Evidence map›Paper›PMID 37138259›Full record

ArticleBMC public health2023

Study on the use and effectiveness of malaria preventive measures reported by employees of chinese construction companies in Western Africa in 2021.

Li Zou, Ke Ning, Wenyu Deng, Xufei Zhang, Mohammad Shahir Sharifi, Junfei Luo, Yin Bai, Xiner Wang, Wenjuan Zhou

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 12% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Li Zou *School of literature and journalism, Central South University, Changsha, Hunan Province, China.
Ke Ning *School of literature and journalism, Central South University, Changsha, Hunan Province, China.
Wenyu DengInsurance Professional College, Changsha, Hunan Province, China.
Xufei ZhangSchool of literature and journalism, Central South University, Changsha, Hunan Province, China.
Mohammad Shahir SharifiSchool of literature and journalism, Central South University, Changsha, Hunan Province, China.
Junfei LuoSchool of literature and journalism, Central South University, Changsha, Hunan Province, China.
Yin BaiSchool of literature and journalism, Central South University, Changsha, Hunan Province, China.
Xiner WangSchool of literature and journalism, Central South University, Changsha, Hunan Province, China.
Wenjuan ZhouSchool of literature and journalism, Central South University, Changsha, Hunan Province, China. wenddi.zhou@csu.edu.cn.
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs malaria continues to be a significant global public health concern, especially in Sub-Saharan Africa, Chinese workers in Africa are at increased risk of malaria. The effectiveness of malaria prevention measures implemented by Chinese companies and workers is a question that may correlate with the malaria infection rate in this population. This study explored the use and effectiveness of malaria prevention measures for Chinese employees in West Africa to provide a reference for companies and individuals on improving malaria prevention and control.

methodsUsing a cross-sectional approach, we surveyed 256 participants in 2021, mainly from Nigeria, Mali, Côte d'Ivoire, Ghana, Guinea, Sierra Leone, and Senegal in West Africa. The survey duration is from July to the end of September 2021. We selected two companies from the 2020 ENR "World's Largest 250 International Contractors" list, which featured 6 Chinese companies, all of which are state-owned and have a 61.9% market share in Africa. The participants were Chinese workers with more than a year of work experience in construction companies in Africa. A 20-minute WeChat-based structured online questionnaire was used to obtain information on malaria infection status and malaria prevention measures. Descriptive statistical analysis, chi-square test, principal components analysis, and ordinal logistic regression analysis are used to analyze the data obtained. The difference in Statistical significance was set at P < 0.05.

resultsNinety six (37.5%) participants contracted malaria more than once within a year. The principal components analysis found a low correlation between public and individual preventive measures. No significant correlation was found between public preventive measures and malaria infection (p > 0.05), while standardized use of mosquito nets (P = 0.016) and pesticide spraying (P = 0.047) contributed significantly to fewer malaria infections at the individual level, but the removal of vegetation around houses (P = 0.028) at the individual level related to higher malaria infection.

conclusionsIn our sample of Chinese construction workers going to Africa, some individual preventive measures had a stronger association with malaria prevention than a variety of public environmental measures. Furthermore, individual and public preventive measures were not associated with each other. Both of these findings are surprising and require further investigation in larger and more diverse samples. This- study provides important clues about the challenges that risk reduction programs face for migrant workers from China and elsewhere.

Indexed as

Construction IndustryMalariaCote d'IvoireEast Asian PeopleGhanaHumansChinese EmployeesInfection RiskInfrastructure ConstructionMalariaWestern Africa

Identifiers

PMID37138259
PMCPMC10158225
OpenAlexW4367842823

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.