Analysis of ‘Malaria Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imdevskp/malaria-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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.

Malaria is a life-threatening disease that kills over 400,000 people each year and affects an estimated 229 million people worldwide. Sub-Saharan Africa bears the greatest health, social and economic impact of malaria, with approximately 370,000 deaths in 2020. It is estimated that approximately 3 billion USD is spent annually on malaria control. We aim to design novel inhibitors for the malaria parasite UAP enzyme that could potentially block the transmission of the parasite from mosquitoes to humans. We will begin by determining the structure of the malaria parasite UAP enzyme by X-ray crystallography or cryogenic electron microscopy

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.

Context:

Understanding the distribution of anopheline vectors of malaria is an important prelude to the design of national malaria control and elimination programmes. A single, geo-coded continental inventory of anophelines using all available published and unpublished data has not been undertaken since the 1960s. We present the largest ever geo-coded database of anophelines in Africa representing a legacy dataset for future updating and identification of knowledge gaps at national levels. The geo-coded and referenced database is made available with the related publication as a reference source for African national malaria control programmes planning their future control and elimination strategies. Information about the underlying research studies can be found at http://kemri-wellcome.org/programme/population-health/.

Content:

Geocoded info on anopheline inventory. See key below.

Acknowledgements:

KEMRI-Wellcome Trust assembled the data and distributed it on Dataverse.

Inspiration:

• Where have malarial mosquito populations grown or decreased?
• Can you predict mosquito population growth trends?
• Do you seen any correlation between mosquito populations and malaria deaths from this dataset?
• Is the banner image mosquito capable of carrying malaria?

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.

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.

There exists great disparity in the number of clinical P. falciparum episodes among children of the same age and living in similar conditions. The epidemiological determinants of such disparity are unclear. We used a data-mining approach to explore a nineteen-year longitudinal malaria cohort study dataset from Senegal and identify variables associated with increased risk of malaria episodes. These were then verified using classical statistics and replicated in a second cohort. In addition to age, we identified a novel high-risk group of children in whom the history of P. falciparum clinical episodes greatly increased risk of further episodes. Age and a high number of previous falciparum clinical episodes not only play major roles in explaining the risk of P. falciparum episodes but also are risk factors for different groups of people. Combined, they explain the majority of falciparum clinical attacks. Contrary to what is widely believed, clinical immunity to P. falciparum does not de facto occur following many P. falciparum clinical episodes. There exist a sub-group of children who suffer repeated clinical episodes. In addition to posing an important challenge for population stratification during clinical trials, this sub-group disproportionally contributes to the disease burden and may necessitate specific prevention and control measures.

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

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.

Background:

• Malaria is caused by small germs carried by mosquitoes. People can get malaria if an infected mosquito bites them. Malaria destroys red blood cells and reduces oxygen in the blood. Most malaria is mild, but severe malaria kills at least 660,000 people each year. About 75% of these are children in Sub-Saharan Africa, most under age 5. Researchers want to find a safe vaccine that helps prevent malaria.

  Objectives:

• To see if a new malaria vaccine is well tolerated and effective.

  Eligibility:

• Healthy adults 18 35 years old who are not pregnant and live in Mali.

  Design:

  • Participants will be screened with medical history, physical exam, and blood test. They will also have an ECG. Soft electrodes will be stuck to the skin. A machine will record the heart s electrical signals.

  • Study participation will last about 1 year.

  • Participants will be randomly placed in 5 groups. Some will get 2 doses of the PfSPZ vaccine weeks apart; some will get 3 or 5 doses of vaccine; some will get 3 or 5 doses of placebo.

  • Doses will be given through a needle in the arm directly into the bloodstream. Then participants must stay at the clinic for 2 hours.

  • After each dose, participants will return to the clinic several times for blood tests and physical exam.

  • A week before the first dose and 2 weeks after the last, participants will take a full course of anti-malaria drugs.

