Prevalence of Obstetric Danger Signs during Pregnancy and Associated Factors among Mothers in Shashemene Rural District, South Ethiopia

Journal of Pregnancy, Volume 2020, Year 2020

Interpretation

Introduction. Obstetric danger signs are those signs that a pregnant woman will see or those symptoms that she will feel which indicate that something is going wrong with her or with the pregnancy. Evidence on the prevalence of obstetric danger signs and contributing factors were crucial in designing programs in the global target of reducing maternal morbidity and mortality. Objective. To assess the prevalence of obstetric danger signs during pregnancy and associated factors among mothers in a Shashemene rural district, South Ethiopia. Methods. A community-based cross-sectional study design was conducted among 395 randomly selected women who gave birth in the last six months. A pretested interviewer-administered questionnaire was utilized. Data were cleaned, coded, and entered into Epi data manager version 4.1 and then exported to SPSS version 20. Bivariable and multivariable logistic regression analyses were employed to assess the association between independent variables with the outcome variable. Statistical significance was declared at p<0.05. Result. One hundred sixty-three (41.3%) of women had a history of obstetric danger signs during pregnancy. The most prevalent obstetric danger signs were vaginal bleeding (15.4%) followed by swelling of the body 12.7% and severe vomiting 5.3%. Women who have less than four times antenatal care visits were 6.7 times more likely to experience obstetric danger signs (AOR 6.7 (95% CI 3.05, 14.85)) compared to those who had antenatal care visit four times and above. Women who have inadequate knowledge of obstetric danger signs were 2.5 times more likely to experience obstetric danger signs during pregnancy (AOR 2.5 (95% CI 1.34, 4.71)), and primigravida women were 6.3 times more likely to have obstetric danger signs during pregnancy (AOR 6.3 (95% CI 2.61, 15.09)) compared to multiparous women. Conclusion. About half of the pregnant mothers have experienced at least one obstetric danger signs. Public health interventions on maternal health should give priority to the prevalent causes of obstetric danger signs, strengthening completion of four antenatal care visits and health education on obstetric danger signs for pregnant mothers at community level especially for primgravid women. The study was conducted in the Shashemene district, located in West Arsi Zone, Oromia regional state, Ethiopia. The total population of the district was estimated to be 265,109 based on the woreda health office report of 2018. The district was 225 km south of Addis Ababa, the capital city of Ethiopia. It was divided into 33 kebeles (small administrative units) with health infrastructures of 8 health centers and 33 health posts. The study was conducted from April 20 to May 21, 2018. The study has a community-based cross-sectional study design. Randomly selected mothers who gave birth in the Shashemene rural district within the last six months during the data collection period were included, while women who are seriously ill and unable to perform interviews and mothers who complain illness of their neonates and/or infants were excluded. The sample size of the study was determined by using the Epi Info version 7.1.1 StatCalc with the assumptions of 95% confidence level, p = prevalence of mothers experienced vaginal bleeding was 19.1% (10), d =4% (marginal errors), Finally, by adding a nonresponse rate of 10%, n = 407. Ten kebeles were selected by using a simple random sampling (lottery) method from thirty-three kebeles in the Shashemene rural district. Then, the census was conducted to register all mothers who gave birth within the last six months to prepare the sampling frame. Proportion to size allocation for each of the ten kebeles based on the number of eligible mothers for the study was done based on census results. Code given for households of eligible mothers during the census was used as a sampling frame for the final selection of the mothers. Finally, computer-generated random numbers were used to recruit study participants. Obstetric danger signs (ODS) refers to the loss of consciousness; persistent vomiting; severe persistent abdominal pain; vaginal bleeding; swelling of face, fingers and feet; blurring of vision; fits of pregnancy; severe recurrent frontal headache; and high-grade fever. Gravidity refers to a total number of pregnancies. Kebele is the lowest administrative structure next to the district. A woman who experienced at least one of the ODS (loss of consciousness; persistent vomiting; severe persistent abdominal pain; vaginal bleeding; swelling of face, fingers, and feet; blurring of vision; fits of pregnancy; severe recurrent frontal headache; and high-grade fever) was categorized as has ODS and no ODS otherwise. Knowledge of ODS was assessed by asking 21 questions, and participants who scored a mean above score was categorized as having adequate knowledge of ODS, otherwise inadequate knowledge [10–13]. The questionnaire was prepared after a review of different literature and modified to suit and relate to the study objective and the area's context from different materials. Questionnaires have sociodemographic factor, maternal factor, and health facility-related factor parts. The questionnaire was partially adapted from the survey tools developed by JHPIEGO Maternal and Neonatal Health program and contextualized according to local contexts. The questionnaire was adapted to fit the study area population context and to meet the objectives of the study. An interviewer-administered structured questionnaire was used to collect the data from mothers who gave birth in the Shashemene