Quality improvement programmes (QIPs) are designed to enhance patient outcomes by systematically introducing evidence-based clinical practices. The CONQUEST QIP focuses on improving the identification and management of patients with COPD in primary care. The process of developing CONQUEST, recruiting, preparing systems for participation, and implementing the QIP across three integrated healthcare systems (IHSs) is examined to identify and share lessons learned.

This review is organized into three stages: 1) development, 2) preparing IHSs for implementation, and 3) implementation. In each stage, key steps are described with the lessons learned and how they can inform others interested in developing QIPs designed to improve the care of patients with chronic conditions in primary care.

Stage 1 was establishing and working with steering committees to develop the QIP Quality Standards, define the target patient population, assess current management practices, and create a global operational protocol. Additionally, potential IHSs were assessed for feasibility of QIP integration into primary care practices. Factors assessed included a review of technological infrastructure, QI experience, and capacity for effective implementation.

Stage 2 was preparation for implementation. Key was enlisting clinical champions to advocate for the QIP, secure participation in primary care, and establish effective communication channels. Preparation for implementation required obtaining IHS approvals, ensuring Health Insurance Portability and Accountability Act compliance, and devising operational strategies for patient outreach and clinical decision support delivery.

Stage 3 was developing three IHS implementation models. With insight into the local context from local clinicians, implementation models were adapted to work with the resources and capacity of the IHSs while ensuring the delivery of essential elements of the programme.

Developing and launching a QIP programme across primary care practices requires extensive groundwork, preparation, and committed local champions to assist in building an adaptable environment that encourages open communication and is receptive to feedback.

Syncope is common among pediatric patients and is rarely pathologic. The mechanisms for symptoms during exercise are less well understood than the resting mechanisms. Additionally, inert gas rebreathing analysis, a non-invasive examination of haemodynamics including cardiac output, has not previously been studied in youth with neurocardiogenic syncope.

This was a retrospective (2017–2023), single-center cohort study in pediatric patients ≤ 21 years with prior peri-exertional syncope evaluated with echocardiography and cardiopulmonary exercise testing with inert gas rebreathing analysis performed on the same day. Patients with and without symptoms during or immediately following exercise were noted.

Of the 101 patients (15.2 ± 2.3 years; 31% male), there were 22 patients with symptoms during exercise testing or recovery. Resting echocardiography stroke volume correlated with resting (r = 0.53, p < 0.0001) and peak stroke volume (r = 0.32, p = 0.009) by inert gas rebreathing and with peak oxygen pulse (r = 0.61, p < 0.0001). Patients with syncopal symptoms peri-exercise had lower left ventricular end-diastolic volume (Z-score –1.2 ± 1.3 vs. –0.36 ± 1.3, p = 0.01) and end-systolic volume (Z-score –1.0 ± 1.4 vs. −0.1 ± 1.1, p = 0.001) by echocardiography, lower percent predicted peak oxygen pulse during exercise (95.5 ± 14.0 vs. 104.6 ± 18.5%, p = 0.04), and slower post-exercise heart rate recovery (31.0 ± 12.7 vs. 37.8 ± 13.2 bpm, p = 0.03).

Among youth with a history of peri-exertional syncope, those who become syncopal with exercise testing have lower left ventricular volumes at rest, decreased peak oxygen pulse, and slower heart rate recovery after exercise than those who remain asymptomatic. Peak oxygen pulse and resting stroke volume on inert gas rebreathing are associated with stroke volume on echocardiogram.

Employment and relationship are crucial for social integration. However, individuals with major psychiatric disorders often face challenges in these domains.

We investigated employment and relationship status changes among patients across the affective and psychotic spectrum – in comparison with healthy controls, examining whether diagnostic groups or functional levels influence these transitions.

The sample from the longitudinal multicentric PsyCourse Study comprised 1260 patients with affective and psychotic spectrum disorders and 441 controls (mean age ± s.d., 39.91 ± 12.65 years; 48.9% female). Multistate models (Markov) were used to analyse transitions in employment and relationship status, focusing on transition intensities. Analyses contained multiple multistate models adjusted for age, gender, job or partner, diagnostic group and Global Assessment of Functioning (GAF) in different combinations to analyse the impact of the covariates on the hazard ratio of changing employment or relationship status.

The clinical group had a higher hazard ratio of losing partner (hazard ratio 1.46, P < 0.001) and job (hazard ratio 4.18, P < 0.001) than the control group (corrected for age/gender). Compared with controls, clinical groups had a higher hazard of losing partner (affective group, hazard ratio 2.69, P = 0.003; psychotic group, hazard ratio 3.06, P = 0.001) and job (affective group, hazard ratio 3.43, P < 0.001; psychotic group, hazard ratio 4.11, P < 0.001). Adjusting for GAF, the hazard ratio of losing partner and job decreased in both clinical groups compared with controls.

