Magnitude, trends and determinants of skilled delivery from Kilite-Awlaelo Health Demographic Surveillance System, Northern Ethiopia, 2009-2017

listen audio

Study Justification:
– The study aims to investigate the magnitude, trend, and determinants of skilled delivery in Kilite-Awlaelo Health Demographic Surveillance System (KA-HDSS), Northern Ethiopia.
– The fundamental approach to improving maternal and neonatal health is increasing the skilled delivery rate.
– Despite a significant decrease in maternal mortality in Ethiopia, a significant number of women still give birth at home.
– Evidence from population-based longitudinal studies on skilled delivery is limited.
Study Highlights:
– The skilled delivery rate among reproductive age women in KA-HDSS has continuously increased from 2010 to 2017.
– The skilled delivery rate becomes high (> = 82) in the period of 2014–2017.
– Education, residence, marital status, occupation, and antenatal care (ANC) visits were the most important determinants for skilled delivery during the period of high skilled delivery rate.
– Urban dwellers had higher odds of delivering by skilled birth attendants compared to rural dwellers.
– Unmarried women who gave birth were more likely to have skilled delivery service compared to those married.
– Women with four or more ANC visits were more likely to undergo skilled delivery service than those with no ANC visits.
– Women with at least a secondary education were more likely to have skilled delivery service compared to those with no formal education.
Recommendations for Lay Reader and Policy Maker:
– Maternal health-related interventions are needed to change women’s attitudes towards skilled delivery.
– ANC coverage should be increased to improve skilled delivery service.
Key Role Players Needed to Address Recommendations:
– Ministry of Health
– Local health authorities
– Health professionals (doctors, nurses, midwives)
– Community health workers
– Non-governmental organizations (NGOs) working in maternal health
Cost Items to Include in Planning the Recommendations:
– Training and capacity building for health professionals and community health workers
– Infrastructure improvement in health facilities
– Outreach programs and awareness campaigns
– ANC services and supplies
– Monitoring and evaluation systems
– Research and data collection

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a population-based longitudinal study design and includes a large sample size. The study analyzes data from nine consecutive years and uses rigorous statistical methods to assess the determinants of skilled delivery. The findings show a significant increase in skilled delivery rate among reproductive age women in Northern Ethiopia. To improve the evidence, the abstract could provide more details on the methodology, such as the sampling strategy and data collection procedures. Additionally, it would be helpful to include information on the limitations of the study and potential biases that may have influenced the results.

Background The fundamental approach to improve maternal and neonatal health is increasing skilled delivery rate. Women giving birth at health institutions can prevent maternal and neonatal deaths by getting skilled birth attendance. In Ethiopia, despite a significant decrease in maternal mortality over the past decade, still a significant number of women give birth at home. Moreover, evidence from population-based longitudinal studies on skilled delivery is limited. Therefore, this study aims to investigate the magnitude, trend, and determinants of skilled delivery in Kilite-Awlaelo Health Demographic Surveillance System (KA-HDSS), Northern Ethiopia. Method Population-based longitudinal study design was conducted by extracting data for nine consecutive years (2009–2017) from KA-HDSS database. In order to measure the trends of skilled delivery, KA-HDSS data sets were analyzed (2009–2017). Bivariate and multivariate analyses were performed using STATA version 16. A multivariable binary logistic regression model was fitted to assess determinants of skilled delivery and odds ratio with 95% CI was used to assess presence of associations at a 0.05 level of significance. Results The skilled delivery rate have continuously increased among reproductive age women from 15.12% (95% CI: 13.30% – 17.09%) in 2010 to 95.85% (95% CI: 94.58% – 96.895%) in 2017. The skilled delivery rate becomes high (> = 82) in the period of 2014–2017. Education, residence, marital status, occupation and antenatal care (ANC) visits were the most important determinants for skilled delivery among reproductive age women during the period of high skilled delivery rate (2014–2017). Women urban dwellers had about 28 times (AOR = 27.66; 95% CI: 3.86–196.97) higher odds to deliver by skilled birth attendants than rural dwellers. Unmarried women who gave birth were 2.18 (AOR: 2.18; 95% CI: 1.30–3.64) times more likely to have skilled delivery service compared to those married. Likewise, women with four or more ANC visits were 3.2 times more likely to undergo skilled delivery service than those having no ANC visits (AOR: 3.16; 95% CI: 2.33–4.28). Moreover, women having at least a secondary education were 2 times more likely to have skilled delivery service compared to those women with no formal education (AOR = 2.10, 95% CI: 1.18–3.74). Conclusion Regardless of the importance of health facility delivery, a significant number of women still deliver at home attended by unskilled birth attendants. There has been a substantial increase in use of health facilities for delivery among women in the reproductive age. The factors affecting skilled delivery among reproductive age women were educational level, residence, marital status, occupation and use of ANC service. Maternal health related interventions are needed to change women’s attitudes towards skilled delivery. Moreover, ANC coverage should be increased to improve skilled delivery service.

