Abstract
Objectives: To distinguish between variation in referral threshold and variation in accurate selection of patients for referral in fast-track referrals for possible cancer. To examine factors associated with threshold and accuracy and model the effects of changing thresholds.
Design: Analysis of national data on cancer referrals from general practices in England over a 5-year period. We developed a new method to estimate specificity of referral to complement existing sensitivity. We used bivariate meta-analysis to produce summary measures and described practices in relation to these.
Setting: 5479 general practitioner (GP) practices with data relating to more than 50 cancer cases diagnosed over the 5 years.
Outcomes: Number of practices whose 95% confidence regions for sensitivity and specificity indicated that they were outliers in terms of either referral threshold or decision accuracy.
Results: 2019 practices (36.8%) were outliers in relation to referral threshold compared with 1205 practices (22%) in relation to decision accuracy. Practice age profile, cancer incidence and deprivation showed a modest association with decision accuracy but not with thresholds. If all practices shared the referral behaviour of those in the highest quintile of age-standardised referral rate, there would be a 3.3% increase in cancers detected through fast-track pathways at the cost of a 36.9% increase in urgent referrals.
Conclusion: This new method permits variation in referral to be described more precisely and quality improvement activities to be targeted. Changing referral thresholds without increasing accuracy will result in modest effects on detection rates and a large increase in demand on diagnostic services.
Design: Analysis of national data on cancer referrals from general practices in England over a 5-year period. We developed a new method to estimate specificity of referral to complement existing sensitivity. We used bivariate meta-analysis to produce summary measures and described practices in relation to these.
Setting: 5479 general practitioner (GP) practices with data relating to more than 50 cancer cases diagnosed over the 5 years.
Outcomes: Number of practices whose 95% confidence regions for sensitivity and specificity indicated that they were outliers in terms of either referral threshold or decision accuracy.
Results: 2019 practices (36.8%) were outliers in relation to referral threshold compared with 1205 practices (22%) in relation to decision accuracy. Practice age profile, cancer incidence and deprivation showed a modest association with decision accuracy but not with thresholds. If all practices shared the referral behaviour of those in the highest quintile of age-standardised referral rate, there would be a 3.3% increase in cancers detected through fast-track pathways at the cost of a 36.9% increase in urgent referrals.
Conclusion: This new method permits variation in referral to be described more precisely and quality improvement activities to be targeted. Changing referral thresholds without increasing accuracy will result in modest effects on detection rates and a large increase in demand on diagnostic services.
| Original language | English |
|---|---|
| Article number | e016439 |
| Number of pages | 10 |
| Journal | BMJ Open |
| Volume | 7 |
| Issue number | 8 |
| Early online date | 21 Aug 2017 |
| DOIs | |
| Publication status | Published - Aug 2017 |
Bibliographical note
Acknowledgements: We wish to thank Alison Avenell, Mairead Black, Jon Dickson,Bruce Guthrie, Tom Love, Angus McLeod, Simon Sawhney and Liz Walton for their
comments on the manuscript.
The data used for this analysis are publicly available at http://fingertips.phe.org.uk/profile/generalpractice/data#mod,1,pyr,2016,pat,19,par,-,are,-,sid1,1938133086,ind1,-,sid2,-,ind2,-.
Keywords
- cancer
- General Practice
- primary care
- variation
- bivariate meta-analysis
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- Distinguishing variation in referral accuracy from referral threshold: analysis of a national dataset of referrals for suspected cancer
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. 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/
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Amanda Lee
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Chair in Medical Statistics
- Institute of Applied Health Sciences
- School of Medicine, Medical Sciences & Nutrition, Medical Statistics
Person: Academic
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David McLernon
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Senior Research Fellow
- School of Medicine, Medical Sciences & Nutrition, Medical Statistics
Person: Academic Related - Research
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Peter Murchie, BSc (Med Sci), MBChB, MSc, MRCGP, CertMgmt (Open), PhD
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Personal Chair (Clinical)
- Institute of Applied Health Sciences
Person: Clinical Academic