Identifying areas that support high malaria risks and where populations lack access to health care is central to reducing the burden in Afghanistan. This study investigated the incidence of Plasmodium vivax and Plasmodium falciparum using routine data to help focus malaria interventions.
To estimate incidence, the study modelled utilisation of the public health sector using fever treatment data from the 2012 national Malaria Indicator Survey. A probabilistic measure of attendance was applied to population density metrics to define the proportion of the population within catchment of a public health facility. Malaria data were used in a Bayesian spatio-temporal conditional-autoregressive model with ecological or environmental covariates, to examine the spatial and temporal variation of incidence.
From the analysis of healthcare utilisation, over 80% of the population was within 2 hours’ travel of the nearest public health facility, while 64.4% were within 30 minutes’ travel. The mean incidence of P. vivax in 2009 was 5.4 (95% Crl 3.2–9.2) cases per 1000 population compared to 1.2 (95% Crl 0.4–2.9) cases per 1000 population for P. falciparum. P. vivax peaked in August while P. falciparum peaked in November. 32% of the estimated 30.5 million people lived in regions where annual incidence was at least 1 case per 1,000 population of P. vivax; 23.7% of the population lived in areas where annual P. falciparum case incidence was at least 1 per 1000.
This study showed how routine data can be combined with household survey data to model malaria incidence. The incidence of both P. vivax and P. falciparum in Afghanistan remain low but the co-distribution of both parasites and the lag in their peak season provides challenges to malaria control in Afghanistan. Future improved case definition to determine levels of imported risks may be useful for the elimination ambitions in Afghanistan.
Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2–10 years of age (PfPR2–10). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR2–10 map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR2–10. Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan’s population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR2–10. Aggregated data suggest reducing risks in some high transmission areas since the 1960s.
Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2–10 years of age (PfPR2–10). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR2–10 map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR2–10. Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan's population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR2–10. Aggregated data suggest reducing risks in some high transmission areas since the 1960s.
Abdisalan Noor discusses new research in PLoS Medicine that used model-based geostatistics to investigate the risks of anemia among preschool-aged children in West Africa that were attributable to malnutrition, malaria, and helminth infections.
In some countries the biological targeting of universal malaria prevention may offer optimal impact on disease and significant cost-savings compared with approaches that presume universal risk. Spatially defined data on coverage of treated nets from recent national household surveys in Kenya were used within a Bayesian geostatistical framework to predict treated net coverage nationally. When combined with the distributions of malaria risk and population an estimated 8.1 million people were not protected with treated nets in 2010 in biologically defined priority areas. After adjusting for the proportion of nets in use that were not long lasting, an estimated 5.5 to 6.3 million long-lasting treated nets would be required to achieve universal coverage in 2010 in Kenya in at-risk areas compared with 16.4 to 18.1 million nets if not restricted to areas of greatest malaria risk. In Kenya, this evidence-based approach could save the national program at least 55 million US dollars.
To systematically evaluate descriptive measures of spatial access to medical treatment, as part of the millennium development goals to reduce the burden of HIV/AIDS, tuberculosis and malaria.
We obtained high-resolution spatial and epidemiological data on health services, population, transport network, topography, land cover and paediatric fever treatment in four Kenyan districts to develop access and use models for government health services in Kenya. Community survey data were used to model use of government health services by febrile children. A model based on the transport network was then implemented and adjusted for actual use patterns. We compared the predictive accuracy of this refined model to that of Euclidean distance metrics.
Higher-order facilities were more attractive to patients (54%, 58% and 60% in three scenarios) than lower-order ones. The transport network model, adjusted for competition between facilities, was most accurate and selected as the best-fit model. It estimated that 63% of the population of the study districts were within the 1 h national access benchmark, against 82% estimated by the Euclidean model.
