J Korean Foot Ankle Soc.  2021 Sep;25(3):126-132. 10.14193/jkfas.2021.25.3.126.

Correlation between Internet Search Query Data and the Health Insurance Review & Assessment Service Data for Seasonality of Plantar Fasciitis

• Hwang SM
• Lee GH
• Oh SY

• KMID: 2520103
• DOI: http://doi.org/10.14193/jkfas.2021.25.3.126

Abstract

Purpose
This study examined whether there are seasonal variations in the number of plantar fasciitis cases from the database of the Korean Health Insurance Review & Assessment Service and an internet search of the volume data related to plantar fasciitis and whether there are correlations between variations.
Materials and Methods
The number of plantar fasciitis cases per month was acquired from the Korean Health Insurance Review & Assessment Service from January 2016 to December 2019. The monthly internet relative search volumes for the keywords ‘‘plantar fasciitis” and ‘‘heel pain” were collected during the same period from DataLab, an internet search query trend service provided by the Korean portal website, Naver. Cosinor analysis was performed to confirm the seasonality of the monthly number of cases and relative search volumes, and Pearson and Spearman correlation analysis was conducted to assess the correlation between them.
Results
The number of cases with plantar fasciitis and the relative search volume for the keywords “plantar fasciitis” and “heel pain” all showed significant seasonality (p<0.001), with the highest in the summer and the lowest in the winter. The number of cases with plantar fasciitis was correlated significantly with the relative search volumes of the keywords “plantar fasciitis” (r=0.632; p<0.001) and “heel pain” (r=0.791; p<0.001), respectively.
Conclusion
Both the number of cases with plantar fasciitis and the internet search data for related keywords showed seasonality, which was the highest in summer. The number of cases showed a significant correlation with the internet search data for the seasonality of plantar fasciitis. Internet big data could be a complementary resource for researching and monitoring plantar fasciitis.

Keyword

Plantar fasciitis; Heel pain; Internet; Big data; Seasonality

Figure

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  Figure. 1 Time series plots for plantar fasciitis from January 2016 to December 2019. (A) The number of plantar fasciitis cases. (B) The relative search volume for “plantar fasciitis”. (C) The relative search volume for “heel pain”.

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  Figure. 2 The plots of the cosinor model for the seasonal variation of plantar fasciitis. (A) The number of plantar fasciitis cases. (B) The relative search volume for “plantar fasciitis”. (C) The relative search volume for “heel pain”.

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