Skip to main content
Top
Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Research article

Subnational estimation of modern contraceptive prevalence in five sub-Saharan African countries: a Bayesian hierarchical approach

Authors: Qingfeng Li, Thomas A. Louis, Li Liu, Chenguang Wang, Amy O. Tsui

Published in: BMC Public Health | Issue 1/2019

Login to get access

Abstract

Background

Global monitoring efforts have relied on national estimates of modern contraceptive prevalence rate (mCPR) for many low-income countries. However, most contraceptive delivery programs are implemented by health departments at lower administrative levels, reflecting a persisting gap between the availability of and need for subnational mCPR estimates.

Methods

Using woman-level data from multiple semi-annual national survey rounds conducted between 2013 and 2016 in five sub-Saharan African countries (Burkina Faso, Ethiopia, Ghana, Kenya, and Uganda) by the Performance, Monitoring and Accountability 2020 project, we propose a Bayesian Hierarchical Model with a standard set of covariates and temporally correlated random effects to estimate the level and trend of mCPR for first level administrative divisions in each country.

Results

There is considerable narrowing of the uncertainty interval (UI) around the model-based estimates, compared to the estimates directly based on the survey data. We find substantial variations in the estimated subnational mCPRs. Uganda, for example, shows a gain in mCPR of 6.4% (95% UI: 4.5–8.3) based on model estimates of 20.9% (19.6–22.2) in mid-2014 and 27.3% (26.0–28.8) in mid-2016, with change across 10 regions ranging from − 0.6 points in Karamoja to 9.4 points in Central 2 region. The lower bound of the UIs of the change over four rounds was above 0 in 6 regions. Similar upward trends are observed for most regions in the other four countries, and there is noticeable within-country geographic variation.

