CPSC's epidemiological data include reports of incidents involving death, injury, or potential injury that are associated with consumer products. The online Clearinghouse posts summary information from death certificates (DTHS), medical examiner reports (MECAP reports), reports published on Saferproducts.gov, Newsclips, and other submissions from consumers, healthcare professionals, state, federal, and local agencies (IPII), and public safety entities.

This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.

Data and code repository for the Open COVID-19 Data Working Group: a global and multi-organizational initative that aims to enable rapid sharing of trusted and open public health data to advance the response to infectious diseases.

The Comprehensive Epidemiologic Data Resource (CEDR) is the U.S. Department of Energy’s (DOE) electronic database comprised of health studies of DOE contract workers and environmental studies of areas surrounding DOE facilities. DOE recognizes the benefits of data sharing and supports the public’s right to know about worker and community health risks. CEDR provides independent researchers and the public with access to de-identified data collected since the Department’s early production years. CEDR’s holdings include more than 80 studies of more than one million workers. CEDR is a national user facility, with a large audience for data that are not available elsewhere.

Most of CEDR’s holdings are derived from epidemiologic studies of DOE workers at many large nuclear weapons plants, such as Hanford, Los Alamos, Oak Ridge, Savannah River Site, and Rocky Flats. These studies primarily use death certificate information to identify excess deaths and patterns of disease among workers to determine what factors contribute to the risk of developing cancer and other illnesses. In addition, many of these studies have radiation exposure measurements on individual workers. Other CEDR collections include historical dose reconstruction studies of past offsite radiologic and chemical exposures around the nuclear weapons facilities. Now a mature system in routine operational use, CEDR’s modern, Internet-based systems respond to thousands of requests to its Web server daily.

CEDR’s library of information, reports, journal articles, and data includes nearly 10,000 citations/documents. CEDR’s bibliographic search feature allows the user to select citations or publications associated with the studies found in the CEDR library.

CEDR’s data collection -- There are two types of data derived from epidemiologic studies:

1) Analytic data files: contain the data that a researcher directly used in conducting the analyses and result in reported findings or publication in a peer-reviewed journal. CEDR’s holdings include more than 200 analytic files.

2) Working data files: files that contain the raw or unedited data from which a researcher selected variables to form an initial analytic data file set. The data in the working data files may contain errors; as such, it is recommended that they be analyzed and results interpreted with caution. There are more than 100 working data files in CEDR’s holdings.

Percentages are relative to the number of primary PCD research studies.

The 2017 nucleic acid and amino acid sequence data for enteroviruses and influenza viruses, as well as corresponding epidemiological data. It can be presented by the counties and cities within the country. The sequence data can facilitate further research by domestic and external researchers.

A collection of data of an epidemiological study of chronic disease in the oldest old based on information collected from Kaiser Permanente facilities in Northern California (KPNC). The initial sample was drawn from the Kaiser''s active membership lists for the years 1971 and 1980. The sample was restricted to members that had a Multiphasic Health Checkup examination (MHC) within 7 years of the baseline date. The sample was stratified to attain equal numbers of observations (1,000 in each) in three sex-age cells for each cohort: 65-69, 70-79, and 80+. Each cohort was followed for 9 years through existing medical records and computerized hospitalization tapes. Mortality data was collected by matching the sampled data with state Vital Statistics data for an additional 3 years for a total follow-up time of 12 years. Part 1 of the data collections consists of Master Records, which includes information from the morbidity review, in which over 35 chronic conditions or diagnoses were abstracted from the member charts, as well as detailed diagnostic criteria for the major conditions. A prevalence review was done, which included the 4 years prior to the baseline date for these same conditions. Recurrent disease is included for the following conditions: cancers, myocardial infarction, and various forms of strokes. A detailed account of outpatient health services use, and data from the multiphasic health checkup, which was administered to each participant during the nine yearly follow-ups, are also included in the Master Records file. The labs and procedures included: chemistry, hematology, urinalysis, bacteriology, chest x-ray, GI x-ray, ultrasound, CT/MRI, mammogram, resting ECG, treadmill ECG, echocardiograms, nuclear scans, outpatient breast biopsy, cystoscopy, and cataract surgery. Inpatient utilization includes all hospitalizations, procedures done during a hospital stay, length of stay, admitting/discharge diagnosis. Part 2, Hospitalization, contains records of causes and dates of hospitalizations and discharges and nursing home admissions. There is also a section on incomplete reviews and the reasons for them. Demographic information and some lifestyle information from the multiphasic health checkup (e.g., smoking, alcohol, and Body Mass Index) are also in this file. Data Availability: These datasets have been documented extensively and are available from the ICPSR (Study No. 4219). * Dates of Study: 1971-1992 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1971 cohort: 2,877 (baseline) ** 1980 cohort: 3,113 (baseline) ** 1971 & 1980: 5,990 ** Hospitalization: 14,730 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04219 * HSRR: http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=381&PROGRAM_CAME=toc_with_source2.cfm

