Breaking down barriers: promoting journals beyond the page with open access journal clubs

In 2020, during the early days of the COVID-19 pandemic, the British Journal of Psychiatry (BJPsych) established a series of free online teaching sessions called BJPsych Journal Clubs. Their educational purpose is two-fold: (a) to provide junior psychiatrists with a friendly but large-scale platform to evaluate and critically appraise recent articles published in the BJPsych and (b) to present new research findings in an open and accessible manner. In this paper, we discuss our framework, the challenges we encountered, how the original model is evolving based on feedback from trainees, and tips for success when delivering international online journal clubs.

Variation in symptoms of common mental disorders in the general population during the COVID-19 pandemic: longitudinal cohort study

BACKGROUND

A significant rise in mental health disorders was expected during the COVID-19 pandemic. However, referrals to mental health services dropped for several months before rising to pre-pandemic levels.

AIMS

To identify trajectories of incidence and risk factors for common mental disorders among the general population during 14 months of the COVID-19 pandemic, to inform potential mental health service needs.

METHOD

A cohort of 33 703 adults in England in the University College London COVID-19 Social Study provided data from March 2020 to May 2021. Growth mixture modelling was used to identify trajectories based on the probability of participants reporting symptoms of depression (Patient Health Questionnaire-9) or anxiety (Generalised Anxiety Disorder-7) in the clinical range, for each month. Sociodemographic and personality-related characteristics associated with each trajectory class were explored.

RESULTS

Five trajectory classes were identified for depression and anxiety. Participants in the largest class (62%) were very unlikely to report clinically significant symptom levels. Other trajectories represented participants with a high likelihood of clinically significant symptoms throughout, early clinically significant symptoms that reduced over time, clinically significant symptoms that emerged as the pandemic unfolded and a moderate likelihood of clinically significant symptoms throughout. Females, younger adults, carers, those with existing mental health diagnoses, those that socialised frequently pre-pandemic and those with higher neuroticism scores were more likely to experience depression or anxiety.

CONCLUSIONS

Nearly 40% of participants followed trajectories indicating risk of clinically significant symptoms of depression or anxiety. The identified risk factors could inform public health interventions to target individuals at risk in future health emergencies.

Stimulant medication and suicide mortality in attention-deficit hyperactivity disorder

Patients diagnosed with attention-deficit hyperactivity disorder (ADHD) are at an elevated risk for suicide. No prior work has assessed the association between stimulant prescriptions and death by suicide in this population. This retrospective cohort study included Department of Veterans Affairs patients with an active ADHD diagnosis that received stimulant medications between 2016 and 2019. We found that months with active stimulant medication prescription was associated with decreased risk of suicide mortality compared with months without stimulant medication (odds ratio 0.57, 95% CI 0.36-0.88). Our results suggest that prescribing stimulant medications for patients diagnosed with ADHD is associated with decreased risk of suicide mortality.

Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer

This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association's ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.

Pre-pandemic trajectories of depressive symptomatology and their relation to depression during the COVID-19 pandemic: longitudinal study of English older people

BACKGROUND

Although the COVID-19 pandemic has affected depression, evidence of the role of pre-pandemic history of depression remains limited.

AIMS

We investigated how long-term trajectories of depressive symptomatology before the COVID-19 pandemic were related to depression during the pandemic, over and above the latest pre-pandemic depression status. Furthermore, we examined whether those experiencing depression closer to the pandemic were at higher risk during the pandemic.

METHOD

Employing data from waves 4-9 of the English Longitudinal Study of Ageing (2008-2009 to 2018-2019), we used group-based trajectory modelling on 3925 English older adults aged 50+ years to identify distinctive trajectories of elevated depressive symptoms (EDS). Fully adjusted logistic models were then used to examine the associations between trajectories and depression during the COVID-19 pandemic (June-July and November-December 2020).

RESULTS

We identified four classes of pre-pandemic trajectories of EDS. About 5% were classed as 'enduring EDS', 8% as 'increasing EDS', 10% as 'decreasing EDS' and 77% as 'absence of EDS'. Compared with respondents with absence of EDS, those with EDS history were more likely to have depression during the COVID-19 pandemic, particularly those with enduring or increasing EDS in the previous 10 years. Moreover, the frequency of EDS was more crucial in predicting the risks of depression during the pandemic than the timing of the latest episode.

CONCLUSIONS

Trajectories of depressive symptomatology are an important risk factor for older adults' mental health, particularly in the context of crisis. Older people with enduring or increasing EDS should receive particular attention from policy makers when provisioning post-pandemic well-being support.

The true effect of lithium is hard to determine

Lithium is the primary choice for preventing bipolar disorder relapses, endorsed by guidelines. A recent systematic review by Ulrichsen et al. showed limitations in assessing its specific impact, but data supports lithium's effectiveness in managing symptoms and preventing relapse. Comprehensive guidelines and research are crucial for its continued use.

The impact of antidepressants and human development measures on the prevalence of sadness, worry and unhappiness: cross-national comparison

Depression is a major public health concern. Depressed individuals have received increasing treatment with antidepressants in Western countries. In this study, we examine the relationship among individual symptoms (sadness, worry and unhappiness), human development factors and antidepressant use in 29 OECD countries. We report that increased antidepressant prescribing is not associated with decreased prevalence of sadness, worry or unhappiness. However, income, education and life expectancy (measured using the Human Development Index) are associated with lower prevalence of all these symptoms. This suggests that increasing spending on depression treatment may not be as effective as general public health interventions at reducing depression in communities.

Gender bias in autism screening: measurement invariance of different model frameworks of the Autism Spectrum Quotient

BACKGROUND

The Autism Spectrum Quotient is a popular autism screening tool recommended for identifying potential cases of autism. However, many women with autism demonstrate a different presentation of traits to those currently captured by screening measures and assessment methods, such as the Autism Spectrum Quotient.

AIMS

Different models of the Autism Spectrum Quotient have been proposed in the literature, utilising different items from the original 50-item scale. Within good-fitting models, the current study aimed to explore whether these items assess autistic traits similarly across men and women.

METHOD

Seventeen Autism Spectrum Quotient models were identified from the literature. Using the responses of a large sample of adults from the UK general population (5246 women, 1830 men), confirmatory factor analysis was used to evaluate the fit of each model. Measurement invariance with respect to gender, adjusting for age, was explored in the 11 model frameworks that were found to have satisfactory fit to our data.

RESULTS

It emerged that only two items were gender invariant (non-biased), whereas for the remaining items, the probability of endorsement was influenced by gender. In particular, women had a higher probability of endorsing items relating to social skills and communication.

CONCLUSIONS

If the items of the Autism Spectrum Quotient indeed reflect autism-related traits, those items should be rephrased to ensure they do not present a gender-related bias. This is vital for ensuring more timely diagnoses and support for all people with autism.

