100+ datasets found
  1. Number of missing persons files in the U.S. 2022, by race

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). Number of missing persons files in the U.S. 2022, by race [Dataset]. https://www.statista.com/statistics/240396/number-of-missing-persons-files-in-the-us-by-race/
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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

    What is the NCIC?

    The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

    Missing people in the United States

    A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

  2. Number of missing person files U.S. 1990-2023

    • statista.com
    Updated Nov 25, 2024
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    Statista (2024). Number of missing person files U.S. 1990-2023 [Dataset]. https://www.statista.com/statistics/240401/number-of-missing-person-files-in-the-us-since-1990/
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    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, the number of missing person files in the United States equaled 563,389 cases, an increase from 2021 which had the lowest number of missing person files in the U.S. since 1990.

  3. Number of missing persons files in the U.S. 2022, by age and gender

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). Number of missing persons files in the U.S. 2022, by age and gender [Dataset]. https://www.statista.com/statistics/240387/number-of-missing-persons-files-in-the-us-by-age/
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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    While the fear of being kidnapped may persist for one’s entire life, in 2022 the number of missing persons under the age of 21 was much higher than those 21 and over, with 206,371 females under 21 reported missing, and 64,956 females over the age of 21 reported missing.

    Why people go missing

    There are many reasons why people go missing; some are kidnapped, some purposefully go missing - in order to escape abuse, for example - and some, usually children, are runaways. What persists in the imagination when thinking of missing persons, however, are kidnapping victims, usually due to extensive media coverage of child kidnappings by the media.

    Demographics of missing persons

    While the number of missing persons in the United States fluctuates, in 2021, this number was at its lowest since 1990. Additionally, while it has been observed that there is more media coverage in the United States of white missing persons, almost half of the missing persons cases in 2022 were of minorities.

  4. g

    Coronavirus (Covid-19) Data in the United States

    • github.com
    • openicpsr.org
    • +2more
    csv
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data
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    csvAvailable download formats
    Dataset provided by
    New York Times
    License

    https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

    Description

    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.

  5. N

    Lost Nation, IA Population Breakdown by Gender and Age Dataset: Male and...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). Lost Nation, IA Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/e1ede495-f25d-11ef-8c1b-3860777c1fe6/
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    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iowa, Lost Nation
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of Lost Nation by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lost Nation. The dataset can be utilized to understand the population distribution of Lost Nation by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lost Nation. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lost Nation.

    Key observations

    Largest age group (population): Male # 50-54 years (27) | Female # 10-14 years (25). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Scope of gender :

    Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

    Variables / Data Columns

    • Age Group: This column displays the age group for the Lost Nation population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Lost Nation is shown in the following column.
    • Population (Female): The female population in the Lost Nation is shown in the following column.
    • Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lost Nation for each age group.

    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 Lost Nation Population by Gender. You can refer the same here

  6. A

    ‘Missing Migrants Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Apr 23, 2019
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2019). ‘Missing Migrants Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-missing-migrants-dataset-c736/2e62d69f/?v=grid
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    Dataset updated
    Apr 23, 2019
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Missing Migrants Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jmataya/missingmigrants on 14 February 2022.

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

    About the Missing Migrants Data

    This data is sourced from the International Organization for Migration. The data is part of a specific project called the Missing Migrants Project which tracks deaths of migrants, including refugees , who have gone missing along mixed migration routes worldwide. The research behind this project began with the October 2013 tragedies, when at least 368 individuals died in two shipwrecks near the Italian island of Lampedusa. Since then, Missing Migrants Project has developed into an important hub and advocacy source of information that media, researchers, and the general public access for the latest information.

    Where is the data from?

    Missing Migrants Project data are compiled from a variety of sources. Sources vary depending on the region and broadly include data from national authorities, such as Coast Guards and Medical Examiners; media reports; NGOs; and interviews with survivors of shipwrecks. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. In other cases, media reports are used. IOM and UNHCR also regularly coordinate on such data to ensure consistency. Data on the U.S./Mexico border are compiled based on data from U.S. county medical examiners and sheriff’s offices, as well as media reports for deaths occurring on the Mexico side of the border. Estimates within Mexico and Central America are based primarily on media and year-end government reports. Data on the Bay of Bengal are drawn from reports by UNHCR and NGOs. In the Horn of Africa, data are obtained from media and NGOs. Data for other regions is drawn from a combination of sources, including media and grassroots organizations. In all regions, Missing Migrants Projectdata represents minimum estimates and are potentially lower than in actuality.

