Evidence map›Paper›PMID 33935523›Full record

ArticleRisk management and healthcare policy2021

Modeling Trade Openness and Life Expectancy in China.

Muhammad Imran Shah, Irfan Ullah, Xiao Xingjian, Huang Haipeng, Alam Rehman, Muhammad Zeeshan, Fakhr E Alam Afridi

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2021. 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
3.0field-weighted citation impact, top 7% of its field
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, 13 citations in OpenAlex.

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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

7 authors at 5 institutions in 2 countries.

Muhammad Imran ShahSchool of Mathematics and Statistics, Wuhan University, Wuhan, People's Republic of China.
Irfan UllahReading Academy, Nanjing University of Information Science and Technology, Nanjing, People's Republic of China.
Xiao XingjianReading Academy, Nanjing University of Information Science and Technology, Nanjing, People's Republic of China.
Huang HaipengReading Academy, Nanjing University of Information Science and Technology, Nanjing, People's Republic of China.
Alam RehmanFaculty of Management Sciences, National University of Modern Languages Islamabad, Islamabad, Pakistan.
Muhammad ZeeshanCollege of Business Administration, Liaoning Technical University, XingCheng, Liaoning Province, 125105, People's Republic of China.
Fakhr E Alam AfridiIslamia College Peshawar, Peshawar, Pakistan.ORCID 0000-0001-9776-5074
Nanjing University of Information Science and Technology · CNIslamia College University · PKLiaoning Technical University · CNNational University of Modern Languages · PKWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study investigates life expectancy and trade openness in China for the period 1960-2018.

methodsWe purposed a theoretical model that is tested for China by applying regime-switching regression.

resultsOur findings suggest that trade openness increases life expectancy in China; trade affects life expectancy from two aspects; firstly, trade expansion and industrialization lead to high economic activities and resulted in raise the income of the people in society leading to improve life expectancy. Secondly, industrial expansion increases the CO

conclusionThus, the net effect of trade liberalization depends on the value of income effect and volume of CO

Indexed as

ChinaCO2 emissionshealth qualitylife expectancyregime-switching regressiontrade

Identifiers

PMID33935523
PMCPMC8079350
OpenAlexW3154822759

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.