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

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.

About Dataset

The dataset you provided, titled "Report Card Enrollment 2023-24 School Year," appears to be a comprehensive collection of information regarding student enrollment and demographics within educational institutions for the specified academic year. Here are some observations about the dataset:

1. Granularity: The dataset seems to be quite granular, providing detailed information not only about overall student enrollment but also about various demographic categories such as gender, race/ethnicity, English language learners, students with disabilities, and socioeconomic status.

2. Demographic Diversity: It captures the diversity of the student population by including counts for various racial/ethnic groups, as well as categories such as gender X, indicating a recognition of diverse gender identities.

3. Socioeconomic Indicators: The dataset includes indicators of socioeconomic status such as students in foster care, homeless students, and those from low-income families, which can provide insights into equity and access issues within the educational system.

4. Special Education and Gifted Programs: It tracks the enrollment of students with disabilities and those identified as highly capable, which are important metrics for evaluating the inclusivity and effectiveness of special education and gifted programs.

5. Geographical Context: The dataset includes information about the county, educational service district, and school district, providing a geographical context for the enrollment data.

6. Temporal Aspect: The "DataAsOf" column indicates that the data has a temporal aspect, suggesting that it may be periodically updated to reflect changes in enrollment and demographics throughout the academic year.

**columns : ** SchoolYear: Indicates the academic year for which the data is reported, in this case, it's 2023-24.

OrganizationLevel: Specifies the level of educational organization, which could be school, district, or any other relevant level within the educational system.

County: Refers to the county where the educational organization is located.

ESDName: Stands for Educational Service District Name, which represents the intermediate level of educational administration in some states.

ESDOrganizationID: ID assigned to the Educational Service District.

DistrictCode: Code assigned to the district within the educational system.

DistrictName: Name of the school district.

DistrictOrganizationId: ID assigned to the district organization.

SchoolCode: Code assigned to the school within the district.

SchoolName: Name of the school.

SchoolOrganizationID: ID assigned to the school organization.

CurrentSchoolType: Indicates the current type of the school, such as elementary, middle, or high school.

GradeLevel: Specifies the grade level(s) served by the school.

All Students: Total number of enrolled students in the school.

Female: Number of female students enrolled.

Gender X: Number of students who identify as gender X, indicating a non-binary or non-conforming gender identity.

Male: Number of male students enrolled.

American Indian/ Alaskan Native: Number of students identifying as American Indian or Alaskan Native.

Asian: Number of students identifying as Asian.

Black/ African American: Number of students identifying as Black or African American.

Hispanic/ Latino of any race(s): Number of students identifying as Hispanic or Latino of any race.

Native Hawaiian/ Other Pacific Islander: Number of students identifying as Native Hawaiian or other Pacific Islander.

Two or More Races: Number of students identifying as belonging to two or more races.

White: Number of students identifying as White.

English Language Learners: Number of students who are learning English as a second language.

Foster Care: Number of students in foster care.

Highly Capable: Number of students identified as highly capable or gifted.

Homeless: Number of students experiencing homelessness.

Low-Income: Number of students from low-income families.

Migrant: Number of students from migrant families.

Military Parent: Number of students with parents serving in the military.

Mobile: Number of students who frequently change residences.

Section 504: Number of students covered under Section 504 of the Rehabilitation Act, which provides accommodations for students with disabilities.

Students with Disabilities: Number of students with disabilities.

Non-English Language Learners: Number of students who are not learning English as a second language.

Non-Foster Care: Number of students who are not in foster care.

Non-Highly Capable: Number of students who are not identified as highly capable or gifted.

Non-Homeless: Number of students wh...

BackgroundPersons experiencing homelessness and vulnerable housing or those with lived experience of homelessness have worse health outcomes than individuals who are stably housed. Structural violence can dramatically affect their acceptance of interventions. We carried out a systematic review to understand the factors that influence the acceptability of social and health interventions among persons with lived experience of homelessness.MethodsWe searched through eight bibliographic databases and selected grey literature sources for articles that were published between 1994 and 2019. We selected primary studies that reported on the experiences of homeless populations interacting with practitioners and service providers working in permanent supportive housing, case management, interventions for substance use, income assistance, and women- and youth-specific interventions. Each study was independently assessed for its methodological quality. We used a framework analysis to identify key findings and used the GRADE-CERQual approach to assess confidence in the key findings.FindingsOur search identified 11,017 citations of which 35 primary studies met our inclusion criteria. Our synthesis highlighted that individuals were marginalized, dehumanized and excluded by their lived homelessness experience. As a result, trust and personal safety were highly valued within human interactions. Lived experience of homelessness influenced attitudes toward health and social service professionals and sometimes led to reluctance to accept interventions. Physical and structural violence intersected with low self-esteem, depression and homeless-related stigma. Positive self-identity facilitated links to long-term and integrated services, peer support, and patient-centred engagement.ConclusionsIndividuals with lived experience of homelessness face considerable marginalization, dehumanization and structural violence. Practitioners and social service providers should consider anti-oppressive approaches and provide, refer to, or advocate for health and structural interventions using the principles of trauma-informed care. Accepting and respecting others as they are, without judgment, may help practitioners navigate barriers to inclusiveness, equitability, and effectiveness for primary care that targets this marginalized population.

