This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

This map service contains the datasets produced and published in the paper "A new world malaria map: Plasmodium falciparum endemicity in 2010" (see credits) which map Plasmodium falciparum malaria for the world in 2010.Please note that for Africa, the PfPR data shown here have been superseded by the 2015 paper which models temporally-varying PfPR across Africa - see the MAP website for further details.

In 2023, there was around *** million dollars invested into research for drugs for malaria worldwide. This statistic shows the investments in malaria research worldwide in 2023, by research area.

Malaria Diagnostics Market Outlook

According to our latest research, the global malaria diagnostics market size reached USD 920 million in 2024, supported by robust advancements in diagnostic technologies and increased funding for malaria control programs. The market is set to expand at a CAGR of 6.8% from 2025 to 2033, with the total market value forecasted to reach USD 1.74 billion by 2033. This growth is primarily driven by the rising incidence of malaria in endemic regions, increasing government and non-governmental initiatives for disease management, and the continuous evolution of diagnostic methods that offer higher sensitivity and specificity.

One of the primary growth factors for the malaria diagnostics market is the escalating prevalence of malaria in tropical and subtropical regions, particularly in sub-Saharan Africa and Southeast Asia. These regions account for the majority of the global malaria burden, prompting governments, non-governmental organizations, and international health agencies to invest heavily in malaria control and elimination programs. The World Health Organization (WHO) and other global health bodies have set ambitious targets for malaria reduction, which has translated into increased funding for the development and deployment of advanced diagnostic tools. The growing awareness regarding early and accurate diagnosis as a critical step in effective malaria management further fuels the demand for innovative diagnostic solutions.

Technological advancements play a pivotal role in driving the malaria diagnostics market forward. The shift from conventional microscopy to rapid diagnostic tests (RDTs) and molecular diagnostic techniques has revolutionized malaria detection, especially in resource-limited settings. RDTs offer quick results and do not require sophisticated laboratory infrastructure, making them ideal for remote and rural areas where malaria is most prevalent. Meanwhile, molecular diagnostics, including PCR-based methods, provide high sensitivity and specificity, enabling the detection of low-level parasitemia and mixed infections. These technological innovations not only enhance diagnostic accuracy but also support large-scale screening and surveillance programs, contributing significantly to market growth.

Another critical growth factor is the increasing collaboration between public and private sector stakeholders to combat malaria. Partnerships between governments, research institutions, diagnostic companies, and funding agencies have led to the development and commercialization of new diagnostic products tailored to the needs of endemic regions. These collaborations have also facilitated the implementation of quality assurance programs, capacity-building initiatives, and training for healthcare workers, ensuring the effective utilization of diagnostic tools. Furthermore, the growing emphasis on point-of-care diagnostics and the integration of digital health technologies are expected to create new avenues for market expansion in the coming years.

From a regional perspective, the Asia Pacific and sub-Saharan Africa dominate the malaria diagnostics market due to their high disease burden and ongoing efforts to strengthen healthcare infrastructure. North America and Europe, while having a lower incidence of malaria, contribute to market growth through research and development activities and the adoption of advanced diagnostic technologies. Latin America and the Middle East & Africa regions are also witnessing increased investments in malaria diagnostics, driven by rising awareness and government-led initiatives. The market outlook remains highly promising, with regional disparities in disease prevalence and healthcare access shaping the demand for diverse diagnostic solutions.

Product Type Analysis

The malaria diagnostics market by product type is segmented into rapid diagnostic tests (RDTs), microscopy, molecular diagnostics, serology, and others. Among these, rapid diagnostic tests have emerged as the leading product s

Collection and species identification of various malarial mosquito specimens for generating a global map of the dominant vector species of malaria. (MapVEu VBP0000027)

Eradication and elimination strategies for lymphatic filariasis (LF) primarily rely on multiple rounds of annual mass drug administration (MDA), but also may benefit from vector control interventions conducted by malaria vector control programs. We aim to examine the overlap in LF prevalence and malaria vector control to identify potential gaps in program coverage. We used previously published geospatial estimates of LF prevalence from the Institute for Health Metrics and Evaluation, as well as publicly available insecticide-treated net (ITN) access (proportion of the total population with access to ITNs) and use (proportion of the total population that slept under an ITN) estimates among the total population and malaria Plasmodium falciparum parasite rates (PfPR) from the Malaria Atlas Project (MAP). We aggregated the 5x5 km2 estimates of LF prevalence estimates and ITN estimates to the implementation unit (IU) level using fractional aggregation, for 33 LF and malaria-endemic locations in Africa, and then overlaid the IU-level aggregates. In this analysis, ITN coverage was low in areas where LF is common, with 51.7% (90/174) of high-LF-prevalence-IUs having both access and use estimates under 40%. Most (67.8%; 61/90) of these low-ITN-coverage, high-LF-prevalence locations were also categorized as high- or highest-prevalence for malaria by PfPR, suggesting suboptimal ITN coverage even in some malaria-co-endemic locations. Even in IUs with high LF prevalence but low malaria prevalence, almost half (48.2%; 39/81) had high levels of access to ITNs. When accounting for population, however, gaps in ITN access in such areas were evident: more individuals lived in high-LF, low-malaria IUs with low ITN access (8.68 million) than lived in high-LF, low-malaria IUs with high ITN access (6.76 million). These results suggest that relying on current malaria vector control programs alone may not provide sufficient ITN coverage for high LF prevalence areas. Opportunities for coordinated vector control programs in places where LF and malaria prevalence are high but ITN coverage is low – or additional ITN distribution in high-LF, low-malaria locations - should be explored to help achieve elimination goals.

