What matters in "multimorbidity"? Arguably resilience and personal health experience are central to quality of life and optimizing survival

Measuring the Effect of the Early assessment Team (MEET) for patients referred to outpatient mental health care: a study protocol for a randomised controlled trial

BACKGROUND

Referrals to specialised mental health care (such as community mental health centres; CMHC) have increased over the last two decades. Patients often have multifaceted problems, which cannot only be solved by such care. Resources are limited, and triaging is challenging. A novel method which approaches patients early and individually upon referral to a CMHC-possibly with a brief intervention-is an Early assessment Team (EaT). In an EaT, two therapists meet the patient early in the process and seek to solve the present problem, often involving community services, primary health care, etc.; attention is paid to symptoms and functional strife, rather than diagnoses. This is in contrast to treatment as usual (TAU), where the patient (after being on a waiting list) meets one therapist, who focuses on history and situation to assign a diagnosis and eventually start a longitudinal treatment. The aim of this study is to describe and compare EaT and TAU regarding such outcomes as work and social adjustment, mental health, quality of life, use of health services, and patient satisfaction. The primary outcome is a change in perceived function from baseline to 12-month follow-up, measured by the Work and Social Adjustment Scale.

METHOD

Patients (18 years and above; n = 588) referred to outpatient health care at a CMHC are randomised to EaT or TAU. Measures (patient self-reports and clinician reports, patients' records, and register data) are collected at baseline, after the first and last meeting, and at 2, 4, 8, 12, and 24 months after inclusion. Some participants will be invited to participate in qualitative interviews.

TRIAL DESIGN

The study is a single-centre, non-blinded, RCT with two conditions involving a longitudinal and mixed design (quantitative and qualitative data).

DISCUSSION

This study will examine an intervention designed to determine early on which patients will benefit from parallel or other measures than assessment and treatment in CMHC and whether these will facilitate their recovery. Findings may potentially contribute to the development of the organisation of mental health services.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05087446. Registered on 21 October 2021.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort

Objective

To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

Materials and Methods

Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline.

Results

We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70.

Discussion

In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct.

Conclusions

These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

Resilience-based Integrated IBD Care Is Associated With Reductions in Health Care Use and Opioids

BACKGROUND & AIMS

Integrated inflammatory bowel disease (IBD) care is effective but not routinely implemented. Validated methods that simultaneously address mind and body targets such as resilience may improve access and outcomes. We describe the development and implementation of the GRITT method and its impact on resilience, health care utilization (HCU), and opioid use in IBD.

METHODS

Consecutive patients from an academic IBD center were evaluated for low resilience on the basis of provider referral. Low resilience patients were invited to participate in the GRITT program. Primary outcome was % reduction in HCU. Secondary outcomes were change in resilience and corticosteroid and opioid use. Patients were allocated into 2 groups for analysis: GRITT participants (GP) and non-participants (NP). Clinical data and HCU in the year before enrollment were collected at baseline and 12 months. One-way repeated measures multivariate analysis of covariance evaluated group × time interactions for the primary outcome. Effect size was calculated for changes in resilience over time.

RESULTS

Of 456 screened IBD patients 394 were eligible, 184 GP and 210 NP. GP had greater reduction in HCU than NP: 71% reduction in emergency department visits, 94% reduction in unplanned hospitalizations. There was 49% reduction in opioid use and 73% reduction in corticosteroid use in GP. Resilience increased by 27.3 points (59%), yielding a large effect size (d = 2.4).

CONCLUSIONS

Mind-body care that focuses on building resilience in the context of IBD care may be a novel approach to reduce unplanned HCU and opioid use, but large, multicenter, randomized controlled trials are needed.

Increases in multimorbidity with weight class in the United States

Little is known regarding how multimorbidity combinations associated with obesity change with increase in body weight. This study employed data from the national Cerner HealthFacts Data Warehouse to identify changes in multimorbidity patterns by weight class using network analysis. Networks were generated for 154 528 middle-aged patients in the following categories: normal weight, overweight, and classes 1, 2, and 3 obesity. The results show significant differences (P-value<0.05) in prevalence by weight class for all but three of 82 diseases considered. The percentage of patients with multimorbidity (excluding obesity) increases from in 55.1% in patients with normal weight, to 57.88% with overweight, 70.39% with Class 1 obesity, 73.99% with Class 2 obesity, and 71.68% in Class 3 obesity, increasing most substantially with the progression from overweight to class 1 obesity. Most prevalent disease clusters expand from only hypertension and dorsalgia in normal weight, to add joint disorders in overweight, lipidemias in class 1 obesity, diabetes in class 2 obesity, and sleep disorders and chronic kidney disease in class 3 obesity. Recognition of multimorbidity patterns associated with weight increase is essential for true precision care of obesity-associated chronic conditions and can help clinicians identify and address preclinical disease before additional complications arise.

Anticipatory Care in Potentially Preventable Hospitalizations: Making Data Sense of Complex Health Journeys

Purpose: Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed.

Application: PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia.

Theory: A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as “particulars” in their unique trajectories.

Evaluation: Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The −3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets—consistent reduction in acute bed days (20–25%) vs. target 10% and high levels of patient satisfaction.

Discussion: Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical.

Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.

