Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations

Corey J A Bradshaw; Claire Perry; Melinda A Judge; Chitra M Saraswati; Jane Heyworth; Peter N Le Souëf

doi:10.1371/journal.pone.0280260

. 2023 Feb 22;18(2):e0280260. doi: 10.1371/journal.pone.0280260

Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations

Corey J A Bradshaw ^1,^2,^#, Claire Perry ^3,^#, Melinda A Judge ^4,^#, Chitra M Saraswati ⁴, Jane Heyworth ³, Peter N Le Souëf ^2,^4,^*,^#

Editor: Joshua Amo-Adjei⁵

PMCID: PMC9946217 PMID: 36812163

Abstract

Although average contraceptive use has increased globally in recent decades, an estimated 222 million (26%) of women of child-bearing age worldwide face an unmet need for family planning—defined as a discrepancy between fertility preferences and contraception practice, or failing to translate desires to avoid pregnancy into preventative behaviours and practices. While many studies have reported relationships between availability/quality of contraception and family planning, infant mortality, and fertility, these relationships have not been evaluated quantitatively across a broad range of low- and middle-income countries. Using publicly available data from 64 low- and middle-income countries, we collated test and control variables in six themes: (i) availability of family planning, (ii) quality of family planning, (iii) female education, (iv) religion, (v) mortality, and (vi) socio-economic conditions. We predicted that higher nation-level availability/quality of family-planning services and female education reduce average fertility, whereas higher infant mortality, greater household size (a proxy for population density), and religious adherence increase it. Given the sample size, we first constructed general linear models to test for relationships between fertility and the variables from each theme, from which we retained those with the highest explanatory power within a final general linear model set to determine the partial correlation of dominant test variables. We also applied boosted regression trees, generalised least-squares models, and generalised linear mixed-effects models to account for non-linearity and spatial autocorrelation. On average among all countries, we found the strongest associations between fertility and infant mortality, household size, and access to any form of contraception. Higher infant mortality and household size increased fertility, whereas greater access to any form of contraception decreased fertility. Female education, home visitations by health workers, quality of family planning, and religious adherence all had weak, if any, explanatory power. Our models suggest that decreasing infant mortality, ensuring sufficient housing to reduce household size, and increasing access to contraception will have the greatest effect on decreasing global fertility. We thus provide new evidence that progressing the United Nation’s Sustainable Development Goals for reducing infant mortality can be accelerated by increasing access to family planning.

Introduction

Although average contraceptive use has increased globally in recent decades, an estimated 222 million (26%) women of child-bearing age worldwide face an unmet need for family planning—defined as a discrepancy between fertility preferences and contraception practice, or failing to translate desires to avoid pregnancy into preventative behaviours and practices [1]. The United Nation’s Sustainable Development Goals 3 and 5 emphasise the basic right to exercise control over sexual and reproductive health through universal access to family planning [2]. While achieving Goal 3 is targeted for 2030, reducing global maternal mortality to < 70 per 100,000 live births and under-5 mortality to ≤ 25 per 1,000 live births are not on track to be met [3]. Providing readily available, high-quality family-planning services is necessary because this is expected to decrease not only fertility, but also the number of unintended pregnancies and infant and maternal deaths [4,5]. Allowing individuals to be able to decide to have fewer children also has the potential to facilitate better investment in the overall health and well-being of families and communities [5].

To date, there is no uniform measure (i.e., set of indicators) for availability and quality of family planning; therefore, gauging the effects these might have on fertility is difficult. Few studies have investigated the relationship between socio-economic conditions and fertility among nations; there are also few studies on the availability and quality of family planning that do more than merely hypothesise a generally negative association with fertility [6,7]. We investigated the association between fertility and the availability and quality of family planning, as well as the potential effects of education, household size (a proxy for population density), religion, infant mortality, and socio-economic conditions identified from the literature. For example, higher maternal education is correlated with reduced total fertility [8]. Religious adherence can also affect fertility patterns [9]—e.g., Catholicism expressly forbids contraception, and predominantly Muslim countries in Sub-Saharan Africa tend to have higher total fertility rates than non-Muslim countries [10]—although the relationship is complex and not always present [11,12]. Infant mortality has been linked to increased fertility due to the ‘insurance’ or ‘replacement’ effect [13], and child morbidity and mortality are themselves exacerbated by higher household sizes (i.e., number of individuals per household) [14–20].

To test the complex potential relationships between these and other hypothesised drivers of human fertility, we specifically tested whether: (1) increasing the availability of family planning is associated with reduced fertility; (2) increasing the quality of family-planning services is associated with reduced fertility; (3) increasing years of female education is associated with reducing fertility; (4) increased fertility is observed in countries with a higher prevalence of adherents of Catholicism or Islam; (5) higher mean household size is positively correlated with fertility, and (6) lower socio-economic conditions and (7) higher mortality (infant and maternal) are associated with higher fertility.

Methods

Data

We used aggregated data at the national level, collating publicly available data from several online sources. These included Demographic and Health Surveys [21], Family Planning Effort Index [22], Multiple Indicator Cluster Surveys [23], National Composite Index on Family Planning [24], the World Bank [25], the World Inequality Database [26], the Association of Religion Data Archives [26,27], and the World Health Organization Global Health Observatory data repositories [28]. We obtained most of the required data from the Demographic and Health Surveys; these nationally representative surveys had uniform methods and included a similar period of reference when measuring indicators.

From these datasets, we derived the following test variables for model construction (additional information in Supporting information Section 2). As the response, we used fertility, which is the mean number of children a woman has between the ages of 15 to 49 years [29], which we sourced from the World Bank (mean from 2010–2020) [25]. We separated the modelling into to two main phases (see Analyses) that first tested relationships to fertility within six separate themes: (i) availability of family planning, (ii) quality of family planning, (iii) female education, (iv) religion, (v) mortality, and (vi) socio-economic conditions. The second phase incorporated the top-ranked indicators into a final model set.

For theme i, availability of family-planning services is measured via the ‘access’ index from the Family Planning Effort Index (2014 version) [22]. We added other indicators from this dataset that measured aspects of family planning-service delivery, including access to community health workers (extent of population visited by healthcare workers who educate about family planning and maternal and child health). For theme ii, we derived quality of family-planning services using the ‘quality’ index from the National Composite Index on Family Planning [24] (2014 version). We also included additional indicators from the Demographic and Health Surveys and Multiple Indicator Cluster Surveys [23] (most recent data per country) to measure other aspects not included in the ‘quality’ index from the National Composite Index on Family Planning database [24]. For theme iii, we obtained mean years of education of women aged 15–49 (weighted) from the Demographic and Health Surveys and Multiple Indicator Cluster Surveys [23]. For theme iv (religion), we combined the percentage of the population adhering either to Catholicism or Islam from the Association of Religion Data Archives [26]. For theme v, infant mortality is higher in areas of low socio-economics [30,31] and a known correlate with fertility [32], so we included the most recent infant mortality data (deaths per 1000 live births) for each country from the World Health Organization Global Health Observatory data repository [28]. We also included the number of maternal deaths per 100,000 live births, and the average number of battle-related deaths (2015–2020) per capita (most recent data from the World Bank) [25]. For theme vi (socio-economics), women in the lowest quintile of household wealth have less availability of family planning compared with women from higher-wealth households [33]. We therefore obtained (a) the net personal wealth of the bottom 50% of the population (p0p50) from the World Inequality Database [26]. We also included the (b) mean number of household members as a proxy for population density, and (c) percentage of households with three generations residing (from the Demographic and Health Surveys and Multiple Indicator Cluster Surveys) [23] (each country’s most recent data).