Against the backdrop of a global malaria epidemic that remains severe, China has eradicated indigenous malaria but still has to be alert to the risk of external importation. Understanding the distribution of vectors can provide an adequate and reliable basis for the development and implementation of vector control strategies. However, with the decline of malaria prevalence in recent years, the capacity of vector monitoring and identification has been greatly weakened. Here we have used new sampling records, climatic data, and topographic data to establish ecological niche models of the three main malaria vectors in China. The model results accurately identified the current habitat suitability areas for the three species of Anopheles and revealed that in addition to precipitation and temperature as important variables affecting the distribution of Anopheles mosquitoes, topographic variables also influenced the distribution of Anopheles mosquitoes. Anopheles sinensis is the most widespread malaria vector in China, with a wide region from the northeast (Heilongjiang Province) to the southwest (Yunnan Province) suitable for its survival. Suitable habitat areas for Anopheles lesteri are concentrated in the central, eastern, and southern regions of China. The suitable habitat areas of Anopheles minimus are the smallest and are only distributed in the border provinces of southern China. On this basis, we further assessed the seasonal variation in habitat suitability areas for these three major malaria vectors in China. The results of this study provide new and more detailed evidence for vector monitoring. In this new era of imported malaria prevention in China, regular reassessment of the risk of vector transmission is recommended.

Background The expansion of global travel has resulted in the importation of African Anopheles mosquitoes, giving rise to cases of local malaria transmission. Here, cases of 'airport malaria' are used to quantify, using a combination of global climate and air traffic volume, where and when are the greatest risks of a Plasmodium falciparum-carrying mosquito being importated by air. This prioritises areas at risk of further airport malaria and possible importation or reemergence of the disease. Methods Monthly data on climate at the World's major airports were combined with air traffic information and African malaria seasonality maps to identify, month-by-month, those existing and future air routes at greatest risk of African malaria-carrying mosquito importation and temporary establishment. Results The location and timing of recorded airport malaria cases proved predictable using a combination of climate and air traffic data. Extending the analysis beyond the current air network architecture enabled identification of the airports and months with greatest climatic similarity to P. falciparum endemic regions of Africa within their principal transmission seasons, and therefore at risk should new aviation routes become operational. Conclusion With the growth of long haul air travel from Africa, the identification of the seasonality and routes of mosquito importation is important in guiding effective aircraft disinsection and vector control. The recent and continued addition of air routes from Africa to more climatically similar regions than Europe will increase movement risks. The approach outlined here is capable of identifying when and where these risks are greatest.

Abstract

The 2020 Kenya Malaria Indicator Survey (2020 KMIS) was a cross-sectional household-based survey with a nationally representative sample of conventional households. The survey targeted women age 15-49 and children age 6 months to age 14 living within conventional households in Kenya. All women age 15-49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. In all sampled households, children age 6 months to age 14 were tested for anaemia and malaria.

The sample for the 2020 KMIS was designed to produce reliable estimates for key malaria indicators at the national level, for urban and rural areas separately, and for each of the five malaria endemic zones.

The 2020 KMIS was designed to provide information on the implementation of core malaria control interventions and serve as a follow-up to the previous malaria indicator surveys. The specific objectives of the 2020 KMIS were as follows: - To measure the extent of ownership of, access to, and use of mosquito nets - To assess coverage of intermittent preventive treatment of malaria during pregnancy - To examine fever management among children under age 5 - To measure the prevalence of malaria and anaemia among children age 6 months to age 14 - To assess knowledge, attitudes, and practices regarding malaria control - To determine the Plasmodium species most prevalent in Kenya

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-14
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and children age 0-14 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 KMIS followed a two-stage stratified cluster sample design and was intended to provide estimates of key malaria indicators for the country as a whole, for urban and rural areas, and for the five malaria-endemic zones (Highland epidemic prone, Lake endemic, Coast endemic, Seasonal, and Low risk).

The five malaria-endemic zones fully cover the country, and each of the 47 counties in the country falls into one or two of the five zones as follows: 1. Highland epidemic prone: Kisii, Nyamira, West Pokot, Trans-Nzoia, Uasin Gishu, Nandi, Narok, Kericho, Bomet, Bungoma, Kakamega, and Elgeyo Marakwet 2. Lake endemic: Siaya, Kisumu, Migori, Homa Bay, Kakamega, Vihiga, Bungoma, and Busia 3. Coast endemic: Mombasa, Kwale, Kilifi, Lamu, and Taita Taveta 4. Seasonal: Tana River, Marsabit, Isiolo, Meru, Tharaka-Nithi, Embu, Kitui, Garissa, Wajir, Mandera, Turkana, Samburu, Baringo, Elgeyo Marakwet, Kajiado, and West Pokot 5. Low risk: Nairobi, Nyandarua, Nyeri, Kirinyaga, Murang’a, Kiambu, Machakos, Makueni, Laikipia, Nakuru, Meru, Tharaka-Nithi, and Embu.

