Early signs of long-term pain: prospective network profiles from late adolescence and lifelong follow-up

Introduction

Long-term pain and mental health problems represent by far the largest share of disability and injury burden today¹. Long-term pain causes disability in patients of all ages, leading to substantial suffering, an enormous economic burden, and loss of productivity in working-age groups². At any given moment, over 20% of the population experiences long-term pain³. However, treatment options are limited because traditional analgesics are typically ineffective for long-term pain⁴. Therefore, to avoid chronicity, identifying those with high risk is essential to provide preventive care and decrease pain incidence, and is a public health priority in the United States⁵. Despite this need, there are currently no definitive demographics or clinical tests to predict who will develop long-term pain, despite previous attempts to forecast the future severity of pain in those already affected^{6, 7–8}.

Epidemiological studies have provided valuable insights into the relationship between sociodemographic factors and pain^9,10, and there is a high overlap between long-term pain and psychiatric conditions, such as depression^{11, 12–13}. Typically, older age and female sex are associated with an increased risk of developing long-term pain¹⁴. Yet, more detailed prospective information about the development of long-term pain is scarce. A large community-based survey conducted in Norway highlighted lifestyle and psychosocial factors, such as obesity, sleeping problems and anxiety, as potential risk factors¹⁵. Given the limited resources in health care and the large population of individuals with pain, it is vital, for both successful prevention and intervention, to find early determinants of long-term pain.

Theories relating to long-term pain include the bio-psycho-social model^{16, 17–18} where the pain condition is seen as the result from a dynamic interaction between biological, psychological, and sociocultural variables. Recent advancements in health sciences advocate network approaches^{19, 20, 21–22} where this approach allows us to explore the dynamic interplay of biological, psychological, and social factors in chronic illness. Network approaches have recently been applied to study fibromyalgia^23,24 and depressive and sleep-related nodes in symptom networks have been related to pain severity²⁵. Here, we performed a network-based analysis that allowed inclusion of a wide array of baseline factors to determine risk factors for future development of...