Factors Associated with Under-5 Mortality in South-South Geopolitical Zone of Nigeria: Evidence from Demographic and Health Survey data

doi:10.21203/rs.3.rs-4017896/v1

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Factors Associated with Under-5 Mortality in South-South Geopolitical Zone of Nigeria: Evidence from Demographic and Health Survey data

https://doi.org/10.21203/rs.3.rs-4017896/v1

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Purpose

Under-5 mortality rates (U5MR) have declined drastically globally. However, it remains high in Nigeria, with variations across the Country’s six geo-political zones. Understanding the factors associated with under-5 mortality (U5M) among lesser-explored subpopulations will inform strategies to curtail U5M. We investigated the factors associated with U5M in the south-south geopolitical zone (SSGZ) of Nigeria and population attributable risk percent (PAR%).

Method

We analysed population-based data from the Nigeria Demographic and Health Survey (NDHS) for 2013 and 2018. The U5MR was determined by survival analysis, and logistic regression analysis was used in SPSS version 26.0 to calculate the Odds Ratios (OR) with 95% Confidence Internals (CI) for U5M associated with various factors, while the PAR% was computed using the Levine’s formula.

Results

The under U5MR for the SSGZ was 73 deaths per 1000 live births. The adjusted logistic regression analysis model showed maternal overweight (OR = 1.39,95% CI:1.12–1.72 p < 0.05), primary education (OR = 2.18. 95%CI:1.18–4.04 p < 0.05) and secondary education (2.05 95%CI:1.13–3.72, p < 0.05) levels, male gender (OR1.22, 95%CI 1..01-1.47,p < 0.05), birth interval of two years (OR = 2.9household (09-4.09, p < 0.001), and small birth size (OR = 2.54, 95% C11.94-3.31, P < 0.001) significantly increased U5M risk, while having seven or more household members (OR = 0.73, 95%CI:0.59–0.89, p < 0.05) and 2 under-five children in the household (OR = 0.66, 95% CI;0.52–0.83, p < 0.001) were protective factors with PAR% ranging from 4.5–47.1%

Conclusion

Increased interventions on maternal weight, maternal education, low birth size, and child spacing could reduce U5M in the SSGZ of Nigeria.

Under-5 mortality

population attributable risk

Nigeria

The mortality of children between 0 and 59 months is an important indicator of global health and social development [1, 2]. The U5MR (number of under-5 deaths per 1000 live births), a proxy for the overall state of paediatric healthcare used to track the results of initiatives to keep children healthy [2] has experienced remarkable progress globally, reducing from 93 deaths per 1000 live births in 1990 to 38 deaths per 1000 live births in 2020 [3, 4, 5] due to global collaboration through Policies and programs [6, 7], changes in economic development, standards of living, social well-being, illness rates, quality and accessibility of medical care, public health practices, and environmental factors [8, 9, 10, 11, 12]. However, the same cannot be said for Nigeria where the U5MR currently stands at 132 deaths per 1000 births [9] with variations across its geopolitical zones (GZ) ranging from 187 deaths per 1000 live births in the northwest to 62 deaths per 1000 live births in the southwest [9] due to socio-economic and cultural inequalities [13, 14].

Globally, conditions such as birth complications, intrapartum-related events, lower respiratory infections, diarrhoea, and other infectious diseases have been implicated as major causes of U5M [4; 15; 16], also several studies in low and middle-income countries (LMIC) have suggested, socioeconomic, household, community, nutritional, environmental, maternal, and child factors to be associated with U5M [17, 18, 19, 20, 21]. However, most of such studies in Nigeria have concentrated on national aggregate data, given the variations in cultural, structural, and socioeconomic development within and across Nigeria's communities, adopting and generalising findings from these studies among GZs could obscure regionally unique health and socioeconomic problems thus providing insufficient evidence for interventional designs for the different GZs. Therefore, to improve U5MR it is necessary to conduct studies that identify risk factors for U5M among Nigeria's disaggregated geographical communities by grouping zones with comparable traits (i.e., socioeconomic, ethnic, cultural, and religious beliefs) together to eliminate reporting discrepancies and enable appropriate intervention designs. Therefore, this study explored the factors associated with U5M in the South-South Geopolitical Zone (SSGZ) of Nigeria and their PAR% using the 2013 & 2018 NDHS. The evidence from this study will guide policies and inform the development of intervention programs.

Study setting and population.

This study is based on data from the 2013 and 2018 NDHS, The population base of this study was live births to respondents of these surveys living within the SSGZ, one of Nigeria's six GZs made up of : Rivers, Akwa Ibom, Bayelsa, Cross River, Delta, and Edo state.

Sources of data

The study employed nationally representative data from the NDHS which are large sample size household surveys implemented by the National Population Commission (NPC) in collaboration with the Federal Ministry of Health Nigeria (FMoH). It employed a two-stage stratified cluster sampling design detailed elsewhere [9, 22] consisting of 904 clusters from which 45 and 30 households were selected in 2013 and 2018 respectively where women aged between 15 and 49 were administered questionnaires to gather information on live births and deaths in the preceding five years. During this period a weighted total of 27,293 live births were reported in the SSGZ, with 12,438 obtained from the 2018 NDHS and 14855 from the 2013 NDHS.

Description of variables

The dependent variable U5M was defined as the death of infants between birth and 59 months of life, while Live births were defined as the complete expulsion of a product of conception from the mother which exhibits signs of life, such as breath, heartbeat, umbilical cord pulsation, or distinct voluntary muscle movement irrespective of the length of pregnancy [23]. The independent variables were socioeconomic and proximate factors identified from the Mosley and Chen framework and previous studies in LMICs [18, 24, 25, 26, 27] adapted to the available datasets.

Data Analysis

All statistical analysis was done on SPSS version 26.0. The distribution of under-fives (U5s) in the SSGZ by survival status and sociodemographic characteristics was summarised using descriptive statistics. The U5MR of the SSGZ was estimated from the 2018 dataset by survival analysis as described by Rutstein and Rojas 2006 [28]. To determine the factors associated with U5M in the SSGZ, the 2013 and 2018 NDHS datasets were pooled, and logistic regression analysis carried out using 3 models to estimate the Odds of mortality at a 95% confidence interval with p-value for statistical significance set at 0.05. In Model 1 univariate (unadjusted) analysis was done for all potential factors, In model 2 multivariate analysis was done for the socioeconomic factors, and proximate factors to adjust for confounding by predictors at the same level of the Mosley and Chen framework, while Model 3 was used to compute the fully adjusted Odd Ratio for each determinant of U5M after accounting for confounding due to proximate and socioeconomic variables together. In model 3 only socioeconomic and proximate factors identified from model 1 to have a statistically significant association with U5M were included. The Population Attributable Risk (PAR)% for factors found to be significantly associated with U5M was computed using the Levine equation which utilised the aOR as an approximation for the relative risk as recommended in literature [29, 30]. The PAR percent and its confidence interval were computed using the formula.

PAR = Pr (aOR-1)

PAR%=$\left[ Pr\times \frac{(aOR-1)}{aOR}\right]$×100

95% CI (%Pop AR) =%PAR ± 1.96 × SE%PAR

Distribution of U5M and U5MR by sociodemographic factors.

A total of 2286 under-5 deaths were recorded with 1304 occurring in males and 982 among females. 30.1% of the sample lived in urban areas while 69.9% lived in rural areas after combining 2013 and 2018 NDHS. Table 1 shows the distribution of under 5 mortalities and U5MR by background characteristics of the study sample. Further details available as Table 1 of supplementary material.

Table 1

Distribution of under 5 mortalities and U5MR by characteristics of the study sample (2013 and 2018 dataset merged)
	Categories	Total participants	Under 5 deaths	U5MR(U5M/1000)
Variable		N	N
Maternal educational level	No education	2834	257	76
	Primary	10073	1001	85
	Secondary	11890	898	67
	Higher	2221	130	53
Wealth index (Income level)	Poorest/poorer	4192	397	85
	Middle	7946	711	77
	Richer/richest	14880	1178	66
Type of place of residence	Urban	8126	597	58
	Rural	18892	1689	79
Mothers age at birth	< 20	5331	535	77
	20–29	14726	1170	72
	30–39	6471	526	67
	40–49	490	55	91
Sex of child	Male	13960	1304	79
	Female	13058	982	65
Birth size	Small/	20953	1950	54
	Average or large	5986	323	53

Under 5 Mortality Rates

Using data from the 2018 NDHS survey which yielded 127,545 live births (51.3% males 48.7% females), with 27,293 live births and 12,317 U5s for the SSG, the national U5MR was determined to be 132 deaths per 1000 live births, while that of the SSGZ was 73 deaths per 1000 live births about 61% and 44.7% less than that of the northwest GZ and the national U5MR. For the U5MR of all GZ see Table 2 of the supplementary material.

Socioeconomic Factors and Under 5 Mortality

As shown in Table 2, results of univariate analysis at the socioeconomic level, showed all individual-level determinants had significantly higher odds of mortality when compared with their reference categories. The odds of U5M were higher (OR = 1.78, 95% CI:1.47–2.14, p < 0.001) amongst mothers with just primary education, and those engaged in manual labour (OR = 1.52, 95%C1:1.25–1.87, p < 0.001), compared to those with higher education and professionals respectively. At the household level U5s in homes with over six family members (OR = 0.86, 95% C1:0.78–0.94, p < 0.05) had reduced odds of mortality while U5s born into the poorest households (OR = 1.22, 95%:1.08-1.37p < 0.05) and rural areas had higher odds of mortality (OR = 1.24, 95% CI:1.12–1.37, p < 0.001).

After adjusting for confounding by other factors at the socioeconomic level (see supplementary material), the association between maternal occupation, sex of household head, wealth index, decision on use of maternal income, place of delivery, religion and U5M was no longer significant. However, the odds of U5M among overweight mothers (OR = 1.28, 95% CI: 1.11–1.48, p < 0.005), and mothers with only primary education increased (OR = 1.93, 95% CI:1.39–2.68 p < 0.0001). When both socioeconomic and proximal factors were included in model 3, the odds of U5M among overweight mothers increased even further (OR = 1.39,95% CI:1.12–1.72 p < 0.05), however, the relationship between obese mothers and U5 mortality was no longer statistically significant. The odds of mortality were increased among U5s born to mothers with primary (OR = 2.18. 95%CI:1.18–4.04 p < 0.05) and secondary education (OR = 2.05 95%CI:1.13–3.72, p p < 0.05), while the odds of U5M in homes with over 7 family members was reduced (OR = 0.73, 95%CI:0.59–0.89, p < 0.05). For further details see supplementary material.

Proximate Factors and Under 5 Mortality

The proximate level univariate analysis (Table 2) showed higher odds of U5M among mother in the 40–49 (OR = 1.427, 95%CI:1.07–1.92 p < 0.0001), less than 20 age groups (OR = 1.26, 95%CI:1.11–1.43, p < 0.05), Males (OR = 1.27, 95%CI:1.16–1.38, P < 0.001), U5s having a small size at birth (OR = 1.80, 95% CI:1.59–2.03, p < 0.001) and U5s born less than 2 years after the preceding birth (OR = 2.44, 95% CI:2.08–2.87, p < 0.001) compared to their reference categories. In model 2, the time of starting breastfeeding, type of toilet facility and mothers' age at birth were found to no longer have a statistically significant association with U5M. However, the odds of U5M among mothers between 40 and 49 years reduced (OR = 0.87,95% CI:0.55–1.38 p > 0.05), while that of mothers less than 20 years old (OR = 1.35, 95% CI:0.90–2.03, p < 0.005) and males increased (see supplementary material).

