In 2023, Nigeria accounted for nearly 26 percent of all malaria cases worldwide, by far the highest share of any country. The Democratic Republic of the Congo had the second-highest share of malaria cases that year with 12.6 percent, followed by Uganda with 4.8 percent. Malaria is an infectious disease spread by female mosquitoes. Symptoms include fever, fatigue, vomiting, and headache and if left untreated the disease may lead to death. The region most impacted by malaria In 2023, there were a total of 263,000 cases of malaria worldwide. The region of Africa accounted for 246,000 of these cases, making it by far the region most impacted by this deadly disease. In comparison, Southeast Asia reported four thousand malaria cases in 2023, while the Americas had just 548. However, incidence rates of malaria have decreased around the world over the past couple decades. In Africa, the incidence rate of malaria decreased from 369 per 1,000 at risk in the year 2000 to 223 per 1,000 at risk in 2022. Worldwide, the incidence rate of malaria decreased from 79 to 60 per 1,000 at risk during this period. How many people die from malaria each year? Although rates of malaria have decreased around the world, hundreds of thousands of people still die from malaria each year, with the majority of these deaths in Africa. In 2023, around 597,000 people died from malaria worldwide, with 569,000 of these deaths occurring in Africa. However, death rates from malaria have decreased in Africa, with a rate of 62.5 per 100,000 at risk in the year 2015 compared to a rate of 52.4 per 100,000 at risk in 2023. In 2023, Nigeria accounted for around 31 percent of all malaria deaths, while 11 percent of such deaths were in the Democratic Republic of the Congo.

Africa is the region most affected by malaria in the world. Over ***** million cases of the disease were reported in the continent in 2022. From a country perspective, the Democratic Republic of the Congo registered the highest number of cases, some **** million, followed by Nigeria, with **** million cases. Overall, the total number of reported deaths due to the disease in Africa was around ****** as of 2022.

Context

• Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes.
• It is preventable and curable.
• In 2018, there were an estimated 228 million cases of malaria worldwide.
• The estimated number of malaria deaths stood at 405 000 in 2018.

• Children aged under 5 years are the most vulnerable group affected by malaria;
• in 2018, they accounted for 67% (272 000) of all malaria deaths worldwide.

• The WHO African Region carries a disproportionately high share of the global malaria burden.
• In 2018, the region was home to 93% of malaria cases and 94% of malaria deaths.

Content

• reported_numbers.csv - Reported no. of cases across the world
• estimated_numbers.csv - Estimated no of cases across the world
• incidence_per_1000_pop_at_risk.csv - Incidence per 1000 people at risk area

Acknowledgements / Data Source

https://apps.who.int/gho/data/node.main.A1363?lang=en

Collection methodology

https://github.com/imdevskp/malaria-data-cleaning

Cover Photo

Photo from https://www.sciencenews.org/article/malaria-parasites-may-have-their-own-circadian-rhythms By JOSEPH TAKAHASHI LAB/UT SOUTHWESTERN MEDICAL CENTER/HHMI

Malaria poses a risk to approximately 3.3 billion people or approximately half of the world's population. Most malaria cases occur in Sub-Saharan Africa. Asia, Latin America, and to a lesser extent the Middle East and parts of Europe are also affected. According to the Global Malaria Report published by the World Health Organization (WHO), malaria was present in 106 countries and territories in 2010; and there were 216 million estimated cases of malaria and nearly 0.7 million deaths - mostly among children living in Africa. In this research, we have estimated current population exposed to malaria - by country. In our computation, we have made the geographical distinction of areas with high, medium, low prevalence ("endemicity") of malaria in each country based on the Global malaria atlas compiled by the Malaria Atlas Project (MAP) of the Oxford University. The data are based on 24,492 parasite rate surveys (Plasmodiumfalciparum. 24,178; Plasmodium vivax. 8,866) from an aggregated sample of 4,373,066 slides prepared from blood samples taken in 85 countries. The MAP study employs a new cartographic technique for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007. These estimates are then compared with those derived under existing surveillance-based approaches to arrive at the final data used in the malaria mapping (Hay et al., 2009). (http://www.map.ox.ac.uk/media/maps/pdf/mean/World_mean.pdf, accessed 2012) Malaria maps generally separate the malaria endemicity into three broad categories by Plasmodium falciparum parasite rate (PfPR), a commonly reported index of malaria transmission intensity: PfPR < 5% as low endemicity, PfPR 5%-40% as medium/intermediate endemicity, and PfPR > 40% as high endemicity. In our research, global mapping techniques were used to estimate population exposed to malaria. The malaria endemicity maps were overlaid on global population maps from Landscan 20051 (Dobson, 2000) and country-level population exposure in the three endemicity areas were computed. Due to the spatial reference of the data and the number of observations in the combined data, the use of Geographic Information Systems functions from ESRI ArcGIS (v 9.3.1) were used and automated in the python (v 2.5) language.

