Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset contains the cumulative number of deaths, average number of deaths annually, average annual crude and adjusted death rates with corresponding 95% confidence intervals, and average annual years of potential life lost per 100,000 residents aged 75 and younger due to selected causes of death, by Chicago community area, for the years 2006 – 2010. A ranking for each measure is also provided, with the highest value indicated with a ranking of 1. See the full description at: https://data.cityofchicago.org/api/views/6vw3-8p6f/files/CqPqfHSv8UUAoXCBjn4_tLqcQHhb36Ih4-meM-4zNzs?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\MORTALITY\Dataset_Description_06_10_PORTAL_ONLY.pdf

2011-2022. The ASTDD Synopses of State Oral Health Programs contain information useful in tracking states’ efforts to improve oral health and contributions to progress toward the national targets for Healthy People objectives for oral health. A subset of the information collected from the most recent five years is provided on the Oral Health Data website. For more information, see http://www.cdc.gov/oralhealthdata/overview/synopses/index.html

RS: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 5.300 Ratio in 2016. This records a decrease from the previous number of 5.600 Ratio for 2015. RS: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 6.800 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 26.400 Ratio in 1990 and a record low of 5.300 Ratio in 2016. RS: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

JP: Births Attended by Skilled Health Staff: % of Total data was reported at 99.900 % in 2015. This records an increase from the previous number of 99.800 % for 2014. JP: Births Attended by Skilled Health Staff: % of Total data is updated yearly, averaging 99.800 % from Dec 1990 (Median) to 2015, with 18 observations. The data reached an all-time high of 100.000 % in 1996 and a record low of 99.800 % in 2014. JP: Births Attended by Skilled Health Staff: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Health Statistics. Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns.; ; UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.; Weighted average; Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.

RS: People Practicing Open Defecation: Rural: % of Rural Population data was reported at 0.163 % in 2015. This records an increase from the previous number of 0.150 % for 2014. RS: People Practicing Open Defecation: Rural: % of Rural Population data is updated yearly, averaging 0.069 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 0.163 % in 2015 and a record low of 0.025 % in 2004. RS: People Practicing Open Defecation: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank: Health Statistics. People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).; Weighted Average;

VAMC-level statistics on the prevalence, mental health utilization, non-mental health utilization, mental health workload, and psychological testing of Veterans with a possible or confirmed diagnosis of mental illness. Information prepared by the VA Northeast Program Evaluation Center (NEPEC) for fiscal year 2015. This dataset is no longer supported and is provided as-is. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set.

This dataset contains information on the names and locations of evidence-based self-management programs delivered by QTAC-NY partners, including street address, city, state, zip code, county, and Delivery System Reform Incentive Payment (DSRIP) Program region. It also contains information about the capacity of each implementation site to deliver specific self-management programs. The data will be updated on a yearly basis.

===###

This synthetic dataset, centred on ART for HIV, was synthesised employing the model outlined in reference [1], incorporating the techniques of WGAN-GP+G_EOT+VAE+Buffer.

This dataset serves as a principal resource for the Centre for Big Data Research in Health (CBDRH) Datathon (see: CBDRH Health Data Science Datathon 2023 (cbdrh-hds-datathon-2023.github.io)). Its primary purpose is to advance the Health Data Analytics (HDAT) courses at the University of New South Wales (UNSW), providing students with exposure to synthetic yet realistic datasets that simulate real-world data.

The dataset is composed of 534,960 records, distributed over 15 distinct columns, and is preserved in a CSV format with a size of 39.1 MB. It contains information about 8,916 synthetic patients over a period of 60 months, with data summarised on a monthly basis. The total number of records corresponds to the product of the synthetic patient count and the record duration in months, thus equating to 8,916 multiplied by 60.

The dataset's structure encompasses 15 columns, which include 13 variables pertinent to ART for HIV as delineated in reference [1], a unique patient identifier, and a further variable signifying the specific time point.

===

This dataset forms part of a continuous series of work, building upon reference [2]. For further details, kindly refer to our papers: [1] Kuo, Nicholas I., Louisa Jorm, and Sebastiano Barbieri. "Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV." arXiv preprint arXiv:2208.08655 (2022). [2] Kuo, Nicholas I-Hsien, et al. "The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms." Scientific Data 9.1 (2022): 693.

===

Latest edit: 16th May 2023.

List of footnotes, notes, and source information for NHIS Adult Summary Statistics. Each row of this dataset contains the accompanying text for a footnote found in the NHIS Adults Summary Statistics Dataset.

"""Local Law 14 (2016) requires that the NYCDOE provide citywide Health Education data, dis aggregated by community school district, city council district, and each individual school. Data reported in this report is from the 2015-16 school year. "" This report provides information about the number and percent of students receiving one semester of health education as defined in Local Law 14 as reported through the 2015-2016 STARS database. It is important to note that schools self-report their scheduling information in STARS.

This report consists of 10 tabs:

1. Health Education Standards
2. HS - School
3. HS-District
4. JS-City Council District
5. MS-School
6. MS-District
7. Ms-City Council District
8. Efficacy
9. Compliance

10. LGBTQ Inclusivity

11. Health Education Standards

This tab provides information on the New York State Health Education Requirements and Standards. These requirements can be found in NYS Education Commissioner’s Regulation Subchapter G Part 135.

