Three in four children age 12–23 months missed opportunities for vaccination in Sub-Saharan African countries: a multilevel mixed effect analysis of demographic health and surveys 2016–2023

Abstract

Background

Despite vaccination being the most cost-effective means to prevent disease and its adverse consequences, missing opportunities for vaccination remains a critical public health challenge. Many SSA countries still couldn’t reach the target endorsed by the Global Vaccine Action Plan. Identifying contributing factors helps policymakers and planners to design potential interventions to avert missing opportunities for vaccination. Thus, this study aimed to assess the prevalence and factors associated with missed opportunities for vaccination.

Methods

This study used nationally representative demographic and health survey data collected from 2016 to 2023 among 44,149 children aged 12 to 23 months in SSA countries. Stata version 14 statistical package was used. Bi-variable and multivariable logistic regression analyses were done to ascertain factors associated with a missed opportunity for vaccination at a p-value less than 0.05 with adjusted odds ratios (AORs) with a 95% confidence interval.

Result

The overall prevalence of missed opportunity for vaccination (MOV) was 75.46% (95% CI: 75.04, 75.87%). The highest prevalence of MOV occurred in Gambia (93.85%), whereas the lowest was reported in Mauritania (41.59%). Children age: 12 to 15 months (AOR = 1.3, 95%CI:1.22, 1.36) and 16 to 18 months (AOR = 1.1, 95%CI: 1.04, 1.16), maternal age 35 to 49 (AOR = 1.1, 95%CI: 1.02, 1.17), married mother (AOR = 0.85, 95%CI: 0.80, 0.90), education: didn’t attend formal education (AOR = 1.13, 95%CI: 1.10, 1.22) and completed primary education (AOR = 1.1, 95%CI: 1.02, 1.16), ANC: never attend (AOR = 0.76, 95%CI:0.71, 0.82) and having four and above ANC visit (AOR = 0.9, 95%CI:0.85, 0.96), health insurance users (AOR = 1.4, 95%CI: 1.25, 1.49), male headed household (AOR = 1.35, 95%CI: 1.27, 1.43), religion: Muslim (AOR = 1.5, 95%CI:1.38, 1.57), Animist (AOR = 1.5, 95%CI:1.31, 1.64), and Catholic followers (AOR = 1.2, 95%CI:1.13, 1.31) and Protestant (AOR = 0.88, 95%CI:0.82, 0.95), home delivery (AOR = 0.79, 95%CI:0.75, 0.84), rural dwellers (AOR = 1.1, 95%CI: 1.04, 1.17), ever had media exposure (AOR = 0.91, 95%CI:0.86, 0.97), big problem to reach health facility (AOR = 1.1, 95%CI: 1.02, 1.14), high community wealth status (AOR = 0.91, 95%CI: 0.83, 0.99), low community level educational status (AOR = 1.2, 95%CI: 1.14, 1.38) and human development index: middle (AOR = 1.4, 95%CI: 1.21, 1.59) were factors associated with MOV.

Conclusion and recommendation

The overall pooled prevalence of missed opportunities for vaccination in Sub-Saharan remains high. Children’s age, maternal age, marital status, education, antenatal care visit, health insurance utilization, sex of household head, religion, place of delivery, residence, media exposure, distance to reach health facility, community wealth status, community educational status, and human development index of the countries were significantly associated with MOV. Policymakers have to encourage technology to promote multimedia exposure to increase community awareness about vaccination, encourage engagement of male partner, and religious leaders in child health. Additionally, potential stakeholders should give great emphasis for accessibility of education and health service for rural and remote areas, and poor segments of population through fostering community health workers and outreach programs, and financial support to increase vaccine coverage by averting missed opportunities for vaccinations. Qualitative research is recommended to explore the facilitators and barriers of missed opportunities for vaccinations among children.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-21273-3.

Background

Immunization has been identified as one of the most cost-effective strategies of disease prevention and has the greatest advantage in public health [1–3]. Additionally, immunization has remarkable societal and economic importance by decreasing catastrophic healthcare-related costs and the lost productivity because of ill health [4, 5]. Even though substantial progress has been made in the expansion of immunization coverage globally since the establishment of EPI in 1974, coverage has stalled nowadays and is still suboptimal in several countries [6]. Sub-Saharan Africa (SSA) countries are still struggling to enhance immunization coverage by ensuring universal access to routine immunization services [7].

Globally, in the last two decades, child mortality has decreased substantially; vaccination plays a vital role in decreasing under-five mortality from 12.3 million in 1990 to 5.3 million in 2018 [8]. However, there is a persistent, huge disparity in child mortality across regions, within and between countries [8]. The SSA region is the leading in under-five mortality rate in the world and accounts for nearly half (52%) of the global total under-five deaths [8]. According to a 2018 report, the SSA region had an average under-five mortality rate of 78 deaths per 1,000 live births, with 1 in 13 children dying before reaching their fifth birthday [8]. This implies the mortality rate is sixteen times higher than the average ratio of 1 out of 99 children in developed countries, and the majority of the deaths are preventable through vaccines, clean water and sanitation, adequate nutrition, and appropriate care by trained health workers as required [8]. This inequality between developed and developing countries can be decreased when all children have equal chances to access vaccines irrespective of their geographical location, socioeconomic status, or demographic status [9, 10]. In most cases, in many countries, children are partially vaccinated or unvaccinated [11]. Due to this, vaccine-preventable diseases remain the main causes of morbidity and mortality in many developing countries [8].

In the world, in 2023, about 84% (108 million) of children received three doses of diphtheria, tetanus, and pertussis (DPT3). However, around 14.5 million children have not received a single dose of vaccine [12].

The World Health Organization (WHO) has identified missed opportunity for vaccination (MOV) as the main contributor to suboptimal immunization coverage in the world [13, 14]. Missed opportunity for vaccination is defined as any contact of an eligible child (e.g., not vaccinated or partially vaccinated and free of contraindication to vaccine) with health service that does not result in the person receiving all the required vaccinations for which s/he is eligible [15]. The WHO strongly recommends the provision of vaccination service at every opportunistic contact with the health facility to increase and sustain immunization coverage progress [13]. In the developing countries, the magnitude of MOV varies widely across countries with an average of 32.2% [16]. Besides, in Sub-Saharan Africa, the magnitude of MOV ranges from 16% in Eswatini to 89% in Gabon [17, 18]. Missed opportunities for vaccination happen due to two major settings, such as [1] during visits for immunization and other preventive services (for instance, growth monitoring, nutritional assessment, and oral rehydration training sessions) and [2] during visits to a health facility for curative service. Avoiding missed opportunities during a visit of health facility will improve the overall immunization coverage in the community if there is availability of adequate resources and health services are high [13].

Scarcity of vaccine, tight clinical schedule that makes it difficult to separate immunization from other health services, providers’ and parents’ perception towards contraindication to vaccine, and failure or inability of health care providers to screen patients for eligibility are the most common enabling factors for MOV [19]. Different studies showed that individual-level factors such as child’s age and birth order, maternal age, educational level, media exposure, antenatal care uptake, and household income were factors associated with MOV [17, 20–23]. Moreover, contextual and health system-related factors such as residence, distance to health facility, number of available health personnel or vaccinators, and absence of vaccines were also associated with MOV [18, 24, 25].

Several Sub-Saharan African countries couldn’t reach the target endorsed by the Global Vaccine Action Plan (GVAP), which aims for at least 90% national coverage of the three-doses Diphtheria-Tetanus-Pertussis (DPT) vaccine [26].

Scholars suggested that intermittent systematic monitoring of immunization programs, identifying the potential barriers for introducing new vaccines, and developing context-specific interventions are crucial to curbing missed opportunities, which in turn improves vaccine coverage.

When the global vaccine coverage was enhanced, an extra one and a half million deaths related to diphtheria, neonatal tetanus, and pertussis could be avoided [27]. Knowing the main determinants of missed opportunity for vaccination at the individual, community, and national levels through multilevel analysis is crucial for setting an intervention and implementation strategies that enhance vaccination coverage. Plenty of previous studies focused on individual-level socio-demographic factors [28–30].

Furthermore, identifying the potential factors is crucial to curbing missed opportunities for vaccination and critical to achieving the Sustainable Development Goals of ensuring good health and well-being for all [31]. To enhance vaccine coverage, ensure Sustainable Development Goals, and decrease vaccine preventable disease needs careful assessment of potential factors of missed opportunities for vaccination, monitoring, and developing targeted interventions. Hence, this study aimed to assess the prevalence and determinants of missed opportunities for vaccination in Sub-Saharan African countries.

Methods

Study design and source of data

This study used a recent national representative demographic and health survey (DHS) secondary data from 2016 to 2023 in 20 Sub-Saharan Africa countries (Benin, Burkina Faso, Cameroon, Cote d’Ivoire, Gabon, Gambia, Ghana, Guinea, Kenya, Liberia, Madagascar, Mali, Mozambique, Mauritania, Nigeria, Senegal, South Africa, Tanzania, Uganda, and Zambia). These countries were selected based on the availability of recent DHS data. All 20 countries were included in this analysis. Countries with DHS before 2016 were excluded due to the long-time data collection period since there has been demographic change over time. The DHS data for all countries was obtained from the official database of the DHS programmer after sending an online request through https://dhsprogram.com by describing the objective of our study in order to get authorization guarantees from each country. Similar requests and processes were applied to access the DHS dataset from all selected countries. The children recode (KR) data set was used to extract the dependent and independent variables. All 90 Low and middle-income countries administered the nationally representative, comparable, and large sample size demographic and health survey every five years [32]. Standardized structured questionnaires were used to collect DHS data. The DHS data constitute the reproductive health, nutritional status, and maternal and child health issues and compute key demographic indicators of missed opportunity for vaccination.

A two-stage stratified sampling design with households as the sampling units was used to select the study participants (samples). In the first stage, enumeration areas were stratified into rural and urban areas to create clusters by using probability proportional allocation. In the second, from each cluster, households were chosen by using an equal probability systematic sampling procedure. Within selected households, all women and men meeting the eligibility criteria were interviewed. Weights are calculated to consider unequal selection probability and for non-response since the surveys are not self-weighting. As a result, the survey findings represent the full target population. The details of the sampling process for the DHS are described by Aliaga and Ruilin [33]. Different quality control measures are used to collect quality DHS data. Consistency across different countries is ensured by using the same variables and instruments (measures) to maintain comparability of the data [34, 35]. However, all countries are free to add specific variables of interest to fix its context. The DHS used standard sampling techniques and designs across all countries. The sampling procedures and design have been developed elsewhere [34]. The DHS data were gathered by trained survey staff that were instructed and adhered to the standard DHS procedures. The standards questionnaires are first prepared based on the countries’ official language and translated into their major local language at different data collection points to ascertain the respondents’ understandability of the survey [36]. In this study, the sample was limited to children aged 12 to 23 months during the survey period, yielding the desired final sample of 43,184 caretakers or mother-child pairs, and it was shown in the below diagram (supplementary Fig. 1).

Study variables

Outcome variables

The outcome variable for this study was missed opportunity for vaccination, which was dichotomized as missed opportunity for vaccination and not missed opportunity for vaccination. According to the World Health Organization (WHO) definition, if a child aged 12–23 months had any contact with health services, who is eligible for vaccination despite the child not receiving any vaccine during his/her visit to a health facility, is considered as a missed opportunity for vaccination that took the value “1” and others are coded as “0” [25]. Contact with health services includes six variables: delivery at the health facility, attendance at postnatal for baby checkup, received vitamin A at the healthcare facility, has health cards, recent medical treatment for fever/cough treatment, and diarrhea at the healthcare facility. However, the variable for receiving vitamin A dose was not used to calculate the missed opportunity for vaccination since the variable has no observation. Missed opportunity for vaccination was ascertained by recoding the variable H10 as missing value, not received vaccine and don’t know response as “missed opportunity for vaccination” and received vaccination as “not received vaccination”. The analysis was limited to one child for a woman in order to minimize over-representation of women with more than one child in the age category [15, 37].

Independent variables

The potential contributing factors were identified for the decomposition analysis by reviewing different literatures [15, 25, 37–39]. Child’s age, sex of a child, birth order, number of under five children in the household, maternal age (15–24, 25–34 and 35 or older), maternal education (no formal education, primary, secondary or higher education), employment status (working or not-working), media access (radio, television and newspaper), residence (rural/urban), marital status (unmarried/married), antenatal contact (no ANC visit, 1–3 ANC visit, and 4 and above ANC visit), size of child at birth(large, average, small), sex of household head (female/male), distance to health facility(big problem, not big problem), visited by filed health worker (yes/no), place of delivery(home/health facility), religion(Muslim, Catholic, Protestant, Animist and no religion), family size (1–3, 4–5 and 6+).

The DHS survey did not collect information directly on household income or expenditure. In the DHS, the wealth indexes are used as a proxy indicator for the socioeconomic status of the households in low- and middle-income countries [40, 41]. Briefly, the wealth index is constructed for every household by using principal components analysis based on the possession of the following household variables: electricity, television, radio, watch, telephone, refrigerator, types of vehicles, and variables describing the water supply and residing, such as the source of drinking water, type of toilet, material of principal flower, walls, roof, cooking fuel, and ownership of agricultural land, type and number of animals owned, and number of rooms per house. Based on these criteria, the wealth index of the household ranked in five quintiles (poorest, poorer, middle, richer, and richest) [15, 42].

Community variable such as community-level education, wealth, media exposure, and antenatal care, were composite variables that were created by aggregating the individual-level characteristics within the community or the clusters classified as low and high according to the percentage value performed for every cluster after checking the distribution through a histogram. The aggregated variable distribution was not uniformly distributed, as the result median (50%) value was used as a cut-off point to classify each community-level factor. A community-level factor that has a proportion value below 50% was considered low; otherwise it was considered as high. The individual and community-level independent factors were extracted by reviewing different literatures [43].

A country-level variable such as the human development index (HDI) was collected from the United Nations Development Program online report with respect to the country’s DHS data collected year and combined together via merging in Stata software [44]. The human development index measures a country’s extent of deprivation, the average percentage of deprivation exhibited by people in multidimensional poverty. Similar to wealth, the intensity of deprivation was computed using principal components by using data on household deprivations in education, health, and living standards. Then, the HDI variable was classified into three levels (low, middle, and high) [17].

Data management and statistical analysis

In this study, for data management and statistical analysis, Stata 14 software was used. The sample data were weighted by using the individual weight (v005) to ensure representativeness of the sample and to generate reliable estimation and standard errors since the overall probability of selecting a household is not the same. In the current study, four serious models were employed. The null model, or the empty model, includes only the outcome variable (MOV) to demonstrate the extent of variation in missed opportunity for vaccination. Model I was applied to measure the effect of merely individual-level factors. Besides, model II was done to measure the effect of merely the community-level factors. Moreover, model III was carried out to demonstrate the effect of both the individual and the community-level factors, and it was fitted. The Intra-class Correlation Coefficient (ICC) and median odds ratio were used for model comparison. Additionally, deviance (-2LLR) values were used to check the goodness of model fitness. Thus, model III was the best-fitted model since this model had the lowest deviance. Bi-variable analysis was employed, and those variables whose p-value was less than 0.2 were candidates for multivariable analysis. In multivariable analysis, those variables whose p-value was less than 0.05 with adjusted odds ratio within a 95% confidence interval were considered statistically associated with MOV.

Missing values were handled based on the DH guideline. The outcome variables that contained the missing value, didn’t know vaccination status, and never had vaccination were considered as missed opportunities for vaccination. Independent variables that had a missing value of more than 5% were excluded from further analysis because the DHS survey was a cross-sectional study.

Patient and public involvement statement

Although caregivers were involved to provide valuable information or insights, they didn’t participate in the study design, development of the protocol, data collection, or dissemination of the results.

Result

Sample characteristics

This study used 20 countries DHS data of children aged 12 to 23 months from Sub-Saharan African countries using 2016 to 2023 DHS data. Gabon and South Africa are countries that had high human development indexes. On the other hand, Cameron, Ghana, Kenya, and Zambia had moderate human development index (Table 1).

Table 1.

Description of demographic and health surveys data by countries, in Sub-saharan Africa 2016 to 2023

Countries	Survey year	Number of children	MOV (%)	Human development index
				Value	Category
Burkina Faso	2021	2358	89.66	0.445	Low HDI
Benin	2017/18	2526	80.42	0.506	Low HDI
Côte d’Ivoire	2021	1881	85.34	0.53	Low HDI
Cameron	2018	1882	80.45	0.581	Moderate HDI
Gabon	2019	1311	79.35	0.702	High HDI
Ghana	2022	1854	90.03	0.602	Moderate HDI
Gambia	2019/20	1517	93.85	0.492	Low HDI
Guinea	2018	1327	78.01	0.464	Low HDI
Kenya	2022	3409	78.32	0.601	Moderate HDI
Liberia	2019/20	982	71.88	0.483	Low HDI
Madagascar	2021	2168	72.40	0.484	Low HDI
Mali	2018	2023	68.45	0.417	Low HDI
Mauritania	2019/20	2124	41.59	0.538	Low HDI
Mozambique	2022	1736	79.37	0.461	Low HDI
Nigeria	2018	5533	57.26	0.53	Low HDI
Senegal	2023	1986	88.67	0.517	Low HDI
Tanzania	2022	2216	85.76	0.532	Low HDI
Uganda	2016	2967	72.11	0.531	Low HDI
South Africa	2016	704	71.93	0.711	High HDI
Zambia	2018	1958	78.74	0.571	Moderate HDI

*HDI = Human Development Index

Individual characteristics

The mean age of women was 28.26 years old and around 46.35% of age ranges from 25 to 34 years. Additionally, the mean age of the child was 17.27 months, and about 38.1% of the child’s age ranges from 19 to 23 months. More than one-third (37.77%) of childbirth orders were second and third. Besides, nearly half a percent of child sex was male. More than three-quarters (76.36%) of mothers give birth at health facilities, and 60.82% of them had four and more antenatal visits. Moreover, about two-thirds (69.25%) of the mothers were married. Similarly, about 36.26% of mothers attended secondary and higher education. Furthermore, more than three-fourths (76.47%) of the mothers didn’t use that internet, and around 70.67% of mothers didn’t have media exposure (Supplement Table 1).

Community level characteristics

More than half (60.99%) of the community caregivers were rural dwellers. Nearly two-thirds (67.15%) of the caregivers didn’t have a big problem getting health facilities. Additionally, more than half (53.92%) of respondents community wealth status was high. Moreover, half of the community caregivers had a high level of media exposure. Furthermore, nearly three-quarters (73.82%) of caregivers were from low human development index countries (Table 2).

Table 2.

Community level characteristics of mother-child pairs /care givers/ in Sub Saharan Africa, 2016–2023 DHS

Variable		Frequency (weighted)	Percentage(weighted)
Residence	Urban	16,566	39.01
	Rural	25,896	60.99
Distance to health facility	Big problem	13,947	32.85
	Not big problem	28,515	67.15
Community wealth status	High	22,894	53.92
	Low	19,569	46.08
Community level antenatal utilization	Low	21,343	50.26
	High	21,119	49.74
Community level media exposure	Low	22,765	53.61
	High	19,697	46.39
Community level education	High	20,647	48.62
	Low	21,815	51.38
HDI	Low	31,343	73.82
	Medium	9104	21.44
	High	2015	4.75

*HDI = Human Development Index

Prevalence of missed opportunity for vaccination

The prevalence of missed opportunity for vaccination in Sub Saharan Africa countries was 75.46% (95% CI: 75.04, 75.87%). The highest prevalence of MOV was reported in Gambia (93.85%), and the lowest prevalence was reported in Mauritania (41.59%) (Supplementary Fig. 2).

Random effect and model fitness

In the present study, around 8.33% of variability on MOV was attributed to cluster level variability. We choose multilevel modeling over standard single-level regression because the ICC value was greater than zero in the null model. Besides, the final model (model III) suggests there was significant inequality in missed opportunities for vaccination among clusters. Around 24.6% of missed opportunities for vaccination in clusters was explained in the final model of model III. By assuming both the individual and community/national level factors in the combined model, the variability decreases to 6.96%. Moreover, the PCV increases from 12.43% in model-I to 18% in model III, which indicates the variability in MOV. Furthermore, the model fitness was demonstrated using the deviance (-2LLR). Hence, the final best-fitted model was model III because it has the lowest deviance value (Table 3).

Table 3.

Model comparison and random effect analysis result of missed opportunity for vaccination in Sub Saharan Africa countries

Random effects	Null model	Model I	Model II	Model III
Variance (%)	30%	26.27%	29%	24.6%
ICC (%)	8.3%	7.4%	7.99%	6.96%
MOR	1.68	1.63	1.67	1.60
Explained variance (PCV)	Ref	12.43%	3.33%	18%
Model fitness
Deviance (-2LL)	46680.3	45765.26	46650.16	45615.6

Fixed effects (measure of association) results

Thus, children whose age from 12 to 15 months and 16 to 18 months were 1.29 and 1.1 times more likely to missed opportunity for vaccination as compared to those children whose age ranges from 19 to 23 months (AOR = 1.29, 95%CI:1.22, 1.36, AOR = 1.1, 95%CI:1.04, 1.16) respectively. Children of mothers whose age ranges from 35 to 49 years were 1.1 times more likely to experience missing an opportunity for vaccination when compared to mothers whose age ranges from 15 to 24 years (AOR = 1.11, 95% CI: 1.02, 1.17). Children of mothers who were married were 16% less likely to miss an opportunity for vaccination when compared to mothers who were separated (AOR = 0.85, 95% CI: 0.80, 0.90). Besides, children of mothers who never attended formal education and completed primary education were 1.13 and 1.1 times more likely to miss opportunities for vaccination when compared to children of mothers who completed secondary and higher education (AOR = 1.13, 95% CI: 1.10, 1.22) and (AOR = 1.1, 95% CI:1.02, 1.16), respectively.

Children of mothers who never attended antenatal care services and who had one to three antenatal visits were 33% and 10% less likely to miss the opportunity for vaccination as compared to children of mothers who had a history of four or more antenatal visits (AOR = 0.76, 95% CI: 0.71, 0.82, and (AOR = 0.9, 95% CI: 0.85, 0.96), respectively.

Children of mothers who had health insurance were 1.4 times more likely to miss opportunities for vaccination when compared to counterparts (AOR = 1.4, 95% CI: 1.25, 1.49). Similarly, children of mothers from male-headed households were 1.35 times more likely to exhibit missed opportunities for vaccination than children from female-headed households (AOR = 1.35, 95%CI: 1.27, 1.43).

Furthermore, children of mothers who were Muslim, animist, and Catholic followers were 1.5, 1.5, and 1.2 times more likely to have missed opportunities for vaccination than mothers who never had religion (AOR = 1.5, 95% CI: 1.38, 1.57, AOR = 1.5, 95% CI:1.31, 1.64, and AOR = 1.2, 95% CI: 1.13, 1.31), respectively. However, children of mothers who were Protestant were 12% less likely to experience missed opportunity for vaccination when compared to children of mothers who never had religion (AOR = 0.88, 95% CI: 0.82, 0.95).

Children of mothers who gave birth at home were 21% less likely to experience missed opportunities for vaccination as compared to children of mothers who gave birth at the health institution (AOR = 0.79, 95% CI: 0.75, 0.84). In addition, children of mothers from rural dwellers were 1.1 times more likely to exhibit missed opportunity for vaccination when compared to counterparts (AOR = 1.04, 95% CI: 1.06, 1.17).

Similarly, children born from mothers who had media exposure were less likely to miss an opportunity for vaccination when compared to mothers who never had media exposure (AOR = 0.91, 95% CI: 0.86, 0.97).

Regarding distance to health facility, children of mothers who had a big problem reaching a health facility had 1.1 times higher risk of missing an opportunity for vaccination than children of mothers who never had a big problem reaching a health facility (AOR = 1.1, 95% CI: 1.02, 1.14).

Children of mothers from high community-level wealth status were 9% less likely to exhibit missed opportunity for vaccination as compared to children of mothers from low community-level wealth status (AOR = 0.91, 95% CI: 0.83, 0.99). Moreover, children of mothers from low community-level educational status were 1.2 times more likely to experience missed opportunity as compared to their counterparts (AOR = 1.2, 95% CI: 1.11, 1.33). Lastly, a child from a country that had a middle human development index was 1.4 times more likely to experience missed opportunity for vaccination as compared to a child whose mother was from a country that had a high human development index (AOR = 1.4, 95% CI: 1.21, 1.59) (supplementary Table 2).

Discussion

This study aimed to assess the prevalence and determinants of missed opportunities for vaccination (MOV) in Sub-Saharan African countries. In this study, the prevalence of MOV was 75.46%, ranging as high as 93.85% in Gambia to as low as in Mauritania (41.59%). Individual-level factors such as child age, maternal age, maternal marital status, maternal education, attending ANC visits, health insurance utilization, media exposure, sex of household head, and religion; community-level factors such as residence, distance to the health facility, community-level wealth, and community-level education; and country-level factors of human development index were statistically associated with MOV.

This prevalence finding is comparable with study reports from Zambia (74.4%) [45], Gozamen, Ethiopia [46], and Chad (77%) [47]. However, it is higher than a study done in a systematic review (27.3%) [48], Gambia (60%), Malawi (66%), and Nigeria (66%). The possible justifications for the variation might be due to sample size, study area, and study period. This study used prevalence of DHS data from different countries with various prevalence of MOV that may greatly contribute to a higher magnitude of MOV than a single country report. Additionally, the present study included DHS data of 2020 and 2021, where Covid-19 outbreak becomes pandemic, and most health facilities services and outreach health service activities were severely compromised. As a result, routine vaccination services may not be delivered as intended at health facilities, which may lead to high missed opportunities for vaccinations [49, 50].

Moreover, this study finding is lower than studies conducted in low- and middle-income countries (90%) [16] and Malawi (92%) [47]. The reason might be due to enhanced implementation of different interventions since 2016, such as the initiation of electronic immunization recording, increasing of social media monitoring related to misinformation regarding vaccines, increasing of community-based approaches of vaccination, fostering Gavi’s vaccine alliance, and strengthening of mobile health applications. Electronic immunization recording in Tanzania and Zambia helps to identify children who do not start immunization and tracing defaulters that may avert missing opportunities for vaccination [51, 52]. Additionally, application of a mobile-based vaccination service platform potentially increases childhood vaccination service due to Short Message Service (SMS) text reminders in developing countries such as Kenya, Benin, Burkina Faso, Nigeria, Ghana, and Zimbabwe [53]. Moreover, now a days some African countries have a policy of vaccines mandatory for school entry as a requirement. This policy may contribute to decrease missed opportunities for vaccination because caregivers or mothers perceive that vaccination is a must for their children’s enrollment in school [54]. This implies technology advancement and social media advocacy influence to avert missing an opportunity for vaccination through clarifying the misinformation regarding vaccines.

Children whose ages were between 12 and 15 months, 16–18 months were more likely to miss an opportunity for vaccination as compared to children whose ages ranged from 19 to 23 months. This finding is similar to a study done in Sub-Saharan Africa, which showed that as the age of a child increases by one month, the probability of missing an opportunity for vaccination decreases by 2% [17].

Children of mothers whose age ranges from 35 to 49 were more likely to miss an opportunity for vaccination as compared to younger mothers (15 to 24 years). This finding is comparable with studies done in Africa [25] and Sub Saharan Africa [16]. However, this finding is in disagreement with study findings in Sub Saharan Africa [17]. The possible justification for the discrepancy might be that older mothers may have larger family sizes and responsibilities than younger mothers; as a result, they are busy, and they may not give attention to their children’s vaccinations. Besides, older mothers may perceived that they could manage their children’ common illnesses. Due to this, mothers may deem that the vaccine is less urgent and less crucial; previous studies showed that caregivers perceptions have associations with vaccination service utilizations [55]. The other possible reason might be that younger mothers may have gotten more attention than older mothers through parental visits, modern communication channels, and campaigns.

Another notable result of the current study is that married caregivers were less likely to miss an opportunity for vaccination as compared to separated caregivers. The possible justification for the discrepancy might be that married women may have stable households, get emotional support from their partners, and participate in joint decision-making with their parents that makes the women to remind vaccination schedule and bring their children to the health facilities for vaccination.

This study also highlighted a surprising finding: children of mothers who were health insurance users were more likely to miss an opportunity for vaccination when compared to nonusers. The reason might be that despite the mother or caretakers being health insurance users, they still may have poor attitude and awareness for vaccination, limited access to vaccines at health facilities, and insured caretakers may seek complex healthcare services over vaccine services.

Another surprising finding of this study is that mothers who never had antenatal care attendance and mothers who had one to three antenatal care visits were less likely to miss an opportunity for vaccination as compared to mothers who had four or more antenatal care attendance. This finding is in contrast with a previous study [37]. The possible reason for the variation might be mothers who never had antenatal care attendance may experience adverse birth outcomes and complications that may provoke crisis-driven action to have vaccinations strongly for their vulnerable infants. The other reason might be the self-motivation of mothers, mother’s perception about the importance of vaccines, and there may be cultural norms that encourage vaccination even though they have no antenatal care visits.

Notably, children of mothers who didn’t attend formal education were at higher risk of missing an opportunity for vaccination than mothers who completed secondary and higher education. This finding is in line with a study done in Sub-Saharan Africa [15], a systematic review [25], Ilorin Metropolis [56], South Africa, Mozambique [57], East Gojjam, Ethiopia [46], Jimma, Ethiopia [58], and Deber tabour, Ethiopia [55]. Similarly, children born to mothers who completed primary education were more likely to miss an opportunity for vaccination than those mothers who attended secondary and higher education. The plausible justification for the discrepancy might be that the illiterate mothers could have poor knowledge regarding the benefit of vaccination for their children. The other possible reason is illiterate mothers could have limited access to immunization messages because they couldn’t read reliable information, including the appointment dates from immunization cards. In addition, uneducated mothers are mostly influenced by social and cultural norms, have limited power in decision-making on maternal and child health-seeking and treatment, and they are financially dependent on parents [55]. However, this finding is in contrast with a study done in Saudi Arabia [59]. This suggests policymakers and planners have to focus on maternal education to curb missing opportunities for vaccination of children.

On the other hand, children born from households headed by males had higher odds of missed opportunities for vaccination than children born from female-headed households. The possible justification might be that men are more focused on their work, and they may not have information regarding the advantages of vaccines because mostly men are not available at home during health workers outreach and campaign activities and may have less time in health engagement. The other reason could be that in male-headed households, women have a low role in decision-making regarding their children’s vaccination. This implies that concerted effort is needed to empower women in order to decrease the risk of missing an opportunity for vaccination since women with high empowerment contributed to decreasing missing opportunities for vaccinations [60].

Children born from Muslim and Catholic followers, and animist mothers were more likely to miss an opportunity for vaccination as compared to children born from mothers who never had religion. This study finding is consistent with a study done in Sub-Saharan Africa [61]. Evidence from a previous study showed that Muslims and Animist were less urbanized, had lower formal education, and lived in poverty [61]. In addition, Animist followers heavily believe in traditional healers and contrast secular community or world, mistrust vaccines and medicine, and have limited access to uptake vaccination service due to strong influence by community elders, spiritual and healer leaders [62]. This indicates that potential stakeholders have to design an intervention that focuses on the religious leaders, community elders, and community civic organization leaders to increase the level of awareness of followers.

This study also highlighted that children of mothers who had media exposure were less likely to miss an opportunities for vaccination when compared to their counterparts. This finding was comparable with a study conducted in Sub-Saharan Africa [15]. Access to media increases mothers or caregivers awareness about the benefit of vaccines and the burden of vaccine-preventable diseases. Additionally, it helps to mobilize community and foster social networks regarding vaccination in the community where there is limited access to vaccination [15]. This implies that media exposure has a great contribution to curbing missing opportunities for vaccination. Thus, the concerned body has to encourage media exposure to the community to enhance caregivers or maternal awareness regarding the merit of vaccination for children.

The other contributing factor for higher missed opportunities for vaccination was residence. This study found that children who lived in rural areas were more likely to miss an opportunity for vaccination as compared to urban dwellers. This result is congruent with studies done in Nigeria [63], Jimma, Ethiopia [58], and Debere Tabor, Ethiopia [55]. The reason for the variation may be due to long-distance walking being the major reason for the missed opportunity for vaccination [63]. The other reason may be that there is a shortage of health care providers in rural health facilities that makes the mother/caregiver wait a long time to receive the service, which in turn leads to missing the vaccine. Additionally, rural mothers/caregivers are less educated, and they are poor, that may have contributed to missing an opportunity for vaccination. This implies that rural women or caregivers need attention to decrease missed opportunities for vaccination.

Moreover, caregiver who had a big problem reaching a health facility were at high risk of missing an opportunity for vaccination as compared to counterparts. The possible justification may be that health facilities that are difficult to reach out to by the clients are mostly understaffed, there is a paucity of vaccine drugs, and difficult to arrange an immunization day at the health facility due to infrequent visit by caregivers. This may contact leads to missing the opportunity for vaccination. In addition, caregivers who live in remote areas are less likely to have awareness about vaccines, and they visit health facilities mostly by focusing on their immediate need of child illness. Due to these reasons, they were more likely to miss the opportunity for vaccination.

