Estimating progress towards meeting women’s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study

Vladimíra Kantorová; Mark C Wheldon; Philipp Ueffing; Aisha N Z Dasgupta

doi:10.1371/journal.pmed.1003026

. 2020 Feb 18;17(2):e1003026. doi: 10.1371/journal.pmed.1003026

Estimating progress towards meeting women’s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study

Vladimíra Kantorová ^1,^*, Mark C Wheldon ¹, Philipp Ueffing ¹, Aisha N Z Dasgupta ¹

Editor: Alison L Drake²

PMCID: PMC7028249 PMID: 32069289

Abstract

Background

Expanding access to contraception and ensuring that need for family planning is satisfied are essential for achieving universal access to reproductive healthcare services, as called for in the 2030 Agenda for Sustainable Development. Monitoring progress towards these outcomes is well established for women of reproductive age (15–49 years) who are married or in a union (MWRA). For those who are not, limited data and variability in data sources and indicator definitions make monitoring challenging. To our knowledge, this study is the first to provide data and harmonised estimates that enable monitoring for all women of reproductive age (15–49 years) (WRA), including unmarried women (UWRA). We seek to quantify the gaps that remain in meeting family-planning needs among all WRA.

Methods and findings

In a systematic analysis, we compiled a comprehensive dataset of family-planning indicators among WRA from 1,247 nationally representative surveys. We used a Bayesian hierarchical model with country-specific time trends to estimate these indicators, with 95% uncertainty intervals (UIs), for 185 countries. We produced estimates from 1990 to 2019 and projections from 2019 to 2030 of contraceptive prevalence and unmet need for family planning among MWRA, UWRA, and all WRA, taking into account the changing proportions that were married or in a union. The model accounted for differences in the prevalence of sexual activity among UWRA across countries.

Among 1.9 billion WRA in 2019, 1.11 billion (95% UI 1.07–1.16) have need for family planning; of those, 842 million (95% UI 800–893) use modern contraception, and 270 million (95% UI 246–301) have unmet need for modern methods. Globally, UWRA represented 15.7% (95% UI 13.4%–19.4%) of all modern contraceptive users and 16.0% (95% UI 12.9%–22.1%) of women with unmet need for modern methods in 2019. The proportion of the need for family planning satisfied by modern methods, Sustainable Development Goals (SDG) indicator 3.7.1, was 75.7% (95% UI 73.2%–78.0%) globally, yet less than half of the need for family planning was met in Middle and Western Africa. Projections to 2030 indicate an increase in the number of women with need for family planning to 1.19 billion (95% UI 1.13–1.26) and in the number of women using modern contraception to 918 million (95% UI 840–1,001).

The main limitations of the study are as follows: (i) the uncertainty surrounding estimates for countries with little or no data is large; and (ii) although some adjustments were made, underreporting of contraceptive use and needs is likely, especially among UWRA.

Conclusions

In this study, we observed that large gaps remain in meeting family-planning needs. The projected increase in the number of women with need for family planning will create challenges to expand family-planning services fast enough to fulfil the growing need. Monitoring of family-planning indicators for all women, not just MWRA, is essential for accurately monitoring progress towards universal access to sexual and reproductive healthcare services—including family planning—by 2030 in the SDG era with its emphasis on ‘leaving no one behind.’

In a Bayesian hierarchical modelling study, Vladimira Kantorova and colleagues estimate progress towards meeting women’s contraceptive needs in 185 countries.

Author summary

Why was this study done?

Increasing availability of modern contraception has permitted greater opportunities for individual choice and responsible decision-making in matters of reproduction.
Global, regional, and national annual estimates of contraceptive prevalence, unmet need for family planning, and the proportion of need for family planning satisfied by modern methods have previously been available only for married or in-union women of reproductive age (15–49 years) (MWRA).
We sought to provide estimates of these family-planning indicators for all women of reproductive age (15–49 years) (WRA), including unmarried women (UWRA), that could be used for global monitoring of the proportion of WRA who have their need for family planning satisfied with modern methods (Sustainable Development Goals [SDG] indicator 3.7.1.)

What did the researchers do and find?

We compiled data on contraceptive use and needs from 1,247 nationally representative surveys and produced harmonised annual estimates and projections that allow monitoring for all WRA, including UWRA.
Among the 1.9 billion WRA worldwide in 2019, 1.1 billion have demand for family planning; of these, 842 million are using modern contraception, and 270 million have an unmet need for modern methods.
UWRA account for an increasing share of users of modern contraceptive methods globally (15.7% in 2019 up from 12.1% in 2000), driven in part by an increase in contraceptive use among UWRA and an increase in the proportion of women who are unmarried.
In 43 countries, of which 32 are low-income countries, less than half of the need for family planning is met by modern methods.

What do these findings mean?

Annual estimates of key family-planning indicators among all women and by marital status, including the degree of uncertainty around them, provide the global health and development community with better tools for monitoring progress in the era of the SDG and other family-planning initiatives.
Although contraceptive use has increased, the progress has been uneven between countries, and large gaps remain in meeting family-planning needs. These gaps should be met by family-planning programmes that enable couples and individuals to decide freely and responsibly the number and spacing of their children, as well as have the information and means to do so.

Introduction

Contraception is one of the most important tools that women and men have for achieving desired family size. The Programme of Action of the International Conference on Population and Development (ICPD), adopted in Cairo, Egypt, by 179 governments in 1994, recognised the basic right of all couples and individuals to decide freely and responsibly the number, spacing, and timing of their children and to have the information and means to do so, as well as the right to attain the highest standard of sexual and reproductive health [1]. Past estimates of family-planning indicators have been largely limited to married or in-union women of reproductive age (15–49 years) (MWRA), and unmarried women of reproductive age (15–49 years) (UWRA) have not been paid adequate attention. Recent changes in the international family-planning field have refocused attention towards all women of reproductive age (15–49 years) (WRA), regardless of marital status. Three ongoing international initiatives reaffirming the commitments made in the Programme of Action—the 2030 Agenda for Sustainable Development, Every Woman Every Child (EWEC), and Family Planning 2020 (FP2020)—demand robust data collection, analysis, and monitoring of family-planning indicators among all WRA, regardless of marital status, to track progress towards expanding access to family planning, evaluate programmatic efforts, and identify funding gaps [2–4]. The 2030 Agenda—anchored around 17 Sustainable Development Goals (SDG)—includes under Goal 3, target 3.7 to ‘ensure universal access to sexual and reproductive healthcare services, including family planning, by 2030.’ EWEC aims to end preventable deaths of women, children, and adolescents and to ensure their health and well-being, which requires universal access to sexual and reproductive healthcare services (including family planning) and rights by 2030. FP2020 aims to expand access to family planning to an additional 120 million women and girls in 69 of the world’s poorest countries by 2020, compared to a baseline in 2012.

Robust analysis of family-planning levels and trends among all WRA is impeded by limited data availability. For the population of women who are married or in a union, this limitation has been addressed by producing annually revised data compilations and model-based estimates and projections of family-planning indicators [5–9]. Efforts to estimate and project family-planning indicators for UWRA or for all WRA have not yet accounted for considerable cross-country variations in reproductive behaviour and sexual activity among unmarried women, nor have they captured changes, by age over time, in the proportions of women who are married or in union. Previous work in this area has been based on a limited number of data sources, focused on selected areas of the world, provided estimates for one point in time, restricted to MWRA, or limited the study to one family-planning indicator without understanding the relationship between need for—and use of—contraception over time and across countries [10–16].

We present here a new data compilation and method to estimate indicators of contraceptive prevalence and unmet need for family planning among all WRA, including UWRA. The method expands a Bayesian hierarchical model developed for MWRA [6,17,18] to produce estimates and projections for UWRA and accounts for differences in the prevalence of sexual activity among UWRA. By combining the UWRA results with those for MWRA, estimates and projections were produced for all WRA while incorporating the underlying compositional changes over time in age structures and the proportions of women who are married or in a union.

