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Is Mental Health Competence in Childhood Associated With Health Risk Behaviors in Adolescence? Findings From the UK Millennium Cohort Study

Open AccessPublished:June 21, 2020DOI:https://doi.org/10.1016/j.jadohealth.2020.04.023

      Abstract

      Purpose

      Promoting positive mental health, particularly through enhancing competencies (such as prosocial behaviors and learning skills), may help prevent the development of health risk behaviors in adolescence and thus support future well-being. Few studies have examined how mental health competencies in childhood are associated with adolescent health risk behaviors, which could inform preventative approaches.

      Methods

      Using UK Millennium Cohort Study data (n = 10,142), we examined how mental health competence (MHC) measured at the end of elementary school (11 years) is associated with self-reported use of cigarettes, e-cigarettes, alcohol, illegal drugs, antisocial behavior, and sexual contact with another young person at age 14 years. A latent measure of MHC was used, capturing aspects of prosocial behavior and learning skills, categorized as high MHC, high–moderate MHC, moderate MHC, and low MHC. Logistic and multinomial regression estimated odds ratios and relative risk ratios for binary and categorical outcomes, respectively, before and after adjusting for confounders. Weights accounted for sample design and attrition and multiple imputation for item missingness.

      Results

      Those with low, moderate, or high-moderate MHC at age 11 years were more likely to have taken part in health risk behaviors at age 14 years compared with those with high MHC. The largest associations were seen for low MHC with binge drinking (relative risk ratio: 1.6 [95% confidence interval: 1.1–2.4]), having tried cigarettes (odds ratio: 2.2 [95% confidence interval: 1.6-3.1]) and tried illegal drugs (odds ratio: 2.0 [95% confidence interval: 1.3-3.1) after adjusting for confounders (which attenuated results but largely maintained significant findings).

      Conclusions

      MHC in late childhood is associated with health risk behaviors in midadolescence. Interventions that increase children's MHC may support healthy development during adolescence, with the potential to improve health and well-being through to adulthood.

      Keywords

      Implications and Contribution
      Higher childhood mental health competence (MHC), reflecting prosocial behaviors and learning skills, is associated with lower likelihood of health risk behaviors in UK adolescents. These competencies have been improved in trials in school and early years' settings and thus hold potential for improving health and well-being across the life course.
      See Related Editorial on p.625
      A number of behaviors such as cigarette smoking, substance abuse, and engaging in risky sexual behaviors are associated with increased risks to health and well-being [
      • Spring B.
      • Moller A.C.
      • Coons M.J.
      Multiple health behaviours: Overview and implications.
      ,
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ]. Although the tendency to explore different behaviors and identities may be driven by normative adolescent developmental processes, many such “health risk behaviors” are initiated during adolescence and may cluster and track into adulthood, affecting health and social outcomes such as education and employment [
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ,
      • Viner R.M.
      • Ross D.
      • Hardy R.
      • et al.
      Life course epidemiology: Recognising the importance of adolescence.
      ,
      • Akasaki M.
      • Ploubidis G.B.
      • Dodgeon B.
      • Bonell C.P.
      The clustering of risk behaviours in adolescence and health consequences in middle age.
      ]. Although the United Kingdom has made progress in reducing the prevalence of particular health risk behaviors in adolescents since the turn of the century, these remain a concern in health research and policy [
      • Viner R.M.
      • Ross D.
      • Hardy R.
      • et al.
      Life course epidemiology: Recognising the importance of adolescence.
      ,
      • Martorano B.
      • de Neubourg C.
      • Natali L.
      • Bradshaw J.
      Child well-being in economically rich countries: changes in the first decade of the 21st century.
      ,
      • Shah R.
      • Hagell A.
      • Cheung R.
      International comparisons of health and wellbeing in adolescence and early adulthood: Research Report February 2019.
      ].
      Positive mental health has been highlighted as a potentially important factor that may support better health and social outcomes throughout the life course [
      • Friedli L.
      • Organization W.H.
      Mental health, resilience and inequalities, Copenhagen, Denmark: World Health Organization Regional Office for Europe.
      ,
      • Goldfeld S.
      • Kvalsvig A.
      • Incledon E.
      • O'connor M.
      Epidemiology of positive mental health in a national census of children at school entry.
      ]. It is a state of well-being that goes beyond the simple absence of illness or infirmity to include strengths and skills such as “efficient perception of reality, self-knowledge, exercise of voluntary control over behavior, self-esteem and self-acceptance, the ability to form affectionate relationships, and productivity” [
      • Barry M.M.
      Addressing the determinants of positive mental health: Concepts, evidence and practice.
      ]. Research has demonstrated that higher levels of competencies, such as social competence, are associated with a lower prevalence of health risk behaviors in young people [
      • Sørlie M.-A.
      • Hagen K.
      • Ogden T.
      Social competence and antisocial behavior: Continuity and distinctiveness across early adolescence.
      ,
      • Hernández Serrano O.
      • Espada J.P.
      • Guillén Riquelme A.
      Relationship of the prosocial behaviour, the problem-solving skills and the use of drugs amongst adolescents.
      ,
      • Simões C.
      • Matos M.
      • Social EdPA.
      Risk behaviors in adolescents with special needs: Are social and emotional competences important?.
      ,
      • Ferreira M.
      • Simões C.
      • Matos M.G.
      • et al.
      The role of social and emotional competence on risk behaviors in adolescence.
      ,
      • Longman-Mills S.
      • Carpenter K.
      Interpersonal competence and sex risk behaviours among Jamaican adolescents.
      ,
      • Stepp S.D.
      • Pardini D.A.
      • Loeber R.
      • Morris N.A.
      The relation between adolescent social competence and young adult delinquency and educational attainment among at-risk youth: The mediating role of peer delinquency.
      ,
      • Carlo G.
      • Crockett L.J.
      • Wilkinson J.L.
      • Beal S.J.
      The longitudinal relationships between rural adolescents’ prosocial behaviors and young adult substance use.
      ]. However, little research has examined how competencies in childhood are related to the development of health risk behaviors in adolescence, which could inform interventions aiming to reduce harmful behaviors and improve long-term well-being. Furthermore, competencies have seldom been explored in combination.
      A multiple component competency-based conceptualization of positive mental health, referred to as mental health competence (MHC), has been developed using a competence framework describing a range of developmental tasks and abilities that children in early to midchildhood should have acquired [
      • Kvalsvig A.
      • O'Connor M.
      • Redmond G.
      • Goldfeld S.
      The unknown citizen: Epidemiological challenges in child mental health.
      ,
      • Masten A.S.
      • Curtis W.J.
      Integrating competence and psychopathology: Pathways toward a comprehensive science of adaptation in development.
      ]. This has been operationalized in early childhood in Australia [
      • Goldfeld S.
      • Kvalsvig A.
      • Incledon E.
      • O'connor M.
      Epidemiology of positive mental health in a national census of children at school entry.
      ,
      • Goldfeld S.
      • Kvalsvig A.
      • Incledon E.
      • et al.
      Predictors of mental health competence in a population cohort of Australian children.
      ,
      • O’Connor E.
      • O’Connor M.
      • Gray S.
      • Goldfeld S.
      Profiles of mental health competence and difficulties as predictors of children’s early learning.
      ] and in later childhood in the United Kingdom [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ]. The UK-based measure of MHC captures a combination of competencies, composed of items exploring prosocial behaviors and learning skills, and has been shown to be cross-sectionally associated with emotional, cognitive, and physical health in children at age 11 years in the Millennium Cohort Study (MCS) [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ]. Notably, this study found that compared with children with high MHC, those with low MHC were more likely to: report unintentional injuries on multiple occasions, be classed as obese, have two or more asthma symptoms, and have lower scores on verbal ability, spatial working memory, and risk taking tests of cognitive development [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ]. Using this measure of MHC [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ], we aimed to establish if MHC in midchildhood was associated with health risk behaviors in adolescence. Specifically, we examined how MHC measured at the end of elementary school (11 years) is related to health risk behaviors at age 14 years in the UK MCS.

