Section 62 of Public Act 21-2, June Special Session, as modified by Section 71 of Public Act 23-204, required the Office of Policy and Management (OPM) to conduct a “Housing and Segregation Study”. This dataset is one of the products of the Housing and Segregation Study. This dataset shows "Matrices of Segregation" calculated for various Connecticut geographies (https://www.census.gov/topics/housing/housing-patterns/guidance/appendix-b.html)

The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

Based on the National Institutes of Health, Centers for Disease Control, American Psychological Association style guide, and American Chemical Society style guide.

The Office for National Statistics Longitudinal Study (ONS-LS) is the largest longitudinal study containing data on ethnicity in the UK and can therefore be used to examine ethnic differences in many health and social outcomes, which are difficult to examine in other longitudinal studies owing to insufficient numbers. This guide aims to ascertain whether the ONS-LS is a suitable dataset for your ethnicity study, specify the sample population for your study and specify the variables that you will need to extract from the ONS-LS for your study.

About this dataset

Context

The dataset tabulates the population of Guide Rock by race. It includes the population of Guide Rock across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Guide Rock across relevant racial categories.

Key observations

The percent distribution of Guide Rock population by race (across all racial categories recognized by the U.S. Census Bureau): 95.93% are white, 2.71% are some other race and 1.36% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Guide Rock
• Population: The population of the racial category (excluding ethnicity) in the Guide Rock is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Guide Rock total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Guide Rock Population by Race & Ethnicity. You can refer the same here

This guidance has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. The ICH Steering Committee has endorsed the final draft and recommended its adoption by the regulatory bodies of the European Union, Japan and USA.

The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model (VetPop). Based on the latest model VetPop2023 and the most recent national survey estimates from the 2023 American Community Survey 1-Year (ACS) data, the projected number of Veterans living in the 50 states, DC and Puerto Rico for fiscal years, 2023 to 2025, are allocated to Urban and Rural areas. As defined by the Census Bureau, Rural encompasses all population, housing, and territory not included within an Urban area (https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html). This table contains the Veteran estimates by urban/rural, sex, age group, and ethnicity. Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

This guidance has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. The ICH Steering Committee has endorsed the final draft and recommended its adoption by the regulatory bodies of the European Union, Japan and USA.

The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

New York City Department of Education 2018 - 19 Guidance Counselor Bill Demographic Data by district, borough, school number and school year providing statistics gender, ethnicity, students with disabilities, poverty and amount of time spent in general education classes.

BackgroundDisparities in cancer outcomes persist between racial, ethnic, and socioeconomic groups. One potential cause is lack of appropriate representation in dose-finding clinical trials. We investigated the extent of disparities in phase I clinical trials and recent changes in the setting of institutional efforts to mitigate disparities, legislative interventions, FDA guidance for sponsors and the COVID-19 pandemic.MethodsWe performed a retrospective review of patients enrolled in phase I clinical trials at the University of Colorado Cancer Center in 2018–2019 and 2022-2023. We collected demographics, area deprivation index (ADI), tumor type and other clinical variables. Differences between cohorts were evaluated with t-tests, chi-Square test, or Fisher exact test. Progression-free survival (PFS) and overall survival (OS) were calculated using the Kaplan-Meier method. Hazard ratios (HR), confidence intervals (CI) and p-values were derived using the Cox-proportional hazards method.ResultsA total of 361 patients were included (209 and 152 in the 2018–2019 and 2022–2023 cohorts, respectively). The population consisted of 85.0% White, 3.3% Asian, 1.4% Black, 0.3% Native Hawaiian or Pacific Islander and no American Indian/Alaskan Native (AIAN) patients by race, and 9.1% Hispanic by ethnicity. The most common tumor type was colorectal cancer (18.3%). Compared to 2018-2019, we observed increases in non-English speakers from 1.9% (4/209) to 6.6% (10/152) (p = 0.028) and in translated informed consent forms (ICFs) from 1.4% (3/209) to 5.9% (9/152) (p = 0.033) in 2022-2023. There were no significant changes in race, ethnicity, insurance, or tumor type, although there was a moderate increase in Hispanic patients from 8.1% to 10.5%. There were no differences in clinical outcomes by race, ethnicity, or ADI scores in the overall study population. However, in the most common cancer type, colorectal cancer, higher ADI scores were associated with decreased median PFS and OS.ConclusionThe interventions resulted in an increase in accrual of non-English speaking patients, however, there was not yet a significant change in overall race and ethnicity. Our study confirms poorer outcomes for patients with higher ADI scores. Further research is warranted to understand disparities in clinical trial accrual, and intervention is needed to improve outcomes for disadvantaged patients.

This statistic shows the results of a survey conducted in the United States in March 2017, by ethnicity. U.S. adults were asked if they could imagine themselves using an app that would give them fitness instructions. In total, 16 percent of Hispanic American or Latino respondents said that they use an app to give them fitness instructions on a regular basis, compared to only 4 percent of White or Caucasian respondents.

Analysis of ‘2018-19 Guidance Counselor Report - Demographic Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/141e1ffa-48d1-4c73-8b2e-1382c71bde4f on 26 January 2022.

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

New York City Department of Education 2018 - 19 Guidance Counselor Bill Demographic Data by district, borough, school number and school year providing statistics gender, ethnicity, students with disabilities, poverty and amount of time spent in general education classes.

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

The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered.Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles.Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups.Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

The BPDA Research Division prepared Census data on total population, population by race and ethnicity, voting-age population, group quarters populations, and housing occupancy for use in the 2022 City Council redistricting process. These data reflect 2020 census block-level data from the 2020 Decennial Census P.L. 94-171 Redistricting Data aggregated to the 275 precincts (as amended April 6, 2022) and the 9 current City Council Districts. Also included are 2010 estimates for these geographies based on 2010 census block-level.

Notes on coding of Race and Ethnicity:

The data presented here follow the conventions recommended by the Department of Justice in their September 1, 2021 guidance on the use of race and ethnicity data in redistricting. This differs from other commonly reported race and ethnicity groupings in that it groups those reporting 2 races, one White and one non-White, as being members of the non-White race reported. Thus a person reporting White and Black would be categorized here as Black. All residents of Hispanic or Latino origin, regardless of reported race, are grouped together. This coding appears on page 12 of the guidance that can be found here: https://www.justice.gov/opa/press-release/file/1429486/download

Notes on 2010 data:

For 2010 data the BPDA Research Division crosswalked 2010 census block data to 2020 boundaries using a combination of block assignment and areal interpolation based on Census Tiger shapefiles and the publicly available boundary files for Boston electoral geographies. For blocks split across 2020 boundaries the entire 2010 population was assigned to one side of the boundary if no residential structures within that block existed on the other side of the boundary. In cases where residential structures were present on both sides of the boundary, areal interpolation was used to assign the block's population and housing units based on the share of the land area of the block falling on either side of the boundary. These numbers will differ from those produced using different crosswalking methods.

According a survey on whether Malaysia is heading toward the right direction, as of October 2022, ** percent of respondents from both the Chinese and Indian ethnic groups said the country was going in the wrong direction. By comparison, ** percent of Malay respondents said it was going in the right direction. Malaysia is slated to hold its 15th general election on November 19, 2022