This dataset contains the All India and Year-wise Malaria Cases and Deaths in India

Note: The data for 2025 is till February only.

In Ghana, around 5.2 million people were diagnosed with malaria in 2022, which was a drop from the previous year which had reported close to 5.8 million of such cases. Within the period studied, the number of confirmed cases of the illness fluctuated heavily. Moreover, malaria is reported as mostly affecting children under the age of five in Ghana. Around 5.2 million people were diagnosed with malaria in Ghana in 2022, a drop from the previous year which had close to 5.8 million such cases. Within the period studied, the number of confirmed cases of the illness fluctuated heavily. Moreover, malaria mostly affects children under the age of five in Ghana. Distinct variation in the reported number of deaths The number of diagnosed malaria cases in Ghana is high but has decreased over the years. This is accompanied by deaths from the illness, although there are significant discrepancies in value based on the methodology used. The World Health Organization (WHO) estimated that approximately 340 individuals in the country died in the same year due to the disease, whereas the Institute for Health Metrics and Evaluation (IHME) estimates that malaria killed 21,600 people. The methods used to determine the cause of death produced different estimations. For example, the IHME often relies on so-called 'verbal autopsies' to compile death-toll estimates, in contrast to the WHO, which relies on the stated cause of death. Malaria’s impact across Africa In 2022, Ghana had the eleventh-highest number of confirmed cases of malaria in Africa. By comparison, DR Congo and Nigeria had the highest number of confirmed cases that year, at about 27 million and 23 million, respectively. Although the overall count of people dying from malaria in Africa has decreased since 2010, the illness ranked seventh among the continent's top ten causes of death in 2019, causing an average of 388 deaths per 100,000 people.

The President’s Malaria Initiative (PMI) is a U.S. Government initiative designed to reduce malaria deaths and illnesses in target countries in sub-Saharan Africa with a long-term vision of a world without malaria. This data asset contains one dataset which reports 2018 summary data for the President's Malaria Initiative (PMI) VectorLink indoor residual spraying (IRS) efforts by country. Reported variables include the number of eligible structures found by the campaign in each country, the number of eligible structures sprayed by the campaign, and the number of people who sleep in those structures.

Context

Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax.

For malaria as well as other microbial infections, manual inspection of thick and thin blood smears by trained microscopists remains the gold standard for parasite detection and stage determination because of its low reagent and instrument cost and high flexibility. Despite manual inspection being extremely low throughput and susceptible to human bias, automatic counting software remains largely unused because of the wide range of variations in brightfield microscopy images. However, a robust automatic counting and cell classification solution would provide enormous benefits due to faster and more accurate quantitative results without human variability; researchers and medical professionals could better characterize stage-specific drug targets and better quantify patient reactions to drugs.

Previous attempts to automate the process of identifying and quantifying malaria have not gained major traction partly due to difficulty of replication, comparison, and extension. Authors also rarely make their image sets available, which precludes replication of results and assessment of potential improvements. The lack of a standard set of images nor standard set of metrics used to report results has impeded the field.

Content

Images are in .png or .jpg format. There are 3 sets of images consisting of 1364 images (~80,000 cells) with different researchers having prepared each one: from Brazil (Stefanie Lopes), from Southeast Asia (Benoit Malleret), and time course (Gabriel Rangel). Blood smears were stained with Giemsa reagent.

Labels

The data consists of two classes of uninfected cells (RBCs and leukocytes) and four classes of infected cells (gametocytes, rings, trophozoites, and schizonts). Annotators were permitted to mark some cells as difficult if not clearly in one of the cell classes. The data had a heavy imbalance towards uninfected RBCs versus uninfected leukocytes and infected cells, making up over 95% of all cells.

A class label and set of bounding box coordinates were given for each cell. For all data sets, infected cells were given a class label by Stefanie Lopes, malaria researcher at the Dr. Heitor Vieira Dourado Tropical Medicine Foundation hospital, indicating stage of development or marked as difficult.

Acknowledgements

Original data available from the Broad Institute Repository at https://data.broadinstitute.org/bbbc/BBBC041/

These images were contributed by Jane Hung of MIT and the Broad Institute in Cambridge, MA.

There is also a Github repository that lists malaria parasite imaging datasets (blood smears): https://github.com/tobsecret/Awesome_Malaria_Parasite_Imaging_Datasets

Published results using this image set These datasets will be evaluated in a publication to be submitted.