The survey utilised the fifth National Sample Survey and Evaluation Programme (NASSEP V) household master sample frame, the same frame used for the 2015 KMIS. The frame was used by KNBS from 2012 to 2020 to conduct household-based sample surveys in Kenya. It was based on the 2009 Kenya Population and Housing Census, and the primary sampling units were clusters developed from enumeration areas (EAs). EAs are the smallest geographical areas created for purposes of census enumeration; a cluster can be an EA or part of an EA. The frame had a total of 5,360 clusters and was stratified into urban and rural areas within each of 47 counties, resulting into 92 sampling strata with Nairobi and Mombasa counties being wholly urban.

The survey employed a two-stage stratified cluster sampling design in which, in the first stage of selection, 301 clusters (134 urban and 167 rural) were randomly selected from the NASSEP V master sample frame using an equal probability selection method with independent selection in each sampling stratum. The second stage involved random selection of a fixed number of 30 households per cluster from a roster of households in the sampled clusters using systematic random sampling.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four types of questionnaires were used for the 2020 KMIS: the Household Questionnaire, the Woman’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires were adapted to reflect issues relevant to Kenya. Modifications were determined after a series of meetings with various stakeholders from DNMP and other government ministries and agencies, nongovernmental organisations, and international partners. The Household and Woman’s Questionnaires in English and Kiswahili were programmed into Android tablets, which enabled the use of computer-assisted personal interviewing (CAPI) for data collection. The Biomarker Questionnaire, in English and Kiswahili, was filled out on hard copy and then entered into the CAPI system.

Cleaning operations

The 2020 KMIS questionnaires were programmed using Census and Survey Processing (CSPro) software. The program was then uploaded into Android-based tablets that were used to collect data via CAPI. The CAPI applications, including the supporting applications and the applications for the Household, Biomarker, and Woman’s Questionnaires, were programmed by ICF. The field supervisors transferred data daily to the CSWeb server, developed by the U.S. Census Bureau and located in Nairobi, for data processing on the central office computer at the KNBS office in Nairobi.

Data received from the field teams were registered and checked for any inconsistencies and outliers on the central office computer at KNBS. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in the central office computer at KNBS head office. The central office held data files which was used to produce final report tables and final data sets. CSPro software was used for data editing, cleaning, weighting, and tabulation.

Response rate

A total of 8,845 households were selected for the survey, of which 8,185 were occupied at the time of fieldwork. Among the occupied households, 7,952 were successfully interviewed, yielding a response rate of 97%. In the interviewed households, 7,035 eligible women were identified for individual interviews and 6,771 were successfully interviewed, yielding a response rate of 96%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2020 Kenya Malaria Indicator Survey (KMIS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2020 KMIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2020 KMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Number of enumeration areas completed, by month and malaria endemicity - Positive rapid diagnostic test (RDT) results, by month and malaria endemicity - Concordance and discordance between RDT and microscopy results - Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

The emergence and spread of drug resistance in the Greater Mekong Subregion (GMS) have added urgency to accelerate malaria elimination while reducing the treatment options. The remaining foci of malaria transmission are often in forests, where vectors tend to bite during daytime and outdoors, thus reducing the effectiveness of insecticide-treated bed nets. Limited periods of exposure suggest that chemoprophylaxis could be a promising strategy to protect forest workers against malaria. Here we discuss three major questions in optimizing malaria chemoprophylaxis for forest workers: which antimalarial drug regimens are most appropriate, how frequently the chemoprophylaxis should be delivered, and how to motivate forest workers to use, and adhere to, malaria prophylaxis.

Malaria Drugs Market Outlook

The global malaria drugs market is projected to grow significantly from a market size of USD 1.2 billion in 2023 to USD 2.1 billion by 2032, at a compound annual growth rate (CAGR) of 6.3% during the forecast period. This growth is primarily driven by the increasing prevalence of malaria, strategic governmental initiatives in endemic regions, and advancements in drug formulations. The market is poised to benefit from concerted efforts to eradicate malaria, increasing the demand for effective anti-malarial medications across the globe. Additionally, the focus on R&D to discover novel compounds and improve existing drug efficacy reinforces the market's upward trajectory.

One of the key growth factors influencing the malaria drugs market is the rising incidence of malaria cases globally. The disease remains a major public health concern in many tropical and subtropical regions, exacerbated by climatic changes and the increasing resistance of malaria parasites to existing drugs. Consequently, there is an urgent need for new therapeutic options and combination therapies that can address resistant strains, sustaining the long-term demand for malaria drugs. Additionally, the growing awareness and educational campaigns regarding malaria prevention and treatment are contributing to the market expansion. Governments and NGOs are collaborating to amplify outreach and improve access to malaria medication, especially in rural and remote areas.

Another significant growth factor is the technological advancements in drug development and manufacturing processes. Innovations in drug formulation, such as Artemisinin-based Combination Therapies (ACTs), have revolutionized malaria treatment, offering more effective and efficient solutions. The pharmaceutical sector's investment in research and development to enhance the efficacy of existing drugs and discover new compounds has further spurred market growth. Furthermore, the increasing adherence to international health standards and regulatory approvals for new drug formulations have facilitated market expansion, ensuring a steady supply of high-quality and safe malaria medications.