Resilience and health (care): A dynamic adaptive perspective

This special forum on resilience explores particular worldviews of resilience-clinical, psychosocial, sociological, complexity science, organizational, and political economy through eight papers. This forum aims to open up the wealth of understandings and implications in health care by taking a transdisciplinary overview.

A commentary on Martin's "What matters in 'multimorbidity'? Arguably resilience and personal health experience are central to quality of life and optimizing survival." J. Eval. Clin. Pract. 2016; 1-3

This is a commentary on Martin's 2016 article on "What matters in 'multimorbidity'." The relationship between self-reported health and resilience is an important recognition of how all health professionals can work productively with their patients within a shared decision framework.

Resilience, health perceptions, (QOL), stressors, and hospital admissions-Observations from the real world of clinical care of unstable health journeys in Monash Watch (MW), Victoria, Australia

RATIONALE, AIMS, AND OBJECTIVES

Monash Watch (MW) aims to reduce potentially preventable hospitalisations in a cohort above a risk "threshold" identified by Health Links Chronic Care (HLCC) algorithms using personal, diagnostic, and service data. MW conducted regular patient monitoring through outbound phone calls using the Patient Journey Record System (PaJR). PaJR alerts are intended to act as a self-reported barometer of stressors, resilience, and health perceptions with more alerts per call indicating greater risk.

AIMS

To describe predictors of PaJR alerts (self-reported from outbound phone calls) and predictors of acute admissions based upon a Theoretical Model for Static and Dynamic Indicators of Acute Admissions.

METHODS

Participants: HLCC cohort with predicted 3+ admissions/year in MW service arm for >40 days; n = 244. Baseline measures-Clinical Frailty Index (CFI); Connor Davis Resilience (CD-RISC): SF-12v2 Health Survey scores Mental (MSC) and Physical (PSC) and ICECAP-O. Dynamic measures: PaJR alerts/call in 10 869 MW records. Acute (non-surgical) admissions from Victorian Admitted Episode database.

ANALYSIS

Logistic regression, correlations, and timeseries homogeneity metrics using XLSTAT.

FINDINGS

Baseline indicators were significantly correlated except SF-12_MCS. SF12-MSC, SF12-PSC and ICECAP-O best predicted PaJR alerts/call (ROC: 0.84). CFI best predicted acute admissions (ROC: 0.66), adding CD-RISC, SF-12_MCS, SF-12_PCS and ICECAP-O with two-way interactions improved model (ROC: 0.70). PaJR alerts were higher ≤10 days preceding acute admissions and significantly correlated with admissions. Patterns in PaJR alerts in four case studies demonstrated dynamic variations signifying risk. Overall, all baseline indicators were explanatory supporting the theoretical model. Timing of PaJR alerts and acute admissions reflecting changing stressors, resilience, and health perceptions were not predicted from baseline indicators but provided a trigger for service interventions.

CONCLUSION

Both static and dynamic indicators representing stressors, resilience, and health perceptions have the potential to inform threshold models of admission risk in ways that could be clinically useful.

Resilience for health-an emergent property of the "health systems as a whole"

Resilience has become a popular term, and its meaning varies widely depending on the context of its use. Its Latin origin, resilire, means "bouncing back"-should bouncing back be understood literally or rather metaphorically in the context of health, illness, dis-ease, and disease? This essay examines ecological, physiological, personal, and health system perspectives inherent in the concept of resilience. It emerges that regardless of the level of aggregation, resilience is a systems property-it is as much a property of each of the subsystems of network physiology, the person, and the health care delivery system as it is a property of the health system as a whole. Given the interdependencies between people, their internal and external environments, and the health service system, strengthening resilience, ie, the ability to positively adapt to challenges and changing circumstances, will require a broad-based public discourse: "How can we strengthen resilience and health for the benefit of people and society at large".

Rapidly developing multimorbidity and disability in older adults: does social background matter?

BACKGROUND

Multimorbidity is among the most disabling geriatric conditions. In this study, we explored whether a rapid development of multimorbidity potentiates its impact on the functional independence of older adults, and whether different sociodemographic factors play a role beyond the rate of chronic disease accumulation.

METHODS

A random sample of persons aged ≥60 years (n = 2387) from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was followed over 6 years. The speed of multimorbidity development was estimated as the rate of chronic disease accumulation (linear mixed models) and further dichotomized into the upper versus the three lower rate quartiles. Binomial negative mixed models were used to analyse the association between speed of multimorbidity development and disability (impaired basic and instrumental activities of daily living), expressed as the incidence rate ratio (IRR). The effect of sociodemographic factors, including sex, education, occupation and social network, was investigated.

RESULTS

The risk of new activity impairment was higher among participants who developed multimorbidity faster (IRR 2.4, 95% CI 1.9-3.1) compared with those who accumulated diseases more slowly overtime, even after considering the baseline number of chronic conditions. Only female sex (IRR for women vs. men 1.6, 95% CI 1.2-2.0) and social network (IRR for poor vs. rich social network 1.7, 95% CI 1.3-2.2) showed an effect on disability beyond the rate of chronic disease accumulation.

CONCLUSIONS

Rapidly developing multimorbidity is a negative prognostic factor for disability. However, sociodemographic factors such as sex and social network may determine older adults' reserves of functional ability, helping them to live independently despite the rapid accumulation of chronic conditions.