Using G-computation to estimate the effect of regionalization of surgical services on the absolute reduction in the occurrence of adverse patient outcomes

Med Care. 2013 Sep;51(9):797-805. doi: 10.1097/MLR.0b013e31829a4fb4.


Background: Numerous studies have found that increased hospital or surgeon operative volumes, as measured by the number of procedures performed, are associated with improved patient outcomes after surgery. These findings have been used to support important health policy decisions about regionalization of surgical services, in which provision of specific surgical services is restricted to hospitals that maintain operative volumes above a specified threshold. The most common statistical approach in volume-outcome studies is to regress patient outcomes on a set of patient characteristics and a variable denoting provider volume. When outcomes are binary, such as operative mortality, logistic regression is used, resulting in the odds ratio being the reported measure of association. However, the odds ratio is a relative measure of effect and does not allow policy makers to estimate the absolute benefit of regionalization.

Objectives: To describe how G-computation can be used to estimate the expected number of lives saved due to regionalization of surgical services.

Research design: Retrospective cohort design of patients undergoing 1 of 3 different surgical procedures in Ontario, Canada.

Results: Regionalization of colorectal cancer surgery, esophagectomy, or pancreaticoduodenectomy in Ontario could reduce the average annual number of perioperative deaths by 20.2, 2.0, and 3.6, for the 3 procedures, respectively.

Conclusions: The absolute reduction in number of operative deaths due to regionalization of surgical procedures can be calculated. This can help inform health policy debate about benefits of regionalization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Interpretation, Statistical*
  • Digestive System Surgical Procedures / mortality*
  • Health Facility Size / statistics & numerical data*
  • Health Planning / statistics & numerical data*
  • Hospital Mortality*
  • Humans
  • Ontario / epidemiology
  • Organizational Case Studies
  • Outcome Assessment, Health Care
  • Retrospective Studies
  • State Medicine / statistics & numerical data