Spatio-Temporal Modelling of Childhood Vaccine Uptake in Nigeria: A Hierarchical Bayesian Zero-Inflated Poisson Approach

doi:10.21203/rs.3.rs-1559574/v1

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Research Article

Spatio-Temporal Modelling of Childhood Vaccine Uptake in Nigeria: A Hierarchical Bayesian Zero-Inflated Poisson Approach

https://doi.org/10.21203/rs.3.rs-1559574/v1

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BACKGROUND

Globally, child mortality and morbidity remain a serious health challenge and infectious diseases are the leading causes. The use of count models together with spatial analysis of the number of vaccines taken is limited in the literature. We used Spatio-temporal in a Bayesian zero-inflated Poisson regression model to assess the number of vaccine uptake among children aged 12–23 months and their associated factors.

METHODS

Data of 19,564 children from 2003, 2008, 2013 and 2018 population-based cross-sectional Nigeria Demographic and Health Survey were used. The childhood vaccines include one dose of Bacillus-Calmette-Guérin; three doses of Diphtheria-Pertussis-Tetanus; three doses of Polio and one dose of Measles. Uptake of all nine vaccines was regarded as full vaccination. We examined the multilevel factors associated with the number of vaccine uptake using descriptive, bivariable and multivariable Bayesian models. Analysis was conducted in Stata version 16 and R statistical packages, and visualization in ArcGIS.

RESULTS

Prevalence of full vaccination was 6.5% in 2003, 14.8% in 2008, 21.8% in 2013 and 23.3% in 2018. Full vaccination coverage ranged from 1.7% in Sokoto to 51.9% in Anambra. Factors associated with the number of vaccines include maternal age (adjusted Incidence “risk” Ratio (aIRR) = 1.05; 95% Credible Interval (CrI) = 1.03–1.07) for 25–34 years and (aIRR = 1.07; 95% CrI = 1.05–1.10) for 35–49 years and education: (aIRR = 1.11, 95% CrI = 1.09–1.14) for primary and (aIRR = 1.16; 95% CrI = 1.13–1.19) for secondary/tertiary education. Other significant factors are wealth status, antenatal care attendance, working status, use of skilled birth attendant, religion, mother’s desire for the child, community poverty rate, community illiteracy, and community unemployment.

CONCLUSION

Although full vaccination has remained low, there have been improvements over the years with wide disparities across the states. Improving uptake of vaccines by educating women on the benefits of hospital delivery and vaccines through radio jingles and posters should be embraced, and state-specific efforts should be made to address inequality in access to routine vaccination in Nigeria.

Child Vaccination

Immunization

Bayesian

Multilevel

MCMC

Zero-Inflated Poisson

Nigeria

One of the most cost-effective public health interventions and major success stories of global health in achieving a reduction of child morbidity and mortality is vaccination. Vaccination is a simple, safe and efficient way of protecting individuals from harmful diseases and a very vital tool of primary health care (1). Vaccination saves millions of lives yearly and reduces the overall “risk” of getting a disease by building protection in the body’s natural immune system, thereby helping people of all ages to live longer and healthier lives (1, 2). The ultimate aim of vaccination programs is to achieve a reduction in the incidence of vaccine-preventable diseases by ensuring a high level of coverage and vaccine administration at appropriate ages and recommended intervals (3).

Globally, child morbidity and mortality are some of the most serious health challenges and infectious diseases are leading causes of under-five deaths (4). In 2019, the child mortality rate was reported to be 38 per 1,000 live birth, the highest of whom were resident in sub-Saharan African countries like Nigeria, Somalia, Chad, Central African Republic, Sierra Leone with 117, 117, 114, 110, 109 per 1,000 live birth respectively (5). Low- and middle-income countries (LMIC) were reported to have an average of 41 per 1,000 live birth, sub-Saharan African countries have about 76 per 1,000 live birth and the least developed countries were reported to have 63 deaths per 1,000 live births (5).

Although factors associated with childhood deaths vary between countries, reducing child morbidity and mortality rates remains an urgent concern, as it is a major indicator of a nation’s progress and development (6, 7). Researchers have established an association between vaccine uptake and child health outcomes (8–13). These studies modelled childhood vaccination as a binary outcome and observed geographical inequality in coverage based on demographic factors.