KA-HDSS is an ongoing open cohort study, located in Northern Ethiopia and hosted by Mekelle University. The site has three climatic zones which includes lowland, midland and high land. Administratively, it was established in 9 rural and 1 urban kebelles in April 2009 (a kebelle is the smallest administrative component in the country). At the beginning of the surveillance, baseline socio-demographic characteristics of 65,848 individuals living in 14,455 households were collected through a census. At the same time, a unique surveillance identification number was given to every enumerated cohort and household to facilitate linking information during longitudinal observation. In 2016, 2 urban kebelles were added as part of the study area and the number of household increased to 21,688. In 2017, the project has made 11 updates rounds with population of 101,146 living in 21,688 households in 12 kebeles (9 rural and 3 urban). A house to house visit is done to capture information regarding individuals, pregnancy observation, pregnancy outcomes, deaths, births and migration. Events are collected as it occurs and updated every six months [19]. The source of data for this study was from KA-HDSS. The study population for this study was all women who had at least one birth in KA-HDSS from April, 2009 to December, 2017. Data regarding the skilled delivery were extracted mainly from pregnancy observation, pregnancy outcome, and relationship tables of KA-HDSS data considering the relevance of each explanatory variable on the prediction of skilled delivery rate in the population. The dependent variable in this study was skilled delivery. It was a dichotomized response as 1 if a woman gave birth by skilled birth attendants and 0 otherwise (if a woman gave birth by unskilled birth attendants). The independent variables were classified as socio-demographic variables, and pregnancy outcome and related variables. The socio-demographic variables are age, ethnicity, religion, marital status, occupation, level of education, and place of residence. The pregnancy outcome and related variables are age at pregnancy, number of ANC visits, bed net use, number of children born alive, number of children dead, number of previous pregnancy and previous pregnancy outcome. Data were cleaned and analysed in STATA version 15 statistical tool. The study population were described using frequency (percentage), mean (±standard deviation (sd)) depending on the nature of data (variables). A line graph was used to observe the trend of institutional delivery (number of skilled deliveries per 100) over time. Moreover, a cross-tabulation between each categorical independent variable and the outcome variable was done to check whether the expected cell counts were adequate or not. Besides, descriptive statistics, a rigorous statistical method was applied to identify the determinants of delivery in the study setting. Bivariate analysis was performed to assess the relationship between the dependent and independent variables. A multivariable binary logistic regression analysis was fitted to identify the adjusted effect of each determinant on the skilled delivery among the study population of the specified study setting. The assumptions of multicollinearity between two or more independent variables were checked. Goodness of fit of the model was assessed using Hosmer-Lemeshow test. Decision regarding the statistical significance effect of independent variables on skilled delivery was made based on either the 95% CIs for AOR or associated P-values. Permission to access the data was obtained from Mekelle University KA HDSS via an agreement on the data sharing policy after ethical approval was obtained from Institutional Review Board (IRB) of Mekelle University, College of Health Sciences. Consent to participate was fully waived as the study participants were not directly involved in the study (i.e. an already existing data were utilized for analysis in the current study). Moreover, the confidentiality of data was kept as there were no personal identifiers used and neither the raw data nor the extracted data were passed to a third person (i.e. it is only used for the purpose of the study).

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with information, reminders, and access to healthcare services. These apps can also facilitate communication between healthcare providers and pregnant women, allowing for remote consultations and monitoring.

2. Telemedicine: Establish telemedicine services that enable pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone calls. This can help address the lack of skilled birth attendants in certain areas and provide timely advice and support during pregnancy and childbirth.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal healthcare services, including antenatal care, education, and support. These workers can also identify high-risk pregnancies and refer women to appropriate healthcare facilities for skilled delivery.

4. Transportation Solutions: Develop innovative transportation solutions, such as ambulances or mobile clinics, to ensure that pregnant women can easily access healthcare facilities for skilled delivery. This can be particularly beneficial for women living in remote or hard-to-reach areas.

5. Financial Incentives: Implement financial incentive programs to encourage pregnant women to seek skilled delivery services. This can include cash transfers, vouchers, or insurance schemes that cover the costs of delivery and transportation to healthcare facilities.

6. Community Awareness Campaigns: Conduct community awareness campaigns to educate and raise awareness about the importance of skilled delivery and the available healthcare services. These campaigns can address cultural beliefs and misconceptions that may discourage women from seeking skilled delivery.