district. The questionnaires were first developed in English and then translated into Afaan Oromo, and then translated back to English again to check its consistency. Six diploma nurses with experiences in survey data collection and two health officers as supervisors participated in the data collection process after two-day training was given by the principal investigator. During the data collection period, the data collectors and supervisors were guided by health development army leaders in each kebele so that they can easily access the houses of each sampled house of women who gave birth within the last six months. The data collectors were given the list of women who gave birth within the last six months in each kebele to be interviewed. The pretest was carried out at Arsi Nagelle district, five days before the actual data collection date, which was outside of the study area and has similar sociodemographic characteristics. During the procedure, the data collectors interview the participants in a private area to increase the confidentiality of the participants. Various activities were performed to assure the quality of data, and data collectors were selected carefully based on clearly established criteria of diploma nurses who were experienced in data collections and currently not working in the kebeles. Before data collection, both interviewers and supervisors were trained in the interview approach, ways to maintain confidentiality, and the privacy of the study participants for two days. The appropriateness of the questionnaire in terms of content, consistency, language, and organization was checked and was modified. The English version prepared questionnaire was translated to the local language (Afaan Oromo) by a person knowing both the languages. Then, another individual who had very good knowledge of both English and Afaan Oromo language translated the Afaan Oromo version back to English to check for its original meaning. The questionnaire was pretested on 21 respondents (5%of sample size) in Arsi Nagelle woreda that had similar characteristics with the study population. The pretest findings were discussed among data collectors, supervisors, and the investigator to ensure a better understanding of the data collection process. Based on the pretest, questions were revised, edited, and those found to be unclear or confusing were modified. To reduce nonresponse rate and unwanted confusion, necessary information and description were given to respondents before initiating the interview. Finally, a structured Afaan Oromo version questionnaire was used for data collection. The principal investigator and supervisor supervised the data collection process. The data quality was controlled by close supervision with aggressive monitoring. Every day, 10% of the completed questionnaires were reviewed and checked for completeness and consistency by the supervisors and principal investigator and the necessary feedback offered to data collectors in the next morning before the data collection begins. To control the quality of the data processing, the data was checked for its completeness before data entry and the inconsistent data was checked to refer to the hard copy of the questionnaire. Quantitative data were entered into Epi data manager version 4.1 and exported to SPSS version 20 for analysis. The cleaning process was done by running a simple frequency after data entry for its consistency. Errors related to inconsistency of data such as missing values and outliers were checked and considered during data cleaning. Descriptive statistics using frequencies, percentages, mean, and standard deviation were used to describe findings. The frequency distributions of the variables were worked out using tables and figures. Bivariable analysis using logistic regression was done and all explanatory variables which have an association with the outcome variable at a p value of less than 0.25 were selected as candidates for multivariable analysis. Multicollinearity between the candidate variables was checked with a minimum tolerance level at 0.2. Hence, variables with a p value of less than 0.25 in the bivariate logistic regression analysis were entered into a multivariable logistic regression model. Then, multivariable analysis using a backward stepwise selection method was done to control for possible confounding variables and to determine the presence of a statistically significant association between explanatory variables and the outcome variable. The level of statistical significance was declared at a p value of < 0.05, and AOR with 95% CI was used to measure the degree of association between independent variables and the outcome variable. Model fitness was checked using Hosmer and Lame show goodness-of-fit test. Finally, the dependent variable was organized as a binary variable with two categories: ODS present (1) and absent (0). The principal component analysis was conducted to set the wealth/economic status of pregnant women. Before analysis, sample adequacy was checked, and after, the assumption of sampling adequacy was fulfilled; then, the appropriateness of the principal component analysis was checked. After that, variables were included and removed where decided. Then, principal component extraction was used to extract variables. The correlation coefficient between the variables (rows) and the principal component was checked. A total of 9 items on household assets were analyzed using the principal component analysis method after checking the fulfillment of assumptions using the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity. Finally, the wealth status of pregnant mothers was classified as low wealth status, medium-high wealth status, and high wealth status depending on the mean value of assets of the mother's score.

