In 2022, **** percent of chronically homeless people in the United States were unsheltered, making them the demographic most likely to be unsheltered when experiencing homelessness. In comparison, homeless people in families were least likely to be unsheltered in that year, at **** percent.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p

The DC Metropolitan Area Drug Study (DCMADS) was conducted in 1991, and included special analyses of homeless and transient populations and of women delivering live births in the DC hospitals. DCMADS was undertaken to assess the full extent of the drug problem in one metropolitan area. The study was comprised of 16 separate studies that focused on different sub-groups, many of which are typically not included or are underrepresented in household surveys. The Homeless and Transient Population study examines the prevalence of illicit drug, alcohol, and tobacco use among members of the homeless and transient population aged 12 and older in the Washington, DC, Metropolitan Statistical Area (DC MSA). The sample frame included respondents from shelters, soup kitchens and food banks, major cluster encampments, and literally homeless people. Data from the questionnaires include history of homelessness, living arrangements and population movement, tobacco, drug, and alcohol use, consequences of use, treatment history, illegal behavior and arrest, emergency room treatment and hospital stays, physical and mental health, pregnancy, insurance, employment and finances, and demographics. Drug specific data include age at first use, route of administration, needle use, withdrawal symptoms, polysubstance use, and perceived risk.This study has 1 Data Set.

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.

Community housing and homeless shelters, mostly small nonprofits, heavily depend on government and charitable funding. According to the Annual Homelessness Assessment Report (AHAR 2023), out % of 653,100 individuals experiencing homelessness, 60.7% were sheltered, while 39.3% remained unsheltered, highlighting a significant underserved market. The pandemic increased unemployment, housing costs and poverty levels, raising demand for shelter services, with government support aiding many establishments. As a result, industry revenue grew at a compound annual growth rate (CAGR) of 5.0%, reaching $21.9 billion by 2024, with a 2.0% climb in 2024 alone. Notably, industry profit rose to 7.0%, with most profit reinvested into operations, as 96.0% of shelters are nonprofits and 98.0% of community housing providers are federally tax-exempt. Individual service needs vary widely. About one-third of shelter services cater to emergency housing. Six out of ten people experiencing homelessness are in urban areas, explaining the concentration of shelters in cities. Also, three out of ten people experiencing homelessness come from a family with children. Catering to a diverse demographic (families, youths, adults, veterans) can restrict economies of scale, but specialized services can attract targeted charitable contributions. Urban shelters face higher rents and costs because of competitive pressures. However, they can gain from group purchasing, network development for better rates and spreading positive information to boost donations. Service provision is expected to remain fragmented, with shelters competing intensely for grants. Donations will fluctuate depending on the economy, increasing during booms and decreasing in downturns. Shelters integrating telehealth, training and security measures may attract a broader group, reducing unsheltered homelessness and increasing revenue for service and infrastructure improvements. Despite favorable economic trends, such as decreasing poverty and unemployment rates and slower housing price growth, revenue will strengthen at a CAGR of only 0.2%, reaching $22.0 billion by 2029.

Abstract copyright UK Data Service and data collection copyright owner. This study was undertaken to establish: the characteristics of single homeless people; the reasons why single people become and remain homeless; the accommodation and support needs and preferences of single homeless people. Main Topics: Demographic details (age, gender, ethnic background, education, employment, income, health, experience of institutions and the armed forces); present and previous accommodation; experience of sleeping rough; reasons for leaving last home; looking for accommodation; accommodation expectations and preferences; need for care and support. Multi-stage stratified random sample Face-to-face interview

2021 is the third time that Prince George has participated in the National Point-in-Time Homeless Count and as with all communities engaging in PiT counts, the COVID-19 pandemic presented a significant set of circumstances to overcome. As there are only two sets of data to compare, it should be cautioned that results and data should be reviewed with this consideration in mind. The basic methodology has not changed from the 2016 count to the 2018 count and on through the 2020/21 count. Training is still required for those participating in surveying respondents. The questions posed on the 2021 PiT Count survey remain the same as previous count questions with just a few minor adjustments. In particular, we determined that some comparative data could now be accessed to ascertain: 1) What impact the pandemic had had on the vulnerable population. 2) What trends or issues may have developed or can be identified through comparative analysis.

Local authorities' action under the homelessness provisions of the 1985 & 1996 Housing Acts, by district. This includes statistics on: Households accepted as being homeless and in prioirty need, by ethnicity. Total decisions made by local housing authorities where the household has been found to be eligible for assistance. Households found to be eligible and in priority need but intentionally homeless Households found to be eligible but not in priority need Households found to be eligible but not homeless Households in temporary accommodation on the last day of the financial year by type of accommodation. Households accepted as being owed a main homelessness duty and for whom arrangements have been made for them, with consent, to remain in their existing accommodation (or to make their own arrangements) for the immediate future. Previously referred to as 'Homeless at Home'.

