Estimating correlations among cardiovascular patients' psychiatric and physical symptom indicators: The biplot in correspondence analysis approach

Int J Methods Psychiatr Res. 2018 Sep;27(3):e1611. doi: 10.1002/mpr.1611. Epub 2018 Mar 2.


Objectives: We employed the correspondence analysis (CA) biplot to estimate correlations between gender-age levels of cardiovascular disease patients and their psychiatric and physical symptoms. Utilization of this correlation estimation can inform clinical practice by elucidating associations between certain psychiatric or physical symptoms and specific gender-age levels.

Method: The CA biplot utilized here was designed to visually inspect row-column category associations in a 2-dimensional plane and then to numerically estimate the category associations with correlations. To do so, we (a) estimated dimensions from row and column categories with CA; (b) verified statistical significance of dimensions with a permutation test; (c) projected row and column categories in a plan constructed with the first 2 dimensions that were statistically significant; (d) visually inspected category associations in the plane; and (e) numerically estimated category associations with correlations.

Results: Consistent with the previous results, female cardiovascular disease patients were more likely to experience psychiatric symptoms than the male patients. However, when examining the results by gender and age, both female and male patients in their 50s and 60s tended to experience elevated rates of the psychiatric symptoms.

Conclusions: The CA biplot can be useful for isolating key clinical concerns among any medical populations.

Keywords: cardiovascular disease; depression; gender; suicide ideation; the biplot in correspondence analysis.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Behavioral Symptoms / epidemiology
  • Behavioral Symptoms / physiopathology*
  • Cardiovascular Diseases / epidemiology
  • Cardiovascular Diseases / physiopathology*
  • Comorbidity
  • Data Interpretation, Statistical*
  • Data Visualization*
  • Female
  • Humans
  • Male
  • Middle Aged
  • New York City / epidemiology
  • Young Adult