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

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Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusion

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.

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Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Acknowledgements

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

Author information

Authors and Affiliations

  1. Unit of PharmacoTherapy, Epidemiology and Economics (PTE2), Department of Pharmacy, University of Groningen, A. Deusinglaan 1, 9713 AV, Groningen, The Netherlands

    Neily Zakiyah, A. D. I. van Asselt, D. Setiawan, Q. Cao & M. J. Postma

  2. Unit of Patient Centered Health Technology Assessment, Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands

    A. D. I. van Asselt & M. J. Postma

  3. Unit Training, Consultancy and Projects, i + Solutions, 3447 GN, Woerden, The Netherlands

    F. Roijmans

  4. Department of Health Sciences, University Medical Center Groningen, University of Groningen, 9713 GZ, Groningen, The Netherlands

    M. J. Postma

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  1. Neily Zakiyah
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  3. D. Setiawan
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Contributions

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.

Corresponding author

Correspondence to Neily Zakiyah.

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Funding

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.

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Zakiyah, N., van Asselt, A.D.I., Setiawan, D. et al. Cost-Effectiveness of Scaling Up Modern Family Planning Interventions in Low- and Middle-Income Countries: An Economic Modeling Analysis in Indonesia and Uganda. Appl Health Econ Health Policy 17, 65–76 (2019). https://doi.org/10.1007/s40258-018-0430-6

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  • DOI: https://doi.org/10.1007/s40258-018-0430-6

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