Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

The recent surge in Internet growth has significantly altered how residents obtain health information and services, underscoring the need to investigate its impact on healthcare perceptions. However, current studies often fail to distinguish between Internet use and involvement, as well as the diverse range of healthcare stakeholders, resulting in incomplete and inconsistent understanding. To address this, this study utilized data from the 2018 China Family Panel Study (CFPS 2018), categorizing attitudes toward healthcare into three dimensions: doctor trust, satisfaction with medical institutions, and perception of systemic healthcare issues. Employing propensity score matching (PSM) to control for thirteen confounding variables, this study examined the Internet’s impact on public attitudes toward healthcare among similar demographic, psychological, and health-related variables. Results revealed that both Internet use and involvement affect residents’ attitudes toward healthcare to some extent, with involvement having a more pronounced effect. While Internet use increased the perception of systemic healthcare issues, Internet involvement enhanced doctor trust, yet reduced satisfaction with medical institutions and exaggerated the perception of systemic healthcare issues. These findings have significant theoretical and practical implications. They enhance the comprehension of diverse levels and purposes of Internet use, thereby advancing our knowledge of its multi-faced influence on public attitudes toward healthcare. Furthermore, they offer insights for medical institutions to improve service quality, assist Internet media in optimizing information delivery, and illuminate the implications for residents who effectively use the Internet to assess health information.

ObjectiveThe purpose of this study was to evaluate the performance of algorithms for identifying cases of severe hypoglycemia in Japanese hospital administrative data.MethodsThis was a multicenter, retrospective, observational study conducted at 3 acute-care hospitals in Japan. The study population included patients aged ≥18 years with diabetes who had an outpatient visit or hospital admission for possible hypoglycemia. Possible cases of severe hypoglycemia were identified using health insurance claims data and Diagnosis Procedure Combination data. Sixty-one algorithms using combinations of diagnostic codes and prescription of high concentration (≥20% mass/volume) injectable glucose were used to define severe hypoglycemia. Independent manual chart reviews by 2 physicians at each hospital were used as the reference standard. Algorithm validity was evaluated using standard performance metrics.ResultsIn total, 336 possible cases of severe hypoglycemia were identified, and 260 were consecutively sampled for validation. The best performing algorithms included 6 algorithms that had sensitivity ≥0.75, and 6 algorithms that had positive predictive values ≥0.75 with sensitivity ≥0.30. The best-performing algorithm with sensitivity ≥0.75 included any diagnoses for possible hypoglycemia or prescription of high-concentration glucose but excluded suspected diagnoses (sensitivity: 0.986 [95% confidence interval 0.959–1.013]; positive predictive value: 0.345 [0.280–0.410]). Restricting the algorithm definition to those with both a diagnosis of possible hypoglycemia and a prescription of high-concentration glucose improved the performance of the algorithm to correctly classify cases as severe hypoglycemia but lowered sensitivity (sensitivity: 0.375 [0.263–0.487]; positive predictive value: 0.771 [0.632–0.911]).ConclusionThe case-identifying algorithms in this study showed moderate positive predictive value and sensitivity for identification of severe hypoglycemia in Japanese healthcare data and can be employed by future pharmacoepidemiological studies using Japanese hospital administrative databases.

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution’s characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution’s IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

The Veterans Health Administration Medical Facilities dataset includes Veteran Affairs hospitals, Veteran Affairs Residential Rehabilitation Treatment Programs (RRTP), Veteran Affairs Nursing Home Care Units (NHCU), Veteran Affairs Outpatient Clinics (VAOC), Vet Centers, and Veteran Affairs Medical Centers (VAMC). It should not include planned and suspended (non-operational) sites and mobile clinics. These definitions were set by the Veterans Health Administration (VHA) Policy Board in December 1998 and are the basis for defining the category and the additional service types for each VHA service site. These definitions cover sites generally owned by the Department of Veterans Affairs (VA) with the exception of leased and contracted community-based outpatient clinics (CBOCs).1. VA HOSPITAL: an institution (health care site) that is owned, staffed and operated by VA and whose primary function is to provide inpatient services. NOTE: Each geographically unique inpatient division of an integrated facility is counted as a separate hospital.2. VA RESIDENTIAL REHABILITATION TREATMENT PROGRAM (RRTP): provides comprehensive health and social services in a VA facility for eligible veterans who are ambulatory and do not require the level of care provided in nursing homes.3. VA NURSING HOME CARE UNITS (NHCU): provides care to individuals who are not in need of hospital care, but who require nursing care and related medical or psychosocial services in an institutional setting. VA NHCUs are facilities designed to care for patients who require a comprehensive care management system coordinated by an interdisciplinary team. Services provided include nursing, medical, rehabilitative, recreational, dietetic, psychosocial, pharmaceutical, radiological, laboratory, dental and spiritual.4. VA OUTPATIENT CLINICS:a. Community-Based Outpatient Clinic (CBOC): a VA-operated, VA-funded, or VA-reimbursed health care facility or site geographically distinct or separate from a parent medical facility. This term encompasses all types of VA outpatient clinics, except hospital-based, independent and mobile clinics. Satellite, community-based, and outreach clinics have been redefined as CBOCs. Technically, CBOCs fall into four Categories, which are: >(i) VA-owned. A CBOC that is owned and staffed by VA. >(ii) Leased. A CBOC where the space is leased (contracted), but is staffed by VA. NOTE: This includes donated space staffed by VA. >(iii) Contracted. A CBOC where the space and the staff are not VA. This is typically a Healthcare Management Organization (HMO)-type provided where multiple sites can be associated with a single station identifier. >(iv) Not Operational. A CBOC which has been approved by Congress, but has not yet begun operating.b. Hospital-Based Outpatient Clinic: outpatient clinic functions located at a hospital.c. Independent Outpatient Clinic: a full-time, self-contained, freestanding, ambulatory care clinic that has no management, program, or fiscal relationship to a VA medical facility. Primary and specialty health care services are provided in an outpatient setting.5. VET CENTER: Provides professional readjustment counseling, community education, outreach to special populations, brokering of services with community agencies, and access to links between the veteran and VA.6. VA MEDICAL CENTER (VAMC): a medical center is a unique VA site of care providing two or more types of services that reside at a single physical site location. The services provided are the primary service as tracked in the VHA Site Tracking (VAST) (i.e., VA Hospital, Nursing Home, Domiciliary, independent outpatient clinic (IOC), hospital-based outpatient clinic (HBOC), and CBOC). The definition of VA medical center does not include the Vet Centers as an identifying service. This dataset is based upon GFI data received from the National Geospatial-Intelligence Agency (NGA). At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 09/21/2007 and the newest record dates from 10/15/2007.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

These data contain counts of vaccine preventable disease cases among California residents by county, disease, and year.

The California Department of Public Health (CDPH) maintains a mandatory, passive reporting system for a list(1) of communicable disease cases and outbreaks. The CDPH Immunization Branch conducts surveillance for vaccine preventable diseases. Health care providers and laboratories are mandated to report cases or suspected cases of these communicable diseases to their local health department (LHD). LHDs are also mandated to report these cases to CDPH.

Materials and Methods

Case data sources and inclusion criteria

Data were extracted on communicable disease cases with an estimated onset or diagnosis date from 2001 through the last year indicated, from California Confidential Morbidity Reports and/or Laboratory Reports that were submitted to CDPH and which met the surveillance case definition for that disease.(2) Because of inherent delays in case reporting and depending on the length of follow-up of clinical, laboratory and epidemiologic investigation, cases with eligible onset dates may be added or rescinded after the date of this report.

Definitions

In general, we defined a case as laboratory and/or clinical evidence of infection or disease in a person that satisfied the communicable disease surveillance case definition published by the United States (US) Centers for Disease Control and Prevention (CDC) or by the Council of State and Territorial Epidemiologists (CSTE) at the time the case was reported.

Limitations

Completeness of reporting

The numbers of disease cases in this report are likely to underestimate the true magnitude of disease. Among factors that may contribute to under-reporting are: delays in notification, limited collection or appropriate testing of specimens, health care seeking behavior among ill persons, limited resources and competing priorities in LHDs, and lack of reporting by clinicians and laboratories. Among factors that may contribute to changes in reporting are disease severity, the availability of new or less expensive diagnostic tests, changes in the case definition by CDC or CDPH, changes in mandatory reporting requirements, recent media or public attention, and active surveillance activities. Differential reporting practices among LHDs may also result in inconsistent reporting of patient information.

