Social and economic determinants of disparities in professional help-seeking for child mental health problems: evidence from a national sample

Health Services Research, Oct, 2005 by Frederick J. Zimmerman

The internalizing symptoms are used to predict specialty visits for internalizing disorders; and the externalizing symptoms are used to predict specialty visits for externalizing disorders. A brief justification is in order for the use of the maximum of the internalizing and externalizing scales in the regression predicting any mental health visit. Often what is used is the full scale of the BPI, which is in effect an average of the two scales. Yet this is unsatisfactory as a driver of the need for a visit. A child may score very high on one subscale, and yet quite low on another subscale. Accordingly the child's average score, or total symptom score, will be average or moderately high, yet his or her need might be much higher than average. Therefore, need is best represented by the maximum of the externalizing and internalizing scales--a high score in any domain constitutes need and should trigger a visit regardless of the child's score on any other domain measured.

For children aged 10 years and over, a child-report depression inventory was administered, including nine questions addressing such issues as how often the child feels sad or blue; how often the child feels tired; how often lonely; and so forth. Summing the scores yielded a depression score that ranges from 0 to 14.

Socioeconomic variables include the parent's income, the mother's education and employment status, and the parents self-reported race/ethnicity. A rich set of insurance variables were available, including dummy variables for whether the child had insurance, whether the child was covered by Medicaid, whether the parents had private insurance or government insurance (as opposed to no insurance), whether the parents were in an HMO, and whether prior authorization was required for a mental health visit. Variables for both the child's insurance status and the parents insurance status were included in the regressions because many children for whom no insurance was reported were in fact covered under their parents' policy.

Because schools are important in the identification, referral, and even treatment, it would be useful to have data on what kinds of mental health programs, if any, are in place in the child's school. Unfortunately, no such data are available in this data set. A single question on whether the child attends private or public school was asked, and is included here as a possible confounder of the socioeconomic variables.

Demographic characteristics of the child and household include the number of children and the number of adults in the household to control for the amount of time and financial resources available for specialty help-seeking. Whether the father was present in the household was included to control for possible gender-based differences in attitudes toward mental health treatment. Since both income and the number of adults in the household are independently controlled, the effect of the "father present" variable should indicate such preference effects, rather than a relaxation of resource constraints. Dummy variables were included for whether the child was a middle, youngest, or only child (oldest, but not only, child being the reference category).