Editorial Perspective: Misaligned incentives in mental health research - the case for Registered Reports.

Current incentive structures reward mental health researchers for producing positive, novel, and clean results. This can promote questionable research practices which contribute to a distorted evidence base, in turn limiting progress in mental health research. Registered Reports (RRs) offer a solution to realign the incentives towards conducting high-quality, rigorous, and accurate studies, by preventing publication and reporting biases. However, the uptake of RRs in mental health research has so far been limited. This editorial perspective highlights the advantages of RRs for mental health research, before discussing potential challenges and how they can be addressed. Greater uptake of RRs in mental health research could help to promote a fairer research culture, limit publication bias and questionable research practices, and ultimately, improve understanding of mental health

Triangulating evidence on the role of perceived versus objective experiences of childhood adversity in psychopathology

Childhood adversities, such as maltreatment, bullying, and socioeconomic deprivation, are well-established risk factors for psychopathology. Recent evidence suggests that it is the perceived, rather than objective (i.e., actual) experience of childhood adversity which is associated with psychopathology (Danese & Widom, 2020). However, it is unclear whether perceptions of childhood adversity cause psychopathology, as this cannot be tested ethically or feasibly with randomised controlled trials. Triangulation can instead be used to improve causal inference in observational research, by integrating evidence across multiple approaches with different sources of bias

Early risk factors for joint trajectories of bullying victimisation and perpetration

Bullying victimisation is a prevalent stressor associated with serious health problems. To inform intervention strategies, it is important to understand children’s patterns of involvement in bullying victimisation and perpetration across development, and identify early risk factors for these developmental trajectories. We analysed data from the Millennium Cohort Study (N = 14,525; 48.6% female, 82.6% White), a representative birth cohort of British children born in 2000–2002 across the UK. Bullying victimisation and perpetration were assessed via child, mother, and teacher reports at ages 5, 7, 11, and 14 years. Early risk factors (child emotional, cognitive, and physical vulnerabilities, and adverse family environments) were assessed at ages 9 months, 3, and 5 years. Using k-means for longitudinal data, we identified five joint trajectories of victimisation and perpetration across ages 5, 7, 11, and 14: uninvolved children (59.78%), early child victims (9.96%), early adolescent victims (15.07%), early child bullies (8.01%), and bully- victims (7.19%). Individual vulnerabilities (e.g., emotional dysregulation, cognitive difficulties) and adverse family environments (maternal psychopathology, low income) in pre-school years independently forecast multiple trajectories of bullying involvement. Compared to victims, bully-victims were more likely to be male, have cognitive difficulties, and experience harsh discipline and low income. Interventions addressing these risk factors (e.g., via accessible mental health care, stigma-based interventions, or programs to support low-income families) may help to prevent bullying involvement and its associated sequelae

Research Review: A guide to computing and implementing polygenic scores in developmental research

The increasing availability of genotype data in longitudinal population- and family-based samples provides opportunities for using polygenic scores (PGS) to study developmental questions in child and adolescent psychology and psychiatry. Here, we aim to provide a comprehensive overview of how PGS can be generated and implemented in developmental psycho(patho)logy, with a focus on longitudinal designs. As such, the paper is organized into three parts: First, we provide a formal definition of polygenic scores and related concepts, focusing on assumptions and limitations. Second, we give a general overview of the methods used to compute polygenic scores, ranging from the classic approach to more advanced methods. We include recommendations and reference resources available to researchers aiming to conduct PGS analyses. Finally, we focus on the practical applications of PGS in the analysis of longitudinal data. We describe how PGS have been used to research developmental outcomes, and how they can be applied to longitudinal data to address developmental questions

Protecting against researcher bias in secondary data analysis:Challenges and potential solutions

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society’s most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data

Childhood Maltreatment and Mental Health Problems:A Systematic Review and Meta-Analysis of Quasi-Experimental Studies