References

1. California Code of Regulations, Title 17, Sections 2500 and 2505 https://www.cdph.ca.gov/Programs/CID/DCDC/CDPH%20Document%20Library/ReportableDiseases.pdf

2. Center for Disease Control and Prevention, National Notifiable Diseases Surveillance System https://ndc.services.cdc.gov/

This data set contains Wisconsin COVID-19 case, death, hospitalization, test data and population information by county boundary. All data are laboratory-confirmed cases of COVID-19 that are frozen once a day to verify and ensure that we are reporting accurate information. These numbers are the official state numbers, though counties may report their own totals independent of Department of Health Services (combining the DHS and local totals may result in inaccurate totals). Deaths are reported by health care providers, medical examiners/coroners, and recorded by local health departments in order to be counted by the state DHS. Detailed data descriptions can be found within the COVID-19 Public Use Data Definitions document: https://www.dhs.wisconsin.gov/publications/p02677.pdf.

Medical HD Video Recorder Market Outlook

The global market size of Medical HD Video Recorders was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% over the forecast period. The primary growth factor driving this market includes the increasing demand for high-quality video recording in medical procedures for improved patient outcomes and documentation.

One significant growth factor for the Medical HD Video Recorder Market is the rising number of surgical procedures worldwide. As the global population ages and the prevalence of chronic diseases increases, the demand for surgeries is escalating. High-definition video recorders play a crucial role in these procedures by providing clear and detailed visual documentation, which aids in better diagnosis, treatment planning, and post-operative care. Additionally, the growing popularity of minimally invasive surgeries, which require precise video recording for effective execution, further propels the market growth.

Technological advancements in medical video recording equipment are also significantly boosting the market. The integration of AI and machine learning in video analytic tools has enhanced the capabilities of HD video recorders, offering better image quality, real-time processing, and improved storage solutions. Moreover, the shift towards digitalization in the healthcare sector, including the adoption of electronic health records (EHR) and telemedicine, has increased the demand for high-definition video recording for accurate data capture and sharing, further driving market expansion.

The increasing investment in healthcare infrastructure, particularly in emerging economies, is another critical growth driver for the Medical HD Video Recorder Market. Governments and private sectors in Asia Pacific, Latin America, and the Middle East are investing heavily in building modern healthcare facilities equipped with advanced medical technologies. These investments are enhancing healthcare delivery and broadening the market opportunities for HD video recorders in these regions. The rising awareness among healthcare professionals about the benefits of high-definition video recording in improving clinical outcomes is also contributing to the market growth.

Regionally, North America currently dominates the Medical HD Video Recorder Market, attributed to the well-established healthcare infrastructure, high adoption rate of advanced medical technologies, and significant presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period due to rapid healthcare advancements, increasing medical tourism, and favorable government initiatives to improve healthcare services. The rising prevalence of chronic diseases and the growing demand for quality medical care in countries like China and India further support the market's regional growth.

Product Type Analysis

The Medical HD Video Recorder Market is segmented into two primary product types: Standalone and Integrated. Standalone HD video recorders are independent devices that can be connected to various medical equipment for video recording purposes. These devices are highly favored in healthcare settings due to their flexibility, ease of use, and cost-effectiveness. The increasing demand for portable and user-friendly recording solutions in medical facilities is driving the growth of this segment. Standalone recorders are particularly popular in smaller healthcare facilities and clinics where budget constraints are a significant consideration.

On the other hand, Integrated HD video recorders are embedded within medical devices such as endoscopes, surgical microscopes, and radiology machines. These recorders offer seamless integration, enhanced functionality, and better space management, making them highly suitable for modern and advanced medical settings. The rising adoption of integrated systems in sophisticated surgical suites and specialized medical centers is expected to drive the growth of this segment. Additionally, integrated HD video recorders are preferred in high-volume hospitals and tertiary care centers where continuous and reliable video recording is essential for complex procedures.

