Cross-sectional relationship between pain intensity and subjective cognitive decline among middle-aged and older adults with arthritis or joint conditions: Results from a population-based study

An Exploratory Analysis of Contributors to Cognitive Functioning Among Sexual and Gender Minority Individuals Who Had COVID-19

Sexual and gender minority (SGM) individuals face mental health disparities. However, research analyzing SGM people's mental health after a COVID-19 diagnosis is scarce. In this secondary analysis of a remote study, we 1) examined associations between cognitive and psychosocial health and 2) explored differences between these health outcomes among SGM (n = 14) and heterosexual cisgender (n = 64) U.S. adults who had COVID-19. We used the Patient Reported Outcome Measures Information System (PROMIS) v2.0 to assess subjective cognition and the BrainCheck cognitive test to analyze objective cognition. We administered the Perceived Stress Scale and PROMIS 57 Profile V.2.0 to measure psychosocial health. SGM COVID-19 survivors had worse scores in depression, anxiety, sleep disturbance, pain, stress, and objective cognition than heterosexual cisgender participants (p-values < .05). Objective cognition was associated with age, SGM classification, racial or ethnic minority classification, income, comorbidities, COVID-19 severity, number of symptoms, and pain (|0.137| < r < |0.373|, p-values < .05). Subjective cognition was associated with comorbidities, number of symptoms, depression, anxiety, sleep disturbance, pain, and stress (|0.158| < r < |0.537|, p-values < .05). Additional studies are needed to expand what is known about post-COVID-19 health disparities and to guide policies and interventions that promote cognitive functioning.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background

Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China.

Methods

Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate.

Results

A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model.

Conclusions

The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.