      Methods

      Sample

      Data were from the UK MCS, a nationally representative contemporary cohort of 18,818 children, born in the United Kingdom between September 2000 and January 2002, of which 18,296 were singletons first surveyed at 9 months. Subsequent surveys have been carried out at 3 (n = 15,381 singletons), 5 (n = 15,041 singletons), 7 (n = 13,681 singletons), 11 (n = 13,112 singletons), and 14 (n = 11,576 singletons) years [
      Centre for longitudinal studies
      Millennium cohort study: A guide to the datasets First, Second, Third, Fourth and Fifth Surveys.
      ,
      Centre for longitudinal studies
      Millennium cohort study sixth sweep (MCS6) Technical report.
      ]. Of the children who had measures of MHC at age 11 years (n = 12,082), 90% (n = 10,142) were present at the 14-year survey. Of these cohort members, 3,266 were missing data on one or more of the outcome or confounding variables; 3,265 had sufficient auxiliary information to have missing information multiply imputed. This was carried out under a missing at random assumption using multivariate imputation by chained equations in 20 datasets, providing an analytic sample of 10,141. Survey weights were used to account for sampling design and attrition at the 14-year survey.
      Ethical approval was received from a Research Ethics Committee at each study survey [
      Centre for longitudinal studies
      Millennium cohort study: A guide to the datasets First, Second, Third, Fourth and Fifth Surveys.
      ,
      Centre for longitudinal studies
      Millennium cohort study sixth sweep (MCS6) Technical report.
      ]. Secondary data analyses do not require additional ethics approval. Further information about the MCS can be found elsewhere (http://www.cls.ioe.ac.uk/MCS). Data were downloaded from the UK Data Service, University of Essex, and University of Manchester, in May 2017.

      Measures

      Exposure: MHC at age 11 years

      We used a categorical measure of MHC at age 11 years, created using a latent class analysis approach described in an earlier paper [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ]. In brief, the measure was developed using maternal reports of eight positively framed questions from the validated Strengths and Difficulties Questionnaire [
      • Goodman R.
      The Strengths and Difficulties Questionnaire: A research note.
      ], and characterizes children according to the combination of prosocial behaviors and learning skills. It consists of four classes: “high MHC” (high prosocial behaviors and learning skills), “high-moderate MHC” (high prosocial behaviors and moderate learning skills), “moderate MHC” (moderate prosocial behaviors and learning skills), and “low MHC” (moderate prosocial behaviors and low learning skills). The methods, including latent class model factors, class selection process, and analysis probability estimates, are given in more detail elsewhere [
      • Hope S.
      • Rougeaux E.
      • Deighton J.
      • et al.
      Mental health competence and indicators of health in eleven year olds: Findings from the UK Millennium Cohort Study.
      ]. In addition, the questions included in the MHC measure and latent class category probabilities are shown in Supplementary file A (Table A1).

      Outcomes: health-risk behaviors at age 14 years

      Several health risk behaviors known to commonly start in early adolescence were included as outcomes.

      E-cigarettes

      E-cigarettes is a binary variable created from answers to a series of statements about e-cigarette usage, categorized as either having never used or tried e-cigarettes or having used them at least once. The latter included having used e-cigarettes in the past (prevalence: 12%), smoking e-cigarettes occasionally (3%), and smoking e-cigarettes every day (.4%).

      Cigarettes

      Cigarettes is a binary variable created from answers to a series of statements about smoking behaviors, categorized as either having never smoked cigarettes or having tried at least once. The latter includes those who have tried only once (8%), used to smoke (2%), sometimes smoke (2%), smoke one to six cigarettes a week (1%), and smoke more than six cigarettes a week (1%).

