Evidence map›Paper›PMID 27921174›Full record

Observational studyInternational journal of biometeorology2017

Spatiotemporal variations of extreme low temperature for emergency transport: a nationwide observational study.

Daisuke Onozuka, Akihito Hagihara

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in International journal of biometeorology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
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

2 authors.

Daisuke OnozukaDepartment of Health Communication, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. onozukad@hcam.med.kyushu-u.ac.jp.ORCID http://orcid.org/0000-0001-9596-9188
Akihito HagiharaDepartment of Health Communication, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. hagihara@hsmp.med.kyushu-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although recent studies have investigated the effect of extreme heat on emergency transport, few have investigated the spatiotemporal variations of extreme low temperature for emergency transport on a national scale. Data pertaining to emergency ambulance transport and weather variation in the 47 prefectures of Japan between 2007 and 2010 were obtained. Nonlinear and delayed relationships between temperature and morbidity were assessed using a two-stage analysis. First, a Poisson regression analysis allowing for overdispersion in a distributed lag nonlinear model was used to estimate the prefecture-specific effects of temperature on morbidity. Second, a multivariate meta-analysis was applied to pool estimates on a national level. Of 15,868,086 emergency transports over the study period, 5,375,621 emergency transports were reported during the winter months (November through February). The overall cumulative relative risk (RR) at the first percentile vs. the minimum morbidity percentile was 1.24 (95 % CI = 1.15-1.34) for all causes, 1.50 (95 % CI = 1.30-1.74) for cardiovascular diseases, and 1.59 (95 % CI = 1.33-1.89) for respiratory diseases. There were differences in the temporal variations between extreme low temperature and respiratory disease morbidity. Spatial variation between prefectures was observed for all causes (Cochran Q test, p < 0.001; I

Indexed as

Cardiovascular DiseasesCold TemperatureEmergency Medical DispatchHumansJapanMorbidityMultivariate AnalysisNonlinear DynamicsRegression AnalysisRespiratory Tract DiseasesRiskDistributed lag non-linear modelEmergency transportExtreme low temperatureMultivariate meta-analysis

Identifiers

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Registered trials

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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.