Conclusions

Reliable subnational estimates of mCPR empower health departments in evidence-based policy making. Despite nationally increasing mCPRs, regional disparities exist within countries suggesting uneven contraceptive access. Raising investments in disadvantaged areas may be warranted to increase equity in access to modern contraceptive methods.
Appendix
Available only for authorised users
Literature
1.
go back to reference Cleland J, Bernstein S, Ezeh A, Faundes A, Glasier A, Innis J. Family planning: the unfinished agenda. Lancet. 2006;368(9549):1810–27.PubMedCrossRef Cleland J, Bernstein S, Ezeh A, Faundes A, Glasier A, Innis J. Family planning: the unfinished agenda. Lancet. 2006;368(9549):1810–27.PubMedCrossRef
2.
go back to reference Fortney JA. The importance of family planning in reducing maternal mortality. Stud Fam Plan. 1987;18(2):109–14.CrossRef Fortney JA. The importance of family planning in reducing maternal mortality. Stud Fam Plan. 1987;18(2):109–14.CrossRef
3.
go back to reference Gwako EL. Conjugal power in rural Kenya families: its influence on women's decisions about family size and family planning practices. Sex Roles. 1997;36(3–4):127–47.PubMedCrossRef Gwako EL. Conjugal power in rural Kenya families: its influence on women's decisions about family size and family planning practices. Sex Roles. 1997;36(3–4):127–47.PubMedCrossRef
4.
go back to reference Morgan SP, Niraula BB. Gender Inequality and Fertility in two Nepali villages. Popul Dev Rev. 1995;21(3):541–61.CrossRef Morgan SP, Niraula BB. Gender Inequality and Fertility in two Nepali villages. Popul Dev Rev. 1995;21(3):541–61.CrossRef
5.
go back to reference Sinha N. Fertility, child work, and schooling consequences of family planning programs: evidence from an experiment in rural Bangladesh. Econ Dev Cult Chang. 2005;54(1):97–128.CrossRef Sinha N. Fertility, child work, and schooling consequences of family planning programs: evidence from an experiment in rural Bangladesh. Econ Dev Cult Chang. 2005;54(1):97–128.CrossRef
6.
go back to reference Chola L, McGee S, Tugendhaft A, Buchmann E, Hofman K. Scaling up family planning to reduce maternal and child mortality: the potential costs and benefits of modern contraceptive use in South Africa. PLoS One. 2015;10(6):e0130077.PubMedPubMedCentralCrossRef Chola L, McGee S, Tugendhaft A, Buchmann E, Hofman K. Scaling up family planning to reduce maternal and child mortality: the potential costs and benefits of modern contraceptive use in South Africa. PLoS One. 2015;10(6):e0130077.PubMedPubMedCentralCrossRef
7.
go back to reference Bailey MJ, Malkova O, Norling J. Do family planning programs decrease poverty? Evidence from public census data. CESifo economic studies. 2014;60(2):312–37.PubMedPubMedCentralCrossRef Bailey MJ, Malkova O, Norling J. Do family planning programs decrease poverty? Evidence from public census data. CESifo economic studies. 2014;60(2):312–37.PubMedPubMedCentralCrossRef
9.
go back to reference Corsi DJ, Neuman M, Finlay JE, Subramanian SV. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602–13.PubMedCrossRef Corsi DJ, Neuman M, Finlay JE, Subramanian SV. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602–13.PubMedCrossRef
10.
go back to reference Fabic MS, Choi Y, Bird S. A systematic review of demographic and health surveys: data availability and utilization for research. Bull World Health Organ 2012; 90(8): 604–612. Fabic MS, Choi Y, Bird S. A systematic review of demographic and health surveys: data availability and utilization for research. Bull World Health Organ 2012; 90(8): 604–612.
11.
go back to reference Gonzalez ME, Hoza C. Small-area estimation with application to unemployment and housing estimates. J Am Stat Assoc. 1978;73(361):7–15.CrossRef Gonzalez ME, Hoza C. Small-area estimation with application to unemployment and housing estimates. J Am Stat Assoc. 1978;73(361):7–15.CrossRef
12.
go back to reference Li W, Kelsey JL, Zhang Z, et al. Small-area estimation and prioritizing communities for obesity control in Massachusetts. Am J Public Health. 2009;99(3):511–9.PubMedPubMedCentralCrossRef Li W, Kelsey JL, Zhang Z, et al. Small-area estimation and prioritizing communities for obesity control in Massachusetts. Am J Public Health. 2009;99(3):511–9.PubMedPubMedCentralCrossRef
13.
go back to reference Asiimwe JB, Jehopio P, Atuhaire LK, Mbonye AK. Examining small area estimation techniques for public health intervention: lessons from application to under-5 mortality data in Uganda. J Public Health Policy. 2011;32(1):1–15.PubMedCrossRef Asiimwe JB, Jehopio P, Atuhaire LK, Mbonye AK. Examining small area estimation techniques for public health intervention: lessons from application to under-5 mortality data in Uganda. J Public Health Policy. 2011;32(1):1–15.PubMedCrossRef
14.
15.
go back to reference Pramanik S, Muthusamy N, Gera R, Laxminarayan R. Vaccination coverage in India: a small area estimation approach. Vaccine. 2015;33(14):1731–8.PubMedCrossRef Pramanik S, Muthusamy N, Gera R, Laxminarayan R. Vaccination coverage in India: a small area estimation approach. Vaccine. 2015;33(14):1731–8.PubMedCrossRef
16.