Recommended citation "We used image set BBBC041v1, available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]."

Copyright The images and ground truth are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License by Jane Hung.

In 2022, nearly 3.42 million cases of malaria were confirmed in Kenya. Although the number of reported infections, including presumed and confirmed cases, declined from over five million in 2019, the disease is still one of the main health issues in the country. Some 219 deaths due to malaria were registered in Kenya as of 2022.

Dataset that shows the percentage of people sleeping under a bed-net, percentage of people who had malaria or fever and the health spending per county in kenya

Data correct at time of upload (5 December 2022). Data maintained at https://www.malariagen.net/resource/34. This Figshare project provides information about the Pf7 dataset which contains genome variation data on over 20,000 worldwide samples of Plasmodium falciparum. The associated publication will be available from the above link once published.
You can browse summary data using the Pf7 data exploration tool.

Background and previous releases This dataset is based on genome variation from the MalariaGEN network, including samples which were previously released through the Pf3k Project, Plasmodium falciparum Community Project and GenRe Mekong Project. It comprises multiple partner studies, each with its own research objectives and led by a local investigator. Genome sequencing is performed centrally, and partner studies are free to analyse and publish the genetic data produced on their own samples, in line with MalariaGEN’s guiding principles on equitable data sharing. This new open dataset is almost three times larger than the last dataset release (Pf6, published 2021), and includes samples from a wider geographic reach. The variants and genotypes described in this publication used version 3 of the analysis pipeline. Data produced using an earlier version of the data analysis pipeline can be explored using an interactive web application.

About the version 7 data pipeline Details of the methods can be found in the accompanying paper.

Content of the data release This release contains details on contributing partner studies, sample metadata and key sample attributes inferred from genomic data, and genomic data including raw sequence reads. Further details and analytical results can be found in the accompanying data release paper These data are available open access. Publications using these data should acknowledge and cite the source of the data using the following format: "This publication uses MalariaGEN data as described in ‘Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples' MalariaGEN et al, (doi to be added on publication).

Study information: Details of the 82 contributing partner studies, including description, contact information and key people. Sample provenance and sequencing metadata: sample information including partner study information, location and year of collection, ENA accession numbers, and QC information for 20,864 samples from 33 countries. Measure of complexity of infections: characterisation of within-host diversity (FWS) for 16,203 QC pass samples. Drug resistance marker genotypes: genotypes at known markers of drug resistance for 16,203 samples, containing amino acid and copy number genotypes at six loci: crt, dhfr, dhps, mdr1, kelch13, plasmepsin 2-3. Inferred resistance status classification: classification of 16,203 QC pass samples into different types of resistance to 10 drugs or combinations of drugs and to RDT detection: chloroquine, pyrimethamine, sulfadoxine, mefloquine, artemisinin, piperaquine, sulfadoxine- pyrimethamine for treatment of uncomplicated malaria, sulfadoxine- pyrimethamine for intermittent preventive treatment in pregnancy, artesunate-mefloquine, dihydroartemisinin-piperaquine, hrp2 and hrp3 gene deletions. Drug resistance markers to inferred resistance status: details of the heuristics utilised to map genetic markers to resistance status classification. Genetic distances: Genetic distance matrix comparing all 20,864 samples. CRT haplotypes: Full crt gene haplotypes for 16,203 QC pass samples CSP C-terminal haplotypes:Full csp C-terminal haplotypes for 16,203 QC pass samples plus 6 lab strains. EBA175 calls: eba175 allelic type calls for 16,203 QC pass samples. Reference genome: the version of the 3D7 reference genome fasta file used for mapping. Annotation file: the version of the 3D7 reference annotation gff file used for genome annotations. Genetic distances: Genetic distance matrix comparing all 20,864 samples. (to be updated) Short variants genotypes: Genotype calls on 10,145,661 SNPs and short indels in all 20,864 samples from 33 countries, available both as VCF (to be updated) and zarr (to be updated) files.

A README file describes in fine detail all the files included in the release, the format and interpretation of each column, and contains some tips and tricks for accessing genotype data in VCF and zarr files.

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.

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

BackgroundDue to the dual critical shortages of acute care and healthcare workers in resource-limited settings, many people suffer or die from conditions that could be easily treated if existing resources were used in a more timely and effective manner. In order to address this preventable morbidity and mortality, a novel emergency midlevel provider training program was developed in rural Uganda. This is the first study that assesses this unique application of a task-shifting model to acute care by evaluating the outcomes of 10,105 patients.MethodsNurses participated in a two-year training program to become midlevel providers called Emergency Care Practitioners at a rural district hospital. This is a retrospective analysis of the Emergency Department’s quality assurance database, including three-day follow-up data. Case fatality rates (CFRs) are reported as the percentage of cases with a specific diagnosis that died within three days of their Emergency Department visit.FindingsOverall, three-day mortality was 2.0%. The most common diagnoses of patients who died were malaria (n=60), pneumonia (n=51), malnutrition (n=21), and trauma (n=18). Overall and under-five CFRs were as follows: malaria, 2.0% and 1.9%; pneumonia, 5.5% and 4.1%; and trauma, 1.2% and 1.6%. Malnutrition-related fatality (all cases

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes 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 sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

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 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

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 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise 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 2021 NMIS 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 2021 NMIS 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 programmes developed by ICF. These programmes use the Taylor linearisation 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 of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• 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.

CR: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data was reported at 8.200 % in 2018. This records a decrease from the previous number of 8.300 % for 2008. CR: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data is updated yearly, averaging 8.250 % from Dec 2008 (Median) to 2018, with 2 observations. The data reached an all-time high of 8.300 % in 2008 and a record low of 8.200 % in 2018. CR: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Data correct at time of upload (16 March 2022). Data maintained at https://www.malariagen.net/resource/30.This Figshare project provides information about data generated by the MalariaGEN Plasmodium vivax Genome Variation project (https://www.malariagen.net/parasite/p-vivax-genome-variation) using the version 4 pipeline for variant discovery and genotype calling. BACKGROUNDThe Plasmodium vivax Genome Variation Project supports groups around the world to establish the landscape of evolution in P. vivax populations and help guide informed control interventions by integrating whole genome sequencing with clinical and epidemiological studies. The Plasmodium vivax Genome Variation Project is comprised of multiple partner studies, uniting around collective ambitions for open genomic data resources, but each with their own respective research objectives and led by independent investigators all over the world. To address the need for comprehensive, large-scale genetic surveillance of P. vivax populations, we combined centralised sequencing capabilities with a standardised analysis pipeline for variant discovery and genotyping that resulted in whole genome sequencing data for partners to conduct downstream analysis in line with MalariaGEN’s guiding principles on equitable data sharing (https://www.malariagen.net/resource/1). This culminated in the open-access Plasmodium vivax Genome Variation project May 2016 data release (https://www.malariagen.net/data/p-vivax-genome-variation-may-2016-data-release) and associated analyses in Pearson et al, 2016 (https://www.nature.com/articles/ng.3599). This platform has provided a foundation to build upon for a second public data resource (v4), which sought to expand on this model to not only integrate more samples from partner studies, but to also include existing sample data generated by the wider scientific community. This is the first large-scale curation of malaria genome variation data across heterogeneous sequencing methodologies and locations, enabling community access to the largest curated dataset for epidemiological inferences across space and time, while simultaneously minimising the potential introduction of biases during the aggregation process with a standardised pipeline. This combined open resource contains a total of 1,895 samples, with the majority of all samples provided by VivaxGEN (1,025), and GlaxoSmith-Kline (GSK) (357), as well as 297 previously published samples from external studies. The data resource collectively represents 14 studies from 27 countries and 88 sampling locations, primarily between 2001-2017. Following on from the initial open data release, we have provided genomic variation data, including SNPs, indels, and tandem duplications. For ease of downstream analysis, we have also included information on population structure, calculated per-sample metrics of within-host diversity, and classified samples into four different types of drug resistance based on a limited set of published genetic markers.ABOUT THE DATA PIPELINEFull details of the methods can be found in the accompanying paper at https://www.nature.com/articles/ng.3599. The major changes from the v1 (May 2016 data release) pipeline are that we now a) map to the PvP01 reference genome rather than PvSal1 and b) use a pipeline based on current GATK best practices which is analogous to the Pf6 pipeline (https://www.malariagen.net/resource/26).CONTENTS OF THE RELEASEThis release contains details on contributing partner studies, sample metadata and key sample attributes inferred from genomic data, and genomic data including raw sequence reads. Further details and analytical results can be found in the accompanying data release paper.These data are available open access. Publications using these data should acknowledge and cite the source of the data using the following format: "This publication uses data from the MalariaGEN Plasmodium vivax Genome Variation Project as described in ‘An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples’. MalariaGEN et al, [DOI]. DATA RESOURCE1) Study information: Details of the 11 contributing partner studies, and 3 external studies, including description, contact information and key people. 2) Sample provenance and sequencing metadata: sample information including partner study information, location and year of collection, ENA accession numbers, and QC information for 1,895 samples from 27 countries. 3) Measure of complexity of infections: characterisation of within-host diversity (FWS) for 1,072 QC pass samples. 4) Drug resistance marker genotypes: genotypes at known markers of drug resistance for 1,895 samples, containing amino acid and copy number genotypes at 3 loci: dhfr, dhps, mdr1. 5) Inferred resistance status classification: classification of 1,072 QC pass samples into different types of resistance to 4 drugs or combinations of drugs: pyrimethamine, sulfadoxine, mefloquine, and sulfadoxine-pyrimethamine combination. 6) Drug resistance markers to inferred resistance status: details of the heuristics utilised to map genetic markers to resistance status classification. 7) Tandem duplication genotypes: genotypes for tandem duplications discovered in four regions of the genome.8) Genome regions and Genome regions index: a bed file (https://genome.ucsc.edu/FAQ/FAQformat.html#format1) classifying genomic regions as core genome or different classes of non-core genome in addition to tabix index file for genome regions file.9) Short variants genotypes: Genotype calls on 4,571,056 SNPs and short indels in 1,895 samples from 27 countries, available both as VCF and zarr files. These are available at: ftp://ngs.sanger.ac.uk/production/malaria/Resource/30.A README file describes in detail all the files included in the release, the format and interpretation of each column, and contains some tips and tricks for accessing the genotype data in VCF and zarr files. The VCF and zarr files in this release can be downloaded from the Wellcome Sanger Institute public FTP site using a freely available FTP client.

