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Modifiable Areal Unit Problems for Infectious Disease Cases Described in Medicare and Medicaid Claims, 2016-2019
1National Library of Medicine, Lister Hill National Centre for Biomedical Communications, Maryland, United States of America.
Introduction:
Modifiable Areal Unit Problems are a major source of spatial uncertainty, but their impact on infectious diseases and epidemic detection is unknown.
Methods:
CMS claims (2016-2019) which included infectious disease codes learned through Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) were extracted and analysed at two different units of geography; states and 'home to work commute extent' mega regions. Analysis was per member per month. Rolling average above the series median within geography and agent of infection was used to assess peak detection. Spatial random forest was used to assess region segmentation by agent of infection.
Results:
Mega-regions produced better peak discovery for most, but not all agents of infection. Variable importance and Gini measures from spatial random forest show agent-location discrimination between states and regions.
Conclusion:
Researchers should defend their geographic unit of report used in peer review studies on an agent by-agent basis.
Insights
Modifiable Areal Unit Problems impact infectious disease detection. Analyzing data by mega-regions, rather than states, improved epidemic peak discovery for most infections, highlighting the importance of geographic scale in spatial analysis.
Area of Science:
- Spatial analysis
- Epidemiology
- Geographic Information Systems (GIS)
Background:
- Modifiable Areal Unit Problems (MAUP) introduce spatial uncertainty.
- The effect of MAUP on infectious disease dynamics and epidemic detection remains unclear.
Purpose of the Study:
- To investigate the impact of different geographic units on infectious disease surveillance.
- To compare epidemic peak detection using states versus mega-regions.
Main Methods:
- Extracted CMS claims data (2016-2019) with infectious disease codes (SNOMED CT).
- Analyzed data at state and mega-region levels using per member per month metrics.
- Employed rolling averages above the series median for peak detection.
- Utilized spatial random forest for agent-location discrimination and region segmentation.
Main Results:
- Mega-regions demonstrated superior peak discovery for most infectious agents compared to states.
- Spatial random forest analysis revealed significant differences in agent-location discrimination between geographic units.
Conclusions:
- The choice of geographic unit significantly influences infectious disease surveillance outcomes.
- Researchers must justify their selected geographic unit of analysis on an agent-by-agent basis in publications.
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