Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models.

BACKGROUND: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. METHODS: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). RESULTS: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. CONCLUSIONS: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

Inference in functional mixed regression models with applications to Positron Emission Tomography imaging data.

In a function-on-scalar regression framework, we present some modeling strategies for functional mixed models and also some approaches for making inference about various aspects of the fixed effects. This is presented in the context of modeling positron emission tomography (PET) data in order to explore the density of various proteins of interest throughout the human brain. For this application, information about the density of the target protein in a given brain region is encapsulated in the impulse response function (IRF) of the region. Previous work on nonparametric estimation of the IRF is limited in that it is only able to model a single brain region at a time. We propose an extension, based on principles of functional data analysis, that will allow modeling of multiple brain regions simultaneously. Applicable more broadly to functional mixed regression modeling, we discuss two general approaches for permutation testing and describe valid strategies for identifying exchangeable units within the model and building corresponding permutation tests. We illustrate our methods with an application to PET data and explore the effects of depression and sex on the IRF.

Low back pain-driven inpatient stays in the United States: a nationwide repeated cross-sectional analysis.

BACKGROUND: Low back pain (LBP)-driven inpatient stays are resource-intensive and costly, yet data on contemporary national trends are limited. MATERIALS AND METHODS: This study used repeated cross-sectional analyses through a nationally representative sample (US National Inpatient Sample, 2016-2019). Outcomes included the rate of LBP-driven inpatient stays; the resource utilization (the proportion of receiving surgical treatments and hospital costs) and prognosis (hospital length of stay and the proportion of nonroutine discharge) among LBP-driven inpatient stays. LBP was classified as overall, nonspecific, and specific (i.e. cancer, cauda equina syndrome, vertebral infection, vertebral compression fracture, axial spondyloarthritis, radicular pain, and spinal canal stenosis). Analyses were further stratified by age, sex, and race/ethnicity. RESULTS: 292 987 LBP-driven inpatient stays (weighted number: 1 464 690) were included, with 269 080 (91.8%) of these for specific LBP and 23 907 (8.2%) for nonspecific LBP. The rate of LBP-driven inpatient stays varied a lot across demographic groups and LBP subtypes (e.g. for overall LBP, highest for non-Hispanic White 180.4 vs. lowest for non-Hispanic Asian/Pacific Islander 42.0 per 100 000 population). Between 2016 and 2019, the rate of nonspecific LBP-driven inpatient stays significantly decreased (relative change: 46.9%); however, substantial variations were found within subcategories of specific LBP-significant increases were found for vertebral infection (relative change: 17.2%), vertebral compression fracture (relative change: 13.4%), and spinal canal stenosis (relative change: 19.9%), while a significant decrease was found for radicular pain (relative change: 12.6%). The proportion of receiving surgical treatments also varied a lot (e.g. for overall LBP, highest for non-Hispanic White 74.4% vs. lowest for non-Hispanic Asian/Pacific Islander 62.8%), and significantly decreased between 2016 and 2019 (e.g. for nonspecific LBP, relative change: 28.6%). Variations were also observed for other outcomes. CONCLUSIONS: In the US, the burden of LBP-driven inpatient stays (i.e. rates of LBP-driven inpatient stays, resource utilization, and prognosis among LBP-driven inpatient stays) is enormous. More research is needed to understand why the burden varies considerably according to the LBP subtype (i.e. nonspecific and specific LBP as well as subcategories of specific LBP) and the subpopulation concerned (i.e. stratified by age, sex, and race/ethnicity).

Trends in prevalence of fractures among adults in the United States, 1999-2020: a population-based study.

BACKGROUND: Population data that examines recent national trends in the prevalence of fractures are lacking in the United States (US). MATERIALS AND METHODS: Analyses were based on 1999-2020 data from the National Health and Nutrition Examination Survey (NHANES). Primary outcomes included the prevalence of hip, wrist, and vertebral fractures among adults aged greater than or equal to 50 years. Changes in the prevalence over time were determined by joinpoint regression analysis. The authors also described the variation by fracture subtypes, sociodemographic characteristics, and their combination. RESULTS: For adults aged greater than or equal to 50 years in NHANES 2017-March 2020, the authors estimated that there was 2.6 million Americans with hip fractures, 14.6 million Americans with wrist fractures, and 5.2 million Americans with vertebral fractures. The prevalence of wrist fractures significantly increased from 8.7% (7.4-9.9%) in 1999-2000 to 12.8% (11.6-14.1%) in 2017-March 2020 among adults aged greater than or equal to 50 years ( P for trend=0.04); significant increases were also observed in fractures that occurred at age less than 50 years, non-Hispanic White, high family income groups, and several combination subgroups (e.g. fractures occurred at age <50 years among women). The prevalence of vertebral fractures increased from 2.2% (1.7-2.8%) in 1999-2000 to 4.6% (3.7-5.5%) in 2017-March 2020 among adults aged greater than or equal to 50 years ( P for trend=0.02); significant increases were also observed in 50-64 years, women, non-Hispanic White, high family income groups and several combination subgroups (e.g. fractures that occurred at age <50 years among women). The authors did not observe significant trend changes in the prevalence of hip fractures among adults aged greater than or equal to 50 years between 1999 and 2020. CONCLUSION: The estimated prevalence of wrist and vertebral fractures significantly increased among US adults aged greater than or equal to 50 years from 1999 to 2020, although hip fractures did not significantly change.

