When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated 73 homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to 653,104 in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded 243,000. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

In 2023, there were about 653,104 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 647,258. 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.

"Ratio of Homeless Population to General Population in major US Cities in 2011. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. A 2011 result is available for Seattle, WA, Miami, FL, and Boston, MA.. 2011 results are not yet available for the other cities, and their 2009 data are displayed in this chart. General population figures are 2010 estimates in New York, San Francisco, and Chicago, and 2009 estimates elsewhere."

Analysis of ‘Directory Of Unsheltered Street Homeless To General Population Ratio 2012’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3938b01a-ab66-4fd1-967a-478702f97b87 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

--- Original source retains full ownership of the source dataset ---

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.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: Yes (Homeless, refugees, camps)

UNIT DESCRIPTIONS: - Dwellings: A building is an independent free-standing structure irrespective of its construction material, composed of one or more rooms. - Households: A household consists of a person or a group of persons who live together in the same housing unit or part of it and who consider themselves as one unit in terms of the provision of food and/or other essentials of living for the group. When most of the members of such a group are related by blood (i.e., biologically) the group shall be referred to as a Private Household for the purpose of the census. On the other hand when the group (i.e., household as defined earlier) consists of members who are not related by blood and they are more than 10, they will be considered as Non-Institutional Collective Household. Note that if the group consists of 10 or less members, it should be considered a private household. - Group quarters: An institution is usually a set of premises used to house a large number of people who are not related by blood or marriage but bound together by a common objective or personal interest (e.g., universities, boarding houses, hospitals, army barracks, camps, prisons, hotels, etc.)

Universe

Residents of South Sudan

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Southern Sudan Centre for Census, Evaluation and Statistics

SAMPLE DESIGN: Long form questionnaire for sedentary households (selected enumeration areas) and a sample of nomad households.

SAMPLE UNIT: Household

SAMPLE FRACTION: 7%

SAMPLE SIZE (person records): 542,765

Mode of data collection

Face-to-face [f2f]

Research instrument

Two forms: Long Questionnaire (for a sample of areas) and Short Questionnaire (for the rest of the country). The information used here is based on the long form questionnaire.

Study Objective: Facemask use is associated with reduced transmission of SARS-CoV-2. Most surveys assessing perceptions and practices of mask use miss the most vulnerable racial, ethnic, and socio-economic populations. These same populations have suffered disproportionate impacts from the pandemic. The purpose of this study was to assess beliefs, access, and practices of mask wearing across 15 urban emergency department (ED) populations. Methods: This was a secondary analysis of a cross-sectional study of ED patients from December 2020 to March 2021 at 15 geographically diverse, safety net EDs across the US. The primary outcome was frequency of mask use outside the home and around others. Other outcome measures included having enough masks and difficulty obtaining them. Results: Of 2,575 patients approached, 2,301 (89%) agreed to participate; nine had missing data pertaining to the primary outcome, leaving 2,292 included in the final analysis. A total of 79% of respondents reported wearing masks “all of the time” and 96% reported wearing masks over half the time. Subjects with PCPs were more likely to report wearing masks over half the time compared to those without PCPs (97% vs 92%). Individuals experiencing homelessness were less likely to wear a mask over half the time compared to those who were housed (81% vs 96%). Conclusions: Study participants reported high rates of facemask use. Respondents who did not have PCPs and those who were homeless were less likely to report wearing a mask over half the time and more likely to report barriers in obtaining masks. The ED may serve a critical role in education regarding, and provision of, masks for vulnerable populations. Methods Study Design and Setting We conducted this secondary analysis of a previously published study regarding ED patients perceptions’ of COVID-19 vaccination.[13] The parent study was a prospective, cross-sectional survey of ED patients at 15 safety net EDs in 14 US cities. The University of California Institutional Review Board approved this study. Verbal consent was obtained. Data Processing Participant ethnicity (Latinx/non-Latinx) and race were self-reported. We categorized those who self-identified as any race other than Latinx as ‘reported race’, non-Latinx (i.e. Black, non-Latinx and White, non-Latinx). If the patient identified themselves as Latinx, they were placed in that category and not in that of any other race. If an individual identified as more than one non-Latinx race, they were categorized as multiracial. Individuals who reported that they were currently applying for health insurance, were unsure if they were insured, or if their response to the question was missing (18 respondents) were categorized as uninsured in a binary variable, and separate analysis was done based on type of insurance reported. The survey submitted in our supplement (S1) is the version used at the lead site. Each of the remaining sites revised their survey to include wording applicable to their community (i.e., the site in Los Angeles changed Healthy San Francisco to Healthy Los Angeles), and these local community health plans were coded together. We identified individuals who reported English and Spanish as their primary language, and grouped those who reported Arabic, Bengali, Cantonese, Tagalog, or Other as “Other” primary language. With regards to gender, we categorized those who identified as gender queer, nonbinary, trans man and trans woman as “other”. Study Outcomes and Key Variables Our primary outcome was subjects’ response to the question, “Do you wear a mask when you are outside of your home when you are around other people?” with answer choices a) always, b) most of the time (more than 50%), c) sometimes, but less than half of the time (less than 50%), and d) I never wear a mask. Respondents were provided with these percentages to help quantify their responses. We stratified respondents into two groups: those who responded always or most of the time as “wears masks over half the time” and those who responded sometimes or never as “wears masks less than half the time. We sorted each of the 15 sites into four geographic regions within the United States. There were 3 sites located in New Jersey, Massachusetts, and Pennsylvania which we categorized in the Northeast region. We categorized 3 sites in Michigan and Iowa as Midwest, and 3 sites in North Carolina, Louisiana, and Maryland as the South. There were 6 sites located on the West Coast from California and Washington State.

