Pooled prevalence and determinants of antenatal care visits in countries with high maternal mortality: A multi-country analysis

Frontiers in Public Health, Volume 11, Year 2023

Interpretation

Background: Complications during pregnancy and childbirth are the leading causes of maternal and child deaths and disabilities, particularly in low- and middle-income countries. Timely and frequent antenatal care prevents these burdens by promoting existing disease treatments, vaccination, iron supplementation, and HIV counseling and testing during pregnancy. Many factors could contribute to optimal ANC utilization remaining below targets in countries with high maternal mortality. This study aimed to assess the prevalence and determinants of optimal ANC utilization by using nationally representative surveys of countries with high maternal mortality. Methods: Secondary data analysis was done using recent Demographic and Health Surveys (DHS) data of 27 countries with high maternal mortality. The multilevel binary logistic regression model was fitted to identify significantly associated factors. Variables were extracted from the individual record (IR) files of from each of the 27 countries. Adjusted odds ratios (AOR) with a 95% confidence interval (CI) and p-value of ≤0.05 in the multivariable model were used to declare significant factors associated with optimal ANC utilization. Result: The pooled prevalence of optimal ANC utilization in countries with high maternal mortality was 55.66% (95% CI: 47.48–63.85). Several determinants at the individual and community level were significantly associated with optimal ANC utilization. Mothers aged 25–34 years, mothers aged 35–49 years, mothers who had formal education, working mothers, women who are married, had media access, households of middle-wealth quintile, richest household, history of pregnancy termination, female household head, and high community education were positively associated with optimal ANC visits in countries with high maternal mortality, whereas being rural residents, unwanted pregnancy, having birth order 2–5, and birth order >5 were negatively associated. Conclusion and recommendations: Optimal ANC utilization in countries with high maternal mortality was relatively low. Both individual-level factors and community-level factors were significantly associated with ANC utilization. Policymakers, stakeholders, and health professionals should give special attention and intervene by targeting rural residents, uneducated mothers, economically poor women, and other significant factors this study revealed. The Demography and Health Surveys employed a cross-sectional study design to collect the data. In this study, we only included countries with high maternal mortality and have publically available DHS data (26). This study is a secondary data analysis using the DHS data conducted in 27 countries. The DHS is a nationally representative survey that is conducted in low- and middle-income countries globally. We used individual record (IR) files to extract the study participants of this study. We weighted the sample using the individual weight of women (v005) to produce the proper representation. Hence, sample weights were generated by dividing (v005) by 1,000,000, and the total weighted sample size from the pooled data was 209,538 (Table 1). Maternal mortality, category, and year of the survey by the country. High, 300–499; Very high, 500–999; Extremely high, >1,000. Women aged 15–49 years with a birth in the last 5 years receiving antenatal care from a skilled provider for the most recent birth were the study population. Sample weight was used to correct for over- and under-sampling and generalizability of the findings. Antenatal care visit was the outcome variable for this study. We dichotomized the ANC visits as inadequate and adequate according to the WHO classification (11). Inadequate ANC is <4 visits, whereas optimal if women had four and more visits. Potential explanatory variables associated with completing optimal ANC visits were considered on two levels. Variables such as mother's age, maternal educational status, parity, marital status, sex of the household head, birth order, and wealth index were used at the individual level, whereas residence, community-level education, community-level poverty, and community-level media exposure were used as community-level variables. Community-level media usage is the proportion of women in the community who use radio, TV, and newsletter, and it was categorized as low community-level media usage and high community-level media usage. “Low” refers to communities in which <50% of respondents had media access, while “high” indicates communities in which ≥50% of respondents had media access. Community-level women's education refers to the proportion of women in the community who have formal education. It was categorized as low if communities in which < 50% of respondents had formal education and high if ≥50% of respondents had attended formal education. Community-level poverty refers to the proportion of women in the community who had low-wealth quintiles. It was categorized as low if the proportion of low-wealth quintile households was < 50% and high if the proportion was ≥50%. STATA version 14.2 was used to clean, recode, and analyze the data. A multilevel binary logistic regression model was fitted to identify significantly associated factors. Both community- and individual-level variables with a p-value of ≤0.2 in the bi-variable analysis were included in the multivariable model. Adjusted OR (AOR) with 95% CI and p < 0.05 were applied to determine significantly associated factors. Four models were applied, comprising the null model (model 0) containing no variables, which is used to check the variability of ANC visits in the community and provide evidence to assess random effect using the interclass correlation coefficient (ICC). Model I was adjusted for individual-level variables, Model II with community-level factors, and Model III with variables from both individual- and community-level variables were fitted with the outcome variable. The fixed effect is a measure of association that estimates the association between independent variables and ANC and is stated as AOR with a 95% confidence interval. The Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and proportional change in variance (PCV) were computed to assess the clustering effect/variability.

