Author:
Hafezparast Nasrin,Bragan Turner Ellie,Dunbar-Rees Rupert,Vusirikala Amoolya,Vodden Alice,de La Morinière Victoria,Yeo Katy,Dodhia Hiten,Durbaba Stevo,Shetty Siddesh,Ashworth Mark
Abstract
Abstract
Background
Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data.
Methods
Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 (‘tier 1’) conditions that almost always results in the individual having chronic pain (2) people with one of 20 (‘tier 2’) conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes.
Results
The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population.
Conclusions
Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.
Funder
Guy's and St Thomas' Charity
Publisher
Springer Science and Business Media LLC
Reference27 articles.
1. National Institute for Health and Care Excellence (NICE) (2021) NG193: Chronic pain (primary and secondary) in over 16s: assessment of all chronic pain and management of chronic primary pain. https://www.nice.org.uk/guidance/ng193. Accessed 08 Dec 2022.
2. Chronic Pain in Adults. 2017. Health Survey for England. London: Public Health England; 2017. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/940858/Chronic_Pain_Report.pdf. Accessed 08 Dec 2022.
3. Bondesson E, Larrosa Pardo F, Stigmar K, Ringqvist Ã, Petersson IF, Jöud A, et al. Comorbidity between pain and mental illness – evidence of a bidirectional relationship. Eur J Pain. 2018;22:1304–11. https://doi.org/10.1002/ejp.1218. Accessed 08 Dec 2022.
4. Fayaz A, Croft P, Langford RM, Donaldson LJ, Jones GT. Prevalence of chronic pain in the UK: a systematic review and meta-analysis of population studies. BMJ Open. 2016;20:e010364. https://doi.org/10.1136/bmjopen-2015-010364. Accessed 08 Dec 2022.
5. Cassell A, Edwards D, Harshfield A, Rhodes K, Brimicombe K, Payne R, et al. The epidemiology of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract. 2018;68:e245-251. https://doi.org/10.3399/bjgp18x695465. Accessed 08 Dec 2022.
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