Use of intraoperative neural monitoring for prognostication of recovery of vocal mobility and reduction of permanent vocal paralysis after thyroidectomy

Introduction: The benefits of intraoperative neural monitoring (IONM) of recurrent laryngeal nerve (RLN) on post-thyroidectomy vocal cord palsy (VCP) rates are contentious. We wished to study impact of IONM on permanent VCP after thyroidectomy. Methods: Retrospective review of prospective series of 1011 (1539 nerves-at-risk) patients undergoing thyroidectomy without (418, group 1) and with (583, group 2) IONM. Results: There were three recognized nerve injuries in group 1, vs one in group 2 (P =.3). There were no differences in overall VCP rates. However, patients in group 2 with immediate postoperative VCP had higher likelihood of full recovery than patients in group 1 (55 of 56 vs 23 of 29 patients, P =.01), and lower incidence of total permanent VCP (2 of 917 vs 9 of 647 patients, P =.01). Conclusion: Among patients with immediate postoperative VCP after thyroidectomy, IONM is associated with a higher likelihood of regaining normal vocal function. This may be related to better identification of RLN branching in IONM cases

Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package, and a comparison with traditional approaches

BEF studies aim at understanding how ecosystems respond to a gradient of species diversity. Diversity-Interactions models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The no. of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. We compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. DI models can capture variation due to species identities, species proportions and species interactions, in addition to richness effects. We also introduce the DImodels R package for implementing DI models. Through worked examples, we show that using DI models can lead to considerably improved model fit over other methods. Collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions. Overall, DI models lead to enhanced inference compared to other approaches. Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends, however, there are multiple ways to quantify the species diversity of a community. DI modelling provides a framework to test the multiple aspects of species diversity and facilitates uncovering a deeper ecological understanding of the BEF relationship

Community-based models of care facilitating the recovery of people living with persistent and complex mental health needs: a systematic review and narrative synthesis

Objective: This study aims to assess the effectiveness of community-based models of care (MoCs) supporting the recovery of individuals who experience persistent and complex mental health needs. Method: We conducted a systematic review and narrative synthesis of MoC studies reporting clinical, functional, or personal recovery from October 2016 to October 2021. Sources were Medline, EMBASE, PsycInfo, CINAHL, and Cochrane databases. Studies were grouped according to MoC features. The narrative synthesis was led by our researchers with lived experience. Results: Beneficial MoCs ranged from well-established to novel and updated models and those explicitly addressing recovery goals and incorporating peer support: goal-focused; integrated community treatment; intensive case management; partners in recovery care coordination; rehabilitation and recovery-focused; social and community connection-focused; supported accommodation; and vocational support. None of our diverse group of MoCs supporting recovery warranted a rating of best practice. Established MoCs, such as intensive case management, are promising practices regarding clinical and functional recovery, with potential for enhancements to support personal recovery. Emerging practice models that support personal and functional recovery are those where consumer goals and priorities are central. Conclusion: Evidence for established models of care shows that there is a need for inevitable evolution and adaptation. Considering the high importance of effective MoCs for people experiencing persistent and complex mental health needs, further attention to service innovation and research is required. Greater emphasis on the inclusion of lived and living experience in the design, delivery, implementation, and research of MoCs is needed, to enhance MOCs' relevance for achieving individual consumer recovery outcomes

“Quitlink”—A Randomized Controlled Trial of Peer Worker Facilitated Quitline Support for Smokers Receiving Mental Health Services: Study Protocol

Introduction: Although smokers with severe mental illnesses (SSMI) make quit attempts at comparable levels to other smokers, fewer are successful in achieving smoking cessation. Specialized smoking cessation treatments targeting their needs can be effective but have not been widely disseminated. Telephone delivered interventions, including by quitlines, show promise. However, few SSMI contact quitlines and few are referred to them by health professionals. Mental health peer workers can potentially play an important role in supporting smoking cessation. This study will apply a pragmatic model using peer workers to engage SSMI with a customized quitline service, forming the “Quitlink” intervention.Methods: A multi-center prospective, cluster-randomized, open, blinded endpoint (PROBE) trial. Over 3 years, 382 smokers will be recruited from mental health services in Victoria, Australia. Following completion of baseline assessment, a brief intervention will be delivered by a peer worker. Participants will then be randomly allocated either to no further intervention, or to be referred and contacted by the Victorian Quitline and offered a targeted 8-week cognitive behavioral intervention along with nicotine replacement therapy (NRT). Follow-up measures will be administered at 2-, 5-, and 8-months post-baseline. The primary outcome is 6 months continuous abstinence from end of treatment with biochemical verification. Secondary outcomes include 7-day point prevalence abstinence from smoking, increased quit attempts, and reductions in cigarettes per day, cravings and withdrawal, mental health symptoms, and other substance use, and improvements in quality of life. We will use a generalized linear mixed model (linear regression for continuous outcomes and logistic regression for dichotomous outcomes) to handle clustering and the repeated measures at baseline, 2-, 5-, and 8-months; individuals will be modeled as random effects, cluster as a random effect, and group assignment as a fixed effect.Discussion: This is the first rigorously designed RCT to evaluate a specialized quitline intervention accompanied by NRT among SSMI. The study will apply a pragmatic model to link SSMI to the Quitline, using peer workers, with the potential for wide dissemination.Clinical Trial Registration:Trial Registry: The trial is registered with ANZCTR (www.anzctr.org.au): ACTRN12619000244101 prior to the accrual of the first participant and updated regularly as per registry guidelines.Trial Sponsor: University of Newcastle, NSW, Australia

