Incidence and causes of maternal near-miss in selected hospitals of Addis Ababa, Ethiopia

PLoS ONE, Volume 12, No. 6, Year 2017

Interpretação

Background: Because maternal mortality is a rare event, it is important to study maternal near-miss as a complement to evaluate and improve the quality of obstetric care. Thus, the study was conducted with the aim of assessing the incidence and causes of maternal near-miss. Methods: A facility-based cross-sectional study was conducted in five selected public hospitals of Addis Ababa, Ethiopia from May 1, 2015 to April 30, 2016. All maternal near-miss cases admitted to the selected hospitals during the study period were prospectively recruited. World Health Organization criteria were used to identify maternal near-miss cases. The number of maternal near-miss cases over one year per 1000 live births occurring during the same year was calculated to determine the incidence of maternal near-miss. Underlying and contributing causes of maternal near-miss were documented from each participant’s record. Results: During the one-year period, there were a total of 238 maternal near-miss cases and 29,697 live births in all participating hospitals, which provides a maternal near-miss incidence ratio of 8.01 per 1000 live births. The underlying causes of the majority of maternal near-miss cases were hypertensive disorders and obstetric hemorrhage. Anemia was the major contributing cause reported for maternal near-miss. Most of the maternal near-miss cases occurred before the women’s arrival at the participating hospitals. Conclusion: The study demonstrated a lower maternal near-miss incidence ratio compared to previous country-level studies. The majority of the near-miss cases occurred before the women’s arrival at the participating hospitals, which underscores the importance of improving pre-hospital barriers. Efforts made toward improvement in the management of life-threatening obstetric complications could reduce the occurrence of maternal near-miss problems that occur during hospitalization. The study was conducted in five selected public hospitals of Addis Ababa, Ethiopia from May 1, 2015 to April 30, 2016. The hospitals were selected based on the number of deliveries they managed per year. Because most critical maternal cases are referred to a hospital known to provide better care, the presence of an Intensive Care Unit (ICU), maternity ward, blood transfusion service and facilities for caesarean section (CS) were also considered in the selection of hospitals. Hence, Tikur Anbessa, St. Paul’s Hospital Millennium Medical College, Zewditu Memorial, Yekatit 12, and Gandhi Memorial Hospitals were selected for the current study. Tikur Anbessa Hospital is the largest referral and teaching hospital in Ethiopia and is operated under the Ministry of Education of Ethiopia. St. Paul’s Hospital Millennium Medical College is the largest referral and teaching hospital among those operated under the Federal Ministry of Health. However, the Gandhi Memorial, Yekatit 12 and Zewditu Memorial Hospitals were among the six governmental referral and teaching hospitals that are managed under the Addis Ababa Administrative Health Office. Together, the five hospitals were responsible for a total of 29,697 live birth deliveries during the year in which this study took place. Apart from Tikur Anbessa Hospital, which received very critical cases from different part of Ethiopia, the hospitals are comparable in terms of the patients they receive for care and treatment (Fig 1). SPHMC (St. Paul’s Hospital Millennium Medical College), TAH (Tikur Anbessa Hospital), Y12H (Yekatit 12 Hospital), ZMH (Zewditu Memorial Hospital) and GMH (Gandhi Memorial Hospital). A facility-based cross-sectional study design was used to address the objective of the current study. All women admitted to the participating hospitals during the study period for the treatment of pregnancy-related complications (such as ectopic pregnancy or abortion), having delivered, or within 42 days of termination of pregnancy, and who fulfilled at least one of the conditions stated in the WHO criteria (S1 Table) [5] were included. Depending on when the near-miss occurred, maternal near-miss cases were further categorized into two groups. Women who were assessed as being in critical condition on arrival to a hospital were classified as near-miss before arrival. However, if the near-miss occurred during hospitalization, it was classified as near-miss after arrival. The sample size was determined by a single population proportion formula by assuming the prevalence of maternal near-miss in Ethiopia to be 7.9% [23]. Considering a 1% margin of error, a 95% confidence interval (CI) and a 10% non-response rate, a minimum of 2795 live births were calculated to be the appropriate sample size for this study. However, during the year of the study, a 10 times larger number of live births than the number required was obtained in the five hospitals (29,697 live births), and we decided to include the entire period of one year to increase the precision of the study. Women who experienced a maternal near-miss event during pregnancy, delivery or the postpartum period were identified prospectively by well-trained midwives