ArticleForensic science, medicine, and pathology2013
Is sudden death random or is it in the weather?
Article in Forensic science, medicine, and pathology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Air Pollution and Temperature: a Systematic Review of Ubiquitous Environmental Exposures and Sudden Cardiac Death.Current environmental health reports · 2023Pooled it
- Short-term exposures to temperature and risk of sudden cardiac death in women: A case-crossover analysis in the Nurses' Health Study.Environmental epidemiology (Philadelphia, Pa.) · 2024Article
- Seasonal variation in medico-legal autopsies in Finland-a nationwide analysis of the period 2016-2021.International journal of legal medicine · 2024Article
- Daily Weather Conditions and Anticipated Death from Cancer.Iranian journal of public health · 2018Article
- Meteorological Variables Associated with Stroke.International scholarly research notices · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has been suggested that the weather may promote some types of death; this study sought to determine if types of death in the region around Adelaide, South Australia, occur in non-random clusters and in relationship to the weather. A Poisson model was used to determine if the occurrence of types of death were random. An exploratory analysis was performed for each death type to see if there was a relationship to weather variables using data supplied by the Bureau of Meteorology. Cases examined at Forensic Science South Australia from 1 January 2008 to 31 December 2009 were reviewed. It was ascertained that cardiovascular deaths were distributed non-randomly; there was statistical evidence to suggest that deaths from ischemic heart disease, pulmonary embolus and drug toxicity had non-random occurrence. Maximum temperatures and increases in temperatures correlated with deaths from natural causes, cardiovascular disease, ischemic heart disease and pulmonary thromboembolus; lower hours of sunlight were statistically significant for deaths due to pulmonary thromboembolus. The distribution pattern of deaths resulting from motor vehicle collision did not fit the Poisson (random) model with variation through the week also being present. Non-random clusters of deaths do occur and weather events, such as increase in temperature, are associated with some types of death. However, analysis indicates that the weather is not responsible for all clustering. With regards to motor vehicle collision deaths temporal variation may be related to social factors, such as holiday periods. Further investigation may assist with health resource planning.
Indexed as
Identifiers
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.