The market's growth is also bolstered by supportive government policies and funding aimed at eradicating malaria. International organizations, such as the World Health Organization (WHO) and the Global Fund, are providing substantial funding and resources to malaria-affected regions, fostering increased access to essential medications. These initiatives are pivotal in enhancing healthcare infrastructure, streamlining drug distribution channels, and improving treatment outcomes. Moreover, the integration of digital health solutions and real-time data analytics in monitoring and managing malaria cases has enabled more efficient tracking and control of outbreaks, promoting the usage of malaria drugs.

In addition to the ongoing efforts to combat malaria, the focus on Filariasis Treatment is gaining momentum in regions where both diseases are prevalent. Filariasis, caused by parasitic worms, poses significant health challenges, often co-existing with malaria in tropical and subtropical areas. The treatment strategies for filariasis are evolving, with an emphasis on mass drug administration and community-based interventions. These efforts are crucial in reducing the burden of filariasis and improving overall public health outcomes. As with malaria, international collaborations and funding are pivotal in supporting research and development for more effective filariasis treatments. The integration of these efforts with malaria control programs can enhance healthcare delivery and resource utilization, ultimately contributing to the eradication of both diseases.

Regionally, the Asia Pacific and Africa are expected to dominate the malaria drugs market due to high disease prevalence and increased investments in healthcare infrastructure. Africa remains the epicenter of malaria cases, accounting for more than 90% of global malaria incidences, thus driving the demand for effective drug therapies. Asia Pacific, on the other hand, benefits from the significant production and export of anti-malarial drugs, particularly from countries like India and China, boosting regional market growth. North America and Europe, although having lower disease incidences, are key players in research and development, contributing to the global market through innovative drug solutions and strategic partnerships with malaria-endemic regions.

Drug Type Analysis

This layer contains the data of state level India Malaria (2015-2020) and contains information about Malaria cases in 2015, Malaria cases in 2016, Malaria deaths in 2016, Malaria cases in 2017 etc.About MalariaMalaria is a potentially life-threatening disease caused by parasites (Plasmodium vivax, Plasmodium falciparum, Plasmodium malaria and Plasmodium ovale) that are transmitted through the bite of infected female Anopheles mosquitoes.Symptoms of Malaria It includes fever and flu-like illness, including shaking chills, headache, muscle aches, and tiredness. Nausea, vomiting, and diarrhea may also occur. Malaria may cause anemia and jaundice (yellow coloring of the skin and eyes) because of the loss of red blood cells. If not promptly treated, the infection can become severe and may cause kidney failure, seizures, mental confusion, coma, and death.Malaria in IndiaAccording to the World malaria report 2019, India represents 3% of the global malaria burden. Despite being the highest malaria burden country of the SEA region, India showed a reduction in reported malaria cases of 49% and deaths of 50.5% compared with 2017.India has a vision of a malaria free country by 2027 and elimination by 2030.The attributes are given below for this web map:Malaria Cases in 2015Malaria Cases in 2016Malaria Deaths in 2016Malaria Cases in 2017Malaria Deaths in 2017Malaria Cases in 2018Malaria Deaths in 2018Malaria Cases in 2019Malaria Deaths in 2019Malaria Cases in 2020Malaria Deaths in 2020This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

Collection and species identification of various malarial mosquito specimens for generating a global map of the dominant vector species of malaria. (MapVEu VBP0000026)

Users can view maps, spatial, and statistical information drawn from different databases around the world. In addition, users can download data sets pertaining to prevalence and location of health facilities. Background The World Health Organization GeoNetwork is a geographic information management system that contains geo-referenced data sets and maps to facilitate the planning and monitoring of health related activities and health conditions. Information is available regarding the prevalence and location of health facilities. User Functionality Users must download the Geographic Information Systems (GIS) and Remote-Sensing (RSS) software applications to interact with the data tools, including digital maps, satellite images, and other geographic information. To obtain maps and other geographic information, users can search by term or geographic location or conduct an advanced search by time frame, year, and geographic location. There is a useful manual located under the “Help” tab, which enables users to learn more about GIS and how to use the GeoNetwork. Data Notes Data sources include: Food and Agriculture Organization of the United Nations (FAO), World Food Programme (WFP), and the United Nations Environment Programme (UNEP). The website announces datasets that have most recently been added to the GeoNetwork, but does not indicate the date it was updated.

BackgroundMalaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.Methodology and FindingsThe lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags.ConclusionThis study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

Abstract

The 2006-07 Angola Malaria Indicator Survey (AMIS) was conducted under the auspices of the National Malaria Control Program (NMCP) within the Ministry of Health (MOH). It was implemented by two private organizations, the Consultoria de Servicos e Pesquisas–COSEP, Consultoria, Lda. and the Consultoria de Gestao e Administracao em Saúde–Consaúde, Lda. This is the first survey of its kind in Angola, and the realization of a standardized household survey constitutes an important landmark in the reinclusion of the country into the international community.

