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Table 4 Sex-adjusted odds ratios for a more frequenta alcohol consumption among alcohol consumers depending on survey design, health, and birth cohort

From: Bias in estimates of alcohol use among older people: selection effects due to design, health, and cohort replacement

  Bivariate Within categories Model 4 Model 5
  OR p-value OR p-value OR p-value OR p-value
Survey design    Model 1     
  Interview succession (linear) 0.96 0.079 0.98 0.353 0.97 0.289 0.99 0.585
  Proxy 0.44 <0.001 0.49 0.001 0.77 0.293 0.80 0.359
  Telephone 0.62 0.004 0.85 0.419 0.84 0.397 0.72 0.106
Health    Model 2     
  Living in an institution (Women) 0.29 <0.001 0.39 0.002 0.46 0.019 0.45 0.020
  Living in an institution (Men) 0.49 0.036 0.68 0.273 0.80 0.544 0.74 0.423
  ADL limitation 0.53 0.000 0.77 0.127 0.81 0.219 0.86 0.400
  Mobility problem 0.66 0.001 0.78 0.056 0.80 0.077 0.77 0.038
Birth cohort    Model 3     
  Period 1992 1.00 ref 1.00 ref    1.00 ref
  2002 1.15 0.395 0.89 0.620    1.18 0.481
  2011 1.88 <0.001 1.16 0.625    1.89 0.044
  Birth cohort (linear) 1.40 <0.001 1.32 0.036    1.04 0.801
  Age (linear) 0.79 0.084 b      
  1. Significant estimates (p<0.05) are in bold aFrom ordered logistic regression models, which provide the average increase of the odds ratio for reporting one higher category, e.g. for weekly rather than monthly. The outcome had three levels (Seldom, monthly or weekly). The assumption of equal effect sizes (also called proportional odds/parallel lines) was tested with partial proportional odds models (Stata command gologit2). The assumption was not violated for any of the independent variables (p > =0.084 in model 5)
  2. bAs age, cohort and period cannot be analysed in the same model, and because the period change was not related to changed age distribution over the years, age was excluded in the full model