# Effects of Conflict and Conflict-Related Sexual Violence on First-Order Marital and Premarital Adolescent Fertility in the Democratic Republic of the Congo

Guerchom Mugisho

► To cite this version:

Guerchom Mugisho. Effects of Conflict and Conflict-Related Sexual Violence on First-Order Marital and Premarital Adolescent Fertility in the Democratic Republic of the Congo. Canadian Studies in Population, 2025, 52 (1), pp.15. 10.1007/s42650-025-00103-w. hal-05464177

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# Effects of Conflict and Conflict-Related Sexual Violence on First-Order Marital and Premarital Adolescent Fertility in the Democratic Republic of the Congo

## Abstract

The conflicts in the Democratic Republic of the Congo (DRC) have resulted in significant conflict-related sexual violence (CRSV) against girls under 18. This study examines the impact of conflict and CRSV on first-order marital and non-marital adolescent fertility in the DRC. We utilized data from the 2007 Demographic and Health Surveys, geo-referenced conflict data (UCDP-GED), and CRSV data (GEO-SVAC). Using the Mundlak approach, we first implemented a binary multilevel discrete-time event history model to assess how exposure to conflict and CRSV impacted the timing of first adolescent conception, both marital and non-marital. Second, we treated first marital and premarital conceptions as two competing events. We employed a multinomial multilevel discrete-time event history model to examine the impact of exposure to conflict and CRSV on each type of event. Our findings reveal that (1) exposure to CRSV increased the risk of premarital adolescent conception; (2) conflict exposure reduced the likelihood of marital adolescent conception; (3) CRSV delayed adolescent conception within marriage. The first two findings align with existing literature, while the third offers new insights into how CRSV may delay the timing of first adolescent pregnancy, including through spousal rejection following extramarital sexual violence and resilience among survivors. These results underscore the need for (1) improved access to contraceptives for girls in conflict zones, (2) strengthened women's empowerment programs, and (3) lasting peace in the DRC to enhance adolescent health outcomes.

**Keywords:** conflict, conflict-related sexual violence, adolescent, fertility, marriage, Democratic Republic of the Congo

# Effet des conflits et des violences sexuelles liées aux conflits sur la première conception adolescente maritale et prémaritale en République démocratique du Congo

## Résumé

Les conflits en République démocratique du Congo (RDC) ont entraîné d'importantes violences sexuelles liées aux conflits (VSLC) à l'encontre des filles de moins de 18 ans. Cette étude analyse l'impact des conflits et des VSLC sur la première conception adolescente maritale et prémaritale. Nous avons fusionné les données de l'Enquête Démographique et de Santé de 2007 (EDS) avec les données géospatiales sur les conflits (UCDP-GED) et les VSLC (GEO-SVAC), en utilisant les coordonnées géographiques des grappes de sondage de l'EDS et les lieux des conflits. En appliquant l'approche de Mundlak, nous avons d'abord estimé un modèle logistique dichotomique à temps discret multiniveau afin de mesurer l'effet de l'exposition aux conflits et aux VSLC sur le risque d'une première conception adolescente, maritale ou prémaritale. Ensuite, nous avons considéré les premières conceptions maritale et prémaritale à l'adolescence comme deux événements concurrents et estimé un modèle logistique multinomial à temps discret multiniveau. Les résultats montrent que (1) l'exposition aux VSLC augmente le risque de conception adolescente prémaritale ; (2) l'exposition aux conflits réduit le risque de conception maritale ; (3) les VSLC retardent la conception adolescente maritale. Ces résultats confirment partiellement la littérature existante et apportent un éclairage nouveau sur les mécanismes par lesquels les VSLC peuvent retarder la première grossesse adolescente, notamment via le rejet conjugal ou la résilience des survivantes. Ils soulignent la nécessité d'améliorer l'accès à la contraception, de renforcer l'autonomisation des femmes et de promouvoir une paix durable pour améliorer la santé des adolescentes en RDC.

**Mots clés:** conflit, violence sexuelle liée aux conflits, fécondité, mariage, République démocratique du Congo
# 1. Introduction

Adolescent motherhood remains highly prevalent in the Democratic Republic of the Congo (DRC). Approximately 19,000 births were recorded annually among women under the age of 15 in 2017 (Schoumaker & Sánchez-Páez, 2022). This figure positioned the DRC as the fourth country in the world (following Nigeria, India, and Bangladesh) with the highest number of births to women aged 10-14 (Schoumaker & Sánchez-Páez, 2022). Before the onset of conflicts in 1996, the fertility rate for girls aged 15-19 in the DRC was 119 births per 1,000 women in 1995, slightly below the Central and East African average of 127. However, during the Second Congo War, this trend reversed, with the fertility rate rising to 126 births per 1,000 by 2011, while the regional average dropped to 109 births per 1,000 for the same age group (World Bank 2022, see Figure 1). In 2017, the fertility rate for girls aged 15 to 19 in the DRC was 109 births for every 1,000 girls. The rate is higher in rural areas, with 136 births per 1,000, compared to 82 births per 1,000 in urban areas (INS, 2017). This high adolescent fertility rate exceeds the target of no more than 50 births per 1,000 girls, placing the country in the red zone for its progress toward achieving the SDGs, specifically target 3.7.2 (Sachs et al., 2023).

Teenage motherhood is a major public health concern. It is associated with higher risks of maternal mortality, stillbirth, neonatal morbidity, and infant mortality (Corcoran, 1998; UNFPA, 2013). Teenage motherhood causes adolescents —here, women aged 10-19 years (WHO, 2024)—to drop out of school, which limits their future career opportunities, restricts their autonomy, and affects their future reproductive choices (Urdinola & Ospino, 2015). In the DRC and across Sub-Saharan Africa, adolescent fertility significantly contributes to the total fertility rate, accounting for approximately 11% (Ministry of Health & ICF International, 2014; Spoorenberg et al., 2024). In addition, most births from teen mothers are unintended (Spoorenberg et al., 2024). Reducing the national level of unintended pregnancy is one of the most important reproductive health goals (Finer & Zolna, 2011). Many of the Sustainable Development Goals (SDGs), including SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth), are directly and negatively impacted by the prevalence of adolescent motherhood (UNFPA, 2013).

Amid conflicts in the DRC, women and children have endured widespread rape, sexual slavery, and killings (Mukwege & Nangini, 2009). Conflict-related sexual violence (CRSV) was used as a strategy of domination (Nelson et al., 2011). Approximately 65% of the victims were reported to be under the age of 18 (Goff, 2010). Healthcare facilities have been destroyed, and healthcare workers have been murdered (Aid Worker Security Database, 2023; Makali et al., 2023). Access to and use of reproductive health care is limited, with about 28% of married women experiencing an unmet need for family planning in 2013 (Ministry of Health & ICF International, 2014; Mpunga et al., 2022). The rate of child marriage is notably high, at approximately 35% (Krafft et al., 2024). Such a context provides a suitable environment for studying the impact of conflict and CRSV on adolescent motherhood.

Most studies outside the field of demography have examined CRSV among adolescents as an isolated phenomenon, without considering its connection to the timing of first marriage or pregnancy in affected populations (Ba & Bhopal, 2017; Nelson et al., 2011). Demographic research on the impact of conflict on adolescent pregnancy or marriage has identified CRSV as a mechanism through which conflict may influence the timing of adolescent motherhood.
However, it has not evaluated the effect of CRSV itself (Singh et al., 2022). One of the few studies identified (DiGiuseppe & Haer, 2023) examined the impact of CRSV on the timing of marriages for girls under 18 in West Africa. This study did not focus on conceptions, precluding differentiation between those occurring within or before. Additionally, it did not include the DRC. The only study that examined the impact of conflict on teenage motherhood in the DRC did not address the role of CRSV and did not distinguish between pregnancies within and before marriage (Mugisho, 2024).

The objectives of this research are to analyze the impact of conflict and CRSV on first conception (both marital and premarital), first marital conception, and first premarital conception during adolescence in the DRC.

This study contributes to the existing literature in three significant ways. First, it clarifies the specific impact of conflict in general, and CRSV in particular, on the likelihood of first-order marital and premarital adolescent fertility. Second, by analyzing first-order marital and premarital adolescent fertility as competing events, the study identifies the relative importance of the main proximate factors through which conflict and CRSV influence adolescent motherhood. These factors include adolescent marriage and the failure to use contraception after unprotected sexual intercourse, whether coerced or consensual. This distinction is crucial, as it helps identify the main targets for policies aimed at combating child and adolescent pregnancy, in line with SDG target 3.7.2. Finally, the study disentangles the within- or longitudinal effect of conflict and CRSV from the contextual effect. This distinction highlights potential mechanisms through which exposure to CRSV may have influenced adolescent motherhood in the DRC.

<figure><img src="image_2.png" /><figcaption>**Figure 1. Trends in adolescent fertility in the DRC and in Central and East African countries**</figcaption></figure>

Source: World Bank (2022).
## 2. Background

### 2.1. High prevalence of CRSV during conflicts, coupled with lower use of modern contraceptive methods

<figure><img src="image_3.png" /></figure>

Note: The large colored dots represent conflict events. Some conflict events (large dots) may not be clearly visible on the graph due to overlapping, as certain geographic locations have experienced multiple conflict events. Each color (refer to the legend) indicates the prevalence of sexual violence associated with each conflict event.

<caption>Figure 2.a: Prevalence of CRSV in the DRC from 1989 to 2007, DRC DHS clusters and their 10 and 50 kilometers buffer zones</caption>

Source: DRC DHS 2007, GEO-SVAC.

The 1990s were a tumultuous period in the DRC's political history. The 1994 Rwandan genocide triggered a large influx of refugees, which exacerbated political instability in the region (Hesselbein, 2007). In October 1996, Laurent-Désiré Kabila, with support from Rwanda, Uganda, and Angola, launched a rebellion (AFDL) against Mobutu Sese Seko. This conflict led to the First Congo War, which culminated in May 1997 when Kabila took control of the country. In August 1998, Rwanda and Uganda initiated another rebellion, sparking the Second Congo War. Kabila was assassinated in January 2001, and his son, Joseph Kabila, succeeded him, working toward international peace (Putzel et al., 2008). Violence decreased
following the peace agreements in 2002, with the war officially ending in July 2003 (Hesselbein, 2007). The death toll from 1998 to 2004 is estimated at 3.9 million (Coghlan et al., 2006). Despite the end of the war, eastern Congo continues to be plagued by periodic violence.

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

Note: The colors gray and orange represent the First and Second Congo Wars, respectively.

<caption>Figure 2.b. Number of fatalities, conflict events and conflict events during which CRSV were reported by calendar year</caption>

Source: UCDP-GED and GEO-SVAC.

These conflicts were characterized by CRSV targeting women and children (Goff, 2010). CRSV was widespread throughout much of the country (Figure 2a); however, it was particularly concentrated in the eastern regions—South Kivu, North Kivu, Maniema, and Province Orientale —as well as in the two Kasai regions and Equateur. Before 1996, only a limited number of conflict events were reported, and no cases of CRSV were documented during this period. The highest number of conflict events and fatalities occurred during the First Congo War, with approximately 209 conflict events leading to over 34,500 fatalities. However, despite the severity of these conflicts, instances of CRSV were relatively few compared to the Second Congo War. The majority of CRSV cases between 1989 and 2007 took place during the Second Congo War (Figure 2.b).

These instances of CRSV occurred in a context of extremely low modern contraceptive prevalence. The rate of modern contraceptive use in the DRC is very low among married women (4.4% in 1984; 2.3% in 1991; 7.7% in 2001; 5.8% in 2007; and 6.6% in 2010) (Figure 2.c). Although statistics on modern contraceptive use among adolescents are not available, it is reasonable to assume that the rate remains low among this group. For instance, a study conducted in the country's largest city, Kinshasa (where the contraceptive use rate among adolescents is expected to be relatively high), reports a modern contraceptive prevalence of approximately 10% among adolescents (Mpunga et al., 2022). In the majority of cities nationwide, this prevalence is likely to be even lower.
<figure><img src="image_5.png" /><figcaption>Figure 2.c. Modern contraceptive use prevalence in the DRC, any method (% of married women aged 15-49)</figcaption></figure>

Source: World Bank (2022).

## 2.2. Literature review and hypotheses formulation

Conflict affects adolescent motherhood through several mechanisms, the most documented in the literature being the collapse of basic socioeconomic structures (Chi et al., 2015), CRSV (DiGiuseppe & Haer, 2023; Muhwezi et al., 2011), family impoverishment (Hoogeveen et al., 2011), and the destruction of the family fabric (Miller et al., 2018). Regardless of the mechanism, adolescent conception can occur within or outside of marriage.

Conflict leads to the collapse of the health system by dismantling healthcare infrastructure and restricting access to maternal health services due to displacement, as well as geographic, security, and financial barriers (Chi et al., 2015). Limited access to reproductive care increases the vulnerability of adolescents—both those who have engaged in consensual sex and those who are victims of CRSV—to unintended pregnancies (Hutchinson et al., 2017). Forcibly displaced victims of sexual violence are more vulnerable to unintended pregnancies (Oladeji et al., 2021). In the DRC, forcibly displaced women who have been victims of rape were found to be less likely to access care following rape (Guerschom et al., 2024). In addition, survivors of sexual violence, particularly those subjected to sexual slavery or prolonged captivity, often face severe restrictions on their mobility and healthcare access, preventing them from seeking contraceptive methods or post-exposure prophylaxis (Denny et al., 2021). A study in the DRC found that women who had been abducted were significantly less likely to receive post-rape medical care, including pregnancy prevention and protection against sexually transmitted infections (Guerschom et al., 2024). Additionally, the stigma surrounding sexual violence further deters survivors from seeking care, exacerbating the risk of adolescent pregnancies before marriage (Amowitz et al., 2002). The GEO-SVAC database<fnref n="1" /> lists 51,893 state-based conflicts worldwide between 1989 and 2009. In Africa, more than anywhere else, 43% of these conflicts were accompanied by CRSV committed by the opposing parties, including 29.6% of conflicts accompanied by a few cases of sexual violence, 9.3% of conflicts in which much sexual violence was committed, and 2.2% of conflicts in which sexual violence was committed on a massive scale (Bahgat et al., 2016). Adolescents are particularly vulnerable to CRSV (Goff, 2010). For instance, a study conducted in Sudan found that conflict and displacement heightened the risk of non-partner sexual violence against adolescents (Murphy et al., 2019). In the DRC, too few of these victims — between 13% and 25% depending on the study — accessed post-rape care as soon as possible to benefit from the prevention of unwanted

<footnote n="1">¹ https://www.prio.org/Data/Armed-Conflict/GEO-SVAC/</footnote>
pregnancy, HIV, and STIs (Banza-Nsungu, 2014; Guerschom et al., 2024). As a result, many
victims of CRSV — up to 43% according to a meta-analysis of 20 countries, including the DRC
— become pregnant as a result of rape (Ba & Bhopal, 2017). Therefore, exposure to CRSV would
be associated with an earlier timing of first-order premarital adolescent conception (*hypothesis H₁*).

CRSV has also been found to increase the risk of child marriage (DiGiuseppe & Haer, 2023) and, by the same token, the risk of adolescent conception. This phenomenon has been observed in armed conflicts where girls have been targeted for recruitment by rebel groups (DiGiuseppe & Haer, 2023). In addition, girls who have experienced CRSV may have limited agency within marital relationships (Bumet, 2012), making it challenging to negotiate contraceptive use or delay childbearing. Hence, *hypothesis H₂* is that exposure to CRSV would increase the risk of first adolescent conception within marriage. From these first two hypotheses, we argue that exposure to CRSV shortens the timing of first adolescent conception (both within and before) (*hypothesis H₃*).

The reduction of household income is one of the primary mechanisms through which conflict adversely affects child and adolescent outcomes (Minoiu & Shemyakina, 2014). Lower income during conflict may encourage transactional and forced unions (Hutchinson et al., 2016; Kramer, 2012). The prevalence of teenage motherhood is negatively associated with income and well-being. For example, Wodon et al. (2017) found that in the DRC, the proportion of women aged 18-22 who were married before 18 increased with income level (from 51% among the poorest to 15.5% among the richest). Similarly, the proportion of women who had their first child before age 18 increased with income (16.7% among the richest and 34% among the poorest). There are at least two reasons for this negative relationship. First, marrying a younger daughter helps reduce the family's investment in her education (Wodon et al., 2017). Second, in intermittent conflicts, such as those in the DRC, households may "smooth" their consumption over time to manage unfavorable economic conditions during periods of heightened conflict (Rosenzweig & Stark, 1989). One strategy families use to manage consumption over time or to cope with difficult periods caused by conflict is offering their daughters, including teenage girls, for marriage, as found in Zimbabwe (Hoogeveen et al., 2011).

In most cases, however, conflict reduces men's material and financial opportunities to enter formal unions (Buvinic et al., 2013; Clifford et al., 2010). Households experiencing a drop in income often postpone spending on marriage and childbearing until the situation improves (Buvinic et al., 2013; Jayaraman et al., 2008). Similarly, forced displacement during conflict may separate women from potential spouses, delaying their marriage, as observed in Tajikistan (Shemyakina, 2013). In addition, shifts in the local sex ratio, driven by elevated male mortality, may reduce the pool of potential spouses, thereby delaying women's entry into marriage (Jayaraman et al., 2008) and, consequently, postponing the timing of first conception within marriage. Furthermore, conflict not only raises the age at first marriage but also prolongs the transition from marriage to first birth through multiple mechanisms, including economic hardship, displacement, and disruptions to healthcare systems. First, conflict often leads to economic insecurity, job losses, and food shortages (Koyame & Clark, 2002; Mercier et al., 2020), all of which can contribute to postponements in first childbearing among already married women. Newly married couples experiencing economic hardship may delay fertility as a coping mechanism, prioritizing financial stability before having children (Lindstrom &
Berhanu, 1999). This is particularly relevant in settings where raising children requires a stable income and access to resources. Second, conflict physically separates partners through forced migration or the involvement of men in combat, thereby decreasing coital frequency and reducing opportunities for conception (Abbasi-Shavazi et al., 2018; Verwimp et al., 2020). Studies on refugees and internally displaced populations have shown that forced migration can delay fertility and disrupt reproductive plans (Saarela & Wilson, 2022). Third, conflict frequently disrupts access to reproductive healthcare, including contraception and maternal health services (Chi et al., 2015). While restricted access to reproductive healthcare may initially imply an increase in unplanned pregnancies (Torrisi, 2024), it can ultimately result in delays in childbearing when women perceive that the health of either their children or themselves is at risk. Women in conflict zones may delay their first conception until they have access to safer healthcare conditions (Svallfors, 2022). Hence, *hypothesis H₄* posits that exposure to conflict would delay the timing of first adolescent conception within marriage.

One significant pathway through which conflict influences adolescent pregnancy is the disruption of family structures. Armed conflict often results in the death or displacement of parents, leaving adolescents without the financial, emotional, and material support that typically helps guide their decision-making. The lack of parental support may push adolescents, particularly girls, toward early informal unions or transactional sex as a way to meet basic needs (Hutchinson et al., 2016; Maclin et al., 2015). Studies such as those by Chae (2013) and Palermo & Peterman (2009) highlight the increased risk of early marriage, sexual debut, and adolescent pregnancy among orphaned. In addition, closeness between parents and children, along with parental supervision, fosters values that discourage teen (or unprotected) intercourse, thereby reducing the risk of adolescent pregnancy (Miller, 2002). Besides disrupting family structures, the collapse of community norms and social cohesion is another key pathway through which conflict affects adolescent pregnancy (Svanemyr, 2020). Armed conflict erodes the traditional structures that guide and protect adolescents, especially girls, from early sexual activity and pregnancy (Matthews & Ritsema, 2004). When communities lose the institutions that regulate behavior, such as elders who traditionally provide oversight, adolescents are more likely to engage in risky sexual behavior as they are no longer subject to the community-based supervision that would traditionally help regulate their sexual activity (Coast et al., 2019; Schlecht et al., 2013; Svanemyr, 2020). Finally, conflict frequently disrupts educational opportunities, particularly for girls. When schools are destroyed or rendered inaccessible, dropout rates tend to increase, thereby limiting adolescents' access to education and the protective role it can play in reducing early pregnancies (Mugisho, 2024). In the same vein, without the protective role of education, girls in conflict zones lack the knowledge to prevent pregnancies, including information on accessing post-rape prevention kits such as post-exposure prophylaxis (Chung et al., 2018). Hence, *hypothesis H₅* posits that exposure to conflict would shorten the timing of adolescent conception before marriage.

According to Schoumaker and Sánchez-Páez (2022), three-quarters of births to adolescents under 15 in developing countries occur within marriage. Given this high proportion of adolescent births within marriage, we assume that the presumed negative effect of conflict on adolescent first-marital conception would also be observed for adolescent conception as a whole, whether within or before marriage (*hypothesis H₆*).
# 3. Data and Methods

## 3.1. Data and data processing

Three data sources were used. The 2007 Demographic and Health Survey (DHS) collected data from 9,995 women aged 15-49 during the survey year (Ministry of Health & ICF International, 2007). This DHS wave implemented 34 strata. In statutory towns, neighborhoods were drawn at the first level and households at the second level. In rural areas, chiefdoms were drawn at the first level, villages at the second level, and households at the third level. A total of 300 sampling clusters were drawn. Their geographic locations are shown in Figure 2.a. The 2007 survey was chosen over the 2013–2014 survey because the latter did not include the variable “number of years in current residence”. This variable allowed us to take women’s mobility into account. This information was crucial because we were interested in the woman’s exposure to conflict in the cluster where she lived during adolescence. In addition, using a survey conducted shortly after the conflicts minimized possible recall bias and selection bias due to emigration and mortality.

The conflict data were retrieved from the Uppsala Conflict Data Program Georeferenced Event Dataset, UCDP-GED (Sundberg & Melander, 2013). UCDP-GED has provided geolocalized data on the prevalence of conflicts worldwide since 1989. UCDP-GED data were preferred to ACLED data because the latter, available since 1997, does not cover the entire conflict period. Detailed information on the UCDP-GED can be found in other papers (Eck, 2012; Sundberg & Melander, 2013). 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). This dataset records a range of information, such as the conflict identifier, geographical coordinates (latitude/longitude), start and end dates, and fatality counts under best, low, and high assumptions. We selected the best estimates of fatalities.

To measure women’s exposure to CRSV, we used GEO-SVAC data, a geo-referenced and dated record of CRSV worldwide between 1989 and 2009, which indicates the prevalence and intensity of sexual violence during each conflict event. The types of sexual violence recorded in these data include rape, sexual slavery, forced prostitution, forced pregnancy, forced abortion or sterilization, genital mutilation, and sexual torture. GEO-SVAC data do not include numerical estimates of sexual violence cases or victims due to validity issues (Cohen & Nordås, 2014).

Each conflict event in the UCDP-GED and GEO-SVAC data has a unique identifier that is the same in both data sources, as GEO-SVAC is a small subset of the UCDP-GED dataset (Bahgat et al., 2016). The two sources were merged using this identifier and the date (month and year) when each event occurred. These data were merged with DHS 2007 data based on geographic coordinates using the *sf*, *rgdal*, and *raster* packages of the R software, applying a 50 km buffer zone centered at the latitude/longitude of the DHS cluster points (see Figure 2.a). A 50-km buffer zone is widely used in the demographic literature (DiGiuseppe & Haer, 2023). A larger buffer zone mitigates misclassification, as cluster locations were shifted by 2 km in urban areas and up to 5 km in rural areas, or even 10 km for 1% of clusters (Boyle et al., 2020). This choice is further substantiated by the observation that conflict may influence the demographic behaviors of populations residing far from areas of active violence through a range of mechanisms, including economic disruption, impoverishment, forced displacement,
and the psychological trauma associated with witnessing the deaths of loved ones (Torrisi, 2020). Using a large geographic area minimizes bias caused by missing data on women's migratory trajectories (Bocquier, 2016).

During the merge process, we ensured that each month of conflict occurrence in the conflict data aligned with the corresponding month of observation in the DHS dataset. The merged dataset (DHS, UCDP-GED, and GEO-SVAC) was converted into a woman-month longitudinal dataset, starting from the first month of age 10 and continuing through the year and month of conception resulting in a live birth during adolescence, or until the last month of age 19 if no conception occurred (right censoring). Using women-month event history data offers significant advantages over women-year data. The finer temporal resolution allows more precise matching of conflict or CRSV occurrence months with conception months, reducing temporal misalignment risk. Additionally, monthly data enables better control of time-varying confounders, producing more reliable estimates and minimizing post-treatment bias. In contrast, averaging events within a year can obscure the dynamic temporal nature of exposure, masking short-term impacts of conflict or CRSV (Hudde & Jacob, 2023).

The start of observation was set at age 10, as this represented the age at which the earliest adolescent conception was observed in our sample. The study period runs from January 1989 to December 2006 (see Figure 3). Observations before January 1989 have been excluded (left truncated), as contextual indicators of conflict and CRSV are available only from 1989. This truncation does not bias our measures of conflict and CRSV exposure, as only a few conflict events occurred before 1990 (three in 1989 and four in 1990) (Sundberg & Melander, 2013). Similarly, women who had their first teenage pregnancy before January 1989 were excluded. Nulliparous women who had turned ten before January 1989 were observed from their exact age in January 1989, with monthly precision, until the month of adolescent conception or the last month of age 19, whichever came first. The generations and time periods covered by the study are presented in Figure 3.

Dealing with mobility is a major methodological challenge in event history analyses with retrospective data grouped into geographical clusters. Mobility was measured by the years spent in the current cluster of residence. From this variable, we calculated the age at which the woman moved into the cluster of residence at the time of the survey. To assess the impact of conflict and CRSV, we focused on women who have lived in the same cluster since turning 10. This approach ensures they all experienced conflict or CRSV in that cluster. Consistent with other studies (Thiede et al., 2020), women who were exposed to conflict during the observation period in clusters other than the one where they resided at the time of the survey (defined here as mobile women) were excluded, as these clusters may have experienced different levels of exposure to conflict or CRSV. The final sample comprises 6,922 women, corresponding to 632,773 women-months.
<figure><img src="image_6.png" /></figure>

Note: The survey was conducted in 2007 among women aged 15-49. The study period spans from January 1989 to December 2006. Observations from years prior to 1989 are excluded (left truncation). The two grey and blue hatched areas represent the first two Congo wars (1996-1997 and 1998-2003). The area at the bottom right corresponds to women who were under the age of 15 at the time of the survey. These women were not included as they were not surveyed. The area between the two diagonal blue dotted lines represents the generations of women who spent their teenage years before 1989 (or who reached the age of 20 before 1989), i.e., women aged 38-49 at the time of the survey. These women were excluded (either left-censored because they had experienced the event before the start of the observation period, or right censored because they were no longer at risk of experiencing teenage motherhood after the age of 20). The women included in the study are those belonging to the generations circled by a solid blue line, i.e., those who reached the age of 10 between 1979 and 2001.

<caption>Figure 3. Lexis diagram illustrating the data used in the study of the effect of conflict and CRSV on adolescent conception in the DRC, based on the 2007 DHS</caption>

## 3.2. Statistical modeling

Event history models were employed to model the risk of first conception during adolescence as a function of time, given that the event has not yet occurred. We used discrete-time models because time is measured on a discrete scale. Additionally, the events are concentrated at specific ages (50% of conceptions during adolescence occurred between the ages of 17 and 19). Since exposure to conflict and CRSV are measured at the cluster level, it is insightful to obtain estimates that can be interpreted as contextual effects. Many prior studies that utilized the same conflict data have employed a fixed-effects model, focusing solely on longitudinal or within-effects (DiGiuseppe & Haer, 2023; Jawad et al., 2020) and overlooking a significant aspect of conflict impact: the between- or contextual effect (Rohrer & Murayama, 2023). We aim to investigate both the within- and contextual-level effects of exposure to conflict and CRSV. The contextual effect refers to the expected difference in the risk of adolescent conception between two otherwise identical women who differ by one unit in their cluster mean exposure to conflict or CRSV variables (Rohrer & Murayama, 2023). These contextual effects are similar to, yet slightly different from, those estimated through within-between modeling (Allison, 2009,
2014) in which time-varying variables are de-meaned for each individual<fnref n="2" />. The within-cluster effects can be interpreted as the changes in the risk of adolescent conception for a specific cluster, attributable to variations in exposure to conflict or CRSV within the same cluster over time (Dieleman & Templin, 2014).

First, we did not differentiate between the types of adolescent conceptions (i.e., marital or premarital). We employed a multilevel event history model, utilizing the Mundlak approach (Mundlak, 1978). In the Mundlak approach, the means of all time-varying variables are incorporated as independent variables to control for unobserved time-invariant characteristics, both at the individual and cluster levels. This model integrates the strengths of both fixed and random effects models. It not only facilitates the estimation of contextual effects but also represents the “best method available to produce within effects that are (relatively) unbiased by omitted higher-level variables” (Bell et al., 2019, p.1067), hence its increasing use in the recent literature.

We initially modeled the baseline hazard as a linear function of age or time:

$$ \ln(P_{itj}/(1-P_{itj})) = \beta_0 + \beta_1^W AGE_{itj} \quad (Equation 1) $$

Next, we specified the full model as follows:

$$ \ln(P_{itj}/(1-P_{itj})) = \beta_0 + \beta_1^W AGE_{itj} + \beta_2^C AGE_{ij} + \beta_3^W CONFLICT_{tj} + \beta_4^W CRSV_{tj} + \beta_5^C CONFLICT_{j} + \beta_6^C CRSV_{j} + \beta_7 PLACE_i + \beta_8 GENERATION_{ij} + v_j \quad (Equation 2) $$

where $P_{itj}$ is the conditional probability of first adolescent conception for woman $i$ living in a cluster $j$ at month- $t$ of a specific year, given that the event has not already occurred. $\beta_0$ is the intercept. $AGE_{itj}$ captures the changing hazard of adolescent conception over time (from age 10 to 19). In this specification, its coefficient $\beta_1^W$ shows how the hazard changes for a given woman as she gets older (Schunck & Perales, 2017). $CONFLICT_{tj}$, $CRSV_{tj}$, $CONFLICT_j$ and $CRSV_j$ are time-varying explanatory variables measured at the cluster-level and their cluster-specific means. $\beta_3^W$, $\beta_4^W$ correspond to the longitudinal or within-effects of exposure to conflict and CRSV respectively, which are close to the fixed-effects modeling estimates (Allison 2014; Bell et al. 2019). $\beta_5^C$, $\beta_6^C$ are estimates of contextual effects of conflict and CRSV (Allison, 2017). $\beta_2, \beta_7, \beta_8$ are effects of control variables. $v_j$ is the cluster-level random effect, assumed to be normally distributed. The random effect at the DHS cluster level $v_j$ was added to account for the correlation of observations for the same cluster. We did not add a random effect at the individual level because the variance of the random effect was not statistically different from 0, according to the Likelihood Ratio test.

Second, we treated first marital and premarital adolescent conceptions as two competing events, where the occurrence of one prevents the other from taking place. This

<footnote n="2">² In this later setting, the coefficient of the means of exposure to conflict or CRSV would represent the expected difference in the mean risk of adolescent conception of two clusters, which differs by one unit in the mean of the conflict or CRSV variables (Rohrer & Murayama, 2023).</footnote>
distinction allowed us to identify the primary mechanisms through which conflict may have influenced adolescent motherhood, such as early marriage or the failure to use contraception. Consequently, the dependent variable consists of three categories. Using the Mundlak approach, we estimated a discrete-time multinomial multilevel logistic model, specified as follows:

$$ \ln(P_{itj,g}/(1-P_{itj,g})) = \beta_{0,g} + \beta_{1,g}^{W} AGE_{itj} + \beta_{2,g}^{C} \overline{AGE}_{ij} + \beta_{3,g}^{W} CONFLICT_{tj} + \beta_{4,g}^{W} CRSV_{tj} + \beta_{5,g}^{C} \overline{CONFLICT}_{j} + \beta_{6,g}^{C} \overline{CRSV}_{j} + \beta_{7,g} PLACE_{i} + \beta_{8,g} GENERATION_{ij} + v_{j} \quad (Equation 3) $$

where $P_{itj,g}$ is the conditional probability that a conception event of type g occurs for woman i in cluster j, at time t, given that no event has occurred before time t. This model allows the effects of the explanatory variables to vary according to whether adolescent conception occurs within or before marriage (Steele et al., 2005). The independent variables and parameters are consistent with those in equation (1). A separate model was estimated for each of the G-1=2 categories of the dependent variable (first marital conception and first premarital adolescent conception), with the remaining category (no conception during adolescence) serving as the reference group. Collinearity between explanatory variables was assessed using generalized variance inflation factors (GVIF). We found collinearity between the time period variable and both age and the spatiotemporal conflict exposure measures (GVIF = 10.3), as most conflicts occurred between 1996 and 2003. Therefore, we excluded the time period variable from our models.

## 3.5. Measures

### 3.5.1. Dependent variables

Consistent with previous studies (Lindstrom & Berhanu, 1999), we focused on the month of conception leading to a live birth, rather than the month in which the live birth occurred, to minimize post-treatment bias. The date of conception was calculated using century month codes (CMC) by subtracting nine months from the CMC date of the first birth.

We defined two dependent variables. The first represents a first conception resulting in a live birth during adolescence. It takes the value 1 for the month of the first conception resulting in a live birth during adolescence, and 0 for all other observation months where the woman did not experience such a conception. The focus on first pregnancies stems from the fact that adolescent first pregnancies are often linked to a higher risk of complications for both the mother and child, due to physiological and psychological immaturity (van Oppenraaij et al., 2009). In addition, many adolescent reproductive health programs and policies are structured to address first pregnancies (Ahinkorah et al., 2022; Estrada et al., 2021), as they are often viewed as the most preventable and have the most extensive ripple effects across the adolescent's life course.

The second distinguishes between marital and premarital adolescent conceptions and has three categories: 1 for the month in which a marital conception occurred, 2 for the month in which a premarital conception occurred, and 0 for any month of observation in which no
conception resulting in a live birth occurred during adolescence. A conception is defined as
marital if the CMC date of conception is later than or equal to the CMC date of marriage, and
non-marital or premarital if the CMC date of conception precedes the CMC date of marriage.

3.5.2. *Explanatory variables*

Two explanatory variables were defined: the first is exposure to conflict, and the second is exposure to CRSV. In the GEO-SVAC data, the prevalence of CRSV is initially measured on an ordinal scale (none, some, many, massive; see Figure 2.a). However, this variable has been converted into a dichotomous measure due to various arithmetic operations performed on it, including summation by cluster and by month. Both variables take the value 1 if a conflict event or CRSV occurred within a 50 km buffer zone around DHS cluster points and within a seven-month window (before and including the index month). The index month was included because exposure to conflict or CRSV during that specific month may directly influence conception within the same month. A six-month window preceding the index month was considered, as it may take time for a girl to conceive following marriage or CRSV. Additionally, families may require time to decide to marry their daughter in response to prior conflict. A shorter interval was selected, however, because the time between exposure to conflict and the decision to marry may be compressed, particularly due to financial pressures or the need for material resources (Sieverding et al., 2020). We extended this definition to encompass the past 12 months in the sensitivity analyses. Additionally, we employed alternative measures of conflict exposure, including the total number of fatalities, tertiles of fatalities, and lagged dummy variables for exposure to conflict and CRSV.

3.5.3. *Control variables*

We controlled for the effects of mean age, birth year, and place of residence (urban or rural). The inclusion of mean age or time, as implemented automatically by the *xthybrid* command in Stata (Schunck & Perales, 2017), serves a methodological purpose: It accounts for interindividual variation and adjusts for unobserved time-invariant factors that may bias the within-individual effect of age and other covariates. Such factors may include differences in biological, psychological, or social development between individuals of the same chronological age. This specification assesses whether women older on average during the observation period have a systematically different conception risk than younger women, independently of the monthly variations in their age (see Schunck & Perales, 2017, p.101-103). We included birth year to account for cohort-specific factors—such as age-related selection into the observation window (e.g., left truncation), generational differences in access to education, or cultural norms around sexual initiation—that may confound the effects of conflict and CRSV exposure. Although residence may change over time, focusing on non-migrant women mitigates bias from the absence of complete migration trajectories. We could not control for region, which would have accounted for regional unobserved characteristics. In the DRC, conflict is highly region-specific (Mugisho, 2025, p.7), with some areas experiencing little or no conflict and others being at the epicenter. Thus, controlling for region would have mitigated or eliminated the effect of conflict or CRSV in our analysis (See Allison, 2017).
# 4. Findings

## 4.1. Sample description

The description of the variables under study, stratified by whether the woman experienced her first adolescent conception within or before marriage, is presented in Tables 1a and 1b. Among the women in the analyzed sample, 2,519 out of 6,922 (36.4%) conceived during adolescence, with 2,000 (28.9%) conceiving within marriage and 519 (7.5%) before marriage. The median age at first adolescent conception was 16 years. Premarital adolescent conception occurred slightly earlier (median age of 15) than marital adolescent conception (median age of 16) (see Table 1b). About 50.6% of the sample lived in rural areas. The proportion with first adolescent conception before marriage was nearly identical in rural (7.6%) and urban (7.4%) settings. However, marital adolescent conception was more common in rural areas (32.7%) than in urban areas (24.9%) (Table 1a).

<table><caption>Table 1a. Sample description (categorical variables)</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Category</th><th rowspan="2">Women-months</th><th rowspan="2">Number of women</th><th colspan="3">Women who had their first conception during adolescence</th></tr><tr><th>Within marriage N (%)<sup>b</sup></th><th>Before marriage N (%)<sup>b</sup></th><th>Within or before marriage N (%)<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="2">Place of residence</td><td>Rural</td><td>316,966</td><td>3,503<sup>a</sup></td><td>1,147 (32.7)<sup>a</sup></td><td>266 (7.6)<sup>a</sup></td><td>1,413 (40.3)<sup>a</sup></td></tr><tr><td>Urban</td><td>315,807</td><td>3,419</td><td>853 (24.9)</td><td>253 (7.4)</td><td>1,106 (32.3)</td></tr><tr><td rowspan="10">Region</td><td>Bandundu</td><td>60,102</td><td>647</td><td>158 (24.4)</td><td>52 (8.1)</td><td>210 (32.5)</td></tr><tr><td>Bas-Congo</td><td>41,733</td><td>447</td><td>106 (23.7)</td><td>55 (12.3)</td><td>161 (36.0)</td></tr><tr><td>Equateur</td><td>58,540</td><td>659</td><td>234 (35.5)</td><td>56 (8.5)</td><td>290 (44.0)</td></tr><tr><td>Kasai Occidental</td><td>39,650</td><td>447</td><td>170 (38.0)</td><td>18 (4.0)</td><td>188 (42.1)</td></tr><tr><td>Kasai Oriental</td><td>49,213</td><td>551</td><td>177 (32.1)</td><td>20 (3.6)</td><td>197 (35.8)</td></tr><tr><td>Katanga</td><td>60,596</td><td>676</td><td>213 (31.5)</td><td>28 (4.1)</td><td>241 (35.7)</td></tr><tr><td>Kinshasa</td><td>129,949</td><td>1,350</td><td>264 (19.6)</td><td>113 (8.4)</td><td>377 (27.9)</td></tr><tr><td>Maniema</td><td>46,483</td><td>525</td><td>187 (35.6)</td><td>35 (6.8)</td><td>222 (32.3)</td></tr><tr><td>Nord-Kivu</td><td>49,916</td><td>553</td><td>159 (28.8)</td><td>59 (10.7)</td><td>218 (39.4)</td></tr><tr><td>Province Orientale</td><td>43,124</td><td>483</td><td>165 (34.2)</td><td>44 (9.1)</td><td>209 (43.3)</td></tr><tr><td>Sud-Kivu</td><td>53,467</td><td>584</td><td>167 (28.6)</td><td>39 (6.9)</td><td>206 (35.3)</td></tr><tr><td rowspan="5">Period</td><td>Pre-war</td><td>335,179<sup>c</sup></td><td>4,979<sup>g</sup></td><td>958<sup>d</sup></td><td>215</td><td>1,173</td></tr><tr><td>First Congo War</td><td>30,387</td><td>4,024<sup>g</sup></td><td>71<sup>d</sup></td><td>26</td><td>97</td></tr><tr><td>Interwar</td><td>45,951</td><td>4,173<sup>g</sup></td><td>113</td><td>44</td><td>157</td></tr><tr><td>Second Congo War</td><td>146,455</td><td>3,589<sup>g</sup></td><td>510</td><td>141</td><td>651</td></tr><tr><td>Post-war</td><td>74,801</td><td>2,320<sup>g</sup></td><td>348</td><td>93</td><td>441</td></tr><tr><td rowspan="2">Exposure to CRSV</td><td>No</td><td>607,596<sup>e</sup></td><td>—<sup>f</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td>Yes</td><td>25,177</td><td>—<sup>f</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">Exposure to conflict<sup>a</sup></td><td>No</td><td>597,261</td><td>—<sup>f</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td>Yes</td><td>35,512<sup>a</sup></td><td>—<sup>f</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td colspan="2">Total number of women-months</td><td>632,773</td><td>6,922</td><td></td><td></td><td></td></tr></tbody></table>

<footnote><sup>a</sup> Of the sample, 3,503 individuals live in rural areas. About 40.3% had their first conception during adolescence, including 1,147 (32.7%) within marriage and 266 (7.6%) before marriage.</footnote>

<footnote><sup>b</sup> Percentage of the total number of women, as shown in the fourth column titled "Number of women".</footnote>

<footnote><sup>c</sup> The figures represent the number of months women spent in each phase of the conflicts.</footnote>

<footnote><sup>d</sup> 958 women had their first child during adolescence within marriage before the war, while 71 women had their first child within marriage during the First Congo War.</footnote>
e The figures represent the number of women-months exposed to conflict or CRSV within a spatiotemporal domain of 50 km and 7 months.

f We did not display the number of women exposed to conflict or CRSV because these variables are time-varying. A given woman may be exposed to conflict or CRSV at one point in time and not exposed at another.

g The figures represent the women who were observed in each phase of the conflicts: Pre-war (from January 1989 to September 1995), First Congo War (from October 1996 to May 1997), Interwar (from June 1997 to July 1998), Second Congo War (from August 1998 to July 2003), and Post-war (from August 2003 onward).

<table><caption>Table 1b. Sample description (quantitative variables)</caption><thead><tr><th rowspan="3">Quantitative variables</th><th colspan="9">Women who had their first conception during adolescence</th></tr><tr><th colspan="3">Within marriage</th><th colspan="3">Before marriage</th><th colspan="3">Within or before marriage</th></tr><tr><th>Min</th><th>Median (sd)</th><th>Max</th><th>Min</th><th>Median (sd)</th><th>Max</th><th>Min</th><th>Median (sd)</th><th>Max</th></tr></thead><tbody><tr><td>Birth cohort</td><td>1969</td><td>1979 (6.8)</td><td>1992</td><td>1969</td><td>1981 (6.1)</td><td>1992</td><td>1969</td><td>1980 (6.8)</td><td>1992</td></tr><tr><td>Age at first adolescent conception</td><td>10.9</td><td>16.1 (2.5)</td><td>19.8</td><td>10</td><td>15.2 (2.4)</td><td>19.5</td><td>10</td><td>15.9 (2.5)</td><td>19.8</td></tr></tbody></table>

## 4.2. Main findings

Table 2 and Figures 4.a to 4.d present the within-effects and contextual effects of exposure to conflict and CRSV on the timing of first marital and first premarital adolescent conception, as well as on the overall timing of adolescent conception, whether within or before marriage.

CRSV has distinct within- and contextual effects on the timing of adolescent motherhood. On the one hand, there is a positive contextual effect of CRSV exposure on the risk of premarital adolescent conception; that is, holding other factors constant, women living in clusters exposed to CRSV experience earlier premarital adolescent conception than those residing in non-exposed clusters ($\beta$ = 3.193, $p$ = 0.036). This result supports *hypothesis H1* that exposure to CRSV is associated with a high risk of first adolescent pregnancy before marriage.

On the other hand, exposure to CRSV has a negative within-cluster (or longitudinal) effect on the risk of first adolescent conception within marriage. Specifically, the probability of first adolescent conception within marriage is lower when a woman's residential cluster is exposed to CRSV than when the same cluster is not exposed ($\beta$ = -0.279, $p$ = 0.037). This result does not support the *H2* *hypothesis* that exposure to CRSV would increase the risk of first adolescent pregnancy within marriage. There was no evidence of a within- or contextual effect of CRSV exposure on adolescent conception (marital and premarital). This finding does not support *hypothesis H3*.

There is a negative contextual effect of exposure to conflict on the probability of first marital adolescent conception; that is, women exposed to conflict experience a later timing of first marital adolescent conception compared to women not exposed to conflict ($\beta$ = -0.423, $p$ = 0.030). This result corroborates *hypothesis H4*, which assumed a negative association between exposure to conflict and the likelihood of a first adolescent conception within marriage.

As first marital conception is the most important component of adolescent conception (see comments on Table 1a), the same negative contextual effect of conflict on the likelihood of adolescent conception (within and before marriage) is observed. Thus, the timing of adolescent
conception (within or before marriage) is later among women exposed to conflict compared to women not exposed to conflict ($\beta = -0.444, p = 0.012$). This result is consistent with *hypothesis H6*.

There is no evidence of the within-effect ($\beta = -0.011, p = 0.940$) and of the contextual effect ($\beta = -0.127, p = 0.757$) of exposure to conflict on first adolescent conception before marriage, contrary to *hypothesis H5*.
<table><caption>Table 2. Effects of exposure to conflict and to CRSV on the risk of first marital conception, first premarital conception, and first conception during adolescence</caption><thead><tr><th rowspan="3">Type of model→<br>Modeled event→</th><th colspan="6">Multinomial multilevel discrete-time event history model</th><th colspan="3">Binary multilevel discrete-time event history model</th></tr><tr><th colspan="3">Marital conception (reference: no conception)</th><th colspan="3">Premarital conception (reference: no conception)</th><th colspan="3">Marital and premarital conceptions (reference: no conception)</th></tr><tr><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th></tr></thead><tbody><tr><td>Characteristic</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age—Within</td><td>0.423</td><td>0.398; 0.448</td><td>&lt;0.001**</td><td>0.632</td><td>0.567; 0.697</td><td>&lt;0.001**</td><td>0.456</td><td>0.433; 0.480</td><td>&lt;0.001**</td></tr><tr><td>Age—Contextual</td><td>-0.893</td><td>-0.961; -0.825</td><td>&lt;0.001**</td><td>-1.748</td><td>-1.884; -1.588</td><td>&lt;0.001**</td><td>1.058</td><td>-1.119; -0.998</td><td>&lt;0.001**</td></tr><tr><td>Exposure to CRSV (Yes)—Within</td><td>-0.279</td><td>-0.555; -0.003</td><td>0.037*</td><td>0.155</td><td>-0.278; 0.589</td><td>0.482</td><td>-0.168</td><td>-0.400; 0.064</td><td>0.156</td></tr><tr><td>Exposure to CRSV—Contextual</td><td>-0.275</td><td>-1.897; 1.347</td><td>0.740</td><td>3.193</td><td>0.054; 6.344</td><td>0.036*</td><td>0.476</td><td>-0.866; 1.818</td><td>0.487</td></tr><tr><td>Exposure to conflict (Yes)—Within</td><td>0.042</td><td>-0.105; 0.193</td><td>0.560</td><td>-0.011</td><td>-0.287; 0.266</td><td>0.940</td><td>0.030</td><td>-0.101; 0.162</td><td>0.648</td></tr><tr><td>Exposure to conflict—Contextual</td><td>-0.423</td><td>-0.826; -0.019</td><td>0.030*</td><td>-0.127</td><td>-0.933; 0.679</td><td>0.757</td><td>-0.444</td><td>-0.789; -0.099</td><td>0.012*</td></tr><tr><td>Year of birth</td><td>0.0002</td><td>-0.0001;0.0006</td><td>0.241</td><td>0.001</td><td>0.0003; 0.001</td><td>0.003**</td><td>0.0003</td><td>0.000; 0.0007</td><td>0.028*</td></tr><tr><td>Place of residence (Rural) —Ref.: Urban</td><td>0.198</td><td>0.089; 0.307</td><td>&lt;0.001**</td><td>0.125</td><td>-0.009; 0.350</td><td>0.276</td><td>0.179</td><td>0.087; 0.270</td><td>&lt;0.001**</td></tr><tr><td>(Intercept)</td><td>0.096</td><td>-0.796; 0.990</td><td>0.832</td><td>5.861</td><td>4.277; 7.444</td><td>&lt;0.001**</td><td>1.918</td><td>1.148; 2.687</td><td>&lt;0.001**</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="3">0.033 [0.015; 0.074]</td><td colspan="3"></td><td colspan="3">0.008 [0.0089; 0.078]</td></tr><tr><td>Sample size: women-months (women)</td><td colspan="3">632,773 (6,922)</td><td colspan="3"></td><td colspan="3">632,773 (6,922)</td></tr><tr><td>Log-likelihood</td><td colspan="3">-16,488.61</td><td colspan="3"></td><td colspan="3">-15,296.371</td></tr></tbody></table>

<footnote n="1">¹CI : Confidence Interval, Coef. : Coefficients</footnote>

<footnote>**P<0.01, *P<0.05, °P<0.10.</footnote>
<figure><img src="image_7.png" /><figcaption>Figures 4.a–4.d. Predicted probabilities of adolescent conception by exposure to conflict and CRSV</figcaption></figure>

4.3. Robustness checks and extended analyses

Additional analyses were conducted to test the robustness of the results to alternative methodological choices (Table 3). First, while maintaining a buffer zone of 50 km around the DHS clusters, we defined conflict exposure using a thirteen-month time window (including the index month). The results (Table 3, Panel A) show that all the effects of conflict and CRSV have the same signs as in Table 2. However, they are generally less pronounced, characterized by lower absolute values of the coefficients and higher p-values compared to those in Table 2. The only exception is the contextual effect of exposure to CRSV on the timing of first premarital adolescent conception, which is more pronounced.

Analyses were conducted using a 20 km buffer radius from the DHS clusters, as the estimated effects of conflict may vary depending on the buffer radius used to define exposure (Kotsadam & Østby, 2019). The within and contextual effects of conflict and CRSV have similar signs to those in Table 2, but the effects are less pronounced. This result suggests that, in the context of this study, measuring conflict with a 20 km buffer around the clusters may not capture all conflict effects.

We measured conflict exposure based on the number of conflict-related fatalities occurring within a spatiotemporal window of 50 km and seven months, including the index
month (Table 3, Panel C). The results show that exposure to CRSV delays the timing of adolescent conception within marriage ($\beta$ = -1.438, *p* = 0.021) and shortens the timing of adolescent conception before marriage ($\beta$ = 2.759, *p* = 0.017). Furthermore, women exposed to conflict have a later timing of adolescent conception (whether marital or premarital) compared to non-exposed women ($\beta$ = -0.0001, *p* = 0.046). These results are very similar to those in Table 2.

Measuring conflict exposure by the number of fatalities can be misleading due to outliers in fatalities recorded in certain clusters (Krafft et al., 2024). Similar to other studies (Krafft et al., 2024; Mugisho, 2025), we measured conflicts using an ordinal categorical variable with four categories, with the first category being 'no deaths' (85.1% of observations in person-months) and the other three categories representing each of the three tertiles. We first used multilevel discrete-time event history models, following the Mundlak approach, treating the ordinal variable of conflict exposure level as quantitative. The coefficients for conflict and CRSV had similar signs to those presented in Table 2, but the effects were no longer statistically significant (results available upon request). This loss of significance could be due to the loss of information from measuring conflict exposure on an ordinal scale or because the effect of conflict may not be linear.

To facilitate interpretation of the effect of this ordinal measure of conflict, we then estimated the effects of exposure to conflict and CRSV on adolescent conception without distinguishing between within- and contextual effects (Table 3, Panel D), i.e., by using a multilevel event history model. As in Table 2, the results show that exposure to CRSV reduces the timing of adolescent conception within marriage ($\beta$ = -0.303, *p* = 0.018)<fnref n="3" />. Women living in clusters with a high level of exposure to conflicts have a later timing for adolescent conception within marriage than women who are not exposed to conflicts ($\beta$ = -0.187, *p* = 0.045). These results are comparable to those in Table 2.

In the main analyses (Table 2), we combined contemporaneous and lagged measures of conflict into a single measure. Drawing on other studies (Kotsadam & Østby, 2019; Krafft et al., 2024), we ran separate models—one using contemporaneous measures (Table 3, Panel E) and the other using lagged measures (Table 3, Panel F)<fnref n="4" />. For ease of reading and comparison with other studies, we did not use Mundlak's approach. The results (Table 3, Panel E) show effects of conflicts similar to those in Table 2, i.e., a negative effect on the risk of adolescent conception as a whole ($\beta$ = -0.239, *p* = 0.021) and the risk of adolescent conception in marriage ($\beta$ = -0.301, *p* = 0.005). There is no evidence of an effect of CRSV exposure in a given month on the risk of conception in the same month. Its effects (of negative sign) are observed only from a longitudinal perspective, i.e., considering the lagged measures (see model using lagged measures, Table 3, Panel F).

<footnote n="3">³ Here, coefficients correspond to the weighted average of the within and between-clusters coefficients (Rabe-Hesketh & Skrondal, 2008).</footnote>

<footnote n="4">⁴ For each lagged measure of conflict and CRSV exposure, we constructed six dummy variables coded 1 if at least one conflict event or conflict-related sexual violence (CRSV) incident occurred within a 50 km radius of the survey cluster, and 0 otherwise.</footnote>
<table><caption>Table 3. Sensitivity of results to alternative modeling and alternative measures of exposure to conflict and CRSV</caption><thead><tr><th rowspan="3">Type of model→<br>Modeled event→<br>Characteristic</th><th colspan="6">Multinomial multilevel discrete-time event history model</th><th colspan="3">Binary multilevel discrete-time event history model</th></tr><tr><th colspan="3">Marital conception (reference: no conception)</th><th colspan="3">Premarital conception (reference: no conception)</th><th colspan="3">Marital and premarital conception (reference: no conception)</th></tr><tr><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th><th>Coef.</th><th>95% CI<sup>1</sup></th><th>p-value</th></tr></thead><tbody><tr><td colspan="10"><strong>Panel A: Exposure to conflict defined by a spatiotemporal domain of a 50 km buffer zone and 13 months before and including the index month</strong></td></tr><tr><td>Exposure to CRSV (Yes)—Within</td><td>-0.252</td><td>-0.519;0.014</td><td>0.064°</td><td>0.127</td><td>-0.285;0.541</td><td>0.545</td><td>-0.153</td><td>-0.377; 0.070</td><td>0.179</td></tr><tr><td>Exposure to CRSV—Contextual</td><td>-0.136</td><td>-1.206; 0.933</td><td>0.802</td><td>2.465</td><td>0.399; 4.531</td><td>0.019*</td><td>0.427</td><td>-0.455; 1.309</td><td>0.343</td></tr><tr><td>Exposure to conflict (Yes)—Within</td><td>-0.006</td><td>-0.142; 0.130</td><td>0.931</td><td>0.037</td><td>-0.217; 0.293</td><td>0.772</td><td>0.0009</td><td>-0.119; 0.121</td><td>0.988</td></tr><tr><td>Exposure to conflict—Contextual</td><td>-0.285</td><td>-0.623; 0.053</td><td>0.098°</td><td>-0.275</td><td>0.951; 0.400</td><td>0.424</td><td>-0.268</td><td>-0.552; 0.015</td><td>0.064°</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="3">0.026 [0.011; 0.057]</td><td colspan="3"></td><td colspan="3">0.000 [0.000; 0.000]</td></tr><tr><td colspan="10"><strong>Panel B: Exposure to conflict and CRSV defined by a spatiotemporal domain of 20 km buffer zone and seven months before and including the index month</strong></td></tr><tr><td>Exposure to CRSV (Yes)—Within</td><td>-0.272</td><td>-0.564; 0.019</td><td>0.067°</td><td>-0.162</td><td>-0.651;0.326</td><td>0.514</td><td>-0.244</td><td>-0.495;0.006</td><td>0.056°</td></tr><tr><td>Exposure to CRSV—Contextual</td><td>-0.484</td><td>-2.569; 1.599</td><td>0.649</td><td>1.909</td><td>-2.463; 6.281</td><td>0.392</td><td>-0.043</td><td>-1.748; 1.661</td><td>0.960</td></tr><tr><td>Exposure to conflict (Yes)—Within</td><td>0.083</td><td>-0.074; 0.242</td><td>0.299</td><td>0.146</td><td>-0.141;0.434</td><td>0.317</td><td>0.094</td><td>-0.044;0.233</td><td>0.180</td></tr><tr><td>Exposure to conflict—Contextual</td><td>-0.441</td><td>-0.922; 0.040</td><td>0.072°</td><td>-0.045</td><td>-1.050;0.959</td><td>0.929</td><td>-0.331</td><td>-0.728;0.066</td><td>0.103</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="3">0.025 [0.011; 0.060]</td><td colspan="3"></td><td colspan="3">0.008 [0.001; 0.077]</td></tr><tr><td colspan="10"><strong>Panel C: Exposure to conflict defined by the number of fatalities occurring within a spatiotemporal domain of a 50 km buffer zone and seven months (before and including the index month)</strong></td></tr><tr><td>Exposure to CRSV (Yes)—Within</td><td>-0.236</td><td>-0.493; 0.020</td><td>0.071</td><td>0.148</td><td>-0.244;0.540</td><td>0.459</td><td>-0.136</td><td>-0.351; 0.0779</td><td>0.212</td></tr><tr><td>Exposure to CRSV—Contextual</td><td>-1.438</td><td>-2.661; -2.215</td><td>0.021*</td><td>2.759</td><td>0.502; 5.017</td><td>0.017*</td><td>-0.485</td><td>-1.488; 0.517</td><td>0.342</td></tr><tr><td>Number of fatalities—Within</td><td>-0.0001</td><td>-0.0003;0.00001</td><td>0.051</td><td>-0.00007</td><td>-0.0003;0.0002</td><td>0.644</td><td>-0.0001</td><td>-0.0003;-0.00001</td><td>0.046*</td></tr><tr><td>Number of fatalities—Contextual</td><td>0.00006</td><td>-0.0006; 0.0007</td><td>0.850</td><td>0.0002</td><td>-0.001;0.002</td><td>0.755</td><td>0.0001</td><td>-0.0004; 0.0006</td><td>0.433</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="3">0.035[0.016; 0.078]</td><td colspan="3"></td><td colspan="3">0.011[0.002; 0.066]</td></tr><tr><td colspan="10"><strong>Panel D: Exposure to conflict measured on an ordinal scale using tertiles of fatalities, grouping non-zero values into three categories (spatiotemporal domain of a 50 km buffer zone and the seven months before and including the index month)</strong></td></tr><tr><td>Exposure to CRSV (Yes)</td><td>-0.303</td><td>-0.556; -0.051</td><td>0.018*</td><td>0.283</td><td>-0.120; 0.687</td><td>0.169</td><td>-0.146</td><td>-0.374;0.081</td><td>0.208</td></tr><tr><td>Exposure to conflict (Ref.: No death)</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>-0.061</td><td>-0.273; 0.152</td><td>0.934</td><td>0.158</td><td>-0.235; 0.550</td><td>0.430</td><td>0.034</td><td>-0.150;0.218</td><td>0.715</td></tr><tr><td>Medium</td><td>0.023</td><td>-0.178; 0.226</td><td>0.817</td><td>0.035</td><td>-0.360;0.429</td><td>0.863</td><td>0.031</td><td>-0.146;0.207</td><td>0.722</td></tr><tr><td>High</td><td>-0.172</td><td>-0.361;0.017</td><td>0.074°</td><td>-0.290</td><td>-0.713; 0.132</td><td>0.178</td><td>-0.187</td><td>-0.370; -0.0038</td><td>0.045°</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="3">0.080 [0.0465; 0.139]</td><td colspan="3"></td><td colspan="3">0.055 [0.033; 0.093]</td></tr></tbody></table>

**Panel E: Exposure to conflict measured by exposure to contemporaneous conflict (spatial domain of 50 km buffer zone)**
<table><tr><td>Exposure to CRSV (Yes)</td><td>-0.312</td><td>-1.036; 0.412</td><td>0.398</td><td>0.090</td><td>-0.906;1.087</td><td>0.859</td><td>-0.187</td><td>-0.716; 0.341</td><td>0.487</td></tr><tr><td>Exposure to conflict (Yes)</td><td>-0.301</td><td>-0.512; -0.090</td><td>0.005**</td><td>-0.120</td><td>-0.539;0.300</td><td>0.300</td><td>-0.239</td><td>-0.442; -0.036</td><td>0.021*</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="6">0.080 [0.0465; 0.139]</td><td colspan="3">0.054 [0.032; 0.091]</td></tr></table>

Panel F: Exposure to conflict and CRSV measured by six lagged dummy measures of exposure to conflict and CRSV (spatial domain of 50 km buffer zone)

<table><tr><td>Exposure to CRSV-lag 1 (Yes)</td><td>0.141</td><td>-0.471; 0.752</td><td>0.652</td><td>0.888</td><td>0.105; 1.672</td><td>0.026*</td><td>0.399</td><td>-0.071; 0.869</td><td>0.096°</td></tr><tr><td>Exposure to CRSV-lag 2 (Yes)</td><td>-0.758</td><td>-1.538; 0.022</td><td>0.057°</td><td>0.15</td><td>-0.722; 1.022</td><td>0.736</td><td>-0.395</td><td>-0.961; 0.172</td><td>0.172</td></tr><tr><td>Exposure to CRSV-lag 3 (Yes)</td><td>0.063</td><td>-0.586; 0.711</td><td>0.850</td><td>0.957</td><td>-0.202; 2.116</td><td>0.106</td><td>0.21</td><td>-0.367; 0.786</td><td>0.476</td></tr><tr><td>Exposure to CRSV-lag 4 (Yes)</td><td>-0.574</td><td>-1.372; 0.225</td><td>0.159</td><td>-0.649</td><td>-2.15; 0.852</td><td>0.397</td><td>-0.56</td><td>-1.293; 0.173</td><td>0.134</td></tr><tr><td>Exposure to CRSV-lag 5 (Yes)</td><td>0.120</td><td>-0.569; 0.808</td><td>0.733</td><td>0.454</td><td>-0.968; 1.875</td><td>0.532</td><td>0.139</td><td>-0.461; 0.739</td><td>0.651</td></tr><tr><td>Exposure to CRSV-lag 6 (Yes)</td><td>-0.139</td><td>-0.913; 0.635</td><td>0.725</td><td>-0.223</td><td>-1.693; 1.246</td><td>0.766</td><td>-0.17</td><td>-0.868; 0.527</td><td>0.632</td></tr><tr><td>Exposure to conflict-lag 1 (Yes)</td><td>-0.358</td><td>-0.644; -0.072</td><td>0.014*</td><td>0.076</td><td>-0.437; 0.589</td><td>0.772</td><td>-0.239</td><td>-0.467; -0.012</td><td>0.039*</td></tr><tr><td>Exposure to conflict-lag 2 (Yes)</td><td>-0.076</td><td>-0.351; 0.199</td><td>0.589</td><td>0.464</td><td>0.028; 0.899</td><td>0.037**</td><td>0.062</td><td>-0.162; 0.287</td><td>0.585</td></tr><tr><td>Exposure to conflict-lag 3 (Yes)</td><td>-0.141</td><td>-0.407; 0.124</td><td>0.297</td><td>-1.101</td><td>-1.876; -0.326</td><td>0.005**</td><td>-0.303</td><td>-0.566; -0.041</td><td>0.024*</td></tr><tr><td>Exposure to conflict-lag 4 (Yes)</td><td>0.154</td><td>-0.134; 0.441</td><td>0.295</td><td>0.133</td><td>-0.389; 0.656</td><td>0.617</td><td>0.153</td><td>-0.085; 0.39</td><td>0.208</td></tr><tr><td>Exposure to conflict-lag 5 (Yes)</td><td>0.108</td><td>-0.179; 0.395</td><td>0.462</td><td>-0.43</td><td>-1.071; 0.212</td><td>0.189</td><td>0.019</td><td>-0.238; 0.276</td><td>0.885</td></tr><tr><td>Exposure to conflict-lag 6 (Yes)</td><td>0.031</td><td>-0.26; 0.323</td><td>0.833</td><td>0.121</td><td>-0.411; 0.653</td><td>0.655</td><td>0.064</td><td>-0.206; 0.333</td><td>0.643</td></tr><tr><td>Variance of the random effect v<sub>j</sub> [CI]</td><td colspan="6">0.078[0.045; 0.137]</td><td colspan="3">0.054[0.032; 0.137]</td></tr><tr><td>Sample size: women-months (women)</td><td colspan="6">632,773 (6,922)</td><td colspan="3">632,773 (6,922)</td></tr></table>

Note. The models control for the effects of the same variables as in Table 2. The models that do not use the Mundlak approach (panels D to F) do not control for the effect of the means of the time-varying variables.

<footnote n="1">¹CI: Confidence Interval, Coef.: Coefficients</footnote>

<footnote>**P<0.01, *P<0.05, °P<0.10.</footnote>
# 5. Discussion

This study aimed to analyze the specific role of conflict in general and CRSV in particular on adolescent conception within marriage, adolescent conception before marriage, and adolescent conception (within or before marriage).

First, we found that women living in clusters exposed to CRSV have an earlier timing of adolescent conception before marriage than women residing in clusters not exposed to CRSV. The types of CRSV documented in the GEO-SVAC data that are likely to increase the risk of adolescent conception before marriage are rape, sexual slavery, forced prostitution, and forced pregnancy (Bahgat et al., 2016). All these types of CRSV have been committed in the DRC (Bahgat et al., 2016; Meger, 2010; Mukwege & Nangini, 2009). We do not interpret this effect as resulting from forced marriages, as, first, forced marriages are not listed among the types of CRSV recorded in the GEO-SVAC data (Bahgat et al., 2016), and second, they are more likely to influence the timing of adolescent conception within marriage. This finding suggests that adolescents living in or near areas where such CRSV occurred were more vulnerable to adolescent pregnancies. This is more likely to be the result of non-access to contraceptive methods or a lack of access to preventive care against the consequences of rape before 72 hours due to barriers of distance, insecurity, or simply a lack of information (Bartels et al., 2012; Guerschom et al., 2024). Indeed, previous research (Banza-Nsungu, 2014; Guerschom et al., 2024) indicates that only a small proportion of rape survivors in eastern DRC—ranging from 13% to 25%—have access to emergency contraception and post-exposure prophylaxis.

Second, we found that the timing of adolescent conception within marriage is significantly later when a cluster is exposed to CRSV compared to when the same cluster is not exposed to CRSV. We have identified at least three mechanisms that may explain this finding. First, the delayed timing of first conception within marriage may result from spousal rejection following extramarital sexual violence. When married women experience sexual violence perpetrated by men other than their husbands, the consequent breach of trust and marital honor—along with concerns that the wife may have been exposed to HIV or other sexually transmitted infections —can lead to spousal rejection. This phenomenon has been particularly documented in Eastern DRC (Agino Foussiakda et al., 2022; Kelly et al., 2011). Husbands may distance themselves from their wives, thereby reducing coital frequency and delaying conception. Second, exposure of a married woman to severe forms of CRSV, such as sexual torture and genital mutilation, can result in direct physical injuries that impair reproductive capacity. The physiological damage incurred from such violence, including scarring, infections, and other complications, may significantly reduce fertility and discourage couples from attempting conception soon after the incident. Moreover, the profound psychological distress associated with these forms of violence may further diminish sexual desire and disrupt the natural progression toward childbearing (Ba & Bhopal, 2017). A study in Eastern DRC has documented that women who experienced CRSV were more likely to have chronic pelvic pain and absence of desire for sex (Dossa et al., 2015). The first two explanations assume that adolescents are exposed to CRSV only after marriage. However, it is also plausible that adolescents experience CRSV prior to marriage. In this scenario, exposure to CRSV may indirectly postpone the onset of childbearing by reducing the likelihood that these girls will marry, thereby delaying the timing of their first conception within marriage. This delay in the
timing of first marriage, and consequently of first conception within marriage, may result from
mechanisms such as diminished attractiveness due to being a rape victim or avoidance of
romantic relationships following sexual violence, as documented in the DRC (Cherlin et al.,
2004). This delay may also stem from women in CRSV-affected areas gaining access to
education and empowerment programs that foster resilience and shift attitudes toward early
marriage and adolescent motherhood. Numerous such programs have been implemented in the
DRC (Amisi et al., 2018; Quattrochi et al., 2019). These explanations are based on the
assumption that adolescents were exposed to CRSV before marriage. Consequently, since our
measure of CRSV exposure accounts for a temporal lag of up to twelve months prior to
conception (see Table 3), these explanations hold only if the interval between marriage and first
conception is less than twelve months. We estimated this interval among women who
experienced their first conception during adolescence within marriage using data from the
2007 DRC DHS to be 9 months (95% CI: 9–10 months)<fnref n="5" />. This finding reinforces the
hypothesis that exposure to CRSV might also contribute to the postponement of first marriage
timing and, consequently, the timing of first conception.

Finally, women exposed to conflict had later timing of first marital adolescent conception and of first adolescent conception (marital and premarital) than unexposed women. The most likely explanation is that the conflicts have reduced the material, financial, or organizational means of achieving formal marriage in the DRC (Koyame & Clark, 2002), delaying marriage and teen motherhood. In this case, the potential husband or his family waits for favorable moments to organize the marriage, as found in Ethiopia (Gurmu & Mace, 2008). Another explanation is that temporary displacement might delay marriage and childbearing due to the instability associated with being a refugee or internally displaced person, as observed in Tajikistan (Shemyakina, 2013). Since this study focuses exclusively on non-migrant women, some may have temporarily relocated during periods of conflict before returning to their usual cluster of residence. Such displacement could reduce coital frequency and delay conception among already married couples.

**6. Strengths and limitations**

This study is the first to analyze the longitudinal and contextual effects of conflict and CRSV on
adolescent pregnancy. It is also the first quantitative study showing that CRSV can reduce the
risk of adolescent marriage and pregnancy in societies with strong traditional norms or
widespread women's empowerment programs. Additionally, it is among the few studies using
geo-localized data to examine conflict and CRSV exposure at a finer temporal scale, helping to
minimize post-treatment bias. Including group means as independent variables—an approach
rarely used in similar research—controls for unobserved time-invariant heterogeneity at both
individual and cluster levels. This mitigates omitted variable bias and allows the inclusion of
time-invariant explanatory variables. This approach gives the study a methodological
advantage over similar studies using fixed or random effects models. By analyzing first marital
and premarital adolescent conceptions as competing events, the study highlights the main

<footnote n="5">⁵ While this interval may be shortened by the desire to conceive a first child—resulting in lower contraceptive use among newly formed couples—it may also be prolonged due to biological factors, such as the period of adolescent sterility (Nath et al., 1995).</footnote>
mechanisms through which exposure to conflict and CRSV impacted adolescent entry into motherhood in the DRC: early marriage or non-use or non-access to contraception.

Our study also has several shortcomings. Data on conflicts may be subject to over- or under-reporting (Cohen & Nordås, 2014; Sundberg & Melander, 2013). Conflict-related deaths are an extreme proxy for the real impact of war (Torrisi, 2024). Conflict can affect adolescent motherhood (looting of property, loss of household assets) even in the absence of deaths. Looking only at deaths may underestimate the true impact of conflict. However, this potential bias is minimized by considering a more expansive buffer zone.

Some rapes in the DRC were followed by the execution of the victims (Brown, 2011). DHS questions are only asked to women who have survived to the time of the survey. Women who have died may be more or less likely to have children as adolescents than those who have survived. This would bias the effect of conflict and CRSV reported in this study.

The date of first conception, and consequently our dependent variables, was measured retrospectively and is thus subject to recall bias. This bias is exacerbated by the fact that we consider both the month and the year of first adolescent conception, rather than just the year. However, it is mitigated by using lagged measures of the cluster-level explanatory variables.

The prevalence of first premarital adolescent conception reported in this study may be underestimated because we considered only conceptions that resulted in live births, thereby excluding induced abortions and miscarriages. These events were excluded because their reporting in the DHS is affected by irregularities, such as underreporting and misclassification (Blencowe et al., 2016).

Another major limitation of the study is that it does not consider temporary mobility. The variable used to measure mobility—the number of years lived in the cluster at the time of the survey—does not indicate whether non-migrant women moved temporarily during conflicts only to return to their usual residence afterward. While in other regions they can be considered immobile throughout the study period, this hypothesis is less likely in the provinces of South Kivu, North Kivu, and Maniema, where a large proportion of the population moved during conflicts to live in refugee camps or with host families. Since most forced migration in these regions occurs internally (Pech & Lakes, 2017), we assume much of the temporal mobility took place within a 50-km radius or in clusters with similar characteristics to their usual residence clusters, reducing potential bias.

Finally, the Mundlak model, like the fixed effects model, controls for time-invariant unobserved heterogeneity but does not account for time-varying variables. These can still cause bias in the estimates of individual and cluster-level variables (Bell et al., 2019), mainly if the aim is to estimate only the direct causal effect of conflict or CRSV. Since conflict impacts adolescent conception indirectly through many mechanisms or unmeasured social processes, our estimated relationships are not worthless (Bell et al., 2019, p.1059).

## 7. Conclusion and policy implications

This study produced three key findings. First, exposure to CRSV delays the timing of first adolescent pregnancy within marriage. Second, CRSV exposure increases the likelihood of adolescent pregnancy before marriage. Finally, conflict delays the timing of adolescent motherhood within marriage. While the latter two findings align with existing literature, the first is novel. It contributes to the literature by suggesting potential mechanisms through which
CRSV may delay adolescent motherhood, such as spousal rejection following extramarital sexual violence or the resilience of girls in CRSV-exposed environments.

These findings underscore the need to provide contraceptive methods and raise awareness among adolescents in general and those living in conflict zones about the need to use contraception in the case of forced or consensual sexual intercourse. Our findings also encourage programs that empower girls living in conflict areas through awareness and enhanced attitudes. Since adolescent marriage is the primary context in which early adolescent pregnancies take place, our findings also point to the need to intensify the fight against adolescent marriages by raising awareness and strictly enforcing the law. Finally, peace in this still-conflicted country is paramount to reducing CRSV and adolescent pregnancy, improving adolescents' current and future health, and achieving SDG target 3.7.2.

## Funding

This research was funded by a French government grant administered by the National Research Agency as part of the Investissements d'Avenir program, reference ANR-17-EURE-0011.

## Ethics approval and consent to participate

We did not need to obtain ethical clearance as all the data we used (DHS, UCDP-GED, and GEO-SVAC) are publicly available.

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