel malaria outbreak identification system in the epidemic prone rural area of Mpumalanga Province, South Africa, for timely identification of malaria outbreaks and guiding integrated public health responses.
Background As surveillance is a key strategy for malaria elimination in South Africa, ensuring strong surveillance systems is a National Department of Health priority. Historically, real time tracking of case trends and reporting within 24 h—a requirement in South Africa’s National surveillance guidelines—has not been possible. To enhance surveillance and response efficiency, a mobile surveillance tool, MalariaConnect, was developed using Unstructured Supplementary Service Data (USSD) technology. It was rolled out in health facilities in malaria endemic areas of South Africa to provide 24-h reporting of malaria cases. Methods To evaluate the efficiency of the mobile tool to detect an outbreak data were extracted from the paper based and MalariaConnect reporting systems in Bushbuckridge from 1 January to 18 June 2017. These data were subject to time series analyses to determine if MalariaConnect provided sufficient data reliably to detect increasing case trends reported through the paper system. The Chi squared test was used to determine goodness of fit between the following indicator data generated using MalariaConnect and paper reporting systems: timeliness, completeness, and precision. Results MalariaConnect adequately tracked case trends reported through the paper system. Timeliness of reporting increased significantly using MalariaConnect with 0.63 days to notification compared to 5.65 days using the paper-system (p < 0.05). The completeness of reporting was significantly higher for the paper system (100% completion; p < 0.05), compared to confirmed MalariaConnect cases (61%). There was a moderate association between data precision and the reporting system (p < 0.05). MalariaConnect provided an effective way of reliably and accurately identifying the onset of the malaria outbreak in Bushbuckridge. Conclusion Timeliness significantly improved using MalariaConnect and in a malaria elimination setting, can be used to markedly improve case investigation and response activities within the recommended 72-h period. Although data completeness and precision were lower compared to paper reporting, MalariaConnect data can be used to trigger outbreak responses.
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