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The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study
Paul Samuel Weiss1, Lance Allyn Waller1
1Rollins School of Public Health, Emory University, Atlanta, GA, United States.
JMIR Public Health and Surveillance
|September 9, 2022
Summary
Nonrandom missing data in public health surveillance can lead to biased population estimates, even with mitigation efforts. This highlights the need for advanced analytical methods to avoid health disparities.
Area of Science:
- Public Health
- Biostatistics
- Survey Methodology
Background:
- Surveillance data are crucial for public health policy and resource allocation.
- Nonrandom sample designs in data collection can introduce bias in population estimates.
- Understanding the impact of nonrandom missingness is vital for accurate public health assessments.
Purpose of the Study:
- To illustrate and quantify the effect of nonrandom missingness on population-based survey estimates.
- To assess the efficacy of common bias control methods against nonrandom missingness.
Main Methods:
- Simulated a population with varying response rates based on satisfaction levels.
- Analyzed the impact of nonrandom missingness on survey estimates.
- Evaluated bias control techniques including sampling weights, sample size inflation, and missingness at random tests.
Main Results:
- Nonrandom missingness introduced significant quantifiable bias, averaging nearly a full point on a 5-point scale.
- Bias mitigation strategies like sample size inflation and sampling weights had negligible effects.
- Hypothesis testing for random missingness frequently failed to detect nonrandom patterns.
Conclusions:
- Assuming missingness at random in surveillance data can lead to significantly inaccurate population estimates.
- Biased estimates can negatively impact public health policy, potentially worsening health disparities.
- Alternative analytical approaches are needed to accurately identify and address the needs of marginalized groups.
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