Evidence map›Paper›PMID 32494236›Full record

ReviewTobacco induced diseases2020

Economic burden of smoking-attributable diseases in China: A systematic review.

Lili Shi, Lumin Zhong, Yuyang Cai

Abstract readReview
In one paragraph

Review in Tobacco induced diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

3 authors.

Lili ShiXinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lumin ZhongDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.
Yuyang CaiSchool of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis review aims to synthesise the studies on smoking-attributable burden of diseases in China to assess the economic burden of smoking and highlight the weakness in these studies to inform future studies.

methodsA systematic search of studies on smoking-attributable burden of disease in seven databases was conducted in 2019 and studies were screened according to inclusion and exclusion criteria. The evaluation of studies was based on the seven key elements for burden of disease studies. Costs were converted into 2013 Renminbi (RMB), with 1000 RMB about 163 US$ in 2013, the year of the first search, using the Consumer Price Index and the then exchange rate.

resultsTwenty studies were identified that estimated the costs of smoking in China, ranging from 57.162 to 368.273 billion RMB in total. The largest proportion of direct costs was allocated to outpatient visits, accounting for 49.17-68.94% of the direct costs. Meanwhile, costs resulting from mortality constituted 64.52-98.82% of the indirect costs. In mainland China, the understanding of PAR% (ratio of population attributable risk and incidence in the total population) in studies is not consistent. Studies on the cost of passive smoking are lacking and the research method for diseases needs to be improved.

conclusionsSmoking-attributable diseases have exerted substantial direct and indirect economic burden on China. The methodologies for future studies should be improved. Hong Kong and Taiwan provide good examples for future research in mainland China and researchers there should use PAR% correctly. More studies on the burden of passive smoking should be conducted. We propose a combination of single and all-disease research methods, if data are sufficient.

Indexed as

economic burdensmokingsystematic review

Identifiers

PMID32494236
PMCPMC7263360

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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.