Back

Prevalence and correlates of joint pain on physical, mental, and social health in women living with and without HIV

Ross, R. D.; Daubert, E.; Cahoon, S. M.; Hernandez, R.; Johnson, D.; Wilson, T. E.; French, A. L.; Cohen, M. H.; Weber, K. M.

2025-12-04 hiv aids
10.64898/2025.12.02.25341477 medRxiv
Show abstract

IntroductionJoint pain is amongst the most common and disabling form of chronic pain globally and is more frequently reported by women than men. Whether joint pain is more prevalent among women with versus without HIV (WWH/WWoH) and differentially contributes to frailty phenotypes and physical, mental, and social functional impairment is unknown. MethodsThe Common Data Elements (CDE) Pain, Enjoyment, and General Activity (PEG) instrument was used to assess and compare joint pain in Chicago midlife WWH versus WWoH and its association with key outcomes (frailty, depressive symptoms, anxiety, and loneliness) using stepwise multivariable models. ResultsPEG was assessed in 179 WWH and 81 WWoH. Overall, 42% of women reported moderate/severe pain, which was more commonly reported in WWH than WWoH (44.1 v. 38.3%) but did not reach statistical significance. Women with moderate/severe joint pain, compared to those with no/mild joint pain (58%) were more likely to be pre-frail/frail (aOR 3.05; CI: 1.65-5.63) and report greater depressive symptomology (aOR 2.42; CI: 1.31-4.47), anxiety (aOR 3.38; CI: 1.52-7.43), and loneliness (aOR 1.84; CI: 1.02-3.29). ConclusionsJoint pain is highly prevalent in mid-life WWH and WWoH and is associated with greater physical, mental, and social health burdens. Incorporating the PEG as a screening tool in clinical practice may help identify women who would benefit most from joint pain interventions to enhance functional capacity.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.