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 graph was created in PowerBi,R and Loocker studio:

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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

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<0.005) were more likely to report positive attitudes, whereas those from France and Poland (p<0.001) were less likely to report positive attitudes.ConclusionThe majority of European citizens hold positive attitudes towards people who are homeless, however there remain significant differences between and within countries. Although it is clear that there is strong support for increased government action and more effective solutions for Europe’s growing homelessness crisis, there also remain public opinion barriers rooted in enduring negative perceptions.

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?

Background: Homeless people are a socially excluded group whose health reflects exposures to intersecting social determinants of health. The aim of this study was to describe and compare the demographic composition, certain social determinants of health, and self-reported health among homeless people in Stockholm, Sweden, in 2006 and 2018.Methods: Analysis of data from face-to-face interviews with homeless people in Stockholm 2006 (n = 155) and 2018 (n = 148), based on a public health survey questionnaire adapted to the group, including the EQ-5D-3L instrument. The chi-squared test was employed to test for statistical significance between groups and the independent t-test for comparison of mean scores and values. Ordinary Least Squares (OLS) regression, with Robust Standard Errors (RSE) was performed on merged 2006 and 2018 data with mean observed EQ VAS score as outcome variable.Results: In 2018 more homeless people originated from countries outside Europe, had temporary social assistance than long-term social insurance, compared to in 2006. In 2018 more respondents reported lack of social support, exposure to violence, and refrained from seeking health care because of economic reasons. Daily smoking, binge drinking, and use of narcotic drugs was lower 2018 than 2006. In 2018 a higher proportion reported problems in the EQ-5D-3L dimensions, the mean TTO index value and the VAS index value was significantly lower than in 2006. In the regression analysis of merged data there was no significant difference between the years.Conclusions: Homeless people are an extremely disadvantaged group, have high rates of illness and disease and report poor health in all EQ-5D-3L dimensions. The EQ VAS score among the homeless people in 2018 is comparable to the score among persons aged 95–104 years in the general Swedish population 2017. The EQ-5D-3L instrument was easily administered to this group, its use allows comparison with larger population groups. Efforts are needed regarding housing, but also intensified collaboration by public authorities with responsibilities for homeless people's health and social welfare. Further studies should evaluate the impact of such efforts by health and social care services on the health and well-being of homeless people.

Don't Patronize Me! -- Data Statement

A. CURATION RATIONALE

We present Don’t Patronize Me!, an annotated dataset with Patronizing and Condescending Language (PCL)towards vulnerable communities. This annotated data is especially aimed at the NLP community in order to help improve the modelling and detection of PCL when referring to vulnerable communities, with the ultimate goal of producing and consuming a more responsible and inclusive communication. The Don’t Patronize Me! dataset (v.1.0) consists of 7,738 paragraphs about vulnerable communities extracted from news stories from the News on Web (NoW) corpus (https://www.english-corpora.org/now/, used with permission). This original corpus contains more than 18 million articles crawled from online media in 20 English-speaking countries from 2010 until 2018. In order to create our own dataset, we automatically selected from the NoW corpus just those articles where at least one word from a list of selected keywords was present. The articles were then divided per country and keyword and split into paragraphs. With the objective of assuring a balanced representation of countries and keywords, we randomly selected 75 paragraphs per keyword and country using theSciKitLearn library [11]. The final dataset will be a collection of 15,000 paragraphs with PCL annotations referring to vulnerable communities (150 per keyword; 750 per country).

Countries represented in the Don’t Patronize Me! dataset: Australia, Hong Kong, Sri Lanka, Pakistan, Bangladesh, Ireland, Malaysia, Singapore, Canada, India, Nigeria, Tanzania, United Kingdom, Jamaica, New Zealand, United States, Ghana, Kenia, Philipines, South Africa.

The keywords include seven potentially vulnerable groups which are widely referred to in general media and are potential recipients of condescending treatment. The remaining three keywords are concepts usually used to describe the former communities or the situations they live.

