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. 2022 Sep 9;21(1):11.
doi: 10.1186/s12942-022-00311-6.

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

Jisu Moon  1 , Inkyung Jung  2
Affiliations

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

Jisu Moon et al. Int J Health Geogr. .

Abstract

Background: In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection.

Methods: We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data.

Results: The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights.

Conclusions: Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.

Keywords: Geographic surveillance; Health survey; Sampling design; Sampling weight; Spatial cluster detection.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Significant spatial clusters detected with high diabetes diagnosis rates of male adults using the KCHS 2018 data. A Individual-level data (frequency). B Individual-level data (weighted frequency). C Aggregate-level data (crude rate)
Fig. 2
Fig. 2
Significant spatial clusters detected with high diabetes diagnosis rates of male adults with age adjustment using the KCHS 2018 data. A Individual-level data (frequency with age adjustment). B Individual-level data (weighted frequency with age adjustment). C Aggregate-level data (age standardized rate)
Fig. 3
Fig. 3
The true simulated cluster models among the 250 districts of South Korea. True cluster model (A). True cluster model (B)
See this image and copyright information in PMC

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