In the final model 3 (Table 2), the mother's age at birth, time of starting breastfeeding, source of drinking water, type of toilet and type of fuel were found to not have a statistically significant association with U5M. However, males had a 22% higher likelihood of U5M (OR1.22, 95%CI 1..01-1.47,p < 0.05), seventh or above births had a 2.5 fold increased odd of death (OR = 2.52, 95% CI; 1.76–1.59, p < 0.001), U5s with less than 2 years interval from successive births had almost 3 times the odds of mortality (OR = 2.93, 95% CI 2.09–4.09, p < 0.001), and U5s with small birth size had a 154% increased odds of mortality (OR2.54, 95% C11.94-3.31, P < 0.001) when compared to their reference groups. For further details see supplementary material.

Table 2

Crude and adjusted odd ratios, 95% confidence interval for factors related to under-5 mortality in the SSGZ, Nigeria
	Categories	Univariate regression analysis			Multivariate regression analysis
Variable		Unadjusted (Crude estimate) model 1			Adjusted Model 3
		OR	95%CI	P-value	OR	95%CI	P-vales
Socio-economic/distal determinants
Individual level determinants
Body mass index	Underweight	1.28	0.98–1.67	0.067	0.69	0.33–1.46	0.334
	Normal	Ref		0.027			0.010
	Overweight	1.17	1.04–1.04	0.007	1.39	1.13–1.72	0.002
	Obese	1.08	0.94–1.25	0.261	1.17	0.87–1.56	0.307
Maternal educational level	No education	1.60	1.29-2.00	0.000	1.55	0.78–3.06	0.209
	Primary	1.78	1.47–2.14	0.000	2.18	1.18–4.04	0.013
	Secondary	1.31	1.09–1.59	0.005	2.05	1.13–3.72	0.018
	Higher	Ref		0.000			0.024
Maternal occupation (Maternal skill/time)	Not working	1.43	1.13–1.82	0.003
	Agriculture/manual labour	1.52	1.25–1.87	0.000	1.18	0.70–1.98	0.532
	Professional/clerical			0.000			0.134
	Services/domestic/others	1.24	1.03–1.51	0.261	0.95	0.58–1.56	0.837
Household level
Sex of household head	Male	0.91	0.89-1.00	0.051	0.83	0.65–1.07	0.151
	Female	Ref
Wealth index (Income level)	Poorest/poorer	1.22	1.08–1.37	0.001	1.04	0.65–1.07	0.802
	Middle	1.14	1.04–1.26	0.007	1.11	0.88–1.42	0.379
	Richer/richest	Ref		0.001			0.672
Decision marking power1 (use of maternal income)	Mother	Ref
	Others	0.86	0.77–0.96	0.008	0.96	0.79–1.18	0.715
Number of household members	Between 1–6	Ref
	7 and above	0.86	0.78-.94	0.001	0.73	0.59–0.90	0.003
Community level determinants
Type of place of residence	Urban	Ref
	Rural	1.24	1.12–1.37	0.000	0.95	0.78–1.21	0.779
Religion	Christian	0.98	0.73–1.29	0.906	1.70	0.74–3.94	0.215
	Islam	Ref		0.026			0.401
	Traditionalist	0.63	0.42–0.96	0.030	2.01	0.71–5.69	0.188
Proximal determinants
Conception/maternal factors
Mothers age at birth	< 20	1.26	1.11–1.43	0.000	1.35	0.90–2.03	0.143
	20–29	0.98	0.88–1.09	0.651	1.05	0.82–1.34	0.726
	30–39	Ref		0.000			0.474
	40–49	1.43	1.07–1.92	0.017	0.92	0.51–1.67	0.783
Sex of child	Male	1.27	1.16–1.38	0.000	1.22	1.01–1.47	0.043
	Female	Ref
Birth order	1	1.08	0.96–1.20	0.193
	2 to 3	Ref		0.000			0.000
	4 to 6	1.14	1.03–1.28	0.016	1.35	1.07–1.71	0.011
	7+	1.99	1.72–2.31	0.000	2.52	1.76–3.60	0.000
Birth interval	< 2	2.44	2.08–2.87	0.000	2.93	2.10–4.10	0.000
	2	1.56	1.33–1.84	0.000	1.87	1.34–2.62	0.000
	3	1.25	1.03–1.52	0.026	1.53	1.04–2.24	0.300
	4+	Ref		0.000
Birth size	Small/very small	1.80	1.59–2.03	0.000	2.54	1.94–3.31	0.000
	Average or large	Ref
Number of children under 5 in household	1	Ref		0.000			0.002
	2	0.78	0.69–0.87	0.000	0.66	0.52–0.83	0.000
	3 and above	0.70	0.70–0.81	0.000	0.80	0.61–1.05	0.111
Environmental contamination & Sanitation
Source of drinking water	Improved	Ref		0.000			0.558
	Unimproved	1.22	1.12–1.34	0.000	1.07	0.86–1.33	0.566
Type of toilet	Improved	Ref		0.011			0.228
	Unimproved	1.09	1.00-1.19	0.042	0.87	0.70–1.09	0.228
Type of cooking fuel	Clean	Ref		0.000			0.411
	Polluting	1.77	1.43–2.18	0.000	0.81	0.50–1.33	0.411

Foot notes

in model 2 socio economic factors (BMI, maternal education level, maternal occupation, sex of house hold head, wealth index, maternal use of healthcare, maternal use of income, number of house hold members, place of residence, place of delivery and religion) were analysed together, while proximate factors (mothers age at birth, sex of child, birth order, birth interval, birth size number of U5s in household, time put to breast, ever vaccinated, source of drinking water, type of toilet, type of cooking fuel) were also analysed to adjust for confounding at the same level. However in model 3 factors found to be significant at both socioeconomic and proximal level (BMI, maternal education level, maternal occupation, sex of house hold head, wealth index, maternal use of income, number of house hold members, place of residence, religion, mothers age at birth, sex of child, birth order, birth interval, birth size number of U5s in household, time put to breast, source of drinking water, type of toilet, type of cooking fuel were analysed to adjust for confounding by factors at both levels.

Population Attributable Risk

The attributable risk which reflects the anticipated decline in mortality if the factor is eliminated [29] is presented in the supplementary material. It shows that 8.28% (95% CI 5.53%-11.01%) of increased U5M in the SSGZ was attributable to overweight mothers, 22.52% (C1;20.82–24.22) attributable to mothers with secondary education 20.18% (95%CI; 17.24–23.12) to primary education, 47.12% (95% CI 37.77–59.47) of U5M was attributable to small birth size among U5s. However, a 19.40% (CI -15.68—23.12) reduction in U5M was attributable to having two under U5s in the household.

Findings from this study showed, that although U5MR in the SSGZ (73 per 1000 live births) was less than the national U5MR (132 per 1000 live births), it was however higher than the global U5MR (38 deaths per 1000 live births) which indicates a need for further interventions to meet the sustainable development goals target of 25 deaths per 1000 live births. The study identified maternal overweight, maternal primary and secondary education levels, male gender, high birth order, and small birth size as significantly associated with increased odds of U5M, while ≥ 7 household members and having 2 U5s in the household were protective. Of the factors identified, low birth size alone accounted for over 47% of the U5M risks. These findings emphasise the role of socioeconomic and proximate level factors in elevating U5M in SSGZ, Nigeria, and support the Mosley and Chen's framework. After adjusting for confounding in model 2 the modification to odds ratio suggests that multiple socioeconomic factors interact to influence the magnitude of the U5M risk, while the change in odds of mortality for socioeconomic factors in the presence of proximate factors support Mosley and Chen’s idea that socioeconomic factors exert their effect on U5M through proximate factors [24].

