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Table 1 Demographic, clinical, and pharmacy-expenditure characteristics of outlier patients according to the identification method (n = 75,574).

From: Does the pharmacy expenditure of patients always correspond with their morbidity burden? Exploring new approaches in the interpretation of pharmacy expenditure

 

General population

Patients with normal pharmacy expenditure

Outliers

  

BXP

Adj. BXP

RESID

BXP

Adj. BXP

RESID

Demographic characteristics

Average age

49.3

49.2a

49.3a

49.3a

50.3abc

48.01ab

47.5ac

Women (%)

55.8

55.8

55.8

55.9a

56.3bc

56.5bd

51.0acd

Patient proportion (%)

100

93.5

98.3

98.8

6.5

1.7

1.2

Pharmacy expenditure characteristics

Annual average expenditure/patient (€)

413.1

334.7a

376.4a

388.19a

1,548.2abc

2,509.2abd

2,434.3acd

Proportion of total expenditure (%)

100

75.8

89.6

92.8

24.2

10.4

7.2

Morbidity bands

Low (%)

22.3

21.7a

22.0a

22.0a

30.5 abc

34.7 abd

39.2 acd

Moderate (%)

57.9

57.7a

58.0 a

57.9a

59.6 abc

53.0abd

55.6acd

High (%)

19.9

20.6 a

20.0 a

20.1 a

9.9abc

12.3abd

5.2acd

Comparison indices

Observed/Average

1

0.8

0.9

0.9

3.8

6.1

5.9

Expected/Average

1

1.1

1.1

1.1

0.7

0.6

0.4

Observed/Expected

1

0.7

0.8

0.9

5.4

10.1

13.3

  1. The p-value corresponds to the chi-square test in the case of percentages or to the Mann-Whitney U test for continuous variables:
  2. a Significant differences between normal and outlier patients (p < 0.05)
  3. b Significant differences between outlier patients identified by BXP and Adj. BXP (p < 0.05)
  4. c Significant differences between outlier patients identified by BXP and RESID (p < 0.05)
  5. d Significant differences between outlier patients identified by Adj.BXP and RESID (p < 0.05)
  6. The associations between the morbidity bands and the type of patient were calculated by analysing standardised residuals of the chi-square test:
  7. BOLD TEXT Significant association between categories (p < 0.05)