A New Measure of Quantified Social Health Is Associated With Levels of Discomfort, Capability, and Mental and General Health Among Patients Seeking Musculoskeletal Specialty Care.

A better understanding of the correlation between social health and mindsets, comfort, and capability could aid the design of individualized care models. However, currently available social health checklists are relatively lengthy, burdensome, and designed for descriptive screening purposes rather than quantitative assessment for clinical research, patient monitoring, or quality improvement. Alternatives such as area deprivation index are prone to overgeneralization, lack depth in regard to personal circumstances, and evolve rapidly with gentrification. To fill this void, we aimed to identify the underlying themes of social health and develop a new, personalized and quantitative social health measure. (1) What underlying themes of social health (factors) among a subset of items derived from available legacy checklists and questionnaires can be identified and quantified using a brief social health measure? (2) How much of the variation in levels of discomfort, capability, general health, feelings of distress, and unhelpful thoughts regarding symptoms is accounted for by quantified social health? In this two-stage, cross-sectional study among people seeking musculoskeletal specialty care in an urban area in the United States, all English and Spanish literate adults (ages 18 to 89 years) were invited to participate in two separate cohorts to help develop a provisional new measure of quantified social health. In a first stage (December 2021 to August 2022), 291 patients rated a subset of items derived from commonly used social health checklists and questionnaires (Tool for Health and Resilience in Vulnerable Environments [THRIVE]; Protocol for Responding to and Assessing Patient Assets, Risks and Experiences [PRAPARE]; and Accountable Health Communities Health-Related Social Needs Screening Tool [HRSN]), of whom 95% (275 of 291; 57% women; mean ± SD age 49 ± 16 years; 51% White, 33% Hispanic; 21% Spanish speaking; 38% completed high school or less) completed all items required to perform factor analysis and were included. Given that so few patients decline participation (estimated at < 5%), we did not track them. We then randomly parsed participants into (1) a learning cohort (69% [189 of 275]) used to identify underlying themes of social health and develop a new measure of quantified social health using exploratory and confirmatory factor analysis (CFA), and (2) a validation cohort (31% [86 of 275]) used to test and internally validate the findings on data not used in its development. During the validation process, we found inconsistencies in the correlations of quantified social health with levels of discomfort and capability between the learning and validation cohort that could not be resolved or explained despite various sensitivity analyses. We therefore identified an additional cohort of 356 eligible patients (February 2023 to June 2023) to complete a new extended subset of items directed at financial security and social support (5 items from the initial stage and 11 new items derived from the Interpersonal Support Evaluation List, Financial Well-Being Scale, Multidimensional Scale of Perceived Social Support, Medical Outcomes Study Social Support Survey, and 6-item Social Support Questionnaire, and "I have to work multiple jobs in order to finance my life" was self-created), of whom 95% (338 of 356; 53% women; mean ± SD age 48 ± 16 years; 38% White, 48% Hispanic; 31% Spanish speaking; 47% completed high school or less) completed all items required to perform factor analysis and were included. We repeated factor analysis to identify the underlying themes of social health and then applied item response theory-based graded response modeling to identify the items that were best able to measure differences in social health (high item discrimination) with the lowest possible floor and ceiling effects (proportion of participants with lowest or highest possible score, respectively; a range of different item difficulties). We also assessed the CFA factor loadings (correlation of an individual item with the identified factor) and modification indices (parameters that suggest whether specific changes to the model would improve model fit appreciably). We then iteratively removed items based on low factor loadings (< 0.4, generally regarded as threshold for items to be considered stable) and high modification indices until model fit in CFA was acceptable (root mean square of error approximation [RMSEA] < 0.05). We then assessed local dependencies among the remaining items (strong relationships between items unrelated to the underlying factor) using Yen Q3 and aimed to combine only items with local dependencies of < 0.25. Because we exhausted our set of items, we were not able to address all local dependencies. Among the remaining items, we then repeated CFA to assess model fit (RMSEA) and used Cronbach alpha to assess internal consistency (the extent to which different subsets of the included items would provide the same measurement outcomes). We performed a differential item functioning analysis to assess whether certain items are rated discordantly based on differences in self-reported age, gender, race, or level of education, which can introduce bias. Last, we assessed the correlations of the new quantified social health measure with various self-reported sociodemographic characteristics (external validity) as well as level of discomfort, capability, general health, and mental health (clinical relevance) using bivariate and multivariable linear regression analyses. We identified two factors representing financial security (11 items) and social support (5 items). After removing problematic items based on our prespecified protocol, we selected 5 items to address financial security (including "I am concerned that the money I have or will save won't last") and 4 items to address social support (including "There is a special person who is around when I am in need"). The selected items of the new quantified social health measure (Social Health Scale [SHS]) displayed good model fit in CFA (RMSEA 0.046, confirming adequate factor structure) and good internal consistency (Cronbach α = 0.80 to 0.84), although there were some remaining local dependencies that could not be resolved by removing items because we exhausted our set of items. We found that more disadvantaged quantitative social health was moderately associated with various sociodemographic characteristics (self-reported Black race [regression coefficient (RC) 2.6 (95% confidence interval [CI] 0.29 to 4.9)], divorced [RC 2.5 (95% CI 0.23 to 4.8)], unemployed [RC 1.7 (95% CI 0.023 to 3.4)], uninsured [RC 3.5 (95% CI 0.33 to 6.7)], and earning less than USD 75,000 per year [RC 2.7 (95% CI 0.020 to 5.4) to 6.8 (95% CI 4.3 to 9.3)]), slightly with higher levels of discomfort (RC 0.055 [95% CI 0.16 to 0.093]), slightly with lower levels of capability (RC -0.19 [95% CI -0.34 to -0.035]), slightly with worse general health (RC 0.13 [95% CI 0.069 to 0.18]), moderately with higher levels of unhelpful thoughts (RC 0.17 [95% CI 0.13 to 0.22]), and moderately with greater feelings of distress (RC 0.23 [95% CI 0.19 to 0.28]). A quantitative measure of social health with domains of financial security and social support had acceptable psychometric properties and seems clinically relevant given the associations with levels of discomfort, capability, and general health. It is important to mention that people with disadvantaged social health should not be further disadvantaged by using a quantitative measure of social health to screen or cherry pick in contexts of incentivized or mandated reporting, which could worsen inequities in access and care. Rather, one should consider disadvantaged social health and its associated stressors as one of several previously less considered and potentially modifiable aspects of comprehensive musculoskeletal health. A personalized, quantitative measure of social health would be useful to better capture and understand the role of social health in comprehensive musculoskeletal specialty care. The SHS can be used to measure the distinct contribution of social health to various aspects of musculoskeletal health to inform development of personalized, whole-person care pathways. Clinicians may also use the SHS to identify and monitor patients with disadvantaged social circumstances. This line of inquiry may benefit from additional research including a larger number of items focused on a broader range of social health to further develop the SHS.

