1 percent of the population under age 20 (Moreno, Laje, et al., 2007). In contrast, the three studies that have assessed rates of mental illness across time in the general population found a prevalence of bipolar disorder of between 1 and 3 per 1,000 children, with no increase over the past two decades (Lewinsohn, Rohde, et al., 1998; Costello, Angold, et al., 1996). The reason for this discrepancy between epidemiological and clinical data may arise from the increased use of psychopharmacological treatments for children. The availability of a treatment may encourage clinicians to make a diagnosis and parents to seek professional help. Thus, the advent of a new drug or greater willingness of parents to bring their children for treatment can greatly increase the number of children seen by professionals, while the baseline prevalence of the disease in the population may remain unchanged.
In order to find out whether population incidence and prevalence are changing we need several longitudinal studies covering different time periods, so that new case rates can be calculated for different historical periods. National surveys like MTF make it possible to chart, for example, the rise and fall of alcohol and cocaine use by adolescents (Banken, 2004). Data like these are not available for other MEB disorders. Although a variety of federal agencies are making efforts to monitor mental, emotional, or behavioral problems, with the exception of substance use disorders, these efforts have not yet produced the repeated estimates over time necessary to plot the rise and fall of disease prevalence and the effects of interventions.
In the language of infectious disease epidemiology, it is possible to talk about various pathogens as “causes” of disease. Epidemiology invented the term “risk factors” in the 1950s when the Framingham Heart Study showed that cardiovascular disease did not have a single cause but many different factors contributing to increased risk, no single factor being either necessary or sufficient. MEB disorders seem to have more in common with chronic diseases like cardiovascular disease than with infectious diseases, in having multiple risk factors.
A mountain of research on environmental risk and protective factors for MEB disorders in young people has identified a large number of predictors, from internal (e.g., intellectual ability, brain development) to familial, educational, communal, and national (see also Chapter 4). Several theorists have developed multilevel risk models that predict complex interactions among the various levels of risk and protection. As with the prevalence and incidence of disorders, the prevalence and incidence of risk factors vary across the nation and at different developmental stages. To take a single example, data from the 2000 decennial census show that the proportion of