Statistical Methods

Survival time can be defined as the time elapsed until the appearance of an event of interest. Indeed, these data are often collected partially, in particular because of the processes of censorship and truncation (Jong & Mara, Reference Jong and Mara2004). The survival function, S(t), is the probability of surviving beyond time t, defined as:

$$S\left( t \right) = P\left( {X > t} \right) = \int_t^\infty {f\left( x \right)dx,t \ge 0} .$$ (1)

S(t) is a decreasing function, and the average survival time expressed follows from the distribution function F(t), which is complementary to S(t), F(t) = 1 – S(t):

$$F\left( t \right) = P\left( {X \le t} \right) = \int_0^t {f\left( x \right)dx}$$

Risk Function λ(t)

Risk function characterizes the probability of dying in a small time interval after t, conditional on having survived up to time t (i.e., the risk of instant death of those who survived).

$$\lambda \left( t \right) = lim{_{h \to 0}}{{P(t \le X \le t + h/X \ge t)} \over h} = {{f(t)} \over {S(t)}} = - {{S{{\left( t \right)}^{'}}} \over {S\left( t \right)}} = - \left[ {linS\left( t \right)} \right]'$$ (2)

An alternative for estimating net survival, that is, survival if the only causes of death are related to being twin, but without the need to know whether the cause of death was attributable to the pathology, was provided by the relative survival model. Indeed, the survival observed in the study group S_o (t) is corrected by the expected survival, S_e (t), estimated from a control group.

$${S_r}\left( t \right) = {{{S_o}\left( t \right)} \over {{S_e}\left( t \right)}}$$ (3)

This represent a ratio between observed survival S_o (t) and expected survival S_e (t) in a given time t in the general population for groups of the same age and same sex. The most common methods for estimating expected survival were the Ederer II method (Ederer & Heise, Reference Ederer and Heise1959) and the Hakulinen method (Hakulinen, Reference Hakulinen1982). These methods are based on the calculation of expected survival from the general population and on nonparametric estimation methods. Later, approaches based on the modeling of the risk function appeared.

Several regression approaches have been proposed in the literature: the additive risk models of Mitry et al. (Reference Mitry, Bouvier, Esteve and Faivre2005) and Hakulinen et al. (Reference Hakulinen, Seppä and Lambert2011), and Breslow’s multiplicative risk models and Andersen et al.’s (Reference Andersen, Borch-Johnsen, Deckert, Green, Hougaard, Keiding and Kreiner1985), which proposed a combined modeling of excess mortality and relative mortality, which is a special case of Aalen’s linear risk-additive regression model. In fact, this method is based on the assumption that the twins are subject to two mortality forces: mortality related to be a twin and mortality related to all other causes of death for children (Figure 1).

Fig. 1.

Estimation methods and models of net survival.

The two main regression models (additive models) of Estève et al. (Reference Estève, Benhamou, Croasdale and Raymond1990) and Hakulinen and Tenkanen (Reference Hakulinen and Tenkanen1987) are both parametric models with proportional hazards, exponential at intervals for the first and discrete for the second. They make it possible to express the excess risk to which the sample studied is subjected. In this study, we used the Estève model, which is based on full maximum likelihood.

Modeling Relative Survival Using a Full Likelihood Approach

Excess mortality is the analogue of mortality in relative survival, and the modeling process was based on this quantity. The total risk of death λ(t; x) at time since birth, t, for twins (with covariate vector x) is modelled as the sum of the expected hazard, λ(t; x)* and the excess hazard due to being a twin, ψ(t; x), that is:

$$\lambda \left( {t;x} \right) = \lambda {(t;{\rm{}}x)^{\rm{*}}} + \psi \left( {t;x} \right){\rm{}}$$ (4)

We obtain λ(t; x)* from a table of instantaneous mortality rates of the general population; generally, these tables are stratified on just one variable (i.e., sex). We note (t_i , δ _i , a_i , x_i ), i = 1, …, n as sample data of n individuals; t_i is the survival time since the birth; the expected hazard λ(t; x)* is obtained from the existing relationship between mortality rates λ _i (a_j ) at a specific age and the probability of survival p_i(j). For the practical needs of this study, it may be considered that

$${p_i}\left( j \right) = {\left( {1 - {\lambda _i}\left( {{a_j}} \right)} \right)^{\left( {{t_j} - {t_{j - 1}}} \right)}}{\rm{}}$$ (5)

For life tables with an age dimension of length, 1, Equation (5) becomes:

$${p_i}\left( j \right) = 1 - {\lambda _i}\left( {{a_j}} \right){\rm{}}$$ (6)

ψ(t; x) is modelled using a proportional rate model, constant at intervals of time, which is written as

$$\psi \left( {t;x} \right) = \eta \left( t \right)exp\left( {\sum\nolimits_{k = 1}^K {{\beta _k}{x_k}} } \right),$$ (7)

With

$$\eta \left( t \right) = \sum\nolimits_j {{\tau _j}{I_j}\left( t \right)} ,and\left\{ {\matrix{{{I_j}\left( t \right) = 1,{t_{j - 1}} \le {t_i} \le {t_j}} \cr{{I_j}\left( t \right) = 0,{t_{i}} > {t_j}.} \cr}}\right.$$$

Where τ _j represents a child for whom $\underline {{x_k}} = 0$ , the specific mortality rate in the jth interval (Estève et al., Reference Estève, Benhamou, Croasdale and Raymond1990) illustrates a method for estimating the model in Equation (4) directly from individual-level data using a full maximum likelihood approach. The likelihood function is given as

$$L\left( {\beta ;{x_i},{t_i},{\delta _i},{a_i}} \right) = \prod\nolimits_{i = 1}^n {exp} \left( { - \int_0^{{t_i}} {\lambda (s,{x_i},{a_i})ds} } \right){\left( {\lambda ({t_i},{x_i},{a_i})} \right)^{{\delta _i}}}$$ (8)

From Equation (1), the log likelihood is given as:

$$L\left( {\beta ;{x_i},{t_i},{\delta _i},{a_i}} \right) = = - \sum\nolimits_{i = 1}^n {\left( {\int_0^{{t_i}} {\lambda {{\left( {s,{x_i},{a_i}} \right)}^*}ds} } \right)} - \sum\nolimits_{i = 1}^n {\left( {\int_0^{{t_i}} {\lambda \left( {s;x} \right)ds} } \right)} + \sum\nolimits_{i = 1}^n {{\delta _i}ln\left( {\lambda {{(t;x)}^*} + \psi \left( {t;x} \right)} \right)}$$ (9)

So,

$$L\left( {\beta ;{x_i},{t_i},{\delta _i},{a_i}} \right) \propto \sum\nolimits_{i = 1}^n {\left( {\int_0^{{t_i}} {\psi \left( {s;x} \right)ds} } \right)} + \sum\nolimits_{i = 1}^n {{\delta _i}ln\left( {\lambda {{(t;x)}^*} + \psi \left( {t;x} \right)} \right)} $$ (10)

The contribution of each individual i to the log likelihood is:

$${L_i}\left( {\beta ;{x_i},{t_i},{\delta _i}} \right) = {\delta _i}ln\left( {\lambda {{(t;x)}^{\rm{*}}} + exp\left( {\mathop \sum \limits_{k = 1}^K {\beta _k}{x_k}} \right)} \right) - {t_i}exp\left( {\mathop \sum \limits_{k = 1}^K {\beta _k}{x_k}} \right).$$

The parameters vector, $\underline {\beta ,}$ are estimated by solving the system equations:

$${{\partial {L_i}\left( {\beta ;{x_i},{t_i},{\rm{}}{\delta _i}} \right)} \over {\partial \beta }} = 0.$$

Similarly, the variance–covariance matrix, Σ _ij $\left( {\underline \beta} \right)$ is given by the following derivatives:

$${{\sum}_i}_j\left( {\underline \beta} \right) = {{{\partial ^2}{L_i}\left( {\beta ;{x_i},{t_i},{\delta _i}} \right)} \over {\partial {\beta _i}\partial {\beta _j}}}$$

$$\propto \mathop \sum \limits_{i = 1}^n \left( {{\delta _i}ln\left( {\lambda {{(t;x)}^{\rm{*}}} + exp\left( {\mathop \sum \limits_{k = 1}^K {\beta _k}{x_k}} \right)} \right) - {t_i}exp\left( {\mathop \sum \limits_{k = 1}^K {\beta _k}{x_k}} \right)} \right)$$

Generally, these models have proportional excess hazards as a special case, but often nonproportional excess hazards are observed. To deal with nonproportionality, we can use a piecewise constant model (Dickman et al., Reference Dickman, Sloggett, Hills and Hakulinen2004), or a fractional polynomials model (Lambert et al., Reference Lambert, Smith, Jones and Botha2005), or splines function (Giorgi et al., Reference Giorgi, Abrahamowicz, Quantin, Bolard, Esteve, Gouvernet and Faivre2003).

Data

The Arab world is located in one of the most strategic regions of the world, extending from the Atlantic Ocean to the Arab Gulf to the east and from the Arabian Sea to Turkey and the Mediterranean to the north (Figure 2). The Arab world covers 10% of the world’s surface (13.5 million km²) and had a population of about 359 million in 2017.

Fig. 2.

Geographical locations of countries.

Note: Sourced from Google Maps.

The population growth rate in the Arab world is 2.3%, with the highest growth rate in Oman at 3.48%, the lowest in Lebanon at 1.38% and in Tunisia at 1.15%. The birth rate in the Arab world is 29.38 per 1,000 inhabitants, while the mortality rate is 7.17 per 1,000 inhabitants. The highest infant mortality rate in Somalia is 123.97 per 1,000 live births, and Djibouti has 101.5 deaths per 1,000 live births. The lowest rate is in Kuwait, with 11.82 deaths per 1,000 live births.

This is the first study in the Arab world that focuses on differences in death rates between twins and singletons. As indicated in the Introduction section, we used the MICS database for estimation. The methodology of MICS, which was established by UNICEF (2018), is focused on helping countries to collect and analyze data to fill the gap in data on indicators to monitor the situation of children and women. It has enabled countries to produce statistically robust and internationally comparable estimates for a range of indicators in the areas of health, education, protection and child health, and HIV/AIDS. The findings of MICS have been widely used as a basis for policy and program development, and to raise awareness of the situation of children and women around the world. A summary description of data from the six Arab countries we studied is shown in Table 1.

Table 1.

Description of MICS database

Note: Date extracted from MICS4 databases.

The choice of these six Arab countries is not arbitrary, but is based on the ranking according to the Human Development Index (HDI) in 2018 (United Nations, 2018); for high human development, we selected Algeria and Tunisia, for medium development, we selected Iraq and Egypt, and for low development, we selected Mauritania and Sudan. Survival analyses were conducted with R software using relsurv and survival packages (Pohar Perme & Pavlic, Reference Pohar Perme and Pavlic2018), which are publicly available on the R CRAN website.

Variables

The variables in our study were selected based on previous studies on the risk factors for deaths of children in general (Fawzi et al., Reference Fawzi, Herrera, Spiegelman, el Amin, Nestel and Mohamed1997; Mishra et al., Reference Mishra, Ram, Singh and Yadav2017; Selemani et al., Reference Selemani, Mwanyangala, Mrema, Shamte, Kajungu, Mkopi, Mahande and Nathan2014; Suchindran & Adlakha, Reference Suchindran and Adlakha1985) and twins compared to singletons (Ananth et al., Reference Ananth, Joseph and Smulian2004; Guang & Laurance, Reference Guang and Grummer-Strawn1993; Steenhaut & Hubinont, Reference Steenhaut, Hubinont and Ezechi2012). Furthermore, for nonhomogeneous measures in the MICS4 databases for the six countries, we focused our research on these variables: sex of the twin (male: 1, female: 2); birth order of twins within the family as a whole (1^st, 2–3, 4–6, +7); mother’s education level (illiterate, primary, secondary, higher); wealth index ¹ ; and mother’s age at birth (e.g., <20, 20–29, 30–39, 40–49).

Mother’s Education

As expected, a strong link was found between the mother’s level of education and her children’s survival chances. However, this relationship is not a simple one, because mortality does not decrease uniformly with increasing level of education. For Egypt, Table 3 shows that twins of illiterate mothers have higher excess mortality, estimated as an HR = exp(^0.775) = 2.17. For Iraq, twins of mothers with a primary education have 40.7% lower excess mortality, 1 − HR = exp(^−0.524) = 0.407, than twins of illiterate mothers; for twins of mothers with secondary education or more, their excess mortality is lower by 37.1%, 1 − HR = exp(^−0.464) = 0.371, than twins of mothers with no education. For Sudan, twins of mothers with a primary education have 24.3% lower excess mortality, 1 − HR = exp(^−0.279) = 0.243, than twins of illiterate mothers.

Finally, it is generally accepted that the education of parents and especially that of the mother is a determining factor in the mortality rate of young children in general and twins in particular.

Mother’s Age and Twins’ Mortality

Part of the increase in infant mortality among young mothers is due to the lack of experience in caring for the infant (Neal et al., Reference Neal, Channon and Chintsanya2018; Selemani et al., Reference Selemani, Mwanyangala, Mrema, Shamte, Kajungu, Mkopi, Mahande and Nathan2014). For Sudan, we found a negative relationship between mother’s age and excess risk of twins’ death; all others being equal, an increase in a mother’s age by one year will reduce the excess risk of death by 8.05%, 1 – HR = exp(^−0.084). For Iraq, age was not a continuous variable; the results showed that in the 20- to 29-year age group, the excess risk of death of twins was reduced by an HR = exp(^−0.470) = 37.5% compared to mothers aged below 20 years; for the 30–39 and 40–49 age groups, the excess risk was reduced, respectively, by HR = exp(^−1.09) = 66.5%, and HR = exp(^−0.737) = 52.1%. For the remaining countries, this variable was not significant.

Birth Order and Twin Excess Risk of Death

Several studies have been conducted to establish the link between birth order and child mortality, (Barclay & Kolk, Reference Barclay and Kolk2015; Black et al., Reference Black, Devereux and Salvanes2005; Mishra et al., Reference Mishra, Ram, Singh and Yadav2017; Sulloway, Reference Sulloway1996; Zajonc & Markus, Reference Zajonc and Markus1975). Although the results are often mixed and controversial, a common finding in most studies is that infants born later have far worse consequences in all areas of life as reported by Mishra et al. (Reference Mishra, Ram, Singh and Yadav2017). The results of these studies were for children in general, and these controversial results were confirmed for the twins in our models (Tables 3–8). The first-order child (i.e., first child born in the household) was taken as a reference class for this variable. For Algeria, the estimates show the excess risk of death of twins if they are the first children is 1.68 times, HR = exp(^0.514), compared to twins who were born second or third order, and 3.61 times, HR = exp(^1.286), for twins who were seventh order or more. For Iraq, this excess risk is 1.92 times, HR = exp(^0.656), compared to twins who were seventh order or more; the rest of the modalities are not significant. For Mauritania, the excess risk of death for the first-order twins is 1.43 times, HR = exp(^0.358), compared to the twins who were fourth to sixth order, and 2.03 times, HR = exp(^0.711), for twins who were seventh order or more.

Model Check Adequacy

In relative survival analysis, the assumption to be satisfied is its proportional (excess) hazard assumption (Stare et al., Reference Stare, Pohar Perme and Henderson2005), According to the p values shown in Tables 4–9 for each variable and the global models, proportional hazard assumption is true because the p value of all variables is more than .05. Among the six estimated models, the estimation results were adequate and more significant in Iraq than in the other five countries; we think that the sample size (N = 136,578) had an important role in this sense.