To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity 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).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Analysis of ‘COVID-19 Cases and Deaths by Race/Ethnicity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3fdc6593-c708-4a6a-8073-5ca862caa279 on 27 January 2022.

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

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More infor

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

NOTE: After 5/20/2021, this dataset will no longer be updated and will be replaced by the new dataset: "COVID-19 Vaccinations by Race/Ethnicity" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/4z97-pa4q).

Cumulative number and percent of people who initiated COVID-19 vaccination and who are fully vaccinated by race/ethnicity for select age groups (ages 16+, ages 65-74, and ages 75+) as reported by providers.

Population estimates are based on 2019 CT population estimates. The 2019 CT population data which is the most recent year available. The tables that show the percent vaccinated by town and age group are an exception. These tables use 2014 CT population estimates. This the most recent year for which reliable estimates by town and age are available.

A person who has received one dose of any vaccine is considered to have received at least one dose. A person is considered fully vaccinated if they have received 2 doses of the Pfizer or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. The number with At Least One Dose and the number Fully Vaccinated add up to more than the total number of doses because people who received the Johnson & Johnson vaccine fit into both categories.

In this data, a person with reported Hispanic or Latino ethnicity is considered Hispanic regardless of reported race. The category Unknown includes unknown race and/or ethnicity.

The percent of people classified as Other race (not specified) and Multiple race in CT WiZ (for COVID-19 vaccine records and all other vaccine records) are higher than would be expected based on census data. Other race, Multiple race and Unknown include people who should be classified as Asian, Black, Hispanic and White. Therefore, the coverage of these groups may be underestimated and should be interpreted with caution.

The estimates for the category Multiple Races are considered unreliable

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

Note: As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

California Commuting Mode Choice

Regional Disparities in Risk of Injury and Death

By Health [source]

About this dataset

This dataset contains data on the modes of transportation used by California residents aged 16 and older to commute to work. It includes data from the U.S. Census Bureau, Decennial Census and American Community Survey, covering all regions, counties, cities/towns, and census tracts in California. With each region showing detailed information regarding how its population travels to work (modes of transportation used), this dataset provides vital insight into the development of transport infrastructure in California over the past decade.

Unlike other states where private cars constitute an overwhelming majority of daily commuters (over 79% nationwide according to a 2015 survey), Californians have built up varied commuting habits – bicycles are commonly reported 5%, public transit stands at 15%, walking alone 4%, and carpooling is at 11%. Commuting plays a significant role on overall health—active modes such as biking or walking lead to healthier lifestyles that lower heart disease risks, obesity rates, diabetes prevalence; passengers on public transport also have a lower chance of injury in collisions compared with pedestrians or cyclists.

The consequences of inadequate planning for human mobility extend beyond physical health – it can also cause huge disparities between different racial groups such as Native Americans who experience four times higher death rate from pedestrian-car collisions than Whites or Asians; African-Americans and Latinos suffer twice as much as White people do when driving privately in their own cars due to air pollution hazards or lack thereof access to reliable public transportation system that could provide them with healthier alternatives. It is our hope that policymakers will use this dataset prominently stated by the Healthy Communities Data & Indicators Project - part of the Office Of Health Equity - while ensuring every resident’s right for safe mobility no matter their background!

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How to use the dataset

This dataset contains information on the percent of Californians aged 16 and older who use different modes of transportation to get to work. The data is collected from the U.S. Census Bureau and American Community Survey, and covers all counties, cities/towns and census tracts in California.

In this dataset, there are several columns of data such as mode (mode of transport), race_eth_name (name of the race/ethnicity), region_code (code for the region) and pop_total (total population). This makes it possible to look at relations between transportation choice and demographic factors like gender or ethnicity, or comparison between regions within California regarding commuting habits.

The purpose of this dataset is to provide information on how Californians travel to their jobs with respect to both geographical area as well as demographic characteristics. It allows studies into why certain areas might have higher usage rates for specific types of transport compared with others, how gender affects travel decisions, or which regions have access issues with public transit compared with driving for example.

