Defining the malaria burden in Nchelenge District, northern Zambia using the World Health Organization malaria indicators survey.
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ABSTRACT:
SUBMITTER: Nambozi M
PROVIDER: S-EPMC4067379 | biostudies-literature | 2014 Jun
REPOSITORIES: biostudies-literature
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Defining the malaria burden in Nchelenge District, northern Zambia using the World Health Organization malaria indicators survey.
Nambozi Michael M Malunga Phidelis P Mulenga Modest M Van Geertruyden Jean-Pierre JP D'Alessandro Umberto U
Malaria journal 20140605
<h4>Background</h4>Malaria is considered as one of the major public health problems and among the diseases of poverty. In areas of stable and relatively high transmission, pregnant women and their newborn babies are among the higher risk groups. A multicentre trial on the safety and efficacy of several formulations of artemisinin-based combination therapy (ACT) during pregnancy is currently on-going in four African countries, including Zambia, whose study site is in Nchelenge district. As the st ...[more]
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Project description:BackgroundThe Foodborne Disease Burden Epidemiology Reference Group (FERG) was established in 2007 by the World Health Organization to estimate the global burden of foodborne diseases (FBDs). This paper describes the methodological framework developed by FERG's Computational Task Force to transform epidemiological information into FBD burden estimates.Methods and findingsThe global and regional burden of 31 FBDs was quantified, along with limited estimates for 5 other FBDs, using Disability-Adjusted Life Years in a hazard- and incidence-based approach. To accomplish this task, the following workflow was defined: outline of disease models and collection of epidemiological data; design and completion of a database template; development of an imputation model; identification of disability weights; probabilistic burden assessment; and estimating the proportion of the disease burden by each hazard that is attributable to exposure by food (i.e., source attribution). All computations were performed in R and the different functions were compiled in the R package 'FERG'. Traceability and transparency were ensured by sharing results and methods in an interactive way with all FERG members throughout the process.ConclusionsWe developed a comprehensive framework for estimating the global burden of FBDs, in which methodological simplicity and transparency were key elements. All the tools developed have been made available and can be translated into a user-friendly national toolkit for studying and monitoring food safety at the local level.
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Project description:BackgroundGlobally in 2015 about 214 million malaria cases and 438,000 deaths were reported with 75% were from Sub-Saharan Africa. Malaria transmission in Ethiopia is unstable, and outbreaks are considered public health emergencies. Understanding the trigger for outbreaks in low-transmission areas can help facilitate malaria elimination. On July 8th malaria outbreak was reported from Laelay Adyabo district. The objective was to investigate the magnitude and associated factors with malaria outbreak.MethodsWe defined a case as confirmed malaria using microscopy or a rapid diagnostic test for Plasmodium parasites in a resident of Laelay-Adyabo District from July 9-28, 2017. We identified cases by reviewing health facility records and conducted a case-control study using randomly-selected cases from a line list, and two neighborhood controls per case. A pretested semi-structured questionnaire adapted from WHO malaria guidelines was used to collect data from case-patients and controls. We calculated crude (COR) and adjusted (AOR) odds ratios to identify factors associated with malaria.ResultA total of 145 confirmed malaria cases (57.9% males) were identified with village attack rate (AR) of 12.1/1000. The AR was higher among males than females (14.1 verses 10.1/1000), children aged 5-14 years (12.9/1000), and in Zelazle Kebelle (13.6/1000 population). Wearing protective clothing (AOR = 0.27, 95% CI 0.11-0.66), having good knowledge of malaria transmission (AOR = 0.25, 95% CI 0.08-0.75), having waste collection material at home (AOR = 0.25 95% CI 0.11-0.61), availability of mosquito breeding sites around home (AOR = 9.08, 95% CI 3.6-22.93), and staying outdoor overnight (AOR = 3.7, 95% CI 1.44-9.56) were independently associated with malaria.ConclusionThe overall attack rate for malaria during this outbreak was high affecting > 1% of the population. Wearing protective clothing at night, knowing about malaria transmission, having mosquito breeding sites around the home, staying outdoors overnight, and having waste collection material in their house were predictors of the infection. Laelay Adyabo district health office should provide health education on malaria transmission and prevention measures and how to clear mosquito breeding sites.
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Project description:BackgroundBayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria.MethodsIn this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana.ResultsThe geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district.ConclusionsThis high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.
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