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# How conflict shapes the impact of infant and child mortality on fertility in the Democratic Republic of the Congo

Guerschom Mugisho

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Guerschom Mugisho. How conflict shapes the impact of infant and child mortality on fertility in the Democratic Republic of the Congo. Advances in Life Course Research, 2025, 65, pp.100692. 10.1016/j.alcr.2025.100692. hal-05193666

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# How conflict shapes the impact of infant and child mortality on fertility in the Democratic Republic of the Congo

Guerschom Mugisho<sup>a,b,c,1</sup> <sup>1</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Keywords:**

Infant mortality

Child mortality

Fertility

Armed conflict

Replacement effect

Democratic Republic of the Congo

## ABSTRACT

This study investigates the impact of infant or child mortality and its interaction with conflict exposure on fertility in the Democratic Republic of the Congo (DRC). Birth histories of 13,859 married women from the 2007 and 2013/2014 Demographic and Health Surveys were analyzed alongside conflict data from the Uppsala Conflict Data Program. A series of conditional frailty survival models were estimated. Results showed that women who lost an index or non-index child during or before the birth interval were at higher risk of giving birth. These findings provide evidence for an immediate volitional replacement effect, i.e., a deliberate or psychological effort to replace a lost child as soon as possible, as well as a long-term replacement effect reflecting the desire to achieve an ideal family size. The immediate replacement effect was more pronounced among women exposed to conflict, while the long-term effect was weaker. These results, which are robust to alternative measures of conflict, reflect how families respond to infant mortality and conflict. In the short run, conflict increases the urgency to replace deceased children, supporting the fast life history or bereavement theories. By contrast, in the long term, conflict disrupts reproductive planning—likely due to resource scarcity, uncertainty about the future, and other constraints—thereby supporting the economic theory of fertility. Short-term interventions should prioritize improving child health through nutrition and immunization, providing mental health support, and offering culturally sensitive reproductive counseling. In contrast, long-term interventions must focus on economic support and family planning programs to empower women in conflict zones.

## 1. Introduction

After nearly three decades of continuous conflict, the Democratic Republic of the Congo (DRC) had the third-highest total fertility rate in the world in 2017, with an average of 5.8 children born per woman. Additionally, it ranked among the ten countries with the highest infant mortality rate, which was 70 deaths per 1000 live births in the same year (Dimbuene et al., 2023; Sachs et al., 2023). With estimates ranging from one to approximately four million deaths between 1998 and 2004 (Kapend et al., 2020; Coghlan et al., 2006), the armed conflicts in the DRC have been among the deadliest in the world since World War II (Reyntjens, 2020). These conflicts continue to this day, but they peaked in 1996–1997 (the First Congo War) and 1998–2003 (the Second Congo War). The First Congo War occurred throughout the country, but the Second Congo War occurred primarily in Kivu, Province Orientale, and Katanga (Reyntjens, 2020). These wars took on the nature of both civil

and international wars. While adults were the primary victims in terms of direct deaths (Coghlan et al., 2006), children also died mainly as a result of the indirect effects of conflicts (Lindskog, 2016). The under-five mortality rate during the Second Congo War increased to 250 deaths per 1000 children in certain areas (Van Herp et al., 2003). Has the high infant and child mortality during or as a result of these armed conflicts impacted fertility in the DRC?

Examining the impact of infant (death before the first birthday) or child mortality (death between the first and fifth birthdays) and conflict on fertility is crucial for several reasons that should draw the attention of demographers and policymakers. First, it helps to explain the role of conflict and infant and child mortality on the high fertility still observed in the DRC. Second, it provides insights into how families adjust their reproductive behavior in response to child loss. This understanding helps policymakers design interventions to reduce fertility in high-mortality settings. Third, it helps policymakers identify and prioritize

<footnote><sup>a</sup> Paris 1 Panthéon-Sorbonne University –Demography Institute, France</footnote>

<footnote><sup>b</sup> Ecole des Hautes Etudes en Démographie, France</footnote>

<footnote><sup>c</sup> Université Evangélique en Afrique, Democratic Republic of the Congo</footnote>

<footnote>E-mail address: mguerschom@gmail.com.</footnote>

<footnote n="1">¹ Address: 5 cours des Humanités, CRIDUP, 93322 Aubervilliers, France</footnote>
assistance for families dealing with the combined effects of child loss and conflict pressures. This is crucial because fertility significantly impacts the well-being of women and households, particularly in very poor economies (Schindler & Brück, 2011). Finally, studying how conflict alters mortality and fertility patterns helps policymakers anticipate population growth or decline in affected regions (Heuveline & Poch, 2007).

The relationship between infant, child, or adult mortality and the timing of a subsequent birth has been studied by demographers through the concept of the replacement effect. This relationship has puzzled demographers for decades (Heuveline & Poch, 2007). Therefore, this topic has been extensively studied in both conflict-free and conflict-affected countries. For example, in Rwanda, Kraehnert et al. (2019) found that having lost a child during the genocide was associated with a higher risk of having a child and a higher number of births in the five years following the genocide. Similarly, a study by Schindler and Brück (2011) found that losing a child during the Rwandan genocide was also associated with a high risk of birth beyond five years after the genocide. Torrisi (2020) found that women who lost their children during periods of heightened conflict in Azerbaijan were more likely to have another child. Verwimp and Van Bavel (2005) found both high fertility and high infant mortality among Rwandan refugees in neighboring countries.

To study the replacement effect, the literature distinguishes between two types of children in a woman's reproductive trajectory: index child and non-index child. The index child is the child whose birth opens up an interval from which the timing of a subsequent birth or conception is counted, that is, the child whose survival status is examined with respect to a subsequent birth (Lindstrom & Kiros, 2007). The non-index child is a child of lower parity than the index child, i.e., whose death is considered in relation to fertility but not as the starting point of the reproductive interval. In studying the replacement effect, this categorization facilitates the distinction between the short-term and long-term replacement effects. The short-term replacement effect follows the death of an index or non-index child in the current interval.<fnref n="2" /> It reflects an effort to quickly replace the lost child (Defo, 1998). The long-term replacement effect is observed after the death of a non-index child before the birth of the index child. It corresponds to a sustained increase in fertility to compensate for earlier child mortality (Lindstrom & Kiros, 2007).

The existing empirical literature on the relationship between exposure to conflict, infant or child mortality, and fertility can be classified into four main categories. The first category, which does not contain any studies on the DRC, includes research that analyzes the effect of infant or child mortality on fertility (Lindstrom & Kiros, 2007; Defo, 1998). The second category comprises studies that have examined the impact of conflicts on infant or child mortality and child health, both in the DRC (Dagnelie et al., 2018; Lindskog, 2016) and in other contexts (Shemyakina, 2021; Wagner et al., 2018). The third category includes research investigating the effect of conflicts on fertility (Thiede et al., 2020; Blanc, 2004; Lindstrom & Berhanu, 1999). No studies in this category have specifically focused on the DRC. The fourth category encompasses studies analyzing the relationship between infant mortality during conflicts and fertility (Heuveline & Poch, 2007; Kraehnert et al., 2019; Nobles et al., 2015). Notably, no research within this category has concentrated on the DRC. Additionally, studies in this group have not distinguished between the short-term and long-term effect of infant or child death on fertility. The present study falls simultaneously within the first, third, and fourth categories of research outlined above, all of which lack any studies focused on the DRC. Its primary contribution lies in examining the roles of infant mortality, armed conflicts, and their interaction on both short-term and long-term fertility in the DRC.

Using data on women's birth histories from the two available Demographic and Health Surveys (DHS) in the DRC and longitudinal conflict data, this research addresses the following questions: (1) Does the death of a child influence the time to the next conception? (2) Is the change in time to conception following a child's death more or less pronounced among women exposed to conflict?

This study has a twofold objective. First, it aims to analyze the effect of infant and child mortality on short-term and long-term fertility in the DRC. Second, it analyzes the role of conflict exposure in the relationship between infant or child mortality and fertility.

This research contributes to the existing literature from theoretical, practical, and methodological perspectives.

From a theoretical standpoint, drawing on theories from multiple disciplines—economics, psychology, evolutionary biology, and demography—this study contributes to a deeper understanding of demographic transitions in conflict-affected settings. It offers insights into how families respond to the immediate and long-term effects of child loss, as well as how prolonged conflict shapes their reproductive decisions and fertility patterns over time.

On a practical level, the study enhances the literature by linking its findings to broader demographic and policy challenges in fragile states, particularly in conflict-affected regions such as the DRC. This study aligns with Sustainable Development Goal (SDG) 3 (Good Health and Well-Being) by providing valuable insights for policymakers addressing population growth and reproductive health in fragile contexts. Furthermore, it contributes to SDG 5 (Gender Equality and Empowerment of Women) by emphasizing the importance of gender-sensitive reproductive health interventions in conflict zones, thereby empowering women to make informed decisions. Additionally, the study supports SDG 16 (Peace, Justice, and Strong Institutions) by examining the impact of conflict on child mortality and fertility, while advocating for policies that improve healthcare access, reduce child mortality, and foster peacebuilding efforts in fragile settings.

Finally, from a methodological standpoint, this study employs innovative approaches that can make meaningful contributions to the field of conflict demography. First, it employs a robust analysis of parity transitions to account for unobserved heterogeneity among women. This approach offers a valuable framework for examining fertility dynamics in conflict-affected settings and can be adapted to other regions experiencing similar demographic and conflict-related challenges. A second methodological contribution lies in assigning a conflict exposure indicator to each woman, tailored to her birth trajectory, even though conflict exposure was initially captured as a geographic or contextual variable. This approach is particularly well-suited to data sources such as the Demographic and Health Surveys (DHS), which document women's birth histories and define interbirth intervals with monthly—or ideally, daily—precision.

## 2. Context

### 2.1. Sustained high fertility in DRC

With an average of over six children per woman from 1983 to 2013 (Fig. 1), the DRC is one of the countries experiencing a very slow fertility transition (Shapiro et al., 2017). Fertility trends are not predictable in the short term, although there has been a slight overall decline since 2001. Fertility in the DRC stabilized between 1984 (6.7 children per woman) and 1992 (about 6.6 children per woman). It increased slightly from 1993. During the First and Second Congo Wars, it remained at this level—about 6.7 children per woman according to retrospective DHS and UN estimates, and 7.3 according to the 1995 and 2001 MICS surveys. A very slight decline began in 2001. Romaniuk (2011) lists several factors that contributed to the high fertility observed in the DRC. First, the patrilineal kinship system creates a culture of high fertility, early marriage, and high marital stability. Second, postpartum abstinence and breastfeeding are losing ground in the cities. Women reported resuming

<footnote n="2">² The period beginning with the birth of the index child and ending with the subsequent birth.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Trends in the average number of children per woman in the DRC. *Note:* The total fertility rate (TFR) for periods away from the survey year has been calculated assuming a constant fertility schedule.
Fertility trends were calculated from the 2007 and 2013/2014 DRC DHS. Estimates for 1984, 1990, 1995, 2001, and 2013 are from the 1984 census, the United Nations, the 1995 and 2001 MICS surveys, and the 2013/2014 DHS report, respectively.</figcaption></figure>

sexual intercourse 4.2 months after childbirth in 2007 and 3.7 months after childbirth in 2013/2014. This has an impact on the birth interval, which was estimated at 30.5 months in 2007 and 30.4 months in 2013/2014. Third, modernity-related factors have a major impact on fertility. Modern contraceptive use remains very low among married women (7.8% in 2013/2014). The educational attainment of women is very low. According to the 2013/2014 DHS, only 2% of women have post-secondary education. This proportion was estimated at 1.4% in 2007. The urbanization rate remains low, with 43% of the urban population in 2007 and 2016 (World Bank, 2018; Romaniuk, 2011). Difficult socio-economic conditions, including high unemployment rates among both men and women, are potential socio-economic factors contributing to high fertility in the DRC (Shapiro et al., 2017; Romaniuk, 2011). Demographic factors such as a high ideal family size (6.3 children per woman in 2007 and 6.1 in 2013/2014), a persistently high unmet need for family planning (26.9% in 2007 and 27.7% in 2013/2014), and elevated levels of infant and child mortality may also help explain the persistently high fertility rates in the DRC (Bongaarts & Casterline, 2013).

## 2.2. Overall decline in infant and child mortality, with a slowdown during the first and second Congo Wars

Overall, the under-five and infant mortality rates decreased over time (Fig. 2). According to Easterlin (1996), this decline is the result of advances in health technology and vaccinations. It is also due to greater awareness of health risks and a willingness to seek medical care rather than relying on traditional healers. This decline has been more pronounced since 2001. This is consistent with the hypothesis of a continuing mortality transition (Lindskog, 2016; Romaniuk, 2011). Infant and child mortality rates stabilized between 1993 and 2000. This may be due to the direct or indirect effects of the First and Second Congo Wars. Most child and adult deaths during the DRC conflicts were indirect deaths (Coghlan et al., 2006). According to Lindskog (2016), these wars impacted under-five mortality in the DRC more through the deterioration of child health (postneonatal and child mortality) than through the deterioration of maternal health (neonatal mortality).

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Trends in infant and under-five mortality rates in the DRC. *Note:* The retrospective estimates of infant and child mortality in 2013 differ from those in the DHS report because the reference periods are different. The 2013/2014 DHS uses 2011 as the reference year. IMR: infant mortality rate (the probability of dying before the first birthday) ; U5MR: under-five mortality rate, UN=United Nations. The colors delineate the First Congo War (1996–1997) and the Second Congo War (1998–2003).
Mortality trends are from DHS DRC 2007 and 2013/2014. Estimates for 1984 and 2001 are from the 1984 census and the 2001 MICS survey, respectively.</figcaption></figure>
# 3. Theoretical/conceptual framework and hypotheses development

## 3.1. Understanding the link between infant/child mortality and fertility

Several theories have attempted to explain the relationship between infant mortality and fertility. I focused on three of them: the price theory, the life history theory, and the terror management theory. These frameworks provide complementary insights from economics, evolutionary biology, and psychology into how child mortality influences fertility. According to these theories, the death of a child can be associated with both increased and decreased fertility.

The price theory (Wolpin, 1997; Ben-Porath, 1976) assumes that children are economic goods in the sense that they have a cost and that parents know exactly how many of the total number of children will survive. Families are assumed to be rational agents who maximize their lifetime utility function as a function of the number of surviving children and a composite consumption good constrained by lifetime income. Their decision is to choose the number of births that maximizes their utility function. In this case, increasing child survival reduces the cost of children and increases their demand. In other words, increasing child mortality increases the cost of a child and decreases the demand for children. An increase in infant mortality increases fertility, as it is usually observed, only if fertility has an inelastic demand (Wolpin, 1997; Ben-Porath, 1976). In a general framework, the sign of the effect of child death on fertility depends on the relative strength of the replacement motive, which tends to increase fertility, and the income effect, which tends to decrease fertility (Kraehnert et al., 2019).

Second, life history theory states that organisms have a finite budget and must choose between the number and quality of their offspring. Increased fertility reduces the time and energy available to invest in each offspring, thereby increasing the risk of mortality (Lawson et al., 2012; Strassmann & Gillespie, 2002). This mechanism is close to the sibling competition hypothesis (Davanzo et al., 2008). When a child dies, the mother has more time and energy to invest in a new replacement child. The effect of an exogenous decrease in offspring mortality risk is determined by how it affects the trade-off between the number and quality of offspring. A decrease in mortality will tend to limit fertility by increasing the number of current offspring dependent on the mother, but if the cause of the decrease in mortality is an increase in available resources, fitness-maximizing organisms may respond by increasing fertility (Lawson et al., 2012; Strassmann & Gillespie, 2002).

Third, terror management theory suggests that awareness of mortality (mortality salience) creates existential anxiety. Individuals cope with this anxiety by adhering to cultural worldviews and seeking both symbolic and literal immortality (Burke et al., 2010; Greenberg et al., 1986). Childbearing is one way to achieve symbolic immortality, as it allows for the continuation of genetic and cultural legacies. When a child dies, some parents may seek to have more children to replace the deceased child and meet cultural or personal expectations (Fritsche et al., 2007). This is particularly common in cultures that value large families. Conversely, the death of a child can lead to decreased fertility. The trauma and existential distress may heighten fears of experiencing another loss, prompting parents to avoid additional pregnancies to prevent further emotional pain (Schwerdtfeger & Shreffler, 2009).

While theoretical frameworks suggest both an increase and decrease in fertility following child death, most empirical studies, however, indicate that the death of a child is associated with increased fertility. The majority of them have examined the impact of the index child's death on the timing of a subsequent birth (Defo, 1998; Park et al., 1998). Two mechanisms have been identified: the physiological effect and the volitional effect (Lindstrom & Kiros, 2007; Zhang, 1990). The physiological effect is an increase in fecundity and the associated risk of conception caused by the cessation of breastfeeding and the return of menstruation following the death of an index child (Ellison, 1995; Guz & Hobcraft, 1991; Wilmsen & Bongaarts, 1986). The volitional effect

consists of parents' desire to replace the deceased child immediately by shortening the time between the death of the index child and the next conception. It is difficult to disentangle the physiological from the volitional replacement effect based solely on data on the death of an index child in populations such as the one studied, where breastfeeding is a common practice. This difficulty stems from the fact that the death of a child results in the end of breastfeeding and return of menstruation (Lindstrom & Kiros, 2007), which shortens the time until the next conception. The death of a child as a result of conflict is thought to influence fertility through the volitional replacement effect (Kraehnert et al., 2019; Schindler & Brück, 2011).

Apart from the index child's death, the death of a non-index child may likewise influence fertility decisions. In this case, couples replace the deceased non-index child by shortening the current birth interval either unconsciously or in order to reach their ideal family size. This short-term replacement behavior is generally expected to occur in countries such as the DRC that are in the pre-transitional or early stages of demographic transition, i.e., where mortality has begun to decline (Adanikin et al., 2019; Lindstrom & Kiros, 2007; Defo, 1998). Accordingly, I hypothesize an immediate or short-term replacement effect, i.e., the death of an index or non-index child within the current interval would be associated with a shortening of the time to subsequent birth (Hypothesis H₁).

In addition to the immediate effects, the death of a child may similarly affect fertility decisions over the long term (Lindstrom & Kiros, 2007). This long-term replacement behavior may be driven by economic considerations, as outlined in price theory, psychological factors (as suggested by the terror management or bereavement theories), or by the expectation of high mortality (indirect replacement effect). The indirect replacement effect refers to the propensity of couples exposed to high infant mortality to have more children than their ideal family size in anticipation of possible future child deaths, as suggested by the insurance hypothesis (Bousmah, 2017; Zhang, 1990). The indirect replacement effect assumes that couples are rational homo demographicus. They wish to have a certain number of surviving children—the ideal number of children—and make decisions to achieve this (Zhang, 1990). The pursuit of target fertility is explained by the fact that children can be a source of future income for parents in old age (Wolpin, 1997; Cain, 1985). Thus, I also hypothesize a long-term replacement effect, that is, the death of a non-index child before the birth of the index child would be associated with a higher risk of subsequent birth (Hypothesis H₂).

## 3.2. Exploring fertility responses to infant and child mortality in crisis settings

The theories described in Section 3.1 are limited in capturing complex behaviors in protracted conflict settings. To understand the potential mechanisms linking conflict-induced child loss and fertility behavior, I incorporate pronatalist ideologies, fast-life history theory, bereavement theories, and the economic theory of fertility.

