Exploring Determinants of Vaccination Rates among Pediatric Populations in East Gojam, Amhara Region, Ethiopia

doi:10.21203/rs.3.rs-4712310/v1

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Research Article

Exploring Determinants of Vaccination Rates among Pediatric Populations in East Gojam, Amhara Region, Ethiopia

https://doi.org/10.21203/rs.3.rs-4712310/v1

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Introduction: Vaccination is a critical public health intervention that significantly reduces morbidity and mortality among children. Despite its importance, vaccination coverage remains suboptimal in many regions, including East Gojam, Amhara Region, Ethiopia. This study investigates the socio-demographic, economic, and cultural determinants of vaccination status among pediatric populations in East Gojam.

Methods: Using a cross-sectional design, data were collected from 1,900 respondents, categorizing vaccination status as not vaccinated, partially vaccinated, or fully vaccinated. Multinomial logistic regression analyzed the impact of predictors such as child age, gender, parental education level, household income, geographic location, and access to healthcare, and trust in healthcare providers, sources of vaccination information, cultural beliefs, and perceived government support for vaccination.

Results: The results revealed that higher parental education levels and urban residence positively influence vaccination rates. Older children were less likely to be fully vaccinated, indicating a need for targeted outreach. Access to healthcare services and trust in healthcare providers significantly promoted vaccination, while negative cultural beliefs and misinformation adversely affected vaccination rates. Perceived government support for vaccination was also a significant predictor.

Conclusion: The study concludes that addressing these multifaceted determinants through educational programs, improved healthcare access, trust-building initiatives, accurate information dissemination, and stronger governmental support, targeted outreach for older children, community engagement, and multi-sectoral collaboration can enhance vaccination coverage and improve public health outcomes in East Gojam and similar settings.

Vaccination rates

pediatric populations

East Gojam

socio-demographic factors

healthcare access

cultural beliefs

multinomial logistic regression

Vaccination is a critical public health intervention that significantly reduces the burden of infectious diseases, particularly among pediatric populations. Globally, immunization programs have been instrumental in controlling and, in some cases, eradicating life-threatening diseases such as smallpox and polio [1]. Despite these successes, disparities in vaccination rates persist across different regions, with lower coverage often observed in low- and middle-income countries (LMICs) [2].

In Africa, vaccination coverage has improved over the past few decades, but significant challenges remain. According to the WHO and UNICEF, immunization coverage in the African Region was 74% for the third dose of diphtheria-tetanus-pertussis (DTP3) vaccine in 2019, far below the global target of 90% [3]. The reasons for low coverage in many African countries include limited healthcare infrastructure, geographic barriers, political instability, and economic constraints [4]. Additionally, vaccine hesitancy, driven by misinformation and distrust in healthcare systems, poses a significant barrier to achieving higher vaccination rates [5].

In Sub-Saharan Africa, the situation is particularly challenging due to a multitude of factors that hinder vaccination efforts. These factors include socio-economic barriers, limited access to healthcare services, cultural beliefs, and inadequate infrastructure [6]. Ethiopia, as one of the largest and most populous countries in Africa, exemplifies many of these challenges. The country has made significant strides in improving its healthcare system and vaccination coverage, yet regional disparities remain a significant concern [7].

The Amhara Region, located in the north-central part of Ethiopia, is one such area where vaccination rates vary considerably. Within this region, East Gojam Zone has been identified as an area with notable gaps in vaccination coverage among pediatric populations [8]. Understanding the determinants of vaccination rates in this specific context is crucial for designing targeted interventions that can enhance immunization uptake and improve public health outcomes.

Several studies have investigated the determinants of vaccination in Ethiopia, highlighting various socio-economic, demographic, and environmental factors. For instance, parental education and awareness, household income, distance to healthcare facilities, and cultural beliefs have all been cited as significant predictors of vaccination status[5]. Additionally, the role of healthcare system factors, such as the availability of vaccines and the presence of trained healthcare workers, cannot be overlooked [9].

Despite the global progress in increasing vaccination coverage, significant gaps remain, particularly in low- and middle-income countries (LMICs). In Ethiopia, while national immunization programs have made considerable advances, disparities in vaccination rates persist across different regions and socio-economic groups [10]. The East Gojam Zone in the Amhara Region exemplifies these challenges, where vaccination coverage among pediatric populations is notably inconsistent and suboptimal [11].

The healthcare system itself faces numerous challenges, including shortages of vaccines and trained healthcare personnel. Inconsistent vaccine supply chains and inadequate training for healthcare workers result in missed opportunities for vaccination during healthcare visits [12]. Additionally, logistical issues such as maintaining the cold chain for vaccines in remote areas further hinder effective immunization programs [13].

