Article

The Devil You Know: Revealing Racial/Ethnic Disparities in the Treatment of Adolescent Depression

Journal of the American Academy of Child and Adolescent Psychiatry (Impact Factor: 6.35). 02/2011; 50(2):106-7. DOI: 10.1016/j.jaac.2010.11.012
Source: PubMed
0 Followers
 · 
67 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: To examine the correlates and consequences of high levels of depressive symptoms among adolescents. Secondary analysis of the 1997 Commonwealth Fund Survey of the Health of Adolescent Girls, a survey of a nationally representative sample of 4648 adolescent boys and girls between the ages of 10 and 18 years, inclusive, conducted in school settings. The self-administered questionnaire contains a screening instrument for depression based on the Children's Depression Inventory. Days of school missed, performance at grade level, alcohol use, drug use, smoking, and bingeing. After controlling for sociodemographics, life events, sexual abuse, physical abuse, and exposure to violence, relative to other children, children and adolescents with high degrees of depressive symptoms missed about 1 day more of school in the month preceding the survey (P<.05) and had higher odds of smoking (odds ratio, 1.84; P<.001), bingeing (odds ratio, 2.02; P<.001), and suicidal ideation (odds ratio, 16.59; P<.001). High levels of depressive symptoms are correlated with serious and significant consequences, even after controlling for life circumstances.
    Archives of Pediatrics and Adolescent Medicine 10/2002; 156(10):1009-14. DOI:10.1001/archpedi.156.10.1009 · 4.25 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Little is known about racial/ethnic differences in the receipt of treatment for major depression in adolescents. This study examined differences in mental health service use in non-Hispanic white, black, Hispanic, and Asian adolescents who experienced an episode of major depression. Five years of data (2004-2008) were pooled from the National Survey on Drug Use and Health to derive a nationally representative sample of 7,704 adolescents (12-17 years old) diagnosed with major depression in the past year. Racial/ethnic differences were estimated with weighted probit regressions across several measurements of mental health service use controlling for demographics and health status. Additional models assessed whether family income and health insurance status accounted for these differences. The adjusted percentages of blacks (32%), Hispanics (31%), and Asians (19%) who received any treatment for major depression were significantly lower than those of non-Hispanic whites (40%; p < .001). Black, Hispanic, and Asian adolescents were also significantly less likely than non-Hispanic whites to receive prescription medication for major depression, to receive treatment for major depression from a mental health specialist or medical provider, and to receive any mental health treatment in an outpatient setting (p < .01). These differences persisted after adjusting for family income and insurance status. Results indicated low rates of mental health treatment for major depression in all adolescents. Improving access to mental health care for adolescents will also require attention to racial/ethnic subgroups at highest risk for non-receipt of services.
    Journal of the American Academy of Child and Adolescent Psychiatry 02/2011; 50(2):160-70. DOI:10.1016/j.jaac.2010.11.004 · 6.35 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To examine the extent to which doctors' rational reactions to clinical uncertainty ("statistical discrimination") can explain racial differences in the diagnosis of depression, hypertension, and diabetes. Main data are from the Medical Outcomes Study (MOS), a 1986 study conducted by RAND Corporation in three U.S. cities. The study compares the processes and outcomes of care for patients in different health care systems. Complementary data from National Health And Examination Survey III (NHANES III) and National Comorbidity Survey (NCS) are also used. Across three systems of care (staff health maintenance organizations, multispecialty groups, and solo practices), the MOS selected 523 health care clinicians. A representative cross-section (21,480) of patients was then chosen from a pool of adults who visited any of these providers during a 9-day period. We analyzed a subsample of the MOS data consisting of patients of white family physicians or internists (11,664 patients). We obtain variables reflecting patients' health conditions and severity, demographics, socioeconomic status, and insurance from the patients' screener interview (administered by MOS staff prior to the patient's encounter with the clinician). We used the reports made by the clinician after the visit to construct indicators of doctors' diagnoses. We obtained prevalence rates from NHANES III and NCS. We find evidence consistent with statistical discrimination for diagnoses of hypertension, diabetes, and depression. In particular, we find that if clinicians act like Bayesians, plausible priors held by the physician about the prevalence of the disease across racial groups could account for racial differences in the diagnosis of hypertension and diabetes. In the case of depression, we find evidence that race affects decisions through differences in communication patterns between doctors and white and minority patients. To contend effectively with inequities in health care, it is necessary to understand the mechanisms behind the problem. Discrimination stemming from prejudice is of a very different character than discrimination stemming from the application of rules of conditional probability as a response to clinical uncertainty. While in the former case, doctors are not acting in the best interests of their patients, in the latter, they are doing the best they can, given the information available. If miscommunication is the culprit, then efforts should be aimed at reducing disparities in the ways in which doctors communicate with patients.
    Health Services Research 03/2005; 40(1):227-52. DOI:10.1111/j.1475-6773.2005.00351.x · 2.49 Impact Factor