  • If a participant gets malaria during the study, they will take another course of anti-malaria drugs.

Goal 3Ensure healthy lives and promote well-being for all at all agesTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsIndicator 3.1.1: Maternal mortality ratioSH_STA_MORT: Maternal mortality ratioIndicator 3.1.2: Proportion of births attended by skilled health personnelSH_STA_BRTC: Proportion of births attended by skilled health personnel (%)Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsIndicator 3.2.1: Under-5 mortality rateSH_DYN_IMRTN: Infant deaths (number)SH_DYN_MORT: Under-five mortality rate, by sex (deaths per 1,000 live births)SH_DYN_IMRT: Infant mortality rate (deaths per 1,000 live births)SH_DYN_MORTN: Under-five deaths (number)Indicator 3.2.2: Neonatal mortality rateSH_DYN_NMRTN: Neonatal deaths (number)SH_DYN_NMRT: Neonatal mortality rate (deaths per 1,000 live births)Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesIndicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsSH_HIV_INCD: Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population)Indicator 3.3.2: Tuberculosis incidence per 100,000 populationSH_TBS_INCD: Tuberculosis incidence (per 100,000 population)Indicator 3.3.3: Malaria incidence per 1,000 populationSH_STA_MALR: Malaria incidence per 1,000 population at risk (per 1,000 population)Indicator 3.3.4: Hepatitis B incidence per 100,000 populationSH_HAP_HBSAG: Prevalence of hepatitis B surface antigen (HBsAg) (%)Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseasesSH_TRP_INTVN: Number of people requiring interventions against neglected tropical diseases (number)Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingIndicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseSH_DTH_NCOM: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)SH_DTH_NCD: Number of deaths attributed to non-communicable diseases, by type of disease and sex (number)Indicator 3.4.2: Suicide mortality rateSH_STA_SCIDE: Suicide mortality rate, by sex (deaths per 100,000 population)SH_STA_SCIDEN: Number of deaths attributed to suicide, by sex (number)Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholIndicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disordersSH_SUD_ALCOL: Alcohol use disorders, 12-month prevalence (%)SH_SUD_TREAT: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%)Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholSH_ALC_CONSPT: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidentsIndicator 3.6.1: Death rate due to road traffic injuriesSH_STA_TRAF: Death rate due to road traffic injuries, by sex (per 100,000 population)Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmesIndicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methodsSH_FPL_MTMM: Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age groupSP_DYN_ADKL: Adolescent birth rate (per 1,000 women aged 15-19 years)Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for allIndicator 3.8.1: Coverage of essential health servicesSH_ACS_UNHC: Universal health coverage (UHC) service coverage indexIndicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or incomeSH_XPD_EARN25: Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%)SH_XPD_EARN10: Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%)Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationIndicator 3.9.1: Mortality rate attributed to household and ambient air pollutionSH_HAP_ASMORT: Age-standardized mortality rate attributed to household air pollution (deaths per 100,000 population)SH_STA_AIRP: Crude death rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_STA_ASAIRP: Age-standardized mortality rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_AAP_MORT: Crude death rate attributed to ambient air pollution (deaths per 100,000 population)SH_AAP_ASMORT: Age-standardized mortality rate attributed to ambient air pollution (deaths per 100,000 population)SH_HAP_MORT: Crude death rate attributed to household air pollution (deaths per 100,000 population)Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)SH_STA_WASH: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (deaths per 100,000 population)Indicator 3.9.3: Mortality rate attributed to unintentional poisoningSH_STA_POISN: Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population)Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriateIndicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and olderSH_PRV_SMOK: Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%)Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allIndicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeSH_ACS_DTP3: Proportion of the target population with access to 3 doses of diphtheria-tetanus-pertussis (DTP3) (%)SH_ACS_MCV2: Proportion of the target population with access to measles-containing-vaccine second-dose (MCV2) (%)SH_ACS_PCV3: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (%)SH_ACS_HPV: Proportion of the target population with access to affordable medicines and vaccines on a sustainable basis, human papillomavirus (HPV) (%)Indicator 3.b.2: Total net official development assistance to medical research and basic health sectorsDC_TOF_HLTHNT: Total official development assistance to medical research and basic heath sectors, net disbursement, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_HLTHL: Total official development assistance to medical research and basic heath sectors, gross disbursement, by recipient countries (millions of constant 2018 United States dollars)Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basisSH_HLF_EMED: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (%)Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesIndicator 3.c.1: Health worker density and distributionSH_MED_DEN: Health worker density, by type of occupation (per 10,000 population)SH_MED_HWRKDIS: Health worker distribution, by sex and type of occupation (%)Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risksIndicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparednessSH_IHR_CAPS: International Health Regulations (IHR) capacity, by type of IHR capacity (%)Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organismsiSH_BLD_MRSA: Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose

Legend: The first column presents transmission settings. The second column presents the total estimated malaria deaths in the all-ages population of 100,000 people per person-year in the absence of vaccination. The estimate is the average over a 10 year period. The remaining columns present the percentage of deaths averted by each delivery strategy, as the median and (range) estimated from 14 models.

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.

Created on 29 Sep 2024 - 16:28 by Lee HainesThe project describes a potential method for reducing the spread of diseases carried by insects, specifically mosquitoes that transmit malaria. The method involves using drugs that make the blood of animals or humans toxic to insects that feed on blood. The key point is that a specific enzyme called 4- hydroxyphenylpyruvate dioxygenase, HPPD, which is important for blood-feeding insects like mosquitoes, can be targeted. A drug called nitisinone, which is already approved by the FDA to treat rare human diseases linked to tyrosine metabolism, can inhibit this enzyme.We further characterise nitisinone's activity - when mosquitoes were fed human blood containing nitisinone, it killed both young and old mosquitoes, and those resistant to other insecticides. In side by side comparisons to another similar drug named ivermectin, nitisinone had a better killing profile for mosquitoes. Additionally, people with a rare genetic condition called alkaptonuria, who therapeutically ingest a low daily dose of nitisinone (2 mg/day), have blood that kills mosquitoes. This suggests that using nitisinone to inhibit this enzyme could be a new way to help control malaria.

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.

Abstract

The Nanoro Health and Demographic Surveillance System (HDSS) was established in 2009 by the Clinical Research Unit of Nanoro – Institut de Recherche en Sciences de la Santé (IRSS-CRUN) with the aim of providing a core framework for clinical trials and also to support the Burkina Faso health authorities in generating epidemiological data that can contribute to the setup and assessment of health interventions. This is achieved by providing an excellent platform for generating epidemiological data fully compliant with international standards. The site activities are currently oriented towards the research (drugs and vaccines) on diseases of public health importance with a specific focus on malaria.

Nanoro is a rural area located in the Centre-West of the country, at approximately 85 Km from the capital city, Ouagadougou. The HDSS area lies between longitudes 1°92537 and 2°3146 W and latitudes 12°57955 and 12°72863 N and covers 24 villages which represents a surface of 594.3 Km2 (~36% of the Nanoro Health District area). In this area, health care is provided by seven peripheral health posts and one referral hospital. Nanaro HDSS is in the Sudano–Sahelian climate which has two main seasons: a rainy season from June to October (average rainfall of 450–700 mm/year, average temperature >30ºC) followed by a dry season from November to May (the temperature may vary from 17ºC in December to a maximum of 43ºC in April).

The population under surveillance is about 63,395 residents (in 2011) with a majority of illiterate people. They are subsistence farmers, cattle-keepers and housewives. The main ethnic groups are Mossi, Gourounsi and Fulani.

Geographic coverage

Nanoro is a rural area located in the Centre-West of the country, at approximately 85 Km from the capital city, Ouagadougou. The HDSS area lies between longitudes 1°92537 and 2°3146 W and latitudes 12°57955 and 12°72863 N and covers 24 villages which represents a surface of 594.3 Km2 (~36% of the Nanoro Health District area). In this area, health care is provided by seven peripheral health posts and one referral hospital.