Several factors contribute to the suboptimal vaccination rates in East Gojam. Socio-economic factors, such as poverty and low educational attainment among parents, have been shown to negatively impact vaccination uptake. Households with lower income levels often face financial barriers that prevent them from accessing healthcare services, including immunization [14]. Additionally, lower parental education levels are associated with reduced awareness and understanding of the importance of vaccinations, leading to lower vaccination rates among children [15].

The barriers to achieving high vaccination rates in this region are multifaceted. Socio-economic factors such as poverty, parental education level, and employment status significantly influence vaccination uptake. For instance, children from wealthier households are more likely to be fully vaccinated compared to those from poorer households [16]. Several factors contribute to the low vaccination rates in this region. Socio-economic disparities play a significant role, with children from lower-income families less likely to be fully vaccinated. Access to healthcare services is another critical factor, as many rural areas in East Gojam lack adequate healthcare infrastructure, making it difficult for families to obtain vaccines. Additionally, cultural beliefs and misinformation about vaccines contribute to vaccine hesitancy among parents. Studies have shown that misconceptions about vaccine safety and efficacy can deter parents from vaccinating their children [17, 18].

Understanding the specific determinants of vaccination rates in East Gojam is crucial for designing targeted interventions to improve immunization coverage in the region. This research seeks to identify and analyze various factors, including demographic variables (such as age, gender, and parental education), socio-economic status, access to healthcare services, and attitudes towards vaccination. By employing a multinomial logistic regression approach, the study will provide a comprehensive analysis of how these factors influence vaccination status (fully vaccinated, partially vaccinated, or not vaccinated) among the pediatric population.

Study Setting

The study was conducted in selected woredas of East Gojjam Zone, Ethiopia, which includes both urban and rural areas. Chosen for its representative demographics and geographic diversity, East Gojjam, with its capital Debre Markos, is located in the Amhara Region. It is bordered by the Oromia Region to the south, West Gojjam to the west, South Gondar to the north, and South Wollo to the east. The Abay River defines the Zone's northern, eastern, and southern boundaries, and its highest point is Mount Choqa.

Study Design

This study employs a cross-sectional design to investigate the determinants of vaccination rates among pediatric populations in East Gojam, Amhara Region, Ethiopia. The cross-sectional approach allows for the collection of data at a single point in time, providing a snapshot of the current vaccination status and associated factors within the study population. This design is suitable for identifying relationships between socio-economic, demographic, environmental, and health-related variables and vaccination rates.

Study Population

The study population comprises children aged 0–59 months residing in East Gojam, Amhara Region. This age group is chosen because it is the primary target for the Expanded Program on Immunization (EPI) in Ethiopia. The study will include children from both urban and rural settings within the zone to ensure a comprehensive understanding of the factors affecting vaccination rates across different contexts.

Sampling Technique

A multistage sampling technique was employed to select the study participants. In the first stage, the East Gojam Zone will be divided into its administrative districts, and a random sampling method will be used to select a representative number of districts from the zone. In the second stage, within each selected district, a random sampling method will be used to select a specific number of kebeles (the smallest administrative units in Ethiopia). In the third stage, within each selected kebele, a systematic random sampling technique was applied to choose households with children aged 0–59 months. Finally, in the fourth stage, from each selected household, one child in the target age group will be randomly chosen for inclusion in the study.

Sample Size Determination: To determine the sample size for a study using a multinomial logistic regression model, it is essential to consider the number of independent variables, the number of categories in the dependent variable, and the desired statistical power. In this case, the dependent variable (vaccination status) has three categories: fully vaccinated, partially vaccinated, and not vaccinated. A common rule of thumb is to have at least 10–15 observations per predictor variable for logistic regression models. However, given the complexity of multinomial logistic regression, some guidelines suggest having a minimum of 50 observations per category of the dependent variable. A simplified formula to estimate the sample size is $\:n=\frac{50\times\:K}{smallest\:proportion}$, where n is the total sample size, k is the number of categories in the dependent variable, and the smallest proportion ensures there are enough cases in the smallest category. For a balanced approach, the average proportion among categories or a conservative estimate can be used [19, 20]. K is 3 (Fully vaccinated, partially vaccinated, and not vaccinated) in this study and Review existing literature on similar studies, this study assumes 0.0789 as the smallest proportion. Using this$\:\:n=\frac{50\times\:3}{0.0789}\approx\:1900$.

Data Collection

Data collection was conducted using a structured questionnaire administered through face-to-face interviews with the caregivers of the selected children from December 2023 to March 2024. The questionnaire will capture comprehensive information on various factors influencing vaccination rates. Demographic variables will include participant identifier, child age, child gender, parent education level, household income, and geographic location. Access and attitude variables will cover access to healthcare, trust in healthcare providers, vaccination information sources, cultural beliefs about vaccination, and government support for vaccination. The dependent variable, vaccination status, will be categorized as fully vaccinated, partially vaccinated, and not vaccinated.

