In 2022, emergency department visit rate was highest among infants under the age of one. Adults 75 years and over had the second-highest ED visit rate, while the average for all ages was 47 visits per 100 people in 2022.

Emergency room visit rates across the United States show significant variation, with a national average of 411 visits per 1,000 population in 2022. This average masks considerable differences between states, ranging from 588 visits per 1,000 population in North Dakota to just 251 in Nevada. Wait times in emergency rooms While ER visit rates provide insight into utilization, wait times offer a glimpse into the efficiency of emergency care delivery. In 2022, ER patients waited an average of 38.1 minutes to see a healthcare provider in emergency departments nationwide. Interestingly, the COVID-19 pandemic temporarily reduced wait times in 2020, but they rebounded to pre-pandemic levels by 2021. Most patients, roughly 70 percent, spend less than an hour in the emergency department before being seen by a medical professional. These figures suggest that despite high utilization in some areas, many emergency departments manage to process patients relatively quickly. Demographic disparities in emergency care Emergency department usage varies significantly across different demographic groups, revealing important healthcare access disparities. Infants under one-year-old and adults 75 years and over have the highest ED visit rates among all age groups. Additionally, racial disparities in ED rates are evident, with non-Hispanic Black individuals having double the ED visit rate of non-Hispanic White individuals in 2022. These patterns underscore the need for targeted healthcare interventions and improved access to curative care for vulnerable populations.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows demographic context for emergency response efforts. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B01001, B08201, B16003, B16004, B17020, B18101, B25040, B25117, B27010 (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

In 2022, there were, on average, 411 hospital emergency room (ER) visits per 1,000 population in the United States. ER visit rates had been steadily increasing from 365 visits per 1,000 population in 1999 till a peak of 445 visits in 2017. There was a drop in ER visit rates during the pandemic, but numbers are slowly increasing to pre-pandemic levels again.

In 2016, there were an estimated 76.8 urban emergency department visits among the black population of the United States, per 100 population. This statistic shows the rate of urban emergency department visits in the U.S. in 2005 and 2016, by race or ethnicity.

Data on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available. SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.

Age-adjusted rate of emergency department visits due to suicide attempts/ideation for Santa Clara County residents. The data are provided for the total county population and by sex and race/ethnicity. The data trends are presented from 2007 to 2014. Source: Office of Statewide Planning and Development,2007-2014 Emergency Department Data; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes and sourceYear (Numeric): Year of emergency department visitCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, and race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only).Age adjusted rate per 100,000 people (Numeric): The Ninth Revision of the International Classification of Diseases codes (ICD-9) are used for coding emergency department visit data. Age-adjusted rate is calculated using 2000 U.S. Standard Population. Rate of emergency department visits due to suicide attempt/ideation is number of related emergency department visits in a year per 100,000 people in the same time period. Data are not presented if the number of emergency department visits is 15 or less.

This layer shows demographic context for emergency response efforts. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dict

In the United States, there were an estimated 46.2 urban emergency department visits for females per 100 population in 2016. This statistic shows the rate of urban emergency department visits in the U.S. in 2005 and 2016, by gender.

The U.S. Freestanding Emergency Department Market size was valued at USD 3.7 billion in 2023 and is projected to reach USD 5.17 billion by 2032, exhibiting a CAGR of 4.9 % during the forecasts period.The U. S. Freestanding Emergency Department (FSED) Market aims at the kind of medical centers that offer out-of-hospital emergency services. FSEDs provide primary medical care for ailments such as injuries, severe diseases, and any other illness that needs prompt attention but not admittance to a hospital. The centers are also placed in a way that they can reach the affected population with a shorter time, adding up to the time taken by patient-crowded hospital emergency departments. Some of the important uses are alleviation of trauma, chest pain, respiratory conditions, and any other emergent health condition. Today’s trends are the expansion of FSEDs because of customer demand for easily accessible emergency care, the integration of telemedicine to handle emergency triage and remote follow-up, and expanded rules and guidelines for working FSEDs’ operational benchmarks and patients’ outcomes in emergency care.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

What is Social Vulnerability?Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.What is the CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.Below, text that describes “tract” methods also refers to county methods.How can the SVI help communities be better prepared for hazardous events?SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.SVI can be used to:Assess community need during emergency preparedness planning.Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.Decide the number of emergency personnel required to assist people.Identify areas in need of emergency shelters.Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not speak English well.Identify communities that will need continued support to recover following an emergency or natural disaster.For more detailed methodology and attribute details, please review this document.

