Evidence map›Paper›PMID 34497077›Full record

ArticleBMJ open2021

Development of influenza-associated disease burden pyramid in Shanghai, China, 2010-2017: a Bayesian modelling study.

Jing Li, Chunfang Wang, Luanqi Ruan, Shan Jin, Chuchu Ye, Huiting Yu, Weiping Zhu, Xiling Wang

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

8 authors.

Jing Li *School of Public Health, Fudan University, Shanghai, Shanghai, China.
Chunfang Wang *Department of Vital Statistics, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Luanqi RuanResearch Base of Key Laboratory of Surveillance and Early Warning on Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Shan JinDepartment of Vital Statistics, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Chuchu YeResearch Base of Key Laboratory of Surveillance and Early Warning on Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Huiting YuDepartment of Vital Statistics, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.ORCID 0000-0002-8979-5238
Weiping ZhuResearch Base of Key Laboratory of Surveillance and Early Warning on Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Shanghai, China erinwang@fudan.edu.cn wpzhu@pdcdc.sh.cn.
Xiling WangSchool of Public Health, Fudan University, Shanghai, Shanghai, China erinwang@fudan.edu.cn wpzhu@pdcdc.sh.cn.ORCID 0000-0002-7164-6189

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesNegative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory disease (R&C), we developed an improved model incorporating Bayes' theorem to estimate the disease burden of influenza in Shanghai, China, from 2010 to 2017.

designA modelling study using aggregated data from administrative systems on weekly R&C mortality and hospitalisation, influenza surveillance and meteorological data. We constrained the regression coefficients for influenza activity to be positive by truncating the prior distributions at zero.

settingShanghai, China.

participantsPeople registered with R&C deaths (450 298) and hospitalisations (2621 787, from 1 July 2013), and with influenza-like illness (ILI) outpatient visits (342 149) between 4 January 2010 and 31 December 2017. PRIMARY OUTCOME MEASURES: Influenza-associated disease burden (mortality, hospitalisation and outpatient visit rates) and clinical severity (outpatient-mortality, outpatient-hospitalisation and hospitalisation-mortality risks).

resultsInfluenza was associated with an annual average of 15.49 (95% credibility interval (CrI) 9.06-22.06) excess R&C deaths, 100.65 (95% CrI 48.79-156.78) excess R&C hospitalisations and 914.95 (95% CrI 798.51-1023.66) excess ILI outpatient visits per 100 000 population in Shanghai. 97.23% and 80.24% excess R&C deaths and hospitalisations occurred in people aged ≥65 years. More than half of excess morbidity and mortality were associated with influenza A(H3N2) virus, and its severities were 1.65-fold to 3.54-fold and 1.47-fold to 2.16-fold higher than that for influenza A(H1N1) and B viruses, respectively.

conclusionsThe proposed Bayesian approach with reasonable prior information improved estimates of influenza-associated disease burden. Influenza A(H3N2) virus was generally associated with higher morbidity and mortality, and was relatively more severe compared with influenza A(H1N1) and B viruses. Targeted influenza prevention and control strategies for the elderly in Shanghai may substantially reduce the disease burden.

Indexed as

Influenza A Virus, H1N1 SubtypeInfluenza, HumanAgedBayes TheoremChinaCost of IllnessHumansInfluenza A Virus, H3N2 SubtypeSeasonsepidemiologypublic healthstatistics & research methods

Identifiers

PMID34497077
PMCPMC8438833

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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.