The Rural Health Clinic (RHC) Enrollments dataset provides enrollment information on all RHCs currently enrolled in Medicare. This data includes information on the RHC's legal business name, doing business as name, organization type and address.

Report to the Appropriations Committee of the United States House of Representatives in Response to Conference Committee Report to PL 110-186. In an effort to provide a snapshot of the quality of care provided at VA health care facilities, this report includes information about waiting times, staffing level, infection rates, surgical volumes, quality measures, patient satisfaction, service availability and complexity, accreditation status, and patient safety. The data in this report have been drawn from multiple sources across VHA. This dataset defines the quality of care at a national level between rural vs urban populations.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Analysis of ‘COVID-19 high risk individuals per ICU bed’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/covid-19-high-risk-individuals-per-icu-bede on 28 January 2022.

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

About this dataset

This dataset contains the data behind the story How One High-Risk Community In Rural South Carolina Is Bracing For COVID-19.

mmsa-icu-beds.csv combines data from the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS), a collection of health-related surveys conducted each year of more than 400,000 Americans, and the Kaiser Family Foundation to show the number of people who are at high risk of becoming seriously ill from COVID-19 per ICU bed in each metropolitan area, micropolitan area or metropolitan division for which we have data.

Being high risk is defined by a number of health conditions and behaviors. Based on the CDC’s list of the relevant underlying conditions that put people at higher risk of serious illness from COVID-19, plus the advice of experts from the Cleveland Clinic, the American Lung Association and the American Heart Association, we counted people as at risk if they’re 65 or older; if they have ever been told they have hypertension, coronary heart disease, a myocardial infarction, angina, a stroke, chronic kidney disease, chronic obstructive pulmonary disease, emphysema, chronic bronchitis or diabetes; if they currently have asthma or a BMI over 40; if they smoke cigarettes every day or some days or use e-cigarettes or vaping products every day or some days; or if they’re currently pregnant. We included every individual who meets at least one of these conditions but counted them only once each, so anyone with multiple conditions doesn’t get counted multiple times. We were not able to include a number of conditions for which we did not have location-based data from the BRFSS, such as liver disease, having smoked, vaped or dabbed marijuana in the last 30 days, and getting cancer treatment or being on immunosuppression medications.

See the data dictionary for column descriptions.

If you find this information useful, please let us know.

License: Creative Commons Attribution 4.0 International License
Source: https://github.com/fivethirtyeight/data/tree/master/covid-geography

This dataset was created by data.world's Admin and contains around 100 samples along with High Risk Per Icu Bed, Icu Beds, technical information and other features such as: - Hospitals - High Risk Per Hospital - and more.

How to use this dataset

• Analyze Total Percent At Risk in relation to High Risk Per Icu Bed
• Study the influence of Icu Beds on Hospitals
• More datasets

Acknowledgements

If you use this dataset in your research, please credit data.world's Admin

Start A New Notebook!

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

Data on Determinants of Adverse Pregnancy Outcome in a Rural American Hospital.

Sample characteristicsa.

Text A. Handling of Missing Values. Table A. Missing Values for Total Household Income at UMMC, Kuala Lumpur and HSNZ, Kuala Terengganu. UMMC, University of Malaya Medical Centre; HSNZ, Hospital Sultanah Nur Zahirah. Table B. Poverty Impact of hospitalization for acute gastroenteritis at UMMC, Kuala Lumpur and HSNZ, Kuala Terengganu. Note: The imputed dataset uses pooled imputed values for Total Household Income. In the complete case analysis, cases with missing values for Total Household Income are deleted. All values are reported in 2009 United States Dollar (US$), as mean (± standard deviation, SD). During the study period, 1 USD was equivalent to 3.36 Malaysian Ringgit (RM). Poverty line income in 2009 for urban regions, Kuala Lumpur US$ 219.03 and rural regions, Kuala Terengganu US$ 211.08 [15]. UMMC, University of Malaya Medical Centre; HSNZ, Hospital Sultanah Nur Zahirah. (DOCX)