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Identification of patients with arthritis and arthritis-related functional limitation using administrative data
Leslie R Harrold1, Wenjun Li, Robert A Yood
1Meyer Primary Care Institute and University of Massachusetts Medical School, Worcester, USA. Leslie.Harrold@umassmed.edu
Objective:
To develop algorithms on the basis of administrative data to identify patients with arthritis and arthritis-related functional limitation (AFL).
Study Design And Setting:
In this retrospective study, 361 enrollees of a health plan underwent a clinical examination to confirm arthritis and assessment of functional limitation on the basis of responses to the health assessment questionnaire. Administrative data were obtained on these subjects and included arthritis drugs dispensed, as well as outpatient and emergency department diagnoses and procedures (including radiographic studies, arthritis procedures, and laboratory tests). Composite risk scores for arthritis and AFL were created on the basis of the association of arthritis-related healthcare utilization with confirmed arthritis and AFL. Algorithms were then developed on the basis of the composite risk scores using logistic regression models.
Results:
The algorithm using the arthritis composite score to identify arthritis patients had an area under the ROC curve (AUC) of 0.74, sensitivity of 75 percent and specificity of 57 percent. Similarly, the algorithm using the AFL composite score to identify patients with AFL had an AUC of 0.73, sensitivity of 62 percent, and specificity of 75 percent.
Conclusion:
The developed algorithms will enable health plans to screen their enrollees for persons with arthritis and AFL. This will facilitate enrollment of patients with arthritis and AFL in disease management programs and/or targeted interventions.
