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.

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

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

Content

Included in this data set are data elements that will help the public identify agencies that are certified to operate programs for runaway and homeless youth. These programs are available to assist runaway and homeless youth in emergency situation and provide independent living skills for youth in transition. Data elements include the agency name, agency business address, phone number, website and type of program offered.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Zac Ong on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Novel and comprehensive cross-sectional datasets were developed to document and measure city level homeless policies across issue area. The dataset is comprised of the 100 largest cities in the United States, including FIPS identifiers and matched Continuum of Care (CoC) level identifiers by CoC number associated with the city. The datasets include city-level homeless policies in the 100 largest cities across the issues of homeless plans; housing plans with mentions of homelessness; homeless outreach teams; and the role of sanitation in homeless policy. Each dataset includes sub-codes to evaluate the governance structure and policy goals of each type of city-level homeless policy. Primary data were collected in 2021 and 2022. Please see the attached publications for complete methodology and data collection procedures for each policy type.

Homeless and battered women's shelters compiled from Reference USA. Reference USA is an internet-based reference service from the Government Division of InfoGroup. This site was designed as a reference to government agencies. ReferenceUSAGov database contains more than 57 million US businesses, 320 million residents, and 855,000 healthcare providers. InfoGroup compiles information from public sources, including yellow pages and business white pages telephone directories, annual reports, federal government data, leading business magazines trade newsletters, major newspapers, industry and specialty directories, and postal service information. Over 350 database specialists make phone calls to verify information on business and healthcare providers in the database, placing in excess of 24 million phone calls annually.

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?

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

This study examines the spatial patterns of homelessness and resources for the homeless population in Louisville, KY with the goal of identifying where homeless populations are located in relation to resources. Working with census data and some of the resources for the homeless, this study uncovers the realities that the homeless face in different parts of the city. This research research was made as a senior thesis for the University of Louisville's department of Geographic and Environmental Sciences. Table 1. Income and Poverty between the United States and Louisville/Jefferson County metro government, Kentucky in 2019 (United States Census Bureau 2021)Homeless people are thought of as less than full citizens. Whether the rest of the city's people agree or disagree, they are citizens, and should have rights to the city as much as everyone else. The opioid crisis, unmanaged mental illnesses, lack of employment, and other issues like limitations on affordable housing have increased the population of homeless people in Louisville in recent years (Reed 2021). More than 1.5 million children experience homelessness in the United States (Poverty USA 2019). The poverty rate in Louisville, Kentucky is 15.9%, and 1 in 10 renters were facing eviction as of 2019. The 2019 Point In Time Count shows that on a randomly picked night in Louisville, 1071 of the city's people are experiencing homelessness, which is an increase of 15% from the 2018 count (Coalition for the Homeless 2019). The previous data compared to the count for 2020 of 1102 people, shows a trend in increasing homeless population (Coalition for the Homeless 2020).

The HELP Act Goals were developed by the California Interagency Council on Homelessness (Cal ICH), pursuant to Homeless Equity for Left Behind Populations (HELP) Act (SB 914). The goals help the state assess its progress toward reducing and ending homelessness for survivors of domestic violence, their children, and unaccompanied women. Measures for each goal are generated using data from the state's Homelessness Data Integration System (HDIS). Values under 11 and values that allow numbers under 11 to be calculated are suppressed by an asterisk. Cells are blank if data is not available for a given year. For more information about the measures and how they are calculated, please see the HELP Act Data Glossary: https://bcsh.ca.gov/calich/documents/help_data_dictionary.pdf For more information about HDIS, please visit https://bcsh.ca.gov/calich/hdis.html.

This page details the data sources and methodology used in the 2023 report by Avery Fairburn, “Examining Jail Data in Hamilton County, TN”. Interactive data visualizations for the report can be found here. A data dictionary is provided in the file section.

Silverdale Detention Center Booking Records

This dataset was collected using a web scraper tool created by Wren Tefft, and the records included in the sample are from August 2nd, 2022 through January 31st, 2023. This data is still being collected daily. The scraper pulls the name, home address, age, charges, and arresting agency for each person booked into the jail. In the file available for download on this page, I have removed the names and street addresses of arrested individuals to maintain their privacy, but have included the city, state, and ZIP code.

