https://doi.org/10.4081/gh.2022.1080
Bayesian spatial modelling of contraception effects on fertility in Mexican municipalities in 2020
Submitted: 9 February 2022
Accepted: 30 April 2022
Published: 17 May 2022
Accepted: 30 April 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The prevalence and use of contraceptive methods is an essential element to explain the behaviour of fertility and population growth. The objective of this study was to analyse the spatial correlation between the use of contraceptive methods in women of childbearing age and fertility levels observed in Mexico’s municipalities in 2020. Data on contraceptive use are from the National Survey of Demographic Dynamics (ENADID) 2018, while fertility rates were estimated from vital statistics and population census data. Three Bayesian spatial models including fixed effects, random effects and spatial effects were employed. The models were estimated using the integral nested Laplace approximation (INLA) package available in the R language. The results reveal the existence of important regional inequalities associated with the use and prevalence of contraceptive methods, which generate marked differences in observed levels of fertility between municipalities.
Blangiardo M, Cameletti M, 2015. Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley & Sons, Chichester, UK.
DOI: https://doi.org/10.1002/9781118950203
Bongaarts J, Casterline J, B, 2018. From fertility preferences to reproductive outcomes in the developing world. Popul Dev Rev 44:793-809.
DOI: https://doi.org/10.1111/padr.12197
Caldwell JC, 1982. Theory of fertility decline. Academic Press, London, UK.
Cavallaro FL, Benova L, Owolabi OO, Ali M, 2020. A systematic review of the effectiveness of counselling strategies for modern contraceptive methods: what works and what doesn’t? BMJ Sex Reprod Health 46:254-69.
DOI: https://doi.org/10.1136/bmjsrh-2019-200377
Chesnais JC, 1992. The demographic transition: stages, patterns, and economic implications. Oxford University Press, Oxford, UK.
Davis K, Blake J, 1956. Social structure and fertility: an analytic framework. Econ Dev Cult Change 4:211-35.
DOI: https://doi.org/10.1086/449714
De la Vara-Salazar E, Hubert C, Saavedra-Avendaño B, Suárez-López L, Villalobos A, Ávila-Burgos L, Hernández-Serrato MI, Schiavon R, Darney BG 2020. Provisión y tipo de métodos anticonceptivos en el posparto inmediato en México, 2018-2019. Salud Publ Mexico 62:637-47.
DOI: https://doi.org/10.21149/11850
Garg S, Singh R, 2014. Need for integration of gender equity in family planning services. Indian J Med Res 140:S147-51.
Gómez RV, 2020. Bayesian inference with INLA. CRC Press, Boca Raton, FL, USA.
Grindlay K, Wollum A, Karver, J, Grossman D, 2021. Over-the-counter oral contraceptive use among women in Mexico: results from a national survey. BMJ Sex Reprod Health 47:205-10.
DOI: https://doi.org/10.1136/bmjsrh-2020-200778
Instituto Nacional de Estadística y Geografía (INEGI), 2018. Encuesta nacional de la dinámica demográfica: diseño muestral. INEGI, México. Available from: https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/702825188504.pdf Accessed: November 15, 2021.
Juarez F, Gayet C, Mejia-Pailles G, 2018. Factors associated with unmet need for contraception in Mexico: evidence from the National Survey of Demographic Dynamics 2014. BMC Public Health 18:1-8.
DOI: https://doi.org/10.1186/s12889-018-5439-0
Lesthaeghe R, van de Kaa DJ, 1986. Twee Demografische Transities? (Two demographic transitions?). In: D.J. van de Kaa and R. Lesthaeghe (Eds.), Bevolking: Groei en Krimp (Population: growth and decline). Van Loghum Slaterus, Deventer, The Netherlands, pp. 9-24.
Mauldin WP, Segal, SJ, 1988. Prevalence of contraceptive use: trends and issues. Stud Fam Plann 19:335-53.
DOI: https://doi.org/10.2307/1966628
Moraga P, 2019. Geospatial health data: modeling and visualization with R-INLA and Shiny. Chapman & Hall/CRC Press, London, UK.
DOI: https://doi.org/10.1201/9780429341823
Notestein F, 1945. Population: the long view. In: T. Schultz (Ed.), Food for the World. University of Chicago Press, Chicago, IL, USA.
Palma Cabrera Y, 2005. Políticas de población y planificación familiar. UNAM Demos 16:24-25.
Pressat R, 2020. Demographic analysis: projections on natality, fertility and replacement. Routledge, London, UK.
DOI: https://doi.org/10.4324/9780203793497
Pritchett L, 1994. Desired fertility and the impact of population policies. Popul Dev Rev 20:1-55.
DOI: https://doi.org/10.2307/2137629
R Core Team, 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/ Accessed: November 12, 2021.
Riebler A, Sørbye SH, Simpson D, Rue A, 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Stat Methods Med Res 25:1145-65.
DOI: https://doi.org/10.1177/0962280216660421
Rue H, Martino S, Chopin N 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc B 71:319-92.
DOI: https://doi.org/10.1111/j.1467-9868.2008.00700.x
Schrödle B, Held L, 2011. Spatio-temporal disease mapping using INLA. Environmetrics 22:725-34.
DOI: https://doi.org/10.1002/env.1065
Sen A, 2001. Population and gender equity. J Public Health Policy 22:169-74.
DOI: https://doi.org/10.2307/3343457
Shuiling K, Likis FE, 2013. Women’s gynecologic health. Jones and Bartlett Learning, Burlington, MA, USA.
Stover J, 1998. Revising the proximate determinants of fertility framework: What have we learned in the past 20 years? Stud Fam Plann 29:255-64.
DOI: https://doi.org/10.2307/172272
Tone, A. 2002. Making room for rubbers: Gender, technology, and birth control before the pill. Hist Technol 18:51-76.
DOI: https://doi.org/10.1080/07341510290028756
Torres-Pereda P, Heredia-Pi IB, Ibanez-Cuevas M, Avila-Burgos L 2019. Quality of family planning services in Mexico: The perspective of demand. PLoS One 14:e0210319.
DOI: https://doi.org/10.1371/journal.pone.0210319
Tuiran R, Partida V, Mojarro O, Zúñiga E, 2002. Fertility in Mexico: trends and forecast. Department of Economic and Social Affairs, Population Division, United Nations, New York, NY, USA, pp. 483-506.
Van de Kaa DJ, 2002. The idea of a second demographic transition in industrialized countries. Birth 35:1-34.
Wang X, Yue YR, Faraway JJ, 2018. Bayesian regression modeling with INLA. Chapman and Hall/CRC Press, London, UK.
DOI: https://doi.org/10.1201/9781351165761
Watkins SC, Menken JA, Bongaarts J, 1987. Demographic foundations of family change. Am Soc Rev 52:346-58.
DOI: https://doi.org/10.2307/2095354
Zavala de Cosío M, 1992. Cambios de fecundidad en México y políticas de población. El Colegio de México/Fondo de Cultura Económica, México.
How to Cite
Núñez Medina, G. . (2022). Bayesian spatial modelling of contraception effects on fertility in Mexican municipalities in 2020. Geospatial Health, 17(1). https://doi.org/10.4081/gh.2022.1080
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