Patterns of site-level periodontal disease and within-mouth correlation among older adults in the Hispanic Community Health Study/Study of Latinos

OBJECTIVES

Clinical measures of periodontal disease such as attachment loss (CAL) and probing depth (PD) vary considerably between and within individuals with periodontitis and are known to be influenced by person-level factors (e.g. age and race/ethnicity) as well as intraoral characteristics (e.g. tooth type and location). This study sought to characterize site-level disease patterns and correlations using both person-level and intraoral factors through a model-based approach.

METHODS

This study used full-mouth, six sites per tooth, periodontal examination data collected from 2301 Hispanic/Latino adults aged 60-74 years in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The presence of site-level CAL ≥3 mm and PD ≥4 mm was estimated using generalized estimating equations (GEE), explicitly modelling pairwise periodontal site correlations, while adjusting for number of teeth, sex and Hispanic/Latino background. Subsequently tooth- and tooth-site patterns of intraoral CAL ≥3 mm and PD ≥4 mm were estimated and visualized in the HCHS/SOL population.

RESULTS

The findings showed that posterior sites had the highest odds of CAL ≥3 mm and PD ≥4 mm. Sites located in the interproximal space had higher odds of PD ≥4 mm but lower odds of CAL ≥3 mm than non-interproximal sites. Mexicans had the lowest odds of CAL ≥3 mm among all Hispanic/Latino backgrounds. While Mexicans had lower odds of PD ≥4 mm than Central Americans and Cubans, they had higher odds than Dominicans and Puerto Ricans. Site-level proportions and pairwise correlations of PD ≥4 mm were generally smaller than those of CAL ≥3 mm.

CONCLUSIONS

The patterns of site-level probabilities of clinical measures of periodontal disease can be defined based on tooth, site and individual-level characteristics. Intraoral correlation patterns, while complex, are quantifiable. The risk factors for site-level CAL ≥3 mm may differ from those of PD ≥4 mm. Likewise, participant risk factors for site-level clinical measures of periodontal disease are distinct from those that affect individual-level periodontitis prevalence. Future epidemiological investigations should consider model-based approaches when examining site-level disease probabilities to identify intra-oral patterns of periodontal disease and make inferences about the larger population.

Role of clinical attachments in psychiatry for international medical graduates to enhance recruitment and retention in the NHS

AIMS AND METHOD

There are numerous challenges in the recruitment and retention of the medical workforce in psychiatry. This mixed-methods study examined the role of psychiatry clinical attachments for international medical graduates (IMGs) to enhance recruitment and retention. An online survey was launched to capture views and perceptions of IMGs about clinical attachments. The quantitative and qualitative responses were analysed to elicit findings.

RESULTS

In total, 92 responses were received, with respondents commonly from India, Pakistan and Egypt. Respondents were mostly aged 25-34, with ≥3 years of psychiatry experience. Over 80% expressed strong interest in completing a psychiatry clinical attachment and believed it would support career progression. Qualitative data indicated that IMGs hoped to gain clinical experience and understanding of the National Health Service (NHS). They wished for a clearer, simpler process for clinical attachments.

CLINICAL IMPLICATIONS

Clinical attachment can be mutually beneficial, providing IMGs with opportunity to confidently start their psychiatry career in the UK and enhance medical recruitment in mental health services across the NHS.

Comprehensive measurement of the prevalence of dementia in low- and middle-income countries: STRiDE methodology and its application in Indonesia and South Africa

BACKGROUND

A core element of the Strengthening Responses to Dementia in Developing Countries (STRiDE) programme was to generate novel data on the prevalence, cost and impact of dementia in low- and middle-income countries, to build better health policy. Indonesia and South Africa are two middle-income countries in need of such data.

AIMS

To present the STRiDE methodology and generate estimates of dementia prevalence in Indonesia and South Africa.

METHOD

We conducted community-based, single-phase, cross-sectional studies in Indonesia and South Africa, randomly sampling participants aged 65 years or older in each country. Dementia prevalence rates for each country were generated by using the 10/66 short schedule and applying its diagnostic algorithm. Weighted estimates were calculated with national sociodemographic data.

RESULTS

Data were collected between September and December 2021 in 2110 people in Indonesia and 408 people in South Africa. The adjusted weighted dementia prevalence was 27.9% (95% CI 25.2-28.9) in Indonesia and 12.5% (95% CI 9.5-16.0) in South Africa. Our results indicate that there could be >4.2 million people in Indonesia and >450 000 people in South Africa who have dementia. Only five participants (0.2%) in Indonesia and two (0.5%) in South Africa had been previously diagnosed with dementia.

CONCLUSIONS

Despite prevalence estimates being high, formal diagnosis rates of dementia were very low across both countries (<1%). Further STRiDE investigations will provide indications of the impact and costs of dementia in these countries, but our results provide evidence that dementia needs to be prioritised within national health and social care policy agendas.

Identifying prognostic indicators for cognitive stimulation therapy for dementia: protocol for a systematic review and individual participant data meta-analysis

BACKGROUND

Cognitive stimulation therapy (CST) is the only non-pharmacological, treatment for dementia recommended by the UK National Institute for Health and Care Excellence, following multiple international trials demonstrating beneficial cognitive outcomes in people with mild-to-moderate dementia. However, there is limited understanding of whether treatment prognosis is influenced by sociodemographic and clinical variables (such as dementia subtype and gender), information which could inform clinical decision-making.

AIM

We describe the protocol for a systematic review and individual patient data meta-analysis assessing the prognostic factors related to CST. In publishing this protocol, we hope to increase the transparency of our work, and keep healthcare professionals aware of the latest evidence for effective CST.

METHOD

A systematic review will be conducted with searches of the bibliographic databases Medline, EMBASE and PsycINFO, from inception to 7 February 2023. Studies will be included if they are clinical trials of CST, use the Alzheimer's Disease Assessment Scale - Cognitive Subscale (gold-standard measure of cognition in dementia in clinical trials) and include participants with mild-to-moderate dementia. Following harmonisation of the data-set, mixed-effect models will be constructed to explore the relationship between the prognostic indicators and change scores post-treatment.

CONCLUSIONS

This is the first individual patient data meta-analyses on CST, and has the potential to significantly optimise patient care. Previous analyses suggest people with advanced dementia could benefit more from CST treatment. Given that CST is currently used post-diagnosis in people with mild-to-moderate dementia, the implications of confirming this finding, among identifying other prognostic indicators, are profound.

Mental health in Germany in the first weeks of the Russo-Ukrainian war

BACKGROUND

In the connected world, although societies are not directly involved in a military conflict, they are exposed to media reports of violence.

AIMS

We assessed the effects of such exposures on mental health in Germany during the military conflict in Ukraine.

