Evidence map›Paper›PMID 29147496›Full record

ArticleJournal of healthcare engineering2017

Markov Chain-Based Acute Effect Estimation of Air Pollution on Elder Asthma Hospitalization.

Li Luo, Fengyi Zhang, Wei Zhang, Lin Sun, Chunyang Li, Debin Huang, Gao Han, Bin Wang

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Characteristics of older adult hospitalized patients with bronchial asthma: a retrospective study.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2021
    Article
  4. 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

8 authors.

Li LuoBusiness School, Sichuan University, Chengdu, Sichuan 610000, China.
Fengyi ZhangBusiness School, Sichuan University, Chengdu, Sichuan 610000, China.
Wei ZhangBig-Data Center of Biomedicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.ORCID 0000-0003-3773-4262
Lin SunBig-Data Center of Biomedicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Chunyang LiBig-Data Center of Biomedicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
Debin HuangChengdu Medical Insurance Administration, Chengdu, Sichuan 610000, China.
Gao HanChengdu Medical Insurance Administration, Chengdu, Sichuan 610000, China.
Bin WangChengdu Medical Insurance Administration, Chengdu, Sichuan 610000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma caused substantial economic and health care burden and is susceptible to air pollution. Particularly, when it comes to elder asthma patient (older than 65), the phenomenon is more significant. The aim of this study is to investigate the Markov-based acute effects of air pollution on elder asthma hospitalizations, in forms of transition probabilities. Methods: A retrospective, population-based study design was used to assess temporal patterns in hospitalizations for asthma in a region of Sichuan province, China. Approximately 12 million residents were covered during this period. Relative risk analysis and Markov chain model were employed on daily hospitalization state estimation. Results: Among PM2.5, PM10, NO Conclusions: SO

Indexed as

Frail ElderlyAgedAged, 80 and overAge FactorsAir PollutantsAir PollutionAsthmaChinaEnvironmental MonitoringFemaleHospitalizationHumansMaleMarkov ChainsRetrospective StudiesRisk FactorsAir PollutantsSulfur Dioxide

Identifiers

PMID29147496
PMCPMC5632917

What OpenQuestion holds

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LicenceCC BY
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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.