Africa is the region most affected by malaria in the world. The total number of reported deaths due to the disease was roughly ****** deaths in 2023. The Democratic Republic of Congo registered the highest amount, with around ****** fatalities, followed by Angola, with some ****** deaths.

BackgroundPrimaquine is a key drug for malaria elimination. In addition to being the only drug active against the dormant relapsing forms of Plasmodium vivax, primaquine is the sole effective treatment of infectious P. falciparum gametocytes, and may interrupt transmission and help contain the spread of artemisinin resistance. However, primaquine can trigger haemolysis in patients with a deficiency in glucose-6-phosphate dehydrogenase (G6PDd). Poor information is available about the distribution of individuals at risk of primaquine-induced haemolysis. We present a continuous evidence-based prevalence map of G6PDd and estimates of affected populations, together with a national index of relative haemolytic risk. Methods and FindingsRepresentative community surveys of phenotypic G6PDd prevalence were identified for 1,734 spatially unique sites. These surveys formed the evidence-base for a Bayesian geostatistical model adapted to the gene's X-linked inheritance, which predicted a G6PDd allele frequency map across malaria endemic countries (MECs) and generated population-weighted estimates of affected populations. Highest median prevalence (peaking at 32.5%) was predicted across sub-Saharan Africa and the Arabian Peninsula. Although G6PDd prevalence was generally lower across central and southeast Asia, rarely exceeding 20%, the majority of G6PDd individuals (67.5% median estimate) were from Asian countries. We estimated a G6PDd allele frequency of 8.0% (interquartile range: 7.4–8.8) across MECs, and 5.3% (4.4–6.7) within malaria-eliminating countries. The reliability of the map is contingent on the underlying data informing the model; population heterogeneity can only be represented by the available surveys, and important weaknesses exist in the map across data-sparse regions. Uncertainty metrics are used to quantify some aspects of these limitations in the map. Finally, we assembled a database of G6PDd variant occurrences to inform a national-level index of relative G6PDd haemolytic risk. Asian countries, where variants were most severe, had the highest relative risks from G6PDd. ConclusionsG6PDd is widespread and spatially heterogeneous across most MECs where primaquine would be valuable for malaria control and elimination. The maps and population estimates presented here reflect potential risk of primaquine-associated harm. In the absence of non-toxic alternatives to primaquine, these results represent additional evidence to help inform safe use of this valuable, yet dangerous, component of the malaria-elimination toolkit. Please see later in the article for the Editors' Summary

In 2022, Uganda had the highest number of confirmed malaria cases and deaths in Southern Africa, with around **** million and *** thousands reported, respectively. Burundi followed with the second-highest amount of cases, with over *** million. However, South Sudan recorded the second-highest number of deaths related to the disease, with ***** fatalities. Africa is the region most affected by malaria in the world, with around 91,300 deaths attributed to the disease in the same period.

Context

The data here is from the Global Health Observatory (GHO) who provide data on malaria incidence, death and prevention from around the world. I have also included malaria net distribution data the Against Malaria Foundation (AMF). The AMF has consistently been ranked as the most cost effective charity by charity evaluators Give Well - http://www.givewell.org/charities/top-charities

Content

GHO data is all in narrow format, with variables for a country in a given year being found on different rows.

