Understanding population and demographic issues in South Sudan
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- Category: Writing aboard the Kenya Airways: A story on coming to Rwanda for the first time
- Published on Monday, 24 October 2011 18:30
- Written by Augustino Ting Mayai, The New Sudan Vision (NSV), newsudanvision.com
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(Juba, South Sudan) – Whereas birth marks the commencement of life, mortality ends it; it is an absorbing state. Therefore human mortality constitutes this state, which is irreversible. Studies of mortality experience provide insights into the state of health and well-being of a population.
Human mortality normally follows two or a mixture of both common laws pioneered by Benjamin Gompertz and William Makeham around the 18th century. The former states that human mortality follows an exponential pattern; that is, it constantly increases with age, beginning around age 30 years. Disregarding the influence of environment, the law inherently assumes that ageing causes death. The below graph illustrates the relationship between mortality and ageing. On the other hand, the latter considers the influence of environmental attributes (age-independent component) in mortality outcome, i.e., wars, disease, nutrition, and stress. The implication of Makeham’s theory is evident in the notable deceleration of human mortality rate starting with the 1950s, probably operating as a fundamental reinforcement of the first classical Demographic Transition theorem.
Courtesy of Arias, 2003
Though all humans are generally mortal, mortality processes and timing are substantially variable depending on relevantly influencing factors, including age, sex, family history, environment, gene, social class, development index, space, nations, time, and more (See Table). Mortality in more developed polities with little influence of age-independent forces reflects the pattern discussed by Gompertz, though contradicted by recent delays in life among near-extinct cohorts. In a barely developing society like South Sudan, it is natural to deduce from Makeham’s theorem to depict mortality behavior in the population.
This brief presentation illustrates basic human mortality theorem and proofs in order to devise understanding of its relevance to population management, particularly in the context of government’s decision-making processes and planning. Mortality, one of the prominent processes influencing population change, is a fundamental variable in population and development items. The following phases, subject to underlying theorems, present South Sudan’s mortality experience.
II. Sources of Data and Analytic Strategies
This presentation uses census and household survey data. Similarly, basic demographic techniques are used to measure human mortality in South Sudan.
III. Infant and Under-five Mortality
Infant and under-five mortality measures are expressed as rates per a thousand live births. We illustrate these rates across two periods in South Sudan as provided below:
Infant
– 102 (household health survey, 2006)
– 119 (Census, 2008)
Under five
– 135 (2006)
– 168 (2008)
Analytic Technique
The above estimates are based on the Brass indirect procedure of childhood mortality estimation pioneered by William Brass and colleagues in the early 1970s. The method uses live birth histories of women aged 15-49, focusing primarily on children born and surviving for a particular age group of women. The method inherently assumes that the probability of dying is a function of the child’s age, subject to differential regional fertility and mortality regimes. The core importance of this procedure is that it adjusts for regional variances in fertility and mortality conditions. Direct techniques are also used but least preferred in societies like South Sudan due to data quality issues.
Policy Implications
Glancing through these estimates, childhood mortality rates in South Sudan seemed to have increased over time, at least as evident by these data, a sign that something terrible has gone wrong with the society’s health system. This readily means that the health conditions of South Sudanese children might have deteriorated immediately following the inauguration of the comprehensive peace agreement.
Normally, these statistics trouble a RESPONSIBLE policy-maker/government. This implies immediate needed investments in population health through increased budgets, increased childhood vaccination services, training and hiring of more qualified health staff, improving sanitary services, and perhaps providing universal health education as a prevention measure.
IV. Maternal Mortality
Maternal mortality is defined as death of a woman during pregnancy or within 42 days of pregnancy termination from related causes. The maternal mortality ratio (MMR) indicates the state of obstetric care provided in the society. Accidental deaths are not included. Unlike the infant and under-five, measured as the total number of deaths divided by the total number of exposure in a specific age group and in a given period, maternal mortality is a ratio of mother’s total deaths to the total number of live births in a particular period (direct method). It is not a probability. South Sudan’s maternal mortality is among the worst in the world. For instance, in 2006, the South Sudan’s MMR was estimated at 2,054 per 100,000 live births, the highest in the world at the time. Two years later, the ratio dropped to around 1,600 per 100,000 births, indeed a significant improvement in maternal health, though still considerably distanced from the Millennium Standards/expectations.
Analytic Techniques
Calibration of maternal mortality is often done in two prominent ways: indirect and direct. The indirect procedure called Sisterhood uses sibling respondents to provide childbirth related mortality histories of sisters. The respondents, organized by age strata, report the number of sisters at maternal mortality risk and the number of deaths to those sisters. Just like the indirect childhood mortality estimation framework, Sisterhood also relies on a set of simulated adjustment factors derived from both fertility and mortality experience of a particular population.
The second framework is very straightforward and directly derives the MMR as a ratio of total maternal deaths to the total number of live births during a particular time period (usually 12 months), multiplied by 100,000.
Policy Implications
The data indicate improvements in South Sudan’s reproductive health perhaps due to improved obstetric care. Still, more work is required for more pronounced improvements. While the progress seems evident, unfortunately, popular expectations for progress remain considerably high.
V. General Data Issues
Generally, estimating population’s representative mortality experience requires quality data. Limited in volume and even scope, South Sudan’s data tend to be very unreliable. Majority of the underlying problems reside in generation and analytic processes of the data. Other major issues include age misstatement and over/under reporting of mortality events either by design or memory loss.
Policy Implications
When wrong data are used in planning processes, wrong policies are easily devised. This invokes investment in quality data so as to enable policy makers develop well-informed public policies.
VI. Conclusion
This work has sensitized the importance of mortality and health information on policy initiatives, making reference to basic theories, suitable analytic frameworks, and historical empirical results. The paper outlined some rudimentary understanding concerned with the relationship transcending human mortality, population health and public policy.
References
Preston et al (2000). Demography: Measuring and Modeling Population Processes
Appendix
Table 1: Infant, under five, and maternal mortality by state, SHH, 2006
|
State |
Infant |
Under five |
Maternal |
|
Jonglei |
74 |
108 |
1,861 |
|
Upper Nile |
82 |
110 |
2,094 |
|
Unity |
64 |
82 |
1,732 |
|
Warrap |
138 |
176 |
2,173 |
|
NB |
129 |
165 |
2,182 |
|
WB |
97 |
134 |
2,216 |
|
Lakes |
90 |
114 |
2,243 |
|
WE |
151 |
192 |
2,327 |
|
CE |
107 |
141 |
1,867 |
|
EE |
83 |
118 |
1,844 |
*Obviously, the levels of mortality are associated with residence, region, maternal education, gender and household’s economic class.
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Augustino Ting Mayai is a doctoral student of Demography at the University of Wisconsin-Madison. He can be reached at n This email address is being protected from spambots. You need JavaScript enabled to view it. ” style=”color: #000000; text-align: left; line-height: 21px; padding: 0px; margin: 0px;”>This email address is being protected from spambots. You need JavaScript enabled to view it. .
