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
... -
《-》
Qualitative evidence synthesis informing our understanding of people's perceptions and experiences of targeted digital communication.
Health communication is an area where changing technologies, particularly digital technologies, have a growing role to play in delivering and exchanging health information between individuals, communities, health systems, and governments.[1] Such innovation has the potential to strengthen health systems and services, with substantial investments in digital health already taking place, particularly in low‐ and middle‐income countries. Communication using mobile phones is an important way of contacting individual people and the public more generally to deliver and exchange health information. Such technologies are used increasingly in this capacity, but poor planning and short‐term projects may be limiting their potential for health improvement. The assumption that mobile devices will solve problems that other forms of communication have not is also prevalent. In this context, understanding people's views and experiences may lead to firmer knowledge on which to build better programs. A qualitative evidence synthesis by Heather Ames and colleagues on clients' perceptions and experiences of targeted digital communication focuses on a particular type of messaging – targeted messages from health services delivered to particular group(s) via mobile devices, in this case looking at communicating with pregnant women and parents of young children, and with adults and teenagers about sexual health and family planning.[2] These areas of reproductive, maternal, newborn, child, and adolescent health (RMNCAH) are where important gains have been made worldwide, but there remains room for improvement. Ames and colleagues sought to examine and understand people's perceptions and experiences of using digital targeted client communication. This might include communication in different formats and with a range of purposes related to RMNCAH – for example, receiving text message reminders to take medicines (e.g. HIV medicines) or go to appointments (such as childhood vaccination appointments), or phone calls offering information or education (such as about breastfeeding or childhood illnesses), support (e.g. providing encouragement to change behaviours) or advice (such as advising about local healthcare services). These communication strategies have the potential to improve health outcomes by communicating with people or by supporting behaviour change. However, changing people's health behaviours to a significant and meaningful degree is notoriously challenging and seldom very effective across the board. There are a multitude of systematic reviews of interventions aiming to change behaviours of both patients and providers, with the overall objective of improving health outcomes – many of which show little or no average effects across groups of people.[3] This evidence synthesis is therefore important as it may help to understand why communicating with people around their health might (or might not) change behaviours and improve consequent health outcomes. By examining the experiences and perspectives of those receiving the interventions, this qualitative evidence synthesis allows us to better understand the interventions' acceptability and usefulness, barriers to their uptake, and factors to be considered when planning implementation. The synthesis looked at 35 studies from countries around the world, focussing on communication related to RMNCAH. Of the 35 studies, 16 were from high‐income countries, mainly the United States, and 19 were from low‐ or middle‐income countries, mainly African countries. Many of the studies presented hypothetical scenarios. The findings from the synthesis are mixed and give us a more nuanced picture of the role of targeted digital communication. People receiving targeted digital communications from health services often liked and valued these contacts, feeling supported and connected by them. However, some also reported problems with the use of these technologies, which may represent barriers to their use. These included practical or technical barriers like poor network or Internet access, as well as cost, language, technical literacy, reading or issues around confidentiality, especially where personal health conditions were involved. Access to mobile phones may also be a barrier, particularly for women and adolescents who may have to share or borrow a phone or who have access controlled by others. In such situations it may be difficult to receive communications or to maintain privacy of content. The synthesis also shows that people's experiences of these interventions are influenced by factors such as the timing of messages, their frequency and content, and their trust in the sender. Identifying key features of such communications by the people who use them might therefore help to inform future choices about how and when such messaging is used. The authors used their knowledge from 25 separate findings to list ten implications for practice. This section of the review is hugely valuable, making a practical contribution to assist governments and public health agencies wishing to develop or improve their delivery of digital health. The implications serve as a list of points to consider, including issues of access (seven different aspects are considered), privacy and confidentiality, reliability, credibility and trust, and responsiveness to the needs and preferences of users. In this way, qualitative evidence is building a picture of how to better communicate with people about health. For example, an earlier 2017 Cochrane qualitative evidence synthesis by Ames, Glenton and Lewin on parents' and informal caregivers' views and experiences of communication about routine childhood vaccination provides ample evidence that may help program managers to deliver or plan communication interventions in ways that are responsive to and acceptable to parents.[4] The qualitative synthesis method, therefore, puts a spotlight on how people's experiences of health and health care in the context of their lives may lead to the design of better interventions, as well as to experimental studies which take more account of the diversity that exists in people's attitudes and decision‐making experiences.[5] In the case of this qualitative evidence synthesis by Ames and colleagues, the method pulled together a substantial body of research (35 data‐rich studies were sampled from 48 studies identified, with the high‐to‐moderate confidence in the evidence for 13 of the synthesized findings). The evidence from this review can inform the development of interventions, and the design of trials and their implementation. While waiting for such new trials or trial evidence on effects to emerge, decision‐makers can build their programs on the highly informative base developed by this review. This qualitative evidence synthesis, alongside other reviews, has informed development by the World Health Organization of its first guideline for using digital technologies for health systems strengthening,[1, 6] part of a comprehensive program of work to better understand and support implementation of such new technologies.
Ryan R
，Hill S
《Cochrane Database of Systematic Reviews》
A conceptual model for advanced/metastatic gastric or gastroesophageal junction cancer: a review of qualitative studies and results from patient interviews.
Despite approvals of new first-line immunotherapies for advanced/metastatic gastric cancer/gastroesophageal junction cancer (aGC/GEJC), patients' median survival is around 14 months and their health-related quality of life (HRQoL) is affected by disease-related symptoms and treatment-related side effects. Using a targeted literature review (TLR) and patient interviews, this study identified disease- and treatment-related concepts that are important to patients with aGC/GEJC and their HRQoL.
A TLR was conducted to identify primary qualitative studies from 2018 to 2021 on patients' experiences with aGC/GEJC. The results, supplemented with the results of two previously identified studies from 2017, were used to develop a preliminary conceptual disease model of aGC/GEJC and an interview guide. Next, one-to-one concept elicitation interviews were conducted where patients with aGC/GEJC were asked about symptoms, impacts on daily life, experience of care, treatment expectations, and clinical trials. The conceptual model was refined using these patient experience data.
Four studies selected from the TLR and the two previously summarized studies identified 47 symptoms (15 disease-related, 20 treatment-related, 12 disease- and treatment-related) and 35 impacts. Interviews with 20 patients identified 36 symptoms. The 12 most important symptoms (mentioned by ≥ 50% of patients; average disturbance ratings: ≥5, scale: 0 'not disturbing' to 10 'extremely disturbing') were: nausea, fatigue, temperature sensitivity, indigestion, weakness, diarrhea, vomiting, early satiety, swallowing difficulties, taste alterations, abdominal pain, general pain. Symptoms were mostly attributed to systemic treatments (chemotherapy, immunotherapy and targeted therapy), followed by surgery. Thirty-one impacts emerged from the interviews, the most common being emotional disturbances, impacts on daily activities and families, and requiring assistance from caregivers. Patients were mostly positive about their experience of care, willing to enroll in clinical trials, and keen to receive innovative treatments with few side effects. The final conceptual disease model details the symptoms and impacts of aGC/GEJC.
The conceptual model provides valuable data on signs/symptoms and impacts of aGC/GEJC affecting patients' lives. This can guide the clinical outcome assessment strategy for the development of innovative treatments more comprehensively than input from physicians alone, to ensure treatments improve both patients' survival and HRQoL. Interviews also help understand patients' perspectives on clinical trials.
Sowell FG
，de Milliano T
，Brady KJS
，Foglia G
，Sasane M
，Bensfia S
，Reaney M
... -
《BMC CANCER》
ResearchGate
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钛学术
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