GHO data (there are a number or superfluous columns):

• GHO (CODE)
• GHO (DISPLAY) - this is the variable being measured
• GHO (URL)
• PUBLISHSTATE (CODE)
• PUBLISHSTATE (DISPLAY)
• PUBLISHSTATE (URL)
• YEAR (CODE)
• YEAR (DISPLAY)
• YEAR (URL)
• REGION (CODE)
• REGION (DISPLAY)
• REGION (URL)
• COUNTRY (CODE) - can be used to join this data with the AMF data
• COUNTRY (DISPLAY)
• COUNTRY (URL)
• Display Value - this is the measured value
• Low - lower confidence interval
• High - higher confidence interval
• Comments

AMF distribution data:

• #_llins - total number of malaria nets distributed
• location - the specific area that received the nets, within the target country
• country - the country in which the nets were distributed
• when - the period the distribution
• by_whom - the organisation(s) which partnered with the AMF to perform the distribution
• country_code - the country's GHO country code (this will allow joining with the GHO data)

For the current version all data was downloaded 20-08-17 The GHO data covers the years from 2000 to 2015 (not all files have data in all years) The AMF data runs from 2006 - the present.

The GHO data is taken as is from the csv (lists) available here: http://apps.who.int/gho/data/node.main.A1362?lang=en The source of the AMF's distribution data is here: https://www.againstmalaria.com/distributions.aspx - it was assembled into a single csv using Excel (mea culpa)

Inspiration

Malaria is one of the world's most devastating diseases, not least because it largely affects some of the poorest people. Over the past 15 years malaria rates and mortality have dropped (http://www.who.int/malaria/media/world-malaria-report-2016/en/), but there is still a long way to go. Understanding the data is generally one of the most important steps in solving any large problem. I'm excited to see what the Kaggle community can find out about the global trends in malaria over this period, and if we can find out anything about the impact of organisations such as the AMF.

Supplementary materials for the peer-reviewed publication: Sogandji, N., Stevenson, A., Luo, M.Y. et al. Systematic review of evidence for the impact and effectiveness of the 1-3-7 strategy for malaria elimination. Malar J 23, 371 (2024). https://doi.org/10.1186/s12936-024-05200-wAbstract Background: The 1-3-7 approach to eliminate malaria was first implemented in China in 2012. It has since been expanded to multiple countries, but no systematic review has examined the evidence for its use. A systematic review was conducted aiming to evaluate the impact and effectiveness of the strategy and identify key challenges and variations in its implementation across different countries. Methods: PUBMED, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CABS Abstracts, LILACS, Global Health, Medrxiv, Biorxiv were searched for all studies containing 1-3-7 and articles included if they contained information on 1-3-7 impact, effectiveness, challenges and/or adaptations for implementation in different countries. Results: 31 studies were included from China (19), Thailand (6), Myanmar (2), Tanzania (1), Cambodia (1), India (1) and Vietnam (1). During 1-3-7 implementation, malaria cases in China decreased by 99.1–99.9%, in Thailand by 66.9% during 2013–19, 65,1% in Cambodia during 2015–17 and 30.3% in India during 2015–16, with some differences in implementation. It was not possible to separate the impact of 1-3-7 from that due to other contemporaneous interventions. Implementing the 1-3-7 policy was largely effective, with reporting within 1 day in 99.8–100% of individuals in China and 36–100% in other countries, investigation within 3 days in 81.5–99.4% in China and 79.4–100% in other countries, and foci investigation within 7 days in 90.1–100% in China and 83.2–100% in other countries. Adaptations to 1-3-7 were described in 5 studies, mostly adjustment of the timing and/or definitions of each component. Key challenges identified included those related to staffing, equipment, process, and patient-provided information. Conclusion: Overall, the 1-3-7 approach was effectively implemented with a concomitant decrease in cases in malaria elimination settings, however, it was not possible to quantify impact as it was not implemented in isolation. Implementing adequate measures for testing, reporting, treatment, and containment is crucial for its success, which is dependent on the availability of resources, infrastructure, staffing, and consistent compliance across regions and throughout the year. However, achieving this nationally and maintaining compliance, especially at borders with malaria-affected countries, poses significant challenges.

