Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors

Declining Burden of Malaria Over two Decades in a Rural Community of Muheza District, North-Eastern Tanzania.

The recently reported declining burden of malaria in some African countries has been attributed to scaling-up of different interventions although in some areas, these changes started before implementation of major interventions. This study assessed the long-term trends of malaria burden for 20 years (1992--2012) in Magoda and for 15 years in Mpapayu village of Muheza district, north-eastern Tanzania, in relation to different interventions as well as changing national malaria control policies.\ud Repeated cross-sectional surveys recruited individuals aged 0 -- 19 years from the two villages whereby blood smears were collected for detection of malaria parasites by microscopy. Prevalence of Plasmodium falciparum infections and other indices of malaria burden (prevalence of anaemia, splenomegaly and gametocytes) were compared across the years and between the study villages. Major interventions deployed including mobile clinic, bed nets and other research activities, and changes in national malaria control policies were also marked. In Magoda, the prevalence of P. falciparum infections initially decreased between 1992 and 1996 (from 83.5 to 62.0%), stabilized between 1996 and 1997, and further declined to 34.4% in 2004. A temporary increase between 2004 and 2008 was followed by a progressive decline to 7.2% in 2012, which is more than 10-fold decrease since 1992. In Mpapayu (from 1998), the highest prevalence was 81.5% in 1999 and it decreased to 25% in 2004. After a slight increase in 2008, a steady decline followed, reaching <5% from 2011 onwards. Bed net usage was high in both villages from 1999 to 2004 (>=88%) but it decreased between 2008 and 2012 (range, 28% - 68%). After adjusting for the effects of bed nets, age, fever and year of study, the risk of P. falciparum infections decreased significantly by >=97% in both villages between 1999 and 2012 (p < 0.001). The prevalence of splenomegaly (>40% to <1%) and gametocytes (23% to <1%) also decreased in both villages.Discussion and conclusionsA remarkable decline in the burden of malaria occurred between 1992 and 2012 and the initial decline (1992 -- 2004) was most likely due to deployment of interventions, such as bed nets, and better services through research activities. Apart from changes of drug policies, the steady decline observed from 2008 occurred when bed net coverage was low suggesting that other factors contributed to the most recent pattern. These results suggest that continued monitoring is required to determine causes of the changing malaria epidemiology and also to monitor the progress towards maintaining low malaria transmission and reaching related millennium development goals

Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003

BACKGROUND: The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. METHODS: The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. RESULTS: The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range +/- 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. CONCLUSION: This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area

Approaches to the evaluation of outbreak detection methods

BACKGROUND: An increasing number of methods are being developed for the early detection of infectious disease outbreaks which could be naturally occurring or as a result of bioterrorism; however, no standardised framework for examining the usefulness of various outbreak detection methods exists. To promote comparability between studies, it is essential that standardised methods are developed for the evaluation of outbreak detection methods. METHODS: This analysis aims to review approaches used to evaluate outbreak detection methods and provide a conceptual framework upon which recommendations for standardised evaluation methods can be based. We reviewed the recently published literature for reports which evaluated methods for the detection of infectious disease outbreaks in public health surveillance data. Evaluation methods identified in the recent literature were categorised according to the presence of common features to provide a conceptual basis within which to understand current approaches to evaluation. RESULTS: There was considerable variation in the approaches used for the evaluation of methods for the detection of outbreaks in public health surveillance data, and appeared to be no single approach of choice. Four main approaches were used to evaluate performance, and these were labelled the Descriptive, Derived, Epidemiological and Simulation approaches. Based on the approaches identified, we propose a basic framework for evaluation and recommend the use of multiple approaches to evaluation to enable a comprehensive and contextualised description of outbreak detection performance. CONCLUSION: The varied nature of performance evaluation demonstrated in this review supports the need for further development of evaluation methods to improve comparability between studies. Our findings indicate that no single approach can fulfil all evaluation requirements. We propose that the cornerstone approaches to evaluation identified provide key contributions to support internal and external validity and comparability of study findings, and suggest these be incorporated into future recommendations for performance assessment

‘The Phone is My Boss and My Helper’ – A Gender Analysis of an mHealth Intervention with Health Extension Workers in Southern Ethiopia

Mobile health (mHealth) provides health services and information via mobile technologies, including mobile phones. There is considerable optimism in mHealth’s potential to overcome health systems’ deficiencies to ensure access to safe, effective and affordable health services. This has led to an ‘explosion of mHealth activities’ and ‘large-scale adoption and deployment of mobile phones’ by Community Health Worker (CHW) programmes. MHealth innovation in relation to CHWs, on which low- and middle-income countries (LMICs) disproportionately depend, has been reported to be ‘particularly promising’. CHWs’ use of mHealth has the potential to improve their motivation; decision-making; training; adherence to guidelines; data entry and quality; planning and efficiency; and communication and health promotion; while also enhancing coverage and timeliness of services and reducing costs. MHealth also allows the monitoring and tracking of health indicators in real time, providing crucial insights to policy makers and enabling CHWs to better serve communities