go back to reference Lin YH, McLain AC, Probst JC, Bennett KJ, Qureshi ZP, Eberth JM. Health-related quality of life among adults 65 years and older in the United States, 2011-2012: a multilevel small area estimation approach. Ann Epidemiol. 2017;27(1):52–8.PubMedCrossRef Lin YH, McLain AC, Probst JC, Bennett KJ, Qureshi ZP, Eberth JM. Health-related quality of life among adults 65 years and older in the United States, 2011-2012: a multilevel small area estimation approach. Ann Epidemiol. 2017;27(1):52–8.PubMedCrossRef
17.
go back to reference Kleinschmidt I, Sharp B, Mueller I, Vounatsou P. Rise in malaria incidence rates in South Africa: a small-area spatial analysis of variation in time trends. Am J Epidemiol. 2002;155(3):257–64.PubMedCrossRef Kleinschmidt I, Sharp B, Mueller I, Vounatsou P. Rise in malaria incidence rates in South Africa: a small-area spatial analysis of variation in time trends. Am J Epidemiol. 2002;155(3):257–64.PubMedCrossRef
18.
go back to reference Devine OJ, Louis TA, Halloran ME. Empirical Bayes methods for stabilizing incidence rates before mapping. Epidemiology (Cambridge, Mass). 1994;5(6):622–30.CrossRef Devine OJ, Louis TA, Halloran ME. Empirical Bayes methods for stabilizing incidence rates before mapping. Epidemiology (Cambridge, Mass). 1994;5(6):622–30.CrossRef
19.
go back to reference New JR, Cahill N, Stover J, Gupta YP, Alkema L. Levels and trends in contraceptive prevalence, unmet need, and demand for family planning for 29 states and union territories in India: a modelling study using the Family Planning Estimation Tool. Lancet Global Health. 2017;5(3):e350–e8. New JR, Cahill N, Stover J, Gupta YP, Alkema L. Levels and trends in contraceptive prevalence, unmet need, and demand for family planning for 29 states and union territories in India: a modelling study using the Family Planning Estimation Tool. Lancet Global Health. 2017;5(3):e350–e8.
20.
go back to reference Alkema L, Kantorova V, Menozzi C, Biddlecom A. National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis. The Lancet. 381(9878):1642–52. Alkema L, Kantorova V, Menozzi C, Biddlecom A. National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis. The Lancet. 381(9878):1642–52.
21.
go back to reference Cahill N, Sonneveldt E, Stover J, et al. Modern contraceptive use, unmet need, and demand satisfied among women of reproductive age who are married or in a union in the focus countries of the Family Planning 2020 initiative: a systematic analysis using the Family Planning Estimation Tool. The Lancet. 2018;391(10123):870–82.CrossRef Cahill N, Sonneveldt E, Stover J, et al. Modern contraceptive use, unmet need, and demand satisfied among women of reproductive age who are married or in a union in the focus countries of the Family Planning 2020 initiative: a systematic analysis using the Family Planning Estimation Tool. The Lancet. 2018;391(10123):870–82.CrossRef
22.
go back to reference Mauro F, Monleon VJ, Temesgen H, Ford KR. Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information. PLoS One. 2017;12(12):e0189401.PubMedPubMedCentralCrossRef Mauro F, Monleon VJ, Temesgen H, Ford KR. Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information. PLoS One. 2017;12(12):e0189401.PubMedPubMedCentralCrossRef
23.
go back to reference Hidiroglou MA, You Y. Comparison of unit level and area level small area estimators. Survey Methodology. 2016;42:41–61. Hidiroglou MA, You Y. Comparison of unit level and area level small area estimators. Survey Methodology. 2016;42:41–61.
24.
go back to reference Zimmerman L, OlaOlorun F, Radloff S. Accelerating and improving survey implementation with mobile technology: lessons from PMA2020 implementation in Lagos, Nigeria. Etude de la Population Africaine. 2015;29(1):1699.CrossRef Zimmerman L, OlaOlorun F, Radloff S. Accelerating and improving survey implementation with mobile technology: lessons from PMA2020 implementation in Lagos, Nigeria. Etude de la Population Africaine. 2015;29(1):1699.CrossRef
25.
go back to reference Zimmerman L, Olson H, Tsui A, Radloff S. PMA2020: rapid turn-around survey data to monitor family planning service and practice in ten countries. Stud Fam Plan. 2017;48(3):293–303.CrossRef Zimmerman L, Olson H, Tsui A, Radloff S. PMA2020: rapid turn-around survey data to monitor family planning service and practice in ten countries. Stud Fam Plan. 2017;48(3):293–303.CrossRef
26.
go back to reference Carlin BP, Louis TA. Bayesian methods for data analysis: CRC Press; 2008. Carlin BP, Louis TA. Bayesian methods for data analysis: CRC Press; 2008.
27.
go back to reference Uganda Bureau of Statistics - UBOS, ICF International. Uganda Demographic and Health Survey 2011. Kampala, Uganda: UBOS and ICF International. p. 2012. Uganda Bureau of Statistics - UBOS, ICF International. Uganda Demographic and Health Survey 2011. Kampala, Uganda: UBOS and ICF International. p. 2012.
28.
go back to reference Hawes M, Safi S, Greenleaf A, et al. Response patterns on behavioral outcomes in relation to use of resident enumerators over multiple survey rounds. 2017. Hawes M, Safi S, Greenleaf A, et al. Response patterns on behavioral outcomes in relation to use of resident enumerators over multiple survey rounds. 2017.
Metadata
Title
Subnational estimation of modern contraceptive prevalence in five sub-Saharan African countries: a Bayesian hierarchical approach
Authors
Qingfeng Li
Thomas A. Louis
Li Liu
Chenguang Wang
Amy O. Tsui
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-6545-3

Other articles of this Issue 1/2019

BMC Public Health 1/2019 Go to the issue