Estimating the Per-Exposure Effect of Infectious Disease Interventions
The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can b...
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| Veröffentlicht in: | Epidemiology (Cambridge, Mass.) Mass.), 2014-01, Vol.25 (1), p.134-138 |
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| container_title | Epidemiology (Cambridge, Mass.) |
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| creator | O'Hagan, Justin J. Lipsitch, Marc Hernán, Miguel A. |
| description | The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142–151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers. |
| doi_str_mv | 10.1097/EDE.0000000000000003 |
| format | Article |
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Hygiene-occupational medicine ; Treatment Outcome ; Vaccination</subject><ispartof>Epidemiology (Cambridge, Mass.), 2014-01, Vol.25 (1), p.134-138</ispartof><rights>Copyright © 2013 Lippincott Williams & Wilkins, Inc</rights><rights>2014 by Lippincott Williams & Wilkins, Inc</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5093-395b91e52fbbf8f2c56a34200aedabd86207f6d877012a30bf882133278800423</citedby><cites>FETCH-LOGICAL-c5093-395b91e52fbbf8f2c56a34200aedabd86207f6d877012a30bf882133278800423</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24759034$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/24759034$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,776,780,799,881,4009,27901,27902,27903,57994,58227</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28157625$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24240656$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>O'Hagan, Justin J.</creatorcontrib><creatorcontrib>Lipsitch, Marc</creatorcontrib><creatorcontrib>Hernán, Miguel A.</creatorcontrib><title>Estimating the Per-Exposure Effect of Infectious Disease Interventions</title><title>Epidemiology (Cambridge, Mass.)</title><addtitle>Epidemiology</addtitle><description>The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142–151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.</description><subject>AIDS vaccines</subject><subject>Analytical estimating</subject><subject>Biological and medical sciences</subject><subject>Communicable Disease Control - methods</subject><subject>Communicable Disease Control - statistics & numerical data</subject><subject>Communicable Diseases - transmission</subject><subject>Disease transmission</subject><subject>Epidemiology</subject><subject>Estimation bias</subject><subject>General aspects</subject><subject>Humans</subject><subject>Infections</subject><subject>Infectious Disease</subject><subject>Infectious diseases</subject><subject>Inference</subject><subject>Medical sciences</subject><subject>Miscellaneous</subject><subject>Models, Statistical</subject><subject>Pathogens</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>Treatment Outcome</subject><subject>Vaccination</subject><issn>1044-3983</issn><issn>1531-5487</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1P3EAMhkcVVaG0_6BFuVTqJXQ-M5NLpQpCi4RED-15NMl62EA2sx0nfPz7Gu2WAhfmMpb92H5tM_ZB8EPBa_ulOW4O-dOnXrE9YZQojXZ2h2yudalqp3bZW8RLzoVVwrxhu1JLzStT7bGTBqd-FaZ-vCimJRQ_IZfN7TrhnKFoYoRuKlIsTsd7q08zFsc9QkAg1wT5GkbyjviOvY5hQHi__ffZ75Pm19GP8uz8--nRt7OyM7xWpMW0tQAjY9tGF2VnqqC05DzAIrQLV0luY7Vw1nIhg-IEOSmUktY5zrVU--zrpu56blew6Kh9DoNfZ5oh3_kUev80MvZLf5GuvXK105WmAp-3BXL6MwNOftVjB8MQRqDpvCDI1aTJEao3aJcTYob40EZwf38CTyfwz09AaQePJT4k_ds5AZ-2QMAuDDGHsevxP-eEsZU0xLkNd5MGWjVeDfMNZL-EMEzLlzR83KRe4pTyIwnW1Fxp9RcELKih</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>O'Hagan, Justin J.</creator><creator>Lipsitch, Marc</creator><creator>Hernán, Miguel A.</creator><general>Lippincott Williams & Wilkins, Inc</general><general>by Lippincott Williams & Wilkins, Inc</general><general>Lippincott Williams & Wilkins</general><scope>IQODW</scope><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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20140101</creationdate><title>Estimating the Per-Exposure Effect of Infectious Disease Interventions</title><author>O'Hagan, Justin J. ; Lipsitch, Marc ; Hernán, Miguel A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5093-395b91e52fbbf8f2c56a34200aedabd86207f6d877012a30bf882133278800423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>AIDS vaccines</topic><topic>Analytical estimating</topic><topic>Biological and medical sciences</topic><topic>Communicable Disease Control - methods</topic><topic>Communicable Disease Control - statistics & numerical data</topic><topic>Communicable Diseases - transmission</topic><topic>Disease transmission</topic><topic>Epidemiology</topic><topic>Estimation bias</topic><topic>General aspects</topic><topic>Humans</topic><topic>Infections</topic><topic>Infectious Disease</topic><topic>Infectious diseases</topic><topic>Inference</topic><topic>Medical sciences</topic><topic>Miscellaneous</topic><topic>Models, Statistical</topic><topic>Pathogens</topic><topic>Public health. 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Hygiene-occupational medicine</topic><topic>Treatment Outcome</topic><topic>Vaccination</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>O'Hagan, Justin J.</creatorcontrib><creatorcontrib>Lipsitch, Marc</creatorcontrib><creatorcontrib>Hernán, Miguel A.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Epidemiology (Cambridge, Mass.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>O'Hagan, Justin J.</au><au>Lipsitch, Marc</au><au>Hernán, Miguel A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the Per-Exposure Effect of Infectious Disease Interventions</atitle><jtitle>Epidemiology (Cambridge, Mass.)</jtitle><addtitle>Epidemiology</addtitle><date>2014-01-01</date><risdate>2014</risdate><volume>25</volume><issue>1</issue><spage>134</spage><epage>138</epage><pages>134-138</pages><issn>1044-3983</issn><eissn>1531-5487</eissn><abstract>The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. 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| ispartof | Epidemiology (Cambridge, Mass.), 2014-01, Vol.25 (1), p.134-138 |
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| source | Jstor Complete Legacy; MEDLINE; Journals@Ovid Complete |
| subjects | AIDS vaccines Analytical estimating Biological and medical sciences Communicable Disease Control - methods Communicable Disease Control - statistics & numerical data Communicable Diseases - transmission Disease transmission Epidemiology Estimation bias General aspects Humans Infections Infectious Disease Infectious diseases Inference Medical sciences Miscellaneous Models, Statistical Pathogens Public health. Hygiene Public health. Hygiene-occupational medicine Treatment Outcome Vaccination |
| title | Estimating the Per-Exposure Effect of Infectious Disease Interventions |
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