(Further recorded data shown in S1.).

This dataset contains data of non-market housing projects - both the buildings owned by City of Vancouver, and the buildings provided by other agencies. Non-market housing is for low and moderate income singles and families, often subsidized through a variety of ways, including senior government support. This housing is managed through various operators, including the public, non-profit, co-op, and urban indigenous sectors. Non-market housing is located throughout Vancouver in the forms of social, supportive, and co-op housing. This dataset includes temporary modular housing, which are demountable structures, not permanently affixed to land and assembled within months. The inventory does not include the following types of housing:Special Needs Residential Facilities - includes community care facilities providing licensed care services, and group residences providing housing as required by law, rehabilitative programs, or temporary housingSingle Room Accommodation - privately-owned single room occupancy (SRO) hotels, rooming houses, and other housing with rooms less than 320 square feet, typically featuring units with a basic cooking setup and shared bathroomsShelters - provide temporary beds, meals, and services to the city's homeless population NoteUnit total (and breakdown) of projects could change over the course of development and are not captured real timeHousing projects with "proposed", "approved" and "under construction" status may not contain unit number breakdown by "Design"Housing projects with "proposed", "approved" and "under construction" status may not contain information on operator names or typeUnit total is the sum of clientele groups (families, seniors, and others) Data currencyThis dataset is updated weekly. Data accuracyData for this dataset is amalgamated from a number of sources. It is possible that some information may not be shown because of data synchronization issues. There may be some loss of quality from data entry errors.Non-housing market projects for which geographic coordinates are not available yet will not show up on the map or in the spatial formats. For a complete list, please consult the XLS or CSV formats. Websites for further informationSocial and market rental housingFind social and co-op housing in Vancouver

IntroductionCompared to stably housed peers, people experiencing homelessness (PEH) have lower rates of ideal glycemic control, and experience premature morbidity and mortality. High rates of behavioral health comorbidities and trauma add to access barriers driving poor outcomes. Limited evidence guides behavioral approaches to support the needs of PEH with diabetes. Lay coaching models can improve care for low-resource populations with diabetes, yet we found no evidence of programs specifically tailored to the needs of PEH.MethodsWe used a multistep, iterative process following the ORBIT model to develop the Diabetes Homeless Medication Support (D-HOMES) program, a new lifestyle intervention for PEH with type 2 diabetes. We built a community-engaged research team who participated in all of the following steps of treatment development: (1) initial treatment conceptualization drawing from evidence-based programs, (2) qualitative interviews with affected people and multi-disciplinary housing and healthcare providers, and (3) an open trial of D-HOMES to evaluate acceptability (Client Satisfaction Questionnaire, exit interview) and treatment engagement (completion rate of up to 10 offered coaching sessions).ResultsIn step (1), the D-HOMES treatment manual drew from existing behavioral activation and lay health coach programs for diabetes as well as clinical resources from Health Care for the Homeless. Step (2) qualitative interviews (n = 26 patients, n = 21 providers) shaped counseling approaches, language and choices regarding interventionists, tools, and resources. PTSD symptoms were reported in 69% of patients. Step (3) trial participants (N = 10) overall found the program acceptable, however, we saw better program satisfaction and treatment engagement among more stably housed people. We developed adapted treatment materials for the target population and refined recruitment/retention strategies and trial procedures sensitive to prevalent discrimination and racism to better retain people of color and those with less stable housing.DiscussionThe research team has used these findings to inform an NIH-funded randomized control pilot trial. We found synergy between community-engaged research and the ORBIT model of behavioral treatment development to develop a new intervention designed for PEH with type 2 diabetes and address health equity gaps in people who have experienced trauma. We conclude that more work and different approaches are needed to address the needs of participants with the least stable housing.

BackgroundFew studies have examined how food insecurity changes over time when living with severe mental disorders or substance use disorders. This study identifies food insecurity trajectories of homeless adults participating in a trial of a housing intervention and examines whether receiving the intervention and having specific mental and substance disorders predict food insecurity trajectories.Materials and methodsWe studied 520 participants in the Toronto site of the At Home/Chez-Soi project. Food insecurity data were collected at seven times during a follow-up period of up to 5.5 years. Mental and substance use disorders were assessed at baseline. Food insecurity trajectories were identified using group based-trajectory modeling. Multinomial logistic regression was used to examine the effects of the intervention and mental and substance use disorders on food insecurity trajectories.ResultsFour food insecurity trajectories were identified: persistently high food insecurity, increasing food insecurity, decreasing food insecurity, and consistently low food insecurity. Receiving the intervention was not a predictor of membership in any specific food insecurity trajectory group. Individuals with major depressive episode, mood disorder with psychotic features, substance disorder, and co-occurring disorder (defined as having at least one alcohol or other substance use disorder and at least one non-substance related mental disorder] were more likely to remain in the persistently high food insecurity group than the consistently low food insecurity group.ConclusionA persistently high level of food insecurity is common among individuals with mental illness who have experienced homelessness, and the presence of certain mental health disorders increases this risk. Mental health services combined with access to resources for basic needs, and re-adaptation training are required to enhance the health and well-being of this population.