Analyses

Data preparation

We applied descriptive analyses to provide an overview of the distribution across all variables. We first transformed all variables and scaled/centred them to improve homoscedasticity and Gaussian behaviour using a logit transformation of the base proportion and then centring and scaling using the scale function in the R programming language [34]. In the case of non-proportional variables (female years of education, battle-related deaths, maternal mortality, net personal wealth in the bottom 50%), we either scaled the raw values (female years of education, battle-related deaths, maternal mortality), or scaled the cube of the variable (net personal wealth) to give approximate-Gaussian behaviour. We examined the transformed explanatory variables for collinearity using a non-parametric (Kendall’s τ) correlation matrix (S1 Table).

Multiple imputation by chained equations

The final dataset included 64 countries with relevant data, although there were some missing values per variable (missing: 19% visitation by community health workers; 6% female years of education; 9% net personal wealth bottom 50%; 9% quality of family planning index; 5% access to any form of contraception). Removing these countries from analysis would have reduced our sample from 64 to 51 countries, a large reduction considering the complexity of the hypotheses to test. We therefore ran multiple imputation by chained equations using the mice library in R [35] to impute any missing values in the predictor variables based on the final-phase dataset (see below) to maintain the highest-possible sample size of 64 countries for subsequent analysis. Multiple imputation using this method is robust for up to 75% missing values [36], and provides stronger inferences than ignoring missing data [37]. Assuming values were missing at random, we employed predictive means matching in the mice function with 50 maximum iterations to impute the missing data. Results were similar whether using imputed or missing-values datasets (see Results).

Linear models

We built general linear models with the glm function in R to identify the contributory (transformed) variable with the most explanatory power in each of the six themes (see Supporting information Sections 4–7). We included various models in each theme and ranked them based on the Bayesian information criterion (BIC) [38,39] given that our focus was on identifying the main drivers of variance in fertility as opposed to prediction [40], with relative model probability equal to its BIC weight (wBIC) (S2–S5 Tables, Supporting information Sections 4–7).

We suspected potential spatial autocorrelation among the country values, so we also constructed general linear mixed-effects models using the lme4 package [41] in R, coding a random effect according to major world region (S1 Fig). Here, we aggregated the 64 countries into the six World Bank regions—Latin America & Caribbean (n = 6 countries); Sub-Saharan Africa (n = 37); Middle East & North Africa (n = 3); Europe & Central Asia (n = 5), South Asia (n = 6); East Asia & Pacific (n = 7)—to account partially for spatial autocorrelation (see Supporting information Section 1, S1 Fig and Analyses). Including the top-ranked variables from each theme into a final model set, we determined both the evidence for a non-random effect of the final variables on fertility, as well as the goodness of fit (percent deviance explained per model).

Boosted regression trees

We also built boosted-regression trees [42] of the final model to account for potential nonlinearity in the relationships between the fertility response and the potential indicators (S2–S5 Figs, Supporting information Sections 4–7). Mixed-effects models developed in the previous section potentially miss sub-regional spatial autocorrelation, so to account for a deeper level of spatial autocorrelation and to quantify uncertainty in the relationships between fertility and each explanatory variable, we resampled countries in the dataset with replacement 1000 times. We then passed each resampled dataset to the boosted regression trees and then calculated the 2.5^th and 97.5^th percentiles for the respective distribution for each predicted fertility as the uncertainty bounds. We applied kappa (κ) limitation to the resampled selections to limit the influence of outliers [43], where we retained only the resampled mean ranks within κσ of the overall average mean (κ = 2). We then recalculated the average and standard deviation of the mean rank, with the process repeated five times.

Generalised least-squares models

Finally, we applied general least-squares models that are designed explicitly to account for spatial autocorrelation among spatial units (countries, in this case) to the final model set. For each country, we coded the centroid coordinates (in latitude/longitude) and determined that a Gaussian correlation was the top-ranked, within-group correlation structure for the saturated model (although the spherical correlation structure was nearly identically supported); we therefore ran the models in the final phase as per the general linear/mixed-effects models with a Gaussian correlation structure. We ranked the ensuing models according to wBIC, and calculated relative goodness-of-fit using three different pseudo-R² metrics: McFadden, Cox and Snell, and Craig and Uhler (using the nagelkerke function in R library rcompanion) [44]. All data and R code to repeat the analyses are provided at github.com/cjabradshaw/humanfertility (doi:10.5281/zenodo.7496142).

Results

Fertility was Normally distributed (Shapiro-Wilk normality test: W = 0.970; p = 0.123) with one outlier (Niger), although that country’s value of 7.14 per woman is the highest national fertility globally [25]. A non-parametric (Kendall’s τ) correlation matrix of the highest-ranked variable from each initial model (S1 Table) indicated that the strongest correlation observed was the relationship between access to any form of contraception and household size (τ = -0.516). Of all regions, countries in sub-Saharan Africa had the highest average number of infant mortalities (45 per 1000 live births), as well as the highest average fertility (4.8 per woman) (Fig 1).

Fig 1 — Violin plots of country-level raw values for a. fertility (children woman^-1) and the highest-ranked variables from each of the six initial modelling phases—b. population with access to community health workers (%), c. female years of education completed, d. population claiming adherence to Catholicism or Islam (%), e. infant mortality (per 1000 births), f. population with access to any form of contraception (%), g. quality of family planning index, h. mean number of household members. The 64 low- and middle-income countries with full data included in the analysis are classed into four world regions (EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = sub-Saharan Africa). Country codes per region (3-letter ISO): **EAP** (KHM, IDN, LAO, MMR, PNG, PHL, TLS); **ECA** (ALB, ARM KGZ, TJK, TUR), **LAC** (COL, DOM, GTM, HTI, HND, PER); **MENA** (EGY, JOR, YEM); SA (AFG, BGD, IND MDV, NPL, PAK); **SSA** (AGO, BEN, BFA, BDI, CMR, TCD, COM, COD, COG, CIV, GNQ, ETH, GAB, GMB, GHA, GIN, KEN, LSO, LBR, MDG, MWI, MLI, MRT, MOZ, NAM, NER, NGA, RWA, STP, SEN, SLE, ZAF, TZA, TGO, UGA, ZMB, ZWE).

General linear and linear mixed-effects models

Visitation by community health workers (CHW) was the most supported variable in theme 1 (S2 Table, S2 Fig), access to any type of contraception (ACC) and quality of family planning index (QUA) in theme 2 (S3 Table, S3 Fig), female years of education completion (FE) in theme 3, percentage Catholic or Muslim in theme 4 (religion), infant mortality (IM) in theme 5 (S4 Table, S4 Fig A-4), and household size in theme 6 (HS) (S5 Table, S5 Fig). Based on this group of seven variables, we constructed 26 candidate general linear and general linear mixed-effects models (Tables 1 and 2, respectively) to determine the most-supported models according to wBIC.

Table 1. Candidate general linear models of the highest-ranked variable from each theme (1. availability of family planning, 2. quality of family planning, 3. education, 4. religion, 5. mortality, 6. socio-economics) in relation to variation in fertility among 64 low- and middle-income countries.

model	k ^a	LL^b	ΔBIC^c	wBIC^d	%DE^e
IM^f+ACC^g	3	-75.53	0.00	0.71	62.9
ALL ^h	8	-66.72	3.16	0.15	71.8
IM+HSⁱ	3	-77.78	4.50	0.07	60.2
FE^j+IM	3	-78.34	5.61	0.04	59.5
FE+ACC	3	-78.89	6.72	0.02	58.8
IM+QUA^k	3	-82.04	13.01	<0.01	54.5
CM^l+IM	3	-82.34	13.61	<0.01	54.1
IM	2	-85.23	15.24	<0.01	49.7
ACC	2	-86.93	18.63	<0.01	47.0
CHW^m+IM	3	-85.23	19.40	<0.01	49.7
FE+HS	3	-85.61	20.15	<0.01	49.2
FE	2	-87.92	20.61	<0.01	45.4
FE+QUA	3	-85.86	20.66	<0.01	48.8
CHW+ACC	3	-86.26	21.44	<0.01	48.1
CHW+FE	3	-86.91	22.75	<0.01	47.0
CM+ACC	3	-86.92	22.78	<0.01	47.0
HS	2	-93.66	32.09	<0.01	34.6
CHW+HS	3	-91.90	32.74	<0.01	38.1
CM+HS	3	-92.49	33.92	<0.01	37.0
*intercept-only*	1	-107.25	55.12	<0.01	-
CHW	2	-105.73	56.23	<0.01	4.7
CHW+QUA	3	-103.81	56.56	<0.01	10.2
QUA	2	-106.21	57.19	<0.01	3.2
CM	2	-106.31	57.39	<0.01	2.9
CHW+CM	3	-104.72	58.37	<0.01	7.6
CM+QUA	3	-104.91	58.76	<0.01	7.1

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^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^e%DE = % deviance explained; ^fIM = infant mortality; ^gACC = access to any form of contraception; ^hALL = saturated model (including the six highest-ranked variables from each phase); ⁱHS = mean number of individuals per household; ^jFE = female years of education completion; ^kQUA = quality of family planning index; ^lCM = % Catholic or Muslim; ^mCHW = visitation from community health workers.

Table 2. Generalised linear mixed-effects models (only 10 top-ranked models according to wBIC_c shown) of the highest-ranked variable from each theme (1. availability of family planning, 2. quality of family planning, 3. education, 4. religion, 5. socio-economics) in relation to variation in fertility among 46 low- and middle-income countries.

model	k ^a	LL^b	ΔBIC^c	wBIC^d	R_m^e	R_c^f
FE^g+ACC^h	9	-62.34	0.00	0.60	43.6	76.6
FE+HSⁱ	11	-62.81	0.96	0.37	40.3	77.2
IM^j+HS	18	-66.36	8.04	0.01	42.0	71.9
FE	20	-68.54	8.25	0.01	34.0	71.1
HS	25	-69.57	10.30	<0.01	29.2	70.6
FE+IM	8	-67.89	11.11	<0.01	41.1	71.4
ACC	23	-70.87	12.91	<0.01	30.4	69.4
IM+ACC	16	-68.81	12.94	<0.01	42.1	69.9
CHW^k+FE	2	-69.83	14.99	<0.01	34.2	70.8
FE+QUA^m	10	-70.01	15.35	<0.01	33.4	70.9
…

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^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^eR_m = marginal R²; ^fR_c = conditional R²; ^gFE = female years of completed education; ^hACC = access to any form of contraception; ⁱHS = mean number of individuals per household; ^jIM = infant mortality; ^kCHW = visitation from community health workers; ^lQUA = quality of family planning index.

According to the final-phase general linear models, the model with infant mortality and access to any form of contraception had the highest model support (wBIC = 0.71) and explained 62.9% of the deviance, followed by the saturated model (wBIC = 0.15; deviance explained = 71.8%) (Table 1). Infant mortality had the highest % deviance explained (49.7%) among the single-variable models (Table 1).

For the generalised linear mixed-effects models accounting for the random effect of region, the model including female years of education and access to any form of contraception (wBIC = 0.60), but the fixed effects accounted for over half (57%) of the total variance explained (R_m = 43.6% of 76.6%; Table 2). Female years of education had the highest explanatory power (R_m = 34.0%) of any single-variable model, followed by mean number of individuals per household (Table 2).

Boosted regression trees

The boosted regression trees using either raw, untransformed values (grey bars in Fig 2A), or the resampled relative contributions across 1000 iterations of the transformed variables (black bars in Fig 2A), clearly indicated that infant mortality had the strongest explanatory power for variance in fertility among countries (Fig 2A), followed by household size, and access to any form of contraception (Fig 2A) (see also S2–S4 Figs, Supporting information Sections 4–8). The relationship between infant mortality and fertility was strongly stepped, suggesting a threshold of a sharp increase in fertility occurring once a country exceeded an infant mortality of ~ 0.0282 (Figs 2B and 3). There was a positive relationship between fertility and household size, with higher household sizes having higher fertility (Fig 2C). Access to contraception had a negative relationship with fertility, suggesting that fertility would decrease as a country improves its citizens’ access to contraception (Fig 2D). Interestingly, the predicted relationship between quality of family planning was opposite to the expected direction: higher ‘quality’ was correlated with higher fertility (Fig 2E) (see Discussion for possible reasons). While the relationships between fertility and education, religion, and health worker visitation were in the hypothesised directions (Fig 2F–2H), their relative contributions were relatively weak (Fig 2A). The relative contribution rank of predictor variables was identical using either the imputed or restricted datasets (S6 Fig).

Fig 3 — Once infant mortality has exceeded a threshold of approximately 0.0282 (28.2/1000 live births), fertility increases precipitously. Country codes provided in Fig 1.

General least-squares models

The country centroids explained 7–14% of the variation in the general least-squares models. After accounting for spatial relationships, female years of education, household size, access to any form of contraception, and infant mortality emerged as the most important correlates to variation in fertility among countries (Table 3).