Methods

Definitions

In this study, contraceptive prevalence was defined as the percentage of women who report themselves or their partners as currently using at least one contraceptive method of any type. For analytical purposes, contraceptive methods are classified as either modern or traditional. Modern methods of contraception include female and male sterilization, the intrauterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap, and spermicidal foam, jelly, cream, and sponge), the lactational amenorrhoea method (LAM), emergency contraception, and other modern methods not reported separately (e.g., the contraceptive patch or vaginal ring). Traditional methods of contraception include rhythm (e.g., fertility awareness-based methods, periodic abstinence), withdrawal, and other traditional methods not reported separately. Unmet need for family planning is the percentage of women who want to stop or delay childbearing for at least 2 years but are not using any contraceptive method. Unmet-need calculation is based on the definition and computation used by the Demographic and Health Surveys (DHS) [19] (Section 2.2 in S1 Appendix). Demand for family planning satisfied by modern methods (SDG indicator 3.7.1 ‘Proportion of women who have their need for family planning satisfied with modern methods’) is modern contraceptive prevalence divided by the sum of contraceptive prevalence and unmet need (also referred to as total demand for family planning). We provide estimates for all WRA, UWRA, and MWRA.

MWRA pertains to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or ‘living together’). UWRA pertains to women who are not married and not in a union and is a complement to MWRA. To the extent possible, this distinction was applied to all survey-based observations, or a bias was assigned to data points that had different definitions of marital or union status.

While family-planning indicators among MWRA are commonly used and readily interpreted, more consideration needs to be given to the calculation and interpretation of the family-planning indicators among UWRA and all WRA.

The major differences in the approaches used for estimating family-planning indicators among UWRA were related to whether the universe of the population was all UWRA or only UWRA who are deemed to be sexually active (defined by sexual activity in past 28 days, past 3 months, past 1 year, or ever sexually active) and so potentially exposed to pregnancy. In this paper, we report indicators for the population of all UWRA. Contraceptive users among UWRA refers to all UWRA who report in a survey using a contraceptive method, irrespective of sexual activity. For unmet need among all UWRA, it is necessary to determine the timing of their most recent sexual activity. UWRA who are not pregnant or postpartum amenorrhoeic are considered currently at risk of pregnancy (and thus could potentially be included in the numerator as having unmet need) if they have had intercourse within the 4 weeks prior to the survey interview. Unmet need for UWRA who are pregnant or postpartum amenorrhoeic is determined regardless of their most recent sexual activity either as pregnant UWRA whose pregnancies were unwanted or mistimed at the time of conception or postpartum amenorrhoeic UWRA who are not using family planning and whose last birth was unwanted or mistimed. Using this approach, estimates of family-planning indicators for all women are directly comparable to those published generally in the survey reports.

Data compilation

The data compilation uses data from multiple sources and employs unified definition and computation of survey-based estimates of family-planning indicators for UWRA. We reviewed all nationally representative household surveys that provided information on contraceptive prevalence. The starting point was the data sources used for estimating contraceptive use for MWRA [18], complemented by queries to national statistical offices, research institutes, and international survey programs. For each survey, we determined whether the question on current use of contraception was asked among all women and whether it was possible to estimate contraceptive prevalence and unmet need for family planning among UWRA. The data compilation includes 1,247 observations of contraceptive prevalence for 195 countries or areas for the period from 1950 to 2019, 540 observations of unmet need for family planning for MWRA across 143 countries, 551 observations across 136 countries or areas for contraceptive prevalence, and 250 observations across 72 countries or areas for unmet need for UWRA (Section 2.3 in S1 Appendix) [5]. For 361 surveys—including DHS; Multiple Indicator Cluster Surveys (MICS); Performance, Monitoring and Accountability 2020 (PMA2020) surveys, Gender and Generations Survey (GGS); and other surveys—we obtained estimates, including sampling errors, from micro-datasets. For other surveys, we used estimates derived from published tabulations, or we obtained specific tabulations from the institutions responsible for data collection. Many of these survey-based estimates had not been previously published. The input data are provided as Supporting Information (S1, S2 and S3 Data).

Ethics approval was not required for this study.

Statistical model

For MWRA, we used existing statistical models [8,18] on the updated data compilation. We expanded the model to UWRA (see Section 3 in S1 Appendix). Both models were fitted to the data described earlier, and we report the model-based estimates and projections. The model provides an assessment of uncertainty in the estimates based on the availability and quality of input data. It allows for greater precision when more and better data are available and indicates the extent of uncertainty in cases in which the data are insufficient or are from sources more susceptible to systematic differences as captured by the biases assigned to each survey-based data point. We describe some key aspects of it subsequently; for a full description, see Section 3 in S1 Appendix. For comparisons between model outputs and the underlying data obtained from surveys for each country, see S2 Appendix.

For each country, the systematic trends over time in total contraceptive prevalence and the ratio of modern-method use to use of any method were modelled with logistic growth curves. The logistic function is appropriate for representing social diffusion processes such as the adoption of contraceptive methods, such that the pace of adoption is slow at first and then is expected to increase at a faster rate, after which it slows down once prevalence approaches higher levels [20,21]. Autocorrelated error processes were added to account for deviations, including reversals, from these smooth trends. Unmet need among UWRA was similarly modelled as a systematic trend plus autocorrelated error but as a function of total contraceptive prevalence rather than time. This function also captures the trend in countries with low sexual activity among UWRA where both contraceptive prevalence and unmet need are very low.

Country-specific estimates of model parameters were obtained by fitting a Bayesian hierarchical model [22], which is fully described in Section 3.6 in S1 Appendix. The output of a Bayesian model is a ‘posterior’ distribution, a high-dimensional probability distribution for the parameters of interest. It is the result of combining information about the parameters contained in the data (encapsulated in the ‘likelihood’ component of the model) with existing prior knowledge (encapsulated by the ‘prior distribution’ component). Standard practice is to summarise the posterior distribution with quantiles of key parameters that provide point estimates and ranges representing the magnitude of uncertainty. We report posterior medians for point estimates and uncertainty intervals (UIs) constructed from the 2.5th and 97.5th percentiles. Under the model, and given the available data, there is a 95% probability that these UIs contain the true value of the parameter.

Many countries had only a few data points, and some countries had none. For example, 59 countries with data on contraceptive prevalence for MWRA had no such data for UWRA. Of the 136 countries that did have data, only 26% of them had any data before 1990 (compared with 63% for MWRA). To improve the precision and accuracy of estimates and projections, a hierarchical structure was added to the Bayesian model. Hierarchical structures cluster countries together based on common characteristics and allow ‘borrowing of strength’ among observations within the same cluster. Estimates for countries with little, biased, or no data are based on data for other countries in the cluster—partly if they have some data, entirely if they have none. The model implicitly weights data from other countries based on the hierarchical structure positions of the countries.

The model for MWRA used a purely geographic clustering [18]; in other words, countries geographically near each other were clustered together. To improve estimation for UWRA in countries with very few—or no—data points and to account for the cross-country variations in reproductive behaviour and sexual activity among UWRA that have an impact on family-planning indicators, we developed a 2-category sexual activity classification and combined it with geographical clusters. The 2 categories were (i) countries with very low levels of sexual activity (Group 0) and (ii) all other countries (Group 1). Eighty-one countries were assigned using information about proportion sexually active from the most recent DHS or MICS; countries with less than 2% sexually active (defined as having sexual intercourse in the past 28 days) among UWRA were assigned to Group 0 and the rest to Group 1. An additional 43 countries were classified using information about the acceptance of sex between unmarried adults reported in the Pew 2013 Global Attitudes Survey (29 countries) or the World Values Survey Wave 6 (14 countries), and the remaining 71 countries (all in Asia and Northern Africa) were assigned on the basis of the proportion religious in the population (Section 2.4 and Fig D in S1 Appendix).

Estimates for countries and indicators with no data were obtained from weighted averages of parameter estimates for certain sets of countries with data. The sets and weights were determined by the hierarchical structure of the model, such that averages based on countries most proximate in the hierarchy were given higher weights (Sections 3.4 and 3.7 in S1 Appendix). We included additional parameters in the model to account for surveys that used different target populations (e.g., nonstandard age groups, omission of subnational geographies) or a different categorization of contraceptive method use (the bias and misclassification parameters are described in Section 3.5 in S1 Appendix). For instance, some surveys include women of different age groups compared to the baseline population of women aged 15 to 49 years (e.g., the GGS, which surveyed only women aged 18 and older). To account for differences in data quality, we estimated total errors by data source type.

Estimates for years outside the periods of data availability were obtained from the fitted Bayesian model by interpolation and extrapolation of the systematic logistic trends and the autocorrelated error processes. The parameters of the systematic trend component are time invariant (Section 3.3 in S1 Appendix) and thus provide estimates of the trend for all years. The autocorrelated error process is also parameterised by time-invariant parameters. Once these were obtained from the model fit, extrapolations beyond the period of data availability were obtained by sequentially sampling from the error process conditional distributions. We labelled all estimates for years 2020 and beyond as ‘projections.’