Keywords: Disable, Homeless, Immigrant, Migrant, Poor families, Women, Hopeless, Vulnerable, In need, Refugee.

B. LANGUAGE VARIETIES

The paragraphs included in the Don’t Patronize Me! dataset are written in English. Twenty English speaking countries are represented in the dataset, thus all their varieties of English are expected to be present in the corpus. See below for the codes of the English varieties as recommended in BCP-47. It is not possible for us to know either the regional varieties of English in each country if any, or if English is the speaker’s first language.

Language varieties in the dataset: en-AU, en-HK, en-LK, en-PK, en-BD, en-IE, en-MY, en-SG, en-CA, en-IN, en-NG, en-TZ, en-GB, en- JM, en-NZ, en-US, en-GH, en-KE, en-PH, en-ZA

C. SPEAKER DEMOGRAPHIC

As the paragraphs of our dataset are extracted from another corpus, we do not have the possibility to trace socio-demographic data of the speakers. Nevertheless, we can assume a) they are journalist, as they work in the media, so they are educated professionals; b) they speak English, although we do not know if this is their first language, and c) there is a wide representation of different races and ethnic origins, as we collect texts from 20 countries. In our dataset, each country contributes with 750 articles, so this is the maximum number of different authors we could have per country. We have not observed any disorder of speech, as the texts are written, probably edited and reviewed before their publication.

D. ANNOTATOR DEMOGRAPHIC

The annotators who collaborated in this dataset are three white females, with ages between 25 and 35 years old. Their first language is Spanish, but they are proficient in English. They all have graduate and postgraduate studies in communication, computer science and data science.

E. SPEECH SITUATION

The news stories from where the paragraphs of our dataset are extracted were published between 2010 and 2018 in 20 countries (see section A). The stories are asynchronous communication, written, edited and probably reviewed before publishing. The texts are published news articles; thus they are likely intended to reach a general audience, although the characteristics of the audience might vary depending on the country of publication.

F. TEXT CHARACTERISTICS

The texts of the dataset belong to the journalism genre and the topics have been previously selected to cover the treatment of the media towards potentially vulnerable groups, as explained in section A.

Background: Homelessness is an increasing problem in Western European countries. In the Netherlands, policy reforms and austerity measures induced an urgent need for management information on local homeless citizens. Municipal authorities initiated cross-sectional reviews of Homeless Service (HS) users. The resulting Homeless People Treatment and Recovery (HOP-TR) study developed a health and needs assessment strategy over different domains to comprehensively assess individuals and care networks with the perspective on recovery.Methods: Dutch HS users were selected using a naturalistic meta-snowball sampling. Semi-structured interviews provided the primary data source. The interview content was partly derived from the InterRAI Community Mental Health questionnaire and the “Homelessness Supplement.” Using the raw interview data, algorithmic summary scores were computed and integrating clinical parameters assessed. The data describe health and needs in a rights-based, recovery-oriented frame of reference. The mental health approach is transdiagnostic. The positive health framework is used for structuring health and needs aspects in relation to the symptomatic (physical and mental health), social (daily living, social participation), and personal (quality of life, meaning) dimensions of recovery.Results: Recruitment (between 2015 and 2017) resulted in a saturated sample of 436 HS users in 16 facilities and seven cities. Most participants were long-term or intermittently homeless. The sample characteristics reveal the multi domain character of needs and the relevance of a broad, comprehensive approach. Local authorities used the reports to reflect and discuss needs, care provision, access, and network cooperation. These dialogs incited to improve the quality of care at various ecosystem levels.Discussion: This paper describes new recruitment strategies and data collections of comprehensive data domains, to improve our knowledge in the field of homelessness. Traditional epidemiological literature on homelessness is often domain specific and relies on administrative sources. The HOP-TR study uses an analytical epidemiological approach. It shifts the assessment focus from problem-centered marginalization processes toward a comprehensive, three-dimensional recovery-oriented vision of health. Different perspectives are integrated to explore the interaction of homeless people with care networks.

