Introduction
The World Health Organization (WHO) has identified birth interval length (the period between two consecutive live births) as a critical determinant of child mortality risks, recommending that women space their births between three and five years apart to reduce health risks to children and mothers (WHO
2007). This recommendation is based on the findings that intervals shorter than 36 months and longer than 60 months are associated with an elevated risk of infant death and other adverse outcomes (Conde-Agudelo et al.
2012; Hobcraft et al.
1985; Rutstein
2005). The relationship between short birth intervals, in particular, and mortality has been remarkably consistent, having been demonstrated repeatedly in a variety of developmental contexts across time and space (Becher et al.
2004; Cleland and Sathar
1984; Curtis et al.
1993; Miller et al.
1992; Millman and Cooksey
1987; Nault et al.
1990; Palloni and Millman
1986; Pebley et al.
1991; Ronsmans
1996; Whitworth and Stephenson
2002). Despite the large body of literature supporting these long-standing conclusions, recent evidence from studies of other perinatal outcomes has called the importance of birth spacing for infant health into question (Ball et al.
2014; Class et al.
2017; Hanley et al.
2017). Identifying whether birth intervals are in fact an important determinant of perinatal outcomes requires confronting two significant shortcomings in the current body of literature: a failure to address potential estimation bias from unmeasured confounding, and a dearth of international comparisons.
Much of the previous literature on the relationship between birth intervals and infant mortality has not adequately addressed the issue of residual confounding by unobservable characteristics. Endogeneity is always a concern when studying the effects of fertility behavior on children’s outcomes (see, e.g., Angrist and Evans
1998; Angrist et al.
2010; Rosenzweig and Wolpin
1980), and this is no different when studying the effects of birth spacing. Unobserved maternal heterogeneity can easily bias estimates of fertility’s effects on child health. The importance of this issue has recently come to the fore: several studies of mothers in affluent countries have shown that after unobserved compositional differences between women are accounted for, birth intervals seem to be inconsequential for children’s perinatal outcomes, such as birth weight, the risk of preterm birth, and being small for gestational age (Ball et al.
2014; Class et al.
2017; Hanley et al.
2017). These research findings call into question whether birth intervals really matter for perinatal outcomes at all (Klebanoff
2017). At the same time, recent research on low-income populations has shown that even after unobserved maternal heterogeneity is adjusted for, birth intervals are still highly consequential for infant mortality in high-mortality populations, such as Bangladesh and sub-Saharan Africa (Kozuki and Walker
2013; Kravdal
2018; Molitoris
2018b).
Because the extant literature is largely composed of case studies, it has been difficult to determine the extent to which differences between findings have been due to methodologies, sample selection procedures, or contextual factors. The primary goal of this study is therefore to investigate how the relationship between preceding birth intervals (the duration of time between the births of the older preceding sibling and the index child) and infant mortality varies across developmental contexts while applying uniform methods that can minimize residual confounding from unobserved heterogeneity. The benefit of a standardized comparative approach is that it allows us to shed light on both the average effects of birth interval length on infant mortality and also whether the importance of birth intervals varies according to contextual conditions. An international comparison may help us to reconcile the apparently discrepant findings in the literature and provide benchmarks for knowing when increasing birth spacing may or may not be a relevant intervention for reducing infant mortality.
Our study addresses the aforementioned issues by using data from 77 countries and more than 200 waves of the Demographic and Health Surveys (DHS). First, we account for the probable endogenous relationship between birth spacing and infant mortality by estimating within-family linear probability models. These models can account for unobservable maternal factors, such as maternal health or shared frailty within the sibling group, which may be correlated with both interval length and infant mortality risks. Second, we explore how the relationship between birth intervals and infant mortality risks varies both within and between populations in order to identify whether specific groups of mothers drive any observed association. Finally, we link our estimates of birth intervals’ effects on infant mortality to several macro-level indicators of development in order to understand the conditions under which birth intervals are more or less important for child survival.
Birth Intervals and Adverse Outcomes: Mechanisms and Findings
A detailed description of the theoretical mechanisms linking preceding birth intervals to children’s outcomes can be found elsewhere (Conde-Agudelo et al.
