Is There an Urban Advantage in Child Survival in Sub-Saharan Africa? Evidence From 18 Countries in the 1990s

Abstract

Evidence of higher child mortality of rural-to-urban migrants compared with urban nonmigrants is growing. However, less attention has been paid to comparing the situation of the same families before and after they migrate with the situation of urban-to-rural migrants. We use DHS data from 18 African countries to compare child mortality rates of six groups based on their mothers’ migration status: rural nonmigrants; urban nonmigrants; rural-to-urban migrants before and after they migrate; and urban-to-rural migrants before and after they migrate. The results show that rural-to-urban migrants had, on average, lower child mortality before they migrated than rural nonmigrants, and that their mortality levels dropped further after they arrived in urban areas. We found no systematic evidence of higher child mortality for rural-to-urban migrants compared with urban nonmigrants. Urban-to-rural migrants had higher mortality in the urban areas, and their move to rural areas appeared advantageous because they experienced lower or similar child mortality after living in rural areas. After we control for known demographic and socioeconomic correlates of under-5 mortality, the urban advantage is greatly reduced and sometimes reversed. The results suggest that it may not be necessarily the place of residence that matters for child survival but, rather, access to services and economic opportunities.

Appendix: Differences Between Actuarial (DHS) and Kaplan-Meier Mortality Estimates in the Presence of Age Heaping

To better understand the differences between the estimates, we illustrate with the help of a Lexis diagram, shown in Fig. 3.

Fig. 3

Lexis diagram for age a and period t before time at survey e

For a given age-specific mortality rate, the computation of the DHS estimate is as follows:

with , i (calendar time: i ∈(α→υ)), and j (age or observation time: (i – α)  ≥  j  >  (i – β)) representing the coordinate of each death counted in the Lexis diagram in a given calendar-time interval t and age interval (a, a + 1); and , counting the survivors of a given cohort at age a. Note that the expressions α→β and τ→υ represent segments on the Lexis diagram, respectively, at age a and a + 1. A segment reduces to a point when the two arguments are the same (e.g., β→β).

In the DHS, an approximation of the deaths occurring in the calendar time interval (e – t)→(e – t + 1) is done by dividing the number of deaths experienced by the cohort S _{(e – t – 1)→(e – t)} by 2, with an hypothesis of equal distribution of the deaths in the age interval (a, a + 1). This approximation in the numerator is mirrored in the denominator by adding one-half of the cohort S _{(e – t −1)→(e – t)} to one-half of the cohort S _{(e – 1)→(e)}.

Using the same notation, the computation of the KM estimate for the same time intervals is:

To compute the KM estimates with left-truncation, only the deaths dated in the interval (e – t)→(e – t + 1) are counted for the cohort S _{(e – t – 1)→(e – t)}; in the denominator, only the survivors at time (e – t) are counted. Survivors at time (e – t) are included in the analysis from the beginning of the age interval even if they spent, on average, only half the time in the age interval. As for right-censoring, among the cohort S _{(e – 1)→(e)}, only those who died in the interval (e – 1)→(e) are counted, in both the numerator and the denominator. The right-censored individuals are discarded from the analysis at the beginning of the age interval, except for those who died. In other words, the left-truncated are supposed to compensate for the right-censored. The hypothesis is of constant death rate over the time interval t.

What are the consequences of age heaping in the computation of the two estimates? In large age intervals where age heaping occurs at the end of the interval, the approximation of deaths for the left-censored cohort by in the DHS equation, following the hypothesis of equal distribution of deaths in the age interval, will tend to underestimate the estimate as compared with the KM estimate computed at exact month. Dividing the deaths of the left-censored cohort by 2 underestimates the actual number of deaths recorded in the reference period t—in particular, in the 6- to 12-months age bracket, where most deaths are declared in the 11th month.

Age heaping has the opposite effect for child mortality estimation when age at death is rounded in years: that is, recorded at the 24th, 36th, 48th, and 60th months. Compared with the DHS estimate, the KM estimate computed using monthly interval will overestimate the person-years at risk over the yearly age interval because all deaths are recorded at the end of the interval while the time at risk runs over the entire interval.

To sum, although the DHS is not sufficiently precise when it comes to left-censoring in the presence of age heaping, the KM monthly estimate is unnecessarily precise when deaths are recorded at only round ages. However, the two effects compensate for each other for the estimation of the overall under-5 mortality rate.