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Background: The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented.Conclusion: Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens. [ABSTRACT FROM AUTHOR]

Background: The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.Methods: We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.Results: Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.Conclusions: In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions. [ABSTRACT FROM AUTHOR]

Background: The causes of childhood anaemia are multifactorial, interrelated and complex. Such causes vary from country to country, and within a country. Thus, strategies for anaemia control should be tailored to local conditions and take into account the specific etiology and prevalence of anaemia in a given setting and sub-population. In addition, policies and programmes for anaemia control that do not account for the spatial heterogeneity of anaemia in children may result in certain sub-populations being excluded, limiting the effectiveness of the programmes. This study investigated the demographic and socio-economic determinants as well as the spatial variation of anaemia in children aged 6 to 59 months in Kenya, Malawi, Tanzania and Uganda.Methods: The study made use of data collected from nationally representative Malaria Indicator Surveys (MIS) and Demographic and Health Surveys (DHS) conducted in all four countries between 2015 and 2017. During these surveys, all children under the age of five years old in the sampled households were tested for malaria and anaemia. A child's anaemia status was based on the World Health Organization's cut-off points where a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. The explanatory variables considered comprised of individual, household and cluster level factors, including the child's malaria status. A multivariable hierarchical Bayesian geoadditive model was used which included a spatial effect for district of child's residence.Results: Prevalence of childhood anaemia ranged from 36.4% to 61.9% across the four countries. Children with a positive malaria result had a significantly higher odds of anaemia [AOR = 4.401; 95% CrI: (3.979, 4.871)]. After adjusting for a child's malaria status and other demographic, socio-economic and environmental factors, the study revealed distinct spatial variation in childhood anaemia within and between Malawi, Uganda and Tanzania. The spatial variation appeared predominantly due to unmeasured district-specific factors that do not transcend boundaries.Conclusions: Anaemia control measures in Malawi, Tanzania and Uganda need to account for internal spatial heterogeneity evident in these countries. Efforts in assessing the local district-specific causes of childhood anaemia within each country should be focused on. [ABSTRACT FROM AUTHOR]

Background: Studies have highlighted the inadequacies of the public health sector in sub-Saharan African countries in providing appropriate malaria case management. The readiness of the public health sector to provide malaria case-management in Somalia, a country where there has been no functioning central government for almost two decades, was investigated. Methods: Three districts were purposively sampled in each of the two self-declared states of Puntland and Somaliland and the south-central region of Somalia, in April-November 2007. A survey and mapping of all public and private health service providers was undertaken. Information was recorded on services provided, types of anti-malarial drugs used and stock, numbers and qualifications of staff, sources of financial support and presence of malaria diagnostic services, new treatment guidelines and job aides for malaria case-management. All settlements were mapped and a semi-quantitative approach was used to estimate their population size. Distances from settlements to public health services were computed. Results: There were 45 public health facilities, 227 public health professionals, and 194 private pharmacies for approximately 0.6 million people in the three districts. The median distance to public health facilities was 6 km. 62.3% of public health facilities prescribed the nationally recommended anti-malarial drug and 37.7% prescribed chloroquine as first-line therapy. 66.7% of public facilities did not have in stock the recommended first-line malaria therapy. Diagnosis of malaria using rapid diagnostic tests (RDT) or microscopy was performed routinely in over 90% of the recommended public facilities but only 50% of these had RDT in stock at the time of survey. National treatment guidelines were available in 31.3% of public health facilities recommended by the national strategy. Only 8.8% of the private pharmacies prescribed artesunate plus sulphadoxine/pyrimethamine, while 53.1% prescribed chloroquine as first-line therapy. 31.4% of private pharmacies also provided malaria diagnosis using RDT or microscopy. Conclusion: Geographic access to public health sector is relatively low and there were major shortages of appropriate guidelines, anti-malarials and diagnostic tests required for appropriate malaria case management. Efforts to strengthen the readiness of the health sector in Somalia to provide malaria case management should improve availability of drugs and diagnostic kits; provide appropriate information and training; and engage and regulate the private sector to scale up malaria control. [ABSTRACT FROM AUTHOR]

Background: A characteristic of Plasmodium falciparum infections is the gradual acquisition of clinical immunity resulting from repeated exposures to the parasite. While the molecular basis of protection against clinical malaria remains unresolved, its effects on epidemiological patterns are well recognized. Accumulating epidemiological data constitute a valuable resource that must be intensively explored and interpreted as to effectively inform control planning. Methodology/Principal Finding: Here we apply a mathematical model to clinical data from eight endemic regions in sub- Saharan Africa. The model provides a quantitative framework within which differences in age distribution of clinical disease are assessed in terms of the parameters underlying transmission. The shorter infectious periods estimated for clinical infections induce a regime of bistability of endemic and malaria-free states in regions of mesoendemic transmission. The two epidemiological states are separated by a threshold that provides a convenient measure for intervention design. Scenarios of eradication and resurgence are simulated. Conclusions/Significance: In regions that support mesoendemic transmission, intervention success depends critically on reducing prevalence below a threshold which separates endemic and malaria-free regimes. [ABSTRACT FROM AUTHOR]

Urbanization is an important demographic phenomenon in sub-Saharan Africa, and rural-urban migration remains a major contributor to urban growth. In a context of sustained economic recession, these demographic processes have been associated with a rise in urban poverty and ill health. Developments in health service provision need to reflect new needs arising from demographic and disease ecology change. In malaria-endemic coastal Kenya, we compared lifelong rural (n = 248) and urban resident (n = 284) Mijikenda mothers' responses to childhood fevers. Despite marked differences between the rural and urban study areas in demographic structure and physical access to biomedical services, rural and urban mothers' treatment-seeking patterns were similar: most mothers sought only biomedical treatment (88%). Shop-bought medicines were used first or only in 69% of the rural and urban fevers that were treated, and government or private clinics were contacted in 49%. A higher proportion of urban informal vendors stocked prescription-only drugs, and urban mothers more likely to contact a private than a government facility. We conclude that improving self-treatment has enormous potential to reduce morbidity and mortality in low-income urban areas, as has frequently been argued for rural areas. However, because of the underlying socio-economic, cultural and structural differences between rural and urban areas, rural approaches to tackle this may have to be modified in urban environments. [ABSTRACT FROM AUTHOR]

Mike English and colleagues argue that we do not have sufficient evidence and health system capacity to abandon presumptive antimalarial treatment in children with fever in sub-Saharan Africa, countering the argument of Blaise Genton and colleagues in a related paper. [ABSTRACT FROM AUTHOR]

Background: The rapid and often uncontrolled rural–urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. Methods: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR2–10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2–10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. Results: The results suggest that the spatial distribution of PfPR2–10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2–10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. Conclusions: The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts. [ABSTRACT FROM AUTHOR]

Background: Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy. Methods: Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. Results: The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. Conclusions: In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests. [ABSTRACT FROM AUTHOR]