Effect of maternity care improvement, fertility decline, and contraceptive use on global maternal mortality reduction between 2000 and 2023: results from a decomposition analysis

Summary

Background

The number of global maternal deaths has reduced 41% from 443 000 in 2000 to 260 000 in 2023. Maternal mortality reduction is possible through improvement in maternity care, which reduces the risk per birth, and a decrease in the number of pregnancies, which reduces women's exposure to the associated mortality risks. We aimed to examine the effects of maternity care improvement, fertility reduction, and increased contraceptive use on maternal mortality decline between 2000 and 2023.

Methods

We conducted two sets of analyses. First, we measured the effects of maternity care improvement and fertility reduction on maternal mortality reduction at the global, regional, and national levels for 195 countries and territories with a simple decomposition method. Second, we employed a counterfactual approach to assess the effects of contraceptive use on maternal mortality reduction through fertility reduction. Data for this analysis came from the most recent database of maternal mortality trends estimations by WHO/Maternal Mortality Estimation Inter-Agency Group for 2000–23 and the UN's World Contraceptive Use 2024 database.

Findings

We estimated that 61·2% of global maternal mortality decline between 2000 and 2023 was attributable to improvements in maternity care and 38·8% was attributable to fertility reduction. An increase in contraceptive prevalence rate during this period prevented 77 400 maternal deaths in 2023 (approximately 24·0% of maternal deaths). The regional estimates showed that the fertility reduction effect on maternal mortality was most pronounced in Latin America and the Caribbean, sub-Saharan Africa, and eastern and southeastern Asia.

Interpretation

Our analysis suggests that both improved maternity care and fertility reduction, primarily through contraceptive use, substantially reduced maternal mortality globally. Accelerated efforts should be given to maternity care and family planning interventions for achieving the UN's Sustainable Development Goals 3.

Funding

US Agency for International Development.

Introduction

Substantial progress has been made between 2000 and 2023 in reducing global maternal mortality, defined as the death of a woman due to pregnancy-related causes while pregnant, during delivery, or within 42 days of the termination of a pregnancy.¹ Global maternal deaths declined by 41% from 443 000 to 260 000, and the maternal mortality ratio (MMR), the key indicator for tracking the UN's Sustainable Development Goal 3.1 (SDG-3.1), declined by approximately 40% from 328 to 197 maternal deaths per 100 000 live births. Historical data from Europe and the USA highlight that maternal mortality reductions began only after medical interventions addressing common obstetric complications were introduced.^{2, 3} Countries with lower maternal mortality consistently show lower fertility rates, higher coverage of antenatal and delivery care, and wider access to emergency health services.⁴

Maternal mortality reduction is possible through two direct mechanisms: improving maternity care to prevent and treat life-threatening complications, and reducing the number of pregnancies that expose women to such risks.⁵

The majority of maternal deaths are due to postpartum haemorrhage, hypertensive disorders, abortion complications, sepsis, embolism, and obstructed labour.⁶ These life-threatening complications are largely preventable, and almost all maternal deaths are avoidable with early diagnosis and timely medical care. Since the Safe Motherhood Initiative,⁷ launched in 1987, concerted efforts have been made to reduce maternal mortality in developing countries through improved antenatal care, safe delivery from skilled birth attendants, postnatal care, and promotion of family planning to reduce unwanted births. A study in 2001 suggested that between 16% and 33% of all maternal deaths could be prevented through skilled attendance at birth.⁸ Community-based interventions have sought to help women delivering at home by preventing major complications and ensuring referral to emergency obstetric facilities. Several countries introduced misoprostol to prevent postpartum haemorrhage at home births, supported community health workers in diagnosing pre-eclampsia and sepsis, and promoted the use of partographs for detecting prolonged labour. Misoprostol has also expanded access to safer abortion, and post-abortion care has improved since the 1994 International Conference on Population and Development.⁹ Collectively, these initiatives have contributed to global mortality decline.

Research in context.

Evidence before this study

Since the beginning of global maternal mortality estimation by WHO in the early 1990s through predictive models, skilled birth attendance and general fertility rate, including gross domestic product as a developmental indicator, have been used as the predictors of maternal deaths at the country level. We did a literature review of publications of any study type in PubMed and Google Scholar in the English language from database inception until March 23, 2025 with key terms (maternal care, fertility, contraceptive use, and maternal mortality) and found that several studies examined the effects of maternity care—primarily through improvements in skilled birth attendance, antenatal care, and postpartum care—and fertility decline, including contraceptive use, on maternal mortality reduction. However, most of these studies examined the effect independently and did not consider any concurrent or overlapping effects. Studies suggested that between 16% and 33% of all maternal deaths could be prevented through skilled attendance at birth. Demographic analysis has suggested that fertility decline prevented about 1·7 million maternal deaths cumulatively between 1990 and 2008. Anrudh K Jain examined maternal mortality decline attributable to both fertility decline and safe motherhood programmes in three South Asian countries (India, Pakistan, and Bangladesh). We found no study that examined the joint effects of maternity care, fertility, and contraceptive use on maternal mortality at global, regional, and country levels.

Added value of this study

To the best of our knowledge, this is the first study to analyse relative contributions of maternity care improvement, fertility decline, and contraceptive use change on maternal mortality decline over nearly a quarter century at global, regional, and country levels using publicly available data. The proposed decomposition method is simple to replicate and can be used by country officials for tracking progress and making decisions on investments for cost-effective interventions. The study brings out the significance of fertility reduction and contraceptive use in tracking progress towards the Sustainable Development Goal 3.1 (SDG-3.1) of reducing global maternal mortality and provides evidence that fertility reduction, in addition to improving maternity care, is essential for meeting the SDG-3.1 target.

Implications of all the available evidence

Our study suggests that improvement in maternity care predominantly contributed to maternal mortality decline globally, but about two-fifths of this decline was due to fertility reduction. Evidence in the literature suggests that investment in family planning programmes to promote contraceptive use and to reduce unwanted fertility has declined substantially over the last few decades. Progress in contraceptive use has faltered during this period compared with earlier decades. Without fertility decline, primarily through voluntary contraceptive use, it will not be possible to achieve the SDG-3.1 goal.

Some studies suggest that contraceptive use, accelerated through family planning programmes, also has a substantial impact on reducing maternal mortality.^{10, 11, 12, 13, 14} It has been estimated that contraceptive use reduced the global overall MMR by 44%.¹⁵ Fertility levels fell from 3·3 births per woman in the 1990s to 2·25 in 2023, substantially lowering women's repeated exposure to pregnancy-related risks. Fertility decline also reduced unwanted and high-risk births, especially those occurring “too early, too close, too late, or too many”.¹⁶ Maternal mortality risk is higher at younger ages (younger than 18 years) when pelvic development is incomplete, and at older ages and with higher parities when a mother's health condition might be more compromised. Studies suggest that eliminating all births in women younger than 20 years and older than 39 years might reduce maternal mortality by 34%, and eliminating births beyond the fifth child could reduce the number of maternal deaths by 58%.¹¹

The relative contributions of maternity care and fertility decline in reducing maternal mortality globally, however, remain unclear. We aimed to examine the relative contributions of maternity care and fertility reduction, primarily through contraceptive usage, on maternal mortality decline between 2000 and 2023 across global, regional, and national levels. Understanding these dynamics could guide resource allocation, support countries lagging in SDG-3.1 progress, and strengthen strategies for reducing unintended pregnancies and improving maternity care.

Methods

Data sources

The data for this analysis are compiled from two databases. The first data source is the 2023 WHO/Maternal Mortality Estimation Inter-Agency Group (MMEIG) maternal mortality estimation database. The database contains input data (eg, skilled attendance at birth, general fertility rate, number of female population aged 15–49 years) and maternal mortality estimates from Bayesian models. The sources of input data and mortality estimation methods are described in the WHO maternal mortality report.¹

Contraceptive use rates data were obtained from the UN World Contraceptive Use 2024 database.¹⁷ These rates were estimated from Bayesian hierarchical models using data from population-level surveys. We used contraceptive prevalence rates for all women, irrespective of marital or cohabitation status.

