Background & Aims
Long-term pain, a prevalent and complex condition, poses challenges in understanding its symptomatology and devising effective treatments [1]. This project employs network approaches to construct multilayer networks from a national registry dataset [2], where we examine 49,132 men who underwent medical and psychological evaluations during conscription during late adolescence in 1969-70. Network analyses in psychopathology have been successful over the last decade in characterizing disorders in terms of interconnected symptoms to elucidate the underlying mechanisms and individual variations [3]. Our aim is to test the network approach in identifying differences between those treated for long-term pain population and individuals without.
Methods
The primary dataset is derived from the Swedish military service registry, encompassing comprehensive information on cognitive and physical abilities, mental and somatic health, personality factors, and lifestyle habits when these individuals were 18. We have 9,459 people treated for long-term pain at some subsequent point in their lives (up until 2019 where they are 68-80 years old). Network analyses involve estimating covariance between nodes (each collected item). Here, we have validated the network approach in general and also identified certain nodes that significantly differ between long-term pain and those without.
Results
Our results show, firstly, that there is a significant global difference between the long-term pain population and the rest of the dataset. Second, after correcting for multiple statistical tests (FDR), we found that three nodes have differing centrality (strength), implying that they correlate with additional variables in the psychological profile, psychological stamina/resilience, and number of friends. This demonstrates that psychological and social variables differ between the long-term pain population and the rest of the dataset.
Conclusions
By leveraging network analyses on a dataset focused on long-term pain, this project contributes to methodological advancements in understanding symptom interactions and predicting outcomes. By identifying differing network profiles, we hope that this can lead toward early and personalized interventions and predicting the effectiveness of different treatment strategies for individuals experiencing long-term pain.
References
1. Andrews, P. M., et al. (2018). Chronic Widespread Pain Prevalence in the General Population: A Systematic Review. European Journal of Pain, 22(1), 5–18.
2. Ludvigsson, J. F., Berglind, D., Sundquist, K., Sundström, J., Tynelius, P., & Neovius, M. (2022). The Swedish military conscription register: opportunities for its use in medical research. European journal of epidemiology, 37(7), 767-777.
3. Bringmann, L. F. (2021). Person-Specific Networks in Psychopathology: Past, Present, and Future. Current Opinion in Psychology, Psychopathology, 41, 59–64.
Presenting Author
William Hedley Thompson
Poster Authors
William Hedley Thompson, PhD
PhD
Department of Applied IT, University of Gothenburg
Lead Author
Emelie Thern
PhD
Karolinska Institute
Lead Author
Filip Gedin
PhD
Karolinska Institute
Lead Author
Anna Andreasson
PhD
Stockholm University
Lead Author
Maria Lalouni
Karolinska Institutet
Lead Author
Topics
- Informatics, Coding, and Pain Registries