Centre-Specific Variation in Atrial Fibrillation Ablation-Treatment Rates in a Universal Single-Payer Healthcare System

Abstract

Background

Disparities in atrial fibrillation ablation rates have been studied previously, with a focus on either patient characteristics or systems factors, rather than geographic factors. The impact of electrophysiology (EP) centre practice patterns on ablation rates has not been well studied.

Methods

This population-based cohort study used linked administrative datasets covering physician billing codes, hospitalizations, prescriptions, and census data. The study population consisted of patients who visited an emergency department with a new diagnosis of atrial fibrillation, in the period 2007-2016, in Ontario, Canada. Patient characteristics, including age, sex, medical history, comorbidities, socioeconomic factors, closest EP centre within 20 km, and distance to the nearest centre, were used as predictors in multivariable logistic regression models to assess the relationship between living in a location around specific EP centres and ablation rates.

Results

The cohort included 134,820 patients, of whom 9267 had an ablation treatment during the study period. Patients undergoing ablation treatment were younger, had a lower Congestive Heart Failure, Hypertension, Age, Diabetes, Stroke/Transient Ischemic Attack (CHADS₂) score, lived closer to EP centres, and had fewer comorbidities than those who did not receive ablation treatment. Wide variation occurred in ablation rates, with adjacent census divisions having ablation rates up to 2.6 times higher. Multivariate regression revealed significant differences in ablation rates for patients who lived in a location around certain EP centres. The odds ratios for living in a location closest to specific centres ranged from 0.78 (95% confidence interval: 0.68-0.89) to 1.60 (95% confidence interval:1.34-1.90).

Conclusions

Living near specific EP centres may significantly affect a patient’s likelihood of receiving ablation treatment, regardless of factors such as age, gender, socioeconomic status, prior medical history, and distance to EP centres.