Neonatal Mortality In Ghana Research Articles

Predictors of neonatal mortality in Ghana: evidence from 2017 Ghana maternal health survey

BackgroundNeonatal mortality contributes about 47% of child mortality globally and over 50% of under-5 deaths in Ghana. There is limited population level analysis done in Ghana on predictors of neonatal mortality.ObjectivesThe objective of the study was to examine the predictors of neonatal mortality in Ghana.MethodThis study utilizes secondary data from the 2017 Ghana Maternal Health Survey (GMHS). The GMHS survey focuses on population and household characteristics, health, nutrition, and lifestyle with particular emphasis on topics that affect the lives of newborns and women, including mortality levels, fertility preferences and family planning methods. A total of 10,624 respondents were included in the study after data cleaning. Descriptive statistical techniques were used to describe important background characteristics of the women and Pearson’s Chi-squares (χ2) test used to assess association between the outcome (neonatal death) and independent variables. Multivariate logistic regression analysis was done to estimate odd ratios and potential confounders controlled. Confidence level was held at 95%, and a p < 0.05 was considered statistically significant. Data analysis was done using STATA 15.ResultsThe prevalence of neonatal mortality was 18 per 1000 live births. ANC attendance, sex of baby, and skin-to-skin contact immediately after birth were predictors of neonatal mortality. Women with at least one ANC visit were less likely to experience neonatal mortality as compared to women with no ANC visit prior to delivery (AOR = 0.11; CI = 0.02–0.56, p = 0.01). Girls were less likely (AOR = 0.68; CI = 0.47–0.98; p = 0.03) to die during the neonatal period as compared to boys. Neonates who were not put skin-to-skin contact immediately after birth were 2.6 times more likely to die within the neonatal period than those who were put skin-to-skin contact immediately after birth (AOR = 2.59; CI = 1.75–3.83, p = 0.00).ConclusionNeonatal mortality remains a public health concern in Ghana, with an estimated rate of 18 deaths per 1,000 live births. Maternal and neonatal factors such as the sex of the newborn, the number of antenatal care visits, and skin-to-skin contact between the newborn and mother immediately after birth are the predictors of neonatal mortality in Ghana.

Multilevel modelling of neonatal mortality in Ghana: Does household and community levels matter?

BackgroundNeonatal mortality accounts for an increasing share of under-five deaths, and they are declining at a slower rate than postnatal deaths. Apparently, neonatal mortality is increasingly becoming a major public health problem in Ghana and the world over. The current study sought to analyze neonatal mortality as a function of predictor variables and to estimate and understand unobserved household and community-level residual effects on neonatal mortality to provide data driven evidence to shape informed policies and interventions aimed at reducing the neonatal mortality burden. MethodsThe current study extracted three-level complex data on 5884 children born in the five years preceding the 2014 Ghana Demographic and Health Survey. A two-level and three-level multilevel logistic models were applied to estimate unobserved household and community-level variations in neonatal mortality in the presence of set of predictor variables. Sampling weights were incorporated in both the descriptive and inferential analysis since the data used emanated from a complex survey. Model fit statistics such as AIC scores for a weighted two-level and three-level random intercept logistic models were compared. The model with the lowest AIC score was considered the most preferred model. ResultsThe household-level random intercept model suggested that the odds of neonatal mortality was higher among multiple births [OR = 3.15 (95% CI: 1.17, 8.50)], babies born to mothers who received prenatal care from non-skilled worker [OR = 5.88 (95% CI: 2.90, 11.91)], babies delivered through caesarian section [OR = 2.47 (95% CI: 1.06, 5.79)], a household with 1–4 members [OR = 10.23 (95% CI: 4.17, 25.50)], a short preceding birth interval (<24 months) [OR = 3.05 (95% CI: 1.18, 7.88)], and preceding birth interval between 24 and 47 months [OR = 2.88 (95% CI: 1.41, 5.91)]. Substantial unobserved household-level residual variations in neonatal mortality were observed. ConclusionThe findings of the current study provide an actionable information to be used by government and other stakeholders in the health sector to renew commitment to reduce neonatal mortality to an acceptable level. There is the need to intensify maternal health education by health providers to encourage pregnant women to visit antenatal clinics at least four times so they could benefit substantially from ANC services.

Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods.

Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and individual-level factors that contribute to neonatal mortality. However, there are relatively few studies that have rigorously assessed geospatial covariates and spatiotemporal variations of neonatal deaths in Ghana. This study focuses on modeling and mapping of spatiotemporal variations in the risk of neonatal mortality in Ghana using Bayesian Hierarchical Spatiotemporal models. This study used data from the Ghana Demographic and Health Surveys (GDHS) conducted in 1993, 1998, 2003, 2008, and 2014. We employed Bayesian Hierarchical Spatiotemporal regression models to identify geospatial correlates and spatiotemporal variations in the risk of neonatal mortality. The estimated weighted crude neonatal mortality rate for the period under consideration was 33.2 neonatal deaths per 1000 live births. The results obtained from Moran's I statistics and CAR model showed the existence of spatial clustering of neonatal mortality. The map of smooth relative risk identified Ashanti region as the most consistent hot-spot region for the entire period under consideration. Small body size babies contributed significantly to an increased risk of neonatal mortality at the regional level [Posterior Mean: 0.003 (95% CrI: 0.00,0.01)]. Hot spot GDHS clusters exhibiting high risk of neonatal mortality were identified by LISA cluster map. Rural residents, small body size babies, parity, and aridity contributed significantly to a higher risk of neonatal mortality at the GDHS cluster level. The findings provide actionable and insightful information to prioritize and distribute the scarce health resources equitably to tackle the menace of neonatal mortality. The regions and GDHS clusters with excess risk of neonatal mortality should receive optimum attention and interventions to reduce the neonatal mortality rate.

Dynamic linear models for policy monitoring. The case of maternal and neonatal mortality in Ghana

Monitoring is a major step in policy analysis used to assess whether a policy is actually working as desired. We provide a general policy monitoring approach based on Bayesian forecasting models. These are employed to predict the evolution of relevant monitoring variables over time and support expected utility calculations to assess the efficiency of the policy. We illustrate the approach by monitoring the Free Maternal Health Care and MDG5 Acceleration Framework policies aimed to reduce maternal and neonatal mortality in Ghana, using dynamic linear models for forecasting purposes. Despite major investments, results at national level suggest no significant improvement in maternal and neonatal survival between pre- and post-policy periods. However, regional analyses show that gains have actually been attained in certain regions, suggesting possible directions for improvements nationwide.

Survival analysis of neonatal mortality in Ghana using three population-based surveys

BackgroundNeonatal mortality in many low-and middle-income countries (LMICs) remains high despite global efforts at addressing this challenge. Tackling neonatal death in LMICs is further complicated by lack of reliable data from individual countries in the region to inform effective context specific interventions. This study investigates the probability of neonatal survival and socio-demographic risk factors of neonatal mortality in Ghana.MethodsPooled data from three population-based surveys (N = 12,148) were analysed using multivariable Cox Proportional Hazards regression models.ResultsThe risk of dying within the first 28 days of life was highest in the first week of life (early neonatal period), it then decreases sharply around the middle of the second week of life and remains low over the late neonatal period. Adjusted hazard ratios (HRs) showed that: rural residency (HR = 1.80, 95% CI: 1.15-2.75); birth order 2-3 (HR = 1.63, 95% CI: 1.10-2.42); birth order ≥7 (HR = 1.89, 95% CI: 1.07-3.33) increased the risk of neonatal death. Additionally, children born to women who were obese had higher risk of neonatal death (HR = 1.69, CI: 1.12-2.56) compared with those of women with optimal weight. Disparities in the risk of neonatal death by geographical regions were also found.ConclusionThe risk of neonatal mortality is highest during the first week of life and it is socio-demographically patterned. The findings emphasise the need to tackle socio-demographic risk factors of neonatal mortality in order to achieve the Sustainable Development Goal 3, which is aimed at reducing neonatal mortality to 12 per 1000 live births by the year 2030.