Sociocultural dynamics, such as pronatalist ideologies, mediate the impact of conflict-related child loss on fertility. In the short term, ethnic or nationalistic conflicts often generate pronatalist pressures, as communities seek to recover lost populations or to strengthen their ethnic identity. This behavior was observed among Iranian couples after the war with Iraq between 1980 and 1988, among Palestinian couples before the end of the twentieth century (Abu-Musa et al., 2008), and in post-genocide Rwanda (Kraehnert et al., 2019).

In addition, the fast life history theory, derived from life history theory, provides a framework for understanding why fertility might increase in response to high mortality caused by violence. It posits that mortality threats can lead to accelerated reproductive decisions (Nettle, 2010; Störmer & Lummaa, 2014). Conflict environments create conditions of uncertainty, insecurity, and elevated mortality, which trigger behaviors aligned with fast life history strategies, such as shortening birth intervals, among others (Mededović, 2019). From these
frameworks, I hypothesize that exposure to armed conflict would exacerbate the short-term replacement behavior (Hypothesis H₃).

Theories of bereavement offer additional explanations of the replacement effect in crisis and crisis-free contexts. Evidence suggests that losing loved ones in conflict has a significantly tougher recovery process compared to losses from non-conflict causes (Alburez-Gutierrez et al., 2024; Kristensen et al., 2012). Consequently, fertility responses vary between losing loved ones (siblings or offspring) in conflict and conflict-free contexts (Kraehnert et al., 2019). However, these responses may change over time. In the short term, the emotional trauma of losing a child often triggers replacement fertility, as parents seek to cope with grief and restore family completeness (Pepper & Nettle, 2013; Yeatman et al., 2013). Previous studies have shown that the negative impacts of bereavement on parental outcomes can last for several years or even longer after child loss (Zhao et al., 2020). Over the long term, chronic grief, and fear of additional loss may lead to reduced fertility, especially in settings where child mortality risks remain high (Nobles et al., 2015; Parker & Douglas, 2010). Furthermore, bereavement may induce stress-related health complications, such as an inability to carry a pregnancy to term and reduced coital frequency. These factors can disrupt hormonal balance and menstrual cycles, thereby delaying conception (James & Singh, 2018).

In conflict-free settings, prospective longitudinal studies linking fertility preferences to actual fertility showed that women who lose a pregnancy or a child before age five try to conceive again shortly, and many succeed (Beringer & Milewski, 2024; Plana-Ripoll et al., 2018). In conflict settings, however, this relationship may depend on how conflict affects proximate determinants of fertility, such as contraceptive use or availability and fertility preferences. In the short term, conflict disrupts healthcare systems and reduces access to contraception, often leading to unintended pregnancies and elevated fertility (Torrisi, 2024; McGinn et al., 2011). In the long term, prolonged exposure to insecurity or economic instability can potentially increase preferences for more children as a strategy for seeking security against potential child loss (Ahmed & Atiqul Haq, 2024), or shift fertility preferences toward having fewer children (Mourtada & Melnikas, 2022), as posited by the economic theory of fertility (Robinson, 1997).

From the perspective of the economic theory of fertility, conflict may shape replacement behavior following child loss by modifying the perceived costs and benefits of childbearing, influencing the quantity-quality tradeoff, and exacerbating economic uncertainty (Becker & Lewis, 1973). In stable environments, parents who experience child loss may engage in replacement behavior, having additional children to compensate for the loss (Nepal et al., 2023; Johnson, 1985). Conflict disrupts this dynamic by exacerbating economic instability, intensifying resource scarcity, and heightening uncertainty about the future (Lindstrom & Berhanu, 1999), as well as by restricting access to healthcare and contraception (Torrisi, 2024). Together, these factors raise the costs of childrearing. In addition, the erosion of educational and economic opportunities resulting from conflict may compel parents to reprioritize, shifting aspirations from larger families toward adaptive strategies centered on survival and immediate well-being (McLanahan, 2004; Caldwell, 1978).

These theoretical perspectives highlight that while immediate fertility increase as a response to crisis-related child loss is common, the long-term response depends on a combination of access to reproductive health services, sociocultural adaptation, and the socio-economic aftermath of conflict. Given that fertility remains high in the DRC, it's likely that exposure to armed conflict would amplify the long-term replacement behavior of child loss (Hypothesis H₄).

# 4. Data and methods

## 4.1. Data and study population

This study utilizes demographic data from the Demographic and

Health Surveys (DHS) and conflict data from the Uppsala Conflict Data Program Georeferenced Event Dataset (UCDP-GED). The DHS is representative at both national and provincial levels. A total of 9,995 and 18,827 women aged 15–49 at the time of the survey responded to the 2007 and 2013/2014 DHS, respectively, representing a combined sample of 28,822 women. Stratified sampling by province was employed. In urban areas, a two-stage sampling procedure was used: the first stage involved selecting statutory towns, followed by a second stage in which households were selected. In rural areas, a three-stage sampling procedure was implemented: the first stage involved selecting areas or sectors; the second stage focused on villages or chiefdoms; and the third stage involved selecting households. These surveys are described in detail in their respective reports (Ministry of Health & ICF International, 2014; 2007). In the birth history section, each woman was asked about every live birth she had experienced in her lifetime. For each child, she reported the name, sex, month and year of birth, survival status (alive or deceased), and, if applicable, age at death: in days (if the child died before 30 days), in months (if before 24 months), or in years in all other cases.

UCDP-GED provides georeferenced and dated data on conflicts worldwide since 1989 (Croicu & Sundberg, 2017). Conflict is defined as “the incidence of the use of armed force by an organized actor against another organized actor, or against civilians, resulting in at least 1 direct death in either the best, low or high estimate categories at a specific location and for a specific temporal duration” (Sundberg & Melander, 2013, p.2). The recorded information includes the conflicts and their identifiers, the start and end dates of each conflict, the actors involved, the region where the conflict occurred, the geographic coordinates of each conflict event site, and the number of fatalities, under the high, best, and low assumptions. In line with existing literature (Torrisi, 2024; Wagner et al., 2018), I selected fatalities corresponding to the best estimates. How these data were integrated into DHS is described in Section 4.3.2.

The study period runs from 1989 to the survey year. Only events (births, deaths) that occurred during this period were included because conflict data were available from 1989 onward, and to minimize recall bias. Analyses are restricted to women who have already had a live birth, because replacing a deceased child presupposes having experienced that child's birth and death. The focus was on married women, similar to other studies (Lindstrom & Kiros, 2007; Defo, 1998), for several reasons. First, married women are more likely to actively decide whether or not to have another child after the loss of a previous child, as childbearing decisions are often closely tied to the marital relationship. In contrast, unmarried women may face more unpredictable reproductive decisions influenced by external factors, such as social stigma (Jain & Mahmoodi, 2022) or unstable partnerships. Second, understanding the timing of reproductive decisions within the context of a marital union is crucial for accurate analysis. When women have experienced multiple unions, the timing and stability of these relationships can introduce complexities that are challenging to account for (Wiegand-Cruz & Salinas, 2025), especially when no data exists on the start and end dates of the unions. To ensure stable estimates, only transitions to parities 2–7 were considered. The sample selected for analysis consists of 13,859 women (Fig. A1).

## 4.2. Data quality assessment

Retrospective survey data on women's reproductive histories are prone to three types of errors: selection bias, errors in dating births and underreporting of deceased children, and errors in age at child death (Hill, 2017; Pullum et al., 2013). Due to data limitations, I cannot study the fertility histories of women who died or emigrated. I also cannot study the survivorship of children whose mothers have died or emigrated. This likely leads to migration or survivorship bias.

Two approaches were employed to assess the quality of birth reporting. First, I calculated and plotted the secondary sex ratio of male
to female births by birth year (Fig. A2). The sex ratio fluctuates over time, which can be interpreted in two ways.

On one hand, these fluctuations may reflect underreporting or overreporting of births. Viewed through this lens, both DHS surveys indicate a slight underrepresentation of female births, particularly during the Second Congo War. Additionally, the 2007 DHS reveals an overrepresentation of female births between 2000 and 2005. Such discrepancies may stem from errors in reporting the year of birth—for instance, births occurring in year *t* may have been misreported as taking place in year *t-1* or *t + 1*. They may also result from the omission of a live birth, especially in cases where the child died in infancy. Another potential source of error is misreporting the child's sex, whether by the interviewer or the respondent. Finally, failure to interview all households in the original sample may contribute to these inconsistencies (Hill, 2017). The overrepresentation of male births during specific periods, which may result from either the aforementioned errors or selective live births due to a preference for sons, can lead to an overestimation of the replacement effect. In contrast, the overreporting of female births may lead to an underestimation of the replacement effect, given the widespread preference for sons in African contexts (Garenne et al., 2023). Over the entire period, the sex ratios of male to female births are 1.03 and 1.015 in the 2007 and 2013/2014 DHS, respectively. These observed sex ratios could indicate sex-selective pregnancy loss, as male fetuses are more likely to be miscarried in settings of conflict and crisis (Valente et al., 2015).

On the other hand, given that the overall sex ratios are close to the expected norm of 1.05–1.06, the observed fluctuations in the secondary sex ratio during certain periods likely reflect random variation rather than systematic underreporting or overreporting of births.

Second, I compared DHS annual retrospective TFR estimates over a long period with MICS or Census estimates (Fig. 1). Overall, the TFR estimates provided by the United Nations and the 2007 and 2013/2014 surveys converged, supporting the quality of the estimates. These estimates are lower than those of the 1995 and 2001 MICS surveys. However, according to Shapiro and Tambashe (2001), the 1995 MICS survey overestimated fertility in 1995.

To assess the quality of infant and child mortality data, a two-step approach was used. First, I assessed possible errors in reporting children's age at death (internal assessment). Over-reporting deaths at 12 months or at ages ending in 0 or 5 are the most common errors in reporting the child's age at death (Hill, 2017). To assess the magnitude of this error, I plotted the distribution of deaths occurring within 19 years prior to the survey by age at death in months (Fig. A3). I found no significant over-reporting of deaths in the twelfth month or in months ending in 0 or 5. Second, I compared the infant and child mortality estimates from different sources (external assessment). Fig. 1 shows that the DHS 2007 and 2013/2014 retrospective annual estimates are comparable to the United Nations estimates, as well as estimates from the 1984 census and the 2001 MICS survey. In addition, estimates of under-5 mortality based on complete reproductive histories from DHS 2007 and 2013/2014 are similar. This consistency suggests the estimates are of acceptable quality and that recall bias in reporting child deaths may not be a major concern. However, under-five mortality estimates from the 2007 and 2013/2014 DHS were lower than those from the 1984 census and the 2001 MICS survey. If the latter estimates are more accurate, this underestimation of under-five mortality is more likely to underestimate the effect of a non-index child death on fertility.

## 4.3. Variables and their description

### 4.3.1. Explanatory variables measuring the replacement effect

Following other studies (Lindstrom & Kiros, 2007; Defo, 1998; Zhang, 1990), three time-varying dummy variables were defined to measure the immediate or long-term replacement effect of the deceased child. Only deaths before age five were examined, as under-five mortality is a key indicator of public health and policy effectiveness,

especially for vulnerable populations (Golding et al., 2017). The first variable (DeathICI) is assigned a value of 1 if the index child's death occurred between their birth and the subsequent conception (i.e., at least nine months before the month of the next birth<fnref n="3" />), and 0 otherwise. This variable serves as a proxy for the immediate replacement effect. This measure does not distinguish between physiological and volitional effects, as the effects of postpartum sterility and prolonged breastfeeding are not controlled for. Data on breastfeeding and postpartum sterility were only available for the last five births before the survey. They are not available during times of heightened conflict. However, the interaction of DeathICI with conflict exposure accounts for the conflict-induced volitional replacement effect. The second, DeathNICI, takes the value 1 if the non-index child's death occurred between the previous birth and 9 months before the month of the next birth, and 0 otherwise. This variable better reflects the short-term or immediate replacement effect free of physiological effect (Lindstrom & Kiros, 2007) because the death of a non-index child is not directly associated with the cessation of breastfeeding or the return of menstruation. The third variable, DeathNICBI, is coded as 1 if the non-index child died at least nine months before the start of the current interval (i.e., at least nine months before the previous birth) and 0 otherwise. This is a proxy for the long-term replacement effect (Helske & Tikka, 2024; Lindstrom & Kiros, 2007).

### 4.3.2. Explanatory variables measuring the effect of conflict

In the core analyses, conflict exposure was operationalized as a categorized measure of the cumulative count of armed conflict events, following the UCDP-GED definition (Sundberg & Melander, 2013), in the woman's province of residence that occurred during each birth interval, that is, between the year and month of one birth and the year and month of the next birth. The variable had four categories. The first category was no conflict event. The three other categories were the tertiles of non-zero values of the cumulative number of conflict events ("Low", "Medium", "High").<fnref n="4" /> The use of discretization for conflict indicators, a method supported by prior research (Krafft et al., 2024; Torrisi, 2024), mitigates the problem of outliers. This approach is also appropriate for our dataset, where conflict events were heavily skewed towards zero.<fnref n="5" /> The total number of conflict events was calculated using the start and end dates of each conflict (from the UCDP-GED), and the start and end dates of each birth interval (from the DHS BIRTH file). The start and end dates of each birth interval in days were unknown because they were calculated from the CMC (Century-Month Code) dates. I assumed that each birth interval began and ended on the first and last days of the month.<fnref n="6" /> The year and month at the beginning and end of each interval were calculated using the year and month of birth and the duration of the birth interval. Consistent with previous research (Svallfors, 2022), I defined conflict exposure using broad geographic areas (regions) to minimize bias arising from the absence of data on women's migration trajectories. Notably, the 2013/2014 DRC DHS did not record information on mobility, such as the duration of residence in the current location. The implications of this methodological choice are discussed in detail later. These measures of conflict intensity and child mortality are particularly suitable for longitudinal studies, as they are observed before the event of interest (birth), helping to mitigate post-treatment bias.

Fig. 3a and b show the yearly distribution of conflict events and related fatalities from 1989 to 2013. On average, there were 101 conflict events per year, with a low of 3 in 1989 and a peak of 546 in 2009. The

<footnote n="3">³ Because a woman cannot conceive during the 9 months of pregnancy as she is already pregnant.</footnote>

<footnote n="4">⁴ Values are grouped as "Low" (below the 33rd percentile), "Medium" (33rd–66th percentiles), and "High" (above the 66th percentile).</footnote>

<footnote n="5">⁵ Results are available upon request.</footnote>

<footnote n="6">⁶ The R scripts are available upon request.</footnote>
<figure><img src="image_7.png" /><figcaption>**Fig. 3a.** Conflict events and conflict-related fatalities in the DRC by year (Fig. 3a) and region (Fig. 3b), considering the “best-estimate” values.</figcaption></figure>

average number of conflict-related fatalities was 3,963, ranging from 28 in 1989 to 34,434 in 1996. Most conflicts and fatalities occurred in the eastern regions, specifically Nord-Kivu, Sud-Kivu, and Province Orientale, particularly between 1996 and 2003, and again in 2009.

### 4.3.3. *Control Variables*

Following the literature (Baynes et al., 2023; Defo, 1998; Lindstrom & Kiros, 2007; Park et al., 1998; Zhang, 1990), I controlled for the effects of age at the previous birth and its square, the length of the previous birth interval, the status of the previous birth (1 = multiple birth, 0 = singleton birth), place of residence (urban or rural), and region. I was unable to control for the number of surviving boys relative to girls—a proxy for son preference that may influence replacement behavior (e.g., Lindstrom & Kiros, 2007)—due to collinearity with other covariates. Age and its square were also highly collinear but were retained because of their strong theoretical relationship with fertility.<fnref n="7" /> The square of age was included to account for the nonlinear relationship between fertility and age (Gayawan et al., 2010). The age at the start of each interval is a time-varying variable calculated by adding the duration of the previous interval (in years) to the age at the start of the previous interval. The mean age of the participants was 21 years old. The length of the previous birth interval was adjusted for to address potential reverse causality between infant mortality and fertility. Shorter birth intervals may elevate the risk of infant mortality, as proposed by the

maternal depletion<fnref n="8" /> and sibling competition hypotheses (Davanzo et al., 2008). I controlled for birth status because the determinants of both child mortality and subsequent fertility following a single and a multiple birth may differ (Robson & Smith, 2011). Women with multiple births (1.1 % of the sample) were not excluded to maintain a large sample size.

The majority of women live in rural areas (66.1 %). The region variable had 11 categories corresponding to the 11 former provinces of the DRC that existed during the survey years: Kinshasa (7.2 %), Bas-Congo (4.1 %), Bandundu (12.2 %), Equateur (12.1 %), Katanga (13.4 %), Kasai Oriental (13.3 %), Kasai Occidental (11 %), Province Orientale (6.9 %), Nord-Kivu (5.5 %), Maniema (7.8 %), and Sud-Kivu (6.6 %).

### 4.4. Statistical modeling and analytical strategy for examining the effects of infant/child mortality and conflict on fertility

Each observation in the DHS BIRTH file represents a birth interval. Unlike other studies (Lindstrom & Kiros, 2007; Defo, 1998), I focus on birth events rather than conceptions, because a woman is at risk of another pregnancy not from conception but from the birth of the previous child. Each woman is represented by several lines of data (intervals) according to the number of births she has had. A set of three variables (*Tstart*, *Tstop*, and *Status*) was used to define the risk time interval. For each woman, the start time (*Tstart*) was equal to zero for the first event and the last recurrence time for subsequent events. The stop time (*Tstop*) equals the start time plus the duration of the interval. The

<footnote n="7">⁷ I conducted a sensitivity analysis by excluding age and age squared in nested models. Their inclusion or exclusion did not affect the effects of other variables (results not shown).</footnote>

<footnote n="8">⁸ The hypothesis predicts that short birth intervals would deplete maternal resources and thus lower the likelihood of a subsequent conception.</footnote>
duration of the interval was equal to the number of months between one birth and the next (Amorim & Cai, 2015). Status is the recurrent event time (status = 1) or censored time (status = 0). The data structure is shown in Table A22. Intervals were measured in months rather than years, because some birth intervals were shorter than 12 months. Intervals that remained open until the time of the survey were right-censored.

To model the hazard of subsequent births in relation to child loss and conflict exposure, I assumed a continuous time assumption, that is, that conception can take place at any time in the interval after the post-partum infertility period and after (or, with less probability, during) breastfeeding. This assumption makes it easier to model intra-subject correlation (Commenges, 2002; Heckman & Singer, 1984).

The variable of interest was the duration in months between one birth and the next. The event of interest is the occurrence of the $(j+1)^{th}$ birth, knowing that the woman has not yet experienced the $(j+1)^{th}$ birth but has already experienced the $j^{th}$ birth. Following other studies (Lindstrom & Kiros, 2007; Defo, 1998), only births that resulted in live births were considered because the reporting and dating of miscarriages, stillbirths, and induced abortions are subject to irregularities in the DHS (Ali et al., 2023; Blencowe et al., 2016).

Kaplan-Meier curves, along with the log-rank test, were used to analyze the timing of each birth since the previous birth as a function of categorical explanatory variables.

In the multivariate analysis, a conditional frailty survival model was used, given that a woman can experience the $(j+1)^{th}$ birth only if she has experienced the $j^{th}$ birth. The frailty model improves accuracy compared to traditional survival models by incorporating unmeasured variables as random effects (Tutz & Schmid, 2016; Box-Steffensmeier & De Boef, 2006).