and nurses in each hospital. Data relating to the most important variables were abstracted from the medical record of the participants using the WHO data abstraction tool, with some modifications [5]. The data were collected from the Delivery Ward, Obstetrics and Gynecology Ward, ICU, and Emergency Gynecology Outpatient Department of each hospital. For each maternal near-miss case, only one underlying cause was identified as per the WHO International Statistical Classification of Diseases and Related Health Problems (ICD). According to the ICD, the underlying cause is the disease or injury which initiated the sequence of events leading directly to death [32]. Because the same classification is used for both maternal death and maternal near-miss [33], the classifications used for maternal near-miss were the same as those listed in the ICD for maternal mortality [34]. However, all possible contributing causes were considered. Information regarding whether the near-miss was present before arrival or developed during hospitalization was also collected in order to determine the place where the near-miss occurred. Data on the total number of live births occurring over one year for each hospital were extracted from the Health Management Information System (HMIS) report of each hospital. The supervisors in all participating hospitals were responsible for checking the completeness of the information. The enumerators filled in the date and signed each questionnaire, which was later checked, edited and signed by the supervisors regularly at each hospital. The data that were collected using hard copies were kept in a locked cabinet by each supervisor until gathered by the principal investigator during supervision. Following this, the data were entered into Epi Info 7 software, and transported to SPSS version 20 and Open Epi computer software for final analysis. The total incidence of maternal near-miss in the hospitals involved in this study was calculated using the maternal near-miss incidence ratio (MNMIR) formula. This was calculated by dividing the number of maternal near-miss cases during one year by the total number of live births during the same year. The incidence ratio in each hospital was also calculated with a 95% CI. In addition, hospital access indicators, such as the number of women with a maternal near-miss condition before arrival at the hospital, were calculated. Intra-hospital care indicators, such as the number of women with near-miss who developed conditions in the hospital, were also calculated. In order to determine the underlying and contributory causes of maternal near-miss, a descriptive frequency for each cause was calculated. The total number and frequency of each cause for all hospitals involved were calculated separately. The causes were categorized into underlying and contributory as per the WHO recommendation [5]. A descriptive frequency of the type of organ dysfunction present in maternal near-miss cases was also calculated. In order to maintain the quality of data, intensive training was given to data collectors and supervisors. All health care workers working in the maternity ward of each participating hospital were also sensitized to the issue so that they would inform the enumerators when they suspected a near-miss case. In addition, inclusion criteria for maternal near-miss were printed and posted on the wall of each ward at all participating hospitals. The data collectors made a daily visit to the Delivery Ward, Obstetrics and Gynecology Ward, ICU, and Emergency Gynecology Outpatient Department to check for potential cases. The data collectors were given training to standardize methods and ensure consistency of data collection. One hospital supervisor, who was responsible for the overall quality of the data, was appointed at each participating hospital. There was frequent supervision of the included hospitals by the principal investigator. The standardized data abstraction form developed by the WHO [5] was used to abstract pertinent information. The questionnaires were also first pre-tested in the participating hospitals to verify the appropriateness of the tool. The standardized WHO criteria were used to identify maternal near-miss cases. Hence, all the above procedures contributed greatly to obtaining quality data. Acceptable ethical standards were strictly adhered to throughout the study process. The study was first approved by the Institutional Review Board of the College of Health sciences, Addis Ababa University (Protocol number: 058/14/SPH, Date: January 2015). It was also approved by the Ethical Review Committee of each hospital. Adequate explanation about the purpose of the study and a letter of support was given to all concerned bodies. For studies that are not clinical trials that involve invasive procedures, taking verbal consent is the standard requirement of the Institutional Review Board of Addis Ababa University. Hence, verbal consent was taken to abstract pertinent information from the participant’s record. The anonymity of the participants was respected via the use of codes rather than the name of the participant. The names of the participants were not reported in the findings of the study to ensure confidentiality.

AI Digest

Study Justification:

The study aimed to assess the incidence and causes of maternal near-miss in selected hospitals of Addis Ababa, Ethiopia. This is important because maternal near-miss cases can provide valuable insights into the quality of obstetric care and help identify areas for improvement. By studying near-miss cases, we can gain a better understanding of the factors contributing to maternal morbidity and mortality and develop strategies to prevent them.

Highlights:

– The study was conducted in five selected public hospitals in Addis Ababa, Ethiopia.
– A total of 238 maternal near-miss cases were identified during the one-year study period.
– The incidence of maternal near-miss was calculated to be 8.01 per 1000 live births.
– Hypertensive disorders and obstetric hemorrhage were found to be the underlying causes of the majority of maternal near-miss cases.
– Anemia was identified as a major contributing cause of maternal near-miss.
– Most maternal near-miss cases occurred before the women’s arrival at the participating hospitals, highlighting the need to improve pre-hospital barriers.
– Efforts to improve the management of life-threatening obstetric complications could help reduce the occurrence of maternal near-miss problems during hospitalization.

Recommendations:

– Improve pre-hospital care and transportation systems to ensure timely access to obstetric care.
– Strengthen antenatal care services to identify and manage high-risk pregnancies.
– Enhance the capacity of healthcare providers to effectively manage hypertensive disorders and obstetric hemorrhage.
– Implement protocols and guidelines for the prevention and management of anemia in pregnant women.
– Conduct further research to explore the reasons behind the lower incidence of maternal near-miss compared to previous studies.

Key Role Players:

– Ministry of Health: Responsible for policy development and implementation of interventions to improve maternal health.
– Hospital Administrators: Responsible for allocating resources and implementing changes within the hospitals.
– Obstetricians and Gynecologists: Provide specialized care and expertise in managing maternal near-miss cases.
– Midwives and Nurses: Play a crucial role in providing care and support to pregnant women and managing complications.
– Community Health Workers: Involved in community outreach and education to promote maternal health.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers.
– Development and implementation of protocols and guidelines.
– Improvement of pre-hospital care and transportation systems.
– Strengthening antenatal care services.
– Procurement of necessary medical equipment and supplies.
– Research funding for further studies.
Please note that the cost items provided are general categories and not actual cost estimates. The actual cost will depend on the specific context and requirements of the interventions.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study was conducted in five selected public hospitals, which provides a reasonable sample size. The study period of one year is also sufficient. The use of World Health Organization criteria to identify maternal near-miss cases adds credibility to the study. However, the abstract lacks information on the methodology used for data collection and analysis. It would be helpful to include details on the training given to data collectors and supervisors, as well as the steps taken to ensure data quality. Additionally, the abstract does not mention any limitations of the study, which could affect the strength of the evidence. To improve the evidence, the abstract should provide more information on the methodology, data collection, and analysis, as well as acknowledge any limitations.