AI Digest

Study Justification:

The study aimed to assess the prevalence of obstetric danger signs during pregnancy and associated factors among mothers in the Shashemene rural district, South Ethiopia. This information is crucial for designing programs to reduce maternal morbidity and mortality globally. By understanding the prevalence and factors associated with obstetric danger signs, public health interventions can be targeted to address the prevalent causes and improve maternal health outcomes.

Study Highlights:

– The study found that 41.3% of women in the Shashemene rural district had a history of obstetric danger signs during pregnancy.
– The most prevalent obstetric danger signs were vaginal bleeding (15.4%), swelling of the body (12.7%), and severe vomiting (5.3%).
– Women who had less than four antenatal care visits were 6.7 times more likely to experience obstetric danger signs compared to those who had four or more visits.
– Women with inadequate knowledge of obstetric danger signs were 2.5 times more likely to experience these signs during pregnancy.
– Primigravida women were 6.3 times more likely to have obstetric danger signs compared to multiparous women.

Recommendations for Lay Readers and Policy Makers:

1. Strengthen completion of at least four antenatal care visits: Encourage pregnant women to attend regular antenatal care visits to identify and manage obstetric danger signs early.
2. Improve health education on obstetric danger signs: Provide comprehensive education to pregnant women and their families about the signs and symptoms of obstetric danger signs, emphasizing the importance of seeking timely medical care.
3. Target interventions for primigravida women: Develop specific interventions to address the increased risk of obstetric danger signs among primigravida women, including targeted education and support.

Key Role Players:

1. Ministry of Health: Responsible for developing and implementing policies and guidelines related to maternal health.
2. Health Centers and Health Posts: Provide antenatal care services and serve as a primary point of contact for pregnant women.
3. Health Development Army Leaders: Assist in guiding data collectors and ensuring access to households for data collection.
4. Data Collectors: Conduct interviews with mothers who gave birth in the Shashemene rural district to collect data for the study.
5. Supervisors: Oversee the data collection process and ensure data quality.

Cost Items for Planning Recommendations:

1. Training: Budget for training sessions for data collectors and supervisors to ensure they are equipped with the necessary skills and knowledge.
2. Materials and Supplies: Allocate funds for printing questionnaires, data collection tools, and other necessary materials.
3. Transportation: Include transportation costs for data collectors and supervisors to reach the study area and conduct interviews.
4. Data Entry and Analysis: Allocate resources for data entry and analysis using appropriate software.
5. Communication and Dissemination: Budget for communication and dissemination activities, such as presenting the study findings to stakeholders and publishing the results in a journal.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and requirements of the study.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is community-based cross-sectional, which allows for data collection from a representative sample. The sample size was determined using statistical calculations. The data collection process was carefully planned and executed, including training of data collectors and supervisors. The statistical analysis was conducted using appropriate methods. However, the study design is cross-sectional, which limits the ability to establish causality. Additionally, the study was conducted in a specific district in Ethiopia, which may limit the generalizability of the findings. To improve the strength of the evidence, future studies could consider using a longitudinal design to establish causality and include a more diverse sample to enhance generalizability.

