title: The possible evidence for an epidemiological transition hypothesis for elderly suicides.
abstract: BACKGROUND
  An epidemiological transition hypothesis has been developed to explain simultaneously wide cross-national variations in elderly suicide rates, trends over time for elderly suicide rates and age-associated trends in suicides rates. This speculative hypothesis suggests that there is a curvilinear (inverted U-shaped curve) relationship between elderly suicide rates and socioeconomic status fitting the quadratic equation y = a + bx - cx2 (where y is the suicide rate, x is the socioeconomic status, and a, b and c are constants).
  
  
  METHODS
  The predicted curvilinear relationship between elderly suicide rates and gross national domestic product (GDP), a measure of socioeconomic status, fitting the above quadratic equation was examined with a curve estimation regression model using data from the World Health Organization.
  
  
  RESULTS
  The relationship between suicide rates in both sexes in the age-bands 65-74 and 75+ years and the GDP was curvilinear (inverted U-shaped curve) and fitted the above quadratic equation, and was statistically significant (at least p<0.05) in all four groups.
  
  
  CONCLUSIONS
  Caution should be exercised in accepting this model of the epidemiological transition hypothesis for elderly suicide rates because it is generated from cross-sectional data using an ecological design. Ideally, this model requires rigorous testing by following selected countries of low socioeconomic status over time as they develop socioeconomically.
authors: Institute for Philosophy, Diversity and Mental Health, International School for Communities, Rights and Inclusion, University of Central Lancashire, Preston and West London Mental Health NHS Trust, London, U.K. ajit.shah@wlmht.nhs.uk
authors: A
authors: A
authors: Shah
mesh_terms: D000367:Age Factors; D000368:Aged; D002648:Child; D046688:Child Mortality; D003430:Cross-Sectional Studies; D004467:Economics / Q000706:statistics & numerical data; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data; D006801:Humans; D008017:Life Expectancy; D016014:Linear Models; D008297:Male; D008960:Models, Psychological; D012044:Regression Analysis; D012737:Sex Factors; D012959:Socioeconomic Factors; D013405:Suicide / Q000523:psychology* / Q000706:statistics & numerical data; D014481:United States / Q000453:epidemiology