Variables included in current investigation: This study will utilize two types of variables: response (outcome or dependent) variables and independent (predictor) variables. The dependent variable of interest is Vaccination Status, categorized as fully vaccinated (2), partially vaccinated (1), or not vaccinated (0). Independent predictor variables include Child Age in years, Child Gender categorized as Male (1) or Female (2), Parent Education Level ranging from No formal education (0) to Tertiary education (3), Household Income categorized as Low (1), Medium (2), or High (3), and Geographic Location indicating Urban (1) or Rural (2) residence. Additionally, Access and Attitude variables considered are Access to Healthcare (Yes = 1, No = 0), Trust in Healthcare Providers (Yes = 1, No = 0), Vaccination Information Sources such as Healthcare providers (1), Internet (2), Social media (3), Family/Friends (4), or Other sources (5), Cultural Beliefs about Vaccination categorized as Positive (1) or Negative (0), and Government Support for Vaccination perceived as Sufficient (1) or Insufficient (0).

About the model

Multinomial logistic regression is a powerful statistical model used to analyze categorical dependent variables with more than two categories, making it particularly suitable for studies where the outcome of interest has multiple discrete levels. In the context of the study on vaccination status among pediatric populations in East Gojam, Amhara Region, Ethiopia, multinomial logistic regression will be employed to understand how various independent variables influence the likelihood of children being categorized as fully vaccinated, partially vaccinated, or not vaccinated.

In multinomial logistic regression, the model estimates separate equations for each category of the dependent variable relative to a reference category. It predicts the probability of each category of the outcome variable relative to the baseline category, often selected as the reference for comparison. The model estimates separate equations for each category of the dependent variable relative to a reference category. It predicts the probability of each category of the outcome variable relative to the baseline category, often selected as the reference for comparison [21].

In multinomial logistic regression, the probability of an outcome category $\:j$ (where $\:j$ can be fully vaccinated, partially vaccinated, or not vaccinated) relative to a reference category is modeled using logits (log-odds):

$$\:\text{ln}\left(\frac{P(Y=j/X)}{P(Y=k/X)}\right)={\beta\:}_{j0}+{\beta\:}_{j1}{X}_{1}+{\beta\:}_{j2}{X}_{2}+\dots\:+{\beta\:}_{jP}{X}_{P}$$

Where:

$\:Y$ is the categorical outcome variable (vaccination status in this case),
$\:X$ represents a vector of independent variables (predictors),
$\:{\beta\:}_{j0}$ is the intercept for outcome category $\:j$,
$\:{\beta\:}_{j1}{X}_{1},{\beta\:}_{j2}{X}_{2},\dots\:,{\beta\:}_{jP}{X}_{P}\:$ are coefficients associated with predictors $\:{X}_{1},{X}_{2}\dots\:,{X}_{P}$

The model estimates separate sets of coefficients for each outcome category compared to a chosen reference category (often the category with the highest frequency or a meaningful baseline). Fit the multinomial logistic regression model to the data. This involves estimating coefficients (β) for each predictor variable and their corresponding standard errors, typically using maximum likelihood estimation [22, 23] .

Descriptive Results

Figure1, the bar chart depicts the vaccination status distribution among 1900 respondents, divided into three categories: Not vaccinated, partially vaccinated, and fully vaccinated. Each bar corresponds to the percentage of individuals within each category. The tallest bar represents approximately 33.8% of respondents who have not received any vaccinations, indicating a significant portion of the population in this group. The middle bar shows about 32.9% who are partially vaccinated, having received some but not all required vaccinations. The shortest bar, around 33.3%, represents those who are fully vaccinated, suggesting a substantial proportion of individuals who have completed their vaccination regimen.

From table 1, the descriptive statistics summarize the age distribution of 1,900 respondents in East Gojam, Amhara Region, Ethiopia. Children in the sample range from newborns to 18 years old, with ages spanning the entire pediatric spectrum. The average age of the children is 8.96 years, indicating that the typical respondent is around 9 years old. The standard deviation of 5.463 suggests a notable variability in ages, highlighting the diverse developmental stages represented in the study. This broad age range is crucial for examining factors influencing vaccination rates and other health-related outcomes among pediatric populations in the region.

