Evidence map›Paper›PMID 37684635›Full record

ArticleBMC public health2023

A study of temperature variability on admissions and deaths for cardiovascular diseases in Northwestern China.

Shan Zheng, Xiaofei Zhang, Wenzhi Zhu, Yonghong Nie, Ximeng Ke, Shaodong Liu, Xue Wang, Jinlong You, Feng Kang, Yana Bai and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

11 authors.

Shan Zheng *Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China. zhengsh@lzu.edu.cn.
Xiaofei Zhang *Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Wenzhi ZhuCenter for Immunological and Metabolic Diseases (CIMD), MED-X Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yonghong NieJinchang Center for Disease Control and Prevention, Jinchang, 737100, China.
Ximeng KeInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Shaodong LiuInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Xue WangInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Jinlong YouInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Feng KangWorkers' Hospital of Jinchuan Group Co., Ltd, Jinchang, 737103, China.
Yana BaiInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
Minzhen WangInstitute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China. wangmzh@lzu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs).

methodThe admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated.

resultsThere was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013-2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season.

conclusionTV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.

Indexed as

Cardiovascular DiseasesAgedChinaEmergency Service, HospitalHeatingHumansMaleTemperatureAdmissions and deathsCardiovascular diseasesDistributed lag nonlinear modelTemperature variability

Identifiers

PMID37684635
PMCPMC10486070

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.