Though the evidence from this study has not been reported previously, the difference in the U5MR of the SSGZ from the national rates and by sociodemographic characteristics supports the suggestions by researchers [18, 31, 32, 33, & 34] that variations exist in U5MR by GZ and sociodemographic characteristics. This reduced U5MR in the SSGZ compared to other regions could be explained by the differences in economic, climatic, and social conditions across the GZs of Nigeria. The SSGZ is home to major industrial centres and receives the largest budgetary allocation increasing resources available to provide basic amenities [35] and has a higher average human development index (0.635) [36] which culminates in a higher survival rate. It also has a longer rainy season and higher precipitation with relatively milder temperatures promoting the variety and availability of food year-round. Finally, the majority of SSGZ residents are Christians which is less restrictive on mothers’ autonomy, access to education, contraceptives, and employment outside the home that could impact their knowledge of appropriate childcare practices, and access to resources. However, when compared to global U5MR the higher U5MR may be explained by the higher level of poverty in Nigeria where 40.1% of the population, live on less than $1.90 per day, [37] and fewer healthcare workers for the population density (1.55 health worker per 1000 people) less than the recommendation of the World Health Organization (WHO) of 4.45 per 1000 [38]. It is also affected by environmental degradation from crude oil spillage and pollution which have been implicated in the poor living conditions linked with child mortalities [39].

The association between maternal education and U5M for the SSGZ was a departure from the findings of other researchers [17, 40, 41, 42]. The increased odds of U5M among mothers with primary or secondary education could be due to a combination of poverty and poor quality of education. Inadequate funding for education (5.4% of the annual budget), poor policy implementation and regulation, and corruption negatively affects the quality and quantity of educational resources in Nigeria [43]. The low level of participation of women in the labour force (48.4%) [44, 45] due to high unemployment and the traditional role of mothers as homemakers may result in a lack of resources to implement knowledge to improve child survival. Also, women with a higher level of education are less likely to engage in certain types of jobs, increasing unemployment among this group [46].

The increased odds of U5M among overweight mothers is consistent with the findings of Creswell et al (2012) [47] but contradict Khan & Awan (2017) [48]. Maternal overweight increases the risk of negative health outcomes for newborn through complications such as pre-eclampsia and diabetes which increase the risk for birth complications, restricted intrauterine growth, congenital anomalies, and birth trauma [49, 50], which is further exacerbated by the unavailability of facilities and resources to manage high-risk births. Also, mothers are the major decision-makers for family nutrition, thus poor nutritional habits may translate to poor nutritional outcomes for U5s. The reduced odds of U5M in households with over seven members mirrored the findings of some researchers [51, 52] but contradicted those of others [53, 54, 55]. This finding could be because the SSGZ like most African communities is communal in nature, and childcare is seen as a community responsibility thus large family sizes are a source of sustenance, knowledge, care, and supervision [56, 57].

The increased odds of U5M among males were similar to the findings of other researchers [20, 26, 17]. This may be attributed to the effects of genetics which result in delayed foetal lung maturation in boys increasing the incidence of respiratory illness and the inhibitory effect of the male hormones on the immune system [58]. Furthermore, the association of U5M with high birth order and short birth interval found in this study is consistent with the findings of past literature [20, 32, 19], and may be explained by maternal reproductive health, parent-child behaviour, and sibling relationships. Women require time to recover after childbirth, however, short intervals between successive pregnancies may result in poor uterine healing, anaemia, and cervical insufficiency hampering the ability to sustain foetal growth increasing the risk for preterm births, foetal malnutrition, infection, and death [59, 60]. A short birth interval may also indicate that a mother does not breastfeed extensively depriving her children of the nutritional and immunological benefits of breastmilk. There may be fewer maternal and household resources available for each successive pregnancy, and higher-order pregnancies are more likely to be unintended conceptions and are linked to a late start of antenatal care and vaccinations [61]. Also, a higher number of older siblings increases the exposure of U5s to communicable infections and developmentally inappropriate activities [62, 63].

The increased odds of mortality for U5s who were smaller at birth were similar to the findings of available literature [25, 26, 31;20; 64]. However, of the factors identified in this study, low birth size alone accounted for over 47% of the U5M risks. Children born with a low birth weight are at a higher risk of morbidity and mortality due to malnutrition, susceptibility to infections, respiratory distress, birth trauma, growth failure, developmental delay, and the development of chronic non-communicable diseases [65; 66]. The reduced odds of U5M in households with two U5s supports the findings of previous studies [24, 40]. Mothers of other U5s might have learned and experienced parenting through time, which could account for the protective effect of this characteristic on child survival, or the presence of multiple mothers with U5s in the same household gives these mothers the chance to support and learn from one another's experiences, as well as share health resources and information.

The results of the PAR% for children born to overweight mothers indicate a change of overweight BMI to within the optimal range would eliminate about 8% of U5M among children born to this group of mothers, while 47.12% (95% CI 37.77% -59.47%) of U5 deaths could be prevented if interventions could prevent small birth size. However, a 19.40% (15.68%-23.12%) reduction in U5M is attributed to having two U5s within a household, but not all these characteristics are amenable to public health interventions.

Strengths and Limitations

To our knowledge, this is the only study on U5M focused on the SSGZ of Nigeria which also provides information on PAR thus providing data relevant to this region. It also used a pooled dataset, and categories increasing its statistical power and made it possible to detect significant associations. However, the quality of the findings may have been undermined because of the retrospective nature of the information collected by the NDHS, also, the unknown confounding effect of variables not considered cannot be accounted for.

Findings from this study showed that mothers being overweight, maternal primary education level and secondary education, male gender, high birth order, and small birth size were significantly associated with increased odds of U5M, while household with seven and above members, and 2 children under five years old in the household were protective against U5M in the SSGZ. The population attributable risk percent (PAR%) findings from this study indicate that policies and strategies to address maternal weight, maternal education, and child spacing could reduce the risk of U5M in SSGZ.

AOR Adjusted Odds Ratio

FMoH Federal ministry of health Nigeria

GZ Geopolitical Zone

LMIC Low- and Middle-Income Countries

NDHS National Demographic and Health Survey

NPC National Population commission

OR Odds Ratio

PAR Population Attributable Risk

SSGZ South-South Geopolitical Zone

U5M Under 5 Mortality

U5MR Under 5 Mortality Rate

WHO World Health Organization

Acknowledgments: The authors would like to acknowledge the contribution to this study from the Demographic and Health Survey Program ICF International, Rockville, MD, USA, for the permission to use the 2013 and 2018 NDHS data sets

Supplementary Information: Information not captured in this manuscript is available in the supplementary material.

Declarations

Funding: No funds, grants, or other support was received.

Competing interest: The authors declare they have no conflict of interest, whether financial or non-financial.

Author contributions I.A and I.D contributed to the study conception and design. The study methodology was developed by I.A and I.D. The formal analysis was conducted by I.A. I.D supervised the study. The First draft of the manuscript was written by I.A, and I.D reviewed and approved the Final manuscript.

Data Availability

The raw dataset used for this study can be accessed from the DHS website. (https://dhsprogram.com)

Ethics approval Ethical clearance and approval were obtained from the Sheffield Hallam University Research Ethics Committee of the College of Social Sciences and Arts, Sheffield Hallam University, and the Demographic Health Survey Program/ICF granted access for the use of the DHS data for study after submitting a formal request. The National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board examined and approved the survey protocol for the NDHS.

Consent to participate: This study is based on secondary analysis of NDHS dataset, and DHS informed consent was obtained from all study participants.

Consent for Publication: Not applicable.

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Factors Associated with Under-5 Mortality in South-South Geopolitical Zone of Nigeria: Evidence from Demographic and Health Survey data

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Abstract

Introduction

Methods

Data Analysis

PAR%=\(\left[ Pr\times \frac{(aOR-1)}{aOR}\right]\)×100

Result

Under 5 Mortality Rates

Socioeconomic Factors and Under 5 Mortality

Proximate Factors and Under 5 Mortality

Population Attributable Risk

Discussion

Strengths and Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1