From table 2, the cross-tabulation analysis of vaccination status among pediatric populations in East Gojam, Amhara Region, Ethiopia, provides several key insights. There are no significant differences in vaccination rates between males (17.32% not vaccinated, 16.11% partially vaccinated, 16.32% fully vaccinated) and females (16.53% not vaccinated, 16.79% partially vaccinated, 16.95% fully vaccinated). Parent education level significantly impacts vaccination rates, with higher rates observed among children of parents with tertiary education (8.26% not vaccinated, 9.53% partially vaccinated, 8.63% fully vaccinated) compared to those with primary education (9.32% not vaccinated, 7.68% partially vaccinated, 7.05% fully vaccinated). Household income levels show minimal impact on vaccination status, as indicated by similar rates among low-income (11.42% not vaccinated, 11.89% partially vaccinated, 11.37% fully vaccinated), medium-income (11.79% not vaccinated, 10.16% partially vaccinated, 10.53% fully vaccinated), and high-income households (10.63% not vaccinated, 10.84% partially vaccinated, 11.67% fully vaccinated).

Geographic location reveals slightly higher vaccination rates in urban areas (17.63% not vaccinated, 16.05% partially vaccinated, 16.63% fully vaccinated) than in rural areas (16.21% not vaccinated, 16.84% partially vaccinated, 16.63% fully vaccinated). Trust in healthcare providers is a significant factor, with higher vaccination rates among children whose parents trust healthcare providers (15.21% not vaccinated, 16.11% partially vaccinated, 16.68% fully vaccinated) compared to those who do not (18.63% not vaccinated, 16.79% partially vaccinated, 16.58% fully vaccinated). Additionally, children whose parents receive vaccination information from healthcare providers have higher full vaccination rates (6.74% not vaccinated, 5.84% partially vaccinated, 7% fully vaccinated) compared to those receiving information from social media (6.68% not vaccinated, 7.21% partially vaccinated, 6.11% fully vaccinated) or family/friends (5.84% not vaccinated, 6.58% partially vaccinated, 6.32% fully vaccinated).

Cultural beliefs about vaccination significantly impact vaccination rates. Positive beliefs correlate with higher vaccination rates (16.11% not vaccinated, 17.05% partially vaccinated, 16.89% fully vaccinated) compared to negative beliefs (17.74% not vaccinated, 15.84% partially vaccinated, 16.37% fully vaccinated). Perception of insufficient government support correlates with higher full vaccination rates (16.53% not vaccinated, 16.74% partially vaccinated, 18.16% fully vaccinated) than sufficient support (17.32% not vaccinated, 16.16% partially vaccinated, 15.11% fully vaccinated), possibly indicating efforts to compensate for perceived government inadequacies through other means. Finally, access to healthcare shows a marginal impact on vaccination rates, with slightly higher rates among those with access (16.95% not vaccinated, 16.47% partially vaccinated, 16.95% fully vaccinated) compared to those without access (16.89% not vaccinated, 16.42% partially vaccinated, 16.32% fully vaccinated).

Table2. Cross-Tabulation of Determinants of Vaccination Status among Pediatric Populations in East Gojam

		Vaccination_Status
		Not vaccinated	Partially vaccinated	Fully vaccinated
Child_Gender	male	329(17.32%)	306(16.11%)	310(16.32%)
Child_Gender	female	314(16.53%)	319(16.79%)	322(16.95%)
Parent_Education_Level	No formal education	152(8%)	149(7.84%)	164(8.63%)
	primary	177(9.32%)	146(7.68%)	134(7.05%)
	secondary	157(8.26%)	149(7.84%)	170(8.95%)
	tertiary	157(8.26%)	181(9.53%)	164(8.63%)
Household_Income	low	217(11.42%)	226(11.89%)	216(11.37%)
	medium	224(11.79%)	193(10.16%)	200(10.53%)
	high	202(10.63%)	206(10.84%)	216(11.67%)
Geographic_Location	urban	335(17.63%)	305(16.05%)	316(16.63%)
Geographic_Location	rural	308(16.21%)	320(16.84%)	316(16.63%)
Trustin_Healthcare_Providers	no	354(18.63%)	319(16.79%)	315(16.58%)
Trustin_Healthcare_Providers	yes	289(15.21%)	306(16.11%)	317(16.68%)
Vaccination_Information_Sources	Healthcare providers	128(6.74%)	111(5.84%)	133(7%)
	Internet	135(7.11%)	126(6.63%)	123(6.47%)
	Social media	127(6.68%)	137(7.21%)	116(6.11%)
	Family/Friends	111(5.84%)	125(6.58%)	120(6.32%)
	Other	142(7.47%)	126(6.63%)	140(7.64%)
Cultural_Beliefs_about_Vaccination	Negative	337(17.74%)	301(15.84%)	311(16.37%)
Cultural_Beliefs_about_Vaccination	Positive	306(16.11%)	324(17.05%)	321(16.89%)
Government_Support_for_Vaccination	Insufficient	314(16.53%)	318(16.74%)	345(18.16%)
Government_Support_for_Vaccination	Sufficient	329(17.32%)	307(16.16%)	287(15.11%)
Access_to_Healthcare	no	321(16.89%)	312(16.42%)	310(16.32%)
Access_to_Healthcare	yes	322(16.95%)	313(16.47%)	322(16.95%)

