title: Child maltreatment and incident mental disorders in middle and older ages: a retrospective UK Biobank cohort study
body: Following the baseline interviews, a series of measurements, including height, weight, and blood pressure, were collected along with urine and blood samples. Body mass index (BMI) was derived from weight/heightmeasured using standardized equipment and used to classify individuals as underweight (<18.5 kg/m), normal weight (18.5 to 24.9 kg/m), overweight (25 to 29.9 kg/m), or obese (>30.0 kg/m). Biomarkers were measured at a dedicated central laboratory between 2014 and 2017. In this study, we have selected triglyceride (TG), a lipid found to be associated with mood disorders [], and C-reactive protein (CRP), a marker of systemic inflammation and indicator of a stress response []. Details of these measures and assay performances can be found online in the UK Biobank showcase and protocol [].
  Recruitment was conducted by sending letters to home addresses obtained from national health records, and follow-up data were obtained via linkage to routine health records, web-based questionnaires and repeat assessment clinics attended by a sub-group ( https://www.ukbiobank.ac.uk/ ). At the baseline assessment clinic, participants completed a self-administered questionnaire on general health and a more in-depth face to face interview on medical history. Self-reported variables included ethnicity, education level, sleep duration, television viewing time, smoking status, and alcohol intake. Deprivation level was measured via the Townsend area deprivation index which sums a score derived from assets, income, and household factors applied to postcode of residence []. The International Physical Activity Questionnaire (IPAQ) was used to assess physical activity levels [].
  This was a retrospective cohort study using data from UK Biobank. Child maltreatment was recalled by participants after they were recruited into the UK Biobank. Incident mental disorders were ascertained through record linkage after the baseline assessment. In this study, participants with prevalent/prior mental disorders at baseline assessment were excluded. This is to reduce recall bias and to focus on incident events in middle and older ages. The timeline of measurements is shown in Supplementary Figure 1
  Child maltreatment was assessed through a web-based questionnaire conducted in August 2017 []. Overall 339,229 participants who provided an email address were invited and approximately half (n = 157,348) of participants completed the online questionnaire. Respondents were generally younger, more likely to be female and white, and had healthier lifestyles compared with non-respondents []. The web-based questionnaire included the Childhood Trauma Screener (CTS) [], a shortened version of the Childhood Trauma Questionnaire (CTQ) []. It consists of a 5-point Likert scale for each of five types of child maltreatment, physical abuse, physical neglect, emotional abuse, emotional neglect, and sexual abuse, and has been validated against the CTQ with good overall correlation (r = 0.88) and satisfactory type-specific correlations (r = 0.55–0.87) []. The CTQ is a widely used instrument for measuring child maltreatment and has been validated against actual records of abuse and neglect []. The threshold values on the Likert scale derived from a validation study [] were used to define the presence or absence of each type of child maltreatment. In this study, the primary exposure variable was the number of types of child maltreatment, and was categorised as 0, 1, 2, and ≥3 as there were not sufficient number of events to analyse participants with 4 or 5 types of maltreatment separately.
  Mental disorders were ascertained through individual-level record linkage to primary care records available for 45% of the UK Biobank cohort until May 2017 for Scotland, September 2017 for Wales, and August 2017 for England. The detailed linkage procedures relating to primary care records are available online ( http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/primary_care_data.pdf ). READ codes in the primary care record were mapped to ICD-10 codes. The outcomes in this study included: all mental disorders (ICD-10 F01-F99), dementia (F01-F03), substances abuse (F10-F19), including alcohol-related, disorders, schizophrenia (F20-F29), affective disorder (F30-F39), depression disorder (F32-F33), anxiety disorder (F40-F48), PTSD (F43.1), and behavioural syndrome (F50-F59) encompassing eating and sleep disorders, as well as syndromes associated with physiological disturbances and physical factors.
