Andrea Verhulst

Julio Romero Prieto

Alam Nurul

Patrick Gerland

Joanne Katz

Bruno Lankoande

Hallie Eilerts

Daniel Erchick

Li Liu

Gilles Pison

Identifying New Age Patterns of Under-5 Mortality Using the Under-5 Mortality Database

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Identifying New Age Patterns of Under-Five Mortality

Using the Under-Five Mortality Database

Andrea Verhulst, Julio Romero Prieto, Nurul Alam, Patrick, Gerland, Joanne Katz, Bruno Lankoande, Li Liu, Gilles Pison, Georges Reniers, Seema Subedi, Francisco Villavicencio, and Michel Guillot

1 Objective of the study

This study brings new insights into the global diversity of the age pattern of under-five mortality. The novelty of our results relies on the newly-compiled Under-Five Mortality Database (U5MD) providing mortality information by detailed age groups -including daily, weekly, and monthly breakdowns. The fine granularity of these data allows the detection of age patterns as well as data quality issues that could not be seen previously in low-and middle-income countries, but also and high-income countries.

The age pattern of under-five mortality refers to variation in the risk of dying between 0 and 5 years of age. This variation reflects underlying causes of death and their changes over time as overall mortality falls. These distributional changes are relatively stable; the existence of typical trajectories in the historical experience of European countries in the 19th and 20th centuries was initially shown by [START_REF] Coale | Regional Model Life Tables and Stable Populations[END_REF]. In particular, Coale and Demeny (CD) identified four families of model life tables relating the level of mortality during infancy (between age 0 and 1) and childhood (between 1 and 5). In Figure 1, we illustrate this relationship between infant mortality denoted q(1y) and child mortality denoted q(1y,5y). The four families of CD models -East, North, South, West -depict trajectories (blue lines) where child mortality tends to decrease faster than infant mortality as mortality falls, but at different paces. These trajectories reflect the increasing proportion of congenital and perinatal causes of deaths concentrated at early age, while infectious diseases tend to decrease at all ages.

The CD model life tables have been used as a historical yardstick to examine the age patten of under-five mortality in low-and middle-income countries [START_REF] Hill | Age patterns of child mortality in the developing world[END_REF][START_REF] Guillot | Child mortality estimation: a global overview of infant and child mortality age patterns in light of new empirical data[END_REF]. These previous systematic comparisons with empirical values showed both substantial agreement as well as systematic deviations. This is shown in Figure 1 using mortality rates obtained with full birth histories from publicly available Demographic and Health Surveys (DHS). While a majority of points from all world regions fall within the range of the CD models, most Sub-Saharan surveys display a strong tendency for child mortality to be above the model range. Epidemiological conditions specific to this world region have been invoked to explain such deviation from European patterns: specific environments characterized by a high prevalence of infectious diseases, inadequate weaning foods, and in some specific cases high prevalence of HIV/AIDS [START_REF] Cantrelle | Breast feeding, mortality in childhood and fertility in a rural zone of senegal[END_REF][START_REF] Garenne | Variations in the age pattern of infant and child mortality with special reference to a case study in Ngayokheme[END_REF][START_REF] Pison | The level and age pattern of mortality in bandafassi (eastern senegal): results from a small-scale and intensive multi-round survey[END_REF][START_REF] Jasseh | Age patterns of mortality within childhood in sub-Saharan Africa[END_REF][START_REF] Guillot | Child mortality estimation: a global overview of infant and child mortality age patterns in light of new empirical data[END_REF]. These factors potentially contribute to increasing mortality at later ages, generating a relatively high level of q(1y,5y) vs. q(1y). However, data quality issues associated with DHS are also commonly acknowledged [START_REF] Pullum | Evidence of omission and displacement in dhs birth histories. dhs methodological reports[END_REF]. In particular, omission of neonatal deaths has been a particular subject of concern and might have contributed to producing the pattern of deviation in q(1y,5y) vs. q(1y) observed in Sub-Saharan DHS surveys.

This study aims to elucidate such deviations. Specifically, the objective of this study is to identify and validate new regional and epidemiological age patterns of under-five mortality that depart from the European and, more broadly, Western experience. To do so, we take advantage of a newly-constituted database (U5MD) to examine age patterns of under-five mortality using unconventional age groups, e.g. relating the neonatal period to the postneonatal period up to 6 months. Indeed, the U5MD includes a large set of high-quality detailed age distributions from low-, middle-, and high-income countries that allow systematic comparisons for these non-conventional age relationships.

This new understanding of the age pattern of under-five mortality is necessary to determine to what extent existing models need to be amended and expanded to take into account epidemiological specificities in low-and middle-income countries. This issue is particularly critical knowing that the CD model life tables are still widely used in these countries for the purpose of estimating infant and under-five mortality (UN IGME, 2018). As shown elsewhere [START_REF] Guillot | Modeling age patterns of under-5 mortality: Results from a log-quadratic model applied to high-quality vital registration data[END_REF], a better modeling of age patterns of under-five mortality in low-and middle-income countries can help evaluate and adjust data that are subject to various types of errors (neonatal omissions, age heaping, etc.). It can also help detect specific vulnerability of children at certain ages. Our results will thus help guide health policy in low-and middle-income countries.

The Under-Five Mortality Database (U5MD)

The U5MD gathers high-quality distributions of deaths and provides mortality rates by sex for low-, middle-, and high-income countries. The U5MD includes daily and weekly age breakdowns up to 28 days of age, monthly age groups up to one year, and trimester groups for the second year. After 2 years, the database provides single year cut-off points up to 5 years.

The first component of the database includes 25 Western countries with high-quality vital registration (VR) systems covering a time period from 1841 to 2016 (1,653 country-years). We used this information as historical yardstick in order to identify deviation in low-and middle-income countries at similar levels of mortality.

As a criterion of data quality, we only selected country-years that were previously included in the Human Mortality Database (HMD). However, further investigation raises serious doubts about the quality of the early neonatal mortality data during the earlier years covered in the VR information (before WWI). Specifically, we found reversals and flattenings of the level of early neonatal mortality associated with the increase of the overall mortality. As discussed elsewhere [START_REF] Guillot | Modeling age patterns of under-5 mortality: Results from a log-quadratic model applied to high-quality vital registration data[END_REF], these reversals and flattenings are clearly related to data quality problems. As a result, we removed 326 country-years for further analysis purpose.

In the present study, we compare directly the DHS to the selected VR data of the U5MD. In order to explain the deviations we find, we use the other components of the database. These other components cover a variety of high-quality data from Asia, Latin America, and Sub-Saharan Africa. They include closely scrutinized data from 12 demographic surveillance sites, 10 cohort studies, and 3 sample registration systems. They will be described in detail in the final paper.

Preliminary results and future work

Preliminary findings show that two distinct regions of the world stand out as having an age pattern of under-five mortality that strongly differs from the European/Western experience: West Africa and South Asia. In both cases, we found that, for a given level of neonatal mortality, the level of postneonatal mortality is below the range of VR data; or inversely, for a given level of postneonatal mortality, the level of neonatal mortality is above range (see Figure 2). Here, we limited the postneonatal period to 6 months to avoid any offsetting effect. In both regions, mortality remain high after 6 months due to acute exposure to infectious diseases. In sum, these regions are characterized by high levels of neonatal mortality and mortality after 6 months, vs. mortality between 28 days and 6 months. This is why this very peculiar age pattern tends to be less visible when the full postneonatal period (up to 12 months) is plotted instead (not shown).

Panel (a) of Figure 2 also shows that the rest of Sub-Saharan surveys is relatively well aligned on the VR data as far as neonatal mortality is concerned. That disproves the hypothesis that the particularly late age pattern found in that region (see Figure 1) is due to omissions of neonatal deaths. This finding supports the hypothesis that specificities of the age pattern observed in parts of Sub-Saharan Africa are real and due to true epidemiological factors. This also shows that the concerns about underestimation of neonatal mortality in DHS may not be warranted.

However, the data misreporting could also be the other way around. Recently, [START_REF] Helleringer | Biases in survey estimates of neonatal mortality: Results from a validation study in urban areas of Guinea-Bissau[END_REF] pointed out the risk that reporting errors in DHS-type survey data might lead to estimates of the neonatal mortality rate that are too high. The main mechanism for such outcome would be the misclassification of stillbirths as early deaths. If it were the case, the early age pattern of mortality we found in West Africa and South Asia could still be spurious. This is why, in the rest of paper, we will compare DHS estimates with the estimates of the U5MD that pertain specifically to low-and middle-income countries. As an example, in the Panel (b) of Figure 2, we plotted the estimates obtained from the Matlab Heath and Demographic Surveillance site (HDSS) in Bangladesh for the period 1966 to 2011. Matlab estimates are in total agreement with the deviation found in South Asian DHS. As a gold standard of data quality, these HDSS data included in the U5MD contribute to validate the existence of specific age pattern of under-five mortality in the region. Similar evidence in support of a specific South Asian pattern were found with the sample registration system from India (not shown). Following this comparative approach, we will examine whether deviations found in the Sub-Saharan African DHS are real or spurious.

Finally, after this analysis of the data, we will characterize the new age patterns of under-five mortality in terms of specific epidemiological patterns and classify regions and countries accordingly, using available information on causes of death and other known epidemiological factors that are related to age patterns of under-five mortality. In the conclusion, we will discuss potential avenues for expanding the scope of existing model life tables to the epidemiological realities of low-and middle-income countries.

Figure 1: Relationship between infant q(1y) and child mortality q(1y,5y). Note: The gray dashed lines indicate isoclines for a given level of q(5y). Figure 2: Relationship between neonatal q(28d) and postneonatal mortality up to 6 months q(28d,6m). Notes: The solid line corresponds to the central prediction of the relationship based on a model derived from the VR data [START_REF] Guillot | Modeling age patterns of under-5 mortality: Results from a log-quadratic model applied to high-quality vital registration data[END_REF]. The dashed lines indicate isoclines for a given level of q(6m).


Adama Ouedraogo
email: adama.ouedraogo@ined.fr

Gilles Pison

Sophie Le Coeur

Abdramane B Soura

Levels, Trends, and Factors Associated with Under-5 Excess Mortality among Twins in Sub-Saharan Africa: Studies of National Surveys from 42 Countries

Keywords: Twins, Under-five mortality, Excess mortality, Associated factors, Socio-cultural status, Sub-Saharan Jumeaux, Mortalité infanto-juvénile, Surmortalité, Facteurs associés, Statut social, Afrique subsaharienne

BACKGROUND

Twin children are more likely to die than singletons. In sub-Saharan African countries, this is an additional burden, as child mortality levels are already higher than anywhere else.

OBJECTIVE

This article presents under-5 mortality rates (U5MR) for twins and singletons in sub-Saharan Africa from 1986 to 2016. It describes the geographical variations and changes over time. It also describes the variation of twins' excess mortality according to age, between 0 and 5 years old. Moreover, it analyses the factors associated with twins' excess mortality.

METHODS

We have used data from 156 national surveys (Demographic and Health Surveys & Multiple Indicator

Cluster Survey) from 42 countries.

RESULTS

The findings show that U5MR are, on average, 3 times higher among twins compared to singletons. The multivariate results show that, all else being equal, the adjusted Hazard Ratio (HRa) of twins is 3.2 times higher (2.9 -3.3; p<0.001) than that of singletons. The main factors associated with excess mortality risks among twins are a set of biomedical and nutritional features, composed of birthweight, caesarean section, antenatal visits and breastfeeding. Among these factors, non-breastfeeding is associated with 4 times higher mortality risks for twins than for singletons; and caesarean delivery is associated with lower mortality risks for twins than for singletons.

CONCLUSION

In sub-Saharan Africa, the health of twin children is a major health challenge.

CONTRIBUTION

The geographical variations and changes over time in twins' excess mortality in sub-Saharan Africa analysed here is a new contribution to the literature.

Introduction

Twins are, compared to singletons, exposed to precarious health conditions and higher mortality rates, at least in the early periods of life (Ouedraogo 2020a). Indeed, twins are more likely to be premature and/or to have low birthweight [START_REF] Schenker | Multiple pregnancies following induction of ovulation[END_REF][START_REF] Pison | Près de la moitié des jumeaux naissent en Afrique[END_REF][START_REF] Blondel | Augmentation des naissances gémellaires et conséquences sur la santé[END_REF][START_REF] Bjerregaard-Andersen | Infant twin mortality and hospitalisations after the perinatal period -a prospective cohort study from Guinea-Bissau[END_REF][START_REF] Boubkraoui | Morbi mortalité périnatale dans les grossesses gémellaires dans une maternité marocaine de niveau 3[END_REF]. In addition, compared to singleton deliveries, twin deliveries are more often prone to complications, which contributes to increased mortality of twin new-borns [START_REF] Santana | Perinatal outcomes in twin pregnancies complicated by maternal morbidity: evidence from the WHO Multicountry Survey on Maternal and Newborn Health[END_REF]. Consequently, the under-five mortality rates (U5MR) are2 to 3 times higher among twins than singletons (Morten [START_REF] Bjerregaard-Andersen | A prospective study of twinning and perinatal mortality in urban Guinea-Bissau[END_REF][START_REF] Blondel | Augmentation des naissances gémellaires et conséquences sur la santé[END_REF].

In the context of developing countries such as those in sub-Saharan Africa, where child mortality is still very high (e.g;, "in 2017, half of all deaths under 5 years of age took place in sub-Saharan Africa", WHO, 2018), twins' health is an additional challenge, the more so because this area has also the world's highest rates of twin births [START_REF] Smits | Twinning across the Developing World[END_REF].

According to [START_REF] Guo | Child Mortality Among Twins in Less Developed Countries[END_REF], in the developing countries, the death probabilities of twins were (at the end of the 1980s) almost 9 times higher in the first day of life compared to singletons. More recently, studying 60 developing countries in Africa, America, Asia and Europe, [START_REF] Bellizzi | Early neonatal mortality in twin pregnancy: Findings from 60 low-and middle-income countries[END_REF] showed that in 2014 (all else being equal) twins were nearly 8 times more likely to die during their first week of life compared to singletons. After the first months of life, the difference in mortality between twins and singletons decreases, but seems to be maintained throughout childhood [START_REF] Guo | Child Mortality Among Twins in Less Developed Countries[END_REF][START_REF] Pison | Près de la moitié des jumeaux naissent en Afrique[END_REF].

According to the study of 30 countries in sub-Saharan Africa by [START_REF] Monden | Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries[END_REF], U5MR levels have declined sharply over the past 20 years. However, the relative differences in mortality rates between twins and singletons seem to be increasing over time, disfavouring twins. Indeed, over the period 1995-2014, the ratio between twins' U5MR and that of singletons in sub-Saharan Africa increased from 2.5 (328‰ vs. 129‰) to 3.4 (213‰ vs. 66‰). These findings reflect the poor health status of twins in the continent.

Nevertheless, the studies mentioned above have only rarely carried out analyses on the temporal dynamics of twins' excess mortality, including cross-country and sub-regional comparisons. This is the first aim of our approach. This work will also lead us to study how the under-5 excess mortality of sub-Saharan twins varies over age. Moreover, this study aims to make an in-depth analysis the factors associated with twins' excess mortality, considering the important role of biological and nutritional factors such as prematurity, weight and breastfeeding. In addition, there are other factors related to the socio-cultural status. Indeed, twin births can be influenced by some beliefs. For example, in Africa, some ethnic groups attribute a divine power to these children and they treat their coming as a source of happiness [START_REF] Adler | Les Jumeaux sont rois[END_REF][START_REF] Pison | Les jumeaux en Afrique au sud du Sahara: fréquence, statut social et mortalité[END_REF][START_REF] Pons | Le guide des jumeaux: la conception, la grossesse[END_REF]. Others, in contrast, see twin births as a curse and mistreat them or even go so far (formerly) as to eliminate them. [START_REF] Pison | Les jumeaux en Afrique au sud du Sahara: fréquence, statut social et mortalité[END_REF][START_REF] Mama | Les jumeaux en Afrique Noire: de la réalité scientifique aux croyances populaires[END_REF]. This can lead to disparities in the care provided to twins [START_REF] Pison | Les jumeaux en Afrique au sud du Sahara: fréquence, statut social et mortalité[END_REF]. For this reason, our analysis will lead us subsidiarily to investigate whether there are still ethnic disparities in twin mortality that would be dependent on their socio-cultural status?

Background

Some factors influencing twins' excess mortality

Here we review in more detail the main factors influencing twins' under-5 excess mortality: low birthweight, prematurity, insufficient breastfeeding, financial "extra cost" and the place of twins in society.

Births often of low weight

Children weighing less than 2,5 kilograms at birth are classified as low birthweight (OMS, 2007). Twin births are the most concerned by low weight at birth [START_REF] Daguet | La fécondité en France au cours du XXe siècle[END_REF]INSERM, 2011 ;[START_REF] Hu | Nationwide Twin Birth Weight Percentiles by Gestational Age in Taiwan[END_REF]; the survival probability of low-birthweight infants is highly compromised. Indeed, low birthweight (whose main factors are prematurity and foetal growth restriction) have health risks on children such as growth deficiency, susceptibility to infections, respiratory problems, neurological problems and behavioural disorders [START_REF] Wardlaw | Low Birthweight: Country, Regional and Global Estimates[END_REF][START_REF] Almond | The Costs of Low Birth Weight[END_REF]. As a result, their risks of morbidity, disability and mortality are increased, especially in the early months of life (Wardlaw, World Health Organization, and UNICEF 2004).

Births that are frequently premature

Any birth occurring before 37 weeks of pregnancy is considered to be premature [START_REF] Chiwanga | Maternal outcome in multiple versus singleton pregnancies in Northern Tanzania: A registrybased case control study[END_REF][START_REF] Quinn | Preterm birth: Case definition & guidelines for data collection, analysis, and presentation of immunisation safety data[END_REF][START_REF] Chabra | Consistent definition of preterm birth: a research imperative![END_REF]WHO, 2018). Twin births are undeniably subject to greater risks of prematurity than singletons [START_REF] Chauhan | Twins: prevalence, problems, and preterm births[END_REF][START_REF] Makrydimas | Prediction of preterm birth in twins[END_REF]. For example, out of 137,085 twins born in 2006 in the USA, 58% were preterm births compared to 11% for singletons [START_REF] Chauhan | Twins: prevalence, problems, and preterm births[END_REF]. According to [START_REF] Couvert | Un siècle de démographie des jumeaux en France : Fréquence,mortalité et parcours de vie[END_REF], this high frequency of preterm birth in twins may be explained by constraints of space, contiguity and relative position imposed by the simultaneous development of two foetuses in the uterus. This makes prematurity one of the main causes of perinatal death, morbidity and, in the long term, neurological deficiencies, especially in twins [START_REF] Althabe | Born Too Soon: The Global Action Report on Preterm Birth[END_REF][START_REF] Makrydimas | Prediction of preterm birth in twins[END_REF][START_REF] Hu | Nationwide Twin Birth Weight Percentiles by Gestational Age in Taiwan[END_REF]. For example, a child born in the 25th week of pregnancy has 50% more risk (than a child born in the 37th week) to die in his or her first few months of life [START_REF] Costeloe | The study: outcomes to discharge from hospital for infants born at the threshold of viability[END_REF], and nearly 50% of those who survive may face serious disabilities in childhood [START_REF] Marlow | Neurologic and Developmental Disability at Six Years of Age after Extremely Preterm Birth[END_REF].

Unfortunately, due to lack of information in our data, prematurity will not be particularly analysed in this study.

A difficult breastfeeding

Twins weakened by low birthweight or prematurity may not always have the strength to breastfeed. This leads to insufficient or inadequate breastfeeding, which makes them even more susceptible to infections [START_REF] Mabiala-Babela | Morbidité et mortalité des jumeaux au CHU de Brazzaville[END_REF]. Moreover, in the event of deterioration in the health status of the mother and/or children resulting from, for example, a complicated delivery, mothers of twins are not always able to practice a correct breastfeeding [START_REF] Guo | Child Mortality Among Twins in Less Developed Countries[END_REF]. Therefore, they sometimes resort to artificial breastfeeding, which is associated with a high risk of morbidity and mortality for twins [START_REF] Mabiala-Babela | Morbidité et mortalité des jumeaux au CHU de Brazzaville[END_REF]. These children are then frequently deprived of exclusive breastfeeding, which is an important source of antibodies and essential nutrients against infections and nutritional deficiencies [START_REF] Van Der Pol | L'influence du type d'allaitement : le cas de Yaoundé[END_REF].

Children involving financial "extra costs"

Many health resources are needed, particularly in sub-Saharan Africa, for the care of twins.

These include: rapid diagnosis of the twin pregnancy, proper antenatal care, safe delivery and special post-natal care for preterm and low birthweight children and their mothers [START_REF] Luke | Specialized prenatal care and maternal and infant outcomes in twin pregnancy[END_REF][START_REF] Jahn | Multiple pregnancy in rural Burkina Faso: frequency, survival, and use of health services[END_REF]Morten Bjerregaard-Andersen et al., 2012 ;[START_REF] Akaba | Review of twin pregnancies in a tertiary hospital in Abuja, Nigeria[END_REF]. These actions are financially expensive for society [START_REF] Almond | The Costs of Low Birth Weight[END_REF]. Moreover, for parents, a twin birth generates a doubling (or even more) of the health and non-health expenses usually incurred for the care of a new-born [START_REF] Bernat | Prise en charge des grossesses gémellaires monoamniotiques[END_REF]. However, health and non-health care (whether individual or institutional) are more difficult to achieve in low-income countries, where poverty is one of the causes of high child mortality (Rao et al., 1983). Not only do the health systems in these countries lack the necessary resources to carry out these tasks, but also the people do not usually make diligent use of the health services that can help anticipating possible complications of twin pregnancies.

A socio-cultural status that can cause discriminations

As explained in the introduction, beliefs and traditions related to twin births can lead to their rejection. Therefore, the socio-cultural status of the twin (which can be understood through ethnic customs and beliefs towards the twin) is seen as a factor that can create discriminations in health care, nutrition and "parental protection" [START_REF] Pison | Les jumeaux en Afrique au sud du Sahara: fréquence, statut social et mortalité[END_REF].

Two types of twins that DHS does not allow to make a distinction

There are two main types of twins: monozygotic (MZ) or identical twins and dizygotic (DZ) or fraternal twins [START_REF] Hall | Twinning[END_REF]. Monozygotic twins are the product of the fertilization of a single egg by a single sperm, the egg splitting in two in the first days after fertilization. These twins are necessarily of the same sex and have an identical genotype. The MZ twinning rate is constant around 3.5 to 4‰, regardless of the woman's age, birth rank (birth order number) and geographical or ethnical origin [START_REF] Pison | Près de la moitié des jumeaux naissent en Afrique[END_REF][START_REF] Long | Twin pregnancy[END_REF]. Dizygotic twins, on the contrary, are the product of the fertilization of two different eggs by two distinct spermatozoa.

Unlike monozygotic twins, dizygotic twins are almost like any two brothers and sisters, in that they have the same sex (or not) in the same proportion as any couple of brothers and sisters.

The frequency of deliveries of dizygotic twins varies under the influence of several factors, mainly the mother's age, birth order, and geographical area [START_REF] Bulmer | 11 Perceptions, knowledge and attitudes regarding twin births in sub-Saharan Africa: the case of Burkina Faso and Senegal (Original title: Perceptions, connaissances et attitudes concernant les naissances gémellaires en Afrique subsaharienne : le cas du Burkina Faso et du Sénégal)[END_REF][START_REF] Pison | Les jumeaux en Afrique au sud du Sahara: fréquence, statut social et mortalité[END_REF].

Sterility treatments also have an effect on dizygotic twin rates [START_REF] Pison | Twinning rates in developed countries: Trends and explanations[END_REF].

The literature shows that there is a difference in mortality between these two types of twins (van den Berg and Drepper 2018). Unfortunately, DHS data do not allow a distinction to be made between fraternal and identical twins. Consequently, this work cannot take into account a possible difference in excess mortality between fraternal and identical twins (relative to singletons) in sub-Saharan Africa.

Materials and Methods

Data

Data sources

This article uses data from national surveys carried out between the years 1986 and 2016 in 42 sub-Saharan African countries1 (the list of countries and surveys is available in Appendix 1).

These data come from two sources. The first source is the Demographic and Health Surveys (DHS). The DHS program is coordinated by the United States Agency for International Development (USAID). The second data source is the Multiple Indicator Cluster Survey (MICS), conducted by the United Nations Children's Fund (UNICEF). In total, data from 156 surveys were used (including 140 DHS and 16 MICS); and depending on the country, the number of surveys (DHS and MICS combined) used ranged from 1 to 10. The DHS and MICS are all retrospective cross-sectional surveys with national coverage. They collect information that can be used to reconstruct the reproductive histories of the women of childbearing age (15-49), as well as information on the health and survival of their children. A variable specifying twin births exists in almost all the databases. However, in cases where this variable is not available, we have created it using a matching method that uses the IDs, years, months, and days of births of children and their mothers.

Description of data use

There were two main uses of the data: the computation of under-5 mortality rates and the multivariate analysis of twins' excess mortality associated factors. The U5MRs were computed for all 156 surveys. This computation included (for each survey) all the deaths before the age of 5 years old that occurred during the 5 years prior to the data collection. It should be noted that a selection based on the 10 years preceding the survey was another possible choice. However, we chose 5-years preceding the survey option because, on the one hand, this choice allows us to have U5MRs that are closer to the real mortality level at the time of the survey. On the other hand, given that these are retrospective surveys, we believe that the longer the period considered, the greater the risk of omissions and under-reporting of deaths. Thus, the dataset we have selected included 2,425,072 children of which 3.47% (84,047) were born from twin pregnancies and 0.06% (1,491) are triplets or more. In total, 10.93% of the children had died before the age of five; of these,11.58% corresponded to singletons, 28.85% to twins and 58.95% to triplets & more.

To enrich our analysis of the dynamics of twins' excess mortality, we also constructed agespecific mortality curves between ages 0 and 5 over different time periods. The aim of the approach was to observe the evolution of twins' excess mortality by age over time. The periods compared were the 1990s (1990-1999), the 2000s (2000-2009) and the 2010s (2010)(2011)(2012)(2013)(2014)(2015)(2016). For this purpose, we selected countries that had carried out at least one survey in each of the three defined periods. This allows us to analyse (for each of these countries and also on an aggregated basis) the changes from one decade to the next. This approach involved 72 surveys from 24 countries, i.e., 3 surveys per country (one survey per decade and per country).

The multivariate analysis of the factors associated with twins' excess mortality involved part of the 156 surveys. In fact, it included only the most recent survey in each country. This analysis therefore involved 25 separate surveys in 25 countries 2 for which information on the mother's ethnicity and on a number of biomedical variables (birthweight in particular) is available. In order to be able to include a large number of variables, we decided to restrict the number of surveys included in the multivariate analysis. Thus, the surveys considered were all carried out between 2010 and 2016. It should also be noted that this multivariate analysis concerned the deaths before the age of 5 years, occurring within the generation of children born during the 5 years preceding the survey (generational cut). Indeed, the biomedical variables (birthweight, antenatal visits, caesarean section, etc.) are only collected by DHS and MICS on the births that occurred in the 5 years preceding each survey. Hence the limitation to the generation of children born during the 5 years preceding each survey. A total of 278,706 children were involved in this analysis, including 268,032 singletons and 10,674 twins, with 17,133 (6.4%) deaths among singletons and 2,362 (22.1%) deaths among twins.

The Lexis diagram below (Figure 1) is a schematic representation of all the deaths covered by the two main types of analysis described above.

Overview on data quality

One of the major challenges in carrying out quantitative studies using data from several countries, particularly in sub-Saharan Africa, is the quality of the collected data.

The DHS in particular have many data quality problems in terms of the accuracy of some of the information collected (current age, age at death, etc.) and the presence of many missing data. countries. This is why this work should be classified as a study carried out in a context of imperfect data. Consequently, the computation of mortality rates has used a method (DHS measure method) that is built to be adapted to this context of imperfect data (see part 3.2.1 below for more information). The presence of important missing data is another DHS data quality problem. For this reason, we have, in the analytical part of the study, adopted a treatment that takes into account their importance in the analysis model (see part 3.2.2 below for more information).

In addition, in order to assess more the data quality, we evaluated the following issues: the reporting of age at death; the reporting of the twin births; and the lack of information on stillbirths in the data.

The age-at-death accuracy assessment consisted in the analysis of age heaping at specific ages at death (12 months, 24 months, 36 months and 48 months). It enabled us to note the existence of peaks of deaths at the relevant ages at death. These peaks are greater among singletons than twins, but these differences are not likely to make a significant impact on the quality of the comparative analysis between twins and singletons.

In order to assess the accuracy of the reporting of twin births, we used a matching method. Indeed, starting from the principle that (in our data) two children with the same mother ID and the same birth dates (or birth dates a few days apart) are twins, we built a second 'twin' variable (the first one being provided by the survey). Then, we compared the numbers of twin births resulting from this second variable with those provided by the surveys. In this way, we were able to confirm the good reporting of twinning statement.

Furthermore, it should be noted that DHS and MICS do not collect information on stillbirths. This leads to an underestimation of mortality rates and an underestimation of twins' excess mortality. Indeed, the proportion of twin births is probably slightly higher among stillbirths than among live births [START_REF] Smits | Twinning across the Developing World[END_REF]). However, this situation is not likely to compromise child mortality studies using DHS data [START_REF] Monden | Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries[END_REF].

Methods

Estimation of the mortality rates

To estimate the mortality rates, we used a method proposed by Measure DHS (the project that coordinates the technical component of the DHS program). First of all, it should be noted that the under-5 mortality rate (U5MR) is defined as the probability that a new-born dies before reaching age 5 [START_REF] Tabutin | COMPARAISON DE DIVERSES APPROCHES POUR LA MESURE DE LA MORTALITE AUX JEUNES AGES[END_REF]. The method proposed by Measure DHS to estimate the U5MR is an adaptation of [START_REF] Rutstein | Infant and child mortality: levels, trends and demographic differentials[END_REF]. According to this method, the U5MR is obtained by combining the death probabilities from the following 8 age intervals: less than 1 month, 1-2 months, 3-5 months, 6-11 months, 12-23 months, 24-35 months, 36-47 months and 48-59 months. It defines the probability of death for any given age interval as the ratio between the number of deaths among children who were exposed to die (within that age interval) and the total number of children of that age interval [START_REF] Rutstein | Infant and child mortality: levels, trends and demographic differentials[END_REF].

Pragmatically, we used a SAS macro built by [START_REF] Atwood | DHS_U5M: A flexible SAS macro to calculate childhood mortality estimates and standard errors from birth histories[END_REF] which is the adaptation of another macro created by Keith Purvis (from Measure DHS) in 1995 and a STATA programme created in 2010 by Kenneth Hill (from the Harvard School of Public Health). Using [START_REF] Rutstein | Infant and child mortality: levels, trends and demographic differentials[END_REF] method presented above, Atwood & Thomson's macro SAS called DHS_U5M (DHS under 5 mortality) calculates the probability of survival for each subinterval and aggregates them to get the U5MR using the following formula:

(𝑛)𝑞(𝑥) = 1 -∐ (1 -𝑞(𝑖)) 𝑖=𝑥+𝑛 𝑖=𝑥
where (n)q(x) is the mortality rate between ages x and x+n (i.e., the U5MR if age is between 0 and 5 years), and q(i) is the subinterval probability of death [START_REF] Atwood | DHS_U5M: A flexible SAS macro to calculate childhood mortality estimates and standard errors from birth histories[END_REF].

However, the method presented above is only suitable for calculating mortality rates for the following periods: neonatal (less than one month), post-neonatal (1-12 months), infant (0-12 months), juvenile (12-60 months) and under-five (0-60 months). It is not suitable for estimating mortality rates by age. Consequently, to construct age-specific mortality curves for ages 0-5, we used the actuarial life table method. This method allows us to determine for a given age interval, [x; x+n[ (x = age and n = interval length), the probability for an individual living at the beginning of the interval to die before the end of the interval. This allows us to have mortality rates by age. Ideally, these age-specific mortality curves would have been constructed using the month as the unit of measure of age. However, the numbers were very low (especially for twins), so we opted for the year.

Methods for analysing the factors associated with twins' excess mortality

The basic principle underlying our analysis of the factors associated with twins' excess mortality (between 0 and 5 years of age), is to determine which variables would 'account for' the difference in mortality risks between twins and singletons. Several methods can help answering this question. Among them, regression methods involving a dependent qualitative variable (e.g., logistic regressions, Cox regression) are more appropriate. Our dependent variable is the death or not of the child before the 5th birthday. Among the independent variables, the child profile variable (twin/singleton), which is the main variable of interest, can be introduced first in the model. Then, by sequentially adding the other variables or groups of variables, we can see their effects on the differential of mortality risk ratio3 between twins and singletons. Naturally, the statistical relevance4 of each step model must be better than the previous one to be valid. Thus, it is possible to know which variables or group of variables have an independent effect on the variable of interest (twin/singleton), all else being equal.

It should be noted that interaction effects between the explanatory variable of interest and the other explanatory variables constitute another approach to study the factors associated with twins' excess mortality. This approach was combined with the one described above.

For the present study, we opted for Cox regression, which is a survival analysis that fits our data. Here, it expresses the probability of death in a small interval of time, Δt, just after t, knowing that the death had not occurred up to t (in our Cox model, the time is expressed in months)5 .

Regarding the choice of the explanatory variables, there are three important steps to consider.

The first is the flat sorting stage (univariate analysis). It allows us to know what information is

available on each variable and to know what kind of coding or re-coding of these variables is to be expected. The second stage consists of cross-sorting (bivariate analyses) between the dependent variable and each of the potential explanatory variables. This makes it possible to highlight the "primary" links between the dependent variable and each variable being explored.

For this, we used "bivariate" Cox regressions, i.e., a Cox regression between each explanatory variable taken separately and the dependent variable. The effects obtained at this stage were socalled unadjusted effects (unadjusted HR). It is also during this second stage that we validated the proportionality hypothesis of the Cox model. The third and last stage in the choice of our explanatory variables is the selection stage. This selection is made according to a step-by-step bottom-up procedure, based on the Akaike information criterion (AIC). Besides the main explanatory variable of interest (twin/singleton), the other explanatory variables selected (control variables) are divided into four groups: biomedical and nutritional variables (birthweight, caesarean section, antenatal visits and breastfeeding), demographic variables (mother's age, birth order, child's sex, mother's marital status, sub-region and child's year of birth), socio-economic variables (household's wealth quintile and urban/rural residence) and socio-cultural variables (mother's education level, the desirability of the pregnancy and the socio-cultural status of the twin according to the mother's ethnicity6 ). Furthermore, in the implementation of the model, missing values were treated in two ways. The first consisted in removing them when we assumed that these represented a very small share.

The second consisted in keeping them as a separate category in the variables where the share of missing values is substantial (see Table 1).

Main Results

Spatial and temporal variations in the differential of U5MR between twins and singletons in sub-Saharan Africa

Aggregate results show that between 1990 and 2010, U5MRs fell considerably in sub-Saharan Africa, for both twins and singletons. In fact, during that period, they were divided by 1.8, from 166‰ to 93‰. However, the ratio between twins' U5MR and that of singletons (i.e., twins' excess mortality) has not decreased. It has even increased over time, rising from 2.3 (365‰ ÷ 159‰; CI 7 =2.2 -2.4) in the 1990s to 2.8 (244‰ ÷ 87‰; CI=2.7 -2.9) in 2010s (see Figure 2). The disaggregated results (by country) confirm the aggregate results by showing that, even if U5MRs have decreased significantly between the 1990s and 2010s, their levels remain very high. However, compared to singletons, twin children, have U5MRs that are on average three times higher. Moreover, this excess mortality among twins is increasing in almost all countries. Figures 3 and4 below (see also Appendix 1) illustrate this point by presenting, for each country, the evolution of twin and singleton mortality rates ( Figure 3) and also the changes in the ratio of twins' U5MR to that for singletons (Figure 4). It is important to note that twins' excess mortality is not necessarily a function of mortality levels, but rather of the importance of the differences in the mortality rates between twins and singletons. When the ratio of twins' mortality rate to that for singletons is greater than unity, there is excess mortality among twins; when it is below unity, there is an under-mortality of twins; when it is equal to unity, there is no difference in mortality between twins and singletons.

Under-five age-specific mortality curves in sub-Saharan Africa

Using only national surveys conducted during the decades of 1990, 2000 and 2010, and considering only those countries that have carried out at least one survey during each of these three decades, here we describe the evolution of the mortality rates by age (between ages 0 and 5). This analysis covers 24 countries8 and here we present the aggregated results (Figure 5).

The specific ages at death analysed are: <12 months old, 12-24 months old, 24-36 months old,36-48 months old and 48-60 months old.

Figure 5 shows that the difference in mortality rates between twins and singletons is very large at the very early stage of life, but it decreases fast with age. After the second year of life, the differences become very small, without disappearing completely. From one decade to the next, the mortality level decreases at all ages for both twins and singletons. However, the amplitude of the differences in mortality by age between twins and singletons seems to remain unchanged from one decade to the next.

Factors associated with excess mortality among twins between ages 0 and 5

The results of the univariate, bivariate and multivariate analysis of the factors associated with twins' excess mortality are presented in Table 1.

Sample description

As indicated before, the sample used to analyse the factors associated with twins' excess mortality involved a total of 278,706 children, including 4% of twins and 96% of singletons.

The percentage of deceased children was 7% overall. However, it was 22% among twins and 6.4% among singletons. The sample's description regarding the other covariates is provided below (see also Table 1).

Biomedical and nutritional covariates

Regarding birthweight, 41% of the children in the sample weighed more than 2.5 kg, 0.4% weighed less than 2.5 kg, and missing values represented 59% (kept in the model). Concerning birth by caesarean section, it concerned 4% of the children in our sample; 79% of the children were born by vaginal births and missing values represented 17% (kept in the model). Moreover, 97% of the children were breastfed, compared to 3% who were not. Regarding antenatal care, 51% of the mothers reported having at least 3 antenatal visits; 5% had no visits; 8% had 1 or 2 visits; and 36% were missing values (kept in the model).

Demographic covariates

In the sample, 49% of the children were female; 45% were of birth order higher than 3; 43% were children whose mothers were under 25 years old; 88% were children of women in union;

4% of the children were from Southern Africa, 22% from Central Africa, 22% from East Africa and 52% from West Africa.

Socio-economic covariates

The sample consisted 48% of children living in very low or low-income households, those living in high or very high-income households represented 33%. In terms of place of residence, 70% of the sample was constituted of children living in rural areas.

Socio-cultural covariates

Regarding desirability, 67% of the children were desired by their mothers, 17% were desired for later, 5% were unwanted and 12% were missing values (kept in the model). Concerning the mothers' education level, 41% of the children were from mothers with no education, 36% from mothers with primary education level, and 23% from mothers with secondary or higher education level. Regarding the socio-cultural status of twins, 72% of the children in our sample had mothers from ethnic groups that used to be ambivalent towards twins (accepting them with great fear). The proportion was 5% for children whose mothers belonged to ethnic groups that traditionally abhorred twins; it was 19% for children whose mothers belonged to ethnic groups that worshipped twins; and 4% were missing values (kept in the model).

Factors associated with twins' excess mortality

Child mortality risk factors (twins as well as singletons)

Our results (presented in Table 1) show that the following factors are associated with significant risks of under-five death in sub-Saharan Africa (years 2010)9 : being a twin (HRa 10 =3.1; p<0.001); birthweight under 2.5 kg (HRa=3.0; p<0.001); caesarean delivery (HRa=1.4; p<0.001); no antenatal visits (HRa=1.13; p<0.01); no breastfeeding (HRa=6.7; p<0.001); male children (HRa=1.2; p<0.001); high birth order (HRa of order 6 or more is 28% higher than that of order 1; p<0.001); a very young mother (HRa of mothers under 20 years of age is at least 20% higher than that of mothers in other age groups; p<0.001); a single mother (HRa=1.13; p<0.01) or divorced/widowed (HRa=1.27; p<0.001); living in West Africa (HRa is at least 10% higher); living in a very low-income household (HRa=1.11; p<0.001); a mother with primary education level or less (HRa=1.13; p<0.001).

Factors associated with twins' excess mortality in sub-Saharan Africa

The results of the Cox model (see Table 1) show that in sub-Saharan Africa in the 2010's (2010-2016), twin children compared to singletons had very high mortality risks. Indeed, for twins, the Hazard Ratio (HR) was 3.9 times higher than that of singletons (Confidence Interval, CI = 3.7 -4.0, p<.0.001). After adjustment in the group of biomedical and nutritional variables (birthweight, caesarean section, antenatal visits and breastfeeding), this ratio changed significantly, dropping to 3.15 (CI=2.9 -3.3). The gradual inclusion of the other groups of covariates in the model no longer significantly changed twins' HR. This confirms that the group of biomedical and nutritional covariates is the predominant group of factors associated with twins' excess mortality.

The interaction effects introduced in the model (between the twinning variable and the other covariates) show that, within the biomedical and nutritional variables, the absence of Caesarean section and of breastfeeding are particularly and independently associated with the excess mortality of twins. Indeed, although caesarean birth is generally associated with a high risk of mortality, among twins it is the non-use of caesarean section that is associated with a higher risk of mortality. Thus, when the delivery was not by caesarean section, the HRa for twins is 3 times higher than that for singletons (CI= 2.9 -3.3; p<0.001); and when the delivery was by caesarean section, it drops to 1.75 (CI= 1.5 -2.1; p<0.001). This interesting result could be understood by the fact that for non-twins, a caesarean section delivery is usually performed following complications of the pregnancy, whereas for a pregnancy of twins (at least those who have been medically attended), the choice of a caesarean section delivery is a planned and generalised practice whose aim is to avoid any complications that could be caused by a vaginal delivery.

Regarding breastfeeding, our results show that the HRa of non-breastfed twins is 4 times higher than that of non-breastfed singletons (CI= 3.6 -4.3; p<0.001); if the children were breastfed, this HRa drops to 3.12 (CI= 2.95 -3.28; p<0.001). This makes the absence of breastfeeding a more important risk factor for mortality among twins than singletons.

Furthermore, with regard to the effect of socio-cultural status on twins' excess mortality, two aspects should be noted. Firstly, taking into account the twins' socio-ethnic status in the Cox model (presented in Table 1) does not contribute to variation in the HRa of twins. This indicates that twins' socio-cultural status (in society) has no independent effect on their excess death risks. But, are there any differences in mortality among twins only, depending on their status in society? The answer is no, according to our results. Indeed, in a second step, by carrying out a

Cox model using only a sample of twin children (see Appendix 3), we did not find socio-ethnic disparities in mortality risks among twins themselves, which would depend on how these ethnic groups receive twins (worshiped, hated, or ambivalent). All these elements seem to demonstrate that the socio-ethnic status of twins in sub-Saharan Africa does not (or no longer) influence their mortality or their excess mortality compared to singletons. However, we must be cautious in this statement, as it cannot be excluded that this result is the consequence of deliberate omissions of twin deaths among ethnic groups that were traditionally unfavourable to them.

Discussion and conclusion

Our analyses show that child mortality remains at high levels in sub-Saharan African countries, and that it is more pronounced when it comes to twins. Indeed, a sub-Saharan twin has an overall under-five mortality rate (U5MR) that is three times higher than that of a singleton. Our findings also show that, from the 1990s to the 2010s, U5MR decreased significantly in sub-Saharan African countries. However, this decrease remains relatively less significant for twins compared to singletons. In addition, the multivariate analysis showed that, all else being equal, twins are 3.2 times more likely to die before their 5th birthday compared to singletons (p<0.001). Biomedical and nutritional variables in general, and the absence of breastfeeding and the non-caesarean section delivery in particular, are the main factors associated with higher mortality risks among twins than singletons. However, this analysis did not demonstrate the existence of socio-ethnic disparities in mortality risks between twins and singletons that would be dependent on the ethno-cultural beliefs and attitudes towards twins in society.

The high mortality rates among twins observed here are in line with the results of several previous studies, the most recent of which are those of [START_REF] Monden | Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries[END_REF] and [START_REF] Bellizzi | Early neonatal mortality in twin pregnancy: Findings from 60 low-and middle-income countries[END_REF]. The first showed that the ratio of U5MR of twins to that of singletons was 3.4 in sub-Saharan Africa in 2014. These authors also found a greater decrease in mortality for singletons compared to twins, as demonstrated by our results. As for [START_REF] Bellizzi | Early neonatal mortality in twin pregnancy: Findings from 60 low-and middle-income countries[END_REF], they found that in 60 developing countries, including those of sub-Saharan Africa, twins' mortality risks are almost 8 times higher than those of singletons in the first week of life. Similarly, albeit to a lesser extent, our results showed that in the first month of life, the risks of death are almost 5 times higher among twins than singletons.

This study is not exempt from limitations in terms of data quality, heterogeneity of countries studied, and over-representation of some countries or some sub-regions in the sample of surveys studied. Firstly, we acknowledge that the ages declared in the national surveys that we used could have problems of accuracy. This could have an impact on the robustness of the estimated mortality rates. Secondly, the heterogeneity of the demographic contexts of the countries included in our study constitutes another limitation. Indeed, from east to west and from centre to south, the demographic contexts of the sub-Saharan countries are plural. As a result, the interpretation of some of our results as those of a 'sub-Saharan African level' hide disparities between these countries. Finally, we note that the over-representation of West African countries in the multivariate analysis can affect the HR estimated for the geographical area and socioethnic variables. Nevertheless, weighting the data contributed to correct this limitation.

Despite some of the limitations noted above, the results of this study confirm the fact that in sub-Saharan Africa, the health of twin children is a major challenge. Thus, targeted actions need to be taken to consolidate the important changes that are gradually taking place. These actions should concern the medical field, in particular the development of obstetrical and paediatric services needed to manage the frequent complications related to twin births. Moreover, they should also involve the social services, which must implement actions to counter possible social threats that would still hang over the health of twins in sub-Saharan Africa. We have already

Figure 1 :

 1 Figure 1: Lexis diagram explaining the deaths concerned by each analysis

  Such problems are known in all the DHS and particularly in the DHS of sub-Saharan African 15 Areas concerned by the computation of under-5 mortality rates (Used all 156 surveys) & the construction of under-5 age-specific mortality curves (Used 72 surveys from 24 countries, spread over 3 decades; including 1 survey per country and per decade) Area covered by multivariate mortality analysis (most recent survey from 25 countries) Age Years 5

Figure 2 :

 2 Figure 2: Change from 1990 to 2010 in U5MR in sub-Saharan Africa -aggregated data from 42 countries

Figure 3 :

 3 Figure 3: Spatial and temporal variations in U5MR in 42 sub-Saharan African countries a

Figure 4 :

 4 Figure 4: Spatial and temporal variations in the ratio of twins' U5MR to that for singletons in 42 sub-Saharan African countries a

Figure 5 :

 5 Figure 5: Age-specific mortality curves between ages 0 and 5 in sub-Saharan Africa: comparison between twins and singletons

Table 1 : Factors associated with twins' excess mortality. Results of the univariate, bivariate and multivariate analyses

 1

								HR adjusted with:
	Variables	Number	Bivariate (unadjusted) HR	(1) = Twinning variable + biomedical and nutritional variables	(2) = (1) + demographic variables	(3) = (2) + socio-economic variables	(4) = (3) + socio-cultural variables
		N	% HR	95% CI HR	95% CI HR		95% CI HR	95% CI HR	95% CI
	Twinning
	Twin	10 674	4 3.9***	3.7 -4.0 3.15*** 2.97 -3.33 3.09***	2.91 -3.27 3.12*** 2.94 -3.30 3.12*** 2.94 -3.31
	Singleton	268 032	96 ref		ref	ref		ref	ref
	Birthweight
	<2.5 Kg	1 101 0.4 4.6***	4.0 -5.4 2.93*** 2.54 -3.38 2.88***	2.50 -3.32 2.92*** 2.53 -3.37 2.97*** 2.57 -3.43
	>=2.5 Kg	114 503	41 ref		ref	ref		ref	ref
	Missing values keep	163 102	59
	Caesarean section
	Yes	10 712	4 1.13*** 1.1 -1.2 1.28*** 1.18 -1.38 1.35***	1.24 -1.46 1.39*** 1.28 -1.51 1.41*** 1.30 -1.52
	No	220 881	79 ref		ref	ref		ref	ref
	Missing values keep	47 113	17
	Ante-natal care
	No ante-natal visit	13 246	5 1.6***	1.5 -1.7 1.22*** 1.14 -1.32 1.16***	1.07 -1.25 1.13**	1.05 -1.22 1.13**	1.04 -1.21
	1 or 2 visits	23 006	8 1.2***	1.1 -1.3 1.12*** 1.07 -1.19 1.09***	1.03 -1.16 1.08*	1.01 -1.15 1.06*	1.00 -1.13
	3 or more ante-natal	141 502	51 ref		ref	ref		ref	ref
	visits Missing values keep	100 952	36
	Breastfeeding
	Yes	269 999	97 ref		ref	ref		ref	ref
	No	8 333	3 7.9***	7.6 -8.2 7.01*** 6.72 -7.31 6.83***	6.55 -7.13 6.87*** 6.59 -7.16 6.70*** 6.42 -7.00
	Missing values supp	374 0.1
	(Twinning) x (Caesarean section)
	Twin vs Singleton / (when caesarean section = no)		3.15*** 2.97 -3.33 3.08***	2.90 -3.27 3.12*** 2.94 -3.30 3.10*** 2.90 -3.31

List of sub-Saharan African countries for which no surveys were available: Botswana, Cape Verde, Equatorial Guinea, Eritrea, Mauritius, and Seychelles.

Benin 2014, Burkina Faso 2010, Cameroon 2014, Chad 2014, Congo 2011, Congo DR 2013, Cote d'Ivoire 2011, Gabon 2012, Gambia 2013, Ghana 2014, Guinea 2012, Guinea-Bissau 2014, Kenya 2014, Liberia 2013, Malawi 2015, Mali 2012, Mauritania 2011, Mozambique 2011, Nigeria 2016, Uganda 2011, Sao Tome 2014, Senegal 2016, Sierra Leone 2013, Togo 2013, Zambia 2013.

Odds Ratio (OR) if it is a logistic regression and Hazard Ratio (HR) if it is a Cox regression.

This relevance can be measured by a lower Akaike information criterion (AIC) or by a smaller -2 Log L (-2 Loglikelihood).

 5 For more details on the Cox model, see the following article: https://www.sciencedirect.com/topics/mathematics/cox-model

The variable 'socio-cultural status of the twin' was based mainly on work by Gilles[START_REF] Pison | Les jumeaux en Afrique au sud du Sahara : fréquence, statut social et mortalité[END_REF], which categorised ethnic groups in sub-Saharan Africa into three groups according to their traditional behaviour towards twins: (i) those who appreciated them, (ii) those who rejected them, and (iii) those who accepted them but feared them (ambivalent). We then conducted a literature search to generalise this classification across all the ethnic groups present in our analyses (see Appendix 2).

Benin, Burkina Faso, Cameroon, Chad, Cote d'Ivoire, Ethiopia, Ghana, Guinea, Kenya, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Tanzania, Togo, Uganda, Zambia and Zimbabwe.

As a reminder, the multivariate analysis focused on surveys carried out in the years 2010.

Appendices