    Updated data and visuals can be found here: https://missingmigrants.iom.int/

    Who is included in Missing Migrants Project data?

    IOM defines a migrant as any person who is moving or has moved across an international border or within a State away from his/her habitual place of residence, regardless of

      (1) the person’s legal status; 
      (2) whether the movement is voluntary or involuntary; 
      (3) what the causes for the movement are; or 
      (4) what the length of the stay is.[1]
    

    Missing Migrants Project counts migrants who have died or gone missing at the external borders of states, or in the process of migration towards an international destination. The count excludes deaths that occur in immigration detention facilities, during deportation, or after forced return to a migrant’s homeland, as well as deaths more loosely connected with migrants’ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. This approach is chosen because deaths that occur at physical borders and while en route represent a more clearly definable category, and inform what migration routes are most dangerous. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, rather tracked as a distinct category.

    How complete is the data on dead and missing migrants?

    Data on fatalities during the migration process are challenging to collect for a number of reasons, most stemming from the irregular nature of migratory journeys on which deaths tend to occur. For one, deaths often occur in remote areas on routes chosen with the explicit aim of evading detection. Countless bodies are never found, and rarely do these deaths come to the attention of authorities or the media. Furthermore, when deaths occur at sea, frequently not all bodies are recovered - sometimes with hundreds missing from one shipwreck - and the precise number of missing is often unknown. In 2015, over 50 per cent of deaths recorded by the Missing Migrants Project refer to migrants who are presumed dead and whose bodies have not been found, mainly at sea.

    Data are also challenging to collect as reporting on deaths is poor, and the data that does exist are highly scattered. Few official sources are collecting data systematically. Many counts of death rely on media as a source. Coverage can be spotty and incomplete. In addition, the involvement of criminal actors in incidents means there may be fear among survivors to report deaths and some deaths may be actively covered-up. The irregular immigration status of many migrants, and at times their families as well, also impedes reporting of missing persons or deaths.

    The varying quality and comprehensiveness of data by region in attempting to estimate deaths globally may exaggerate the share of deaths that occur in some regions, while under-representing the share occurring in others.

    What can be understood through this data?

    The available data can give an indication of changing conditions and trends related to migration routes and the people travelling on them, which can be relevant for policy making and protection plans. Data can be useful to determine the relative risks of irregular migration routes. For example, Missing Migrants Project data show that despite the increase in migrant flows through the eastern Mediterranean in 2015, the central Mediterranean remained the more deadly route. In 2015, nearly two people died out of every 100 travellers (1.85%) crossing the Central route, as opposed to one out of every 1,000 that crossed from Turkey to Greece (0.095%). From the data, we can also get a sense of whether groups like women and children face additional vulnerabilities on migration routes.

    However, it is important to note that because of the challenges in data collection for the missing and dead, basic demographic information on the deceased is rarely known. Often migrants in mixed migration flows do not carry appropriate identification. When bodies are found it may not be possible to identify them or to determine basic demographic information. In the data compiled by Missing Migrants Project, sex of the deceased is unknown in over 80% of cases. Region of origin has been determined for the majority of the deceased. Even this information is at times extrapolated based on available information – for instance if all survivors of a shipwreck are of one origin it was assumed those missing also came from the same region.

    The Missing Migrants Project dataset includes coordinates for where incidents of death took place, which indicates where the risks to migrants may be highest. However, it should be noted that all coordinates are estimates.

    Why collect data on missing and dead migrants?

    By counting lives lost during migration, even if the result is only an informed estimate, we at least acknowledge the fact of these deaths. What before was vague and ill-defined is now a quantified tragedy that must be addressed. Politically, the availability of official data is important. The lack of political commitment at national and international levels to record and account for migrant deaths reflects and contributes to a lack of concern more broadly for the safety and well-being of migrants, including asylum-seekers. Further, it drives public apathy, ignorance, and the dehumanization of these groups.

    Data are crucial to better understand the profiles of those who are most at risk and to tailor policies to better assist migrants and prevent loss of life. Ultimately, improved data should contribute to efforts to better understand the causes, both direct and indirect, of fatalities and their potential links to broader migration control policies and practices.

    Counting and recording the dead can also be an initial step to encourage improved systems of identification of those who die. Identifying the dead is a moral imperative that respects and acknowledges those who have died. This process can also provide a some sense of closure for families who may otherwise be left without ever knowing the fate of missing loved ones.

