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Measuring "Fearonomic Effects" in Valuing Therapies: An Application to COVID-19 in China.

Identifieur interne : 000119 ( Main/Exploration ); précédent : 000118; suivant : 000120

Measuring "Fearonomic Effects" in Valuing Therapies: An Application to COVID-19 in China.

Auteurs : Siyu Ma [États-Unis] ; David D. Kim [États-Unis] ; Joshua T. Cohen [États-Unis] ; Peter J. Neumann [États-Unis]

Source :

RBID : pubmed:33127009

Descripteurs français

English descriptors

Abstract

OBJECTIVES

To develop a checklist that helps quantify the economic impact associated with fear of contagion and to illustrate how one might use the checklist by presenting a case study featuring China during the coronavirus disease 2019 (COVID-19) outbreak.

METHODS

Based on "fearonomic effects," a qualitative framework that conceptualizes the direct and indirect economic effects caused by the fear of contagion, we created a checklist to facilitate empirical estimation. As a case study, we first identified relevant sectors affected by China's lockdown policies implemented just before the Lunar New Year (LNY) week. To quantify the immediate impact, we then estimated the projected spending levels in 2020 in the absence of COVID-19 and compared these projections with actual spending during the LNY week. Data sources used include Chinese and global websites. To characterize uncertainty, we reported upper and lower bound estimates and calculated midpoints for each range.

RESULTS

The COVID-19 epidemic is estimated to cost China's economy $283 billion ($196-369 billion), that is, ¥2.0 trillion renminbi (¥1.4-¥2.6 trillion), during the LNY week. Reduced restaurant and movie theater business ($106 [$103-$109] billion, 37.5% [36.4%-38.5%]) and reduced public transportation utilization ($96 [$13-$179] billion dollars, 33.9% [4.6%-63.3%]) explain most of this loss, followed by travel restrictions and the resulting loss of hotel business and tourism ($80.36 billion, 28.4%).

CONCLUSION

Our checklist can help quantify the immediate and near-term impact of COVID-19 on a country's economy. It can also help researchers and policy makers consider the broader economic and social consequences when valuing future vaccines and treatments.


DOI: 10.1016/j.jval.2020.06.002
PubMed: 33127009
PubMed Central: PMC7384788


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<b>OBJECTIVES</b>
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<b>METHODS</b>
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<p>Based on "fearonomic effects," a qualitative framework that conceptualizes the direct and indirect economic effects caused by the fear of contagion, we created a checklist to facilitate empirical estimation. As a case study, we first identified relevant sectors affected by China's lockdown policies implemented just before the Lunar New Year (LNY) week. To quantify the immediate impact, we then estimated the projected spending levels in 2020 in the absence of COVID-19 and compared these projections with actual spending during the LNY week. Data sources used include Chinese and global websites. To characterize uncertainty, we reported upper and lower bound estimates and calculated midpoints for each range.</p>
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<b>RESULTS</b>
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<p>The COVID-19 epidemic is estimated to cost China's economy $283 billion ($196-369 billion), that is, ¥2.0 trillion renminbi (¥1.4-¥2.6 trillion), during the LNY week. Reduced restaurant and movie theater business ($106 [$103-$109] billion, 37.5% [36.4%-38.5%]) and reduced public transportation utilization ($96 [$13-$179] billion dollars, 33.9% [4.6%-63.3%]) explain most of this loss, followed by travel restrictions and the resulting loss of hotel business and tourism ($80.36 billion, 28.4%).</p>
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<b>CONCLUSION</b>
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<Citation>Value Health. 2018 Feb;21(2):131-139</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">29477390</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Value Health. 2014 Nov;17(7):A450</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">27201236</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Health Policy. 2008 Oct;88(1):110-20</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">18436332</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>BMC Med. 2016 Jan 05;14:2</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">26732586</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>BMJ Glob Health. 2016 Nov 9;1(3):e000111</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">28588965</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>JAMA. 2020 Apr 7;323(13):1239-1242</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">32091533</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Patient. 2019 Dec;12(6):631-638</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">31347011</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Proc Natl Acad Sci U S A. 2014 Aug 26;111(34):12313-9</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">25136129</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Med Decis Making. 2020 Apr;40(3):251-253</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">32428432</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
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<name sortKey="Neumann, Peter J" sort="Neumann, Peter J" uniqKey="Neumann P" first="Peter J" last="Neumann">Peter J. Neumann</name>
</country>
</tree>
</affiliations>
</record>

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