Determinants of default from full completion of vaccination among children between 12 and 23 months old in Yilmana Densa district, west Gojam zone, Ethiopia, 2019

listen audio

Study Justification:
– Vaccination is a cost-effective public health intervention to protect children from vaccine-preventable diseases.
– The prevalence of default to full completion of child immunization is high in Ethiopia.
– The determinants of default to full completion have not been thoroughly investigated in the study area.
Study Highlights:
– Unmatched case-control study design conducted in Yilmana Densa district, Ethiopia.
– 343 children between 12 and 23 months old were included in the study.
– Factors associated with defaulting from full completion of vaccination were identified.
– Mothers who did not attend ANC follow-up, did not receive information about vaccinations, and had longer travel time to vaccination sites were at higher risk of defaulting.
– Maternal waiting time for child vaccination and home delivery were also risk factors for defaulting.
– The study recommends promoting institutional delivery services utilization and considering the distribution of vaccination sites to reduce waiting time for mothers.
Study Recommendations:
– Promote institutional delivery services utilization to improve access to vaccination.
– Provide health education and information about vaccinations to mothers.
– Improve transportation infrastructure to reduce travel time to vaccination sites.
– Consider opening new outreach vaccination sites to reduce waiting time for mothers.
Key Role Players:
– District health office
– Health centers
– Health posts
– Outreach vaccination teams
– Community health workers
– Maternal and child health program coordinators
Cost Items for Planning Recommendations:
– Training and capacity building for health workers
– Health education materials and campaigns
– Transportation and logistics for outreach vaccination teams
– Infrastructure development for new vaccination sites
– Monitoring and evaluation activities
– Data collection and analysis tools

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is appropriate for investigating the determinants of default to full completion of vaccination. The sample size calculation and sampling method are clearly described. The statistical analysis is well-defined, including descriptive analysis and logistic regression. However, there are some areas for improvement. Firstly, the abstract does not provide information on the response rate or any potential biases in the sample. Secondly, the abstract does not mention the specific vaccines included in the routine immunization program. Lastly, the abstract does not provide information on the limitations of the study or any potential confounding factors that were controlled for in the analysis. To improve the evidence, it would be helpful to include these missing details in the abstract.

Background: Vaccination is one of the best cost-effective public health interventions to safeguard children from vaccine-preventable diseases. In Ethiopia, the prevalence of default to the full completion of child immunization is high. However, the determinants of default to full completion have not been thoroughly investigated in this study area. Therefore, this study assessed the determinants of default to the full compilation of vaccination among children between 12 and 23 months old in Yilmana Densa District, west Gojam, northwest Ethiopia. Methods: A community-based unmatched case-control study design was employed in the Yilmana Densa district among 343 (111 cases and 232 controls) randomly selected 12–23 months old children. Face-to-face interviews were used to collect data using a multistage sampling method. For analysis, data were entered into epidata version 3.1 and exported to SPSS 23 software. Descriptive analysis followed by binary and multivariable logistic regression analysis was conducted. The statistical significance was declared at a p-value of 0.05. Result: This study identified that mothers who had not attended ANC follow-up [AOR = 5.55, 95% CI: (1.789–17.217)], mothers who had not gotten information about vaccinations [AOR = 8.589, 95% CI: (4.414–16.714)], and mothers whose time taken to reach vaccination site is more than 39 min were at higher risk to default from completion of vaccination [AOR = 3.252, 95% CI: (1.952–5.417)]. Furthermore, maternal waiting time (>45 min) for child vaccination [AOR = 2.674, 95% CI: (1.517–4.714)] and home delivery [AOR 3.19, 95% CI: (1.751–5.814)] were risk factors to default child from full completion of vaccination. Conclusion: Mothers delivered at home, mothers not attending ANC follow-up, mothers who did not get health information about the vaccine, mothers taking longer time to reach the vaccination site, and staying longer time for child vaccination are causes of default. Motivated institutional delivery services utilization is recommended. The district office should consider the distribution of vaccination sites by the opening of new outreach site to reduce the waiting time of mothers.

