The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

Download reports from the Massachusetts Department of Public Health (DPH), January 2022-September 2023.

COVID-19 deeply impacted communities across Massachusetts, but people of color are bearing a higher burden of cases and deaths relative to their population size.

As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 PCR diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity). A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case. These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
. This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description(updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN hospital data reporting guidance at https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6 , 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics

This table will no longer be updated after 5/30/2024 given the end of the 2023-2024 viral respiratory vaccine season. This table shows the cumulative number and percentage of CT residents who have received an updated COVID-19 vaccine during the 2023-2024 viral respiratory season by age group (current age). CDC recommends that people get at least one dose of this vaccine to protect against serious illness, whether or not they have had a COVID-19 vaccination before. Children and people with moderate to severe immunosuppression might be recommended more than one dose. For more information on COVID-19 vaccination recommendations, click here. • Data are reported weekly on Thursday and include doses administered to Saturday of the previous week (Sunday – Saturday). All data in this report are preliminary. Data from the previous week may be changed because of delays in reporting, deduplication, or correction of errors. • These analyses are based on data reported to CT WiZ which is the immunization information system for CT. CT providers are required by law to report all doses of vaccine administered. CT WiZ also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT Wiz electronically. Electronic data exchange is being added jurisdiction-by-jurisdiction. Currently, this includes Rhode Island and New York City but not Massachusetts and New York State. Therefore, doses administered to CT residents in neighboring towns in Massachusetts and New York State will not be included. A full list of the jurisdiction with which CT has established electronic data exchange can be seen at the bottom of this page (https://portal.ct.gov/immunization/Knowledge-Base/Articles/Vaccine-Providers/CT-WiZ-for-Vaccine-Providers-and-Training/Query-and-Response-functionality-in-CT-WiZ?language=en_US) • Population size estimates used to calculate cumulative percentages are based on 2020 DPH provisional census estimates*. • People are included if they have an active jurisdictional status in CT WiZ at the time weekly data are pulled. This excludes people who live out of state, are deceased and a small percentage who have opted out of CT WiZ. DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020, State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

United States Excess Death excl COVID: Predicted: Single Excess Est: Massachusetts data was reported at 0.000 Number in 16 Sep 2023. This stayed constant from the previous number of 0.000 Number for 09 Sep 2023. United States Excess Death excl COVID: Predicted: Single Excess Est: Massachusetts data is updated weekly, averaging 0.000 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 209.000 Number in 13 Jan 2018 and a record low of 0.000 Number in 16 Sep 2023. United States Excess Death excl COVID: Predicted: Single Excess Est: Massachusetts data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

The dashboard includes COVID-19 cases, testing, and hospitalizations data. It also contains data on: city/town specific metrics; confirmed and probable cases; testing; age groups, race and ethnicity, and sex of cases; hospitalizations and deaths; hospital capacity.

View daily updates and historical trends for Massachusetts Coronavirus Deaths Per Day (DISCONTINUED). Source: Center for Disease Control and Prevention. T…

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

This dataset tracks the updates made on the dataset "COVID-19 State Profile Report - Massachusetts" as a repository for previous versions of the data and metadata.

This dataset is no longer being updated as of 6/30/2022. It is being retained on the Open Data Portal for its potential historical interest.

In November 2020, the City of Cambridge began collecting and analyzing COVID-19 data from municipal wastewater, which can serve as an early indicator of increased COVID-19 infections in the city. The Cambridge Public Health Department and Cambridge Department of Public Works are using technology developed by Biobot, a Cambridge based company, and partnering with the Massachusetts Water Resources Authority (MWRA). This Cambridge wastewater surveillance initiative is funded through a $175,000 appropriation from the Cambridge City Council.

This dataset indicates the presence of the COVID-19 virus (measured as viral RNA particles from the novel coronavirus per ml) in municipal wastewater. The Cambridge site data here were collected as a 24-hour composite sample, which is taken weekly. The MWRA site data ere were collected as a 24-hour composite sample, which is taken daily. MWRA and Cambridge data are listed here in a single table.

An interactive graph of this data is available here: https://cityofcambridge.shinyapps.io/COVID19/?tab=wastewater

All areas within the City of Cambridge are captured across four separate catchment areas (or sewersheds) as indicated on the map viewable here: https://cityofcambridge.shinyapps.io/COVID19/_w_484790f7/BioBot_Sites.png. The North and West Cambridge sample also includes nearly all of Belmont and very small areas of Arlington and Somerville (light yellow). The remaining collection sites are entirely -- or almost entirely -- drawn from Cambridge households and workplaces.