However, this approach does not account for the number of vaccines taken as the binary logistics regression undercounts the number of vaccines since they would be collapsed into two categories: either “none” versus “at least one” or “all” versus “not all”. Rather count models will be more appropriate in the identification of factors associated with the number of vaccines taken. Moreover, failure to account for the hierarchical nature of the associated factors as well as the spatial and temporal distribution of the number of vaccines taken, may undermine the reliability of the findings. We are not aware of any study that assessed all these.

In Nigeria, researchers have also explored factors associated with vaccination coverage (11, 13–16). Demographic and socio-economic factors such as child-specific, parental and household characteristics have been identified as important predictors of child vaccination uptake (9, 11, 17, 18). Furthermore, birth order, age and place of birth of a child has been found to influence vaccination uptake (18–20). Parental factors such as literacy, media exposure and educational attainment are factors that have been found to have a strong relationship with vaccination uptake (21).

We hypothesized that the number of vaccine uptake vary across the individual-, community- and state-level factors. The knowledge of factors associated with the number of childhood vaccination received and the spatial and temporal distribution of the level of the uptakes are very useful in providing evidence-based information for child and maternal health programmers. Therefore, this study aimed to explore the Spatio-temporal distribution and the factors associated with the number of vaccines uptake among children aged 12–23 months between 2003 and 2018 in Nigeria.

Study Area

The study area is Nigeria, the largest country in Africa. In Nigeria, the National Primary Health Care Development Agency (NPHCDA) is responsible for controlling vaccine-preventable diseases through the provision of vaccines and its rules and guidelines. The National Social Mobilization Working Group is nationally responsible for the development of communication strategies and interventions, with State and Local Government representation. These groups carry out campaigns, alongside other private and public health organizations regularly, at intervals or as the need arises.

Study Population

The study population of participants consists of children aged 12–23 months, which represents the youngest cohort of children who are supposed to take all vaccines recommended in the first year of life.

Study Design

The study used data from a cross-sectional and nationally representative household sample survey for Nigeria; pooled from 4 consecutive datasets of the National Demographic and Health Survey (NDHS) (a population cross-sectional survey) conducted in 2003, 2008, 2013 and 2018.

Sampling

The NDHS used a multi-stage, stratified sampling design in all the 36 states in Nigeria and the FCT with the clusters as the primary sampling units, and households as sampling units. At the first stage, clusters (enumeration areas) were selected from already identified rural and urban local government areas. The second stage involved the selection of households within the selected clusters using the latest Nigeria Population Census at the time of the respective survey as the sampling frame. All eligible women of reproductive age (15–49 years) living in the selected households were interviewed. Sampling weights were added to account for unequal probability of selection at the cluster levels and non-response since the samples are not self-weighting. These weights help to minimize non-response and selection biases.

Study Variables

Dependent variable

Our dependent variable is the number of vaccines taken by the children. Data on each vaccine uptake was taken from the women, and the response was binary with children who had taken the vaccine coded as “1” and “0” otherwise. The data on each of the 9 vaccines were then merged into one variable showing the number of vaccines a child took. The vaccines are one dose of Bacillus-Calmette-Guérin (BCG); three doses of Diphtheria-Pertussis-Tetanus (DPT); three doses of Polio and one dose of Measles.

Independent variables

Explanatory variables were selected based on findings in the literature (9, 11, 18, 22) and availability of data at individual, community and state levels. Three levels of explanatory variables were used for the hierarchical nature of the study

Individual variables such as indicators of socio-demographic characteristics of the child and mother including mother’s age, educational level, religion, working status, desire for the last child (wanted or not), skilled birth attendant presence on delivery, exposure to media, birth order, wealth status (divided into three quartiles), number of antenatal care attendance, sex of the child, and healthcare decision-maker, were included in the analysis. Exposure to media, in this study, was defined as the mother’s access to information through any of newspapers/magazines, radio or television (i.e., if the mother reads or watches any, at least once a week).

Community-level variables such as place of residence, community poverty rate, illiteracy rate and unemployment, and State level variables such as rural population and health facilities per 10000 population were included in the model.

Data Management

Data were extracted from the DHS website and pooled for analysis. The data was cleaned, to handle missing observations and other irrelevances. A weighted univariate and bivariate (Chi-square) analysis was done in Stata version 16. The essence of the sample weighting, which was provided in the data, was to adjust for unequal cluster sizes, stratifications and ensure that the findings in the study properly represent the population of interest for each state (2). Statistical significance was set to 0.05. ArcGIS PRO version 2.8 was used to draw maps, while BayesX in R was used for Spatio-temporal regression and to create the posterior maps.

Statistical Analysis

Weighted descriptive and univariate analyses were carried out in the study. A p-value < 0.05 was deemed significant. To identify the association/relationship between the individual, community (contextual) and state-level characteristics and vaccination, a chi-square test was carried out on association between explanatory variables and the number of vaccines taken using Stata version 16; after which the count modelling was carried out in R programming language.

Before the regression modelling, a multicollinearity test was performed on the variables and a high correlation was variables with a variance inflation factor > 10 was dropped from the multivariable analysis. Using all the 3-level model for the count response, which was defined as children, i, who took vaccination (at level 1), from a community, j (at level 2) and living in a state, k (at level 3), the models the study worked with is the generalized linear mixed model (GLMM) with both fixed and random effects.

Two count modelling strategies were considered, including the Bayesian Poisson and Zero-inflated Poisson. Previous studies have compared the performance of the frequentist and Bayesian and have found the Bayesian approach is more robust and gives a better result while assessing convergence (23–28). Model selection and comparison was done using − 2 Log-Likelihood and Deviance Information Criterion. The Zero-inflated Poisson model performed better judging with the lowest value of the measure of goodness.

The fully adjusted model was then used to control for the effects of all the levels (individual-, community-, and state-level characteristics). The median posterior estimates of the models and standard error were presented. Model selection was based on the models’ goodness of fit parameters stated earlier.

In this study, the presence of non-linear effects for some covariates indicates that strictly linear predictors cannot be assumed. The geographical patterns of vaccine uptake and the possible non-linear effects were simultaneously explored using a geo-additive, semi-parametric hierarchical model that controlled for spatial dependence and nonlinear time-varying effects of covariates. The models are of the form:

$${{\eta }}_{{ijk}}={\beta }_{0}+ \sum _{p=1}^{P}{\beta }_{p}{x}_{pijk}+{U}_{0jk}+ {V}_{0k}$$

Where η is the link function; ${\beta }_{0}$ is the intercept; ${\beta }_{p}$ is the regression coefficient for the p parameters; ${x}_{pijk}$ are the covariates; ${U}_{0jk}$ is the random component for children from community j, in state k; ${V}_{0k}$ is the random component for children in state k. The linear predictor is the log link function with dynamic and spatial effects and a geo-additive semi-parametric predictor:

$${{\eta }}_{{ijk}}={f}_{1}{x}_{i1}+{f}_{spat}\left({s}_{i}\right)+ \sum _{p=1}^{P}{\beta }_{p}{x}_{pijk}+{U}_{0jk}+ {V}_{0k}$$

Where ${f}_{1}$ is the non-linear smoothed effect of the metrical covariate of mothers age, and ${f}_{spat}\left({s}_{i}\right)$ is the effect of the spatial covariate labelling the state in Nigeria.

About half (49%) of the mothers were aged 25–34 years, ranging from 15 to 49 years. Only 7% of the respondents had tertiary education while a larger percentage (46%) of the women had no formal education; this pattern was also observed across each of the years of the interview. The percentage of women who had a skilled birth attendant present at delivery was 37%, and about 36% were not exposed to media at all (Table 1). Prevalence of full vaccination was 6.5% in 2003, 14.8% in 2008, 21.8% in 2013 and 23.3% in 2018.

Table 1

Weighted Distribution of respondent’s characteristics by survey year and number of vaccine uptake
	2003 (n = 1151) [n (%)]	2008 (n = 5570) [n (%)]	2013 (n = 6281) [n (%)]	2018 (n = 6562) [n (%)]	Total (n = 19564) [n (%)]	${\chi }^{2}$ (No of vaccine uptake)
Level 1 (Individual Level Characteristics)
Age (28; (23–33))*
15–24 years	345 (30.0)	1,618 (29.1)	1,795 (28.6)	1,877 (28.6)	5,635 (28.8)	< 0.001
25–34 years	555 (48.2)	2,720 (48.8)	3,063 (48.8)	3,207 (48.9)	9,545 (48.8)
35–49 years	251 (21.8)	1,232 (22.1)	1,423 (22.7)	1,478 (22.5)	4,384 (22.4)
Level of Education
No formal education	550 (47.8)	2,779 (49.9)	2,862 (45.6)	2,872 (43.8)	9,063 (46.3)	< 0.001
Primary education	297 (25.8)	1,257 (22.6)	1,231 (19.6)	983 (15.0)	3,768 (19.3)
Secondary education	265 (23.0)	1,261 (22.6)	1,736 (27.6)	2,175 (33.5)	5,437 (28.0)
Tertiary education	39 (3.4)	273 (4.9)	452 (7.2)	532 (8.1)	1,296 (7.2)
Religion
Christian	231 (20.6)	2,407 (44.2)	2,403 (37.9)	2,503 (37.5)	7,544 (38.5)	< 0.001
Islam	116 (14.9)	2,960 (54.4)	3,843 (60.6)	4,146 (62.0)	11,116 (56.7)
Others	724 (64.5)	79 (1.5)	101 (1.6)	34 (1.0)	938 (4.8)
Working Status
Not currently working	427 (37.1)	2,040 (36.6)	1,931 (30.7)	2,201 (33.5)	6,599 (33.7)	< 0.001
Currently Working	724 (62.9)	3,530 (63.4)	4,350 (69.3)	4,361 (66.5)	12,965 (66.3)	< 0.001
Wanted last child
Wanted then	967 (84.0)	4,960 (89.1)	5,550 (88.4)	5,648 (86.1)	17,125 (87.5)	< 0.001
Wanted later Wanted no more	184 (16.0)	610 (11.0)	731 (11.6)	914 (13.9)	2,439 (12.5)	< 0.001
Attendant on delivery
Unskilled	714 (62.0)	3,851 (69.1)	3,885 (61.9)	3,823 (58.3)	12,273 (62.7)	< 0.001
Skilled	437 (38.0)	1,719 (30.9)	2,396 (38.2)	2,739 (41.7)	7,291 (37.3)	< 0.001
Exposure to media
Not exposed at all	290 (25.2)	2,000 (35.9)	2,218 (35.3)	2,628 (40.1)	7,136 (36.5)	< 0.001
Partially exposed	530 (46.1)	2,078 (37.3)	2,600 (41.4)	2,740 (41.8)	7,948 (40.6)
Very Exposed	331 (28.8)	1,492 (26.8)	1,463 (23.3)	1,194 (18.2)	4,480 (22.9)
Birth order of child (3; (2–5))*
First	212 (208)	1,033 (18.6)	1,223 (19.5)	1,250 (19.1)	3,718 (19.0)	< 0.001
Second	208 (18.1)	937 (16.8)	1,075 (17.1)	1,209 (18.4)	3,429 (17.5)
Third	166 (14.4)	832 (14.9)	893 (14.2)	1,022 (15.6)	2,913 (14.9)
Fourth and above	565 (49.1)	2,768 (49.7)	3,090 (49.2)	3,081 (47.0)	9,504 (48.6)
Wealth Status
Bottom 33%	306 (26.6)	1,936 (34.8)	2,169 (34.5)	2,111 (32.2)	6,522 (33.3)	< 0.001
Average	484 (42.1)	1,908 (34.3)	2,023 (32.2)	2,106 (32.1)	6,521 (33.3)
Top 33%	361 (31.4)	1,726 (31.0)	2,089 (33.3)	2,345 (35.7)	6,521 (33.3)
Number of ANC visit (4; (0–7))*
No visit	460 (40.0)	2,843 (51.0)	2,508 (39.9)	1,974 (30.1)	7,785 (39.8)	< 0.001
1–3 visits	158 (13.7)	584 (10.4)	707 (11.3)	1,048 (16.0)	2,497 (12.8)
4–7 visits	258 (22.4)	1,157 (20.8)	1,578 (25.1)	2,405 (36.7)	5,398 (27.6)
8 or more visits	275 (23.9)	986 (17.7)	1,488 (23.7)	1,135 (17.3)	3,884 (19.9)
Sex of the child
Male	586 (50.9)	2,792 (50.1)	3,302 (52.6)	3,411 (52.0)	10.091 (51.6)	0.210
Female	565 (49.1)	2,778 (49.9)	2,979 (47.4)	3,151 (48.0)	9,473 (48.4)	0.210
Healthcare decision maker
Self	144 (12.5)	395 (7.1)	313 (5.0)	511 (7.8)	1,363 (7.0)	< 0.001
Husband alone	831 (72.2)	3,177 (57.0)	3,731 (59.4)	3,753 (57.2)	11,492 (58.7)
Joint	117 (10.2)	1,723 (30.9)	1,879 (29.9)	1,920 (29.3)	5,639 (28.8)
Other/Unknown	59 (5.1)	275 (4.9)	358 (5.7)	378 (5.8)	1,070 (5.5)
Level 2 (Community Level Characteristics)
Place of residence
Urban	430 (37.4)	1,478 (26.6)	2,064 (32.9)	2,259 (34.4)	6,231 (31.9)	< 0.001
Rural	721 (62.6)	4,092 (73.5)	4,217 (67.1)	4,303 (65.6)	13,333 (68.2)	< 0.001
Community poverty rate
Low	225 (19.6)	1634 (29.3)	1795 (28.6)	2967 (45.2)	6621 (33.8)	< 0.001
Average	518 (45.0)	2038 (36.6)	2156 (34.3)	1737 (26.5)	6449 (33.0)
High	408 (35.5)	1898 (34.1)	2330 (37.1)	1858 (28.3)	6494 (33.2)
Community illiteracy rate
Low	116 (10.1)	1581 (28.4)	1843 (29.3)	2986 (45.5)	6526 (33.4)
Average	587 (51.0)	2073 (37.2)	2009 (32.0)	1862 (28.4)	6531 (33.4)	< 0.001
High	448 (38.9)	1916 (34.4)	2429 (38.7)	1714 (26.1)	6507 (33.3)
Community unemployment
Low	98 (8.5)	1396 (25.1)	1832 (29.2)	3362 (51.2)	6688 (34.2)
Average	340 (29.5)	1951 (35)	2248 (35.8)	1846 (28.1)	6385 (32.6)	< 0.001
High	713 (62)	2223 (39.9)	2201 (35)	1354 (20.6)	6491 (33.2)
Level 3 (State Level Characteristics)
Rural proportion
Low rural proportion	191 (16.6)	1008 (18.1)	1166 (18.6)	1262 (19.2)	3627 (18.5)	< 0.001
Average rural proportion	351 (30.5)	1403 (25.2)	1466 (23.3)	1623 (24.7)	4843 (24.8)
High rural proportion	609 (52.9)	3159 (56.7)	3649 (58.1)	3677 (56.0)	11094 (56.7)
Health facility per 100,000
< 15	271 (23.5)	1327 (23.8)	1567 (25.0)	1625 (24.8)	4790 (24.5)	< 0.001
15–25	683 (59.3)	3158 (56.7)	3522 (56.1)	3576 (54.5)	10939 (55.9)
> 25	197 (17.1)	1085 (19.5)	1192 (19.0)	1361 (20.7)	3835 (19.6)
Distal Characteristic
Region of residence
North Central	201 (17.5)	938 (16.8)	922 (14.7)	1135 (17.3)	3196 (16.3)	< 0.001
North East	268 (23.3)	1271 (22.8)	1277 (20.3)	1433 (21.8)	4249 (21.7)
North West	357 (31.0)	1583 (28.4)	1932 (30.8)	1889 (28.8)	5761 (29.5)
South East	99 (8.6)	497 (8.9)	605 (9.6)	739 (11.3)	1940 (9.9)
South South	111 (9.6)	641 (11.5)	788 (12.6)	667 (10.2)	2207 (11.3)
South West	115 (10.0)	640 (11.5)	757 (12.1)	699 (10.7)	2211 (11.3)

Figure 1 shows the percentage of the children who took each of the vaccines across the survey years. The percentage of children that took no vaccine across each of the years dropped from 35% in 2003 to 25% in 2018. Figure 2 presents the number of vaccines received among all the cohorts of children in the study. The distribution of vaccine uptake was similar across the survey years.

Furthermore, Fig. 3 disaggregated the spatial prevalence of full vaccine uptake by each of the survey years. In 2003, a larger percentage of the states in the Northern region had 0% prevalence of full vaccination, except Kano, Kaduna and Bauchi which had above 0% but less than 5% prevalence. Some states in the Southern region also had a 0% prevalence of full vaccination. The highest prevalence was observed in Lagos and Delta states.

In 2008, an improvement in full vaccine uptake was observed with only Jigawa reporting a 0% prevalence of full vaccination. Although there were improvements across states, only Osun had a prevalence above 50%. In 2013 and 2018, no state was found to have a 0% prevalence while Anambra and Lagos had the highest prevalence (> 50%) in 2013 and Abuja had above 50% full vaccine uptake in 2018. Overall Full vaccination coverage ranged from 1.7% in Sokoto to 51.9% in Anambra.

Figure 4 presents the smoothed map of the association between the age of mothers and vaccine uptake, adjusted for year and spatial effect. As the age of mothers increased, the likelihood of vaccine uptake also increased. Table 2 presents the output of smoothed Zero-inflated Poisson regression model for the number of vaccine uptake among children aged 12–23 months in Nigeria, adjusted for spatial effects and other confounders.

