Evidence map›Paper›PMID 31752192›Full record

ArticleInternational journal of environmental research and public health2019

Valuing Acute Health Risks of Air Pollution in the Jinchuan Mining Area, China: A Choice Experiment with Perceived Exposure and Hazardousness as Co-Determinants.

Zhengtao Li

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Zhengtao LiSchool of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper analyzes the choice of illness-cure combinations to estimate people's willingness to pay (WTP) for the reduction of acute health risks correlated with air pollution caused by mining and smelting in the Jinchuan mining area, China. To improve explaining the power of choice experiment (CE), a random parameter logit model (RPL) was employed and extended by considering rank ordered choice sets and non-linear effects of health risk perception on choice behaviors. The results of this study indicated that the ordered RPL approach produced better results than the unordered alternative after comparing different modeling techniques. Perceived health risk, illness attributes, and the residents' external characteristics: income, education, age, family health experience, work environment and proximity to pollution source are important determinants of the Jinchuan people's choice mode for avoiding acute health risks caused by air pollution. Taking all acute illnesses investigated together, the mean Jinchuan household WTP for reducing acute health risk caused by air pollution is 146.69 RMB (abbreviation of Chinese yuan) per year (US$23.38, 0.31% of average yearly household income). On the basis of our findings, we conclude that virtually Jinchuan residents perceive air pollution as a serious health risk. To assist the residents to take appropriate preventive action, the local government should develop counseling and educational campaigns and institutionalize disclosure of air quality conditions.

Indexed as

Health BehaviorModels, StatisticalAir PollutionChinaEnvironmental PollutionFemaleHealthHumansIncomeMaleMiningWorkplaceair pollutionChinamodel under-specificationperceived health riskrandom parameter logit modelwillingness to pay

Identifiers

PMID31752192
PMCPMC6887755

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.