05.07.2020 UPDATE – Starting with data for May 6th, New York Times dataset shows the total combined number of confirmed and probable Covid-19 cases and deaths where available. Many states and localities have started to report this data using criteria that were developed by states and the federal government. This will cause a spike in the cases and deaths data for some areas while the New York Times works to revise their historical data with those probable cases and deaths.
The Dartmouth Atlas Project has long based its analyses on the natural markets where residents of the United States receive their care. The 306 Hospital Referral Regions (HRR) have become a widely used standard for health care policy and research because they correspond to local travel patterns, which often cross county or state lines.
On this page, we have applied these methods to the COVID-19 epidemic, using the county-level data that are being collected, organized and updated daily by the New York Times. County case and death rates were aggregated to the 306 Hospital Referral Regions. We present the following: population-based rates of reported COVID-19 cases, population-based rates of reported deaths from COVID-19, and average growth rates in reported cases over the prior week. The maps and data tables are updated on a daily basis.
These maps and data provide 4 helpful insights:
- They account for differences in population size across states and regions, revealing the proportion (rather than the absolute number) of the population that has been diagnosed with COVID-19.
- They reveal differences across regions within states, which can be dramatic, particularly in large states such as California and Texas.
- Many counties are very small. A substantial number have no cases, leaving the false impression that the virus is not present in the region. Small numbers provide statistically imprecise estimates of prevalence. HRRs improve statistical precision and reveal that COVID-19 is present in every HRR.
- As HRRs were constructed originally to measure health care catchment areas, they provide a more accurate tool than geographic boundaries for assessing the relative capacity of local health care systems. Data on hospital capacity, easily linked by HRR, can be found here.
For more information relevant to the COVID-19 pandemic, see our COVID-19 page.
New COVID-19 Cases per 100,000 for the Past 14 Days: By Hospital Referral Region (HRR)
The map displays the rolling sum of newly reported COVID-19 cases over the past 14 days per 100,000 residents of each of the 306 United States Hospital Referral Regions (HRR). Roll over an HRR to see additional information, including the name of the HRR, the number of new cases in the HRR during the past 14 days , the average daily growth rate over the past 7 days and the rank in growth rates in the US, from highest (1) to lowest (306). The bar graph below can be scrolled down to show rates for each U.S. HRR.
Current Average Weekly Growth Rates of Reported COVID-19 Cases by US Hospital Referral Region
Timelapse animation of average daily growth rate of covid-19 cases from March 1 – April 30, 2020.
The map below displays the current average weekly growth rates for reported COVID-19 cases per 100,000 residents of each of the 306 United States Hospital Referral Regions (HRRs), based on county-level data files maintained and made available by the New York Times. Growth rates were calculated for HRRs with at least 25 cases. Roll over an HRR to see additional information, including the name of the HRR, average daily growth rate over the past 7 days, the rank of growth rates in the U.S. from highest (1) to lowest (306), the current prevalence of COVID-19 (reported cases per 100,000), and the rank in prevalence in the US, from highest (1) to lowest (306). The bar graph below can be scrolled down to show rates for each U.S. HRR.
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Current COVID-19 Cases per 100,000 Population by US Hospital Referral Region
The map below displays the current number of reported COVID-19 cases per 100,000 residents of each of the 306 United States Hospital Referral Regions (HRRs), based on county-level data files maintained and made available by the New York Times. Roll over an HRR to see additional information, including the name of the HRR, the number of cases in the HRR, the current prevalence of COVID-19, and the rank in prevalence in the US, from highest (1) to lowest (306). The bar graph below can be scrolled down to show rates for each U.S. HRR.
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Current COVID-19 Deaths per 100,000 Population by US Hospital Referral Region
The map below displays the mortality rate (COVID-19 reported deaths per 100,000 population) for each of the 306 United States Hospital Referral Regions (HRRs), based on county-level data files maintained and made available by the New York Times. Roll over an HRR to see additional information, including the name of the HRR, the current mortality rate in the HRR, and the rank of mortality rates, from highest (1) to lowest (306). The bar graph below can be scrolled down to show rates for each U.S. HRR.
Data for these analyses were drawn from two sources, the New York Times daily update of county case and death counts, and the MABLE datafile from the Missouri Census Data Center, which provides a crosswalk between each U.S. county and the Dartmouth Atlas of Health Care-defined Hospital Referral Regions. This crosswalk file reports the total population of each county and the number of residents of that county who are within a given HRR, allowing the proportion of residents of a county within a given HRR to be determined.
The New York Times case and death counts were used to calculate the county rates based on population data from the US Census. HRR rates were then calculated as the weighted average of county-level rates, with the weight equal to the proportion of residents of each county within a given HRR. Growth rates were calculated using the following formula:
Average Growth rate = ((Rate today / Rate 7 days ago)(1/7)) - 1
(includes data pulled daily from New York Times COVID-19 Data github repository)
Alignment of Counties and HRRs
Although many counties fall within HRRs, some span HRR boundaries. The HRR rates are thus an estimate and will only be exactly correct when the constituent counties aligns with the HRR, or when the per-capita rates are similar across counties. We have calculated an accuracy index to let readers understand for a specific HRR how strongly these align. (This is calculated as the weighted average of the proportions of the county populations that fall within an HRR, where the weight is the ratio of the “in-HRR” county population to the total HRR population. A value of 1 means perfect alignment, a value closer to 0 implies that there is less confidence in the estimate. 90% of HRRs have accuracy index values over 0.75. The four HRRs with values under 0.25 are Evanston IL (0.16), Blue Island IL (0.15), Palm Springs CA (0.11) and Sun City AZ (0.08). The download file includes this measure.
Counties vary in the characteristics of their populations, including differences in age, gender, race and comorbidities. Because we do not know the demographic or health characteristics of the cases or deaths, we could not adjust for these differences. It is likely that these differences have less influence on spread of the disease, and much more influence on case fatality rates, as has been made clear in a recent Health Affairs Blog using Dartmouth HRRs.
Limitations of New York Times Data
The New York Times data are based on the efforts of multiple journalists working to analyze data releases from states and local health departments, seeking to clarify the data, and correct and update this data wherever possible. Additional details on their methods can be found in their github repository’s README file. Their files are updated daily with corrections to earlier time periods with each update. The Atlas calculations are rerun each day, including the corrected data for prior days.
Looking Back vs Looking Forward
The calculation of the growth rates is based on the prior seven days and should not be assumed to predict future rates.
Analysis and mapping by Anoop Nanda, Sukdith Punjasthitkul, Jonathan Skinner and Elliott Fisher