Applied Health Economics and Health Policy

, Volume 17, Issue 1, pp 65–76 | Cite as

Cost-Effectiveness of Scaling Up Modern Family Planning Interventions in Low- and Middle-Income Countries: An Economic Modeling Analysis in Indonesia and Uganda

  • Neily ZakiyahEmail author
  • A. D. I. van Asselt
  • D. Setiawan
  • Q. Cao
  • F. Roijmans
  • M. J. Postma
Original Research Article



The aim was to estimate the long-term cost-effectiveness of improved family planning interventions to reduce the unmet need in low- and middle-income countries, with Indonesia and Uganda as reference cases.


The analysis was performed using a Markov decision analytic model, where current situation and several scenarios to reduce the unmet need were incorporated as the comparative strategies. Country-specific evidence was synthesized from the demographic and health survey and published studies. The model simulated the sexual and reproductive health experience of women in the reproductive age range over a time horizon of women’s reproductive years, from the healthcare payer perspective. Modeled outcomes included clinical events, costs and incremental cost-effectiveness ratios (ICERs) expressed as cost per disability-adjusted life year (DALY) averted. Deterministic and probabilistic sensitivity analyses were conducted to assess the impact of parameter uncertainty on modeled outcomes.


In the hypothetical cohort of 100,000 women, scenarios to reduce the unmet need for family planning would result in savings within a range of US$230,600–US$895,100 and US$564,400–US$1,865,900 in Indonesia and Uganda, respectively. The interventions would avert an estimated 1859–3780 and 3705–12,230 DALYs in Indonesia and Uganda, respectively. The results of our analysis indicate that scaling up family planning dominates the current situation in all scenarios in both countries, with lower costs and fewer DALYs. These results were robust in sensitivity analyses.


Scaling up family planning interventions could improve women’s health outcomes substantially and be cost-effective or even cost saving across a range of scenarios compared to the current situation.



The model used in this study was provided to the journal’s peer reviewers for their reference when reviewing the manuscript.

Author Contribution

NZ, FR and MJP designed the study. NZ, ADIvA, DS and MJP developed and analyzed the economic model. NZ and ADIvA validated the model and interpreted the results. DS and QC contributed to the development and analysis of the statistical approach. NZ prepared the first draft of the report. All authors edited and approved the final version.

Compliance with Ethical Standards


No sources of funding were used to conduct this study or to prepare this manuscript.

Conflict of Interest

NZ was a part-time researcher at i + Solutions. FR is employed by i + Solutions. MJP has received grants and honoraria from various pharmaceutical companies, including companies that might be interested in the content of this article. ADIvA, DS and QC report no conflicts of interest that are relevant to the content of this article.

Supplementary material

40258_2018_430_MOESM1_ESM.docx (775 kb)
Supplementary material 1 (DOCX 774 kb)


