Evidence map›Paper›PMID 38773497›Full record

SynthesisBMC public health2024

Lag effect of ambient temperature on respiratory emergency department visits in Beijing: a time series and pooled analysis.

Xuan Li, Yongming Zhang, Zhenbiao Tian, Jianping Wang, Jinhua Zhao, Yuanjun Lyu, Ying Ni, Yuming Guo, Zhuang Cui, Wenyi Zhang and 1 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

11 authors.

Xuan Li *Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China.
Yongming Zhang *Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Beijing, China.
Zhenbiao Tian *Beijing Red Cross Emergency Center, Beijing, 100085, China.
Jianping WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China.
Jinhua ZhaoDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China.
Yuanjun LyuDepartment of Endocrinology, Tianjin Hospital, Tianjin, China.
Ying NiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China.
Yuming GuoDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Zhuang CuiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China.
Wenyi ZhangChinese PLA Center for Disease Control and Prevention, 20 Dong-Da Street, Fengtai District, Beijing, 100071, People's Republic of China. zwy0419@126.com.
Changping LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Heping District, Tianjin, 300070, P.R. China. ChangpingLi417@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough the association between ambient temperature and mortality of respiratory diseases was numerously documented, the association between various ambient temperature levels and respiratory emergency department (ED) visits has not been well studied. A recent investigation of the association between respiratory ED visits and various levels of ambient temperature was conducted in Beijing, China.

methodsDaily meteorological data, air pollution data, and respiratory ED visits data from 2017 to 2018 were collected in Beijing. The relationship between ambient temperature and respiratory ED visits was explored using a distributed lagged nonlinear model (DLNM). Then we performed subgroup analysis based on age and gender. Finally, meta-analysis was utilized to aggregate the total influence of ambient temperature on respiratory ED visits across China.

resultsThe single-day lag risk for extreme cold peaked at a relative risk (RR) of 1.048 [95% confidence interval (CI): 1.009, 1.088] at a lag of 21 days, with a long lag effect. As for the single-day lag risk for extreme hot, a short lag effect was shown at a lag of 7 days with an RR of 1.076 (95% CI: 1.038, 1.114). The cumulative lagged effects of both hot and cold effects peaked at lag 0-21 days, with a cumulative risk of the onset of 3.690 (95% CI: 2.133, 6.382) and 1.641 (95% CI: 1.284, 2.098), respectively, with stronger impact on the hot. Additionally, the elderly were more sensitive to ambient temperature. The males were more susceptible to hot weather than the females. A longer cold temperature lag effect was found in females. Compared with the meta-analysis, a pooled effect of ambient temperature was consistent in general. In the subgroup analysis, a significant difference was found by gender.

conclusionsTemperature level, age-specific, and gender-specific effects between ambient temperature and the number of ED visits provide information on early warning measures for the prevention and control of respiratory diseases.

Indexed as

Emergency Service, HospitalRespiratory Tract DiseasesAdolescentAdultAgedAged, 80 and overAir PollutionBeijingChildChild, PreschoolEmergency Room VisitsFemaleHumansInfantInfant, NewbornMaleAmbient temperatureDistributed lag non-linear modelEmergency department visitsMeta-analysisMeteorological factorsRespiratory diseases

Identifiers

PMID38773497
PMCPMC11106889

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.