Modeling breast cancer screening strategies

New recommendations by the U.S. Preventive Services Task Force about routine mammograms are getting lots of attention (see statement published today in Annals of Internal Medicine).

Although this topic is not directly related to public health preparedness, it is interesting that the justification for the new recommendations includes the results of six mathematical models. The modeling is discussed in Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms by Mandelblatt et al. (This article also cites the articles describing the details of each model.)

The mathematical models are similar and all estimate the impact of different screening strategies. The benefits are measured in life-years gained because of averted or delayed breast cancer death due to a screening strategy; the harms include false-positive mammograms, unnecessary biopsies, and overdiagnosis.

Mandelblatt et al. examined 20 different strategies and identified eight “non-dominated” strategies – that is, for each of these eight, there was no other strategy that gave more benefits with fewer overall mammograms. Among these eight, there is a tradeoff: more benefits will necessarily require more mammograms.

The models also estimate that false-positives occur more often in those strategies that include screening for women between the ages of 40 to 49 years.

Promising Practices for Pandemics

The University of Minnesota Center for Infectious Disease Research & Policy maintains a web site called Promising Practices: Pandemic Preparedness Tools. One can download the practices directly from this web site.

The practices, developed by different states and local jurisdictions, are organized into the following topics: types of patient care, communication, community disease mitigation, and helping at-risk groups. The practices are also organized by the states that developed them.