Abstract

Malaria is a significant public health problem in Kenya. More than 70% of the population is at constant risk from malaria, including those most vulnerable to the disease, specifically children and pregnant women. In the past 5 years, there has been a concerted effort by the government and malaria partnerships to fight the disease through prevention and treatment interventions such as mass and routine mosquito net distribution programs to attain universal coverage, intermittent preventive treatment for malaria during pregnancy, and parasitological diagnosis and management of malaria cases. The Kenya Malaria Indicator Survey is one of the key performance monitoring tools periodically used to provide an in-depth assessment of malaria control efforts over time. Kenya has in the past undertaken three Malaria Indicator Surveys, in 2007, 2010, and 2015. The results from these surveys provide information on the performance of the key malaria control interventions as experienced by communities across the country; and are crucial to evaluation of interventions. Moreover, they enable effective planning and malaria control programming and facilitate a good understanding of the factors, dynamics, and impediments that affect control efforts. The reports also provide evidence for comparison with other malaria control programs globally and allow for benchmarking to meet international standards and practices for combating the disease. In this regard, it is incumbent upon all partners and stakeholders in malaria control and elimination to embrace this report and assess the implications for malaria programming over the next few years.The report, therefore, has come at an opportune time when we are in the midst of implementing the Kenya Malaria Strategy 2019-2023. The results will form a basis for redirecting efforts and reorienting both technical and operational perspectives to address the challenges and strengthen the successes observed. The Ministry of Health is committed to further reducing the malaria burden in the coming years. Thus, I urge all players in malaria control to rededicate efforts and investments to enable delivery of sound malaria interventions and drive the burden further down towards our ambitious vision of a malaria-free Kenya within the shortest time possible

Geographic coverage

National

Analysis unit

Household and individuals

Universe

Sampled individuals and households

Sampling procedure

A sample is a group of people who have been selected for a survey. In the KMIS, the sample is designed to represent the national population age 15-49. In addition to national data, most countries want to collect and report data on smaller geographical or administrative areas. However, doing so requires a minimum sample size per area. For the 2020 KMIS the survey sample is representative at the national level, malaria endemicity zone, and for urban and rural areas. To generate statistics that are representative of the country as a whole and the five malaria endemicity zones, the number of women surveyed in each malaria endemicity zone should contribute to the size of the total (national) sample in proportion to size of the malaria endemicity zone. However, if some malaria endemicity zones have small populations, then a sample allocated in proportion to each malaria endemicity zone’s population may not include sufficient women from each district for analysis. To solve this problem, malaria endemicity zones with small populations are oversampled. For example, let’s say that you have enough money to interview 6,771 women and want to produce results that are representative of Kenya as a whole and its malaria endemicity zones (as in Table 2.11). However, the total population of Kenya is not evenly distributed among the malaria endemicity zones: some malaria endemicity zones, such as Low risk zone, are heavily populated while others, such as Coast endemic zone are not. Thus, Coast endemic zonemust be oversampled

Mode of data collection

Face to face using CAPI

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.

Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical component of the Global Technical Strategy for Malaria from 2016 to 2030. While traditional differential equation models can perform basic forecasting, supervised machine learning algorithms provide more accurate predictions, as demonstrated by a recent study using an elastic net model (REMPS). Nevertheless, current short-term prediction systems do not achieve the required accuracy levels for routine clinical practice. To improve in this direction, stacked hybrid models have been proposed, in which the outputs of several machine learning models are aggregated by using a meta-learner predictive model. In this paper, we propose an alternative specialist hybrid approach that combines a linear predictive model that specializes in the linear component of the malaria prevalence signal and a recurrent neural network predictive model that specializes in the non-linear residuals of the linear prediction, trained with a novel asymmetric loss. Our findings show that the specialist hybrid approach outperforms the current state-of-the-art stacked models on an open-source dataset containing 22 years of malaria prevalence data from the city of Ibadan in southwest Nigeria. The specialist hybrid approach is a promising alternative to current prediction methods, as well as a tool to improve decision-making and resource allocation for malaria control in high-risk countries.

In 2023, Brazil ranked first with ***** percent of malaria cases, making it one of the most affected countries in the region. Venezuela accounted for nearly one-quarter of all malaria cases reported in Latin America and the Caribbean, while in Colombia, the estimated distribution of malaria cases reached ***** percent. As of that year, there were over ******* reported cases of malaria in Brazil.

Coordinates of distribution points of Anopheles mosquitoes after purification based on ENMTools software.