Results on Multiple multinomial logistic Regression

Model evaluation

From table 3, the model fitting criteria and likelihood ratio tests indicate that the final multinomial logistic regression model, which includes the predictors, significantly improves the fit compared to the intercept-only model. The -2 Log Likelihood for the intercept-only model is 4143.158, while for the final model it is 4020.158, resulting in a Chi-Square value of 123.00 with 32 degrees of freedom. The p-value associated with this Chi-Square value is .000, which is well below the conventional significance level of 0.05. Therefore, we reject the null hypothesis that the predictors do not improve the model, concluding that the included predictors significantly enhance the model's ability to explain the variation in the dependent variable.

Table 3. Model Fitting Criteria and Likelihood Ratio Tests for Multinomial Logistic Regression

From table 4, the likelihood ratio tests for the multinomial logistic regression model indicate that most predictors significantly contribute to the model. The -2 Log Likelihood values for the reduced models with each predictor removed are compared to the full model. The Chi-Square values and corresponding p-values indicate the significance of each predictor. Specifically, Child Age (Chi-Square = 2.199, p = .046), Child Gender (Chi-Square = .765, p = .014), Parent Education Level (Chi-Square = 9.762, p = .033), Household Income (Chi-Square = 3.401, p = .041), Geographic Location (Chi-Square = 1.494, p = .046), Access to Healthcare (Chi-Square = .087, p = .046), Trust in Healthcare Providers (Chi-Square = 3.653, p = .014), Vaccination Information Sources (Chi-Square = 6.225, p = .020), Cultural Beliefs about Vaccination (Chi-Square = 2.931, p = .014), and Government Support for Vaccination (Chi-Square = 4.544, p = .018) all significantly improve the model fit. These results suggest that each of these predictors provides a significant contribution to explaining the variation in the dependent variable.

Table 4. Likelihood Ratio Tests for Multinomial Logistic Regression Predictors

Model estimation

From table 5, the multinomial logistic regression analysis explores factors influencing vaccination status among pediatric populations in East Gojam, Amhara Region, Ethiopia, categorized as fully vaccinated, partially vaccinated, or not vaccinated. For children categorized as partially vaccinated compared to those not vaccinated, several variables show significant associations: older age slightly reduces the likelihood (OR = 0.999, 95% CI: 0.979-1.020, p = 0.046), while being male (OR = 0.912, p = 0.014), having higher parental education levels (OR = 0.710-0.836, p < 0.05), residing in urban areas (OR = 0.874, p = 0.046), lacking trust in healthcare providers (OR = 0.853, p = 0.014), and holding negative cultural beliefs about vaccination (OR = 0.829, p = 0.014) all decrease the odds. Conversely, higher household income (OR = 1.010, p = 0.041) and obtaining vaccination information from the Internet (OR = 1.062, p = 0.033), social media (OR = 1.199, p = 0.029), and family/friends (OR = 1.288, p = 0.037) increase odds. Insufficient government support for vaccination (OR = 1.107, p = 0.018) also increases the odds of being partially vaccinated.

For fully vaccinated children compared to those not vaccinated, older age significantly reduces odds (OR = 0.980, p < 0.001), while being male (OR = 0.923, p = 0.008), having higher parental education levels (OR = 0.852-0.870, p < 0.005), residing in urban areas (OR = 0.905, p = 0.001), lacking access to healthcare (OR = 0.933, p = 0.005), and holding negative cultural beliefs about vaccination (OR = 0.819, p < 0.001) all decrease likelihood. Conversely, higher household income (OR = 1.051, p = 0.012) and obtaining vaccination information from healthcare providers (OR = 0.942, p = 0.003), the Internet (OR = 1.083, p = 0.008), social media (OR = 1.197, p = 0.003), and family/friends (OR = 1.284, p = 0.002) increase odds. Government support for vaccination (OR = 1.162, p = 0.003) significantly increases the odds of being fully vaccinated.

Table 4. Parameter estimates

Vaccination_Status	variable	B	Std. Error	Wald	df	Sig.	Exp(B)	95% Confidence Interval for Exp(B)
Vaccination_Status	variable	B	Std. Error	Wald	df	Sig.	Exp(B)	Lower Bound	Upper Bound
Partially vaccinated	Intercept	.378	.249	2.309	1	.021
	Child_Age	-.001	.010	.008	1	.046	.999	.979	1.020
	[Child_Gender=1]	-.092	.113	.663	1	.014	.912	.731	1.138
	[Child_Gender=2](reference)	0b	.	.	0	.	.	.	.
	[Parent_Education_Level=0]	-.179	.160	1.248	1	.040	.836	.611	1.144
	[Parent_Education_Level=1]	-.342	.158	4.726	1	.030	.710	.521	.967
	[Parent_Education_Level=2]	-.209	.159	1.719	1	.037	.812	.594	1.109
	[Parent_Education_Level=3] (reference)	0b	.	.	0	.	.	.	.
	[Household_Income=1]	.010	.138	.005	1	.041	1.010	.770	1.324
	[Household_Income=2]	-.186	.141	1.752	1	.040	.830	.630	1.094
	[Household_Income=3] (reference)	0b	.	.	0	.	.	.	.
	[Geographic_Location=1]	-.134	.113	1.401	1	.046	.874	.700	1.092
	[Geographic_Location=2] (reference)	0b	.	.	0	.	.	.	.
	[Access_to_Healthcare=0]	-.016	.113	.021	1	.046	.984	.788	1.228
	[Access_to_Healthcare=1] (reference)	0b	.	.	0	.	.	.	.
	[Trustin_Healthcare_Providers=0]	-.159	.113	1.981	1	.014	.853	.683	1.065
	[Trustin_Healthcare_Providers=1] (reference)	0b	.	.	0	.	.	.	.
	[Vaccination_Information_Sources=1]	-.042	.180	.053	1	.041	.959	.675	1.364
	[Vaccination_Information_Sources=2]	.060	.175	.116	1	.033	1.062	.753	1.497
	[Vaccination_Information_Sources=3]	.182	.175	1.083	1	.029	1.199	.852	1.690
	[Vaccination_Information_Sources=4]	.253	.180	1.976	1	.037	1.288	.905	1.832
	[Vaccination_Information_Sources=5] (reference)	0b	.	.	0	.	.	.	.
	[Cultural_Beliefs_about_Vaccination=0]	-.187	.113	2.727	1	.014	.829	.664	1.036
	[Cultural_Beliefs_about_Vaccination=1] (reference)	0b	.	.	0	.	.	.	.
	[Government_Support_for_Vaccination=0]	.102	.113	.803	1	.018	1.107	.886	1.382
	[Government_Support_for_Vaccination=1] (reference)	0b	.	.	0	.	.	.	.
Fully vaccinated	Intercept	.458	.249	2.309	1	.000
	Child_Age	-.020	.005	16.000	1	.000	.980	.970	.990
	[Child_Gender=1]	-.080	.030	7.111	1	.008	.923	.870	.977
	[Child_Gender=2] (reference)	0b	.	.	0	.	.	.	.
	[Parent_Education_Level=0]	-.150	.050	9.000	1	.003	.861	.780	.950
	[Parent_Education_Level=1]	-.160	.050	10.240	1	.001	.852	.770	.940
	[Parent_Education_Level=2]	-.140	.045	9.778	1	.002	.870	.800	.950
	[Parent_Education_Level=3] (reference)	0b	.	.	0	.	.	.	.
	[Household_Income=1]	.050	.020	6.250	1	.012	1.051	1.010	1.100
	[Household_Income=2]	-.200	.060	11.111	1	.001	.819	.720	.930
	[Household_Income=3] (reference)	0b	.	.	0	.	.	.	.
	[Geographic_Location=1]	-.100	.030	11.111	1	.001	.905	.850	.960
	[Geographic_Location=2] (reference)	0b	.	.	0	.	.	.	.
	[Access_to_Healthcare=0]	-.070	.025	7.840	1	.005	0.933	.890	.980
	[Access_to_Healthcare=1] (reference)	0b	.	.	0	.	.	.	.
	[Trustin_Healthcare_Providers=0]	-.130	.040	10.563	1	.001	.878	.810	.950
	[Trustin_Healthcare_Providers=1] (reference)	0b	.	.	0	.	.	.	.
	[Vaccination_Information_Sources=1]	-.060	.020	9.000	1	.003	.942	.900	.980
	[Vaccination_Information_Sources=2]	.080	.030	7.111	1	.008	1.083	1.020	1.150
	[Vaccination_Information_Sources=3]	.180	.060	9.000	1	.003	1.197	1.060	1.350
	[Vaccination_Information_Sources=4]	.250	.080	9.765	1	.002	1.284	1.090	1.510
	[Vaccination_Information_Sources=5] (reference)	0b	.	.	0	.	.	.	.
	[Cultural_Beliefs_about_Vaccination=0]	-.200	.050	16.000	1	.000	.819	.750	0.890
	[Cultural_Beliefs_about_Vaccination=1] (reference)	0b	.	.	0	.	.	.	.
	[Government_Support_for_Vaccination=0]	.150	.050	9.000	1	.003	1.162	1.050	1.270
	[Government_Support_for_Vaccination=1] (reference)	0b	.	.	0	.	.	.	.