Addresses

The addresses in these records are provided by arrestees upon being booked, and recorded by jail staff. The raw data contained a considerable number of errors, so I tested the validity of addresses by using Google’s Address Validation API, and categorized them based on the results:

Address Status - Valid w/ No Errors: Address was able to be identified by the Google API and confirmed as a known address of record with USPS, and included no errors. Non-address values (such as “Homeless”) that had no errors are included in this category as well. - Valid w/ Errors: Address was confirmed but included errors (such as a misspelled street name or city name, or an incorrect ZIP code). Non-address values (such as “Homeless”) that contained errors are included in this category as well. - Invalid: Address was not able to be confirmed, due to either too many errors, missing address components, or non-existent address components (such as a street number that did not correspond with any real location). - No Apt. Number: Address was confirmed, but is invalid due to a missing unit number. These addresses are included in analysis, as the street address is correct, but otherwise considered invalid as they are undeliverable. - None: No address or other value was listed by jail staff.

I also categorized addresses by type, to account for the fact that a large number of arrestees were listed as homeless, living at a hotel or homeless shelter, or living at a commercial address. Categories are detailed below:

Address Type - Single-Unit Residential: Valid residential addresses that do not contain a unit number. - Multi-Unit Residential Residential addresses that contain (or should contain) a unit number. Addresses that were missing a unit number are included in this category. - Commercial: Valid non-residential addresses not listed in another category. - Hotel: Valid addresses of hotels. - Community Kitchen: The address of a homeless service provider in Chattanooga, listed as the home address for a significant portion of arrestees. - Homeless: Arrestees that had “Homeless”, “Transient”, or variations listed instead of an address. - P.O. Box: P.O. boxes that were listed as home addresses. - Invalid: Addresses that were not able to be confirmed, due to either too many errors, missing address components, or non-existent address components (such as a street number that did not correspond with any real location). - None: No address or other value was listed by jail staff.

Primary Charges

To choose the primary charge in arrests that included multiple different charges, I used this method: Charges were ranked first by classification, from highest (Class A felony) to lowest (Class C misdemeanor). Out of a group of multiple charges, the primary charge would be the one with the highest classification. If there were multiple charges with the same classification (e.g. two class A misdemeanor charges), then the one listed first in the booking record was identified as the primary charge.

I made exceptions to this method for Violation of Probation, Failure to Appear charges, and Resisting or Evading Arrest charges, which I did not list as the primary charge except when there were no other charges. This was to account for the fact that Failure to Appear charges are typically issued as warrants, and the fact that being charged with another crime while on probation typically constitutes a probation violation.

There was also a group of charges that I did not list as primary unless they were the sole charge, due to the fact that their classification or definition is dependent on other charges. These charges were Possession of Firearm During a Felony, Contributing to the Deli...