Patients face an increased hazard of job loss and relationship dissolution compared with healthy controls, and this is partially conditioned by the diagnosis and functional level. These findings underscore a high demand for destigmatisation and support for individuals in managing their functional limitations.

Observational studies consistently report associations between tobacco use, cannabis use and mental illness. However, the extent to which this association reflects an increased risk of new-onset mental illness is unclear and may be biased by unmeasured confounding.

A systematic review and meta-analysis (CRD42021243903). Electronic databases were searched until November 2022. Longitudinal studies in general population samples assessing tobacco and/or cannabis use and reporting the association (e.g. risk ratio [RR]) with incident anxiety, mood, or psychotic disorders were included. Estimates were combined using random-effects meta-analyses. Bias was explored using a modified Newcastle–Ottawa Scale, confounder matrix, E-values, and Doi plots.

Seventy-five studies were included. Tobacco use was associated with mood disorders (K = 43; RR: 1.39, 95% confidence interval [CI] 1.30–1.47), but not anxiety disorders (K = 7; RR: 1.21, 95% CI 0.87–1.68) and evidence for psychotic disorders was influenced by treatment of outliers (K = 4, RR: 3.45, 95% CI 2.63–4.53; K = 5, RR: 2.06, 95% CI 0.98–4.29). Cannabis use was associated with psychotic disorders (K = 4; RR: 3.19, 95% CI 2.07–4.90), but not mood (K = 7; RR: 1.31, 95% CI 0.92–1.86) or anxiety disorders (K = 7; RR: 1.10, 95% CI 0.99–1.22). Confounder matrices and E-values suggested potential overestimation of effects. Only 27% of studies were rated as high quality.

Both substances were associated with psychotic disorders and tobacco use was associated with mood disorders. There was no clear evidence of an association between cannabis use and mood or anxiety disorders. Limited high-quality studies underscore the need for future research using robust causal inference approaches (e.g. evidence triangulation).

The question of how science can become a lever in achieving the Sustainable Development Goals permeates most recent sustainability research. Wide-ranging literature calling for a transformative approach has emerged in recent years. This ‘transformative turn’ is fueled by publications from fields such as sustainability science, social-ecological research, conservation science, sustainability transitions, or sustainability governance studies. However, there is a lack of a shared understanding specifically of what is meant for research to be transformative in this developing discourse around doing science differently to tackle sustainability problems. We aim to advance transformative research for sustainability. We define transformative research and outline six of its characteristics: (1) interventional nature and a theory of change focus; (2) collaborative modes of knowledge production, experimentation and learning; (3) systems thinking literacy and contextualization; (4) reflexivity, normative and inner dimensions; (5) local agency, decolonization, and reshaping power; (6) new quality criteria and rethinking impact. We highlight three tensions between transformative research and traditional paradigms of academic research: (1) process- and output-orientation; (2) accountability toward society and toward science; (3) methodologies rooted in scientific traditions and post-normal methodologies. We conclude with future directions on how academia could reconcile these tensions to support and promote transformative research.

Dominant ways of doing research are not enough to achieve the UN Sustainable Development Goals. The typical response of science to dealing with the current local and global sustainability crises is to produce and accumulate more knowledge. Transformative research seeks to couple knowledge production with co-creating change. This paper defines the transformative way of doing research to pro-actively support society's fight against pressing societal and environmental problems. We present six characteristics of transformative research. We reflect on the challenges related to implementing these characteristics in scientific practice and on how academia can play its part.

Sustainability transformation needs to be reflected in science, but what makes sustainability research transformative?

It has been posited that alcohol use may confound the association between greater concussion history and poorer neurobehavioral functioning. However, while greater alcohol use is positively correlated with neurobehavioral difficulties, the association between alcohol use and concussion history is not well understood. Therefore, this study investigated the cross-sectional and longitudinal associations between cumulative concussion history, years of contact sport participation, and health-related/psychological factors with alcohol use in former professional football players across multiple decades.