Analysis unit

Individual

Universe

All individual residents in the HDSS

Kind of data

Event history data

Frequency of data collection

3 round for year

Sampling procedure

No sampling is done

Sampling deviation

Not Applicable

Mode of data collection

Proxy Respondent [proxy]

Research instrument

The questionnaires are designed by the Demographer in collaboration with the other member of the HDSS team. They contain the household questionnaire, the compound questionnaire and all the event forms needed for the data capture concerning the events (marriage, pregnancy, birth, in/out-migration, death, verbal autopsy…) which must be registered during the round. After the validation of the questionnaires, the data management team make the copy of each questionnaire for the field team. Then, they print the household register by cluster for the field teambefore starting the next round.

Cleaning operations

The forms are controlled by supervisor before moving for data entry.

Response rate

No refusals

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate BF021 MicroDataCleaned Starts 95866 2017-05-18 13:44
BF021 MicroDataCleaned Transitions 0 230712 230712 0 2017-05-18 13:44
BF021 MicroDataCleaned Ends 95866 2017-05-18 13:44
BF021 MicroDataCleaned SexValues 230712 2017-05-18 13:44
BF021 MicroDataCleaned DoBValues 230712 2017-05-18 13:45

Abstract

The 2005 Guyana HIV/AIDS Indicator Survey (GAIS) is the first household-based, comprehensive survey on HIV/AIDS to be carried out in Guyana. The 2005 GAIS was implemented by the Guyana Responsible Parenthood Association (GRPA) for the Ministry of Health (MoH). ORC Macro of Calverton, Maryland provided technical assistance to the project through its contract with the U.S. Agency for International Development (USAID) under the MEASURE DHS program. Funding to cover technical assistance by ORC Macro and for local costs was provided in their entirety by USAID/Washington and USAID/Guyana.

The 2005 GAIS is a nationally representative sample survey of women and men age 15-49 initiated by MoH with the purpose of obtaining national baseline data for indicators on knowledge/awareness, attitudes, and behavior regarding HIV/AIDS. The survey data can be effectively used to calculate valuable indicators of the President’s Emergency Plan for AIDS Relief (PEPFAR), the Joint United Nations Program on HIV/AIDS (UNAIDS), the United Nations General Assembly Special Session (UNGASS), the United Nations Children Fund (UNICEF) Orphan and Vulnerable Children unit (OVC), and the World Health Organization (WHO), among others. The overall goal of the survey was to provide program managers and policymakers involved in HIV/AIDS programs with information needed to monitor and evaluate existing programs; and to effectively plan and implement future interventions, including resource mobilization and allocation, for combating the HIV/AIDS epidemic in Guyana.

Other objectives of the 2005 GAIS include the support of dissemination and utilization of the results in planning, managing and improving family planning and health services in the country; and enhancing the survey capabilities of the institutions involved in order to facilitate the implementation of surveys of this type in the future.

The 2005 GAIS sampled over 3,000 households and completed interviews with 2,425 eligible women and 1,875 eligible men. In addition to the data on HIV/AIDS indicators, data on the characteristics of households and its members, malaria, infant and child mortality, tuberculosis, fertility, and family planning were also collected.

Geographic coverage

National

Analysis unit

• Individuals;
• Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the 2005 GAIS is to provide estimates with acceptable precision for important population characteristics such as HIV/AIDS related knowledge, attitudes, and behavior. The population to be covered by the 2005 GAIS was defined as the universe of all women and men age 15-49 in Guyana.

The major domains to be distinguished in the tabulation of important characteristics for the eligible population are: • Guyana as a whole • The urban area and the rural area each as a separate major domain • Georgetown and the remainder urban areas.