An unmatched community-based case-control study design was used. This study was conducted in Yilmana Densa, West Gojam zone, Ethiopia, from 30 March to 15 May 2019. Yilmana Densa district is the second-most populated district in the west Gojam Zone with an estimated number of 275,187 populations. Of which, 136,218 (49.5%) were female and 5,200 (5.1%) were children between 12 and 23 months of age. The district has 5 urban and 30 rural kebeles. EPI is provided by all health centers, health posts, and outreach sessions. Health service coverage was 89%. According to the 2017/18 district health office report, the full completion vaccination coverage was 66%, which is below the WHO standard (80%). All children between 12 and 23 months of age, and who had started at least one dose of the routine immunization program in the Yilmana Densa district were the source population. All children between 12 and 23 months of age residing in randomly selected kebeles were the study population. All children aged 12–23 months lived in the study area for the past 2 years and the children at least received one vaccination exposure. All children aged 12–23 months have completed all the recommended vaccines. Children whose parents or guardians struggle to communicate immunization information about their child succinctly. The sample size was calculated using EPI info version 7.2.1.0 and is based on the following assumptions: A power of 80% with a 95% confide level (CL), a maximum tolerable error of 5%, and the one case to two control (1:2) ratio with Odds ratio of 5.7. With a 10% non-response rate, 345 people were the estimated sample size (115 cases and 230 controls). Proportions of maternal health service utilization among cases (97.7%) and controls (88.1%) were obtained from the previous literature (13). The study participants were selected using a stratified sampling technique. The district was classified into two strata: urban and rural residents. Then, one urban and seven rural kebeles were chosen at random to provide valid study subjects. Cases and controls in the kebeles were identified using child vaccination cards and a vaccine registration book from the health posts. Cases were children aged 12–23 months who have missed at least one dose from the recommended schedule (except for polio zero). The total sample size of 345 (115 cases and 230 controls) was allocated proportionally to each selected kebele. Finally, the study participants were selected randomly by the lottery method from all the selected kebeles and households. A data extraction checklist form was used to extract secondary data. Primary data were collected using a structured and interviewer-administered questionnaire. Defaulting from full completion of vaccination (Yes/No) were the dependent variables. Whereas, the independent variables were socio-demographic factors such as age of mother, pregnancy status, residency, caretaker, maternal occupation, sex of the child, birth order, child’s father, and paternal behavior; government-related factors: health budget policy and vaccine demand/vaccine supply; health service accessibility: time taken to get health post, waiting time for vaccination, place of delivery, antenatal care service, vaccine mode of transport to health facility/outreach site, and stock out vaccination place/time; maternal health service utilization: ANC/PNC, TT vaccination, antenatal conference participation, and inconvenient vaccination place/time, waiting time, appointment, and getting of health education about vaccination. Secondary data were extracted using a data extraction checklist form. Face-to-face interviews were used to collect primary data using a structured and pretested Amharic version questionnaire. The questionnaire was first prepared in English and then translated to Amharic. The questionnaire had designed to measure socio-demographic characteristics, maternal health service utilization, health facility access, paternal behavior, vaccination status of a child, and reasons for defaulting from full completion of vaccination. Children’s vaccination cards were used to collect information about their vaccination status. The questionnaire was pretested on 5% of the study participants in another area. Data collectors and supervisors each received 1 day of training to ensure that they all had a common understanding of the study’s objectives and each of the questionnaire’s questions. Daily, data were checked for completeness, consistency, accuracy, and clarity. Communication with data collectors, supervisors, and principal investigators was maintained throughout the study period. Before data entry, the data collectors, supervisors, and principal investigator checked the returned collected data for completeness. The data were cleaned, coded, and entered into Epi data version 3.1 and exported to SPSS version 23 for analysis. The simple frequency with percentage, figure, and tables were used to display the descriptive part of the result. A bivariable logistic regression model was used to identify the determinant variables. A variable with p ≤ 0.2 in bivariable logistic regression was eligible for the multivariable logistic regression analysis model to control the confounding effect. Both bivariable and multivariable logistic regression models were used to identify the determinant factors of default to full completion vaccination. Odds ratio (OR) with a 95% confidence interval was used to identify the strength of associations. A p < 0.05% was considered a statistically significant association.

N/A

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide information and reminders about maternal health services, including vaccination schedules. These apps can also offer educational resources and allow for easy communication with healthcare providers.

2. Community Health Workers: Train and deploy community health workers who can provide education and support to mothers in remote or underserved areas. These workers can help increase awareness about the importance of vaccination and assist in scheduling and accessing maternal health services.

3. Telemedicine: Establish telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide timely advice and guidance on vaccination and other maternal health concerns.

4. Transportation Support: Improve transportation infrastructure and provide transportation subsidies or incentives to pregnant women, especially those living in rural areas, to ensure they can easily access vaccination sites and other maternal health services.

5. Outreach Programs: Organize regular outreach programs in communities to provide vaccinations and other maternal health services. These programs can be conducted in collaboration with local community organizations and can help reach women who may face challenges in accessing healthcare facilities.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of vaccination and other maternal health services. These campaigns can utilize various communication channels, such as radio, television, social media, and community gatherings, to reach a wide audience.