Extrapolating the results from the best-fit model in study districts to the national level shows that approximately six million people are currently incorrectly estimated to have access to government health services within 1 h. Simple Euclidean distance assumptions, which underpin needs assessments and against which millennium development goals are evaluated, thus require reconsideration.
millennium development goals; health services; access; use; distance models; kenya
Insecticide-treated bednets (ITNs) provide a means to improve child survival across Africa. Sales figures of these nets and survey coverage data presented nationally mask inequities in populations at biological and economic risk, and do not allow for precision in the estimation of unmet commodity needs. We gathered subnational ITN coverage sample survey data from 40 malaria-endemic countries in Africa between 2000 and 2007.
We computed the projected ITN coverage among children aged less than 5 years for age-adjusted population data that were stratified according to malaria transmission risks, proximate determinants of poverty, and methods of ITN delivery.
In 2000, only 1·7 million (1·8%) African children living in stable malaria-endemic conditions were protected by an ITN and the number increased to 20·3 million (18·5%) by 2007 leaving 89·6 million children unprotected. Of these, 30 million were living in some of the poorest areas of Africa: 54% were living in only seven countries and 25% in Nigeria alone. Overall, 33 (83%) countries were estimated to have ITN coverage of less than 40% in 2007. On average, we noted a greater increase in ITN coverage in areas where free distribution had operated between survey periods.
By mapping the distribution of populations in relation to malaria risk and intervention coverage, we provide a means to track the future requirements for scaling up essential disease-prevention strategies. The present coverage of ITN in Africa remains inadequate and a focused effort to improve distribution in selected areas would have a substantial effect on the continent's malaria burden.
To investigate the association, if any, between child mortality and distance to the nearest hospital.
The study was based on data from a 1-year study of the cause of illness in febrile paediatric admissions to a district hospital in north-east Tanzania. All villages in the catchment population were geolocated, and travel times were estimated from availability of local transport. Using bands of travel time to hospital, we compared admission rates, inpatient case fatality rates and child mortality rates in the catchment population using inpatient deaths as the numerator.
Three thousand hundred and eleven children under the age of 5 years were included of whom 4.6% died; 2307 were admitted from <3 h away of whom 3.4% died and 804 were admitted from ≥3 h away of whom 8.0% died. The admission rate declined from 125/1000 catchment population at <3 h away to 25/1000 at ≥3 h away, and the corresponding hospital deaths/catchment population were 4.3/1000 and 2.0/1000, respectively. Children admitted from more than 3 h away were more likely to be male, had a longer pre-admission duration of illness and a shorter time between admission and death. Assuming uniform mortality in the catchment population, the predicted number of deaths not benefiting from hospital admission prior to death increased by 21.4% per hour of travel time to hospital. If the same admission and death rates that were found at <3 h from the hospital applied to the whole catchment population and if hospital care conferred a 30% survival benefit compared to home care, then 10.3% of childhood deaths due to febrile illness in the catchment population would have been averted.
The mortality impact of poor access to hospital care in areas of high paediatric mortality is likely to be substantial although uncertainty over the mortality benefit of inpatient care is the largest constraint in making an accurate estimate.
access; hospital; mortality; child; Africa
To evaluate barriers preventing pregnant women from using insecticide-treated nets (ITN) and intermittent presumptive treatment (IPT) with sulphadoxine-pyrimethamine (SP) 5 years after the launch of the national malaria strategy promoting these measures in Kenya.
All women aged 15–49 years were interviewed during a community survey in four districts between December 2006 and January 2007. Women pregnant in the last 12 months were asked about their age, parity, education, use of nets, ITN, antenatal care (ANC) services and sulphadoxine-pyrimethamine (SP) (overall and for IPT) during pregnancy. Homestead assets were recorded and used to develop a wealth index. Travel time to ANC clinics was computed using a geographic information system algorithm. Predictors of net and IPT use were defined using multivariate logistic regression.