2012), but we briefly outline some of the leading explanations for why short birth intervals may be detrimental in some contexts but not in others. These mechanisms, which are not mutually exclusive, are maternal depletion, infection transmission, and sibling competition.
The maternal depletion hypothesis argues that shorter birth intervals do not allow women to fully physically recuperate from the previous pregnancy, which subsequently results in suboptimal fetal development and a higher risk of mortality for the child born following the short interval (Winkvist et al.
1992). In a context of chronic, continuous, and sustained foot shortages, a woman’s body prioritizes its own well-being over that of the fetus in distributing energy and nutrients (Ellison
2003; Peacock
1991). Such a physiological response is thought to preserve a woman’s potential for future reproduction as well as for lactation. While research continues to explore specifically what is depleted by one pregnancy and not sufficiently restored by the next (e.g., fat, micronutrients, muscle mass), some facts are well understood. For example, folate (vitamin B
9), which is critical for the growth and development of the fetus and is generally replenished in the postnatal period, is less likely to return to optimal levels during shorter intervals (Greenberg et al.
2011).
Infection transmission is the second mechanism that may link birth intervals to infant mortality risks. The horizontal transmission hypothesis holds that closely spaced births will place the younger of the siblings at a greater risk of mortality (Boerma and Bicego
1992). The younger sibling will be exposed to a similar set of diseases as the older sibling while also having a less-developed immune system, which will increase the ease of transmission from the older to the younger sibling. The weaker immune system of the latter can also increase the lethality of infectious diseases. Some evidence indicates that for certain communicable childhood diseases, such as measles, secondary infections acquired by an index child from their older sibling tend to have significantly higher case fatality rates (Aaby et al.
1984,
1986; Garenne and Aaby
1990).
The final mechanism linking intervals to mortality is sibling competition, which implies that closely spaced children are more likely to compete for the same resources, such as parental time and investment. Generally, competition for most resources would not be so much a result of the interval length per se but rather a result of an increase in family size, leading to a decrease in parental attention and investment in the first years of life for the index child. However, direct competition for one critical resource—breastmilk—would be directly related to the length of a birth interval. Some evidence from low-income countries suggests that breastfeeding-pregnancy overlap is not uncommon (Boerma and Bicego
1992; Molitoris
2018a; Ramachandran
2002) and may result in a lower quality and quantity of breastmilk for the child born following the interval, leading to diminished neonatal growth (Marquis et al.
2002,
2003).
Our discussion thus far has centered on mechanisms that would explain why
shorter preceding birth intervals may cause adverse perinatal outcomes. This focus has been intentional given that the literature on the topic has overwhelmingly shown that shorter intervals are associated with higher rates of mortality, stillbirth, low birth weight, and other poor outcomes. However, a smaller literature shows that
long intervals (i.e., longer than 60 months) are also disproportionately associated with higher risks of adverse maternal outcomes, such as preeclampsia and eclampsia, which are known to be associated with fetal loss and preterm birth (Conde-Agudelo and Belizán
2000; Conde-Agudelo et al.
2006; Skjærven et al.
2002; Zhu et al.
1999). Why longer intervals would be detrimental has not yet been firmly established, but one explanation—maternal regression—is that the longer a woman goes without conceiving a subsequent child, the more her physiology (and consequently her perinatal outcomes) resembles that of a woman during her first pregnancy (Zhu et al.
1999). Nevertheless, it is important to recognize that the exposure to intervals beyond 60 months is much smaller than the exposure to intervals shorter than, say, 24 months. In developing countries, approximately 25 % of births occur within 24 months of the preceding birth, but only about 6 % of births occur after 60 months (Rutstein
2005). Short birth intervals therefore pose a considerably greater risk in most populations.
The literature has consistently found that short interbirth intervals are predictive of adverse infant outcomes, but this is not a universal finding. Some recent studies of high-income populations in Sweden, Canada, and Australia have found that when controlling for unobserved maternal heterogeneity via sibling fixed effects, short birth intervals did not lead to higher risks of low birth weight, being small for gestational age, or preterm birth (Ball et al.