The other notable finding of this study was that caregivers from high-wealth communities had a lower risk of missed opportunities for vaccination as compared to caregivers from low-wealth communities. The reason might be that wealthier communities are more likely to have higher education level awareness regarding vaccination service, may have transportation access, and are more likely to have health insurance that makes them have more frequent visits to health facilities without hesitation, even though vaccination service is an exempted service in most developing countries. This implies that multi-sectoral collaboration efforts are needed enhance community wealth levels, which greatly attribute to increasing immunization coverage by decreasing missed opportunities for vaccination.

On the other hand, caregivers from communities that had low levels of education had a high risk of missing an opportunity for vaccination as compared to counterparts. The possible justification might be that caregivers in low-educated communities may have low awareness and be more influenced by social and cultural norms; as a result, they are less likely to uptake vaccination services while they visit the health facility. The other reason may be bad experience from neighbors and fear of adverse events of vaccines, which may hinder them to using vaccine services [64]. This implies raising community-level education is the key means to increase societal health awareness, especially the preventive health intervention of vaccination service.

Furthermore, caregivers from countries that had a middle human development index were more likely to miss an opportunity for vaccination when compared to caregivers from countries that had a high human development index. This finding is comparable with a previous study [17]. The plausible justification for the discrepancy may be due to countries that have a middle human development indexes having poor educational and wealth status, limited health care access, and weak public health policy regarding immunization. These reasons may hinder a caregiver from uptake vaccination service for their children. This suggests countries with middle human development index needs substantial increasing in reaching education-deprived societies, financially unsecured communities, strengthening equity of health care access, and designing an intervention that focuses on vaccination with realistic implementation strategies.

Strength and limitation

This study used a national representative demographic and health survey data of large sample size using different countries appended data. Nevertheless, the respondents were asked few questions that assessed events that happened in the past five years prior to the survey. Due to this, participants may not remember the events that may lead to recollection bias. Besides, the current study was unable to show a cause-effect relationship since this study used a cross-sectional study design. Additionally, in this study, some important variables related to miss opportunities for vaccination were not included because of high missing values in the data. Moreover, comparability of the DHS data might be affected since using pooled DHS data collected from different countries in different years may lead to potential changes in demographics, health policies, and other key factors over time. Furthermore, this finding may be underestimated, since this study did not include second year of life (beyond 23 months). Hence, some vaccination and booster doses were not considered in this study.

Conclusion

In this study, the overall prevalence of missed opportunities for vaccination among children 12 to 23 months in Sub-Saharan remains high. Children’s age, maternal age, marital status, education, antenatal care visit, health insurance utilization, sex of household head, religion, place of delivery, residence, media exposure, distance to reach health facility, community wealth status, community educational status, and human development index of the countries were significantly associated with MOV. Policymakers have to leverage technology to promote multimedia exposure to increase community awareness about vaccination, encourage engagement of male partner, and religious leaders in child health. Additionally, potential stakeholders should give great emphasis for accessibility of education and health service for rural and remote areas, and poor segments of population through fostering community health workers and outreach programs, and financial support to increase vaccine coverage by averting missed opportunities for vaccinations. Qualitative research is recommended to explore the facilitators and barriers of missed opportunities for vaccination among children.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

ANC

Antenatal care

AOR

Adjusted odds ratio

CI

Confidence interval

DHS

Demography health survey

ICC

Inter-class correlation coefficient

GVAP

Global Vaccine Action Plan

HDI

Human development index

and MOV

Missed opportunity for vaccination

Author contributions

MJ had commenced the research concept, wrote up the research proposal, extracted and analyzed the DHS data, presented the results, and prepared the manuscript. KAD participated in the data analysis and the interpretation of the result and was involved in writing the manuscript. TZA, MGT, AH, WDN, GT, and MMT participated in the data extraction, data analysis, and interpretation of the findings. TZT, MGT, AH, GT, MMT, WDN, and KAD were involved in the discussion, review, and summarization of the manuscript. All the authors read and approved the final manuscript.

Funding

No funding was secured for this study.

Data availability

The current study data can be accessed upon a winnable reason request from the corresponding author, and anyone who is interested can obtain the data freely via the online website www.measuredhs.com.

Declarations

Ethical approval

We sent an online free request to the DHS programmer to obtain ethical approval and permission through www.measuredhs.com. The original DHS data of each country was collected by adhering to the international and ethical guidelines after receiving approved ethical clearance from the ICF Macro Institute Review Board, the Center for Disease Control and Prevention (CDC), and the Institute Review Board (IRB) from every country in line with the United States Department of Health and Human Services requirement for human participant protection. In addition, during the data collection process. All the study participants and/or their legal guardians of minors (age less than 16) gave written informed consent to participate. All the procedures were done according to the Helsinki declaration. Confidentiality was maintained through not disclosing the information obtained from the DHS dataset to a third party. Furthermore, the present study is not an experimental study. A further detailed description of how the DHSs uses data and its ethical clearance can be found athttp://goo.gl/ny8T6X.

Consent for publication

It is not applicable for this study since the current study used a secondary data analysis done by the Central Statistical Agency of the countries.

Competing interests

The authors declare no competing interests.