BackgroundMalaria remains a significant public health challenge in Ethiopia, hindering the country’s productivity and development. While malaria incidence had decreased by 2018, and Ethiopia is working towards eliminating the disease by 2030, outbreaks still occur even in areas of low endemicity. Therefore the aim of this study was to assess the ten-year trend in malaria prevalence from 2015 to 2024 at Bichena Primary Hospital in the Amhara region of northwestern Ethiopia.Materials and methodsA retrospective review of malaria blood film examination results was conducted using laboratory registration logbooks at Bichena Primary Hospital. Data collection was carried out from December 30, to January 14. The data were collected using a data collection sheet and entered into the Statistical Package for Social Sciences (SPSS) version for analysis. Bi-variable and multi-variable regression analyses and Pearson’s chi-square test were used to examine associations and differences in malaria prevalence trends across factors such as sex, age, year, and season and Plasmodium species. Descriptive statistics were also used to summarize the sociodemographic characteristics of the study participants and the results were presented in graphs, tables and texts.ResultsOut of the 24,107 malaria blood films examined, 4,322 (17.9%, 95% CI: 17.4%-18.4%) tested positive for Plasmodium infections. Of the confirmed cases, 58.7% were P. vivax, 28.6% were P. falciparum, and 12.2% were mixed infections. P. vivax was the predominant species throughout the study period (2015-2024), except for the years 2016 and 2018, when P. falciparum was more prevalent. Subsequently, an increase in malaria cases was reported, with the highest proportion recorded in 2024 (26.8%) and the lowest in 2018 (4%). The likelihood of malaria prevalence was 1.28 times higher in males than in females. Additionally, the chance of malaria prevalence was 1.27 times higher in the 15-24 age group compared to other age groups. The study revealed a significant rise in malaria prevalence, highlighting that malaria remains a major public health issue in the study area. There were also pronounced seasonal variations in malaria cases each year, with males and younger adults being more affected than females, older age groups, and children under five.Conclusions and recommendationsMalaria prevention and control efforts need to be strengthened, focusing on regional differences. Ongoing research on diagnostic challenges, parasite elimination, and mosquito infectivity after malaria treatment is essential.

Abstract

The 2014 MIS used a standard set of instruments and protocol developed by RBM Monitoring and Evaluation Reference Group (MERG). These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardised survey management and data collection methodology. The package also includes standardised measurement of malaria parasite and anaemia prevalence among target populations to derive the malariarelated burden at the community level.

The key objectives of the 2014 MIS were to: • Measure the level of ownership and use of mosquito nets • Assess coverage of intermittent preventive treatment for pregnant women • Identify treatment practises, including the use of specific antimalarial medications to treat malaria among children under 5 • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess knowledge, attitudes, and practises of malaria in the adult population • Measure trends in key malaria indicators since the 2012 MIS

The 2014 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions in Malawi: Northern, Central, and Southern.

Geographic coverage

National coverage

Analysis unit

• Women age 15-49 years
• Children age 6-59 months

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 2014 MIS sample was designed to produce most of the key indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions.

The survey utilised a two-stage sample design. The first stage involved selecting 140 clusters with probability proportional to size from the list of approximately 12,474 EAs covered in the 2008 National Population and Housing Census. The EA size was the number of residential households in the EA recorded in the census. Among the 140 clusters selected, 50 were in urban areas and 90 were in rural areas. Urban areas were over-sampled within regions in order to produce robust estimates for that domain. Therefore, the MIS sample was not proportional to the population for urbanrural residence and required a final weighting adjustment to provide valid estimates for every domain of the survey. In the second stage, in each of the selected EAs, 25 households were selected, using systematic sampling, from a list of households in the EA.

All women age 15-49 who were either permanent residents of the selected households or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months who were listed in the household were eligible for anaemia and malaria testing.

Note: The sample design is described in details in Appendix A.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2014 Malawi MIS: a Household Questionnaire, a Biomarker Questionnaire, and a Woman’s Questionnaire. The Household and Woman’s questionnaires were based on the model MIS questionnaires developed by the RBM and DHS programmes, as well as the 2010 and 2012 MIS. The model questionnaires were modified to reflect relevant issues of malaria in Malawi in consultation with the Steering Committee, the NMCP, and staff from ICF International. The questionnaires were translated into the two main local languages of Malawi—Chichewa and Tumbuka.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some 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 age 6-59 months who were eligible for anaemia and malaria testing. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, roof, and walls of the house, ownership of various durable goods, and ownership and use of mosquito nets.

The Biomarker Questionnaire was used to record haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5 years. The questionnaire was filled in by the health technician and transcribed into the tablet computer by the team supervisor.