2014; Class et al.
2017; Hanley et al.
2017), suggesting that the apparent relationship between interval length and children’s outcomes may be attributable to the nonrandom distribution of birth intervals across mothers. Nevertheless, other recent research on infant and child mortality using the same statistical approach has found quite different results. Two studies of poor, high-mortality populations—specifically, nineteenth century Stockholm, Sweden, and contemporary Bangladesh—have shown that shorter birth intervals increased the risk of neonatal, postneonatal, and child mortality (Molitoris
2017,
2018b). Furthermore, the latter two studies presented results that may explain the discrepancy in findings mentioned earlier. First, the effects of birth interval length on mortality risks decreased over time as the overall level of mortality declined in Sweden (Molitoris
2017). Second, even within a high-mortality context, the size of the effects of interval length on mortality varied inversely with the educational level of the mother in Bangladesh (Molitoris
2018b).
Taken together, all these findings may fit into the same picture. Given the mechanisms outlined earlier in this section, one should expect that as economic and epidemiological conditions improve, short birth intervals should become a less significant predictor of infant mortality. Maternal depletion, infection transmission, and resource competition should all become relatively less important as the general nutrition and health of the population improves, thereby making birth intervals a weaker determinant of infant mortality. To examine whether this is indeed the case, we apply uniform statistical methods that can account for unobserved heterogeneity to data from a variety of low- to middle-income contexts, and we explicitly examine whether the association varies across their respective levels of development.
Methods
To analyze the effects of birth spacing on infant mortality, we estimated the following linear probability model:
$$ {Y}_{ij}={S}_{ij}{\upbeta}_{1, ij}+{\mathbf{X}}_{ij}{\upbeta}_{k, ij}+{\uptheta}_j+{\upvarepsilon}_{ij}. $$
(1)
The dependent variable,
Y, is binary and indicates whether child
i of mother
j died in the first year of life. Our main independent variable,
S, is the length of the preceding interbirth interval (i.e., the time between the birth of the older adjacent sibling and the birth of the index child). We treated it as a continuous variable with a quartic functional form in order to account for the possibility of a nonlinear relationship between interval length and mortality risks (Hobcraft et al.
1985; Rutstein
2005) (see Fig.
A12 in the online appendix for evaluation of functional form). Because a major goal of this study is to provide comparable estimates across many populations, we adopted parsimonious models that control for basic demographic characteristics that may vary across siblings. The controls,
X, include the sex of the index child, (centered) birth year, survival status of the previous child at the time of the index child’s birth, and birth order. Summary statistics of the model’s covariates may be found in Table
1.
Table 1
Distribution of index children’s selected characteristics
Infant Deaths | 369,227 | 8.1 | | | |
Preceding Interval (in months) |
<12 | 147,128 | 3.2 | 210.1 | 0.84 | 0.08 |
12–14 | 265,978 | 5.8 | 156.5 | 1.09 | 0.07 |
15–17 | 280,031 | 6.1 | 124.6 | 1.34 | 0.07 |
18–20 | 344,059 | 7.5 | 104.7 | 1.59 | 0.07 |
21–23 | 474,945 | 10.4 | 92.9 | 1.84 | 0.07 |
24–26 | 533,548 | 11.7 | 81.5 | 2.08 | 0.07 |
27–29 | 423,655 | 9.3 | 75.5 | 2.33 | 0.07 |
30–32 | 338,117 | 7.4 | 67.5 | 2.58 | 0.07 |
33–35 | 301,998 | 6.6 | 59.5 | 2.83 | 0.07 |
36–38 | 265,019 | 5.8 | 53.3 | 3.08 | 0.07 |
39–41 | 195,262 | 4.3 | 51.7 | 3.33 | 0.07 |
42–44 | 149,155 | 3.3 | 50.4 | 3.58 | 0.07 |
45–47 | 128,310 | 2.8 | 44.9 | 3.83 | 0.07 |
48–50 | 112,670 | 2.5 | 41.1 | 4.08 | 0.07 |
51–53 | 86,132 | 1.9 | 41.0 | 4.33 | 0.07 |
54–56 | 69,504 | 1.5 | 40.9 | 4.58 | 0.07 |
57–59 | 62,972 | 1.4 | 38.7 | 4.83 | 0.07 |
60–62 | 57,423 | 1.3 | 37.6 | 5.08 | 0.07 |
63–65 | 44,476 | 1.0 | 38.6 | 5.33 | 0.07 |
66–68 | 37,336 | 0.8 | 37.2 | 5.58 | 0.07 |
69–71 | 34,062 | 0.8 | 36.9 | 5.83 | 0.07 |
72–74 | 31,416 | 0.7 | 36.3 | 6.08 | 0.07 |
75–77 | 25,078 | 0.6 | 36.6 | 6.33 | 0.07 |
78–80 | 20,942 | 0.5 | 36.5 | 6.58 | 0.07 |
81–83 | 19,617 | 0.4 | 38.2 | 6.83 | 0.07 |
84+ | 115,295 | 2.5 | 37.2 | 8.11 | 0.83 |
Sex |
Male | 2,328,349 | 51.0 | 85.5 | 2.73 | 1.50 |
Female | 2,235,779 | 49.0 | 76.1 | 2.74 | 1.50 |
Survival Status of Previously Born Sibling |
Alive | 3,868,540 | 84.8 | 63.7 | 2.82 | 1.51 |
Died | 695,588 | 15.2 | 176.5 | 2.27 | 1.32 |
Birth Order |
2 | 1,140,772 | 25.0 | 86.1 | 2.58 | 1.40 |
3 | 1,149,562 | 25.2 | 71.0 | 2.85 | 1.60 |
4 | 803,513 | 17.6 | 75.4 | 2.81 | 1.54 |
5 | 548,504 | 12.0 | 80.4 | 2.78 | 1.50 |
6 | 368,638 | 8.1 | 85.2 | 2.73 | 1.46 |
7 | 239,333 | 5.2 | 90.2 | 2.70 | 1.42 |
8+ | 313,806 | 6.9 | 100.9 | 2.60 | 1.35 |
Maternal Education |
No education | 2,094,677 | 45.9 | 101.1 | 2.63 | 1.36 |
Primary | 1,635,935 | 35.9 | 73.2 | 2.75 | 1.52 |
Secondary | 713,587 | 15.6 | 47.2 | 2.93 | 1.70 |
Tertiary | 118,939 | 2.61 | 32.1 | 3.08 | 1.87 |
Missing/unknown | 990 | 0.02 | 82.8 | 2.66 | 1.47 |
UN Subregion |
Caribbean | 166,187 | 3.6 | 61.55 | 2.64 | 1.57 |
Central America | 155,316 | 3.4 | 57.66 | 2.64 | 1.52 |
Central Asia | 36,549 | 0.8 | 56.58 | 2.87 | 1.73 |
Eastern Africa | 782,653 | 17.2 | 88.55 | 2.73 | 1.37 |
Middle Africa | 208,658 | 4.6 | 79.34 | 2.76 | 1.42 |
Northern Africa | 289,526 | 6.3 | 81.02 | 2.66 | 1.55 |
South America | 533,459 | 11.7 | 68.58 | 2.83 | 1.73 |
Southeastern Asia | 502,336 | 11.0 | 72.75 | 2.88 | 1.68 |
Southern Africa | 66,759 | 1.5 | 61.15 | 3.26 | 1.76 |
Southern and Eastern Europe | 689,361 | 15.1 | 85.10 | 2.62 | 1.39 |
Southern Asia | 8,948 | 0.2 | 44.14 | 3.22 | 1.85 |
Western Africa | 939,886 | 20.6 | 98.16 | 2.75 | 1.35 |
Western Asia | 184,490 | 4.0 | 54.91 | 2.41 | 1.47 |
|
N
| Mean | SD | Min. | Max. |
Birth Year | 4,564,128 | 1990.67 | 10.48 | 1952 | 2014 |
Preceding Interval (in years) | 4,564,128 | 2.73 | 1.50 | 0.50 | 9.92 |
Most previous studies on this topic in low-income countries have not addressed the probable endogeneity of birth interval length when studying its effects on infant health. Interval length may be correlated with a host of characteristics that may be unobserved, such as maternal breastfeeding preferences or health behaviors, and may themselves influence the probability of infant mortality. Recent work has called attention to the importance of accounting for unobserved factors that may bias estimates of the effect of birth spacing on child outcomes (Ball et al.
2014; Barclay and Kolk
2017; DaVanzo et al.
2008; Kravdal
2018; Molitoris
2017,
2018b). We therefore partitioned the error term into a mother-specific component, θ, and an individual-specific component, ε, by subtracting the within-mother means of all variables from their observed values. This allowed us to estimate within-family models by controlling for sibling fixed effects (FE). Thus, our models compared children born to the same mother. Our results therefore should not be driven by unobserved, time-invariant differences between mothers that correlate with interval length, such as religious affiliation; ever-born number of children; age at first birth; ethnicity; country and survey effects; or, insofar as it is time-invariant, socioeconomic status, among other factors. Sibling FE also allowed us to control for the shared propensity for infant mortality within a given family (i.e., shared frailty). Recent work has compared cousins for the same reasons mentioned earlier (Class et al.
2017), but this was not feasible in the present study because cousins cannot be identified in the DHS data.
The within-family approach is not without limitations, however. First, we were unable to control for any source of endogeneity that emerges as a result of time-varying unobserved heterogeneity that is not captured by birth order or birth cohort. With that in mind, our modelling strategy does, however, offer a more robust control strategy than has generally been applied. Second, the within-family approach necessarily restricted our analysis sample to only women with three or more births. However, because we are studying high-fertility populations, the problem this restriction poses for the generalizability of the findings is not severe. Nearly 77 % of all children in the DHS come from family sizes of three or more. Because we are interested in only higher-order births, our analysis actually captures the vast majority of infants who could be affected by birth spacing: one-child sibling groups do not contribute any observations to the universe of birth intervals, and two-child sibling groups contribute only one birth interval. In contrast, a three-child sibling group contributes twice as many birth intervals to the universe of birth intervals as a two-child group, a four-child group contributes three times as many, and so on. Given the high fertility in our data, we calculated that our focus on sibling groups with at least three children includes 91.5 % of the measurable birth intervals in the surveys. Finally, a within-family analysis will also disproportionately exclude more recent maternal cohorts (with respect to the interview date) who have not yet had three or more children, although it will not exclude women who gave birth at younger ages in older cohorts.
In our analysis, we first compared the between-family estimates (ordinary least squares (OLS)) with the within-family estimates (FE) using the pooled sample of surveys to identify whether the relationship between preceding interval length and infant mortality persists after minimizing residual confounding from maternal heterogeneity. We then stratified the sample by United Nations subregion (see Table
A6 in the online appendix for grouping) and maternal education to identify whether the relationship varies between or within populations. This exercise is valuable because it can highlight whether the aggregated patterns are being driven by a few exceptional parts of the world and can reveal whether infant mortality is more strongly linked to birth spacing in some groups than others. Based on the theoretical mechanisms described previously, we would expect that children born to women with less education would be more vulnerable to infection or resource scarcity than those born to more highly educated women. Recent evidence from Bangladesh has indeed shown this to be the case (Molitoris
2018b), and it is important to identify whether this finding is generalizable to the rest of the world. More precise targeting of vulnerable groups by family planning programs may be required in order to offset recent funding cuts to international aid organizations (Bingenheimer and Skuster
2017; Starrs
2017).
