Article Text

Download PDFPDF

Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis
  1. Kristine Kroeker1,
  2. Jessica Widdifield2,3,
  3. Saman Muthukumarana4,
  4. Depeng Jiang1,
  5. Lisa M Lix1
  1. 1 Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
  2. 2 Institute for Clinical Evaluative Sciences, Toronto, Canada
  3. 3 Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada
  4. 4 Department of Statistics, University of Manitoba, Winnipeg, Canada
  1. Correspondence to Dr. Lisa M Lix; lisa.lix{at}med.umanitoba.ca

Abstract

Objective This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study.

Study design and setting Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data. We considered: (A) separate univariate models for sensitivity and specificity, (B) univariate model for Youden’s summary index and (C) bivariate (ie, joint) mixed-effects model for sensitivity and specificity. Model covariates included the number of diagnoses in physician, hospital and emergency department records, physician diagnosis observation time, duration of time between physician diagnoses and number of RA-related prescription medication records.

Results The most common case definition attributes were: 1+ hospital diagnosis (65%), 2+ physician diagnoses (43%), 1+ specialist physician diagnosis (51%) and 2+ years of physician diagnosis observation time (27%). Statistically significant improvements in sensitivity and/or specificity for separate univariate models were associated with (all p values <0.01): 2+ and 3+ physician diagnoses, unlimited physician diagnosis observation time, 1+ specialist physician diagnosis and 1+ RA-related prescription medication records (65+ years only). The bivariate model produced similar results. Youden’s index was associated with these same case definition criteria, except for the length of the physician diagnosis observation time.

Conclusion A model-based method provides valuable empirical evidence to aid in selecting a definition(s) for ascertaining diagnosed disease cases from administrative health data. The choice between univariate and bivariate models depends on the goals of the validation study and number of case definitions.

  • administrative health data
  • chronic disease
  • diagnosis
  • regression
  • rheumatoid arthritis
  • validation

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 and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Footnotes

  • Contributors KK led the conception and design of the study, analysis and interpretation of the data and drafted the article. LML provided guidance on the conception and design of the study, assisted in the analysis and interpretation of the data and was involved in revising the article. JW provided access to the study data, assisted in interpretation of the data and was involved in revising the article. DJ and SM provided guidance on the conception and design of the study, assisted in the analysis and interpretation of the data and were involved in revising the article. All authors read and approved the final manuscript.

  • Funding The first author was supported by the Canadian Institutes of Health Research (CIHR) Drug Safety and Effectiveness Network grant TD3.137716 through the scholarship from the Canadian Network for Advanced Interdisciplinary Methods for comparative effectiveness research (CAN.AIM) team. This work was supported by the Canadian Institutes of Health Research (CIHR) (www.cihr.irsc.gc.ca) through Canadian Master’s Scholarship funding from CIHR to the first author.

  • Competing interests None declared.

  • Patient consent This study does not involve human subjects.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement The datasets used and analysed during the current study are available from JW on reasonable request.

  • Correction notice This paper has been amended since it was published Online First. Owing to a scripting error, some of the publisher names in the references were replaced with 'BMJ Publishing Group'. This only affected the full text version, not the PDF. We have since corrected these errors and the correct publishers have been inserted into the references.