Table 3. General least-squares models of the highest-ranked variable from each theme (i. family-planning availability, ii. family-planning quality, iii. education, iv. religion, v. mortality, vi. socio-economics) in relation to variation in fertility among 64 low- and middle-income countries.

model	BIC^a	wBIC^b	psR²mcf^c	psR²cs^d	psR²cu^e
FE^f+HS^g	129.40	0.687	0.33	0.55	0.61
FE+ACC^h	131.24	0.273	0.32	0.54	0.59
IMⁱ+HS	136.90	0.016	0.28	0.50	0.54
ALL ^j	137.45	0.012	0.28	0.49	0.54
FE+IM	139.72	0.004	0.24	0.44	0.48
CHW^k+HS	140.21	0.003	0.26	0.47	0.51
FE	139.99	0.003	0.24	0.44	0.48
CHW+FE	179.18	<0.001	0.14	0.30	0.32
CHW+CM^l	168.69	<0.001	0.08	0.17	0.19
CHW+IM	161.52	<0.001	0.12	0.26	0.28
CHW+ACC	146.90	<0.001	0.22	0.41	0.45
CHW+QUA^m	170.65	<0.001	0.07	0.15	0.16
FE+QUA	196.68	<0.001	-0.10	-0.28	-0.31
CM+IM	196.16	<0.001	-0.10	-0.27	-0.30
CM+ACC	156.57	<0.001	0.16	0.31	0.35
CM+QUA	149.35	<0.001	0.20	0.39	0.43
CM+HS	234.77	<0.001	-0.35	-1.33	-1.45
IM+ACC	144.08	<0.001	0.24	0.44	0.48
IM+QUA	147.39	<0.001	0.21	0.41	0.45
CHW	162.52	<0.001	0.12	0.25	0.27
CM	168.85	<0.001	0.05	0.11	0.12
IM	171.01	<0.001	0.04	0.08	0.09
ACC	158.53	<0.001	0.12	0.25	0.27
QUA	150.32	<0.001	0.17	0.34	0.37
HS	172.12	<0.001	0.03	0.07	0.07
*intercept-only*	172.40	<0.001	-	-	-

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^aBIC = Bayesian information criterion; ^bwBIC = Bayesian information criterion weight (≈ model probability); ^cpsR²mcf = pseudo-R² (McFadden metric); ^dpsR²cs = pseudo-R² (Cox & Snell metric); ^epsR²cu = pseudo-R² (Craig & Uhler metric); ^fFE = female years of completed education; ^gHS = mean number of individuals per household; ^hACC = access to any form of contraception; ⁱIM = infant mortality; ^jALL = saturated model (including all highest-ranked variables); ^kCHW = visitation from community health workers; ^lCM = % Catholic or Muslim; ^mQUA = quality of family planning index.

Discussion

Our study is the first to investigate the potential associations between the availability and quality of family planning and fertility, while simultaneously considering other potential contributory variables among 64 low- and middle-income countries. We found that high infant mortality was most strongly related to high fertility, but nonlinearly, followed by high household size (i.e., mean number of individuals per household) and reduced access to contraception. However, the relative contribution of each of these variables to reducing fertility potentially also needs to consider the correlation between access to contraception and decreased infant mortality. Increased access to contraception has been previously associated with decreased infant mortality and is thought to act through increasing space between births, and avoiding the increased infant mortality found with higher birth order [45,46]. We also found that other factors considered to be important in reducing fertility, including community health worker visits, female education, and religion (Catholicism or Islam) had only weak associations, at least at the spatial scale across nations.

The counter-intuitive relationship we found between the index of family-planning quality (increasing quality leading to higher fertility) might be explained in part by the dominance of nations in sub-Saharan Africa in our sample. A recent study [47] comparing contraceptive use and the quality index from the National Composite Index on Family Planning [24] indeed found a positive correlation between the two variables, yet the relationship was strongest for sub-Saharan Africa, despite this region having the lowest overall contraceptive use, highest fertilities, and highest infant mortality (Fig 1). In fact, sub-Saharan Africa scored highest across all five dimensions, as well as overall, on the National Composite Index on Family Planning, which might indicate biases that are a known limitation of the methodology used [47]. This potentially suggests that the combined metric of ‘quality’ could be interpreted differently in African nations relative to elsewhere.

Infant mortality as a strong predictor of variation in fertility is supported elsewhere [32]. Overall, our findings support the notion that to decrease global fertility, both infant survival rates plus access to contraception need to be increased. Recommendations for measures to decrease infant mortality emphasise improving the quality of antenatal care, increasing the number of trained healthcare staff at births, and improving postnatal care for both infants and mothers [48,49]. Given the evidence that household composition, including the number of individuals, can worsen child health [14–20], improving living conditions to ameliorate high-density living could also indirectly result in lower fertility. A greater emphasis on providing access to contraception as a direct contribution to decreasing infant mortality is important for such guidelines.

There are multiple factors that can influence fertility, some of the subtleties of which we could not test directly with the available data. Responsible family planning requires the effort of both men and women, yet male contraceptive use is decreasing globally [29]. Potential reasons could be local views on gender equality, respect and dignity for women, and the proportion of women participating in the workforce [50]. Child marriage and gender-based violence are positively correlated with low contraceptive use and increased fertility in some conditions [51,52]; however, women who experience child-marriage have a higher modern contraceptive use (e.g., female and male sterilisation, intra-uterine device, oral hormonal pills, vaginal barrier methods) compared to adult married women [53]. Our findings also suggest that increasing the availability of contraceptives can be effective in reducing fertility where parents do not have access to secondary education. Fundamentally, contraceptive use is closely linked to infant mortality. Allowing citizens to choose family planning by providing readily available, modern methods of contraception, could improve infant survival because parents can plan and space their births, thereby investing in higher-quality care and increased available resources for each child. This could also improve maternal and child-health outcomes globally.

Strengths and limitations

A major strength of our study is using nationally representative surveys from the Demographic and Health Surveys and other databases, which suggests general applicability to low- and middle-income nations. Further, applying several different modelling frameworks that confirmed the main contributors added robustness to our findings. Our design meant that possible biases exist, such as potentially hiding spatial variation within countries when relying on national averages. There was also the potential for systematic differences between countries in terms of data collection and reporting (i.e., unstated variation in the number of surveys completed for each country). Other limitations include using only the most recent, complete data, which perhaps masks temporal variation in the underlying relationships, and the potential mismatching of some variables derived from surveys occurring at different times.

In conclusion, efforts to increase infant survival, particularly in low- and middle-income countries, would in turn reduce fertility and improve child and maternal health outcomes. An important component of these activities is to provide both women and men the choice to access non-coercive, quality family-planning services. Overall, there is more that can be done to aid in meeting the initiatives of the Sustainable Development Goals of the United Nations, which, if unmet, will see global increases in fertility, more child deaths, and more birth-related deaths among women.

Supporting information

S1 Fig. Regionalization map.

World map showing four regional classes used as a random effect in the general linear mixed-effects models (n = 64 countries). Country codes per region (3-letter ISO): Latin America & Caribbean (COL, DOM, GTM, HTI, HND, PER); Sub-Saharan Africa (AGO, BEN, BFA, BDI, CMR, TCD, COM, COD, COG, CIV, GNQ, ETH, GAB, GMB, GHA, GIN, KEN, LSO, LBR, MDG, MWI, MLI, MRT, MOZ, NAM, NER, NGA, RWA, STP, SEN, SLE, ZAF, TZA, TGO, UGA, ZMB, ZWE); Middle East & North Africa (EGY, JOR, YEM); Europe & Central Asia (ALB, ARM, KGZ, TJK, TUR); South Asia (AFG, BGD, IND, MDV, NPL, PAK), East Asia & Pacific (KHM, IDN, LAO, MMR, PNG, PHL, TLS). Map generated in QGIS Version 3.26.1-Buenos Aires using an open-licence layer (https://hub.arcgis.com/datasets/esri::world-countries-generalized/explore?location=-0.684955%2C0.000000%2C2.40).

(EPS)

Click here for additional data file.^{(36.4MB, eps)}

S2 Fig. Theme 1 results—availability of family planning.

