http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6874 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. 2012-03-12T06:48:03.811Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:9602 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. 2011-12-06T00:50:07.551Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:4365 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. 2010-04-27T04:54:27.350Z ]]>