      Alcohol consumption

      A single, three-category measure of alcohol consumption was created using questions about whether the cohort member had ever consumed an alcoholic drink (more than a few sips) and if they ever had five or more alcoholic drinks at a time (a drink defined as is half a pint of lager, beer or cider, one alcopop, a small glass of wine, or a measure of spirits). It comprised having never tried drinking alcohol, having ever consumed an alcoholic drink but never tried binge drinking (defined as consuming five or more alcoholic drinks at a time; 39%), and having ever consumed an alcoholic drink including binge drinking (11%).

      Illegal drug use

      Illegal drug use is defined as having ever tried cannabis (4%) or other illegal drugs (such as ecstasy, cocaine, speed; .6%).

      Antisocial behavior

      A single binary measure of antisocial behavior captured whether the cohort member had ever engaged in one or more of the following: theft from a shop (prevalence 3%) or a person (such as a mobile phone or money 1%), graffiti (3%), public property damages (3%), carrying a knife or weapon (2%), using or hitting someone with a weapon (1%), or breaking and entering (.2%).

      Sexual contact

      Respondents were asked if they had ever kissed or cuddled another young person; if they said yes, they were asked if they had done any of the following: touched the other person's private parts (prevalence: 5%), had their private parts touched (5%), performed oral sex (2%), received oral sex (2%), and had sexual intercourse (2%). If they answered yes to any of these, we classed this as having had sexual contact.

      Confounding

      We adjusted for a series of potential confounders; these were defined as measures that were associated with both the exposure and the outcomes but were not on the causal pathway between the two. Confounders that remained unchanged or stable during the time of the cohort study were assessed at 9 months: cohort member's sex, cohort member's ethnicity (white, mixed, Indian, Pakistani and Bangladeshi, Black/Black British, and other ethnic group), maternal age at cohort member's birth (14–19, 20–24, 25–29, 30–34, and 35+ years), maternal academic attainment (degree+, diploma, A levels, General Certificate of Secondary Education [GCSE] grade A∗ to C, GCSE D to G, other, and none), and any maternal smoking in pregnancy.
      Confounders that were more likely to change over time were assessed at 7 years (the most recent sweep collected before both MHC and the outcomes): family structure (couple, reconstituted, or lone parent), household income (Organisation for Economic Co-operation and Development [OECD] equivalized income quintiles), maternal mental health (Kessler-6 scale [
      • Kessler R.C.
      • Andrews G.
      • Colpe L.J.
      • et al.
      Short screening scales to monitor population prevalences and trends in non-specific psychological distress.
      ], summed and dichotomized as none-low and moderate-severe psychological distress [
      • Hansen K.
      • Joshi H.
      Millennium cohort study second survey: A user's guide to initial findings, London, UK: Centre for Longitudinal Studies, Institute of Education, University of London.
      ]), cohort member siblings (has any siblings in the household at age 7 years, parent report), main respondent alcohol consumption at age 7 years (self-reported number of drinks per day: never drinks, 1–2, 3–4, 5–6, 7+).
      Two further confounders were also included: parent–child relationship quality, assessed only at age 3 years (using the Pianta Child-Parent Relationship Scale score, as reported by the main caregiver [
      • Johnson J.
      • Atkinson M.
      • R R.
      Millennium cohort study: Psychological, developmental and health inventories user guide to the data.
      ]), and puberty reported at age 11 years (using the Petersen Puberty scale [
      • Petersen A.C.
      • Crockett L.
      • Richards M.
      • Boxer A.
      A self-report measure of pubertal status: Reliability, validity, and initial norms.
      ], parental report).

      Statistical analyses

      Descriptive analyses investigated associations between MHC, health risk behaviors at age 14 years, and confounders. Logistic regression was used to estimate odds ratios (ORs) for binary outcomes (cigarettes, e-cigarettes, illegal drugs, antisocial behavior, and sexual contact), and multinomial regression was used to estimate relative risk ratios (RRRs) for the three-category outcome (alcohol), according to MHC classes. We estimated ORs and RRRs (and 95% confidence intervals [CIs]) before and after adjusting for confounding. Analyses were carried out in Stata/SE 13.1 (StataCorp LP, TX).