First, a model estimating for each covariate a single effect for all events was estimated:

$$ \lambda(t) = \lambda_{0j}(t - t_{j-1})e^{\delta(t^* + \delta)} \quad (1) $$

where $j$ represents the event order; $\lambda_{0j}$ is the baseline hazard, which varies across events, to allow stratification by event, but the effects of covariates are constant from one event order to the next; $(t-t_{j-1})$ incorporates the gap time structure of the data, so that the hazard gives the risk for birth $j$ since the previous birth $(j-1)$; $X$ is the vector of independent variables, some of which vary over time; $\delta$ is the frailty term or random effect.

The frailty term accounts for unobserved heterogeneity among women, as some may have higher or lower risks of conceiving due to unknown, unmeasured, or unmeasurable time-invariant factors (Defo, 1998). These factors are particularly numerous in the study of timing to birth. These include, among other things, fecundity, nutrition, genetic factors. Failure to account for these factors may bias estimates of baseline risk and covariate effects (Qin & Guan, 2023). The frailty term was assumed to be log-Gaussian (with a mean of 1 and a variance of $\theta$), because the Gaussian distribution “forms a simpler framework for multiple and correlated random effects” (Therneau & Mayo, 2024, p.9). The frailty variance quantifies the extent to which unobserved factors affect the hazard of a subsequent birth. Its statistical significance was assessed using the likelihood ratio test comparing the conditional Cox models with and without a frailty term (Therneau & Mayo, 2024). A p-value lower than 0.05 means the frailty variance is statistically significantly different from 0. A frailty variance significantly different from 0 suggests that failing to account for woman-specific heterogeneity could bias the estimated effects of the key variables (Therneau & Mayo, 2024; Balan & Putter, 2020).

The fixed covariate effects were expressed as hazard ratios. The hazard ratio captures the instantaneous risk of transitioning from no event to an event, such as from a previous birth to a subsequent birth. It represents a transition rate. A hazard ratio greater (or less) than 1 indicates a shorter (or longer) waiting time for the next birth (Sashegyi &

Ferry, 2017). An interaction hazard ratio significantly above (or below) 1 indicates that conflict exposure amplifies (or attenuates) the replacement effect.

Because the determinants of fertility vary by parity (Lerch & Spoorenberg, 2017; Lindstrom & Kiros, 2007; Defo, 1998), I also estimated the Cox frailty models for each event. This approach yields more informative results than the all-parity model. Modeling for each event also has the advantage of observing how the effects evolve with each subsequent event (Lin et al., 2018; Box-Steffensmeier & De Boef, 2006). The risk function for each event $j$ is:

$$ \lambda_j(t) = \lambda_{0j}(t - t_{j-1})e^{\delta_j(t^* + \delta)} \quad (2) $$

In Eq. (2), both the baseline hazard $\lambda_{0j}$ and the effects of covariates vary by event order.

Parameters were estimated by penalized partial likelihood (Palmgren & Ripatti, 2002) using the coxme and survival packages of R (Therneau & Lumley, 2024; Therneau & Mayo, 2024). This model accounts for the dependency of events by stratifying by event (estimating a different baseline risk for each event) and a gap time formulation of duration (Box-Steffensmeier & De Boef, 2006). Event dependence may occur in this study because a prior birth may reduce the risk of subsequent births (e.g., a medical prescription after a caesarean section). Failure to account for this time dependence may result in overestimated standard errors of the coefficients and erroneous rejection of the null hypothesis (Amorim & Cai, 2015).

To analyze a potential causal relationship between child death and conflict exposure, a three-step modeling approach was used.

First, models were estimated to assess the effect of infant or child death on birth transitions, both with and without control variables. The hazard ratio between the categories of DeathICI (index child death during the interval vs. no index child death during the interval) and DeathNICI (non-index child death during the interval vs. no non-index child death during the interval) was used to test hypothesis $H_1$.

Second, models were estimated to examine the effect of conflict, both with and without control variables. The hazard ratio of DeathNICBI (non-index child death before the interval vs. no non-index child death before the interval) was used to assess hypothesis $H_2$.

Third, models were estimated with interaction terms between conflict exposure and infant or child death indicators, both with and without control variables. In these models, three interaction terms were included. The interaction between the death of an index or non-index child in an interval and the number of conflict events in the same interval evaluated hypothesis $H_3$. To assess hypothesis $H_4$, an interaction term between each woman's experience of child death in the previous interval and her individual exposure to conflict events during that same interval (i.e., a woman-specific lag-1 conflict measure) was included. To enhance clarity and facilitate interpretation, forest plots were used to present hazard ratios for infant and child mortality, as well as for exposure to conflict. In addition, interaction effects were illustrated using predicted risk scores, calculated as the exponential of the linear predictor. The risk score is proportional to the hazard ratio. It represents an individual's overall predicted risk of experiencing the event. Higher risk scores indicate a higher risk of the event (Therneau, 2024).

In the core analyses, the interaction term between child death in the intervals before the preceding one (i.e., lag 2 or higher) and cumulative conflict events during those same intervals was not included in the same model as the lag 1 measures due to collinearity and reduced sample size. However, in the sensitivity analyses, I included the interactions between the death of a non-index child in the two preceding intervals and conflict exposure during those intervals for illustrative purposes.<fnref n="9" />

<footnote n="9">⁹ Exposure to conflict in all prior intervals was not measured using cumulative conflict events across all previous intervals to avoid serial correlation and dynamic feedback, which could bias the estimates.</footnote>
I performed multiple robustness checks (see Appendix). Conflict exposure was measured using tertiles of the cumulative number of fatalities and conflict events occurring during each birth interval within a 75 km buffer radius around DHS cluster points.<fnref n="10" /> Several alternative measures of conflict exposure were used. These included the logarithms of cumulative conflict events and cumulative deaths, each with adjustment (adding 1 prior to log transformation).

Several extended analyses were conducted to test the observed effects of conflict and infant/child mortality in different populations and to explore some mechanisms through which conflict exposure influences the replacement effect. To assess the generalizability of the main findings to unmarried or cohabiting women, I conducted analyses including both married and unmarried women. Separate analyses were performed for the 2007 and 2013/2014 DRC DHS datasets to evaluate potential recall bias. Additional analyses excluded women with multiple births. To address potential concerns about overlapping variance or masking of effects, each of the infant/child mortality indicators—*DeathICI*, *DeathNICI*, and *DeathNICBI*—was also included in separate models. To isolate the effect of conflict from general time trends (such as secular fertility declines or policy changes), I included dummy variables for the year at the start of each birth interval as covariates. Finally, to understand the role of fertility preferences in the relationship between conflict exposure and the replacement effect of child loss, I analyzed the effects of child loss, conflict exposure, and their interaction on fertility preferences.

# 5. Results

## 5.1. Timing of births in relation to infant or child mortality and conflict exposure

Fig. 4 depicts the timing of birth according to the experience of the death of an index or non-index child, all parities combined. The median interval between consecutive births was 29 months. Women who lost a child had shorter birth intervals than those who did not. The median birth interval was 27 months for those who lost an index child, compared to 32 months for those who did not. Among women who lost a non-index child during the interval, the median birth interval was 18 months, versus 31 months for those who did not. When the loss occurred before the interval, the median birth interval was 23 months, compared to 32 months. These findings suggest both immediate and longer-term replacement effects.

Fig. 5 illustrates the association between birth timing and three indicators of conflict exposure. Women who experienced conflict exhibit longer median birth intervals (33 months) compared to those who were not exposed (27 months) (Fig. 5a). An overall trend of increasing birth intervals is observed with rising conflict intensity, regardless of whether conflict exposure is captured by the number of events or conflict-related fatalities. For instance, the median birth intervals are 27, 32, 33, and 34 months for women with no, low, medium, and high levels of conflict exposure, respectively (Fig. 5b). This evidence points to a pattern of delayed childbirth among women experiencing conflict.

Fig. 6a complements Figs. 4 and 5 by examining the effect of the interaction between index child death and conflict exposure on the timing of subsequent births. Among women not exposed to conflict, child loss leads to shorter birth intervals, with a median of 24 months for those who experienced child loss compared to 28 months for those who did not (a 4-month difference). For women exposed to conflict, the same pattern is evident, but the effect is slightly stronger: the median birth interval is 29 months with child loss and 33 months without (a 4-month difference). This indicates a clear replacement effect following child loss in both groups. However, there is insufficient evidence to suggest that conflict exposure affects index child replacement. This result holds

considering the transition to subsequent births regardless of parity (Fig. 6a) or considering each parity-specific transition (Fig. 7a). This finding suggests that exposure to conflict may not influence the replacement of an index child loss, regardless of parity.

Fig. 6b shows the link between conflict exposure and replacement behavior related to non-index child loss in the current interval. Among women not exposed to conflict, the loss of a non-index child is associated with a 12-month gap in the timing of subsequent births between those who experienced such a loss (median: 16 months) and those who did not (median: 28 months). This gap increases to 14 months among women exposed to conflict (median interval of 19 months with loss vs. 33 months without loss), suggesting that conflict may strengthen the short-term replacement effect. A similar trend is observed across all parities (Fig. 7b), indicating that the amplifying effect of conflict on short-term replacement behavior may be present across all parity levels.

Fig. 6c illustrates the relationship between conflict and long-term replacement behavior. Among women who have not experienced conflict, the death of a non-index child before the interval results in a shorter median birth interval (22 months compared to 28 months). In contrast, among women exposed to conflict, while there is still a decrease in birth interval (23 months for those who experienced a child death versus 28 months for those who did not), the effect is slightly less pronounced and still non statistically significant. The same pattern is observed across all parities (Fig. 7c). This preliminary finding suggests that conflict may exert a negative, though statistically non-significant, influence on the long-term replacement effect.

In summary, these descriptive analyses show that women who experience the loss of a child tend to replace them both in the short and long term, with conflict only amplifying the short-term replacement behavior.

## 5.2. Impact of infant/child mortality and conflict on the risk of subsequent births

### 5.2.1. Analysis of the effects of infant/child mortality, conflict, and their interaction on the risk of birth across all parities

Figs. 8–10 and Table A1 (model with controls) show the results of the Cox frailty model examining the effects of infant or child death, conflict, and their interaction on the risk of subsequent births across all parities.

The deaths of an index child and a non-index child within the current interval were both strongly associated with a higher risk of subsequent birth (see Figs. 8–9 or Model 1 in Table A1). These findings, which support hypothesis $H_1$, provide evidence of a short-term volitional replacement effect. The immediate volitional replacement following the death of a non-index child within an interval was the strongest of the three effects analyzed (high HRs; see Fig. 8). The death of a non-index child before the current interval increased the risk of subsequent birth (see Fig. 8 or Model 1 in Table A1), suggesting a long-term replacement effect. This finding supports hypothesis $H_2$.

In addition, conflict intensity in the woman's region of residence during the birth interval was negatively associated with the risk of subsequent birth (see Fig. 9 or Model 2 in Table A1).

The interaction analysis for the all-parity model is shown in Fig. 10 and in Model 3 of Table A1. There is a negative effect of the interaction between index child death and conflict exposure, although it is not statistically significant (Fig. 10a or Model 3 in Table A1). This suggests insufficient evidence to conclude that conflict exposure alters the volitional or physiological replacement effect following the death of an index child.

Exposure to a medium level of conflict amplifies the volitional replacement effect (Fig. 10b or Model 3 in Table A1). This finding supports hypothesis $H_3$, which posited that the risk of birth following the death of a non-index child in the current interval would be higher among women exposed to conflict. Exposure to low and high-level conflict was not associated with the replacement effect. The positive effect of medium-level conflict exposure, contrasted with the lack of effects at

<footnote n="10">¹⁰ Similar to the core analysis, a large buffer radius was used to mitigate potential bias arising from the lack of mobility data.</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Timing of subsequent birth according to the death of a child, all parities combined (Kaplan-Meier estimator. Note: A lower survival curve indicates an early birth timing. Vertical lines represent the median interval. P-values are derived from the log-rank test. P-value < 0.05 indicates significant differences in birth timing among categories.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Timing of subsequent birth according to conflict exposure, all parities combined (Kaplan-Meier estimator).</figcaption></figure>

low and high levels of exposure, suggests a non-linear relationship between conflict and the short-term child replacement effect.

Fig. 10c and Model 3 of Table A1 illustrate the effect of the interaction between conflict intensity and non-index child loss prior to the current interval on birth transitions. Higher levels of conflict exposure are associated with a lower risk of replacing a child lost in the previous birth interval. This finding suggests that conflict exposure mitigates the long-term replacement behavior, contrary to hypothesis H₄.

Three control variables—previous birth interval, whether the previous birth was a multiple birth, and region—were significantly associated with birth risk (Table A1). A longer previous birth interval was negatively associated with subsequent birth risk, consistent with the maternal depletion hypothesis. Moreover, birth risk varied significantly by region. The DRC regions, classified by birth risk, are as follows: Sud-Kivu, Nord-Kivu, Kasaï Occidental, Katanga, Kasaï Oriental, Equateur, Maniema, Province Orientale, Bandundu, Bas-Congo, and Kinshasa.

The frailty variance θ was statistically different from 0 (Table A1), meaning that accounting for frailty improves the model fit. This also implies that woman-specific time-invariant reproductive behaviors, as well as biological or genetic factors that are not captured by the observed variables, play a significant role in shaping fertility outcomes.

### 5.2.2. Analysis of the effect of infant/child mortality, conflict exposure and their interaction on birth transitions by parity

Figs. 11–13 and Tables A2–4 complement Figs. 8–10 and Table A1 by showing the effects of infant/child mortality (Fig. 11 and Table A2), conflict (Fig. 12 and Table A3), and their interaction (Fig. 13 and Table A4) on birth risk by parity (see Eq. 2).

The death of an index child increased the risk of birth for transitions to parities 2 and 3 (Fig. 11). Similarly, the death of a non-index child in the current interval was associated with an increased risk of birth for all transitions (Fig. 11). Finally, the death of a non-index child prior to the
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Timing of birth following child loss among conflict-exposed and non-exposed women, all parities combined (Kaplan-Meier estimator). **Note:** The effect of conflict on the replacement effect can be identified by comparing the median birth interval differences between women who experienced child loss and those who did not, in both conflict-exposed and non-exposed groups. A reduced replacement effect due to conflict results in a smaller birth interval gap, whereas a stronger effect leads to a larger gap.</figcaption></figure>

current interval increased the risk of birth across nearly all parities. This last finding suggests that bereaved women tend to replace children who died years earlier, regardless of parity, reflecting a persistent desire to maintain family size independent of overall fertility levels.

In terms of conflict effects, exposure to conflict reduces the risk of subsequent births for women, regardless of parity (Fig. 12).

Fig. 13 complements Figs. 11 and 12 by analyzing the interaction between infant/child mortality and conflict exposure across parity. Conflict exposure slightly attenuates the replacement effect following the death of the index child, but only for the transition from parity 1–2. A similar pattern is observed at higher parities, although the estimates are not statistically significant (Fig. 13a). In contrast, exposure to medium levels of conflict amplifies the short-term replacement effect for transitions to parities 3–4 and 6–7 (Fig. 13b), indicating that conflict reinforces short-term replacement behavior regardless of parity. Finally, conflict attenuates long-term replacement behavior across all parities (Fig. 13c): at higher parities (transitions to parities 3–7), exposure to high levels of conflict weakens the replacement effect, while at lower parities (transition from parity 2–3), even low levels of conflict reduce the replacement effect.

### 5.3. Robustness checks and extended analyses

Several robustness analyses were conducted, as presented in the appendix. The effects of infant or child loss, conflict, and their interaction, as shown in Table A1 and Fig. 8, remained consistent with and without controls (Table A5), indicating that the associations between conflict, child death, and subsequent births are robust and not sensitive to the inclusion of control variables. In other words, controls do not significantly confound these relationships.

The effects of conflict—whether measured by tertiles of conflict events (Table A6) or fatalities (Table A7)—and its interaction with infant or child mortality on birth transitions remained consistent when conflict exposure was defined using a 75 km buffer around DHS cluster points.<fnref n="11" /> In other words, accounting for potential cross-border effects

using a 75 km buffer around DHS clusters yields results consistent with those obtained using province-level conflict measures. This suggests that the province-level measure adequately captures overall conflict exposure, including spillover effects, or that such spillovers do not significantly influence the relationship.

Findings similar to those presented in the core analyses (Figs. 11–12 or Table A1) were observed when conflict exposure was measured using tertiles of cumulative fatalities in the region of residence within each birth interval (Table A8), the logarithm of cumulative conflict events within each birth interval (Tables A9 and A10), and the logarithm of cumulative fatalities within each birth interval (Tables A11 and A12). The comparability of results from measuring conflict by event counts or fatalities reinforces the construct validity and reliability of the conflict exposure indicator used in the core analyses. Moreover, the convergence between categorical and quantitative measures of conflict exposure enhances the internal validity of the analysis and suggests that the cutoffs used to categorize conflict exposure capture substantively meaningful variation rather than arbitrary thresholds.

Regressions examining the interaction between the death of a non-index child in the two preceding intervals and cumulative conflict events within those intervals (Table A13) yielded results comparable to those in Fig. 10. In all these analyses, conflict amplified the short-term replacement effect and weakened the long-term effect. These consistent findings across different measures of conflict exposure further reinforce the robustness of the results.

Analyses were conducted on different populations. When unmarried women were included (Table A14), the findings were similar to those for married women (Figs. 8–9). Similarly, excluding women who had multiple births (Table A15) produced results consistent with those in Figs. 8–9. This suggests that the main findings are generalizable to both unmarried or cohabiting women and to those who had a singleton birth. In addition, analyses of the effects of infant or child mortality and conflict on fertility using the DHS 2007 and DHS 2013/2014 (Table A16) yielded results consistent with the pooled results (Fig. 8). The observed consistency implies that recall bias is unlikely to significantly affect the findings.

When the child/infant mortality indicators (DeathICI, DeathNICI, and DeathNICBI) were analyzed separately, the results remained consistent across models (Table A17). The death of either an index or non-index child during or prior to the current interval was associated with a

<footnote n="11">¹¹ Considering a 50 km buffer radius around DHS cluster points resulted in similar findings, with weak but statistically significant effects at the 5% threshold. Results are available upon request.</footnote>
<figure><img src="image_11.png" /><figcaption>Fig. 7. Kaplan-Meier estimates of median birth intervals between the first and seventh births, by child death and conflict exposure.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Effects of infant/child mortality on subsequent birth risk: hazard ratios from a conditional frailty Cox model (combined parities).
*Note:* The points represent the hazard ratios along with their 95 % confidence intervals. The model, stratified by parity, controls for the effects of age and its square, the duration of the previous birth interval, having had multiple births, place of residence, and region. The effects of the control variables are presented in Table A1. The model is stratified by parity.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Effects of conflict exposure on subsequent birth risk: hazard ratios from a conditional frailty Cox model (combined parities).</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Predicted risk scores of interactions between infant or child death and conflict intensity on subsequent births. *Note:* The predicted risk scores were computed from Model 3 (Table A1). *DeathICI:* Death of an index child in the current interval, *DeathNICBI:* death of a non-index child before the current interval (in all previous intervals), *DeathNICBIPrev1:* death of a non-index child in the previous interval.</figcaption></figure>

shorter time to the next birth. These findings reinforce the conclusions drawn from the main model (Fig. 8), where all indicators were simultaneously adjusted. The consistency of results across separate and combined models suggests that there is no substantial overlapping variance or masking of effects among the three indicators of child loss, further strengthening the robustness of the conclusions.

Similar effects of conflict exposure and child mortality indicators on birth transitions were observed with and without controlling for year dummies marking the start of each birth interval (Table A18). This suggests that time-related factors—such as secular fertility trends or policy changes in the year initiating each interval—do not confound the relationship between infant or child death, conflict exposure, and subsequent births.

Tables A19-21 show how fertility preferences influence the

relationship between infant/child mortality, conflict exposure, their interaction, and fertility. Conflict exposure reduces the desire to have children both in the short term (within two years) and in the medium to long term (beyond two years). This finding suggests that the negative relationship between conflict exposure and fertility (Fig. 9) is partly driven by fertility preferences. In contrast, the loss of a child is associated with an increased desire to have another child in both the short term and the medium to long term, indicating that the observed replacement effect is partly explained by heightened fertility preferences.

## 6. Strengths and limitations

This study has many strengths. It contributes to policy by identifying
<figure><img src="image_15.png" /><figcaption>**Fig. 11.** Effects of infant/child mortality on subsequent birth risk: hazard ratios from parity-specific conditional frailty Cox models. *Note:* The model controls for the effects of age and its square, the duration of the previous birth interval, having had multiple births, place of residence, and region. The effects of the control variables are presented in Table A2. *DeathICI:* Death of an index child in the current interval, *DeathNICI:* Death of a non-index child in the current interval, *DeathNICBI:* Death of a non-index child before the current interval (in all previous intervals).</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Effects of conflict exposure on subsequent birth risk: predicted risk scores from parity-specific conditional frailty Cox models. *Note:* The effects of the control variables are presented in Table A3.</figcaption></figure>

when and where interventions targeting fertility behaviors are most needed, particularly during and after conflict. It also refines demographic transition theory by illustrating how conflict disrupts the relationship between mortality and fertility in pre-transitional societies.

The use of frailty survival models tailored to parity transitions provides a sophisticated method for disentangling replacement effects. Additionally, the integration of geo-referenced conflict data with demographic surveys offers a replicable model for studying fertility in other conflict-affected regions.

Despite these strengths, certain limitations remain. In addition to the selection bias discussed in the methodology section, this study has several other limitations. First, the measures of conflict intensity used may be biased because they do not account for women’s migration trajectories, as the variable length of time spent in the current residence, which could help account for mobility, was not collected in the 2013/2014 DHS. This bias, due to the lack of information on women’s migratory trajectory, would be greater the smaller the geographical areas considered. For this reason, I did not use the geolocalized
a) Interaction: Conflict Exposure x DeathICI

<figure><img src="image_17.png" /></figure>

b) Interaction: Conflict Exposure x DeathNICI

<figure><img src="image_18.png" /></figure>

c) Interaction: Lag-1 Conflict Exposure x DeathNICBIPrev1

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Transition from parity 3→4

<figure><img src="image_21.png" /></figure>

Transition from parity 3→4

<figure><img src="image_22.png" /></figure>

Lag-1 Conflict Exposure

<figure><img src="image_23.png" /></figure>

Transition from parity 4→5

<figure><img src="image_24.png" /></figure>

Transition from parity 4→5

Lag-1 Conflict Exposure

<figure><img src="image_25.png" /></figure>

Transition from parity 5→6

<figure><img src="image_26.png" /></figure>

Transition from parity 5→6

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

Transition from parity 6→7

<figure><img src="image_29.png" /></figure>

Transition from parity 6→7

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /><figcaption>**Fig. 13.** Predicted risk scores of interactions between infant or child death and conflict intensity on subsequent births. **Note:** The predicted risk scores were computed from Table A4. **DeathICI:** Death of an index child in the current interval, **DeathNICI:** death of a non-index child before the current interval (in all previous intervals), **DeathNICBIPrev1:** death of a non-index child in the previous interval.</figcaption></figure>
information provided by the UCDP-GED in the core analyses, as some studies have done (Thiede et al., 2020; Kotsadam & Østby, 2019). To minimize this bias, conflict intensity variables were measured at the level of very large geographic areas, i.e., at the provincial level, as in other studies (Svallfors, 2022). It should be noted, however, that even if this geo-localized information was used, it would not consider short-term mobilities. Forced migration, the most common, occurs within regions or to neighboring countries (Pech & Lakes, 2017; Tamm & Lauterbach, 2011). Moreover, non-conflict-related migration between regions is rare in sub-Saharan Africa as a whole (Corno et al., 2020) and would be less frequent in the DRC due to the lack of roads connecting the provinces (Ali et al., 2015). A study of Kinshasa, the most attractive region in terms of jobs, shows that women born outside Kinshasa account for 15 % of all women (Anglewicz et al., 2017). This proportion is more likely to be made up of women from neighboring regions that, like Kinshasa, have experienced less conflict. From that, it can be inferred that the proportion of migrant women is likely to be much lower than 15 % in other regions.

Second, the UCDP-GED data use a restrictive definition of conflict, only including armed conflicts that resulted in at least one death (Sundberg & Melander, 2013). This can lead to an underestimate of the real level of conflict in the DRC. This limitation emphasizes the importance of comparative studies using UCPD and ACLED data, the two leaders in geo-localized conflict data. This comparative analysis was not conducted because the ACLED data do not cover the entire wartime period in the DRC.

Third, while the conditional frailty model effectively controls for time-invariant factors, it may overlook potential time-varying omitted variables, such as changes in marital status or shifts in women's professional trajectories. However, this limitation is unlikely to compromise the study's core findings, since these factors may operate indirectly and are correlated with the primary determinants examined here. For instance, age and age squared capture lifecycle effects that are closely linked with both marital transitions and career progression.

Fourth, as Torrisi (2020) notes, studying the transition from parity *j* to *j* + 1 creates a sample selectivity problem since each transition is analyzed among women who have reached parity *j*. However, this may not be a major concern, as the analysis focuses on understanding birth transitions among women at risk of having the next birth, who constitute the relevant population for each transition.

Fifth, accurate data on pregnancy duration were unavailable. Therefore, infant and child mortality indicators within a birth interval were measured assuming a uniform nine-month gestation for all pregnancies. In conflict settings, research has shown that harmful prenatal exposures increase the risk of adverse outcomes, including preterm birth and low birth weight (Torche & Nobles, 2024). Consequently, assuming a uniform nine-month gestation may bias estimates by misclassifying the timing of conception relative to child death and underestimating the incidence of infant and child loss. This misclassification can lead to an underestimation of the short-term replacement effect.

Finally, this study considered only live births. In cases where abortions and miscarriages occur within a birth interval, the interval between two live births—the variable of interest in this study—may be extended, even if bereaved couples intended to replace a deceased child, thereby potentially underestimating the replacement effect.

# 7. Discussion and conclusion

The objectives of this study were to analyze the impact of infant and child mortality on the timing of subsequent births, and to examine the role of conflict in this relationship. It used data on women's birth histories and the number of conflict events and conflict-related deaths recorded in the woman's province of residence within each birth interval.

The results show that the death of an index child, the death of a non-index child within the current interval, and the death of a non-index child before the current interval were all associated with a high risk of subsequent birth. Because I did not control for the effects of postpartum amenorrhea and breastfeeding, the effect of the death of the index child on fertility is the result of both the return of menses due to the cessation of breastfeeding by the deceased child and the effect of volitional child replacement. The hazard ratios for births following the first- to sixth-order births in women who had lost an index child and in those who had not lost an index child ranged from 1 to 1.39. These estimates are comparable to those reported in other studies using a similar methodology: 1.33 in the study by Hossain et al. (2007) in Bangladesh. They are comparable but lower than those found in the study by Lindstrom and Kiros (2007) in Ethiopia (1.66–2.29).

The effect of the death of the non-index child within an interval on the risk of subsequent birth is a better proxy for the volitional replacement effect (Lindstrom & Kiros, 2007), because the death of a non-indexed child in an interval cannot directly interrupt breastfeeding and, therefore, the return of menstruation. This replacement effect was the strongest among the three replacement effects analyzed, suggesting that the replacement behavior would be primarily driven by volitional factors rather than biological ones. It is worth noting that the increase in fertility following the death of an index or non-index child may be due to unintended pregnancies resulting from disruptions in healthcare and unmet needs for family planning. However, as indicated in Tables A19 and A20, women who had recently experienced the loss of one or more children expressed a short-term desire for an additional child. This result, which contrasts with a study conducted in the Colombian context (Svallfors, 2022), suggests that the observed increase in fertility following the loss of an index or non-index child was driven by intentional reproductive decisions. This indicates that, in contexts like the DRC where fertility remains high, parents consciously respond immediately to infant or child mortality by shortening the interval to subsequent birth, thus confirming hypothesis H₁. This finding resonates with aspects of bereavement theory, which posits that the emotional impact of child loss can prompt immediate efforts to restore family balance (Pepper & Nettle, 2013; Yeatman et al., 2013). This finding is similar to that of Hossain et al. (2007), who found a 1.87 times higher risk of birth among women who had lost a non-index child in the current interval than among those who had not lost a non-index child. It does not support Lindstrom and Kiros (2007), who found no significant effect of the death of a non-index child in the current interval on the risk of birth in Ethiopia.

I found a high risk of birth among women who lost a non-index child before the start of the current interval. This means that couples tend to replace children who have died several years ago. This result confirms the hypothesis of a long-term replacement effect (H₂). It aligns with the insurance hypothesis and price theory, which view couples as rational agents seeking an ideal number of surviving children. In this framework, losing a child raises the effective "cost" of reaching that target, prompting couples to shorten the birth interval to replace the lost child (Wolpin, 1997; Zhang, 1990; Ben-Porath, 1976). This ideal family size hypothesis is supported by the observation that the index child replacement effect in this study is generally diminished or absent at higher parities, suggesting that couples have reached or are nearing their desired family size. This is inconsistent with Lindstrom and Kiros (2007), who found a more pronounced replacement effect at higher parities in Ethiopia, reflecting the fact that couples put more effort into replacing deceased children as they approach their ideal family size. Couples can use two main mechanisms to shorten the interval between births to replace a deceased child. First, they could discontinue any modern (used by 7.8 % of married women) or traditional contraception, such as the withdrawal method (used by approximately 4.5 % of married women) and periodic abstinence (used by about 7.2 % of married women). Second, they can reduce the duration of postpartum abstinence, which averaged 3.7 months in 2013/2014 and 4.2 months in 2007 (Ministry of Health & ICF International, 2007; 2014).

Regarding conflict, I found a negative association between conflict
intensity and birth risk at all parity levels. This may result from reduced financial and material resources, combined with increased uncertainty about the future as described by the uncertainty reduction theory (Caldwell, 2006; Friedman et al., 1994). The unpredictability of outcomes like healthcare access and income in fragile environments may lead women to delay or cease childbearing if they perceive risks to their health or that of their children. Additionally, this finding resonates with terror management theory, where instead of promoting fertility as a coping mechanism, the overwhelming mortality salience in conflict settings may trigger existential distress that discourages childbearing (Burke et al., 2010). This finding is consistent with that of Clifford et al. (2010), who found that fertility declined in Tajikistan during the Civil War. It also corroborates the findings of Lindstrom and Berhanu (1999), who found a short-term decline in births in Ethiopia during periods of crisis (conflict and famine), and Torrisi (2020), who found a decline in fertility, especially third births, in Azerbaijan during the conflict.

In line with hypothesis H₃, conflict exposure in the DRC strengthened the volitional replacement effect, suggesting that conflict may act as a catalyst for short-term reproductive decisions. This finding aligns with Kraehnert et al. (2019) and Schindler and Brück (2011), who found a high risk of having a child within five years and more than five years after the genocide in Rwanda, respectively, among women who lost a child during the genocide. The death of a child influences women's subsequent fertility by affecting, among other factors, their fertility preferences (Plana-Ripoll et al., 2018). Thus, women who had lost one or more children prior to the survey expressed a desire to have another child in the short term (Tables A19-20). Two interrelated hypotheses may explain the increased propensity to replace deceased children among women exposed to conflict. First, this pattern may reflect a psychological response to loss, wherein childbearing serves as a coping mechanism to mitigate grief and reestablish a sense of family continuity, as proposed by bereavement theory (Pepper & Nettle, 2013). Second, it may represent an adaptive reproductive strategy aimed at ensuring lineage survival amid elevated mortality risks, aligning with fast life history theory (Joyner & Beaver, 2023).

Although seemingly contradictory, these findings—a negative association between conflict intensity and overall birth risk, and a conflict-induced strengthening of the volitional replacement effect—reflect distinct yet coexisting fertility responses to conflict. While conflict intensity is associated with an overall decline in birth risk, it also intensifies short-term reproductive responses among women who experience child loss. In such cases, conflict may act as a catalyst for volitional replacement. This is a localized behavioral adaptation. Micro-level evidence suggests that neither physiologically induced nor volitional replacement tends to generate substantial population-level impacts on fertility (Nobles et al., 2015). Unlike in stable settings where child mortality is more predictable, the DRC's volatile context heightens the urgency of reproductive decision-making while constraining long-term planning, resulting in a divergence between overall fertility trends and subgroup-specific dynamics.

Finally, I found that, in the long term, conflict attenuates the replacement effect, contrary to hypothesis H₄. This finding is consistent with several theories. First, it supports bereavement theory, which suggests that prolonged grief following child loss, coupled with the persistent fear of further loss, reduces the willingness to engage in replacement fertility over time (Dahlberg, 2018). Additionally, this finding aligns with the economic theory of fertility (Robinson, 1997), which suggests that the chronic socio-economic hardships associated with conflict—such as resource scarcity and economic instability—increase the costs of childrearing, thereby reducing both the capacity and the incentive for replacement births. This theory appears suitable for explaining the long-term decline in replacement behavior

following child loss during conflict, as the study focuses on a country where protracted conflicts have significantly reduced families' standard of living (Koyame & Clark, 2002). This combination of prolonged emotional trauma (as posited by the bereavement theory) and enduring socio-economic constraints (see economic theory of fertility) contributes to a long-term decline in replacement fertility. Notably, this finding challenges the demographic transition theory, which posits a positive relationship between mortality and fertility, suggesting that higher child mortality should lead to increased fertility as a compensatory response. In contrast, the results indicate that, in protracted conflict settings, increased child mortality ultimately leads to a reduction in long-term fertility, as the fear of further loss and the burden of ongoing insecurity reshape reproductive strategies over time. The two last findings may seem contradictory at first, but they reveal distinct temporal responses to conflict. In the short term, conflict-induced child loss triggers an urgent replacement response which amplifies immediate fertility. Over the long term, however, the sustained insecurity, chronic stress, and economic hardships associated with protracted conflict discourage further childbearing, thereby attenuating the replacement effect.

The external validity of these findings depends on the stage of demographic transition and country-specific context. In high-fertility regions like parts of Africa and Asia, the immediate response to child loss may mirror this study's findings (Hossain et al., 2007). In contrast, in developed regions with low fertility, responses may differ due to socio-economic factors or better access to family planning. However, the methodology used in this study can be applied to other conflict-affected regions with protracted conflict and available birth history data, like DHS surveys. Finally, while the negative effect of conflict on fertility identified in this study applies to all women, both married and, to some extent, unmarried, it may not be observed across all age groups. Previous studies have shown that conflicts, particularly during the Second Congo War, increased the risk of teenage first motherhood in the DRC (Mugisho, 2024).

This study is the first to examine the impact of conflict on the replacement effect in the DRC, contributing to the existing literature in two ways. First, it provides evidence of a long-term replacement effect. Second, it demonstrates that in conflict settings, the death of a child does not always lead to an immediate increase in fertility aimed at replacing the deceased child. While conflict-induced child loss triggers an immediate replacement response, amplifying short-term fertility, long-term effects such as bereavement-related stress, trauma, fear of the future, and economic constraints may reduce the desire to replace children lost during or as a result of conflict.

The findings of this study have important policy implications for reproductive health and family planning in conflict-affected areas, particularly in the DRC. First, given that child loss increases fertility both in the short and long term, it is essential to provide post-bereavement counseling and mental health services to support families in managing grief and reproductive decisions. Additionally, interventions to reduce child mortality, such as improving healthcare, expanding vaccination programs, and ensuring better nutrition, would decrease the need for replacement births. The amplifying role of conflict on short-term replacement behavior calls for emergency reproductive health services, including postpartum care, family planning, and trauma-informed counseling, while access to modern contraceptives can prevent unplanned pregnancies after child loss. Finally, the attenuated long-term replacement behavior emphasizes the importance of long-term economic support, such as financial stability programs and job training, alongside continued access to reproductive health services to address the ongoing emotional and financial challenges families face. These interventions are essential for managing fertility dynamics and improving the well-being of this still conflict-affected population.
### CRediT authorship contribution statement

**Guerschom Mugisho:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

### Declaration of Generative AI and AI-assisted technologies in the writing process

The author declares having used Grammarly and ChatGPT during the writing process to improve the language and readability. The author reviewed and edited the suggested content and takes full responsibility for the final published article.

### Ethics approval and consent to participate

Ethical clearance to use DHS data was obtained from The Demographic and Health Surveys (DHS) Program.

### Consent for publication

Not applicable.

### Declaration of Competing Interest

The author declares that he has no competing interests.

### Acknowledgements

I would first like to thank Professors Christophe Bergouignan and Armelle Andro for their valuable guidance. I sincerely thank the two reviewers of this article and the handling editor for their expertise and for their insightful comments, which have significantly improved the soundness of the manuscript. This work benefited from a French government grant managed by the National Research Agency under the Investissements d'avenir programme with the reference ANR-17-EURE-0011.

## Appendix

<caption>**Table A1**</caption>

Cox frailty model of the effect of infant or child mortality and conflict exposure, and their interaction on birth transitions among married women in the DRC (all parities combined)

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant/child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between infant/child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI<sup>c</sup> (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.32</td><td>0.028</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>1.55</td><td>0.034</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>4.39</td><td>0.054</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>4.02</td><td>0.089</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.08</td><td>0.036</td><td>0.030 *</td><td></td><td></td><td></td><td>1.80</td><td>0.071</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>0.63</td><td>0.016</td><td>&lt; 0.001 **</td><td>0.64</td><td>0.021</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>0.49</td><td>0.017</td><td>&lt; 0.001 **</td><td>0.51</td><td>0.016</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>0.31</td><td>0.021</td><td>&lt; 0.001 **</td><td>0.32</td><td>0.034</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99</td><td>0.017</td><td>0.730</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.95</td><td>0.018</td><td>0.014*</td></tr><tr><td>High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.94</td><td>0.021</td><td>0.005*</td></tr><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI<sup>c</sup> * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.91</td><td>0.054</td><td>0.091°</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.92</td><td>0.054</td><td>0.149</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.97</td><td>0.056</td><td>0.598</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.01</td><td>0.137</td><td>0.840</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.56</td><td>0.134</td><td>0.004**</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99</td><td>0.755</td><td>0.373</td></tr><tr><td>DeathNICBIPrev1<sup>c</sup> * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.834</td><td>0.110</td><td>0.102</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.949</td><td>0.110</td><td>0.635</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.739</td><td>0.109</td><td>0.005**</td></tr><tr><td><strong>Age</strong></td><td>0.99</td><td>0.058</td><td>0.914</td><td>0.98</td><td>0.057</td><td>0.838</td><td>1.00</td><td>0.058</td><td>0.936</td></tr><tr><td><strong>Age<sup>2</sup></strong></td><td>1.04</td><td>0.055</td><td>0.418</td><td>1.04</td><td>0.054</td><td>0.427</td><td>1.03</td><td>0.054</td><td>0.538</td></tr><tr><td><strong>Length of the previous birth interval</strong></td><td>0.99</td><td>0.00</td><td>&lt; 0.001 **</td><td>0.99</td><td>0.001</td><td>&lt; 0.001 **</td><td>0.99</td><td>0.001</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Multiple births (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.89</td><td>0.045</td><td>0.019*</td><td>0.95</td><td>0.044</td><td>0.255</td><td>0.94</td><td>0.333</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Place of residence (Ref.:Rural)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.99</td><td>0.014</td><td>0.546</td><td>0.98</td><td>0.014</td><td>0.361</td><td>0.98</td><td>0.014</td><td>0.323</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.12</td><td>0.03</td><td>0.002 **</td><td>1.29</td><td>0.036</td><td>&lt; 0.001 **</td><td>0.76</td><td>0.034</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>1.08</td><td>0.04</td><td>0.088°</td><td>1.67</td><td>0.025</td><td>&lt; 0.001 **</td><td>0.97</td><td>0.042</td><td>0.145</td></tr><tr><td>Equateur</td><td>1.37</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.48</td><td>0.026</td><td>&lt; 0.001 **</td><td>1.23</td><td>0.033</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Oriental</td><td>1.38</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.72</td><td>0.025</td><td>&lt; 0.001 **</td><td>1.09</td><td>0.034</td><td>0.002 **</td></tr><tr><td>Kasai Occidental</td><td>1.45</td><td>0.03</td><td>&lt; 0.001 **</td><td>2.13</td><td>0.025</td><td>&lt; 0.001 **</td><td>1.27</td><td>0.032</td><td>&lt; 0.001 **</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A1 (continued)</caption><thead><tr><th>Variables</th><th colspan="3">Model 1 (effect of infant/child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between infant/child loss and conflict exposure)</th></tr></thead><tbody><tr><td>Katanga</td><td>1.41</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.31</td><td>0.033</td><td>&lt; 0.001 **</td><td>1.57</td><td>0.032</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.31</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.63</td><td>0.029</td><td>&lt; 0.001 **</td><td>1.19</td><td>0.036</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>1.51</td><td>0.03</td><td>&lt; 0.001 **</td><td>3.17</td><td>0.035</td><td>&lt; 0.001 **</td><td>2.21</td><td>0.040</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.26</td><td>0.03</td><td>&lt; 0.001 **</td><td>2.39</td><td>0.033</td><td>&lt; 0.001 **</td><td>1.70</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>1.62</td><td>0.03</td><td>&lt; 0.001 **</td><td>3.21</td><td>0.032</td><td>&lt; 0.001 **</td><td>2.21</td><td>0.038</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ, χ²b and p-valueb</strong></td><td>0.04</td><td>74.8b</td><td>&lt; 0.001**b</td><td>0.05</td><td>47.2b</td><td>&lt; 0.001**b</td><td>0.054</td><td>75.7</td><td>&lt; 0.001**b</td></tr><tr><td>Number of women</td><td colspan="3">13,859</td><td colspan="3">13,859</td><td colspan="3">13,859</td></tr><tr><td>Integrated log of partial likelihood and χ² value</td><td colspan="3">-243,658 (1,445.0)</td><td colspan="3">-242,425.9 (3 910)</td><td colspan="3">-242,036.6 (4,690)</td></tr><tr><td>Akaike Information Criterion (AIC)</td><td colspan="3">1,407</td><td colspan="3">3,872.3</td><td colspan="3">4,799.6</td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity.

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ²value and p-value from the likelihood ratio test comparing the conditional Cox models with and without a frailty term. A p-value lower than 0.05 means the frailty variance is statistically significantly different from 0.

c DeathICI: Death of an index child in the current interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICBIPrev1: death of a non-index child in the previous interval, Lag-1 Conflict exposure: exposure to conflict in the previous interval

<caption>Table A2</caption>

Parity-specific conditional frailty models of the effects of infant/child mortality on subsequent birth risk among married women in the DRC

<table><thead><tr><th rowspan="2">Variables</th><th colspan="2">1st to 2nd birth</th><th colspan="2">2nd to 3rd birth</th><th colspan="2">3rd to 4th birth</th><th colspan="2">4th to 5th birth</th><th colspan="2">5th to 6th birth</th><th colspan="2">6th to 7th birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.40</td><td>&lt; 0.001 **</td><td>1.17</td><td>&lt; 0.001 **</td><td>1.17</td><td>0.003*</td><td>1.16</td><td>0.279</td><td>1.10</td><td>0.374</td><td>1.16</td><td>0.279</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>a</td><td>a</td><td>3.76</td><td>&lt; 0.001 **</td><td>3.76</td><td>&lt; 0.001 **</td><td>3.82</td><td>&lt; 0.001**</td><td>4.34</td><td>&lt; 0.001**</td><td>3.82</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td>1.50</td><td>&lt; 0.001 **</td><td>1.51</td><td>&lt; 0.001 **</td><td>1.42</td><td>0.014*</td><td>1.42</td><td>0.003**</td><td>1.43</td><td>0.014*</td></tr><tr><td><strong>Age</strong></td><td>9.86</td><td>&lt; 0.001 **</td><td>7.98</td><td>&lt; 0.001 **</td><td>7.98</td><td>&lt; 0.001 **</td><td>2.77</td><td>&lt; 0.001**</td><td>3.88</td><td>&lt; 0.001**</td><td>2.77</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age²</strong></td><td>0.15</td><td>&lt; 0.001 **</td><td>0.19</td><td>&lt; 0.001 **</td><td>0.19</td><td>&lt; 0.001 **</td><td>0.51</td><td>0.025*</td><td>0.36</td><td>&lt; 0.001**</td><td>0.51</td><td>0.025*</td></tr><tr><td><strong>Previous birth interval</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Multiple births (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.96</td><td>0.688</td><td>0.82</td><td>0.047*</td><td>0.82</td><td>0.047*</td><td>0.92</td><td>0.605</td><td>1.00</td><td>0.960</td><td>0.92</td><td>0.605</td></tr><tr><td><strong>Place of residence (Ref.: Rural)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.97</td><td>0.272</td><td>0.99</td><td>0.828</td><td>0.99</td><td>0.826</td><td>1.00</td><td>0.951</td><td>1.03</td><td>0.517</td><td>1.00</td><td>0.951</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.12</td><td>0.029*</td><td>1.24</td><td>&lt; 0.001**</td><td>1.24</td><td>&lt; 0.001**</td><td>1.02</td><td>0.779</td><td>1.12</td><td>0.301</td><td>0.96</td><td>0.829</td></tr><tr><td>Bas-Congo</td><td>1.14</td><td>0.064°</td><td>1.24</td><td>0.005**</td><td>1.24</td><td>0.005**</td><td>1.07</td><td>0.467</td><td>1.17</td><td>0.241</td><td>0.99</td><td>0.958</td></tr><tr><td>Equateur</td><td>1.54</td><td>&lt; 0.001 **</td><td>1.67</td><td>&lt; 0.001 **</td><td>1.67</td><td>&lt; 0.001 **</td><td>1.29</td><td>&lt; 0.001**</td><td>1.65</td><td>&lt; 0.001**</td><td>1.32</td><td>0.046*</td></tr><tr><td>Kasai Oriental</td><td>1.57</td><td>&lt; 0.001 **</td><td>1.62</td><td>&lt; 0.001 **</td><td>1.62</td><td>&lt; 0.001 **</td><td>1.26</td><td>0.003**</td><td>1.66</td><td>&lt; 0.001**</td><td>1.46</td><td>0.006**</td></tr><tr><td>Kasai Occidental</td><td>1.69</td><td>&lt; 0.001 **</td><td>1.75</td><td>&lt; 0.001 **</td><td>1.75</td><td>&lt; 0.001 **</td><td>1.38</td><td>&lt; 0.001**</td><td>1.71</td><td>&lt; 0.001**</td><td>1.56</td><td>0.001**</td></tr><tr><td>Katanga</td><td>1.74</td><td>&lt; 0.001 **</td><td>1.77</td><td>&lt; 0.001 **</td><td>1.77</td><td>&lt; 0.001 **</td><td>1.31</td><td>&lt; 0.001**</td><td>1.77</td><td>&lt; 0.001**</td><td>1.51</td><td>0.002**</td></tr><tr><td>Maniema</td><td>1.46</td><td>&lt; 0.001 **</td><td>1.58</td><td>&lt; 0.001 **</td><td>1.58</td><td>0.008**</td><td>1.26</td><td>0.005**</td><td>1.59</td><td>&lt; 0.001**</td><td>1.35</td><td>0.042*</td></tr><tr><td>Nord Kivu</td><td>1.76</td><td>&lt; 0.001 **</td><td>1.77</td><td>&lt; 0.001 **</td><td>1.77</td><td>&lt; 0.001 **</td><td>1.44</td><td>&lt; 0.001**</td><td>1.83</td><td>&lt; 0.001**</td><td>1.45</td><td>0.015*</td></tr><tr><td>Province Orientale</td><td>1.43</td><td>&lt; 0.001 **</td><td>1.56</td><td>&lt; 0.001 **</td><td>1.56</td><td>&lt; 0.001 **</td><td>1.19</td><td>0.040*</td><td>1.44</td><td>0.002*</td><td>1.32</td><td>0.069°</td></tr><tr><td>Sud Kivu</td><td>2.00</td><td>&lt; 0.001 **</td><td>1.95</td><td>&lt; 0.001 **</td><td>1.95</td><td>&lt; 0.001 **</td><td>1.58</td><td>&lt; 0.001**</td><td>2.04</td><td>&lt; 0.001**</td><td>1.65</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ and p-valueb</strong></td><td>0.03b</td><td>&lt; 0.001**b</td><td>&lt; 10<sup>-3</sup></td><td>0.943</td><td>&lt; 10<sup>-3</sup></td><td>0.945</td><td>&lt; 10<sup>-3</sup></td><td>0.942</td><td>&lt; 10<sup>-3</sup></td><td>0.944</td><td>&lt; 10<sup>-3</sup></td><td>0.952</td></tr><tr><td>Number of events</td><td colspan="2">11,915</td><td colspan="2">9,677</td><td colspan="2">7,594</td><td colspan="2">5,585</td><td colspan="2">3,811</td><td colspan="2">2,450</td></tr><tr><td>Integrated log of partial likelihood and χ² value</td><td colspan="2">-69,816.5<br>(1,928)</td><td colspan="2">-77,019.1 (1,659)</td><td colspan="2">-48,221.7<br>(1,105.8)</td><td colspan="2">-39,755 (227.1)</td><td colspan="2">-23,924.6 (529.9)</td><td colspan="2">-14,914 (358.0)</td></tr><tr><td>AIC</td><td colspan="2">1,896</td><td colspan="2">1,621</td><td colspan="2">1,067.8</td><td colspan="2">189.1</td><td colspan="2">491.9</td><td colspan="2">320.0</td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

DeathICI: Death of an index child in the current interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICBIPrev1: death of a non-index child in the previous interval, Lag-1 Conflict exposure: exposure to conflict in the previous interval

a Variables not included because women with parity 1 do not have non-index children

b p-value from the likelihood ratio test.

c The previous birth interval was not included, as women at parity 1 have no prior birth and, consequently, no prior birth interval.
<caption>Table A3</caption>

Parity-specific conditional frailty models examining the effects of conflict exposure on subsequent birth risk among married women in the Democratic Republic of the Congo

<table><thead><tr><th rowspan="2">Variables</th><th colspan="8">Transition from</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th></th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td><strong>Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0.63</td><td>&lt; 0.001 **</td><td>0.66</td><td>&lt; 0.001 **</td><td>0.66</td><td>&lt; 0.001 **</td><td>0.64</td><td>&lt; 0.001 **</td><td>0.64</td><td>&lt; 0.001 **</td><td>0.55</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td>0.50</td><td>&lt; 0.001 **</td><td>0.51</td><td>&lt; 0.001 **</td><td>0.50</td><td>&lt; 0.001 **</td><td>0.46</td><td>&lt; 0.001 **</td><td>0.49</td><td>&lt; 0.001 **</td><td>0.47</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td>0.32</td><td>&lt; 0.001 **</td><td>0.34</td><td>&lt; 0.001 **</td><td>0.35</td><td>&lt; 0.001 **</td><td>0.32</td><td>&lt; 0.001 **</td><td>0.32</td><td>&lt; 0.001 **</td><td>0.28</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age</strong></td><td>9.18</td><td>&lt; 0.001 **</td><td>6.98</td><td>&lt; 0.001 **</td><td>7.36</td><td>&lt; 0.001 **</td><td>6.04</td><td>&lt; 0.001 **</td><td>4.12</td><td>&lt; 0.001 **</td><td>3.13</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age<sup>2</sup></strong></td><td>0.16</td><td>&lt; 0.001 **</td><td>0.20</td><td>&lt; 0.001 **</td><td>0.20</td><td>&lt; 0.001 **</td><td>0.24</td><td>&lt; 0.001 **</td><td>0.34</td><td>&lt; 0.001 **</td><td>0.46</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Previous birth interval</strong></td><td>a</td><td>a</td><td>0.98</td><td>&lt; 0.001 **</td><td>0.98</td><td>&lt; 0.001 **</td><td>0.98</td><td>&lt; 0.001 **</td><td>0.99</td><td>&lt; 0.001 **</td><td>0.98</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Multiple births (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.99</td><td>0.923</td><td>0.85</td><td>0.099°</td><td>0.94</td><td>0.608</td><td>0.97</td><td>0.849</td><td>1.09</td><td>0.455</td><td>0.91</td><td>0.597</td></tr><tr><td><strong>Place of residence (Ref.: Rural)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.97</td><td>0.318</td><td>0.97</td><td>0.404</td><td>0.95</td><td>0.118</td><td>1.00</td><td>0.870</td><td>1.04</td><td>0.305</td><td>1.01</td><td>0.807</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>0.77</td><td>&lt; 0.001 **</td><td>0.86</td><td>0.024*</td><td>0.88</td><td>0.097°</td><td>0.76</td><td>0.002**</td><td>0.77</td><td>0.027*</td><td>0.67</td><td>0.007*</td></tr><tr><td>Bas-Congo</td><td>1.04</td><td>0.615</td><td>1.11</td><td>0.161</td><td>1.09</td><td>0.306</td><td>1.06</td><td>0.546</td><td>1.10</td><td>0.475</td><td>0.95</td><td>0.799</td></tr><tr><td>Equateur</td><td>1.48</td><td>&lt; 0.001 **</td><td>1.59</td><td>&lt; 0.001 **</td><td>1.51</td><td>&lt; 0.001 **</td><td>1.45</td><td>&lt; 0.001 **</td><td>1.56</td><td>&lt; 0.001 **</td><td>1.28</td><td>0.076°</td></tr><tr><td>Kasai Oriental</td><td>1.30</td><td>&lt; 0.001 **</td><td>1.35</td><td>&lt; 0.001 **</td><td>1.33</td><td>&lt; 0.001 **</td><td>1.29</td><td>&lt; 0.001 **</td><td>1.42</td><td>&lt; 0.001 **</td><td>1.27</td><td>0.091°</td></tr><tr><td>Kasai Occidental</td><td>1.54</td><td>&lt; 0.001 **</td><td>1.62</td><td>&lt; 0.001 **</td><td>1.62</td><td>&lt; 0.001 **</td><td>1.55</td><td>&lt; 0.001 **</td><td>1.59</td><td>&lt; 0.001 **</td><td>1.48</td><td>0.004**</td></tr><tr><td>Katanga</td><td>2.01</td><td>&lt; 0.001 **</td><td>2.04</td><td>&lt; 0.001 **</td><td>2.00</td><td>&lt; 0.001 **</td><td>1.96</td><td>&lt; 0.001 **</td><td>2.09</td><td>&lt; 0.001 **</td><td>1.77</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.35</td><td>&lt; 0.001 **</td><td>1.53</td><td>&lt; 0.001 **</td><td>1.53</td><td>&lt; 0.001 **</td><td>1.45</td><td>&lt; 0.001 **</td><td>1.52</td><td>&lt; 0.001 **</td><td>1.38</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>2.70</td><td>&lt; 0.001 **</td><td>2.70</td><td>&lt; 0.001 **</td><td>2.52</td><td>&lt; 0.001 **</td><td>2.61</td><td>&lt; 0.001 **</td><td>3.04</td><td>&lt; 0.001 **</td><td>2.57</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.84</td><td>&lt; 0.001 **</td><td>2.09</td><td>&lt; 0.001 **</td><td>2.09</td><td>&lt; 0.001 **</td><td>2.06</td><td>&lt; 0.001 **</td><td>2.22</td><td>&lt; 0.001 **</td><td>2.15</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>2.81</td><td>&lt; 0.001 **</td><td>2.85</td><td>&lt; 0.001 **</td><td>2.76</td><td>&lt; 0.001 **</td><td>2.66</td><td>&lt; 0.001 **</td><td>3.14</td><td>&lt; 0.001 **</td><td>2.85</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ and p-value<sup>b</sup></strong></td><td>0.04</td><td>&lt; 0.001**<sup>b</sup></td><td>&lt; 10<sup>-3</sup></td><td>0.937</td><td>&lt; 10<sup>-3</sup></td><td>0.945</td><td>&lt; 10<sup>-3</sup></td><td>0.950</td><td>&lt; 10<sup>-3</sup></td><td>0.956</td><td>&lt; 10<sup>-3</sup></td><td>0.963</td></tr><tr><td>Number of events</td><td>11,915</td><td></td><td>9,677</td><td></td><td>7,594</td><td></td><td>5,585</td><td></td><td>3,811</td><td></td><td>2,450</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td>-75,536.4 (2,827)</td><td></td><td>-61,877 (2,136)</td><td></td><td>-48,033.8 (1,381)</td><td></td><td>-34,889 (1,055)</td><td></td><td>-23,842 (694)</td><td></td><td>-14,845 (494)</td><td></td></tr><tr><td>AIC</td><td>2,791</td><td></td><td>2,098</td><td></td><td>1,443</td><td></td><td>1,017</td><td></td><td>656</td><td></td><td>456</td><td></td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a The previous birth interval was not included, as women at parity 1 have no prior birth and, consequently, no prior birth interval.

b p-value from the likelihood ratio test.

<caption>Table A4</caption>

Parity-specific Cox frailty models of the interactions between infant/child mortality and conflict exposure on subsequent birth risk among married women in the DRC

<table><thead><tr><th rowspan="2">Variables</th><th colspan="8">Transition from</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th></th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI<sup>a</sup> * Conflict exposure—Low</td><td>0.81</td><td>0.033*</td><td>0.93</td><td>0.552</td><td>0.96</td><td>0.806</td><td>0.90</td><td>0.473</td><td>1.05</td><td>0.739</td><td>0.65</td><td>0.051°</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td>0.85</td><td>0.138</td><td>0.96</td><td>0.746</td><td>1.09</td><td>0.480</td><td>1.04</td><td>0.759</td><td>0.72</td><td>0.077°</td><td>0.91</td><td>0.616</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td>1.05</td><td>0.620</td><td>0.86</td><td>0.190</td><td>0.78</td><td>0.065°</td><td>0.97</td><td>0.862</td><td>0.78</td><td>0.153</td><td>0.80</td><td>0.271</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td>1.11</td><td>0.122</td><td>0.89</td><td>0.049*</td><td>1.08</td><td>0.221</td><td>0.82</td><td>0.035*</td><td>1.00</td><td>0.981</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td>1.94</td><td>0.020*</td><td>1.66</td><td>0.079°</td><td>0.99</td><td>0.891</td><td>0.95</td><td>0.641</td><td>2.11</td><td>&lt; 0.001**</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td>0.98</td><td>0.095°</td><td>0.99</td><td>0.911</td><td>0.95</td><td>0.711</td><td>0.74</td><td>0.031*</td><td>0.92</td><td>0.640</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td></td><td></td><td>0.47</td><td>&lt; 0.001**</td><td>0.63</td><td>0.040*</td><td>1.05</td><td>0.841</td><td>1.22</td><td>0.493</td><td>0.91</td><td>0.344</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td>0.90</td><td>0.604</td><td>0.74</td><td>0.203</td><td>1.16</td><td>0.531</td><td>1.51</td><td>0.150</td><td>0.92</td><td>0.348</td></tr><tr><td>DNIBCIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td>1.00</td><td>0.963</td><td>0.74</td><td>0.192</td><td>0.53</td><td>0.012*</td><td>0.69</td><td>0.215</td><td>0.64</td><td>0.040*</td></tr><tr><td><strong>Frailty variance θ and p-value<sup>b</sup></strong></td><td>0.02<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>&lt; 10<sup>-3</sup></td><td>0.943</td><td>&lt; 10<sup>-3</sup></td><td>0.975</td><td>&lt; 10<sup>-3</sup></td><td>0.945</td><td>&lt; 10<sup>-3</sup></td><td>0.951</td><td>&lt; 10<sup>-3</sup></td><td>0.943</td></tr><tr><td>Number of events</td><td>11,915</td><td></td><td>9,677</td><td></td><td>7,594</td><td></td><td>5,585</td><td></td><td>3,811</td><td></td><td>2,450</td><td></td></tr><tr><td>AIC</td><td>2,861.8</td><td></td><td>1,835</td><td></td><td>733.8</td><td></td><td>851</td><td></td><td>361</td><td></td><td>101.4</td><td></td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

Note: The model controls for the effect of the same variables presented in Table A2.

a DeathICI: Death of an index child in the current interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICBIPrev1: death of a non-index child in the previous interval, Lag-1 Conflict exposure: exposure to conflict in the previous interval

b p-value from the likelihood ratio test.
<caption>**Table A5**</caption>

Cox frailty model without controls: Effect of infant/child mortality, conflict exposure, and their interaction on birth transitions among married women in the DRC (all parities)

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant and child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.32</td><td>0.029</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>1.49</td><td>0.037</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>4.53</td><td>0.054</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>4.66</td><td>0.056</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.09</td><td>0.036</td><td>0.010*</td><td></td><td></td><td></td><td>1.11</td><td>0.038</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>0.65</td><td>0.016</td><td>&lt; 0.001 **</td><td>0.66</td><td>0.056</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>0.56</td><td>0.017</td><td>&lt; 0.001 **</td><td>0.56</td><td>0.058</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>0.47</td><td>0.017</td><td>&lt; 0.001 **</td><td>0.86</td><td>0.056</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.01</td><td>0.016</td><td>0.525</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.07</td><td>0.017</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.36</td><td>0.018</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI<sup>c</sup> * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.91</td><td>0.044</td><td>0.082<sup>d</sup></td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.92</td><td>0.047</td><td>0.152</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.97</td><td>0.051</td><td>0.601</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.01</td><td>0.137</td><td>0.823</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.56</td><td>0.130</td><td>0.001**</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99</td><td>0.612</td><td>0.402</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.81</td><td>0.109</td><td>0.221</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.92</td><td>0.112</td><td>0.590</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.73</td><td>0.107</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ, χ<sup>2</sup><sup>b</sup> and p-value<sup>b</sup></strong></td><td>0.08</td><td>161<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.11</td><td>286<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.10</td><td>315</td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td>13,859</td><td></td><td></td><td>13,859</td><td></td><td></td><td>13,859</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td>-243,871 (1,019)</td><td></td><td></td><td>-243,368 (2,026)</td><td></td><td></td><td>-243,111 (2,114)</td><td></td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td>1,011</td><td></td><td></td><td>2,018</td><td></td><td></td><td>6,230.4</td><td></td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity. Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ²-value and p-value from the likelihood ratio test comparing the conditional Cox models with and without a frailty term. A *p-value* lower than 0.05 means the frailty variance is statistically significantly different from 0.

c. *DeathICI*: death of an index child in the (current) interval, *DeathNICI*: death of a non-index child in the interval, *DeathNICBI*: death of a non-index child before the current interval (in all previous intervals), *DeathNICBIPrev1*: death of a non-index child in the previous interval

<caption>**Table A6**</caption>

Cox frailty model of the effects of conflict exposure within a 75 km buffer around DHS clusters (measured by tertiles of cumulative conflict events) and its interaction with infant/child mortality on transitions to higher-order births

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.31</td><td>0.03</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>5.83</td><td>0.07</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.26</td><td>0.04</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Conflict exposure (Ref.: No event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0.74</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.73</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td>0.66</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.63</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td>0.43</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.4</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>1.06</td><td>0.02</td><td>0.002 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>1.09</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>1.31</td><td>0.03</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI<sup>c</sup> * Conflict exposure—Low</td><td></td><td></td><td></td><td>0.96</td><td>0.05</td><td>0.501</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>1.02</td><td>0.06</td><td>0.736</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td>1.08</td><td>0.06</td><td>0.235</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td>1.00</td><td>0.16</td><td>0.998</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>1.13</td><td>0.18</td><td>0.509</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td>1.39</td><td>0.20</td><td>0.045*</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td>0.87</td><td>0.08</td><td>0.093°</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A6 (continued)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td>0.82</td><td>0.09</td><td>0.029 *</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td>0.63</td><td>0.09</td><td>&lt; 0.001 **</td></tr><tr><td>Age</td><td>1.09</td><td>0.04</td><td>0.034 *</td><td>1.10</td><td>0.04</td><td>0.023 *</td></tr><tr><td>Age<sup>2</sup></td><td>1.02</td><td>0.04</td><td>0.619</td><td>1.01</td><td>0.04</td><td>0.782</td></tr><tr><td>Length of the previous birth interval</td><td>0.99</td><td>0</td><td>0.006 **</td><td>0.99</td><td>0</td><td>0.257</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.93</td><td>0.04</td><td>0.083 °</td><td>0.89</td><td>0.04</td><td>0.005 **</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>1.04</td><td>0.01</td><td>0.003 **</td><td>1.03</td><td>0.01</td><td>0.022 *</td></tr><tr><td>Region (Ref.: Kinshasa)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>0.71</td><td>0.03</td><td>&lt; 0.001 **</td><td>0.72</td><td>0.03</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>0.75</td><td>0.04</td><td>&lt; 0.001 **</td><td>0.77</td><td>0.04</td><td>&lt; 0.001 **</td></tr><tr><td>Equateur</td><td>0.91</td><td>0.03</td><td>0.002 **</td><td>0.94</td><td>0.03</td><td>0.041 *</td></tr><tr><td>Kasai Oriental</td><td>0.94</td><td>0.03</td><td>0.038 *</td><td>0.96</td><td>0.03</td><td>0.179</td></tr><tr><td>Kasai Occidental</td><td>1.03</td><td>0.03</td><td>0.348</td><td>1.06</td><td>0.03</td><td>0.044 *</td></tr><tr><td>Katanga</td><td>0.97</td><td>0.03</td><td>0.369</td><td>1.00</td><td>0.03</td><td>0.898</td></tr><tr><td>Maniema</td><td>0.97</td><td>0.03</td><td>0.288</td><td>1.01</td><td>0.03</td><td>0.736</td></tr><tr><td>Nord Kivu</td><td>1.66</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.69</td><td>0.04</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.02</td><td>0.03</td><td>0.559</td><td>1.05</td><td>0.03</td><td>0.132</td></tr><tr><td>Sud-Kivu</td><td>1.90</td><td>0.03</td><td>&lt; 0.001 **</td><td>1.87</td><td>0.03</td><td>&lt; 0.001 **</td></tr><tr><td>Frailty variance θ, χ<sup>2</sup>b and p-value<sup>b</sup></td><td>0.063</td><td>73<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.076</td><td>110<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td>13,859</td><td></td><td></td><td>13,859</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td>-324,400 (2,446)</td><td></td><td></td><td>-324,885 (2,4877)</td><td></td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td>2,408</td><td></td><td></td><td>6,961</td><td></td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity.

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ²-value and p-value from the likelihood ratio test.

c DeathICI: Death of an index child in the current interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICI: death of a non-index child in the interval

<caption>Table A7</caption>

Cox frailty model of the effects of conflict exposure within a 75 km buffer around DHS clusters (measured using tertiles of fatalities) and its interaction with infant/child mortality on transitions to higher-order births (second to seventh)

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td>Death of an index child, DeathICI<sup>C</sup> (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.30</td><td>0.023</td><td>&lt; 0.001 **</td></tr><tr><td>Death of a non-index child, DeathNICI (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>5.9°</td><td>0.071</td><td>&lt; 0.001 **</td></tr><tr><td>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.27</td><td>0.042</td><td>&lt; 0.001 **</td></tr><tr><td>Conflict exposure (Ref.: No death)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0.78</td><td>0.017</td><td>&lt; 0.001 **</td><td>0.78</td><td>0.018</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td>0.65</td><td>0.018</td><td>&lt; 0.001 **</td><td>0.65</td><td>0.017</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td>0.43</td><td>0.021</td><td>&lt; 0.001 **</td><td>0.43</td><td>0.016</td><td>&lt; 0.001 **</td></tr><tr><td>Lag-1 Conflict exposure (Ref.: No death)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>1.09</td><td>0.021</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>1.12</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>1.16</td><td>0.019</td><td>&lt; 0.001 **</td></tr><tr><td>Interaction between child death and conflict intensity</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI * Conflict exposure—Low</td><td></td><td></td><td></td><td>0.96</td><td>0.056</td><td>0.504</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>1.04</td><td>0.061</td><td>0.444</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td>1.11</td><td>0.063</td><td>0.081 °</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td>0.93</td><td>0.171</td><td>0.696</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>0.88</td><td>0.180</td><td>0.481</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td>1.94</td><td>0.212</td><td>0.001 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td>1.12</td><td>0.101</td><td>0.266</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td>0.77</td><td>0.082</td><td>0.002 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td>0.61</td><td>0.080</td><td>&lt; 0.001 **</td></tr><tr><td>Age</td><td>1.06</td><td>0.041</td><td>0.030*</td><td>1.11</td><td>0.041</td><td>0.014 *</td></tr><tr><td>Age<sup>2</sup></td><td>1.02</td><td>0.041</td><td>0.401</td><td>1.01</td><td>0.043</td><td>0.900</td></tr><tr><td>Length of the previous birth interval</td><td>0.99</td><td>0.001</td><td>0.007 **</td><td>0.99</td><td>0.000</td><td>0.105</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.94</td><td>0.041</td><td>0.162</td><td>0.89</td><td>0.039</td><td>0.007 **</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>1.04</td><td>0.011</td><td>0.006 **</td><td>1.04</td><td>0.011</td><td>0.009 **</td></tr><tr><td>Region (Ref.: Kinshasa)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table A7 (continued)**</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td>Bandundu</td><td>0.76</td><td>0.029</td><td>&lt; 0.001 **</td><td>0.79</td><td>0.032</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>0.83</td><td>0.038</td><td>&lt; 0.001 **</td><td>0.84</td><td>0.038</td><td>&lt; 0.001 **</td></tr><tr><td>Equateur</td><td>0.99</td><td>0.030</td><td>0.711</td><td>1.02</td><td>0.031</td><td>0.601</td></tr><tr><td>Kasai Oriental</td><td>1.02</td><td>0.027</td><td>0.563</td><td>1.03</td><td>0.029</td><td>0.313</td></tr><tr><td>Kasai Occidental</td><td>1.15</td><td>0.028</td><td>&lt; 0.001 **</td><td>1.17</td><td>0.028</td><td>&lt; 0.001 **</td></tr><tr><td>Katanga</td><td>1.06</td><td>0.031</td><td>0.059 °</td><td>1.08</td><td>0.029</td><td>0.011 °</td></tr><tr><td>Maniema</td><td>1.12</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.12</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>1.85</td><td>0.032</td><td>&lt; 0.001 **</td><td>1.80</td><td>0.037</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.15</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.15</td><td>0.027</td><td>&lt; 0.001 **</td></tr><tr><td>Sud-Kivu</td><td>2.04</td><td>0.033</td><td>&lt; 0.001 **</td><td>1.97</td><td>0.025</td><td>&lt; 0.001 **</td></tr><tr><td>Frailty variance θ, χ<sup>2</sup>b and p-value<sup>b</sup></td><td>0.061</td><td>131<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.067</td><td>602<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td>13,859</td><td></td><td></td><td>13,859</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td>-324,221 (2,806)</td><td></td><td></td><td>-323,654 (3,938)</td><td></td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td>2,806</td><td></td><td></td><td>3,870</td><td></td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity.

Statistical significance: **p < 0.01, * p < 0.05, ° p < 0.10.

a standard error of coefficients b χ² value and p-value from the likelihood ratio test.

c DeathICI: Death of an index child in the current interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICBIPrev1: death of a non-index child in the previous interval, Lag-1 Conflict exposure: exposure to conflict in the previous interval

<caption>**Table A8**</caption>

Cox frailty model of the effects of conflict exposure (measured by tertiles of fatalities in the region of residence) and its interaction with infant or child mortality on transitions to 2nd –7th births among married women in the DRC

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.38</td><td>0.03</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>4.96</td><td>0.09</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td></td><td></td><td></td><td>1.39</td><td>0.05</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Conflict exposure (Ref.: No death)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0.65</td><td>0.01</td><td>&lt; 0.001 **</td><td>0.65</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td>0.54</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.53</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td>0.31</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.31</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No death)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>0.93</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>1.02</td><td>0.02</td><td>0.256</td></tr><tr><td>High</td><td></td><td></td><td></td><td>0.89</td><td>0.02</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI * Conflict exposure—Low</td><td></td><td></td><td></td><td>0.93</td><td>0.05</td><td>0.081 °</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>1.04</td><td>0.05</td><td>0.405</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td>0.97</td><td>0.05</td><td>0.506</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td>0.94</td><td>0.16</td><td>0.678</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td>1.16</td><td>0.14</td><td>0.302</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td>1.65</td><td>0.15</td><td>&lt; 0.001 **</td></tr><tr><td>DeathNICBI* Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td>0.85</td><td>0.07</td><td>0.030 °</td></tr><tr><td>DeathNICBI* Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td>0.89</td><td>0.07</td><td>0.098 °</td></tr><tr><td>DeathNICBI* Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td>0.57</td><td>0.07</td><td>&lt; 0.001 **</td></tr><tr><td>Frailty variance θ, χ<sup>2</sup>b and p-value<sup>b</sup></td><td>0.03</td><td>43.5<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.03</td><td>39.1</td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td>13,859</td><td></td><td></td><td>13,859</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td>-322,715.4 (5,817)</td><td></td><td></td><td>-322,109.4 (7,029)</td><td></td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td>5,529</td><td></td><td></td><td>6,961</td><td></td></tr></tbody></table>

Statistical significance: **p < 0.01, * p < 0.05, ° p < 0.10.

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity. The model controls for the effect of the same variables presented in Table A1.

a standard error of coefficients

b χ² value and p-value from the likelihood ratio test.

c. DeathICI: Death of an index child in the current interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval (in all previous intervals), DeathNICBIPrev1: death of a non-index child in the previous interval, Lag-1 Conflict exposure: exposure to conflict in the previous interval
<caption>**Table A9**</caption>

Parity-specific conditional frailty models examining the effects of conflict exposure (measured as the log of conflict events) on subsequent birth risk

<table><thead><tr><th rowspan="3">Variables</th><th colspan="12">Transition from</th><th colspan="2">All transitions combined</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td>Log of the cum. number of conflict events</td><td>0.55</td><td>&lt; 0.001 **</td><td>0.72</td><td>&lt; 0.001 **</td><td>0.84</td><td>&lt; 0.001 **</td><td>0.88</td><td>&lt; 0.001 **</td><td>0.88</td><td>&lt; 0.001 **</td><td>0.94</td><td>&lt; 0.001 **</td><td>0.72</td><td>&lt; 0.001 **</td></tr><tr><td>Age</td><td>0.98</td><td>0.878</td><td>0.89</td><td>0.166</td><td>0.81</td><td>0.027 *</td><td>0.75</td><td>0.011 *</td><td>0.84</td><td>0.198</td><td>0.89</td><td>0.525</td><td>1.06</td><td>0.158</td></tr><tr><td>Age<sup>2</sup></td><td>1.09</td><td>0.333</td><td>1.23</td><td>0.012 *</td><td>1.39</td><td>&lt; 0.001 **</td><td>1.53</td><td>&lt; 0.001 **</td><td>1.39</td><td>0.016 *</td><td>1.32</td><td>0.123</td><td>1.05</td><td>0.203</td></tr><tr><td>Previous birth interval</td><td></td><td></td><td>0.99</td><td>&lt; 0.001 **</td><td>1.00</td><td>0.341</td><td>0.99</td><td>0.001 **</td><td>1.00</td><td>0.224</td><td>1.00</td><td>0.811</td><td>0.99</td><td>0.002 **</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.9</td><td>0.159</td><td>0.94</td><td>0.435</td><td>0.98</td><td>0.838</td><td>0.90</td><td>0.394</td><td>0.91</td><td>0.493</td><td>1.06</td><td>0.77</td><td>0.97</td><td>0.394</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.97</td><td>0.154</td><td>0.96</td><td>0.103</td><td>1.00</td><td>0.936</td><td>1.00</td><td>0.965</td><td>1.01</td><td>0.848</td><td>1.05</td><td>0.284</td><td>0.99</td><td>0.245</td></tr><tr><td>Region (Ref.: Kinshasa)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>0.57</td><td>&lt; 0.001 **</td><td>0.85</td><td>0.002 **</td><td>0.80</td><td>&lt; 0.001 **</td><td>0.84</td><td>0.021 *</td><td>0.82</td><td>0.057 °</td><td>0.82</td><td>0.184</td><td>0.76</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>1.01</td><td>0.844</td><td>1.01</td><td>0.829</td><td>0.95</td><td>0.474</td><td>0.94</td><td>0.521</td><td>0.95</td><td>0.698</td><td>0.81</td><td>0.255</td><td>0.96</td><td>0.226</td></tr><tr><td>Equateur</td><td>1.20</td><td>0.001 **</td><td>1.39</td><td>&lt; 0.001 **</td><td>1.26</td><td>&lt; 0.001 **</td><td>1.22</td><td>0.008 **</td><td>1.18</td><td>0.081 °</td><td>1.04</td><td>0.759</td><td>1.28</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Oriental</td><td>1.18</td><td>0.003 **</td><td>1.24</td><td>&lt; 0.001 **</td><td>1.12</td><td>0.069 °</td><td>1.13</td><td>0.124</td><td>1.11</td><td>0.288</td><td>1.09</td><td>0.54</td><td>1.15</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Occidental</td><td>1.27</td><td>&lt; 0.001 **</td><td>1.45</td><td>&lt; 0.001 **</td><td>1.27</td><td>&lt; 0.001 **</td><td>1.25</td><td>0.002 **</td><td>1.21</td><td>0.042 *</td><td>1.17</td><td>0.26</td><td>1.31</td><td>&lt; 0.001 **</td></tr><tr><td>Katanga</td><td>2.05</td><td>&lt; 0.001 **</td><td>1.69</td><td>&lt; 0.001 **</td><td>1.49</td><td>&lt; 0.001 **</td><td>1.43</td><td>&lt; 0.001 **</td><td>1.37</td><td>&lt; 0.001 **</td><td>1.15</td><td>0.295</td><td>1.77</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.57</td><td>&lt; 0.001 **</td><td>1.37</td><td>&lt; 0.001 **</td><td>1.16</td><td>0.024 *</td><td>1.21</td><td>0.021 *</td><td>1.20</td><td>0.087 °</td><td>1.08</td><td>0.592</td><td>1.3</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>7.51</td><td>&lt; 0.001 **</td><td>3.41</td><td>&lt; 0.001 **</td><td>1.95</td><td>&lt; 0.001 **</td><td>1.73</td><td>&lt; 0.001 **</td><td>1.58</td><td>&lt; 0.001 **</td><td>1.25</td><td>0.139</td><td>3.3</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>6.59</td><td>&lt; 0.001 **</td><td>2.62</td><td>&lt; 0.001 **</td><td>1.61</td><td>&lt; 0.001 **</td><td>1.38</td><td>&lt; 0.001 **</td><td>1.30</td><td>0.016 *</td><td>1.04</td><td>0.799</td><td>2.4</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>8.10</td><td>&lt; 0.001 **</td><td>3.47</td><td>&lt; 0.001 **</td><td>2.19</td><td>&lt; 0.001 **</td><td>1.82</td><td>&lt; 0.001 **</td><td>1.59</td><td>&lt; 0.001 **</td><td>1.53</td><td>0.004 **</td><td>3.27</td><td>&lt; 0.001 **</td></tr><tr><td>Frailty variance θ</td><td>0.24</td><td>&lt; 0.001 **</td><td>0.001</td><td>&lt; 0.001 **</td><td>0.001</td><td>&lt; 0.001 **</td><td>0.001</td><td>&lt; 0.001 **</td><td>&lt; 10<sup>-3</sup></td><td>0.943</td><td>&lt; 10<sup>-3</sup></td><td>&lt; 0.001 **</td><td>0.05</td><td>&lt; 0.001 **</td></tr><tr><td>Number of events</td><td>11,915</td><td></td><td>9,577</td><td></td><td>7,594</td><td></td><td>5,585</td><td></td><td>3,811</td><td></td><td>2,450</td><td></td><td>37,447</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td>-98,501<br>(2,8141)</td><td></td><td>-75,213 (1,512)</td><td></td><td>-57,107 (548)</td><td></td><td>-39,869 (318)</td><td></td><td>-25,395 (227)</td><td></td><td>-14,571 (113)</td><td></td><td>322,349 (6,548)</td><td></td></tr><tr><td>AIC</td><td>2,999</td><td></td><td>1,478</td><td></td><td>514</td><td></td><td>286</td><td></td><td>193</td><td></td><td>79</td><td></td><td>6,716</td><td></td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a The previous birth interval was not included, as women at parity 1 have no prior birth and, consequently, no prior birth interval.

<caption>**Table A10**</caption>

Parity-specific frailty models assessing the interaction between conflict exposure (log of cumulative conflict events) and infant/child mortality on subsequent birth risk

<table><thead><tr><th rowspan="3">Variables</th><th colspan="12">Transition from</th><th colspan="2">All transitions combined</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td>DeathICI<sup>b</sup> (Yes)*Log (Cumulated conflict events)</td><td>0.54</td><td>&lt; 0.001 **</td><td>1.02</td><td>0.450</td><td>1.04</td><td>0.074 °</td><td>0.99</td><td>0.969</td><td>1.00</td><td>0.830</td><td>1.00</td><td>0.860</td><td>0.99</td><td>0.620</td></tr><tr><td>DeathNICI (Yes)*Log (Cumulated conflict events)</td><td>a</td><td>a</td><td>1.14</td><td>0.098 °</td><td>1.05</td><td>0.469</td><td>1.13</td><td>0.230</td><td>1.13</td><td>0.210</td><td>1.03</td><td>0.830</td><td>1.12</td><td>0.004 **</td></tr><tr><td>DeathNICBIPrev1 (Yes)*Log (Lag 1 of cumulated conflict events)</td><td>a</td><td>a</td><td>0.85</td><td>&lt; 0.001 **</td><td>0.97</td><td>&lt; 0.001 **</td><td>0.94</td><td>0.140</td><td>0.98</td><td>0.740</td><td>0.910</td><td>0.051 °</td><td>0.87</td><td>&lt; 0.001 **</td></tr><tr><td>Frailty variance θ</td><td>0.262</td><td>&lt; 0.001 **</td><td>0.001</td><td>0.947</td><td>0.001</td><td>0.941</td><td>0.001</td><td>0.936</td><td>&lt; 10<sup>-3</sup></td><td>0.943</td><td>&lt; 10<sup>-3</sup></td><td>0.952</td><td>0.05</td><td>&lt; 0.001 **</td></tr><tr><td>Number of events</td><td>11,915</td><td></td><td>9,677</td><td></td><td>7,594</td><td></td><td>5,585</td><td></td><td>3,811</td><td></td><td>2,450</td><td></td><td>37,447</td><td></td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td>-98 101 (2 811)</td><td></td><td>-75,927 (2,183)</td><td></td><td>-56,952 (857)</td><td></td><td>-39,678<br>(381)</td><td></td><td>-25,367<br>(281)</td><td></td><td>-14,547 (160)</td><td></td><td>321,827 (7,592)</td><td></td></tr><tr><td>AIC</td><td>3,304</td><td></td><td>2,135</td><td></td><td>809</td><td></td><td>333</td><td></td><td>234</td><td></td><td>112</td><td></td><td>7,544</td><td></td></tr></tbody></table>

a. Variables not included because women with parity 1 do not have non-index children. Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

Note: The model controls for the effect of the same variables presented in Table A9.

b. *DeathICI*: death of an index child in the (current) interval, *DeathNICI*: death of a non-index child in the interval, *DeathNICBIPrev1*: death of a non-index child in the previous interval

<caption>**Table A11**</caption>

Parity-specific and combined-parity conditional frailty models of the effects of conflict exposure (measured as the log of cumulative fatalities) on subsequent birth risk

<table><thead><tr><th rowspan="3">Variables</th><th colspan="12">Transition from</th><th colspan="2">All transitions combined</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td>Log of the cum. number of fatalities</td><td>0.74</td><td>&lt; 0.001 **</td><td>0.86</td><td>&lt; 0.001 **</td><td>0.91</td><td>&lt; 0.001 **</td><td>0.95</td><td>&lt; 0.001 **</td><td>0.95</td><td>&lt; 0.001 **</td><td>0.98</td><td>&lt; 0.001 **</td><td>0.87</td><td>&lt; 0.001 **</td></tr><tr><td>Age</td><td>0.98</td><td>0.633</td><td>0.88</td><td>0.126</td><td>0.78</td><td>0.011 *</td><td>0.75</td><td>0.011 *</td><td>0.85</td><td>0.245</td><td>0.89</td><td>0.51</td><td>1.1</td><td>0.020 *</td></tr><tr><td>Age<sup>2</sup></td><td>1.02</td><td>0.541</td><td>1.26</td><td>0.007 **</td><td>1.44</td><td>&lt; 0.001 **</td><td>1.53</td><td>&lt; 0.001 **</td><td>1.37</td><td>0.022 *</td><td>1.32</td><td>0.118</td><td>1.01</td><td>0.723</td></tr><tr><td>Previous birth interval</td><td></td><td></td><td>0.99</td><td>&lt; 0.001 **</td><td>1.00</td><td>0.403</td><td>0.99</td><td>0.002 **</td><td>1.00</td><td>0.197</td><td>1.00</td><td>0.797</td><td>0.99</td><td>&lt; 0.001 **</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.95</td><td>0.136</td><td>0.95</td><td>0.493</td><td>0.98</td><td>0.866</td><td>0.91</td><td>0.441</td><td>0.90</td><td>0.469</td><td>1.06</td><td>0.775</td><td>0.96</td><td>0.353</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td>0.99</td><td>0.56</td><td>0.98</td><td>0.422</td><td>1.00</td><td>0.856</td><td>1.00</td><td>0.957</td><td>1.01</td><td>0.866</td><td>1.05</td><td>0.273</td><td>1.00</td><td>0.683</td></tr><tr><td>Region (Ref.: Kinshasa)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>Table A11 (continued)</caption><thead><tr><th rowspan="3">Variables</th><th colspan="12">Transition from</th><th colspan="2">All transitions combined</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td>Bandundu</td><td>0.70</td><td>&lt; 0.001**</td><td>0.85</td><td>0.002**</td><td>0.81</td><td>&lt; 0.001**</td><td>0.89</td><td>0.146</td><td>0.90</td><td>0.289</td><td>0.85</td><td>0.276</td><td>0.81</td><td>&lt; 0.001**</td></tr><tr><td>Bas-Congo</td><td>1.15</td><td>0.030*</td><td>1.13</td><td>0.068°</td><td>0.96</td><td>0.600</td><td>0.97</td><td>0.734</td><td>0.98</td><td>0.884</td><td>0.83</td><td>0.303</td><td>1.02</td><td>0.660</td></tr><tr><td>Equateur</td><td>1.04</td><td>0.540</td><td>1.30</td><td>&lt; 0.001**</td><td>1.25</td><td>&lt; 0.001**</td><td>1.25</td><td>0.003**</td><td>1.22</td><td>0.043*</td><td>1.06</td><td>0.66</td><td>1.22</td><td>&lt; 0.001**</td></tr><tr><td>Kasai Oriental</td><td>1.04</td><td>0.464</td><td>1.18</td><td>0.002**</td><td>1.09</td><td>0.149</td><td>1.16</td><td>0.054°</td><td>1.16</td><td>0.136</td><td>1.11</td><td>0.448</td><td>1.12</td><td>&lt; 0.001**</td></tr><tr><td>Kasai Occidental</td><td>1.16</td><td>0.031*</td><td>1.40</td><td>&lt; 0.001**</td><td>1.21</td><td>0.001**</td><td>1.26</td><td>0.002**</td><td>1.23</td><td>0.027*</td><td>1.19</td><td>0.217</td><td>1.23</td><td>&lt; 0.001**</td></tr><tr><td>Katanga</td><td>1.90</td><td>&lt; 0.001**</td><td>1.55</td><td>&lt; 0.001**</td><td>1.48</td><td>&lt; 0.001**</td><td>1.41</td><td>&lt; 0.001**</td><td>1.36</td><td>0.001**</td><td>1.16</td><td>0.281</td><td>1.62</td><td>&lt; 0.001**</td></tr><tr><td>Maniema</td><td>1.27</td><td>&lt; 0.001**</td><td>1.40</td><td>&lt; 0.001**</td><td>1.22</td><td>0.003**</td><td>1.26</td><td>0.004**</td><td>1.27</td><td>0.021*</td><td>1.12</td><td>0.445</td><td>1.32</td><td>&lt; 0.001**</td></tr><tr><td>Nord Kivu</td><td>4.95</td><td>&lt; 0.001**</td><td>2.74</td><td>&lt; 0.001**</td><td>1.83</td><td>&lt; 0.001**</td><td>1.66</td><td>&lt; 0.001**</td><td>1.58</td><td>&lt; 0.001**</td><td>1.25</td><td>0.127</td><td>2.5</td><td>&lt; 0.001**</td></tr><tr><td>Province Orientale</td><td>4.08</td><td>&lt; 0.001**</td><td>2.25</td><td>&lt; 0.001**</td><td>1.57</td><td>&lt; 0.001**</td><td>1.35</td><td>&lt; 0.001**</td><td>1.32</td><td>0.010*</td><td>1.05</td><td>0.725</td><td>1.93</td><td>&lt; 0.001**</td></tr><tr><td>Sud Kivu</td><td>4.73</td><td>&lt; 0.001**</td><td>2.82</td><td>&lt; 0.001**</td><td>2.03</td><td>&lt; 0.001**</td><td>1.74</td><td>&lt; 0.001**</td><td>1.56</td><td>&lt; 0.001**</td><td>1.53</td><td>0.004**</td><td>2.43</td><td>&lt; 0.001**</td></tr><tr><td><strong>Frailty variance θ</strong></td><td>0.11</td><td>&lt; 0.001**</td><td>0.001</td><td>938</td><td>&lt; 10<sup>-3</sup></td><td>0.970</td><td>&lt; 10<sup>-3</sup></td><td>0.937</td><td>&lt; 10<sup>-3</sup></td><td>0.934</td><td>&lt; 10<sup>-3</sup></td><td>0.951</td><td>0.03</td><td>&lt; 0.001**</td></tr><tr><td>Number of events</td><td colspan="2">11,915</td><td colspan="2">9,677</td><td colspan="2">7,594</td><td colspan="2">5,585</td><td colspan="2">3,811</td><td colspan="2">2,450</td><td colspan="2">37,447</td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td colspan="2">-98,644 (2,499)</td><td colspan="2">-76 250 (1,538)</td><td colspan="2">-57,067 (628)</td><td colspan="2">-39,721 (295)</td><td colspan="2">-25,401 (215)</td><td colspan="2">-14,571 (112)</td><td colspan="2">322,872 (5,502)</td></tr><tr><td>AIC</td><td colspan="2">2,503</td><td colspan="2">1,504</td><td colspan="2">594</td><td colspan="2">261</td><td colspan="2">183</td><td colspan="2">78</td><td colspan="2">5,468</td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a The previous birth interval was not included, as women at parity 1 have no prior birth and, consequently, no prior birth interval.

<caption>Table A12</caption>

Parity-specific and combined-parity conditional frailty models of the effect of the interaction between conflict exposure (measured as the log of cumulative fatalities) and infant/child mortality on the transition to higher-order births

<table><thead><tr><th rowspan="3">Variables</th><th colspan="12">Transition from</th><th colspan="2">All transitions combined</th></tr><tr><th colspan="2">1<sup>st</sup> to 2<sup>nd</sup> birth</th><th colspan="2">2<sup>nd</sup> to 3<sup>rd</sup> birth</th><th colspan="2">3<sup>rd</sup> to 4<sup>th</sup> birth</th><th colspan="2">4<sup>th</sup> to 5<sup>th</sup> birth</th><th colspan="2">5<sup>th</sup> to 6<sup>th</sup> birth</th><th colspan="2">6<sup>th</sup> to 7<sup>th</sup> birth</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td>DeathIC<sup>b</sup> (Yes)*Log (Cumulated fatalities)</td><td>0.96</td><td>0.011*</td><td>1.02</td><td>0.100</td><td>1.04</td><td>0.007**</td><td>0.99</td><td>0.880</td><td>1.01</td><td>0.535</td><td>0.99</td><td>0.999</td><td>1.01</td><td>0.292</td></tr><tr><td>DeathNICI (Yes)*Log (Cumulated fatalities)</td><td>a</td><td>a</td><td>1.09</td><td>0.029*</td><td>1.04</td><td>0.300</td><td>1.06</td><td>0.160</td><td>1.07</td><td>0.120</td><td>1.00</td><td>0.940</td><td>1.06</td><td>0.001**</td></tr><tr><td>DeathNICBIPrev1 (Yes)*Log (Lag 1 of cumulated fatalities)</td><td>a</td><td>a</td><td>0.94</td><td>0.001**</td><td>0.94</td><td>&lt; 0.001**</td><td>0.97</td><td>0.149</td><td>0.99</td><td>0.970</td><td>0.96</td><td>0.140</td><td>0.94</td><td>&lt; 0.001**</td></tr><tr><td><strong>Frailty variance θ</strong></td><td>0.292</td><td>&lt; 0.001**</td><td>&lt; 10<sup>-3</sup></td><td>0.942</td><td>&lt; 10<sup>-3</sup></td><td>0.971</td><td>&lt; 10<sup>-3</sup></td><td>0.937</td><td>&lt; 10<sup>-3</sup></td><td>0.943</td><td>&lt; 10<sup>-3</sup></td><td>0.952</td><td>0.03</td><td>&lt; 0.001**</td></tr><tr><td>Number of events</td><td colspan="2">11 915</td><td colspan="2">9 677</td><td colspan="2">7 594</td><td colspan="2">5 585</td><td colspan="2">3 811</td><td colspan="2">2 450</td><td colspan="2">37 447</td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td colspan="2">-98 110 (2 522)</td><td colspan="2">-76 021 (1 994)</td><td colspan="2">-56 921 (920)</td><td colspan="2">-39 670 (397)</td><td colspan="2">-25 370 (276)</td><td colspan="2">-14 550 (156)</td><td colspan="2">321 827 (7 592)</td></tr><tr><td>AIC</td><td colspan="2">2 492</td><td colspan="2">1 946</td><td colspan="2">873</td><td colspan="2">349</td><td colspan="2">228</td><td colspan="2">108</td><td colspan="2">7 544</td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a. Variables not included because women with parity 1 do not have non-index children

Note: The model controls for the effect of the same variables presented in Table A11.

b. *DeathNICI:* death of an index child in the (current) interval, *DeathNICI:* death of a non-index child in the interval, *DeathNICBIPrev1:* death of a non-index child in the previous interval

<caption>Table A13</caption>

Cox frailty model of the effect of interactions between the death of a non-index child in each of the two preceding intervals and conflict exposure in the respective intervals on the transition to higher-order births

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>2.26</td><td>0.060</td><td>&lt; 0.001**</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0.98</td><td>0.012</td><td>0.382</td></tr><tr><td>Medium</td><td>0.96</td><td>0.020</td><td>0.025*</td></tr><tr><td>High</td><td>0.92</td><td>0.021</td><td>&lt; 0.001**</td></tr><tr><td><strong>Lag-2 Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>1.01</td><td>0.019</td><td>0.499</td></tr><tr><td>Medium</td><td>0.95</td><td>0.020</td><td>0.026*</td></tr><tr><td>High</td><td>1.05</td><td>0.029</td><td>0.061°</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td>1.04</td><td>0.060</td><td>0.069°</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td>0.98</td><td>0.020</td><td>0.382</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td>0.96</td><td>0.021</td><td>0.025*</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A13 (continued)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td>DeathNICBIPrev2* Lag-2 Conflict exposure—Low</td><td>0.92</td><td>0.023</td><td>&lt; 0.001 **</td></tr><tr><td>DeathNICBIPrev2* Lag-2 Conflict exposure—Medium</td><td>1.01</td><td>0.023</td><td>0.499</td></tr><tr><td>DeathNICBIPrev2* Lag-2 Conflict exposure—High</td><td>0.95</td><td>0.04</td><td>0.026 *</td></tr><tr><td>Age</td><td>1.02</td><td>0.045</td><td>0.662</td></tr><tr><td>Age<sup>2</sup></td><td>1.13</td><td>0.059</td><td>0.026 *</td></tr><tr><td>Length of the previous birth interval</td><td>1.00</td><td>0.001</td><td>0.194</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.89</td><td>0.045</td><td>0.027 *</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>1.01</td><td>0.019</td><td>0.561</td></tr><tr><td>Region (Ref.: Kinshasa)</td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.04</td><td>0.039</td><td>0.270</td></tr><tr><td>Bas-Congo</td><td>1.04</td><td>0.040</td><td>0.381</td></tr><tr><td>Equateur</td><td>1.28</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Oriental</td><td>1.29</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Occidental</td><td>1.42</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Katanga</td><td>1.41</td><td>0.031</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.33</td><td>0.040</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>1.51</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.25</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>1.04</td><td>0.039</td><td>0.270</td></tr><tr><td>Frailty variance θ, χ<sup>2</sup>b and p-value<sup>b</sup></td><td>0.05</td><td>48</td><td>&lt; 0.001 **<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td></td><td>7,594</td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td></td><td>-225,093 (1,222)</td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td></td><td>1,164</td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity. Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ²value and p-value from the likelihood ratio test.

DeathNICBIPrev1: death of a non-index child in the previous interval

DeathNICBIPrev2: death of a non-index child in the interval preceding the previous interval

<caption>Table A14</caption>

Cox frailty model of the effects of infant/child mortality, conflict exposure, and their interaction on transitions to second through seventh births among married and unmarried women in the DRC

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant and child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup> (coef.)</th><th>p</th></tr></thead><tbody><tr><td>Death of an index child, DeathICI (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.3</td><td>0.011</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>1.36</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>Death of a non-index child, DeathNICI (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>5.75</td><td>0.030</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>4.68</td><td>0.081</td><td>&lt; 0.001 **</td></tr><tr><td>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.19</td><td>0.022</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>1.34</td><td>0.039</td><td>&lt; 0.001 **</td></tr><tr><td>Conflict exposure (Ref.: No conflict event)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>0.65</td><td>0.011</td><td>&lt; 0.001 **</td><td>0.65</td><td>0.011</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>0.48</td><td>0.010</td><td>&lt; 0.001 **</td><td>0.47</td><td>0.010</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>0.27</td><td>0.019</td><td>&lt; 0.001 **</td><td>0.27</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>Lag-1 Conflict exposure (Ref.: No conflict event)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.94</td><td>0.021</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.93</td><td>0.021</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.98</td><td>0.020</td><td>0.321</td></tr><tr><td>Interaction between child death and conflict intensity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.91</td><td>0.046</td><td>0.053 °</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99</td><td>0.039</td><td>0.831</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.93</td><td>0.039</td><td>0.094 °</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.14</td><td>0.130</td><td>0.317</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.34</td><td>0.121</td><td>0.020 *</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.49</td><td>0.139</td><td>0.004 **</td></tr><tr><td>DeathNICBIPrev1<sup>c</sup> * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.95</td><td>0.059</td><td>0.443</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.83</td><td>0.041</td><td>0.001 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.68</td><td>0.060</td><td>&lt; 0.001 **</td></tr><tr><td>Age</td><td>1.09</td><td>0.018</td><td>0.013 *</td><td>1.00</td><td>0.030</td><td>0.923</td><td>1.02</td><td>0.032</td><td>0.636</td></tr><tr><td>Age<sup>2</sup></td><td>1.03</td><td>0.017</td><td>0.442</td><td>1.12</td><td>0.031</td><td>0.001 **</td><td>1.10</td><td>0.031</td><td>0.006 **</td></tr><tr><td>Length of the previous birth interval</td><td>0.99</td><td>0.001</td><td>0.017 *</td><td>1.00</td><td>0.000</td><td>0.003 **</td><td>1.00</td><td>0.000</td><td>0.109</td></tr><tr><td>Multiple births (Ref.:No)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.90</td><td>0.037</td><td>0.002 **</td><td>0.95</td><td>0.038</td><td>0.186</td><td>0.91</td><td>0.041</td><td>0.007 **</td></tr><tr><td>Place of residence (Ref.:Rural)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>Table A14 (continued)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant and child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child loss and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th></tr></thead><tbody><tr><td>Urban</td><td>0.97</td><td>0.012</td><td>0.003 **</td><td>0.97</td><td>0.011</td><td>0.001 **</td><td>0.97</td><td>0.011</td><td>0.004 **</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.07</td><td>0.021</td><td>0.007 **</td><td>0.73</td><td>0.020</td><td>&lt; 0.001 **</td><td>0.72</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>1.03</td><td>0.020</td><td>0.351</td><td>0.95</td><td>0.031</td><td>0.082 °</td><td>0.94</td><td>0.029</td><td>0.038 *</td></tr><tr><td>Equateur</td><td>1.27</td><td>0.021</td><td>&lt; 0.001 **</td><td>1.23</td><td>0.020</td><td>&lt; 0.001 **</td><td>1.22</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Oriental</td><td>1.33</td><td>0.021</td><td>&lt; 0.001 **</td><td>1.10</td><td>0.031</td><td>&lt; 0.001 **</td><td>1.08</td><td>0.031</td><td>0.002 **</td></tr><tr><td>Kasai Occidental</td><td>1.51</td><td>0.019</td><td>&lt; 0.001 **</td><td>1.32</td><td>0.020</td><td>&lt; 0.001 **</td><td>1.32</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>Katanga</td><td>1.35</td><td>0.021</td><td>&lt; 0.001 **</td><td>1.67</td><td>0.020</td><td>&lt; 0.001 **</td><td>1.67</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.27</td><td>0.020</td><td>&lt; 0.001 **</td><td>1.24</td><td>0.027</td><td>&lt; 0.001 **</td><td>1.22</td><td>0.028</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>1.5</td><td>0.020</td><td>&lt; 0.001 **</td><td>2.5</td><td>0.028</td><td>&lt; 0.001 **</td><td>2.48</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.25</td><td>0.020</td><td>&lt; 0.001 **</td><td>2.06</td><td>0.031</td><td>&lt; 0.001 **</td><td>2.02</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>1.55</td><td>0.019</td><td>&lt; 0.001 **</td><td>2.69</td><td>0.030</td><td>&lt; 0.001 **</td><td>2.64</td><td>0.001</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ, χ²b and p-valueb</strong></td><td>0.05</td><td>105<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.06</td><td>176<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.06<sup>b</sup></td><td>210<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td colspan="3">21,231</td><td colspan="3">21,231</td><td colspan="3">21,231</td></tr><tr><td>Integrated log of partial likelihood and χ² value</td><td colspan="3">-462,773 (2,826.0)</td><td colspan="3">-460,086.9 (8,199.8)</td><td colspan="3">-459,385.4 (9,602.9)</td></tr><tr><td>Akaike Information Criterion (AIC)</td><td colspan="3">2,788</td><td colspan="3">8,161</td><td colspan="3">9,534.9</td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity.

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ²value and p-value from the likelihood ratio test.

c DeathICI: death of an index child in the (current) interval, DeathNICI: death of a non-index child in the interval, DeathNICBI: death of a non-index child before the current interval, DeathNICBIPrev1: death of a non-index child in the previous interval

<caption>Table A15</caption>

<table><caption>Cox frailty model of the effects of infant/child mortality and conflict exposure (measured by tertiles the cumulative number of conflict events) on transitions to 2nd–7th births among married women (excluding those who had multiple births) in the DRC</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant and child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child death and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.31</td><td>0.020</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>1.36</td><td>0.040</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>6.01</td><td>0.058</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td>4.17</td><td>0.089</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI (Ref.:No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.17</td><td>0.033</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td>0.65</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.66</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td>0.49</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.51</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td>0.30</td><td>0.02</td><td>&lt; 0.001 **</td><td>0.32</td><td>0.020</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Lag-1 Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.14</td><td>0.011</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.18</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.25</td><td>0.300</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Interaction between child death and conflict intensity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeathICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.87</td><td>0.050</td><td>0.053 °</td></tr><tr><td>DeathICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99</td><td>0.050</td><td>0.765</td></tr><tr><td>DeathICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.97</td><td>0.050</td><td>0.477</td></tr><tr><td>DeathNICI * Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.3</td><td>0.152</td><td>0.072 °</td></tr><tr><td>DeathNICI * Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.61</td><td>0.139</td><td>&lt; 0.001 **</td></tr><tr><td>DeathNICI * Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.78</td><td>0.158</td><td>&lt; 0.001 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Low</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.83</td><td>0.067</td><td>0.008 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—Medium</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.75</td><td>0.067</td><td>&lt; 0.001 **</td></tr><tr><td>DeathNICBIPrev1 * Lag-1 Conflict exposure—High</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.57</td><td>0.067</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age</strong></td><td>1.15</td><td>0.039</td><td>&lt; 0.001 **</td><td>1.07</td><td>0.04</td><td>0.096 °</td><td>1.28</td><td>0.040</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age<sup>2</sup></strong></td><td>0.96</td><td>0.039</td><td>0.361</td><td>1.04</td><td>0.04</td><td>0.301</td><td>0.86</td><td>0.040</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Length of the previous birth interval</strong></td><td>0.99</td><td>0.010</td><td>0.020 *</td><td>0.99</td><td>&lt; 0.01</td><td>&lt; 0.001 **</td><td>0.99</td><td>0.002</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Place of residence (Ref.:Rural)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.99</td><td>0.010</td><td>0.344</td><td>0.99</td><td>0.01</td><td>0.285</td><td>0.99</td><td>0.012</td><td>0.328</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.09</td><td>0.029</td><td>0.003 **</td><td>0.72</td><td>0.029</td><td>&lt; 0.001 **</td><td>0.77</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>Bas-Congo</td><td>1.06</td><td>0.033</td><td>0.091 °</td><td>0.93</td><td>0.039</td><td>0.048 *</td><td>0.95</td><td>0.033</td><td>0.134</td></tr><tr><td>Equateur</td><td>1.32</td><td>0.032</td><td>&lt; 0.001 **</td><td>1.23</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.23</td><td>0.032</td><td>&lt; 0.001 **</td></tr><tr><td>Kasai Oriental</td><td>1.36</td><td>0.029</td><td>&lt; 0.001 **</td><td>1.08</td><td>0.030</td><td>0.010 *</td><td>1.09</td><td>0.029</td><td>0.003 **</td></tr><tr><td>Kasai Occidental</td><td>1.45</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.29</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.28</td><td>0.030</td><td>&lt; 0.001 **</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A15 (continued)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Model 1 (effect of infant and child death)</th><th colspan="3">Model 2 (effect of conflict exposure)</th><th colspan="3">Model 3 (effect of interactions between child death and conflict exposure)</th></tr><tr><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th><th>HR</th><th>Se<sup>a</sup><br>(coef.)</th><th>p</th></tr></thead><tbody><tr><td>Katanga</td><td>1.39</td><td>0.029</td><td>&lt; 0.001 **</td><td>1.62</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.55</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Maniema</td><td>1.31</td><td>0.029</td><td>&lt; 0.001 **</td><td>1.22</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.19</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Nord Kivu</td><td>1.49</td><td>0.029</td><td>&lt; 0.001 **</td><td>2.44</td><td>0.038</td><td>&lt; 0.001 **</td><td>2.2</td><td>0.029</td><td>&lt; 0.001 **</td></tr><tr><td>Province Orientale</td><td>1.25</td><td>0.030</td><td>&lt; 0.001 **</td><td>1.8</td><td>0.031</td><td>&lt; 0.001 **</td><td>1.65</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td>Sud Kivu</td><td>1.61</td><td>0.030</td><td>&lt; 0.001 **</td><td>2.52</td><td>0.029</td><td>&lt; 0.001 **</td><td>2.28</td><td>0.030</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Frailty variance θ, χ<sup>2</sup> and p-value<sup>b</sup></strong></td><td>0.05</td><td>84<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.05</td><td>97<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td><td>0.04</td><td>46<sup>b</sup></td><td>&lt; 0.001**<sup>b</sup></td></tr><tr><td>Number of women</td><td></td><td>13,578</td><td></td><td></td><td>13,578</td><td></td><td></td><td>13,578</td><td></td></tr><tr><td>Integrated log of partial likelihood (χ<sup>2</sup> value)</td><td></td><td>-319,727 (2,029)</td><td></td><td></td><td>-316,997 (5,460)</td><td></td><td></td><td>-384,919 (6,171)</td><td></td></tr><tr><td>Akaike Information Criterion (AIC)</td><td></td><td>1,993</td><td></td><td></td><td>5,424</td><td></td><td></td><td>6,105</td><td></td></tr></tbody></table>

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity. Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

a standard error of coefficients

b χ² value and p-value from the likelihood ratio test.

c *DeathICI*: Death of an index child in the current interval, *DeathNICI*: death of a non-index child in the interval, *DeathNICBI*: death of a non-index child before the current interval (in all previous intervals), *DeathNICBIPrev1*: death of a non-index child in the previous interval, *Lag-1 Conflict exposure*: exposure to conflict in the previous interval

<caption>Table A16</caption>

Separate models for DHS 2007 and DHS 2013/2014 of the effect of infant/child death and conflict exposure on birth transitions

<table><thead><tr><th rowspan="3">Variables</th><th colspan="4">Effect of child death on subsequent births</th><th colspan="4">Effect of conflict exposure on subsequent births</th></tr><tr><th colspan="2">DHS 2007</th><th colspan="2">DHS 2013/2014</th><th colspan="2">DHS 2007</th><th colspan="2">DHS 2013/2014</th></tr><tr><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th><th>HR</th><th>p</th></tr></thead><tbody><tr><td><strong>Death of an index child, DeathICI<sup>C</sup> (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.39</td><td>&lt; 0.001 **</td><td>1.23</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Death of a non-index child, DeathNICI (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>3.94</td><td>&lt; 0.001 **</td><td>5.30</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Death of a non-index child before the interval, DeathNICBI(Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.11</td><td>0.060°</td><td>1.39</td><td>&lt; 0.001 **</td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Conflict exposure (Ref.: No conflict event)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td></td><td></td><td></td><td></td><td>0.60</td><td>&lt; 0.001 **</td><td>0.63</td><td>&lt; 0.001 **</td></tr><tr><td>Medium</td><td></td><td></td><td></td><td></td><td>0.44</td><td>&lt; 0.001 **</td><td>0.42</td><td>&lt; 0.001 **</td></tr><tr><td>High</td><td></td><td></td><td></td><td></td><td>0.28</td><td>&lt; 0.001 **</td><td>0.26</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Age</strong></td><td>0.99</td><td>0.962</td><td>0.98</td><td>0.424</td><td>1.06</td><td>0.161</td><td>1.06</td><td>0.698</td></tr><tr><td><strong>Age<sup>2</sup></strong></td><td>1.04</td><td>0.710</td><td>1.06</td><td>0.855</td><td>0.97</td><td>0.075°</td><td>0.97</td><td>0.682</td></tr><tr><td><strong>Previous birth interval</strong></td><td>0.99</td><td>&lt; 0.001 **</td><td>1.02</td><td>&lt; 0.001 **</td><td>0.99</td><td>0.002**</td><td>0.99</td><td>&lt; 0.001 **</td></tr><tr><td><strong>Multiple births (Ref.: No)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>0.88</td><td>0.246</td><td>0.93</td><td>0.453</td><td>0.93</td><td>0.512</td><td>0.93</td><td>0.381</td></tr><tr><td><strong>Place of residence (Ref.: Rural)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>0.99</td><td>0.811</td><td>0.98</td><td>0.319</td><td>0.98</td><td>0.483</td><td>0.98</td><td>0.506</td></tr><tr><td><strong>Region (Ref.: Kinshasa)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bandundu</td><td>1.14</td><td>0.003 **</td><td>0.96</td><td>0.421</td><td>0.80</td><td>&lt; 0.001 **</td><td>0.74</td><td>&lt; 0.001**</td></tr><tr><td>Bas-Congo</td><td>1.06</td><td>0.225</td><td>1.25</td><td>&lt; 0.001 **</td><td>0.91</td><td>0.122</td><td>1.05</td><td>0.329</td></tr><tr><td>Equateur</td><td>1.27</td><td>&lt; 0.001 **</td><td>1.31</td><td>&lt; 0.001 **</td><td>1.29</td><td>&lt; 0.001 **</td><td>1.28</td><td>&lt; 0.001**</td></tr><tr><td>Kasai Oriental</td><td>1.27</td><td>&lt; 0.001 **</td><td>1.34</td><td>&lt; 0.001 **</td><td>1.07</td><td>0.209</td><td>1.14</td><td>0.002**</td></tr><tr><td>Kasai Occidental</td><td>1.42</td><td>&lt; 0.001 **</td><td>1.30</td><td>&lt; 0.001 **</td><td>1.34</td><td>&lt; 0.001 **</td><td>1.30</td><td>&lt; 0.001**</td></tr><tr><td>Katanga</td><td>1.34</td><td>&lt; 0.001 **</td><td>0.90</td><td>0.012*</td><td>1.76</td><td>&lt; 0.001 **</td><td>1.60</td><td>&lt; 0.001**</td></tr><tr><td>Maniema</td><td>1.24</td><td>&lt; 0.001 **</td><td>1.25</td><td>&lt; 0.001 **</td><td>1.22</td><td>&lt; 0.001 **</td><td>1.25</td><td>&lt; 0.001**</td></tr><tr><td>Nord Kivu</td><td>1.48</td><td>&lt; 0.001 **</td><td>1.36</td><td>&lt; 0.001 **</td><td>2.41</td><td>&lt; 0.001 **</td><td>2.47</td><td>&lt; 0.001**</td></tr><tr><td>Province Orientale</td><td>1.22</td><td>&lt; 0.001 **</td><td>1.18</td><td>&lt; 0.001 **</td><td>1.61</td><td>&lt; 0.001 **</td><td>1.99</td><td>&lt; 0.001**</td></tr><tr><td>Sud Kivu</td><td>1.49</td><td>&lt; 0.001 **</td><td>1.55</td><td>&lt; 0.001 **</td><td>2.28</td><td>&lt; 0.001 **</td><td>2.66</td><td>&lt; 0.001**</td></tr><tr><td><strong>Frailty variance θ</strong></td><td>0.082</td><td>&lt; 0.001 **</td><td>0.056</td><td>&lt; 0.001 **</td><td>0.083</td><td>&lt; 0.001 **</td><td>0.027</td><td>&lt; 0.001 **</td></tr><tr><td>Number of women</td><td></td><td>5,154</td><td></td><td>8,705</td><td></td><td>5,154</td><td></td><td>8,705</td></tr><tr><td>Integrated log of partial likelihood and χ<sup>2</sup> value</td><td></td><td>-66,820 (643)</td><td></td><td>-156,610 (846.3)</td><td></td><td>-66,820 (643)</td><td></td><td>-155,652 (2,760)</td></tr><tr><td>AIC</td><td></td><td>605</td><td></td><td>808.3</td><td></td><td>605</td><td></td><td>2,722</td></tr></tbody></table>

Statistical significance: **p < 0.01, *p < 0.05, °p < 0.10.

Note: The effect of parity does not appear in the list of variables because the model was stratified by parity.

c *DeathICI*: death of an index child in the (current) interval, *DeathNICI*: death of a non-index child in the interval, *DeathNICBI*: death of a non-index child before the current interval (in all previous intervals), *DeathNICBIPrev1*: death of a non-index child in the previous interval