    Identification and tracing of the dead and missing

    As mentioned above, the challenge remains to count the numbers of dead and also identify those counted. Globally, the majority of those who die during migration remain unidentified. Even in cases in which a body is found identification rates are low. Families may search for years or a lifetime to find conclusive news of their loved one. In the meantime, they may face psychological, practical, financial, and legal problems.

    Ultimately Missing Migrants Project would like to see that every unidentified body, for which it is possible to recover, is adequately “managed”, analysed and tracked to ensure proper documentation, traceability and dignity. Common forensic protocols and standards should be agreed upon, and used within and between States. Furthermore, data relating to the dead and missing should be held in searchable and open databases at local, national and international levels to facilitate identification.

    For more in-depth analysis and discussion of the numbers of missing and dead migrants around the world, and the challenges involved in identification and tracing, read our two reports on the issue, Fatal Journeys: Tracking Lives Lost during Migration (2014) and Fatal Journeys Volume 2, Identification and Tracing of Dead and Missing Migrants

    Content

    The data set records

  7. N

    2016-17 - 2020-23 Citywide End-of-Year Attendance and Chronic Absenteeism...

    • data.cityofnewyork.us
    • catalog.data.gov
    application/rdfxml +5
    Updated Apr 5, 2022
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    Department of Education (DOE) (2022). 2016-17 - 2020-23 Citywide End-of-Year Attendance and Chronic Absenteeism Data [Dataset]. https://data.cityofnewyork.us/Education/2016-17-2020-23-Citywide-End-of-Year-Attendance-an/sgsi-66kk
    Explore at:
    application/rssxml, csv, json, application/rdfxml, xml, tsvAvailable download formats
    Dataset updated
    Apr 5, 2022
    Dataset authored and provided by
    Department of Education (DOE)
    Description

    Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an assessment, in whichever manner the school is collecting • Student participation in an online forum, chat log, or discussion thread • Student/family phone call, email or response to teacher email • Phone, email, and/or other digital communication with a family member which confirms student interaction/engagement • Other evidence of participation as determined by the principal. Interactions data are attributed to students' school of record on a given day. A student participating in a Shared Instruction (SHIN) model may have recorded interactions at multiple schools on a given day, but only one record is counted for the interaction rate, attributed to students' school of record for that day. Due to the shift to hybrid learning, attendance data for the 2020-21 school year include both in-person and remote instruction. Total days, days absent, and days present fields include both in-person and remote attendance. More information on attendance policies can be found here: https://www.schools.nyc.gov/school-life/rules-for-students/attendance

  8. d

    2016-17 - 2020-21 End-of-Year Borough Attendance and Chronic Absenteeism...

    • catalog.data.gov
    • data.cityofnewyork.us
    • +1more
    Updated Nov 29, 2024
    + more versions
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    data.cityofnewyork.us (2024). 2016-17 - 2020-21 End-of-Year Borough Attendance and Chronic Absenteeism Data [Dataset]. https://catalog.data.gov/dataset/2016-17-2020-21-end-of-year-borough-attendance-and-chronic-absenteeism-data
    Explore at:
    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an

  9. N

    Lost Springs, KS Population Breakdown by Gender and Age Dataset: Male and...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). Lost Springs, KS Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lost-springs-ks-population-by-gender/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kansas, Lost Springs
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of Lost Springs by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lost Springs. The dataset can be utilized to understand the population distribution of Lost Springs by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lost Springs. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lost Springs.

    Key observations

    Largest age group (population): Male # 50-54 years (7) | Female # 70-74 years (7). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Scope of gender :

    Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

    Variables / Data Columns

    • Age Group: This column displays the age group for the Lost Springs population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Lost Springs is shown in the following column.
    • Population (Female): The female population in the Lost Springs is shown in the following column.
    • Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lost Springs for each age group.

