Malaria in Uganda: Challenges to control on the long road to elimination

Abstract

Malaria remains one of the leading health problems of the developing world, and Uganda bears a particularly large burden from the disease. Our understanding is limited by a lack of reliable data, but it is clear that the prevalence of malaria infection, incidence of disease, and mortality from severe malaria all remain very high. Uganda has made progress in implementing key malaria control measures, in particular distribution of insecticide impregnated bednets, indoor residual spraying of insecticides, utilization of artemisinin-based combination therapy to treat uncomplicated malaria, and provision of intermittent preventive therapy for pregnant women. However, despite enthusiasm regarding the potential for the elimination of malaria in other areas, there is no convincing evidence that the burden of malaria has decreased in Uganda in recent years. Major challenges to malaria control in Uganda include very high malaria transmission intensity, inadequate health care resources, a weak health system, inadequate understanding of malaria epidemiology and the impact of control interventions, increasing resistance of parasites to drugs and of mosquitoes to insecticides, inappropriate case management, inadequate utilization of drugs to prevent malaria, and inadequate epidemic preparedness and response. Despite these challenges, prospects for the control of malaria have improved, and with attention to underlying challenges, progress toward the control of malaria in Uganda can be expected.

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

“…In Uganda, the greatest incidence rates were estimated in the northern part of the country characterised by lowlands as suggested by UBOS andICF International (2009) andUSAID (2013b). However, the lowest rates were estimated in the central region as observed in our malaria dataset, and not in the highland regions as previously reported (UBOS and ICF International, 2009;USAID, 2013b;Yeka et al, 2012). Table 3 shows the results of the analysis conducted to assess the contribution of weather to the predictive ability of the model.…”

“…Factors such as human behaviour (e.g. irrigation activities), immunity levels, and access to preventive measures such as sanitation and window screening (Baeza et al, 2011;Pindolia et al, 2013;Reiter, 2001Reiter, , 2008, may all contribute to such heterogeneity in addition to differences in the criteria, and skills to use diagnostic tools (Kyabayinze et al, 2012;Yeka et al, 2012). It is noted, however, that malaria incidence tends to be greater in the northern and eastern half of the country where the mean temperature, poverty rate, and rural population densities are greater, and the mean altitude is lower in agreement with previous reports (UBOS and ICF International, 2009;USAID, 2013b).…”

“…Thus, the estimated incidence rates may differ from those occurring in the population. This situation has been previously detected and acknowledged in a range of settings (Choge et al, 2014;Kyabayinze et al, 2012;Nankabirwa et al, 2009;WHO, 2013;Yeka et al, 2012). However, according to WHO (2013), epidemiological surveillance systems do not need to detect all cases to allow the assessment of malaria incidence.…”

“…Laboratory confirmed cases would have been preferred for conducting this study; however, only time series of clinically diagnosed data were available to cover the entirety of both countries with a time span of about a decade. Laboratory confirmation is still limited in the region in terms of tools, skills and infrastructure although efforts are being taken to improve coverage (Kyabayinze et al, 2012;Yeka et al, 2012). For example, laboratory confirmation is routinely conducted in six sentinel sites across Uganda (Okello et al, 2006;Zinszer et al, 2015); however, the geographical and temporal coverage of the related dataset is considerably low with data freely available only for the period October 2006 to March 2013 for the longest time series.…”

“…Pathogen-related factors such as parasite species also influence the spatiotemporal patterns of malaria (for example Chowell et al, 2009;Lin et al, 2010). Differences in the tools, criteria, and skills for diagnosis; and errors in data management may contribute to the spatiotemporal variability observed in malaria data as well (Kyabayinze et al, 2012;Yeka et al, 2012). Climate data are readily available at spatial and temporal resolutions that allow their incorporation in statistical disease models (Bouzid et al, 2014;Colón-González et al, 2013;Garske et al, 2013;Lowe et al, 2013).…”