1. HS - School

This tab includes school level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - District

This tab includes district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - City Council District

This tab includes city council district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. Ms - School

This tab includes school level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - District

This tab includes district level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - City Council District

This tab includes City Council district level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. Compliance

This tab provides information on how the DOE complies with the State and City health education requirements.

1. Efficacy

This tab provides information about the DOE's recommended health education curricula.

1. LGBTQ Inclusivity

This tab provides information about how the DOE supports health education that is inclusive and supportive of LGBTQ students.

Additional Information

YABC, D75 home and hospital, D79 and charter schools are excluded from this report."

Snapshot img

Healthcare Analytics Market Size 2025-2029

The healthcare analytics market size is forecast to increase by USD 81.28 billion, at a CAGR of 25% between 2024 and 2029.

The market is experiencing significant growth due to several key trends. The integration of big data with healthcare analytics is a major growth factor, enabling healthcare providers to make data-driven decisions and improve patient outcomes.
Another trend is the increasing use of Internet-enabled mobile devices in healthcare services, allowing for remote monitoring and real-time data access. However, data security and privacy concerns remain a challenge, with the need for strong security measures to protect sensitive patient information. These trends are shaping the future of patient engagement and driving growth in the global healthcare analytics market as well.

What will be the Size of the Healthcare Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of digital solutions for improving patient care and reducing treatment costs. Healthcare organizations are leveraging descriptive analytics to gain insights from clinical data, while predictive and prescriptive analytics enable the development of personalized treatment plans and optimal therapeutic strategies. Financial analytics help manage healthcare expenses, ensuring cost-effective patient care. The National Institutes of Health (NIH) and other research institutions are driving innovation in health data analytics, leading to advancements in areas such as patient compliance, medication selection, and disease management. Industry leaders are utilizing artificial intelligence and machine learning to enhance clinical care, outreach, and disease management, ultimately leading to better treatment consistency and optimal outcomes for patients.

How is this Healthcare Analytics Industry segmented and which is the largest segment?

The healthcare analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Services
  Software
  Hardware


Deployment

  On-premise
  Cloud-based


Type

  Descriptive Analysis
  Predictive Analysis
  Prescriptive and Diagnostics


Application

  Financial Analytics
  Clinical Analytics
  Operations and Administrative Analytics
  Population Health Analytics


End-User

  Insurance Company
  Government Agencies
  Healthcare Providers
  Pharmaceutical and Medical Device Companies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Component Insights

The services segment is estimated to witness significant growth during the forecast period. Healthcare analytics services encompass consulting, learning and training, development and integration, hardware maintenance and support, IT management, process management, and software support. The consulting and software support segments are experiencing significant growth due to the increasing demand for advanced healthcare delivery systems and cost-effective models. The healthcare sector's ongoing transition from on-premises to cloud-based software and IT infrastructure deployment is another growth driver. This shift is expected to increase the demand for IT education and training services. End-users of these services range from individual doctor offices to full-service hospitals and multi-location clinics, including large hospitals and tissue and blood processing organizations.

Get a glance at the share of various segments. Request Free Sample

The services segment was valued at USD 6.7 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market is driven by the increasing demand for secure data access and effective patient information management. The US and Canada are the primary contributors to this market due to their early adoption of advanced technologies, such as machine learning, predictive analytics, and quantum computing, across various industries. These technologies enable the healthcare sector to optimize patient compliance, medication selection, and therapeutic strategies and, ultimately, achieve optimal outcomes. Major companies in this market provide solutions to help healthcare organizations manage and

In 2020, the National Center for Health Statistics (NCHS) partnered with the Census Bureau on an experimental data system called the Household Pulse Survey. This survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about how emergent issues are impacting American households. Beginning in Phase 4.0 (on January 9, 2024), questions on social support, loneliness, and social isolation were added to the survey. These questions have been included on other nationally representative surveys. Briefly, the question on social support was included on the National Health Interview Survey (NHIS) from July 2020-December 2021 and was added to the 2024 NHIS. The question on loneliness was added to the 2024 NHIS. The questions on social isolation are adapted from the Berkman-Syme Social Network Index and were included on an earlier cycle of the National Health and Nutrition Examination Survey. For more information, please visit: https://www.cdc.gov/nchs/covid19/pulse/lack-socialconnection.htm

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. This file lists the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility beds file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations.

These 21 regions are aggregations of counties developed by the Health Statistics Section in partnership with state and local public health professionals. The regions were developed using statistical and demographic criteria:

Food Safety and Public Health Statistics (2021-2024)

KZ: Domestic Private Health Expenditure: % of Current Health Expenditure data was reported at 39.463 % in 2015. This records an increase from the previous number of 37.746 % for 2014. KZ: Domestic Private Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 38.039 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 49.062 % in 2000 and a record low of 31.419 % in 2011. KZ: Domestic Private Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kazakhstan – Table KZ.World Bank: Health Statistics. Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Contains data from World Health Organization's data portal covering various indicators (one per resource).

List of footnotes, notes, and source information for NHIS Child Summary Statistics. Each row of this dataset contains the accompanying text for a footnote found in the NHIS Child Summary Statistics Dataset.

Interactive Summary Health Statistics for Children provide annual estimates of selected health topics for children under age 18 years based on final data from the National Health Interview Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.