The Veterans Health Administration (VHA) has now collaborated with the Centers for Medicare & Medicaid Services (CMS) to present information to consumers about the quality and safety of health care in VHA. VHA has approximately 50 percent of Veterans enrolled in the healthcare system who are eligible for Medicare and, therefore, have some choice in how and where they receive inpatient services. VHA has adopted healthcare transparency as a strategy to enhance public trust and to help Veterans make informed choices about their health care.VHA currently reports the following types of quality measures on Hospital Compare:Timely and effective care.Behavioral health.Readmissions and deaths.Patient safety.*Experience of care.

The dataset provides performance ratings for coronary artery bypass graft (CABG) surgery, inpatient mortality indicators (IMIs), and elective percutaneous coronary intervention (PCI). The outcome measures include: operative mortality for isolated CABG; inpatient mortality for acute stroke, acute myocardial infarction, heart failure, gastrointestinal hemorrhage, hip fracture, pneumonia, abdominal aortic aneurysm repair, carotid endarterectomy, esophageal resection, pancreatic resection, percutaneous coronary intervention; three outcome measures for elective PCI without on-site cardiac surgery: mortality, post-PCI stroke, and post-PCI emergency coronary artery bypass graft surgery; postoperative sepsis following elective surgeries. It includes risk-adjusted rates, number of adverse events and cases.

This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance rates and scores for the Clinical Process of Care Pneumonia measures. This dataset is no more updated now a days and has been retired from hospital compare datasets.

This data package contains information about hospital readmission and deaths as well as hospital excess readmission reduction program. It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.

Provides basic information for general acute care hospital buildings such as height, number of stories, the building code used to design the building, and the year it was completed. The data is sorted by counties and cities. Structural Performance Categories (SPC ratings) are also provided. SPC ratings range from 1 to 5 with SPC 1 assigned to buildings that may be at risk of collapse during a strong earthquake and SPC 5 assigned to buildings reasonably capable of providing services to the public following a strong earthquake. Where SPC ratings have not been confirmed by the Department of Health Care Access and Information (HCAI) yet, the rating index is followed by 's'. A URL for the building webpage in HCAI/OSHPD eServices Portal is also provided to view projects related to any building.

Comprehensive Care for Joint Replacement Model - provider data. This data set includes provider data for two quality measures tracked during an episode of care: complication rate for hip/knee replacement patients and the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Survey

Complications and deaths - state data. This data set includes state-level data for the hip/knee complication measure, the CMS Patient Safety Indicators, and 30-day death rates.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 5 procedures performed (Abdominal Aortic Aneurysm Repair, Unruptured/Open, Abdominal Aortic Aneurysm Repair, Unruptured/Endovascular, Carotid Endarterectomy, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2022 IMIs were generated using AHRQ Version 2023, while previous years' IMIs were generated with older versions of AHRQ software (2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Why Not the Best VA or WNTBVA is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. These .ZIP files are no longer supported and are in an 'as-is' state. They were accurate at time of publication.

Data for seismic compliance in general acute care hospitals is grouped by counties. All general acute care hospital buildings are assigned a Structural Performance Category (SPC) which measures the probable seismic performance of building structural systems. Building structural systems include beams, columns, shear walls, slabs, and foundations. SPC ratings range from 1 to 5 with SPC 1 assigned to buildings that may be at risk of collapse during a strong earthquake and SPC 5 assigned to buildings reasonably capable of providing services to the public following a strong earthquake. State law requires all SPC 1 buildings to be removed from providing general acute care services by January 1, 2020, unless an approved extension has been granted, and all SPC 2 buildings to be removed from providing general acute care services by January 1, 2030. A hospital facility meets the January 1, 2030 requirements if all the general acute care buildings on campus are SPC and NPC compliant. 2030 compliant SPC ratings are either SPC 3, 4, 4D, or 5. 2030 compliant NPC ratings is NPC 5. Data is provided for both hospital facilities and hospital buildings. Data is updated approximately every two weeks.

The Hospital Price Transparency Enforcement Activities and Outcomes dataset contains information related to enforcement actions taken by CMS following a compliance review of a hospital's obligation to establish, update and make public a list of the hospital’s standard charges for items and services provided by the hospital, in accordance with regulation (45 CFR 180). This data set includes the name of each hospital or hospital location, the hospital or hospital location address, the outcome or action following a CMS compliance review and the date of the outcome or action taken.

This data package contains information about Abeyance, Dispersal and GP Practice Codes for Prescribing Cost Centres and Relevant Health Trusts. It comprises of datasets on Hospital Compare by General Practitioners and Long Term Care Hospitals by General Information and Provider data. It also includes datasets on Nurse Prescribers, Pharmacy Headquarters and Private Controlled Drug Prescribers in England as well as Medical Center Location of US Hospital.

This dataset contains the location of hospitals that performed Coronary Artery Bypass Graft (CABG) surgery in California from 2011 to 2022. It also provides performance ratings for the following risk-adjusted outcome measures related to the CABG surgery: operative mortality for isolated CABG and CABG plus valve, post-operative stroke, and 30-day all cause readmission. The dataset also includes the number of cases and adverse events for each CABG outcome measure. The methodology, including risk models that generated the rates and performance ratings differed among years. Users should exercise caution when comparing trends of rates over time (see metadata for more information).

Why Not the Best VA or WNTBVA is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. These .ZIP files are no longer supported and are in an 'as-is' state. They were accurate at time of publication.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement. Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information. There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

This dataset lists the Structural Performance Category or “SPC rating” and Nonstructural Performance Category or “NPC rating” of all General Acute Care Hospitals in California, including a building’s probability of collapse in a significant earthquake as assessed by Multi-Hazard Loss Estimation Technology (HAZUS). To link the HCAI IDs with those from other Departments, like CDPH, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old. End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Health Administrative Datasets (SAIL) for Wales held under SN 9310
  
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The Millennium Cohort Study: Linked Health Administrative Data (Scottish Medical Records), Child Health Reviews, 2000-2015: Secure Access includes data files from the NHS Digital Hospital Episode Statistics database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland. The Scottish Medical Records database contains information about all hospital admissions in Scotland. This study concerns the Child Health Reviews (CHR) from first visit to school reviews.

Other datasets are available from the Scottish Medical Records database, these include:

• Prescribing Information System (PIS) held under SN 8710
• Scottish Immunisation and Recall System (SIRS) held under SN 8711
• Scottish Birth Records (SMR11) held under <a

BackgroundPopulation-based, prospective studies can provide important insights into Parkinson’s disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.MethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.ResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPV ranged from 56–90% in hospital datasets, 53–87% in prescription datasets, 81–90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPV ranged from 36–88% in hospital datasets, 40–74% in prescription datasets, and was 94% in mortality datasets. Sensitivity ranged from 15–73% in single datasets for PD and 43–63% in single datasets for parkinsonism.ConclusionsIn many settings, routinely collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.