To use this dataset you should start by familiarizing yourself with descriptive statistics such as percentages, hazard ratios etc., in order to understand each variable's contribution towards commuting trends more effectively. It might also help if you filter data by geographic area or personal characteristics first before performing more detailed analysis for more insightful results that can be used in policy-making when planning effective infrastructure investments related to transportation options over time or among differing populations within California state population levels noted here year-by-year across a decade period provided here

Research Ideas

• Creating interactive maps to visualize and compare the transportation methods of different race/ethnicities in California.
• Analyzing the transportation trends across regions, counties, cities/towns, and census tracts to forecast and plan for infrastructure investments.
• Comparing the risk ratio of pedestrian-car fatalities across different ethnic groups in order to address safety issues within underserved populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [Open Database License (ODbL) v1.0](https://opendatacommons.org/lice...

Age-adjusted rate of death from diseases of the heart by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (Numeric): Year of dataCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: <1, 1 to 4, 5 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United StatesRate per 100,000 people (Numeric): Rate of deaths from diseases of the heart. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Number of lives saved in each race/ethnicity group by vaccinating 1,000 individuals, either allocating doses to individuals 65–74 (LSj) or to front-line workers (LFj).

Historical Dataset of Idea Health Professions Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2021-2023),Total Classroom Teachers Trends Over Years (2021-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2021-2023),Asian Student Percentage Comparison Over Years (2021-2023),Hispanic Student Percentage Comparison Over Years (2021-2023),Black Student Percentage Comparison Over Years (2021-2023),White Student Percentage Comparison Over Years (2021-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2021-2023),Free Lunch Eligibility Comparison Over Years (2021-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2021-2023),Reading and Language Arts Proficiency Comparison Over Years (2021-2022),Math Proficiency Comparison Over Years (2021-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2021-2022)

Explore the number of employees in government sectors dataset for insights on labor and employment trends in Saudi Arabia. Analyze data collected by SAMA Annual.

Labor, Government, employment, SAMA Annual

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Note:The Statistics include men and women working under the following ladders: (Public Ladder of Employee Salaries, Ladder of Health Jobs, Ladder of Educational Jobs, Ladder of the Teaching staff, lecturers and teaching assistants, Judge Ladder, Ladder of Investigation staff and public prosecutors, and Ladder of Wage - earners).

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

This table contains data on the rate of violent crime (crimes per 1,000 population) for California, its regions, counties, cities and towns. Crime and population data are from the Federal Bureau of Investigations, Uniform Crime Reports. Rates above the city/town level include data from city, university and college, county, state, tribal, and federal law enforcement agencies. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Ten percent of all deaths in young California adults aged 15-44 years are related to assault and homicide. In 2010, California law enforcement agencies reported 1,809 murders, 8,331 rapes, and over 95,000 aggravated assaults. African Americans in California are 11 times more likely to die of assault and homicide than Whites. More information about the data table and a data dictionary can be found in the About/Attachments section.

Summary

The State of Early Education and Care in Boston: Supply, Demand, Affordability, and Quality, is the first in what is planned as a recurrent landscape survey of early childhood, preschool and childcare programs in every neighborhood of Boston. It focuses on potential supply, demand and gaps in child-care seats (availability, quality and affordability). This report’s estimates set a baseline understanding to help focus and track investments and policy changes for early childhood in the city.

This publication is a culmination of efforts by a diverse data committee representing providers, parents, funding agencies, policymakers, advocates, and researchers. The report includes data from several sources, such as American Community Survey, Massachusetts Department of Early Education and Care, Massachusetts Department of Elementary & Secondary Education, Boston Public Health Commission, City of Boston, among others. For detailed information on methodology, findings and recommendations, please access the full report here

The first dataset contains all Census data used in the publication. Data is presented by neighborhoods:

• Population 0 – 5 years;
• Population 0 – 2 years;
• Population 3 – 5 years;
• Race/ethnicity for children 0 – 4 years (White, non-Hispanic; Black; Asian; Hispanic/Latinx);
• Family type (married couples, female householder, male householder);
• Poverty status;
• Family median income in the past 12 months;
• Average cost of care as a percentage of median family income (infant, preschool);
• Share of families that cannot afford care (infant, preschool)

The Boston Planning & Development Agency Research Division analyzed 2013-2017 American Community Survey data to estimate numbers by ZIP-Code. The Boston Opportunity Agenda combined that data by the approximate neighborhoods and estimated cost of care and affordability.

