Health insurance system and resilience to epidemics
We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID‐19. Our findings can be summarized as follows....
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Veröffentlicht in: | Risk analysis 2023-01, Vol.43 (1), p.97-114 |
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description | We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID‐19. Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost‐efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction. |
doi_str_mv | 10.1111/risa.14005 |
format | Article |
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Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost‐efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.</description><identifier>ISSN: 0272-4332</identifier><identifier>EISSN: 1539-6924</identifier><identifier>DOI: 10.1111/risa.14005</identifier><identifier>PMID: 36089331</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Adaptation, Psychological ; Communicable Disease Control ; Contagion ; Coping ; COVID-19 ; COVID-19 - epidemiology ; Decentralization ; Disease control ; epidemic ; Epidemics ; Health insurance ; Humans ; Infections ; Infectious diseases ; Insurance ; Insurance, Health ; market insurance ; Markets ; Optimization ; public insurance ; Resilience ; Risk assessment ; social distancing/lockdown ; Technology ; Tests ; trace and test</subject><ispartof>Risk analysis, 2023-01, Vol.43 (1), p.97-114</ispartof><rights>2022 Society for Risk Analysis.</rights><rights>2023 Society for Risk Analysis.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2875-a93794a0d01d04097e37348fabfc3856b89e143f56622d2e0b88ec3094b552d13</citedby><cites>FETCH-LOGICAL-c2875-a93794a0d01d04097e37348fabfc3856b89e143f56622d2e0b88ec3094b552d13</cites><orcidid>0000-0003-1470-0115</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Frisa.14005$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Frisa.14005$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36089331$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hong, Jimin</creatorcontrib><creatorcontrib>Seog, Sung Hun</creatorcontrib><title>Health insurance system and resilience to epidemics</title><title>Risk analysis</title><addtitle>Risk Anal</addtitle><description>We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID‐19. Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost‐efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.</description><subject>Adaptation, Psychological</subject><subject>Communicable Disease Control</subject><subject>Contagion</subject><subject>Coping</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Decentralization</subject><subject>Disease control</subject><subject>epidemic</subject><subject>Epidemics</subject><subject>Health insurance</subject><subject>Humans</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Insurance</subject><subject>Insurance, Health</subject><subject>market insurance</subject><subject>Markets</subject><subject>Optimization</subject><subject>public insurance</subject><subject>Resilience</subject><subject>Risk assessment</subject><subject>social distancing/lockdown</subject><subject>Technology</subject><subject>Tests</subject><subject>trace and test</subject><issn>0272-4332</issn><issn>1539-6924</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90M1LwzAYx_EgipvTi3-AFLyI0JnkSdrkOIa6wUDw5RzS9ilm9GUmLbL_3s5NDx7MJRA-_AhfQi4ZnbLh3HkX7JQJSuURGTMJOk40F8dkTHnKYwHAR-QshDWlbDDpKRlBQpUGYGMCC7RV9x65JvTeNjlGYRs6rCPbFJHH4CqHu9eujXDjCqxdHs7JSWmrgBeHe0LeHu5f54t49fS4nM9Wcc5VKmOrIdXC0oKyggqqU4QUhCptVuagZJIpjUxAKZOE84IjzZTCHKgWmZS8YDAhN_vdjW8_egydqV3Isapsg20fDE8ZAFWg-UCv_9B12_tm-N2g0kRBIocwE3K7V7lvQ_BYmo13tfVbw6jZpTS7lOY75YCvDpN9VmPxS3_aDYDtwaercPvPlHlevsz2o1_ApHur</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Hong, Jimin</creator><creator>Seog, Sung Hun</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U7</scope><scope>7U9</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>FQK</scope><scope>FR3</scope><scope>H94</scope><scope>JBE</scope><scope>JQ2</scope><scope>KR7</scope><scope>M7N</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1470-0115</orcidid></search><sort><creationdate>202301</creationdate><title>Health insurance system and resilience to epidemics</title><author>Hong, Jimin ; Seog, Sung Hun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2875-a93794a0d01d04097e37348fabfc3856b89e143f56622d2e0b88ec3094b552d13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptation, Psychological</topic><topic>Communicable Disease Control</topic><topic>Contagion</topic><topic>Coping</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>Decentralization</topic><topic>Disease control</topic><topic>epidemic</topic><topic>Epidemics</topic><topic>Health insurance</topic><topic>Humans</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Insurance</topic><topic>Insurance, Health</topic><topic>market insurance</topic><topic>Markets</topic><topic>Optimization</topic><topic>public insurance</topic><topic>Resilience</topic><topic>Risk assessment</topic><topic>social distancing/lockdown</topic><topic>Technology</topic><topic>Tests</topic><topic>trace and test</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hong, Jimin</creatorcontrib><creatorcontrib>Seog, Sung Hun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>International Bibliography of the Social Sciences</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Risk analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hong, Jimin</au><au>Seog, Sung Hun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Health insurance system and resilience to epidemics</atitle><jtitle>Risk analysis</jtitle><addtitle>Risk Anal</addtitle><date>2023-01</date><risdate>2023</risdate><volume>43</volume><issue>1</issue><spage>97</spage><epage>114</epage><pages>97-114</pages><issn>0272-4332</issn><eissn>1539-6924</eissn><abstract>We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID‐19. 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subjects | Adaptation, Psychological Communicable Disease Control Contagion Coping COVID-19 COVID-19 - epidemiology Decentralization Disease control epidemic Epidemics Health insurance Humans Infections Infectious diseases Insurance Insurance, Health market insurance Markets Optimization public insurance Resilience Risk assessment social distancing/lockdown Technology Tests trace and test |
title | Health insurance system and resilience to epidemics |
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