Boosted regression tree results (variable relative performance) for availability of family planning among 52 low- and middle-income countries (available countries in non-imputed dataset). Cross-validation % deviance explained by the final model of 32750 trees: 52.4 ± 7.0%. Community health workers = visitation by a community health worker; social media marketing = social marketing of subsidised contraceptives; community-based distribution = community-based distribution of family-planning; access = ‘access’ index comprising of indicators for availability of family-planning from the Family Planning Effort Index [1]; logistics and transport = logistics and transport; private sector = involvement of private-sector agencies and groups.

(TIFF)

Click here for additional data file.^{(1.1MB, tiff)}

S3 Fig. Theme 2 results—quality of family planning.

Boosted regression tree results (variable relative performance) for quality of family planning among 56 low- and middle-income countries (available countries in non-imputed dataset). Cross-validation % deviance explained by the final model of 28650 trees: 53.4 ± 10.3%. Any form of contraception = proportion of a population who have access to any form (modern and/or traditional) contraception; quality = ‘quality’ index of family-planning indicators from the National Composite Index on Family Planning [2]; no contraception = proportion of a population who have no access to contraception; traditional contraception = proportion of a population who have access to traditional forms of contraception only; modern contraception = proportion of a population who have access to modern forms of contraception.

(TIFF)

Click here for additional data file.^{(1.1MB, tiff)}

S4 Fig. Theme 5 results—mortality.

Boosted regression tree results (variable relative performance) for mortality among 29 low- and middle-income countries (available countries in non-imputed dataset). Cross-validation % deviance explained by the final model of 39800 trees: 62.9 ± 14.7%. Infant mortality (deaths per 1000 births); battle-related mortality (deaths per capita); maternal mortality (deaths per 100,000 live births)

(TIFF)

Click here for additional data file.^{(1.2MB, tiff)}

S5 Fig. Theme 6 results—socio-economics.

Boosted regression tree results (variable relative performance) for socio-economic indicators among 61 low- and middle-income countries (available countries in non-imputed dataset). Cross-validation % deviance explained by the final model of 39900 trees: 75.3 ± 4.8%. Three generations = households with at least three generations residing; house size = mean number of household members; wealth inequality = bottom 50% of net personal wealth.

(TIFF)

Click here for additional data file.^{(488.5KB, tiff)}

S6 Fig. Final phase results.

Boosted regression tree results (variable relative performance) of the final phase variables based on multiple-imputed versus missing-data datasets for 64 and 51 low- and middle-income countries, respectively. Cross-validation % deviance explained by the final model: 85.4% ± 3.7% (25100 trees) and 82.6% ± 3.4% (27150 trees) for the imputed and missing datasets, respectively. House size = mean number of individuals per household; community health workers = visitation from community health workers.

(TIFF)

Click here for additional data file.^{(1.9MB, tiff)}

S1 Table. Variable correlation matrix.

Correlation (Kendall’s τ) matrix of the highest-ranked variable(s) from each of the six thematic modelling phases (1. family-planning availability, 2. family-planning quality, 3. education, 4. religion, 5. mortality, 6. socio-economics) among 64 low- and middle-income countries. ^acomm work = visitation by a community healthcare worker; ^beducation = female years of education; ^cCathol+Musl = % of population Catholic or Muslim; ^dinfant mort = infant mortality; ^eaccess = access to any form of contraception; ^fquality = quality of family planning index.

(DOCX)

Click here for additional data file.^{(14.1KB, docx)}

S2 Table. Theme 1 results—availability of family planning.

General linear models for indicators of availability of family-planning in relation to variation in fertility among 52 low- and middle-income countries (available countries in non-imputed dataset). ^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^e%DE = % deviance explained; ^fACC = ‘access’ index comprising indicators for availability of family planning from the Family Planning Effort Index [1]; ^gCBDT = community-based distribution of family planning; ^hSOCM = social marketing of subsidised contraceptives; ⁱCHW = visitation by a community health worker; ^jLOGT = logistics and transport; ^kPRIV = involvement of private-sector agencies and groups; ^lALL = saturated model (all variables included).

(DOCX)

Click here for additional data file.^{(16.5KB, docx)}

S3 Table. Theme 2 results—quality of family planning.

General linear models for indicators of quality of family-planning in relation to variation in fertility among 56 low- and middle-income countries (available countries in non-imputed dataset). ^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^e%DE = % deviance explained; ^fQUA = ‘quality’ index from the National Composite Index of Family Planning [2]; ^gNC = access to no form of contraception; ^hAC = access to any form of contraception; ⁱMC = access to modern contraceptives; ^kTC = access to traditional contraceptives.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S4 Table. Theme 5 results—mortality.

General linear models for indicators of mortality in relation to fertility among 29 low- and middle-income countries (available countries in non-imputed dataset). ^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^e%DE = % deviance explained; ^fIM = infant mortality (deaths per 1000 births); ^gBRD = battle-related deaths per capita; ^hMM = maternal mortality (deaths per 100,000 live births).

(DOCX)

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

S5 Table. Theme 6 results—socio-economics.

General linear models for indicators of socio-economics in relation to fertility among 61 low- and middle-income countries (available countries in non-imputed dataset). ^ak = number of parameters; ^bLL = log-likelihood; ^cΔBIC = difference in Bayesian information criterion between model and top-ranked model; ^dwBIC = Bayesian information criterion weight (≈ model probability); ^e%DE = % deviance explained; ^fGEN = proportion of household with three generations residing; ^gHS = household size (number of members); ^hWLTH = bottom 50% of net personal wealth from the World Inequality Database.

(DOCX)

Click here for additional data file.^{(14.2KB, docx)}

S1 File. Description of indicators within each index.

(DOCX)

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

Data Availability

Demographic and Health Surveys (dhsprogram.com) and Multiple Indicator Cluster Surveys (mics.unicef.org) data from the following countries were used (3-letter ISO): Latin America & Caribbean (COL, DOM, GTM, HTI, HND, PER); Sub-Saharan Africa (AGO, BEN, BFA, BDI, CMR, TCD, COM, COD, COG, CIV, GNQ, ETH, GAB, GMB, GHA, GIN, KEN, LSO, LBR, MDG, MWI, MLI, MRT, MOZ, NAM, NER, NGA, RWA, STP, SEN, SLE, ZAF, TZA, TGO, UGA, ZMB, ZWE); Middle East & North Africa (EGY, JOR, YEM); Europe & Central Asia (ALB, ARM, KGZ, TJK, TUR); South Asia (AFG, BGD, IND, MDV, NPL, PAK), East Asia & Pacific (KHM, IDN, LAO, MMR, PNG, PHL, TLS). Also incorporated was data from the Family Planning Effort database (2017 version) (track20.org/pages/data_analysis/policy/FPE.php), National Composite Index on Family Planning (track20.org/pages/data_analysis/policy/NCIFP.php), World Bank (data.worldbank.org), Association of Religion Data Archives (thearda.com), the World Inequality Database (wid.world/data) and World Health Organization Global Health Observatory data repositories (who.int/data/gho). A detailed description of the data used is included in the supplementary information, section 2. Supplementary figure 1 was created using the freely available program QGIS Version 3.26.1-Buenos Aires with an open-licence layer (https://hub.arcgis.com/datasets/esri::world-countries-generalized/explore?location=-0.684955%2C0.000000%2C2.40).

