Testing for fertility stalls in demographic and health surveys
Identifieur interne : 000367 ( Pmc/Checkpoint ); précédent : 000366; suivant : 000368Testing for fertility stalls in demographic and health surveys
Auteurs : Michel L. Garenne [France, Afrique du Sud]Source :
- Population Health Metrics [ 1478-7954 ] ; 2011.
Abstract
This study compares two methods for testing fertility trends and fertility stalls using Demographic and Health Surveys data. The first method is based on linear regression and uses the equivalence of period and cohort estimates with the same cumulative fertility at age 40, the same number of births, and the same distribution of women by parity. The second method is based on logistic regression. It assumes that the age pattern of fertility is constant over short periods of time. Both methods were applied to fertility trends in several African countries (Ghana, Kenya, Madagascar, Nigeria, Rwanda, Senegal, Tanzania, and Zambia). The two methods were found to predict similar values of cumulative fertility, to produce consistent slopes, to document fertility trends the same way, and to characterize fertility stalls with similar statistical evidence. They can also be used to refute apparent fertility stalls obtained when comparing two point estimates from two successive surveys.
Url:
DOI: 10.1186/1478-7954-9-59
PubMed: 22151961
PubMed Central: 3271990
Affiliations:
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Garenne</name><affiliation wicri:level="3"><nlm:aff id="I1">Institut de Recherche pour le Développement, UMI Résiliences, Paris, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Institut de Recherche pour le Développement, UMI Résiliences, Paris</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:aff id="I2">Institut Pasteur, Unité d'Epidémiologie des Maladies Emergentes, Paris, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Institut Pasteur, Unité d'Epidémiologie des Maladies Emergentes, Paris</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="I3">Witwatersrand University, School of Public Health, Johannesburg, South Africa</nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Witwatersrand University, School of Public Health, Johannesburg</wicri:regionArea><wicri:noRegion>Johannesburg</wicri:noRegion></affiliation></author></analytic><series><title level="j">Population Health Metrics</title><idno type="eISSN">1478-7954</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This study compares two methods for testing fertility trends and fertility stalls using Demographic and Health Surveys data. The first method is based on linear regression and uses the equivalence of period and cohort estimates with the same cumulative fertility at age 40, the same number of births, and the same distribution of women by parity. The second method is based on logistic regression. It assumes that the age pattern of fertility is constant over short periods of time. Both methods were applied to fertility trends in several African countries (Ghana, Kenya, Madagascar, Nigeria, Rwanda, Senegal, Tanzania, and Zambia). The two methods were found to predict similar values of cumulative fertility, to produce consistent slopes, to document fertility trends the same way, and to characterize fertility stalls with similar statistical evidence. They can also be used to refute apparent fertility stalls obtained when comparing two point estimates from two successive surveys.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Coale, Aj" uniqKey="Coale A">AJ Coale</name></author><author><name sortKey="Watkins, Sc" uniqKey="Watkins S">SC Watkins</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van De Walle, E" uniqKey="Van De Walle E">E van de Walle</name></author><author><name sortKey="Knodel, J" uniqKey="Knodel J">J Knodel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pantelides, Ea" uniqKey="Pantelides E">EA Pantelides</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bongaarts, J" uniqKey="Bongaarts J">J Bongaarts</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bongaarts, J" uniqKey="Bongaarts J">J 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Popul Health Metr</journal-id><journal-title-group><journal-title>Population Health Metrics</journal-title></journal-title-group><issn pub-type="epub">1478-7954</issn><publisher><publisher-name>BioMed Central</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22151961</article-id><article-id pub-id-type="pmc">3271990</article-id><article-id pub-id-type="publisher-id">1478-7954-9-59</article-id><article-id pub-id-type="doi">10.1186/1478-7954-9-59</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research</subject></subj-group></article-categories><title-group><article-title>Testing for fertility stalls in demographic and health surveys</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Garenne</surname><given-names>Michel L</given-names></name><xref ref-type="aff" rid="I1">1</xref><xref ref-type="aff" rid="I2">2</xref><xref ref-type="aff" rid="I3">3</xref><email>mgarenne@pasteur.fr</email></contrib></contrib-group><aff id="I1"><label>1</label>Institut de Recherche pour le Développement, UMI Résiliences, Paris, France</aff><aff id="I2"><label>2</label>Institut Pasteur, Unité d'Epidémiologie des Maladies Emergentes, Paris, France</aff><aff id="I3"><label>3</label>Witwatersrand University, School of Public Health, Johannesburg, South Africa</aff><pub-date pub-type="collection"><year>2011</year></pub-date><pub-date pub-type="epub"><day>8</day><month>12</month><year>2011</year></pub-date><volume>9</volume><fpage>59</fpage><lpage>59</lpage><history><date date-type="received"><day>9</day><month>12</month><year>2010</year></date><date date-type="accepted"><day>8</day><month>12</month><year>2011</year></date></history><permissions><copyright-statement>Copyright ©2011 Garenne; licensee BioMed Central Ltd.</copyright-statement><copyright-year>2011</copyright-year><copyright-holder>Garenne; licensee BioMed Central Ltd.</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:href="http://www.pophealthmetrics.com/content/9/1/59"></self-uri><abstract><p>This study compares two methods for testing fertility trends and fertility stalls using Demographic and Health Surveys data. The first method is based on linear regression and uses the equivalence of period and cohort estimates with the same cumulative fertility at age 40, the same number of births, and the same distribution of women by parity. The second method is based on logistic regression. It assumes that the age pattern of fertility is constant over short periods of time. Both methods were applied to fertility trends in several African countries (Ghana, Kenya, Madagascar, Nigeria, Rwanda, Senegal, Tanzania, and Zambia). The two methods were found to predict similar values of cumulative fertility, to produce consistent slopes, to document fertility trends the same way, and to characterize fertility stalls with similar statistical evidence. They can also be used to refute apparent fertility stalls obtained when comparing two point estimates from two successive surveys.</p></abstract><kwd-group><kwd>Demographic transition</kwd><kwd>Fertility decline</kwd><kwd>Fertility stall</kwd><kwd>Statistical methods</kwd><kwd>Linear regression</kwd><kwd>Logistic regression</kwd><kwd>Poisson regression</kwd><kwd>sub-Saharan Africa</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>Afrique du Sud</li><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Garenne, Michel L" sort="Garenne, Michel L" uniqKey="Garenne M" first="Michel L" last="Garenne">Michel L. Garenne</name></region><name sortKey="Garenne, Michel L" sort="Garenne, Michel L" uniqKey="Garenne M" first="Michel L" last="Garenne">Michel L. Garenne</name></country><country name="Afrique du Sud"><noRegion><name sortKey="Garenne, Michel L" sort="Garenne, Michel L" uniqKey="Garenne M" first="Michel L" last="Garenne">Michel L. Garenne</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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