The Fortune Society, a private not-for-profit organization located in New York City, provides a variety of services that are intended to support former prisoners in becoming stable and productive members of society. The purpose of this evaluation was to explore the extent to which receiving supportive services at the Fortune Society improved clients' prospects for law abiding behavior. More specifically, this study examined the extent to which receipt of these services reduced recidivism and homelessness following release. The research team adopted a quasi-experimental design that compared recidivism outcomes for persons enrolled at Fortune (clients) to persons released from New York State prisons and returning to New York City and, separately, inmates released from the New York City jails, none of whom went to Fortune (non-clients). All -- clients and non-clients alike -- were released after January 1, 2000, and before November 3, 2005 (for state prisoners), and March 3, 2005 (for city jail prisoners). Information about all prisoners released during these time frames was obtained from the New York State Department of Correctional Services for state prisoners and from the New York City Department of Correction for city prisoners. The research team also obtained records from the Fortune Society for its clients and arrest and conviction information for all released prisoners from the New York State Division of Criminal Justice Services' criminal history repository. These records were matched and merged, producing a 72,408 case dataset on 57,349 released state prisoners (Part 1) and a 68,614 case dataset on 64,049 city jail prisoners (Part 2). The research team obtained data from the Fortune Society for 15,685 persons formally registered as clients between 1989 and 2006 (Part 3) and data on 416,943 activities provided to clients at the Fortune Society between September 1999 and March 2006 (Part 4). Additionally, the research team obtained 97,665 records from the New York City Department of Homeless Services of all persons who sought shelter or other homeless services during the period from January 2000 to July 2006 (Part 5). Part 6 contains 96,009 cases and catalogs matches between a New York State criminal record identifier and a Fortune Society client identifier. The New York State Prisons Releases Data (Part 1) contain a total of 124 variables on released prison inmate characteristics including demographic information, criminal history variables, indicator variables, geographic variables, and service variables. The New York City Jails Releases Data (Part 2) contain a total of 92 variables on released jail inmate characteristics including demographic information, criminal history variables, indicator variables, and geographic variables. The Fortune Society Client Data (Part 3) contain 44 variables including demographic, criminal history, needs/issues, and other variables. The Fortune Society Client Activity Data (Part 4) contain seven variables including two identifiers, end date, Fortune service unit, duration in hours, activity type, and activity. The Homelessness Events Data (Part 5) contain four variables including two identifiers, change in homeless status, and date of change. The New York State Criminal Record/Fortune Society Client Match Data (Part 6) contain four variables including three identifiers and a variable that indicates the type of match between a New York State criminal record identifier and a Fortune Society client identifier.

The Local Employment Dynamics (LED) Partnership is a voluntary federal-state enterprise created for the purpose of merging employee, and employer data to provide a set of enhanced labor market statistics known collectively as Quarterly Workforce Indicators (QWI). The QWI are a set of economic indicators including employment, job creation, earnings, and other measures of employment flows. For the purposes of this dataset, LED data for 2018 is aggregated to Census Summary Level 070 (State + County + County Subdivision + Place/Remainder), and joined with the Emergency Solutions Grantee (ESG) areas spatial dataset for FY2018. The Emergency Solutions Grants (ESG), formally the Emergency Shelter Grants, program is designed to identify sheltered and unsheltered homeless persons, as well as those at risk of homelessness, and provide the services necessary to help those persons quickly regain stability in permanent housing after experiencing a housing crisis and/or homelessness. The ESG is a non-competitive formula grant awarded to recipients which are state governments, large cities, urban counties, and U.S. territories. Recipients make these funds available to eligible sub-recipients, which can be either local government agencies or private nonprofit organizations. The recipient agencies and organizations, which actually run the homeless assistance projects, apply for ESG funds to the governmental grantee, and not directly to HUD. Please note that this version of the data does not include Community Planning and Development (CPD) entitlement grantees. LED data for CPD entitlement areas can be obtained from the LED for CDBG Grantee Areas feature service. To learn more about the Local Employment Dynamics (LED) Partnership visit: https://lehd.ces.census.gov/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_LED for ESG Grantee Areas

Date of Coverage: ESG-2021/LED-2018

This letter includes general preparation information, funding to states to mitigate COVID-19, guidance to prepare homeless shelters, and more. Browse All COVID-19 Resources Metadata-only record linking to the original dataset. Open original dataset below.

May 29, 2014

Dear Colleague:

May is National Foster Care Month, a time for our Nation to reaffirm its commitment to America’s children. Last year, roughly 200,000 young people entered into foster care because of abuse and/or neglect. Inadequate housing was a factor in many of these cases. In fact, every year, inadequate housing contributes to the removal of 22,000 children from their families. This can have lasting consequences for young people.

Research shows that children facing housing instability, homelessness, and poverty are more likely to be involved in the child welfare system. When a family is living in distressed conditions or experiencing homelessness, it can affect their ability to care for their kids, and it can have a negative impact on the ability of kids to learn in school, maintain good health, and keep their hope for the future. With this in mind, it is critical that we do everything we can to provide them with the safe and stable housing they need to succeed.

To achieve this goal, it is critical that all of us—Federal agencies, public housing authorities, Continuums of Care, and local child welfare agencies—closely collaborate with each other. The needs of families are diverse. Some need intensive support and long-term access to appropriate services. Others simply need financial assistance to care for their children. In many cases, neither child welfare agencies nor programs aimed at preventing homelessness can meet all of these needs alone.

