Background & Aims
Pain and its relationship with mental health are important research topics. Pain imposes significant burdens on society through medical costs and lost productivity [1], with complex, multifaceted intersections across physical, psychological, social, and biological domains [2].
Electronic mental health records are a valuable data source for investigating the intersection and relationship between pain and mental health. They also enable researchers to investigate changes in how pain is recorded based on demographic and diagnostic criteria.
The objective of this study is to determine distributions of documented physical pain across demographic and diagnostic groups in the clinical notes of an electronic mental health records database by using natural language processing methods, and to examine the level of overlap in recorded physical pain between primary care and secondary care services.
Methods
The Clinical Record Interactive Search (CRIS) database, used in this study, contains a de-identified version of electronic health record data from The South London and Maudsley NHS Foundation Trust (SLaM), one of Europe’s largest mental healthcare organisations, serving a catchment of 1.3M residents in south London [3]. CRIS contains about 30 million free text documents, with an average of 90 documents per patient.
A cohort of patients was extracted from the CRIS database. This included active patients (i.e., under an accepted referral) aged 18+ on the index date of July 1, 2018, and whose record contained at least one document (>= 30 characters) within a window of 2017 to 2019.
An NLP application (described in detail at [4]) was run on the sentences from documents of patients within this cohort to identify patients who had relevant pain mentions. This cohort was compared to linked primary care records from a local government area in South London, Lambeth DataNet (LDN)[5].
Results
A total of 27,211 patients were retrieved based on the extraction criteria. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15-1.19), female (OR 1.42, 95% CI 1.35-1.49), of Asian (OR 1.30, 95% CI 1.16-1.45) or Black (OR 1.49, 95% CI 1.40-1.59) ethnicities, and living in deprived neighbourhoods i.e., Index of Multiple Deprivation <= 5 (OR 1.64, 95% CI 1.55-1.73) showed higher odds of recorded pain.
Amongst the 14,202 patients with any recorded pain, 53% patients included mentions of anatomy associated with pain. The most common body parts mentioned were lower limbs (20%).
Patients with severe mental illnesses were found to be less likely to have recorded pain (OR 0.43, 95% CI 0.41-0.46, p<0.001). When comparing the overlap between primary and secondary care, 17% of the cohort from secondary care also had records within primary care, and 31% of this overlapping group had recorded pain in both records.
Conclusions
The findings of this study show the sociodemographic and diagnostic differences in recorded pain and have significant implications for the assessment and management of physical pain in patients with mental health disorders.
This study utilises the CRIS database to investigate the recorded mentions of pain in patients with mental health conditions. The results reflect current literature findings that pain is a common issue among mental health patients, with 52% of the cohort containing sentences with relevant mentions of pain. The findings of this study have significant implications for the assessment and management of pain in mental health patients and highlight the importance of utilising electronic health records for research purposes. More research in this area can help towards these issues and provide safer and equitable access to good-quality pain management.
References
1.Phillips CJ. The cost and burden of chronic pain. Rev Pain 2009;3:2–5. doi:10.1177/204946370900300102
2. Meints SM, Edwards RR. Evaluating psychosocial contributions to chronic pain outcomes. Prog Neuropsychopharmacol Biol Psychiatry 2018;87:168–82. doi:10.1016/j.pnpbp.2018.01.017
3. Stewart R, Soremekun M, Perera G, Broadbent M, Callard F, Denis M, et al. The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 2009 Aug 12;9:51.
4. Chaturvedi J, Velupillai S, Stewart R, Roberts A. Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach. arXiv Published Online First: 2023. doi:10.48550/arxiv.2304.01240
5. NHS. Lambeth DataNet [Internet]. 2021. Available from: https://www.lambethccg.nhs.uk/your-health/Information-for-patients/lambeth-datanet/Pages/default.aspx
Presenting Author
Jaya Chaturvedi
Poster Authors
Topics
- Access to Care