Maternal multimorbidity and preterm birth in Scotland : an observational record-linkage study

This work was funded by Northwood Charitable Trust and by the Strategic Priority Fund “Tackling multimorbidity at scale” programme (grant number MR/W014432/1) delivered by the Medical Research Council and the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council.Background Multimorbidity is common in women across the life course. Preterm birth is the single biggest cause of neonatal mortality and morbidity. We aim to estimate the prevalence of multimorbidity in pregnant women and to examine the association between maternal multimorbidity and PTB. Methods This is a retrospective cohort study using electronic health records from the Scottish Morbidity Records. All pregnancies among women aged 15 to 49 with a conception date between 1 January 2014 and 31 December 2018 were included. Multimorbidity was defined as the presence of two or more pre-existing long-term physical or mental health conditions, and complex multimorbidity as the presence of four or more. It was calculated at the time of conception using a predefined list of 79 conditions published by the MuM-PreDiCT consortium. PTB was defined as babies born alive between 24 and less than 37 completed weeks of gestation. We used Generalised Estimating Equations adjusted for maternal age, socioeconomic status, number of previous pregnancies, BMI, and smoking history to estimate the effect of maternal pre-existing multimorbidity. Absolut rates are reported in the results and tables, whilst Odds Ratios (ORs) are adjusted (aOR). Results Thirty thousand five hundred fifty-seven singleton births from 27,711 pregnant women were included in the analysis. The prevalence of pre-existing multimorbidity and complex multimorbidity was 16.8% (95% CI: 16.4–17.2) and 3.6% (95% CI: 3.3–3.8), respectively. The prevalence of multimorbidity in the youngest age group was 10.2%(95% CI: 8.8–11.6), while in those 40 to 44, it was 21.4% (95% CI: 18.4–24.4), and in the 45 to 49 age group, it was 20% (95% CI: 8.9–31.1). In women without multimorbidity, the prevalence of PTB was 6.7%; it was 11.6% in women with multimorbidity and 15.6% in women with complex multimorbidity. After adjusting for maternal age, socioeconomic status, number of previous pregnancies, Body Mass Index (BMI), and smoking, multimorbidity was associated with higher odds of PTB (aOR = 1.64, 95% CI: 1.48–1.82). Conclusions Multimorbidity at the time of conception was present in one in six women and was associated with an increased risk of preterm birth. Multimorbidity presents a significant health burden to women and their offspring. Routine and comprehensive evaluation of women with multimorbidity before and during pregnancy is urgently needed.Publisher PDFPeer reviewe

Community-based models of care facilitating the recovery of people living with persistent and complex mental health needs: a systematic review and narrative synthesis

ObjectiveThis study aims to assess the effectiveness of community-based models of care (MoCs) supporting the recovery of individuals who experience persistent and complex mental health needs.MethodWe conducted a systematic review and narrative synthesis of MoC studies reporting clinical, functional, or personal recovery from October 2016 to October 2021. Sources were Medline, EMBASE, PsycInfo, CINAHL, and Cochrane databases. Studies were grouped according to MoC features. The narrative synthesis was led by our researchers with lived experience.ResultsBeneficial MoCs ranged from well-established to novel and updated models and those explicitly addressing recovery goals and incorporating peer support: goal-focused; integrated community treatment; intensive case management; partners in recovery care coordination; rehabilitation and recovery-focused; social and community connection-focused; supported accommodation; and vocational support. None of our diverse group of MoCs supporting recovery warranted a rating of best practice. Established MoCs, such as intensive case management, are promising practices regarding clinical and functional recovery, with potential for enhancements to support personal recovery. Emerging practice models that support personal and functional recovery are those where consumer goals and priorities are central.ConclusionEvidence for established models of care shows that there is a need for inevitable evolution and adaptation. Considering the high importance of effective MoCs for people experiencing persistent and complex mental health needs, further attention to service innovation and research is required. Greater emphasis on the inclusion of lived and living experience in the design, delivery, implementation, and research of MoCs is needed, to enhance MOCs' relevance for achieving individual consumer recovery outcomes