The Woman’s Questionnaire was used to collect information from all women age 15-49 years and covered the following topics: • Background characteristics (age, residential history, education, literacy, religion, dialect) • Reproductive history and child mortality for births in the last six years • Prenatal care and preventive malaria treatment for most recent birth • Prevalence and treatment of fever among children under 5 years • Knowledge about malaria (symptoms, causes, and ways to prevent it) and messages on malaria • Cost incurred for the treatment of fever in children under 5 years

No formal field pre-test was done for the survey questionnaires because most of the 2014 MIS questions were included in previous surveys in Malawi and the field staff were experienced in anaemia and malaria testing in the field and in the use of PDAs for data collection.

Cleaning operations

Data for the 2014 Malawi MIS were collected through questionnaires programmed onto computer tablets. ICFI data processing specialists loaded the Household, Biomarker, and Woman’s Questionnaires in English and the two main local languages, Chichewa and Tumbuka, in the computer tablets and installed data entry and processing programmes. The tablets were Bluetooth-enabled to facilitate electronic transfer of files, e.g., data from the Household Questionnaires transferred among survey team members and transfer of completed questionnaires to the team supervisor’s tablet. The field supervisors transferred data on a daily basis to the central data processing unit using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.

The Census Survey Processing Software (CSPro) was used for data editing, weighting, cleaning, and tabulation. In the NMCP central office, data received from the supervisors’ tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structure and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were resent to the central processing unit.

Response rate

Of the 3,501 households selected for the sample, 3,415 were occupied at the time of fieldwork. Eighty-six dwellings were abandoned and, therefore, were not included in the response rate. Among the occupied households, 3,405 were successfully interviewed, yielding a total household response rate above 99 percent. In the interviewed households, 2,927 eligible women were identified as eligible for the individual interview and 2,897 were successfully interviewed, yielding a response rate of 99 percent.

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 2014 Malawi Malaria Indicator Survey (Malawi MIS) 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 2014 Malawi MIS 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 among 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 percent 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 2014 Malawi MIS 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 either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios like the ones in the Malawi MIS survey.

The Taylor linearisation method treats any

Artesunate Used For Malaria Market Outlook

The global market size for Artesunate used for malaria treatment was valued at approximately USD 400 million in 2023 and is expected to reach around USD 800 million by 2032, growing at a compound annual growth rate (CAGR) of 7.5%. This growth is primarily driven by the increasing prevalence of malaria in tropical and subtropical regions, coupled with rising awareness about the effectiveness of artesunate in treating severe malaria cases. Key growth factors include the need for more effective antimalarial treatments in the face of drug resistance, supportive government policies, and increased funding for malaria eradication programs.

One significant growth factor for the Artesunate market is the rising incidence of malaria in many parts of the world, particularly in sub-Saharan Africa and Southeast Asia. Malaria remains a major public health issue, affecting hundreds of millions of people each year and causing significant morbidity and mortality. As a result, there is a substantial demand for effective antimalarial medications like artesunate, which has been proven to be highly effective against severe malaria, especially when compared to older treatments like quinine. The effectiveness of artesunate in reducing mortality rates has been a critical driver for its increased adoption in both hospital and outpatient settings.

Additionally, the growing awareness about malaria prevention and treatment among healthcare providers and the general public is contributing to market expansion. International organizations such as the World Health Organization (WHO) and various non-governmental organizations (NGOs) are actively involved in promoting the use of artesunate and other effective antimalarial drugs. These organizations provide guidelines, training, and resources to healthcare professionals in malaria-endemic regions, ensuring that artesunate is available and properly utilized. Furthermore, educational campaigns aimed at the general public help increase awareness about the importance of timely and effective malaria treatment, thereby boosting the demand for artesunate.

Moreover, supportive government policies and increased funding for malaria control programs are playing a crucial role in the growth of the Artesunate market. Many countries in malaria-endemic regions have implemented national malaria control programs that prioritize the distribution of effective antimalarial medications, including artesunate. These programs are often supported by international donors and partnerships, which provide the necessary financial and technical assistance. As a result, there is improved access to artesunate in areas where it is most needed, further driving market growth.