Data are corrected for wastewater flow rate, which adjusts for population in general. Data listed are expected to reflect the burden of COVID-19 infections within each of the four sewersheds. A lag of approximately 4-7 days will occur before new transmissions captured in wastewater data would result in a positive PCR test for COVID-19, the most common testing method used. While this wastewater surveillance tool can provide an early indication of major changes in transmission within the community, it remains an emerging technology. In assessing community transmission, wastewater surveillance data should only be considered in conjunction with other clinical measures, such as current infection rates and test positivity.

Each location is selected because it reflects input from a distinct catchment area (or sewershed) as identified on the color-coded map. Viral data collected from small catchment areas like these four Cambridge sites are more variable than data collected from central collection points (e.g., the MWRA facility on Deer Island) where wastewater from dozens of communities are joined and mixed. Data from each catchment area will be impacted by daily activity among individuals living in that area (e.g., working from home vs. traveling to work) and by daytime activities that are not from residences (businesses, schools, etc.) As such, the Regional MWRA data provides a more stable measure of regional viral counts. COVID wastewater data for Boston North and Boston South regions is available at https://www.mwra.com/biobot/biobotdata.htm

Community specific data reports for vaccine administration results, updated weekly, and data from the Public Health (DPH) COVID Community Impact Survey to help target approaches.

Context

Massachusetts has been publishing city and town COVID-19 case rate data for about three weeks now. I've been making maps out of it with R (e.g., here), and have written up a bit about how in my blog.

Content

Each week's data is complete for January 1st through the given date (April 14th, 22nd, and 29th, so far). The state publishes a case count and a rate (per 100,000) per city, plus a count for where the city was unknown and the state totals (last two rows). Raw state data columns are marked with the date. Adjusted columns have some estimates in them of case counts (where the state was vague), and rates (where the state omitted them), but otherwise match the state data. The county and population are included for each city.

Note that the population data is from the US Census and is not the same as the populations the state used to calculate their rates.

Acknowledgements

Massachusetts Department of Public Health data: current edition. I have archived the previous versions to do the diffs, but you can probably get your own copies from the WayBack Machine. (If you do so, avoid the bad data from the evening of April 22nd.) Data was extracted using the R package docxtractor.

Census data: This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. Data was obtained from the estimates API (2018) using the R package tidycensus.

Inspiration

I made some maps from this data, both static and with Leaflet, to try to get a better sense of what was going on in the state and where. The latest one is here. Suggestions for improvement are welcome.

View daily updates and historical trends for Massachusetts Coronavirus Tests Administered. Source: US Department of Health & Human Services. Track economi…

United States SB: Massachusetts (MA): COVID-19 Impact: Large Negative Effect data was reported at 18.600 % in 11 Apr 2022. This records an increase from the previous number of 18.400 % for 04 Apr 2022. United States SB: Massachusetts (MA): COVID-19 Impact: Large Negative Effect data is updated weekly, averaging 21.200 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 26.000 % in 07 Mar 2022 and a record low of 18.400 % in 04 Apr 2022. United States SB: Massachusetts (MA): COVID-19 Impact: Large Negative Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S: Small Business Pulse Survey: by State: Northeast Region: Weekly, Beg Monday (Discontinued).

Overview

As coronavirus cases have exploded across the country, states have struggled to obtain sufficient personal protective equipment such as masks, face shields, gloves and ventilators to meet the needs of healthcare workers. FEMA began distributing PPE from the national stockpile as well as PPE obtained from private manufacturers to states in March.

Initially, FEMA distributed materials based primarily on population. By late March, Its methods changed to send more PPE to hotspot locations, and FEMA claimed these decisions were data-driven and need-based. By late spring, the agency was considering requests from states as well.

Although all U.S. states and territories have received some amount of PPE from FEMA, the amounts of PPE states have per capita and per positive COVID-19 case vary widely.

The AP used this data in a story that ran July 7.

