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A simulation study for geographic cluster detection analysis on population-based health survey data using spatial

Jisu Moon1, Inkyung Jung2

  • 1Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.

International Journal of Health Geographics
|September 9, 2022
PubMed
Summary

For spatial cluster detection in health surveys, aggregated data provides higher accuracy than individual-level data. This finding is crucial for improving epidemiological analyses using spatial scan statistics.

Keywords:
Geographic surveillanceHealth surveySampling designSampling weightSpatial cluster detection

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Area of Science:

  • Public Health
  • Epidemiology
  • Spatial Statistics

Background:

  • Spatial scan statistics identify health outcome clusters in population-based surveys.
  • Complex survey data analysis often overlooks sampling weights and design.
  • This oversight can impact the accuracy of spatial cluster detection studies.

Purpose of the Study:

  • To compare the suitability of different data types for spatial cluster detection using complex survey data.
  • To evaluate the impact of using individual-level, weighted individual-level, and aggregated data in spatial scan statistics.
  • To determine the optimal data aggregation strategy for identifying spatial health patterns.

Main Methods:

  • A simulation study was conducted to compare three data approaches: individual-level, weighted individual-level, and aggregated data.
  • Spatial cluster detection was performed using spatial scan statistics on each data type.
  • Performance was evaluated using sensitivity and positive predictive value (PPV) over 100 iterations.

Main Results:

  • Spatial cluster detection results differed significantly based on the data type used.
  • All three approaches showed high average sensitivity.
  • Aggregated data yielded a higher average positive predictive value (PPV) compared to individual-level data (weighted or unweighted).

Conclusions:

  • Aggregated data is the most appropriate data type for spatial cluster detection with spatial scan statistics in population-based health surveys.
  • Using aggregated data enhances the reliability of identifying spatial patterns in health outcomes.
  • This finding has significant implications for epidemiological research and public health surveillance.