Statistical analysis

We conducted two sets of analyses. First, we employed a simple decomposition method to disaggregate the effects of maternity care and fertility reduction on maternal mortality between 2000 and 2023. Second, we examined the effect of contraceptive use on maternal mortality changes through fertility reduction during this period with a counterfactual approach.^{18, 19}

Decomposition of maternal mortality changes due to maternity care and fertility changes

We exploited the interrelationship between two indicators of maternal mortality—the MMR and the maternal mortality rate (MMRate)—and fertility indicator, the general fertility rate (GFR), to disaggregate the total effects of maternity care and fertility reduction on maternal mortality changes. MMRate is defined as the number of maternal deaths divided by the number of reproductive-aged women and expressed per 100 000 women per year. MMR is defined as the number of maternal deaths per 100 000 live births. The MMRate measures the maternal mortality risk per woman, and the MMR measures the maternal mortality risk per birth. Improvement in maternity care reduces risks on a per-birth basis, and it is generally agreed that declines in MMR reflect improved maternity care.^{14, 20} Because fertility decline reduces women's exposure to pregnancy risks, changes in the MMRate reflect fertility's separate path of effects on maternal mortality. The GFR is the annual births per 1000 women of reproductive age.

These three indicators are expressed mathematically as:

$$\text{MMRate}=\frac{\text{D}}{\text{W}};\text{MMR}=\frac{\text{D}}{\text{B}};\text{GFR}=\frac{\text{B}}{\text{W}}$$

where D=number of maternal deaths, W=women of reproductive age (15–49 years), and B=number of births. Consequently, they are interdependent through the relationship of: MMRate=MMR × GFR.We compared the relative changes in these three indicators between 2000 and 2023 through rate ratio (RR) estimators:

$$\frac{{\text{MMRate}}_{2023}}{{\text{MMRate}}_{2000}}=\frac{{\text{MMR}}_{2023}}{{\text{MMR}}_{\text{2000}}}\times \frac{{\text{GFR}}_{2023}}{{\text{GFR}}_{2000}}$$

To change the multiplicative relationship in the above equation to an additive one, so that the relative contribution can be attributed separately to each component on the right-hand side of the equation, we log-transformed the equation to:

$$log\left(\frac{{\text{MMRate}}_{2023}}{{\text{MMRate}}_{2000}}\right)=log\left(\frac{{\text{MMR}}_{2023}}{{\text{MMR}}_{\text{2000}}}\right)+log\left(\frac{{\text{GFR}}_{2023}}{{\text{GFR}}_{2000}}\right)$$

ie, log(MMRate₂₀₂₃) – log(MMRate₂₀₀₀) = [log(MMR₂₀₂₃) – log(MMR₂₀₀₀)] + [log(GFR₂₀₂₃) – log(GFR₂₀₂₀)]

This implies that the difference in the log rates of MMRates between 2000 and 2023 equals the sum of changes in the log of MMR and log of GFR. Through this specification, the relative contributions of maternal health-care improvement and fertility declines can be measured. We capture the contribution of improvement in maternity care by the relative changes in MMR, which shows the risk per birth, and the contribution of fertility changes on maternal mortality by the relative changes in the GFR. In the calculation of the percentage contributions of MMR and GFR reduction to MMRate reduction, we used the absolute values, |X|, of the log of MMR and GFR ratios in the denominator but the original value in the numerator so that the summative values are equal to 100% and the direction of the change (increased or decreased during the observation period) is preserved for convenient interpretation.

The interpretation of the RRs

We show below that the RRs of MMRates between the two periods of 2000 and 2023 essentially show the effect of fertility changes and MMR decline on the number of maternal deaths averted. Consider,

A. No. of observed maternal deaths in 2023 = B₂₀₂₃×MMR₂₀₂₃ = W₂₀₂₃ × GFR₂₀₂₃ × MMR₂₀₂₃

B. Expected no. of maternal deaths in 2023 with no change in 2000 MMR = W₂₀₂₃ × GFR₂₀₂₃ × MMR₂₀₀₀

C. Expected no. of maternal deaths in 2023 with no change in 2000 GFR = W₂₀₂₃ × GFR₂₀₀₀ × MMR₂₀₂₃

D. Expected no. of maternal deaths in 2023 with no change in 2000 MMR and GFR = W₂₀₂₃ ×GFR₂₀₀₀ × MMR₂₀₀₀

So,

1. Relative reduction in the no. of maternal deaths due to MMR changes:

$$\frac{\text{A}}{\text{B}}=\frac{{\text{W}}_{2023}\times {\text{GFR}}_{2023}\times {\text{MMR}}_{2023}}{{\text{W}}_{2023}\times {\text{GFR}}_{2023}\times {\text{MMR}}_{2000}}=\frac{{\text{MMR}}_{2023}}{{\text{MMR}}_{2000}}$$

2. Relative reduction in the no. of maternal deaths due to GFR changes:

$$\frac{\text{A}}{\text{C}}=\frac{{\text{W}}_{2023}\times {\text{GFR}}_{2023}\times {\text{MMR}}_{2023}}{{\text{W}}_{2023}\times {\text{GFR}}_{2000}\times {\text{MMR}}_{2023}}=\frac{{\text{GFR}}_{2023}}{{\text{GFR}}_{2000}}$$

3. Relative reduction in the no. of maternal deaths due to both MMR and GFR changes:

$$\frac{\text{A}}{\text{D}}=\frac{{\text{W}}_{2023}\times {\text{GFR}}_{2023}\times {\text{MMR}}_{2023}}{{\text{W}}_{2023}\times {\text{GFR}}_{2000}\times {\text{MMR}}_{2000}}=\frac{{\text{MMR}}_{2023}}{{\text{MMR}}_{2000}}\times \frac{{\text{GFR}}_{2023}}{{\text{GFR}}_{2000}}=\frac{{\text{MMRate}}_{2023}}{{\text{MMRate}}_{2000}}$$

Decomposition of the effects of contraceptive use on maternal mortality reduction through fertility decline

Contraceptive use is the major determinant of fertility. Other factors, such as abortion, delayed marriage, abstinence, and prolonged breastfeeding, also affect fertility but to a lesser extent than contraception.²¹ For policy translation of the study results, including programmatic strategies for family planning interventions, we also examined the relative contribution of contraceptive use on fertility decline.

It is possible to use Bongaarts' proximate determinants of fertility method to decompose the effects of contraceptive use on fertility²¹ and consequently on maternal mortality. However, the method requires information on all the proximate determinants. Country-level estimates of the proximate determinants, notably abortion, are not reliably available. Instead, we exploit the relationship between population-level fertility and contraceptive use.

A simple linear regression model with a quadratic term for contraceptive prevalence rates fitted the data with the following equation:

GFR_i = β₀ + β₁CPR_i + β₂CPR_i² + ε_i

The error term (ε) on the right-hand side captures the unexplained, residual parts of the model, which cannot be explained by contraceptive use changes, such as abortion. The first part on the right side of the equation captures the effect of contraceptive use on fertility reduction between 2000 and 2023.

We recognise that the relationship between GFR and contraceptive use is context dependent. Contraceptive use effectiveness varies significantly across countries, depending on the practice of more effective methods such as sterilisation, implants, injectables, and intrauterine devices. To address such heterogeneity across countries i over time (year) t, we used a random coefficient model²² as follows for the counterfactual analysis^{18, 19} to determine the expected level of GFR in 2023 with the level of contraceptive prevalence rates in 2000:

GFR_it = β₀ + β₁CPR_it + ϑ_iCPR_it + ν_i + ε_it

where the variances of the random intercept ν^∼N(0,σ²_v) and the random slope ϑ_i^∼N(0,σ²_υ) are distributed normally with mean 0 and variance σ²_υ. The random slope heterogeneity parameter ϑ captures any deviations of the fixed effect of contraceptive prevalence rates on GFR. In the final model, we added a quadratic contraceptive prevalence rates term of statistical significance to better fit the model. The model was fitted with the maximum-likelihood method, and the specification for the best fit was selected based on mean squared error, Akaike information criterion, and Bayesian information criterion statistics. The model was used to predict the level of GFR in 2023 with the value of contraceptive prevalence rates in 2000, and these estimates were used to calculate the expected number of maternal deaths in 2023 with a counterfactual fertility rate (ie, with the contraceptive prevalence level of 2000). The number of maternal deaths averted by contraceptive use was calculated by subtracting the observed number of maternal deaths (W₂₀₂₃ × GFR₂₀₂₃ × MMR₂₀₂₃) from the expected number of maternal deaths with the counterfactual fertility rate (W₂₀₂₃ × GFR_{counterfactual}× MMR₂₀₂₃). The results reflect the overall average changes between 2000 and 2023.

Note that WHO/MMEIG estimated the global and regional numbers of maternal deaths, MMRs, and MMRates from the median of the posterior distribution of the Bayesian model results, not from the aggregated country-specific results. Global estimates based on the median of the posterior distribution of the Bayesian model results are about 5·6% higher than the country-level aggregate results. Our global and regional results are based on WHO/MMEIG estimates. However, to assess the contraceptive prevalence rates effect, we fitted the model at the country level. Consequently, maternal deaths averted by change in the contraceptive prevalence rates between 2000 and 2023 are presented at the country level only. All statistical analyses were performed with Stata version 18.5.²³ This study was based on published and publicly available population-level data and as such, no ethical approval was required.

Role of the funding source

The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.

Results

Globally, the MMRate reduced by approximately 56%, MMR by 40%, and GFR by 28% between 2000 and 2023 (table 1). For the decrease in the number of maternal deaths, 61·2% was attributable to improvement in maternity care and 38·8% was attributable to fertility reduction (these estimates were 66·5% and 33·5%, respectively, if country-level aggregated deaths were considered for the global maternal mortality estimates).

Table 1.

Percentage distribution of the contribution of maternity care improvement and fertility decline on maternal mortality reduction at global and regional levels, 2000–2023

		MMRate per 100 000 women		MMR per 100 000 births		GFR per 1000 women		Rate ratio			Percent change in maternal deaths due to
		2000	2023	2000	2023	2000	2023	MMRate	MMR	GFR	Maternity care improvement	Fertility reduction
World		25·6	11·2	328	197	78	57	0·44	0·60	0·72	−61·2%	−38·8%
Sub-Saharan Africa		125·6	55·1	748	454	168	121	0·44	0·61	0·72	−60·6%	−39·4%
	Eastern Africa	117·5	30·4	665	263	177	116	0·26	0·40	0·66	−68·7%	−31·3%
	Middle Africa	130·5	64·2	709	415	184	155	0·49	0·58	0·84	−75·8%	−24·2%
	Southern Africa	15·4	8·8	200	137	77	64	0·57	0·68	0·83	−67·9%	−32·1%
	Western Africa	161·5	88	912	691	177	127	0·54	0·76	0·72	−45·6%	−54·5%
Northern Africa and western Asia		15·8	5·6	161	78	98	72	0·35	0·48	0·74	−70·5%	−29·5%
	Northern Africa	23·4	8	239	101	98	79	0·34	0·42	0·81	−80·8%	−19·3%
	Western Asia	8·6	3·5	87	52	99	68	0·41	0·60	0·68	−57·7%	−42·3%
Central and southern Asia		42	7	395	112	106	63	0·17	0·28	0·59	−70·5%	−29·5%
	Central Asia	3·7	1·8	49	21	75	86	0·49	0·43	1·14	−87·0%	13·0%
	Southern Asia	43·5	7·2	405	117	107	62	0·17	0·29	0·57	−69·2%	−30·9%
Eastern and southeastern Asia		6·2	2·1	119	65	52	31	0·33	0·55	0·60	−54·0%	−46·0%
	Eastern Asia	2·4	0·4	54	17	45	24	0·17	0·32	0·53	−64·0%	−36·0%
	Southeastern Asia	43·5	7·2	405	117	107	62	0·17	0·29	0·57	−69·2%	−30·9%
Latin America and the Caribbean		7·1	3·6	92	77	77	47	0·50	0·83	0·61	−26·7%	−73·3%
	The Caribbean	14·3	10	191	190	75	52	0·70	0·99	0·70	−2·0%	−98·0%
	Central America	7	2·7	77	49	91	55	0·39	0·65	0·61	−46·7%	−53·3%
	South America	6·4	3·4	88	77	73	44	0·52	0·87	0·60	−21·3%	−78·8%
Oceania (excluding Australia and New Zealand)		34·7	14·8	274	173	126	85	0·43	0·63	0·67	−53·8%	−46·2%
	Melanesia	36·2	15·1	284	176	127	86	0·42	0·62	0·67	−54·9%	−45·2%
	Micronesia	22·2	9·6	194	126	114	76	0·43	0·65	0·67	−51·5%	−48·5%
	Polynesia	11·8	8·6	101	98	117	88	0·73	0·97	0·76	−9·9%	−90·1%
Australia and New Zealand		0·3	0·1	7	3	46	44	0·40	0·42	0·94	−93·3%	−6·7%
Europe and northern America		0·6	0·4	17	11	39	35	0·59	0·65	0·91	−82·4%	−17·7%
	Eastern Europe	1·1	0·3	36	9	30	31	0·25	0·25	1·03	−97·9%	2·1%
	Northern Europe	0·5	0·3	11	7	42	38	0·59	0·65	0·91	−82·0%	−18·0%
	Southern Europe	0·3	0·2	9	6	35	28	0·56	0·70	0·80	−61·7%	−38·3%
	Western Europe	0·3	0·2	9	5	40	37	0·56	0·62	0·91	−83·2%	−16·8%
	Northern America	0·6	0·7	12	16	48	41	1·17	1·37	0·85	66·4%	−33·6%

GFR=general fertility rate. MMR=maternal mortality ratio. MMRate=maternal mortality rate.

In 2000, high-income regions (Europe, northern America, Australia, and New Zealand) had low maternal mortality and fertility compared with other regions, and by 2023 little had changed. Reductions mainly reflected better maternal care, except in northern America, where MMR increased but GFR declined (48 to 41 per 1000 women). In low-income and middle-income countries (LMICs), maternal mortality fell between 2000 and 2023 due to improved maternity care and reduced GFRs.

Maternal mortality reduced significantly in all the sub-regions of Asia and sub-Saharan Africa. Similarly, fertility also reduced significantly in these regions. In sub-Saharan Africa, MMRates were reduced by approximately 56%, MMR by approximately 39%, and GFR by approximately 28% (table 1). Overall, maternity care improvement contributed to 60·6% and fertility reduction contributed to 39·4% reduction in maternal mortality between 2000 and 2023. The contribution of maternity care was highest in the middle Africa region countries (75·8%) and least in western Africa (45·6%).

Fertility reduction was low in northern Africa (19·3%), whereas maternity care improvement predominantly drove the reduction in maternal deaths. In southern Asian countries, maternity care improvement and fertility reduction contributed to 69·2% and 30·9% reduction in maternal deaths, respectively. In Latin American and the Caribbean countries, fertility reduction was the main driver for maternal mortality reduction during this period (73·3%), ranging from 53·3% to 98·0% across countries.

Many sub-Saharan African countries achieved remarkable success in substantially reducing MMRates, often 75% or more (Angola, Cabo Verde, Djibouti, Ethiopia, Mozambique, Rwanda, Sierra Leone, South Sudan, Swaziland, Uganda, and Zambia; table 2). Reductions of maternal mortality in most of these countries were primarily attributable to improvements in maternity care. Many countries, however, such as Benin (92·5%), Botswana (56·3%), Côte d'Ivoire (88·2%), Kenya (61·7%), Lesotho (62·9%), Liberia (62·5%), Mauritius (55·8%), Nigeria (70·3%), Seychelles (70·7%), and Zimbabwe (59·0%) reduced maternal deaths primarily through fertility reduction.

Table 2.

Percentage distribution of the contribution of maternity care improvement and fertility decline on maternal mortality reduction in 195 countries, 2000–2023