      Results

      Table 1 presents characteristics of the observed (unimputed) MCS sample, the complete case sample, and the imputed sample that was used in the main analysis. Compared with the observed MCS sample, the complete case sample was marginally less likely to display risk taking behaviors and to be from minority ethnic groups and more likely to be from a disadvantaged socioeconomic background. The analytic (imputed) sample resembled the observed MCS sample (Table 1).
      Table 1Prevalence of mental health competence (MHC), health-risk behaviors, and confounders in 14-year-olds in the UK Millennium Cohort Study
      Observed sample (unimputed; n = 10,142)Complete case (n = 6,876)Analytic (imputed) sample (n = 10,141)
      NWeighted % (95% CI)NWeighted % (95% CI)Weighted % (95% CI)
      MHC at age 11 years
       High MHC (high PS; high LS)3,99736.4 (35.0–37.8)2,72537.3 (35.8–38.9)36.4 (35.0–37.8)
       High-moderate MHC (high PS; moderate LS)3,60436.1 (34.5–37.4)2,52136.8 (35.3–38.2)36.1 (34.8–37.4)
       Moderate MHC (moderate PS; moderate LS)1,85719.0 (18.0–20.1)1,25119.3 (19.2–20.4)19.0 (17.9–20.1)
       Low MHC (moderate PS; low LS)6848.5 (7.7–9.5)3796.6 (5.9–7.5)8.5 (7.6–9.4)
       Missing1,422
       Total not at 11 year sweep (n)5,868
      Health risk behaviors at age 14 years (CM)
       Ever consumed alcohol
      Yes but not binge drinking3,48138.6 (37.1–40.1)2,60242.2 (40.9–43.6)38.6 (37.1–40.1)
      Yes including binge drinking92310.6 (9.7–11.7)64210.3 (9.3–11.4)10.8 (9.8–11.8)
      Missing397
       Ever tried cigarettes1,39516.6 (16.6–17.6)90915.2 (13.9–16.6)16.9 (15.9–18.0)
      Missing431
       Ever tried e-cigarettes1,46617.5 (16.4–18.6)98917.0 (15.8–18.3)17.8 (16.6–18.9)
      Missing426
       Ever tried illegal drugs4345.5 (5.0–6.2)2704.8 (4.1–5.5)5.7 (5.1–6.4)
      Missing402
       Antisocial behavior8369.5 (8.8–10.3)5529.1 (8.2–10.1)9.7 (8.9–10.5)
      Missing434
       Ever had sexual contact with another young person5306.2 (5.5–6.9)3585.8 (5.1–6.7)6.2 (5.5–7.0)
      Missing424
      Total not at 14 year sweep (n)7,404
      Confounders (M)
       Cohort member sex
      Male5,06351.8 (50.6–53.0)3,37950.5 (48.9–52.0)51.8 (50.6–53.1)
      Female5,07948.2 (47.0–49.4)3,49749.5 (48.0–51.1)48.2 (47.0–49.4)
      Missing0
       Cohort member ethnicity
      White8,30582.9 (80.1–85.3)6,19290.1 (88.3–91.7)82.9 (80.3–85.4)
      Mixed4685.3 (4.6–6.1)2674.5 (3.8–5.3)5.3 (4.6–6.1)
      Indian2452.0 (1.5–2.6)961.0 (.8–1.4)2.0 (1.4–2.5)
      Pakistani and Bangladeshi6174.2 (2.8–6.2)1311.3 (.9–2.1)4.2 (2.5–5.8)
      Black or Black British2893.5 (2.5–4.7)1222.2 (1.4–3.2)3.5 (2.4–4.6)
      Other ethnic group2172.2 (1.7–2.8)68.9 (.6–1.3)2.2 (1.7–2.8)
      Missing1
       Maternal age at cohort member birth (years)
      14–196029.8 (8.7–10.9)3708.6 (7.5–9.8)9.7 (8.6–10.8)
      20–241,59219.0 (17.6–20.6)92615.6 (14.2–17.2)19.1 (17.6–20.6)
      25–292,73528.3 (27.1–29.6)1,92428.9 (27.3–30.5)28.3 (27.0–29.5)
      30–343,16227.9 (26.7–29.2)2,37430.8 (29.4–32.2)27.9 (26.6–29.2)
      35+1,74015.0 (13.9–16.1)1,28216.2 (15.0–17.5)15.0 (13.9–16.2)
      Missing311
       Maternal smoking in pregnancy
      No7,90775.7 (74.2–77.2)5,58777.7 (76.1–79.2)75.4 (73.9–76.9)
      Yes1,91324.3 (22.8–25.8)1,28922.3 (20.8–23.9)24.6 (23.1–26.1)
      Missing322
       Maternal academic attainment (at 9 months)
      Supplemented with information collected at 3 years if not complete at 9 months.
      Degree+2,07215.0 (13.3–16.9)1,60717.8 (15.9–19.9)15.0 (13.2–16.8)
      Diploma9898.0 (7.4–8.7)7719.6 (8.8–10.4)8.0 (7.3–8.7)
      A-levels1,0318.6 (8.0–9.2)79110.1 (9.3–11.0)8.6 (7.9–9.2)
      GCSE grade A
      Supplemented with information collected at 3 years if not complete at 9 months.
      -C
      3,33134.7 (33.0–36.4)2,35337.2 (35.3–39.2)34.7 (33.0–36.3)
      GCSE D-G99712.5 (11.4–13.8)63611.4 (10.3–12.7)12.5 (11.3–13.7)
      Other2712.5 (2.1–3.1)1001.3 (1.0–1.8)2.5 (2.0–3.0)
      None1,42818.7 (17.2–20.4)61812.5 (11.1–13.9)18.8 (17.2–20.4)
      Missing23
       Parent–child relationship (at age 3 years)
      In the analyses as continuous variables.
      Mean Pianta score8,46963.9 (63.7–64.2)6,87664.2 (63.9–64.4)63.7 (63.5–64.0)
      Missing1,673
       Family structure (at age 7 years)
      Natural parents7,20370.0 (68.4–71.5)5,34672.4 (70.7–74.1)68.3 (66.7–69.9)
      Reconstituted5407.3 (6.6–8.2)3837.2 (6.4–8.0)7.8 (6.8–8.9)
      Lone parent1,72922.7 (21.3–24.1)1,14820.4 (18.9–22.0)24.0 (22.6–25.5)
      Missing670
       Cohort member has siblings in the household (at age 7 years)
      No1,06411.7 (10.8–12.5)77011.6 (10.7–12.6)11.5 (10.6–12.3)
      Yes8,41288.4 (87.5–89.2)6,10688.4 (87.4–89.4)88.6 (87.7–89.4)
      Missing666
       Income quintiles (at age 7 years)
      1st (highest income)2,03118.8 (17.0–20.7)1,61921.0 (19.1–23.1)17.0 (15.2–18.7)
      2nd2,02919.9 (18.6–21.2)1,68623.0 (21.7–24.5)17.8 (16.7–19.0)
      3rd1,94920.5 (19.2–21.9)1,47721.2 (19.8–22.7)19.6 (18.3–20.9)
      4th1,80720.5 (19.2–21.9)1,19818.8 (17.4–20.3)21.7 (20.5–23.0)
      5th (lowest income)1,65120.4 (18.7–22.1)89616.0 (14.6–17.4)24.0 (22.1–25.8)
      Missing675
       Maternal mental health (at age 7 years)
      No-low distress6,07567.4 (66.0–68.7)4,89569.2 (67.7–70.6)66.2 (64.7–67.6)
      Medium/high distress2,64532.7 (31.3–34.0)1,98130.8 (29.4–32.3)33.8 (32.3–35.3)
      Missing1,422
       Main parent's alcohol consumption (drinks per day, at age 7 years)
      Never drinks2,33723.5 (21.5–25.6)1,09316.2 (14.8–17.6)23.6 (21.5–25.6)
      1–24,02639.0 (37.4–40.6)3,14044.1 (42.4–45.8)39.0 (37.4–40.6)
      3–42,21422.6 (21.4–23.8)1,70424.4 (23.3–25.6)22.5 (21.3–23.7)
      5+1,30014.9 (13.8–16.1)93915.3 (14.1–16.6)14.9 (13.8–16.1)
      Missing265
       Cohort member's level of pubertal development (at age 11 years)
      In the analyses as continuous variables.
      Not started3333.2 (2.7–3.8)2243.2 (2.7–3.9)3.2 (2.7–3.8)
      Barely started8,84893.7 (93.0–94.4)6,46093.9 (93–94.6)93.7 (93.0–94.4)
      Definitely started2823.1 (2.7–3.5)1922.9 (2.5–3.5)3.0 (2.6–3.5)
      Missing679
      CI = confidence interval; CM = cohort member respondent; LS = learning skills; M = main respondent; PS = prosocial behaviors.
      a Supplemented with information collected at 3 years if not complete at 9 months.
      b In the analyses as continuous variables.
      In the analytic sample, approximately half of cohort members reported that they had tried alcohol by age 14 years, with 11% having engaged in binge drinking. Approximately 17% of cohort members had ever tried smoking cigarettes, and 18% had ever tried e-cigarettes. Trying illegal drugs, displaying antisocial behaviors, and having sexual contact with another young person were less prevalent, ranging from 6% to 10% (Table 1). Most cohort members were categorized as having high MHC (36%) or high-moderate MHC (36%) at age 11 years, with 19% and 9% as having low-moderate and low MHC, respectively.
      Cohort members with low MHC were more likely to be male, have younger mothers (aged <24 years), have mothers with low (GCSE D-G) or no academic qualifications, to be in reconstituted or single-parent families, not to have siblings, to have reached puberty by age 11 years, to be in low-income families, to have mothers with moderate-severe mental distress, to have lower parent–child relationship scores, to have mothers who had smoked in pregnancy, and to have parents who drink three or more drinks on a normal drinking day (Supplementary file A, Table A2).

      MHC and health risk behaviors at 14 years

      Fourteen-year-olds with lower levels of MHC (low, low-moderate, or moderate MHC) at age 11 years were overall more likely to have taken part in health risk behaviors compared with those with high MHC. The likelihood was highest in those with low MHC for all outcomes assessed with the exception of sexual contact with another young person. For example, those with low MHC had twice the relative risk of binge drinking (RRR: 2.0 [95% CI: 1.4–2.9]) and three times the odds of having tried smoking cigarettes (OR: 3.2 [2.3–4.3]). However, results also indicated that 14-year-olds with high-moderate MHC may be somewhat more likely to take part in most health risk behaviors compared with those with moderate MHC. Adjusting for potential confounding factors partially attenuated these results, but risks generally remained elevated (Table 2).
      Table 2Association between mental health competence (MHC) at 11 years and health risk behaviors at 14 years in the Millennium Cohort Study (imputed n = 10,141)
      High MHC (high PS; high LS)High-moderate MHC (high PS; moderate LS)Moderate MHC (moderate PS; moderate LS)Low MHC (moderate PS; low LS)
      Prevalence, weighted, % (95% CIs)
      Ever consumed alcohol
       No56.0 (53.5–58.4)46.8 (44.5–49.0)49.7 (46.7–52.7)46.3 (40.6–52.1)
       Yes-low or moderate consumption35.6 (33.4–37.8)40.8 (38.7–42.9)39.6 (36.7–42.5)40.0 (34.4–45.6)
       Yes-high consumption (binge drinking)8.5 (7.3–9.6)12.4 (10.9–14.0)10.7 (8.8–12.5)13.7 (9.8–17.6)
      Ever tried cigarettes
       No88.3 (86.9–89.6)80.7 (79.0–82.5)83.4 (81.1–85.7)71.2 (66.2–76.3)
       Yes11.7 (10.4–13.1)19.3 (17.5–21.0)16.6 (14.4–18.9)28.8 (23.7–33.9)
      Ever tried e-cigarettes
       No86.1 (84.6–87.7)80.6 (78.8–82.4)81.9 (79.4–84.3)74.3 (69.6–79.0)
       Yes13.9 (12.3–15.4)19.4 (17.6–21.2)18.1 (15.7–20.6)25.7 (21.0–30.5)
      Ever tried illegal drugs
       No96.0 (95.3–96.7)93.4 (92.2–94.5)94.8 (93.3–96.4)89.8 (86.6–93.1)
       Yes4.0 (3.3–4.8)6.6 (5.5–7.8)5.2 (3.7–6.7)10.2 (6.9–13.4)
      Antisocial behavior
       No92.6 (91.5–93.7)89.7 (88.4–91.0)89.6 (88.4–91.0)84.2 (87.5–91.7)
       Yes7.4 (6.3–8.5)10.3 (9.0–11.6)10.4 (8.4–12.5)15.8 (11.9–19.7)
      Ever had sexual contact with another young person
       No95.1 (94.2–96.0)93.0 (91.8–94.2)92.8 (91.4–94.3)93.3 (90.9–95.7)
       Yes4.9 (4.0–5.9)7.0 (5.8–8.2)7.2 (5.7–8.6)6.7 (4.3–9.1)
      Unadjusted regression results
       Relative risk ratios (95% CIs)
       Ever consumed alcohol
      No----
      Yes-low or moderate consumption-1.4 (1.2–1.6)1.3 (1.1–1.5)1.3 (1.0–1.7)
      Yes-high consumption (binge drinking)-1.7 (1.4–2.1)1.4 (1.1–1.8)2.0 (1.4–2.9)
      Odds ratios (95% CIs)
       Ever tried cigarettes
      No----
      Yes1.8 (1.5–2.1)1.5 (1.2–1.8)3.2 (2.3–4.3)
       Ever tried e-cigarettes
      No----
      Yes-1.5 (1.3–1.8)1.4 (1.1–1.7)2.2 (1.6–2.9)
       Ever tried illegal drugs
      No---
      Yes1.7 (1.3–2.3)1.3 (.9–1.8)2.8 (1.8–4.2)
       Antisocial behavior
      No----
      Yes-1.4 (1.2–1.8)1.4 (1.1–1.8)2.5 (1.7–3.5)
       Ever had sexual contact with another young person
      No----
      Yes-1.4 (1.1–1.9)1.5 (1.1–2.0)1.4 (1.0–2.2)
      Adjusted regression results
      Adjusted for cohort member (CM) sex, CM ethnicity, maternal age at cohort member's birth, income at 7 years, maternal academic attainment at 9 months, family structure at 7 years, parent–child relationship at 3 years, maternal mental health at 7 years, having siblings at 7 years, main parent respondent alcohol consumption (drinks per day at 7 years), smoking in pregnancy, and puberty at 11 years.
       Relative risk ratios (95% CIs)
       Ever had an alcoholic drink
      No----
      Yes-low or moderate consumption-1.2 (1.1–1.4)1.1 (1.0–1.3)1.3 (.9–1.7)
      Yes-high consumption (binge drinking)-1.4 (1.1–1.8)1.2 (1.0–1.6)1.6 (1.1–2.4)
      Odds ratios (95% CIs)
       Ever tried cigarettes
      No----
      Yes-1.5 (1.3–1.8)1.3 (1.0–1.6)2.2 (1.6–3.1)
       Ever tried e-cigarettes
      No----
      Yes-1.2 (1.0–1.5)1.1 (.9–1.4)1.4 (1.0–2.0)
       Ever tried illegal drugs
      No----
      Yes-1.4 (1.1–1.9)1.1 (.7–1.6)2.0 (1.3–3.1)
       Any antisocial behavior
      No----
      Yes-1.2 (1.0–1.5)1.2 (.9–1.5)1.9 (1.3–2.7)
       Ever had sexual contact with another young person
      No----
      Yes-1.2 (1.0–1.6)1.3 (.9–1.8)1.1 (.7–1.7)
      CI = confidence interval; LS = learning skills; PS = prosocial behaviors.
      a Adjusted for cohort member (CM) sex, CM ethnicity, maternal age at cohort member's birth, income at 7 years, maternal academic attainment at 9 months, family structure at 7 years, parent–child relationship at 3 years, maternal mental health at 7 years, having siblings at 7 years, main parent respondent alcohol consumption (drinks per day at 7 years), smoking in pregnancy, and puberty at 11 years.
      Analyses were repeated in the observed (unimputed) and complete case samples (not shown). For all outcomes, the results were very similar with patterns remaining the same as those found in the unadjusted results for the imputed sample (reported in Table 2 and the results above).

      Discussion

      A measure of MHC reflecting learning skills and prosocial behaviors in late childhood was associated with health risk behaviors at age 14 years. Those with low MHC, comprising 9% of the sample, were more likely to take part in the health risk behaviors assessed with the exception of sexual contact (which covered a range of sexual behaviors). The results nonetheless indicated a nonlinear association between MHC and health risk behaviors, with a slightly higher likelihood of taking part in health risk behaviors among those with high-moderate MHC compared with moderate MHC. Particular skills or competencies may play different roles in the development of health risk behaviors in adolescence. More research is needed, however, to disentangle these patterns. Overall although results were attenuated, significant differences remained after adjusting for confounders.
      A number of studies have explored the association between similar aspects of competence as those included in our measure of MHC and adolescent health risk behaviors [
      • Sørlie M.-A.
      • Hagen K.
      • Ogden T.
      Social competence and antisocial behavior: Continuity and distinctiveness across early adolescence.
      ,
      • Hernández Serrano O.
      • Espada J.P.
      • Guillén Riquelme A.
      Relationship of the prosocial behaviour, the problem-solving skills and the use of drugs amongst adolescents.
      ,
      • Simões C.
      • Matos M.
      • Social EdPA.
      Risk behaviors in adolescents with special needs: Are social and emotional competences important?.
      ,
      • Ferreira M.
      • Simões C.
      • Matos M.G.
      • et al.
      The role of social and emotional competence on risk behaviors in adolescence.
      ,
      • Longman-Mills S.
      • Carpenter K.
      Interpersonal competence and sex risk behaviours among Jamaican adolescents.
      ,
      • Stepp S.D.
      • Pardini D.A.
      • Loeber R.
      • Morris N.A.
      The relation between adolescent social competence and young adult delinquency and educational attainment among at-risk youth: The mediating role of peer delinquency.
      ,
      • Carlo G.
      • Crockett L.J.
      • Wilkinson J.L.
      • Beal S.J.
      The longitudinal relationships between rural adolescents’ prosocial behaviors and young adult substance use.
      ], although all focused on competence in adolescence (not childhood) and none were UK-based. Several cross-sectional studies have shown that higher levels of prosocial behaviors were associated with lower prevalence of health risk behaviors, such as antisocial behavior, illegal drug use, and sexual contact [
      • Hernández Serrano O.
      • Espada J.P.
      • Guillén Riquelme A.
      Relationship of the prosocial behaviour, the problem-solving skills and the use of drugs amongst adolescents.
      ,
      • Simões C.
      • Matos M.
      • Social EdPA.
      Risk behaviors in adolescents with special needs: Are social and emotional competences important?.
      ,
      • Ferreira M.
      • Simões C.
      • Matos M.G.
      • et al.
      The role of social and emotional competence on risk behaviors in adolescence.
      ,
      • Longman-Mills S.
      • Carpenter K.
      Interpersonal competence and sex risk behaviours among Jamaican adolescents.
      ]. Cross-sectional evidence of an association for problem solving (which relates to critical thinking learning skills) and health risk behaviors was, however, mixed [
      • Hernández Serrano O.
      • Espada J.P.
      • Guillén Riquelme A.
      Relationship of the prosocial behaviour, the problem-solving skills and the use of drugs amongst adolescents.
      ,
      • Simões C.
      • Matos M.
      • Social EdPA.
      Risk behaviors in adolescents with special needs: Are social and emotional competences important?.
      ,
      • Ferreira M.
      • Simões C.
      • Matos M.G.
      • et al.
      The role of social and emotional competence on risk behaviors in adolescence.
      ]. Three studies used longitudinal data to investigate temporal associations between aspects of competence and health risk behaviors. Of these, one found that social and learning competence (as a combined measure) in early adolescence was associated with decreased involvement with deviant peers in later adolescence and less delinquency in adulthood [
      • Stepp S.D.
      • Pardini D.A.
      • Loeber R.
      • Morris N.A.
      The relation between adolescent social competence and young adult delinquency and educational attainment among at-risk youth: The mediating role of peer delinquency.
      ]. Two other studies found that lower social skills in early and mid-adolescence were associated with self-reported antisocial behavior in later adolescence and higher illegal drug use in adulthood, respectively [
      • Sørlie M.-A.
      • Hagen K.
      • Ogden T.
      Social competence and antisocial behavior: Continuity and distinctiveness across early adolescence.
      ,
      • Carlo G.
      • Crockett L.J.
      • Wilkinson J.L.
      • Beal S.J.
      The longitudinal relationships between rural adolescents’ prosocial behaviors and young adult substance use.
      ].
      In terms of prevalence of health risk behaviors in the United Kingdom, no comparable data were identified. Although figures have been reported for some health risk behaviors, these have used different measures of assessment, in other ages groups, or did not cover the whole of the United Kingdom [
      NHS Digital
      Smoking, drinking and drug use among young people in England–2016. Leeds, UK: The Health and Social Care Information Centre (NHS Digital).
      ,
      • Brooks F.
      • Magnusson J.
      • Klemera E.
      • et al.
      HBSC England National Report: Findings from the 2014 HBSC Study for England.
      ,
      • Roe S.
      • Ashe J.
      Young people and crime: Findings from the 2006 Offending, Crime and Justice Survey. Home Office Statistical Bulletin.
      ].

      Strengths and limitations

      We have examined, for the first time in the United Kingdom, the relationship between childhood MHC and adolescent health risk behaviors. There is a paucity of research exploring the association between competencies in childhood and adolescent health behaviors, particularly using combinations of competencies. Our study aimed to address this gap, using contemporary, UK-representative longitudinal data, a range of important health risk behaviors, and a measure of MHC that captured a number of mental health competencies, broadly reflecting prosocial behaviors and learning skills. The range of information available in the MCS enabled us to account for a wide range of possible confounding factors. The majority of the health behaviors examined were unlikely to have been present at age 11 years, thus minimizing the possibility of reverse causality. Sample design and attrition were accounted for with survey weights, and item missingness using multiple imputation and comparisons between complete case and imputed and nonimputed datasets showed similar patterns. The availability of questionnaire items relevant to MHC was relatively limited, only allowing the examination of two domains of MHC to be assessed (prosocial behaviors and learning skills). Nevertheless, these are important components of MHC, which may be amenable to change through intervention and as such are the focus of early years' programs [
      Department for Education
      Statutory framework for the early years foundation stage: Setting the standards for learning, development and care for children from birth to five.
      ]. The MHC items and the health risk behaviors were generated from self-reported maternal and cohort member response, respectively, which could have been affected by response bias because of influences of socioeconomic and contextual factors, memory, health, social desirability, or, in the case of the health risk behavior outcomes, the sensitive nature of the questions [
      • Brener N.D.
      • Billy J.O.G.
      • Grady W.R.
      Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: Evidence from the scientific literature.
      ,
      • Najman J.M.
      • Williams G.M.
      • Nikles J.
      • et al.
      Bias influencing maternal reports of child behaviour and emotional state.
      ]. The health risk behaviors were, however, completed by the cohort members on computer devices, ensuring anonymity and privacy, and this should have minimized the risk of bias [
      • Tourangeau R.
      • Yan T.
      Sensitive questions in surveys.
      ,
      • Bowling A.
      Mode of questionnaire administration can have serious effects on data quality.
      ]. Our research aimed to assess whether MHC was associated with behaviors that may potentially lead to subsequent harms to health and well-being. A range of behaviors was explored and where possible, with varying levels of risk (e.g., we were able to differentiate binge drinking from lower alcohol consumption). We were not, however, able to do this for all outcomes because of the low prevalence of more extreme behaviors (e.g., regular smoking [2%] or having had sexual intercourse by age 14 years [2%]), as this did not allow sufficient power to carry out analyses by level of MHC. The measures used may, therefore, not all have equal implications in the longer term.

      Implications for policy and research

      Our research shows that MHC at the end of elementary school is associated with a number of health risk behaviors at age 14 years, particularly for those with the lowest levels of MHC. Although health risk behaviors may reflect normative adolescent development, behaviors such as smoking, illegal drug use, binge drinking, antisocial behavior, and sexual risk taking are important predictors of later health risk behaviors and/or may have adverse consequences for later health (including greater likelihood of cardiovascular disease and cancer) and social well-being [
      • Spring B.
      • Moller A.C.
      • Coons M.J.
      Multiple health behaviours: Overview and implications.
      ,
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ,
      • Viner R.M.
      • Ross D.
      • Hardy R.
      • et al.
      Life course epidemiology: Recognising the importance of adolescence.
      ,
      • Akasaki M.
      • Ploubidis G.B.
      • Dodgeon B.
      • Bonell C.P.
      The clustering of risk behaviours in adolescence and health consequences in middle age.
      ,
      • Leather N.C.
      Risk-taking behaviour in adolescence: A literature review.
      ,
      • Sawyer S.M.
      • Afifi R.A.
      • Bearinger L.H.
      • et al.
      Adolescence: A foundation for future health.
      ,
      • Paradis A.D.
      • Koenen K.C.
      • Fitzmaurice G.M.
      • Buka S.L.
      Impact of persistent and adolescent-limited antisocial behaviour on adult health outcomes.
      ]. Research has also suggested that health risk behaviors in adolescence are likely to cluster and covary and may influence the development of more extreme or new behaviors over time [
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ]. It has also been found that early onset of smoking and sexual intercourse compared with peers is associated with greater involvement in risky behaviors in early adulthood [
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ,
      • Akasaki M.
      • Ploubidis G.B.
      • Dodgeon B.
      • Bonell C.P.
      The clustering of risk behaviours in adolescence and health consequences in middle age.
      ]. School-based life skills and positive youth development programs may help reduce the likelihood of certain health risk behaviors, such as alcohol and drug consumption in young adolescents [
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ,
      • Sawyer S.M.
      • Afifi R.A.
      • Bearinger L.H.
      • et al.
      Adolescence: A foundation for future health.
      ,
      • Botvin G.J.
      • Griffin K.W.
      Life skills training: Preventing substance misuse by enhancing individual and social competence.
      ,
      • Spaeth M.
      • Weichold K.
      • Silbereisen R.K.
      • Wiesner M.
      Examining the differential effectiveness of a life skills program (IPSY) on alcohol use trajectories in early adolescence.
      ,
      • Taylor R.D.
      • Oberle E.
      • Durlak J.A.
      • Weissberg R.P.
      Promoting positive youth development through school-based social and emotional learning interventions: A meta-analysis of follow-up effects.
      ]. Prosocial behaviors and learning skills are not only amenable to intervention, but their improvement may also offer wider benefits on well-being and life chances [
      • Santelli J.S.
      • Sivaramakrishnan K.
      • Edelstein Z.R.
      • Fried L.P.
      Adolescent risk-taking, cancer risk, and life course approaches to prevention.
      ,
      • Barry M.M.
      Addressing the determinants of positive mental health: Concepts, evidence and practice.
      ,
      • Taylor R.D.
      • Oberle E.
      • Durlak J.A.
      • Weissberg R.P.
      Promoting positive youth development through school-based social and emotional learning interventions: A meta-analysis of follow-up effects.
      ]. However, more research is needed to explore the link between MHC and health risk behaviors as adolescents transition into adulthood, and more widely, in different countries and cultures, and at different periods. MHC provides a measure of positive mental health which can easily be reported by parents, teachers, or young people using data from existing surveys measured from infancy through to adolescence. As the MCS cohort members age, future work should track changes in MHC and health risk behaviors over time.

      Acknowledgments

      The authors would like to thank all the Millennium Cohort Study families for their participation and the director of the Millennium Cohort Study and colleagues in the management team at the Centre for Longitudinal Studies, UCL Institute of Education. The data are held by the UK Data Service for all the sweeps (first survey https://doi.org/10.5255/UKDA-SN-4683-1; second survey http://doi.org/10.5255/UKDA-SN-5350-3; third survey http://doi.org/10.5255/UKDA-SN-5795-3; fourth survey http://doi.org/10.5255/UKDA-SN-6411-6; fifth survey http://doi.org/10.5255/UKDA-SN-7464-2; sixth survey http://doi.org/10.5255/UKDA-SN-8156-2). The authors would also like to thank members of the Policy Research Unit in the Health of Children, Young People and Families for their comments on the manuscript.
      Authors’ contributions: All authors contributed to the study design. E.R. carried out the analyses, with input from A.P., S.H., C.L., R.V., and J.D. All authors contributed to the interpretation of the results. E.R. wrote the article. All authors commented on drafts of the article and have approved the final version.

      Funding Sources

      The authors received support from the Policy Research Unit in the Health of Children, Young People and Families (funding reference 10090001). The Policy Research Unit in the Health of Children, Young People and Families is funded by the Department of Health and Social Care Policy Research Programme. This is an independent piece of research commissioned and funded by the Department of Health and Social Care, United Kingdom. The views expressed are not necessarily those of the Department. J.D. was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care North Thames at Bart's Health NHS Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. AP was supported by the Wellcome Trust (205412/Z/16/Z), the Medical Research Council (MC_UU_12017/13), and the Scottish Government Chief Scientist Office (SPHSU13). Research at the UCL Institute of Child Health and Great Ormond Street Hospital for Children receives a proportion of the funding from the Department of Health and Social Care's National Institute for Health Research Biomedical Research Centres funding scheme. The Millennium Cohort Study is funded by grants to former and current directors of the study from the Economic and Social Research Council (Professor Heather Joshi, Professor Lucinda Platt, and Professor Emla Fitzsimons) and a consortium of government funders. The study sponsors played no part in the design, data analysis, and interpretation of this study; the writing of the article or the decision to submit the article for publication, and the authors' work was independent of their funders.

      Supplementary Data

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