We used a Markov chain Monte Carlo (MCMC) algorithm to generate samples from the joint posterior distribution of model parameters [22,23,24]. The MCMC sampling algorithm was implemented using JAGS 4.2.0 Open Source software [24], and the analysis was carried out in R version 3.5.2 [25]. The source code is available at https://github.com/FPcounts/FPEMglobal.

The samples from the posterior were transformed to produce probabilistic results for other indicators, such as need for family planning satisfied by modern methods. Results on the count scale, such as the number of contraceptive users or women with unmet need, were produced by multiplying posterior sample elements by country-specific estimates and projections of the number of UWRA or MWRA, as appropriate [26,27]. For instance, the posterior distribution of the number of UWRA using any method of contraception was obtained by multiplying each element of the posterior sample for contraceptive prevalence by the number of UWRA in the appropriate year, within country. Estimates for country aggregates (e.g., sub-Saharan Africa) were obtained by summing the posterior distributions of the counts across the countries in the aggregate. Estimates for UWRA and MWRA were combined to provide WRA estimates in the same way. That is, for each country at each year, UWRA and MWRA estimates were summed on the count scale to produce WRA estimates.

A key contribution of this article is the data compilation and statistical model for estimating contraceptive prevalence and unmet need for family planning among UWRA itself, therefore a prespecified analysis plan was not prepared, and all analyses are non-prespecified. The statistical model for MWRA [8,18] is applied to a larger and more recent dataset. As in Alkema and colleagues’ work [18], model development began with substantive reasoning about the prevalence of contraceptive use and needs and how they might differ between MWRA and UWRA. We identified sexual activity among UWRA as a major factor to be included in the hierarchical structure of the model for UWRA.

The validity of the model was assessed with 3 cross-validation exercises [22]. These entailed leaving out certain sets of observations (depending on the exercise), refitting the model, and checking model results against the left-out observations for calibration and predictive accuracy. The results indicated that the model was robust and adequately calibrated to generate the estimates for family-planning indicators among UWRA (Section 4.3 in S1 Appendix). We also tested the sexual activity classification for UWRA. Because there were 2 levels (‘low’ and ‘other’), we identified countries with data that were close to the threshold of 2% sexually active among UWRA. The country closest to the threshold was Indonesia, which was assigned to the ‘low’ sexual activity group. A sensitivity analysis was conducted, assigning Indonesia to the ‘other’ group, but its relatively abundant data resulted in no meaningful change in the results.

Indicators used

We computed the medians and 95% UIs for all indicators of interest using the 2.5th and 97.5th percentiles of the posterior distributions for the following 7 indicators: contraceptive prevalence (any, modern, traditional), unmet need for family planning, unmet need for modern methods, total demand for family planning, and demand for family planning satisfied with modern methods.

We generated posterior estimates of change in family-planning indicators over periods of years at the trajectory level so as to obtain probabilistic estimates of change with posterior probabilities of an increase (PPIs) or decrease (PPDs; such that PPD = 1 − PPI). These probabilities reflect the certainty regarding the reported change: a higher posterior probability corresponds to greater certainty about the result. For example, PPI > 0.9 means that there is more than 90% probability that the actual change was greater than 0. These posterior estimates of change and PPIs account for correlation between estimates of the start and end years and are more accurate than, for example, comparing the separately generated posterior estimates (including uncertainty) at the start and end years of the period.

We produced national, regional, and global results for the period from 1990 to 2030 and discuss the changes in estimates from 2000 to 2019 and projections to 2030 in line with the requirements for SDG monitoring. Results quoted for 2030 are model-based predictions, and we use different visual elements to distinguish these in plots. We present results for the 185 countries with at least 90,000 inhabitants in 2017 [26]. Estimates from an additional 10 countries and areas with total population below 90,000 inhabitants are included in the aggregated results. The classifications used to form regions, subregions, and other country aggregates are defined in S1 Appendix Table G. The results are reported according to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement [28] (S1 Checklist).

Results

Family-planning trends among MWRA

Modern contraceptive prevalence among MWRA increased worldwide between 2000 and 2019 from 55.0% (95% UI 53.7%–56.3%) to 57.1% (95% UI 54.6%–59.5%), or 2.1 percentage points (95% UI −0.7 to 4.8, PPI > 0.93) (Table 1 and Fig 1; Section 5 in S1 Appendix, S4 and S5 Results for country-specific results; Table 2 for corresponding numbers of women). Prevalence in 2019 was highest in Eastern Asia (80.8% [95% UI 74.8%–85.4%]). Median estimates for Western Europe, South America, and Northern Europe were above 70%, while those for Northern America, Central America, and Australia and New Zealand were above 60%. In contrast, prevalence was lowest in 2019 in Middle Africa (13.7% [95% UI 10.5%–17.9%]) and Western Africa (20% [95% UI 17.8%–22.5%]). These were, however, increases over the 2000 levels by, respectively, 7.1 percentage points (95% UI 3.5–11.5) and 11.7 percentage points (95% UI 9.3–14.4), with PPI = 1 in both cases. The increases were also large in Eastern Africa, greater by 23.7 percentage points (95% UI 20.9–26.7, PPI = 1), and Eastern Europe, greater by 10.5 percentage points (95% UI 0.6–19.9, PPI > 0.98).

Table 1. Global estimates of the proportion (%) of WRA using a modern contraceptive method, having unmet need for modern methods, and having demand for family planning, and the proportion of need for family planning satisfied by modern methods, for 1990, 2000, 2010, 2019, and 2030.

		All WRA (%)	MWRA (%)	UWRA (%)
Indicator	Year	Median (95% UI)	Median (95% UI)	Median (95% UI)
Contraceptive use (any modern method)	1990	35.9 (34.0–39.3)	47.5 (45.1–49.6)	12.3 (8.9–22.0)
	2000	42.0 (40.5–44.6)	55.0 (53.7–56.3)	15.4 (12.0–23.1)
	2010	43.5 (41.6–46.4)	56.7 (54.7–58.6)	18.3 (14.5–25.8)
	2019	44.3 (42.1–47.0)	57.1 (54.6–59.5)	20.1 (16.3–26.3)
	2030	45.1 (41.2–49.1)	59.2 (54.3–63.7)	21.5 (17.1–27.8)
Contraceptive use (any traditional method)	1990	6.1 (5.3–8.0)	7.7 (6.8–8.6)	3.0 (1.4–8.3)
	2000	5.2 (4.6–6.3)	6.6 (5.9–7.3)	2.4 (1.4–5.6)
	2010	4.5 (3.9–5.5)	5.9 (5.2–6.9)	1.9 (1.1–4.3)
	2019	4.2 (3.5–5.2)	5.6 (4.6–6.9)	1.6 (1.0–3.3)
	2030	3.9 (3.0–5.3)	5.4 (4.0–7.4)	1.5 (0.9–3.0)
Unmet need for modern methods	1990	17.6 (16.3–20.1)	22.7 (21.3–24.2)	7.3 (4.6–14.3)
	2000	15.1 (14.1–16.8)	19.1 (18.2–20.0)	7.0 (4.8–11.9)
	2010	14.3 (13.2–15.9)	18.2 (17.1–19.6)	6.7 (4.7–11.0)
	2019	14.2 (12.9–15.8)	18.2 (16.7–20.0)	6.6 (4.8–10.1)
	2030	13.3 (11.6–15.5)	17.3 (14.9–20.3)	6.6 (4.9–9.9)
Total need for family planning	1990	53.6 (51.5–57.6)	70.2 (68.4–71.8)	19.6 (14.6–31.2)
	2000	57.1 (55.4–60.0)	74.1 (73.0–75.1)	22.4 (17.7–31.1)
	2010	57.8 (55.8–60.7)	74.9 (73.4–76.4)	25.0 (20.1–33.1)
	2019	58.5 (56.5–61.1)	75.3 (73.6–77.0)	26.7 (22.1–33.2)
	2030	58.4 (55.3–61.9)	76.5 (73.3–79.3)	28.1 (23.0–34.7)
Proportion of the need for family planning satisfied by modern methods (SDG indicator 3.7.1)	1990	67.1 (63.8–69.7)	67.7 (65.4–69.7)	62.7 (45.6–76.4)
	2000	73.5 (71.3–75.3)	74.3 (73.0–75.4)	68.6 (56.2–78.6)
	2010	75.3 (72.9–77.3)	75.7 (73.8–77.3)	73.2 (61.9–81.7)
	2019	75.7 (73.2–78.0)	75.8 (73.4–78.0)	75.4 (66.1–82.6)
	2030	77.2 (73.2–80.5)	77.4 (73.1–80.9)	76.6 (67.7–83.4)

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Abbreviations: MWRA, women of reproductive age (15–49 years) who are married or in a union; SDG, Sustainable Development Goals; UI, uncertainty interval; UWRA, women of reproductive age (15–49 years) who are unmarried and not in a union; WRA, women of reproductive age (15–49 years)

Fig 1 — The country classification by income level is based on June 2019 gross national income per capita from the World Bank. Estimates are indicated for the years 1990, 2000, 2010, and 2019 by solid circles and interpolated by solid lines. Projections for the year 2030 are indicated by open circles and interpolated by dashed lines. Shaded ribbons indicate 95% UIs; intervals for projection years are shaded lighter. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

Table 2. Global estimates of the number (millions) of WRA using a modern contraceptive method, having unmet need for modern methods, and having demand for family planning, and the proportion unmarried among all women, for 1990, 2000, 2010, 2019, and 2030.

		All WRA, N (in millions)	MWRA, N (in millions)	UWRA, N (in millions)	Proportion unmarried among all women (%)
Indicator	Year	Median (95% UI)	Median (95% UI)	Median (95% UI)	Median (95% UI)
Contraceptive use (any modern method)	1990	473 (448–518)	420 (399–438)	53 (39–95)	11.2 (8.6–18.4)
	2000	660 (637–701)	580 (566–593)	80 (62–120)	12.1 (9.7–17.1)
	2010	779 (745–830)	667 (642–689)	113 (89–159)	14.4 (12.0–19.1)
	2019	842 (800–893)	709 (678–740)	133 (107–174)	15.7 (13.4–19.4)
	2030	918 (840–1,001)	754 (692–812)	164 (130–212)	17.9 (15.5–21.2)
Contraceptive use (any traditional method)	1990	81 (70–105)	68 (60–76)	13 (6–36)	16.1 (9.0–34.2)
	2000	82 (72–99)	69 (62–77)	12 (7–29)	15.0 (9.8–29.3)
	2010	81 (70–99)	70 (61–81)	11 (7–26)	14.1 (9.7–26.8)
	2019	80 (66–99)	69 (57–85)	11 (7–22)	13.5 (10.0–21.9)
	2030	80 (61–109)	69 (51–95)	12 (7–23)	14.5 (11.1–21.0)
Unmet need for modern methods	1990	232 (215–264)	200 (188–214)	32 (20–62)	13.7 (9.3–23.4)
	2000	237 (222–264)	201 (192–211)	36 (25–61)	15.3 (11.3–23.2)
	2010	255 (237–285)	214 (201–230)	41 (29–68)	16.1 (12.3–23.8)
	2019	270 (246–301)	226 (207–248)	43 (32–66)	16.0 (12.9–22.1)
	2030	271 (236–317)	221 (190–258)	50 (37–75)	18.5 (15.7–23.8)
Total need for family planning	1990	705 (678–758)	620 (604–635)	85 (63–135)	12.0 (9.3–17.8)
	2000	897 (870–944)	781 (770–792)	116 (92–161)	12.9 (10.5–17.1)
	2010	1,035 (999–1,087)	881 (863–897)	154 (123–203)	14.9 (12.3–18.7)
	2019	1,112 (1,073–1,161)	936 (915–957)	176 (146–219)	15.8 (13.6–18.8)
	2030	1,189 (1,126–1,261)	975 (934–1,011)	214 (175–265)	18.0 (15.6–21.0)

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Abbreviations: MWRA, women of reproductive age (15–49 years) who are married or in a union; UI, uncertainty interval; UWRA, women of reproductive age (15–49 years) who are unmarried and not in a union; WRA, women of reproductive age (15–49 years)

Over the same period, the proportion of the need for family planning satisfied by modern methods among MWRA worldwide changed from 74.3% (95% UI 73.0%–75.4%) to 75.8% (95% UI 73.4%–78.0%) (Table 1 and Fig 2; Table 2 for corresponding numbers of women). The median estimate of change was 1.5% (95% UI −1% to 3.9%, PPI = 0.88). Eastern Asia and Western Europe had the highest proportions of need satisfied (92.9% [95% UI 88.9%–95.6%] and 89.7% [95% UI 83.5%–93.6%], respectively). Middle Africa (27.3% [95% UI 21.9%–33.6%]) and Western Africa (42.3% [95% UI 38.2%–46.6%]) had the lowest estimated proportions of need satisfied, although these were increases on 2000 levels—as for modern contraceptive prevalence—of 12.4 percentage points (95% UI 6.2–19.4, PPI = 1) and 20.1 percentage points (95% UI 15.3–24.9, PPI = 1), respectively. Eastern Africa had the third lowest estimated proportions of need satisfied in 2000 (32.7% [95% UI 31.1%–34.4%]) but a substantial increase of 29.8 percentage points (95% UI 26.3%—33.3%, PPI = 1) in the 2000–2019 period, which resulted in an estimate of 62.5% (95% UI 59.3%–65.6%) in 2019.

Fig 2 — The country classification by income level is based on June 2019 gross national income per capita from the World Bank. Estimates are indicated for the years 1990, 2000, 2010, and 2019 by solid circles and interpolated by solid lines. Projections for the year 2030 are indicated by open circles and interpolated by dashed lines. Shaded ribbons indicate 95% UIs; intervals for projection years are shaded lighter. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

Family-planning trends among UWRA

Between 2000 and 2019, the proportion of UWRA using modern contraceptive methods increased worldwide from 15.4% (95% UI 12.0%–23.1%) to 20.1% (95% UI 16.3%–26.3%), or 4.6 percentage points (95% UI 0.2–8.7, PPI > 0.98) (see Section 5 in S1 Appendix, S2 and S3 Results for country-specific results). Contraceptive prevalence and the scale of its increase varied widely across geographic subregions. In 2019, more than a third of UWRA were using a modern method in Australia/New Zealand, the Caribbean, Northern America, Northern Europe, South America, Southern Africa, Southern Europe, and Western Europe (Table 1 and Fig 1; Table 2 for corresponding numbers of women). The 2 subregions with the largest increase since 2000 were Central America and South America, greater by 12.7 percentage points (95% UI 3.0–23.4, PPI > 0.99) and 16.8 points (95% UI 6.3–28.7, PPI > 0.99), respectively (Fig 3). Although UWRA contraceptive use generally remained low in sub-Saharan Africa (except Southern Africa), the proportion of UWRA using a modern contraceptive method increased with posterior probability of 99% in Eastern Africa by 6.7 percentage points (95% UI 4.6–9.1), in Middle Africa by 8.9 percentage points (95% UI 4.0–14.7), and in Western Africa by 7.0 percentage points (95% UI 4.2–10.0).

Fig 3 — The 95% UIs are displayed by horizontal lines. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

The proportion of UWRA using a modern contraceptive method remained very low in Northern Africa, South-Eastern Asia, and Western Asia (median estimates for the subregions are less than 3%) (Fig 4A). These are subregions in which overall need for family planning and sexual activity among UWRA is low.

Fig 4 — Median estimates of (A) modern contraceptive prevalence, (B) unmet need for a modern method, and (C) the proportion of need for family planning satisfied by modern methods. For countries marked with ‘*,’ no survey-based observations are available for UWRA. The base map was obtained from Natural Earth (https://naturalearthdata.com). The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. UWRA, women of reproductive age (15–49 years) who are unmarried and not in a union.

The use of traditional methods is not common among UWRA. Globally, 1.6% (95% UI 1.0%–3.3%) of UWRA used a traditional method in 2019. Middle Africa is an exception (5.4% [95% UI 2.6%–10.3%]) with the highest prevalence of traditional methods in Congo (11.5% [95% UI 3.1%–25.1%]).

The proportion of UWRA with unmet need for modern methods (including traditional-method users) remained unchanged since 2000 and stood at 6.6% (95% UI 4.8%–10.1%) globally in 2019. High proportions of UWRA continued to experience unmet need in Middle and Western Africa, exceeding 15% in Angola, Benin, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Gabon, Guinea-Bissau, Liberia, Madagascar, and Sierra Leone (Fig 4B).

The proportion of need for family planning satisfied by modern methods among UWRA in 2019 was estimated at 75.4% (95% UI 66.1%–82.6%) globally, increasing from 68.6% (95% UI 56.2%–78.6%) in 2000. It remained low in Middle Africa (48.1% [95% UI 37.5%–57.9%]), Western Africa (58.1% [95% UI 52.0%–63.6%]), and Eastern Africa (63.0% [95% UI 58.4%–67.5%]), though it has increased since 2000 by more than 10 percentage points in all 3 subregions. Estimates of the proportion of need for family planning satisfied by modern methods among UWRA is presented for 140 countries (excluding countries classified as having low sexual activity among unmarried women) and among them, the proportion of need satisfied was below 50% in 11 countries and below 75% in an additional 74 countries in 2019 (Fig 4C, Section 5 in S1 Appendix).

UWRA contraceptive users in the context of all users

UWRA account for an increasing proportion of modern users (from 12.1% [95% UI 9.7%–17.1%] in 2000 to 15.7% [95% UI 13.4%–19.4%] in 2019; Table 2). In Northern Africa, Southern Asia, South-Eastern Asia, and Western Asia, less than 5% of all modern users were UWRA in 2019, compared to the highest share of 58.5% (95% UI 52.2%–63.7%) in Southern Africa (Fig 5). More than a third of modern contraceptive users are UWRA in Australia/New Zealand, Middle Africa, Northern America, Northern Europe, and Southern Europe. Globally, UWRA accounted for 16.0% (95% UI 12.9%–22.1%) of women with unmet need for modern methods in 2019.

Fig 5 — The 95% UIs are displayed by horizontal lines. In the United Nations classification, the more developed regions comprise all countries of Europe, Northern America, Australia and New Zealand, and Japan. The less developed regions comprise all other countries. The country classification by income level is based on June 2019 gross national income per capita from the World Bank. UI, uncertainty interval; UWRA, women of reproductive age (15–49 years) who are unmarried and not in a union; WRA, women of reproductive age (15–49 years).

Contraceptive prevalence and need for family planning among WRA

Among all WRA, use of modern contraceptives increased worldwide between 2000 and 2019 from 42.0% (95% UI 40.5%–44.6%) to 44.3% (95% UI 42.1%–47.0%) (PPI > 0.97) and in absolute number of users from 660 million (95% UI 637–701 million) to 842 million (95% UI 800–893 million) (Tables 1 and 2, Section 5 in S1 Appendix, S6 and S7 Results for country-specific results).

Global unmet need for modern methods declined marginally from 15.1% (95% UI 14.1%–16.8%) in 2000 to 14.2% (95% UI 12.9%–15.8%) in 2019. In absolute numbers, 270 million (95% UI 246–301 million) women have an unmet need for modern methods in 2019, up from 237 million (95% UI 222–264 million) in 2000. The subregion of Southern Asia has the highest number of women with unmet need for modern methods in 2019 at 87 million (95% UI 71–109 million), with 4 other subregions above 15 million: 22 million (95% UI 18–21 million) of Eastern Africa, 20 million (95% UI 18–22 million) of Western Africa, 23 million (95% UI 20–27 million) of South-Eastern Asia, and 28 million (18–48 million) of Eastern Asia (Fig 6).

Fig 6 — The 95% UIs are displayed by vertical lines. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

The proportion of need for family planning satisfied by modern methods among WRA globally increased from 73.5% (95% UI 71.3%–75.3%) in 2000 to 75.7% (95% UI 73.2%–78.0%) in 2019, or 2.2 percentage points (95% UI −0.3 to 4.7, PPI > 0.95) (Fig 7, Table 1). Of 76 countries that in 2000 had less than half of the demand satisfied by modern methods, 34 countries had demand satisfied above 50% in 2019, with the highest increases in Rwanda, from 13.3% (95% UI 11.4%–15.6%) in 2000 to 66.8% (95% UI 56.1%–75.9%) in 2019, and in Ethiopia, from 15.7% (95% UI 13.7%–18.2%) in 2000 to 62.5% (95% UI 55.5%–69.4%) in 2019 (PPI = 1.0) (Fig 8). Our probabilistic projections indicate that countries and regions with currently small proportions of need satisfied would achieve higher levels by 2030 as projected based on country-specific experiences together with experiences in other countries in the region and the world.

Fig 7 — Estimates are shown for the years 2000 and 2019. The 95% UIs are displayed by solid horizontal lines and median estimates by the solid circles. Projections for 2030 are shown using open circles and dashed lines. In United Nations classification, the more developed regions comprise all countries of Europe, Northern America, Australia and New Zealand, and Japan. The less developed regions comprise all other countries. The country classification by income level is based on June 2019 gross national income per capita from the World Bank. Note: results for all countries presented in S2 Appendix. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

Fig 8 — The 95% UIs are displayed by solid horizontal lines and median estimates by the solid circles. Projections for 2030 are shown using open circles and dashed lines. UI, uncertainty interval; WRA, women of reproductive age (15–49 years).

The proportion of need satisfied by modern methods remains smallest in countries with low income, despite the large increase from 32.1% (95% UI 30.5%–34.0%) in 2000 to 52.6% (95% UI 50.1%–55.2%) in 2019 (PPI > 0.99) (Fig 7), with the smallest proportions of need satisfied by modern methods in Chad 23.6% (95% UI 15.9%–33.5%), Somalia 17.7% (95% UI 4.6%–44.8%), and South Sudan 17.8% (95% UI 9.5%–35.4%). Among 43 countries with less than half of their demand satisfied by modern methods in 2019, 32 are low and lower-middle income.

Discussion

Our findings indicate that, among the 1.9 billion WRA worldwide in 2019, 1.1 billion have demand for family planning; of these, 842 million are using modern contraception, and 270 million have an unmet need for modern methods. The majority of WRA are in developing countries. In low- and middle-income countries (as defined by the World Bank classification of gross national income per capita as of June 2019), 708 million women are using modern contraception, and 234 million women have an unmet need for modern methods. In less developed regions (as used in the United Nations classification), 698 million women are using modern contraception, and 232 million women have an unmet need for modern methods. In Middle and Western Africa, high proportions of WRA (both among married and unmarried women) continue to experience unmet need for modern methods, highlighting the need for increased government commitment to, and greater international investment in, family-planning programming in these 2 subregions.

The proportion of all modern contraceptive users who are UWRA rose from 12.1% in 2000 to 15.7% in 2019, driven by an increase in modern contraceptive prevalence among UWRA as well as an increasing share of women who are unmarried or not in a union (from 32.9% in 2000 to 34.7% in 2019), the latter due to changes in marital patterns and age structures [26,27]. The contribution of UWRA to the total demand for family planning will likely continue to increase because of further postponement of marriage and union formation; however, it will also depend on the changes in sexual activity among UWRA. A previous study of trends in sexual activity among UWRA [29] observed no generalised pattern over time and across countries.

Despite three-quarters of WRA globally having their need for family planning satisfied by modern methods in 2019, there is great diversity across countries and subregions. Most countries with currently low proportions of need for family planning satisfied by modern methods are low-income and lower-middle–income countries, and for many of them, the population of WRA is projected to increase by more than a third by the end of 2030 compared to 2019 [27]. This will create challenges to expand family-planning services fast enough to fulfil the growing need for family planning and will likely generate additional development-related challenges associated with rapid population growth. Even with the projected increases in the proportion of need satisfied by modern methods in each country from 2019 to 2030, the global figure is projected to increase only slightly to 77.2% (95% UI 73.2%–80.5%) in 2030 (Table 1) because of the changing composition of WRA worldwide that reflects increasing representation of women from countries with a lower proportion of need for family planning satisfied with modern methods.

In a comprehensive and systematic way, we generated annual estimates and projections of contraceptive prevalence, unmet need, and demand for family planning among UWRA and all WRA for the period 1990 to 2030. We took into account a wide range of surveys, systematically accounting for variability in errors across data sources, and potential biases in observations that departed from standard measures or reference groups for contraceptive prevalence. The Bayesian approach generated UIs for all estimates and projections, enabled assessments of the probability of actual change over time, and allowed cross-country comparisons and calculation of aggregates for specific time periods. The model accounts for variations in the prevalence of sexual activity among UWRA across countries and incorporates underlying compositional changes in marital/union status over time. For countries or areas that had no data on contraceptive use among UWRA, we provided estimates based on a novel hierarchical classification derived from information on sexual activity among UWRA and geographical clusters.

The most recent comparable study—Adding It Up 2017 by the Guttmacher Institute [13]—estimated 671 million modern contraceptive users among WRA in developing countries for 2017 (compared to our estimate of 685 million [95% UI 655–728 million] for 2017), and they estimated 214 million women with unmet need for modern methods (compared to our estimate of 230 million [95% UI 212–256 million]); both Guttmacher estimates are within our 95% UIs. For sub-Saharan Africa, the differences in the numbers of modern contraceptive users are larger. The Adding It Up 2017 estimate of 51 million users of modern contraception is lower than the 95% UI of our estimate for year 2017 of 58 million (95% UI 56–60 million), whereas the estimate for unmet need for modern methods is comparable—51 million in Adding It Up compared to our 51 million (95% UI 48–53 million). Adding It Up 2017 did not publish results for individual countries, and therefore we cannot directly compare the results. There are 2 main reasons for discrepancies between the 2 sources. First, more input data have become available describing the situation in 2017. Whereas the most recent surveys in Adding It Up 2017 were from 2016 (and only 2 of the 48 countries of sub-Saharan Africa had a 2016 survey available), our 2019 data compilation contains surveys that took place in 2016 or after for 18 sub-Saharan African countries. Second, the approach of producing current estimates is different. The Adding It Up 2017 study used estimates from surveys in years prior to 2017 as estimates for the situation in 2017. Therefore, in the countries with increasing contraceptive use, the most recent survey-based observation of contraceptive prevalence—especially when time since last survey is several years—might underestimate the current situation. However, our model-based estimates use short-term projections since the last observation, taking into account past trends in the country and regional and global trends.

Nevertheless, some data limitations remain. For 59 of the 195 countries, there are no survey data available for contraceptive use among UWRA (marked with ‘*’ in the maps in Fig 4 and indicated in Tables H–K in S1 Appendix). The models used here provide reasonable estimates of contraceptive prevalence and unmet need for countries without such data, determined by the hierarchical structure of the model, such that averages based on countries most proximate in the hierarchy were given greater weight. However, the uncertainty surrounding those estimates is large. Even for countries where survey data are available, contraceptive use and sexual activity among UWRA are thought to be underreported, and data may be susceptible to other biases. The extent of and methods for adjustment of underreporting require further research. The largest gap in the data compilation for UWRA was China, which represented 14% of the global population of UWRA. In China, only ever-married women were asked about contraceptive use, even in light of growing evidence from studies (though not nationally representative) that sexual activity and contraceptive use among unmarried women is increasingly common (Section 2.3.2 in S1 Appendix). Therefore, the 95% UI around the estimated proportion of UWRA modern contraceptive users is large for Eastern Asia (see Fig 1).

Our priority was to provide estimates for all WRA (comparable to estimates generally published in survey reports), and our study included all contraceptive users irrespective of their sexual activity status. Not all contraceptive users are sexually active. Among MWRA, in many populations more than 10% of women who report currently using a contraceptive method also report no sexual intercourse in the past 28 days. Among UWRA, this proportion was observed to be more than 50% depending on the population and contraceptive method used [29]. Different approaches could have analysed only the population of women who are sexually active and include only those who report being sexually active in the counts of contraceptive users. While a previous study analysed survey data for the population of sexually active WRA only, it did not produce annual estimates and projections or the aggregate results [15]. To produce such aggregates would require estimates of populations of sexually active women across countries and over time.

For estimates of current need for family planning among nonusers (excluding those who are pregnant, postpartum, or infecund), it is commonly assumed that all MWRA are sexually active despite the fact that, in many populations, 20% or more report no sexual intercourse in the past 4 weeks. For UWRA, such an assumption is untenable because there are large differences in the prevalence of recent sexual activity among UWRA [29]; thus, a criterion of sexual exposure to risk of pregnancy is needed. Previous studies have used a variety of criteria: sex in last 4 weeks, sex in past 3 months, sex in past 1 year, and ever had sex. This has an important implication for the calculation of family-planning indicators among unmarried women, such as unmet need and demand satisfied, in which sexual activity is used to determine exposure to the risk of pregnancy for this group of women. In this paper, the 4-weeks criterion is applied following the unmet need for family planning algorithm in DHS [19]. The definition of current sexual activity as sexual intercourse within the last 4 weeks misses many UWRA who were sexually active within a longer period of time (e.g., within the last year), who may be at risk of pregnancy even if they are only sporadically sexually active.

The estimates presented here will enable monitoring of progress towards universal access to reproductive health and enable assessment of the impact of commitments made and actions taken in the 2030 Agenda under SDG indicator 3.7.1, as well as FP2020 reporting of the number of additional users, contraceptive prevalence, unmet need, and demand for family planning satisfied among all WRA [7,9]. The projections can be used in policy and programme planning. For example, probabilistic projections can be used to set ambitious yet achievable country-level targets, and the model could be applied to subnational data [30,31]. Our results would allow this work to be extended to all women.

Furthermore, the results of our study are needed to underpin models of the impact of increased use of family planning on other reproductive health outcomes [13,32] and models estimating the wider impact on health, schooling, and economic outcomes. Worldwide, an estimated 44% of pregnancies were unintended in 2010–2014, and some 56% of all unintended pregnancies ended in abortion [33] these findings underscore the continuing need for investments to meet women’s and couples’ contraceptive needs. Globally, UWRA account for a significant share of unintended pregnancies and abortions, and these events could potentially lead to more serious consequences for UWRA than for MWRA. The annual number of abortions worldwide is estimated at 56.3 million in 2010–2014, of which 27% were obtained by UWRA [34]. Both studies [33,34] pointed out that the lack of estimates of contraceptive use and unmet need for family planning among UWRA limited their ability to determine the extent to which these trends were associated with need for contraception. Our study will help close this gap.

The findings support recent calls to increase investments in family planning, especially in regions of the world where contraceptive prevalence is still low, unmet need is high, and the growth in the number of WRA is rapid. We show that more than half of the women with demand for family planning in 2030 will be in low-income and lower-middle–income countries, making it particularly important for the international community to support family-planning programmes here.

Supporting information

S1 Appendix. Technical appendix providing supplementary information on input data and methodology, plus supplementary results.

(PDF)

Click here for additional data file.^{(2.6MB, pdf)}

S2 Appendix. Supplementary figures; country-specific estimates and projections of family-planning indicators.

(PDF)

Click here for additional data file.^{(11.8MB, pdf)}

S1 Data. Input data for MWRA.

(CSV)

Click here for additional data file.^{(229.2KB, csv)}

S2 Data. Input data for UWRA.

(CSV)

Click here for additional data file.^{(117KB, csv)}

S3 Data. Table G in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(12.1KB, csv)}

S1 Results. Tables 1 and 2 in xlsx format.

(XLSX)

Click here for additional data file.^{(15.1KB, xlsx)}

S2 Results. Table H in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(20.4KB, csv)}

S3 Results. Table I in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(16.9KB, csv)}

S4 Results. Table J in S1 Appendix provided in csv format.

(CSV)

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S5 Results. Table K in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(21.5KB, csv)}

S6 Results. Table L in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(16.7KB, csv)}

S7 Results. Table M in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(17KB, csv)}

S1 Checklist. Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist.

(DOCX)

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Acknowledgments

Disclosure: The views expressed herein are those of the authors and do not necessarily reflect the views of the United Nations.

The authors gratefully acknowledge Ann Biddlecom, Jorge Bravo, Patrick Gerland, and John R. Wilmoth for their helpful comments on the paper. In addition, we thank Helena Cruz Castanheira, Stephen Kisambira, Kyaw-Kyaw Lay, Natalie Lin, and Nadia Soerjanto for their help with compiling the datasets of key family-planning indicators and Leontine Alkema, Win Brown, Niamh Cahill, Emily Sonneveldt, John Stover, and Michelle Weinberger for their valuable contributions to discussions regarding model development.

Abbreviations

DHS: Demographic and Health Surveys
EWEC: Every Woman Every Child
FP2020: Family Planning 2020
GATHER: Guidelines for Accurate and Transparent Health Estimates Reporting
GGS: Gender and Generations Survey
ICPD: International Conference on Population and Development
IUD: intrauterine device
LAM: lactational amenorrhoea method
MCMC: Markov chain Monte Carlo
MICS: Multiple Indicator Cluster Surveys
MWRA: women of reproductive age (15–49 years) who are married or in a union
PMA2020: Performance, Monitoring and Accountability 2020
PPD: posterior probability of a decrease
PPI: posterior probability of an increase
SDG: Sustainable Development Goals
UI: uncertainty interval
UWRA: women of reproductive age (15–49 years) who are unmarried and not in a union
WRA: women of reproductive age (15–49 years)

Data Availability

The input data are included in S1 and S2 Data. Data and metadata are contained in files. Data are also available from the online open-access publication: United Nations, Department of Economic and Social Affairs, Population Division. World Contraceptive Use 2019. New York: 2019. Available from: https://www.un.org/en/development/desa/population/.

Funding Statement

VK, MCW, PU, and ANZD received funding from the Bill and Melinda Gates Foundation at https://www.gatesfoundation.org/ (Grants Nos. OPP1183453 and OPP1110679). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003026.r001

Decision Letter 0

Louise Gaynor-Brook

27 Oct 2019

Dear Dr. Kantorova,

Thank you very much for submitting your manuscript "Contraceptive prevalence, unmet need for family planning, and the need for family planning satisfied by modern contraceptive methods: National, regional and global trends for all women of reproductive age from 1990 to 2030" (PMEDICINE-D-19-03179) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Nov 08 2019 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Louise Gaynor, MBBS PhD

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Title – it’s quite long and we also need a study descriptor, can we instead change to: Progress in 2030 sustainable development goals for contraceptive access and use: predictive model estimates from 185 countries

Abstract – Please provide summary demographic details of women included in the survey; please use 95% Cis and p values here (and throughout including the main text and tables, etc) for any quantifiable data.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please

see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please avoid using itals font for emphasis

Refs in the main text, please use square brackets instead of superscript and the brackets and refs should occur before the full stop.

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Please ensure the doc is clean and clear of track changes – page 8.

Please clarify how many countries are included – abstract you say 185, under the ‘Data compilation’ section you say 195.

Please clarify in the main text what traditional and modern contraceptives are, with examples for relevance. Please place this ahead of the first mention in table 1.

Fig 4 – more developed / less developed countries. This is rather vague. Is this common terminology? If not please use something more specific.

Please provide a completed GATHER checklist as a Supp file.

Comments from the reviewers:

Reviewer #1: The paper is well written and organized and addresses and important global health issue. It will likely be widely cited. Past studies of FP indicators (eg. by the UN) have often focused on married/in union women, thus neglecting a growing population of unmarried women. The present study provides for the first time estimates and projections of FP indicators by country and region for both married and unmarried women.

There are no issues that are problematic or require major revision. However I have several suggestions for clarifying the presentation.

1) The abstract and introduction should state clearly that you will present estimates from 1990 to 2019 and projections from 2019 to 2030

2) Introduction: between the first and second sentence add something like "past estimates of FP indicators have been largely limited to married/in union women but recent changes in ….."

3) To provide the reader with a better understanding of the importance of unmet need and unsatisfied demand, note that there x million abortions and Y million unplanned pregnancies annually with wide ranging adverse consequences.

4)The term "posterior" (used from page 8 onwards) should be defined for readers who are not familiar with Baysian methods

5) The PPI measure should be defined and the reader should be given some guidance as to how to interpret a value of say 0.9.

6) p13 line 5 and 6 says: " ...are largely a reflection of differences in sexual activity among UWRA" This statement is probably correct but how do you determine this?

7) Page 23 first para: Isn't a rise in sexual activity among UWRA part of the explanation?

Reviewer #2: Monitoring progress towards universal access to family planning, and associated indicators has been identified as a global priority. Unmarried women are often excluded from studies and demographic health surveys, which biases reporting of key family planning indicators. The authors present a model to help quantify the contribution of unmarried adolescents to these FP indicators, which is helpful for programs and policymakers. However, the authors miss a few opportunities to be more transparent in the main text on how the indicators were derived for unmarried women, particularly sexual activity which is used to create the indicators, and the sources of information that were used to make this estimate. In addition, the authors could make more comparisons to put their results in context. For example, some countries have included unmarried adolescents in their DHS surveys. How do the results compare for these specific countries. Also, it is unclear what the intention of including the married women indicators are, and how these indicators are different than prior estimates. It would be plausible to compare the unmarried/married, and also the total (All women) to show the impact of excluding the unmarried women is. Overall the analysis is interesting, and transparency in key methods in the primary text (not appendices) would improve comprehension about the methods used to create the model. Finally, the article is quite long and it would be helpful for the authors to more briefly describe the methods (omitting some of the modeling jargon but adding details critical to understanding how estimates were derived), cut the text to highlight key results and make more comparisons between unmarried/married/all women, and synthesize results more concisely in the discussion. These edits would greatly improve the manuscript.

Specific comments are included below:

Abstract

1. The conclusions state the CPR and demand for FP differ by marital status yet only unmarried women are described in findings. Add in findings from married women.

2. The last sentence of the conclusion is essentially same as the background. The data do not clearly indicate why "accurate monitoring" is essential.

Introduction

3. The last sentence could be shortened to more concisely describe what the objective was and the other details can only be mentioned in methods.

4. More details on unmet need for UWRA who have not initiated sexual activity should be briefly discussed as you include this specific group in your methods.

Methods

5. Please specify the duration of delaying future childbearing in your definition. Also be clear that when you say delay you mean future children, not necessarily delay first pregnancy.

6. Omit the rational "caution" in the methods. "While FP indicators…"

7. It would be helpful if definitions were applied universally when indicated, ie at risk of pregnancy, rather than described for one group and referred to as being calculated in the same way.

8. Suggest re-defining calculation of unmet need rather than just citation.

9. It would be helpful to briefly describe how you determined if "social values shows most of the population regarded sexual activity among unmarried as not acceptable/justifiable". This is a critical component of parameterizing your model and should be described in methods not just in appendix.

10. The methods would benefit from more specificity rather than general approaches. Ie "estimates for countries and indicators with no data….", which countries or how many where there? Which indicators? More transparency in the methods would improve readability in the methods, yet also ensure description of methods is appropriate for the journal audience (most of whom aren't modelers/statisticians).

11. How did the authors determine if the model was "robust and adequately calibrated"? Be more specific.

12. Define traditional method use.

13. I suggest adding prior published estimates of indicators on graphs where available, as data points or lines. This would improve interpretation of how the model for married women compares with prior estimates, as it is unclear what added value the model has for calculated indicators for married women rather than as a comparison using similar methodology for unmarried women.

Results

14. The authors begin by summarizing the changes in FP indicates among married women over time and geographically. However the primary objective is to show unmarried women, this should be the focus of the results and comparisons made with married women.

15. Figure 1 shows data through 2030 but the text refers to 2019. Suggest harmonizing presentation to include 2030 for consistency, even though it is projected.

16. Results could be presented more succinctly to highlight key points rather than more comprehensive text on results (ie perhaps not all geographic regions need to be mentioned in text for all indicators).

17. Presentation of married/unmarried women separately in the text makes it more difficult to compare findings. I suggest reorganizing to pair the indicators and present married/unmarried together.

18. Figure 4, need legend to understand income group. Also, suggest moving to appendix.

19. Avoid interpreting results in results section, ie interpreting the explanation why total need for FP increases slightly. It is also redundant with the discussion.

20. The comparison of indicates with Guttmacher is helpful; there appear to be a few nuances of the data prior to or including 2017. Could you do a sensitivity analysis and compare your 2016 estimates to their 2017 to help account for this difference?

Reviewer #3:

This is an interesting paper concerned with unmet need for family planning. It is well written and represents the result of many laborious tasks. The methodological aspects of the work are well selected and suitable for the applied problem.

Specific comments are given below.

Linking the evidence on MWRA and UWRA is a vital aspect of the work. The way this is performed is described in the appendix where the details of the hierarchical model which allows for borrowing of strength are outlined. However, it would be illuminating if the principles of this aspect are also given in the main manuscript, especially the intuition of how is this crucial task accomplished, including the flow of information.

It would be useful to add the priors for the (co)variance matrices (like in 3.47), especially since their density and parameters may affect the results, particularly in hierarchical models with borrowing of information and sparse data, as in the present paper.

Also, please discuss the choice of priors when restricted ranges are employed and informative selections are made, like in 3.120, say.

Please discuss the selection of two (high/low) sexual activity categories. Would a third (say intermediate) category be feasible in terms of data availability? Would this be expected to improve the accuracy of the results?

Please add the process of reviewing the evidence which resulted in the data collection. Was that done via a systematic review? Add the details where necessary, including the databases searched and the keywords used

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Feb 18;17(2):e1003026. doi: 10.1371/journal.pmed.1003026.r002

Author response to Decision Letter 0

13 Nov 2019

Attachment

Submitted filename: Replies to reviewers&editors_20191109.docx

Click here for additional data file.^{(53.5KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003026.r003

Decision Letter 1

Louise Gaynor-Brook

10 Dec 2019

Dear Dr. Wheldon,

Thank you very much for re-submitting your manuscript "Progress towards meeting contraceptive needs in the SDG era: Bayesian hierarchical model estimates and projections for 185 countries" (PMEDICINE-D-19-03179R1) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Dec 17 2019 11:59PM.

