Observational studyBMC medical research methodology2022
Empirical assessment of alternative methods for identifying seasonality in observational healthcare data.
Observational study in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Epidemiological insights into canine rabies in Chennai: Trends, forecasting and One Health implications.One health (Amsterdam, Netherlands) · 2025Article
- Temporal and Seasonal Trends in Pediatric Eye Trauma in California.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018.Heliyon · 2024Article
- Intervention Effect of a Soybean-Based Complementary Food Supplement on Anemic Infants in a Poor Rural Region in China: Evidence from Quasi-RCT.Children (Basel, Switzerland) · 2023Article
- Seasonal and Temporal Trends of Infectious Keratitis in Northern Vietnam.The American journal of tropical medicine and hygiene · 2023Article
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Authors and funding
2 authors.
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Abstract
backgroundSeasonality 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.
methodsWe 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.
resultsAcross 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.
conclusionMethods 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.
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