AI Digest

Study Justification:

– Complications during pregnancy and childbirth are leading causes of maternal and child deaths and disabilities, especially in low- and middle-income countries.
– Timely and frequent antenatal care (ANC) can prevent these burdens by promoting disease treatments, vaccination, iron supplementation, and HIV counseling and testing during pregnancy.
– However, optimal ANC utilization remains below targets in countries with high maternal mortality.
– This study aims to assess the prevalence and determinants of optimal ANC utilization in countries with high maternal mortality.

Study Highlights:

– Pooled prevalence of optimal ANC utilization in countries with high maternal mortality was 55.66%.
– Several determinants at the individual and community level were significantly associated with optimal ANC utilization.
– Factors positively associated with optimal ANC visits include mothers aged 25-34 and 35-49, mothers with formal education, working mothers, married women, those with media access, households of middle-wealth quintile and richest households, history of pregnancy termination, female household head, and high community education.
– Factors negatively associated with optimal ANC visits include rural residence, unwanted pregnancy, birth order 2-5, and birth order >5.

Recommendations:

– Policymakers, stakeholders, and health professionals should give special attention to and intervene in targeting rural residents, uneducated mothers, economically poor women, and other significant factors identified in this study.
– Interventions should focus on improving access to ANC services, especially in rural areas, and addressing barriers related to education and economic status.

Key Role Players:

– Policymakers
– Stakeholders
– Health professionals

Cost Items for Planning Recommendations:

– Infrastructure development for ANC services in rural areas
– Training programs for health professionals
– Educational initiatives for women
– Outreach programs to reach economically poor women
– Awareness campaigns and media initiatives

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a secondary data analysis of nationally representative surveys from 27 countries with high maternal mortality. The study used a multilevel binary logistic regression model to identify significantly associated factors. The prevalence of optimal ANC utilization was reported with a 95% confidence interval. The abstract also provides information on the study design, sample size, and statistical analysis methods. To improve the evidence, the abstract could include more details on the specific variables included in the analysis and their definitions, as well as the specific statistical tests used to determine significance.

Abstract

The Demography and Health Surveys employed a cross-sectional study design to collect the data. In this study, we only included countries with high maternal mortality and have publically available DHS data (26). This study is a secondary data analysis using the DHS data conducted in 27 countries. The DHS is a nationally representative survey that is conducted in low- and middle-income countries globally. We used individual record (IR) files to extract the study participants of this study. We weighted the sample using the individual weight of women (v005) to produce the proper representation. Hence, sample weights were generated by dividing (v005) by 1,000,000, and the total weighted sample size from the pooled data was 209,538 (Table 1). Maternal mortality, category, and year of the survey by the country. High, 300–499; Very high, 500–999; Extremely high, >1,000. Women aged 15–49 years with a birth in the last 5 years receiving antenatal care from a skilled provider for the most recent birth were the study population. Sample weight was used to correct for over- and under-sampling and generalizability of the findings. Antenatal care visit was the outcome variable for this study. We dichotomized the ANC visits as inadequate and adequate according to the WHO classification (11). Inadequate ANC is <4 visits, whereas optimal if women had four and more visits. Potential explanatory variables associated with completing optimal ANC visits were considered on two levels. Variables such as mother's age, maternal educational status, parity, marital status, sex of the household head, birth order, and wealth index were used at the individual level, whereas residence, community-level education, community-level poverty, and community-level media exposure were used as community-level variables. Community-level media usage is the proportion of women in the community who use radio, TV, and newsletter, and it was categorized as low community-level media usage and high community-level media usage. “Low” refers to communities in which <50% of respondents had media access, while “high” indicates communities in which ≥50% of respondents had media access. Community-level women's education refers to the proportion of women in the community who have formal education. It was categorized as low if communities in which < 50% of respondents had formal education and high if ≥50% of respondents had attended formal education. Community-level poverty refers to the proportion of women in the community who had low-wealth quintiles. It was categorized as low if the proportion of low-wealth quintile households was < 50% and high if the proportion was ≥50%. STATA version 14.2 was used to clean, recode, and analyze the data. A multilevel binary logistic regression model was fitted to identify significantly associated factors. Both community- and individual-level variables with a p-value of ≤0.2 in the bi-variable analysis were included in the multivariable model. Adjusted OR (AOR) with 95% CI and p < 0.05 were applied to determine significantly associated factors. Four models were applied, comprising the null model (model 0) containing no variables, which is used to check the variability of ANC visits in the community and provide evidence to assess random effect using the interclass correlation coefficient (ICC). Model I was adjusted for individual-level variables, Model II with community-level factors, and Model III with variables from both individual- and community-level variables were fitted with the outcome variable. The fixed effect is a measure of association that estimates the association between independent variables and ANC and is stated as AOR with a 95% confidence interval. The Intra-class Correlation Coefficient (ICC), Median Odds Ratio (MOR), and proportional change in variance (PCV) were computed to assess the clustering effect/variability.