Overall 68% of pregnant women used a net; 52% used an ITN; 84% attended an ANC clinic at least once and 74% at least twice. Fifty-three percent of women took at least one dose of IPT-SP, however only 22% took two or more doses. Women from the least poor homesteads (OR = 2.53, 1.36–4.68) and those who used IPT services (OR = 1.73, 1.24–2.42) were more likely to sleep under any net. Women who used IPT were more likely to use ITNs (OR = 1.35, 1.03–1.77), while those who lived more than an hour from an ANC clinic were less likely (OR = 0.61, 0.46–0.81) to use ITN. Women with formal education (1.47, 1.01–2.17) and those who used ITN (OR: 1.68, 1.20–2.36) were more likely to have received at least one dose of IPT-SP.
Although the use of ITN had increased 10-fold and the use of IPT fourfold since last measured in 2001, coverage remains low. Provider practices in the delivery of protective measures against malaria must change, supported by community awareness campaigns on the importance of mothers’ use of IPT.
malaria; pregnancy; antenatal care; intermittent presumptive treatment; sulphadoxine-pyrimethamine; insecticide-treated nets; Kenya
Over a decade ago, the Roll Back Malaria Partnership was launched, and since then there has been unprecedented investment in malaria control. We examined the change in malaria transmission intensity during the period 2000–10 in Africa.
We assembled a geocoded and community Plasmodium falciparum parasite rate standardised to the age group 2–10 years (PfPR2–10) database from across 49 endemic countries and territories in Africa from surveys undertaken since 1980. The data were used within a Bayesian space–time geostatistical framework to predict PfPR2–10 in 2000 and 2010 at a 1 × 1 km spatial resolution. Population distribution maps at the same spatial resolution were used to compute populations at risk by endemicity class and estimate population-adjusted PfPR2–10 (PAPfPR2–10) for each of the 44 countries for which predictions were possible for each year.
Between 2000 and 2010, the population in hyperendemic (>50% to 75% PfPR2–10) or holoendemic (>75% PfPR2–10) areas decreased from 218·6 million (34·4%) of 635·7 million to 183·5 million (22·5%) of 815·7 million across 44 malaria-endemic countries. 280·1 million (34·3%) people lived in areas of mesoendemic transmission (>10% to 50% PfPR2–10) in 2010 compared with 178·6 million (28·1%) in 2000. Population in areas of unstable or very low transmission (<5% PfPR2–10) increased from 131·7 million people (20·7%) in 2000 to 219·0 million (26·8%) in 2010. An estimated 217·6 million people, or 26·7% of the 2010 population, lived in areas where transmission had reduced by at least one PfPR2–10 endemicity class. 40 countries showed a reduction in national mean PAPfPR2–10. Only ten countries contributed 87·1% of the population living in areas of hyperendemic or holoendemic transmission in 2010.
Substantial reductions in malaria transmission have been achieved in endemic countries in Africa over the period 2000–10. However, 57% of the population in 2010 continued to live in areas where transmission remains moderate to intense and global support to sustain and accelerate the reduction of transmission must remain a priority.
Mobile phone data are increasingly being used to quantify the movements of human populations for a wide range of social, scientific and public health research. However, making population-level inferences using these data is complicated by differential ownership of phones among different demographic groups that may exhibit variable mobility. Here, we quantify the effects of ownership bias on mobility estimates by coupling two data sources from the same country during the same time frame. We analyse mobility patterns from one of the largest mobile phone datasets studied, representing the daily movements of nearly 15 million individuals in Kenya over the course of a year. We couple this analysis with the results from a survey of socioeconomic status, mobile phone ownership and usage patterns across the country, providing regional estimates of population distributions of income, reported airtime expenditure and actual airtime expenditure across the country. We match the two data sources and show that mobility estimates are surprisingly robust to the substantial biases in phone ownership across different geographical and socioeconomic groups.
