There is a significant mortality gap between the general population and people with psychosis. Completion rates of regular physical health assessments for cardiovascular risk in this group are suboptimal. Point-of-care testing (POCT) for diabetes and hyperlipidaemia – providing an immediate result from a finger-prick – could improve these rates.

To evaluate the impact on patient–clinician encounters and on physical health check completion rates of implementing POCT for cardiovascular risk markers in early intervention in psychosis (EIP) services in South East England.

A mixed-methods, real-world evaluation study was performed, with 40 POCT machines introduced across EIP teams in all eight mental health trusts in South East England from March to May 2021. Clinician training and support was provided. Numbers of completed physical health checks, HbA1c and lipid panel blood tests completed 6 and 12 months before and 6 months after introduction of POCT were collected for individual patients. Data were compared with those from the South West region, which acted as a control. Clinician questionnaires were administered at 2 and 8 months, capturing device usability and impacts on patient interactions.

Post-POCT, South East England saw significant increases in HbA1c testing (odds ratio 2.02, 95% CI 1.17–3.49), lipid testing (odds ratio 2.38, 95% CI 1.43–3.97) and total completed health checks (odds ratio 3.61, 95% CI 1.94–7.94). These increases were not seen in the South West. Questionnaires revealed improved patient engagement, clinician empowerment and patients’ preference for POCT over traditional blood tests.

POCT is associated with improvements in the completion and quality of physical health checks, and thus could be a tool to enhance holistic care for individuals with psychosis.

To explore the source, message, channel, and receiver effects on patient concern for antibiotic resistance, willingness to reduce antibiotic use, and expectations for an antibiotic prescription in a prepandemic sample.

We used data reported from a national cross-sectional survey of adults who had visited an urgent care center within the last year. Data were collected from April 4 to April 9, 2017. The survey included an embedded experimental design to test changing effects before versus after message exposure.

A national sample of adult participants (n = 610) who had used urgent care at least once in the past year were recruited through GfK’s KnowledgePanelTM. KnowledgePanel survey response rates are typically about 65%. Respondents ranged in age from 18 to 85 and were more likely to be female (377/610; 62%), White (408/610; 67%), and covered by private insurance (414/610; 68%).

Outcome variables were measured on 4-point scales 1–4 scale, and t-tests were conducted for measures that were collected pre and postmessaging. The majority of participants trusted their doctor and desired them as the source for information regarding antibiotic resistance, followed by field experts (eg, CDC). Direct messaging (eg, email) and targeted advertisements were least preferred.

This study provides foundational data on patient communication preferences in terms of source, message content, and channel when receiving information on antibiotics and antibiotic resistance, as well as how these factors affect patient concern, willingness, and expectations. Follow-up work is needed to replicate these findings in a postpandemic sample.

Poor air quality is associated with poor health. Little attention is given to the complex array of environmental exposures and air pollutants that affect mental health during the life course.

We gather interdisciplinary expertise and knowledge across the air pollution and mental health fields. We seek to propose future research priorities and how to address them.

Through a rapid narrative review, we summarise the key scientific findings, knowledge gaps and methodological challenges.

There is emerging evidence of associations between poor air quality, both indoors and outdoors, and poor mental health more generally, as well as specific mental disorders. Furthermore, pre-existing long-term conditions appear to deteriorate, requiring more healthcare. Evidence of critical periods for exposure among children and adolescents highlights the need for more longitudinal data as the basis of early preventive actions and policies. Particulate matter, including bioaerosols, are implicated, but form part of a complex exposome influenced by geography, deprivation, socioeconomic conditions and biological and individual vulnerabilities. Critical knowledge gaps need to be addressed to design interventions for mitigation and prevention, reflecting ever-changing sources of air pollution. The evidence base can inform and motivate multi-sector and interdisciplinary efforts of researchers, practitioners, policy makers, industry, community groups and campaigners to take informed action.

There are knowledge gaps and a need for more research, for example, around bioaerosols exposure, indoor and outdoor pollution, urban design and impact on mental health over the life course.

It is unknown how much variation in adult mental health problems is associated with differences between societal/cultural groups, over and above differences between individuals.

To test these relative contributions, a consortium of indigenous researchers collected Adult Self-Report (ASR) ratings from 16 906 18- to 59-year-olds in 28 societies that represented seven culture clusters identified in the Global Leadership and Organizational Behavioral Effectiveness study (e.g. Confucian, Anglo). The ASR is scored on 17 problem scales, plus a personal strengths scale. Hierarchical linear modeling estimated variance accounted for by individual differences (including measurement error), society, and culture cluster. Multi-level analyses of covariance tested age and gender effects.

