Spatio-temporal patterns of under 5 mortality in Nigeria

Ayoade, Modupe Alake

doi:10.1016/j.ssmph.2018.09.004

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…The spatial autocorrelation analysis result indicated that U5MR had a spatial dependency in 2000, 2005, 2011 and 2016 (Moran's I: 0.046, 0.047; 0.09 and 0.096, respectively at p value 0.01). This result is supported by the findings in Nigeria 32 33 and Bangladesh. 34 The spatial heterogeneity of under-five mortality clustering was detected mainly in Benishangul and Afar regions.…”

Section: Open Accesssupporting

confidence: 85%

Exploring spatiotemporal distribution of under-five mortality in Ethiopia: further analysis of Ethiopian Demographic and Health Surveys 2000, 2005, 2011 and 2016

Liyew

Kassie

Teshale

et al. 2021

bmjpo

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ObjectiveThe aim of this study was to explore spatiotemporal distribution of under-five mortality in Ethiopia using data from four (2000, 2005, 2011 and 2016) consecutive demographic and health surveys.MethodsA total of 41 498 children were included from four consecutive Ethiopian Demographic and Health Surveys. The geospatial analysis was conducted by using ArcGIS V.10.7 and saTScan V.9.6. Thus, spatial autocorrelation, hotspot analysis, spatial interpolation and spatial scan statistics were carried out for each survey separately to show the temporal pattern of geographically risk areas of under-five mortality in Ethiopia. Finally, the most under-five mortality rate (U5MR) risk areas in each survey period were mapped.ResultsUnder-five mortality was spatially clustered in Ethiopia (Moran’s Index: 0.046–0.096, p<0.01). The Benishangul-Gumuz region was consistently at a higher risk in the last two decades. Additional hotspot areas were detected at Afar and Amhara (in 2000, 2005, 2016), at Gambala (in 2011) and the South Nation Nationality and People’s (SNNP) Region (in 2016). Moreover, 160 primary clusters were identified. Of these, 85 clusters (log-likelihood ratio (LLR)=13.10, p<0.01) were from Benishangul-Gumuz and Amhara regions (in 2000); 67 clusters (LLR=12.93, p<0.01) were from Afar and Amhara regions (in 2005); 4 clusters (LLR=10.54, p<0.01) were from Benishangul-Gumuz region (in 2011); and another 4 clusters (LLR=11.85, p<0.01) were from Afar region (in 2016).ConclusionHigh-risk areas were detected mainly in the Benishangul-Gumuz and Afar regions. As a result, designing under-five population targeted intervention programmes in those high-risk geographical regions was vital to reduce under-five mortality in Ethiopia.

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Section: Open Accesssupporting

confidence: 85%

Exploring spatiotemporal distribution of under-five mortality in Ethiopia: further analysis of Ethiopian Demographic and Health Surveys 2000, 2005, 2011 and 2016

Liyew

Kassie

Teshale

et al. 2021

bmjpo

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“…Similarly, the urban advantage in under-5 mortality over time prevails in SSA, and the growth of urban populations predicts the magnitude of under-5 disparities [ 55 ]. Regional differences in mortality risk can be observed in both countries with slow [ 56 ] or rapid gains in child survival or that have met the MDGs [ 48 – 50 , 55 , 57 , 58 ]. In Nigeria, for example, wide variations in under-5 mortality are observed across states over time, with higher levels in northern regions.…”

Section: Discussionmentioning

confidence: 99%

Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study

et al. 2019

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BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countr...

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“…[10] determined the socioeconomic indicators contributing to child mortality in Nigeria, and surprisingly, concluded that attendance of skilled health workers during delivery was associated with an increased risk of neonatal death. [11] examined the Spatio-temporal distribution of under-five mortality rate in Nigeria and found that there is spatial clustering and geographical disparities between states in Nigeria. Recently, [12] examined the effect of spatial distribution and other covariates on under-five mortality deaths in Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Under Age Five Children Survival Times in Nigeria: A Bayesian Spatial Modelling Approach

Egbon

Bogoni

Babalola³

et al. 2021

Preprint

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Background: Nigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparities in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. Methods: The approach commonly adopted in multilevel modeling of the under-five mortality rate in Nigeria is to consider child death status as a binary outcome variable. In this manner, information about the time localization of mortality risk is lost in the process. This study quantified the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria using a Bayesian hierarchical hazard model with time to death outcomes. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a mixed effect framework with spatial components were considered, and the best model was selected for inference. In addition, the Kaplan Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. Results: Findings show that gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kanu, Kogi, Nasarawa, Plateau, Sokoto states in Nigeria. Conclusions: This study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal health care service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria.

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Spatio-temporal patterns of under 5 mortality in Nigeria

Cited by 9 publications

References 12 publications

Exploring spatiotemporal distribution of under-five mortality in Ethiopia: further analysis of Ethiopian Demographic and Health Surveys 2000, 2005, 2011 and 2016

Exploring spatiotemporal distribution of under-five mortality in Ethiopia: further analysis of Ethiopian Demographic and Health Surveys 2000, 2005, 2011 and 2016

Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study

Under Age Five Children Survival Times in Nigeria: A Bayesian Spatial Modelling Approach

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