mobile phone; human mobility; socio-economic status
The transmission of malaria across the Arabian Peninsula is governed by the diversity of dominant vectors and extreme aridity. It is likely that where malaria transmission was historically possible it was intense and led to a high disease burden. Here, we review the speed of elimination, approaches taken, define the shrinking map of risk since 1960 and discuss the threats posed to a malaria-free Arabian Peninsula using the archive material, case data and published works. From as early as the 1940s, attempts were made to eliminate malaria on the peninsula but were met with varying degrees of success through to the 1970s; however, these did result in a shrinking of the margins of malaria transmission across the peninsula. Epidemics in the 1990s galvanised national malaria control programmes to reinvigorate control efforts. Before the launch of the recent global ambition for malaria eradication, countries on the Arabian Peninsula launched a collaborative malaria-free initiative in 2005. This initiative led a further shrinking of the malaria risk map and today locally acquired clinical cases of malaria are reported only in Saudi Arabia and Yemen, with the latter contributing to over 98% of the clinical burden.
•Incidence is modelled using HMIS data in Namibia.•Assembled data include parasitological and clinical diagnosed case. The clinical cases are adjusted using slide positivity rates at each facility.•Denominator catchment population adjusted for probability for seeking treatment when sick with fever.•Bayesian spatio-temporal model was implemented at facility level, adjusting for missing data using INLA.•Spatio-temporal monthly maps of incidence are produced and a mean prediction for 2009 for Namibia.
As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.
ACD, active case detection; CAR, conditional auto-regressive; CPO, conditional predictive ordinate; DIC, deviance information criterion; ESRI, Environmental System Research Institute; EVI, enhanced vegetation index; GF, Gaussian field; GIS, geographic information system; GMRF, Gaussian markov random field; GPS, global positioning system; GRUMP, Global Rural and Urban Mapping Project; HMIS, Health Management Information System; INLA, Integrated Nested Laplace Approximation; JAXA, Japan Aerospace Exploration Agency; MAUP, Modifiable Areal Unit Problem; MCMC, Markov Chain Monte Carlo; MODIS, MODerate-resolution Imaging Spectro-radiometer; MoHSS, Ministry of Health and Social Services; NASA, National Aeronautics and Space Administration; NVBDCP, National Vector-Borne and Disease Control Programme; PCD, passive case detection; PHS, public health sector; RDT, Rapid Diagnostic Test; SPA, Service Provision Assessments; TRMM, Tropical Rainfall Measuring Mission; TSI, temperature suitability index; WHO, World Health Organisation; ZIP, Zero-Inflated Poisson; Namibia; Malaria; Spatio-temporal; Conditional-autoregressive
Substantial development assistance has been directed towards reducing the high malaria burden in Malawi over the past decade. We assessed changes in transmission over this period of malaria control scale-up by compiling community Plasmodium falciparum rate (PfPR) data during 2000–2011 and used model-based geostatistical methods to predict mean PfPR2–10 in 2000, 2005, and 2010. In addition, we calculated population-adjusted prevalences and populations at risk by district to inform malaria control program priority setting. The national population-adjusted PfPR2–10 was 37% in 2010, and we found no evidence of change over this period of scale-up. The entire population of Malawi is under meso-endemic transmission risk, with those in districts along the shore of Lake Malawi and Shire River Valley under highest risk. The lack of change in prevalence confirms modeling predictions that when compared with lower transmission, prevalence reductions in high transmission settings require greater investment and longer time scales.
The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations.
National population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined.
Patterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20–30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10–20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use.
Census and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.
Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies specific importation routes that contribute to malaria epidemiology on regional spatial scales.
The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.
Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation.
Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.
Population; Demography; Mapping; Millenium development goals
Historical evidence of the levels of intervention scale up and its relationships to changing malaria risks provides important contextual information for current ambitions to eliminate malaria in various regions of Africa today.
Community-based Plasmodium falciparum prevalence data from 3,260 geo-coded time-space locations between 1969 and 1992 were assembled from archives covering an examination of 230,174 individuals located in northern Namibia. These data were standardized the age-range 2 to less than 10 years and used within a Bayesian model-based geo-statistical framework to examine the changes of malaria risk in the years 1969, 1974, 1979, 1984 and 1989 at 5×5 km spatial resolution. This changing risk was described against rainfall seasons and the wide-scale use of indoor-residual house-spraying and mass drug administration.
