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Table 4 Variables and effect estimates from the model

From: Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates

Characteristics Categories Effect estimate 95% CI
Mean age Years 0.052 0.044, 0.061
Number of age groups 1 0
2 −2.7 −3.69, −1.71
3 0.391 −0.14, 0.92
4 0.474 −0.10, 1.05
5 0.102 −0.47, 0.67
6 0.001 −0.87, 0.87
Disease classification Names of specific disease/disease groups 0
Diseases based on ICD-10 or ICD-9 codes 1.26 0.039, 2.49
Diseases based on ICPC-2 or CIRS −0.789 −1.64, 0.067
No. of diseases in the classification 5–9 0 -
10–24 0.516 0.0017, 1.03
25–74 1.22 0.43, 2.01
≥75 0.806 −0.47, 2.08
Setting General population 0
Primary care practice 0.015 −0.70, 0.73
Hospital/nursing home 1.41 0.39, 2.44
Health insurance 1.05 −0.66, 2.76
Data source Self-report 0
Medical record −0.863 −1.71, −0.019
Self-report + medical record −0.75 −1.43, −0.071
Administrative data −0.497 −2.39, 1.39
Data collection period Up to one year 0
One year or more −0.349 −0.79, 0.09
Data reporting quality P2+ given in paper 0
P2+ calculated from paper −0.4 −0.94, 0.14
P2+ read from graph in paper −1.76 −2.52, −0.99
Constant   −3.305 −4.01, −2.60
  1. Legend: Random effects model with response: logit P2+, sampling weights inverse to binomial variance of logit P2+; average sampling weight: 0.0182. N = 108; adjusted R2 = 70.6%; residual variance τ2 = 0.5812; overall F(20,87) = 12.61; p < 0.00005
  2. Categories with “Effect estimate” = 0 are reference categories. Gender analyses were done using a different data set (see text). Mean age: Effect estimate gives the change in logit P2+ when changing mean age by one year. Other variables: Effect estimate quantifies the effect on logit P2+ of going from the reference category to the category of interest – e.g., going from one age group to two changes logit P2+ by −2.70, when keeping all other variables fixed (also see Discussion)