Across the 17 problem scales, the variance accounted for by individual differences ranged from 80.3% for DSM-oriented anxiety problems to 95.2% for DSM-oriented avoidant personality (mean = 90.7%); by society: 3.2% for DSM-oriented somatic problems to 8.0% for DSM-oriented anxiety problems (mean = 6.3%); and by culture cluster: 0.0% for DSM-oriented avoidant personality to 11.6% for DSM-oriented anxiety problems (mean = 3.0%). For strengths, individual differences accounted for 80.8% of variance, societal differences 10.5%, and cultural differences 8.7%. Age and gender had very small effects.

Overall, adults' self-ratings of mental health problems and strengths were associated much more with individual differences than societal/cultural differences, although this varied across scales. These findings support cross-cultural use of standardized measures to assess mental health problems, but urge caution in assessment of personal strengths.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Implicit in the UN's Sustainable Development Goal (SDG) Agenda is the notion that environmental sustainability is intertwined with, and underpins, the 17 Goals. Yet the language of the Goals, and their Targets and indicators is blind to the myriad ways in which nature supports people's health and wealth – which we argue represents a key impediment to progress. Using examples of nature–human wellbeing linkages, we assess the language of all 169 Targets to identify urgent research, policy, and action needed to spotlight and leverage nature's foundational role, to help enable truly sustainable development for all.

Nature's foundational role in helping achieve the SDGs is implicit rather than explicit in the language of SDGs Goals, Targets, and indicators. Drawing from the scientific literature describing how nature underpins human wellbeing, we carry out a systematic assessment of the language of all 169 Targets, categorizing which Targets are dependent upon nature for their achievement, could harm nature if attained through business-as-usual actions, or may synergistically benefit nature through their attainment. We find that half are dependent upon nature for their achievement – yet for more than two-thirds of those nature's role goes unstated and risks being downplayed or ignored. Moreover, while achieving the overwhelming majority of the 169 Targets could potentially benefit nature, more than 60% are likely to deliver ‘mixed outcomes’ – benefitting or harming nature depending on how they're achieved. Furthermore, of the 241 official indicators <5% track nature's role in achieving the parent Target. Our analysis provides insights important for increasing effectiveness across the SDG agenda regarding where to invest, how to enhance synergies and limit unanticipated impacts, and how to measure success. It also suggests a path for integrating the ‘nature that people need’ to achieve the SDGs into the CBD's post-2020 Global Biodiversity Framework.

Harmonizing links between the SDGs and the CBD's post-2020 Global Biodiversity Framework is vital for promoting sustainable development

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

To identify factors associated with breast-feeding initiation and continuation in Canadian-born and non-Canadian-born women.

Prospective cohort of mothers and infants born from 2008 to 2012: the Canadian Healthy Infant Longitudinal Development (CHILD) Cohort Study.

General community setting in four Canadian provinces.

In total, 3455 pregnant women from Vancouver, Edmonton, Winnipeg and Toronto between 2008 and 2012.

Of 3010 participants included in the current study, the majority were Canadian-born (75·5 %). Breast-feeding initiation rates were high in both non-Canadian-born (95·5 %) and Canadian-born participants (92·7 %). The median breast-feeding duration was 10 months in Canadian-born participants and 11 months in non-Canadian-born participants. Among Canadian-born participants, factors associated with breast-feeding initiation and continuation were older maternal age, higher maternal education, living with their partner and recruitment site. Rooming-in during the hospital stay was also associated with higher rates of breast-feeding initiation, but not continuation at 6-month postpartum. Factors associated with non-initiation of breast-feeding and cessation at 6-month postpartum were maternal smoking, living with a current smoker, caesarean birth and early-term birth. Among non-Canadian-born participants, maternal smoking during pregnancy was associated with lower odds of breast-feeding initiation and lower odds of breast-feeding continuation at 6 months, and older maternal age and recruitment site were associated with breast-feeding continuation at 6 months.

Although Canadian-born and non-Canadian-born women in the CHILD cohort have similar breast-feeding initiation rates, breast-feeding initiation and continuation are more strongly associated with socio-demographic characteristics in Canadian-born participants. Recruitment site was strongly associated with breast-feeding continuation in both groups and may indicate geographic disparities in breast-feeding rates nationally.