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.

Data

The dataset contains 2 folders

• Test
• Train

In Those two folder we have 2 folders - Infected - Uninfected

• there are total 550 images

Inspiration

Save humans by detecting and deploying Image Cells that contain Malaria or not!

preview of data

https://www.mdpi.com/asi/asi-04-00082/article_deploy/html/images/asi-04-00082-g001-550.jpg" alt="">

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.

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.

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

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.

The data shows the number of Malaria Cases in children of age 0-5 years in yearly distributions in different states of India . Note:-(1)Malaria is a disease caused by a parasite. The parasite is spread to humans through the bites of infected mosquitoes. People who have malaria usually feel very sick with a high fever and shaking chills.

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.

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

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 Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

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

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 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

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.

Context

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs). DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise)

• 01. Country/Territory - Name of the Country/Territory
• 02. Code - Country/Territory Code
• 03. Year - Year of the Incident
• 04. Meningitis - No. of People died from Meningitis
• 05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
• 06. Parkinson's Disease - No. of People died from Parkinson's Disease
• 07. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
• 08. Malaria - No. of People died from Malaria
• 09. Drowning - No. of People died from Drowning
• 10. Interpersonal Violence - No. of People died from Interpersonal Violence
• 11. Maternal Disorders - No. of People died from Maternal Disorders
• 12. Drug Use Disorders - No. of People died from Drug Use Disorders
• 13. Tuberculosis - No. of People died from Tuberculosis
• 14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
• 15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
• 16. Neonatal Disorders - No. of People died from Neonatal Disorders
• 17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
• 18. Self-harm - No. of People died from Self-harm
• 19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
• 20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
• 21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
• 22. Neoplasms - No. of People died from Neoplasms
• 23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
• 24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
• 25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
• 26. Poisonings - No. of People died from Poisoning
• 27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
• 28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
• 29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
• 30. Digestive Diseases - No. of People died from Digestive Diseases
• 31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
• ...

BackgroundThis study aimed to synthesize available evidence on the efficacy of dihydroartemisinin-piperaquine (DHP) in treating uncomplicated Plasmodium vivax malaria in people living in endemic countries. Methodology and Principal FindingsThis is a meta-analysis of randomized controlled trials (RCT). We searched relevant studies in electronic databases up to May 2013. RCTs comparing efficacy of (DHP) with other artemisinin-based combination therapy (ACT), non-ACT or placebo were selected. The primary endpoint was efficacy expressed as PCR-corrected parasitological failure. Efficacy was pooled by hazard ratio (HR) and 95% CI, if studies reported time-to-event outcomes by the Kaplan-Meier method or data available for calculation of HR Nine RCTs with 14 datasets were included in the quantitative analysis. Overall, most of the studies were of high quality. Only a few studies compared with the same antimalarial drugs and reported the outcomes of the same follow-up duration, which created some difficulties in pooling of outcome data. We found the superiority of DHP over chloroquine (CQ) (at day > 42-63, HR:2.33, 95% CI:1.86-2.93, I2: 0%) or artemether-lumefentrine (AL) (at day 42, HR:2.07, 95% CI:1.38-3.09, I2: 39%). On the basis of GRADE criteria, further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Discussion/ConclusionFindings document that DHP is more efficacious than CQ and AL in treating uncomplicated P. vivax malaria. The better safety profile of DHP and the once-daily dosage improves adherence, and its fixed co-formulation ensures that both drugs (dihydroartemisinin and piperaquine) are taken together. However, DHP is not active against the hypnozoite stage of P. vivax. DHP has the potential to become an alternative antimalarial drug for the treatment uncomplicated P. vivax malaria. This should be substantiated by future RCTs with other ACTs. Additional work is required to establish how best to combine this treatment with appropriate antirelapse therapy (primaquine or other drugs under development).

