Identifying Psoriasis and Psoriatic Arthritis Patients in Retrospective Databases When Diagnosis Codes Are Not Available: A Validation Study Comparing Medication/Prescriber Visit-Based Algorithms with Diagnosis Codes.

BACKGROUND: Using diagnosis code-based algorithms is the primary method of identifying patient cohorts for retrospective studies; nevertheless, many databases lack reliable diagnosis code information. OBJECTIVES: To develop precise algorithms based on medication claims/prescriber visits (MCs/PVs) to identify psoriasis (PsO) patients and psoriatic patients with arthritic conditions (PsO-AC), a proxy for psoriatic arthritis, in Canadian databases lacking diagnosis codes. METHODS: Algorithms were developed using medications with narrow indication profiles in combination with prescriber specialty to define PsO and PsO-AC. For a 3-year study period from July 1, 2009, algorithms were validated using the PharMetrics Plus database, which contains both adjudicated medication claims and diagnosis codes. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the developed algorithms were assessed using diagnosis code as the reference standard. Chosen algorithms were then applied to Canadian drug databases to profile the algorithm-identified PsO and PsO-AC cohorts. RESULTS: In the selected database, 183,328 patients were identified for validation. The highest PPVs for PsO (85%) and PsO-AC (65%) occurred when a predictive algorithm of two or more MCs/PVs was compared with the reference standard of one or more diagnosis codes. NPV and specificity were high (99%-100%), whereas sensitivity was low (≤30%). Reducing the number of MCs/PVs or increasing diagnosis claims decreased the algorithms' PPVs. CONCLUSIONS: We have developed an MC/PV-based algorithm to identify PsO patients with a high degree of accuracy, but accuracy for PsO-AC requires further investigation. Such methods allow researchers to conduct retrospective studies in databases in which diagnosis codes are absent.

Severe, eosinophilic asthma in primary care in Canada: a longitudinal study of the clinical burden and economic impact based on linked electronic medical record data.

BACKGROUND: Stratification of patients with severe asthma by blood eosinophil counts predicts responders to anti-interleukin (IL)-5 (mepolizumab and reslizumab) and anti-IL-5 receptor α (benralizumab) therapies. This study characterized patients with severe asthma who could qualify for these biologics in a primary care setting. METHODS: We retrospectively selected patients from July 1, 2010, to June 30, 2014, using a linked electronic medical records (EMR) database (IMS Evidence 360 EMR Canada) for > 950,000 patients in primary care in Ontario, Canada. Patients aged ≥ 12 years with ≥ 2 documented asthma diagnoses were identified as having severe asthma based on prescriptions for high-dosage inhaled corticosteroids (ICS) plus either a leukotriene receptor antagonist, long-acting ß2-agonist (LABA), or theophylline filled on the same day. Patients' asthma was considered severe also if they received a prescription for ICS with oral corticosteroids (OCS) or an additional prescription for omalizumab. Patient characteristics, asthma-related medications, and blood eosinophil counts were captured using observed care patterns for the year prior to ICS/LABA and/or OCS prescription. Health care resource use (HCRU) and costs were captured throughout the 1-year follow-up period. RESULTS: We identified 212 patients who met the criteria for severe asthma. These patients required an average of 6.5 physician visits during the 1-year follow-up period (95% confidence interval 5.7-7.3), and 20 (9%) were referred to respiratory specialists. Overall, 56 patients (26%) with severe asthma had complete blood counts, of whom 23 (41%) had blood eosinophil counts ≥ 300 cells/µL and might be considered for anti-eosinophil therapies. Patients with severe asthma and blood eosinophil counts ≥ 300 cells/µL had more respiratory specialist referrals (17% vs. 12%) than patients with blood eosinophils < 300 cells/µL. CONCLUSIONS: Our data suggest that during 2010-2014, Ontario primary care patients with severe asthma and high blood eosinophil counts had greater HRCU than those with lower counts. Approximately 41% of patients with severe asthma could qualify for anti-eosinophil drugs based on blood eosinophil counts. However, the eosinophilic status of most patients was unknown. It is appropriate to increase awareness of the use of blood eosinophil counts to identify patients who could be considered for anti-eosinophil therapies.

The Direct Economic Burden of Gout in an Elderly Canadian Population.

OBJECTIVE: To estimate the direct healthcare cost and resource use from the public payer perspective between patients with incident gout and matched gout-free patients in Ontario. METHODS: Patients with incident gout aged ≥ 66 with uninterrupted Ontario Health Insurance Plan (OHIP) coverage in the 1-year baseline period were included in the study. Patients with gout were indexed at first gout diagnosis or prescription over the study period April 1, 2008, to March 31, 2014. Gout-free patients with no gout diagnosis within history were matched (up to 5:1) to each patient with gout. Linked medical records were analyzed until end of study, death, or OHIP ineligibility. Bang and Tsiatis adjusted healthcare costs and resource use were compared using bootstrap p-values and 95% CI. RESULTS: A total of 29,894 patients with gout and 148,231 gout-free patients were included in the study. Patients were 56% male, had a median Adjusted Clinical Group healthcare resource use band of moderate morbidity, and had a median age of 75-79 years. Baseline comorbidities were similar between groups except for renal disease. Analyzing 5-year total healthcare costs, patients with gout ($44,297) incurred a significantly higher average healthcare cost compared to gout-free patients ($33,965), for an incremental cost of $10,332 (95% CI $9617-$11,039; p < 0.01). Similar trends were observed in all individual healthcare component cost and use metrics. CONCLUSION: Following onset of gout, patients in Ontario incur significantly greater healthcare costs and resource use compared to matched gout-free patients. Alternative gout management strategies should be investigated to reduce the incremental burden of gout borne by the Ontario healthcare system.