{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:12:52Z","timestamp":1790651572768,"version":"4.1.0"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2014,5,20]],"date-time":"2014-05-20T00:00:00Z","timestamp":1400544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AI085073"],"award-info":[{"award-number":["R01 AI085073"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2014,9]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>Interference occurs when the treatment of one person affects the outcome of another. For example, in infectious diseases, whether one individual is vaccinated may affect whether another individual becomes infected or develops disease. Quantifying such indirect (or spillover) effects of vaccination could have important public health or policy implications. In this article we use recently developed inverse\u2010probability weighted (IPW) estimators of treatment effects in the presence of interference to analyze an individually\u2010randomized, placebo\u2010controlled trial of cholera vaccination that targeted 121,982 individuals in Matlab, Bangladesh. Because these IPW estimators have not been employed previously, a simulation study was also conducted to assess the empirical behavior of the estimators in settings similar to the cholera vaccine trial. Simulation study results demonstrate the IPW estimators can yield unbiased estimates of the direct, indirect, total, and overall effects of vaccination when there is interference provided the untestable no unmeasured confounders assumption holds and the group\u2010level propensity score model is correctly specified. Application of the IPW estimators to the cholera vaccine trial indicates the presence of interference. For example, the IPW estimates suggest on average 5.29 fewer cases of cholera per 1000 person\u2010years (95% confidence interval 2.61, 7.96) will occur among unvaccinated individuals within neighborhoods with 60% vaccine coverage compared to neighborhoods with 32% coverage. Our analysis also demonstrates how not accounting for interference can render misleading conclusions about the public health utility of vaccination.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/biom.12184","type":"journal-article","created":{"date-parts":[[2014,5,20]],"date-time":"2014-05-20T07:52:56Z","timestamp":1400572376000},"page":"731-741","source":"Crossref","is-referenced-by-count":61,"title":["Assessing effects of cholera vaccination in the presence of interference"],"prefix":"10.1093","volume":"70","author":[{"given":"Carolina","family":"Perez\u2010Heydrich","sequence":"first","affiliation":[{"name":"Department of Biological Sciences Meredith College  Raleigh, North Carolina U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael G.","family":"Hudgens","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina  Chapel Hill, North Carolina U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Elizabeth","family":"Halloran","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington  Seattle, Washington U.S.A."},{"name":"Fred Hutchinson Cancer Research Center  Seattle, Washington U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John D.","family":"Clemens","sequence":"additional","affiliation":[{"name":"Department of Epidemiology University of California  Los Angeles, California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of International Health Johns Hopkins University  Baltimore, Maryland U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael E.","family":"Emch","sequence":"additional","affiliation":[{"name":"Department of Geography University of North Carolina  Chapel Hill, North Carolina U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2014,5,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(05)66550-6"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vaccine.2009.03.085"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2008.11.004"},{"key":"e_1_2_8_5_1","unstructured":"Bates D. Maechler M. andBolker B.(2011).lme4: Linear mixed\u2010effects models using S4 classes. R package version 0.999375\u201342.http:\/\/CRAN.R\u2010project.org\/package=lme4"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/158.1.60"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470977811"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-199503000-00010"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000292"},{"key":"e_1_2_8_10_1","first-page":"241","article-title":"Estimation of vaccine efficacy for both susceptibility to infection and reduction in infectiousness for prophylactic HIV vaccines with partner augmentation","author":"Longini I. M.","year":"2002","journal-title":"The Quantitative Evaluation of HIV Prevention Programs"},{"key":"e_1_2_8_11_1","unstructured":"R Core Team. (2013).R: A Language and Environment for Statistical Computing. Vienna Austria: R Foundation for Statistical Computing.http:\/\/www.R\u2010project.org"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0022971"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000636"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1198\/000313002753631330"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/93.4.1003"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280210386779"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01619.x"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2Fbiom.12184","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/biom.12184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T13:00:33Z","timestamp":1706274033000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/70\/3\/731-741\/7419918"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,5,20]]},"references-count":17,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,9]]}},"alternative-id":["10.1111\/biom.12184"],"URL":"https:\/\/doi.org\/10.1111\/biom.12184","archive":["Portico"],"relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"value":"0006-341X","type":"print"},{"value":"1541-0420","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,5,20]]}}}