The standalone segment is anticipated to hold a significant market share due to its widespread application across various medical procedures and its affordability. However, the integrated segment is expected to grow at a faster rate during the forecast period,

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

This data set contains Wisconsin COVID-19 data by county boundary. Data is updated at 2:00PM CDT daily.Detailed data descriptions can be found within the COVID-19 Public Use Data Definitions document.All data are laboratory-confirmed cases of COVID-19 that we freeze once a day to verify and ensure that we are reporting accurate information. These numbers are the official state numbers, though counties may report their own totals independent of DHS. Combining the DHS and local totals may result in inaccurate totals.Data included in these tables are subject to change. As individual cases are investigated by public health, there may be corrections to the status and details of cases that result in changes to this information.Deaths must be reported by health care providers, medical examiners/coroners, and recorded by local health departments in order to be counted.Starting on March 30, 2020, the number of people with negative test results was changed to include only Wisconsin residents. The number of people with negative test results includes only Wisconsin residents who had their results reported electronically to DHS. As a result, this number underestimates the total number of Wisconsin residents with negative test results."-999" values represent fewer than 5 cases, including 0 cases.For more information on the COVID-19 outbreak please visit https://www.dhs.wisconsin.gov/outbreaks/index.htm.

This data set contains Wisconsin COVID-19 data by census tract boundary. Data is updated at 2:00PM CDT daily.Detailed data descriptions can be found within the COVID-19 Public Use Data Definitions document.All data are laboratory-confirmed cases of COVID-19 that we freeze once a day to verify and ensure that we are reporting accurate information. These numbers are the official state numbers, though counties may report their own totals independent of DHS. Combining the DHS and local totals may result in inaccurate totals.Data included in these tables are subject to change. As individual cases are investigated by public health, there may be corrections to the status and details of cases that result in changes to this information.Deaths must be reported by health care providers, medical examiners/coroners, and recorded by local health departments in order to be counted.Starting on March 30, 2020, the number of people with negative test results was changed to include only Wisconsin residents. The number of people with negative test results includes only Wisconsin residents who had their results reported electronically to DHS. As a result, this number underestimates the total number of Wisconsin residents with negative test results."-999" values represent fewer than 5 cases, including 0 cases.For more information on the COVID-19 outbreak please visit https://www.dhs.wisconsin.gov/outbreaks/index.htm.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Problem Statement

👉 Download the case studies here

An automotive manufacturer was experiencing high levels of defects in weld seams during the production process. These defects were affecting the quality of the final products, leading to increased rework, higher production costs, and delays. The manufacturer required a reliable solution to detect and inspect weld seams automatically, ensuring consistent product quality while improving production efficiency.

Challenge

Manual analysis of medical images is labor-intensive and susceptible to inconsistencies.

Diagnostic delays often occur due to the lack of skilled radiologists and the increasing volume of medical imaging data.

Detecting subtle anomalies in images like X-rays, MRIs, and CT scans requires advanced expertise, making it challenging to maintain consistent accuracy.

There was a need for an automated, real-time diagnostic tool to support healthcare professionals in decision-making.

Solution Provided

An AI-based weld defect detection system was developed to capture, analyze, and identify defects in real-time on the production line. Utilizing a convolutional neural network deployed on an NVIDIA Jetson Nano, the system efficiently classifies defects, integrates with monitoring systems for automatic flagging, and continuously improves through ongoing data feedback.

Development Steps

Data Collection

Installed high-definition cameras to capture and label weld seam images.

Preprocessing

Normalized images and enhanced key features to prepare for training.

Model Training

Developed a CNN using transfer learning and augmented the dataset for robustness.

Validation

Tested the model on unseen data to ensure accuracy and reliability before deployment.

Deployment

Implemented the trained model on an NVIDIA Jetson Nano for real-time defect detection.

Integration & Improvement

Connected the system to production monitoring and established a feedback loop for continuous model enhancement.

Results

Increased Conversion Rates

Personalized product recommendations led to a 15 increase in conversion rates, as guests were more likely to find products that matched their preferences.

Advanced customer Retention

By delivering applicable and engaging exploits, the platform erected stronger connections with guests, enhancing dedication and duplication purchases.

Advanced Average Order Value

Adapted suggestions encouraged guests to add complementary particulars to their carts, performing in a conspicuous increase in average order value.

Enhanced customer Satisfaction

Substantiated marketing created a indefectible and enjoyable shopping experience, leading to positive customer feedback and bettered brand character.

Real- time severity

The system’s capability to adapt to real- time relations assured those recommendations remained applicable, indeed as customer preferences changed.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Compared clinical, therapeutic, and outcome characteristics between RT-PCR positive to at least one respiratory pathogen, and RT-PCR negative patients, among SARI cases during the influenza season 2022/2023.

RT-PCR results of nasal swabs and tracheal aspirates in MICU-admitted SARI patients from week 39/2022 to week 19/2023.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.