Evidence map›Paper›PMID 33794444›Full record

ArticleWaste management (New York, N.Y.)2021

Quantitative evaluation of infectious health care wastes from numbers of confirmed, suspected and out-patients during COVID-19 pandemic: A case study of Wuhan.

Jia Wang, Zhuqi Chen, Xiaojuan Lang, Songlin Wang, Lie Yang, Xiaolong Wu, Xinquan Zhou, Zhulei Chen

Open access · greenAbstract read
In one paragraph

Article in Waste management (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 5% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Does medical waste research during COVID-19 meet the challenge induced by the pandemic to waste management?Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2024
    Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi.Environmental science and pollution research international · 2022
    Article
  7. Article
  8. Article
  9. Review
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 at 4 institutions in 2 countries.

Jia WangSchool of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
Zhuqi ChenSchool of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China; Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education; Hubei Key Laboratory of Material Chemistry and Service Failure, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China. Electronic address: zqchen@hust.edu.cn.
Xiaojuan LangSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430074, PR China.
Songlin WangSchool of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
Lie YangHubei Key Laboratory of Mineral Resources Processing and Environment, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, PR China.
Xiaolong WuHubei Solid Waste and Chemical Pollution Prevention Center, Wuhan 430000, PR China.
Xinquan ZhouSchool of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
Zhulei ChenSchool of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China.
Huazhong University of Science and Technology · CNMinistry of Education · ROWuhan University of Technology · CNZhongnan University of Economics and Law · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fight against coronavirus disease 2019 (COVID-19) is still running its courses. Proper management and disposal of health care wastes (HCWs) are critical to win the fight. To achieve aforementioned tasks, prediction of their production is highly desired. In this study, primary data of production of three kinds of HCWs collected from Wuhan, the first epidemic epicenter worldwide and a mega city with more than 10 million population who has went through a lockdown period of 78 days, were reported for their first time. HCWs were classified into routine HCWs, infectious HCWs (IHCWs) and infectious municipal solid wastes. Among them, infectious HCWs from designated hospitals for COVID-19 were recognized as the most dangerous one. A multiple linear regression (MLR) model was built to predict the production of IHCWs with high significance. Numbers of patients were demonstrated high correlations with the production of IHCWs in an order of confirmed patients > out-patients > suspected patients. By the MLR model, production rates of IHCWs by confirmed, suspected and out patients were determined as 3.2, 1.8 and 0.1 kg/patient, respectively. In addition, constant production of IHCWs during the pandemic period was determined as 13 tons/d. This is the first study on quantitative evaluation of infectious HCWs during COVID-19 pandemic. The achievements in this study have potentials to shed light on global efforts to the prediction, management and disposal of vast HCWs generated in the war against COVID-19.

Indexed as

COVID-19PandemicsCommunicable Disease ControlDelivery of Health CareHealth PersonnelHumansOutpatientsSARS-CoV-2COVID-19Infectious Health Care WastesMultiple Linear RegressionPatientsWuhan

Identifiers

PMID33794444
PMCPMC7983547
OpenAlexW3137635534

What OpenQuestion holds

Textmetadata
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.