Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark

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Abstract

Purpose

This paper describes the open cohort CROSS-TRACKS, which comprises population-based data from primary care, secondary care and national registries to study patient pathways and transitions across sectors while adjusting for sociodemographic characteristics.

Participants

A total of 221 283 individuals resided in the four Danish municipalities that constituted the catchment area of Horsens Regional Hospital in 2012–2018. A total of 96% of the population used primary care, 35% received at least one transfer payment and 66% was in contact with a hospital at least once in the period. Additional clinical information is available for hospital contacts (eg, alcohol intake, smoking status, body mass index and blood pressure). A total of 23% (n=8191) of individuals aged ≥65 years had at least one potentially preventable hospital admission, and 73% (n=5941) of these individuals had more than one.

Findings to date

The cohort is currently used for research projects in epidemiology and artificial intelligence. These projects comprise a prediction model for potentially preventable hospital admissions, a clinical decision support system based on artificial intelligence, prevention of medication errors in the transition between sectors, health behaviour and sociodemographic characteristics of men and women prior to fertility treatment, and a recently published study applying machine learning methods for early detection of sepsis.

Future plans

The CROSS-TRACKS cohort will be expanded to comprise the entire Central Denmark Region consisting of 1.3 million residents. The cohort can provide new knowledge on how to best organise interventions across healthcare sectors and prevent potentially preventable hospital admissions. Such knowledge would benefit both the individual citizen and society as a whole.

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Most cited references 31

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Mary Charlson, Peter Pompei, Kathy Ales … (1987)

The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.

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The Danish National Patient Registry: a review of content, data quality, and research potential

Morten Schmidt, Sigrun Alba Johannesdottir Schmidt, Jakob Sandegaard … (2015)

Background The Danish National Patient Registry (DNPR) is one of the world’s oldest nationwide hospital registries and is used extensively for research. Many studies have validated algorithms for identifying health events in the DNPR, but the reports are fragmented and no overview exists. Objectives To review the content, data quality, and research potential of the DNPR. Methods We examined the setting, history, aims, content, and classification systems of the DNPR. We searched PubMed and the Danish Medical Journal to create a bibliography of validation studies. We included also studies that were referenced in retrieved papers or known to us beforehand. Methodological considerations related to DNPR data were reviewed. Results During 1977–2012, the DNPR registered 8,085,603 persons, accounting for 7,268,857 inpatient, 5,953,405 outpatient, and 5,097,300 emergency department contacts. The DNPR provides nationwide longitudinal registration of detailed administrative and clinical data. It has recorded information on all patients discharged from Danish nonpsychiatric hospitals since 1977 and on psychiatric inpatients and emergency department and outpatient specialty clinic contacts since 1995. For each patient contact, one primary and optional secondary diagnoses are recorded according to the International Classification of Diseases. The DNPR provides a data source to identify diseases, examinations, certain in-hospital medical treatments, and surgical procedures. Long-term temporal trends in hospitalization and treatment rates can be studied. The positive predictive values of diseases and treatments vary widely (<15%–100%). The DNPR data are linkable at the patient level with data from other Danish administrative registries, clinical registries, randomized controlled trials, population surveys, and epidemiologic field studies – enabling researchers to reconstruct individual life and health trajectories for an entire population. Conclusion The DNPR is a valuable tool for epidemiological research. However, both its strengths and limitations must be considered when interpreting research results, and continuous validation of its clinical data is essential.

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The Danish Civil Registration System as a tool in epidemiology.

Morten Schmidt, Lars Østergaard Pedersen, Henrik Toft Sørensen (2014)

The methodological advances in epidemiology have facilitated the use of the Danish Civil Registration System (CRS) in ways not previously described systematically. We reviewed the CRS and its use as a research tool in epidemiology. We obtained information from the Danish Law on Civil Registration and the Central Office of Civil Registration, and used existing literature to provide illustrative examples of its use. The CRS is an administrative register established on April 2, 1968. It contains individual-level information on all persons residing in Denmark (and Greenland as of May 1, 1972). By January 2014, the CRS had cumulatively registered 9.5 million individuals and more than 400 million person-years of follow-up. A unique ten-digit Civil Personal Register number assigned to all persons in the CRS allows for technically easy, cost-effective, and unambiguous individual-level record linkage of Danish registers. Daily updated information on migration and vital status allows for nationwide cohort studies with virtually complete long-term follow-up on emigration and death. The CRS facilitates sampling of general population comparison cohorts, controls in case-control studies, family cohorts, and target groups in population surveys. The data in the CRS are virtually complete, have high accuracy, and can be retrieved for research purposes while protecting the anonymity of Danish residents. In conclusion, the CRS is a key tool for epidemiological research in Denmark.

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Author and article information

Journal

Journal ID (nlm-ta): BMJ Open

Journal ID (iso-abbrev): BMJ Open

Journal ID (hwp): bmjopen

Journal ID (publisher-id): bmjopen

Title: BMJ Open

Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )

ISSN (Electronic): 2044-6055

Publication date Collection: 2020

Publication date (Electronic): 29 October 2020

Volume: 10

Issue: 10

Electronic Location Identifier: e039996

Affiliations

[1 ]departmentDepartment of Research , Horsens Regional Hospital , Horsens, Denmark

[2 ]Enversion A/S , Aarhus, Denmark

[3 ]departmentDepartment of Clinical Epidemiology , Aarhus University Hospital , Aarhus, Denmark

[4 ]departmentDepartment of Clinical Medicine , Aarhus University , Aarhus, Denmark

Author notes

[Correspondence to ] Mr Anders Hammerich Riis; anderiis@ 123456rm.dk

Author information

Anders Hammerich Riis http://orcid.org/0000-0002-6684-4068

Article

Publisher ID: bmjopen-2020-039996

DOI: 10.1136/bmjopen-2020-039996

PMC ID: 7597526

PubMed ID: 33122323

SO-VID: 89edbc71-261a-48bb-975b-a02df52d45bb

License:

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

History

Date received : 01 May 2020

Date revision received : 28 September 2020

Date accepted : 07 October 2020

Funding

Funded by: Fund for the advancement of health research in Central Denmark Region, the development and resource funds under Danish Regions (Udviklings- og forskningspuljen ved Danske Regioner);

Funded by: Public Health in the Central Denmark Region – a collaboration between municipalities and the region (Folkesundhed i Midten);

Funded by: Horsens Regional Hospital;

Funded by: Circular Co-Creation under the MedTec Innovation Consortium;

Funded by: The Danish Health Confederation (Sundheds-kartellet);

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ScienceOpen disciplines: Medicine

Keywords: epidemiology,health informatics,quality in health care

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ScienceOpen disciplines: Medicine

Keywords: epidemiology, health informatics, quality in health care

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Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark

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Abstract

Purpose

Participants

Findings to date

Future plans

Related collections

AIP Publishing: Coronavirus

Most cited references 31

A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

The Danish National Patient Registry: a review of content, data quality, and research potential

The Danish Civil Registration System as a tool in epidemiology.

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Cited by 8

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