Homelessness Report May 2025. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).Homelessness data Official homelessness data is produced by local authorities through the Pathway Accommodation and Support System (PASS). PASS was rolled-out nationally during the course of 2013. The Department’s official homelessness statistics are published on a monthly basis and refer to the number of homeless persons accommodated in emergency accommodation funded and overseen by housing authorities during a specific count week, typically the last full week of the month. The reports are produced through the Pathway Accommodation & Support System (PASS), collated on a regional basis and compiled and published by the Department. Homelessness reporting commenced in this format in 2014. The format of the data may change or vary over time due to administrative and/or technology changes and improvements. The administration of homeless services is organised across nine administrative regions, with one local authority in each of the regions, “the lead authority”, having overall responsibility for the disbursement of Exchequer funding. In each region a Joint Homelessness Consultative Forum exists which includes representation from the relevant State and non-governmental organisations involved in the delivery of homeless services in a particular region. Delegated arrangements are governed by an annually agreed protocol between the Department and the lead authority in each region. These protocols set out the arrangements, responsibilities and financial/performance data reporting requirements for the delegation of funding from the Department. Under Sections 38 and 39 of the Housing (Miscellaneous Provisions) Act 2009 a statutory Management Group exists for each regional forum. This is comprised of representatives from the relevant housing authorities and the Health Service Executive, and it is the responsibility of the Management Group to consider issues around the need for homeless services and to plan for the implementation, funding and co-ordination of such services. In relation to the terms used in the report for the accommodation types see explanation below: PEA - Private Emergency Accommodation: this may include hotels, B&Bs and other residential facilities that are used on an emergency basis. Supports are provided to services users on a visiting supports basis. STA - Supported Temporary Accommodation: accommodation, including family hubs, hostels, with onsite professional support. TEA - Temporary Emergency Accommodation: emergency accommodation with no (or minimal) support....

Local authorities' action under the homelessness provisions of the 1985 & 1996 Housing Acts, by district. This includes statistics on: Households accepted as being homeless and in prioirty need, by ethnicity. Total decisions made by local housing authorities where the household has been found to be eligible for assistance. Households found to be eligible and in priority need but intentionally homeless Households found to be eligible but not in priority need Households found to be eligible but not homeless Households in temporary accommodation on the last day of the financial year by type of accommodation. Households accepted as being owed a main homelessness duty and for whom arrangements have been made for them, with consent, to remain in their existing accommodation (or to make their own arrangements) for the immediate future. Previously referred to as 'Homeless at Home'.

Over time, HIV status reporting has improved among the homeless, while it has remained at low levels, and almost constant, among the non-homeless (third column under each population category). At the same time, the percentage of HIV+ TB cases has increased among the homeless, but it has slightly declined among the non-homeless (second column under each population category). To compare against the CDC estimates for 2005 (18), related numbers from the SFDPH TBCP data are also provided for that year.

Homelessness has been a consistent problem for the city of Louisville for decades now. Despite efforts from the city government and local nonprofits, homelessness increased 139% last year alone. The Covid-19 pandemic significantly worsened the crisis, but the risk factors that contribute to homelessness are still endemic across the city: lack of affordable housing, lack of access to physical and mental healthcare, stagnant wages, etc. Homelessness has negative effects on mortality, personal health of the homeless, and public health in general (also see here, no paywall). When I recently attended a strategy meeting for the Louisville Downtown Partnership, one of the top issues voted by attendees was the rise of homelessness downtown. This could come from genuine care or that many Americans associate homeless people with crime. Everyone benefits when the issues that cause homelessness are addressed effectively, and a vital part of that is knowing what areas are most at-risk.The app above was made to map certain risk factors across Jefferson County. The risk factors include percent of households with 50%+ income going to rent, persons without health insurance coverage, percent of households at or below the poverty line, percent of households using public assistance, percent of persons reporting extensive physical and mental distress, unemployment, along with other economic and health-based factors. This doesn’t include every possible factor that could cause homelessness, but many that have strong effects. A dummy census tract was made with all the worst possible outcomes for risk factors, which was then used to rank the similarity of every census tract in Jefferson County; the lower the rank, the more at-risk the tract is. The app allows you to click through every tract in the county and see the ten most at-risk ones.The most at-risk places tend to line up with the west end and areas of the city that were historically redlined. These areas also saw mass amounts of “urban renewal” in the 60s and 70s. They also tend to line up with areas of the city that face the highest eviction rates (thanks to Ryan Massey for pointing this out).

