Evidence map›Paper›PMID 35627516›Full record

ArticleInternational journal of environmental research and public health2022

A Longitudinal Study on the Addictive Behaviors of General Population before and during the COVID-19 Pandemic in China.

Xiaoyu Wang, Zaifei Ma, Chunan Wang

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
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 at 3 institutions in 1 country.

Xiaoyu WangInstitute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing 100006, China.
Zaifei MaSchool of Statistics, Renmin University of China, Beijing 100872, China.
Chunan WangSchool of Economics and Management, Beihang University, Beijing 100191, China.ORCID 0000-0003-0810-6400
Beihang University · CNInstitute of Population and Labor Economics · CNRenmin University of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By using nationally representative longitudinal data, this study investigates the effects of the COVID-19 pandemic on the addictive behaviors (smoking and drinking) of the general population in China. From the China Family Panel Studies (CFPS) 2018 and 2020, we extract a sample of individuals over 16 years of age in China, consisting of 14,468 individuals and 28,936 observations. We decompose the sample into three age groups, that is, ages between 16 and 39, ages between 40 and 59 and ages above 60. The bootstrap method is used to estimate the confidence interval of the difference in the mean of addictive behaviors, and logit models are used in the regression analysis. Our results show that the COVID-19 pandemic reduces the smoking behavior of individuals above 40 years of age, and that it reduces the drinking behavior of individuals above 16 years of age. However, the pandemic increases the smoking behavior of individuals between 16 and 39 years of age. These results may be closely related to the characteristics of COVID-19 (that is, a respiratory system disease), the working and economic pressures of young Chinese and the role of drinking alcohol in building and maintaining social networks in China.

Indexed as

Behavior, AddictiveCOVID-19AdolescentAdultChinaHumansLongitudinal StudiesPandemicsYoung Adultaddictive behaviorChinaCOVID-19 pandemicdrinkingsmoking

Identifiers

PMID35627516
PMCPMC9141667
OpenAlexW4280590302

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.