The impact of endemic and epidemic malaria on the risk of stillbirth in two areas of Tanzania with different malaria transmission patterns

BACKGROUND: The impact of malaria on the risk of stillbirth is still under debate. The aim of the present analysis was to determine comparative changes in stillbirth prevalence between two areas of Tanzania with different malaria transmission patterns in order to estimate the malaria attributable component. METHODS: A retrospective analysis was completed of stillbirth differences between primigravidae and multigravidae in relation to malaria cases and transmission patterns for two different areas of Tanzania with a focus on the effects of the El Niño southern climatic oscillation (ENSO). One area, Kagera, experiences outbreaks of malaria, and the other area, Morogoro, is holoendemic. Delivery and malaria data were collected over a six year period from records of the two district hospitals in these locations. RESULTS: There was a significantly higher prevalence of low birthweight in primigravidae compared to multigravidae for both data sets. Low birthweight and stillbirth prevalence (17.5% and 4.8%) were significantly higher in Kilosa compared to Ndolage (11.9% and 2.4%). There was a significant difference in stillbirth prevalence between Ndolage and Kilosa between malaria seasons (2.4% and 5.6% respectively, p < 0.001) and during malaria seasons (1.9% and 5.9% respectively, p < 0.001). During ENSO there was no difference (4.1% and 4.9%, respectively). There was a significant difference in low birthweight prevalence between Ndolage and Kilosa between malaria seasons (14.4% and 23.0% respectively, p < 0.001) and in relation to malaria seasons (13.9% and 25.2% respectively, p < 0.001). During ENSO there was no difference (22.2% and 19.8%, respectively). Increased low birthweight risk occurred approximately five months following peak malaria prevalence, but stillbirth risk increased at the time of malaria peaks. CONCLUSION: Malaria exposure during pregnancy has a delayed effect on birthweight outcomes, but a more acute effect on stillbirth risk

Climate prediction of El Niño malaria epidemics in north-west Tanzania

Malaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics. A regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season. It was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years. These results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region

Monitoring and evaluation of malaria in pregnancy – developing a rational basis for control

Monitoring and evaluation of malaria control in pregnancy is essential for assessing the efficacy and effectiveness of health interventions aimed at reducing the major burden of this disease on women living in endemic areas. Yet there is no currently integrated strategic approach on how this should be achieved. Malaria control in pregnancy is formulated in relation to epidemiological patterns of exposure. Current emphasis is on intermittent preventive treatment (IPTp) during pregnancy with sulphadoxine-pyrimethamine in higher transmission areas, combined with insecticide treated bed nets (ITNs) and case management. Emphasis in lower transmission areas is primarily on case management. This paper discusses a rational basis for monitoring and evaluation based on: assessments of therapeutic and prophylactic drug efficacy; proportional reductions in parasite prevalence; seasonal effects; rapid assessment methodologies; birthweight and/or anaemia nomograms; case-coverage methods; maternal mortality indices; operational and programmatic indicators; and safety and pharmacovigilance of antimalarials in pregnancy. These approaches should be incorporated more effectively within National Programmes in order to facilitate surveillance and improve identification of high-risk women. Systems for utilizing routinely collected data should be strengthened, with greater attention to safety and pharmacovigilance with the advent of artemisinin combination therapies, and prospects of inadvertent exposures to artemisinins in the first trimester. Integrating monitoring activities within malaria control, reproductive health and adolescent-friendly services will be critical for implementation. Large-scale operational research is required to further evaluate the validity of currently proposed indicators, and in order to clarify the breadth and scale of implementation to be deployed

Malaria Journal 2007, 6:162 doi:10.1186/1475-2875-6-162

PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Climate prediction of El Nino malaria epidemics in north-west Tanzani

Correction: Monitoring and evaluation of malaria in pregnancy - developing a rational basis for control

ABSTRACT: I have noted since publication of our paper [Brabin BJ, et al. Monitoring and evaluation of malaria in pregnancy - developing a rational basis for control. Malar J. 2008, 7(Suppl 1):S6] that my surname is incorrect. My surname has been spelt incorrectly as Wasame as it is actually spelt Warsam

Climate prediction of El Niño malaria epidemics in north-west Tanzania-1

<p><b>Copyright information:</b></p><p>Taken from "Climate prediction of El Niño malaria epidemics in north-west Tanzania"</p><p>http://www.malariajournal.com/content/6/1/162</p><p>Malaria Journal 2007;6():162-162.</p><p>Published online 6 Dec 2007</p><p>PMCID:PMC2228309.</p><p></p>anuary period and the log malaria anomalies from October to March, while the second season corresponds to the climatic anomalies during the February to July period and the log malaria anomalies from April to September. The anomalies were standardized by subtracting the mean and dividing by the standard deviation

Climate prediction of El Niño malaria epidemics in north-west Tanzania-3

<p><b>Copyright information:</b></p><p>Taken from "Climate prediction of El Niño malaria epidemics in north-west Tanzania"</p><p>http://www.malariajournal.com/content/6/1/162</p><p>Malaria Journal 2007;6():162-162.</p><p>Published online 6 Dec 2007</p><p>PMCID:PMC2228309.</p><p></p>t the upper and lower quartiles of the 63 DEMETER ensembles. The ends of the whiskers show the minimum and maximum values of the DEMETER re-forecasts. First, the DEMETER forecasts of maximum temperature anomalies for the Aug-Jan season are compared to the observed temperature and malaria anomalies (a). Second, the DEMETER forecasts of total rainfall during the same period are compared to the corresponding observed rainfall and malaria anomalies (b). Third, the DEMETER forecasts of total rainfall during the second rainy season (February starting date) are compared to the corresponding observed rainfall and malaria anomalies (c). Fourth, the six-month DEMETER forecasts of total rainfall during the same period are combined with the observed maximum temperature anomalies during the first rainy season using the 'C3' linear regression model. These resulting malaria forecasts anomalies are compared to the weather observation predicted malaria and observed malaria anomalies (d). The anomalies were standardized by subtracting the mean and dividing by the standard deviation