Table contains count of reported lyme disease cases among county residents. Count includes both confirmed and probable cases. Source: Santa Clara County Public Health Department, Automated Vital Statistics System (AVSS), January 1 - June 30, 2011; Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), July 1, 2011 - 2021; Data as of 4/8/2022.METADATA:notes (String): Lists table title, notes, sourcesyear (String): Year of diagnosiscount (Numeric): Number of cases reported

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

This table contains the daily 7-day average case rates for Santa Clara County residents by age and vaccination status as well as the 7-day average case counts and population estimates to calculate these rates. Residents are considered fully vaccinated two weeks after the final dose of the vaccine, and residents are considered unvaccinated if they have not received any doses of a COVID-19 vaccine. People who are partially vaccinated are not included in either category.

Counties are experiencing temporary fluctuations in vaccination data received from the State of California due to a data reconciliation process involving integration of vaccination data systems by the State. The data reconciliation process and associated fluctuations in county vaccination are expected to be resolved by mid-April 2022

Table contains county of reported west nile virus cases among county residents. Count includes both confirmed and probable cases. West nile virus includes aymptomatic, non-neuroinvasive, and neuroinvasive cases. Source: Santa Clara County Public Health Department, Automated Vital Statistics System (AVSS), January 1 - June 30, 2011; Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), July 1, 2011 - 2021; Data as of 4/8/2022.METADATA:notes (String): Lists table title, notes, sourcesyear (Numeric): Year of diagnosiscount (Numeric): Number of cases reported

Graph and download economic data for All Employees: Retail Trade: Health and Personal Care Stores in San Jose-Sunnyvale-Santa Clara, CA (MSA) (DISCONTINUED) (SMU06419404244600001SA) from Jan 1990 to Dec 2022 about hygiene, San Jose, health, retail trade, CA, sales, retail, employment, and USA.

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

The dataset provides number of county residents who are Second booster vaccinated by gender. Number of people vaccinated (completed primary series) is the number of county residents who either received a vaccine requiring a single dose (Janssen/Johnson & Johnson) or received the second dose of a vaccine requiring two doses (Pfizer or Moderna). Source: California Immunization Registry. This table is updated Monday-Friday and will not be updated on holidays.

Counties are experiencing temporary fluctuations in vaccination data received from the State of California due to a data reconciliation process involving integration of vaccination data systems by the State. The data reconciliation process and associated fluctuations in county vaccination are expected to be resolved by May 2022

Table contains count of reported zika virus infections among county residents. Count includes both confirmed and probable cases. Source: Santa Clara County Public Health Department, Automated Vital Statistics System (AVSS), January 1 - June 30, 2011; Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), July 1, 2011 - 2021; Data as of 4/8/2022.METADATA:notes (String): Lists table title, notes, sourcesyear (Numeric): Year of diagnosiscount (Numeric): Number of cases reported

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

The dataset provides number of county residents who are vaccinated by Race/Ethnicity. Number of people vaccinated (completed primary series) is the number of county residents who either received a vaccine requiring a single dose (Janssen/Johnson & Johnson) or received the second dose of a vaccine requiring two doses (Pfizer or Moderna). Source: California Immunization Registry. This table is updated Monday-Friday and will not be updated on holidays.

Counties are experiencing temporary fluctuations in vaccination data received from the State of California due to a data reconciliation process involving integration of vaccination data systems by the State. The data reconciliation process and associated fluctuations in county vaccination are expected to be resolved by May 2022

All Employees: Retail Trade: Health and Personal Care Stores in San Jose-Sunnyvale-Santa Clara, CA (MSA) was 5.70000 Thous. of Persons in December of 2022, according to the United States Federal Reserve. Historically, All Employees: Retail Trade: Health and Personal Care Stores in San Jose-Sunnyvale-Santa Clara, CA (MSA) reached a record high of 6.10000 in December of 2000 and a record low of 3.60000 in May of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Retail Trade: Health and Personal Care Stores in San Jose-Sunnyvale-Santa Clara, CA (MSA) - last updated from the United States Federal Reserve on September of 2025.

Table contains count of reported valley fever (coccidioidomycosis) cases among county residents. Count includes both confirmed and probable cases. Source: Santa Clara County Public Health Department, Automated Vital Statistics System (AVSS), January 1 - June 30, 2011; Santa Clara County Public Health Department, California Reportable Diseases Information Exchange (CalREDIE), July 1, 2011 - 2021; Data as of 4/8/2022.METADATA:notes (String): Lists table title, notes, sourcesyear (Numeric): Year of diagnosiscount (Numeric): Number of cases reported

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.

