4 Results
4.1. Non-parametric findings
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model 2, and t will be, similar to the models above, the survival time. Additionally, the Weibull model will be used for by-group definitions of the variables described in Table 2, with the purpose of finding evidence on which causal mechanism is more impactful.
3.2.3. Expected results
In line with the objectives of this study presented in Section 1.2., the methods described in this section aim to provide a basis to corroborate or deny the followings premises:
• There is a deleterious effect of spacing childbirth in less than 18 months for the Nicaraguan case, evidenced to a higher hazard rate for children born in this interval.
• Spacing childbirth in 18-35 months or more than 36 months (WHO recommended interval) is beneficial for children survival, it reduces the hazard rate for children born in these intervals.
• There is evidence of two causal mechanism found in the literature: maternal depletion syndrome and sibling competition: variables such as death of the preceding child, birth order and singleton/multiple birth find a higher hazard of infant death.
• The model can provide information on the most impactful of those mechanisms:
there’s a clearer effect of larger magnitude for one of the mechanisms.
4 Results
4.1. Non-parametric findings
Firstly, the distribution of variables depicted in Table 2 was found comparatively for the group that died while being under-one-year old and the group that survived past their first year of life. These differences provide initial insight on potential effects of different covariates across survivor and non-survivor groups (see Table 3 below).
Among the main effects that a priori we can observe while comparing groups, regarding our main independent variable, interbirth interval, for children who died as an infant the short intervals had a larger representative share of 28.45%, while in the survivor group only a 13.4% had had the shorter interval. Conversely, those that were born with a longer interval (more than 18 months) were more prevalent among the survivor group (56.1%) that among children that passed away before reaching one year of age (39%). On the other hand, with slight differences, those who died were primarily male, while the surviving children are primarily female. The death of the preceding child seems to be a
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previous sibling passed away, as opposed to a 5% in the surviving group.Table 3: Distribution of selected socioeconomic variables among Nicaraguan families, 1998-2011
Variable All births Infant mortality (Under 1 years old)
Death of preceding child 0.000
No 129,223 94.41 6,163 84.48 123,060 94.97
Secondary/Tertiary 44,098 28.15 1,037 14.22 43,061 28.83
Mother’s age at birth (years) 0.000
<18 21,903 16.07 1,775 24.93 20,128 15.58
Remoteness to health services 0.000
Not far 136,096 86.87 6,047 82.89 130,049 87.06
*** This variable contains information on pregnancy terminations and miscarriages, as the data contains
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Regarding mother-level covariates, these initial distributional data also point an important issue historical and current for Nicaraguan woman: poor educational attainments. Women who either attain a primary level of education or none at all account for almost a third of the women in the sample. Mother’s education has been found to have a strong relationship with child mortality, as lower educated mothers have less access to sexual and reproductive health information, and this is observable in the Nicaraguan data, as there is a higher incidence of low educated mothers among those who died (85.78%).
In respect to mother’s age, a quarter of children that suffered from infant mortality where born to teenage moms (younger than 18 years old), as opposed to a 15.58% of survivors past one-year-old born to the same age-segment mothers.
Additionally, variables at the household-level show that the lack of readily resources negatively reflect the most among children that died. For instance, while examining the measurement of household wealth, it’s noticeable the greater share of low-income families among infants that died (68%) than those who didn’t. Conversely, high-income households can provide with better health care and resources thus being more prominently among the survivor group (22%) than poor families that have no way but to access primary care in the public system and have less resources to take better care of both the mother and the child before and after childbirth (15%). In second place, the results for the long-rank of each covariate are presented in the last column to the right of Table 3. The test leads to the rejection of the null hypothesis that there is no difference between two or more survival curves in all but one of the covariates.
Lastly on the non-parametric results for the survival of infants in Nicaragua, the graphical depictions of the survival and cumulative hazards by categories of interbirth intervals are presented. Figure 1 presents the Kaplan-Meier survival curves for infants according to the interbirth interval they have with their preceding sibling. In this sense, the KM curves clearly show that the lowest survival probability, and clearly separated from other categories, belongs to the children born in the shortest birth interval (less than 18 months); which also accelerates the decline of survival probabilities within the first periods of time to survival analysis.
Additionally, children born first have the second lowest probability of survival than those born of intervals of 18 to 35 months, or more than 36 months. This group being the one with the greater survival curve, which corresponds to the recommended longer intervals suggested by the WHO; all have a flatter behavior, i.e. do not present a constant reduction of survival probability as the children born to the shortest interval do.
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Figure 1: Kaplan Meier survival curves for Interbirth intervals
Conversely, Figure 2 in Appendix presents the Nelson Aalen cumulative hazard curves for infants according to their preceding interbirth interval, which is the opposite function to the survival probability depicted in the Kaplan-Meier curves of Figure 1.
The cumulative hazard curves serve as a graphical check on the fit of a potential parametric model. Particularly, the “slope” of the hazard curve is steeper and accelerates more quickly for those children aged one month old or less. As mentioned above, the curves behaviors should be the complete opposite of the KM curves. Therefore, the group with the highest cumulative hazard is, again, children born to less than 18 months of interbirth interval; while first born, interbirth intervals of 18 to 35 months and notably, longer intervals (36+ months) have lower cumulative hazard rate.
4.2. Semi-parametric model findings