Supporting Research in Pain Management for Veterans and Military Service Members
Supporting Research in Pain Management for Veterans and Military Service Members

Evaluating Public Health Interventions 10

Let’s learn as we go, and minimize large scale public health intervention failures:
The LAGO Design for optimizing complex multi-component interventions


Donna Spiegelman1 2, Daniel Nevo2 3, Arhit Chakrabarti4, Dale Barnhart2, Bhaswati Ganguli5, Judith J. Lok2 6

  1. Yale School of Public Health, Connecticut, USA
  2. Harvard T.H. Chan School of Public Health, Massachusetts, USA
  3. Tel Aviv University, Tel Aviv-Yafo, Israel
  4. Novartis Healthcare Pvt. Ltd., Hyderabad, India
  5. University of Calcutta, Calcutta, India
  6. Boston University, Massachusetts, USA


In the face of vast numbers of preventable deaths around the world and gaping disparities in their distribution, we cannot afford to run null trials of proven interventions. The gold standard in biomedicine, the individually randomized clinical trial, is ill-suited in its role as the primary tool for knowledge generation for large scale complex public health interventions of multi-component interventions. Here, we discuss the new Learn as You Go (LAGO) design. In LAGO trials, the components of the complex package are repeatedly optimized in pre-planned stages, until the intervention package is optimized both in terms of its target goal and cost. In this column, we elucidate key features of the LAGO’ design, illustrated by the null BetterBirth study, a large-scale public health intervention trial aimed at reducing maternal and neonatal mortality through the use of WHO’s best practices checklist. (DRAFT)

April 2019