Dihydroartemisinin, a derivative of artemisinin, plays a pivotal role in the treatment of malaria, often used in combination therapies to enhance efficacy. It is particularly effective in reducing the parasite load in patients, making it a crucial component in the fight against malaria. The use of Dihydroartemisinin in Artemisinin-based Combination Therapies (ACTs) has been instrumental in addressing drug resistance, which is a growing concern in malaria treatment. By combining Dihydroartemisinin with other antimalarial agents, healthcare providers can offer a more robust treatment regimen that not only targets the malaria parasite effectively but also helps in preventing the development of resistance. This approach has been endorsed by the World Health Organization as a standard treatment for uncomplicated malaria, highlighting its importance in global health strategies.

From a regional perspective, Asia Pacific is expected to dominate the Artesunate market due to the high burden of malaria in countries such as India, Indonesia, and Papua New Guinea. This region is followed by sub-Saharan Africa, where countries like Nigeria and the Democratic Republic of Congo have some of the highest malaria incidence rates in the world. North America and Europe also contribute to the market, primarily through funding and research initiatives aimed at developing new antimalarial treatments and improving access to existing medications. The Middle East and Latin America represent smaller market segments but still play a role in the global fight against malaria.

Product Type Analysis

The Artesunate market can be segmented by product type into injectable artesunate and oral artesunate. Injectable artesunate is particularly critical in the treatment of severe malaria

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.

Malaria Vaccines Market Outlook

The malaria vaccines market, which had a projected market size of approximately USD 132 million in 2023, is anticipated to expand significantly, reaching an estimated USD 345 million by 2032. This growth trajectory reflects a compound annual growth rate (CAGR) of 11.2% from 2024 to 2032. The primary growth drivers for this market include increased awareness of malaria prevention, strategic initiatives by governments across malaria-prone regions, and technological advancements in vaccine development. These factors are collectively driving the demand and development of effective vaccines aimed at reducing the global malaria burden.

One of the main growth factors in the malaria vaccines market is the increasing global awareness of the disease's impact, particularly in endemic regions. Malaria continues to pose a significant public health challenge, affecting millions each year, primarily in Sub-Saharan Africa and parts of Asia. Efforts by international health organizations, such as the World Health Organization (WHO), and various Non-Governmental Organizations (NGOs) have been pivotal in raising awareness and driving vaccination campaigns. Funding from global health initiatives like the Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance, also plays a crucial role in supporting the development and distribution of malaria vaccines, thereby expanding the overall market size.

Technological advancements in vaccine research and development constitute another significant growth factor. The advent of novel vaccine technologies, such as recombinant and attenuated vaccines, has been crucial in creating more effective and long-lasting malaria vaccines. Innovations in biotechnology facilitate the development of vaccines with improved efficacy, which has been crucial for gaining regulatory approvals and ensuring widespread adoption. Collaborations between biotech firms and research institutions further foster innovation, leading to the rapid development of next-generation vaccines that target multiple strains of the malaria parasite, thereby enhancing disease management strategies.

Furthermore, strategic governmental policies and initiatives in malaria-endemic countries are important contributors to market growth. Many governments are investing in healthcare infrastructure and vaccine procurement programs to combat malaria, recognizing it as a priority in public health agendas. Efforts to integrate malaria vaccination into national immunization programs have also gained momentum, supported by increased funding and international cooperation. Such policy-driven initiatives have generated a robust demand for malaria vaccines, accelerating market growth and enhancing accessibility to vaccines in underserved regions.

The regional outlook for the malaria vaccines market indicates substantial growth potential in Asia Pacific and Africa, where malaria prevalence remains high. Africa, in particular, is expected to be the largest market due to the high disease burden and ongoing vaccination campaigns. Meanwhile, Asia Pacific countries such as India and Indonesia are investing heavily in disease eradication programs, contributing significantly to market growth. North America and Europe are also witnessing growth due to research and development investments and increasing awareness about malaria prevention among travelers to endemic regions. These regional dynamics are shaping the future of the malaria vaccines market, with targeted interventions and strategic partnerships driving significant market expansion.

Vaccine Type Analysis

The malaria vaccines market is segmented by vaccine type into pre-erythrocytic vaccines, erythrocytic vaccines, and multi-antigen vaccines. Pre-erythrocytic vaccines, which target the liver-stage infection of the Plasmodium parasite, hold significant promise in the prevention of malaria. These vaccines are designed to inhibit the parasite before it enters the bloodstream, thereby providing an initial line of defense. Recent advancements in this segment have led to the development of candidates with higher efficacy rates. As research continues, pre-erythrocytic vaccines are expected to gain substantial traction, driven by their potential to reduce disease incidence significantly.