Dear Partners,

This month, the Administration for Children and Families (ACF) observed World Day Against Child Labor by spotlighting and encouraging those, who could, to join the Within and Beyond Our Borders: Collective Action to Address Hazardous Child Labor organized by the U.S. Department of Labor (DOL) on June 12, 2023. If you missed it, or would like to rewatch it, you can find it here

.

Since 2018, the DOL has seen a 69 percent increase in children being employed illegally by companies. In the last fiscal year, the department found that 835 companies it investigated had employed more than 3,800 children in violation of labor laws. There has been a 26 percent increase in children employed in hazardous occupations. These numbers tell us that we have work to do as the human services sector to learn more and become engaged in preventing unlawful child labor and supporting youth.

As I have said before, child labor exploitation can disrupt a youth’s health, safety, education, and overall well-being, which are unacceptable consequences for any child. The Administration for Children and Families (ACF) supports a broad network of resources for vulnerable youth. We know that migrant and immigrant youth are especially vulnerable to exploitation, and it is often youth in or exiting the child welfare system who are targeted for various forms of exploitation. Child labor exploitation can impact children and youth across demographics.

On March 24, 2023, the DOL and the U.S. Department of Health and Human Services (HHS) announced a Memorandum of Agreement - PDF

to advance ongoing efforts to address child labor exploitation. In addition, DOL and HHS are collaborating on training and educational materials.

As we expand this work, we know how important our partners throughout the country are in this effort. The Administration for Children and Families (ACF) is committed to addressing the increased presence of child labor exploitation through a variety of actions including equipping partners with materials and educational resources to build knowledge about child labor laws and rights, and remedies. This information is important for our human services sector and the children and families who may be most at risk.

Please join ACF in increasing awareness and distributing resources to address child labor exploitation including the following:

ACF resources may be also useful when working with a youth who has concerns about their safety. This includes the Family and Youth Services Bureau (FYSB)’s program on Runaway and Homeless Youth which provides a hotline for youth, concerned adults, and providers to access resources. At, www.1800runaway.org

, their 24/7 crisis connection allows for calls, texts, live chat, and email to get information and resources.

In addition, ACF’s Office of Trafficking In-Persons (OTIP) is an important resource for identifying and supporting survivors of trafficking. The National Human Trafficking Hotline

provides a 24/7, confidential, multilingual hotline for victims, survivors, and witnesses of human trafficking. While labor exploitation should not be conflated with labor trafficking, in some cases labor exploitation may rise to meet the legal definitions of trafficking. The OTIP website

contains many resources for grantees and communities on labor trafficking.

Again, I hope you will continue to build awareness for yourself, your organization, or your community on child labor exploitation. It takes a whole community effort to support our children and youth.

Most sincerely,

January Contreras

Metadata-only record linking to the original dataset. Open original dataset below.

Background: Homelessness is an increasing problem in Western European countries. Dutch local authorities initiated cross-sectional reviews to obtain accurate health and needs information on Homeless Service (HS) users.Methods: The Homeless People Treatment and Recovery (HOP-TR) study uses a comprehensive assessment strategy to obtain health data. Using a naturalistic meta-snowball sampling in 2015–2017, 436 Dutch HS users were assessed. The lived experience of HS users was the primary data source and was enriched with professional assessments. The InterRAI Community Mental Health questionnaire and “Homelessness Supplement” provided information in different areas of life. The approach for mental health assessments was transdiagnostic. Raw interview data were recoded to assess health and needs. The positive health framework structured symptomatic, social, and personal health domains relevant to recovery.Results: Most subjects were males, low educated, with a migration background. The majority were long-term or intermittently homeless. Concurrent health problems were present in two domains or more in most (95.0%) subjects. Almost all participants showed mental health problems (98.6%); for a significant share severe (72.5%). Frequent comorbid conditions were addiction (78%), chronic physical conditions (59.2%), and intellectual impairments (39.9%).Conclusion: The HOP-TR study reveals significant concurrent health problems among Dutch HS users. The interdependent character of different needs requires an integrated 3-D public health approach to comprehensively serve symptomatic, social, and personal dimensions, required to facilitate recovery.