Comparing Models for Early Warning Systems of Neglected Tropical Diseases

Early Warning Systems (EWS) are management tools to predict the occurrence of epidemics. They are based on the dependence of a given infectious disease on environmental variables. Although several neglected tropical diseases are sensitive to the effect of climate, our ability to predict their dynamics has been barely studied. In this paper, we use several models to determine if the relationship between cases and climatic variability is robust—that is, not simply an artifact of model choice. We propose that EWS should be based on results from several models that are to be compared in terms of their ability to predict future number of cases. We use a specific metric for this comparison known as the predictive R2, which measures the accuracy of the predictions. For example, an R2 of 1 indicates perfect accuracy for predictions that perfectly match observed cases. For cutaneous leishmaniasis, R2 values range from 72% to77%, well above predictions using mean seasonal values (64%). We emphasize that predictability should be evaluated with observations that have not been used to fit the model. Finally, we argue that EWS should incorporate climatic variables that are known to have a consistent relationship with the number of observed cases

Early clinical development of artemether-lumefantrine dispersible tablet: palatability of three flavours and bioavailability in healthy subjects

BACKGROUND\ud \ud Efforts to ease administration and enhance acceptability of the oral anti-malarial artemether-lumefantrine (A-L) crushed tablet to infants and children triggered the development of a novel dispersible tablet of A-L. During early development of this new formulation, two studies were performed in healthy subjects, one to evaluate the palatability of three flavours of A-L, and a second one to compare the bioavailability of active principles between the dispersible tablet and the tablet (administered crushed and intact).\ud \ud METHODS\ud \ud Study 1 was performed in 48 healthy schoolchildren in Tanzania. Within 1 day, all subjects tasted a strawberry-, orange- and cherry-flavoured oral A-L suspension for 10 seconds (without swallowing) in a randomized, single-blind, crossover fashion. The palatability of each formulation was rated using a visual analogue scale (VAS). Study 2 was an open, randomized crossover trial in 48 healthy adults given single doses of A-L (80 mg artemether + 480 mg lumefantrine) with food. The objectives were to compare the bioavailability of artemether, dihydroartemisinin (DHA) and lumefantrine between the dispersible tablet and the tablet administered crushed (primary objective) and intact (secondary objective).\ud \ud RESULTS\ud \ud Study 1 showed no statistically significant difference in VAS scores between the three flavours but cherry had the highest score in several ratings (particularly for overall liking). Study 2 demonstrated that the dispersible and crushed tablets delivered bioequivalent artemether, DHA and lumefantrine systemic exposure (area under the curve [AUC]); mean ± SD AUC0-tlast were 208 ± 113 vs 195 ± 93 h.ng/ml for artemether, 206 ± 81 vs 199 ± 84 h.ng/ml for DHA and 262 ± 107 vs 291 ± 106 h x μg/ml for lumefantrine. Bioequivalence was also shown for peak plasma concentrations (Cmax) of DHA and lumefantrine. Compared with the intact tablet, the dispersible tablet resulted in bioequivalent lumefantrine exposure, but AUC and Cmax values of artemether and DHA were 20-35% lower.\ud \ud CONCLUSIONS\ud \ud Considering that cherry was the preferred flavour, and that the novel A-L dispersible tablet demonstrated similar pharmacokinetic performances to the tablet administered crushed, a cherry-flavoured A-L dispersible tablet formulation was selected for further development and testing in a large efficacy and safety study in African children with malaria

Effect of meteorological factors on clinical malaria risk among children: an assessment using village-based meteorological stations and community-based parasitological survey

<p>Abstract</p> <p>Background</p> <p>Temperature, rainfall and humidity have been widely associated with the dynamics of malaria vector population and, therefore, with spread of the disease. However, at the local scale, there is a lack of a systematic quantification of the effect of these factors on malaria transmission. Further, most attempts to quantify this effect are based on proxy meteorological data acquired from satellites or interpolated from a different scale. This has led to controversies about the contribution of climate change to malaria transmission risk among others. Our study addresses the original question of relating meteorological factors measured at the local scale with malaria infection, using data collected at the same time and scale.</p> <p>Methods</p> <p>676 children (6–59 months) were selected randomly from three ecologically different sites (urban and rural). During weekly home visits between December 1, 2003, and November 30, 2004, fieldworkers tested children with fever for clinical malaria. They also collected data on possible confounders monthly. Digital meteorological stations measured ambient temperature, humidity, and rainfall in each site. Logistic regression was used to estimate the risk of clinical malaria given the previous month's meteorological conditions.</p> <p>Results</p> <p>The overall incidence of clinical malaria over the study period was 1.07 episodes per child. Meteorological factors were associated with clinical malaria with mean temperature having the largest effect.</p> <p>Conclusion</p> <p>Temperature was the best predictor for clinical malaria among children under five. A systematic measurement of local temperature through ground stations and integration of such data in the routine health information system could support assessment of malaria transmission risk at the district level for well-targeted control efforts.</p