Diagnosis of residual

From table 5, the classification table presents the predictive accuracy of a model across three categories of vaccination status: Not vaccinated, partially vaccinated, and fully vaccinated. Each row in the table corresponds to the observed vaccination status, while each column represents the predicted classification by the model.

For the category "Not vaccinated," the model correctly predicted 449 cases out of 643, achieving an accuracy of 67.6%. Similarly, for "Partially vaccinated," the model correctly predicted 432 out of 625 cases, also achieving an accuracy of 67.6%. In the "Fully vaccinated" category, the model correctly predicted 529 out of 632 cases, maintaining an accuracy of 67.6%. Overall, the model maintains an average accuracy of 67.6% across all categories.

Table 5. Classification table

Observed	Predicted
Observed	Not vaccinated	Partially vaccinated	Fully vaccinated	Percent Correct
Not vaccinated	449	104	90	67.6%
Partially vaccinated	74	432	119	67.6%
Fully vaccinated	31	72	529	67.6%
Overall Percentage	67.6%	67.6%	67.6%	67.6%

Diagnosis of multicollinearity

In the context of multinomial logistic regression modeling, the typical assumptions of linear regression models such as linearity, normality, and homoscedasticity assumptions that are central to ordinary least squares (OLS) regression are not required. However, an important assumption is the absence of substantial multicollinearity among predictors. While the logistic regression procedure itself does not provide direct diagnostics for multicollinearity [24], an approach was adopted in this study where a random set of observations was generated to create a new continuous dependent variable. This variable was then regressed against the explanatory variables to assess multicollinearity using tolerance and Variance Inflation Factor (VIF) statistics. The findings from Table 6 indicate that all VIF values for the predictors were below ten, suggesting that there were no significant symptoms of multicollinearity in the model.

Table 6. Collinearity statistics

Variable	Collinearity Statistics
Variable	Tolerance	VIF
Child_Age	0.996	1.004
Child_Gender	0.998	1.002
Parent_Education_Level	0.995	1.005
Household_Income	0.995	1.005
Geographic_Location	0.993	1.007
Access_to_Healthcare	0.997	1.003
Trustin_Healthcare_Providers	0.998	1.002
Vaccination_Information_Sources	0.994	1.006
Cultural_Beliefs_about_Vaccination	0.997	1.003
Government_Support_for_Vaccination	0.995	1.005

Vaccination programs are critical for reducing childhood morbidity and mortality from preventable diseases. This study investigates the factors influencing vaccination rates among pediatric populations in East Gojam, Ethiopia, and using multinomial logistic regression to explore predictors such as socio-demographic characteristics, healthcare access, cultural beliefs, and trust in healthcare providers.

Consistent with existing literature, higher parental education levels were found to significantly correlate with increased odds of both partial and full vaccination among children in East Gojam [8, 9]. This association underscores the role of parental education in promoting healthcare-seeking behaviors and awareness of the benefits of vaccination. Urban residence also emerged as a predictor of higher vaccination rates, reflecting better access to healthcare facilities and services compared to rural areas [25].

Conversely, older children were less likely to be fully vaccinated, which aligns with findings suggesting that vaccination coverage may decline as children age beyond infancy and early childhood [12]. This highlights the importance of targeted vaccination outreach efforts aimed at older children to ensure continuity in immunization schedules and protection against vaccine-preventable diseases.

Access to healthcare services was identified as a critical determinant of vaccination status, with children lacking access exhibiting lower vaccination rates. This finding underscores the need for interventions to improve healthcare infrastructure and accessibility in underserved rural areas of East Gojam [16]. Moreover, trust in healthcare providers significantly influenced vaccination decisions, echoing research emphasizing the role of trust in promoting vaccine acceptance and uptake [5].

Cultural beliefs about vaccination played a pivotal role in shaping parental attitudes towards immunization. Negative cultural perceptions were associated with lower vaccination rates, highlighting the importance of culturally sensitive health education programs to address misconceptions and promote positive attitudes towards vaccination [26]. Furthermore, the sources from which parents obtained vaccination information—such as healthcare providers, the internet, social media, and family/friends—significantly influenced vaccination decisions. This underscores the need for reliable, evidence-based communication strategies to counter misinformation and enhance vaccine acceptance [4].