Funding Statement

CB was supported in part by a Rockefeller Foundation Bellagio Writer’s Fellowship. The authors received no other specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0280260.r001

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5 Jul 2022

PONE-D-22-06894Lower infant mortality and access to contraception reduce fertility in low- and middle-income nationsPLOS ONE

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Reviewer #1: Dear Editor,

Thank you for the opportunity to review the manuscript by Bradshaw entitled “Lower infant mortality and access to contraception reduce fertility in low- and middle-income nations”. The authors obtained publicly available datasets from different sources. I find the study to be novel. The authors also adopted several robust statistical analytical techniques and their main finding is that reduction in infant mortality and improved access to contraception could lead to reduction in global fertility. The authors have acknowledged some of the key limitations in their study. That notwithstanding I have provided some suggestions that could improve the paper. Kindly see below my comments.

Key questions

1. I might be wrong but I am tempted to believe that the manuscript was not intended for Plos One. Per whatever reason, including rejection from a different journal which is more likely to be from BMJ Global Health (see page 19, lines 405-409) the authors could have taken their time to reformat it to suite the submission requirements of this journal. That said Line 89 “Hence, our study suggests that to decrease global fertility, activities that decrease infant mortality and increase access to quality, non-coercive family-planning services will have the greatest effect”. In as much as this is a great suggestion, already available data from the World Bank for instance (see https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=XO) has shown that the average fertility rate for LMICs is about 2.5 (2020) which is even expected to reduce further due to several reasons/interventions including your findings. The bone of contention here is that now some countries are devising several strategies to increase their fertility because it is now below replacement levels (see https://www.euro.who.int/__data/assets/pdf_file/0010/73954/EN63.pdf).So making a general suggestion to decrease global fertility is an argument that will be out of place. This needs to be contextualised.

Introduction

2. Page 6 line, 104, it seems the word ‘services’ is missing

3. Kindly provide references for the few studies “Few studies have investigated the relationship between socio-economic conditions and fertility among nations”(page 6 line 110-112).

4. Although the background is well situated in the SDGs, critical issues are missing

a. The authors have failed to expand the literature review on infant mortality and fertility rate

b. They have also failed to situate the study in a given framework. For example, the proximate determinants model could be used

Methods

5. Well described methods but a minor comment, please provide justification for using only datasets from the DHS between 2010 and 2018. There are countries with relatively new datasets https://dhsprogram.com/data/available-datasets.cfm. I am inferring from the data used http://track20.org/pages/data_analysis/policy/NCIFP.php which has the most recent version to be 2017, if that is the reason then it should be explicitly stated.

6. It will also be great fill the STROBE checklist and add it as a supplementary file.

Results

7. The first part of the results could be moved to the methods section (page 11, line 230-241)

Discussion

8. To a greater extent the authors have discussed the key findings. If the authors take on board my suggestion on the need to incorporate a framework in the study, then this should also reflect in their discussion.

9. It will be great to create a sub-heading to discuss the strength and limitations of the study in detail. Some of the limitations worth discussing are: the timeframe for the study, the different survey years for some of the DHSs, the cross-sectional nature of some of the datasets etc.

10. My main concern with the framing of the paper is “reduction in global fertility”. That is true for most low-and middle-income countries but this is not the case in every country. Some countries are even struggling with replacement levels and this should also be discussed in detail.

11. The conclusions and policy implications are not explicitly discussed. This should be the thrust of the paper. A sub-heading/section should be created to discuss this also.

**********

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Reviewer #1: No

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PLoS One. 2023 Feb 22;18(2):e0280260. doi: 10.1371/journal.pone.0280260.r002

Author response to Decision Letter 0

6 Sep 2022

Response to reviewers

Reviewer #1

RESPONSE #1: We have now formatted for PLOS ONE. We acknowledge our oversight.

That said Line 89 “Hence, our study suggests that to decrease global fertility, activities that decrease infant mortality and increase access to quality, non-coercive family-planning services will have the greatest effect”. In as much as this is a great suggestion, already available data from the World Bank for instance (data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=XO) has shown that the average fertility rate for LMICs is about 2.5 (2020) which is even expected to reduce further due to several reasons/interventions including your findings. The bone of contention here is that now some countries are devising several strategies to increase their fertility because it is now below replacement levels (see euro.who.int/__data/assets/pdf_file/0010/73954/EN63.pdf). So making a general suggestion to decrease global fertility is an argument that will be out of place. This needs to be contextualised.

RESPONSE #2: We respectfully disagree. There is little chance of global population decline this century. For example, the United Nations Population Division’s latest cohort-component models projects a median scenario of reaching 10.4 billion by the 2080s, and maintaining approximately that size until 2100 (assuming fertility rates continue declining on average).

The contention is therefore based on the observation a few high-income nations are bucking the global trend by experiencing slight population declines and changing age structures, and the misplaced notion that this represents a problem.

But to slow and perhaps even reverse climate change [1], as well as mitigate the extinction crisis underway [2], we are obliged to reduce consumption globally [3, 4] all this within an economic system that is essentially ‘broken’ [5]. Shrinking human populations will contribute to that goal (provided we simultaneously reduce per-capita consumption). The oft-touted ‘crisis’ of ageing populations is founded on the erroneous notion that it will lead to economic catastrophes for the affected countries. Indeed, countries like South Korea and Japan have declining populations [6], others like Italy are stable and will be declining soon [7], and some countries like Australia are only growing because of net immigration [8].

The reason for exaggeration of a ‘crisis’ generally comes down to the overly simplistic ‘dependency ratio’ [9], which has several different forms but generally compares the number of people in the labour force against those who have retired from it. The idea here is that once the number of people no longer in the labour force exceeds the number of those in the labour force, the latter can no longer support the entirety of the former.

This simplistic 1:1 relationship essentially assumes that you need one person working to support one retired person. But this is a flawed interpretation for several reasons. First, in any country experiencing population decline (i.e., mainly high-income nations), there is almost always a form of national superannuation (retirement savings). This means that labourers save money in a special investment fund (usually guaranteed or supported by government co-contributions) such that by the time of retirement, there is sufficient funds to persist until the end of life. Certainly, some superannuation schemes are better than others, but the idea that the working support the non-working is not only simplistic, it is mostly wrong.

The dependency ratio also assumes that anyone too young to be in the labour force is irrelevant for a nation’s economy. But this too is incorrect. A declining population also has a changing age structure, meaning that there are fewer young people (children). With fewer children comes fewer societal expenses in many aspects (e.g., education, transport, housing, etc.). It turns out that once you include children, dependency ratios do not change as much as those including only adults [8, 10].

The argument tends to focus instead on the increasing ratio of retired people requiring increased medical spending. This too is an oversimplification. People are today living longer and have more years of healthy life than they have ever before [9], and public healthcare is a self-sustaining concept given that support for healthy people in their younger years reduces the time spent unhealthy later. Dependency ratios also assume a static set of conditions between labourers and retirees. But this is so simplistic as to be patently ridiculous. No longer do most people retire at the age of 55 and cease any meaningful contribution to the economy. Forgetting unpaid volunteer work for the moment (which is a sizeable, yet non-valued aspect of most economies) [11], people are working much later in life, have flexible work arrangements (COVID has emphasised this), and are generally contributing to economies well into their retirement years. Assuming fixed conditions is an ageist concept — it essentially treats retirees as useless members of society.