Objective: Childhood maltreatment is associated with mental health problems, but the extent to which this relationship is causal remains unclear. To strengthen causal inference, the authors conducted a systematic review and meta-analysis of quasi-experimental studies examining the relationship between childhood maltreatment and mental health problems. Methods: A search of PubMed, PsycINFO, and Embase was conducted for peer-reviewed, English-language articles from database inception until January 1, 2022. Studies were included if they examined the association between childhood maltreatment and mental health problems using a quasi-experimental method (e.g., twin/sibling differences design, children of twins design, adoption design, fixed-effects design, random-intercept cross-lagged panel model, natural experiment, propensity score matching, or inverse probability weighting). Results: Thirty-four quasi-experimental studies were identified, comprising 54,646 independent participants. Before quasi-experimental adjustment for confounding, childhood maltreatment was moderately associated with mental health problems (Cohen’s d=0.56, 95% CI=0.41, 0.71). After quasi-experimental adjustment, a small association between childhood maltreatment and mental health problems remained (Cohen’s d=0.31, 95% CI=0.24, 0.37). This adjusted association between childhood maltreatment and mental health was consistent across different quasi-experimental methods, and generalized across different psychiatric disorders. Conclusions: These findings are consistent with a small, causal contribution of childhood maltreatment to mental health problems. Furthermore, the findings suggest that part of the overall risk of mental health problems in individuals exposed to maltreatment is due to wider genetic and environmental risk factors. Therefore, preventing childhood maltreatment and addressing wider psychiatric risk factors in individuals exposed to maltreatment could help to prevent psychopathology

Population vs Individual Prediction of Poor Health from Results of Adverse Childhood Experiences Screening

Importance: Adverse childhood experiences (ACEs) are well-established risk factors for health problems in a population. However, it is not known whether screening for ACEs can accurately identify individuals who develop later health problems. Objective: To test the predictive accuracy of ACE screening for later health problems. Design, Setting, and Participants: This study comprised 2 birth cohorts: the Environmental Risk (E-Risk) Longitudinal Twin Study observed 2232 participants born during the period from 1994 to 1995 until they were aged 18 years (2012-2014); the Dunedin Multidisciplinary Health and Development Study observed 1037 participants born during the period from 1972 to 1973 until they were aged 45 years (2017-2019). Statistical analysis was performed from May 28, 2018, to July 29, 2020. Exposures: ACEs were measured prospectively in childhood through repeated interviews and observations in both cohorts. ACEs were also measured retrospectively in the Dunedin cohort through interviews at 38 years. Main Outcomes and Measures: Health outcomes were assessed at 18 years in E-Risk and at 45 years in the Dunedin cohort. Mental health problems were assessed through clinical interviews using the Diagnostic Interview Schedule. Physical health problems were assessed through interviews, anthropometric measurements, and blood collection. Results: Of 2232 E-Risk participants, 2009 (1051 girls [52%]) were included in the analysis. Of 1037 Dunedin cohort participants, 918 (460 boys [50%]) were included in the analysis. In E-Risk, children with higher ACE scores had greater risk of later health problems (any mental health problem: relative risk, 1.14 [95% CI, 1.10-1.18] per each additional ACE; any physical health problem: relative risk, 1.09 [95% CI, 1.07-1.12] per each additional ACE). ACE scores were associated with health problems independent of other information typically available to clinicians (ie, sex, socioeconomic disadvantage, and history of health problems). However, ACE scores had poor accuracy in predicting an individual's risk of later health problems (any mental health problem: area under the receiver operating characteristic curve, 0.58 [95% CI, 0.56-0.61]; any physical health problem: area under the receiver operating characteristic curve, 0.60 [95% CI, 0.58-0.63]; chance prediction: area under the receiver operating characteristic curve, 0.50). Findings were consistent in the Dunedin cohort using both prospective and retrospective ACE measures. Conclusions and Relevance: This study suggests that, although ACE scores can forecast mean group differences in health, they have poor accuracy in predicting an individual's risk of later health problems. Therefore, targeting interventions based on ACE screening is likely to be ineffective in preventing poor health outcomes

Gene-environment correlations and causal effects of childhood maltreatment on physical and mental health: a genetically informed approach.

BACKGROUND: Childhood maltreatment is associated with poor mental and physical health. However, the mechanisms of gene-environment correlations and the potential causal effects of childhood maltreatment on health are unknown. Using genetics, we aimed to delineate the sources of gene-environment correlation for childhood maltreatment and the causal relationship between childhood maltreatment and health. METHODS: We did a genome-wide association study meta-analysis of childhood maltreatment using data from the UK Biobank (n=143 473), Psychiatric Genomics Consortium (n=26 290), Avon Longitudinal Study of Parents and Children (n=8346), Adolescent Brain Cognitive Development Study (n=5400), and Generation R (n=1905). We included individuals who had phenotypic and genetic data available. We investigated single nucleotide polymorphism heritability and genetic correlations among different subtypes, operationalisations, and reports of childhood maltreatment. Family-based and population-based polygenic score analyses were done to elucidate gene-environment correlation mechanisms. We used genetic correlation and Mendelian randomisation analyses to identify shared genetics and test causal relationships between childhood maltreatment and mental and physical health conditions. FINDINGS: Our meta-analysis of genome-wide association studies (N=185 414) identified 14 independent loci associated with childhood maltreatment (13 novel). We identified high genetic overlap (genetic correlations 0·24-1·00) among different maltreatment operationalisations, subtypes, and reporting methods. Within-family analyses provided some support for active and reactive gene-environment correlation but did not show the absence of passive gene-environment correlation. Robust Mendelian randomisation suggested a potential causal role of childhood maltreatment in depression (unidirectional), as well as both schizophrenia and ADHD (bidirectional), but not in physical health conditions (coronary artery disease, type 2 diabetes) or inflammation (C-reactive protein concentration). INTERPRETATION: Childhood maltreatment has a heritable component, with substantial genetic correlations among different operationalisations, subtypes, and retrospective and prospective reports of childhood maltreatment. Family-based analyses point to a role of active and reactive gene-environment correlation, with equivocal support for passive correlation. Mendelian randomisation supports a (primarily bidirectional) causal role of childhood maltreatment on mental health, but not on physical health conditions. Our study identifies research avenues to inform the prevention of childhood maltreatment and its long-term effects. FUNDING: Wellcome Trust, UK Medical Research Council, Horizon 2020, National Institute of Mental Health, and National Institute for Health Research Biomedical Research Centre.This work was supported by the Wellcome Trust (Grant refs: 214322/Z/18/Z, 104036/Z/14/Z, 204623/Z/16/Z, and 217065/Z/19/Z). VW was funded by the Bowring Research Fellowship from St. Catharine’s College, Cambridge and Wellcome Trust Collaborative Award (Grant Ref: 214322/Z/18/Z). ASFK and AM are supported by Wellcome Trust Grant 104036/Z/14/Z. ASFK is also supported by an ESRC Postdoctoral Fellowship (Grant ref: ES/V011650/1). ML is supported by the scholarship from the China Scholarship Council (No. 201706990036). The work of CC has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 707404 and grant agreement No 848158 (EarlyCause Project). MHvIJ is supported by the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant No. 024.001.003, Consortium on Individual Development) and by a Spinoza Prize of the Netherlands Organization for Scientific Research. HMS and MRM are supported by the Medical Research Council and the University of Bristol (MC_UU_00011/7) and by the National Institute for Health Research (NIHR) Biomedical Research Centre at the University Hospitals Bristol National Health Service Foundation Trust and the University of Bristol. HMS is also supported by the European Research Council (Grant ref: 758813 MHINT). CMN is supported by the National Institute for Mental Health NIMH R01MH106595 and the Center of Excellence for Stress and Mental Health (CESAMH), Veterans Affairs San Diego. AJG and SB are supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 204623/Z/16/Z). TMM and RB are supported by the NIMH (R01MH117014, TMM; K23MH120437, RB).The research was conducted in association with the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, and the NIHR Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care. This research was possible due to two applications to the UK Biobank: Projects 20904 and 23787. This research was co-funded by the NIHR Cambridge Biomedical Research Centre and a Marmaduke Sheild grant. The UK Medical Research Council and Wellcome (Grant Ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The study website contains details of all data available through a fully searchable data dictionary (http://www.bristol.ac.uk/alspac/researchers/our-data/). Part of this data was collected using REDCap, see the REDCap website for details https://projectredcap.org/resources/citations/). The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Centre, Rotterdam; the Erasmus University Rotterdam; ZonMw; the Netherlands Organization for Scientific Research (NWO); and the Ministry of Health, Welfare and Sport. The authors gratefully acknowledge the contribution of all children and parents, general practitioners, hospitals, midwives and pharmacies involved in the Generation R Study. The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Erasmus School of Social and Behavioural Sciences at Erasmus University Rotterdam; the Municipal Health Service Rotterdam area, Rotterdam; the Rotterdam Homecare Foundation, Rotterdam; and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam