Co-occurrence of multiple psychosocial risk factors in childhood is commonplace. We used latent class analysis to identify common patterns of risk co-occurrence. The five replicable risk classes yielded from our analyses represent more complexity than a single cumulative risk score, but considerable parsimony over approaches that enter each of multiple individual risk factors and their interactions into one statistical model. The usefulness of cumulative risk approaches has been firmly established (Appleyard, Egeland, Van Dulmen, & Sroufe, 2005
). Building on this knowledge to establish which risk factor combinations from a large array of potential risk factors are most deleterious for youth is an important next step in risk research.
Approximately 49% of the sample was categorized in one of two low risk classes, another substantial minority (42.8%) fell into one of the three moderate risk classes, and 8.6% percent of the sample was classified in the high risk class. These relative proportions are remarkably similar to those obtained in the Menard et al. study (2004)
(47% low risk, 46.2% moderate risk and 6.8% high risk). On the one hand, the consistency of these rates across studies with different risk variables suggests that only one in 12 to 15 youth contend with the highest levels of psychosocial risk. On the other hand, moderate levels of risk seem to be as common as low levels.
To date, risk factor selection across configurational studies has varied, limiting our ability to compare our risk factor configurations with those that emerged from previous person-oriented studies. For example, Menard and colleagues (2004)
used separate emotional, physical, and sexual abuse categories of trauma (which we had to aggregate on account of their low prevalences in our sample), but left out other, non-traumatic, childhood risk factors. Furthermore, the Add Health study (2006) used only one or two composite risk factors to represent four distinct risk domains, which might account for the fact that, unlike our risk classes, the Add Health risk classes mostly appeared to represent additive risk effects. The heterogeneity in risk factor selection across studies may, however, also may increase the likelihood of identifying particularly deleterious combinations of risk factors.
Our two replicable moderate risk classes were both characterized by socioeconomic disadvantage, one by non-nuclear family structure and the other by higher rates of parental crime, all of which may be plausible targets for public efforts at alleviating psychosocial risk. Although children from the high risk class also had somewhat elevated item endorsement probabilities on some of the socioeconomic variables, they were primarily characterized by higher rates of parent-based (e.g., parental mental illness and crime) and family relational dysfunction (e.g. parent–child conflict and interparental problems). Children from this class had the highest rates of all psychiatric problems. Simultaneously addressing all of the risk factors that were elevated in the high risk configuration may present great challenges to clinical therapies and public policies.
As expected, the high and low risk classes had the strongest and weakest links with psychopathology. This is generally consistent with findings from cumulative risk approaches. Yet, there were several instances in which youth with similar levels of risk had different rates of psychiatric outcomes based on their unique configuration of risk factors, suggesting some specificity in risk class-outcome associations. For example, youth in Moderate/Single/Poor/Crime had higher rates of disruptive disorders than did youth in the Moderate/Uneducated/Poor class. This pattern was reversed for emotional disorders. Furthermore, our first follow-up analysis comparing youth in the high risk class to other youth with a similar number of risk factors suggested that the specific pattern of risk factors was as important as the sheer number of risk factors. Our second follow-up analyses suggested that simultaneously considering both configurational and cumulative risk models strikes the best balance between parsimony and complexity. That approach effectively summarized in six parameters the effects of the original 17 risk variables.