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The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study.

Paul Samuel WeissLance Allyn Waller
Published in: JMIR public health and surveillance (2022)
Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.
Keyphrases
  • public health
  • healthcare
  • mental health
  • deep learning
  • climate change
  • health promotion
  • artificial intelligence
  • health insurance