Prevalence of conduct problems and social risk factors in ethnically diverse inner-city schools

Data are from the baseline assessments conducted as part of an accelerated cohort study based in inner-city London boroughs, Southwark and Lambeth (REACH - Resilience, Ethnicity, and Adolescent mental Health) [23]. These boroughs are both ethnically and socio-economically highly diverse [21, 24]_. We selected 14 state-funded secondary schools based on borough-representative proportions of pupils a) in minority ethnic groups and b) eligible for free school meals. Twelve of these schools participated; one school declined due to time constraints, one school was not included as they agreed after recruitment targets were met. At each participating school, all pupils in school years 7–9 (age 11–14) were eligible to take part (n, 4945).

To obtain informed consent, two weeks prior to data collection, researchers delivered school assemblies and distributed information packs to all eligible pupils and their parents/carers. Study information was available via the study website, and where possible, school websites and mailing lists. We asked parents/carers to return a form or contact the school or research team if they did not want their child to participate. Pupils gave written assent before completing the hour-long in-class questionnaire. Researchers were present in all sessions. Baseline questionnaires were administered between February 2016 and January 2018.

We performed analyses in Stata v.15 using weighted data. We developed sample weights using the National Pupil Database (Spring 2016/17 School Census in Lambeth and Southwark).

Analyses were conducted using multilevel logistic regression (melogit and margins commands) with school fitted as a second-level variable. To compare groups, we used the marginalised delta method (nlcom command) to calculate risk ratios (RR) from odds ratios [35].

Without a clear reference category for ethnic group comparisons, we used the overall sample prevalence. We conducted multilevel logistic regressions including ethnic group as a variable and used postestimation command to generate risk estimates for each ethnic group (in these regressions white British was a reference group, see results in Additional File 1). We also conducted each multilevel logistic regression with the relevant covariates and multilevel structure included but no ethnic group variable and used postestimation command to generate a risk estimate for the overall sample. Based on these regressions, we calculated predicted cases and non-cases from each ethnic group and the overall population. The number of predicted cases based on membership of each ethnic group was then compared with the number of predicted cases overall to generate standardised risk ratios. Hence, the estimate of the overall sample prevalence was used as our standard population in each comparison.

To test hypothesis 1a, we used intercept only models to estimate weighted prevalence using imputed data. To test hypothesis 1b, we estimated risk ratios to quantify associations between ethnicity and behavioural outcomes (conduct problems, fighting/bullying, rule-breaking), adjusted for potential confounders (year group, free school meals and gender). To test hypothesis 1c, we estimated associations between gender and behavioural outcomes overall, adjusted for confounders (year group, free school meals and ethnicity), and stratified by ethnic group.

To test hypothesis 2, we estimated risk ratios to quantify associations between putative risk factors (racial discrimination, free school meals, troublesome friends, high parental control, low parental care) and behavioural outcomes adjusted for confounders (year group, ethnicity, gender).

To account for bias due to item non-response we carried out multi-level multiple imputation in REALCOM [36]. We performed imputation on two data sets, one including pilot schools n = 4353 and one excluding pilot schools n = 3535. In each imputation model, we included all available variables used in analyses with school fitted as a second-level variable. 20 imputations were used.

A total of 4353 pupils (51% girls, 85% minority ethnic groups) participated (Fig. 1). The mean age was 12·4(SD 0·96) years old. Sample demographics are presented in Table 1. For demographics stratified by gender, see Additional File 2.

Around 16% (95% CI: 15·2–17·5) scored above the threshold for conduct problems. This is over three times higher than the estimated national prevalence of conduct problems using the same measure in the UK Millennium Cohort (MCS) at age 14 (5·2% [95%CI 4·6–5·9]) (personal communication), and the MHCYP³ age 11–16 (6.2% [CI’s not available]) (Hypothesis 1a). Overall, an estimated 35·4% (95% CI: 31·2–39·5) reported often starting fights and/or bullying or threatening people and 38·7% (95% CI 34·9–42·6) reported engaging in other rule-breaking behaviours in the last year (estimates reported on weighted and imputed data).

There was variation by ethnic group in prevalence of conduct problems (Hypothesis 1b; Table 2). Generally, conduct problems were less common among Indian/Pakistani/Bangladeshi (8·8% [95% CI: 3·0–14·5]), and white British (10·6% [95% CI: 7·9–13·3]) groups and more common among black Caribbean (23·2% [95%CI: 19·6–26·9]) and mixed white and black ethnic groups (21·9% [95%CI: 17·5–26·3]). The prevalence was close to the overall estimate among black African (16·4% [95%CI:13·4–19·5]), Latin American (15·6% [95%CI: 10·3–21·0]), and other mixed groups (17·4% [95% CI: 13·7–21·2]). It follows that the prevalence in all other ethnic groups was higher than in Indian/Pakistani/Bangladeshi and white British groups. Indeed, compared with overall prevalence, all behavioural problems were less common among white British adolescents, e.g., conduct problems RR 0·65 (95% CI 0·51–0·82), fighting/bullying RR 0·71 (95% CI 0·60–0·83), and rule-breaking RR 0·80 (95% CI 0·69–0·91). Among black Caribbean adolescents, there was a higher prevalence of conduct problems (RR 1·39 [95% CI 1·19–1·62]), rule-breaking behaviours (RR 1·16 [95% CI 1·02–1·32]), and, to a lesser extent, fighting/bullying behaviours (RR 1·10 [95% CI 0·95–1·26]). Among the Latin American group, fighting/bullying were more common (RR 1·33 [95% CI 1·03–1·67]), but conduct problems were not (RR 0·97 [95%CI 0·68–1·35]).

Compared with boys, prevalence of conduct problems (RR 0·81 [95% CI 0·67–0·95]) and rule-breaking behaviours (RR 0·85 [95% CI 0·71–1·00]) were lower among girls (Hypothesis 1c). There was no evidence of a gender difference in fighting/bullying (RR 1·02 [95% CI 0·84–1·19]).

There were notable variations by ethnic group in gender differences. There was a lower prevalence of conduct problems among girls in the other white (RR 0·38 [95%CI 0·10–0·65]), and Latin American (RR 0.38 [95%CI 0.12–0.64]) groups. However, there were no differences in black African and black Caribbean groups (Table 3). Similarly, there were gender differences in rule-breaking in some groups (e.g., white British (RR 0·66 [95% CI 0·46–0·86]), other mixed (RR 0.58 [95% CI 0·41–0·76]), but not others. There was no evidence of a gender difference in risks of fighting/bullying within any ethnic group – estimates were high for girls and boys.

Each putative risk factor – racial discrimination, free school meals, troublesome friends, and parental care and control – was associated with increased risk of each outcome (Hypothesis 2; Table 4). For example, among those who experienced racial discrimination the risk of conduct problems was two times greater than among those who had not (RR 1·95 [95%CI 1·59–2·31]). Among those who reported many or all their friends got into trouble, the risk of conduct problems was around 5 times greater than for those who reported none of their friends got into trouble (RR 4.80 [95% CI 3.75–5.85]).

The occurrence of putative risk factors was largely similar among most ethnic groups included (Hypothesis 3; Table 5). The white British group was the exception. Every putative risk factor (except parental low care) was less common among the white British group. For instance, exposure to discrimination was similar among most ethnic groups including Latin American, black Caribbean, black African, other black, and mixed white and black. By contrast, exposure to racial discrimination was lower for the white British group (17% vs. 28% overall), (RR 0·60 [95% CI 0·49–0·72]). There was also some evidence of putative risks being more common in the mixed/multiple, mixed white and black groups, and the black Caribbean groups. Indeed, racial discrimination was most common among the mixed/multiple group relative to the overall sample. (37% vs 28% overall), (RR 1.36 [95% CI 1.07–1.69]). The mixed white and black group were more likely to receive free school melas than the overall sample (33% vs 24% overall), (RR 1.36 [95% CI 1.13–1.62]), and the black Caribbean group were more likely to have a few or many troublesome friends (76% vs. 70% overall), (RR 1·10 [95% CI 0·99–1·21]).

We next considered whether these risk factors might explain observed variations in conduct problems by ethnic group. When we added these putative explanatory factors, the magnitude of variations in prevalence by ethnic group generally remained similar (Table 6). However, for the white British group, when models were adjusted, the estimated RR approached 1, i.e. no difference in risk between white British adolescents and the overall prevalence. This suggests these factors account for some of the disparity in conduct problems and antisocial behaviours between the white British group and the overall prevalence. For example, in the base model (adjusted for age and free school meals) there was strong evidence that the risk of conduct problems for white British adolescents was lower than the overall sample (RR 0·65 [95%CI 0·50–0·82]). In Model 4 (adjusted for racial discrimination, troublesome friends, parental control and care) there was weaker evidence for a difference in risk of conduct problems among white British adolescents compared with the overall sample (RR 0·81 [95%CI 0·64–1·01]). This pattern may reflect attenuation of cumulative advantage of white British adolescents (who are exposed to fewer risk factors for conduct problems than the overall sample) as we adjust the model. In addition, there was some evidence of attenuated risks of conduct problems as models were adjusted in those groups who were more commonly exposed to putative risk factors. For example, in the base model the black Caribbean group were at elevated risk of conduct problems compared with the overall sample (RR 1·37 [95%CI 1·12–1·64]) and this estimate was reduced in model 3 through adjusting for racial discrimination, friends who get into trouble, and high parental control (RR 1.25 [95%CI 1.02–1.52]).

The inclusion of two pilot schools in analyses of conduct problems and racial discrimination did not substantively alter any of the reported associations [see sensitivity analyses in Additional File 3].

Several methodological limitations should be considered before interpreting our findings. For example, not all pupils participated, which may have biased the sample. The primary reason for non-participation was absence from school and, as conduct problems are associated with school absence, our sample may underestimate prevalence. Conversely, self-report single-informant SDQ scores, while sensitive to true negatives (95%), tend to be over-inclusive [25] and may overestimate conduct problems. The relative impact of each potential bias is unclear. Still, it seems likely that conduct problems are substantially more common in inner-city schools given the high threshold we used for conduct problems, and the comparison with MCS prevalence estimates using the same self-report measure at the same threshold.

Our self-report measure of conduct problems may not reflect the same construct for different ethnic groups. Conduct problems are a multifaceted and heterogeneous group of behaviours which may present differently across ethnic groups. Moreover, self-report measures are susceptible to information bias. Adolescents may diminish or exaggerate behaviours according to social acceptability or desirability, which may vary by cultural background. This noted, a strength of self-report measures, compared with teacher-report, is that they are not (directly) impacted by implicit gendered or racial biases from teachers. Still, the potential for heterogeneity within our conduct problem measure, inclusive of the potential for information bias, should be considered in interpreting our results.

Additional limitations include the depth and variety of social risk factors we could consider. First, we used a basic measure of racial discrimination which does not necessarily capture insidious or pervasive forms of structural or implicit racism. Consequently, our results likely underestimate exposure to, and impact of, discrimination. Second, our measure of parental control may conflate harmful coercive parenting with protective parental monitoring, hence should be interpreted with caution. Third we use a simple self-report measure of troublesome friends. Future work should include more complex and ecologically valid peer friendship network approaches. Further, it is likely that additional risk factors (e.g. adverse life-events) and protective factors (e.g. religion), also vary by ethnic group and contribute to differences. For example, despite high exposure to discrimination and parental control, the Indian/Pakistani/Bangladeshi group had fewer conduct problems. This suggests an important avenue for further analyses.

Finally, at present, analyses are cross-sectional; therefore, we cannot make strong conclusions about the direction of effects.

Our estimates of the prevalence of conduct problems are higher than in London sub-samples of national studies, i.e., 16% vs. 2% in London-wide estimates from the MHCYP [3] and vs. 4% in the MCS (16% vs 4%)(personal communication). This may reflect biases in the London sub-samples of national studies. For example, the MHCYP and MCS are limited by low participation rates (MCHYP, 52%; MCS-wave 6 [age 14], 61%) [3, 37] and probably under-sampled those from minority and marginalised groups. By comparison, REACH had an 88% participation rate. Studies, like REACH, which were similarly localised – i.e., The Research with East London Adolescents Community Health Survey [38] (RELACHS) and DASH [14] – reported prevalence estimates similar to ours. For example, using the same self-report-SDQ conduct problem measure (“0–3” = low risk, “4–10” = slightly raised to very high risk) RELACHS reported 30·7% (95%CI: 28·2–33·2) of boys had slightly raised to very high risk of conduct problems, almost identical to our estimate 30·6% (95%CI: 28·6–32·6) [38] when scored at the same threshold. Together these data do suggest conduct problems are markedly more common in London schools.

We replicated anticipated associations between social risk factors - racial discrimination, perceived parenting, and troublesome friends - and conduct problems [8‐10, 39]. These risk factors are markers of interactive ecological systems, from intimate parent-child interaction to wider friendships in school, all enmeshed within socio-historical context of racism in the UK. Whilst specific mechanisms of action are beyond the scope of the present paper, it is possible each risk factor explored here, though from disparate domains and acting through different developmental pathways, may interactively and cumulatively increase risk of conduct problems. The high prevalence of conduct problems in inner-city schools, alongside strong associations with risk factors for conduct problems, imply clustered social risk factors may be more common in inner-city schools than nationally, and therefore, contribute to a higher prevalence of conduct problems.

Previous localised studies have also found evidence for a second level of inequality - inequality between ethnic groups. Compared with white British peers, greater risks of conduct problems and antisocial behaviour among black Caribbean and mixed white and black ethnicity adolescents have been reported in a 2008 systematic review of ethnic differences and in reports on MCS data [6, 7, 14]. The mean conduct problem scores among both boys and girls in black Caribbean, mixed white and black, and black African groups reported in DASH [40] were also high, and similar to our findings for these groups. The lack of gender differences among black and mixed ethnicity groups found in both REACH and DASH may indicate that the inner-city environment persistently disproportionately impacts black and mixed ethnicity girls. However, for the white British group, mean conduct problem scores were – to a modest extent – higher in DASH than in REACH. For example, mean conduct problem scores among white British boys was 2.72 in DASH (95%CI 2.58–2.85) vs 2.28 in REACH (95%CI 2.09–2.46). For white British girls, the mean conduct problem scores in DASH were 2.30 (95%CI 2.16–2.44) vs REACH 1.79 (95%CI 1.65–2.04). This suggests a possible reduction in prevalence of conduct problems overtime for white British boys and girls, perhaps indicating changes in social risk factors for the white British group, but not all ethnic groups over the last 15 years. Our findings are consistent with one literal manifestation of what has been termed a “white privilege effect”, whereby the white groups were exposed to fewer social risk factors for conduct problems. The greater exposure to these risks among black and mixed groups may push more adolescents along developmental pathways to conduct problems. For example, initial exposure to racial discrimination may cause distress and increase challenging behaviours [18, 39]; parental control may then increase to harsh or coercive control in response; this, in turn, may further increase and entrench conduct problems [41, 42]. As conduct problems develop because of exposure to social risks, peer friendships can exacerbate or mitigate them. In adolescence, typical selection of friends in the same area, often from the same-ethnic group may aggregate or dissipate risk exposures in friendship groups [9]. Peer influence in a friendship group, with either concentrated or diluted risk factors for conduct problems, may amplify ethnic differences in conduct problems. Reduced exposure to the social risks included in the present study, including racial discrimination and troublesome friends, largely accounted for the lower risk of conduct problems among the white British group relative to the rest of the sample.