Geospatial analysis of determinants of neonatal mortality in Ghana

BackgroundGhana did not meet the Millennium Development Goal 4 of reducing child mortality by two-thirds and may not meet SDG (2030). There is a need to direct scarce resources to mitigate the impact of the most important risk factors influencing high neonatal deaths. This study applied both spatial and non-spatial regression models to explore the differential impact of environmental, maternal, and child associated risk factors on neonatal deaths in Ghana.MethodsThe study relied on data from the Ghana Demographic and Health Surveys (GDHS) and the Ghana Maternal Health Survey (GMHS) conducted between 1998 and 2017 among 49,908 women of reproductive age and 31,367 children under five (GDHS-1998 = 3298, GDHS-2003 = 3844, GDHS-2008 = 2992, GDHS-2014 = 5884, GMHS-2017 = 15,349). Spatial Autoregressive Models that account for spatial autocorrelation in the data at the cluster-level and non-spatial statistical models with appropriate sampling weight adjustment were used to study factors associated with neonatal deaths, and a p-value less than 0.05 was considered statistically significant.ResultsPopulation density, multiple births, smaller household sizes, high parity, and low birth weight significantly increased the risk of neonatal deaths over the years. Among mothers who had multiple births, the risk of having neonatal deaths was approximately four times as high as the risk of neonatal deaths among mothers who had only single birth [aRR = 3.42, 95% CI: 1.63–7.17, p < 0.05]. Neonates who were perceived by their mothers to be small were at a higher risk of neonatal death compared to very large neonates [aRR = 2.08, 95% CI: 1.19–3.63, p < 0.05]. A unit increase in the number of children born to a woman of reproductive age was associated with a 49% increased risk in neonatal deaths [aRR = 1.49, 95% CI: 1.30–1.69, p < 0.05].ConclusionNeonatal mortality in Ghana remains relatively high, and the factors that predisposed children to neonatal death were birth size that were perceived to be small, low birth weight, higher parity, and multiple births. Improving pregnant women’s nutritional patterns and providing special support to women who have multiple deliveries will reduce neonatal mortality in Ghana.

Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana.

BackgroundOne of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To understand the risk factors of neonatal mortality in Ghana, the current study carefully evaluated and compared the predictive accuracy and performance of two classification models.MethodsThis study reviewed the birth history data collected on 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS). The 2014 GDHS is a cross‐sectional nationally representative household sample survey. The relevant variables were selected using leaps‐and‐bounds method, and the area under curves were compared to evaluate the predictive accuracy of unweighted penalized and weighted single‐level multivariable logistic regression models for predicting neonatal mortality using the 2014 GDHS data.ResultsThe study found neonatal mortality prevalence of 2.8%. A sample of 4514 children born in the 5 years preceding the 2014 GDHS was included in the inferential analysis. The results of the current study show that for the unweighted penalized single‐level multivariable logistic model, there is an increased risk of neonatal death among babies born to mothers who received prenatal care from non‐skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single‐level multivariable logistic regression models was 82% and 80%, respectively.ConclusionThe study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2.

Neonatal mortality in the central districts of Ghana: analysis of community and composition factors

BackgroundCommunities and their composition have an impact on neonatal mortality. However, considering the smallest health administrative units as communities and investigating the impact of these communities and their composition on neonatal mortality in Ghana have not been studied. Therefore, this study aimed to investigate the effect of community-, household- and individual-level factors on the risk of neonatal mortality in two districts in Ghana.MethodsThis was a longitudinal study that used the Kintampo Health and Demographic Surveillance System as a platform to select 30,132 neonatal singletons with 634 deaths. Multilevel cox frailty model was used to examine the effect of community-, household- and individual-level factors on the risk of neonatal mortality.ResultsRegarding individual-level factors, neonates born to mothers with previous adverse pregnancy (aHR = 1.38, 95% CI: 1.05–1.83), neonates whose mothers did not receive tetanus toxoid vaccine (aHR = 1.32, 95% CI: 1.08–1.60) and neonates of mothers with Middle, Junior High School or Junior Secondary School education (aHR = 1.30, 95% CI: 1.02–1.65) compared to mothers without formal education, had a higher risk of neonatal mortality. However, female neonates (aHR = 0.61, 95% CI: 0.51–0.73) and neonates whose mother had secondary education or higher (aHR = 0.37, 95% CI: 0.18–0.75) compared to those with no formal education had a lower risk of mortality. Neonates with longer gestation period (aHR = 0.95, 95% CI: 0.94–0.97) and those who were delivered at home (aHR = 0.56, 95% CI: 0.45–0.70), private maternity home (aHR = 0.45, 95% CI: 0.30–0.68) or health centre/clinic (aHR = 0.40, 95% CI: 0.26–0.60) compared to hospital delivery had lower risk of mortality. Regarding the household-level, neonates belonging to third quintile of the household wealth (aHR = 0.70, 95% CI: 0.52–0.94) and neonates belonging to households with crowded sleeping rooms (aHR = 0.91, 95% CI: 0.85–0.97) had lower risk of mortality.ConclusionThe findings of the study suggest the risk of neonatal mortality at the individual- and household-levels in the Kintampo Districts. Interventions and strategies should be tailored towards the high-risk groups identified in the study.

Can an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?

