Evidence map›Paper›PMID 27984849›Full record

ArticleAlcoholism, clinical and experimental research2017

Composition of Unrecorded Distilled Alcohol (bai jiu) Produced in Small Rural Factories in Central China.

Ian Newman, Ling Qian, Niran Tamrakar, Yonghua Feng, Ganrong Xu

Open access · bronzeAbstract read
In one paragraph

Article in Alcoholism, clinical and experimental research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Alcohol Use in China: Unrecorded and Recorded Bai Jiu in Three Rural Regions.International journal of environmental research and public health · 2021
    Article
  5. Article
  6. Growing burden of alcoholic liver disease in China: A review.World journal of gastroenterology · 2019
    Review
  7. Chemical Composition and Safety of Unrecorded Grain Alcohol (Bai Jiu) Samples from Three Provinces in China.International journal of environmental research and public health · 2018
    Article
  8. 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

5 authors at 3 institutions in 2 countries.

Ian NewmanDepartment of Educational Psychology, Nebraska Prevention Center for Alcohol and Drug Abuse, University of Nebraska-Lincoln, Lincoln, Nebraska.
Ling QianDepartment of Guidance & Training, Chinese Center for Health Education, Beijing, China.
Niran TamrakarDepartment of Educational Psychology, Nebraska Prevention Center for Alcohol and Drug Abuse, University of Nebraska-Lincoln, Lincoln, Nebraska.
Yonghua FengDepartment of Educational Psychology, Nebraska Prevention Center for Alcohol and Drug Abuse, University of Nebraska-Lincoln, Lincoln, Nebraska.
Ganrong XuKey Laboratory of Industrial Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China.
University of Nebraska–Lincoln · USChinese Center For Disease Control and Prevention · CNJiangnan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnrecorded traditional distilled spirits (bai jiu, ) are made and used throughout rural China for everyday use and special occasions. Nearly every town or village has a distiller to supply the demand. In rural China, distilling bai jiu is legal and regulated lightly or not at all. The World Health Organization estimates that as much as 25% of all alcohol consumed in China is unrecorded alcohol, of which an unknown portion is unrecorded bai jiu. Little is known about the composition of unrecorded Chinese spirits from rural parts of the country. This study focused on white spirits because the high ethanol (EtOH) concentration makes them more likely to contribute to health risks compared to other types of lower alcohol by volume (ABV) Chinese unrecorded alcohol.

methodsResearchers purchased samples of Chinese white spirits from small-factory distillers in central China. An independent laboratory conducted the analysis. Alcohol strength (ABV) was determined by hydrometer. Gas chromatography was used to determine the concentration of volatile organic compounds: EtOH, methanol, acetaldehyde, ethyl acetate, and higher alcohols. Samples were tested for 3 heavy metals-arsenic, cadmium, and lead. We used the guidelines developed by the Alcohol Measures for Public Health Research Alliance (AMPHORA) of the European Commission to assess risk.

resultsABV ranged from 35.7 to 61.4%, and 58 of the 61 samples exceeded 40% ABV. The concentration of methanol, ethyl acetate, lead, arsenic, and cadmium was below AMPHORA guideline. The sum of higher alcohols exceeded the AMPHORA maximum in just 1 sample. Forty of the 61 samples had acetaldehyde levels beyond the AMPHORA guideline.

conclusionsThe unrecorded Chinese alcohols we analyzed had a high EtOH concentration-a public health concern that is also presented by recorded alcohols. The high percentage of samples (65.5%) that had elevated acetaldehyde suggests the need to investigate the causes for this result and the need for steps to reduce acetaldehyde levels. The cumulative long-term risks of using high EtOH and high acetaldehyde Chinese spirits are heightened by the percentage of people in China who have a genetic trait for impaired acetaldehyde metabolism.

Indexed as

DistillationRural PopulationAcetaldehydeAlcoholic BeveragesChinaEthanolHumansMetals, HeavyMethanolAcetaldehydeEthanolMetals, HeavyMethanolAcetaldehydebai jiuChinese White SpiritsUnrecorded Alcohol白酒

Identifiers

PMID27984849
PMCPMC6680223
OpenAlexW2563176026

What OpenQuestion holds

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LicenceCC BY-NC
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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.