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Cross-country comparisons of self-reported vs. DSM-IV diagnosed depressive disorders:  Results from the  S tudy of Global  Age ing and Adult Health (SAGE) Tristan Gorrindo, MD Dept of Psychiatry Massachusetts General Hospital [email_address]
SAGE Wave 1 data from 5 countries
Study Goals: Estimate prevalence (1 year, DSM criteria) Estimate treatment (1 year) Examine correlates
DSM-IV-TR Criteria Classify respondents as depressed if they endorsed a two week period in the last year of depressed mood or anhedonia and had 4 or more symptoms of the following list: loss of appetite, difficulty falling asleep or insomnia, fatigue or low energy, restlessness or slow movement, negative feelings about oneself or loss of confidence, diminished concentration, or thoughts of suicide.
Treatment in the Past Year Respondent’s report of taking medications or having other treatment in the past year, or having sought in- or out-patient treatment for anxiety or depression in the past year.
Correlates Age Sex Education  <Primary Primary Secondary >Secondary Marital Status  Married or cohabiting,  Never married,  Divorced, separated,  Widowed Quintile of permanent income “ Stigma”  Would a sad person admit to being sad? Self-Assessed Health  Good or very good, Moderate Bad or very bad
Results
Overall Prevalence  (Treated  or  Diagnosed, percentages)
Model Sex Controlling for age, marital status, permanent income, education, and stigma: Women are significantly more likely to report being depressed than men in India, Mexico, and South Africa; not in Ghana or China
Model Age Controlling for sex, marital status, permanent income, education, and stigma: Older age is significantly associated with a DSM diagnosis of depression in India, Mexico, and Ghana; not in South Africa or China
Model Marital Status Controlling for sex, age, permanent income, education, and stigma: Being widowed is significantly associated with a DSM diagnosis of depression in India and South Africa; it is significantly and  negatively  associated with depression in Mexico; no association in China
No consistent story with respect to income, education, or stigma
Self-Assessed Health It matters a lot, but we need to think carefully about reverse causality
“ Unmet Need” Country Not Depressed & Not Treated % of Depressed who are not treated Untreated Depressed as % of Sample Population India 91.6 93.5 7.2 Mexico 91.3 74.8 4.3 South Africa 93.5 25.4 1.3 Ghana 95.5 92.2 4.0 China 98.9 85.5 0.7
1.  Why is China so low (the estimate is consistent with other WHO estimates in China)? 2.  Can we use the vignettes to understand differences across countries? 3.  Do the explanations of sex differentials vary by country? 4.  What will other health correlates (that might have fewer problems with reverse causality) tell us? Questions and Next Steps
WHO’s Study on Global Aging and Adult Health is supported by the US National Institute on Aging through an Interagency Agreement with WHO and through the NIH grant R01 AG034479 Somnath Chatterji Paul Kowal Zachary Epstein Maxine Weinstein

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WPA Presentation 2011

  • 1. Cross-country comparisons of self-reported vs. DSM-IV diagnosed depressive disorders: Results from the S tudy of Global Age ing and Adult Health (SAGE) Tristan Gorrindo, MD Dept of Psychiatry Massachusetts General Hospital [email_address]
  • 2. SAGE Wave 1 data from 5 countries
  • 3. Study Goals: Estimate prevalence (1 year, DSM criteria) Estimate treatment (1 year) Examine correlates
  • 4. DSM-IV-TR Criteria Classify respondents as depressed if they endorsed a two week period in the last year of depressed mood or anhedonia and had 4 or more symptoms of the following list: loss of appetite, difficulty falling asleep or insomnia, fatigue or low energy, restlessness or slow movement, negative feelings about oneself or loss of confidence, diminished concentration, or thoughts of suicide.
  • 5. Treatment in the Past Year Respondent’s report of taking medications or having other treatment in the past year, or having sought in- or out-patient treatment for anxiety or depression in the past year.
  • 6. Correlates Age Sex Education <Primary Primary Secondary >Secondary Marital Status Married or cohabiting, Never married, Divorced, separated, Widowed Quintile of permanent income “ Stigma” Would a sad person admit to being sad? Self-Assessed Health Good or very good, Moderate Bad or very bad
  • 8. Overall Prevalence (Treated or Diagnosed, percentages)
  • 9. Model Sex Controlling for age, marital status, permanent income, education, and stigma: Women are significantly more likely to report being depressed than men in India, Mexico, and South Africa; not in Ghana or China
  • 10. Model Age Controlling for sex, marital status, permanent income, education, and stigma: Older age is significantly associated with a DSM diagnosis of depression in India, Mexico, and Ghana; not in South Africa or China
  • 11. Model Marital Status Controlling for sex, age, permanent income, education, and stigma: Being widowed is significantly associated with a DSM diagnosis of depression in India and South Africa; it is significantly and negatively associated with depression in Mexico; no association in China
  • 12. No consistent story with respect to income, education, or stigma
  • 13. Self-Assessed Health It matters a lot, but we need to think carefully about reverse causality
  • 14. “ Unmet Need” Country Not Depressed & Not Treated % of Depressed who are not treated Untreated Depressed as % of Sample Population India 91.6 93.5 7.2 Mexico 91.3 74.8 4.3 South Africa 93.5 25.4 1.3 Ghana 95.5 92.2 4.0 China 98.9 85.5 0.7
  • 15. 1. Why is China so low (the estimate is consistent with other WHO estimates in China)? 2. Can we use the vignettes to understand differences across countries? 3. Do the explanations of sex differentials vary by country? 4. What will other health correlates (that might have fewer problems with reverse causality) tell us? Questions and Next Steps
  • 16. WHO’s Study on Global Aging and Adult Health is supported by the US National Institute on Aging through an Interagency Agreement with WHO and through the NIH grant R01 AG034479 Somnath Chatterji Paul Kowal Zachary Epstein Maxine Weinstein