Sincerely,

Louise Gaynor-Brook, MBBS PhD

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Title: The title is still rather long. We suggest “"Estimating progress towards meeting women's contraceptive needs in 185 countries from 1990-2019: a Bayesian hierarchical modelling study" or similar.

Abstract: Background - The final sentence should clearly state the study question.

Please avoid assertions of primacy ("This study is the first..."); we suggest adding ‘To our knowledge...’

Please begin your Abstract: Conclusions with "In this study, we observed ..." or similar

Please add the title ‘Author Summary’ ahead of your author summary

At the start of your methods section, please adapt the text to "In this study, contraceptive prevalence was defined ..." or similar.

At the top of p.11 of the PDF, you state that "a protocol or analysis plan was not pre-published". Please adapt this wording to state whether or not a prespecified plan was prepared, and if so attach the relevant document as a supplementary file. Please highlight non-prespecified analyses.

Please add a brief statement to your methods section to note (for example) that ethics approval was not required for this study.

To begin the first paragraph of your discussion section, please add "Our findings indicate that ..." or similar.

Please revise any references in superscript and present in square brackets e.g. line 284

Line 322 - Please add a space between ‘changewith’

Line 334 - please revise to ‘Estimates from an additional’

Fig 1a, 1b, 4 - please define GNI in the figure legend

Line 463 - please revise to ‘Table 1’

Line 513 - please revise to ‘The majority...’

References - please ensure that references are appropriately formatted e.g. removal of ‘The ‘ before ‘Lancet’ in ref 8 and others

Please remove quotation marks from reference titles, e.g., reference 31.

Where necessary (e.g., reference 33), please ensure that 6 author names are listed, followed by "et al.".

Please adapt the format of reference 24 to match the other references.

Comments from Reviewers:

Reviewer #2: The revised manuscript has improved the flow and clarity. Only one comment on the revision:

Abstract - while I understand the rationale for not including married women in results of abstract, the conclusion of the abstract is based on this comparison of unmarried/married. I suggest revising your conclusion, "Trends in contraceptive prevalence and demand for family planning differ substantially by marital status" or showing the results on married women in the abstract. Otherwise your primary conclusion is not substantiated with evidence in abstract.

Reviewer #3: The authors have sufficiently revised an already very good paper so this article is now acceptable for publication

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Feb 18;17(2):e1003026. doi: 10.1371/journal.pmed.1003026.r004

Author response to Decision Letter 1

8 Jan 2020

Attachment

Submitted filename: Replies to reviewers&editors_20191212.docx

Click here for additional data file.^{(33.7KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003026.r005

Decision Letter 2

Louise Gaynor-Brook

9 Jan 2020

Dear Dr Kantorová,

On behalf of my colleagues and the academic editor, Dr. Alison L Drake, I am delighted to inform you that your manuscript entitled "Estimating progress towards meeting women's contraceptive needs in 185 countries: a Bayesian hierarchical modelling study" (PMEDICINE-D-19-03179R2) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Louise Gaynor-Brook, MBBS PhD

Associate Editor

PLOS Medicine

plosmedicine.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Technical appendix providing supplementary information on input data and methodology, plus supplementary results.

(PDF)

Click here for additional data file.^{(2.6MB, pdf)}

S2 Appendix. Supplementary figures; country-specific estimates and projections of family-planning indicators.

(PDF)

Click here for additional data file.^{(11.8MB, pdf)}

S1 Data. Input data for MWRA.

(CSV)

Click here for additional data file.^{(229.2KB, csv)}

S2 Data. Input data for UWRA.

(CSV)

Click here for additional data file.^{(117KB, csv)}

S3 Data. Table G in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(12.1KB, csv)}

S1 Results. Tables 1 and 2 in xlsx format.

(XLSX)

Click here for additional data file.^{(15.1KB, xlsx)}

S2 Results. Table H in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(20.4KB, csv)}

S3 Results. Table I in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(16.9KB, csv)}

S4 Results. Table J in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(21.3KB, csv)}

S5 Results. Table K in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(21.5KB, csv)}

S6 Results. Table L in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(16.7KB, csv)}

S7 Results. Table M in S1 Appendix provided in csv format.

(CSV)

Click here for additional data file.^{(17KB, csv)}

S1 Checklist. Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist.

(DOCX)

Click here for additional data file.^{(30.9KB, docx)}

Attachment

Submitted filename: Replies to reviewers&editors_20191109.docx

Click here for additional data file.^{(53.5KB, docx)}

Attachment

Submitted filename: Replies to reviewers&editors_20191212.docx

Click here for additional data file.^{(33.7KB, docx)}

Data Availability Statement

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[pmed.1003026.ref002] 2.United Nations General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015 (A/RES/70/1). New York (NY): United Nations; 2015. https://sustainabledevelopment.un.org/post2015/transformingourworld. [cited 22 Jan 2020].

[pmed.1003026.ref003] 3.Every Woman Every Child. The Global Strategy for Women’s, Children’s and Adolescents Health (2016–2030). New York (NY): Every Woman Every Child; 2015. http://globalstrategy.everywomaneverychild.org/. [cited 22 Jan 2020].

[pmed.1003026.ref004] 4.Brown W, Druce N, Bunting J, Radloff S, Koroma D, Gupta S, et al. Developing the “120 by 20” Goal for the Global FP2020 Initiative. Stud Fam Plann. 2014;45: 73–84. 10.1111/j.1728-4465.2014.00377.x [DOI] [PubMed] [Google Scholar]

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[pmed.1003026.ref006] 6.United Nations, Department of Economic and Social Affairs, Population Division. World Family Planning 2017 Highlights. New York (NY): United Nations; 2017. https://www.un.org/en/development/desa/population/publications/pdf/family/WFP2017_Highlights.pdf. [cited 22 Jan 2020].

[pmed.1003026.ref007] 7.United Nations. The Sustainable Development Goals report 2018. New York (NY): United Nations; 2018.

[pmed.1003026.ref008] 8.Cahill N, Sonneveldt E, Stover J, Weinberger M, Williamson J, Wei C, 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. Lancet. 2017; 391: 870–882. 10.1016/S0140-6736(17)33104-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[pmed.1003026.ref010] 10.Mauldin WP, Segal SJ. Prevalence of contraceptive use: Trends and issues. Stud Fam Plann. 1988; 19: 335–353. [PubMed] [Google Scholar]

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PERMALINK

Estimating progress towards meeting women’s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study

Vladimíra Kantorová

Mark C Wheldon

Philipp Ueffing

Aisha N Z Dasgupta

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Definitions

Data compilation

Statistical model

Indicators used

Results

Family-planning trends among MWRA

Table 1. Global estimates of the proportion (%) of WRA using a modern contraceptive method, having unmet need for modern methods, and having demand for family planning, and the proportion of need for family planning satisfied by modern methods, for 1990, 2000, 2010, 2019, and 2030.

Fig 1. Percentage of WRA who used a modern contraceptive method by marital group for the period 1990–2030, by World Bank income group and subregion.

Table 2. Global estimates of the number (millions) of WRA using a modern contraceptive method, having unmet need for modern methods, and having demand for family planning, and the proportion unmarried among all women, for 1990, 2000, 2010, 2019, and 2030.

Fig 2. Proportion of need for family planning satisfied by modern methods among WRA by marital group for the period 1990–2030, by World Bank income group and subregion.

Family-planning trends among UWRA

Fig 3. Percentage-points change between 2000 and 2019 in the proportion of WRA who used a modern contraceptive method, by marital status.

Fig 4. 2019 estimates for family-planning indicators among UWRA.

UWRA contraceptive users in the context of all users

Fig 5. Proportion of UWRA among WRA using modern contraception by World Bank income group and by subregion in 2000 and 2019.

Contraceptive prevalence and need for family planning among WRA

Fig 6. Number of WRA having unmet need for modern methods by subregion in 2000 and 2019.

Fig 7. Proportion of need for family planning satisfied by modern contraceptive methods among WRA, median and 95% UIs, by subregion.

Fig 8. Proportion of need for family planning satisfied by modern contraceptive methods among WRA, median and 95% UIs, by country, among countries where proportion of need for family planning satisfied by modern contraceptive methods was less than 50% in 2000.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Louise Gaynor-Brook

Roles

Author response to Decision Letter 0

Decision Letter 1

Louise Gaynor-Brook

Roles

Author response to Decision Letter 1

Decision Letter 2

Louise Gaynor-Brook

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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