	MMRate per 100 000 women		MMR per 100 000 births		GFR per 1000 women		Rate ratio			Percent change in maternal deaths due to
	2000	2023	2000	2023	2000	2023	MMRate	MMR	GFR	Maternity care improvement	Fertility reduction
Sub-Saharan Africa
Angola	126·8	28·0	659	183	192	153	0·22	0·28	0·80	−84·8%	−15·2%
Benin	92·3	69·3	529	518	174	134	0·75	0·98	0·77	−7·5%	−92·5%
Botswana	13·6	13·1	136	155	100	84	0·96	1·14	0·84	43·7%	−56·3%
Burkina Faso	99·2	29·8	524	242	189	123	0·30	0·46	0·65	−64·3%	−35·7%
Burundi	157·1	53·3	860	392	183	136	0·34	0·46	0·74	−72·8%	−27·3%
Cabo Verde	12·1	1·7	110	40	110	44	0·14	0·36	0·40	−52·3%	−47·7%
Cameroon	115·5	33·6	678	258	170	130	0·29	0·38	0·76	−78·2%	−21·8%
Central African Republic	255·1	131·1	1490	692	171	190	0·51	0·46	1·11	−88·4%	11·7%
Chad	271·8	132·5	1208	748	225	177	0·49	0·62	0·79	−66·8%	−33·2%
Comoros	71·4	19·5	476	179	150	109	0·27	0·38	0·73	−75·4%	−24·6%
Congo (Brazzaville)	72·2	28·7	509	241	142	119	0·40	0·47	0·84	−81·1%	−18·9%
Côte d’Ivoire	69·2	45·0	378	359	183	125	0·65	0·95	0·68	−11·8%	−88·2%
Democratic Republic of the Congo	111·3	74·6	585	427	190	175	0·67	0·73	0·92	−78·7%	−21·3%
Djibouti	62·8	11·2	501	162	125	69	0·18	0·32	0·55	−65·7%	−34·3%
Equatorial Guinea	67·7	21·3	406	174	167	122	0·31	0·43	0·73	−73·2%	−26·8%
Eritrea	115·2	31·5	741	291	155	108	0·27	0·39	0·70	−72·0%	−28·0%
Eswatini	44·4	9·9	361	118	123	84	0·22	0·33	0·68	−74·3%	−25·7%
Ethiopia	170·8	23·4	870	195	196	120	0·14	0·22	0·61	−75·4%	−24·6%
Gabon	23·9	24·5	178	233	134	105	1·02	1·31	0·78	52·1%	−47·9%
The Gambia	138·2	41·7	810	354	171	118	0·30	0·44	0·69	−69·0%	−31·0%
Ghana	66·7	22·5	472	234	141	96	0·34	0·50	0·68	−64·5%	−35·5%
Guinea	166·0	64·5	951	494	174	131	0·39	0·52	0·75	−69·3%	−30·7%
Guinea-Bissau	215·8	56·2	1295	505	167	111	0·26	0·39	0·67	−69·9%	−30·1%
Kenya	33·9	14·5	206	149	164	97	0·43	0·72	0·59	−38·3%	−61·7%
Lesotho	44·0	39·8	413	478	107	83	0·90	1·16	0·78	37·1%	−62·9%
Liberia	135·2	74·3	786	628	172	118	0·55	0·80	0·69	−37·5%	−62·5%
Madagascar	110·4	54·4	638	445	173	122	0·49	0·70	0·71	−50·9%	−49·1%
Malawi	93·2	26·0	506	225	184	115	0·28	0·44	0·63	−63·5%	−36·5%
Mali	153·1	62·1	741	367	207	169	0·41	0·50	0·82	−77·7%	−22·3%
Mauritania	100·5	50·9	644	381	156	134	0·51	0·59	0·86	−77·1%	−22·9%
Mauritius	2·4	2·1	43	66	55	32	0·90	1·52	0·59	44·2%	−55·8%
Mozambique	93·0	14·6	547	99	170	147	0·16	0·18	0·87	−92·2%	−7·8%
Namibia	45·5	13·1	384	139	119	94	0·29	0·36	0·79	−81·5%	−18·5%
Niger	196·7	63·9	845	350	233	182	0·32	0·41	0·78	−78·3%	−21·7%
Nigeria	203·4	129·3	1136	993	179	130	0·64	0·87	0·73	−29·7%	−70·3%
Rwanda	142·8	23·5	885	229	161	102	0·16	0·26	0·64	−74·8%	−25·2%
São Tomé and Príncipe	28·6	8·0	180	75	159	106	0·28	0·42	0·66	−68·1%	−31·9%
Senegal	91·6	26·3	581	237	158	111	0·29	0·41	0·70	−71·8%	−28·2%
Seychelles	2·8	2·2	45	42	62	54	0·81	0·94	0·86	−29·3%	−70·7%
Sierra Leone	295·4	40·3	1603	354	184	114	0·14	0·22	0·62	−75·9%	−24·1%
Somalia	246·9	102·2	1150	563	215	182	0·41	0·49	0·85	−81·1%	−18·9%
South Africa	12·8	7·3	175	118	73	62	0·57	0·67	0·85	−71·1%	−28·9%
South Sudan	326·9	73·6	1658	692	197	106	0·23	0·42	0·54	−58·6%	−41·4%
Tanzania	95·0	37·9	568	276	167	138	0·40	0·49	0·82	−78·6%	−21·4%
Togo	74·0	42·0	496	349	149	120	0·57	0·70	0·81	−61·9%	−38·1%
Uganda	99·9	23·3	475	170	210	137	0·23	0·36	0·65	−70·6%	−29·4%
Zambia	63·1	10·6	340	85	185	124	0·17	0·25	0·67	−77·6%	−22·4%
Zimbabwe	52·7	39·0	405	358	130	109	0·74	0·88	0·84	−41·0%	−59·0%
Northern Africa and western Asia
Algeria	8·7	4·6	123	62	71	73	0·53	0·51	1·03	−95·4%	4·6%
Armenia	1·5	0·8	40	19	38	41	0·53	0·49	1·08	−90·2%	9·8%
Azerbaijan	2·3	0·7	46	18	49	41	0·33	0·39	0·84	−84·9%	−15·1%
Bahrain	2·1	0·9	25	17	85	52	0·41	0·67	0·61	−45·2%	−54·8%
Cyprus	0·5	0·5	12	14	44	38	1·01	1·15	0·87	50·9%	−49·1%
Egypt	5·4	1·3	53	17	103	77	0·24	0·32	0·75	−79·6%	−20·4%
Georgia	1·8	0·9	43	20	42	44	0·49	0·46	1·05	−94·0%	6·0%
Iraq	20·9	6·3	146	66	144	95	0·30	0·45	0·66	−65·6%	−34·4%
Israel	0·7	0·2	9	2	77	73	0·27	0·29	0·94	−95·0%	−5·0%
Jordan	7·2	2·3	61	31	117	74	0·32	0·50	0·63	−60·3%	−39·7%
Kuwait	1·0	0·3	11	8	88	40	0·31	0·69	0·45	−31·6%	−68·4%
Lebanon	1·7	0·9	24	15	73	58	0·51	0·63	0·80	−67·1%	−33·0%
Libya	5·6	3·5	71	59	79	58	0·62	0·84	0·73	−36·4%	−63·6%
Morocco	21·6	4·1	271	70	80	59	0·19	0·26	0·74	−82·0%	−18·0%
Occupied Palestinian territory	9·7	1·6	59	16	165	100	0·17	0·27	0·61	−72·2%	−27·8%
Oman	2·0	0·9	19	13	110	75	0·46	0·68	0·68	−50·3%	−49·7%
Qatar	1·0	0·2	11	4	89	52	0·20	0·34	0·58	−66·1%	−33·9%
Saudi Arabia	2·3	0·5	20	7	114	67	0·22	0·37	0·59	−65·6%	−34·5%
Sudan	107·3	32·5	645	256	166	127	0·30	0·40	0·76	−77·5%	−22·5%
Syria	3·8	1·5	31	20	122	75	0·41	0·66	0·62	−45·7%	−54·3%
Tunisia	3·2	1·7	56	36	57	48	0·55	0·64	0·86	−74·0%	−26·0%
Türkiye	2·4	0·6	32	15	75	43	0·26	0·46	0·57	−58·3%	−41·7%
United Arab Emirates	0·7	0·1	8	3	81	39	0·15	0·31	0·48	−61·7%	−38·3%
Yemen	44·3	16·0	241	118	184	135	0·36	0·49	0·74	−69·9%	−30·1%
Central and southern Asia
Afghanistan	314·9	73·2	1372	521	230	141	0·23	0·38	0·61	−66·4%	−33·6%
Bangladesh	57·5	7·6	523	115	110	66	0·13	0·22	0·60	−74·9%	−25·1%
Bhutan	32·9	2·0	324	47	102	43	0·06	0·14	0·42	−69·0%	−31·0%
India	37·8	4·5	362	80	104	56	0·12	0·22	0·54	−70·7%	−29·3%
Iran	2·4	0·7	43	16	57	44	0·28	0·37	0·77	−79·0%	−21·0%
Kazakhstan	3·0	0·7	58	10	52	76	0·24	0·17	1·47	−82·4%	17·6%
Kyrgyzstan	5·6	3·3	72	42	77	78	0·59	0·58	1·01	−97·9%	2·1%
Maldives	7·5	1·5	94	32	80	47	0·20	0·34	0·58	−66·6%	−33·4%
Nepal	59·5	8·8	480	142	124	62	0·15	0·30	0·50	−63·8%	−36·2%
Pakistan	64·5	16·5	419	155	154	106	0·26	0·37	0·69	−72·9%	−27·1%
Sri Lanka	2·4	0·9	39	18	61	50	0·39	0·47	0·82	−79·4%	−20·6%
Tajikistan	6·3	1·3	58	14	109	95	0·21	0·24	0·87	−91·1%	−9·0%
Turkmenistan	2·1	0·3	25	5	85	76	0·16	0·18	0·89	−93·8%	−6·2%
Uzbekistan	3·5	2·6	42	26	82	97	0·74	0·62	1·19	−73·3%	26·7%
Eastern and southeastern Asia
Brunei	3·3	1·7	48	36	69	47	0·51	0·76	0·68	−41·6%	−58·4%
Burma/Myanmar	30·2	10·5	375	185	80	57	0·35	0·49	0·70	−66·8%	−33·2%
Cambodia	50·2	10·0	476	137	105	73	0·20	0·29	0·69	−76·9%	−23·1%
China	2·5	0·4	56	16	46	23	0·15	0·29	0·51	−64·8%	−35·2%
Indonesia	23·3	7·8	311	140	75	55	0·33	0·45	0·74	−72·2%	−27·8%
Japan	0·3	0·1	9	3	35	27	0·27	0·35	0·76	−79·5%	−20·5%
Laos	82·4	8·2	609	112	135	73	0·10	0·18	0·54	−73·4%	−26·6%