7. Improving Health Facility Infrastructure: Invest in improving the infrastructure and capacity of health facilities to ensure they can accommodate the increasing demand for maternal health services. This includes expanding vaccination sites, reducing waiting times, and ensuring an adequate supply of vaccines.

8. Collaboration and Coordination: Strengthen collaboration and coordination among different stakeholders, including government agencies, healthcare providers, community organizations, and international partners, to ensure a comprehensive and integrated approach to improving access to maternal health services.

It is important to note that the specific implementation of these innovations would require further research, planning, and collaboration with local stakeholders to address the unique challenges and context of the Yilmana Densa district in Ethiopia.
AI Innovations Description
Based on the study conducted in Yilmana Densa district, West Gojam zone, Ethiopia, the following recommendations can be made to improve access to maternal health:

1. Increase awareness and education: Provide information and education about the importance of maternal health and vaccination to mothers and their families. This can be done through community health workers, health education sessions, and outreach programs.

2. Improve antenatal care (ANC) utilization: Encourage pregnant women to attend ANC follow-up visits regularly. ANC visits provide an opportunity to educate mothers about vaccination and address any concerns or misconceptions they may have.

3. Enhance vaccination information dissemination: Ensure that mothers receive accurate and timely information about vaccinations. This can be done through various channels such as health facilities, community health workers, and mobile text messaging services.

4. Reduce waiting time: Take measures to minimize the waiting time for mothers at vaccination sites. This can be achieved by increasing the number of vaccination sites, improving the efficiency of service delivery, and optimizing the scheduling of appointments.

5. Promote institutional delivery: Encourage mothers to deliver their babies at health facilities rather than at home. Institutional delivery provides an opportunity for mothers to receive comprehensive maternal health services, including vaccination, in a safe and controlled environment.

6. Strengthen outreach services: Expand the reach of vaccination services through the establishment of new outreach sites. This will help reduce the travel time for mothers and improve access to vaccinations, especially in remote areas.

By implementing these recommendations, it is expected that access to maternal health services, including vaccination, will be improved, leading to better health outcomes for both mothers and children.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthen Antenatal Care (ANC) Services: Increase awareness and utilization of ANC services by implementing community-based education programs, providing incentives for ANC attendance, and ensuring the availability of quality ANC services in both urban and rural areas.

2. Improve Health Information Dissemination: Develop targeted health education campaigns to provide accurate and comprehensive information about maternal health, including the importance of vaccinations, to mothers and caregivers. Utilize various communication channels such as community health workers, mobile health apps, and radio programs to reach a wider audience.

3. Enhance Vaccine Delivery and Accessibility: Improve the accessibility of vaccination services by establishing more vaccination sites, particularly in remote areas. Strengthen the supply chain management system to ensure a consistent supply of vaccines and reduce stockouts. Additionally, explore innovative approaches such as mobile vaccination clinics to reach underserved populations.

4. Reduce Waiting Time: Implement strategies to minimize waiting time for vaccination, such as optimizing appointment systems, increasing the number of healthcare providers, and streamlining the vaccination process. This can help reduce the likelihood of mothers defaulting from completing their child’s vaccination.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that measure access to maternal health, such as ANC attendance rates, vaccination coverage, waiting time for vaccination, and knowledge about maternal health.

2. Collect baseline data: Gather data on the current status of the indicators in the target population. This can be done through surveys, interviews, and data extraction from health records.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the identified determinants and their relationships with the indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and socio-economic characteristics.

4. Input intervention scenarios: Define different scenarios based on the recommendations mentioned above. For each scenario, modify the relevant variables in the model to reflect the expected changes resulting from the interventions.

5. Simulate outcomes: Run the simulation model using the baseline data and the intervention scenarios. This will generate projected outcomes for each indicator, allowing for a comparison of the potential impact of the recommendations.

6. Analyze results: Analyze the simulated outcomes to assess the effectiveness of each intervention scenario in improving access to maternal health. Compare the results across different scenarios to identify the most impactful recommendations.

7. Refine and validate the model: Validate the simulation model by comparing the simulated outcomes with real-world data, if available. Refine the model based on feedback and further iterations to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. Use the results to advocate for the implementation of the recommended interventions and inform decision-making processes.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available resources.

Share this:
Facebook
Twitter
LinkedIn
WhatsApp
Email
DIMA AI Care
×
This site is registered on wpml.org as a development site. Switch to a production site key to remove this banner.