Administratively, Guyana is divided into 10 major regions. For census purposes, each region is further subdivided in enumeration districts (EDs). Each ED is classified as either urban or rural. There is a list of EDs that contains the number of households and population for each ED from the 2002 census. The list of EDs is grouped by administrative units as townships. The available demarcated cartographic material for each ED from the last census makes an adequate sample frame for the 2005 GAIS.

The sampling design had two stages with enumeration districts (EDs) as the primary sampling units (PSUs) and households as the secondary sampling units (SSUs). The standard design for the GAIS called for the selection of 120 EDs. Twenty-five households were selected by systematic random sampling from a full list of households from each of the selected enumeration districts for a total of 3,000 households. All women and men 15-49 years of age in the sample households were eligible to be interviewed with the individual questionnaire.

The database for the recently completed 2002 Census was used as a sampling frame to select the sampling units. In the census frame, EDs are grouped by urban-rural location within the ten administrative regions and they are also ordered in each administrative unit in serpentine fashion. Therefore, this stratification and ordering will be also reflected in the 2005 GAIS sample.

Based on response rates from other surveys in Guyana, around 3,000 interviews of women and somewhat fewer of men expected to be completed in the 3,000 households selected.

Several allocation schemes were considered for the sample of clusters for each urban-rural domain. One option was to allocate clusters to urban and rural areas proportionally to the population in the area. According to the census, the urban population represents only 29 percent of the population of the country. In this case, around 35 clusters out of the 120 would have been allocated to the urban area. Options to obtain the best allocation by region were also examined. It should be emphasized that optimality is not guaranteed at the regional level but the power for analysis is increased in the urban area of Georgetown by departing from proportionality. Upon further analysis of the different options, the selection of an equal number of clusters in each major domain (60 urban and 60 rural) was recommended for the 2005 GAIS. As a result of the nonproportionalallocation of the number of EDs for the urban-rural and regional domains, the household sample for the 2005 GAIS is not a self-weighted sample.

The 2005 GAIS sample of households was selected using a stratified two-stage cluster design consisting of 120 clusters. The first stage-units (primary sampling units or PSUs) are the enumeration areas used for the 2002 Population and Housing Census. The number of EDs (clusters) in each domain area was calculated dividing its total allocated number of households by the sample take (25 households for selection per ED). In each major domain, clusters are selected systematically with probability proportional to size.

The sampling procedures are more fully described in "Guyana HIV/AIDS Indicator Survey 2005 - Final Report" pp.135-138.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey, namely: the Household Questionnaire and the Individual Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS program. In consultation with USAID/Guyana, MoH, GRPA, and other government agencies and local organizations, the model questionnaires were modified to reflect issues relevant to HIV/AIDS in Guyana. The questionnaires were finalized around mid-May.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. For each person listed, information was collected on sex, age, education, and relationship to the head of the household. An important purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview.

The Household Questionnaire also collected non-income proxy indicators about 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; and ownership of various durable goods and land. As part of the Malaria Module, questions were included on ownership and use of mosquito bednets.

The Individual Questionnaire was used to collect information from women and men age 15-49 years and covered the following topics: • Background characteristics (age, education, media exposure, employment, etc.) • Reproductive history (number of births and—for women—a birth history, birth registration, current pregnancy, and current family planning use) • Marriage and sexual activity • Husband’s background • Knowledge about HIV/AIDS and exposure to specific HIV-related mass media programs • Attitudes toward people living with HIV/AIDS • Knowledge and experience with HIV testing • Knowledge and symptoms of other sexually transmitted infections (STIs) • The malaria module and questions on tuberculosis

Cleaning operations

The processing of the GAIS questionnaires began in mid-July 2005, shortly after the beginning of fieldwork and during the first visit of the ORC Macro data processing specialist. Questionnaires for completed clusters (enumeration districts) were periodically submitted to GRPA offices in Georgetown, where they were edited by data processing personnel who had been trained specifically for this task. The concurrent processing of the data—standard for surveys participating in the DHS program—allowed GRPA to produce field-check tables to monitor response rates and other variables, and advise field teams of any problems that were detected during data entry. All data were entered twice, allowing 100 percent verification. Data processing, including data entry, data editing, and tabulations, was done using CSPro, a program developed by ORC Macro, the U.S. Bureau of Census, and SERPRO for processing surveys and censuses. The data entry and editing of the questionnaires was completed during a second visit by the ORC Macro specialist in mid-September. At this time, a clean data set was produced and basic tables with the basic HIV/AIDS indicators were run. The tables included in the current report were completed by the end of November 2005.

Response rate

• From a total of 3,055 households in the sample, 2,800 were occupied. Among these households, interviews were completed in 2,608, for a response rate of 93 percent. • A total of 2,776 eligible women were identified and

BackgroundThe emergence and spread of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion (GMS) threatens global malaria elimination efforts. Mass drug administration (MDA), the presumptive antimalarial treatment of an entire population to clear the subclinical parasite reservoir, is a strategy to accelerate malaria elimination. We report a cluster randomised trial to assess the effectiveness of dihydroartemisinin-piperaquine (DP) MDA in reducing falciparum malaria incidence and prevalence in 16 remote village populations in Myanmar, Vietnam, Cambodia, and the Lao People’s Democratic Republic, where artemisinin resistance is prevalent.Methods and findingsAfter establishing vector control and community-based case management and following intensive community engagement, we used restricted randomisation within village pairs to select 8 villages to receive early DP MDA and 8 villages as controls for 12 months, after which the control villages received deferred DP MDA. The MDA comprised 3 monthly rounds of 3 daily doses of DP and, except in Cambodia, a single low dose of primaquine. We conducted exhaustive cross-sectional surveys of the entire population of each village at quarterly intervals using ultrasensitive quantitative PCR to detect Plasmodium infections. The study was conducted between May 2013 and July 2017. The investigators randomised 16 villages that had a total of 8,445 residents at the start of the study. Of these 8,445 residents, 4,135 (49%) residents living in 8 villages, plus an additional 288 newcomers to the villages, were randomised to receive early MDA; 3,790 out of the 4,423 (86%) participated in at least 1 MDA round, and 2,520 out of the 4,423 (57%) participated in all 3 rounds. The primary outcome, P. falciparum prevalence by month 3 (M3), fell by 92% (from 5.1% [171/3,340] to 0.4% [12/2,828]) in early MDA villages and by 29% (from 7.2% [246/3,405] to 5.1% [155/3,057]) in control villages. Over the following 9 months, the P. falciparum prevalence increased to 3.3% (96/2,881) in early MDA villages and to 6.1% (128/2,101) in control villages (adjusted incidence rate ratio 0.41 [95% CI 0.20 to 0.84]; p = 0.015). Individual protection was proportional to the number of completed MDA rounds. Of 221 participants with subclinical P. falciparum infections who participated in MDA and could be followed up, 207 (94%) cleared their infections, including 9 of 10 with artemisinin- and piperaquine-resistant infections. The DP MDAs were well tolerated; 6 severe adverse events were detected during the follow-up period, but none was attributable to the intervention.ConclusionsAdded to community-based basic malaria control measures, 3 monthly rounds of DP MDA reduced the incidence and prevalence of falciparum malaria over a 1-year period in areas affected by artemisinin resistance. P. falciparum infections returned during the follow-up period as the remaining infections spread and malaria was reintroduced from surrounding areas. Limitations of this study include a relatively small sample of villages, heterogeneity between villages, and mobility of villagers that may have limited the impact of the intervention. These results suggest that, if used as part of a comprehensive, well-organised, and well-resourced elimination programme, DP MDA can be a useful additional tool to accelerate malaria elimination.Trial registrationClinicalTrials.gov NCT01872702

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.