    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 Lost Springs Population by Gender. You can refer the same here

  10. d

    COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE

    • catalog.data.gov
    • data.ct.gov
    • +1more
    Updated Aug 12, 2023
    + more versions
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    data.ct.gov (2023). COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-and-deaths-by-race-ethnicity
    Explore at:
    Dataset updated
    Aug 12, 2023
    Dataset provided by
    data.ct.gov
    Description

    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

  11. z

    Counts of Influenza reported in UNITED STATES OF AMERICA: 1919-1951

    • zenodo.org
    • data.niaid.nih.gov
    json, xml, zip
    Updated Jun 3, 2024
    + more versions
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    Willem Van Panhuis; Willem Van Panhuis; Anne Cross; Anne Cross; Donald Burke; Donald Burke (2024). Counts of Influenza reported in UNITED STATES OF AMERICA: 1919-1951 [Dataset]. http://doi.org/10.25337/t7/ptycho.v2.0/us.6142004
    Explore at:
    json, xml, zipAvailable download formats
    Dataset updated
    Jun 3, 2024
    Dataset provided by
    Project Tycho
    Authors
    Willem Van Panhuis; Willem Van Panhuis; Anne Cross; Anne Cross; Donald Burke; Donald Burke
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Oct 26, 1919 - Dec 8, 1951
    Area covered
    United States
    Description

    Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

    Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

    Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

    • Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
    • Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

  12. N

    Lost Nation, IA Age Group Population Dataset: A Complete Breakdown of Lost...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
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    Neilsberg Research (2025). Lost Nation, IA Age Group Population Dataset: A Complete Breakdown of Lost Nation Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lost-nation-ia-population-by-age/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iowa, Lost Nation
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Lost Nation population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Lost Nation. The dataset can be utilized to understand the population distribution of Lost Nation by age. For example, using this dataset, we can identify the largest age group in Lost Nation.

    Key observations

    The largest age group in Lost Nation, IA was for the group of age 10 to 14 years years with a population of 47 (11.96%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Lost Nation, IA was the 35 to 39 years years with a population of 8 (2.04%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Content

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

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Lost Nation is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Lost Nation 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 Lost Nation Population by Age. You can refer the same here

  13. United States COVID-19 Community Levels by County

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Mar 3, 2022
    + more versions
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    CDC COVID-19 Response (2022). United States COVID-19 Community Levels by County [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-Community-Levels-by-County/3nnm-4jni
    Explore at:
    application/rdfxml, application/rssxml, csv, tsv, xml, jsonAvailable download formats
    Dataset updated
    Mar 3, 2022
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    CDC COVID-19 Response
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

    This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

    The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

    Using these data, the COVID-19 community level was classified as low, medium, or high.

    COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

    For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

    Archived Data Notes:

    This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

    March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

    March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

    March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

    March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

    March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

    March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

    April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

    April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

    May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

    May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

    May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

    June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

    June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

    June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

    July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

    July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

    July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

    July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

    July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

    August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

    August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

    August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

    August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

    August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

    August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

    August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

    September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

    September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

  14. N

    Lost Nation, IA Population Breakdown by Gender Dataset: Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). Lost Nation, IA Population Breakdown by Gender Dataset: Male and Female Population Distribution // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/b240975d-f25d-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iowa, Lost Nation
    Variables measured
    Male Population, Female Population, Male Population as Percent of Total Population, Female Population as Percent of Total Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of Lost Nation by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Lost Nation across both sexes and to determine which sex constitutes the majority.

    Key observations

    There is a slight majority of female population, with 52.93% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

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

    Scope of gender :

    Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

    Variables / Data Columns

    • Gender: This column displays the Gender (Male / Female)
    • Population: The population of the gender in the Lost Nation is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each gender as a proportion of Lost Nation 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 Lost Nation Population by Race & Ethnicity. You can refer the same here

  15. COVID-19 Reported Patient Impact and Hospital Capacity by State (RAW)

    • healthdata.gov
    • datahub.hhs.gov
    • +2more
    Updated Dec 14, 2020
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    U.S. Department of Health & Human Services (2020). COVID-19 Reported Patient Impact and Hospital Capacity by State (RAW) [Dataset]. https://healthdata.gov/dataset/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/6xf2-c3ie
    Explore at:
    xml, csv, application/rssxml, application/rdfxml, tsv, application/geo+json, kml, kmzAvailable download formats
    Dataset updated
    Dec 14, 2020
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    U.S. Department of Health & Human Services
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

    The following dataset provides state-aggregated data for hospital utilization. These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

    The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

    No statistical analysis is applied to account for non-response and/or to account for missing data.

    The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

    On June 26, 2023 the field "reporting_cutoff_start" was replaced by the field "date".

