Aaron Mabuza

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

BackgroundMpumalanga 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.MethodsSaTScan™ 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 discussionSaTScan 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.ConclusionExploring 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.

Increased Gametocytemia after Treatment: An Early Parasitological Indicator of Emerging Sulfadoxine-Pyrimethamine Resistance in Falciparum Malaria

BACKGROUND Although malaria treatment aims primarily to eliminate the asexual blood stages that cause illness, reducing the carriage of gametocytes is critical for limiting malaria transmission and the spread of resistance. METHODS Clinical and parasitological responses to the fixed-dose combination of sulfadoxine and pyrimethamine in patients with uncomplicated falciparum malaria were assessed biannually since implementation of this treatment policy in 1998 in Mpumalanga Province, South Africa. RESULTS Despite sustained cure rates of > 90% (P = .14), the duration of gametocyte carriage increased from 3 to 22 weeks (per 1000 person-weeks) between 1998 and 2002 (P < .001). The dhfr and dhps mutations associated with sulfadoxine-pyrimethamine resistance were the most important drivers of the increased gametocytemia, although these mutations were not associated with increased pretreatment asexual parasite density or slower asexual parasite clearance times. The geometric mean gametocyte duration and area under the gametocyte density time curve (per 1000 person-weeks) were 7.0 weeks and 60.8 gametocytes/microL per week, respectively, among patients with wild-type parasites, compared with 45.4 weeks (P = .016) and 1212 gametocytes/microL per week (P = .014), respectively, among those with parasites containing 1-5 dhfr/dhps mutations. CONCLUSIONS An increased duration and density of gametocyte carriage after sulfadoxine-pyrimethamine treatment was an early indicator of drug resistance. This increased gametocytemia among patients who have primary infections with drug-resistant Plasmodium falciparum fuels the spread of resistance even before treatment failure rates increase significantly.

Using a geographical information system to plan a malaria control programme in South Africa.

INTRODUCTION Sustainable control of malaria in sub-Saharan Africa is jeopardized by dwindling public health resources resulting from competing health priorities that include an overwhelming acquired immunodeficiency syndrome (AIDS) epidemic. In Mpumalanga province, South Africa, rational planning has historically been hampered by a case surveillance system for malaria that only provided estimates of risk at the magisterial district level (a subdivision of a province). METHODS To better map control programme activities to their geographical location, the malaria notification system was overhauled and a geographical information system implemented. The introduction of a simplified notification form used only for malaria and a carefully monitored notification system provided the good quality data necessary to support an effective geographical information system. RESULTS The geographical information system displays data on malaria cases at a village or town level and has proved valuable in stratifying malaria risk within those magisterial districts at highest risk, Barberton and Nkomazi. The conspicuous west-to-east gradient, in which the risk rises sharply towards the Mozambican border (relative risk = 4.12, 95% confidence interval = 3.88-4.46 when the malaria risk within 5 km of the border was compared with the remaining areas in these two districts), allowed development of a targeted approach to control. DISCUSSION The geographical information system for malaria was enormously valuable in enabling malaria risk at town and village level to be shown. Matching malaria control measures to specific strata of endemic malaria has provided the opportunity for more efficient malaria control in Mpumalanga province.

Malaria control in South Africa 2000–2010: beyond MDG6

BackgroundMalaria is one of the key targets within Goal 6 of the Millennium Development Goals (MDGs), whereby the disease needs to be halted and reversed by the year 2015. Several other international targets have been set, however the MDGs are universally accepted, hence it is the focus of this manuscript.MethodsAn assessment was undertaken to determine the progress South Africa has made against the malaria target of MDG Goal 6. Data were analyzed for the period 2000 until 2010 and verified after municipal boundary changes in some of South Africa’s districts and subsequent to verifying actual residence of malaria positive cases.ResultsSouth Africa has made significant progress in controlling malaria transmission over the past decade; malaria cases declined by 89.41% (63663 in 2000 vs 6741 in 2010) and deaths decreased by 85.4% (453 vs 66) in the year 2000 compared to the year 2010. Coupled with this, malaria cases among children under five years of age have also declined by 93% (6791 in 2000 vs 451 in 2010). This has resulted in South Africa achieving and exceeding the malaria target of the MDGs. A series of interventions have attributed to this decrease, these include: drug policy change from monotherapy to artemisinin combination therapy, insecticide change from pyrethroids back to DDT; cross border collaboration (South Africa with Mozambique and Swaziland through the Lubombo Spatial Development Initiative– LSDI) and financial investment in malaria control. The KwaZulu-Natal Province has seen the largest reduction in malaria cases and deaths (99.1% cases- 41786 vs 380; and 98.5% deaths 340 vs 5), when comparing the year 2000 with 2010. The Limpopo Province recorded the lowest reduction in malaria cases compared to the other malaria endemic provinces (56.1% reduction- 9487 vs 4174; when comparing 2000 to 2010).ConclusionsSouth Africa is well positioned to move beyond the malaria target of the MDGs and progress towards elimination. However, in addition to its existing interventions, the country will need to sustain its financing for malaria control and support programmed reorientation towards elimination and scale up active surveillance coupled with treatment at the community level. Moreover cross-border malaria collaboration needs to be sustained and scaled up to prevent the re-introduction of malaria into the country.

The feasibility of malaria elimination in South Africa

BackgroundFollowing the last major malaria epidemic in 2000, malaria incidence in South Africa has declined markedly. The decrease has been so emphatic that South Africa now meets the World Health Organization (WHO) threshold for malaria elimination. Given the Millennium Development Goal of reversing the spread of malaria by 2015, South Africa is being urged to adopt an elimination agenda. This study aimed to determine the appropriateness of implementing a malaria elimination programme in present day South Africa.MethodsAn assessment of the progress made by South Africa in terms of implementing an integrated malaria control programme across the three malaria-endemic provinces was undertaken. Vector control and case management data were analysed from the period of 2000 until 2011.ResultsBoth malaria-related morbidity and mortality have decreased significantly across all three malaria-endemic provinces since 2000. The greatest decline was seen in KwaZulu-Natal where cases decreased from 42,276 in 2000 to 380 in 2010 and deaths dropped from 122 in 2000 to six in 2010. Although there has been a 49.2 % (8,553 vs 4,214) decrease in the malaria cases reported in Limpopo Province, currently it is the largest contributor to the malaria incidence in South Africa. Despite all three provinces reporting average insecticide spray coverage of over 80%, malaria incidence in both Mpumalanga and Limpopo remains above the elimination threshold. Locally transmitted case numbers have declined in all three malaria provinces but imported case numbers have been increasing. Knowledge gaps in vector distribution, insecticide resistance status and drug usage were also identified.ConclusionsMalaria elimination in South Africa is a realistic possibility if certain criteria are met. Firstly, there must be continued support for the existing malaria control programmes to ensure the gains made are sustained. Secondly, cross border malaria control initiatives with neighbouring countries must be strongly encouraged and supported to reduce malaria in the region and the importation of malaria into South Africa. Thirdly, operational research, particularly on vector distribution and insecticide resistance status must be conducted as a matter of urgency, and finally, the surveillance systems must be refined to ensure the information required to inform an elimination agenda are routinely collected.

Household and microeconomic factors associated with malaria in Mpumalanga, South Africa

A household matched case-control study design was used to explore associations between household characteristics and malaria risk in seven study towns in the hypoendemic area of Mpumalanga Province, South Africa. Controls were identified from neighboring households of each case. Principle component analysis was used to calculate a wealth index for households to allow comparison across socioeconomic groups. Conditional univariate and multiple logistic regression analyses were used to assess associations between household malaria risk and potential risk factors. Univariate analysis demonstrated an increased household malaria risk for people living in mud-walled houses compared with those in brick dwellings (OR=5.10, 95% CI 2.03-12.80, P=0.001). Multivariate analysis confirmed the association between malaria risk and mud-wall construction (OR=6.12, 95% CI 2.26-16.59, P=0.001) and demonstrated an association with opening windows after retiring to sleep (OR=4.01, 95% CI 1.32-12.18, P=0.014). An inverse association between household wealth, third (OR=0.24, 95% CI 0.09-0.65, P=0.005) and fourth quartiles (OR=0.27, 95% CI 0.10-0.79, P=0.016), and malaria risk was observed. Associations found here include increased household malaria risk and mud-wall construction, the practice of opening of windows at night and relative household poverty. Education campaigns targeting risk behavior may reduce malaria risk, but economic development is a more important intervention.

Malaria deaths as sentinel events to monitor healthcare delivery and antimalarial drug safety

Objectives  To identify case management, health system and antimalarial drug factors contributing to malaria deaths.

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

Background and objectiveTo 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.MethodsUsing 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.ResultsEighty 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.ConclusionThe 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.

Malaria pharmacovigilance in Africa : Lessons from a pilot project in mpumalanga province, South Africa

Abstractp ]Background and objectives: Prior to the introduction of artemisinin-based combination antimalarial therapy in Mpumalanga province, South Africa, a pharmacovigilance strategy was developed to pilot locally relevant surveillance methods for detecting serious adverse drug reactions (ADRs) and signals related to artesunate plus sulfadoxine/pyrimethamine. Study design: From 1 March 2002 to 30 June 2004, five methods for detecting ADRs in patients receiving antimalarials were piloted in the rural communities of Mpumalanga province in South Africa: (i) home follow-up of patients by malaria control staff; (ii) enhanced spontaneous reporting of suspected ADRs by health professionals at clinics and hospitals; (iii) active hospital surveillance for malaria-related admissions and patients recently treated for malaria; (iv) a confidential enquiry into malaria-related deaths; and (v) adverse events monitoring during two therapeutic efficacy studies conducted in 2002 and 2004. Results: During the study period, the malaria control programme was notified of 4778 cases of malaria while sulfadoxine/pyrimethamine monotherapy was the recommended treatment and 7692 cases after the introduction of artesunate plus sulfadoxine/pyrimethamine in January 2003. Of 2393 home follow-up visits of reported cases of malaria, three fatal adverse events were identified where recent use of artesunate plus sulfadoxine/pyrimethamine treatment was reported. Two cases were attributed to poor response to treatment, while one case was considered possibly related to artesunate plus sulfadoxine/pyrimethamine treatment. Clinic and hospital surveillance reported six ADRs in association with sulfadoxine/pyrimethamine treatment, five being treatment failures and one being a non-serious rash. During active hospital surveillance, 38 inpatients exposed to sulfadoxine/pyrimethamine were identified, including one child who experienced pancytopenia following treatment with sulfadoxine/pyrimethamine 11 days before admission; this adverse effect was considered to be possibly due to sulfadoxine/pyrimethamine treatment. The confidential enquiry into malaria- 900 related deaths identified three adverse events, including a death where the contribution of treatment could not be excluded. A therapeutic efficacy study of 95 patients followed over 42 days identified one case of repeated vomiting possibly associated with artesunate plus sulfadoxine/pyrimethamine. Conclusion: Multifaceted monitoring throughout the malaria patient journey is necessary in developing countries implementing new treatments to safeguard against missing serious complications associated with malaria treatment.

Exploring the Seasonality of Reported Treated Malaria Cases in Mpumalanga, South Africa

South Africa, having met the World Health Organisations 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.