Malaysia	3·0	1·1	37	26	82	43	0·37	0·71	0·53	−35·0%	−65·0%
Mongolia	10·3	2·8	144	41	71	70	0·28	0·28	0·99	−98·9%	−1·1%
North Korea	7·8	3·1	129	67	61	47	0·40	0·52	0·77	−71·5%	−28·5%
Philippines	13·8	4·6	127	84	109	55	0·33	0·66	0·50	−37·6%	−62·4%
Singapore	0·7	0·2	17	6	39	27	0·25	0·36	0·71	−74·9%	−25·1%
South Korea	0·6	0·1	15	4	42	17	0·10	0·25	0·42	−61·6%	−38·4%
Thailand	2·3	1·0	49	34	48	29	0·43	0·71	0·61	−41·1%	−59·0%
Timor-Leste	125·2	15·5	796	192	157	80	0·12	0·24	0·51	−68·0%	−32·0%
Viet Nam	5·0	2·3	83	48	61	48	0·45	0·58	0·78	−69·3%	−30·7%
Latin America and the Caribbean
Antigua and Barbuda	2·3	1·4	37	35	62	40	0·60	0·93	0·64	−13·5%	−86·6%
Argentina	4·1	1·3	58	33	71	40	0·32	0·57	0·56	−48·6%	−51·4%
Barbados	2·3	1·5	46	35	50	42	0·64	0·76	0·83	−59·8%	−40·2%
Belize	9·0	3·9	78	67	115	59	0·44	0·86	0·51	−19·0%	−81·0%
Bolivia	33·9	10·8	287	146	118	74	0·32	0·51	0·63	−58·9%	−41·1%
Brazil	4·6	2·8	69	67	67	42	0·61	0·97	0·62	−5·1%	−94·9%
Chile	1·9	0·3	33	10	56	31	0·17	0·30	0·56	−67·3%	−32·7%
Colombia	7·3	2·7	95	59	76	45	0·37	0·62	0·59	−47·0%	−53·0%
Costa Rica	2·5	0·8	36	24	70	35	0·34	0·68	0·51	−36·2%	−63·8%
Cuba	2·1	1·2	48	35	43	34	0·56	0·72	0·78	−56·4%	−43·6%
Dominica	4·2	1·4	61	36	68	40	0·34	0·58	0·58	−49·9%	−50·1%
Dominican Republic	6·9	7·9	76	124	91	63	1·14	1·65	0·69	57·5%	−42·5%
Ecuador	11·5	2·8	123	55	94	51	0·25	0·45	0·55	−56·9%	−43·1%
El Salvador	4·6	2·0	48	39	97	51	0·43	0·82	0·53	−23·7%	−76·3%
Grenada	3·1	2·0	46	48	69	41	0·64	1·06	0·60	10·3%	−89·7%
Guatemala	23·2	6·7	166	94	140	71	0·29	0·57	0·51	−45·9%	−54·2%
Guyana	16·1	5·3	171	75	94	71	0·33	0·44	0·75	−73·9%	−26·1%
Haiti	55·0	25·0	440	328	125	76	0·45	0·75	0·61	−37·2%	−62·8%
Honduras	11·1	3·5	82	47	136	75	0·32	0·57	0·55	−48·4%	−51·6%
Jamaica	5·9	4·8	82	130	72	37	0·83	1·59	0·52	41·5%	−58·5%
Mexico	4·7	2·2	56	42	84	52	0·46	0·75	0·62	−38·1%	−61·9%
Nicaragua	22·2	3·9	213	60	104	65	0·18	0·28	0·62	−72·7%	−27·3%
Panama	5·3	2·1	64	37	83	57	0·40	0·58	0·69	−59·6%	−40·4%
Paraguay	21·4	4·1	207	58	104	70	0·19	0·28	0·68	−76·6%	−23·4%
Peru	9·8	2·8	115	51	85	54	0·28	0·45	0·64	−64·3%	−35·7%
Puerto Rico*	1·2	0·3	22	11	54	23	0·21	0·51	0·42	−44·2%	−55·8%
Saint Kitts and Nevis	8·9	2·8	143	74	62	38	0·32	0·51	0·62	−58·3%	−41·7%
Saint Lucia	5·6	1·6	83	44	67	37	0·29	0·53	0·55	−51·4%	−48·6%
Saint Vincent and the Grenadines	4·8	2·6	68	56	71	46	0·53	0·83	0·64	−30·4%	−69·6%
Suriname	25·6	5·2	282	84	91	62	0·20	0·30	0·68	−75·9%	−24·2%
The Bahamas	3·0	2·7	51	76	59	36	0·90	1·48	0·61	43·9%	−56·1%
Trinidad and Tobago	3·5	2·0	76	54	47	37	0·57	0·72	0·80	−59·4%	−40·6%
Uruguay	1·4	0·6	23	15	59	37	0·40	0·65	0·61	−46·8%	−53·2%
Venezuela	7·3	12·5	86	227	84	55	1·72	2·63	0·65	69·4%	−30·6%
Oceania (excluding Australia and New Zealand)
Cook Islands	0	0	0	0	91	49	0·17	0·32	0·54	−64·4%	−35·6%
Federated States of Micronesia	24·7	10·5	204	129	121	81	0·42	0·63	0·67	−53·4%	−46·6%
Fiji	4·1	1·9	47	30	86	62	0·47	0·64	0·72	−57·8%	−42·3%
Kiribati	13·8	7·3	119	80	116	92	0·53	0·67	0·79	−62·8%	−37·2%
Marshall Islands	37·0	12·4	251	155	147	80	0·33	0·62	0·54	−44·1%	−55·9%
Nauru	28·1	26·7	278	273	101	98	0·95	0·98	0·97	−34·4%	−65·6%
Palau	6·1	3·9	119	89	51	44	0·64	0·75	0·85	−64·0%	−36·0%
Papua New Guinea	43·0	16·6	323	189	133	88	0·39	0·58	0·66	−56·5%	−43·5%
Samoa	12·3	10·6	93	101	133	104	0·86	1·09	0·79	26·8%	−73·2%
Solomon Islands	24·3	12·4	166	123	146	101	0·51	0·74	0·69	−45·4%	−54·6%
Tonga	11·5	5·5	100	67	115	83	0·48	0·67	0·72	−54·6%	−45·4%
Tuvalu	23·2	16·1	237	170	98	95	0·69	0·72	0·97	−91·3%	−8·7%
Vanuatu	17·4	10·8	133	100	131	108	0·62	0·75	0·83	−60·2%	−39·8%
Australia and New Zealand
Australia	0·3	0·1	7	2	45	44	0·34	0·35	0·96	−96·4%	−3·6%
New Zealand	0·5	0·3	11	7	51	44	0·56	0·66	0·86	−73·3%	−26·7%
Europe and northern America
Albania	0·9	0·3	15	7	61	38	0·28	0·45	0·62	−63·1%	−37·0%
Andorra	0·5	0·3	14	11	38	25	0·49	0·76	0·65	−38·9%	−61·1%
Austria	0·2	0·2	7	6	35	34	0·89	0·92	0·97	−73·4%	−26·6%
Belarus	0·6	0	20	1	31	27	0·05	0·05	0·89	−96·0%	−4·0%
Belgium	0·3	0·1	8	4	41	35	0·41	0·48	0·85	−81·4%	−18·6%
Bosnia and Herzegovina	0·4	0·2	11	6	34	32	0·50	0·54	0·93	−89·6%	−10·4%
Bulgaria	0·6	0·2	19	6	32	38	0·34	0·29	1·17	−88·9%	11·1%
Canada	0·3	0·4	9	12	36	35	1·27	1·31	0·97	90·1%	−9·9%
Croatia	0·3	0·1	8	3	34	34	0·40	0·40	0·99	−98·4%	−1·6%
Czech Republic	0·2	0·1	7	3	30	34	0·46	0·41	1·11	−89·2%	10·8%
Denmark	0·4	0·1	9	4	46	39	0·35	0·42	0·85	−84·4%	−15·6%
Estonia	0·9	0·2	27	5	33	33	0·18	0·18	1·00	−100·0%	0%
Finland	0·3	0·3	7	8	40	33	0·91	1·10	0·83	33·5%	−66·5%
France	0·4	0·3	9	7	47	39	0·65	0·77	0·84	−59·3%	−40·7%
Germany	0·2	0·1	7	4	35	37	0·57	0·54	1·05	−92·2%	7·8%
Greece	0·1	0·1	4	5	36	30	1·12	1·35	0·83	62·0%	−38·0%
Hungary	0·5	0·4	14	12	33	34	0·86	0·84	1·02	−90·2%	9·8%
Iceland	0·4	0·1	7	3	54	42	0·36	0·46	0·79	−76·2%	−23·8%
Ireland	0·4	0·1	8	4	49	38	0·36	0·47	0·77	−73·9%	−26·1%
Italy	0·4	0·2	11	6	34	27	0·48	0·61	0·80	−68·3%	−31·7%
Latvia	1·0	0·6	34	19	30	31	0·57	0·56	1·02	−96·2%	3·8%
Lithuania	0·5	0·2	16	8	34	30	0·43	0·48	0·88	−85·5%	−14·6%
Luxembourg	0·8	0·4	18	11	45	38	0·54	0·65	0·83	−70·4%	−29·6%
Malta	0·5	0·3	13	8	39	31	0·52	0·66	0·79	−63·2%	−36·9%
Moldova	1·5	0·8	38	19	38	40	0·52	0·49	1·06	−93·0%	7·0%
Monaco	0·5	0·3	10	5	49	51	0·54	0·51	1·05	−92·8%	7·2%
Montenegro	0·6	0·2	11	6	49	43	0·44	0·51	0·86	−81·8%	−18·2%
Netherlands	0·6	0·2	13	4	46	37	0·26	0·32	0·81	−84·2%	−15·9%
North Macedonia	0·6	0·1	11	3	49	35	0·18	0·25	0·72	−80·7%	−19·3%
Norway	0·3	0	5	1	49	36	0·18	0·24	0·75	−83·0%	−17·0%
Poland	0·2	0	7	2	33	31	0·21	0·22	0·94	−96·1%	−3·9%
Portugal	0·4	0·5	9	15	40	34	1·32	1·59	0·83	71·4%	−28·6%
Romania	1·6	0·4	45	12	36	38	0·27	0·26	1·05	−96·2%	3·8%
Russia	1·5	0·3	51	9	28	33	0·22	0·18	1·17	−91·4%	8·6%
San Marino	0·6	0·2	14	8	40	24	0·33	0·55	0·61	−54·5%	−45·5%
Serbia	0·5	0·4	16	11	32	35	0·75	0·69	1·08	−82·3%	17·8%
Slovakia	0·3	0·2	8	4	34	36	0·58	0·54	1·07	−90·5%	9·5%
Slovenia	0·2	0·1	7	3	31	36	0·53	0·46	1·15	−85·1%	14·9%
Spain	0·2	0·1	5	3	34	28	0·48	0·58	0·82	−73·6%	−26·4%
Sweden	0·2	0·2	5	4	39	38	0·72	0·74	0·97	−91·7%	−8·3%
Switzerland	0·3	0·2	9	5	38	37	0·61	0·62	0·97	−94·4%	−5·6%
Ukraine	0·9	0·3	32	15	27	21	0·37	0·47	0·79	−75·7%	−24·3%
UK	0·5	0·3	12	8	42	40	0·65	0·69	0·95	−87·1%	−12·9%
USA	0·6	0·7	12	17	49	42	1·16	1·38	0·85	65·6%	−34·4%