  2.4 Statistical analyses
  [7] Maughan B
  Rutter M Retrospective reporting of childhood adversity: Issues in assessing long-term recall. Cox proportional hazard models were used to analyse the associations between child maltreatment and incident mental disorders. The results are reported as hazard ratios (HRs) with 95% confidence intervals (CIs). In the primary analysis we only included participants who did not have any mental disorder diagnosis at or before baseline assessment. This can reduce reverse causation because prevalent mental disorders could affect child maltreatment recall []. The models were adjusted for age at baseline assessment, sex, and ethnicity as potential confounders. Deprivation and education could be the consequence of child maltreatment (mediator) or affecting child maltreatment recall (confounder), but were primarily hypothesised to be mediators in the analysis. The hypothetical causal assumption is shown in Supplementary Figure 2 . Since excluding participants who had mental disorder diagnosis prior to baseline assessment may induce selection bias, we conducted sensitivity analyses including all participants with complete data. In these sensitivity analyses, follow-up was assumed to start when they were at the age of 30 years and all mental disorder diagnoses after that were included.
  Moderation analyses were conducted by: sex, age group (38–50, 51–60, and 61–72 years; approximate tertiles), education level (with vs. without university degree), and area-based deprivation index (≥ vs. < median), physical activity (<600 vs ≥600 MET-min/week), obesity (BMI ≥30kg/m2 vs <30kg/m2), alcohol drinking (≤14 vs >14 units/week), binge drinking (binge drinker [average drinking unit per occasion >6 for female and >8 for male] vs non-binger drinker), ability to confide (never/almost never vs sometimes or more), and frequency of social visits (never/almost never vs sometimes or more), and household factors (household size, living with partner, living with children, living with relatives, living with unrelated person/people). We selected these factors because age could affect the reporting of maltreatment, sex could affect the type of maltreatment, and socioeconomic and lifestyle factors might influence the long-term effects of maltreatment experiences. Both stratified (sub-group) and interaction analyses were conducted for mental disorders, depression disorder, anxiety disorder, and behavioural syndrome as these outcomes had sufficient events for subgroup analyses. We also conducted sensitivity analyses including all mental disorder diagnoses for the moderation analysis of sex, age group, and education level as these factors should be the same or very similar between age of 30 and the baseline assessment.
  We studied five groups of potential mediators: PA and TV (physical activity >600 MET-min/week [binary variable], time spent watching television [continuous variable]), smoking and drinking (any smoking and alcohol drinking >14 units a week [binary variable]), social factors (able to confide and social visit frequency [binary variables]), CVD risk factors (obesity and blood pressure [continuous variable]), and CRP and TG (continuous variable). All potential mediators were selected because of their potential associations with both child maltreatment and mental health. These groups of mediators were adjusted for in the Cox models to examine whether, and to what extent, the HRs between child maltreatment and mental disorders were attenuated.
  [23] Tingley D
  Yamamoto T
  Hirose K
  Keele L
  Imai K Mediation: R package for causal mediation analysis. [24] Richiardi L
  Bellocco R
  Zugna D Mediation analysis in epidemiology: methods, interpretation and bias. [25] Gelfand LA
  MacKinnon DP
  DeRubeis RJ
  Baraldi AN Mediation analysis with survival outcomes: accelerated failure time vs. proportional hazards models. Formal mediation analysis based on counterfactual framework was then conducted []. Counterfactual framework defines direct (non-mediated) and indirect (mediated) effects and are more robust against various limitations of traditional adjustment-based mediation analysis, such as mediator-outcome confounding affected by exposure []. To reduce multicollinearity and overadjustment, the potential mediators were selected using a stepwise approach. Firstly, mental disorder was regressed by child maltreatment and all potential mediators and confounders in a Weibull regression model with robust standard errors. Weibull regression was chosen because of its superior statistical properties in mediation analysis []. Potential mediators were then selected based on their associations with mental disorder after mutual adjustments. The selected potential mediators were then regressed by child maltreatment count and other covariates (mediator model) in either logistic (for binary mediators) or linear (for other mediators) models adjusting for other mediators and confounders. The outcome and mediator models were then combined to compute the mediation proportions based on natural indirect effect (NIE) divided by total effect (TE) for each participant which was then averaged. Quasi-Bayesian estimation with 1,000 iterations were used for estimating the 95% CI and p-values of the NIE and TE.
  Analyses were conducted in R version 4.0.2 using packages survival and mediation.