Simplistic dependency ratios used to justify a looming demographic ‘crisis’ are also inherently xenophobic and racist. Because the Earth’s human population is nowhere near reaching a peak or decline, there is a plentiful pool of able-bodied people of working age in most of the world. The problem of insufficient number of labourers in any one country is then entirely based on a distribution issue — limited or suffocating immigration policies (including welcoming and open refugee policies) could ‘fix’ any labour shortages anywhere with the right policies. There is ample evidence now that migrants provide net benefits to the receiving economies [12], not the other way around.

Invoking the idea of a demographic crises also ignores the overwhelming benefits population reductions have for the average person (mitigation of climate change and biodiversity loss notwithstanding). Fewer people clambering for insufficient housing means that lifestyles improve and become more affordable. Having fewer people alleviates potential food-supply and -distribution problems. Having fewer people means fewer cars on the streets, easier access to public transportation, more affordable medical services, and less-competitive educational opportunities. For the average person, fewer people = better life.

Frankly, politicians (and their corporate backers) echo the trope that population declines are bad mainly because fewer consumers mean lower net profits for shareholders. The mega-rich will be slightly less mega-rich if there is a moderate drop in total number of consumers. Corporate capture of governments worldwide perpetuates the myth that an ageing population is bad for us, when it is in fact the opposite.

As such, we are confident in our wording as expressed.

2. Page 6 line, 104, it seems the word ‘services’ is missing

RESPONSE #3: Corrected.

3. Kindly provide references for the few studies “Few studies have investigated the relationship between socio-economic conditions and fertility among nations”(page 6 line 110-112).

RESPONSE #4: We have added Janowitz [13] and Bollen et al. [14].

4. Although the background is well situated in the SDGs, critical issues are missing

a. The authors have failed to expand the literature review on infant mortality and fertility rate

RESPONSE #5: We chose to pose hypotheses based on evidence from existing research about the link between fertility and infant mortality.

However, we have elected to expand the justification and references in the Introduction with the following:

“Infant mortality has been linked to increased fertility due to the ‘insurance’ or ‘replacement’ effect [15, 16], and child morbidity and mortality are themselves exacerbated by higher household sizes (i.e., number of individuals per household) [17-23].”

b. They have also failed to situate the study in a given framework. For example, the proximate determinants model could be used

RESPONSE #6: As we stated in Response #5, we have updated our hypothesis with appropriate citations about the well-established link. While Bongaarts {Bongaarts, 1978 #17209}was examining family-level determinants and individual choices made within certain socio-economic contexts, we acknowledge that citing Bongaarts study is wise, so we have included it in the new text highlighted in Response #5.

RESPONSE #7: Most of the time we have taken to revise the manuscript has been dedicated to overhaul the dataset almost completely by:

i. obtaining the most complete, up-to-date data

ii. improving the data in certain modelling phases by obtaining better indicators of particular variables,

iii. adding more countries to the sample, and

iv. providing a multiple imputation procedure to increase the sample size of countries by robustly imputing some missing data.

We outline these changes briefly here.

• Total fertility is now the mean from 2010 to 2020 (from the World Bank) [24];

• We replaced school completion data with the mean years of education of women aged 15–19 from the Demographic and Health Surveys [25];

• We combined Catholic and Muslim adherents data per country from the Association of Religion Data Archives [26] (previously, we only include Catholics, despite evidence of higher fertility in Muslim-majority nations in Sub-Saharan Africa) [27];

• We now use the newest estimates of infant mortality (2020) from the World Health Organization [28];

• We replaced the poverty variables with the net personal wealth of the bottom 50% from the World Inequality Database [29] as a better measure of mean poverty (including debt);

• We replaced conflict-related deaths with battle-related deaths (World Bank) [24];

• We updated the percentage of household with three generations residing, mean number of household members, and all the family-planning availability variables (access) from the Demographic and Health Surveys Program [25] and the Multiple Indicator Cluster Surveys [30].

• The new sample of countries in the raw dataset is now 64, yet there were still some countries missing data (up to 19% in some variables). We therefore applied multiple imputation by chained equations to maintain the full sample of 64 countries to improve our strength of inference and robustness of results.

• We re-ran all models (general linear/mixed-effects, general least-squares, boosted regression trees) with these new data. Our conclusions hold, yet we did find a few small differences (e.g., the inclusion of household size as an important explanatory variable, and the counter-intuitive positive relationship between family-planning quality and fertility – discussed in the main text).

6. It will also be great fill the STROBE checklist and add it as a supplementary file.

RESPONSE #8: We have not included a STROBE checklist because our study did not directly involve human participants. We did not do any trials, did no patient interventions, and required no ethics approval to access publicly available data.

7. The first part of the results could be moved to the methods section (page 11, line 230-241)

RESPONSE #9: We respectfully disagree. These are important results to help interpret the subsequent modelling outputs.

RESPONSE #10: Please see Response #6. There is a major disconnect between the family/individual-level proximate determinants model with our national-scale investigation. We argue that we have provided an expansive justification for posing the hypothesis and interpreting the results, especially in the revision with a broader coverage of the literature.

RESPONSE #10: We have now added a ‘Strengths and limitations’ section as recommended.

RESPONSE #11: We have addressed this comment at length in Response #2.

11. The conclusions and policy implications are not explicitly discussed. This should be the thrust of the paper. A sub-heading/section should be created to discuss this also.

RESPONSE #12: We contend that while our results have strong policy implications that we discuss at the end of the paper, our manuscript does not analyse policies per se. Furthermore, policies are generally rolled out at subnational scales, and so require bespoke, context-specific language and legislation to be effective. We cannot possibly prescribe such an array of national and sub-national bespoke policies, even if we were policy experts. However, our results do broadly support the notion that improving family planning and reducing child mortality will have some of the greatest benefits to low- and middle-income nations in the future, regardless of what policies are invoked to achieve those broad aims.

References

1. IPCC. AR6 Climate Change 2021. UK: Cambridge Univ Press, 2021.

2. Bradshaw CJA, Ehrlich PR, Beattie A, Ceballos G, Crist E, Diamond J, et al. Underestimating the challenges of avoiding a ghastly future. Front Conserv Sci. 2021;1:9. doi: 10.3389/fcosc.2020.615419.

3. Wackernagel M, Lin D, Evans M, Hanscom L, Raven P. Defying the footprint oracle: implications of country resource trends. Sustainability. 2019;11(7):2164-. doi: 10.3390/su11072164.

4. Wackernagel M, Hanscom L, Jayasinghe P, Lin D, Murthy A, Neill E, et al. The importance of resource security for poverty eradication. Nat Sustain. 2021;4:731-8. doi: 10.1038/s41893-021-00708-4.

5. Dasgupta P. The Economics of Biodiversity: The Dasgupta Review. London: HM Treasury, 2021.

6. ourworldindata.org/grapher/future-population-projections-by-country?country=BRA~USA~JPN~ITA~KOR.