GFR=general fertility rate. MMR=maternal mortality ratio. MMRate=maternal mortality rate.

^*Puerto Rico is a Caribbean territory of the USA.

In most southern Asian countries, the MMRate and MMR reduced significantly (Bangladesh, Bhutan, India, Iran, Maldives, and Nepal), and were accompanied by a large drop in their GFRs. About two-thirds of maternal death prevention in these countries was attributable to maternity care improvement and one-third to fertility reduction. Maternity care improvement was the primary contributing factor for maternal mortality reduction in Sri Lanka (79·4%) and all central Asia countries (73–98%; table 2).

Maternal deaths were comparatively low in northern Africa and western Asia countries, except Sudan, although more than half of the maternal mortality reduction was achieved through declines in fertility, notably in Bahrain, Kuwait, Libya, and Syria.

Countries in Oceania showed substantial heterogeneity. The largest reduction in maternal mortality was achieved through maternity care in Tuvalu (91·3%), and by fertility reduction in Samoa (73·2%) and Nauru (65·6%; table 2).

Table 3 summarises the decomposition effects of contraceptive prevalences at the country level. Our estimate suggests increase in contraceptive use averted about 77 400 maternal deaths globally in 2023 (about 24%). Contraceptive use increased predominantly in high maternal mortality countries, which led to such an effect. In India, the contraceptive prevalence rates among all women increased from 38·6% to 50·3% between 2000 and 2023, which averted 16 464 maternal deaths (46·8%) in 2023. Nigeria, which had the highest number of maternal deaths globally (74 559) in 2023, had a modest increase in contraceptive prevalence rates for all women from 13·0% to 18·9% between 2000 and 2023; the country averted 16 697 maternal deaths (18·3%).

Table 3.

Percentage distribution of the contribution of contraceptive use on fertility decline at global and regional levels (with WHO input data), 2000–2023