After estimating these models, we then adopted a comparative perspective. Once again using the within-family approach, we estimated the association between birth intervals and mortality for each country-cohort combination in the pooled DHS sample. Because country does not vary between siblings and therefore cannot be included as a covariate in the model, we estimated separate models for each country and included an interaction term between the preceding birth interval and the birth year of the index child. We then estimated the effect of increasing the interval from 12 to 24 months on infant mortality for each birth cohort with at least 30 observations in each country-cohort combination. Next, we linked the estimates to World Bank data to examine whether the effects of birth intervals vary according to the level of development, proxied using data on the IMR and total fertility rate (TFR) for each country-birth cohort combination. These two indicators were chosen because they serve as good general proxies for social and economic development, and information on these indicators was also consistently available across countries and years.
Discussion
Our study produced several important findings. First, we showed that the relationship between birth interval length and infant mortality in low- and middle-income countries persists even after a within-family methodology is applied to account for unobserved heterogeneity between mothers. We found that the probability of dying is much higher at intervals below 24 months, and this pattern was highly consistent across regions of the world. Second, we found no evidence that intervals longer than 60 months are associated with an elevated probability of dying. On the contrary, the evidence presented here suggests that the probability of dying either plateaus or continues to decline, albeit at a slower pace, at longer birth intervals. Finally, and most significantly, the results from our international comparison showed that the importance of birth spacing as a determinant of infant mortality declines at more advanced levels of development. These findings have a number of important implications.
First, in contrast to recent studies using the same approach to analyze perinatal outcomes in populations from high-income countries, our study found that birth spacing does indeed have significant implications for infant survival, especially when intervals are shorter than 24 months. Because we adopted a within-family design, this pattern cannot be explained by unobserved heterogeneity between mothers.
Second, our results only partially support the WHO recommendation for spacing births between three and five years apart. The largest improvements in the probability of survival consistently come from increasing spacing until at least 36 months. Where our findings differ from the current recommendation is that we found little evidence that
longer birth intervals will be detrimental for infant mortality. In most of our analyses, the probability of infant mortality plateaued at intervals of 36 to 48 months or even continued to decline at intervals longer than 48 months. In some of the UN subregions, we found evidence of a reversal in mortality risks followed by a continued decline, but these increases were often statistically indistinguishable from zero or were so slight as to be of little practical significance. Thus, although our results certainly support the idea of diminishing returns to longer spacing for mitigating infant mortality risks, they do not consistently support any upper bound for safe spacing. This finding is in line with recent work on low-income countries that has come to the same conclusion (Kozuki and Walker
2013), suggesting that guidelines for optimal spacing may need to be revised.
Third, in our international comparison, we showed that as the level of development increases, as measured by the level of infant mortality and total fertility, the average beneficial effect of increasing a birth interval from 12 to 24 months approaches zero. This finding was entirely consistent with the variation that we observed within populations, which showed that birth intervals were less consequential for infant mortality at higher levels of maternal education.
Finally, because we showed that the strength of the relationship between birth interval length and infant mortality declines as mortality and fertility fall, the comparative results here help to reconcile the differences in findings reported elsewhere. Recent research using data from high-income populations with low mortality and fertility cast doubt on the importance of interpregnancy intervals for poor perinatal outcomes, such as preterm birth and low birth weight (Ball et al.
2014; Class et al.
2017; Hanley et al.
2017). These studies also applied the same sibling FE approach used in this study in order to account for unobserved maternal heterogeneity. Consequently, it was unclear whether the discrepant findings in those studies were due to differences in methodologies, data, or context. Based on our comparative findings, it seems to be the latter. The null results from high-income contexts are entirely consistent with the patterns observed in low-income contexts. As development progresses, birth intervals become less significant for child health. Considering the causal mechanisms involved with this relationship, it would indeed be a surprise to find that birth intervals are significant for infant survival in contexts where infant mortality is extremely rare. In such populations, the average level of nutrition is high, and the burden of infectious diseases is low. Furthermore, the wide availability of both antenatal and postnatal medical interventions can save many vulnerable young lives. In low-income populations, however, where childhood stunting and wasting may be common, infectious disease is prevalent, family sizes are larger, and access to any modern medical care may be limited, infant mortality may be more sensitive to all inputs, including factors such as birth spacing. An additional implication of this finding is that it underscores the importance of promoting exclusive breastfeeding, especially in high-mortality populations. Breastfeeding has many known benefits, one of which is its ability to inhibit conception when practiced exclusively for up to six months (Kennedy et al.
