Skip to main content

Table 3 The results of interrupted time series analysis for DDDc

From: The impact of the national volume-based procurement policy on the use of policy-related drugs in Nanjing: an interrupted time-series analysis

Categories

Constant

β0(Yt0)

Secular trend

β1(Yt1)

Level change

β2(Yt2)

Trend change

β3(Yt3)

Drugs under the same generic name

 Bid-winning drugs

28.20*** (29.99)

-0.59* (24.64)

-7.12*** (15.25)

0.56* (13.84)

 Generic drugs

25.04*** (25.54)

0.03 (25.21)

-7.62*** (19.55)

0.05 (18.32)

 Branded drugs

82.04*** (80.92)

-1.65*** (72.60)

-3.08* (57.21)

1.54*** (56.57)

 Total

46.72*** (46.61)

-0.89*** (41.69)

-3.01**(31.17)

0.60** (30.53)

Alternative drugs under different generic names

 Perfect clinical equivalence

9.31*** (9.32)

0.00 (9.33)

0.03 (9.33)

-0.05* (9.13)

 Fundamental clinical equivalence

26.91*** (26.61)

-0.05* (26.60)

-6.02*** (20.34)

0.02 (20.12)

 Limited clinical equivalence

49.41*** (49.02)

-0.17** (48.37)

0.65 (48.39)

0.14 (47.61)

 Total

40.12*** (39.78)

-0.13** (39.36)

-1.28** (37.59)

0.09 (37.01)

  1. ***p-value < 0.001
  2. **p-value < 0.01
  3. *p-value < 0.05
  4. Yt0 refers to the Yt value of DDDc in the first month included in the study
  5. Yt1 refers to the average DDDc before policy intervention
  6. Yt2 refers to the value of DDDc in the month of policy intervention
  7. Yt3 refers to the average DDDc after policy intervention