    On April 27, 2022 the following pediatric fields were added:

  16. all_pediatric_inpatient_bed_occupied
  17. all_pediatric_inpatient_bed_occupied_coverage
  18. all_pediatric_inpatient_beds
  19. all_pediatric_inpatient_beds_coverage
  20. previous_day_admission_pediatric_covid_confirmed_0_4
  21. previous_day_admission_pediatric_covid_confirmed_0_4_coverage
  22. previous_day_admission_pediatric_covid_confirmed_12_17
  23. previous_day_admission_pediatric_covid_confirmed_12_17_coverage
  24. previous_day_admission_pediatric_covid_confirmed_5_11
  25. previous_day_admission_pediatric_covid_confirmed_5_11_coverage
  26. previous_day_admission_pediatric_covid_confirmed_unknown
  27. previous_day_admission_pediatric_covid_confirmed_unknown_coverage
  28. staffed_icu_pediatric_patients_confirmed_covid
  29. staffed_icu_pediatric_patients_confirmed_covid_coverage
  30. staffed_pediatric_icu_bed_occupancy
  31. staffed_pediatric_icu_bed_occupancy_coverage
  32. total_staffed_pediatric_icu_beds
  33. total_staffed_pediatric_icu_beds_coverage

    On January 19, 2022, the following fields have been added to this dataset:
  34. inpatient_beds_used_covid
  35. inpatient_beds_used_covid_coverage

    On September 17, 2021, this data set has had the following fields added:
  36. icu_patients_confirmed_influenza,
  37. icu_patients_confirmed_influenza_coverage,
  38. previous_day_admission_influenza_confirmed,
  39. previous_day_admission_influenza_confirmed_coverage,
  40. previous_day_deaths_covid_and_influenza,
  41. previous_day_deaths_covid_and_influenza_coverage,
  42. previous_day_deaths_influenza,
  43. previous_day_deaths_influenza_coverage,
  44. total_patients_hospitalized_confirmed_influenza,
  45. total_patients_hospitalized_confirmed_influenza_and_covid,
  46. total_patients_hospitalized_confirmed_influenza_and_covid_coverage,
  47. total_patients_hospitalized_confirmed_influenza_coverage

    On September 13, 2021, this data set has had the following fields added:
  48. on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses,
  49. on_hand_supply_therapeutic_b_bamlanivimab_courses,
  50. on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses,
  51. previous_week_therapeutic_a_casirivimab_imdevimab_courses_used,
  52. previous_week_therapeutic_b_bamlanivimab_courses_used,
  53. previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used

    On June 30, 2021, this data set has had the following fields added:
  54. deaths_covid
  55. deaths_covid_coverage

    On April 30, 2021, this data set has had the following fields added:
  56. previous_day_admission_adult_covid_confirmed_18-19
  57. previous_day_admission_adult_covid_confirmed_18-19_coverage
  58. previous_day_admission_adult_covid_confirmed_20-29_coverage
  59. previous_day_admission_adult_covid_confirmed_30-39
  60. previous_day_admission_adult_covid_confirmed_30-39_coverage
  61. previous_day_admission_adult_covid_confirmed_40-49
  62. previous_day_admission_adult_covid_confirmed_40-49_coverage
  63. previous_day_admission_adult_covid_confirmed_40-49_coverage
  64. previous_day_admission_adult_covid_confirmed_50-59
  65. previous_day_admission_adult_covid_confirmed_50-59_coverage
  66. previous_day_admission_adult_covid_confirmed_60-69
  67. previous_day_admission_adult_covid_confirmed_60-69_coverage
  68. previous_day_admission_adult_covid_confirmed_70-79
  69. previous_day_admission_adult_covid_confirmed_70-79_coverage
  70. previous_day_admission_adult_covid_confirmed_80+
  71. previous_day_admission_adult_covid_confirmed_80+_coverage
  72. previous_day_admission_adult_covid_confirmed_unknown
  73. previous_day_admission_adult_covid_confirmed_unknown_coverage
  74. previous_day_admission_adult_covid_suspected_18-19
  75. previous_day_admission_adult_covid_suspected_18-19_coverage
  76. previous_day_admission_adult_covid_suspected_20-29
  77. previous_day_admission_adult_covid_suspected_20-29_coverage
  78. previous_day_admission_adult_covid_suspected_30-39
  79. previous_day_admission_adult_covid_suspected_30-39_coverage
  80. previous_day_admission_adult_covid_suspected_40-49
  81. previous_day_admission_adult_covid_suspected_40-49_coverage
  82. previous_day_admission_adult_covid_suspected_50-59
  83. previous_day_admission_adult_covid_suspected_50-59_coverage
  84. previous_day_admission_adult_covid_suspected_60-69
  85. previous_day_admission_adult_covid_suspected_60-69_coverage
  86. previous_day_admission_adult_covid_suspected_70-79
  87. previous_day_admission_adult_covid_suspected_70-79_coverage
  88. previous_day_admission_adult_covid_suspected_80+
  89. previous_day_admission_adult_covid_suspected_80+_coverage
  90. previous_day_admission_adult_covid_suspected_unknown
  91. previous_day_admission_adult_covid_suspected_unknown_coverage