Most areas of Northern Namibia experienced low intensity transmission during a ten-year period of wide-scale control activities between 1969 and 1979. As control efforts waned, flooding occurred, drug resistance emerged and the war for independence intensified the spatial extent of moderate-to-high malaria transmission expanded reaching a peak in the late 1980s.
Targeting vectors and parasite in northern Namibia was likely to have successfully sustained a situation of low intensity transmission, but unraveled quickly to a peak of transmission intensity following a sequence of events by the early 1990s.
Countries aiming for malaria elimination need to define their malariogenic potential, of which measures of both receptive and current transmission are major components. As Namibia pursues malaria elimination, the importation risks due to cross-border human population movements with higher risk neighboring countries has been identified as a major challenge. Here we used historical and contemporary Plasmodium falciparum prevalence data for Namibia to estimate receptive and current levels of malaria risk in nine northern regions. We explore the potential of these risk maps to support decision-making for malaria elimination in Namibia.
Age-corrected geocoded community P. falciparum rate PfPR2-10 data from the period 1967–1992 (n = 3,260) and 2009 (n = 120) were modeled separately within a Bayesian model-based geostatistical (MBG) framework. A full Bayesian space-time MBG model was implemented using the 1967–1992 data to make predictions for every five years from 1969 to 1989. These maps were used to compute the maximum mean PfPR2-10 at 5 x 5 km locations in the northern regions of Namibia to estimate receptivity. A separate spatial Bayesian MBG was fitted to the 2009 data to predict current risk of malaria at similar spatial resolution. Using a high-resolution population map for Namibia, population at risk by receptive and current endemicity by region and population adjusted PfPR2-10 by health district were computed. Validations of predictions were undertaken separately for the historical and current risk models.
Highest receptive risks were observed in the northern regions of Caprivi, Kavango and Ohangwena along the border with Angola and Zambia. Relative to the receptive risks, over 90% of the 1.4 million people across the nine regions of northern Namibia appear to have transitioned to a lower endemic class by 2009. The biggest transition appeared to have occurred in areas of highest receptive risks. Of the 23 health districts, 12 had receptive PAPfPR2-10 risks of 5% to 18% and accounted for 57% of the population in the north. Current PAPfPR2-10 risks was largely <5% across the study area.
The comparison of receptive and current malaria risks in the northern regions of Namibia show health districts that are most at risk of importation due to their proximity to the relatively higher transmission northern neighbouring countries, higher population and modeled receptivity. These health districts should be prioritized as the cross-border control initiatives are rolled out.
Many patients with suspected malaria in sub-Saharan Africa seek treatment from private providers, but this sector suffers from sub-standard medicine dispensing practices. To improve the quality of care received for presumptive malaria from the highly accessed private retail sector in western Kenya, subsidized pre-packaged artemether-lumefantrine (AL) was provided to private retailers, together with a one day training for retail staff on malaria diagnosis and treatment, job aids and community engagement activities.
The intervention was assessed using a cluster-randomized, controlled design. Provider and mystery-shopper cross-sectional surveys were conducted at baseline and eight months post-intervention to assess provider practices. Data were analysed based on cluster-level summaries, comparing control and intervention arms.
On average, 564 retail outlets were interviewed per year. At follow-up, 43% of respondents reported that at least one staff member had attended the training in the intervention arm. The intervention significantly increased the percentage of providers knowing the first line treatment for uncomplicated malaria by 24.2% points (confidence interval (CI): 14.8%, 33.6%; adjusted p=0.0001); the percentage of outlets stocking AL by 31.7% points (CI: 22.0%, 41.3%; adjusted p=0.0001); and the percentage of providers prescribing AL for presumptive malaria by 23.6% points (CI: 18.7%, 28.6%; adjusted p=0.0001). Generally outlets that received training and job aids performed better than those receiving one or none of these intervention components.
