Archives of Gynecology and Obstetrics

, Volume 288, Issue 6, pp 1285–1293

Can major systematic reviews influence practice patterns? A case study of episiotomy trends

  • Yu-Chu Shen
  • Wee Chung Sim
  • Aaron B. Caughey
  • David H. Howard
Maternal-Fetal Medicine

DOI: 10.1007/s00404-013-2904-y

Cite this article as:
Shen, YC., Sim, W.C., Caughey, A.B. et al. Arch Gynecol Obstet (2013) 288: 1285. doi:10.1007/s00404-013-2904-y

Abstract

Purpose

Episiotomy is one of the most commonly performed procedures among women of childbearing age in the United States. In 2005, a major systematic review conducted by Hartmann and colleagues recommended against routine use of episiotomy and was widely covered in the media. We assessed the impact of the Hartman et al. study on episiotomy trend.

Methods

Based on 100 % hospital discharge data from eight states in 2003–2008, we used interrupted time series regression models to estimate the impact of the Hartman et al. review on episiotomy rates. We used mixed-effects regression models to assess whether interhospital variation was reduced over time.

Results

After controlling for underlying trend, episiotomy rates dropped by 1.4 percentage points after Hartman et al. publication (p < 0.01 for spontaneous delivery; p < 0.1 for operative delivery). The publication has smaller effect on government hospitals as compared to private hospitals. Mixed effects models estimated negative correlation between cross-time and cross-hospital variations in episiotomy rates, indicating reduced cross-hospital variation over time.

Conclusions

Our results suggested that there has been a gradual decline in episiotomy rates over the period 2003–2008, and that synthesis of evidence showing harms from routine episiotomy had limited impact on practice patterns in the case of episiotomy. The experience of episiotomy illustrates the challenge of using comparative effectiveness and evidenced-based medicine to reduce use of unnecessary procedures.

Keywords

Episiotomy Practice pattern Interrupted time series regression models 

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2013

Authors and Affiliations

  • Yu-Chu Shen
    • 1
    • 2
  • Wee Chung Sim
    • 1
  • Aaron B. Caughey
    • 3
  • David H. Howard
    • 4
  1. 1.Graduate School of Business and Public PolicyNaval Postgraduate SchoolMontereyUSA
  2. 2.National Bureau of Economic ResearchCambridgeUSA
  3. 3.Department of Obstetrics and GynecologyOregon Health and Science UniversityPortlandUSA
  4. 4.Department of Health Policy and ManagementEmory UniversityAtlantaUSA