The AMIS includes key information on household characteristics, such as the composition of the population, levels of water and sanitation, and possession of goods. It also collected information on the education and literacy of women as well as fertility and reproductive health (antenatal care and delivery).

Since the survey is specific to malaria, it asked questions on indoor residual spraying and on the availability and use of mosquito nets in the household. Women were asked whether they were given medicine for prevention and treatment of malaria during pregnancy. Finally, women were asked whether their children had recently had fever and what medicines they were given.

The survey collected blood samples for two important biomarkers: anemia and malaria. Anemia was assessed among women age 15-49 and children under age five, using a portable photometer. Malaria was assessed among pregnant women age 15-49 and children under age five using a rapid diagnostic test and a microscopic test in a subsample. All individuals who tested positive for malaria were given treatment on the spot.

Geographic coverage

National

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2006-07 Angola Malaria Indicator Survey (2006-07 AMIS) is based on a representative probability sample of households. The sample provides information on women of reproductive age (15-49) and children under five, specifically for malaria-related indicators. The survey covered the entire country.

The sample was designed to provide estimates with acceptable levels of precision for key malaria-related indicators and for two sub-populations: pregnant women age 15-49 and children under five. The major sample domains for which these estimates are computed are: 1. Angola at a national level, 2. Total urban areas and total rural areas of Angola, and 3. Major malaria epidemiologic regions, defined as: a) Hyperendemic, b) Mesoendemic Stable, c) Mesoendemic Unstable, and d) Luanda, which was extracted from the Mesoendemic Stable region and represents the capital city.

SAMPLE FRAME Angola is divided into 18 provinces, and they can be grouped into eight sub-regions (e.g., North, East, and Center) according to factors that make some provinces homogeneous among themselves. Each province is subdivided into municipalities (161 in total); each municipality is subdivided into communes (635 in total); and each commune is classified as either urban or rural. Each urban commune is subdivided into administrative areas called censal sections (CSs). Each rural commune has a list of villages, with estimated populations in each village. Therefore, the list of CSs in each urban commune and the list of villages in each rural commune constitute the sample frames for the 2006-07 AMIS.

STRATIFICATION The communes were grouped by major regions, by rural and urban location, by sub-regions, and by provinces, in order to find homogeneous sampling units. In addition, within each urban commune, several CSs were grouped together to take advantage of the existence of bairros (sub-districts). These groupings were used to stratify the sample.

SAMPLE SIZE The sample size for the AMIS was estimated based on the minimum size needed to obtain malaria-related indicators with acceptable levels of precision. The precision levels were calculated for each domain. Since the maximum accepted number of domains for Angola was four (i.e., the three epidemiologic regions plus Luanda), the sample size estimate would have to be multiplied by four.

The key indicator selected for the survey was malaria prevalence. Since little was known about its actual level, an assumption was made about its nationwide level. It was estimated between 25 and 30 percent, and the lower level was selected for increased confidence.

Given an estimated malaria prevalence of 25 percent in each domain, at a relative error level of 15 percent, the sample would require 533 children under age five. This is roughly equivalent to 630 households per domain or about 2,500 households nationwide. However, it was possible that the survey would find other indicators at lower percentage values, e.g., children under five sleeping under a bednet. Also, some indicators would be obtained from a smaller sub-population, such as pregnant women. Therefore, the recommended sample size per domain was 750 households, or 3,000 households nationwide. With a sample of this size, depending on the values found, it was possible that some indicators might not be susceptible to analysis at the domain level, but only at the national and urban-rural levels.

SAMPLE SELECTION The 2006-07 AMIS sample was selected using a stratified three-stage cluster design providing 120 clusters, 48 in urban and 72 in rural areas. In each urban or rural area in a given domain, clusters were selected systematically with probability proportional to size. The selection was done using the following formulas at different stages.

In the first sampling stage, communes were stratified by urban-rural area and by province in each major domain. Then communes were selected with probability proportional to their estimated population using the following formula: P1i = (30 x mi/ mi)

In each selected commune, the second sampling stage selected clusters (censal sections in urban communes and villages in rural communes) with probability proportional to their estimated population size using the following formula: P2ji = (ai x mji/j mji)

The third stage constituted the final selection of households in a given cluster, using the following formula: P3ji = (c/Lji)

The sampling procedures are fully described in Appendix A of " Angola Malaria Indicator Survey 2006-2007 - Final Report" pp.41-45.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires prepared by the Survey and Indicator Guidance Task Force of the Monitoring and Evaluation Reference Group (MERG) for the Roll Back Malaria Partnership were adapted for the 2006-07 AMIS. There were two main questionnaires: a household questionnaire and an individual woman’s questionnaire.

The Household Questionnaire was used to list all the usual members and visitors in selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for individual interviews. The Household Questionnaire also collected information on characteristics of a household’s dwelling, including the water source, toilet facilities, and flooring materials; the household’s ownership of durable goods and mosquito nets; and the use of mosquito nets and indoor residual spraying.

In addition, the Household Questionnaire provided for the collection of blood samples for two biomarkers: hemoglobin and the presence of malaria parasites. Hemoglobin tests were performed on all children under age five and women age 15-49, while malaria tests were performed on children under age five and pregnant women.