Former professional American football players completed general health questionnaires in 2001 and 2019, including demographic information, football history, concussion/medical history, and health-related/psychological functioning. Alcohol use frequency and amount was reported for three timepoints: during professional career (collected retrospectively in 2001), 2001, and 2019. During professional career and 2001 alcohol use frequency included none, 1-2, 3-4, 5-7 days/week, while amount included none, 12, 3-5, 6-7, 8+ drinks/occasion. For 2019, frequency included never, monthly or less, 2-4 times/month, 2-3 times/week, >4 times/week, while amount included none, 1-2, 3-4, 5-6, 7-9, 10+ drinks/occasion. Scores on a screening measure for Alcohol Use Disorder (CAGE) were also available at during professional career and 2001 timepoints. Concussion history was recorded in 2001 and binned into five groups: 0, 1-2, 3-5, 6-9, 10+. Depression and pain interference were assessed via PROMIS measures at all timepoints. Sleep disturbance was assessed in 2001 via separate instrument and with PROMIS Sleep Disturbance in 2019. Spearman’s rho correlations tested associations between concussion history and years of sport participation with alcohol use across timepoints, and whether poor health functioning (depression, pain interference, sleep disturbance) in 2001 and 2019 were associated with alcohol use both within and between timepoints.

Among the 351 participants (Mage=47.86[SD=10.18] in 2001), there were no significant associations between concussion history or years of contact sport participation with CAGE scores or alcohol use frequency/amount during professional career, 2001, or 2019 (rhos=-.072-.067, ps>.05). In 2001, greater depressive symptomology and sleep disturbance were related to higher CAGE scores (rho=.209, p<.001; rho=.176, p<.001, respectively), while greater depressive symptomology, pain interference, and sleep disturbance were related to higher alcohol use frequency (rho=.176, p=.002; rho=.109, p=.045; rho=.132, p=.013, respectively) and amount/occasion (rho=.215, p<.001; rho=.127, p=.020; rho=.153, p=.004, respectively). In 2019, depressive symptomology, pain interference, and sleep disturbance were not related to alcohol use (rhos=-.047-.087, ps>.05). Between timepoints, more sleep disturbance in 2001 was associated with higher alcohol amount/occasion in 2019 (rho=.115, p=.036).

Increased alcohol intake has been theorized to be a consequence of greater concussion history, and as such, thought to confound associations between concussion history and neurobehavioral function later in life. Our findings indicate concussion history and years of contact sport participation were not significantly associated with alcohol use cross-sectionally or longitudinally, regardless of alcohol use characterization. While higher levels of depression, pain interference, and sleep disturbance in 2001 were related to greater alcohol use in 2001, they were not associated cross-sectionally in 2019. Results support the need to concurrently address health-related and psychological factors in the implementation of alcohol use interventions for former NFL players, particularly earlier in the sport discontinuation timeline.

To assess the relative risk of hospital-onset Clostridioides difficile (HO-CDI) during each month of the early coronavirus disease 2019 (COVID-19) pandemic and to compare it with historical expectation based on patient characteristics.

This study used a retrospective cohort design. We collected secondary data from the institution’s electronic health record (EHR).

The Ohio State University Wexner Medical Center, Ohio, a large tertiary healthcare system in the Midwest.

All adult patients admitted to the inpatient setting between January 2018 and May 2021 were eligible for the study. Prisoners, children, individuals presenting with Clostridioides difficile on admission, and patients with <4 days of inpatient stay were excluded from the study.

After controlling for patient characteristics, the observed numbers of HO-CDI cases were not significantly different than expected. However, during 3 months of the pandemic period, the observed numbers of cases were significantly different from what would be expected based on patient characteristics. Of these 3 months, 2 months had more cases than expected and 1 month had fewer.

Variations in HO-CDI incidence seemed to trend with COVID-19 incidence but were not fully explained by our case mix. Other factors contributing to the variability in HO-CDI incidence beyond listed patient characteristics need to be explored.

Psychotic disorders develop gradually along a continuum of severity. Understanding factors associated with psychosis development, such as sleep, could aid in identification of individuals at elevated risk. This study aimed to assess (1) the dynamic relationship between psychotic experiences (PEs) and sleep quality and quantity, and (2) whether this relationship differed between different clinical stages along the psychosis continuum.

We used daily diary data (90 days) of individuals (N = 96) at early stages (i.e. before a first diagnosis of psychosis) along the psychosis continuum. Multilevel models were constructed with sleep quality and sleep quantity as predictors of PEs and vice versa. Post-hoc, we constructed a multilevel model with both sleep quality and quantity as predictors of PEs. In addition, we tested whether associations differed between clinical stages.

Within persons, poorer sleep predicted next day PEs (B = −0.02, p = 0.01), but not vice versa. Between persons, shorter sleep over the 90-day period predicted more PEs (B = −0.04, p = 0.002). Experiencing more PEs over 90-days predicted poorer (B = −0.02, p = 0.02) and shorter (B = −1.06, p = 0.008) sleep. We did not find any significant moderation effects for clinical stage.

We found a bidirectional relationship between sleep and PEs with daily fluctuations in sleep predicting next day PEs and general patterns of more PEs predicting poorer and shorter sleep. Our results highlight the importance of assessing sleep as a risk marker in the early clinical stages for psychosis.