Materials

N/A

Innovations

The study “Pooled prevalence and determinants of antenatal care visits in countries with high maternal mortality: A multi-country analysis” identified several factors associated with optimal antenatal care (ANC) utilization in countries with high maternal mortality. These factors include:

1. Individual-level factors:
– Mothers aged 25-34 years and 35-49 years
– Mothers with formal education
– Working mothers
– Married women
– Women with media access
– Women from households of middle-wealth quintile and richest households
– Women with a history of pregnancy termination
– Female household heads
– Women from communities with high levels of education

2. Community-level factors:
– Women residing in urban areas
– Women from communities with high levels of media usage
– Women from communities with low levels of poverty

Based on these findings, potential recommendations to improve access to maternal health could include:

1. Targeted interventions: Policymakers and health professionals should develop targeted interventions to reach specific groups identified as having lower ANC utilization, such as rural residents, uneducated mothers, and economically poor women. These interventions could include mobile clinics, community health workers, and educational campaigns tailored to the needs of these populations.

2. Education and awareness: Enhancing education and awareness about the importance of ANC among women and communities can help increase utilization. This can be achieved through community-based education programs, media campaigns, and partnerships with local organizations.

3. Improving access to healthcare facilities: Efforts should be made to improve access to healthcare facilities, particularly in rural areas. This can involve increasing the number of healthcare facilities, improving transportation infrastructure, and providing financial support for transportation costs.

4. Empowering women: Promoting women’s empowerment and gender equality can contribute to improved access to maternal health. This can be achieved through initiatives that support women’s education, economic empowerment, and decision-making power in reproductive health matters.

5. Strengthening healthcare systems: Investing in healthcare infrastructure, training healthcare providers, and ensuring the availability of essential supplies and equipment are crucial for improving access to maternal health services. This includes ensuring the availability of skilled providers for ANC services and promoting quality care.

It is important to note that these recommendations should be tailored to the specific context and needs of each country and community. Collaboration between policymakers, stakeholders, and healthcare professionals is essential for implementing effective strategies to improve access to maternal health.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study findings is to implement targeted interventions that address the identified determinants of optimal antenatal care (ANC) utilization.

Specifically, policymakers, stakeholders, and health professionals should focus on the following areas:

1. Targeting rural residents: Implement strategies to improve ANC access and utilization in rural areas, where optimal ANC utilization was found to be lower. This could include mobile clinics or outreach programs to bring ANC services closer to rural communities.

2. Providing education for uneducated mothers: Develop programs that promote education and awareness about the importance of ANC among mothers with no formal education. This could involve community-based education campaigns, providing information through local media channels, or integrating ANC education into existing community programs.

3. Addressing economic barriers: Implement initiatives to overcome economic barriers that prevent economically poor women from accessing ANC services. This could involve providing financial support for ANC visits, subsidizing transportation costs, or integrating ANC services into existing social welfare programs.

4. Strengthening community-level education: Invest in improving community-level education to increase awareness and understanding of the importance of ANC. This could involve supporting community-based education programs, promoting female education, and engaging community leaders in advocating for ANC utilization.

By targeting these specific determinants, it is expected that access to maternal health services, particularly ANC, can be improved, leading to better maternal and child health outcomes.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile Health (mHealth) Solutions: Utilize mobile technology to provide pregnant women with access to information, reminders for antenatal care visits, and telemedicine services for remote consultations.

2. Community Health Workers: Train and deploy community health workers to provide antenatal care services, education, and support to pregnant women in remote or underserved areas.

3. Telemedicine: Implement telemedicine platforms to connect pregnant women with healthcare providers for virtual antenatal care visits, reducing the need for travel and improving access in rural areas.

4. Transportation Support: Establish transportation systems or subsidies to help pregnant women reach healthcare facilities for antenatal care visits, especially in areas with limited transportation options.

5. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, enabling them to access quality antenatal care services at healthcare facilities.

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

1. Define the target population: Identify the specific population group (e.g., pregnant women in a certain region or country) for which access to maternal health needs to be improved.

2. Collect baseline data: Gather data on the current state of access to maternal health services, including the number of antenatal care visits, distance to healthcare facilities, and any existing barriers or challenges.

3. Define indicators: Determine key indicators to measure the impact of the recommendations, such as the increase in the number of antenatal care visits, reduction in travel time to healthcare facilities, or improvement in maternal health outcomes.

4. Develop a simulation model: Create a simulation model that incorporates the potential recommendations and their expected effects on the defined indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and resource availability.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the impact of each recommendation on the defined indicators. Adjust the parameters of the recommendations (e.g., coverage, effectiveness) to explore different scenarios.

6. Analyze results: Analyze the simulation results to assess the potential impact of each recommendation on improving access to maternal health. Compare the outcomes of different scenarios to identify the most effective strategies.

7. Validate and refine the model: Validate the simulation model by comparing the predicted outcomes with real-world data or expert opinions. Refine the model based on feedback and additional data, if necessary.

8. Communicate findings: Present the findings of the simulation study, including the estimated impact of the recommendations on improving access to maternal health. Use the results to inform decision-making, policy development, and resource allocation for maternal health programs.

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

Author

Dima Health

Identifiers:

DOI: 10.3389/fpubh.2023.1035759

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female