Findings

• Overall, low population, rural states have the most PPE per positive case as of mid-June. This generally held true across types of equipment.
• The states that had the highest number of total PPE items per coronavirus case as of mid-May were, in descending order: Alaska, Montana, Vermont, Hawaii, Wyoming, and North Dakota. The highest was Alaska with 1,579 PPE items per coronavirus case.
• The states that had the highest number of total items per case as of mid-June were largely the same states — Montana, Alaska, Hawaii, Vermont, Wyoming, and West Virginia. The highest was Montana with 1,125 PPE items per coronavirus case.
• Conversely, the states that had the lowest amounts of PPE per positive case in mid-May included hotspot states — Massachusetts, New York, Virginia, California, Nebraska, and Iowa. New Jersey was just a couple spots further down. The lowest was Massachusetts with 36 PPE items per coronavirus case.
• The states that had the lowest amounts of PPE per case as of mid-June were largely the same as well — Massachusetts, New York, Iowa, California, and Nebraska. The lowest was Massachusetts with 32 PPE items per coronavirus case.
• When evaluated on a per-capita basis rather than per positive coronavirus case, the picture is different. The District of Columbia received the most PPE per capita in both May and June, although the vast majority of the PPE it received was distributed as of mid-May. Vermont, Kansas, New Jersey, and North Dakota had the next highest numbers of PPE per capita as of both mid-May and mid-June.
• There is no clear pattern of FEMA distribution by party control of states.

About the data

These numbers include material distributed by FEMA and also those sold by private distributors under direction from FEMA. They include materials both delivered to and en route to states.

States have purchased PPE directly in addition to receiving PPE from FEMA or directed there by the agency, and this data only includes the latter categories.

FEMA also distributed and directed the distribution of gear to U.S. territories in addition to states, which are included in FEMA’s release linked below, but not are not included in this data.

FEMA has publicly distributed its breakdown of PPE delivery by state for May and June. FEMA did not provide comprehensive numbers for each state before May.

These numbers are cumulative, meaning that the numbers for May include items of PPE distributed prior to May 14, dating to when the agency began allocations on March 1. The June numbers include the May numbers and any new PPE distributions since then.

The population column, which was used to calculate the numbers of PPE items per state, came from data from the U.S Census Bureau. Since the Census releases annual population data, population data from 2019 was used for each state.

The numbers of coronavirus cases were pulled from the data released daily by Johns Hopkins University as of the dates that FEMA released its distribution numbers — May 14 and June 10.

Caveats

The data includes amounts of gear that had been delivered to the states or were en route as of the reporting dates.

All PPE item numbers above 1 million were rounded to the nearest hundred thousand by FEMA, but numbers lower than that were not rounded.

In some cases, gear headed to a state was rerouted because it was needed more somewhere else or a state decided it did not need it. In some instances, that resulted in states having higher numbers for certain supplies in May than in June.

The overall goal for this project was to reduce the incidence of COVID-19, hospitalization, and mortality among adults with serious mental illness (SMI) and intellectual disabilities/developmental disabilities (IDD) in congregate living settings (i.e., group homes) in Massachusetts, as well as to reduce COVID-19 incidence among staff who work in these settings. The research team was guided by two comparative effectiveness questions: With the goal of prioritizing and making actionable best practices available as resources, what is the comparative effectiveness of various types and intensities of preventative interventions (e.g., screening, isolation, contact tracing, hand hygiene, physical distancing, use of face masks) in reducing rates of COVID-19, related hospitalizations, and related mortality in this population? With the goal of effectively implementing best practices, what is the most effective implementation strategy to reduce rates of COVID-19 in this population: using tailored best practices (TBP) with SMI/IDD residents and staff of group homes in mind, or general best practices (GBP) from state and federal standard guidelines for all congregate care settings? The specific aims of this study were as follows: Aim 1a. Synthesize existing baseline data collected by 6 state behavioral health agencies on COVID-19 rates, hospitalization, mortality, and use of infection prevention practices. Aim 1b. Collect stakeholder input via surveys and virtual focus groups on staff and resident experiences and on barriers/facilitators to implementing recommended preventative practices. Aims 2a and 2b. Determine the comparative effectiveness of various COVID-19 preventative practices by (Aim 2a) using a validated simulation model to estimate COVID-19 spread in group homes and (Aim 2b) obtaining stakeholder input on prioritizing and defining tailored best practices for implementation. Aim 3. Compare the effectiveness of TBPs with GBPs by using a hybrid effectiveness-implementation cluster randomized controlled trial. Data collected to answer Aims 1 and 2 served as the foundation for designing the Aim 3 trial. Data for the trial were collected in 3-month intervals beginning January 2021 (baseline) until October 2022 (15-month follow-up). Residents and staff were sampled from approximately 400 group homes. Primary implementation outcome measures were COVID-19 vaccination rates and fidelity scores. The primary effectiveness outcome measure was COVID-19 infection. Notes: This collection contains only data from Aim 1a and Aim 3. Throughout the data and documentation, "intellectual and/or developmental disabilities" is abbreviated as both IDD and ID/DD.

View daily updates and historical trends for Massachusetts Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts …