Risk
factors for infant mortality in rural and urban Nigeria: evidence from the
national household survey
Emmanuel
O Adewuyi1, 2* Yun Zhao1 Reeta Lamichhane1
This is the accepted version. Access
the published version on the Scandinavian Journal of Public Health website: http://journals.sagepub.com/doi/abs/10.1177/1403494817696599?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed.
Cite as:
Adewuyi EO, Zhao Y and Lamichhane R. Risk factors for infant mortality in rural
and urban Nigeria: evidence from the national household survey. Scand J
Public Health, 2017. doi: 1403494817696599 (Epub ahead of print).
1 Department of Epidemiology and Biostatistics, School
of Public Health, Faculty of Health Sciences, Curtin University, Bentley
Campus, 6102, Perth, WA, Australia.
2 Federal Ministry
of Defense, 2 Division Hospital, Adekunle Fajuyi Cantonment, Ojoo Area, Ibadan,
Nigeria.
* Correspondence:
Emmanuel Adewuyi. Email: e.adewuyi@postgrad.curtin.edu.au
Abstract
Aims: This
study investigates the rural-urban differences in infant mortality rates (IMR)
and the associated risk factors in Nigeria.
Methods: The
dataset from the 2013 Nigeria demographic
and health survey (NDHS), disaggregated by rural-urban residence was analyzed using complex samples statistics. A multivariable
logistic regression analysis was computed to explore the adjusted relationship
and identify risk factors for infant mortality.
Results: In
rural and urban Nigeria, IMR were 70 and 49 deaths per 1000 live births,
respectively. Risk factors in rural residence were past maternal marital union
(Adjusted OR [AOR]: 1.625, P = 0.020); small birth size
(AOR: 1.550, P < 0.001); birth interval <24 2.057="" months="" p="" span=""> < 0.001); residence in
North-East (AOR: 1.346, P = 0.038), and North-West
(AOR: 1.653, P < 0.001) regions and caesarean
delivery (AOR: 2.922, P = 0.001). Risk factors in urban residence were poor
wealth index (AOR: 2.292, P < 0.001);
small birth size (AOR: 2.276, P < 0.001); male gender
(AOR: 1.416, P = 0.022); birth interval <24 months="" span=""> 1.605,
P=
0.002);
maternal obesity (AOR: 1.641, P = 0.008); and caesarean delivery (AOR: 1.947, P
=
0.032).
Conclusions: Infants
in rural residence had higher rates of mortality than their urban counterparts
and disparities in risk factors exist between the residences.
Keywords:
Determinants,
infant mortality, maternal obesity, mode of delivery, Nigeria, risk factors, rural-urban
disparities in Nigeria.
Background
Through the millennium
development goals (MDG), a significant reduction in infant mortality rate (IMR)
has been recorded, worldwide [1]. To demonstrate
the impressive progress made through such a global initiative, reports indicate
that infant mortality fell from an estimated 8.9 million in 1990 to
approximately 4.5 million in 2015 [1]. In all, 62
countries, including 12 in the low-income category, realized MDG 4: two-third
reduction in under-five mortality (U5M) rate by 2015 [1]. This substantial
progress was made possible through the implementation of innovative,
evidence-based and context-specific programs. For instance, the scale-up of cost-effective interventions like vaccination,
HIV control, obstetric care, nutritional support, and integrated management of
diseases like diarrhea and pneumonia have
proven effective in many countries – Malawi, Ethiopia and so on [1-3].
Like many countries
around the world, Nigeria has implemented a number of programs including the
Midwives Service Scheme, and the Ward Health System, all aimed at improving
child survival chances in the country [4]. These
interventions notwithstanding, the country maintains one of the highest numbers of under-five and infant mortalities in
the world [1]. Unarguably, IMR has reduced substantially over
the years in Nigeria – from 126 deaths per 1000 live births in 1990, to 69
deaths per 1000 live births in 2015, representing about 45% decreased mortality
[1]. However, with
over 53% contribution to the rate of U5M, the burden of infant mortality remains
considerably high in Nigeria [5, 6]. Going by the current
rate of 69/1000 live births, one in every fifteen children in Nigeria dies
before completing one year of existence
[4]. It is no
surprise, therefore, that the country did not realize
MDG 4 by the set deadline of 2015.
With the recent commencement
of the sustainable development goals (SDG), there is a critical need for an accelerated
reduction in the rate of infant and, hence, U5M in Nigeria. SDG 3.2, with an
ambitious aim of consolidating on the gains of MDG 4, seeks to achieve a global
U5M rate as low as 25 deaths per 1000 live births by the year 2030 [1]. To be on track
for this target, Nigeria requires a two to three-fold increased reduction in U5M
rate [1], and one of the
priority focus should be on improving infant survival. The need for a speedy
reduction of IMR in Nigeria is equally critical given that infant mortality is
often used to judge the level of socioeconomic
development of a nation [4]. Although the necessity for a greater attention on neonatal
mortality is being canvassed, globally [7-9]; a
holistic approach that accords infant and other childhood survival a
commensurate priority is key to a speedy realization of this goal.
Reducing IMR, and realizing SDG 3.2 in Nigeria, as in any other
country, requires a greater prioritization
of the most disadvantaged populations through an unwavering commitment to
equity in intervention coverage [1]. In light of this
premise, there is a growing consensus on using high quality disaggregated data in identifying and eliminating disparities in
child survival [1, 10]. Disaggregating
childhood mortality studies along rural-urban residence, for instance, may help
in capturing the most disadvantaged groups alongside the associated risk
factors which otherwise could be lost to the use of a ‘one-size-fits-all approach’ of pooled datasets. In support of this
position, a recent Nigerian study reported a lack
of ‘access to electricity’ as a significant risk factor for neonatal mortality
only in urban residence [9]; whereas the same factor did not make any statistical difference
in the rural [9] and in the
overall residence [8]. Brazil, which
has already achieved SDG 3.2 represents a striking example of using
evidence-based data disaggregation approach in closing childhood survival
equity gaps [1].
Regrettably, the use of
disaggregated data in infant mortality studies is limited in Nigeria. Hence,
gaps in knowledge exist on the rates and risk factors for infant mortality in
rural and urban Nigeria. By exploring the latest available evidence, this study aims to bridge the gaps. To the best of knowledge, this is the first study
to investigate the determinants of infant mortality in rural and urban Nigeria
using a disaggregated dataset that is nationally representative. Given the
current economic challenges in Nigeria, this study may help in priority setting
and in designing target-specific interventions aimed at addressing infant
mortality in the country.
Aims
To investigate IMR and
the associated risk factors in Nigeria with a special focus on the differences
between the rural and urban residence.
Methods
Data sources
The data
analyzed in this study was from the 2013 edition of the Nigerian demographic
and health survey (NDHS), a public domain data that are freely available online
with permission from ICF International. The survey which was cross-sectional in
design is nationally representative of the Nigerian population. The survey was
implemented by the Nigerian population commission with support from many
development partners including technical assistance from ICF International, USA
[4]. The major objective of the survey was the provision of
an up-to-date information on nutritional status of children and women, family
planning awareness, maternal and childhood mortalities, among other factors [4]. There have been four previous editions of NDHS: 1990,
1999, 2003 and 2008, before the 2013 which is the latest and most current in
the series.
The 2013
NDHS applied a three-stage stratified cluster sampling technique for sample
selection. There was a total of 904 clusters in the design for the survey – 532
in rural areas and 372 in urban residence [4]. Men and women were eligible for interview if they
were aged 15 to 49 years, willing and had resided in the selected households
for at least one night before the survey [4]. Structured questionnaires adapted from the Measure
Demographic and Health Survey program were the instruments for data collection.
The questionnaires were of three types – households’, women’s and men’s – developed initially in English language but
later translated into the three major Nigerian languages (Hausa, Igbo, and
Yoruba). The questionnaires were thereafter pretested and used for the survey by
the trained interviewers [4].
Out of
40 680 representative households selected for the survey, 38 904 were occupied
as at fieldwork time (22 834 in rural residence and 16 070 in urban residence).
Of the households found to be occupied, a total of 38 522 , consisting of 22
663 in rural areas and 15 859 in urban areas was interviewed successfully,
representing a 99% households’ response rate – 99.3% in rural residence and
98.7% in urban residence [4]. A comprehensive information on the sampling techniques
and the setting for the survey has previously being published [4]. Information on births and deaths of children within
five years preceding the 2013 NDHS was analyzed in this study. Mortality data
were self-reported and analyses were restricted to the available information on
singleton live births. Multiple births may produce misleading results as they
are associated with increased risk of infant mortality [11],
hence, they were excluded in analyses.
Study variables
The main outcome variable for this
study was infant mortality, defined as the probability of dying before the
first birthday [4], and expressed as
the number of deaths per 1000 live births. Children who died before completing
their first year of existence were compared to those who survived the same
period of time. Explanatory variables were selected based on Mosley and Chen’s conceptual
framework [12], slightly
modified cognizant of the available information in NDHS dataset. Three categories
of variables were investigated as follows: distal (socioeconomic), proximate (bio-demographic)
and intermediate (health/behavioral [corresponds
to Mosley and Chen’s “Personal Illness Control”]). This classification agrees
with practice in previous studies [8, 9, 13], and is supported by the method of
data collection used in NDHS – at households and individual levels [4].
Socioeconomic
variables were further classified into three – households, maternal and paternal
(partner’s) factors. Households socioeconomic factors comprised of wealth index,
the composite indicator of socioeconomic status reported in the 2013 NDHS which
was recoded from five to three categories (poor = “poorest” and “poorer”,
middle = “middle”, rich = “richer” and “richest”). The new categorization gives
a better reflection of the socioeconomic status classification in Nigeria. .
Other household socioeconomic factors assessed included: cooking fuel, toilet
facility, access to electricity, and drinking water source [9]. Maternal socioeconomic factors, on the other
hand, were: maternal education level, literacy, and working status
(occupation). Paternal education level and occupation were sub-grouped under
the paternal socioeconomic factors.
Bio-demographic
(proximate) factors were likewise sub-divided into demographic (maternal
marital status, religion, residence, and region of residence) as well as
biological factors (maternal age, body mass index [BMI], age at first birth,
child’s birth order, child’s birth size [a proxy for birthweight], gender of child,
and preceding birth interval) [9]. Last,
health/behavioral (intermediate) factors were sub-divided into pre-delivery
(desire for pregnancy and antenatal care attendance), delivery (place of
delivery, delivery assistance and mode of delivery) and post-delivery
(breastfeeding initiation)[9].
Statistical analysis
To assess the unadjusted
association of each variable with IMR, frequency tabulation and Chi-square (χ2)
test were carried out. A simple logistic regression analysis (SLR) was
conducted to examine the unadjusted
likelihood of dying within one year of life, expressed as crude odds ratio
(COR). In line with the recommended hierarchical approach
[14],
a
multivariable logistic regression analysis was conducted to identify factors associated
with infant mortality. This approach permits
the assessment of distal variables with appropriate adjustment for proximate
and intermediate variables [14] .
To be selected for multivariable model building, variables must satisfy the inclusion criterion of P < 0.20 in the SLR. Three sets of parsimonious models (I, II, III) were built separately for data disaggregated by rural-urban residence. Model I assessed the adjusted relationship between the outcome variable and socioeconomic factors; and, variables associated with the outcome at the 10% significance level (P <0 .1="" 0.01="" 0.05="" 10="" 5="" 95="" a="" along="" and="" at="" backward="" behavioral="" bio-demographic="" br="" built="" combined="" confidence="" elimination="" for="" health="" i="" ii="" iii="" in="" interval="" level="" method="" model.="" model="" next="" obtaining="" p="" parsimonious="" ratained="" reported="" retained="" significance="" significant="" similarly="" subsequent="" the="" their="" used="" values.="" variables.="" variables="" was="" were="" with="">
Collinearity was checked using
the variance inflation factors (VIF). ‘Maternal education level’ and ‘maternal
literacy level’ were strongly correlated, hence, ‘maternal literacy level’ was
excluded in multivariable analyses. Also, ‘antenatal attendance’ was not
included in multivariable models because a substantial part of its information
was missing. Given the multi-stage
cluster sampling technique used in NDHS, all statistical analyses were computed
using the complex samples statistics. This statistical method adjusts for the
unequal probability selection by incorporating the sample design into the
survey analysis such that results are representative and bias in estimates are minimized. All data management and analyses
were performed using IBM SPSS Statistics for Windows, version 21.0 (Released
2012; IBM, Armonk, NY, USA).
Results
Profile of the study
population, IMR and SLR
Table 1 presents the
background characteristics of the study populations (rural and urban) together
with the IMR. In rural and urban residences, IMR were 70 and 49 per 1000 live
births, respectively (P < 0.001). Generally, urban residents fared better in
all of the study variables. For instance, 63.70% of women in rural residence had
no education compared to 22.40% in urban residence. Similarly, more women in
urban residence (67%) had access to skilled delivery services than those in the
rural residence (24.8%). Table 2 presents the result of the unadjusted
relationship between outcome and explanatory variables. In the SLR, several factors were found to be associated
with infant mortality both in rural and urban residence. Also, using the SLR,
the likelihood of infant mortality (based on the overall 2013 NDHS data) was
1.45 times higher in rural compared to urban residence (P < 0.001) [result not
shown on Table].
Risk factors for infant
mortality in rural and urban residence
Based on the results of the
multivariable models disaggregated by rural-urban residence (Table 3), no
socioeconomic factor was statistically significant as a risk factor for infant mortality in rural Nigeria. However, two
bio-demographic factors, maternal marital status and region of residence, were
significantly associated with infant mortality in the (rural) residence only.
Rural infants whose mothers were formerly but no longer married had 62.5%
increased risk of mortality compared to
those whose mothers were married or cohabiting (Table 3). Also, infants whose
mothers resided in the North-East and North-West regions had 34.6% and 65.3% increased
risk of mortality, respectively, compared to their counterparts in the
North-Central region of the country.
In urban residence, wealth index, a proxy for
socioeconomic status, was significantly associated with infant mortality. Based
on this result, poor households had 2.29 times increased risk of infant mortality compared to households
in the rich wealth index. Further, maternal obesity was associated with 64.1%
increased infant mortality risk while ‘maternal underweight’ was protective,
reducing infant mortality risk by 63.9% (Table 3) only in urban residence. Also,
male infants in urban residence were nearly 42% more at risk of mortality than
their female counterparts.
Regardless of residence
type (rural or urban), three factors – small birth size, preceding birth
interval < 24 months and caesarean delivery – attained statistical
significance as risk factors for infant mortality. Compared to those perceived
as having large birth size, infants reported as having small birth size had
1.55 times and nearly 2.3 times increased mortality risk in rural and urban
residences, respectively. Similarly, preceding birth interval < 24 months
was associated with about two folds increased risk of infant mortality in rural
residence and about 60% increased risk of mortality in urban residence. Last, infants
delivered through a caesarean section had increased likelihood of mortality
compared to those with a non-caesarean delivery, whether in rural or urban
residence (Table 3).
Discussion
From the results of the multivariable analyses, three factors – small
birth size, preceding birth interval < 24 months and caesarean mode of delivery – were significantly associated with infant
mortality, irrespective of rural-urban residence. Other risk factors differ
considerably with residence type. These included poor wealth index, maternal
obesity and male gender in urban residence; as well as maternal marital status
and the region of residence in rural residence. These findings support the
importance of disaggregated studies/data for identifying population-specific differences
in the rates and determinants of infant mortality.
In urban residence, infants from poor households were more at risk
of mortality than their counterparts in the middle wealth index category, showing that low socioeconomic
status was a significant risk for infant
mortality in urban Nigeria. This survival disadvantage in poor urban households
is comparable to the high infant mortality rates
in rural residence (Tables 1). While, contrary to expectation, wealth index did
not attain statistical significance as a
predictor of infant mortality in rural residence,
the results of IMR (Table 1) indicate that infants living in urban areas (IMR =
49 deaths per 1000 live births) generally had lower rate of mortality than their rural
counterparts (IMR = 70 deaths per 1000 live births, P < 0.001). This finding
agrees with trends in studies showing that disparities
exist in childhood mortalities across socioeconomic and geographic devides [8, 15], in this instance, rural-urban, with greater risks
for rural infants. The finding may be blamed on several factors
including inequities in intervention coverage between rural and urban residence
as well as between poor and rich households [6, 15, 16]. Similar to those in poor households, infants
born in rural areas are often disadvantaged by socioeconomic factors – access
to safe drinking water, improved toilet facility, electricity, among others
factors [1, 4]. This is
particularly likely in Nigeria given the high poverty and rural-urban migration
(a possible perception of better living conditions in urban areas) level in the
country [17]. Also, compared
to urban Nigeria, access to healthcare services is poorer in many rural
communities, consequent upon ill-equipped facilities, traditional
practices/beliefs, distance barriers, and inadequately
skilled workers [4, 17, 18].
Interestingly, the
effects of all socioeconomic factors
disappeared in rural residence following adjustment for other
factors/confounders (Table 3). The result is similar to the finding of a study on neonatal mortality where the impacts of all
socioeconomic factors were lost in rural Nigeria following adjustment for other
factors [9]. This may be due
to the masking effect of ‘maternal marital status’ and the ‘region of
residence’ which were overwhelmingly significant in rural residence (Table 3).
It is equally possible that the disparities in the region of residence found in rural Nigeria had accounted for the
impact of some of the factors, especially, those in the socioeconomic category.
The statistical significance of some of the factors in the SLR (without
adjustment) [Table 2] which was lost in the multivariable analysis (with
adjustment) may be an evidence in support of this premise.
Infants in rural
residence whose mothers were married or cohabiting
were found to have a lower risk of
mortality compared to those whose mothers were formerly married. This finding
is consistent with the result of a study in rural Ghana indicating that marital
union was protective against childhood
mortality [18]. The emotional
and financial support that marital relationship affords in a developing country
like Nigeria may explain the present result [8, 18]. The fact that infants whose mothers
were previously but no longer married – divorcees, widows, and the separated –
had the greater risk of mortality lends credence to this argument. Women in the
named categories would be expected to experience greater psychological and/or
financial difficulties than their colleagues in a stable marriage, and this may contribute in some ways to making
their infants more vulnerable. This is more so as marriage is universally
perceived as a social and economic security in Nigeria [4].
Similar to the findings
in other studies, infants with small birth size (a substitute for low
birthweight) had higher risks of mortality
in this study [8, 16, 19], regardless of the residence type. Genetic
factors, malnutrition in pregnancy, as well as obstetric factors have largely
been blamed for this result [9, 20]. Intra-uterine
fetal monitoring and nutrition support services in pregnancy are possible
interventions for this finding [9, 20]. Also, preceding
birth interval < 24 months was a striking
risk factor in all residences (Table 3). This finding agrees with other studies
and may be explained by the exhaustion of maternal biological resources (theory
of maternal depletion syndrome) as well as possible competition among siblings
for attention and resources [5, 8, 19]. The impact of short birth interval
(< 24 months) on infant mortality as found in this study, however, differs
between the residences with greater risks in rural Nigeria. This possibly reflects
lesser knowledge and poorer use of family planning services among rural women in
Nigeria. With evidence showing an overall contraceptive prevalence of 16% –
nine percent in rural areas and 23 percent in urban residence – family planning
services are poorly utilized in Nigeria [4]. Interventions
focusing on improved use of family
planning services, therefore, may prove useful in speeding up the reduction of
IMR in the country. The higher mortality risk found among male infants is
equally consistent with previous studies [5, 8, 13]. This has been linked with the
delayed maturation of fetal lungs in male newborns predisposing them to a greater likelihood of respiratory tract
infections [5, 8, 13].
Further, maternal BMI was
significantly associated with infant mortality in urban residence, and while obesity assumed statistical significance
as a risk factor, maternal underweight was protective. This protective role
does not agree with the popular opinion
in the literature [21], however,
a mixed effect of this variable has been reported in respect of neonatal outcomes
[22, 23]. In any case, there is evidence that maternal obesity
increases the risk of pregnancy-related complications such as stillbirth,
preeclampsia, gestational hypertension, preterm delivery, low birthweight, cesarean
section, congenital abnormalities and so on [24, 25].
These may contribute to the increased risks of infant
mortality among obese
mothers.
The finding that residence
in the North-West and the North-East regions of Nigeria increased the risks of
infant mortality in rural residence may be connected in some ways with the on-going insurgency in parts of the named regions
[26]. This is
particularly likely in the North-East considering that survey did not hold in
six clusters of the region (four in Borno and two in Yobe state) due to
security reason [4]. In light of this
report, infant mortality may have been underestimated in the North-East region;
and this limitation needs to be considered when interpreting the results of
this study. Other contributory factors would be the impact of low level of
socioeconomic development, education, breastfeeding, and utilization of health services in northern Nigeria [4, 27, 28]; especially, in the rural residence
of the North-East region which for years has been the epicenter of insurgency [26].
Last, caesarean delivery was associated with
increased risk of infant mortality both in rural
and urban residences. This result is consistent with previous
studies reporting an association between cesarean
delivery and infant mortality [5, 29] . In the Nigerian
context, emergency caesarean section in women with life-threatening complications, possibly evidenced by the low uptake of the obstetric
intervention (Table 1), have been linked
with this finding [5, 9, 30]. The low uptake of cesarean section in the country may be due to
misconceptions about the mode of delivery among Nigerian women [5, 30]. However, as suggested by Adewuyi & Zhao [9] in respect of
neonatal mortality, the high cost of the obstetric intervention in the country
could be a contributory factor. While cesarean
delivery is known to be lifesaving,
particularly, in complicated pregnancies, its possible contribution to late
breastfeeding initiation, shortening of
gestation duration, and the practice of emergency caesarean section (due to late presentation), among other factors
could limit or reverse its beneficial effects on infants’ survival [29].
Strengths
and limitations
One remarkable strength
of this study is the large sample size and the national representativeness of
the dataset used; hence, data disaggregation
does not undermine generalizability of estimates. High response rate, low
missing data, rural-urban disaggregation, and application of complex samples
statistics in all analyses are some of the other strengths. A few limitations,
however, need to be considered when the results of this study are being
interpreted. First, the cross-sectional design does not allow estimation of causality. Second, obstetric complications, antenatal
attendance, postnatal care, and small for gestational age were not assessed/not
included in the multivariable models due to either substantially missing
information or their non-availability in the NDHS dataset. Third, underestimation
of infant mortality is possible given that only surviving
women participated in NDHS. Fourth, underestimation of infant mortality may
also occur due to recall bias since estimates were based on retrospective birth
histories. Last, there was heaping of mortalities at 12th month of age in the 2013 NDHS; and this may result
in a slight underestimation of infant mortality [4].
Conclusions
and recommendations
IMRs were 70 and 49 deaths
per 1000 live births in rural and urban residence, respectively. This reveals
the existence of disparities in the rate of infant mortality in rural and urban
Nigeria; and indicates that rural infants were more at risk of mortality.
Similar disparities in risk factors for infant mortality were observed in the
two residences. Hence, interventions aimed at speeding up the reduction of IMR
in Nigeria would need to prioritize
findings in this study. First, rural infants, generally, and, infants in the
rural North-East and North-West regions, in particular, should be accorded a
priority attention. Given the possible effects of insurgency, low education, breastfeeding and socioeconomic levels,
on infant mortality risks in the two named regions, multidimensional/sectorial approaches that address these and
similar factors should be adopted.
Second, improved
utilization of family planning services (for enhanced child spacing) need to be
pursued as part of a holistic approach to speeding up the reduction of IMR both
in rural and urban Nigeria. Being a known cost-effective means of promoting
child spacing (through lactational amenorrhea), early initiation and exclusive
breastfeeding practices should be further promoted in the country. Closely
related is the imperative of safer and affordable cesarean deliveries both in rural and urban residences. Possible misconceptions
on caesarean section need to be addressed
just as it is important to promote better access to emergency obstetric care
services. Third, this study further recommends, as a matter of priority in
urban Nigeria, the need for policies/programs on poverty and maternal obesity
reduction which may form components of long-term
approaches to reducing IMR. Infant mortality associated with small birth size,
as found in urban areas may equally benefit from target-specific interventions
that prioritize the provision of
intra-uterine feta monitoring and
nutrition support services. Last, this study reveals that the risk of infant
mortality was significantly higher among rural mothers who were previously but
no longer married/cohabiting. Future intervention efforts would need to put
this finding in perspective, for instance, by focusing on education in matters
of sexual behaviors and by promoting family oriented/supportive
programs/services in rural Nigeria.
Acknowledgements
The authors gratefully
appreciate ICF International, USA, for providing the NDHS dataset for this
study.
Funding:
None
Conflict of interest: None
Ethical approval: This study is a secondary analysis of NDHS dataset, as
such, no ethical approval is required. Permission to use the dataset was
obtained from ICF International Inc., USA.
References
1. UNICEF, Committing to Child Survival: A Promise Renewed, Progress Report 2014.
2014. New York, USA: UNICEF, 2015.
2. Kuruvilla, S., et al., Success factors for reducing maternal and
child mortality. Bulletin of the World Health Organization, 2014. 92(7): p. 533-544.
3. Molyneux, M. and E. Molyneux, Reaching Millennium Development Goal 4.
The Lancet Global Health, 2016. 4(3):
p. e146-e147.
4. National Population Commission (NPC)
[Nigeria] and ICF International, Nigeria
Demographic and Health Survey 2013. 2014: Abuja, Nigeria and Rockville,
Maryland, USA: NPC and ICF International.
5. Ezeh, O.K., et al., Risk factors for postneonatal, infant, child
and under-5 mortality in Nigeria: a pooled cross-sectional analysis. BMJ
open, 2015. 5(3): p. e006779.
6. Susuman, A.S., et al., High infant mortality rate, high total
fertility rate and very low female literacy in selected African countries.
Scandinavian journal of public health, 2016. 44(1): p. 2-5.
7. Wardlaw, T., et al., UNICEF Report: enormous progress in child
survival but greater focus on newborns urgently needed. Reproductive
health, 2014. 11(1): p. 82.
8. Adewuyi, E.O., Y. Zhao, and R.
Lamichhane, Socioeconomic,
bio-demographic and health/behavioral determinants of neonatal mortality in
Nigeria: a multilevel analysis of 2013 demographic and health survey. Int J
Contemp Pediatr, 2016. 3(2).
9. Adewuyi, E.O. and Y. Zhao, Determinants of neonatal mortality in rural
and urban Nigeria: evidence from a population-based national survey.
Pediatrics International, 2016: doi: 10.1111/ped.13086.
10. Hosseinpoor, A.R., et al., Equity-oriented monitoring in the context of
universal health coverage. PLoS Med, 2014. 11(9): p. e1001727.
11. Miyahara, R., et al., The large contribution of twins to neonatal
and post-neonatal mortality in The Gambia, a 5-year prospective study. BMC
pediatrics, 2016. 16(1): p. 1.
12. Mosley, W.H. and L.C. Chen, An analytical framework for the study of
child survival in developing countries. Population and development review,
1984: p. 25-45.
13. Mustafa, H.E. and C. Odimegwu, Socioeconomic determinants of infant
mortality in Kenya: analysis of Kenya DHS 2003. J Humanit Soc Sci, 2008. 2: p. 1934-722.
14. Victora, C.G., et al., The role of conceptual frameworks in
epidemiological analysis: a hierarchical approach. International journal of
epidemiology, 1997. 26(1): p.
224-227.
15. Arntzen, A. and A.M.N. Andersen, Social determinants for infant mortality in
the Nordic countries, 1980-2001. Scandinavian journal of public health,
2004. 32(5): p. 381-389.
16. Khadka, K.B., et al., The socio-economic determinants of infant
mortality in Nepal: analysis of Nepal Demographic Health Survey, 2011. BMC
pediatrics, 2015. 15(1): p. 1.
17. Abbass, I.M., Trends of rural-urban migration in Nigeria. European Scientific
Journal, 2012. 8(3).
18. Kanmiki, E.W., et al., Socio-economic and demographic determinants
of under-five mortality in rural northern Ghana. BMC international health
and human rights, 2014. 14(1): p. 1.
19. Lamichhane, R., et al., Factors associated with infant mortality in
Nepal: a comparative analysis of Nepal demographic and health surveys (NDHS)
2006 and 2011. BMC Public Health, 2017. 17(1): p. 53.
20. Lawn, J.E., et al., 4 million neonatal deaths: when? Where? Why?
The Lancet, 2005. 365(9462): p.
891-900.
21. Aune, D., et al., Maternal body mass index and the risk of fetal death, stillbirth, and
infant death: a systematic review and meta-analysis. Jama, 2014. 311(15): p. 1536-1546.
22. Blomberg, M., Maternal Obesity, Mode of Delivery, and Neonatal Outcome.
Obstetrics & Gynecology, 2013. 122(1):
p. 50-55.
23. Cresswell, J.A., et al., Effect of maternal obesity on neonatal death
in sub-Saharan Africa: multivariable analysis of 27 national datasets. The
Lancet. 380(9850): p. 1325-1330.
24. Siega-Riz, A.M., A.M. Siega-Riz, and
B. Laraia, The implications of maternal
overweight and obesity on the course of pregnancy and birth outcomes.
Matern Child Health J, 2006. 10(5
Suppl): p. S153-6.
25. Chen, A., et al., Maternal obesity and the risk of infant death in the United States.
Epidemiology (Cambridge, Mass.), 2009. 20(1):
p. 74.
26. Eme, O.I. and J. Ibietan, The cost of Boko Haram activities in
Nigeria. Arabian Journal of Business and Management Review (OMAN Chapter),
2012. 2(2): p. 10.
27. Doctor, H.V., et al., Northern Nigeria maternal, newborn and child
health programme: selected analyses from population-based baseline survey.
The Open Demography Journal, 2011. 4(11-12):
p. 11.
28. Adewuyi, E.O. and K. Adefemi, Breastfeeding in Nigeria: a systematic
review. Int J Community Med Public Health, 2016. 3(2): p. 385-396.
29. Shah, A., et al., Cesarean delivery outcomes from the WHO global survey on maternal and
perinatal health in Africa. International Journal of Gynecology &
Obstetrics, 2009. 107(3): p.
191-197.
30. Sunday-Adeoye, I.
and C. Kalu, Pregnant Nigerian women’s
view of cesarean section. Nigerian journal of clinical practice, 2011. 14(3): p. 276-279.
Table 1: Characteristics of study
variables and infant mortality rates
Variables
|
Rural
|
Urban
|
||||||||
n (unweighted)+
|
n (%) **
|
IMR
|
n
(unweighted)
|
n (%)
**
|
IMR
|
|||||
Socioeconomic Variables
Maternal
education level
None
Primary
Secondary/Higher
|
12117
4058
4274
|
13031 (63.70)
3681 (18.00)
3737 (18.30)
|
74
68
60
|
2171
2108
5656
|
2229 (22.40)
2106 (21.20)
5600 (56.40)
|
58
62
41
|
||||
Maternal
literacy level
Cannot read at all
Able to read
Missing
|
14391
5826
232
|
15112 (73.90)
5092 (24.90)
245 (1.20)
|
74
59
|
3127
6763
45
|
3159 (31.80)
6736 (67.80)
50 (0.50)
|
64
43
|
||||
Maternal
occupation
Not working
Working
Missing
|
6459
13883
107
|
6605 (32.30)
13783 (67.40)
61 (0.30)
|
71
70
|
2386
7508
41
|
2414 (24.30)
7481 (75.30)
40 (0.40)
|
48
50
|
||||
Paternal
education level
None
Primary
Secondary/Higher
Missing
|
9654
3933
6267
595
|
10368 (50.70)
3783 (18.50)
5767 (28.20)
511 (2.50)
|
76
69
59
|
1584
1798
6279
274
|
1639 (16.50)
1798 (18.10)
6239 (62.80)
258 (2.60)
|
52
56
47
|
||||
Paternal
occupation
Not Working
Working
Missing
|
132
19840
477
|
123 (0.60)
19938 (97.50)
389 (1.90)
|
32
70
|
134
9508
293
|
109 (1.10)
9557 (96.20)
268 (2.70)
|
19
50
|
||||
Wealth
index (SES)
Poor
Middle
Rich
|
12909
4302
3238
|
13537 (66.20)
4110 (20.10)
2802 (13.70)
|
76
59
61
|
1097
1726
7112
|
1053 (10.60)
1619 (16.30)
7262 (73.10)
|
82
48
45
|
||||
Cooking
fuel
Solid fuels
Non-solid fuels
Missing
|
19133
1139
177
|
19365 (94.70)
961 (4.70)
123 (0.60)
|
71
57
|
5783
4046
106
|
5524 (55.60)
4332 (43.60)
79 (0.80)
|
55
43
|
||||
Toilet
facility
Unimproved
Improved
Missing
|
12690
7579
180
|
12719 (62.20)
7587 (37.10)
143 (0.70)
|
71
68
|
2724
7103
108
|
2434 (24.50)
7412 (74.60)
89 (0.90)
|
60
46
|
||||
Drinking
water source
Unimproved sources
Improved sources
Missing
|
11034
9213
202
|
11051 (54.00)
9242 (45.20)
156 (0.80)
|
69
70
|
2555
7273
107
|
2431 (24.50)
7416 (74.60)
87 (0.90)
|
62
45
|
||||
Electricity
access
No
Yes
Missing
|
14180
6078
191
|
14212 (69.50)
6094 (29.80)
143 (0.70)
|
71
68
|
1856
7971
108
|
1788 (18.00)
8067 (81.20)
79 (0.80)
|
68
45
|
||||
Bio-demographic variables
Maternal
age at first childbirth
Below 20 years (Teen)
20 years or more (Non-teen)
|
13837
6612
|
14028 (68.60)
6421 (31.40)
|
72
67
|
4253
5682
|
4332 (43.60)
5603 (56.40)
|
50
49
|
||||
Maternal
marital status
Unmarried
Formerly married/cohabited
Married/cohabiting
|
383
524
19542
|
307 (1.50)
491 (2.40)
19651 (96.10)
|
97
98
69
|
209
332
9394
|
189 (1.90)
308 (3.10)
9448 (95.10)
|
34
57
50
|
||||
Religion
Traditionalist/other
Islam
Christianity
Missing
|
221
13156
6968
104
|
225 (1.10)
14110 (69.00)
5971 (29.20)
123 (0.60)
|
63
73
64
|
75
4622
5190
48
|
50 (0.50)
4878 (49.10)
4948 (49.80)
50 (0.50)
|
39
45
54
|
||||
Residence
Rural
Urban
|
20449
-
|
19810 (65.20)
-
|
70++
-
|
-
9935
|
-
10574 (34.80)
|
-
49++
|
||||
Maternal
age
< 20 years
36 or more years
20 - 35 years
|
1258
3742
15449
|
1329 (6.50)
3619 (17.70)
15500 (75.80)
|
87
72
68
|
247
2010
7678
|
248 (2.50)
1977 (19.90)
7710 (77.60)
|
105
63
44
|
||||
Birth
order
1
2-3
≥4
|
3755
6247
10447
|
3763 (18.40)
6360 (31.10)
10327 (50.50)
|
83
62
71
|
2282
3530
4123
|
2265 (22.80)
3507 (35.30)
4163 (41.90)
|
56
41
53
|
||||
Size
of child at birth
Small
Average
Large
Missing
|
3186
8187
8642
434
|
3272 (16.00)
8098 (39.60)
8670 (42.40)
409 (2.00)
|
92
65
60
|
1101
4091
4590
153
|
1143 (11.50)
4173 (42.00)
4461 (44.90)
159 (2.60)
|
80
46
36
|
||||
Gender
of child
Male
Female
|
10341
10108
|
10284 (50.30)
10165 (49.70)
|
75
66
|
5073
4862
|
5030 (50.60)
4905 (49.40)
|
54
44
|
||||
Preceding
birth interval
< 24 months
≥ 24 months
Missing
|
3901
12793
3755
|
3906 (19.10)
12781 (62.50)
3763 (18.40)
|
109
54
|
1724
5929
2282
|
1768 (17.80)
5901 (59.40)
2265 (22.80)
|
74
40
|
||||
Maternal
BMI
Obese
Overweight
Underweight
Normal weight
|
1106
2950
2036
14357
|
1084 (5.30)
2740 (13.40)
1984 (9.70)
14641 (71.60)
|
76
70
74
69
|
1273
2520
571
5571
|
1272 (12.80)
2494 (25.10)
566 (5.70)
5603 (56.40)
|
64
49
26
49
|
||||
Region
of residence
North-Central
North-East
North-West
South-East
South-South
South-West
|
3118
5034
7718
941
2495
1143
|
3292 (16.10)
4192 (20.50)
9059 (44.30)
838 (4.10)
1881 (9.20)
1186 (5.80)
|
53
72
81
73
58
53
|
1326
1325
1888
1743
1104
2549
|
864 (8.70)
1242 (12.50)
2394 (24.10)
1739 (17.50)
904 (9.10)
2782 (28.00)
|
49
45
45
73
47
41
|
||||
Health/behavioral variables
Mode
of delivery
Caesarean
Non-caesarean
Missing
|
207
20122
120
|
204 (1.00)
20142 (98.50)
123 (0.60)
|
124
69
|
384
9366
185
|
358 (3.60)
9,379 (94.40)
199 (2.00)
|
84
49
|
||||
Breastfeeding
initiation
Beyond first one hour
Within first one hour
Missing
|
13111
6430
908
|
13292 (65.00)
6216 (30.40)
961 (4.70)
|
61
57
|
5400
4110
425
|
5454 (54.90)
4034 (40.60)
437 (4.40)
|
39
35
|
||||
Desire
for pregnancy
Then
No more
Later
Missing
|
18599
289
1334
227
|
18793 (91.90)
245 (1.20)
1186 (5.80)
245 (1.20)
|
68
67
62
|
8790
236
825
84
|
8822 (88.90)
229 (2.30)
795 (8.00)
89 (0.90)
|
47
49
34
|
||||
Place
of delivery
Home
Private Facility
Public Facility
Missing
|
15523
1202
3468
256
|
15807 (77.30)
1186 (5.80)
3170 (15.50)
266 (1.30)
|
68
69
64
|
3552
2531
3767
85
|
3755 (37.80)
2613 (26.30)
3477 (35.00)
89 (0.90)
|
54
40
44
|
||||
Delivery
assistance
Skilled
No assistance
TBA/combined
Missing
|
5383
3177
11568
321
|
5071 (24.80)
3538 (17.30)
11513 (56.30)
348 (1.70)
|
67
73
66
|
6803
561
2477
94
|
6656 (67.00)
656 (6.60)
2543 (25.60)
89 (0.90)
|
42
50
57
|
||||
Antenatal
care attendance
No
Yes
Missing
|
5847
7065
7537
|
6237 (30.50)
6728 (32.90)
7484 (36.60)
|
52
47
|
709
5648
3578
|
725 (7.30)
5633 (56.70)
3577 (36.00)
|
52
32
|
||||
**
= Weighted for the sampling probability with the use of complex sample
statistics. + = without complex samples statistics. n = rural or urban sample
size. IMR = infant mortality rate per
1000 live births. IMR was not calculated for missing values. BMI: body mass
index. TBA: traditional birth attendants. SES: socioeconomic status. ++: IMR in
rural and urban residence were compared using the un-disaggregated data to
obtain P-Value (Pearson X2 test) of (P < 0.001) for the purpose
of testing for significant difference.
Table 2: Results of the unadjusted
relationship between infant mortality and explanatory variables
Variables
|
Rural
|
Urban
|
||||||||||||||
COR
|
95% CI
|
P-Value
|
COR
|
95% CI
|
P-Value
|
|||||||||||
Socioeconomic variables
Maternal
education level
None
Primary
Secondary/Higher
(ref)
|
-
1.260
1.141
1.000
|
-
1.049 - 1.512
0.894 - 1.457
-
|
0.038*
0.013*
0.289
-
|
-
1.428
1.540
1.000
|
-
1.058
- 1.928
1.182
- 2.007
-
|
0.002*
0.020*
0.001*
-
|
||||||||||
Maternal
literacy level
Cannot read at all
Able to read (ref)
|
-
1.280
1.000
|
-
1.086 - 1.509
-
|
0.003*
0.003*
-
|
-
1.549
1.000
|
-
1.222 - 1.965
-
|
< 0.001*
< 0.001*
-
|
||||||||||
Maternal
occupation
Not working
Working (ref)
|
-
1.006
1.000
|
-
0.880 - 1.152
-
|
0.925
0.925
-
|
-
0.951
1.000
|
-
0.740 - 1.223
-
|
0.697
0.697
-
|
||||||||||
Paternal
education level
None
Primary
Secondary/Higher (ref)
|
-
1.296
1.170
1.000
|
-
1.103 - 1.523
0.948 - 1.444
-
|
0.007*
0.002*
0.144
-
|
-
1.106
1.208
1.000
|
-
0.785 - 1.558
0.865 - 1.685
-
|
0.534
0.564
0.267
-
|
||||||||||
Paternal
occupation
Not Working
Working (ref)
|
-
0.440
1.000
|
-
0.148 - 1.307
-
|
0.139
0.139
-
|
-
0.371
1.000
|
-
0.119 - 1.152
-
|
0.086
0.086
-
|
||||||||||
Wealth
index
Poor
Middle
Rich (ref)
|
-
1.257
0.958
1.000
|
-
0.996 - 1.585
0.737 - 1.245
-
|
0.005*
0.054
0.748
-
|
-
1.909
1.083
1.000
|
-
1.433 - 2.544
0.771 - 1.521
-
|
< 0.001*
< 0.001*
0.646
-
|
||||||||||
Cooking
fuel
Solid fuels
Non-solid fuels (ref)
|
-
1.254
1.000
|
-
0.920 - 1.711
-
|
0.152
0.152
-
|
-
1.294
1.000
|
-
1.037 - 1.615
-
|
0.023*
0.023*
-
|
||||||||||
Toilet
facility
Unimproved
Improved (ref)
|
-
1.038
1.000
|
-
0.909 - 1.185
-
|
0.582
0.582
-
|
-
1.344
1.000
|
-
1.027 1.759
-
|
0.031*
0.031*
-
|
||||||||||
Drinking
water source
Unimproved sources
Improved sources (ref)
|
-
0.989
1.000
|
-
0.840 - 1.163
-
|
0.890
0.890
-
|
-
1.392
1.000
|
-
1.088 - 1.782
-
|
0.009*
0.009*
-
|
||||||||||
Electricity
access
No
Yes (ref)
|
-
1.038
1.000
|
-
0.869 - 1.239
-
|
0.682
0.682
-
|
-
1.537
1.000
|
-
1.167 - 2.025
-
|
0.002*
0.002*
-
|
||||||||||
Bio-demographic variables
Maternal
age at first child birth
Below 20 years [teen]
20 years or more [non-teen] (ref)
|
-
1.066
1.000
|
-
0.924 - 1.229
-
|
0.381
0.381
-
|
-
1.027
1.000
|
-
0.808 - 1.305
-
|
0.825
0.825
-
|
||||||||||
Maternal
marital status
Unmarried
Formerly married/co-habited
Married/co-habiting (ref)
|
-
1.448
1.458
1.000
|
-
0.935 2.243
1.026 2.073
-
|
0.027*
0.097
0.035*
-
|
-
0.680
1.165
1.000
|
-
0.292 - 1.585
0.627 - 2.164
-
|
0.589
0.372
0.629
-
|
||||||||||
Religion
Traditionalist/other
Islam
Christianity (ref)
|
-
0.968
1.140
1.000
|
-
0.573 - 1.636
0.965 - 1.346
-
|
0.245
0.904
0.123
-
|
-
0.724
0.833
1.000
|
-
0.272 - 1.929
0.648 - 1.070
-
|
0.337
0.518
0.153
-
|
||||||||||
Maternal
age
< 20 years
36 or more years
20 - 35 years (ref)
|
-
1.303
1.061
1.000
|
-
1.028 - 1.652
0.896 - 1.257
-
|
0.083
0.029*
0.492
|
-
2.542
1.440
1.000
|
-
1.630 - 3.965
1.095 - 1.893
-
|
< 0.001*
< 0.001*
0.009*
-
|
||||||||||
Birth
order
1
2-3
≥4 (ref)
|
-
1.194
0.868
1.000
|
-
1.017 - 1.402
0.742 - 1.014
-
|
0.003*
0.031*
0.074
-
|
-
1.064
0.762
1.000
|
-
0.813 1.392
0.581 1.000
-
|
0.066
0.651
0.050
-
|
||||||||||
Size
of child at birth
Small
Average
Large (ref)
|
-
1.593
1.093
1.000
|
-
1.329 - 1.910
0.939 - 1.272
-
|
< 0.001*
< 0.001*
0.250
-
|
-
2.344
1.288
1.000
|
-
1.756 - 3.127
0.991 - 1.674
-
|
< 0.001*
< 0.001*
0.059
-
|
||||||||||
Gender
of child
Male
Female (ref)
|
-
1.141
1.000
|
-
1.007 - 1.292
-
|
0.038*
0.038*
-
|
-
1.242
1.000
|
-
0.988 - 1.561
-
|
0.063
0.063
-
|
||||||||||
Preceding
birth interval
< 24
≥ 24 (ref)
|
-
2.133
1.000
|
-
1.866 - 2.439
-
|
< 0.001*
< 0.001*
-
|
-
1.935
1.000
|
-
1.462 - 2.561
-
|
< 0.001*
< 0.001*
-
|
||||||||||
Maternal
BMI
Obese
Overweight
Underweight
Normal weight (ref)
|
-
1.102
1.018
1.073
1.000
|
-
0.725 - 1.675
0.830 - 1.248
0.868 - 1.325
-
|
0.902
0.649
0.864
0.515
-
|
-
1.339
1.009
0.521
1.000
|
-
0.982 1.825
0.779 1.306
0.263 1.033
-
|
0.073
0.065
0.946
0.062
-
|
||||||||||
Region
of residence
South-West
North-East
North-West
South-East
South-South
North-Central (ref)
|
-
1.009
1.389
1.580
1.428
1.121
1.000
|
-
0.588 – 1.733
1.085 – 1.778
1.247 – 2.002
0.969 – 2.103
0.848 – 1.481
-
|
0.001*
0.974
0.009*
<0 .001="" span="">
0.072
0.423
-
|
-
0.846
0.930
0.931
1.532
0.962
1.000
|
-
0.532 – 1.345
0.552 – 1.566
0.594 – 1.458
1.013 – 2.317
0.593 – 1.561
-
|
0.022*
0.479
0.784
0.753
0.043*
0.875
-
|
||||||||||
Health/behavioral variables
Mode
of delivery
Caesarean
Non-caesarean (ref)
|
-
1.907
1.000
|
-
1.174 - 3.095
-
|
0.009*
0.009*
-
|
-
1.792
1.000
|
-
1.145 - 2.805
-
|
0.011*
0.011*
-
|
||||||||||
Breastfeeding
initiation
Beyond first one hour
Within first one hour (ref)
|
-
1.071
1.000
|
-
0.904 - 1.269
-
|
0.425
0.425
-
|
-
1.122
1.000
|
-
0.877 - 1.434
-
|
0.359
0.359
-
|
||||||||||
Desire
for pregnancy
Then
No more
Later (ref)
|
-
1.108
1.082
1.000
|
-
0.844 - 1.454
0.588 - 1.992
-
|
0.760
0.460
0.800
-
|
-
1.429
1.465
1.000
|
-
0.950 - 2.149
0.680 - 3.156
-
|
0.225
0.087
0.329
-
|
||||||||||
Place
of delivery
Home
Private Facility
Public Facility (ref)
|
-
1.079
1.099
1.000
|
-
0.895 - 1.301
0.795 - 1.519
-
|
0.702
0.424
0.568
-
|
-
1.235
0.908
1.000
|
-
0.935 - 1.630
0.671 - 1.231
-
|
0.128
0.137
0.535
-
|
||||||||||
Delivery
assistance
Skilled
No assistance
TBA/combined (ref)
|
-
1.016
1.112
1.000
|
-
0.855 - 1.209
0.931 - 1.327
-
|
0.491
0.853
0.241
-
|
-
0.734
0.875
1.000
|
-
0.558 - 0.967
0.549 - 1.395
-
|
0.088
0.028
0.574
-
|
||||||||||
Antenatal
care attendance
No
Yes (ref)
|
-
1.124
1.000
|
-
0.939 - 1.346
-
|
0.202
0.202
-
|
-
1.662
1.000
|
-
1.103 - 2.503
-
|
0.015*
0.015*
-
|
||||||||||
*Statistically
significant at 5% significance level. COR: crude odds ratio. CI: confidence
interval. ref: reference value
Table 3: Factors
associated with infant mortality in rural and urban Nigeria
Variables
|
Rural
|
Urban
|
||||
AOR
|
95% CI
|
P-Value
|
AOR
|
95% CI
|
P-Value
|
|
Wealth
index (SES)
Poor
Middle
Rich (ref)
|
-
-
-
|
-
-
-
|
-
-
-
|
-
2.292
1.202
1.000
|
-
1.589
– 3.308
0.839
– 1.722
-
|
< 0.001*
< 0.001*
0.316
-
|
Maternal
marital status
Unmarried
Formerly married/cohabited
Married/cohabiting
(ref)
|
-
1.853
1.625
1.000
|
-
0.759 - 4.523
1.079 - 2.447
-
|
0.029*
0.175
0.020*
-
|
-
-
-
|
-
-
-
|
-
-
-
|
Size
of child at birth
Small
Average
Large (ref)
|
-
1.550
1.044
1.000
|
-
1.266 - 1.898
0.873 - 1.247
-
|
< 0.001*
< 0.001*
0.638
-
|
-
2.276
1.314
1.000
|
-
1.585 - 3.270
0.979 - 1.764
-
|
< 0.001*
< 0.001*
0.069
-
|
Gender
of child
Male
Female (ref)
|
-
-
|
-
-
|
-
-
|
-
1.416
1.000
|
-
1.052 - 1.907
-
|
0.022*
0.022*
-
|
Preceding
birth interval (Months)
< 24
≥ 24 (ref)
|
-
2.057
1.000
|
-
1.784 - 2.371
-
|
< 0.001*
< 0.001*
-
|
-
1.605
1.000
|
-
1.191 - 2.161
-
|
0.002*
0.002*
-
|
Region
of residence
South-West
North-East
North-West
South-East
South-South
North-Central (ref)
|
-
1.109
1.346
1.653
1.337
1.005
1.000
|
-
0.568 - 2.169
1.017 - 1.783
1.271 - 2.148
0.811 - 2.202
0.703 - 1.437
-
|
0.001*
0.761
0.038*
< 0.001*
0.254
0.977
-
|
-
-
-
-
|
-
-
-
-
|
-
-
-
-
|
Maternal
BMI
Obese
Overweight
Underweight
Normal weight (ref)
|
-
-
-
|
-
-
-
|
-
-
-
|
-
1.641
1.081
0.361
1.000
|
-
1.139 - 2.365
0.784 - 1.490
0.135 - 0.963
-
|
0.032*
0.008*
0.634
0.042*
-
|
Mode
of delivery
Caesarean
Non-caesarean (ref)
|
-
2.922
1.000
|
-
1.569 - 5.443
-
|
0.001*
0.001*
-
|
-
1.947
1.000
|
-
1.059 - 3.581
-
|
0.032*
0.032*
-
|
*Statistically
significant at 5% significance level. AOR: adjusted odds ratio. CI: confidence
interval. ref: reference value