The programs authorized by title IV-B of the Social Security Act provide a limited pool of funds to prevent the removal of children from their homes or to help those in foster care reunite with their families. In general, states use title IV-B funds for short-term, crisis-driven interventions and services, which may include one-time assistance with housing, utilities, or other related housing costs. For many of these families, gaining access to reliable housing supports, such as provided through HUD’s Housing Choice Voucher (HCV) or public housing programs, can provide the key to a stable future.

We know that families are more likely to remain housed if they have a targeted service paired with appropriate housing that meets their needs. Through close collaboration, child welfare agencies and public housing agencies can provide these paired services to keep families and youth in safe and appropriate housing. One example is HUD’s Family Unification Program (FUP).

A special purpose voucher program, FUP demonstrates how local partnerships can address housing needs for families using child welfare services and youth aging out of foster care. Similarly, public housing agencies and child welfare agencies can come together to establish a local preference for families referred by child welfare and couple this housing assistance with supportive services. Child welfare agencies can also collaborate with private multifamily housing owners that provide HUD-assisted rental assistance, or by partnering with state or local housing agencies to develop local affordable housing through the Low-Income Housing Tax Credit (LIHTC) and HUD’s HOME Investment Partnerships Program. Together, child welfare agencies, housing agencies, and Continuums of Care can create an array of housing interventions to serve these children, youth, and families better.

Currently, The Children’s Bureau has two sets of grants aimed at providing more information about successful housing interventions for these vulnerable families. One develops strategies for homeless youth and the other targets homeless families. HUD and the U.S. Department of Health and Human Services’ Administration for Children and Families will continue working together to develop and disseminate information about promising practices and strategies for serving this population.

Opening Doors: The Federal Strategic Plan to End Homelessness recognizes the critical needs of youth and families by designating them as two prio

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.

The Local Employment Dynamics (LED) Partnership is a voluntary federal-state enterprise created for the purpose of merging employee, and employer data to provide a set of enhanced labor market statistics known collectively as Quarterly Workforce Indicators (QWI). The QWI are a set of economic indicators including employment, job creation, earnings, and other measures of employment flows. For the purposes of this dataset, LED data for 2018 is aggregated to Census Summary Level 070 (State + County + County Subdivision + Place/Remainder), and joined with the Continuum of Care Program grantee areas spatial dataset for FY2017. The Continuum of Care (CoC) Homeless Assistance Programs administered by HUD award funds competitively and require the development of a Continuum of Care system in the community where assistance is being sought. A continuum of care system is designed to address the critical problem of homelessness through a coordinated community-based process of identifying needs and building a system to address those needs. The approach is predicated on the understanding that homelessness is not caused merely by a lack of shelter, but involves a variety of underlying, unmet needs - physical, economic, and social. Funds are granted based on the competition following the Notice of Funding Availability (NOFA). Please note that this version of the data does not include Community Planning and Development (CPD) entitlement grantees. LED data for CPD entitlement areas can be obtained from the LED for CDBG Grantee Areas feature service. To learn more about the Local Employment Dynamics (LED) Partnership visit: https://lehd.ces.census.gov/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_LED for CoC Grantee Areas

Date of Coverage: CoC-2021/LED-2018

ABSTRACT This study aimed to identify the state of the art on the health care of homeless persons in the daily life of Primary Health Care. The scoping review method proposed by the Joanna Briggs Institute (JBI) was adopted, and the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist was used for greater methodological transparency and rigor in the presentation of results. The database search took place in October 2021, and included PubMed, LILACS, Scopus, Cochrane Central, Web of Science and CINAHL. A total of 21.940 articles were found in the six databases, of which 31 articles constituted the final sample of this study. This review corroborated that the health care of homeless persons is a public health challenge and requires more professional investment and cross-cutting policies. As the health needs of these people have a different configuration and call for immediate attention, building a bond and developing health promotion actions is a challenge, considering the multifactorial and multifaceted aspects that involve homeless persons.