Evidence map›Paper›PMID 35639562›Full record

ArticleInternational journal of epidemiology2022

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial.

Lina Madaniyazi, Aurelio Tobias, Yoonhee Kim, Yeonseung Chung, Ben Armstrong, Masahiro Hashizume

Abstract read
In one paragraph

Article in International journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Building the model: a review of input structures in extreme temperature-health.Environmental health : a global access science source · 2026
    Pooled it
  2. Article
  3. Article
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  5. Seasonality and effects of climatic exposures on community-acquired Legionnaires' disease incidence, Italy, 2005 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026
    Article
  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lina MadaniyaziSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.ORCID 0000-0001-9067-2422
Aurelio TobiasSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Yoonhee KimDepartment of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yeonseung ChungDepartment of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.ORCID 0000-0002-1501-2747
Ben ArmstrongCentre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK.
Masahiro HashizumeDepartment of Pediatric Infectious Diseases, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.

Indexed as

SeasonsHumansRisk Factorsattributable fractionmortalitypeak to troughSeasonalitytemperaturetime-series

Identifiers

PMID35639562
PMCPMC9557844

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.