Examining Predictors of Psychological Distress Among Youth Engaging with Jigsaw for a Brief Intervention

Risk factors for psychological distress among help-seeking youth are poorly understood. Addressing this gap is important for informing mental health service provision. This study aimed to identify risk factors among youth attending Jigsaw, a youth mental health service in Ireland. Routine data were collected from N = 9,673 youth who engaged with Jigsaw (Mean age = 16.9 years, SD = 3.14), including presenting issues, levels of psychological distress, age, and gender. Confirmatory Factor Analysis identified thirteen factors of clustering issues. Several factors, including Self-criticism and Negative Thoughts, were strongly associated with items clustering as psychological distress, however these factors were poorly predictive of distress as measured by the CORE (YP-CORE: R2 = 14.7%, CORE-10: R2 = 6.9%). The findings provide insight into associations between young people's identified presenting issues and self-identified distress. Implications include applying appropriate therapeutic modalities to focus on risk factors and informing routine outcome measurement in integrated youth mental health services.

The quality of family relationships in dementia: Mixed methods to unravel mixed feelings

Objective: Close relationships influence health and quality of life outcomes for people with dementia and their families. Yet, we know little on the role of different relationship domains with quantitative methods having proved to have limitations in this research field. We aimed to study these relationship domains over time, contrasting the views of people with dementia and their family carers, making use of both quantitative and qualitative approaches.Methods: A convergent mixed methods design was adopted, analysing longitudinal data (four time points over three years) from 66 dyads of Portuguese community-dwelling people with dementia and their primary carers, from the EU-Actifcare project sample. Quantitative assessments used sociodemographic and clinical variables, and Positive Affect Index scores, with descriptive and inferential analyses. Qualitative data, collected through individual and joint semi-structured interviews, were explored using thematic analysis.Results: Both quantitative and qualitative findings demonstrated that some domains of relationship quality are affected in different ways, with changes occurring at different stages. Some (e.g., 'communication') may even improve after initial decline. 'Closeness' was consistently altered over time, from carers' perspectives, and played an important protective role regarding institutionalisation. Overall, changes in the relationship quality were perceived differently by people with dementia and their carers, and these divergent perspectives often led to tension. Qualitative data revealed that 'mixed feelings' (ambivalence) involve complex experiences, arguably more difficult to manage than negative feelings alone. Furthermore, perceived informal support, particularly from the extended family, and receiving formal services' assistance, seemed to facilitate positive (re)appraisals of the relationship.Conclusions: A deeper understanding of relationship quality and its domains as dementia progresses may help tailoring interventions to tackle modifiable aspects of relationships, meeting the needs and cherishing the resources of dyads and families. Timely assessments could identify relationships at risk and need for support, including for alternative caring arrangements.

Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review

OBJECTIVES

To identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?

DESIGN

A scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.

SETTING

Hospital setting.

PARTICIPANT

Any type of clinician caring for any type of patient.

INTERVENTION

Machine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or "'model-based'" decision support systems.

PRIMARY AND SECONDARY OUTCOME MEASURES

Patient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.

RESULTS

After screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).

CONCLUSIONS

We found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.

FUNDING

Canadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442). SCOPING REVIEW REGISTRATION: https://osf.io/e2mna.

Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

Using predicted length of stay to define treatment and model costs in hospitalized adults with serious illness: an evaluation of palliative care

BACKGROUND

Economic research on hospital palliative care faces major challenges. Observational studies using routine data encounter difficulties because treatment timing is not under investigator control and unobserved patient complexity is endemic. An individual's predicted LOS at admission offers potential advantages in this context.

METHODS

We conducted a retrospective cohort study on adults admitted to a large cancer center in the United States between 2009 and 2015. We defined a derivation sample to estimate predicted LOS using baseline factors (N = 16,425) and an analytic sample for our primary analyses (N = 2674) based on diagnosis of a terminal illness and high risk of hospital mortality. We modelled our treatment variable according to the timing of first palliative care interaction as a function of predicted LOS, and we employed predicted LOS as an additional covariate in regression as a proxy for complexity alongside diagnosis and comorbidity index. We evaluated models based on predictive accuracy in and out of sample, on Akaike and Bayesian Information Criteria, and precision of treatment effect estimate.

RESULTS

Our approach using an additional covariate yielded major improvement in model accuracy: R² increased from 0.14 to 0.23, and model performance also improved on predictive accuracy and information criteria. Treatment effect estimates and conclusions were unaffected. Our approach with respect to treatment variable yielded no substantial improvements in model performance, but post hoc analyses show an association between treatment effect estimate and estimated LOS at baseline.

CONCLUSION

Allocation of scarce palliative care capacity and value-based reimbursement models should take into consideration when and for whom the intervention has the largest impact on treatment choices. An individual's predicted LOS at baseline is useful in this context for accurately predicting costs, and potentially has further benefits in modelling treatment effects.

