Making data map-worthy-enhancing routine malaria data to support surveillance and mapping of Plasmodium falciparum anti-malarial resistance in a pre-elimination sub-Saharan African setting: a molecular and spatiotemporal epidemiology study.

BACKGROUND: Independent emergence and spread of artemisinin-resistant Plasmodium falciparum malaria have recently been confirmed in Africa, with molecular markers associated with artemisinin resistance increasingly detected. Surveillance to promptly detect and effectively respond to anti-malarial resistance is generally suboptimal in Africa, especially in low transmission settings where therapeutic efficacy studies are often not feasible due to recruitment challenges. However, these communities may be at higher risk of anti-malarial resistance. METHODS: From March 2018 to February 2020, a sequential mixed-methods study was conducted to evaluate the feasibility of the near-real-time linkage of individual patient anti-malarial resistance profiles with their case notifications and treatment response reports, and map these to fine scales in Nkomazi sub-district, Mpumalanga, a pre-elimination area in South Africa. RESULTS: Plasmodium falciparum molecular marker resistance profiles were linked to 55.1% (2636/4787) of notified malaria cases, 85% (2240/2636) of which were mapped to healthcare facility, ward and locality levels. Over time, linkage of individual malaria case demographic and molecular data increased to 75.1%. No artemisinin resistant validated/associated  Kelch-13 mutations were detected in the 2385 PCR positive samples. Almost all 2812 samples assessed for lumefantrine susceptibility carried the wildtype mdr86ASN and crt76LYS alleles, potentially associated with decreased lumefantrine susceptibility. CONCLUSION: Routine near-real-time mapping of molecular markers associated with anti-malarial drug resistance on a fine spatial scale provides a rapid and efficient early warning system for emerging resistance. The lessons learnt here could inform scale-up to provincial, national and regional malaria elimination programmes, and may be relevant for other antimicrobial resistance surveillance.

Effectiveness of 24-h mobile reporting tool during a malaria outbreak in Mpumalanga Province, South Africa.

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.

Timelier notification and action with mobile phones-towards malaria elimination in South Africa.

BACKGROUND: Surveillance 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. METHODS: A 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. RESULTS: Using 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. CONCLUSIONS: SMS 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.

Exploring the seasonality of reported treated malaria cases in Mpumalanga, South Africa.

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.

Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes.

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. METHODS: SaTScan software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. RESULTS AND DISCUSSION: SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. CONCLUSION: Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out.

Evaluation of an operational malaria outbreak identification and response system in Mpumalanga Province, South Africa.

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. METHODS: Using five years of historical notification data, two binomial thresholds were determined for each primary health care facility in the highest malaria risk area of Mpumalanga province. Whenever the thresholds were exceeded at health facility level (tier 1), primary health care staff notified the malaria control programme, which then confirmed adequate stocks of malaria treatment to manage potential increased cases. The cases were followed up at household level to verify the likely source of infection. The binomial thresholds were reviewed at village/town level (tier 2) to determine whether additional response measures were required. In addition, an automated electronic outbreak identification system at town/village level (tier 2) was integrated into the case notification database (tier 3) to ensure that unexpected increases in case notification were not missed.The performance of these binomial outbreak thresholds was evaluated against other currently recommended thresholds using retrospective data. The acceptability of the system at primary health care level was evaluated through structured interviews with health facility staff. RESULTS: Eighty four percent of health facilities reported outbreaks within 24 hours (n = 95), 92% (n = 104) within 48 hours and 100% (n = 113) within 72 hours. Appropriate response to all malaria outbreaks (n = 113, tier 1, n = 46, tier 2) were achieved within 24 hours. The system was positively viewed by all health facility staff. When compared to other epidemiological systems for a specified 12 month outbreak season (June 2003 to July 2004) the binomial exact thresholds produced one false weekly outbreak, the C-sum 12 weekly outbreaks and the mean + 2 SD nine false weekly outbreaks. Exceeding the binomial level 1 threshold triggered an alert four weeks prior to an outbreak, but exceeding the binomial level 2 threshold identified an outbreak as it occurred. CONCLUSION: The malaria outbreak surveillance system using binomial thresholds achieved its primary goal of identifying outbreaks early facilitating appropriate local public health responses aimed at averting a possible large-scale epidemic in a low, and unstable, malaria transmission setting.