  • Communities and Crime Dataset (Unnormalized Data)

    • kaggle.com
    Updated Feb 9, 2023
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    John (2023). Communities and Crime Dataset (Unnormalized Data) [Dataset]. https://www.kaggle.com/datasets/johnp47/communities-and-crime-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 9, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    John
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Source:

    Creator: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law Enforcement Management and Administrative Statistics Survey, available from ICPSR at U of Michigan. -- Donor: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- Date: July 2009

    Data Set Information:

    Many variables are included so that algorithms that select or learn weights for attributes could be tested. However, clearly unrelated attributes were not included; attributes were picked if there was any plausible connection to crime (N=122), plus the attribute to be predicted (Per Capita Violent Crimes). The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units.

    The per capita violent crimes variable was calculated using population and the sum of crime variables considered violent crimes in the United States: murder, rape, robbery, and assault. There was apparently some controversy in some states concerning the counting of rapes. These resulted in missing values for rape, which resulted in incorrect values for per capita violent crime. These cities are not included in the dataset. Many of these omitted communities were from the midwestern USA.

    Data is described below based on original values. All numeric data was normalized into the decimal range 0.00-1.00 using an Unsupervised, equal-interval binning method. Attributes retain their distribution and skew (hence for example the population attribute has a mean value of 0.06 because most communities are small). E.g. An attribute described as 'mean people per household' is actually the normalized (0-1) version of that value.

    The normalization preserves rough ratios of values WITHIN an attribute (e.g. double the value for double the population within the available precision - except for extreme values (all values more than 3 SD above the mean are normalized to 1.00; all values more than 3 SD below the mean are normalized to 0.00)).

    However, the normalization does not preserve relationships between values BETWEEN attributes (e.g. it would not be meaningful to compare the value for whitePerCap with the value for blackPerCap for a community)

    A limitation was that the LEMAS survey was of the police departments with at least 100 officers, plus a random sample of smaller departments. For our purposes, communities not found in both census and crime datasets were omitted. Many communities are missing LEMAS data.

    Attribute Information:

    '(125 predictive, 4 non-predictive, 18 potential goal) ', ' communityname: Community name - not predictive - for information only (string) ', ' state: US state (by 2 letter postal abbreviation)(nominal) ', ' countyCode: numeric code for county - not predictive, and many missing values (numeric) ', ' communityCode: numeric code for community - not predictive and many missing values (numeric) ', ' fold: fold number for non-random 10 fold cross validation, potentially useful for debugging, paired tests - not predictive (numeric - integer) ', ' population: population for community: (numeric - expected to be integer) ', ' householdsize: mean people per household (numeric - decimal) ', ' racepctblack: percentage of population that is african american (numeric - decimal) ', ' racePctWhite: percentage of population that is caucasian (numeric - decimal) ', ' racePctAsian: percentage of population that is of asian heritage (numeric - decimal) ', ' racePctHisp: percentage of population that is of hispanic heritage (numeric - decimal) ', ' agePct12t21: percentage of population that is 12-21 in age (numeric - decimal) ', ' agePct12t29: percentage of population that is 12-29 in age (numeric - decimal) ', ' agePct16t24: percentage of population that is 16-24 in age (numeric - decimal) ', ' agePct65up: percentage of population that is 65 and over in age (numeric - decimal) ', ' numbUrban: number of people living in areas classified as urban (numeric - expected to be integer) ', ' pctUrban: percentage of people living in areas classified as urban (numeric - decimal) ', ' medIncome: median household income (numeric - may be integer) ', ' pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal) ', ' pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal) ', ' pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal) ', ' pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal) ', ' pctWPubAsst: pe...