A binary dasymetric analysis, using National Land Cover Databases (NLCD) as an auxiliary, is applied to US Census Tracts. American Community Survey (ACS) Demographic data is often joined and used to generate impacted population with a hazard/AOI.

This layer shows demographic context for emergency response efforts. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.

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Personal Emergency Response System Market Size 2024-2028

The personal emergency response system (PERS) market size is forecast to increase by USD 3.66 billion at a CAGR of 7.81% between 2023 and 2028.

The market is experiencing significant growth due to the increasing aging population and the development of advanced wearable devices. These systems, which include standalone PERS and transmitters, enable elderly individuals to call for help in case of an emergency. One trend In the market is the integration of Rcube monitoring and standalone voice capabilities, allowing for more effective and efficient response times. Additionally, the PERS market includes smoke detectors, bed sensors, and pullcord residential systems. Another trend is the implementation of wandering systems, which help prevent elders from wandering off and getting lost. However, a lack of awareness about the benefits of PERS technology remains a challenge for market growth. In summary, the PERS market is driven by the aging population and technological advancements but faces challenges in increasing awareness and adoption.

What will be the Size of the Personal Emergency Response System (PERS) Market During the Forecast Period?

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The market is witnessing significant growth due to the increasing safety concerns for the elderly population in developed economies. With an aging population, the need for reliable and efficient healthcare infrastructure to cater to their unique needs is becoming increasingly important. The PERS marketplace caters to various user demographics, including seniors living independently, those with chronic conditions, and individuals at risk of falls. These systems are designed to provide quick assistance during emergencies, ensuring the safety and well-being of the elderly population. The PERS market offers various solutions, including standalone systems and those integrated with GPS technology, fall detection, and wearable devices. Standalone systems come with transmitters that users can wear around their necks or wrists and press in case of an emergency. These systems connect to a monitoring center, which can dispatch emergency services or contact a predefined contact. Moreover, the market also includes wander systems that help locate individuals with cognitive impairments who tend to wander away from their homes. These systems use GPS technology and can alert caregivers or family members if the individual leaves a designated area.
Medical alarm systems, another segment of the PERS market, are landline-based and require a phone line to function. However, with the increasing preference for wireless and mobile solutions, the market is witnessing a shift towards mobile PERS, which are wireless, cellular, and offer the flexibility of being used anywhere. These solutions cater to different user needs and preferences, ensuring comprehensive coverage for elderly safety. Healthcare expenditure in developed economies is on the rise, and PERS solutions offer a cost-effective way to provide essential healthcare services to the elderly population. The market is expected to continue growing, driven by the increasing awareness of personal safety and the need to cater to the unique healthcare requirements of the aging population. Thus, the PERS market is an essential component of the healthcare infrastructure in developed economies, providing reliable and efficient emergency response solutions for the elderly population. With various solutions available, including standalone systems, GPS-enabled devices, and medical alarm systems, the market offers a range of options to cater to diverse user needs and preferences. The market's growth is driven by the increasing awareness of personal safety, the rising healthcare expenditure, and the unique healthcare requirements of the aging population.

How is this Personal Emergency Response System (PERS) Industry segmented and which is the largest segment?

The personal emergency response system (PERS) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Mobile
  Landline
  Standalone


End-user

  Home healthcare
  Assisted living facilities
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  Asia

    China
    Japan


  Rest of World (ROW)

By Type Insights

The mobile segment is estimated to witness significant growth during the forecast period. Personal Emergency Response Systems (PERS), also known as medical alarm systems, have gained significant traction In the healthcare industry due to their ability to provide timely assistance during emergencies. These systems offer various types of devices, including landline, wireless, and mobile versions. Landline PERS syste

Colorado county-level and state data on rates of hospitalizations among Colorado residents for multiple years as published by the Colorado Environmental Public Health Tracking project. Current years published include 2004-2018.Numerator/denominator informationEvent/numerator data:Hospital discharges, Hospital Discharge Data Set, Colorado Hospital Association.Emergency department discharges, Emergency Department Discharge Data Set, Colorado Hospital Association.Population/denominator data:Midyear resident population estimates. Source: State Demography Office, Colorado Department of Local Affairs.Interpreting the dataWhat these data tell us:These data tell us rates of hospitalizations and emergency department visits among Colorado residents over time and across counties. The rate is the number of hospitalizations or emergency department visits per state or county population in a calendar year.What these data do not tell us:These data do not tell us the number of people who currently have or experience each condition. The data may reflect more severe cases of each condition since people who are hospitalized or admitted to the emergency room often have a more severe illness.Comparisons of these rates of hospitalization and emergency department visits to environmental measures should be done with caution.Elevated rates of hospitalizations and emergency department visits in a geographic area with higher than average environmental exposure do not necessarily indicate that the environmental exposure is causing the higher rate.There may be other factors that lead to increased disease rates within a geographic area. Rates may differ due to factors such as access to medical care which can affect the likelihood of a person being hospitalized for asthma.Calculation methodsCase definition for hospitalizations and emergency department visits occurring:before October 1, 2015 are based on diagnosis codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM).on or after October 1, 2015 are based on diagnosis codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10CM).Age-specific rates in each age group and geographic population are calculated:per 10,000 population for asthma, chronic obstructive pulmonary disease (COPD), and heart attack.per 100,000 population for carbon monoxide poisoning and heat-related illness.Age-adjusted rates are calculated:per 10,000 population for asthma, chronic obstructive pulmonary disease, and heart attackper 100,000 population for carbon monoxide poisoning and heat-related illness.Rates are adjusted for differences across age and sex by the direct method using the Year 2000 U.S. Standard Population.Limitations of the dataThe hospital and emergency department visits datasets do not include all cases. Those who do not receive medical care, receive medical treatment in outpatient settings (other than emergency department), or die without being admitted to a hospital are not included in these datasets. Differences in rates by year or county may reflect differences or changes in medical coding or billing for hospitalizations and emergency department visits, or changes in access to medical care. Although exact duplicate records are excluded, the measures are based upon events, not individuals. If the same person is admitted to the hospital or emergency department multiple times for the same condition in the same year, these events would be counted as separate events, even though it was the same person. If people are being counted more than once, the reported rate may be higher than the true rate. Reporting rates at the state and county level is a broad measure. This means the data will not show the true disease burden at a more local level, such as the neighborhood. These data are not geographically specific enough to be linked with many types of environmental exposure, which may vary across the county.Data not includedThese data do not include hospital or emergency department discharges from Federal facilities in Colorado, such as U.S. Department of Veterans Affairs Medical Centers.

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.

This layer provides generalized county (or county equivalent) boundaries for the United States in the 50 states and the District of Columbia, developed by Esri from U.S. Census Bureau sources and updated as boundaries change.Attribute fields include 2020 total population from the U.S. Census Public Law 94 data. The geometry was originally extracted from ArcUSA 1:25M and slightly generalized for optimum performance. Geometry updates are made when major boundary changes occur such as a county splitting or merging with an adjacent unit. The new county equivalent planning regions for Connecticut are included without population values since these areas did not exist in the 2020 Census..This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

In 2016, there were an estimated 45.7 urban emergency department visits by adults aged 65 years and older per 100 population in the U.S. This statistic shows the rate of urban emergency department visits in the U.S. in 2005 and 2016, by age group.