The Women’s Questionnaire was used to interview all women age 15-49. It covered the following topics: background characteristics, education, reproduction, pregnancy and intermittent preventive treatment (IPT) of malaria, and treatment of fever in children.

The questionnaires were translated into Portuguese and six national languages: Kikongo, Kimbunda, Umbundu, Kiokwé, Nganguela, and Kuanhama.

The survey protocol was submitted to and approved by the Ethical Review Committee at the National Malaria Control Program and the Institutional Review Board (IRB) of Macro International.

Cleaning operations

Data entry began two weeks after the start of data collection. Four data entry operators entered data under the supervision of a data processing manager, a questionnaire organizer, and a questionnaire editor. Check tables on the performance of individual interviewers and teams were assessed periodically, especially during the early weeks of fieldwork. Such checks showed initial weaknesses in certain teams, which required extra supervisory field trips. Once all data were entered, a consultant verified completeness of the forms and internal consistency between data entry and initial results.

Response rate

A total of 2,809 households were selected, of which 2,675 proved to be occupied. The total number of households interviewed was 2,599, yielding a household response rate of 97 percent.

A total of 3,136 eligible women were identified in these households, and interviews were completed for 2,973 women, yielding a response rate of 95 percent. Response rates were slightly higher in rural areas than urban areas.

Sampling error estimates

The sample of respondents selected in the 2006-07 AMIS is only one of many samples that could have

A detailed understanding of the human infectious reservoir is essential for improving malaria transmission-reducing interventions. Here we report a multi-regional assessment of population-wide malaria transmission potential based on 1209 mosquito feeding assays in endemic areas of Burkina Faso and Kenya. Across both sites, we identified 39 infectious individuals. In high endemicity settings, infectious individuals were identifiable by research-grade microscopy (92.6%; 25/27), whilst one of three infectious individuals in the lowest endemicity setting was detected by molecular techniques alone. The percentages of infected mosquitoes in the different surveys ranged from 0.05 (4/7716) to 1.6% (121/7749), and correlate positively with transmission intensity. We also estimated exposure to malaria vectors through genetic matching of blood from 1094 wild-caught bloodfed mosquitoes with that of humans resident in the same houses. Although adults transmitted fewer parasites to mosquitoes than children, they received more mosquito bites, thus balancing their contribution to the infectious reservoir.

Abstract

The 2011 Malaria Indicator Survey in Angola (2011 AMIS) was conducted by Cosep Consultoria, Consaúde Lda., and the Programa Nacional de Controle da Malária, with technical assistance from ICF Macro. Fieldwork took place from January 2011 through May 2011. The Angola Malara Indicator Survey (AMIS) is part of the Demographic and Health Surveys (MEASURE DHS) program and the Malaria Indicator Surveys (MIS) programs, implemented by ICF International under contract with USAID Washington. The objectives of the 2011 AMIS are (1) to evaluate behavior related to the prevention and treatment of malaria and (2) to estimate the prevalence of malaria among children under age 5. Additional questions were included to facilitate the estimation of fertility and infant mortality.

Fieldwork for the 2011 AMIS took place between January 2011 and May 2011, amidst heavy rains and floods typical of the period of high transmission of malaria. The survey collected data from 8,030 households and 8,589 women age 15-49. The sample was designed to represent populations at the national level, at urban and rural levels, and in four recognized malaria epidemiological regions: Hyperendemic, Mesoendemic Stable, Mesoendemic Unstable, and the Province of Luanda.

Geographic coverage

National

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under age 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

OBJECTIVES OF THE SAMPLING DESIGN (1) The 2011 AMIS survey was designed to determine reliable malaria prevalence estimates among children under age 5 at the various domains of interest (when feasible) and mortality estimates for children under age 5. (2) The major domains to be distinguished in the tabulation of key indicators are: - Angola at the national level - The majority of indicators for each of the four domains defined for Angola and classified as the following regions: 1) Hyperendemic region, high malaria prevalence 2) Mesoendemic Stable region, medium malaria prevalence 3) Mesoendemic Unstable region, medium malaria prevalence, though prevalence is affected by the amount of rain 4) Luanda province - Urban and rural areas of Angola (each as a separate domain) - Any contiguous group of provinces with an adequate sample size of at least 1,500 households (3) The primary objective of the 2011 AMIS is to provide estimates with acceptable precision for important population indicators associated with each domain, such as: a. Ownership and use of mosquito bednets. b. Practices to treat malaria among children under age 5 and the use of specific antimalarial drugs c. Prevalence of malaria and anemia among children age 6-59 months d. Knowledge, attitudes, and practices regarding malaria in the general population

SAMPLE FRAME Administratively, Angola is divided into 18 provinces, which can be grouped into eight subregions depending on how they share some common factors.2 In turn, each province is subdivided into municipalities (164 in total), and each municipality is divided into communes (532 in total). Each commune is classified as either urban or rural. In addition to these administrative units, in preparation for the last population census, each urban commune was subdivided into segments named census sections (CSs) that were equivalent to enumeration areas. The National Statistical Institute (INE) had been preparing cartographic materials, including a count of rooms and dwellings, for each CS in the urban areas. This material became an appropriate sampling frame for the 2011 AMIS. However, INE does not have updated cartographic material for the rural areas. To compensate for this lack, INE uses its regional offices to collect a list of villages, with estimated populations in each village, for most of the rural communes,. To develop the sample frame for the 2011 AMIS, the list of CSs was used for the urban communes and the list of villages was used for the rural communes.