METHOD

We used the German population-based cohort for digital health research, DigiHero, launching a survey on the eighth day of the Russo-Ukrainian war. Of the 27 509 cohort participants from the general population, 19 444 (70.7%) responded within 17 days. We measured mental health and fear of the impact of war compared with other fears (natural disasters or health-related).

RESULTS

In a subsample of 4441 participants assessed twice, anxiety in the population (measured by the Generalised Anxiety Disorder-7 screener) was higher in the first weeks of war than during the strongest COVID-19 restrictions. Anxiety was elevated across the whole age spectrum, and the mean was above the cut-off for mild anxiety. Over 95% of participants expressed various degrees of fear of the impact of war, whereas the percentage for other investigated fears was 0.47-0.82. A one-point difference in the fear of the impact of war was associated with a 2.5 point (95% CI 2.42-2.58) increase in anxiety (11.9% of the maximum anxiety score). For emotional distress, the increase was 0.67 points (0.66-0.68) (16.75% of the maximum score).

CONCLUSIONS

The population in Germany reacted to the Russo-Ukrainian war with substantial distress, exceeding reactions during the strongest restrictions in the COVID-19 pandemic. Fear of the impact of war was associated with worse mental health.

Development and validation of the facilitative interpersonal skills scale for clients

OBJECTIVE

Psychotherapy studies have revealed that therapist characteristics are responsible for 5% to 9% of outcome variance. The therapist-facilitative interpersonal skills (FIS) have been shown to predict both alliance and outcomes, indicating that higher FIS therapists are more effective than lower FIS therapists. The current study focused on the development and validation of the FIS-client version (FIS-C) instrument, aimed at collecting the clients' perspectives on relevant therapist characteristics.

METHOD

The clinical outcomes in routine evaluation-outcome measures, the session rating scale, and the FIS questionnaire-client version were filled out by psychotherapy clients. Exploratory, confirmatory factor, and test-retest analysis were conducted.

RESULTS

Results indicate robust psychometric characteristics, in terms of validity (factorial, convergent, discriminant, and nomological), reliability, and sensitivity.

CONCLUSION

The validation of the FIS-C represents an important contribution to clinical research and practice, namely to the field of client feedback and therapist expertise.

Relationship between adjustment disorder symptoms and probable diagnosis before and after second lockdown in Israel: longitudinal symptom network analysis

BACKGROUND

There is cumulative evidence of the importance of exploring the change of dynamics between symptoms over time as reflective of consolidation of psychopathology.

AIMS

To explore the interactions between symptoms of ICD-11 adjustment disorder before and after the second lockdown of the COVID-19 pandemic in Israel and identify the most central symptoms and their concurrent and prospective associations with probable adjustment disorder.

METHOD

This is a population-based study drawn from a probability-based internet panel. A representative sample of the adult Israeli population was assessed at two time points (T1, pre-second lockdown, n = 1029, response rate 76.17%; T2, post-second lockdown, n = 764, response rate 74.24%). Symptoms of adjustment disorder were assessed by the International Adjustment Disorder Questionnaire (IADQ).

RESULTS

Although the overall strength of associations at the two measurement points was similar and two same communities were found, there was a significant change in their structure, with a more consolidated network at T2. The most central item was 'difficult to relax' in both networks. Cross-sectionally, all symptoms of failure to adapt significantly predicted adjustment disorder. 'Worry a lot more' (preoccupation) and 'difficult to adapt to life' (failure to adapt) at T1 significantly predicted this diagnosis at T2.

CONCLUSIONS

Adjustment disorder symptoms consolidated during the second lockdown of the pandemic. In line with the ICD-11 conceptualisation of adjustment disorder, both preoccupation and failure-to-adapt symptoms have prognostic validity. This highlights the importance of identifying and targeting adjustment disorder symptoms during a period of stress such as the COVID-19 pandemic.

Impact of early intervention on the population prevalence of common mental disorders: 20-year prospective study

BACKGROUND

The potential for early interventions to reduce the later prevalence of common mental disorders (CMD) first experienced in adolescence is unclear.

AIMS

To examine the course of CMD and evaluate the extent to which the prevalence of CMD could be reduced by preventing adolescent CMD, or by intervening to change four young adult processes, between the ages of 20 and 29 years, that could be mediating the link between adolescent and adult disorder.

METHOD

This was a prospective cohort study of 1923 Australian participants assessed repeatedly from adolescence (wave 1, mean age 14 years) to adulthood (wave 10, mean age 35 years). Causal mediation analysis was undertaken to evaluate the extent to which the prevalence of CMD at age 35 years in those with adolescent CMD could be reduced by either preventing adolescent CMD, or by intervening on four young adult mediating processes: the occurrence of young adult CMD, frequent cannabis use, parenting a child by age 24 years, and engagement in higher education and employment.

RESULTS

At age 35, 19.2% of participants reported CMD; a quarter of these participants experienced CMD during both adolescence and young adulthood. In total, 49% of those with CMD during both adolescence and young adulthood went on to report CMD at age 35 years. Preventing adolescent CMD reduced the population prevalence at age 35 years by 3.9%. Intervening on all four young adult processes among those with adolescent CMD, reduced this prevalence by 1.6%.

CONCLUSIONS

In this Australian cohort, a large proportion of adolescent CMD resolved by adulthood, and by age 35 years, the largest proportion of CMD emerged among individuals without prior CMD. Time-limited, early intervention in those with earlier adolescent disorder is unlikely to substantially reduce the prevalence of CMD in midlife.

Predicting relapse or recurrence of depression: systematic review of prognostic models

BACKGROUND

Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence.

AIMS

To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder.

METHOD

We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST).

RESULTS

We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility.

CONCLUSIONS

Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

Psychosocial markers of age at onset in bipolar disorder: a machine learning approach

BACKGROUND

Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the psychosocial predictors associated with AAO are unknown.

AIMS

We aim to identify psychosocial factors associated with bipolar disorder AAO.

METHOD

Using data from the Bipolar Disorder Research Network UK, we employed least absolute shrinkage and selection operator regression to identify psychosocial factors associated with bipolar disorder AAO. Twenty-eight factors were entered into our model, with AAO as our outcome measure.

RESULTS

We included 1022 participants with bipolar disorder (μ = 23.0, s.d. ± 9.86) in our model. Six variables predicted an earlier AAO: childhood abuse (β = -0.2855), regular cannabis use in the year before onset (β = -0.2765), death of a close family friend or relative in the 6 months before onset (β = -0.2435), family history of suicide (β = -0.1385), schizotypal personality traits (β = -0.1055) and irritable temperament (β = -0.0685). Five predicted a later AAO: the average number of alcohol units consumed per week in the year before onset (β = 0.1385); birth of a child in the 6 months before onset (β = 0.2755); death of parent, partner, child or sibling in the 6 months before onset (β = 0.3125); seeking work without success for 1 month or more in the 6 months before onset (β = 0.3505) and a major financial crisis in the 6 months before onset (β = 0.4575).

