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INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.
"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."
In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.
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 po
The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)
Field Name Data Type Definition Notes (optional)
Census Blockgroup Text San Francisco Census Block Groups
Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.
Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.
Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.
NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.
Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.
Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.
English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.
SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.
Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.
Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.
MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.
Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.
Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.
Disability_wNULLvalues
Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.
Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.
LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.
LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.
FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.
FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood
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Over time, HIV status reporting has improved among the homeless, while it has remained at low levels, and almost constant, among the non-homeless (third column under each population category). At the same time, the percentage of HIV+ TB cases has increased among the homeless, but it has slightly declined among the non-homeless (second column under each population category). To compare against the CDC estimates for 2005 (18), related numbers from the SFDPH TBCP data are also provided for that year.
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Analysis of ‘Directory Of Unsheltered Street Homeless To General Population Ratio 2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/783fe600-3a75-46d8-8b91-4b626e273ffd 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 2010. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. A 2010 result is only available for Seattle, WA. Other cities either did not conduct a count in 2010, or their 2010 results are not yet available. 2009 unsheltered census figures were used for Los Angeles, San Francisco, Miami, and Washington, DC, and Boston; the 2007 estimate is used for Chicago. General population figures are the latest estimates from the U.S. Census Bureau."
--- Original source retains full ownership of the source dataset ---
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”.
To access the dataset that continues to refresh daily, navigate to this page: COVID-19 Deaths by Population Characteristics Over Time. The dataset contains data on the following population characteristics that are no longer being reported publicly:
B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate. Data on the population characteristics of COVID-19 deaths are from: * Case interviews * Laboratories * Medical providers These multiple streams of data are merged, deduplicated, and undergo data verification processes. 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 deaths 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 Virtual Assistant information gathering starting December 2021. The California Department of Public Health, Virtual Assistant is only sent to adults who are 18+ years old. 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 will only update when any population characteristics are archived. Data for existing characteristic types will not change but new characteristic types may be added. D. HOW TO USE THIS DATASET This dataset may include 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 deaths on each date.
New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.
E. CHANGE LOG
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Individual shelter participation per event and total census population. This data is shown in Fig 2B. Percentage of tests taken. Individual datapoints shown in Fig 2A. (XLSX)
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Sociodemographic, substance use and mental health characteristics among marginally housed and homeless transitional age youth in San Francisco, CA (N = 100).
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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.
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Pairwise correlation coefficients between syndemic factors among marginally housed and homeless transitional age youth in San Francisco, CA (N = 100).
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The prevalence of those at risk of clinical depression by number of syndemic factors among marginally housed and homeless transitional age youth in San Francisco, CA (N = 100).
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Bivariate and multivariable modified poisson regression models examining factors associated with being at risk of clinical depression among marginally housed and homeless transitional age youth in San Francisco, CA (N = 100).
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INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.