1989). The continued promotion of exclusive breastfeeding could both directly reduce the risk of infant mortality by providing infants with optimal nutrition and indirectly reduce the risk of infant mortality by shifting the distribution of birth intervals in a population away from shorter intervals. This may be especially important for the populations of Central Africa, where recent declines in the durations of breastfeeding and postpartum abstinence have been responsible for stalls or reversals in respective fertility transitions (Rogers and Stephenson
2018).
Our study does have limitations to consider. First, we were able to consider the effects of only birth intervals, not interpregnancy intervals (i.e., the duration from the birth of one child to the conception of the next), on the risk of infant mortality. In our view, defining birth spacing in terms of the interpregnancy interval has two advantages: (1) it may provide a slightly better representation of women’s recuperative potential, and (2) it may also help to avoid misattributing the effects of preterm birth to those of short birth intervals. Nevertheless, the measure certainly would have drawbacks if it is self-reported, as it is in the DHS. If systematic differences exist in the misreporting of pregnancy durations or miscarriages, this would introduce greater uncertainty into our main exposure of interest. Furthermore, although the DHS includes information on time and length of pregnancies for a subset of children listed in the birth histories (usually the most recent pregnancy within the five years preceding the survey), its use would exclude many cases from our analysis, and its reliability is less clear. In addition, its use would eliminate the possibility of controlling for sibling FE, which was a central goal of this study. Second, because our estimates are based on within-family models, we were able to show that the relationship between birth intervals and infant mortality in low-income contexts is not attributable to time-invariant compositional differences between women. Nevertheless, our approach cannot remove the influence of within-family
time-varying unobserved heterogeneity that is not captured by birth order or birth year and can be correlated with both interval length and infant mortality risks. Examples of such factors might include negative shocks to maternal health or socioeconomic resources in the household that could be correlated with infant mortality and reproductive behavior. In addition, the DHS data are based on self-reported fertility histories, which will undoubtedly introduce a certain degree of measurement error (Pullum and Becker
2014; Schoumaker
2014). However, we checked the sensitivity of our results to misreporting of births and found that the only difference in the findings appears to be that in the restricted sample, the estimated probability of dying plateaued after intervals longer than 48 months instead of continuing to decline. Finally, the development indicators that we drew from the World Bank refer to the national level of infant mortality and total fertility and thus may not closely correspond to the local conditions that the respondents to the survey actually experienced.
Nevertheless, our study also has important strengths. To our knowledge, this study is the first to apply a methodology that can account for unobserved heterogeneity in a comparative framework to identify the effects of birth spacing on infant mortality. In doing so, we confirmed many of the findings of previous research while also uncovering new details that can help revise general recommendations for birth spacing practices. By adopting a comparative approach, our study helps to reconcile some of the supposed inconsistencies in the current body of literature.
The findings presented here also offer several promising paths for future research. First, future research ought to focus more explicitly on identifying the causal mechanisms connecting birth interval length to infant mortality. Although our study sought to identify whether the relationship between birth spacing and mortality holds when adopting a robust control strategy, it was beyond its scope to identify which mechanisms facilitate this relationship. To explicitly identify the relative importance of the mechanisms linking short intervals to infant mortality, longitudinal data that include detailed information on factors such as biomarkers, household spending, and medical care would be required. Second, future work should also consider potentially different associations between birth spacing and mortality occurring at various times during infancy (i.e., early neonatal, late neonatal, postneonatal). Because the DHS is based on retrospective birth histories, deaths occurring in the early neonatal period may be especially prone to misreporting, and it is for this reason that we focused our study on infant mortality as a whole. Third, more comparative work that also includes wealthier populations would help to fill in the gaps regarding why birth intervals seem to matter a great deal in low-income contexts but much less in high-income contexts. Finally, it would be worthwhile to investigate whether the relationships between birth spacing and other outcomes are similarly moderated by the level of population health or other development indicators. Further comparative research may therefore help us to understand the conditions under which birth intervals matter for child health.
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