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Article Information
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Author:
Diddy Antai1,2
Affiliations:
1Division of Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Sweden
2Division of Global Health & Inequalities, The Angels Trust, Nigeria
Correspondence to:
Diddy Antai
Email:
Diddy.Antai@ki.se
Postal address:
SE 171 76 Stockholm, Sweden
Dates:
Received: 08 Oct. 2010
Accepted: 13 Mar. 2011
Published: 22 Sept. 2011
How to cite this article:
Antai D. Rural-Urban Inequities in Childhood Immunisation in Nigeria: The Role of Community Contexts. Afr J Prm Health Care Fam Med. 2011;3(1), Art. #238, 8 pages.
doi:10.4102/phcfm.v3i1.238
Copyright Notice:
© 2011. The Authors. Licensee: AOSIS OpenJournals. This work is licensed under the Creative Commons Attribution License.
ISSN: 2071-2928 (print)
ISSN: 2071-2930 (online)
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Rural-urban inequities in childhood immunisation in Nigeria: The role of community contexts
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In This Original Research...
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Open Access
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• Abstract
• Introduction
• Setting
• Significance of the study
• Ethical considerations
• Methods
• Data collection
• Measurements
• Outcome
• Exposures
• Community-level risk factors
• Individual-level risk factors
• Analysing
• Results
• Proportion of children by place of residence and vaccination type: Table 1
• Multilevel logistic regression analysis
• Measures of association (fixed effects): Table 2
• Measures of variation (random effects): Table 3
• Discussion
• Conclusion
• Acknowledgements
• Competing interests
• References
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Context: Childhood vaccinations are one of the most cost-effective means of reducing negative child health outcomes. Despite the benefits of immunisation,
inequities persist both between and within rural-urban areas in Nigeria.
Objectives: To assess the role of community contexts on rural-urban inequities in full immunisation uptake amongst children 12 months of age and older.
Methods: Data from the 2003 Nigeria Demographic and Health Survey including 6029 live born children from 3725 women aged 15–49 years were examined
using multilevel regression analysis.
Results: Rural children were disadvantaged both in the proportion receiving full immunisation and individual vaccines. Contextual or community-level factors
such as community prenatal care by doctor, community hospital delivery, and region of residence accounted for significant rural-urban inequities in full immunisation.
Conclusion: This study stresses the need for community-level interventions aimed at closing rural-urban inequities in the provision of maternal and child
health care services.
Setting
Vaccinations are effective, inexpensive, and cost-effective means for preventing several infectious diseases.1 It is estimated that about 27 million
children and 40 million pregnant women do not receive the full complement of vaccines, out of which over 2 million people die worldwide yearly from vaccine preventable
diseases.2Vaccine-preventable diseases (VPDs) constitute major causes of morbidity and mortality in Africa. In spite of this, vaccination coverage rates
for the various childhood vaccines in Nigeria are amongst the lowest in the world,3 with Nigeria being amongst the ten countries worldwide having vaccine
coverage below 50%.4
Individual, community and systemic factors have been shown to influence the equitable uptake of childhood immunisation in Nigeria and other countries in sub-Saharan
Africa.5 Whilst much is known about systemic barriers (vaccine supply, distribution, costs, and provider skills)6,7 and individual-level factors
(poor understanding of immunisation, suspicions, myths, and rumours,8 low maternal education,9,10 maternal employment and working outside the
home,11 younger maternal age,9 delivering away from a health facility and not possessing an immunisation card12,13) that determine
immunisation uptake within rural areas of developing countries, such as Nigeria, much less is known about the role of community-level characteristics on rural-urban
inequities in childhood immunisation. Nigeria’s population is largely rural with more than 53% of the population living in rural areas,14 and with
the widespread rural-urban inequities in immunisation coverage to the disadvantage of rural children, rural-urban disparities are of particular relevance for
immunisation services.
Significance of the study
An individual’s socio-economic position and health-seeking behaviour are influenced by various infrastructural and institutional characteristics at the
community level such as the availability of healthcare services, distance to health care facilities, lack of transportation.15,16 These community-level
factors in turn depend on the availability of resources within different community and the broader geographic area, which may decrease or increase the accessibility
of healthcare services, such as childhood immunisation uptake within the community.17,18 This study hypothesises that the community contexts in which an
individual resides influences the likelihood of childhood immunisation uptake.19 On this basis, this study aims to examine the effect of contextual or
community-level factors on rural-urban inequities in full immunisation uptake, whilst controlling for individual-level characteristics.
This study was based on secondary data with all participant identifiers removed. Survey procedures and instruments were approved by the National Ethics Committee
in the Federal Ministry of Health, Nigeria and by the Ethics Committee of the Opinion Research Corporation Macro International Incorporated (ORC Macro Inc.),
Calverton, USA. Ethical permission for use of the data in the present study was obtained from ORC Macro Inc.
Data collection
Data for the study were obtained from the 2003 Nigeria Demographic Survey (DHS). This is a nationally-representative probability sample, collected using a
stratified two-stage cluster sampling procedure, according to the list of enumeration areas developed from the 1991 Population Census sampling frame. Initial
sampling involved the selection of 365 clusters or primary sampling units (PSUs) with a probability proportional to the size. Subsequent sampling involved
systematically selecting households from the previously selected clusters, resulting in a sample of 7864 households. Data were collected by face-to-face interviews
from the 3725 women aged 15–49 years in these households. These women contributed a total of 6029 live born children born to the survey. Information collected
included birth histories, in-depth demographic and socio-economic information on illnesses, medical care, immunisations, and anthropometric details of
children.20 Immunisation status of a child was determined from vaccination cards shown to the DHS interviewer. In the absence of vaccination cards,
mothers were asked to recall whether the child had received BCG, polio, DPT (including the number of doses for each) and measles vaccinations. How was recall
bias handled?
Measurements
Outcome
The outcome variable is the likelihood of a child 12 months of age and older having received all of the eight required vaccinations (full immunisation) according to the
national immunisation schedule.
Exposures
Community-level risk factors
Four community-level variables were assessed:
• community mother’s education, defined as the percentage of mothers with secondary or higher education in the primary sampling unit (PSU), and
categorised as low, middle, and high
• community hospital delivery, defined as the percentage of mothers who delivered their child in the hospital, and categorised as: low, middle, and high
• community prenatal care by doctor, defined as the percentage of mothers who received prenatal care by a doctor categorised as low and high
• mother’s region of residence, categorised according to the six geopolitical zones in Nigeria, that is, North-Central, North-East, North-West,
South-East, South-South, and South-West Nigeria.
Community-level variables were estimated at the level of the primary sampling unit (PSU), (n = 365). Clusters or PSUs are administratively-defined
areas used as proxies for ‘neighbourhoods’ or ‘communities’.21,22 PSUs are small and fairly homogenous units with respect
to population socio-demographic characteristics, economic status and living conditions. They consist of one or more enumeration areas (EAs), which are the
smallest geographic units for which census data are available in Nigeria. Each PSU was made up of a minimum of 50 households; in the case of less than 50
households, a contiguous EA was added.20
Individual-level risk factors
Eight additional child-level and mother-level variables of interest were examined:
• the gender of the child, assessed as male and female
• birth order and interval between births, a variable created by merging ‘birth order’ and ‘preceding birth interval’ because
first births are frequently omitted in analyses of preceding birth interval and survival of the preceding child because they are not preceded by another birth.
In order to enable the inclusion of first births in the analysis, birth order was merged with birth interval; the resulting variable was classified into seven
categories as: first births, birth order 2–4 with short birth interval (< 24 months), birth order 2–4 with medium birth interval (24–47 months),
birth order 2–4 with long birth interval (48+ months), birth order 5+ with short birth interval (< 24 months), birth order 5+ with medium birth interval
(24–47 months), and birth order 5+ with long birth interval (48 months)
• mother’s age, grouped as: 15–18, 19–23, 24–28, 29–33, and 34 years and older
• ethnicity, categorised as: Hausa/Fulani/Kanuri, Igbo, Yoruba, and other minority ethnic groups
• mothers’ education, categorised as: no education, primary, and secondary or higher education
• mother’s occupation, categorised as: professional/technical/managerial, clerical/sales/services/skilled manual, agricultural
self-employed or agricultural employee or household and domestic/unskilled manual occupations, and not working
• prenatal care by doctor, categorised as yes and no
• place of delivery of child, categorised as home and hospital facility.
Analysing
The rural-urban distribution of the children by full immunisation status was calculated. A three-level multilevel logistic regression model was used to
identify the determinants of rural-urban immunisation uptake at the individual (children and mothers) and community levels.23 Children (level 1),
were nested within mothers (level 2), who were in turn nested within communities (level 3), and six models were fitted into the analysis. Model 0
(empty model) contained no exposure variable, and only focused on decomposing the total variance into its mother and community components.
Model 1 contained place of residence as the only exposure variable, and Model 2 included child-level characteristics (sex of the child, and birth
order or birth interval). Model 3 added mother-level characteristics (mothers’ age, ethnicity, mothers’ education, and mothers’ occupation).
Model 4 additionally included health care utilisation characteristics (place of delivery of child, and prenatal care by doctor). Finally, Model 5
adjusted for community-level characteristics (community mother’s education, community hospital delivery, community prenatal care by doctor, and region of
residence).
The intercept or average probability of full immunisation is assumed to vary randomly across mothers and communities. Measures of association (fixed effects) are
expressed as odds ratio (OR) and 95% confidence intervals (95% CI). Measures of variation (random effects) are expressed as Variance Partition Coefficient (VPC)
and percentage change in variance (PCV). The VPC measures the clustering of infection or disease amongst individuals with a specific covariate pattern, that is,
it is a measure of the extent that siblings resemble each other more than they resemble children from other families in relation to the risk of full immunisation.
A large VPC value (close to 1) would indicate maximally segregated clusters in the risk of full immunisation, whilst a low VPC value (0 or close to zero) would
suggest homogeneous risk of full immunisation amongst clusters i.e. that households are similar with respect to full immunisation risks and that households are
irrelevant for understanding differences (variation) in full immunisation within this population. Statistical testing of the population variance was performed
using the Wald statistic, that is, the ratio of the estimate variance to its standard error,18 and p-values were calculated. MLwiN software
package 2.0.2 was used for the multilevel analyses,24 with Binomial, Penalised Quasi-Likelihood (PQL) procedures.25
Proportion of children by place of residence and vaccination type: Table 1
Only 18% (N = 937) of the total number of children had received full immunisation. Of these, only a total of 296 (9%) rural children and 641 (34%) urban
children had received full immunisation. Regarding the individual vaccines, a higher proportion of the rural children in comparison to urban children had not
received BCG, DPT1, DPT2, DPT3, OPV3, and measles vaccines.
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TABLE 1: Proportion of children by place of residence and vaccination type.
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Multilevel logistic regression analysis
Measures of association (fixed effects): Table 2
Place of residence was introduced in Model 1, and results show that children residing in rural areas had a 42% (OR = 0.58, 95% CI = 0.47–0.72) lower
likelihood of receiving full immunisation compared with children residing in urban areas. With the introduction of sex of the child and birth order or birth interval
in Model 2, the likelihood of receiving full immunisation for children residing in rural areas (OR = 0.59, 95% CI = 0.47–0.72) remained basically
unchanged. In addition, children of 5+ birth order after short birth interval 24 months or less (OR = 0.52, 95% CI = 0.33–0.82) had significantly lower
likelihood of being fully immunised than children of 2–4 birth order and medium birth interval 24–47 months.
Model 3 included mother-level characteristics (mothers’ age, mothers’ ethnicity, mothers’ education, and mothers’ occupation)
resulting in a further increase in the likelihood of receiving full immunisation for children residing in rural areas (OR = 0.69, 95% CI = 0.55–0.86).
Children of mothers from Igbo (OR = 1.70, 95% CI = 1.18–2.45), Yoruba (OR = 2.04, 95% CI = 1.39–2.99), and Other (OR = 1.72,
95% CI = 1.30–2.28) ethnic groups had increased likelihood of receiving full immunisation compared with children of mothers from the Hausa/Fulani/Kanuri
ethnic groups. Children of mothers with no education had 31% lower likelihood (OR = 0.69, 95% CI = 0.52–0.93) of receiving full immunisation than children of
mothers with secondary or higher education. Model 4 controlled for health care utilisation with attenuation of the likelihood of receiving full
immunisation amongst children residing in rural areas (OR = 0.65, 95% CI = 0.44–0.95) compared with children of mothers in urban areas. Children
of mothers who were 34 years or older had a 58% (OR = 1.58, 95% CI = 1.07–2.33) higher likelihood of receiving full immunisation compared with children of
mothers in the reference group (24–28 years). Furthermore, children belonging to the Other ethnic group had a 74% (OR = 1.74, 95% CI = 1.23–2.45)
higher likelihood of receiving full immunisation than children of Hausa/Fulani/Kanuri mothers.
Finally, Model 5 included community-level covariates (community mothers’ education, community hospital delivery, community prenatal care by
doctor, and region of residence). Children of mothers residing in rural areas remained at higher likelihood (OR = 0.61, 95% CI = 0.43–0.86) of receiving full
immunisation compared with children of mothers resident in urban areas. Children of mothers 34 years or older still had significantly higher likelihood of receiving
full immunisation (OR = 1.59, 95% CI = 1.02–2.49) than children of mothers 24–28 years of age. Children of mothers that did not receive prenatal care by
doctor during pregnancy had 42% (OR = 0.58, 95% CI = 0.39–0.86) lower likelihood of receiving of full immunisation compared with children of mothers who received
prenatal care by doctor. Children whose mothers were resident in communities with low proportion of hospital delivery had 45% lower likelihood of receiving full
immunisation (OR = 0.55, 95% CI = 0.33–0.92) compared to children of mothers residing in communities with the proportion of hospital delivery at the median
level. In addition, children of mothers living in communities with a high proportion of mothers who received prenatal care by doctor had a 79% (OR = 1.79, 95% CI =
1.06–3.02) higher likelihood of receiving full immunisation compared to children of mothers who received prenatal care by doctor. In addition, children of
mothers living in the South-South region had 56% lower likelihood (OR = 0.44, 95% CI = 0.22–0.90) of receiving full immunisation than children of mothers living
in the South-West region.
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TABLE 2: Measures of association (fixed effects i.e. odds ratios and 95% confidence intervals) for multilevel logistic regression models of the factors associated with rural-urban differences in full immunisation.
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Measures of variation (random effects): Table 3
Model 0 provides an indication of the extent of spatial clustering of full immunisation and indicates that the community-level variance (τ = 0.593,
p = 0.008) and mother-level variance (τ = 0.280, p = 0.010) were significant. The VPC indicated that the correlation between communities and
between mothers were 15.3% and 7.3%, respectively. After adjusting for place of residence in Model 1, the community-level variance in full immunisation
(τ = 0.437, p = 0.006) and mother-level variance (τ = 0.509, p = 0.021) remained significant. The VPC indicates that controlling for the
place of residence slightly increases the proportion of variance in full immunisation that exists between mothers (12.0%) and decreases that between communities
(10.3%). The PCV in this model shows that 26.3% and -82% in the odds of full immunisation across communities and mothers respectively, were explained by rural-urban
residence. This indicates that part of the clustering of full immunisation within areas or communities resulted from composition of the households by place of
residence. This is a composition effect.
After adjusting for child-level variables in Model 2, the community-level variance in full immunisation (τ = 0.421, p = 0.005) and mother-level
variance (τ = 0.506, p = 0.021) remained significant. The correlation between communities and between mothers remained basically unchanged, whilst the
change in variance (PCV) in the odds of full immunisation was 3.7% across communities and -10% across mothers, indicating that part of the clustering of full
immunisation within areas or communities were due to a composition effect of mother’s characteristics within communities. After adjusting for mother-level
variables in Model 3, the community-level variance in full immunisation (τ = 0.207, p = 0.002) and mother-level variance (τ = 0.788, p =
0.037) also remained significant. The VPC indicated that controlling for mother-level variables decreased the correlation between communities to 4.8% and increased
the correlation between mothers to 18.4%. According to the PCV, 50.8% and -55.7% of the variance in the odds of full immunisation across communities and mothers
respectively were explained by mother-level characteristics a composition effect of mothers’ characteristics clustering within communities.
After adjusting for health care utilisation variables in Model 4, only the community-level variance in full immunisation remained significant (τ = 0.202,
p = 0.004). The correlation between communities increased to 5.8%, whilst the correlation between mothers was zero, suggesting that mothers are similar with
respect to likelihood of their children receiving full immunisation, and that mothers are nonrelevant for understanding differences (variation) in full immunisation
after adjusting for health care utilisation within this population. The PCV in the odds of full immunisation of 2.4% across communities indicates the clustering of
full immunisation within areas or communities were due to the effect of health care utilisation characteristics within communities; indicating a variation in the
characteristics of the communities, that is, a contextual effect. Finally, after adjusting for community-level variables in Model 5, only the community-level
variance in full immunisation remained significant (τ = 0.197, p = 0.005). The correlation between communities was 5.6%, meaning that community-level
characteristics (region of residence, community hospital delivery, and community prenatal care by doctor) account for the variation in full immunisation. The
correlation between mothers remained at zero. The PCV in the odds of full immunisation of 2.5% across communities was explained by the aforementioned community-level
characteristics, and is indicative of a contextual effect of community characteristics. Successively smaller values in Deviance Information Criterion (DIC) with each
subsequent model indicate that for the most part, the model was a good-fit.
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TABLE 3: Measures of variation (random effects).
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A summary of the findings in this study is that:
• there is significant variation in full immunisation across individual-level and community-level contexts
• there is an association between place (rural-urban) of residence and the likelihood of children receiving full immunisation; this association
remained only slightly attenuated even after sequentially adjusting for possible confounders
• full immunisation varies significantly across communities
• contextual or community-level factors account for rural-urban variation in full immunisation over and above the individual-level characteristics of the
mother or child
• community-level characteristics (community hospital delivery, community prenatal care by doctor, and region of residence) play an influential role in the
effect of place of residence on the likelihood of full immunisation.
Contextual or community-level characteristics were important predictors of full immunisation uptake, as evidenced by the findings that living in a communities with
low proportion of mothers who had hospital delivery was associated with significantly lower likelihood of children receiving full immunisation, whilst living in a
community with high proportion of mothers who had prenatal care by doctor was associated with significantly higher likelihood of children receiving full immunisation.
Possible explanations for this finding may be the increased confidence in the value of child immunisation and institutional delivery amongst mothers who attend
prenatal care by doctor and amongst those who delivered in a hospital setting, which may be developed from counselling during prenatal care. Developing a familiarity
with health care systems tends to increase the likelihood of subsequently utilising health care services such as child immunisation and institutional delivery amongst
mothers. Similar findings have been reported in other studies,26,28 based on data from the 1992–1993 National Family Health Survey (NFHS) of India.
Like the present study, these studies were cross-sectional and nationally representative samples, which either assessed the determinants of the use of prenatal care
and child immunisation in rural India,26 estimated the effect of demand for immunisation and the effect of immunisation coverage on the probability of
child survival in rural areas of India,27 or the effect maternal and child services utilisation on the likelihood of institutional delivery in rural
India.28 However, the present study differs in its use of multilevel logistic regression analysis and in its consideration of the effect of rural
community contexts.
In addition, residence in the South-South region of Nigeria was associated with significantly lower likelihood of a child receiving full immunisation. Because the
six geopolitical regions in Nigeria represent different levels of social development and population densities, different economic, religious, and political
situations,29 it is not unexpected that these regional differences would influence child immunisation campaign effectiveness, a fact reported in a
previous study.30 That children resident in the South-South (or Niger Delta) region of Nigeria had lower risks of being fully immunised is not an
unexpected finding, given that this region is severely economically deprived and is characterised by extensive mangrove forest, lagoons and swamps, stretching
over 100 km inland. This region is characterised largely by rural hard-to-reach communities, with extensive poverty, as well as poor healthcare and social
infrastructure. It is a region undergoing conflict, with armed militias interfering with vaccination processes, thus preventing vaccination officers from reaching
children in remote settlements.31
The significantly higher likelihood of children of mothers 34 years or older receiving full immunisation is consistent with findings from a recent cross-sectional
DHS studies from Nigeria,30 that assessed the individual-level and community-level explanatory factors associated with child immunisation uptake between
migrant and nonmigrant groups, and from Bangladesh,32 which showed that older mothers were more likely to fully immunise their children than the youngest
and oldest age groups, because maternal age may serve as a proxy for the women’s accumulated knowledge of healthcare services, which may in turn have a positive
influence on acceptance of full immunisation of children. Findings in the present study may be a consequence of the development of modern medicine and the improvement
in educational opportunities available to women in recent years, whereby women in the middle age group might have more knowledge about modern healthcare services and
value modern medicine more than the older women. It is plausible that older mothers are more enlightened about child-bearing matters as well as the benefits of
immunisation programmes. Access to prenatal care at the individual level was also an important predictor of full immunisation uptake. This, in addition to the
aforementioned explanation, is also an indication of the quality of care received by mothers and infants during delivery. Women lacking prenatal care are less likely
to be informed of the importance of childhood immunisation and other important health-promoting programmes. This added to the fact that rural areas are often deficient
in healthcare facilities and skilled healthcare workers help to explain the resulting rural-urban inequity in childhood immunisation. Significant
‘unexplained’ variance remaining between communities in the measures of variation strongly indicates that other possible unobserved or unobservable
factors are likely to influence immunisation uptake. Lower DIC values with successive models indicate a good-fit of the analytic model.
Some limitations need to be considered in relation to this study. Firstly, other unaddressed community-level factors, such as such as distance to immunisation
centres, and quality of immunisation services may be important determinants of full immunization immunisation by place of residence. Secondly, defining neighbourhoods
according to administratively defined boundaries may nondifferentially misclassify individuals into an inappropriate administrative boundary, which could generate
information biases and reduce the validity of analyses.33
The strengths of this study worthy of mention include firstly, the DHS surveys are nationally-representative and allow for generalisation of the results across the
country.34 Secondly, variables in the DHS surveys are defined similarly across countries making results easily comparable across countries,35
and thirdly, using administrative boundaries gives the possibility of comparing any set of data on the same geographic frame, or of presenting complex data in a
simple way.
The results of this study indicate the presence of rural-urban inequities in full immunisation, attributable to contextual or community-level factors within rural
areas. It stresses the need to close rural-urban gaps in community-level healthcare infrastructure, with emphasis being placed on reducing rural-urban inequities
in the provision of maternal and child health care service.
The relevance of these findings lies in the reinforcement of the role of community contexts in the fight against vaccine preventable diseases in developing
countries, which are vital to the success of immunisation campaigns.
The authors are grateful to Measure Demographic and Health Survey (ORC Macro) for the data used in this study, and the anonymous reviewers for their very
useful comments and my advisor and mentor.
Competing interests
The author declares that there were no competing interests in the writing of this article.
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