Enhancing malaria control using a computerised management system in southern Africa

BACKGROUND: Malaria control programmes utilising indoor residual spraying are only effective if a high coverage of targeted structures is achieved and an insecticide that is effective against the specific mosquito vector is correctly applied. Ongoing monitoring of spraying operations is essential to assure optimal programme performance and early corrective action, where indicated. METHODS: Successful development and application of a computerised spraying operations management system in Mpumalanga Province, South Africa during 1998 resulted in its adaptation and introduction in neighbouring Maputo Province, southern Mozambique during 2000. The structure and components of this computerised management system are described, and its' operational benefit in southern Mozambique, where community-based spray operators apply intradomiciliary insecticide, are reviewed. CONCLUSIONS: The computerised management system allowed malaria programme management and field supervisors to monitor spraying coverage, insecticide consumption and application rates on an ongoing basis. The system supported a successful transition to community-based spraying, while assuring correct insecticide application and spraying completion according to schedule

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

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. 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. 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. 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

Community knowledge and perceptions about malaria and practices influencing malaria control in Mpumalanga province, South Africa

Objective. To assess community knowledge and perceptions about malaria and its control in a rural setting.Design. Descriptive cross-sectional survey.Setting. Tonga district with a population of 116 418, seasonal malaria with an annual incidence of 3 260 cases.Subjects. Female heads of 299 randomly selected households.Methodology. A total of 299 households were selected from a random sample of 30 dusters. Community knowledge and perceptions about malaria and its control were assessed by interviews with the female head of each of the 299 selected households.Results. Respondents ranked malaria as the third most serious health problem facing the community after TB and AIDS. Seventy-two per cent (214/299) of respondents reported that they knew what malaria disease was and of these, 92.1% (197/214) mentioned mosquito bites as the cause of malaria. The respondents' understanding of the causal role of mosquitoes in malaria was significantly related to their knowledge about disease symptoms (P < 0.(01). Reported community compliance with the malaria control programme (MCP) was satisfactory; 86.6% (259/299) of respondents . reported that their homes had been sprayed during the past 2 years but 10.0% (30/299) did not know why homes were sprayed. Hospitals or clinics were the facilities where respondents most commonly sought treatment for fever; 66.9% (200/299) reported that they would seek treatment immediately after the onset of high fever. Specific practices such as replastering or washing of inside walls compromised the effectiveness of the MCP. Personal p~ventivemeasures were sometimes used against malaria (50.8%,152/299) and use was positively associated with education level (P = 0.001). Respondents expressed their desire for more information about malaria and their willingness to contribute to the control of malaria in their community.Conclusion. The survey collected information which was directly relevant to the development of health education messages to increase community awareness of the problem of malaria, to emphasise the importance of early diagnosis and prompt treatment of malaria, to improve community understanding of the function of indoor residual spraying, and to enlighten the population of the role of mosquitoes in malaria transmission and the availability and benefits of personal protection measures against mosquito bites

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

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