Epidemiologic data.

This interactive tool allows users to generate tables and graphs on information relating to pregnancy and childbirth. All data comes from the CDC's PRAMS. Topics include: breastfeeding, prenatal care, insurance coverage and alcohol use during pregnancy. Background CPONDER is the interaction online data tool for the Center's for Disease Control and Prevention (CDC)'s Pregnancy Risk Assessment Monitoring System (PRAMS). PRAMS gathers state and national level data on a variety of topics related to pregnancy and childbirth. Examples of information include: breastfeeding, alcohol use, multivitamin use, prenatal care, and contraception. User Functionality Users select choices from three drop down menus to search for d ata. The menus are state, year and topic. Users can then select the specific question from PRAMS they are interested in, and the data table or graph will appear. Users can then compare that question to another state or to another year to generate a new data table or graph. Data Notes The data source for CPONDER is PRAMS. The data is from every year between 2000 and 2008, and data is available at the state and national level. However, states must have participated in PRAMS to be part of CPONDER. Not every state, and not every year for every state, is available.

The database compiles aggregated time series data from the National Center for Epidemiology, Disease Prevention, and Control (CDC) of Peru. This comprehensive national data, collected annually, originates from the National Epidemiological Surveillance Network and the system's reporting channels. Time series have been constructed in parallel, covering the period from 1983 to 2023.

Data products about the epidemiological, social and economic dimensions of the outbreak. Includes datasets, dashboards, statistics, analyses, trends, charts and maps. Also includes a list of locations where people may have been exposed to the virus.

WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

COVID-19 integrated surveillance data provided by the Italian Institute of Health and processed via UnrollingAverages.jl to deconvolve the weekly moving averages.

Research integrity and research fairness have gained considerable momentum in the past decade and have direct implications for global health epidemiology. Research integrity and research fairness principles should be equally nurtured to produce high quality impactful research – but bridging the two can lead to practical and ethical dilemmas. In order to provide practical guidance to researchers and epidemiologist, we set out to develop good epidemiological practice guidelines specifically for global health epidemiology, targeted at stakeholders involved in the commissioning, conduct, appraisal and publication of global health research.

We developed preliminary guidelines based on targeted online searches on existing best practices for epidemiological studies and sought to align these with key elements of global health research and research fairness. We validated these guidelines through a Delphi consultation study, to reach a consensus among a wide representation of stakeholders.

A total of 45 experts provided input on the first round of GEP e-Delphi consultation, and 40 in the second. Respondents covered a range of organisations (including for example academia, ministries, NGOs, research funders, technical agencies) involved in epidemiological studies from countries around the world. A selection of eight experts were invited for a face-to-face meeting. The final guidelines consists of a set of six standards and 42 accompanying criteria including study preparation, study protocol and ethical review, data collection, data management, analysis, reporting and dissemination.

This database only includes anonymised responses of participants who agreed to their data being shared in this depository , i.e.19 out of the 45 (Round 1) and 40 (Round 2) participants.