  1. 1.
    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. Lancet. 2013;381:1642–52. Scholar
  2. 2.
    Ahmed S, Li Q, Liu L, Tsui AO. Maternal deaths averted by contraceptive use: an analysis of 172 countries. Lancet. 2012;380:111–25. Scholar
  3. 3.
    Prata N, Sreenivas A, Vahidnia F, Potts M. Saving maternal lives in resource-poor settings: facing reality. Health Policy. 2009;89:131–48. Scholar
  4. 4.
    Tsui AO, McDonald-Mosley R, Burke AE. Family planning and the burden of unintended pregnancies. Epidemiol Rev. 2010;32:152–74. Scholar
  5. 5.
    Singh S, Darroch J, Ashford L, Vlassoff M. Adding it up: the costs and benefits of investing in family planning and maternal and newborn health. New York, NY: Guttmacher Institute and United National Population Fund; 2009.Google Scholar
  6. 6.
    Zakiyah N, van Asselt ADI, Roijmans F, Postma MJ, Rosen S, Vassall A. Economic evaluation of family planning interventions in low and middle income countries; a systematic review. PLoS ONE. 2016;11:e0168447. Scholar
  7. 7.
    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Value Health. 2013;16:e1–5. Scholar
  8. 8.
    Statistics Indonesia (Badan Pusat Statistik-BPS) Kementrian Kesehatan (Kemenkes- MOH), and ICF International NP and FPB (BKKBN). Indonesia Demographic and Health Survey 2012. Jakarta, Indonesia BPS, BKKBN, Kemenkes, ICF Int. 2013.Google Scholar
  9. 9.
    Uganda Bureau of Statistics (UBOS) and ICF International. Uganda Demographic and Health Survey 2011. Kampala, Uganda: UBOS Calverton, Maryl ICF Int Inc. 2012.Google Scholar
  10. 10.
    WHO. Macroeconomics and health: investing in health for economic development. Geneva: WHO; 2001.Google Scholar
  11. 11.
    Vemer P, Ramos IC, van Voorn GAK, Al MJ, Feenstra TL. AdViSHE: a validation-assessment tool of health-economic models for decision makers and model users. Pharmacoeconomics. 2016;34:349–61.CrossRefGoogle Scholar
  12. 12.
    Trussell J. Understanding contraceptive failure. Best Pract Res Obstet Gynaecol. 2009;23:199–209. Scholar
  13. 13.
    Kennedy KI, Visness CM. Contraceptive efficacy of lactational amenorrhoea. Lancet. 1992;339:227–30. Scholar
  14. 14.
    Van der Wijden C, Manion C. Lactational amenorrhoea method for family planning. Cochrane Database Syst Rev. 2015;10:CD001329. Scholar
  15. 15.
    Babigumira JB, Stergachis A, Veenstra DL, Gardner JS, Ngonzi J, Mukasa-Kivunike P, et al. Potential cost-effectiveness of universal access to modern contraceptives in Uganda. PLoS ONE. 2012;7:e30735. Scholar
  16. 16.
    van de Kassteele J, Hoogenveen RT, Engelfriet PM, Baal PH, Boshuizen HC. Estimating net transition probabilities from cross-sectional data with application to risk factors in chronic disease modeling. Stat Med. 2012;31:533–43. Scholar
  17. 17.
    Indonesia National Population and Family Planning Board. Maternal health; number of pregnant women 2014.  Accessed 5 Jan 2016.
  18. 18.
    WHO. Health statistics and information systems, disease and injury estimates, burden of disease. WHO. 2016.Google Scholar
  19. 19.
    Kementrian Kesehatan Republik Indonesia. Standar Tarif Pelayanan Kesehatan pada Fasilitas Kesehatan Tingkat Pertama dan Fasilitas Kesehatan Tingkat Lanjutan dalam Penyelenggarakan Program Jaminan Kesehatan. Jakarta: Kementrian Kesehatan Republik Indonesia; 2014.Google Scholar
  20. 20.
    Babigumira JB, Stergachis A, Veenstra DL, Gardner JS, Ngonzi J, Mukasa-Kivunike P, et al. Estimating the costs of induced abortion in Uganda: a model-based analysis. BMC Public Health. 2011;11:904. Scholar
  21. 21.
    Levin A, Dmytraczenko T, McEuen M, Ssengooba F, Mangani R, Van Dyck G. Costs of maternal health care services in three anglophone African countries. Int J Health Plann Manage. 2003;18:3–22. Scholar
  22. 22.
    Black R, Laxminarayan R, Temmerman M, Walker N. Disease control priorities, 3rd edition, vol 2. Reproductive, maternal, newborn, and child health. Washington, DC: World Bank; 2016.
  23. 23.
    Horton S, Levin C. Cost-efectiveness of interventions for reproductive, maternal, neonatal, and child health. In: Disease Control Priorities, vol. 2, 3rd Edn. Reproductive, Maternal, Newborn, and Child Health. Washington, DC: World Bank; 2016.
  24. 24.
    Vlassoff M, Sundaram A, Bankole A, Remez L, Mugisha F. Benefits of meeting the contraceptive needs of Ugandan women. Issues Brief (Alan Guttmacher Inst) 2009:1–8.Google Scholar
  25. 25.
    Hu D, Bertozzi SM, Gakidou E, Sweet S, Goldie SJ. The costs, benefits, and cost-effectiveness of interventions to reduce maternal morbidity and mortality in Mexico. PLoS ONE. 2007;2:e750. Scholar
  26. 26.
    Goldie SJ, Sweet S, Carvalho N, Natchu UCM, Hu D. Alternative strategies to reduce maternal mortality in India: a cost-effectiveness analysis. PLoS Med. 2010;7:e1000264. Scholar
  27. 27.
    Carvalho N, Salehi AS, Goldie SJ. National and sub-national analysis of the health benefits and cost-effectiveness of strategies to reduce maternal mortality in Afghanistan. Health Policy Plan. 2013;28:62–74. Scholar
  28. 28.
    Erim DO, Resch SC, Goldie SJ. Assessing health and economic outcomes of interventions to reduce pregnancy-related mortality in Nigeria. BMC Public Health. 2012;12:786. Scholar
  29. 29.
    Mohllajee AP, Curtis KM, Morrow B, Marchbanks PA. Pregnancy intention and its relationship to birth and maternal outcomes. Obstet Gynecol. 2007;109:678–86. Scholar
  30. 30.
    Sedgh G, Ball H. Abortion in Indonesia. Issues Brief (Alan Guttmacher Inst). 2008;2:1–6.Google Scholar
  31. 31.
    Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: OUP; 2006.Google Scholar
  32. 32.
    Horton R, Peterson HB. The rebirth of family planning. Lancet. 2012;380:77. Scholar
  33. 33.
    Peterson HB, Darmstadt GL, Bongaarts J. Meeting the unmet need for family planning: now is the time. Lancet. 2013;381:1696–9. Scholar
  34. 34.
    UN. Transforming our world: the 2030 Agenda for Sustainable Development 2015;2017. ​Accessed 9 Oct 2016.
  35. 35.
    Ronsmans C, Scott S, Adisasmita A, Deviany P, Nandiaty F. Estimation of population-based incidence of pregnancy-related illness and mortality (PRIAM) in two districts in West Java, Indonesia. BJOG. 2009;116:82–90. Scholar
  36. 36.
    Casterline JB. Collecting data on pregnancy loss: a review of evidence from the world fertility survey. Stud Fam Plann. 1989. Scholar
  37. 37.
    Masfiah S, Anandari D, Budi Aji H. Does prenatal care package in Indonesia reduce miscarriage/stillbirth? ​Manag Heal. 2015;19(1).Google Scholar
  38. 38.
    Asiki G, Baisley K, Newton R, Marions L, Seeley J, Kamali A, et al. Adverse pregnancy outcomes in rural Uganda (1996–2013): trends and associated factors from serial cross sectional surveys. BMC Pregnancy Childbirth. 2015;15:1–12. Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Unit of PharmacoTherapy, Epidemiology and Economics (PTE2), Department of PharmacyUniversity of GroningenGroningenThe Netherlands
  2. 2.Unit of Patient Centered Health Technology Assessment, Department of Epidemiology, University Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
  3. 3.Unit Training, Consultancy and Projectsi + SolutionsWoerdenThe Netherlands
  4. 4.Department of Health Sciences, University Medical Center GroningenUniversity of GroningenGroningenThe Netherlands

Personalised recommendations