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

Homelessness is a costly and traumatic condition that affects hundreds of thousands of people each year in the U.S. alone. Most homeless programs focus on assisting people experiencing homelessness, but research has shown that predicting and preventing homelessness can be a more cost-effective solution. Of the few studies focused on predicting homelessness, most focus on people already seeking assistance; however, these methods necessarily cannot identify those not actively seeking assistance. Providing aid before conditions become dire may better prevent homelessness. Few methods exist to predict homelessness on the general population, and these methods use health and criminal history information, much of which may not be available or timely. We hypothesize that recent financial health information based on utility payment history is useful in predicting homelessness. In particular, we demonstrate the value of utility customer billing records to predict homelessness using logistic regression models based on this data. The performance of these models is comparable to other studies, suggesting such an approach could be productionalized due to the ubiquity and timeliness of this type of data. Our results suggest that utility billing records would have value for screening a broad section of the general population to identify those at risk of homelessness.

Homelessness is a costly and traumatic condition that affects hundreds of thousands of people each year in the U.S. alone. Most homeless programs focus on assisting people experiencing homelessness, but research has shown that predicting and preventing homelessness can be a more cost-effective solution. Of the few studies focused on predicting homelessness, most focus on people already seeking assistance; however, these methods necessarily cannot identify those not actively seeking assistance. Providing aid before conditions become dire may better prevent homelessness. Few methods exist to predict homelessness on the general population, and these methods use health and criminal history information, much of which may not be available or timely. We hypothesize that recent financial health information based on utility payment history is useful in predicting homelessness. In particular, we demonstrate the value of utility customer billing records to predict homelessness using logistic regression models based on this data. The performance of these models is comparable to other studies, suggesting such an approach could be productionalized due to the ubiquity and timeliness of this type of data. Our results suggest that utility billing records would have value for screening a broad section of the general population to identify those at risk of homelessness.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes - Special populations: Yes (Homeless)

UNIT DESCRIPTIONS: - Dwellings: A dwelling unit may be defined as any structure; permanent, semi permanent or traditional where people live and sleep. It may be a hut, house, stores with a sleeping room or rooms at the back or sides, a shelter of reeds/straw such as those used by fishermen, or any other structure where people sleep. - Households: A household consists of one or more persons, related or unrelated, who live together and make common provision for food. They regularly take all their food from the same pot, and/or share the same grain store (nkhokwe) or pool their incomes for the purpose of purchasing food. Persons in a household may live in one or more dwelling units. - Group quarters: Collective household refers to a large group of people who live together and sharing common facilities such as kitchen, toilet, lounge, and dormitories. In such situation the residents may not have complete independent quarters that qualify as housing units as their living quarters during the census period.

Universe

All persons present in Malawi at the time of census. These include foreigners with acknowledged status as refugees, and citizens of Malawi who at the time of census are absent temporarily (less than 6 months). However, diplomatic personnel of the foreign diplomatic and consular representative offices, foreign military personnel and their family members, and the members and representatives of the international organizations and communities located in Malawi are not enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Statistical Office

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 1,343,078

Mode of data collection

Face-to-face [f2f]

Research instrument

Census questionnaire containing questions on demographic and socio-economic characteristics of the population, dwelling unit characteristics, emigration, and maternal and general deaths