Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects

Omariba, D. Walter Rasugu; Beaujot, Roderic; Rajulton, Fernando

doi:10.1007/s11113-007-9031-z

Cited by 72 publications

(108 citation statements)

References 38 publications

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“…We observed that the risk of infant mortality is about 5 times more likely for children of mother greater 35 years old at their first birth compared with children of whose their mother's age as at the time of their first birth was less than 20 years. This is in consistent with the study in Kenya by Omariba [9]. This high effect of maternal age at first birth to infant mortality may be due to physiological and sociopsychological factors.…”

Section: Discussionsupporting

confidence: 93%

“…Mortality in human population follow Weibull, because it is generally high in the first years of life, it declines in other ages of childhood, then increasing slowly in adult ages to old age [17]. Based on this point, many researches on childhood mortality used Weibull model [9,11].…”

Section: Discussionmentioning

confidence: 99%

“…These results, was in agreement with previous findings. [9,15,22] The fact that the risk of childhood death is not totally captured by NDHS makes frailty model more interesting and points to unobserved/unmeasured factors. The exclusion of important variables like healthcare, breastfeeding and HIV/AIDS increased the level of the frailty effect, as these are known determinants of child mortality [22].…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have shown that infants and child mortality rates vary by socio-economic and bio-demographic characteristics [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…86 deaths per 1,000 live births) than in urban areas (60 deaths per 1,000 live births) and child mortality is 89 deaths per 1,000 live births in the rural areas as against 42 deaths per 1,000 live births in urban areas [14]. Data from a different environment indicate that the incidence of death in children are not randomly distributed families but, rather, that there is a positive association of sibling deaths [9,15]. A natural explanation of this would be that families in which child deaths are concentrated share some environmental or genetic risk factors that predispose their children to higher risks of death.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Frailty on Infant and Child Mortality in Rural Nigeria

Wegbom¹,

Akinyemi²,

Edet³

2018

AJPHR

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Background: Infant and child mortality remains a major public health challenge in Nigeria and other parts of the developing world with rural areas sharing the largest burden which of course have devastating effects on concerned mothers and the population at large. This study was conducted to determine the effect of frailty and which of infant or child mortality is most affected by unobserved heterogeneity in Rural Nigeria. Methods: Data from 2013 Nigeria Demographic and Health Survey were analyzed. Weibull frailty models were fitted. The frailty effects, Hazard ratio (HR) and its 95% confidence interval (CI) were estimated. Results: The frailty value in infant and child mortality are 51.8 and 56.5 percent respectively, which means that the covariates in infant and child models explained 48.2 percent and 43.5 percent family variation in infant and child deaths in rural Nigeria. Conclusion: Child mortality is more affected by unobserved heterogeneity than infant mortality in rural Nigeria.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Many studies have shown that infants and child mortality rates vary by socio-economic and bio-demographic characteristics [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Influence of Frailty on Infant and Child Mortality in Rural Nigeria

Wegbom¹,

Akinyemi²,

Edet³

2018

AJPHR

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Infant mortality and intra‐household competition in the Northern Islands of Orkney, Scotland, 1855–2001

Sparks

Wood

Johnson

2013

American J Phys Anthropol

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This study applies principles from the theory of household life cycles to the study of early childhood mortality in the population of the Northern Orkney Islands, Scotland. The primary hypothesis is that unfavorable household economic conditions resulting from changes in household demographic composition increase the risk of death for children under the age of 5 years because of limited resources and intra-household competition. We apply Cox proportional hazards models to nearly 5,000 linked birth and death records from the Northern Orkney Islands, Scotland, from the period 1855 to 2001. The dependent variable is the child's risk of death before age 5. Findings suggest that children in households with unfavorable age compositions face higher risk of death. This elevated risk of death continues once heterogeneity among children, islands, and households is controlled. Results also show differential risk of death for male children, children of higher birth orders, and twin births. The analyses present evidence for intra-household competition in this historic setting. The most convincing evidence of competition is found in the effects of household consumer/producer ratios and twinning on child mortality risks.

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Clustering of child deaths among families in low and middle‐income countries: A meta‐analysis

Dwivedi

Ranjan

Mishra

et al. 2023

Health Science Reports

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Background and Aims: Several studies have examined the phenomenon of "death clustering," in which two or more children born to the same mother or from the same family die at an early age. Therefore, a scientific examination of the results is essential to understand how the survival status of the older siblings affects the survival of the younger siblings. By using meta-analysis, this study aims to provide a quantitative synthesis of the results of studies on "child death clustering" in low-and middle-income countries (LMICs).Methods: This study followed the PRISMA-P 2015 guidelines. We used four electronic databases-PubMed, Medline, Scopus, and Google Scholar with search and citation analysis capabilities. Initially, 140 studies were identified, but only 27 met the eligibility criteria eventually. These were studies that had used the death of a previous child as a covariate to determine the survival status of the index child. The heterogeneity and the publication bias of the studies were examined using the Cochran test, I 2 statistic, and Egger's meta-regression test. Results:The pooled estimate of 114 study estimates for LMICs contains some bias.India's 37 study estimates were distributed more or less equally along the middle line, indicating no publication bias, while there was a slight bias in the estimates for Africa, Latin America, and Bangladesh. The odds of experiencing the death of the index child in the selected LMICs were 2.3 times higher for mothers who had lost any prior child as compared to those mothers who had not had any prior child loss.For African mothers, the odds were five times higher, whereas for Indian mothers, the odds were 1.66 times higher. Mothers' characteristics, such as education, occupation, health-seeking behavior, and maternal competence, significantly affect the child's survival status. Conclusion:Achieving the sustainable development goals would not be possible if mothers in countries experiencing high levels of under-five mortality are not provided with better health and nutrition facilities. Mothers who have lost multiple children should be targeted for assistance.

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Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects

Cited by 72 publications

References 38 publications

The Influence of Frailty on Infant and Child Mortality in Rural Nigeria

The Influence of Frailty on Infant and Child Mortality in Rural Nigeria

Infant mortality and intra‐household competition in the Northern Islands of Orkney, Scotland, 1855–2001

Clustering of child deaths among families in low and middle‐income countries: A meta‐analysis

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