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Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010-16: a subnational analysis of cross-sectional surveys

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dc.contributor.authorPezzulo, Carlaen
dc.contributor.authorNilsen, Kristineen
dc.contributor.authorCarioli, Alessandraen
dc.contributor.authorTejedor-Garavito, Nataliaen
dc.contributor.authorHanspal, Sophie E.en
dc.contributor.authorHilber, Theodoren
dc.contributor.authorJames, William H. M.en
dc.contributor.authorRuktanonchai, Corrine W.en
dc.contributor.authorAlegana, Victoren
dc.contributor.authorSorichetta, Alessandroen
dc.contributor.authorWigley, Adelle S.en
dc.contributor.authorHornby, Graeme M.en
dc.contributor.authorMatthews, Zoeen
dc.contributor.authorTatem, Andrew J.en
dc.date.accessioned2022-04-26T12:55:32Zen
dc.date.available2022-04-26T12:55:32Zen
dc.date.issued2021-06en
dc.description.abstractBackground Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. Methods We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. Findings TFR varied from below replacement (2.1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. Interpretation Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.en
dc.description.notesWellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.en
dc.description.sponsorshipWellcome TrustWellcome TrustEuropean Commission; UK Foreign, Commonwealth and Development Office; Bill & Melinda Gates FoundationBill & Melinda Gates FoundationCGIARen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/S2214-109X(21)00082-6en
dc.identifier.issn2214-109Xen
dc.identifier.issue6en
dc.identifier.pmid34019836en
dc.identifier.urihttp://hdl.handle.net/10919/109746en
dc.identifier.volume9en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleGeographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010-16: a subnational analysis of cross-sectional surveysen
dc.title.serialLancet Global Healthen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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