Seroprevalence of Toxoplasma gondii has been extensively studied in a variety of different human populations. However, no study has focused on homeless populations. Accordingly, the present study aimed to assess the seroprevalence of anti-T. gondii antibodies and the risk factors associated in homeless persons from homeless shelter of São Paulo city, southeastern Brazil. In addition, anti-HIV antibodies and associated risk of T. gondii and HIV coinfection have been evaluated. Anti-T. gondii antibodies were detected by indirect fluorescent antibody test. In addition, anti-HIV levels were tested by chemiluminescence enzyme immunoassay, with positive samples confirmed by rapid immunoblot assay. Overall, IgG anti-T. gondii seropositivity was found in 43/120 (35.8%) homeless persons, with endpoint titers varying from 16 to 1,024. The only two pregnant women tested were negative for IgM by chemiluminescence enzyme immunoassay, with normal parturition and clinically healthy newborns in both cases. There were no statistical differences in the risk factors for anti-T. gondii serology (p > 0.05). Anti-HIV seropositivity was found in 2/120 (1.7%) homeless persons, confirmed as HIV-1. One HIV seropositive individual was also sero-reactive to IgG anti-T. gondii, and both were negative to IgM anti-T. gondii. This is the first study that reports the serosurvey of T. gondii in homeless persons worldwide. Despite the limited sample size available in the present study, our findings have shown that the prevalence of anti-T. gondii antibodies in homeless persons herein was lower than the general population, probably due to homeless diet habit of eating mainly processed food intake. No statistical differences were found regarding risk factors for anti-T. gondii exposure in homeless persons. Future studies should be conducted to fully establish risk factors for anti-T. gondii exposure in homeless persons.

This dataset "Global hotspots of climate related disasters" shows the number of people impacted by climate-related disasters recorded in the EM-DAT database between 2000 and 2020. This dataset was used to prepare the maps and the analysis of the paper Donatti C.I., Nicholas K., Fedele G., Delforge D., Speybroeck N., Moraga P., Blatter J., Below R., Zvoleff A. 2024. Global hotspots of climate-related disasters. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2024.104488. This dataset includes information on people impacted by Drought, tropical cyclones, flash flood, riverine flood, forest fire, land fire, heat wave, landslide and mudslide. Data on coastal flood was not included because the database only had recordings until 2013. Data on disaster sub-types “landslides” and “mudslides” as presented in the EM-DAT were further combined as one single climate-related disaster (“land and mudslides”) for the analyses. Likewise, data on disaster sub-types “forest fire” and “land fire” were further combined as one climate-related disaster (“wildfire”). The data was accessed directly from the EM-DAT database and then summarized as show in the dataset. We used this database, downloaded on June 2nd 2021, to access data on “total affected” people and the “total deaths” per disaster event impacting a country (i.e., an entry in the EM-DAT), which were combined in this study to create the variable “total people impacted”. In the EM-DAT database, “total affected” represents the sum of people “injured,” “affected,” and “homeless” resulting from a particular event. “Injured” were considered those that have suffered from physical injuries, trauma, or an illness requiring immediate medical assistance, including people hospitalized, as a direct result of a disaster, “affected” were considered people requiring immediate assistance during an emergency and “homeless” were considered those whose homes were destroyed or heavily damaged and therefore needed shelter after an event. “Total deaths” include people that have died or were considered missing, those whose whereabouts since the disaster were unknown and presumed dead based on official figures. More details can be found under “documentation, data structure and content description” at emdat.be. In the dataset, "ADM-CODE" refers to the code used to identify each administrative area, which refers to the code of FAO's Global Administrative Unit Layer, GAUL.

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.