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Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded
Nasrin Hafezparast1, Ellie Bragan Turner1, Rupert Dunbar-Rees1
1Outcomes Based Healthcare, 11-13 Cavendish Square, Marylebone, London, W1G 0AN, UK.
BMC Primary Care
|September 10, 2023
Summary
A new algorithm combining diagnostic and medication codes improves chronic pain prevalence estimates from primary care data. This approach offers more representative figures, aiding in better management of chronic pain populations.
Area of Science:
- Epidemiology
- Health Informatics
Background:
- Primary care data underestimates chronic pain prevalence, often relying solely on analgesic prescriptions.
- Non-drug pain management strategies are increasingly common, further skewing medication-based prevalence estimates.
- Accurate chronic pain prevalence data is crucial for understanding its complex nature and effective management.
Approach:
- Developed and tested an algorithm integrating medication and specific diagnostic codes to identify chronic pain.
- Utilized 4 criteria and 8 logic rules, incorporating 1,932 SNOMED CT codes.
- Applied the algorithm to primary care data from 41 GP practices, covering 386,238 adults.
Key Points:
- Identified 16.6% (64,135 individuals) with chronic pain in the study population.
- Observed higher prevalence rates in Black ethnicity females, the most deprived, and older populations.
- The algorithm provides more representative chronic pain prevalence estimates compared to previous methods.
Conclusions:
- The developed algorithm enhances the accuracy of chronic pain prevalence estimation using coded primary care data.
- This approach can facilitate systematic identification and population-based management of chronic pain.
- Improved prevalence data supports better management of symptom burden for individuals with chronic pain.
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