Evidence map›Paper›PMID 37650029›Full record

ArticleInternational journal of population data science2022

Lessons in Linkage: combining administrative data using deterministic linkage for surveillance of sports and recreation injuries in Florida, United States.

Charlotte Baker, Quinton Nottingham, Jonathan Holloway

Abstract read
In one paragraph

Article in International journal of population data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Charlotte BakerVirginia Polytechnic Institute and State University, Virginia-Maryland College of Veterinary Medicine, Department of Population Health Sciences, 205 Duck Pond Drive (0442), Blacksburg, Virginia, USA 24061-0442.
Quinton NottinghamVirginia Polytechnic Institute and State University, Pamplin College of Business, Department of Business Information Technology, 880 W Campus Drive (0235), Blacksburg, Virginia, USA 24061-0235.
Jonathan HollowayVirginia Polytechnic Institute and State University, Virginia-Maryland College of Veterinary Medicine, Department of Population Health Sciences, 205 Duck Pond Drive (0442), Blacksburg, Virginia, USA 24061-0442.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Previous and ongoing epidemiological surveillance of sports and recreation injuries (SRI) has been cross-sectional in nature, utilised a subset of injuries based on athletic trainer availability, or focused on elite and professional athletes. In the United States, surveillance is often prohibitively expensive and not well funded by national organisations or agencies, except for the case of some professional and elite sports. This paper details the methodology, barriers, and successes of using deterministic linkage to combine emergency department and hospitalisation data with a single identifier for use in surveilling sports injuries for persons aged 5 to 18 years. Design: Data linkage of a population cohort. Methods: We performed deterministic linkage of administrative emergency department and hospitalisation data from the state of Florida in the US. Data was acquired from the Florida Agency for Health Care Administration. With limited identifiers available due to privacy, we combined data across multiple years using a near universal identifier. We identified sport and recreation injuries using a modified External Cause of Injury Morbidity Matrix and ICD codes across all possible diagnoses. Finally, we obtained descriptive statistics of records that were successfully linked and those that were not to assess similarities between the groups. Results: We found 384,731 visits for SRI over a seven-year period. We were able to link approximately 70% of the records using a single identifier. There were statistically significant differences by age, sex, payer, and race/ethnicity for the records that were linked compared to the records that were not linked. Conclusions: This study is significant because while similar methods have been used to examine other conditions (e.g. asthma), few have linked multiple types of administrative data especially with nearly no identifiers to examine sports and recreation injuries. This method was found useful to identify injuries over time for the same individuals seeking care in emergency departments, or in hospital inpatient settings, though future work will need to address the limitations of this method. If we expect to move health surveillance forward as budgets for it become even more limited, we must develop and improve methods to do it with fewer resources, including using data that has great limitations.

Indexed as

AsthmaAthletic InjuriesSportsCross-Sectional StudiesFloridaHumansbiostatisticsdata cleaningdata linkagedeterministic linkagedisease surveillanceepidemiological methodsepidemiologyhealth surveillanceinjury surveillancepublic healthPythonreformattingSASsurveillance

Identifiers

PMID37650029
PMCPMC10464874

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.