Abstract

The study was conducted in the Shashemene district, located in West Arsi Zone, Oromia regional state, Ethiopia. The total population of the district was estimated to be 265,109 based on the woreda health office report of 2018. The district was 225 km south of Addis Ababa, the capital city of Ethiopia. It was divided into 33 kebeles (small administrative units) with health infrastructures of 8 health centers and 33 health posts. The study was conducted from April 20 to May 21, 2018. The study has a community-based cross-sectional study design. Randomly selected mothers who gave birth in the Shashemene rural district within the last six months during the data collection period were included, while women who are seriously ill and unable to perform interviews and mothers who complain illness of their neonates and/or infants were excluded. The sample size of the study was determined by using the Epi Info version 7.1.1 StatCalc with the assumptions of 95% confidence level, p = prevalence of mothers experienced vaginal bleeding was 19.1% (10), d =4% (marginal errors), Finally, by adding a nonresponse rate of 10%, n = 407. Ten kebeles were selected by using a simple random sampling (lottery) method from thirty-three kebeles in the Shashemene rural district. Then, the census was conducted to register all mothers who gave birth within the last six months to prepare the sampling frame. Proportion to size allocation for each of the ten kebeles based on the number of eligible mothers for the study was done based on census results. Code given for households of eligible mothers during the census was used as a sampling frame for the final selection of the mothers. Finally, computer-generated random numbers were used to recruit study participants. Obstetric danger signs (ODS) refers to the loss of consciousness; persistent vomiting; severe persistent abdominal pain; vaginal bleeding; swelling of face, fingers and feet; blurring of vision; fits of pregnancy; severe recurrent frontal headache; and high-grade fever. Gravidity refers to a total number of pregnancies. Kebele is the lowest administrative structure next to the district. A woman who experienced at least one of the ODS (loss of consciousness; persistent vomiting; severe persistent abdominal pain; vaginal bleeding; swelling of face, fingers, and feet; blurring of vision; fits of pregnancy; severe recurrent frontal headache; and high-grade fever) was categorized as has ODS and no ODS otherwise. Knowledge of ODS was assessed by asking 21 questions, and participants who scored a mean above score was categorized as having adequate knowledge of ODS, otherwise inadequate knowledge [10–13]. The questionnaire was prepared after a review of different literature and modified to suit and relate to the study objective and the area's context from different materials. Questionnaires have sociodemographic factor, maternal factor, and health facility-related factor parts. The questionnaire was partially adapted from the survey tools developed by JHPIEGO Maternal and Neonatal Health program and contextualized according to local contexts. The questionnaire was adapted to fit the study area population context and to meet the objectives of the study. An interviewer-administered structured questionnaire was used to collect the data from mothers who gave birth in the Shashemene district. The questionnaires were first developed in English and then translated into Afaan Oromo, and then translated back to English again to check its consistency. Six diploma nurses with experiences in survey data collection and two health officers as supervisors participated in the data collection process after two-day training was given by the principal investigator. During the data collection period, the data collectors and supervisors were guided by health development army leaders in each kebele so that they can easily access the houses of each sampled house of women who gave birth within the last six months. The data collectors were given the list of women who gave birth within the last six months in each kebele to be interviewed. The pretest was carried out at Arsi Nagelle district, five days before the actual data collection date, which was outside of the study area and has similar sociodemographic characteristics. During the procedure, the data collectors interview the participants in a private area to increase the confidentiality of the participants. Various activities were performed to assure the quality of data, and data collectors were selected carefully based on clearly established criteria of diploma nurses who were experienced in data collections and currently not working in the kebeles. Before data collection, both interviewers and supervisors were trained in the interview approach, ways to maintain confidentiality, and the privacy of the study participants for two days. The appropriateness of the questionnaire in terms of content, consistency, language, and organization was checked and was modified. The English version prepared questionnaire was translated to the local language (Afaan Oromo) by a person knowing both the languages. Then, another individual who had very good knowledge of both English and Afaan Oromo language translated the Afaan Oromo version back to English to check for its original meaning. The questionnaire was pretested on 21 respondents (5%of sample size) in Arsi Nagelle woreda that had similar characteristics with the study population. The pretest findings were discussed among data collectors, supervisors, and the investigator to ensure a better understanding of the data collection process. Based on the pretest, questions were revised, edited, and those found to be unclear or confusing were modified. To reduce nonresponse rate and unwanted confusion, necessary information and description were given to respondents before initiating the interview. Finally, a structured Afaan Oromo version questionnaire was used for data collection. The principal investigator and supervisor supervised the data collection process. The data quality was controlled by close supervision with aggressive monitoring. Every day, 10% of the completed questionnaires were reviewed and checked for completeness and consistency by the supervisors and principal investigator and the necessary feedback offered to data collectors in the next morning before the data collection begins. To control the quality of the data processing, the data was checked for its completeness before data entry and the inconsistent data was checked to refer to the hard copy of the questionnaire. Quantitative data were entered into Epi data manager version 4.1 and exported to SPSS version 20 for analysis. The cleaning process was done by running a simple frequency after data entry for its consistency. Errors related to inconsistency of data such as missing values and outliers were checked and considered during data cleaning. Descriptive statistics using frequencies, percentages, mean, and standard deviation were used to describe findings. The frequency distributions of the variables were worked out using tables and figures. Bivariable analysis using logistic regression was done and all explanatory variables which have an association with the outcome variable at a p value of less than 0.25 were selected as candidates for multivariable analysis. Multicollinearity between the candidate variables was checked with a minimum tolerance level at 0.2. Hence, variables with a p value of less than 0.25 in the bivariate logistic regression analysis were entered into a multivariable logistic regression model. Then, multivariable analysis using a backward stepwise selection method was done to control for possible confounding variables and to determine the presence of a statistically significant association between explanatory variables and the outcome variable. The level of statistical significance was declared at a p value of < 0.05, and AOR with 95% CI was used to measure the degree of association between independent variables and the outcome variable. Model fitness was checked using Hosmer and Lame show goodness-of-fit test. Finally, the dependent variable was organized as a binary variable with two categories: ODS present (1) and absent (0). The principal component analysis was conducted to set the wealth/economic status of pregnant women. Before analysis, sample adequacy was checked, and after, the assumption of sampling adequacy was fulfilled; then, the appropriateness of the principal component analysis was checked. After that, variables were included and removed where decided. Then, principal component extraction was used to extract variables. The correlation coefficient between the variables (rows) and the principal component was checked. A total of 9 items on household assets were analyzed using the principal component analysis method after checking the fulfillment of assumptions using the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity. Finally, the wealth status of pregnant mothers was classified as low wealth status, medium-high wealth status, and high wealth status depending on the mean value of assets of the mother's score.

Materials

N/A

Innovations

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 pregnant women with information on obstetric danger signs, antenatal care visits, and health education on maternal health. These apps can also send reminders for appointments and provide access to teleconsultations with healthcare providers.

2. Community Health Workers: Train and deploy community health workers to educate pregnant women in rural areas about obstetric danger signs, the importance of antenatal care visits, and the need for timely referrals to healthcare facilities. These workers can also provide basic prenatal care and support to pregnant women in their communities.

3. Telemedicine: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare providers through video calls or phone calls. This can help address the shortage of healthcare providers in rural areas and improve access to timely and accurate medical advice.

4. Maternal Health Vouchers: Implement a voucher system that provides pregnant women with financial assistance to cover the costs of antenatal care visits, transportation to healthcare facilities, and emergency obstetric care. This can help reduce financial barriers and ensure that pregnant women can access the necessary healthcare services.

5. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness among pregnant women and their families about obstetric danger signs, the importance of antenatal care visits, and the benefits of skilled birth attendance. These campaigns can use various mediums such as radio, television, and community meetings to reach a wide audience.

6. Strengthening Health Infrastructure: Invest in improving the health infrastructure in rural areas, including the construction and equipping of health centers and maternity wards. This can help ensure that pregnant women have access to quality maternal healthcare services closer to their homes.

7. Maternal Transport Systems: Establish or strengthen maternal transport systems that provide pregnant women with reliable and timely transportation to healthcare facilities for antenatal care visits and emergency obstetric care. This can help overcome geographical barriers and ensure that pregnant women can reach healthcare facilities in a timely manner.

8. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs that specialize in maternal health to implement innovative programs and interventions that improve access to maternal healthcare services. These partnerships can leverage the expertise and resources of NGOs to reach underserved populations and address specific challenges in maternal health.

It is important to note that the implementation of these innovations should be context-specific and tailored to the local healthcare system and needs of the community.

AI Innovations Description

Based on the study conducted in the Shashemene rural district, South Ethiopia, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Strengthen antenatal care visits: The study found that women who had less than four antenatal care visits were more likely to experience obstetric danger signs during pregnancy. To improve access to maternal health, it is important to prioritize and encourage pregnant women to complete at least four antenatal care visits. This can be done through community-based interventions, such as health education campaigns and outreach programs.

2. Health education on obstetric danger signs: The study also found that women with inadequate knowledge of obstetric danger signs were more likely to experience them during pregnancy. Therefore, it is crucial to provide comprehensive health education to pregnant women, especially primigravida women, on the common obstetric danger signs and when to seek medical help. This can be done through various channels, such as community health workers, mobile health clinics, and educational materials.

3. Targeted interventions for primigravida women: The study revealed that primigravida women were more likely to have obstetric danger signs during pregnancy compared to multiparous women. To address this issue, targeted interventions should be developed specifically for primigravida women, focusing on early detection and management of obstetric danger signs. This can include regular check-ups, personalized counseling, and support groups.

4. Strengthening health facilities in rural areas: The study was conducted in a rural district, highlighting the importance of strengthening health facilities in such areas. This can be achieved by improving infrastructure, ensuring availability of skilled healthcare providers, and providing necessary medical equipment and supplies. Additionally, efforts should be made to increase the accessibility and affordability of maternal health services in rural areas.

Overall, the recommendations from this study emphasize the need for comprehensive and targeted interventions to improve access to maternal health. By implementing these recommendations, it is possible to reduce maternal morbidity and mortality rates, ultimately improving the overall well-being of pregnant women and their communities.

AI Innovations Methodology

Based on the study “Prevalence of Obstetric Danger Signs during Pregnancy and Associated Factors among Mothers in Shashemene Rural District, South Ethiopia,” here are some potential recommendations to improve access to maternal health:

1. Increase the number of antenatal care visits: The study found that women who had less than four antenatal care visits were more likely to experience obstetric danger signs during pregnancy. Encouraging and ensuring that pregnant women attend the recommended number of antenatal care visits can help identify and address any potential complications early on.

2. Strengthen health education on obstetric danger signs: The study also found that women with inadequate knowledge of obstetric danger signs were more likely to experience them during pregnancy. Providing comprehensive health education to pregnant women, especially primigravida women, can help them recognize and seek timely care for any danger signs they may experience.

3. Improve access to healthcare facilities: Enhancing the availability and accessibility of healthcare facilities, particularly in rural areas like Shashemene, can help pregnant women access timely and appropriate care. This can include increasing the number of health centers and health posts, as well as improving transportation infrastructure to facilitate easier access to healthcare facilities.

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 specific indicators that can measure the impact of the recommendations, such as the percentage of pregnant women attending the recommended number of antenatal care visits, the percentage of pregnant women with adequate knowledge of obstetric danger signs, and the percentage of pregnant women accessing healthcare facilities within a certain distance.

2. Collect baseline data: Gather data on the current status of the indicators before implementing the recommendations. This can be done through surveys, interviews, or existing data sources.

3. Implement the recommendations: Roll out the recommended interventions, such as increasing the number of antenatal care visits, conducting health education sessions, and improving healthcare infrastructure.

4. Monitor and evaluate: Continuously monitor the progress and impact of the interventions. Collect data on the indicators at regular intervals to assess any changes or improvements. This can involve conducting follow-up surveys or interviews with pregnant women, healthcare providers, and community members.

5. Analyze the data: Use statistical analysis techniques to analyze the collected data and determine the impact of the recommendations on improving access to maternal health. Compare the baseline data with the post-intervention data to identify any significant changes or improvements.

6. Draw conclusions and make recommendations: Based on the analysis, draw conclusions about the effectiveness of the recommendations in improving access to maternal health. Identify any challenges or barriers that may have affected the outcomes. Make recommendations for further improvements or modifications to the interventions based on the findings.

7. Disseminate the findings: Share the results of the impact assessment with relevant stakeholders, including policymakers, healthcare providers, and community members. Use the findings to advocate for continued support and investment in initiatives that improve access to maternal health.

By following this methodology, it is possible to simulate the impact of the recommendations on improving access to maternal health and make informed decisions for future interventions.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 3

Identifiers:

DOI: 10.1155/2020/6153146

ISSN: 20902727

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, Grounded Theory, Phenomenological Study

Study Approach:

Quantitative

Participants Gender:

Female