WHO Malaysia Health Indicators

Malaria, HIV/STIs, Suicide, CVD, Mortality, and more

By Humanitarian Data Exchange [source]

About this dataset

This dataset contains a range of indicators related to health, health systems, and sustainable development from the World Health Organization's data portal. It covers topics ranging from mortality and global health estimates to essential health technologies, youth engagement, mental health initiatives, and infectious diseases. With data points including publich state codes and display values, this dataset provides detailed insight into how healthcare is managed all around the globe. From tracking malaria outbreaks to exploring various international agreements on public healthcare initiatives, this dataset offers a wide array of powerful information for machine learning projects that are designed to improve our understanding of global healthcare trends. Explore the correlations between different countries' universal healthcare coverage measures or investigate any discrepancies between developed and developing nations - unlock deeper insights with the WHO's extensive data!

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How to use the dataset

Getting Started: First, you need to download the dataset from Kaggle. Once you have it saved in your computer, open it with a spreadsheet software such as Excel or Google Sheets.

Exploring the Data: The dataset contains columns that offer information about indicators related to health in Malaysia including mortality rates, prevention programs and providers, financing information, human resource information, and more. To explore particular aspects of this data you should filter the rows using any of these column values. For example if you want results for a specific year or region you can filter by ‘year’ or ‘region’ accordingly. It’s important to note that some columns have relation between them (e.g., country code corresponds with country display name).

Data Outputs:
Using this dataset allows users to generate visual representations such as graphs which can help display trends over time regarding our stability goals concerning human resources funding rates or pregnancies outcomes among other variables included in our report summary outputs on WHO dashboard at global level specifically representing data coming from our members countries likeMalaysia making sense out these actions performed by several governments highlights where we still have areas lacking risk mitigation efforts and core elements when tryingto achieve better life quality around world aiming better efficiency through good governance practices supported on demand reduction strategies coming from healthcare professionals expertise frame work .

Conclusion:

Research Ideas

• Analysis of health coverage and services in Malaysia, allowing comparison between different public health organizations and the effect of specific prevention programs.
• Identification of gaps between existing healthcare access and provide a standardized data-driven reference point to ensure equitable access across different regions in the country.
• Creation of interactive geographical dashboards that display comparisons among relevant indicators, providing visual representation on how to best target distribution resources for optimal impact

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rsud-service-organization-and-delivery-prevention-programs-and-providers-indicators-for-malaysia-38.csv | Column name | Description | |:--------------------------------------|:----------------------------------------------------------------| | GHO (CODE) | The Global Health Observatory code for the indicator. (String) | | GHO (DISPLAY) | The name of the indicator. (String) | | GHO (URL) | The URL for the indicator. (URL) | | PUBLISHSTATE (CODE) | The code for the publishing state of the indicator. (String) | | PUBLISHSTATE (DISPLAY) | The name of the publishing state of the indicator. (String) | | PUBLISHSTATE (URL) | The URL for the publishing state of the indicator. (URL) | | YEAR (CODE) | The code for...

About Dataset

Context:

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs).

DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content:

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise):

1. Country/Territory - Name of the Country/Territory
2. Code - Country/Territory Code
3. Year - Year of the Incident
4. Meningitis - No. of People died from Meningitis
5. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
6. Parkinson's Disease - No. of People died from Parkinson's Disease
7. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
8. Malaria - No. of People died from Malaria
9. Drowning - No. of People died from Drowning
10. Interpersonal Violence - No. of People died from Interpersonal Violence
11. Maternal Disorders - No. of People died from Maternal Disorders
12. Drug Use Disorders - No. of People died from Drug Use Disorders
13. Tuberculosis - No. of People died from Tuberculosis
14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
16. Neonatal Disorders - No. of People died from Neonatal Disorders
17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
18. Self-harm - No. of People died from Self-harm
19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
22. Neoplasms - No. of People died from Neoplasms
23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
26. Poisonings - No. of People died from Poisoning
27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
30. Digestive Diseases - No. of People died from Digestive Diseases
31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
32. Acute Hepatitis - No. of People died from Acute Hepatitis Structure of the Dataset

Acknowledgement:

This Dataset is created from Our World in Data. This Dataset falls under open access under the Creative Commons BY license. You can check the FAQ for more informa...

BackgroundMalaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010. MethodsPlasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006–2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985–2009). After quality control, 2,516 were included into a national database of age-standardized 2–10 year old PfPR data (PfPR2–10) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR2–10 endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface. ResultsWe estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas. ConclusionWhile further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies.