Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 11/13/2025.

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply ��_��_��_shelter��_�� and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

Homeless Students in Arkansas (2024–25): What the data says

TL;DR: 10.9k Arkansas students experienced homelessness in 2024–25 (0.8% of enrollment). Most are “Doubled Up”, sharing housing because of loss of housing or economic hardship. Geography matters: large, fast-growing counties report the highest counts even when they aren’t the poorest, and poverty explains much but not all of variation in homelessness.

Data & Method

Sources: Arkansas Department of Education 2024–2025; NIH poverty estimates (see workbook notes).

Unit of analysis: county-level counts of students

Tools: Tableau Public dashboard + worksheets; regression overlay on county scatter.

What to look at in the dashboard

County Map – Homeless students by county. Use the map to spot hotspots, hover for counts and enrollment context.

Housing Type Breakdown – Statewide composition: Doubled-Up 89.3%, Awaiting Foster Care 4.9%, Hotels/Motels 3.9%, Unsheltered 1.9%. Hidden homelessness dominates the lived experience of students.

Poverty vs. Homeless Students (Scatter) – A clear positive relationship (R² ≈ 0.59, p < 0.0001) indicates poverty is a strong driver, but not the whole story—some populous counties sit above/below the line.

County Comparison Bars – For larger counties (e.g., Benton, Pulaski, Washington), most identified students are Doubled-Up, and that share typically ranges 80–92%, underscoring the need for family-stability interventions.

Key findings

Scale: ~10,872 students (≈0.8% of 1.46M enrollment) were identified as experiencing homelessness statewide.

Geography ≠ poverty alone: Benton County reports the highest count despite not being among the highest poverty counties, reflecting population growth and housing pressure.

Mechanism: “Doubled Up” is the dominant pathway into homelessness for students. It's far more common than shelters, motels, or unsheltered situations. Supports that keep families stably housed (rent/utility assistance, eviction prevention, rapid re-housing) are likely to reach the largest group.

How analysts can use this

Targeting: Combine county counts with local enrollment to compute rates and flag counties that are high count and high rate for prioritization.

Program design: Given the 89% Doubled Up share, expect needs around transportation, documentation, and quick stabilization rather than shelter capacity alone.

Further work: Add rental vacancy, eviction filings, and new construction permits to the model to explain outliers.

Caveats

Counts reflect identification, not true prevalence; under identification is common for Doubled Up students.

County differences may reflect district identification practices and local resources.

Exploration tips: Use the dashboard’s tooltips, legend toggles (to isolate housing types), and the regression line on the scatter to compare counties to the statewide trend.

Each year, homeless coalitions across the country conduct a Point in Time Count (PIT) during the same 24-hour period in January to estimate the number of persons experiencing homelessness living in their region. The PIT count includes those living in emergency shelters, transitional housing programs, and those living unsheltered on the street. The PIT count does not include homeless families and youth who are doubled up with family or friends, or those at imminent risk of becoming homeless. The numbers are a “snapshot” on a single day rather than a definitive count. Despite these limitations, the count helps communities plan for programs and services, identifies gaps in the homeless system, and provides demographic information about populations who experience homelessness.

This dataset includes key measures that have been counted during each PIT since 2019. This dataset will be updated annually.

According to U.S. Department of Housing and Urban Development's definition, homelessness includes individuals and families who lack a fixed, regular, and adequate nighttime residence. A homeless count provides a "snapshot in time" to quantify the size of the homeless population at a specific point during the year. Regardless of how successful outreach efforts are, an undercount of people experiencing homelessness is possible. Counts includes persons experiencing unsheltered and sheltered homelessness. Greater Los Angeles Homeless Count occurred in the nights of February 22, 23 and 24, 2022. Glendale's count occurred in the morning and evening of February 25, 2022. Long Beach's count occurred in the early morning of February 24, 2022. Pasadena's count occurred in the evening of February 22, 2022 and morning of February 23, 2022. Data not available for Los Angeles City neighborhoods and unincorporated Los Angeles County; LAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels.Housing affordability is a major concern for many Los Angeles County residents. Housing burden can increase the risk for homelessness. Individuals experiencing homelessness experience disproportionately higher rates of certain health conditions, such as tuberculosis, HIV infection, alcohol and drug abuse, and mental illness. Barriers to accessing care and limited access to resources contribute greatly to these observed disparities.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_homeless_population. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

People experiencing homelessness have historically had high mortality rates compared to housed individuals in Canada, a trend believed to have become exacerbated during the COVID-19 pandemic. In this matched cohort study conducted in Toronto, Canada, we investigated all-cause mortality over a one-year period by following a random sample of people experiencing homelessness (n = 640) alongside matched housed (n = 6,400) and low-income housed (n = 6,400) individuals. Matching criteria included age, sex-assigned-at-birth, and Charlson comorbidity index. Data were sourced from the Ku-gaa-gii pimitizi-win cohort study and administrative databases from ICES. People experiencing homelessness had 2.7 deaths/100 person-years, compared to 0.7/100 person-years in both matched unexposed groups, representing an all-cause mortality unadjusted hazard ratio (uHR) of 3.7 (95% CI, 2.1–6.5). Younger homeless individuals had much higher uHRs than older groups (ages 25–44 years uHR 16.8 [95% CI 4.0–70.2]; ages 45–64 uHR 6.8 [95% CI 3.0–15.1]; ages 65+ uHR 0.35 [95% CI 0.1–2.6]). Homeless participants who died were, on average, 17 years younger than unexposed individuals. After adjusting for number of comorbidities and presence of mental health or substance use disorder, people experiencing homelessness still had more than twice the hazard of death (aHR 2.2 [95% CI 1.2–4.0]). Homelessness is an important risk factor for mortality; interventions to address this health disparity, such as increased focus on homelessness prevention, are urgently needed.

The most recent rate of homelessness is calculated using ACS population estimates from the previous year, unless otherwise noted.

Data Source: HUD's Annual Homeless Assessment Report (AHAR) Point-in-Time (PIT) Estimates by State and American Community Survey (ACS) 1-Year Estimates

Why this MattersSafe, adequate, and stable housing is a human right and essential for the health and well-being of individuals, families, and communities.People who experience homelessness also struggle to maintain access to healthcare, employment, education, healthy relationships, and other basic necessities in life, according to the DC Interagency Council on Homelessness Strategic Plan.BIPOC populations are disproportionately affected by homelessness due to housing discrimination, mass incarceration, and other policies that have limited socioeconomic opportunities for Black, Latino, and other people of color.

The District's Response Strategic investments in proven strategies for driving down homelessness, including the Career Mobility Action Plan (Career MAP) program, operation of non-congregate housing, and expansion of the District’s shelter capacity.Homelessness prevention programs for at-risk individuals and families, such as emergency rental assistance, targeted affordable housing, and permanent supporting housing.Programs and services to enhance resident’s economic and employment security and ensure access to affordable housing.

Context:

I was finishing the Google Analytics Certificate and had to work on a Capstone Project. I decided to create my own case study and work with data related to homeless people, since I think it's a topic we all have to be more aware of. While looking for some datasets I encountered the California State open datasets, and I picked the data related to the Project Roomkey.

"Project Roomkey gives people who are experiencing homelessness and are recovering from COVID-19 or have been exposed to COVID-19 a place to recuperate and properly quarantine outside of a hospital. It also provides a safe place for isolation for people who are experiencing homelessness and at high risk for medical complications should they to become infected." https://www.cdss.ca.gov/inforesources/cdss-programs/housing-programs/project-roomkey

Content:

It contains a copy of the original dataset, along with metadata and descriptions of variables. It also contains the data cleaning process and the analysis

Acknowledgements:

I want to thank Mark Hovarth and his work, which I was able to see through the Youtube Channel: https://www.youtube.com/user/invisiblepeopletv Thanks for your work and for the inspiration!

Inspiration:

I wanted to answer very specific questions with the help of this data

1. What is the county in California with the largest total quantity of rooms unoccupied between April, 2020 and April 2021?

2. What are the counties in California with the lowest ratios of rooms occupied to rooms and trailers_delivered to trailer_requested between April, 2020 and April, 2021?

3. Which has been the most solidary county in California regarding trailers donated between April, 2020 and April, 2021?

4. What is the day in which the most numbers of rooms were occupied in California between April, 2020 and April 2021?

Decisions on whether a household is homeless and in priority need. The term "Homelessness" is often considered to apply only to people "sleeping rough". However, most of our statistics on homelessness relate to the statutorily homeless i.e. those households which meet specific criteria of priority need set out in legislation, and to whom a homelessness duty has been accepted by a local authority. Such households are rarely homeless in the literal sense of being without a roof over their heads, but are more likely to be threatened with the loss of, or are unable to continue with, their current accommodation. All households that apply for assistance under the Housing and Homelessness Acts are referred to as "decisions". However, these do not include households found to be ineligible for assistance (some persons from abroad are ineligible for assistance). This dataset provides statistics on the numbers of decisions made on applications for assistance. The data is broken down by local authority and according to the outcome of the decision: either rejected, together with reason for rejection, or accepted. The numbers are presented in terms of households, not individuals. A household is defined as: one person living alone, or a group of people living at the same address who share common housekeeping or a living room. Values of less than five households have been suppressed. In addition, some values of five or greater have been suppressed to prevent other suppressed values being calculated This data is also available in Table 784a, available for download as an Excel spreadsheet.

The term "Homelessness" is often considered to apply only to people "sleeping rough". However, most of our statistics on homelessness relate to the statutorily homeless i.e. those households which meet specific criteria of priority need set out in legislation, and to whom a homelessness duty has been accepted by a local authority. Such households are rarely homeless in the literal sense of being without a roof over their heads, but are more likely to be threatened with the loss of, or are unable to continue with, their current accommodation. A "main homelessness duty" is owed where the authority is satisfied that the applicant is eligible for assistance, unintentionally homeless and falls within a specified priority need group. Such statutorily homeless households are referred to as "acceptances". This dataset provides statistics on the numbers of households accepted as statutorily homeless and presented in terms of acceptances per 1000 households in each local authority area. The total number of acceptances is broken down further according to ethnicity in the related dataset, Homelessness Acceptances. The numbers are presented in terms of households, not individuals. A household is defined as: one person living alone, or a group of people living at the same address who share common housekeeping or a living room. Values of less than five households have been suppressed. In addition, some values of five or greater have been suppressed to prevent other suppressed values being calculated This data is also available in Table 784a, available for download as an Excel spreadsheet.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

this graph was created in PowerBi,R and Loocker studio:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff21bb298c472dbc4bed21ef6dda71d5e%2Fgraph1.jpg?generation=1715375554075996&alt=media" alt="">

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This topic page studies available data and empirical evidence on homelessness, focusing specifically on how it affects people in high-income countries. Homeless people are among the most vulnerable groups in high-income countries.

You can read our topic page on Extreme Poverty if you are interested in a broader perspective on economic deprivation and a perspective beyond high-income countries.

Homeless people in the US What data is available? One of the most common ways to measure homelessness is through so-called 'point-in-time' counts of people who are sleeping in shelters or on the streets. These are figures that are intended to reflect the number of people who are homeless 'on any given night'.

The main source of point-in-time estimates in the US is the Department of Housing and Urban Development, which releases the Annual Homeless Assessment Report to Congress (AHARC). They calculate 'point-in-time' estimates by counting homeless people in late January of each year.

The main underlying sources of data used to produce the figures published in the AHARC are (i) registries from shelters and (ii) counts and estimates of sheltered and unsheltered homeless persons provided by care organizations, as part of their applications for government funding.

The counts from the care organizations (called 'Continuums of Care' in the US) come from active counts that are undertaken at the community level, by walking around the streets, using pre-established methodologies.1

In these figures, 'Sheltered Homelessness' refers to people who are staying in emergency shelters, transitional housing programs, or safe havens. 'Unsheltered Homelessness', on the other hand, refers to people whose primary nighttime residence is a public or private place not designated for, or ordinarily used as, a regular sleeping accommodation for people – for example, the streets, vehicles, or parks.2

PurposeHomeless persons have a high risk for tuberculosis. The prevalence of latent tuberculosis infection and the risk for a progression to active tuberculosis is higher in the homeless than in the general population. The objective was to assess the prevalence and risk factors of tuberculosis/latent tuberculosis infection in a homeless population in Germany.MethodsHomeless individuals (n = 150) were enrolled in a cross-sectional study at three shelters in Münster, Germany (October 2017–July 2018). All participants were screened using an ELISPOT interferon-γ release assay (IGRA). Those participants tested positive/borderline by IGRA provided three sputa for microbiological analysis (line probe assay, microscopy, culture) and underwent a chest X-ray to screen for active pulmonary TB. Risk factors for tuberculosis/latent tuberculosis infection were analysed using a standardized questionnaire.ResultsOf the 142 evaluable IGRA, 21 (15%) were positive and two (1%) were borderline. No participant with a positive/borderline IGRA had an active tuberculosis as assessed by chest X-ray and microbiology. A negative IGRA was associated with a citizenship of a low-incidence country for tuberculosis (according to WHO, p = 0.01), low-incidence country of birth (p<0.001) or main residence in a low-incidence country in the past five years (p = 0.002).ConclusionsThe prevalence of latent tuberculosis infection (diagnosed by a positive/borderline IGRA) was 16%; no active tuberculosis was detected. The highest risk for latent tuberculosis infection was found in patients from high-incidence countries. This population at risk should be either treated for latent tuberculosis infection or need to be monitored to early detect a progression into active disease.

IntroductionCertain living conditions, such as homelessness, increase health risks in epidemic situations. We conducted a prospective observational cohort study to investigate the impact of the COVID-19 pandemic on morbidity and mortality in adult people who were homeless.MethodsThe study population comprised around 40% of the entire population experiencing homelessness in Marseille. They were enrolled at 48 different locations during the first pandemic wave (June to August 2020) and were followed up 3 and 6 months later. Rapid serological screening for SARS-CoV-2 was performed by community outreach teams at each follow-up, who also conducted interviews. Death registers and hospital administrative databases were consulted.ResultsA total of 1,332 participants [mean age 40.1 years [SD 14.2], women 339 (29.9%)] were enrolled in the cohort. Of these, 192 (14.4%) participants were found positive for COVID-19 and were propensity score matched (1:3) and compared with 553 non-COVID-19 cases. Living in emergency shelters was associated with COVID-19 infection. While 56.3% of the COVID-19-infected cohort reported no symptoms, 25.0% were hospitalized due to the severity of the disease. Presence of three or more pre-existing comorbidities was associated with all-cause hospitalization. Among COVID-19 cases, only older age was associated with COVID-19 hospitalization. Three deaths occurred in the cohort, two of which were among the COVID-19 cases.ConclusionThe study provides new evidence that the population experiencing homelessness faces higher risks of infection and hospitalization due to COVID-19 than the general population. Despite the efforts of public authorities, the health inequities experienced by people who are homeless remained major. More intensive and appropriate integrated care and earlier re-housing are needed.

BackgroundHomelessness is one of the most disabling and precarious living conditions. The objective of this Delphi consensus study was to identify priority needs and at-risk population subgroups among homeless and vulnerably housed people to guide the development of a more responsive and person-centred clinical practice guideline.MethodsWe used a literature review and expert working group to produce an initial list of needs and at-risk subgroups of homeless and vulnerably housed populations. We then followed a modified Delphi consensus method, asking expert health professionals, using electronic surveys, and persons with lived experience of homelessness, using oral surveys, to prioritize needs and at-risk sub-populations across Canada. Criteria for ranking included potential for impact, extent of inequities and burden of illness. We set ratings of ≥ 60% to determine consensus over three rounds of surveys.FindingsEighty four health professionals and 76 persons with lived experience of homelessness participated from across Canada, achieving an overall 73% response rate. The participants identified priority needs including mental health and addiction care, facilitating access to permanent housing, facilitating access to income support and case management/care coordination. Participants also ranked specific homeless sub-populations in need of additional research including: Indigenous Peoples (First Nations, Métis, and Inuit); youth, women and families; people with acquired brain injury, intellectual or physical disabilities; and refugees and other migrants.InterpretationThe inclusion of the perspectives of both expert health professionals and people with lived experience of homelessness provided validity in identifying real-world needs to guide systematic reviews in four key areas according to priority needs, as well as launch a number of working groups to explore how to adapt interventions for specific at-risk populations, to create evidence-based guidelines.