Concern that self-harm and mental health conditions are increasing in university students may reflect widening access to higher education, existing population trends and/or stressors associated with this setting.

To compare population-level data on self-harm, neurodevelopmental and mental health conditions between university students and non-students with similar characteristics before and during enrolment.

This cohort study linked electronic records from the Higher Education Statistics Agency for 2012–2018 to primary and secondary healthcare records. Students were undergraduates aged 18 to 24 years at university entry. Non-students were pseudo-randomly selected based on an equivalent age distribution. Logistic regressions were used to calculate odds ratios. Poisson regressions were used to calculate incidence rate ratios (IRR).

The study included 96 760 students and 151 795 non-students. Being male, self-harm and mental health conditions recorded before university entry, and higher deprivation levels, resulted in lower odds of becoming a student and higher odds of drop-out from university. IRRs for self-harm, depression, anxiety, autism spectrum disorder (ASD), drug use and schizophrenia were lower for students. IRRs for self-harm, depression, attention-deficit hyperactivity disorder, ASD, alcohol use and schizophrenia increased more in students than in non-students over time. Older students experienced greater risk of self-harm and mental health conditions, whereas younger students were more at risk of alcohol use than non-student counterparts.

Mental health conditions in students are common and diverse. While at university, students require person-centred stepped care, integrated with local third-sector and healthcare services to address specific conditions.

People under the care of mental health services are at increased risk of suicide. Existing studies are small in scale and lack comparisons.

To identify opportunities for suicide prevention and underpinning data enhancement in people with recent contact with mental health services.

This population-based study includes people who died by suicide in the year following a mental health services contact in Wales, 2001–2015 (cases), paired with similar patients who did not die by suicide (controls). We linked the National Confidential Inquiry into Suicide and Safety in Mental Health and the Suicide Information Database – Cymru with primary and secondary healthcare records. We present results of conditional logistic regression.

We matched 1031 cases with 5155 controls. In the year before their death, 98.3% of cases were in contact with healthcare services, and 28.5% presented with self-harm. Cases had more emergency department contacts (odds ratio 2.4, 95% CI 2.1–2.7) and emergency hospital admissions (odds ratio 1.5, 95% CI 1.4–1.7), but fewer primary care contacts (odds ratio 0.7, 95% CI 0.6–0.9) and out-patient appointments (odds ratio 0.2, 95% CI 0.2–0.3) than controls. Odds ratios were larger in females than males for injury and poisoning (odds ratio: 3.3 (95% CI 2.5–4.5) v. 2.6 (95% CI 2.1–3.1)).

We may be missing existing opportunities to intervene, particularly in emergency departments and hospital admissions with self-harm presentations and with unattributed self-harm, especially in females. Prevention efforts should focus on strengthening routine care contacts, responding to emergency contacts and better self-harm care. There are benefits to enhancing clinical audit systems with routinely collected data.

Approximately 6.5 million Americans ages 65 and older have Alzheimer’s disease and related dementias, a prevalence projected to triple by 2060. While subtle impairment in cognition and instrumental activities of daily living (IADLs) arises in the mild cognitive impairment (MCI) phase, early detection of these insidious changes is difficult to capture given limitations. Traditional IADL assessments administered infrequently are less sensitive to early MCI and not conducive to tracking subtle changes that precede significant declines. Continuous passive monitoring of IADLs using sensors and software in home environments is a promising alternative. The purpose of this study was to determine which remotely monitored IADLs best distinguish between MCI and normal cognition.

Participants were 65 years or older, independently community-dwelling, and had at least one daily medication and home internet access. Clinical assessments were performed at baseline. Electronic pillboxes (MedTracker) and computer software (Worktime) measured daily medication and computer habits using the Oregon Center for Aging and Technology (ORCATECH) platform. The Survey for Memory, Attention, and Reaction Time (SMART; Trail A, Trail B, and Stroop Tests) is a self-administered digital cognitive assessment that was deployed monthly. IADL data was aggregated for each participant at baseline (first 90 days) in each domain and various features developed for each. The receiver operating characteristic area under the curve (ROC-AUC) was calculated for each feature.