  • G

    Kidnapping rate by country, around the world | TheGlobalEconomy.com

    • theglobaleconomy.com
    csv, excel, xml
    Updated Jan 17, 2015
    + more versions
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    Globalen LLC (2015). Kidnapping rate by country, around the world | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/rankings/kidnapping/
    Explore at:
    xml, csv, excelAvailable download formats
    Dataset updated
    Jan 17, 2015
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 2003 - Dec 31, 2017
    Area covered
    World, World
    Description

    The average for 2017 based on 65 countries was 1.8 kidnappings per 100,000 people. The highest value was in Belgium: 10.3 kidnappings per 100,000 people and the lowest value was in Bermuda: 0 kidnappings per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

  • DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area

    • data.cityofnewyork.us
    • catalog.data.gov
    Updated Apr 27, 2021
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    Department of Health and Mental Hygiene (DOHMH) (2021). DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area [Dataset]. https://data.cityofnewyork.us/dataset/DOHMH-COVID-19-Antibody-by-Modified-ZIP-Code-Tabul/6qs8-44ki
    Explore at:
    csv, application/rssxml, tsv, xml, application/rdfxml, kmz, application/geo+json, kmlAvailable download formats
    Dataset updated
    Apr 27, 2021
    Dataset provided by
    New York City Department of Health and Mental Hygienehttps://nyc.gov/health
    Authors
    Department of Health and Mental Hygiene (DOHMH)
    Description

    This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.
    These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

    In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders)

    Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

    Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

    Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.
    For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.pagehttps://github.com/nychealth/coronavirus-datahttps://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

  • The Mississippi Repository for Missing and Unidentified Persons

    • figshare.com
    Updated Mar 7, 2025
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    Jesse Goliath; Sarajane Smith-Escudero; Boman Shelton; Jordan Lynton Cox (2025). The Mississippi Repository for Missing and Unidentified Persons [Dataset]. http://doi.org/10.6084/m9.figshare.28555151.v1
    Explore at:
    Dataset updated
    Mar 7, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Jesse Goliath; Sarajane Smith-Escudero; Boman Shelton; Jordan Lynton Cox
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mississippi River
    Description

    The Mississippi Repository for Missing and Unidentified Persons (MS Repository) was developed in January 2022 to help identify, resolve, and archive Mississippi’s missing and unidentified persons cases. The MS Repository, housed at Mississippi State University, serves as a statewide missing and unidentified persons clearinghouse database. The MS Repository is under the purview of the Cobb Institute of Archaeology (including the Department of Anthropology and Middle Eastern Cultures) and the MSU Police Department (MSUPD). In collaboration with law enforcement agencies throughout the state, the goals of the MS Repository are to:1. Provide a centralized location for data on missing and unidentified persons from Mississippi2. Increase missing persons public access for all Mississippians3. Visualize socioeconomic and medicolegal disparities affecting missing persons through geospatial analysis4. Partner with neighboring states to facilitate data sharing of missing and unidentified persons information.The lack of comprehensive missing and unidentified persons repository data at the state and national levels continues to hinder identifying missing and unidentified people. The MS Repository is the only secure, formalized, searchable Mississippi data repository for unidentified and missing persons information. It includes missing and unidentified persons information from the National Missing and Unidentified Persons System (NamUS), law enforcement missing persons reports on social media, cases from non-profit missing persons advocacy groups, and reports from families with missing loved ones. Like NamUS, the MS Repository provides demographic information about the missing individual and case circumstances, including last seen date and location. Each profile has a built-in capacity for holding copies of medical records and DNA records results (including family reference samples). All profiles (current and resolved) are stored electronically and available in perpetuity, regardless of case status. In addition to the database, there is a searchable clearinghouse website accessible to the public (missinginms.msstate.edu).

  • d

    DOHMH COVID-19 Antibody-by-Neighborhood Poverty

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Jul 7, 2024
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    data.cityofnewyork.us (2024). DOHMH COVID-19 Antibody-by-Neighborhood Poverty [Dataset]. https://catalog.data.gov/dataset/dohmh-covid-19-antibody-by-neighborhood-poverty
    Explore at:
    Dataset updated
    Jul 7, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain

  • Share
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    Statista (2024). Number of missing persons files in the U.S. 2022, by race [Dataset]. https://www.statista.com/statistics/240396/number-of-missing-persons-files-in-the-us-by-race/
    Organization logo

    Number of missing persons files in the U.S. 2022, by race

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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

    What is the NCIC?

    The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

    Missing people in the United States

    A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

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