Overachieving Municipalities in Public Health: A Machine-learning Approach.

Journal: Epidemiology (Cambridge, Mass.)
PMID: 30212386

Abstract

BACKGROUND: Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict life expectancy at birth, and then compare health-related characteristics of the under- and overachievers (i.e., municipalities that have a worse and better outcome than predicted, respectively).

Authors

  • Alexandre Dias Porto Chiavegatto Filho
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.
  • Hellen Geremias Dos Santos
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.
  • Carla Ferreira do Nascimento
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.
  • Kaio Massa
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.
  • Ichiro Kawachi
    Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA.

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