Retention of patients in opioid substitution treatment: A systematic review

Background

Retention in opioid substitution (OST) treatment is associated with substantial reductions in all cause and overdose mortality. This systematic review aims to identify both protective factors supporting retention in OST, and risk factors for treatment dropout.

Methods

A systematic search was performed using MEDLINE, Embase, PsycInfo, CINAHL and Web of Science (January 2001 to October 2019). Randomised controlled trials (RCTs) and observational cohort studies reporting on retention rates and factors associated with retention in OST were included. Factors associated with treatment retention and dropout were explored according to the Maudsley Addiction Profile. A narrative synthesis is provided.

Results

67 studies were included in this review (4 RCTs and 63 observational cohort studies; N = 294,592), all assessing factors associated with retention in OST or treatment dropout. The median retention rate across observational studies was approximately 57% at 12 months, which fell to 38.4% at three years. Studies included were heterogeneous in nature with respect to treatment setting, type of OST, risk factor assessment, ascertainment of outcome and duration of follow-up. While the presence of such methodological heterogeneity makes it difficult to synthesise results, there is limited evidence to support the influence of a number of factors on retention, including age, substance use, OST drug dose, legal issues, and attitudes to OST.

Conclusions

Younger age, substance use particularly cocaine and heroin use, lower doses of methadone, criminal activity/incarceration, and negative attitudes to MMT appear to be associated with reduced retention in OST. A consensus definition of retention is required to allow for comparability across future studies.

Characterising the prognostic potential of HLA-DR during colorectal cancer development

HLA-DR, an MHC class II molecule that mediates antigen presentation, is a favourable prognostic indicator in colorectal cancer (CRC). However, the dynamics and location of HLA-DR expression during CRC development are unclear. We aimed to define HLA-DR expression by immunohistochemistry in colorectal epithelium and stromal tissue at different stages of cancer development, assessing non-neoplastic colorectal adenocarcinoma-adjacent tissue, adenomas and carcinoma tissues, and to associate HLA-DR levels with clinical outcomes. Patients with higher than median HLA-DR expression survived at least twice as long as patients with lower expression. This association was significant for HLA-DR staining in the colorectal carcinoma epithelium (n = 152, p = 0.011, HR 1.9, 95% CI 1.15-3.15) and adjacent non-neoplastic epithelium (n = 152, p < 0.001, HR 2.7, 95% CI 1.59-4.66), but not stroma. In stage II cases, however, the prognostic value of HLA-DR expression was significant only in adjacent non-neoplastic tissues, for both epithelium (n = 63, p = 0.015, HR 3.6, 95% CI 1.279-10.25) and stroma (n = 63, p = 0.018, HR 5.07, 95% CI 1.32-19.49). HLA-DR was lower in carcinoma tissue compared to matched adenomas (n = 35), in epithelium (p < 0.01) and stroma (p < 0.001). HLA-DR was further reduced in late-stage carcinoma (n = 101) compared to early stage (n = 105), in epithelium (p < 0.001) and stroma (p < 0.01). HLA-DR expression was lower (p < 0.05) in the adjacent non-neoplastic epithelium of patients with cancer recurrence. We demonstrate a progressive loss of HLA-DR in epithelial and stromal tissue compartments during CRC development and show prognostic ability in carcinoma-adjacent non-neoplastic tissues, highlighting the importance of this molecule in the anti-cancer immune response. These findings may have wider implications for immunotherapeutic interventions.

Migrant health research in the Republic of Ireland: a scoping review

BACKGROUND

Migration to European countries has increased in number and diversity in recent years. Factors such as access to healthcare, language barriers and legal status can impact the health outcomes of migrant groups. However, little is known about the evidence base on the health status of migrants in the Republic of Ireland. Our aim was to scope existing peer-reviewed research on the health of migrants in Ireland and identify any gaps in the evidence.

METHODS

We conducted a scoping review of peer-reviewed research on the health of migrants in the Republic of Ireland. Eleven electronic databases were searched for peer-reviewed, empirical articles published between 2001 and 2017. Search terms were adapted from a World Health Organisation review. Findings were analysed using the 2016 World Health Organisation Strategy and Action Plan for Refugee and Migrant Health in the World Health Organisation European region, which outlines nine strategic areas that require collaborative action.

RESULTS

Of 9396 articles retrieved, 80 met inclusion criteria, with the majority (81%) published since 2009. More than half of the studies had a quantitative design (65%). Migrants studied came from Eastern Europe, Asia and Africa and included labour migrants, refugees and asylum seekers. Most studies related to two World Health Organisation strategic areas; 4: "achieving public health preparedness and ensuring an effective response", and 5: "strengthening health systems and their resilience".

CONCLUSION

There is growing attention to migrant health in Ireland with a balance of qualitative and quantitative research. While much of the identified research is relevant to three of the World Health Organisation strategic areas, there are significant gaps in the other six areas. The study design could be replicated in other countries to examine and inform migrant health research.