	CPR 2000	CPR 2023	GFR 2023	GFR expected with CPR of 2000 in 2023*	Numbers of maternal deaths 2023	Expected maternal deaths with CPR of 2000	Numbers of maternal deaths prevented by CPR change	% reduction in maternal deaths due to contraceptive change between 2000 and 2023
Afghanistan	7·3	19·1	141	219	7646	11 896	4250	35·7%
Albania	45·4	46·3	38	32	2	2	0	0%
Algeria	34·2	33·0	73	62	566	484	−82	−17·0%
Angola	9·6	17·0	153	183	2531	3036	506	16·6%
Antigua and Barbuda	32·8	40·4	40	59	0	1	0	0%
Argentina	47·6	57·9	40	55	167	231	63	27·4%
Armenia	36·1	37·0	41	40	7	7	0	0%
Australia	56·5	57·9	44	45	7	7	0	0%
Austria	58·2	65·8	34	38	5	5	1	0%
Azerbaijan	32·0	34·4	41	57	22	31	9	25·8%
Bahrain	31·9	37·0	52	93	3	6	3	40·0%
Bangladesh	41·0	52·6	66	107	4017	6465	2448	37·8%
Barbados	42·3	49·8	42	49	1	1	0	0%
Belarus	48·6	51·7	27	27	1	1	0	0%
Belgium	54·8	60·5	35	36	4	4	0	0%
Belize	36·8	42·8	59	113	5	9	5	44·4%
Benin	16·9	18·5	134	145	2477	2689	211	7·8%
Bhutan	19·7	39·4	43	118	5	13	8	66·7%
Bolivia	34·8	44·3	74	100	381	513	132	25·7%
Bosnia and Herzegovina	37·8	38·4	32	30	2	1	0	0%
Botswana	45·1	57·6	84	101	95	115	19	16·7%
Brazil	59·7	66·6	42	58	1741	2418	677	28·0%
Bulgaria	69·0	65·9	38	33	3	3	0	0%
Burkina Faso	11·2	31·2	123	183	1762	2615	854	32·6%
Burundi	9·4	20·3	136	171	1807	2271	464	20·4%
Cabo Verde	37·3	44·1	44	83	3	5	2	50·0%
Cambodia	16·1	42·5	73	131	497	898	401	44·6%
Cameroon	25·1	23·9	130	127	2472	2422	−50	−2%
Canada	70·7	72·6	35	38	42	46	4	6·7%
Central African Republic	17·3	20·7	190	191	1652	1661	9	0·5%
Chad	3·9	7·4	177	216	6123	7444	1321	17·7%
Chile	44·2	65·2	31	51	17	28	11	35·7%
China	67·7	67·2	23	22	1446	1349	−98	−7·2%
Colombia	52·3	63·0	45	71	419	658	239	36·2%
Comoros	16·5	11·9	109	130	44	52	8	15·4%
Congo (Brazzaville)	36·0	41·9	119	129	457	496	40	7·9%
Cook Islands	46·2	48·2	49	71	0	0	0	0%
Costa Rica	50·4	54·5	35	56	13	20	7	36·8%
Côte d’Ivoire	19·5	28·0	125	177	3579	5046	1468	29·1%
Croatia	50·5	46·2	34	33	1	1	0	0%
Cuba	62·4	63·2	34	34	33	34	1	0%
Czech Republic	59·6	63·4	34	31	2	2	0	0%
Democratic Republic of the Congo	19·2	26·3	175	181	18 661	19 362	702	3·6%
Denmark	64·2	62·7	39	39	2	2	0	0%
Djibouti	5·0	15·2	69	142	39	79	40	50·6%
Dominica	36·7	47·4	40	80	0	1	0	0%
Dominican Republic	48·8	56·4	63	81	253	324	71	21·9%
Ecuador	39·9	53·4	51	84	150	246	96	39·2%
Egypt	36·7	41·2	77	91	408	482	74	15·2%
El Salvador	42·5	50·8	51	92	39	70	31	43·5%
Equatorial Guinea	11·4	18·2	122	167	95	129	34	26·4%
Eritrea	5·7	8·5	108	156	289	415	127	30·4%
Estonia	52·9	63·0	33	30	1	0	0	0%
Eswatini	29·6	46·6	84	112	35	47	12	23·9%
Ethiopia	5·4	26·7	120	192	8003	12 782	4778	37·4%
Fiji	24·9	25·0	62	62	5	5	0	0%
Finland	73·5	78·7	33	38	3	4	0	0%
France	64·2	66·7	39	41	47	48	2	2·1%
Gabon	33·0	24·9	105	106	160	162	2	1·2%
Georgia	23·4	31·6	44	45	9	9	0	0%
Germany	58·8	54·8	37	36	26	26	0	0%
Ghana	18·6	29·6	96	144	2083	3116	1033	33·1%
Greece	48·1	50·2	30	33	4	4	0	0%
Grenada	32·2	44·1	41	79	1	1	1	0%
Guatemala	26·2	41·8	71	131	353	648	294	45·4%
Guinea	7·6	14·3	131	168	2409	3097	688	22·2%
Guinea-Bissau	9·8	31·8	111	166	327	490	163	33·1%
Guyana	26·7	28·2	71	78	13	14	1	7·7%
Haiti	18·4	28·8	76	115	846	1277	431	33·7%
Honduras	39·3	49·8	75	121	109	177	67	38·1%
Hungary	50·1	49·4	34	34	10	10	0	0%
India	38·6	50·3	56	105	18 687	35 151	16 464	46·8%
Indonesia	38·9	37·9	55	53	6298	6047	−250	−4·1%
Iran	50·3	57·1	44	74	186	314	128	40·9%
Iraq	25·4	38·0	95	126	765	1016	251	24·7%
Ireland	64·6	64·9	38	38	2	2	0	0%
Israel	41·1	39·8	73	70	4	4	0	0%
Italy	53·2	59·0	27	30	25	27	2	7·4%
Jamaica	38·0	35·9	37	30	43	34	−9	−23·5%
Japan	44·7	50·6	27	34	23	30	6	20·7%
Jordan	32·4	33·1	74	79	73	77	5	5·2%
Kazakhstan	45·3	42·9	76	69	39	35	−4	−8·6%
Kenya	28·6	46·1	97	158	2234	3615	1381	38·2%
Kiribati	29·5	23·5	92	107	3	3	0	0%
Kuwait	29·8	35·5	40	78	4	8	4	42·9%
Kyrgyzstan	34·3	25·8	78	85	63	68	5	7·4%
Laos	20·9	39·0	73	136	183	340	157	46·0%
Latvia	58·2	58·2	31	31	3	3	0	0%
Lebanon	34·0	32·5	58	69	14	17	3	12·5%
Lesotho	27·2	49·6	83	106	266	339	72	21·3%
Liberia	12·0	27·5	118	173	1068	1558	491	31·5%
Libya	21·0	16·3	58	77	74	98	24	24·5%
Lithuania	45·7	47·0	30	28	2	1	0	0%
Madagascar	18·8	40·7	122	168	4460	6130	1670	27·2%
Malawi	23·3	49·2	115	162	1490	2096	606	28·9%
Malaysia	32·0	29·5	43	74	115	197	82	41·6%
Maldives	27·1	16·5	47	72	2	3	1	0%
Mali	8·2	18·7	169	200	3495	4128	634	15·3%
Malta	47·6	53·2	31	43	0	0	0	0%
Marshall Islands	32·1	38·9	80	130	1	2	1	0%
Mauritania	5·5	11·1	134	155	659	765	107	13·9%
Mauritius	46·7	36·2	32	59	8	14	6	42·9%
Mexico	47·1	52·9	52	71	858	1179	321	27·2%
Moldova	50·7	48·5	40	42	6	6	0	0%
Mongolia	44·1	41·3	70	56	26	21	−5	−23·8%
Montenegro	37·8	22·6	43	47	0	0	0	0%
Morocco	36·9	40·9	59	71	439	524	84	16·1%
Mozambique	11·4	26·5	147	165	1250	1399	148	10·6%
Myanmar	20·1	34·3	57	82	1669	2403	734	30·6%
Namibia	38·4	52·9	94	114	107	129	22	17·2%
Nauru	26·7	31·4	98	97	1	1	0	0%
Nepal	27·4	42·0	62	115	816	1515	700	46·1%
Netherlands	62·4	63·1	37	37	7	7	0	0%
New Zealand	61·0	64·0	44	50	4	5	1	0%
Nicaragua	43·1	58·9	65	102	80	126	46	36·0%
Niger	8·4	11·7	182	194	3839	4092	253	6·2%
Nigeria	13·0	18·9	130	159	74 559	91 256	16 697	18·3%
North Korea	50·8	60·9	47	61	229	299	71	23·4%
North Macedonia	44·3	43·7	35	37	0	0	0	0%
Norway	69·8	67·9	36	35	1	1	0	0%
Occupied Palestinian territory	33·2	39·3	100	151	24	36	12	31·4%
Oman	17·5	21·2	75	106	11	15	4	26·7%
Pakistan	17·0	26·0	106	146	10 669	14 664	3994	27·2%
Palau	25·0	28·5	44	65	0	0	0	0%
Panama	42·3	46·8	57	76	26	35	9	25·7%
Papua New Guinea	20·6	28·3	88	129	482	710	228	32·2%
Paraguay	44·1	57·6	70	100	80	113	33	29·5%
Peru	43·8	53·6	54	83	278	426	149	34·7%
Philippines	32·1	34·3	55	73	1541	2061	520	25·2%
Poland	49·7	54·3	31	30	5	5	0	0%
Portugal	59·0	58·0	34	32	13	12	0	0%
Puerto Rico	50·7	51·5	23	26	2	2	0	0%
Qatar	26·8	32·6	52	102	1	2	1	50·0%
Romania	51·3	53·4	38	39	21	22	1	0%
Russia	47·8	49·7	33	30	122	111	−11	−10%
Rwanda	8·0	38·8	102	167	908	1477	569	38·5%
Saint Kitts and Nevis	35·8	47·0	38	66	0	1	0	0%
Saint Lucia	38·1	47·9	37	82	1	2	1	100%
Saint Vincent and the Grenadines	40·7	47·8	46	74	1	1	0	0%
Samoa	16·1	13·8	104	105	6	6	0	0%
São Tomé and Príncipe	22·3	37·6	106	151	5	7	2	28·6%
Saudi Arabia	15·2	22·2	67	102	40	61	20	33·3%
Senegal	9·1	19·8	111	152	1263	1727	464	26·9%
Serbia	52·1	47·0	35	33	6	6	0	0%
Sierra Leone	7·2	28·1	114	181	915	1453	537	37·0%
Singapore	37·2	32·6	27	39	3	4	1	25·0%
Slovakia	55·4	55·1	36	35	2	2	0	0%
Slovenia	51·1	49·3	36	31	1	1	0	0%
Solomon Islands	23·2	23·6	101	104	26	27	1	0%
Somalia	5·5	7·1	182	199	4437	4858	421	8·6%
South Africa	50·0	50·9	62	65	1395	1466	71	4·8%
South Korea	53·1	50·8	17	12	9	6	−3	−33·3%
South Sudan	3·6	6·6	106	196	2271	4185	1914	45·7%
Spain	58·2	63·2	28	28	9	9	0	0%
Sri Lanka	43·7	45·3	50	54	59	64	5	6·2%
Sudan	5·5	11·2	127	177	4301	5996	1695	28·3%
Suriname	29·9	34·0	62	83	9	12	3	25·0%
Sweden	59·7	58·6	38	37	4	4	0	0%
Switzerland	72·7	72·8	37	38	5	5	0	0%
Syria	28·1	34·2	75	114	106	161	55	34·2%
Tajikistan	23·7	22·9	95	99	38	39	1	2·6%
Tanzania	20·9	31·6	138	152	6472	7153	681	9·5%
Thailand	49·0	41·4	29	34	204	238	34	14·3%
The Bahamas	35·3	44·2	36	66	3	6	3	33·3%
The Gambia	11·8	14·3	118	134	291	331	40	11·8%
Timor-Leste	12·2	19·4	80	107	59	78	20	24·4%
Togo	18·7	24·5	120	151	1011	1270	259	20·3%
Tonga	17·4	c18·1	83	91	2	2	0	0%
Trinidad and Tobago	27·1	31·0	37	47	9	11	2	20·0%
Tunisia	33·4	30·7	48	32	60	40	−20	−47·5%
Türkiye	45·6	43·1	43	36	157	133	−24	−17·3%
Turkmenistan	33·7	36·0	76	90	7	8	1	12·5%
Tuvalu	20·8	19·6	95	95	0	0	0	0%
Uganda	17·8	39·5	137	196	2917	4183	1266	30·3%
Ukraine	55·2	53·1	21	21	32	32	0	0%
United Arab Emirates	21·6	36·0	39	104	3	7	4	57·1%
UK	74·5	70·8	40	37	57	54	−3	−5·7%
USA	62·7	60·4	42	44	608	634	26	3·9%
Uruguay	51·5	56·9	37	56	5	8	3	28·6%
Uzbekistan	46·2	47·2	97	102	250	263	13	5·0%
Vanuatu	25·3	37·6	108	136	9	11	2	18·2%
Venezuela	45·6	54·0	55	75	968	1324	356	26·9%
Viet Nam	49·3	58·0	48	69	661	945	284	30·0%
Yemen	13·5	30·0	135	209	1642	2531	888	35·1%
Zambia	21·6	37·5	124	162	586	765	179	23·3%
Zimbabwe	38·0	50·6	109	122	1777	1978	201	10·2%