This is a dataset from the GBD database (http://ghdx.healthdata.org/gbd-results-tool), including the disability-adjusted life years (DALYs), deaths, prevalence, incidence, DALYs and deaths due to short gestation and low birth weight in early and late neonates with neonatal sepsis in China and the United States. Date Submitted: 2023-06-27

The German Centre for Cancer Registry Data (ZfKD) provides the topical cancer statistics for Germany. In an interactive database query you will get information on incidence and mortality rates as well as for prevalence and survival rates for different types of cancer.

This repository contains the largest COVID-19 epidemiological database available in addition to a powerful set of expansive covariates. It includes open sourced data with a permissive license (enabling commercial use) relating to vaccinations, epidemiology, hospitalizations, demographics, economy, geography, health, mobility, government response, weather, and more. Moreover, the data merges daily time-series from hundreds of data sources at a fine spatial resolution, containing over 20,000 locations and using a consistent set of region keys. This dataset is available in both the US and EU regions of BigQuery at the following links: COVID-19 Open Data: US Region COVID-19 Open Data: EU Region All data in this dataset is retrieved automatically. When possible, data is retrieved directly from the relevant authorities, like a country's ministry of health. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

After about 5 years of data gathering in the field of Shahrekord PERSIAN cohort study, a big database has been produced read about 20 million variable figures. PIs invites name researchers from all over the world to collaborate in interpretation and research by using this valuable database. Epidemiological research on disease prevalence and the risk factors of NCDs, data mining studies, using artificial intelligence and augmented reality, statistical modeling, and longitudinal studies to follow cause and effect Relations in health events or among the subjects can generally be practiced for these collaborative studies. To read the terms and conditions of these studies and any other information needed you can be referred to http://persiancohort.com/cohortsites/shahrekord/ And https://cohort.skums.ac.ir/

This data repository contains wastewater-based and epidemiological data collected in Kisumu, Kenya, between March 29, 2022 and March 8, 2023. Wastewater-based data was generated from samples (n=161) collected and extracted by trained professionals from centralized wastewater treatment sites (n=41) and from decentralized sites: hospitals (n=50), public toilets in markets (n=31), and public toilets at transportation hubs (n=39). The presence or absence of SARS-CoV-2 RNA and its concentration if present were measured using RT-qPCR. Epidemiological data was gathered from two sources, COVID-Dx platform and the Kisumu County Health Information Office. This epidemiological data includes the number of SARS-CoV-2 tests conducted, the number of positive tests, and the number of COVID-19 related admissions from the four largest hospitals in Kisumu: JOOTRH, Kisumu County Referral Hospital, Avenue Hospital, and Aga Khan Hospital. The odds of detecting one or more COVID-19 cases within 4-7 days after a positive WBS sample were 9 times higher than on days with negative WBS samples.T_min : Minimum daily temperatureT_max: Maximum daily temperatureAverage_WBS_conc : Average Wastewater based SARS CoV-2 concentration Gene copies/mlCat_WBS : Absence or presence of SARS CoV-2 in Wastewater samples logconc : Log concentration WWTP : Average WBS concentration in wastewater treatment plant samples Transport_public_toilets : Average WBS concentration in bus terminal wastewater samples Market_public_toilets : Average WBS concentration in market wastewater samples Hospitals : Average WBS concentration in hospital wastewater samples Total_tests : Total number of COVID-19 tests conducted in the four hospitalsTotal_positives : Total number of positive COVID-19 tests from the four hospitalsTotal_admissions : Total number of COVID-19 related admissions in the four hospitalsCum_pos_3daysbefore : Cumulative number of positive COVID-19 tests within 3 days before a wastewater sample collection dateCum_pos_7daysbefore: Cumulative number of positive COVID-19 tests within 7 days before a wastewater sample collection dateCum_pos_15daysbefore : Cumulative number of positive COVID-19 tests within 15 days before a wastewater sample collection dateCum_pos_3daysafter : Cumulative number of positive COVID-19 tests within 3 days after a wastewater sample collection dateCum_pos_7daysafter : Cumulative number of positive COVID-19 tests within 7 days after a wastewater sample collection dateCum_pos_15daysafter : Cumulative number of positive COVID-19 tests within 15 days after a wastewater sample collection date