Tanzania HIV/Malaria Indicator Survey (THMIS) presents the proportion of the population that could sleep under an insecticide-treated net (ITN) if each ITN in the household were used by up to two people. This population is referred to as having access to an ITN. Coupled with mosquito net usage, ITN access can provide useful information on the magnitude of the behavioural gap in ITN ownership and use, or, in other words, the population with access to an ITN but not using it. If the difference between these indicators is substantial, the programme may need to focus on behaviour change and how to identify the main drivers/barriers to ITN use in order to design an appropriate intervention.Percent distribution of the de facto household population by number of ITNs the household owns, according to number of persons who stayed in the household the night before the survey.

CR: Prevalence of Severe Wasting: Weight for Height: % of Children under 5: Male data was reported at 0.100 % in 2018. CR: Prevalence of Severe Wasting: Weight for Height: % of Children under 5: Male data is updated yearly, averaging 0.100 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 0.100 % in 2018 and a record low of 0.100 % in 2018. CR: Prevalence of Severe Wasting: Weight for Height: % of Children under 5: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Health Statistics. Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

BackgroundIncreasing the distribution and use of insecticide-treated nets (ITNs) in Sub-Saharan Africa has made controlling malaria with ITNs more practical. We evaluated community effects induced by ITNs, specifically long-lasting insecticidal nets (LLINs), under ordinary conditions in an endemic malaria area of Western Kenya. MethodsUsing the database from Mbita Health and Demographic Surveillance System (HDSS), children younger than 5 years old were assessed over four survey periods. We analyzed the effect of bed net usage, LLIN density and population density of young people around a child on all-cause child mortality (ACCM) rates using Cox PH models. ResultsDuring the study, 14,554 children were followed and 250 deaths were recorded. The adjusted hazard ratios (HRs) for LLIN usage compared with no net usage were not significant among the models: 1.08 (95%CI 0.76–1.52), 1.19 (95%CI 0.69–2.08) and 0.92 (95%CI 0.42–2.02) for LLIN users, untreated net users, and any net users, respectively. A significant increasing linear trend in risk across LLIN density quartiles (HR = 1.25; 95%CI 1.03–1.51) and a decreasing linear trend in risk across young population density quartiles among non-net user children (HR = 0.77; 95%CI 0.63–0.94) were observed. ConclusionsAlthough our data showed that current LLIN coverage level (about 35%) could induce a community effect to protect children sleeping without bed nets even in a malaria-endemic area, it appears that a better system is needed to monitor the current malaria situation globally in order to optimize malaria control programs with limited resources.