Global Health Expenditure Database provides comparable data on health expenditure for 194 countries and territories since 2000 with open access to the public. WHO works collaboratively with Member States to update the database (Explore the Data) annually, using available information such as health accounts data, government expenditure records and official statistics.

IntroductionStrong and efficient institutions are vital to the development of well-functioning governments and strong societies. The term “institution building” encompasses the creation, support, development, and strengthening of organizations and institutions. Still, there is little aggregated evidence on “institution building” considering a wider system-thinking approach, best practices, or development cooperation specifically in the field of public health. In 2007, the International Association of National Public Health Institutes (IANPHI) created a guiding Framework that countries may use for developing National Public Health Institutes (NPHIs). This Framework is currently being revised.MethodsIn this context, we conducted a systematic review to facilitate this revision with recent evidence on institution building and its potential contribution to NPHI. We followed the PRISMA guidelines for systematic reviews, searching for relevant publications in seven scientific databases (Pubmed, VHL/LILACS, EconLit, Google Scholar, Web of Science, World Affairs Online, ECONBIZ) and four libraries (World Bank; European Health for All database of the World Health Organization European Region, WHO; Organization for Economic Cooperation and Development, OECD; and the African Union Common Repository). The search was carried out in October 2021. We used the “framework analysis” tool for systematically processing documents according to key themes.ResultsAs a result, we identified 3,015 records, of which we included 62 documents in the final review. This systematic review fills a major gap of aggregated information on institution building in the field of public health and National Public Health Institutes. It is to our knowledge the first systematic review of this kind. The overriding result is the identification and definition of six domains of institution building in the health sector: “governance,” “knowledge and innovation,” “inter-institutional cooperation,” “monitoring and control,” “participation,” and “sustainability and context-specific adaptability.”DiscussionOur results show that the described domains are highly relevant to the public health sector, and that managers and the scientific community recognize their importance. Still, they are often not applied consistently when creating or developing NPHIs. We conclude that organizations engaged in institution building of NPHIs, including IANPHI, may greatly benefit from state-of-the-art research on institution building as presented in this study.

IntroductionStrong and efficient institutions are vital to the development of well-functioning governments and strong societies. The term “institution building” encompasses the creation, support, development, and strengthening of organizations and institutions. Still, there is little aggregated evidence on “institution building” considering a wider system-thinking approach, best practices, or development cooperation specifically in the field of public health. In 2007, the International Association of National Public Health Institutes (IANPHI) created a guiding Framework that countries may use for developing National Public Health Institutes (NPHIs). This Framework is currently being revised.MethodsIn this context, we conducted a systematic review to facilitate this revision with recent evidence on institution building and its potential contribution to NPHI. We followed the PRISMA guidelines for systematic reviews, searching for relevant publications in seven scientific databases (Pubmed, VHL/LILACS, EconLit, Google Scholar, Web of Science, World Affairs Online, ECONBIZ) and four libraries (World Bank; European Health for All database of the World Health Organization European Region, WHO; Organization for Economic Cooperation and Development, OECD; and the African Union Common Repository). The search was carried out in October 2021. We used the “framework analysis” tool for systematically processing documents according to key themes.ResultsAs a result, we identified 3,015 records, of which we included 62 documents in the final review. This systematic review fills a major gap of aggregated information on institution building in the field of public health and National Public Health Institutes. It is to our knowledge the first systematic review of this kind. The overriding result is the identification and definition of six domains of institution building in the health sector: “governance,” “knowledge and innovation,” “inter-institutional cooperation,” “monitoring and control,” “participation,” and “sustainability and context-specific adaptability.”DiscussionOur results show that the described domains are highly relevant to the public health sector, and that managers and the scientific community recognize their importance. Still, they are often not applied consistently when creating or developing NPHIs. We conclude that organizations engaged in institution building of NPHIs, including IANPHI, may greatly benefit from state-of-the-art research on institution building as presented in this study.

JHU Has Stopped Collecting Data As Of 03/10/2023

After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations.

The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics.

Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.

Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.

On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:

• time_series_covid19_confirmed_global.csv
• time_series_covid19_deaths_global.csv
• time_series_covid19_recovered_global.csv

---DEPRECATION WARNING---
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:

• time_series_19-covid-Confirmed.csv
• time_series_19-covid-Deaths.csv
• time_series_19-covid-Recovered.csv

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger. Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time. The 2017 GHI has been calculated for 119 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators. This year's GHI reflects the most recent available country-level data and from 2012 through 2016. It, therefore, reflects the hunger levels during this period rather than solely capturing conditions in 2017. The 1992, 2000, 2008, and 2017 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available. The four component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1992, 2000, 2008, and 2017 GHI scores. Undernourishment data for the 2017 GHI are for 2014-2016. 2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; and statistical tables from UNICEF. For the 2017 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2012-2016. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1992, 2000, 2008, and 2017 GHI scores. For the 2017 GHI, data on child mortality are from 2015. Resources related to 2017 Global Hunger Index 2017 Global Hunger Index Website 2017 Global Hunger Index Linked Open Data (LOD) 2017 Global Hunger Index Report

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

Journal of Political Economy Acceptance Rate - ResearchHelpDesk - The Journal of Political Economy is a monthly peer-reviewed academic journal published by the University of Chicago Press. Established by James Laurence Laughlin in 1892, it covers both theoretical and empirical economics. In the past, the journal published quarterly from its introduction through 1905, ten issues per volume from 1906 through 1921, and bimonthly from 1922 through 2019. The editor-in-chief is Magne Mogstad (University of Chicago). Abstract & Indexing Articles that appear in the Journal of Political Economy are indexed in the following abstracting and indexing services: Ulrich's Periodicals Directory (Print) Ulrichsweb (Online) J-Gate HINARI Association for Asian Studies Bibliography of Asian Studies (Online) Business Index CABI Abstracts on Hygiene and Communicable Diseases (Online) Agricultural Economics Database CAB Abstracts (Commonwealth Agricultural Bureaux) Dairy Science Abstracts (Online) Environmental Impact Global Health Leisure Tourism Database Nutrition and Food Sciences Database Rural Development Abstracts (Online) Soil Science Database Soils and Fertilizers (Online) Tropical Diseases Bulletin (Online) World Agricultural Economics and Rural Sociology Abstracts (Online) Clarivate Analytics Current Contents Social Sciences Citation Index Web of Science De Gruyter Saur Dietrich's Index Philosophicus IBZ - Internationale Bibliographie der Geistes- und Sozialwissenschaftlichen Zeitschriftenliteratur Internationale Bibliographie der Rezensionen Geistes- und Sozialwissenschaftlicher Literatur EBSCOhost America: History and Life ATLA Religion Database (American Theological Library Association) Biography Index: Past and Present (H.W. Wilson) Book Review Digest Plus (H.W. Wilson) Business Source Alumni Edition (Full Text) Business Source Complete (Full Text) Business Source Corporate (Full Text) Business Source Corporate Plus (Full Text) Business Source Elite (Full Text) Business Source Premier (Full Text) Business Source Ultimate (Full Text) Current Abstracts EBSCO MegaFILE (Full Text) EBSCO Periodicals Collection (Full Text) EconLit with Full Text (Full Text) ERIC (Education Resources Information Center) GeoRef Historical Abstracts (Online) Humanities & Social Sciences Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Source Humanities Source Ultimate Index to Legal Periodicals Retrospective: 1908-1981 (H.W. Wilson) Legal Source Library & Information Science Source MLA International Bibliography (Modern Language Association) OmniFile Full Text Mega (H.W. Wilson) Poetry & Short Story Reference Center Political Science Complete Public Affairs Index Readers' Guide Retrospective: 1890-1982 (H.W. Wilson) Russian Academy of Sciences Bibliographies Social Sciences Abstracts Social Sciences Full Text (H.W. Wilson) Social Sciences Index Retrospective: 1907-1983 (H.W. Wilson) SocINDEX SocINDEX with Full Text TOC Premier Women's Studies International Elsevier BV GEOBASE Scopus ERIC (Education Resources Information Center) ERIC (Education Resources Information Center) Gale Academic ASAP Academic OneFile Advanced Placement Government and Social Studies Book Review Index Plus Business & Company ProFile ASAP Business ASAP Business ASAP International Business Collection Business Insights: Essentials Business Insights: Global Business, Economics and Theory Collection Expanded Academic ASAP General Business File ASAP General OneFile General Reference Center Gold General Reference Centre International InfoTrac Custom InfoTrac Student Edition MLA International Bibliography (Modern Language Association) Popular Magazines US History Collection H.W. Wilson Social Sciences Index National Library of Medicine PubMed OCLC ArticleFirst Periodical Abstracts Sociological Abstracts (Online), Selective Ovid EconLit ERIC (Education Resources Information Center) GeoRef ProQuest ABI/INFORM Collection ABI/INFORM Global (American Business Information) ABI/INFORM Research (American Business Information) Business Premium Collection EconLit ERIC (Education Resources Information Center) GeoRef Health Management Database Health Research Premium Collection Hospital Premium Collection International Bibliography of the Social Sciences, Core MLA International Bibliography (Modern Language Association) PAIS Archive Professional ABI/INFORM Complete Professional ProQuest Central ProQuest 5000 ProQuest 5000 International ProQuest Central ProQuest Pharma Collection Research Library Social Science Database Social Science Premium Collection Sociological Abstracts (Online), Selective Worldwide Political Science Abstracts, Selective SCIMP (Selective Cooperative Index of Management Periodicals) Taylor & Francis Educational Research Abstracts Online Wiley-Blackwell Publishing Asia Asian - Pacific Economic Literature (Online)