Traditional IADL Questionnaires.

At baseline, 103 participants (normal n = 59, Mage = 73.6±5.5; MCI n = 44, Mage = 76.0±6.1) completed three functional questionnaires (Functional Activities Questionnaire; Measurement of Everyday Cognition (ECog), both self-report and informant). The Informant ECog demonstrated the highest AUC (72% AUC, p = <.001).

Remotely monitored in-home IADLs and self-administered brief online cognitive test performance.

Eighty-four had medication data (normal n = 48, Mage = 73.2±5.4; MCI n = 36, Mage = 75.6±6.9). Four features related to pillbox-use frequency (73% AUC) and four features related to pillbox-use time (62% AUC) were developed. The discrepancy between self-reported frequency of use versus actual use was the most discriminating (67% AUC, p = .03).

Sixty-six had computer data (normal n = 38, Mage = 73.6±6.1; MCI n = 28, Mage = 76.6±6.8). Average usage time showed 64% AUC (p = .048) and usage variability showed 60% AUC (p = .18).

One hundred and two completed the SMART (normal n = 59, Mage = 73.6±5.5; MCI n = 43, Mage = 75.9±6.2). Eleven features related to survey completion time demonstrated 80% AUC in discriminating cognition. Eleven features related to the number of clicks during the survey demonstrated 70% AUC. Lastly, seven mouse movement features demonstrated 71% AUC.

Pillbox use frequency combined features and self-administered brief online cognitive test combined features (e.g., completion times, mouse cursor movements) have acceptable to excellent ability to discriminate between normal cognition and MCI and are relatively comparable to informant rated IADL questionnaires. General computer usage habits demonstrated lower discriminatory ability. Our approach has applied implications for detecting and tracking older adults’ declining cognition and function in real world contexts.

Subjective cognitive decline (SCD, i.e., perceived cognitive decline without neuropsychological deficits) is associated with Alzheimer’s disease pathology and increased risk for cognitive impairment but is heterogenous in etiology and has been linked to other factors including personality and depression. Mental wellbeing (i.e., the perception and functioning of social, emotional, and health-related aspects of one’s life) has been associated with subjective memory complaints, but its relationship with other subjective cognitive domains is poorly understood. Further characterizing the relationship between mental wellbeing and SCD could refine understanding of SCD and inform development of interventions that prevent progression to objective cognitive decline. This study aimed to describe the relationship between mental wellbeing and subjective decline in multiple cognitive domains and examine whether this relationship differs between older adults with normal cognition and those with mild cognitive impairment (MCI).

Community-dwelling older adults (normal: n = 58, Mage = 73.7±5.6; MCI: n = 43, Mage = 75.9±6.1) completed the Everyday Cognition scale, a validated self-report measure of SCD, and the RAND-36 Health Survey, a validated self-report measure of health-related quality of life which includes a mental wellbeing subscale. Spearman’s rank correlations were conducted between self-reported mental wellbeing and each self-reported cognitive domain (i.e., memory, language, visuospatial, and executive function) for the Normal Cognition and MCI groups.

Worse mental wellbeing was associated with worse subjective language and executive function in the normal group, rs(56) = -.42, p =.001; rs(56) = -.37, p =.005, but not for the MCI group, rs(41) = -.23, p =.15; rs(41) = -.12, p =.46. Worse mental wellbeing was associated with worse subjective visuospatial function in the MCI group, rs(41) = -.39, p =.009, but not in the normal group, rs(56) = -.11, p =.39. For both groups, worse mental wellbeing was associated with worse subjective memory, rs(56) = -.45, p < .001; rs(41) = -.37, p =.02. While this correlation was greater in the normal group, the difference was not significant (z = 0.38, p =.71).

