Empirical assessment of alternative methods for identifying seasonality in observational healthcare data.

Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE

Original Publication: London : BioMed Central, [2001-

Delivery of Health Care* ; Research Design*; Data Collection ; Databases, Factual ; Humans ; Seasons

Background: Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data residing in databases that vary in size, type, and provenance.
Methods: We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations.
Results: Across all databases and levels of significance, concordance ranged from 20.2 to 40.2%. Across all databases and levels of significance, the proportion of time series classified seasonal ranged from 4.9 to 88.3%. For each database and level of significance, we computed the difference between the maximum and minimum proportion of time series classified seasonal by all methods. The median within-database difference was 54.8, 34.7, and 39.8%, for p < 0.01, 0.05, and 0.1, respectively.
Conclusion: Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. The methods exhibit considerable discord within all databases, implying that the discord is a result of the difference between the methods themselves and not due to the choice of database. The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable and thus the choice of method can affect the generalizability of their work. Seasonality determination is highly dependent on the method chosen.
(© 2022. The Author(s).)

PLoS Pathog. 2018 Nov 8;14(11):e1007327. (PMID: 30408114)
Medicine (Baltimore). 2021 May 28;100(21):e25820. (PMID: 34032695)
Int J Environ Res Public Health. 2020 Feb 18;17(4):. (PMID: 32085630)
Drug Healthc Patient Saf. 2016 Mar 31;8:39-48. (PMID: 27099532)
BMC Med Res Methodol. 2008 Oct 22;8:67. (PMID: 18945366)
Clin Microbiol Infect. 2012 Oct;18(10):946-54. (PMID: 22817528)
Ann Hum Genet. 1961 May;25:83-7. (PMID: 13725808)
Annu Rev Public Health. 2007;28:127-43. (PMID: 17222079)

Keywords: ACHILLES; ARIMA; CASTOR; Classification; Common data model; Cyclical; OHDSI; OMOP CDM; Observational data; R; Seasonality; Time series

Date Created: 20220702 Date Completed: 20220707 Latest Revision: 20220716

20231215

PMC9250712

10.1186/s12874-022-01652-3

35780114