CPR=contraceptive prevalence rate. GFR=general fertility rate.

^*Estimated from the random-coefficient model.

Discussion

This study assessed the impact of improvement in maternity health care, fertility reduction, and contraceptive use increase on maternal mortality reduction between 2000 and 2023 in 195 countries and territories. We used a simple method of decomposition analysis in this paper for directly ascertaining the attributable contribution of two direct maternal mortality determinants, maternity care and fertility reduction, from the readily available WHO/MMEIG's maternal mortality database.

This study also showed maternal mortality reduction from different perspectives. For example, the WHO/MMEIG estimate in sub-Saharan Africa showed that the number of maternal deaths declined from 205 400 in 2000 to 182 000 in 2023, a 11·4% reduction. During the same period, global maternal deaths reduced by 41·3%, from 443 000 to 260 000.¹ This gives the perception that maternal mortality reduction in sub-Saharan Africa was slow. However, the MMRate in sub-Saharan Africa reduced by 56% during this period, which is the same rate as globally. MMR reductions were almost similar, but substantially lower than the MMRate reduction.

This study has some limitations. Direct measures of maternal deaths in developing countries are notoriously poor due to weak vital registration systems and misclassification of pregnancy-related deaths. The available databases of maternal mortality are derived from model-based estimates. These estimates are driven by assumptions about data distributions, quality, imputation of missing data, and relevant covariates. WHO/MMEIG's maternal mortality estimates have high uncertainty levels, particularly for LMICs.²⁴ The uncertainty in errors in such estimates is also likely to affect our results. The available compiled data on contraceptive prevalence rates and GFR are also derived from model estimates, which themselves are subjected to varying levels of precision and are likely to influence the precision of our results. Overall, those estimates that are modelled are similarly subjected to large uncertainty.

Our results suggest that 61·2% of the reduction in maternal mortality globally between 2000 and 2023 is attributable to improvement in maternity care and 38·8% to fertility reduction. The leading causes of maternal mortality are haemorrhage, hypertensive disorders, sepsis, obstructed labour, and complications from unsafe abortion, all of which require basic and emergency obstetrical care²⁵ to prevent death. Providing universal access to quality maternal health services and ensuring skilled attendance at delivery are key action strategies initiated by the Safe Motherhood programme,⁷ which has had a major role in reducing maternal mortality globally. MMEIG's compiled data suggest that skilled birth attendance increased from 60·8% in 2000 to 86·0% in 2023. However, in many countries with high maternal mortality, a large proportion of women deliver at home and the progress in improving delivery care by skilled birth attendants has been slow which hinders the reduction of maternal mortality.²⁶

Although the number of women increased by almost 28·6% between 2000 and 2023, the number of births decreased by approximately 2·6% during the period due to GFR reductions.²⁷ The fertility decline reduces the proportions of high-risk births of women too young, of high parity, and with short birth intervals. This study shows that about two-fifths of the maternal mortality reduction globally was attributable to fertility rate decline. There are at least four causal pathways through which fertility decline directly reduces the number of maternal deaths: reducing women's exposure to the incidence of pregnancies; reducing vulnerability to unsafe abortions; delaying first births in young women when pelvic development is premature; and reducing the hazards of frailty from high-parity and closely spaced pregnancies. Every year nearly 73 million women undergo abortions to terminate unwanted pregnancies and about 8–14% of maternal deaths are due to abortion complications.^{6, 28, 29} The reduction of unwanted pregnancies through fertility reduction with contraceptive use and safe abortion directly and indirectly contributes to reducing maternal mortality. We show that an increase in the contraceptive use rate between 2000 and 2023 has prevented an additional 77 400 maternal deaths in 2023 alone through fertility reduction. Contraceptive prevalence rates increased very modestly between 2000 and 2023 in sub-Saharan Africa countries, where maternal mortality is high, which also suggests the potential scope of reducing maternal deaths with expanded family planning programmes. India averted about 47% of maternal deaths in 2023 with its contraceptive prevalence rates increase. Improving access to safer abortion is also critical for reducing maternal mortality. Our results showed a large contribution of fertility reduction on the decline in maternal deaths, beyond the contribution from contraceptive use, which might imply that women's access to safer abortion services has also improved globally. Increased availability of misoprostol and abortion medications might have further facilitated women's access to safer and more convenient abortion choices.

Since the early 1990s, funding has been drastically reduced for family planning programmes for developing countries, partly due to the channelling of funds to address HIV epidemic challenges. During this period, the contraceptive use rate increased modestly, from 55% to 64% for people who were married or in union.¹⁷ Concerted efforts must be taken to support and improve contraceptive use levels in low-income countries, where contraceptive use is low, especially in sub-Saharan Africa. Among the 186 countries where contraceptive prevalence rate data were available, 50 countries had a contraceptive prevalence rate value for all women of less than 30%. Unwanted fertility rates were also very high in low contraceptive prevalence rate countries. Women are repeatedly and unnecessarily exposed to life-threatening pregnancy complications and thus remain vulnerable to mortality and morbidity risks over their reproductive years in these disadvantaged countries. Some women compound these risks by accessing unsafe abortions to avoid unwanted births, which can be partly prevented with access to effective contraception to manage unplanned pregnancies by spacing and limiting births. As it stands, the countries where maternal mortality ratios are highest are also those where contraceptive prevalence rates are low.

Maternity care programmes, with the launch of the Safe Motherhood Initiative in 1987, identified the following four core strategies, commonly referred to as the four pillars of safe motherhood, for reducing maternal mortality in developing countries: antenatal care, safe delivery, postnatal care, and family planning. However, family planning interventions are often not integrated tightly with maternity care. We strongly call for universal integration of family planning programmes with maternity health care. US Agency for International Development (USAID) has contributed heavily to funding maternal health care and family planning programmes globally. However, the recent dismantling of USAID and loss of funding are likely to jeopardise progress in achieving the SDG-3 goals. Renewed enforcement of policies towards abortion restrictions likely further affects the progress. Concerted, reinvigorated efforts are needed to bolster both maternity care and family planning programmes globally to eliminate all preventable maternal deaths.

Data sharing

The study used the publicly available WHO/MMEIG 2023 maternal mortality database and UN's World Contraceptive Use 2024 database.

Declaration of interests

All authors declare no competing interests.