Table 2

Zero-Inflated Poisson three-level variance components model showing the adjusted posterior median distribution of the number of vaccine uptake
	POSTERIOR aIRR (95% CrI)
CONTROL VARIABLE
Year
2003	Reference
2008	1.08 (1.03–1.13)
2013	1.16 (1.11–1.21)
2018	1.22 (1.17–1.28)
Level 1 (Individual Level Characteristics)
Age
15–24 years	Reference
25–34 years	1.05 (1.03–1.07)
35–49 years	1.07 (1.05–1.10)
Level of Education
No formal education	Reference
Primary education	1.11 (1.09–1.14)
Secondary/Tertiary education	1.16 (1.13–1.19)
Religion
Christian	Reference
Islam	0.95 (0.93–0.97)
Others	0.83 (0.78–0.87)
Working Status
Not currently working	Reference
Currently working	1.03 (1.01–1.04)
Wanted last child
Wanted then	Reference
Did not want then	0.99 (0.97–1.02)
Attendant at delivery
Unskilled	Reference
Skilled	1.10 (1.09–1.12)
Exposure to media
Not exposed at all	Reference
Partially exposed	1.01 (0.99–1.03)
Ver exposed	1.04 (1.02–1.07)
Birth order of child
First	Reference
Second	0.99 (0.97–1.02)
Third	0.97 (0.95–0.99)
Fourth and above	0.97 (0.95–0.99)
Wealth Status
Bottom 33%	Reference
Average	1.09 (1.07–1.12)
Top 33%	1.16 (1.13–1.19)
Number of ANC visits
No visit	Reference
1–3 visits	1.16 (1.13–1.19)
4–7 visits	1.23 (1.21–1.26)
8 or more visits	1.20 (1.18–1.23)
Sex of the child
Male	Reference
Female	1.00 (0.99–1.01)
Healthcare decision maker
Self
Husband alone	0.99 (0.97–1.01)
Joint	1.01 (0.98–1.03)
Other	0.98 (0.95–1.02)
Level 2 (Community Level Characteristics)
Place of residence
Urban	Reference
Rural	0.99 (0.97–1.01)
Community poverty rate
Low	Reference
Average	1.04 (1.02–1.06)
High	1.01 (0.99–1.04)
Community illiteracy rate
Low	Reference
Average	0.99 (0.97–1.02)
High	0.96 (0.94–0.99)
Community unemployment
Low	Reference
Average	1.03 (1.01–1.04)
High	0.96 (0.93–0.99)
Level 3 (State Level Characteristics)
Rural proportion
Low rural proportion	Reference
Average rural proportion	0.94 (0.86–1.03)
High rural proportion	0.91 (0.83–1.01)
Health facility per 100,000
< 15	Reference
15–25	1.02 (0.93–1.13)
> 25	1.04 (0.93–1.16)
RANDOM EFFECTS
Individual-level Variance
Mean (95% CrI)	0.0058 (0.0009)
Community-level Variance
Mean (95% CrI)	0.0020 (0.0016)
State-level Variance
Mean (95% CrI)	0.0021 (0.0018)

There was a significant variation in the number of vaccine uptake, across communities, and also across states. The community-level variance was estimated 0.0020, and 0.0021 at the state level. The credible intervals of these estimates are significant, so the hypothesis that the regression slopes for the number of vaccine uptake vary across the individual-, community- and state-level is supported by the data although the majority of variations can be attributed or explained by individual-level effects (0.0058).

In 2008, 2013 and 2018 survey years, after adjusting for spatial effects and other confounders, children had a higher “risk” of a higher number of vaccine uptake than in 2003. Children whose mothers were aged 25–34 years and 35–49 years age group had 8% higher “risk” of a higher number of vaccine uptake relative to those aged 15–24 years (adjusted Incidence “risk” Ratio (aIRR: 1.05, 1.07; 95% credible interval (CrI): 1.03–1.07, 1.05–1.10). Similarly, an increase in the level of formal education increased the incidence of vaccine uptake; children whose mothers had primary education or secondary/tertiary education had a higher incidence than those whose mothers had no formal education (aIRR: 1.11, 1.16; 95% CrI: 1.09–1.14, 1.13–1.19).

Individuals from better-off households had a higher number of vaccine uptake than those individuals from households in poorer categories (aIRR: 1.09, 1.16; 95% CrI: 1.07–1.12, 1.13–1.19 respectively), and those who had between 1–3 antenatal care (ANC) visits, 4–7 ANC visits and 8 or more ANC visits had a higher incidence of vaccine uptake than those who never attended ANC services (aIRR = 1.16, 1.23, 1.20; 95% CrI = 1.13–1.19, 1.21–1.26 and 1.18–1.23 respectively). Other significant factors associated with the number of vaccines taken are working status, religion, attendant on delivery (which acted as a surrogate for postnatal care attendance), religion and birth order.

Children whose mothers lived in communities with average poverty rates have a higher “risk” of vaccinating their children (aIRR: 1.04; 95% CrI: 1.02–1.06) and communities with high illiteracy rates had a lower odds of child vaccine uptake (aIRR: 0.96; 95% CRI: 0.94–0.99). The high unemployment rate in communities corresponded to a lower “risk” of vaccine uptake (aIRR: 0.96; 95% CrI: 0.93–0.99). Communities with average unemployment rates had a higher “risk” of child vaccine uptake than communities with low unemployment (aIRR: 1.03; 95% CrI: 1.01–1.04) while communities with high unemployment rates had lower “risk” (aIRR: 0.96; 95% CrI: 0.93–0.99).

Notably, at the state level, none of the variables considered was significantly associated with the number of vaccine uptake. However, the posterior median map in Fig. 5 (adjusted for spatial effects) showed low uptake of vaccines in the Northern region compared to a high likelihood in the Southern region.

A Zero-Inflated Poisson Regression model was fitted to the data of 19,564 children aged 12–23 months from the 2003, 2008, 2013 and 2018 children recode data of the Nigeria Demographic and Health Survey while applying the Bayesian hierarchical modelling for parameter estimation.

There was a general improvement in vaccine uptake over time, as the percentage of children who were fully vaccinated increased between 2003 and 2018 just as the average number of vaccines received increased during the same period. This was in agreement with a previous study (29) but at variance with findings from other African countries such as Burundi, Rwanda and Kenya (10, 30, 31). Despite the observed increase in vaccine uptake, the percentage of non-vaccinated children remained high. A persistently low uptake was observed in the Northern region of the country throughout the period covered in this study.

Maternal age was a very important socio-economic and individual-level predictor of the number of vaccine uptake, as an increase in the mother’s age increases the number of vaccine uptake. As mothers increased in age, they plausibly would have had prior childbirth experience and known the importance of vaccines. This stance was also supported by previous studies (10, 32).

Also, the mother’s educational level was positively correlated with the number of vaccine uptake; a higher level of education corresponds to an increased number of vaccine uptake. Mothers who were educated would be well informed of the relevant benefits and advantages of taking vaccines, which in turn leads to an increase in uptake. Similar results were found in previous studies, as educated mothers (which may be extended to occupation) are more informed on issues relating to family planning and child health (9, 30). Household wealth status was found to significantly predict vaccine uptake as women from households in the richer quantiles may be more educated and have well-paying jobs than in poorer households (9). This could be ascribed to higher awareness and education among women in richer households than the poorer ones.

Religion is a very important determinant of health-seeking behaviours and health outcomes (9), as evidenced from the study. It is not impossible that some religion promote vaccination than others. Bearing in mind that the North, with the lowest vaccine uptake, is predominantly Muslims and of Hausa/Fulani culture, poor vaccine uptake in that zone could be ascribed to cultural hesitance and religious influence. Having a skilled birth attendant increased the likelihood of a child receiving all immunizations at birth; this is also reflected in an increased incidence of vaccine uptake among women who attended antenatal care, plausibly the trust in the health system is increased by previous experience. Attendance of antenatal and postnatal clinics has been linked with increased confidence and positive inclination towards the health system (11).

The association between the number of vaccine uptake and access to media was significant. Several studies have suggested that regular exposure through mass media and other community dissemination platforms are key channels to promoting vaccines (13, 30, 33). Birth order was significantly associated with vaccine uptake and findings from previous literature have reported that increased demand in competition for family resources is associated with an increased number of children (34). The relationship between these maternal characteristics and the number of vaccine uptake indicates the importance of media access and family planning in the uptake of childhood vaccines.

Previous studies have linked health facility access and place of residence (rural and urban) to health outcomes, including vaccine uptake (9, 13, 34, 35). Although the place of residence was not significant in the current study, other variables such as community poverty, illiteracy and unemployment are a direct derivation of residence. Notably, the gender of the children did not affect the uptake of vaccines, as other studies pointed out in Nigeria and Ghana, but not in some other countries like India (9, 10, 36).