Assessing the mediating role of iron status on associations between an industry-relevant metal mixture and verbal learning and memory in Italian adolescents.

BACKGROUND: Metals, including lead (Pb), manganese (Mn), chromium (Cr) and copper (Cu), have been associated with neurodevelopment; iron (Fe) plays a role in the metabolism and neurotoxicity of metals, suggesting Fe may mediate metal-neurodevelopment associations. However, no study to date has examined Fe as a mediator of the association between metal mixtures and neurodevelopment. OBJECTIVE: We assessed Fe status as a mediator of a mixture of Pb, Mn, Cr and Cu in relation to verbal learning and memory in a cohort of Italian adolescents. METHODS: We used cross-sectional data from 383 adolescents (10-14 years) in the Public Health Impact of Metals Exposure Study. Metals were quantified in blood (Pb) or hair (Mn, Cr, Cu) using ICP-MS, and three markers of Fe status (blood hemoglobin, serum ferritin and transferrin) were quantified using luminescence assays or immunoassays. Verbal learning and memory were assessed using the California Verbal Learning Test for Children (CVLT-C). We used Bayesian Kernel Machine Regression Causal Mediation Analysis to estimate four mediation effects: the natural direct effect (NDE), natural indirect effect (NIE), controlled direct effect (CDE) and total effect (TE). Beta (ß) coefficients and 95 % credible intervals (CIs) were estimated for all effects. RESULTS: The metal mixture was jointly associated with a greater number of words recalled on the CVLT-C, but these associations were not mediated by Fe status. For example, when ferritin was considered as the mediator, the NIE for long delay free recall was null (ß = 0.00; 95 % CI = -0.22, 0.23). Conversely, the NDE (ß = 0.23; 95 % CI = 0.01, 0.44) indicated a beneficial association of the mixture with recall that operated independently of Fe status. CONCLUSION: An industry-relevant metal mixture was associated with learning and memory, but there was no evidence of mediation by Fe status. Further studies in populations with Fe deficiency and greater variation in metal exposure are warranted.

Association of chronic musculoskeletal pain with mortality among UK adults: A population-based cohort study with mediation analysis.

BACKGROUND: We aimed to quantify the association between chronic musculoskeletal pain and all-cause mortality, and to investigate the extent to which this association is mediated by physical activity, smoking status, alcohol consumption, and opioid use. METHODS: For this population-based cohort study, we used data from UK Biobank, UK between baseline visit (2006-2010) to 18th December 2020. We assessed the associations between chronic musculoskeletal pain and all-cause mortality using a Cox proportional hazards model. We performed causal mediation analyses to examine the proportion of the association between chronic musculoskeletal pain and all-cause mortality. FINDINGS: Of the 384,367 included participants, a total of 187,269 participants reported chronic musculoskeletal pain. Higher number of pain sites was associated with increased risk of all-cause mortality compared to having no pain (e.g., four sites vs no site of pain, Hazard Ratio [HR] 1.46, 95% Confidence Interval [CI] 1.35 to 1.57). The multiple mediator analyses showed that the mediating proportions of all four mediators ranged from 53.4% to 122.6%: among participants with two or more pain sites, the effect estimate reduced substantially, for example, HR reduced from 1.25 (95% CI: 1.21 to 1.30; two pain sites) to 1.07 (95% CI: 1.01 to 1.11; two pain sites). INTERPRETATION: We found that higher number of pain sites was associated with increased risk of all-cause mortality compared to having no pain, and at least half of the association of chronic musculoskeletal pain with increased all-cause mortality may be accounted for by four mediators. FUNDING: Twins Research Australia.