Annual figures for each local authority in England on the outcomes of actions to prevent or relieve homelessness.

Under the Homelessness Act 2002, local housing authorities must have a strategy for preventing homelessness in their district. The strategy must apply to everyone at risk of homelessness, including cases where someone is found to be homeless but not in priority need and cases where someone is found to be intentionally homeless. This means that prevention can be offered to any household, rather than a subset of the population considered eligible for assistance under the statutory homeless legislation.

Homelessness prevention refers to positive action taken by the local authority which provides someone who considers themselves at risk of homelessness with a solution for at least the next six months. This is done by either assisting them to obtain alternative accommodation or enabling them to remain in their existing home.

Homelessness relief occurs when an authority has been unable to prevent homelessness but helps someone to secure accommodation, even though the authority is under no statutory obligation to do so.

This data set brings together the returns for each local authority, from 2009-10 to 2017-18 in one worksheet, based on the government's live table number 792 which provides each year's figures in a separate tab. The data is exactly the same from both sources, this re-formatting simply aims to help use and re-use the data more quickly and easily.

In 2017-18 we have added region codes for each district, and have published a separate data set which gives the totals for each region and the whole of England. The 2017-18 version ​replaces previous versions, as it simply adds seven columns for 2017-18, alongside the columns for each previous financial year.

People experiencing homelessness have disproportionately high rates of traumatic brain injury (TBI), yet services remain inaccessible or poorly adapted to their needs. Limited research has explored the barriers, facilitators and potential solutions to improve healthcare for this population. The objectives were to identify the individual- and environment-level barriers to healthcare for people experiencing homelessness who have sustained a TBI, identify the environment-level facilitators to care for this population, and identify potential solutions to improve care. A qualitative descriptive study was conducted and four focus groups were held (n = 20), consisting of healthcare professionals (n = 10), community workers (n = 6), and managers from both sectors (n = 4). Data were analyzed using Braun and Clarke’s thematic analysis approach. Participants reported: (1) healthcare structures misaligned with the realities of people experiencing homelessness; (2) reduced trust in health services by people experiencing homelessness; (3) reliance on overburdened community organizations lacking TBI expertise; and (4) transforming care requires cross-sector collaborations and rethinking current healthcare delivery to provide more flexible TBI services. Healthcare for this population is not optimal and fails to meet their needs. Implementing low-threshold service models, fostering collaboration, and providing targeted training could significantly improve TBI care for this population. Possible solutions to the current siloed approach to care for people experiencing homelessness (PEH) include the development of mobile health services that include traumatic brain injury (TBI)-specific expertise, and housing services adapted to the needs of people experiencing homelessness living with cognitive and behavioral impairments.Cross-sector collaborative training and initiatives have shown potential for other populations with complex health needs and could be adapted to bridge the gap between TBI-specific and homelessness-specific services.Healthcare policies need to include access to healthcare and rehabilitation services for underserved populations to support transition from homelessness to more humane and adequate housing situations. Possible solutions to the current siloed approach to care for people experiencing homelessness (PEH) include the development of mobile health services that include traumatic brain injury (TBI)-specific expertise, and housing services adapted to the needs of people experiencing homelessness living with cognitive and behavioral impairments. Cross-sector collaborative training and initiatives have shown potential for other populations with complex health needs and could be adapted to bridge the gap between TBI-specific and homelessness-specific services. Healthcare policies need to include access to healthcare and rehabilitation services for underserved populations to support transition from homelessness to more humane and adequate housing situations.

IntroductionPeople experiencing homelessness (PEH) are affected by poor mental and physical health. Crucial healthcare remains inaccessible. In urgent need, people seek assistance in hospitals. The length of stay (LOS) can be used as an indicator of quality in inpatient healthcare. This study aimed to reveal factors influencing the LOS of PEH.MethodsA retrospective secondary data analysis of hospital discharge letters was conducted. Descriptive analyses were used to examine sociodemographics and the LOS in relation to individual disease groups according to the ICD-10. Disease burden was evaluated using a modified Elixhauser Comorbidity Score (ECS). Analyses were conducted separately by sex. Multiple linear regression was used to identify factors influencing the LOS.ResultsThe analysis included 807 hospital discharge letters from 521 PEH. The majority of letters were from men (89.2%). Both groups differed significantly in terms of age, with more women under the age of 30 years (27.1% versus 10.3%, p