Encountering the Evidence: Child Mortality and Per Capita Income in Africa
Bibliographic record
Abstract
(ProQuest: ... denotes formulae omitted.)INTRODUCTIONChild mortality is not a new phenomenon. It has been a major concern especially among policy makers and experts at national and international levels. It is therefore not surprising that it is one of the Millennium Development Goals. The Millennium Development Goal 4 aims at reducing by two thirds, between 1990 and 2015, the under-five mortality rate. The highest rates of child mortality continue to be found in sub-Saharan Africa.In 2008, one in seven children died in this region before their fifth birthday; the highest levels were in Western and Central Africa, where one in six children died before age five (169 deaths per 1,000 live births). All 34 countries with under-five mortality rates exceeding 100 per 1,000 live births in 2008 are in sub-Saharan Africa, except Afghanistan [12].Thus the interest in child mortality in Africa is fully justifiable especially when one takes into account the economic and social implications of this high under-five mortality rate. The current trend is in sharp contrast with the great optimism expressed in the 1980s about child survival in high mortality developing countries [13]. This tragic situation has led to an avalanche of literature on the determinants of child mortality in Africa with authors having divergent views as concerns the way forward [14].The aim of this paper is to go beyond the determinants of child mortality to examine the correlation between under-five mortality and per capita income in Africa. Ssewanyana and Younger investigated the lack of correlation between income and infant mortality in Uganda [15]Their investigation was prompted by the fact that infant mortality had not improved in spite of the rapid economic growth in Uganda. The authors also sort to find out whether the same lack of a correlation in the national data also applies to individual level data. That is to say are infants in richer households no less likely to die than those in poorer households in Uganda? This paper investigates whether other African countries follow the trend in Uganda where economic growth does not lead to a corresponding decrease in under-five mortality.The rest of this paper is organized as follows. Section 2 provides the conceptual framework and literature review on child mortality and per capita income. Section 3 outlines our research methodology and setting. Section 4 focuses on the analysis and discussion of the results.CONCEPTUAL FRAMEWORK AND LITERATURELike other studies on child mortality, we make use of the Mosley-Chen framework. Henry Mosley and Lincoln Chen argued in their widely cited 1984 article that research efforts to identify the most cost-effective uses for health sector resources were hindered by the lack of clear conceptual models for the study of child health [16]. They noted that while both the social and medical sciences have made significant contributions to the understanding of child mortality in developing countries, the differing methodologies have compartmentalized such knowledge and therefore obstructed the development of potentially more useful approaches to understanding child survival [17]. It is within this background that they came up with an analytical framework incorporating both medical and social science methodologies into a more coherent framework of child survival.The Mosley-Chen framework distinguishes between proximate and socioeconomic determinants of child mortality. Proximate determinants directly affect child mortality risk, such as maternal factors (age, birth interval), nutrition, injuries, environmental contamination (water, sanitation, and hygienic factors), as well as personal illness control. On the other hand, socioeconomic variables have to operate through the proximate determinants and hence indirectly affect child mortality risk [18]. Socioeconomic determinants are categorised into individual, household, and community levels [19]. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".