STRATIFICATION The communes were grouped by major region, by rural or urban location, by sub-region, and by province as a way to identify homogeneous sampling units. In addition, within each urban commune, several CSs were grouped, taking advantage of the existing neighborhoods (sub-districts) for stratification of the sample.

SAMPLE SIZE The following table includes different scenarios used to select a sample size in a populationbased survey. In the absence of domains, the numbers are valid for the entire population; however, if analyses are expected for more than one domain, then the numbers should be interpreted as required for each domain.

SAMPLE ALLOCATION The clusters for the implementation of the 2011 AMIS are defined on the basis of census sections (CSs) for urban communes and on the basis of villages for rural communes. The 240 clusters considered for the 2011 AMIS were equally allocated at 60 clusters in each domain. The target for the 2011 AMIS was to select about 8,800 households. Therefore, the sample take is on average 36 selected households per cluster (i.e., 8,800/240). Clusters are distributed as 96 in the urban areas and 144 in the rural areas.

Under the final sample allocation, it is expected that each of the four major malaria regions in Angola will provide a minimum of about 2,200 completed women interviews, 2,100 children under age 5, and 2,000 births in the last five years. Neither the distribution of the 240 clusters among major regions nor the distribution of households in the sample is proportional to the estimated population distribution. This is due to the disproportional number of CSs among major regions. As a result, the sample for the 2011 AMIS is not a selfweighted household sample. Therefore, the 2011 AMIS sample is unbalanced for residence areas and regions and will require the design of a final weighting adjustment procedure to provide representative estimates for all the study domains.

SAMPLE SELECTION The sample for the 2011 AMIS was selected using a stratified three-stage cluster design consisting of 240 clusters, with 96 in urban areas and 144 in rural areas. In each urban or rural area in a given region, clusters are selected systematically with probability proportional to size.

The sampling procedures are fully described in Appendix A of " Angola Malaria Indicator Survey 2011 - Final Report" pp.43-48.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used for the 2011 AMIS: a household questionnaire and another questionnaire for women age 15-49 in the households selected for the survey. The questionnaires were developed from the ones used for the 2006-07 malaria indicator survey, which followed the methodology of the Roll Back Malaria and MEASURE DHS programs.

The Household Questionnaire was used to list all the usual members and visitors who stayed in the selected households the night before the survey. It also identified women eligible for interviewing and children age 6-59 months eligible for anemia and malaria tests.

Basic information collected on the characteristics of each person included age, sex, and relationship to head of household. The Household Questionnaire was also used to collect information on characteristics of the household dwelling, such as the water source; type of toilet facilities; materials used for the roof, floors, and walls; possession of durable goods; and possession and use of mosquito nets.

The Woman’s Questionnaire, used to collect information for all women age 15-49, covered the following topics: - Sociodemographic characteristics of the respondent - Birth history - Prenatal care and intermittent preventive treatment (IPT) of malaria during pregnancy for the most recent birth - Treatment of malaria symptoms in children - Malaria knowledge

The survey protocol was submitted to and approved by the National Ethical Review Committee of the National Malaria Control Program and by the Institutional Review Board (IRB) of ICF Macro.

Cleaning operations

Data entry started two weeks after the beginning of fieldwork. Twelve data entry operators were used, six in the morning and six in the afternoon. They were supervised by the data processing manager, the questionnaire organizer, and the questionnaire editor. Control tables with data on interviewer and team performance were assessed periodically, especially during the first two weeks of fieldwork. The tables helped identify mistakes some teams made at the beginning of fieldwork; these mistakes resulted in extra supervisory field visits. Once the data entry was finalized, a consultant verified completeness of the questionnaires and consistency betwen data entry and the initial results.

Response rate

A total of 8,806 households were selected, of which 8,493 were occupied. The total number of households interviewed was 8,030, yielding a household response rate of 95 percent.

A total of 8,746 eligible women were identified in these households, and interviews were completed for 8,589 women, yielding a response rate of 98 percent. Household response rates were 97 percent in urban areas and 93 percent in rural areas, and response rates for eligible women were 97 percent in urban areas and 99 percent in rural areas.

Sampling error estimates

The sample of respondents selected in the 2011 AMIS is only one of many samples that could have been selected from the same population, using the same sample design and expected size. Each of these samples would yield results that differ somewhat from

Abstract

Since 1995, the National Malaria Control Programme (NMCP) and its partners have been implementing and scaling up malaria interventions in all parts of the country. To determine the progress made in malaria control and prevention in Uganda, the Uganda Malaria Indicator Survey (UMIS) was implemented in 2009 and again in 2014-15 to provide data on key malaria indicators including mosquito net ownership and use, as well as prompt treatment using ACT.

The main objective of the UMIS is to obtain population-based estimates on malaria indicators including the prevalence of malaria and anaemia to inform strategic planning and programme evaluation. Specific objectives are: 1. To obtain estimates of the magnitude and distribution of anaemia and malaria parasitemia among children age 0-59 months 2. To estimate core malaria programme coverage indicators • Measure the extent of ownership and use of mosquito bed nets • Assess coverage of the intermittent preventive treatment programme for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Assess percentage of children under five with fever for whom advice or treatment was sought • Determine the species of plasmodium parasite most prevalent in children age 0-59 months 3. To measure indicators of knowledge, attitudes, and behaviour related to malaria control 4. To determine the factors associated with malaria parasitemia and anaemia

Geographic coverage

National

Analysis unit

• Household
• Children age 0-5
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) was designed to provide most of the key malaria indicators for the country as a whole, for urban and rural areas, and for 10 survey regions.

In addition, three study domains based on malaria endemicity were created to provide selected malaria indicators addressing NMCP/MOH programmatic needs: 1) to evaluate the effect of interventions such as indoor residual spraying (IRS) in the 10 districts in the north, 2) to provide baseline indicators for the 14 districts planned for future IRS programmes, and 3) provide estimates separately for high altitude areas with low malaria burden. The three study domains are arranged as follows: Domain 1: ten (10) districts in which IRS programmes are currently implemented; Domain 2: fourteen (14) districts planned for future IRS programmes (to provide baseline estimates); Domain 3: ten (10) high-altitude districts (low malaria burden areas).

Apart from the three study domains above, the region of Karamoja was over-sampled in order to be comparable to a DHS region, and the urban areas of Wakiso and Mukono districts, together with Kampala, were combined to form a special 'Greater Kampala' zone.

Each of the 10 regions and the 3 study domains comprise multiple administrative districts that share a similar malaria burden or have specific malaria prevention efforts. The capital city, Kampala, comprises its own district and is entirely urban.

The sampling frame used for the 2014-15 UMIS was the preparatory frame for the Uganda Population and Housing Census, which was conducted in August 2014. Provided by the Uganda Bureau of Statistics (UBOS), the sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks, and prisons.

The 2014-15 UMIS sample was selected using a stratified two-stage cluster design consisting of 210 clusters, with 44 in urban areas and 166 in rural areas. In the first stage, 20 sampling strata were created and clusters were selected independently from each stratum by a probability-proportional-to-size selection. In the selected clusters, a complete listing of households and a mapping exercise was conducted from 25 October to 20 November 2014, with the resulting list of households serving as the sampling frame for the selection of households in the second stage.

In the second stage of the selection process, 28 households were selected in each cluster by equal probability systematic sampling. Because of the nonproportional allocation of the sample to the different regions and study domains, the sample is not self-weighting. Weighting factors have been added to the data file so that the results will be representative at the national and regional level as well as the survey domain level.

All women age 15-49 who were either permanent residents of the households in the 2014-15 UMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 0-59 months who were either permanent residents of the sampled households or visitors present in the households on the night before the survey were eligible to be tested for malaria and anaemia.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 UMIS used two questionnaires: a Household Questionnaire and a Woman’s Questionnaire for women age 15-49 in the selected households. Both of these instruments were based on the model Malaria Indicator Survey questionnaires developed by the Roll Back Malaria Monitoring and Evaluation Research Group, as well as other questionnaires from previous surveys conducted in Uganda, including the 2009 UMIS. The Technical Working Group organised stakeholders’ meetings in Kampala to review the draft questionnaires. Stakeholders comprised a range of potential users, including government institutions, nongovernmental organisations, and interested donor groups. The questionnaires were translated from English into six local languages (Ateso/Karamajong, Luganda, Lugbara, Luo, Runyankole/Rukiga, and Runyoro/Rutoro).

The Household Questionnaire captured data on all usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for the individual interview and children eligible for anaemia and malaria testing. The Household Questionnaire was also used to collect responses on indicators of ownership and use of mosquito bed nets. In addition, the Household Questionnaire collected data on housing conditions and assets to calculate the measures of household wealth.

The Woman’s Questionnaire was used to collect data from women age 15-49 years, including: background characteristics (age, education, etc.); reproductive history (number of births, survival of births, etc.); current pregnancy status, intermittent preventive treatment for malaria during recent pregnancies; and antimalarial treatment for children under five with recent fever). It also collected information on knowledge about malaria.

Cleaning operations

All questionnaires for the 2014-15 UMIS were returned to the data processing centre at the UBOS headquarters in Kampala. Activities performed included office editing, data entry, and editing of computeridentified inconsistencies. The data were processed by a team consisting of one data entry supervisor, one assistant supervisor, 24 data entry operators, and 7 staff who performed tasks related to questionnaire administration, office editing, and secondary editing. Data entry and editing were accomplished using CSPro software. The process of office editing and data processing was initiated in January 2015 and completed in mid- February 2015.

Response rate

A total of 5,802 households were selected for the sample, of which 5,494 were occupied. Of the occupied households, 5,345 were successfully interviewed, yielding a response rate of 97 percent. The response rate among households in rural areas was slightly higher (98 percent) than the response rate in urban areas (96 percent).

In the interviewed households, 5,494 women were identified as eligible for the individual interview; interviews were completed with 5,322 women, yielding a response rate of 97 percent. The eligible women’s response rate does not differ by urban or rural residence. The principal reason for non-response among eligible women was failure to find individuals at home despite repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the selected household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2014-15 UMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a