Table contains estimated percentage of adults ages 18 years and older who reported ever being diagnosed with diabetes by a healthcare provider. Data are presented at zip code level. Data are downloaded from the AskCHIS Neighborhood Edition and are not direct estimates. For more information on the methodology used to calculate estimates, please visit healthpolicy.ucla.edu. Data for zip code 95053 are not available. Source: California Health Interview Survey, AskCHIS Neighborhood Edition, 2018 CHIS data. Exported on June 1, 2022.METADATA:notes (String): Lists table title, notes, sourceszip_code (Numeric): Geography IDestimate (Numeric): Estimate of adults with diabetesunit (String): Unit used for the estimate (Percent)CI (Numeric): 95% confidence interval for the estimate

Table contains county residents without health insurance. Data are summarized as people of all ages and those 19 to 64 years old. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B27001; data accessed on June 30, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population for whom health insurance coverage was assessedt_uninsured (Numeric): Number of people (all ages) who were without health insurancep_uninsured (Numeric): Percent of people (all ages) who were without health insurancet_19_64 (Numeric): Population ages 19 to 64 years for whom health insurance coverage was assessedt_unins_19_64 (Numeric): Number of people ages 19 to 64 years who were without health insurancep_unins_19_64 (Numeric): Percent of people ages 19 to 64 years who were without health insurance

Table contains total population and population density summarized at county, city, zip code, and census tract level. Population density is defined as number of people residing per square mile of area. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B01001; data accessed on April 11, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationpop_density (Numeric): Area in square milesarea (Numeric): Population density

Table contains estimated percentage of adults ages 18 years and older who report ever being diagnosed with asthma by a healthcare provider. Data are at zip code level. Data are downloaded from the AskCHIS Neighborhood Edition and are not direct estimates. For more information on the methodology used to calculate estimates, please visit healthpolicy.ucla.edu. Data for zip codes 94305 and 95053 are not available. Source: California Health Interview Survey, AskCHIS Neighborhood Edition, 2018 CHIS data. Exported on June 1, 2022.METADATA:notes (String): Lists table title, notes, sourceszip_code (Numeric): Geography IDestimate (Numeric): Estimate of adults with asthmaunit (String): Unit used for the estimate (Percent)CI (Numeric): 95% confidence interval for the estimate

Table contains count and percentage of county residents living with an ambulatory disability. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table S1801; data accessed on July 20, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population for whom disability was assessedt_disability (Numeric): Number of people living with any type of disability (total)pct_t_disability (Numeric): Percent of people living with any type of disability (total)ambulatory_diff (Numeric): Number of people living with ambulatory disabilitypct_ambulatory (Numeric): Percent of people living with ambulatory disability

Table contains count and percentage of households in the county that have at least a smartphone but have no other type of computing device at home. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table S2801; data accessed on August 23, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographytotal_HH (Numeric): Total householdssmartphone_no_oth_compt (Numeric): Number of households with smartphone with no other type of computing devicepct_smartphone_no_oth_compt (Numeric): Percent of households with smartphone with no other type of computing device

Table contains median household income for households residing in Santa Clara County. Data are presented at county, city, zip code and census tract level. Notes: Data are presented for zip codes (ZCTAs) fully within the county. Data are capped at $250,001 for geographies with median household income of $250,000 or higher. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B19013; data accessed on May 16, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographymedHHinc (Numeric): Median household income

Table contains count and percent of county residents ages 25 years and older with less than high school education attainment. The measure is summarized at county, city, zip code and census tract. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B15002; data accessed on May 17, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population ages 25 and olderpct_lt_HS (Numeric): Number of people ages 25 and older with less than high school educationlt_highschool (Numeric): Percent of people ages 25 and older with less than high school education

Table contains count and percentage of households with an annual household income of less than $100,000. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B19001; data accessed on May 16, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographytotalHH (Numeric): Total householdslt100k (Numeric): Number of households with less than $100,000 annual incomepct_lt100k (Numeric): Percent of households with less than $100,000 annual income

Table contains count and percentage of housing units in the county that were built before 1980. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B25034; data accessed on July 20, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographyHU_total (Numeric): Total housing unitsHU_before1980 (Numeric): Number of housing units built before 1980pct_before1980 (Numeric): Percent of housing units built before 1980