CORRELATES OF SELF-DIAGNOSIS OF CHRONIC MEDICAL AND MENTAL HEALTH CONDITIONS IN UNDER-SERVED AFRICAN AMERICAN AND LATINO POPULATIONS

2008 
Objective: This study examines the correlates of self-diagnosis of chronic medical and mental health conditions in under-served minority populations. The Behavioral Model for Vulnerable Populations was employed to compare the predisposing and enabling characteristics of two groups: the first group consisted of individuals who self-reported their medical conditions without a presumptive or definitive physician diagnosis, while the second group consisted of individuals who self-reported their medical conditions with a presumptive or definitive physician diagnosis of their condition. Study Setting: The sample consisted of 287 African American and Latino heads of household. This sample was obtained from a geographically defined random sample of 418 households from three urban public housing communities in Los Angeles County, California. Study Design: This study was a cross-sectional, face-to-face, semistructured interview survey. Results: Using logistic regression techniques and controlling for demographic characteristics, the results indicate that accessibility, affordability, continuity of medical care, and financial strains were the core concepts that explain the gap between self vs physician diagnosis of medical conditions. Conclusion: This study identifies unique characteristics of minority persons who claimed that their medical conditions had not been presented to or diagnosed by a medical provider in comparison to those who are formally diagnosed by medical providers. The study provides an entry point for further examination of correlates and sequels of self-diagnosis and its resultant effects on professional treatment-seeking in minority populations with certain medically important chronic conditions. (Ethn Dis. 2008;18[Suppl 2]:S2-105–S2-111)
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    18
    References
    11
    Citations
    NaN
    KQI
    []