These results suggest that perceptions of mental wellbeing are related to perceptions of cognitive decline in multiple domains, and that the specific domains involved differ between normal and MCI groups. The differential associations may mean perception of specific cognitive domains more strongly affect mental wellbeing, or mental wellbeing more acutely influences perception of those domains. The overall observed relationship between SCD and mental wellbeing may have several explanations: the impact of broader health perceptions may extend to cognitive perception, behavioral changes associated with poor wellbeing may reduce subjective cognitive function, or worse subjective cognitive function may lead to negative experiences of wellbeing. Future longitudinal investigation could inform causal inferences. The more limited associations between mental wellbeing and SCD among MCI individuals may point to the role of decreased self-awareness (due to cognitive impairment) precluding detection of subtle changes in cognition or wellbeing. This study highlights the importance of better understanding mental wellbeing in experiences of SCD in both normal and MCI older adults to improve cognitive and mental health outcomes.

The cost effectiveness of treating Bell’s palsy with prednisolone in children is unknown. This study aimed to assess the cost effectiveness of prednisolone, compared with placebo, in treating Bell’s Palsy in children from a healthcare sector perspective.

This economic evaluation was a prospectively planned secondary analysis of a triple-blind randomized superiority trial conducted from 2015 to 2020 that compared prednisolone with placebo. The time horizon was six months after randomization. The 180 participants were aged from six months to 17 years and presented within 72 hours of onset of clinician diagnosed Bell’s palsy. Interventions were oral prednisolone (1 mg per kg daily) or taste-matched placebo administered for ten days. Incremental cost-effectiveness ratios comparing prednisolone with placebo were estimated. Costs included medication costs, doctor visits, and medical tests over the six-month study period. Effectiveness was measured using quality-adjusted life-years (QALYs) derived from the Child Health Utility 9D instrument. Nonparametric bootstrapping was performed to capture uncertainties. Prespecified subgroup analyses by age (12 to 17 years versus <12 years) were performed.

The mean cost per patient was USD188 in the prednisolone group and USD121 in the placebo group over the six-month period (difference USD66, 95% confidence interval [CI]: 47, 179). The mean QALYs gained over six months were 0.45 in the prednisolone group and 0.44 in the placebo group (difference 0.01, 95%CI: -0.01, 0.03). Prednisolone was very likely cost effective given a conventional willingness-to-pay threshold of USD 50,000 per QALY gained (the cost per additional QALY gained was USD6,625 using prednisolone compared with placebo). Subgroup analysis suggested that this was primarily driven by the high probability of prednisolone being cost effective in children aged 12 to 17 years (98%), compared with those younger than 12 years (51%).

This study provides new evidence to stakeholders and policy makers who are considering whether to make prednisolone available for treating Bell’s palsy in children aged 12 to 17 years.

The number of patient preference studies in health has increased dramatically. There is growing use of patient preferences in a wide variety of contexts, including health technology assessment. Patient preference studies can help inform decision makers on the needs and priorities of patients and the tradeoffs they are willing to make about health technologies.

This International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force included international experts, health preference researchers and others from diverse backgrounds, including regulatory, health technology assessment, medicine, patient advocacy, and the pharmaceutical industry. The report underwent two rounds of written reviews by ISPOR Preferences Special Interest Group members until a final consensus was reached. The Task Force focused on developing a roadmap that would: (i) apply to the wide variety of preference methods, (ii) identify key domains to guide researchers and other stakeholders in making patient preference studies more useful to decision makers, and (iii) detail important questions to guide researchers conducting preference studies and those critically appraising them.

This Task Force report provides a novel roadmap that invites patient-preference researchers to work with decision makers, patients and other stakeholders to do even more to ensure that studies are useful and impactful. The ISPOR Roadmap consists of five key elements: (i) Context; (ii) Purpose; (iii) Population; (iv) Method; and (v) Impact. In this report, we define these five elements and provide good practices on how patient-preference researchers can actively contribute to increasing the usefulness and impact of patient preference studies in decision-making. We also present a set of key questions that can support researchers and other stakeholders in assessing efforts that promote preference studies’ intended and unintended impact.

This roadmap can help increase the usefulness and impact of patient preference studies in decision-making by challenging researchers to engage and partner with decision makers, patients and others, and together consider the intended and unintended impacts of patient preference studies on decision-making while actively fostering positive impact.