Children whose mothers reside in communities with high rural illiteracy and unemployment rates are at lower “risk” of a high number of vaccine uptake. This can be attributed to poor awareness and lower access to health services in rural areas (possibly by distance), and an insufficient number of facilities to cover for the large population in those areas. Although state-level characteristics were not significant in this study, we postulate that children whose mothers reside in states with a high rural proportion are at risk of taking a lower number of vaccines. This can be attributed to lower access to health services in rural areas from community-level variables – plausibly by distance and an insufficient number of facilities to cover the disadvantaged areas.

STRENGTH AND LIMITATIONS

A major strength of this study was that the study pooled data between 2003 and 2018 in Nigeria to model the number of vaccine uptake among children aged 12–23 months in Nigeria. Furthermore, this study applied the Bayesian framework against the frequentist approach in establishing the number of vaccine uptake. Also, we assessed the hierarchical nature of the data as well as the spatial-temporal distribution of the vaccine uptakes. However, a major drawback in this study is the cross-sectional design which is limited in establishing causality. Also, there may have been under-reporting in the number of vaccine uptake; the NDHS assumes that vaccination has not been obtained when the record of vaccination was missing or the mother does not remember if the child has taken the vaccine. The secondary nature of the data also limited the choices of explanatory variables.

Different individual-, community-, and state-level variations were observed to significantly affect vaccine uptake among children aged 12-23months in Nigeria. Improvements in vaccine uptake were recorded across the survey years and the analysis showed that vaccine uptake differed across the individual-level (including age, education, religion, occupation, attendant on delivery, exposure to media, wealth, antenatal visits) and communities (poverty, illiteracy and unemployment rates). This points to the influence of demographic, socio-economic and environmental factors, as well as cultural factors. The posterior predict, from evidence in the estimates, poses a stern call to action and to prevent a reduction in the number of future vaccine uptake.

Furthermore, since vaccines have been proven to be an effective strategy in reducing child morbidity and mortality across countries of the world, programmes aimed at educating women on the benefits of hospital delivery and vaccines should be implemented to increase uptake. There are need to improve overall community awareness on vaccination. There may be a need for special intervention where the numbers of vaccines received are low compared with the south. The northern states may benchmark what practices enhance higher uptake in the south. Government should involve non-governmental organizations and other relevant organizations in promoting and sustaining vaccine uptake among children, especially in locations where uptake is low.

LMIC

Low- and middle-income countries

NPHCDA

National Primary Health Care Development Agency

NDHS

National Demographic and Health Survey

BCG

Bacillus-Calmette-Guérin

DPT

Diphtheria-Pertussis-Tetanus

GLMM

Generalized linear mixed model

CrI

Credible Interval

IRR

Incidence risk ratio

ANC

Antenatal care

ETHICAL APPROVAL AND CONSENT TO PARTICIPATE

The study was based on the analysis of openly available secondary data and Ethical approvals were obtained from the Ethics Committee of the ICF Macro at Fairfax, Virginia in the USA and by the National Ethics Committees in Nigeria. Written and signed informed consent was obtained from each parent and/or legal guardians of the children who participated in the study were told that the interviews have minimal risks and potential benefits. All information was collected anonymously and held confidentially. All methods were carried out in accordance with relevant guidelines and regulations.

CONSENT FOR PUBLICATION

Not applicable

AVAILABILITY OF DATA AND MATERIALS

Data used for this study are available on the DHS website, https://dhsprogram.com

COMPETING INTERESTS

The authors declare that they have no competing interests.

FUNDING

None declared

AUTHORS’ CONTRIBUTIONS

TVL and AFF conceptualised and designed the study. TVL and KAA analysed and interpreted the data while ASA and AFF were senior authors overseeing the analysis and interpretation of the results. TVL, KAA, ASA and AFF drafted, reviewed and edited the manuscript. All authors read and approved the final version of the manuscript.

ACKNOWLEDGEMENTS

The authors are grateful to ICF Macro, USA, for granting the authors the request to use the Demographic and Health Survey data.

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No competing interests reported.

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Spatio-Temporal Modelling of Childhood Vaccine Uptake in Nigeria: A Hierarchical Bayesian Zero-Inflated Poisson Approach

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Abstract

BACKGROUND

METHODS

RESULTS

CONCLUSION

Figures

Introduction

Materials And Methods

Study Variables

Data Management

Statistical Analysis

Results

Discussion

STRENGTH AND LIMITATIONS

Conclusion

Abbreviations

Declarations

References

Competing Interests

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