BackgroundThis study assessed health workers’ adherence to neonatal health protocols before and during the implementation of a mobile health (mHealth) clinical decision-making support system (mCDMSS) that sought to bridge access to neonatal health protocol gap in a low-resource setting.MethodsWe performed a cross-sectional document review within two purposively selected clusters (one poorly-resourced and one well-resourced), from each arm of a cluster-randomized trial at two different time points: before and during the trial. The total trial consisted of 16 clusters randomized into 8 intervention and 8 control clusters to assess the impact of an mCDMSS on neonatal mortality in Ghana. We evaluated health workers’ adherence (expressed as percentages) to birth asphyxia, neonatal jaundice and cord sepsis protocols by reviewing medical records of neonatal in-patients using a checklist. Differences in adherence to neonatal health protocols within and between the study arms were assessed using Wilcoxon rank-sum and permutation tests for each morbidity type. In addition, we tracked concurrent neonatal health improvement activities in the clusters during the 18-month intervention period.ResultsIn the intervention arm, mean adherence was 35.2% (SD = 5.8%) and 43.6% (SD = 27.5%) for asphyxia; 25.0% (SD = 14.8%) and 39.3% (SD = 27.7%) for jaundice; 52.0% (SD = 11.0%) and 75.0% (SD = 21.2%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. In the control arm, mean adherence was 52.9% (SD = 16.4%) and 74.5% (SD = 14.7%) for asphyxia; 45.1% (SD = 12.8%) and 64.6% (SD = 8.2%) for jaundice; 53.8% (SD = 16.0%) and 60.8% (SD = 11.7%) for cord sepsis protocols in the pre-intervention and intervention periods respectively. We observed nonsignificant improvement in protocol adherence in the intervention clusters but significant improvement in protocol adherence in the control clusters. There were 2 concurrent neonatal health improvement activities in the intervention clusters and over 12 in the control clusters during the intervention period.ConclusionWhether mHealth interventions can improve adherence to neonatal health protocols in low-resource settings cannot be ascertained by this study. Neonatal health improvement activities are however likely to improve protocol adherence. Future mHealth evaluations of protocol adherence must account for other concurrent interventions in study contexts.

The effect of an mhealth clinical decision-making support system on neonatal mortality in Ghana

Abstract Background Mhealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310). Results There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47·3% of deliveries and 56·9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4·5 to 6·4 deaths and, from 3·9 to 4·3 deaths per 1,000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was non-significantly higher in the intervention arm compared to the control arm (odds ratio 2·10; 95% CI (0·77;5·77); p = 0·15). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0·71 (p = 0·05). Conclusions Non-significant higher risk of neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mhealth intervention implementation and effects. Key messages Supposedly effective interventions must be evaluated in context before they are scaled-up. Mechanisms influencing outcomes in context must be considered in the design and evaluation of interventions.

Burden of Neonatal Surgical Conditions in Northern Ghana

BackgroundCongenital anomalies have risen to become the fifth leading cause of under-five mortality globally. The majority of deaths and disability occur in low- and middle-income countries including Ghana. This 3-year retrospective review aimed to define, for the first time, the characteristics and outcomes of neonatal surgical conditions in northern Ghana.MethodsA retrospective study was conducted to include all admissions to the Tamale Teaching Hospital (TTH) neonatal intensive care unit (NICU) with surgical conditions between January 2014 and January 2017. Data were collected on demographics, diagnosis and outcomes. Descriptive analysis was performed on all data, and logistic regression was used to predict determinants of neonatal mortality. p < 0.05 was deemed significant.ResultsThree hundred and forty-seven neonates were included. Two hundred and sixty-one (75.2%) were aged 7 days or less at presentation, with males (n = 177, 52%) slightly higher than females (n = 165, 48%). The majority were delivered by spontaneous vaginal delivery (n = 247, 88%); 191 (58%) were born in hospital. Congenital anomalies accounted for 302 (87%) of the neonatal surgical cases and 45 (96%) deaths. The most common anomalies were omphalocele (n = 48, 13.8%), imperforate anus (n = 34, 9.8%), intestinal obstruction (n = 29, 8.4%), spina bifida (n = 26, 7.5%) and hydrocephalus (n = 19, 5.5%). The overall mortality rate was 13.5%. Two-thirds of the deaths (n = 30) from congenital anomalies were conditions involving the digestive system with gastroschisis having the highest mortality of 88%. Omphalocele (n = 11, 23.4%), gastroschisis (n = 7, 14.9%) and imperforate anus (n = 6, 12.8%) contributed to the most deaths. On multivariate analysis, low birthweight was significantly associated with mortality (OR 3.59, CI 1.4–9.5, p = 0.009).ConclusionCongenital anomalies are a major global health problem associated with high neonatal mortality in Ghana. The highest burden in terms of both caseload and mortality is attributed to congenital anomalies involving the digestive system, which should be targeted to improve outcomes.