7. Strengthening Healthcare Infrastructure: Invest in improving healthcare infrastructure, including the availability and quality of healthcare facilities, equipment, and skilled healthcare providers. This can help increase the confidence and trust of pregnant women in the healthcare system, leading to higher utilization of skilled delivery services.

It is important to note that the specific implementation of these innovations should be tailored to the local context and needs of the community.
AI Innovations Description
Based on the information provided, here is a recommendation that can be used to develop an innovation to improve access to maternal health:

1. Increase awareness and education: Implement targeted educational campaigns to raise awareness about the importance of skilled delivery and the risks associated with home births. This can be done through community outreach programs, radio broadcasts, and educational materials distributed in health facilities and community centers.

2. Improve access to antenatal care (ANC): Strengthen ANC services and promote early and regular attendance. This can be achieved by expanding the availability of ANC clinics, ensuring adequate staffing and resources, and providing incentives for women to attend ANC visits, such as transportation vouchers or free check-ups.

3. Enhance transportation infrastructure: Improve transportation networks in rural areas to facilitate access to health facilities. This can include building or upgrading roads, providing ambulances or other means of transportation, and establishing referral systems to ensure timely access to emergency obstetric care.

4. Address socio-economic barriers: Implement interventions to address socio-economic factors that hinder access to skilled delivery, such as poverty, lack of education, and cultural beliefs. This can involve providing financial support for transportation and delivery costs, offering scholarships or vocational training for women, and engaging community leaders and traditional birth attendants to promote skilled delivery.

5. Strengthen health systems: Invest in strengthening health systems, including training and capacity building for healthcare providers, improving infrastructure and equipment in health facilities, and ensuring the availability of essential drugs and supplies for safe deliveries.

6. Monitor and evaluate progress: Establish a robust monitoring and evaluation system to track the progress of interventions and identify areas for improvement. This can involve regular data collection on skilled delivery rates, ANC attendance, and other relevant indicators, as well as conducting periodic assessments and surveys to assess the impact of interventions.

By implementing these recommendations, it is possible to improve access to skilled delivery and reduce maternal and neonatal mortality rates in the study area and similar settings.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Strengthening Health Facilities: Enhance the capacity and resources of health facilities to provide quality maternal health services, including skilled birth attendance, emergency obstetric care, and postnatal care.

2. Community-Based Interventions: Implement community-based programs that promote awareness and education on the importance of skilled delivery, encourage women to seek care at health facilities, and provide support and transportation for pregnant women.

3. Mobile Health Technologies: Utilize mobile health technologies, such as text messaging and mobile applications, to provide information and reminders about antenatal care visits, skilled delivery options, and postnatal care.

4. Task-Shifting: Train and empower midwives, nurses, and community health workers to provide skilled birth attendance and basic emergency obstetric care, especially in remote and underserved areas.

5. Financial Incentives: Implement financial incentives, such as cash transfers or health insurance schemes, to encourage women to deliver at health facilities and reduce financial barriers to accessing maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Data Collection: Gather baseline data on the current access to maternal health services, including the percentage of women delivering at health facilities, the availability of skilled birth attendants, and the utilization of antenatal care.

2. Modeling: Develop a simulation model that incorporates the potential impact of the recommendations on various factors, such as the increase in skilled delivery rate, the reduction in home births, and the improvement in access to antenatal care.

3. Parameter Estimation: Estimate the parameters of the simulation model based on available data, research studies, and expert opinions. This may involve conducting surveys, interviews, or focus group discussions with key stakeholders.

4. Sensitivity Analysis: Perform sensitivity analysis to assess the robustness of the simulation model by varying the input parameters and evaluating the resulting changes in the outcomes. This helps identify the most influential factors and uncertainties in the model.

5. Scenario Testing: Simulate different scenarios by adjusting the input parameters according to the potential impact of each recommendation. This allows for the comparison of different intervention strategies and their effects on improving access to maternal health.

6. Evaluation: Analyze the simulation results to evaluate the potential impact of the recommendations on improving access to maternal health. This may include assessing changes in skilled delivery rates, reduction in home births, and improvements in antenatal care utilization.

7. Policy Recommendations: Based on the simulation findings, provide evidence-based policy recommendations on the most effective interventions to improve access to maternal health. Consider the feasibility, cost-effectiveness, and sustainability of the recommended strategies.

It is important to note that the methodology described above is a general framework and may need to be tailored to the specific context and data availability of the Kilite-Awlaelo Health Demographic Surveillance System (KA-HDSS) in Northern Ethiopia.

Partilhar isto:
Facebook
Twitter
LinkedIn
WhatsApp
Email