Risk Factors Of Under-five Mortality Research Articles

Predictive model and determinants of under-five child mortality: evidence from the 2014 Ghana demographic and health survey

BackgroundGlobally, millions of children aged below 5 years die every year and some of these deaths could have been prevented. Though a global problem, under-five mortality is also a major public health problem in Ghana with a rate of 60 deaths per 1000 live births. Identification of drivers of mortality among children aged below 5 years is an important problem that needs to be addressed because it could help inform health policy and intervention strategies aimed at achieving the United Nations SDG Goal 3 target 2. The aim of this study is to develop a predictive model and to identify determinants of under-five mortality.MethodThe 2014 Ghana Demographic and Health Survey data was used in this study. Analyses were conducted on 5884 children. The outcome variable is child survival status (alive or dead). Single level binary logistic and multilevel logistic regression models were employed to investigate determinants of under-five mortality. The fit of the model was checked using Variance Inflation Factor and Likelihood Ratio tests. The Receiver Operating Characteristic curve was used to assess the predictive ability of the models. A p-value< 0.05 was used to declare statistical significance.ResultsThe study observed 289 (4.91%) deaths among children aged below 5 years. The study produced a good predictive model and identified increase in number of total children ever born, number of births in last 5 years, and mothers who did not intend to use contraceptive as critical risk factors that increase the odds of under-five mortality. Also, children who were born multiple and residing in certain geographical regions of Ghana is associated with increased odds of under-five mortality. Maternal education and being a female child decreased the odds of under-five mortality. No significant unobserved household-level variations in under-five mortality were found. The spatial map revealed regional differences in crude under-five mortality rate in the country.ConclusionThis study identified critical risk factors for under-five mortality and strongly highlights the need for family planning, improvement in maternal education and addressing regional disparities in child health which could help inform health policy and intervention strategies aimed at improving child survival.

Robustness of Poisson Mixture models in identifying risk factors for Under-Five mortality in Nigeria

Background: Estimates of Under-Five mortality (U5M) have taken advantage of indirect methods but U5M risk factors have been identified using fixed statistical models with little considerations for the potentials of mixture models. Mixture models such as Poisson-Mixture models exhibit flexibility tendency, which is an attribute of robustness lacking in fixed models. Objective: To examine the robustness of Poisson-Mixture models in identifying reliable determinants of U5M. Methods: The data on 18,855 women used in this study were obtained from the 2008 Nigeria Demographic and Health Survey (NDHS). Six different Poisson-Mixture models namely: Poisson (PO), Zero-Inflated Poisson (ZIP), Poisson Hurdle (PH), Negative Binomial (NBI), Zero-Inflated Negative Binomial (ZINBI) and Negative Binomial Hurdle (NBIH) were fitted separately to the data. The Akaike Information Criteria (AIC) and diagnostic check for normality were used to select robust models. All tests were conducted at p = 0.05. Results: The models and AIC values for U5M were: 38763.47 (PO), 38654.55 (ZIP), 44270.77 (PH), 38526.26 (NBI), 38513.71 (ZINBI) and 44269.30 (NBIH). The PO, ZIP, PH and NBIH met normality test criteria, and the ZIP model was of best fit. The model identified breastfeeding, paternal education, toilet type, maternal education, place of delivery, birth-order and antenatal-visits as significant determinants of U5M at the national level. Conclusion: The Zero-Inflated Poisson model provided the best robust estimates of Under-five Mortality in Nigeria, while maternal education and birth-order were identified as the most important determinants. The Poisson-mixture models are recommended for modelling Under-five Mortality in Nigeria.

Investigating risk factors for under-five mortality in an HIV hyper-endemic area of rural South Africa, from 2000-2014.

IntroductionDespite global progress, there remains a disproportionate burden of under-five year old deaths in sub-Saharan Africa (SSA), where four out of five child deaths occur. Substantial progress has been made in improving sanitation, controlling communicable diseases and the spread of HIV in most parts of the world. However, significant strides to address some key risk factors related to under-five mortality are still needed in rural SSA if they are to attain relevant 2030 SDG targets. The aim of this study is to investigate the risk factors for under-five mortality in an HIV hyper-endemic area of rural South Africa, from 2000–2014. Some of the key risk factors investigated are, for example: household wealth, source of drinking water, distance to the national road and birth order.MethodsWe conducted a statistical analysis of 759 births from a population-based cohort in rural KwaZulu-Natal Province, South Africa, from 2000 to 2014. A Cox Proportional Hazards model was used to identify the risk factors and key socio-demographic correlates of under-five mortality leveraging the longitudinal structure of the population cohort.ResultsChild mortality rates declined by 80 per cent from 2000 to 2014, from >140 per 1,000 persons in years 2001–2003 to 20 per 1,000 persons in the year 2014. The highest under-five mortality rate was recorded in 2002/2003, which decreased following the start of antiretroviral therapy rollout in 2003/4. The results indicated that under-five and infant mortality are significantly associated with a low wealth index of 1.49 (1.007–2.48) for under-fives and 3.03 (1.72–5.34) for infants. Children and infants with a lower wealth index had a significantly increased risk of mortality as compared to those with a high wealth index. Other significant factors included: source of household drinking water (borehole) 3.03 (1.72–5.34) for under-fives and 2.98 (1.62–5.49) for infants; having an HIV positive mother 4.22 (2.68–6.65) for under-fives and 3.26 (1.93–5.51) for infants, and period of death 9.13 (5.70–14.6) for under-fives and 1.28 (0.75–2.20) for infants. Wealth index had the largest population attributable fraction of 25.4 per cent.ConclusionsThe research findings show a substantial overall reduction in under-five mortality since 2003. Unsafe household water sources and having an HIV-positive mother were associated with an increased risk of under-five mortality in this rural setting. The significant risk factors identified align well with the SDG 2030 targets for reducing child mortality, which include improved nutrition, sanitation, hygiene and reduced HIV infections. Current trajectories suggest that there is some hope for meeting the 2030 SGD targets in rural South Africa and the region if the identified significant risk factors are adequately addressed.

Under five mortality patterns and associated maternal risk factors in sub-Saharan Africa: A multi-country analysis.

BackgroundUnder-5 mortality rate in the sub-Saharan region has remained unabated. Worse still, information on the regional trend and associated determinants are not readily available. Knowledge of the trend and determinants of under-5 mortality are essential for effective design of intervention programmes that will enhance their survival. We aimed to examine the mortality patterns in under-5 children and maternal factors associated with under-5 deaths.MethodsDemographic and Health Survey (DHS) data from five sub-Sahara Africa countries; Chad, Democratic Republic of Congo, Mali, Niger and Zimbabwe were used in this study. The sample size consisted of 68,085 women aged 15–49 years with at least one history of childbirth. The outcome variable was under-five mortality rate. Relevant information on maternal factors were extracted for analysis. Multivariable Cox proportional hazards regression was used to model maternal factors associated with under-five mortality.ResultsThe current under-5 mortality rate (per 1,000 live births) was; 133 in Republic of Chad, 104 in Democratic Republic of Congo, 95 in Mali, 127 in Niger, and 69 in Zimbabwe. Several maternal and child level factors were found to be significantly associated with under-five mortality. Lack of spousal support (not currently married) resulted to increase in under-five mortality (Chad- Hazard Ratio [HR] = 1.11, 95%CI = 0.97–1.25; DR Congo- HR = 1.24, 95%CI = 1.11–1.40; Mali- HR = 2.43, 95%CI = 1.63–3.64; Niger- HR = 1.59, 95%CI = 1.24–2.03; Zimbabwe- HR = 1.33, 95%CI = 1.06–1.67). Delivery by caesarean section was significantly associated with under-five mortality (Chad- HR = 1.32, 95%CI = 1.00–1.77; DR Congo- HR = 1.20, 95%CI = 1.01–1.43; Mali- HR = 1.42, 95%CI = 1.08–1.85; Niger- HR = 1.43, 95%CI = 1.06–1.92; Zimbabwe- HR = 1.49, 95%CI = 1.03–2.15).ConclusionDespite concerted effort by government and several stakeholders in health to improve childhood survival, the rate of under-5 mortality is still high. Our findings provided evidence on the contribution of maternal age, place of residence, household wealth index, level of education, employment, marital status, religious background, birth type, birth order and interval, sex and size of child, place and mode of delivery, to Under-5 mortality rate in SSA. The position of prominent risk factors for under-five mortality should be addressed through effective design of timely and efficient intervention aimed at reducing childhood mortality.

Socio-Economic and Demographic Determinants of Under-Five Mortality in Ethiopia, 2011

Background: Although the economic conditions in Ethiopian households have recently improved, under-five mortality has remained one of the major problems in the country. Identification of the risk factors for under-five mortality is an important problem that needs to be addressed. This study aimed to estimate the effect of socio-economic and demographic factors on under-five mortality in Ethiopia. Methods: Data consisting of a nationally representative sample of 26370 children in the 2011 Ethiopia Demographic and Health Survey was analyzed. The survey logistic regression model was adopted for analysis. The outcome variable was the child’s survival status i.e. the child being either alive or dead. The explanatory variables were the socio-economic and demographic factors for mother and child, respectively. Results: The overall significance of all variables was investigated, and the following variables were found to have significant effect on the under-five mortality in Ethiopia: the sex of child, mother’s age at birth, the region, area of residence and educational of the mother. Conclusion: This study suggests that improving the educational level of mothers and reducing the regional disparity can reduce child mortality in Ethiopia.

Comparison of under-five mortality for 2000, 2005 and 2011 surveys in Ethiopia

BackgroundThough the socio-economic situation of the Ethiopian household is improving along with the decrease in under-five child mortality. But, under-five mortality is still one of the major problems. Identification of the risk factors change over time which mismatches with the diminishing rate of under-five mortality is important to address the problems.MethodsThe survey data used for this research was taken from three different Ethiopian Demographic and Health Surveys (2000, 2005 and 2011). This data was used to identify the effect of time varying under-five mortality risk factors. The Cox proportional hazard model was adapted for the analysis.ResultsThe effect of respondent’s current age, age at first birth and educational level on the under-five mortality rate significantly diminishes in the recent surveys. On the other hand, the effect of the number of births in the last 5 years increases more in 2011 than in the earlier two surveys. Similarly, number of household members in the house and the number of under-five children in the house demonstrated a difference through years. Regarding total children ever born, child death is more for the year 2000 followed by 2005 and 2011.ConclusionBased on the study, our findings confirmed that under-five mortality is a serious problem in the country. The analysis displayed that the hazard of under-five mortality has a decreasing pattern in years. The result for regions showed that there was an increase in years for some of the regions. This research work gives necessary information to device improved teaching for family planning and children health care to change the child mortality circumstance in the country. Our study suggests that the impact of demographic characteristics and socio-economic factors on child mortality should account for their integral changes over time.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3601-0) contains supplementary material, which is available to authorized users.

Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data.

BackgroundUnder-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality—and its social and environmental risk factors—at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana.Methods and FindingsWe used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0) for each of Ghana’s 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from <5% in some northern districts, where 5q0 had been higher in 2000, to >40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis.ConclusionsUnder-five mortality has declined in all of Ghana’s districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends.

Childhood mortality spatial distribution in Ethiopia

ABSTRACTThe risk of a child dying before completing five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. For this study, the 2000, 2005 and 2011 Ethiopian Demographic Survey (EDHS) was used. Generalized linear mixed model with spatial covariance structure was adapted. The model allowed for spatial correlation, and leads to the more realistic estimate for under-five mortality risk factors. The analysis showed that the risk of under-five mortality shows decline in years. But, some regions showed increase in years. The study highlights the need to implement better education for family planning and child care to improve the under-five mortality situation in some administrative areas.

Risk Factors for Under-Five Mortality in Secondary Level Hospitals in Ibadan, South-Western Nigeria.

Risk Factors for Under-Five Mortality in Secondary Level Hospitals in Ibadan, South-Western Nigeria.

Structured additive regression models with spatial correlation to estimate under-five mortality risk factors in Ethiopia.

BackgroundThe risk of a child dying before reaching five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. It is important to identify factors affecting under-five mortality.MethodsA structured additive logistic regression model which accounts the spatial correlation was adopted to estimate under-five mortality risk factors. The 2011 Ethiopian Demographic and Health Survey data was used for this study.ResultsThe analysis showed that the risk of under-five mortality increases as the family size approaches seven and keeps increasing. With respect to socio-economic factors, the greater the household wealth, the lower the mortality. Moreover, for older mothers, the chance of their child to dying before reaching five is diminishes.ConclusionThe model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity. Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

The distribution and effects of child mortality risk factors in Ethiopia: A comparison of estimates from DSS and DHS

Objectives: To conduct a comparative analysis of the distribution and effects of under-five mortality correlates using Demographic and Health Survey (DHS) and Demographic Surveillance System (DSS) data from Ethiopia, and to investigate the methodological bias in DHS-based childhood mortality rates due to the impossibility of including children whose mothers were deceased. Methods: Using all-cause under-5 mortality as an outcome variable, the distribution and effects of risk factors weremodeled using survival analysis. All live births in rural Ethiopia in the 5-year period before the 2005 DSS+ survey and between 01/01/2000 and 31/12/2004 in the DSS in the Butajira Rural Health Program (in the Southern Nations, Nationalities, and People's (SNNP) region of Ethiopia) were included. Results: Overall, similar estimates of hazard rate ratios were derived from both DHS and DSS data and the child mortality risk profile is similar between each data source, with multiple births and living in less populous households being significant risk factors for under-five mortality. Nevertheless, some notable differences were observed. The DSS data was more sensitive to local variations in population composition and health status, whilst the more dispersed DHS approach tended to average out local variation across the country. Excluding children whose mothers were deceased from the DSS analysis had no important effect on risk profiles or estimates of survival functions at age 5 years. DHS survival functions were somewhat lower than DSS estimates (BRHP=0.87, DHS rural Ethiopia=0.67, DHS SNNP=0.66). Conclusion: Despite differing methodologies, cross-sectional DHS and longitudinal DSS data produce estimates of the distribution and effects of under-five mortality risk factors that are broadly similar. The differing methodological characteristics of DHS and DSS mean that when combined, these two data sources have the potential to provide a comprehensive picture of national population composition and health status as well as the extent of local variation –both of which are important for health monitoring and planning.