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Table 1 Correlation coefficient of variables and weighting of predictors

From: Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Variable Correlation coefficient p value    Weighting
Age 0.42 <0.001    
12-month period after enrolment
Health behaviour     Broad adherence  
 Secondary prevention 1 Secondary prevention (Integrated) 8.18
  Health check-ups 0.122 <0.01   *  
  Item of health check-ups 0.128 <0.001   *  
 Tertiary prevention    
  Rehabilitation intensity 0.012 0.43 2 Rehabilitation intensity 0.81
  Guidance 0.156 <0.001 3 Guidance 1.04
  PDC 0.079 <0.001 4 PDC 5.35
 Overlapping outpatient service    
  Outpatient visits 0.001 0.938 5 Overlapping outpatient visits 3.03
  Clinical laboratory and physiological tests 0.049 <0.05 6 Overlapping clinical laboratory and physiological test 5.02
 Medical attendance 7 Medical attendance (Integrated) 2.76
  Inpatient days 0.52 <0.001   **  
  Outpatients visits 0.352 <0.001   **  
  Dispensing 0.354 <0.001   **  
 Public behaviour    
  Generic drug rate 0.209 <0.001 8 Generic drug rate 6.58
Long-term care
 In-home services, number per year 0.224 <0.001    
 Community-based services, number per year 0.011 0.466    
 Facility services, number per year 0.086 <0.001    
48-month follow-up period after index 12-month enrolment
 Follow-up period 0.442 <0.001    
 Medical expense 0.938 <0.001    
 Long-term care expense 0.269 <0.001    
  1. Integrated into an adherence 1* or adherence 7** index by machine learning
  2. Abbreviations: PDC proportion of days covered