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