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

International Data & Economic Analysis (IDEA) is USAID's comprehensive source of economic and social data and analysis. IDEA brings together over 12,000 data series from over 125 sources into one location for easy access by USAID and its partners through the USAID public website. The data are broken down by countries, years and the following sectors: Economy, Country Ratings and Rankings, Trade, Development Assistance, Education, Health, Population, and Natural Resources. IDEA regularly updates the database as new data become available. Examples of IDEA sources include the Demographic and Health Surveys, STATcompiler; UN Food and Agriculture Organization, Food Price Index; IMF, Direction of Trade Statistics; Millennium Challenge Corporation; and World Bank, World Development Indicators. The database can be queried by navigating to the site displayed in the Home Page field below.

The purpose of the investigation is to provide information on selected venereal diseases to assess the development of the epidemiological situation in the Czech Republic, to monitor the health status of the population and to manage the health care provided. The results are forwarded to the World Health Organization and ECDC.

RPN is a continuous continuation of long-term statistical monitoring (since 1959) in the ÚZIS CR. Since 2003, RPN has been operated as a web application with a central database. Regional hygiene stations enter data into the register via an internet connection via the secure https protocol. Access to the registry and assignment of a user role is approved by the administrator.

RPN - its internet part - is established by the Ministry of Health of the Czech Republic and is part of the hygiene service information system. The hygiene service is responsible for the protection of public health and contributes to the fulfillment of Act No. 258/2000 Coll. , on the protection of public health and Decree of the Ministry of Health No. 306/2012 Coll. , on the prevention of the emergence and spread of infectious diseases and hygienic requirements for the operation of medical facilities and social care institutions.

Statistical unit of inquiry

A statistical unit is each detected sexually transmitted disease that is subject to reporting, including reinfections. The register includes all epidemiological reports on venereal disease, on death with venereal disease, suspected disease or transmission of venereal disease, and marked sources of venereal disease infection, including cases detected in foreigners.

The following diseases are subject to mandatory reporting of venereal diseases:

Congenital syphilis (A50), early syphilis (A51), late syphilis (A52), other and unspecified syphilis (A53), gonococcal infection (A54), lymphopogranuloma venereum (A55), chancroid-ulcer molle (A57).

Example African Swine Fever dataset generated by programs described in following publication

Title: Web-scraping programmatic techniques in aggregating difficult to access OIE WAHIS animal disease outbreak information; using African Swine Fever in Europe as an example.

Short running title: Methods for web-scraping OIE WAHIS data.

Abstract: This study describes and makes available new methods for acquiring difficult to access, publicly available, disease surveillance data. It uses World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in Belarus and its neighbouring European countries to showcase the importance of adequate disease surveillance data to inform decision-making. The data acquired from these methods allow for large-scale, geospatial outbreak mapping and summary statistics of any terrestrial disease listed on the OIE World Animal Health Information System (WAHIS) database. These techniques will make important epidemiological data more accessible to the scientific community and aid in gaining further insight into the occurrence and spread of OIE listed diseases in a timely manner, fulfilling an important function of disease surveillance.