Overall, subsidizing ACT and retailer training can significantly increase the percentage of outlets stocking and selling AL for the presumptive treatment of malaria, but further research is needed on strategies to improve the provision of counselling advice to retail customers.
Community case-management of malaria; Artemisinin-based combination therapy; Antimalarial subsidy programme; Drug retailers
Rational decision making on malaria control depends on an understanding of the epidemiological risks and control measures. National Malaria Control Programmes across Africa have access to a range of state-of-the-art malaria risk mapping products that might serve their decision-making needs. The use of cartography in planning malaria control has never been methodically reviewed.
Materials and Methods
An audit of the risk maps used by NMCPs in 47 malaria endemic countries in Africa was undertaken by examining the most recent national malaria strategies, monitoring and evaluation plans, malaria programme reviews and applications submitted to the Global Fund. The types of maps presented and how they have been used to define priorities for investment and control was investigated.
91% of endemic countries in Africa have defined malaria risk at sub-national levels using at least one risk map. The range of risk maps varies from maps based on suitability of climate for transmission; predicted malaria seasons and temperature/altitude limitations, to representations of clinical data and modelled parasite prevalence. The choice of maps is influenced by the source of the information. Maps developed using national data through in-country research partnerships have greater utility than more readily accessible web-based options developed without inputs from national control programmes. Although almost all countries have stratification maps, only a few use them to guide decisions on the selection of interventions allocation of resources for malaria control.
The way information on the epidemiology of malaria is presented and used needs to be addressed to ensure evidence-based added value in planning control. The science on modelled impact of interventions must be integrated into new mapping products to allow a translation of risk into rational decision making for malaria control. As overseas and domestic funding diminishes, strategic planning will be necessary to guide appropriate financing for malaria control.
Malaria rapid diagnostic tests (RDTs) are known to yield false-positive results, and their use in epidemiologic surveys will overestimate infection prevalence and potentially hinder efficient targeting of interventions. To examine the consequences of using RDTs in school surveys, we compared three RDT brands used during a nationwide school survey in Kenya with expert microscopy and investigated the cost implications of using alternative diagnostic approaches in identifying localities with differing levels of infection. Overall, RDT sensitivity was 96.1% and specificity was 70.8%. In terms of classifying districts and schools according to prevalence categories, RDTs were most reliable for the < 1% and > 40% categories and least reliable in the 1–4.9% category. In low-prevalence settings, microscopy was the most expensive approach, and RDT results corrected by either microscopy or polymerase chain reaction were the cheapest. Use of polymerase chain reaction-corrected RDT results is recommended in school malaria surveys, especially in settings with low-to-moderate malaria transmission.
Understanding the historical, temporal changes of malaria risk following control efforts in Africa provides a unique insight into what has been and might be archived towards a long-term ambition of elimination on the continent. Here, we use archived published and unpublished material combined with biological constraints on transmission accompanied by a narrative on malaria control to document the changing incidence of malaria in Africa since earliest reports pre-second World War. One result is a more informed mapped definition of the changing margins of transmission in 1939, 1959, 1979, 1999 and 2009.
Malaria rapid diagnostic tests (RDTs) are known to yield false-positive results, and their use in epidemiologic surveys will overestimate infection prevalence and potentially hinder efficient targeting of interventions. To examine the consequences of using RDTs in school surveys, we compared three RDT brands used during a nationwide school survey in Kenya with expert microscopy and investigated the cost implications of using alternative diagnostic approaches in identifying localities with differing levels of infection. Overall, RDT sensitivity was 96.1% and specificity was 70.8%. In terms of classifying districts and schools according to prevalence categories, RDTs were most reliable for the < 1% and > 40% categories and least reliable in the 1–4.9% category. In low-prevalence settings, microscopy was the most expensive approach, and RDT results corrected by either microscopy or polymerase chain reaction were the cheapest. Use of polymerase chain reaction–corrected RDT results is recommended in school malaria surveys, especially in settings with low-to-moderate malaria transmission.
Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.