We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.

The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.

There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.

A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.

Dementia is a common and progressive condition whose prevalence is growing worldwide. It is challenging for healthcare systems to provide continuity in clinical services for all patients from diagnosis to death.

To test whether individuals who are most likely to need enhanced care later in the disease course can be identified at the point of diagnosis, thus allowing the targeted intervention.

We used clinical information collected routinely in de-identified electronic patient records from two UK National Health Service (NHS) trusts to identify at diagnosis which individuals were at increased risk of needing enhanced care (psychiatric in-patient or intensive (crisis) community care).

We examined the records of a total of 25 326 patients with dementia. A minority (16% in the Cambridgeshire trust and 2.4% in the London trust) needed enhanced care. Patients who needed enhanced care differed from those who did not in age, cognitive test scores and Health of the Nation Outcome Scale scores. Logistic regression discriminated risk, with an area under the receiver operating characteristic curve (AUROC) of up to 0.78 after 1 year and 0.74 after 4 years. We were able to confirm the validity of the approach in two trusts that differed widely in the populations they serve.

It is possible to identify, at the time of diagnosis of dementia, individuals most likely to need enhanced care later in the disease course. This permits the development of targeted clinical interventions for this high-risk group.

Describing our institution’s off-label use of gabapentin to treat irritability after superior cavopulmonary connection surgery and its impact on subsequent opiate and benzodiazepine requirements.

This is a single-center retrospective cohort study including infants who underwent superior cavopulmonary connection operation between 2011 and 2019.

Gabapentin was administered in 74 subjects (74/323, 22.9%) during the observation period, with a median (IQR) starting dose of 5.7 (3.3, 15.0) mg/kg/day and a maximum dose of 10.7 (5.5, 23.4) mg/kg/day. Infants who underwent surgery in 2015–19 were more likely to receive gabapentin compared with those who underwent surgery in 2011–14 (p < 0.0001). Infants prescribed gabapentin were younger at surgery (137 versus 146 days, p = 0.007) and had longer chest tube durations (1.8 versus 0.9 days, p < 0.001), as well as longer postoperative intensive care (5.8 versus 3.1 days, p < 0.0001) and hospital (11.5 versus 7.0 days, p < 0.0001) lengths of stays. The year of surgery was the only predisposing factor associated with gabapentin administration in multivariate analysis. In adjusted linear regression, infants prescribed gabapentin on postoperative day 0–4 (n = 64) had reduced benzodiazepine exposure in the following 3 days (−0.29 mg/kg, 95% CI −0.52 – −0.06, p = 0.01) compared with those not prescribed gabapentin, while no difference was seen in opioid exposure (p = 0.59).

Gabapentin was used with increasing frequency during the study period. There was a modest reduction in benzodiazepine requirements associated with gabapentin administration and no reduction in opioid requirements. A randomised controlled trial could better assess gabapentin’s benefits postoperatively in children with congenital heart disease.

Cross-national neuropsychological research is needed to understand the social, economic, and cultural factors associated with cognitive risk and resilience across global aging populations. Memory and language have been shown to be sensitive to age-related cognitive decline and pathological cognitive aging processes and may be more sensitive to subtle cognitive decline than measures of global cognitive function. Thus, we aimed to derive and validate harmonized cognitive domain scores for memory and language across population-based studies in the US and Mexico.

Data came from the Health and Retirement Study (HRS) Harmonized Cognitive Assessment Protocol (HCAP) and the Mexican Health and Aging Study (MHAS) Ancillary Study on Cognitive Aging (Mex-Cog). We used confirmatory factor analysis methodology to create statistically co-calibrated cognitive domains of memory and language. We performed differential item functioning (DIF) analysis to evaluate measurement differences across studies, using a cultural neuropsychological approach to identify comparable items across studies (i.e., cross-study anchors). We evaluated harmonized scores by examining their relationship to age and education in each study.

We included 3347 participants from the HRS-HCAP study [Mage=76.6(7.5), 60% female] and 2042 participants from the Mex-Cog study [Mage=68.1(9.0), 59% female]. Education was classified according to the International Standard Classification of Education in the following categories (HRS-HCAP and Mex-Cog, respectively): none or early childhood education: (0.7%; 50.5%), primary education (4.1%; 22.3%), lower secondary education (7.1%; 15.7%), upper secondary education (41.1%; 3.0%), and any college (47.1%; 8.5%). DIF analyses revealed that 5 out of the 7 memory items and 1 out of the 12 language items demonstrated statistical evidence of measurement differences across studies, meaning that these items measured each underlying cognitive construct differently across studies. After adjusting for DIF by not allowing the items with DIF to be cross-study anchors, harmonized memory and language scores showed generally the expected associations with age and education in each study. Increasing age was associated with lower memory (r=-0.40 in HRS-HCAP; r=-0.44 in Mex-Cog) and language (r=-0.31 in HRS-HCAP and r=-0.67 in Mex-Cog) scores. Increasing years of education was associated with better memory and language scores, with mean scores ranging from z=-0.86 and z=-0.29 among those with a primary education or lower to z=0.33 and z=0.90 among those with any college, for HRS-HCAP and Mex-Cog, respectively.

A cultural neuropsychology approach to statistical harmonization facilitates the generation of harmonized measures of cognitive functioning in cross-national studies. Future work can utilize these harmonized cognitive scores to investigate determinants of late-life cognitive decline and dementia in the US and Mexico.

