Dataset Information

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.

ABSTRACT: The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.

SUBMITTER: Ramanathan K

PROVIDER: S-EPMC7068504 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.

Ramanathan Kavitha K Thenmozhi Mani M George Sebastian S Anandan Shalini S Veeraraghavan Balaji B Naumova Elena N EN Jeyaseelan Lakshmanan L

International journal of environmental research and public health 20200218 4

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such speci ...[more]

PMID: 32085630

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Project description:IntroductionReturning to work and sustaining employment can be a significant challenge for traumatic brain injury (TBI) survivors. Within the literature, there is recurring support for the role of workplace accommodations in effective and early return-to-work (RTW). To date, however, there has been a lack of systematic reviews exploring the specific role of workplace accommodations within the context of RTW after TBI. The primary objective of this protocol is to outline the methodological approach that will be undertaken to systematically review the literature and to assess the effectiveness of workplace accommodations in facilitating RTW.Methods and analysisA total of nine databases will be searched systematically using the concepts 'Brain injury,' 'RTW' and 'Job Accommodations.' Study selection will be performed independently by three reviewers, based on predetermined eligibility criteria through two rounds of screening using, first, the title and abstract, followed by a full-text search. Extracted information will include the study's purpose, design, and setting; the data source and type; the severity of TBI and the diagnostic criterion used; a comprehensive description of the intervention provided; the RTW outcome variables and the statistical methods used, etc. The data will be tabulated and narratively synthesised. Systematic review registration: This protocol has been registered with International Prospective Register of Systematic Reviews.Ethics and disseminationAs this review intends to use pre-existing published studies hence research ethics board approvals will not be required. Nevertheless, this review will follow the ethical and governance standards in the data management and presentation of results. The findings from this review will potentially be published in a peer-reviewed scientific journal (electronically and in print). The results of this review will be presented at both national/international conferences and shared with stakeholders influencing RTW practices.Prospero registration numberCRD42016043517.

Dataset Information

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.

Publications

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.

Similar Datasets

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