Additional notes:

• Record Type: Each record represents a ZIP-Code defined neighborhood. See list below for detailed information on Boston ZIP-Codes used to create each one of the 15 neighborhoods.
• Data Quality: Numbers presented here came from 2013-2017 American Community Survey data. Therefore, these are ESTIMATES and have margin of errors. The smaller the geographical unit, the greater the margin of error. The Boston Planning & Development Agency analyzed the data to estimate numbers by ZIP-Code.
• Race/Ethnicity: Non-White Hispanics may be double counted due to data limitations.
• Cost of Care: The average cost of care as a percentage of median family income was computed assuming the annual average cost of infant care was $19,877 and the average cost of preschool care was $ 13,771 (Childcare Aware of America, 2019). For each neighborhood we estimated the impact of child care (infant and preschool) on its median annual family income.
• Affordability: The Department of Health and Human Services (DHHS) sets a standard regarding the affordability of child care, where the annual cost of child care should not exceed 10 percent of household annual income. Using this 10% threshold, we estimated that to afford market rate infant care, a family’s annual income would have to be at least $198,770. The census income bracket closest to this income was a family income of $150,000– 199,999. To afford preschool care, a family's annual income should be at least $137,710. Thus, the census income bracket that encompass this income is $125,000 - 149,999. For both infant and preschool care, we underestimated the number of families that can afford care.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsPolicy Development: Helps policymakers develop targeted interventions to address the needs of vulnerable populations.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability.Research: Provides a robust foundation for academic and applied research in socio-economic and demographic studies.Community Planning: Aids in the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities.Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2013-2017 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

For questions about this data please contact ITOpenData@minneapolismn.gov2014 Minneapolis Community Technology Survey Data

Thanks to the 3,015 residents for their participation, the third year's results are in on a survey the City of Minneapolis conducted to understand how Minneapolis residents use computers, mobile devices and the Internet. Access to computers and the Internet, along with the skills to use these tools is critical as technology becomes more and more a part of our daily lives and is integrated in our economic, educational, health, and workforce systems. The results will inform priorities for the City’s digital inclusion initiatives, and help engage businesses, neighborhood and community groups, public sector partners, and funders to more effectively address community technology and economic development needs. In addition, the survey provides data to measure changes in the community over time.

The City of Minneapolis Information Technology Department contracted with National Research Center, Inc. (NRC) to conduct a survey of residents to inform the City’s efforts to overcome the digital equity gap between individuals and groups in their access to and use and knowledge of information and communication technologies. This is the third iteration of the Minneapolis Community Technology Survey; the first was conducted in 2012 and the second in 2013.Summary of Data Fields:Field 1 – Overall percentage of respondents who have lived in Minneapolis for 5 years or less by community and user levelField 2 – Overall percentage of foreign-born respondents by community and user levelField 3 – Overall percentage of respondents who rent their homes by community and user levelField 4 – Overall percentage of respondents who live in attached homes by community and user levelField 5 – Overall percentage of respondents living in households with three or more people by community and user levelField 6 – Overall percentage of respondents living in households with children under the age of 18 by community and user levelField 7 – Overall percentage of female respondents by community and user levelField 8 – Overall percentage of respondents aged 55 years or older by community and user levelField 9 – Overall percentage of respondents who are hispanic and/or any race other than white by community and user levelField 10 – Overall percentage of respondents who prefer to speak a language other than English at home by community and user levelField 11 – Overall percentage of respondents having annual household incomes of less than $50,000 by community and user levelField 12 – Overall percentage of respondents who do not work full- or part-time by community and user level

Field 13 – Overall percentage of respondents who do not have a 4-year degree by community and user level

Full data set (Raw data and data dictionary in Excel format)

The workbook has two tabs, the first is the data dictionary that is needed to translate the data; the second is the raw data.

See data summarized in a variety of formats at: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865

For additional details about the survey, the survey questionnaire, methodology and more, see: http://www.minneapolismn.gov/it/inclusion/WCMS1P-118865 or contact: Elise Ebhardt, 612-673-2026, City of Minneapolis IT Department

See also: 2012 and 2013 survey results

The City's IT Vision includes a component for addressing the digital divide in Minneapolis: All City residents, institutions and businesses will have the tools, skills and motivation to gain value from the digital society. Our residents and businesses need to be equipped to effectively compete with others around the world—to be smarter, more creative, more knowledgeable, and more innovative. Leveraging technology is a necessary ingredient of success.