7. Raj A, Ghule M, Battala M, Dasgupta A, Ritter J, Nair S, et al. Brief report: parent–adolescent child concordance in social norms related to gender equity in marriage – findings from rural India. J Adolesc. 2014;37(7):1181-4. doi: 10.1016/j.adolescence.2014.08.006.

8. Bradshaw CJA, Brook BW. Implications of Australia's population policy for future greenhouse-gas emissions targets. Asia Pac Pol Stud. 2016;3:249-65. doi: 10.1002/app5.135.

9. ourworldindata.org/age-structure.

10. Bradshaw CJA, Brook BW. Human population reduction is not a quick fix for environmental problems. Proc Natl Acad Sci USA. 2014;111(46):16610-5. doi: 10.1073/pnas.1410465111.

11. probonoeconomics.com/News/the-economic-value-of-volunteering.

12. brookings.edu/blog/up-front/2018/06/19/refugees-are-a-win-win-win-formula-for-economic-development.

13. Janowitz BS. An empirical study of the effects of socioeconomic development on fertility rates. Demography. 1971;8(3):319-30. doi: 10.2307/2060620.

14. Bollen KA, Glanville JL, Stecklov G. Socioeconomic Status and Class in Studies of Fertility and Health in Developing Countries. Annual Review of Sociology. 2001;27:153-85.

15. van Soest A, Saha UR. Relationships between infant mortality, birth spacing and fertility in Matlab, Bangladesh. PLoS One. 2018;13(4):e0195940. doi: 10.1371/journal.pone.0195940.

16. Bongaarts J. A framework for analyzing the proximate determinants of fertility. Pop Dev Rev. 1978;4(1):105-32. doi: 10.2307/1972149.

17. Bradshaw CJA, Otto SP, Mehrabi Z, Annamalay AA, Heft-Neal S, Wagner Z, et al. Testing the socioeconomic and environmental determinants of better child-health outcomes in Africa: a cross-sectional study among nations. BMJ Open. 2019;9(9):e029968. doi: 10.1136/bmjopen-2019-029968.

18. Annim SK, Awusabo-Asare K, Amo-Adjei J. Household nucleation, dependency and child health outcomes in Ghana. J Biosoc Sci. 2015;47(5):565-92. doi: 10.1017/S0021932014000340.

19. Root G. Population density and spatial differentials in child mortality in Zimbabwe. Soc Sci Med. 1997;44(3):413-21. doi: 10.1016/S0277-9536(96)00162-1.

20. Anand A, Roy N. Transitioning toward Sustainable Development Goals: the role of household environment in influencing child health in Sub-Saharan Africa and South Asia using recent demographic health surveys. Front Pub Health. 2016;4:87. doi: 10.3389/fpubh.2016.00087.

21. Wolfe BL, Behrman JR. Determinants of child mortality, health, and nutrition in a developing country. J Dev Econ. 1982;11(2):163-93. doi: 10.1016/0304-3878(82)90002-5.

22. Lolekha S, Tanthiphabha W, Sornchai P, Kosuwan P, Sutra S, Warachit B, et al. Effect of climatic factors and population density on varicella zoster virus epidemiology within a tropical country. Am J Trop Med Hyg. 2001;64(3):131-6. doi: 10.4269/ajtmh.2001.64.131.

23. Greiner KA, Li C, Kawachi I, Hunt DC, Ahluwalia JS. The relationships of social participation and community ratings to health and health behaviors in areas with high and low population density. Soc Sci Med. 2004;59(11):2303-12. doi: 10.1016/j.socscimed.2004.03.023.

24. data.worldbank.org.

25. dhsprogram.com.

26. thearda.com.

27. Westoff CF, Bietsch K. Religion and Reproductive Behavior in Sub-Saharan Africa. DHS Analytical Studies No. 48. Rockville, Maryland, USA: ICF International, 2015.

28. who.int/data/gho.

29. wid.world/data.

30. mics.unicef.org.

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PLoS One. doi: 10.1371/journal.pone.0280260.r003

Decision Letter 1

Joshua Amo-Adjei

26 Dec 2022

Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations

PONE-D-22-06894R1

Dear Dr. Judge,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have satisfactorily addressed all the major issues I raised in the previous version of the manuscript. Subject to the Editor's decision, the manuscript is potentially acceptable for publication at this stage.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Abdul-Aziz Seidu

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PLoS One. doi: 10.1371/journal.pone.0280260.r004

Acceptance letter

Joshua Amo-Adjei

30 Jan 2023

PONE-D-22-06894R1

Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations

Dear Dr. Judge:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Associated Data

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

Supplementary Materials

S1 Fig. Regionalization map.

(EPS)

Click here for additional data file.^{(36.4MB, eps)}

S2 Fig. Theme 1 results—availability of family planning.

(TIFF)

Click here for additional data file.^{(1.1MB, tiff)}

S3 Fig. Theme 2 results—quality of family planning.

(TIFF)

Click here for additional data file.^{(1.1MB, tiff)}

S4 Fig. Theme 5 results—mortality.

(TIFF)

Click here for additional data file.^{(1.2MB, tiff)}

S5 Fig. Theme 6 results—socio-economics.

(TIFF)

Click here for additional data file.^{(488.5KB, tiff)}

S6 Fig. Final phase results.

(TIFF)

Click here for additional data file.^{(1.9MB, tiff)}

S1 Table. Variable correlation matrix.

(DOCX)

Click here for additional data file.^{(14.1KB, docx)}

S2 Table. Theme 1 results—availability of family planning.

(DOCX)

Click here for additional data file.^{(16.5KB, docx)}

S3 Table. Theme 2 results—quality of family planning.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S4 Table. Theme 5 results—mortality.

(DOCX)

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

S5 Table. Theme 6 results—socio-economics.

(DOCX)

Click here for additional data file.^{(14.2KB, docx)}

S1 File. Description of indicators within each index.

(DOCX)

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

Attachment

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Data Availability Statement

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PERMALINK

Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations

Corey J A Bradshaw

Claire Perry

Melinda A Judge

Chitra M Saraswati

Jane Heyworth

Peter N Le Souëf

Roles

Abstract

Introduction

Methods

Data

Analyses

Data preparation

Multiple imputation by chained equations

Linear models

Boosted regression trees

Generalised least-squares models

Results

Fig 1.

General linear and linear mixed-effects models

Table 1. Candidate general linear models of the highest-ranked variable from each theme (1. availability of family planning, 2. quality of family planning, 3. education, 4. religion, 5. mortality, 6. socio-economics) in relation to variation in fertility among 64 low- and middle-income countries.

Boosted regression trees

Fig 2.

Fig 3. Predicted fertility as a function of variation in infant mortality (raw data from 64 countries for which both variables were available superimposed onto boosted regression tree relationship).

General least-squares models

Table 3. General least-squares models of the highest-ranked variable from each theme (i. family-planning availability, ii. family-planning quality, iii. education, iv. religion, v. mortality, vi. socio-economics) in relation to variation in fertility among 64 low- and middle-income countries.

Discussion

Strengths and limitations

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Joshua Amo-Adjei

Roles

Author response to Decision Letter 0

Decision Letter 1

Joshua Amo-Adjei

Roles

Acceptance letter

Joshua Amo-Adjei

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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