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Three contraceptive acceptance strategies

dc.contributor.authorPotter, R. G.en_US
dc.contributor.authorKobrin, F.en_US
dc.contributor.authorLangsten, R.en_US
dc.date.accessioned2006-04-07T17:36:58Z
dc.date.available2006-04-07T17:36:58Z
dc.date.issued1979-02en_US
dc.identifier.citationPotter, R. G., Kobrin, F., Langsten, R. (1979/02)."Three contraceptive acceptance strategies." Mathematical Biosciences 43(1-2): 1-22. <http://hdl.handle.net/2027.42/23630>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6VHX-45GWN52-26/2/4c74140e7c688b3db4508688d64d8c1fen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/23630
dc.description.abstractThree main classes of contraceptive acceptance strategy may be distinguished: "fixed duration T" (women counseled to accept T months after childbirth); "postamenorrheic" (accept directly after the first postpartum menses), and "mixed T" (accept T months after childbirth or after first menses, whichever occurs sooner). Any two strategies may be compared by means of a probability model simulating the first passage times from childbirth to next pregnancy of two cohorts of mothers identical in their fecundity and in the effectiveness and continuation with which contraception is practiced, but contrasting in their acceptance regimens. Of particular interest is the class of mixed-T strategies, which have not previously been analyzed. The efficiency of the mixed-T rule at least equals, and for most T-values exceeds that of the corresponding fixed-duration rule both in the short run (lower cumulative pregnancy rate during the first few months) and in the long run (greater mean interval to next conception). Conditions for the superiority of the mixed-T over the postamenorrheic are also given. Several results are illustrated with reference to a Bangladesh subpopulation.en_US
dc.format.extent1295427 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleThree contraceptive acceptance strategiesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumPopulation Studies Center, University of Michigan, Ann Arbor, Michigan 48109, USAen_US
dc.contributor.affiliationotherDepartment of Sociology, Brown University, Providence, Rhode Island 02912, USAen_US
dc.contributor.affiliationotherDepartment of Sociology, Brown University, Providence, Rhode Island 02912, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/23630/1/0000593.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0025-5564(79)90101-9en_US
dc.identifier.sourceMathematical Biosciencesen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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