Erythrocytic vaccines, on the other hand, are developed to target the blood-stage of the parasite, where symptoms become apparent. This segment is crucial because it directly addresses the symptomatic phase of malaria, aiming to prevent the clinical manifestation of the disease. Although less prevalent than pre-erythrocytic vaccines, eryt

Abstract

The 2022 Cameroon Malaria Indicator Survey (2022 MIS) was implemented by the National Institute of Statistics (NIS). Data collection took place from August 22 to December 1, 2022. The survey is a national sample survey designed to provide information on topics such as availability and use of insecticide-treated nets (ITNs), prophylactic and therapeutic use of antimalarials, diagnostic testing for malaria in children presenting with fever, and the prevalence of malaria among children under age 5 (based on a rapid diagnostic test carried out at home).

The primary objective of the 2022 CMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria among pregnant women, and care seeking for and treatment of fever among children. In addition, young children were tested for anemia and for malaria. Community knowledge, perceptions, and practices regarding malaria prevention and control were also assessed.

The information collected through the 2022 CMIS is intended to help policymakers and program managers in evaluating and implementing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2022 CMIS targeted individuals in households throughout the country. A national sample of 6,580 households (3,598 in 257 urban clusters and 2,982 in 213 rural clusters) was planned for the survey. The sample was distributed to ensure adequate representation of urban and rural areas as well as the following 12 regions: Adamawa, Centre (excluding Yaoundé), Douala, East, Far North, Littoral (excluding Douala), North, North-West, West, South, South-West, and Yaoundé. In each of the regions (excluding Yaoundé and Douala, which are considered as having no rural sections), two layers were created: the urban layer and the rural layer.

A stratified, two-stage survey was implemented. In the first stage, 470 enumeration areas (EAs) or clusters were selected systematically with probability proportional to household size. The EAs were derived from the mapping work of the fourth General Census of Population and Housing (GRPH), carried out in 2017–18 by the Central Bureau of Population Censuses and Studies (BUCREP). A mapping exercise and enumeration of households in the clusters selected were implemented on tablet PCs by NIS from May 11 to August 14, 2022, to establish an updated list of households in each EA to serve as the basis for the second-degree draw. In the second stage, a sample of 14 households per cluster was selected using a systematic draw with equal probability.

All women age 15–49 who were residents of selected households or visitors who spent the night preceding the interview in the household were eligible to be interviewed. In addition, all children age 6–59 months were eligible for malaria and anemia tests.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2022 CMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires were based on standard DHS Program templates and adapted to reflect Cameroon’s specific population and malaria control needs. Information on survey data collectors was also gathered via a self-administered Fieldworker Questionnaire. All questionnaires were prepared in French and English.

Cleaning operations

In the interviews, responses were recorded directly on tablets using the appropriate computer application, developed using CSPro software. This application has several menus and includes internal controls and interview guides. Then data collected in the field were sent to the central server via the Internet using a quality control program, allowing almost instantaneous detection of the main collection errors for each team and each fieldworker. This information was immediately sent to the field teams to improve data quality, including returning to households for necessary checks. Regular activities of the chief supervisor focused mainly on teams for which there were specific concerns regarding data quality tables.

Once all of the field data were sent to the server, the survey data file was checked and cleaned and the weighting coefficients applied. All original identifiers were deleted from the data file. After checking that the data file was in its final format, the findings shown here were produced. All cover pages of the paper questionnaires containing identifiers were wiped out.

Response rate

Of the 6,580 households initially scheduled to be surveyed, 6,290 were actually selected. Of these 6,290 households, 6,080 were occupied at the time of the survey. Of the occupied households, 6,031 were successfully surveyed, for a response rate of 99%. In the surveyed households, 6,647 women age 15–49 were eligible for the individual women’s survey and 6,532 were successfully interviewed, for a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in 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, or incorrect data entry. Although numerous efforts were made during the implementation of the 2022 Cameroon Malaria Indicator Survey (2022 CMIS) 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 2022 CMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), 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 2022 CMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programs developed by ICF. These programs use the Taylor linearization method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month and region
• Positive rapid diagnostic test (RDT) results by month and region

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