Evidence map›Paper›PMID 38882249›Full record

ArticleDigital health

Change in disease burden associated with influenza and air pollutants during the COVID-19 pandemic in Hong Kong.

Yanwen Liu, Xie Jingyu, Cai Cihan, Hilda Tsang, Shuya Lu, Daihai He, Lin Yang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yanwen LiuDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China.
Xie JingyuJC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong SAR, China.
Cai CihanDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China.
Hilda TsangSchool of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China.
Shuya LuSchool of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China.
Daihai HeDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China.
Lin YangSchool of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-5964-3233

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aimed to estimate the variation in disease burden associated with air pollutants and other respiratory viruses during the COVID-19 pandemic. Methods: We adopted a machine learning approach to calculate the excess mortality attributable to air pollutants and influenza, during the pre-pandemic and pandemic period. Results: In the first 2 years of the COVID-19 pandemic, there were 8762 (95% confidence interval, 7503-9993), and 12,496 (11,718-13,332) excess all-cause deaths in Hong Kong. These figures correspond to 117.4 and 167.9 per 100,000 population, and 12.6% and 8.5% of total deaths in 2020 and 2021, respectively. Compared to the period before the pandemic, excess deaths from all-causes, cardiovascular and respiratory diseases, pneumonia and influenza attributable to influenza A and B significantly decreased in all age groups. However, excess deaths associated with ozone increased in all age-disease categories, while the relative change of nitrogen dioxide (NO Conclusions: A notable shift in disease burden attributable to influenza and air pollutants was observed in the pandemic period, suggesting that both direct and indirect impacts shall be considered when assessing the global and regional burden of the COVID-19 pandemic.

Indexed as

air pollutionCOVID-19influenzamachine learningmortality

Identifiers

PMID38882249
PMCPMC11179508

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.