A new study reported that using multi-level negative binomial regression to account for over-dispersion of data, this study demonstrated statistical modelling that will allow for a more refined understanding of the influence of patient, physician, practice and jurisdictional levels on referrals.

A referral from a family physician (FP) to a specialist is an inflection point in the patient journey, with potential implications for clinical outcomes and health policy. Primary care electronic medical record (EMR) databases offer opportunities to examine referral patterns. Until recently, software techniques were not available to model these kinds of multi-level count data.

The objective of the study was to establish a methodology for determining referral rates from family physicians (FPs) to medical specialists using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) EMR database.

Retrospective cohort study, mixed effects and multi-level negative binomial regression modelling with 87,258 eligible patients between 2007 and 2012. Mean referrals compared by patient sex, age, chronic conditions, FP visits, and urban/rural practice location. The proportion of variance in referral rates attributable to the patient and practice levels.

On average, males had 0.26 and females had 0.31 referrals in a 12-month period. Referrals were significantly higher for females, increased with age, FP visits and the number of chronic conditions (p < 0.0001). Overall, 14% of the variance in referrals could be attributed to the practice level, and 86% to patient-level characteristics.

In conclusion, both the patient and practice characteristics influenced referral patterns. The methodologic insights gained from this study have relevance to future studies on many research questions that utilise count data, both within primary care and broader health services research. The utility of the CPCSSN database will continue to increase in tandem with data quality improvements, providing a valuable resource to study Canadian referral patterns over time.