We identify a set of essential recent advances in climate change research with high policy relevance, across natural and social sciences: (1) looming inevitability and implications of overshooting the 1.5°C warming limit, (2) urgent need for a rapid and managed fossil fuel phase-out, (3) challenges for scaling carbon dioxide removal, (4) uncertainties regarding the future contribution of natural carbon sinks, (5) intertwinedness of the crises of biodiversity loss and climate change, (6) compound events, (7) mountain glacier loss, (8) human immobility in the face of climate risks, (9) adaptation justice, and (10) just transitions in food systems.

The Intergovernmental Panel on Climate Change Assessment Reports provides the scientific foundation for international climate negotiations and constitutes an unmatched resource for researchers. However, the assessment cycles take multiple years. As a contribution to cross- and interdisciplinary understanding of climate change across diverse research communities, we have streamlined an annual process to identify and synthesize significant research advances. We collected input from experts on various fields using an online questionnaire and prioritized a set of 10 key research insights with high policy relevance. This year, we focus on: (1) the looming overshoot of the 1.5°C warming limit, (2) the urgency of fossil fuel phase-out, (3) challenges to scale-up carbon dioxide removal, (4) uncertainties regarding future natural carbon sinks, (5) the need for joint governance of biodiversity loss and climate change, (6) advances in understanding compound events, (7) accelerated mountain glacier loss, (8) human immobility amidst climate risks, (9) adaptation justice, and (10) just transitions in food systems. We present a succinct account of these insights, reflect on their policy implications, and offer an integrated set of policy-relevant messages. This science synthesis and science communication effort is also the basis for a policy report contributing to elevate climate science every year in time for the United Nations Climate Change Conference.

We highlight recent and policy-relevant advances in climate change research – with input from more than 200 experts.

Patients with Fontan failure are high-risk candidates for heart transplantation and other advanced therapies. Understanding the outcomes following initial heart failure consultation can help define appropriate timing of referral for advanced heart failure care.

This is a survey study of heart failure providers seeing any Fontan patient for initial heart failure care. Part 1 of the survey captured data on clinical characteristics at the time of heart failure consultation, and Part 2, completed 30 days later, captured outcomes (death, transplant evaluation outcome, and other interventions). Patients were classified as “too late” (death or declined for transplant due to being too sick) and/or “care escalation” (ventricular assist device implanted, inotrope initiated, and/or listed for transplant), within 30 days. “Late referral” was defined as those referred too late and/or had care escalation.

Between 7/2020 and 7/2022, 77 Fontan patients (52% inpatient) had an initial heart failure consultation. Ten per cent were referred too late (6 were too sick for heart transplantation with one subsequent death, and two others died without heart transplantation evaluation, within 30 days), and 36% had care escalation (21 listed ± 5 ventricular assist device implanted ± 6 inotrope initiated). Overall, 42% were late referrals. Heart failure consultation < 1 year after Fontan surgery was strongly associated with late referral (OR 6.2, 95% CI 1.8–21.5, p=0.004).

Over 40% of Fontan patients seen for an initial heart failure consultation were late referrals, with 10% dying or being declined for transplant within a month of consultation. Earlier referral, particularly for those with heart failure soon after Fontan surgery, should be encouraged.

Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

Suicidal acts may cluster in time and space and lead to community concerns about further imitative suicidal episodes. Although suicide clusters have been researched in previous studies, less is known about the clustering of non-fatal suicidal behaviour (self-harm). Furthermore, most previous studies used crude temporal and spatial information, e.g., numbers aggregated by month and residence area, for cluster detection analysis. This study aimed to (i) identify space–time clusters of self-harm and suicide using daily incidence data and exact address and (ii) investigate the characteristics of cluster-related suicidal acts.

Data on emergency department presentations for self-harm and suicide deaths in Taipei City and New Taipei City, Taiwan, were used in this study. In all-age and age-specific analyses, self-harm and suicide clusters were identified using space–time permutation scan statistics. A cut-off of 0.10 for the p value was used to identify possible clusters. Logistic regression was used to investigate the characteristics associated with cluster-related episodes.

A total of 5,291 self-harm episodes and 1,406 suicides in Taipei City (2004–2006) and 20,531 self-harm episodes and 2,329 suicides in New Taipei City (2012–2016) were included in the analysis. In the two cities, two self-harm clusters (n [number of self-harm episodes or suicide deaths in the cluster] = 4 and 8 in Taipei City), four suicide clusters (n = 3 in Taipei City and n = 4, 11 and 4 in New Taipei City) and two self-harm and suicide combined clusters (n = 4 in Taipei City and n = 8 in New Taipei City) were identified. Space–time clusters of self-harm, suicide, and self-harm and suicide combined accounted for 0.05%, 0.59%, and 0.08% of the respective groups of suicidal acts. Cluster-related episodes of self-harm and suicide were more likely to be male (adjusted odds ratio [aOR] = 2.22, 95% confidence interval [CI] 1.26, 3.89) and young people aged 10–29 years (aOR = 2.72, 95% CI 1.43, 5.21) than their cluster-unrelated counterparts.

Space–time clusters of self-harm, suicide, and self-harm and suicide combined accounted for a relatively small proportion of suicidal acts and were associated with some sex/age characteristics. Focusing on suicide deaths alone may underestimate the size of some clusters and/or lead to some clusters being overlooked. Future research could consider combining self-harm and suicide data and use social connection information to investigate possible clusters of suicidal acts.