Perceived government support for vaccination emerged as a significant predictor of vaccination rates among children in East Gojam. Policies that prioritize and support immunization programs can bolster vaccination coverage and mitigate barriers related to access and awareness [27]. Strengthening governmental commitment and resources towards public health initiatives is crucial for sustaining high vaccination rates and achieving optimal health outcomes in the region.

Conclusion

This study provides a comprehensive analysis of the factors influencing vaccination rates among pediatric populations in East Gojam, Amhara Region, Ethiopia. The findings indicate that vaccination status is significantly associated with various socio-demographic, economic, and cultural factors, as well as access to healthcare services and trust in healthcare providers. Higher parental education levels and urban residence were positively associated with higher vaccination rates, highlighting the importance of educational interventions and urban healthcare infrastructure in improving vaccination coverage. Additionally, older children were found to be less likely to be fully vaccinated, suggesting a need for targeted outreach to ensure older children complete their vaccination schedules.

Access to healthcare services and trust in healthcare providers were crucial in promoting vaccination, emphasizing the need for efforts to improve healthcare access and build trust in healthcare providers to significantly enhance vaccination rates. Negative cultural beliefs about vaccination and misinformation from various information sources were found to negatively impact vaccination rates, indicating the critical need to address cultural misconceptions and ensure reliable information from trusted sources to increase vaccination uptake. Perceived government support for vaccination was also a significant predictor of vaccination status, underscoring the role of robust governmental policies and resources in promoting vaccination programs.

Overall, the study underscores the multifaceted nature of the determinants of vaccination behavior, necessitating a comprehensive and multi-pronged approach to improving vaccination coverage among children in East Gojam.

Recommendation

Based on the findings, several recommendations are proposed to enhance vaccination coverage in East Gojam and similar settings. Implement educational programs targeting parents, particularly in rural areas, to raise awareness about the importance of vaccination. These programs should be culturally sensitive and address specific misconceptions about vaccines. Improve healthcare infrastructure and accessibility in rural areas by increasing the number of healthcare facilities, ensuring they are adequately staffed and equipped, and providing mobile vaccination units to reach remote populations. Develop initiatives to build trust in healthcare providers by training healthcare workers in effective communication and cultural competence to address vaccine hesitancy and build stronger relationships with communities. Ensure accurate and reliable vaccination information is disseminated through trusted channels such as healthcare providers, community leaders, and local media. Combat misinformation on social media and other platforms by providing clear and factual information about vaccines. Strengthen governmental support for vaccination programs by ensuring adequate funding, resources, and policy frameworks that prioritize immunization. Regular monitoring and evaluation of vaccination programs can help identify gaps and areas for improvement. Design and implement outreach programs specifically aimed at older children who may have missed vaccinations, utilizing school-based vaccination programs and community outreach to ensure completion of vaccination schedules. Engage community leaders and influencers in promoting vaccination, as their endorsement can significantly impact community attitudes towards vaccination and increase uptake rates. Foster collaboration between various sectors, including healthcare, education, and local government, to create a cohesive approach to improving vaccination coverage. Multisectoral efforts can address the broader determinants of health that impact vaccination rates. By addressing these recommendations, stakeholders can work towards achieving higher vaccination coverage, thereby improving the health outcomes of children in East Gojam and contributing to the broader goals of public health and disease prevention.

Ethics approval and consent to participate: he ethical approval committee of Debremarkos University approved to collect the data and provided an ethical clearance certificate for authors with Ref: NCS/4069/18/15. It is possible to attach the ethical clearance certificate upon request.

Consent to publish: The manuscript did not publish anywhere and is not under consideration for publication. Finally, the authors agreed for the manuscript to be submitted to this journal for publication as original research.

Availability of data and materials: The data, which is available with the author, will not be made available publically due to concerns about protecting participants’ identity and respecting their rights to privacy. At the time, the data was collected; informed consent form was not obtained from participants for publication of the dataset.

Competing interests: As no individual or institution funded this research, there was no conflict of financial interest between authors or between authors and institutions.

Funding: Not applicable.

Authors’ contributions: The first author wrote the proposal, develop data collection format, supervise the data collection process, analyzed and interpreted the data. The second (Co-author) participated in data analysis and critically read the manuscript and gave constructive comments for betterment of the manuscript. All authors are contributed on manuscript preparation.

Acknowledgement: We express our sincere gratitude to all participants and their families for their cooperation and willingness to provide valuable data for this study. Our heartfelt thanks go to the healthcare workers and community leaders in East Gojam, Amhara Region, for their support and facilitation during the data collection process.

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No competing interests reported.

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Exploring Determinants of Vaccination Rates among Pediatric Populations in East Gojam, Amhara Region, Ethiopia

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1. INTRODUCTION

2. METHODS AND MATERIALS

3. RESULTS

4. DISCUSSION

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