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

BackgroundMHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. MethodsIn the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310) and Pan African Clinical Trials Registry (PACTR20151200109073). FindingsThere were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47∙3% of deliveries and 56∙9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4∙5 to 6∙4 deaths and, from 3∙9 to 4∙3 deaths per 1000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was 2⋅09 (95% CI (1∙00;4∙38); p = 0∙051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0∙71 (p = 0∙05). InterpretationThe higher risk of institutional neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects. FundingNetherlands Foundation for Scientific Research - WOTRO, Science for Global Development; Utrecht University.

Determinants and the impact of the National Health Insurance on neonatal mortality in Ghana

The national health insurance was established to increase access to health care services and the maternal component was later introduced to improve the health outcomes of both mother and child. The main objectives of this study are to investigate the factors that affect neonatal deaths as well as examine the effect of the Ghana Health Insurance on neonatal deaths in Ghana. Using the most recent round of the Ghana Demographic and Health Survey, the study estimates the probit model with interaction effects to account for the heterogeneity in outcomes. Additionally, the study employs the propensity score matching approach to account for the possible endogeneity in the insurance enrolment decision. Results from the estimations, after controlling for relevant individual and household characteristics suggest that the national health insurance significantly reduces the likelihood of neonatal deaths. Estimates remain consistent even after more robust estimators are employed. Estimates from the interaction between place of residence and health insurance indicate that health insurance beneficiaries who reside in urban areas are at a higher risk of neonatal deaths compared to other women. Access to medical facilities proxied by distance to the nearest health post emerged as an important predictor of neonatal death. The study also suggests significant regional differences in neonatal deaths. We, therefore, conclude that the national health insurance may have the potential to substantially improve the health outcomes of neonates and have policy implications for increasing coverage to more mothers and their neonates, as well as coverage in critical neonatal services and drugs.

Evaluating the Cost-Effectiveness of an Integrated Program to Reduce Maternal and Neonatal Mortality in Ghana

Evaluating the Cost-Effectiveness of an Integrated Program to Reduce Maternal and Neonatal Mortality in Ghana

Individual and community determinants of neonatal mortality in Ghana: a multilevel analysis

BackgroundNeonatal mortality is a global challenge; identification of individual and community determinants associated with it are important for targeted interventions. However in most low and middle income countries (LMICs) including Ghana this problem has not been adequately investigated as the impact of contextual factors remains undetermined despite their significant influence on under-five mortality and morbidity.MethodsBased on a modified conceptual framework for child survival, hierarchical modelling was deployed to examine about 6,900 women, aged 15 – 49 years (level 1), nested within 412 communities (level 2) in Ghana by analysing combined data of the 2003 and 2008 Ghana Demographic and Health Survey. The aim was to identify individual (maternal, paternal, neonatal, antenatal, delivery and postnatal) and community (socioeconomic disadvantage communities) determinants associated with neonatal mortality.ResultsThe results showed both individual and community characteristics to be associated with neonatal mortality. Infants of multiple-gestation [OR 5.30; P-value < 0.001; 95% CI 2.81 – 10.00], neonates with inadequate birth spacing [OR 3.47; P-value < 0.01; 95% CI 1.60 – 7.57] and low birth weight [OR 2.01; P-value < 0.01; 95% CI 1.23 – 3.30] had a lower chance of surviving the neonatal period. Similarly, infants of grand multiparous mothers [OR 2.59; P-value < 0.05; 95% CI 1.03 – 6.49] and non-breastfed infants [OR 142.31; P-value < 0.001; 95% CI 80.19 – 252.54] were more likely to die during neonatal life, whereas adequate utilization of antenatal, delivery and postnatal health services [OR 0.25; P-value < 0.001; 95% CI 0.13 – 0.46] reduced the likelihood of neonatal mortality. Dwelling in a neighbourhood with high socioeconomic deprivation was associated with increased neonatal mortality [OR 3.38; P-value < 0.01; 95% CI 1.42 – 8.04].ConclusionBoth individual and community characteristics show a marked impact on neonatal survival. Implementation of community-based interventions addressing basic education, poverty alleviation, women empowerment and infrastructural development and an increased focus on the continuum-of-care approach in healthcare service will improve neonatal survival.