CONCLUSIONS

The identified predictor variables have the potential to help stratify high-risk individuals into likely AAO groups, to inform treatment provision and early intervention.

[Types of clinical data and statistical methodology of human use experience in traditional Chinese medicine]

Application experience in humans, a summary of the clinical practice of traditional Chinese medicine(TCM), serves as an important data source for evaluating the safety, effectiveness, and clinical value of drugs in the development of new Chinese medicine. The collected data serving as the evaluation evidence through statistical analysis are critical to the research on the application experience in humans. This article summarized and analyzed the data characteristics and statistical methodology of application experience of Chinese medicine in humans, and concluded the data types, outcome evaluation, bias evaluation, confounding factors, and missing values. Furthermore, the article emphasized the importance of data analysis of application experience of Chinese medicine in humans for TCM evidence and put forward the current difficulties, such as low data quality and large internal bias, lack of individualized data processing methods, and lack of methods for &quot;disease-syndrome combination&quot; data. We believe that with the development of methodology, the application experience of Chinese medicine in humans can strongly support the development of new drugs in TCM.

Changes in the identification and management of mental health and domestic abuse among pregnant women during the COVID-19 lockdown: regression discontinuity study

BACKGROUND

Domestic violence and abuse (DVA) and mental illness during pregnancy have long-lasting and potentially serious consequences, which may have been exacerbated during the COVID-19 pandemic.

AIMS

To investigate how the UK COVID-19 lockdown policy influenced the identification of DVA and depressive symptoms during pregnancy in health services in South-East London in Spring 2020, using eLIXIR (Early-Life Data Cross-Linkage in Research) maternity and mental routine healthcare data.

METHOD

We used a regression discontinuity approach, with a quasi-experimental study design, to analyse the effect of the transition into and out of the COVID-19 lockdown on the rates of positive depression screens, DVA recorded in maternity and secondary mental health services, and contact with secondary mental health services during pregnancy.

RESULTS

We analysed 26 447 pregnancies from 1 October 2018 to 29 August 2020. The rate of DVA recorded in maternity services was low throughout the period (<0.5%). Within secondary mental health services, rates of DVA dropped by 78% (adjusted odds ratio 0.219, P = 0.012) during lockdown, remaining low after lockdown. The rate of women screening positive for depression increased by 40% (adjusted odds ratio 1.40, P = 0.023), but returned to baseline after lockdown lifted.

CONCLUSIONS

Rates of DVA identification in secondary mental health services dropped during and after lockdown, whereas overall rates of DVA identified in maternity services were concerningly low. Healthcare services must adopt guidance to facilitate safe enquiry, particularly in remote consultations. Further research is vital to address the longer-term impact on women's mental health caused by the increase in depression during the lockdown.

Cross-cultural comparison of depressive symptoms on the Beck Depression Inventory-II, across six population samples

BACKGROUND

The Beck Depression Inventory (BDI) and BDI-II (revised version) are some of the most widely used and comparable self-report scales for assessing the presence and severity of depressive symptoms in many countries. However, although the relative mean score of each symptom in different countries may vary, the cultural differences of BDI-II symptoms for each item have not been previously studied.

AIMS

To examine the overall picture of the magnitude of the symptoms in the Finnish population, and compare the relative mean score of each symptom between all published population-based samples from different countries fulfilling the search criteria.

METHOD

We conducted a search for population-based studies reporting BDI-II item, using Scopus, PsycINFO and PubMed, and five population-based samples were identified. Relative average scores for each item of the scale were calculated for the Finnish population and five populations from other countries. Meta-regression methods were used to test the differences in the relative score of each symptom between each country separately, and results were then visually compared with spider charts.

RESULTS

We found significant differences in several BDI-II item scores between countries: lower indecisiveness, higher changes in sleep pattern and higher irritability in Finland; higher loss of pleasure in Norway; higher loss of interest in the Dominic Republic; higher self-criticalness and feelings of punishment in Mexico; and higher sadness in Japan.

CONCLUSIONS

Based on the study fundings and including all currently published population-based samples with BDI-II scores, cultural differences in depressive symptoms should be considered when interpreting BDI-II item scores.

Effect of frequent assessment of suicidal thinking on its incidence and severity: high-resolution real-time monitoring study

Researchers, clinicians and patients are increasingly using real-time monitoring methods to understand and predict suicidal thoughts and behaviours. These methods involve frequently assessing suicidal thoughts, but it is not known whether asking about suicide repeatedly is iatrogenic. We tested two questions about this approach: (a) does repeatedly assessing suicidal thinking over short periods of time increase suicidal thinking, and (b) is more frequent assessment of suicidal thinking associated with more severe suicidal thinking? In a real-time monitoring study (n = 101 participants, n = 12 793 surveys), we found no evidence to support the notion that repeated assessment of suicidal thoughts is iatrogenic.

Attention-deficit hyperactivity disorder in people with intellectual disability: statistical approach to developing a bespoke screening tool

BACKGROUND

Attention-deficit hyperactivity disorder (ADHD) is common among people with intellectual disability. Diagnosing ADHD in this clinically and cognitively complex and diverse group is difficult, given the overlapping psychiatric and behavioural presentations. Underdiagnoses and misdiagnoses leading to irrational polypharmacy and worse health and social outcomes are common. Diagnostic interviews exist, but are cumbersome and not in regular clinical use.

AIMS

We aimed to develop a screening tool to help identify people with intellectual disability and ADHD.

METHOD

A prospective cross-sectional study, using STROBE guidance, invited all carers of people with intellectual disability aged 18-50 years open to the review of the psychiatric team in a single UK intellectual disability service (catchment population: 150 000). A ten-item questionnaire based on the DSM-V ADHD criteria was circulated. All respondents' baseline clinical characteristics were recorded, and the DIVA-5-ID was administered blinded to the individual questionnaire result. Fisher exact and multiple logistic regressions were conducted to identify relevant questionnaire items and the combinations that afforded best sensitivity and specificity for predicting ADHD.

RESULTS

Of 78 people invited, 39 responded (26 men, 13 women), of whom 30 had moderate-to-profound intellectual disability and 38 had associated comorbidities and on were medication, including 22 on psychotropics. Thirty-six screened positive for ADHD, and 24 were diagnosed (16 men, eight women). Analysis showed two positive responses on three specific questions to have 88% sensitivity and 87% specificity, and be the best predictor of ADHD.

CONCLUSIONS

The three-question screening is an important development for identifying ADHD in people with intellectual disability. It needs larger-scale replication to generate generalisable results.

Cultural research deconstructs the psychosocial construct 'expressed emotion'

Expressed emotion (EE) is a highly researched psychosocial construct. Cultural research challenges the assumption that high family criticism is a universal determinant of poor outcome, especially for chronic illness. The concept of warmth, an original component of EE, was dropped owing to the complexity of its measurement. Warmth has now been resurrected as an important predictor of good patient outcome. Cultural scrutiny and appropriate adaptation of any psychosocial construct is necessary before its acceptance into the medical lexicon of healthcare.

Network structure of ICD-11 adjustment disorder: a cross-cultural comparison of three African countries

BACKGROUND

Adjustment disorder is one of the most widespread mental disorders worldwide. In ICD-11, adjustment disorder is characterised by two main symptom clusters: preoccupation with the stressor and failure to adapt. A network analytic approach has been applied to most ICD-11 stress-related disorders. However, no study to date has explored the relationship between symptoms of adjustment disorder using network analysis.

AIMS

We aimed to explore the network structure of adjustment disorder symptoms and whether its structure replicates across questionnaire versions and samples.

METHOD

A network analysis was conducted on adjustment disorder symptoms as assessed by the Adjustment Disorder-New Module (ADNM-8) and an ultra-brief version (ADNM-4) using data from 2524 participants in Nigeria (n = 1006), Kenya (n = 1018) and Ghana (n = 500).

RESULTS

There were extensive connections between items across all samples in both ADNM versions. Results highlight that preoccupation symptoms seem to be more prominent in terms of edges strengths (i.e. connections) and had the highest centrality in all networks across samples and ADNM versions. Comparisons of network structure invariance revealed one difference between Nigeria and Ghana in both ADNM versions. Importantly, the ADNM-8 global strength was similar in all networks whereas in the ADNM-4 Kenya had a higher global strength score compared with Nigeria.

CONCLUSIONS

Results provide evidence of the coherence of adjustment disorder in ICD-11 as assessed by the ADNM questionnaire. The prominence of preoccupation symptoms in adjustment disorder highlights a possible therapeutic target to alleviate distress. There is a need to further replicate the network structure of adjustment disorder in non-African samples.

Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach

OBJECTIVE

Patterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to generate personalized predictions of symptom trajectories among patients receiving treatment for EDs, and compared model performance to a simpler logistic regression prediction model.

METHOD

Participants were adolescent girls and adult women (N = 333) presenting for residential ED treatment. Self-report progress assessments were completed at admission, discharge, and weekly throughout treatment. Latent growth mixture modeling previously identified three latent treatment response trajectories (Rapid Response, Gradual Response, and Low-Symptom Static Response) and assigned a trajectory type to each patient. Machine learning models (support vector, k-nearest neighbors) and logistic regression were applied to these data to predict a patient's response trajectory using data from the first 2 weeks of treatment.

RESULTS

The best-performing machine learning model (evaluated via area under the receiver operating characteristics curve [AUC]) was the radial-kernel support vector machine (AUCRADIAL = 0.94). However, the more computationally-intensive machine learning models did not improve predictive power beyond that achieved by logistic regression (AUCLOGIT = 0.93). Logistic regression significantly improved upon chance prediction (MAUC[NULL] = 0.50, SD = .01; p <.001).

DISCUSSION

Prediction of ED treatment response trajectories is feasible and achieves excellent performance, however, machine learning added little benefit. We discuss the need to explore how advance knowledge of expected trajectories may be used to plan treatment and deliver individualized interventions to maximize treatment effects.

Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

Background

Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out.

Objective

This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR.

Methods

We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach).

Results

Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects.

Conclusions

MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

Matching research and practice: Prediction of individual patient progress and dropout risk for basic routine outcome monitoring

OBJECTIVE

Despite evidence showing that systematic outcome monitoring can prevent treatment failure, the practical conditions that allow for implementation are seldom met in naturalistic psychological services. In the context of limited time and resources, session-by-session evaluation is rare in most clinical settings. This study aimed to validate innovative prediction methods for individual treatment progress and dropout risk based on basic outcome monitoring.

METHODS

Routine data of a naturalistic psychotherapy outpatient sample were analyzed (N = 3902). Patients were treated with cognitive behavioral therapy with up to 95 sessions (M = 39.19, SD = 16.99) and assessment intervals of 5-15 sessions. Treatment progress and dropout risk were predicted in two independent analyses using the nearest neighbor method and least absolute shrinkage and selection operator regression, respectively.

RESULTS

The correlation between observed and predicted patient progress was r = .46. Intrinsic treatment motivation, previous inpatient treatment, university-entrance qualification, baseline impairment, diagnosed personality disorder, and diagnosed eating disorder were identified as significant predictors of dropout, explaining 11% of variance.

CONCLUSIONS

Innovative outcome prediction in naturalistic psychotherapy is not limited to elaborated progress monitoring. This study demonstrates a reasonable approach for tracking patient progress as long as session-by-session assessment is not a valid standard.

Using dynamical systems mathematical modeling to examine the impact emotional expression on the therapeutic relationship: A demonstration across three psychotherapeutic theoretical approaches

Objective: The purpose of this paper is to describe an approach to dynamical systems (DS) using a set of differential equations, and how an application of these equations can be used to address a critical element of the therapeutic relationship. Using APA's Three Approaches to Psychotherapy with a Female Client: The Next Generation and Three Approaches to Psychotherapy with a Male Client: The Next Generation videos, DS models were created for each of the six sessions with expert clinicians (Judith Beck, Leslie Greenberg, and Nancy McWilliams) from the three theoretical approaches. Method: A second-by-second observational coding system of the emotional exchanges of the therapists and clients was used as the data for the equations. Results: DS modeling allowed for a side-by-side comparison between the three approaches as well as between the two clients. Examining the graphs created by plotting the results of the DS equations (in particular, phase-space portraits) revealed that there were similarities among the three theoretical approaches, and there were notable differences between the two clients. Conclusions: DS modelling can provide researchers and clinicians with a powerful tool to investigate the complex phenomenon that is psychotherapy.

Methodological Challenges and Proposed Solutions for Evaluating Opioid Policy Effectiveness

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when evaluating the effects of opioid policies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms.

Smoking cessation in severe mental illness: combined long-term quit rates from the UK SCIMITAR trials programme

Smoking contributes to health inequalities for people with severe mental illness (SMI). Although smoking cessation interventions are effective in the short term, there are few long-term trial-based estimates of abstinence. The SCIMITAR trials programme includes the largest trial to date of a smoking cessation intervention for people with SMI, but this was underpowered to detect anticipated long-term quit rates. By pooling pilot and full-trial data we found that quit rates were maintained at 12 months (OR = 1.67, 95% CI 1.02-2.73, P = 0.04). Policymakers can now be confident that bespoke smoking cessation interventions produce successful short- and long-term quitting.

Accuracy of individual and combined risk-scale items in the prediction of repetition of self-harm: multicentre prospective cohort study

BACKGROUND

Individuals attending emergency departments following self-harm have increased risks of future self-harm. Despite the common use of risk scales in self-harm assessment, there is growing evidence that combinations of risk factors do not accurately identify those at greatest risk of further self-harm and suicide.

AIMS

To evaluate and compare predictive accuracy in prediction of repeat self-harm from clinician and patient ratings of risk, individual risk-scale items and a scale constructed with top-performing items.

METHOD

We conducted secondary analysis of data from a five-hospital multicentre prospective cohort study of participants referred to psychiatric liaison services following self-harm. We tested predictive utility of items from five risk scales: Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS, Modified SAD PERSONS, Barratt Impulsiveness Scale and clinician and patient risk estimates. Area under the curve (AUC), sensitivity, specificity, predictive values and likelihood ratios were used to evaluate predictive accuracy, with sensitivity analyses using classification-tree regression.

RESULTS

A total of 483 self-harm episodes were included, and 145 (30%) were followed by a repeat presentation within 6 months. AUC of individual items ranged from 0.43-0.65. Combining best performing items resulted in an AUC of 0.56. Some individual items outperformed the scale they originated from; no items were superior to clinician or patient risk estimations.

CONCLUSIONS

No individual or combination of items outperformed patients' or clinicians' ratings. This suggests there are limitations to combining risk factors to predict risk of self-harm repetition. Risk scales should have little role in the management of people who have self-harmed.

Predicting future courses of psychotherapy within a grouped LASSO framework

Objective: There is a paucity of studies examining the experience of clients who undergo multiple courses of psychotherapy. Conducted within a large practice research network, this study demonstrated that returning therapy clients comprise a considerable portion of the clinical population in university counseling settings, and identified variables associated with return to therapy. Method: Utilizing data spanning 2013 to 2017, statistical variable selection for predicting return to therapy was conducted via grouped least absolute shrinkage and selection operator (grouped LASSO) applied to logistic regression. The grouped LASSO approach is described in detail to facilitate learning and replication. The paper also addresses methodological considerations related to this approach, such as sample size, generalizability, as well as general strengths and limitations. Results: Attendance rate, duration of initial treatment course, social anxiety, perceived social support, academic distress, and alcohol use were identified as predictive of return to therapy. Conclusions: Findings could help inform more cost-effective policies for session limits (e.g., extending session limits for clients with social anxiety), referral decisions (e.g., for clients with alcohol use problems), and appointment reminders (based on the association between poor attendance rate and return to therapy). Taking into account the many reasons that can explain why clients do or do not return to therapy, these findings also could inform clinicians' early case conceptualizations and treatment interventions.

Trajectories of changes in distress in counseling center clients: a replication study

Objective: Latent growth mixture modeling (LGMM) and latent class growth analysis (LCGA) are methods of identifying subgroups of individuals with similar trajectories during the course of psychotherapy. Due to inconsistent methodology, previous LGMM/LCGA psychotherapy research has led to inconsistent findings. The purpose of this study was to contribute to our understanding of individual differences in change trajectories during psychotherapy using LGMM/LCGA by attempting to replicate a previous study by Owen et al. (2015. Trajectories of change in psychotherapy. Journal of Clinical Psychology, 71(9), 817-827). Method: This study used LGMM/LCGA to model trajectories of change in a sample of 2538 psychotherapy clients at a university student counseling center. This was a secondary analysis of naturalistically-collected outcome data using The Behavioral Health Measure. Results: LGMM models did not converge. A 2-class LCGA model was selected based on fit statistics and parsimony. One class was labeled as Slow and Steady Change Before Plateau, whereas the other was labeled as Early Rapid Change Before Plateau. We also extended these findings by considering variables associated with class membership. Conclusions: These classes followed similar trajectories to two of the classes identified by Owen et al. These results indicate that latent trajectory modeling may lead to replicable findings. Furthermore, these results have implications for managing expectations about change in psychotherapy.

The Dobson-Rawlins pact and the National Institute for Health and Care Excellence: impact of political independence on scientific and legal accountability

This analysis considers whether the independence of the National Institute for Health and Care Excellence (NICE), while safeguarding guidelines from commercial lobbying, may render NICE legally and scientifically unaccountable. The analysis examines the role of judicial reviews and stakeholder consultations in place of peer review in light of current debates concerning the depression guideline.

Statistical power in partially nested designs probing multilevel mediation

Objective: Analysis of the intermediate behaviors and mechanisms through which innovative therapies come to shape outcomes is a critical objective in many areas of psychotherapy research because it supports the iterative exploration, development and refinement of theories and therapies. Despite widespread interest in the intermediate behaviors and mechanisms that convey treatment effects, there is limited guidance on how to effectively and efficiently design studies to detect such mediated effects in the types of partially nested designs that commonly arise in psychotherapy research. In this study, we develop statistical power formulas to identify requisite sample sizes and guide the planning of studies probing mediation under two- and three-level partially nested designs. Method: We investigate multilevel mediation in partially nested structures and models for two- and three-level designs. Results: Well-powered studies probing mediation using partially nested designs will typically require moderate to large sample sizes or moderate to large effects. Discussion: We implement these formulas in the R package PowerUpR and a simple Shiny web application (https://poweruprshiny.shinyapps.io/PartiallyNestedMediationPower/) and demonstrate their use to plan studies using partially nested designs.

Trajectories of depressive symptoms and adult educational and employment outcomes

BACKGROUND

Depressive symptoms show different trajectories throughout childhood and adolescence that may have different consequences for adult outcomes.

AIMS

To examine trajectories of childhood depressive symptoms and their association with education and employment outcomes in early adulthood.

METHOD

We estimated latent trajectory classes from participants with repeated measures of self-reported depressive symptoms between 11 and 24 years of age and examined their association with two distal outcomes: university degree and those not in employment, education or training at age 24.

RESULTS

Our main analyses (n = 9399) yielded five heterogenous trajectories of depressive symptoms. The largest group found (70.5% of participants) had a stable trajectory of low depressive symptoms (stable-low). The other four groups had symptom profiles that reached full-threshold levels at different developmental stages and for different durations. We identified the following groups: childhood-limited (5.1% of participants) with full-threshold symptoms at ages 11-13; childhood-persistent (3.5%) with full-threshold symptoms at ages 13-24; adolescent onset (9.4%) with full-threshold symptoms at ages 17-19; and early-adult onset (11.6%) with full-threshold symptoms at ages 22-24. Relative to the majority 'stable-low' group, the other four groups all exhibited higher risks of one or both adult outcomes.

CONCLUSIONS

Accurate identification of depressive symptom trajectories requires data spanning the period from early adolescence to early adulthood. Consideration of changes in, as well as levels of, depressive symptoms could improve the targeting of preventative interventions in early-to-mid adolescence.

Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems

Concomitant use of multiple drugs for therapeutic purposes is known as “polypharmacy situations,” which has been recognized as an important social problem recently. In polypharmacy situations, each drug not only induces adverse events (AEs) but also increases the risk of AEs due to drug–drug interactions (DDIs). The proportion of AEs caused by DDIs is estimated to be around 30% of unexpected AEs. The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of DDI, and therefore, patients on multiple drugs are usually excluded. However, unlike pre-marketing randomized clinical trials, in clinical practice (= post marketing), many patients use multiple drugs. The spontaneous reporting system is one of the significant sources drug safety surveillance in post-marketing. Commonly, signals of potential drug-induced AEs detected from this source are validated in real-world settings. Recently, not only methodological studies on signal detection of “single” drug, but also on several methodological studies on signal detection of DDIs have been conducted. On the other hand, there are few articles that systematically summarize the statistical methodology for signal detection of DDIs. Therefore, this article reviews the studies on the latest statistical methodologies from classical methodologies for signal detection of DDIs using spontaneous reporting system. This article describes how to calculate for each detection method and the major findings from the published literatures about DDIs. Finally, this article presented several limitations related to the currently used methodologies for signal detection of DDIs and suggestions for further studies.

Average area under the curve: An alternative method for quantifying the dental caries experience in longitudinal studies

OBJECTIVES

Field-traditional decayed, missing, filled surfaces (dmfs) increments require complete follow-up, only using initial and final visits. Repeated dmfs scores complicate sophisticated statistical models, limiting their utility. Elsewhere, area under the curve (AUC) uses all repeated measures to summarize data. This study applied AUC methodology to caries data, creating average AUCs for dmfs trajectories (dmfs^aAUC ) and comparing increments and dmfs^aAUC values.

METHODS

Longitudinal data were obtained from high-caries risk (i.e. poor, rural, African American community in Perry County, Alabama) infants, 8 to 18 months at baseline. Baseline and five annual visual oral examinations provided dmfs scores. Differences in baseline and final dmfs scores constituted increments. The trapezoidal rule was applied to dmfs trajectories to calculate AUC values which were adjusted for varying follow-up times, producing dmfs^aAUC values. Participants sharing incremental or dmfs^aAUC values had their trajectories and second caries measurements compared. Within-participant increment and dmfs^aAUC differences were evaluated (paired t test, α = 0.05). Comparative analyses required complete follow-up.

RESULTS

The dmfs^aAUC provided forty-eight additional person-years, increasing the potential sample size by 20% (N = 85). Sixty-six children, 5.7 to 6.3 year-olds at study's end, contributed 121 331 person-days to five-year increment and dmfs^aAUC calculations. Trajectories and dmfs^aAUC values varied for participants with equivalent increments; comparable trajectories and different increments resulted from participants with similar dmfs^aAUC values. Within-participant disease amounts were similar.

CONCLUSIONS

When desired, dmfs^aAUC can replace increments as a more data-inclusive summary of longitudinal caries burden, incorporating intermediate visits, incomplete follow-up and time.

Suicidal behaviour among persons with attention-deficit hyperactivity disorder

BACKGROUND

Persons diagnosed with attention-deficit hyperactivity disorder (ADHD) have been found to have an increased risk of suicidal behaviour, but the pathway remains to be thoroughly explored.AimsTo determine whether persons with ADHD are more likely to present with suicidal behaviour (i.e. suicide attempts and deaths by suicide) if they have a comorbid psychiatric disorder.

METHOD

Using nationwide registers covering the entire population of Denmark, this cohort study of 2.9 million individuals followed from 1 January 1995 until 31 December 2014, covers more than 46 million person-years. All persons aged ≥10 years with Danish-born parents were identified and persons with a diagnosis of ADHD were compared with persons without. Incidence rate ratios (IRRs) were calculated by Poisson regression, with adjustments for sociodemographics and parental suicidal behaviour.

RESULTS

Persons with ADHD were followed for 164 113 person-years and 697 suicidal outcomes were observed. This group was found to have an IRR of suicidal behaviour of 4.7 (95% CI, 4.3-5.1) compared with those without ADHD. Persons with ADHD only had a 4.1-fold higher rate (95% CI, 3.5-4.7) when compared with those without any psychiatric diagnoses. For persons with ADHD and comorbid disorders the IRR was higher yet (IRR: 10.4; 95% CI, 9.5-11.4).

CONCLUSIONS

This study underlines the link between ADHD and an elevated rate of suicidal behaviour, which is significantly elevated by comorbid psychiatric disorders. In sum, these results suggest that persons with ADHD and comorbid psychiatric disorders are targets for suicide preventive interventions.Declaration of interestNone.

Statistical methodology for constructing gestational age-related charts using cross-sectional and longitudinal data: The INTERGROWTH-21st project as a case study

Most studies aiming to construct reference or standard charts use a cross-sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z-scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness-of-fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age-specific reference or standard centiles for cross-sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH-21^st Project, ie, newborn weight (cross-sectional) and fetal head circumference (longitudinal) data as examples.

Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)

BACKGROUND

Ultrasound measures are valuable for epidemiologic studies of risk factors for growth restriction. Longitudinal measurements enable investigation of rates of change and identification of windows where growth is impacted more acutely. However, missing data can be problematic in these studies, limiting sample size, ability to characterise windows of vulnerability, and in some instances creating bias. We sought to compare a parametric linear mixed model (LMM) approach to multiple imputation in this setting with multiple imputation by chained equation (MICE) methodology.

METHODS

Ultrasound scans performed for clinical purposes were abstracted from women in the LIFECODES birth cohort (n = 1003) if they were close in time to three study visits (median 18, 26, and 35 weeks' gestation). We created imputed datasets using LMM and MICE and calculated associations between demographic factors and ultrasound parameters cross-sectionally and longitudinally. Results were compared with a complete-case analysis.

RESULTS

Most participants had ultrasounds at 18 weeks' gestation, and ~50% had measurements at 26 and 35 weeks; 100% had birthweight. Associations between demographic factors and ultrasound measures were similar in magnitude, but more precise, when either imputed datasets were used, compared with a complete-case analysis, in both the cross-sectional or longitudinal analyses.

CONCLUSIONS

MICE, though ignoring the non-linear features of the trajectory and within subject correlation, is able to provide reasonable imputation of foetal growth data when compared to LMM. Because it simultaneously imputes missing covariate data and does not require specification of variance structure as in LMM, MICE may be preferable for imputation in this setting.

A relational perspective on the association between working alliance and treatment outcome

Objective: Evidence is inconclusive on whether variability in alliance ratings within or between therapists is a better predictor of treatment outcome. The objective of the present study was to explore between and within patient and therapist variability in alliance ratings, reciprocity among them, and their significance for treatment outcome. Method: A large primary care psychotherapy sample was used. Patient and therapist ratings of the working alliance at session three and patient ratings of psychological distress pre-post were used for analyses. A one-with-many analytical design was used in order to address problems associated with nonindependence. Results: Within-therapist variation in alliance ratings accounted for larger shares of the total variance than between-therapist variation in both therapist and patient ratings. Associations between averaged patient and therapist ratings of the alliance for the individual therapists and their average treatment outcome were weak but the associations between specific alliance ratings and treatment outcome within therapies were strong. Conclusions: The results indicated a substantial dyadic reciprocity in alliance ratings. Within-therapist variation in alliance was a better predictor of treatment outcome than between-therapist variation in alliance ratings.

Daily dynamic assessment and modelling of intersession processes in ambulatory psychotherapy: A proof of concept study

Background: the typical mode of assessment in studies on intersession processes (ISP) in psychotherapy is using cross-sectional or weekly measurements. Daily dynamics of intersession processes have not yet been studied. Method: intersession process data from 22 ambulatory psychotherapy cases were collected in a naturalistic study with high temporal resolution, resulting in a total of 1026 daily measurements. Multilevel vector autoregressive (VAR) modelling was applied to discover the temporal course and causal influences among intersession processes. Centrality analysis was applied to discover unique functions of various intersession process variables. Results: a group-level network structure was discovered, offering first insights on the role of different intersession processes during psychotherapy. Centrality analysis revealed unique roles for various aspects of the intersession process. Temporal distance from the last session had only weak influence on the ISP. Conclusions: using short, daily measures, the unique role of various aspects of the ISP were uncovered. Some aspects of the ISP, like recalling session contents or reflection on future session contents, are facilitators of overall ISP intensity. Other aspects like thoughts on payment or appointments or negative treatment-related emotions are likely to suppress ISP.

Accounting for twin births in sample size calculations for randomised trials

BACKGROUND

Including twins in randomised trials leads to non-independence or clustering in the data. Clustering has important implications for sample size calculations, yet few trials take this into account. Estimates of the intracluster correlation coefficient (ICC), or the correlation between outcomes of twins, are needed to assist with sample size planning. Our aims were to provide ICC estimates for infant outcomes, describe the information that must be specified in order to account for clustering due to twins in sample size calculations, and develop a simple tool for performing sample size calculations for trials including twins.

METHODS

ICCs were estimated for infant outcomes collected in four randomised trials that included twins. The information required to account for clustering due to twins in sample size calculations is described. A tool that calculates the sample size based on this information was developed in Microsoft Excel and in R as a Shiny web app.

RESULTS

ICC estimates ranged between -0.12, indicating a weak negative relationship, and 0.98, indicating a strong positive relationship between outcomes of twins. Example calculations illustrate how the ICC estimates and sample size calculator can be used to determine the target sample size for trials including twins.

CONCLUSIONS

Clustering among outcomes measured on twins should be taken into account in sample size calculations to obtain the desired power. Our ICC estimates and sample size calculator will be useful for designing future trials that include twins. Publication of additional ICCs is needed to further assist with sample size planning for future trials.

A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes

Objective: Routine outcome monitoring benefits treatment by identifying potential no change and deterioration. The present study compared two methods of identifying early change and their ability to predict negative outcomes on self-report symptom and wellbeing measures. Method: 1467 voluntary day patients participated in a 10-day group Cognitive Behaviour Therapy (CBT) program and completed the symptom and wellbeing measures daily. Early change, as defined by (a) the clinical significance method and (b) longitudinal modelling, was compared on each measure. Results: Early change, as defined by the simpler clinical significance method, was superior at predicting negative outcomes than longitudinal modelling. The longitudinal modelling method failed to detect a group of deteriorated patients, and agreement between the early change methods and the final unchanged outcome was higher for the clinical significance method. Conclusions: Therapists could use the clinical significance early change method during treatment to alert them of patients at risk for negative outcomes, which in turn could allow therapists to prevent those negative outcomes from occurring.

SLC6A3 polymorphism and response to methylphenidate in children with ADHD: A systematic review and meta-analysis

Methylphenidate (MPH) is the most commonly used treatment for attention-deficit hyperactivity disorder (ADHD) in children. However, the response to MPH is not similar in all patients. This meta-analysis investigated the potential role of SLC6A3 polymorphisms in response to MPH in children with ADHD. Clinical trials or naturalistic studies were selected from electronic databases. A meta-analysis was conducted using a random-effects model. Cohen's d effect size and 95% confidence intervals (CIs) were determined. Sensitivity analysis and meta-regression were performed. Q-statistic and Egger's tests were conducted to evaluate heterogeneity and publication bias, respectively. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was used to assess the quality of evidence. Sixteen studies with follow-up periods of 1-28 weeks were eligible. The mean treatment acceptability of MPH was 97.2%. In contrast to clinical trials, the meta-analysis of naturalistic studies indicated that children without 10/10 repeat carriers had better response to MPH (Cohen's d: -0.09 and 0.44, respectively). The 9/9 repeat polymorphism had no effect on the response rate (Cohen's d: -0.43). In the meta-regression, a significant association was observed between baseline severity of ADHD, MPH dosage, and combined type of ADHD in some genetic models. Sensitivity analysis indicated the robustness of our findings. No publication bias was observed in our meta-analysis. The GRADE evaluations revealed very low levels of confidence for each outcome of response to MPH. The results of clinical trials and naturalistic studies regarding the effect size between different polymorphisms of SLC6A3 were contradictory. Therefore, further research is recommended.

The Multitheoretical List of Therapeutic Interventions - 30 items (MULTI-30)

Objective: To develop a brief version of the Multitheoretical List of Therapeutic Interventions (MULTI-60) in order to decrease completion time burden by approximately half, while maintaining content coverage. Study 1 aimed to select 30 items. Study 2 aimed to examine the reliability and internal consistency of the MULTI-30. Study 3 aimed to validate the MULTI-30 and ensure content coverage. Method: In Study 1, the sample included 186 therapist and 255 patient MULTI ratings, and 164 ratings of sessions coded by trained observers. Internal consistency (Chronbach's alpha and McDonald's omega) was calculated and confirmatory factor analysis was conducted. Psychotherapy experts rated content relevance. Study 2 included a sample of 644 patient and 522 therapist ratings, and 793 codings of psychotherapy sessions. In Study 3, the sample included 33 codings of sessions. A series of regression analyses was conducted to examine replication of previously published findings using the MULTI-30. Results: The MULTI-30 was found valid, reliable, and internally consistent across 2564 ratings examined across the three studies presented. Conclusion: The MULTI-30 a brief and reliable process measure. Future studies are required for further validation. Clinical or methodological significance of this article: The MULTI-30, developed and validated in this study, is a valid, reliable, and cost-effective brief measure which could be used to assess patients, therapists, and observers' perceptions of use of interventions from eight major therapeutic approaches. The MULTI-30 could be used to examine the role of use of specific interventions on process and outcome of different treatment modalities. It could also be used as a clinical tool in teaching, training, and supervision.