ArticleEpidemiology and infection2014
A relationship between acute respiratory illnesses and weather.
Article in Epidemiology and infection, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan.BMC public health · 2025Article
- Common cold.Frontiers in allergy · 2023Review
- Valuing Acute Health Risks of Air Pollution in the Jinchuan Mining Area, China: A Choice Experiment with Perceived Exposure and Hazardousness as Co-Determinants.International journal of environmental research and public health · 2019Article
- Applicability of the universal thermal climate index for predicting the outbreaks of respiratory tract infections: a mathematical modeling approach.International journal of biometeorology · 2019Article
- Seasonality and selective trends in viral acute respiratory tract infections.Medical hypotheses · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Weekly data from 7 years (2004-2010) of primary-care counts of acute respiratory illnesses (ARIs) and local weather readings were used to adjust a multivariate time-series vector error correction model with covariates (VECMX). Weather variables were included through a partial least squares index that consisted of weekly minimum temperature (coefficient = - 0·26), weekly median of relative humidity (coefficient = 0·22) and weekly accumulated rainfall (coefficient = 0·5). The VECMX long-term test reported significance for trend (0·01, P = 0·00) and weather index (1·69, P = 0·00). Short-term relationship was influenced by seasonality. The model accounted for 76% of the variability in the series (adj. R 2 = 0·76), and the co-integration diagnostics confirmed its appropriateness. The procedure is easily reproducible by researchers in all climates, can be used to identify relevant weather fluctuations affecting the incidence of ARIs, and could help clarify the influence of contact rates on the spread of these diseases.
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What OpenQuestion holds
Registered trials
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.