Brinkman N ，Broekman M ，Teunis T ，Choi S ，Ring D ，Jayakumar P ... -  《-》

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling.

Elwenspoek MM ，Thom H ，Sheppard AL ，Keeney E ，O'Donnell R ，Jackson J ，Roadevin C ，Dawson S ，Lane D ，Stubbs J ，Everitt H ，Watson JC ，Hay AD ，Gillett P ，Robins G ，Jones HE ，Mallett S ，Whiting PF ... -  《-》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Are Levels of Loneliness Associated With Levels of Comfort and Capability in Musculoskeletal Illness?

Variation in levels of pain intensity and incapability among patients with musculoskeletal conditions is associated with measures of mindset (unhelpful thoughts [such as hurt equals harm] and feelings of distress [overwhelm, rumination] regarding bodily sensations) and circumstances (social health as well as security in finances, roles, home, and support) as much or more so than pathophysiology severity. Loneliness is an important aspect of social health, it is associated with worse mental health, and it has been identified as worthy of attention and intervention by health authorities in several countries. It is estimated that up to one-third of adults older than 45 years of age experience loneliness. Given that a large percentage of people seeking musculoskeletal specialty care are older than 45 years, identification of notable levels of loneliness and an association with greater levels of pain intensity and incapability would support screening and treatment of feelings of loneliness as part of comprehensive, whole-person, musculoskeletal care strategies. In a cross-sectional study of people seeking musculoskeletal specialty care for upper and lower extremity conditions, we asked: (1) Are there factors associated with levels of capability including greater feelings of loneliness? (2) Are there factors associated with levels of pain intensity including greater feelings of loneliness? We recruited 146 new and returning, English-speaking, adult patients without cognitive deficiencies seeking care in metropolitan musculoskeletal specialty offices. Three patients were excluded because they did not complete the measures of pain intensity and incapability, and 143 were analyzed, including 57% (82) women with a mean age ± SD of 56 ± 17 years and 71% (102) with an upper extremity condition. Participants completed validated measures of feelings of loneliness (University of California, Los Angeles [UCLA] Loneliness Scale), thoughts and feelings regarding sensations (three items each validated in a factor analysis of commonly used measures), and levels of incapability (PROMIS Physical Function computer adaptive test), and pain intensity (pain intensity on an 11-point ordinal scale between 0 [no pain] and 10 [the most intense possible pain]). In the multivariable analysis, we measured the relationship between levels of incapability and pain intensity and feelings of loneliness, accounting for demographic factors and thoughts and feelings regarding sensations. Accounting for potential confounding variables such as income level and insurance status, we found that lower levels of capability were moderately associated with greater feelings of distress regarding symptoms (such as rumination or a sense of overwhelm; regression coefficient [RC] -0.99 [95% confidence interval (CI) CI -1.5 to - 0.51]; p < 0.001) and that higher levels of capability were more modestly associated with having an upper rather than lower extremity condition (RC 4.4 [95% CI 1.5 to 7.3]; p = 0.003) and an income between USD 46,000 and USD 75,000 (RC 6.7 [95% CI 1.4 to 12]; p = 0.01) compared with an income less than USD 24,000 a year. Levels of capability were not associated with levels of loneliness (RC = -0.15 [95% CI -0.38 to 0.086]; p = 0.22), even though the mean level of loneliness was 54, representing moderate to high levels of loneliness. Higher levels of pain intensity were moderately associated with greater feelings of distress regarding symptoms (RC 0.35 [95% CI 0.22 to 0.47]; p < 0.001) and also modestly associated with greater level of unhelpful thoughts about symptoms (such as pain equating to injury) (RC 0.19 [95% CI 0.036 to 0.34]; p = 0.002), having a 4-year college degree (RC -1.4 [95% CI -2.4 to -0.26]; p = 0.02), and having a postcollege graduate degree (RC -1.35 [95% CI -2.4 to -0.26]; p = 0.02) compared with high school or less education but not with higher levels of loneliness. The observation that levels of musculoskeletal incapability and pain intensity have limited association with loneliness reinforces the evidence that other cognitive and emotional factors are the key modifiable personal factors in musculoskeletal illness. Our findings do not discount the importance of addressing loneliness in musculoskeletal care, but efforts to tackle loneliness alone may be less effective than efforts to focus on loneliness in addition to thoughts and feelings regarding bodily sensations. Level II, prognostic study.

Ponce H ，Cordero R ，Ring D ，Sayegh G ，Azarpey A ，Jayakumar P ... -  《-》

"I've Spent My Whole Life Striving to Be Normal": Internalized Stigma and Perceived Impact of Diagnosis in Autistic Adults.

Huang Y ，Trollor JN ，Foley KR ，Arnold SRC ... -  《-》