Resumo

The study was conducted in five selected public hospitals of Addis Ababa, Ethiopia from May 1, 2015 to April 30, 2016. The hospitals were selected based on the number of deliveries they managed per year. Because most critical maternal cases are referred to a hospital known to provide better care, the presence of an Intensive Care Unit (ICU), maternity ward, blood transfusion service and facilities for caesarean section (CS) were also considered in the selection of hospitals. Hence, Tikur Anbessa, St. Paul’s Hospital Millennium Medical College, Zewditu Memorial, Yekatit 12, and Gandhi Memorial Hospitals were selected for the current study. Tikur Anbessa Hospital is the largest referral and teaching hospital in Ethiopia and is operated under the Ministry of Education of Ethiopia. St. Paul’s Hospital Millennium Medical College is the largest referral and teaching hospital among those operated under the Federal Ministry of Health. However, the Gandhi Memorial, Yekatit 12 and Zewditu Memorial Hospitals were among the six governmental referral and teaching hospitals that are managed under the Addis Ababa Administrative Health Office. Together, the five hospitals were responsible for a total of 29,697 live birth deliveries during the year in which this study took place. Apart from Tikur Anbessa Hospital, which received very critical cases from different part of Ethiopia, the hospitals are comparable in terms of the patients they receive for care and treatment (Fig 1). SPHMC (St. Paul’s Hospital Millennium Medical College), TAH (Tikur Anbessa Hospital), Y12H (Yekatit 12 Hospital), ZMH (Zewditu Memorial Hospital) and GMH (Gandhi Memorial Hospital). A facility-based cross-sectional study design was used to address the objective of the current study. All women admitted to the participating hospitals during the study period for the treatment of pregnancy-related complications (such as ectopic pregnancy or abortion), having delivered, or within 42 days of termination of pregnancy, and who fulfilled at least one of the conditions stated in the WHO criteria (S1 Table) [5] were included. Depending on when the near-miss occurred, maternal near-miss cases were further categorized into two groups. Women who were assessed as being in critical condition on arrival to a hospital were classified as near-miss before arrival. However, if the near-miss occurred during hospitalization, it was classified as near-miss after arrival. The sample size was determined by a single population proportion formula by assuming the prevalence of maternal near-miss in Ethiopia to be 7.9% [23]. Considering a 1% margin of error, a 95% confidence interval (CI) and a 10% non-response rate, a minimum of 2795 live births were calculated to be the appropriate sample size for this study. However, during the year of the study, a 10 times larger number of live births than the number required was obtained in the five hospitals (29,697 live births), and we decided to include the entire period of one year to increase the precision of the study. Women who experienced a maternal near-miss event during pregnancy, delivery or the postpartum period were identified prospectively by well-trained midwives and nurses in each hospital. Data relating to the most important variables were abstracted from the medical record of the participants using the WHO data abstraction tool, with some modifications [5]. The data were collected from the Delivery Ward, Obstetrics and Gynecology Ward, ICU, and Emergency Gynecology Outpatient Department of each hospital. For each maternal near-miss case, only one underlying cause was identified as per the WHO International Statistical Classification of Diseases and Related Health Problems (ICD). According to the ICD, the underlying cause is the disease or injury which initiated the sequence of events leading directly to death [32]. Because the same classification is used for both maternal death and maternal near-miss [33], the classifications used for maternal near-miss were the same as those listed in the ICD for maternal mortality [34]. However, all possible contributing causes were considered. Information regarding whether the near-miss was present before arrival or developed during hospitalization was also collected in order to determine the place where the near-miss occurred. Data on the total number of live births occurring over one year for each hospital were extracted from the Health Management Information System (HMIS) report of each hospital. The supervisors in all participating hospitals were responsible for checking the completeness of the information. The enumerators filled in the date and signed each questionnaire, which was later checked, edited and signed by the supervisors regularly at each hospital. The data that were collected using hard copies were kept in a locked cabinet by each supervisor until gathered by the principal investigator during supervision. Following this, the data were entered into Epi Info 7 software, and transported to SPSS version 20 and Open Epi computer software for final analysis. The total incidence of maternal near-miss in the hospitals involved in this study was calculated using the maternal near-miss incidence ratio (MNMIR) formula. This was calculated by dividing the number of maternal near-miss cases during one year by the total number of live births during the same year. The incidence ratio in each hospital was also calculated with a 95% CI. In addition, hospital access indicators, such as the number of women with a maternal near-miss condition before arrival at the hospital, were calculated. Intra-hospital care indicators, such as the number of women with near-miss who developed conditions in the hospital, were also calculated. In order to determine the underlying and contributory causes of maternal near-miss, a descriptive frequency for each cause was calculated. The total number and frequency of each cause for all hospitals involved were calculated separately. The causes were categorized into underlying and contributory as per the WHO recommendation [5]. A descriptive frequency of the type of organ dysfunction present in maternal near-miss cases was also calculated. In order to maintain the quality of data, intensive training was given to data collectors and supervisors. All health care workers working in the maternity ward of each participating hospital were also sensitized to the issue so that they would inform the enumerators when they suspected a near-miss case. In addition, inclusion criteria for maternal near-miss were printed and posted on the wall of each ward at all participating hospitals. The data collectors made a daily visit to the Delivery Ward, Obstetrics and Gynecology Ward, ICU, and Emergency Gynecology Outpatient Department to check for potential cases. The data collectors were given training to standardize methods and ensure consistency of data collection. One hospital supervisor, who was responsible for the overall quality of the data, was appointed at each participating hospital. There was frequent supervision of the included hospitals by the principal investigator. The standardized data abstraction form developed by the WHO [5] was used to abstract pertinent information. The questionnaires were also first pre-tested in the participating hospitals to verify the appropriateness of the tool. The standardized WHO criteria were used to identify maternal near-miss cases. Hence, all the above procedures contributed greatly to obtaining quality data. Acceptable ethical standards were strictly adhered to throughout the study process. The study was first approved by the Institutional Review Board of the College of Health sciences, Addis Ababa University (Protocol number: 058/14/SPH, Date: January 2015). It was also approved by the Ethical Review Committee of each hospital. Adequate explanation about the purpose of the study and a letter of support was given to all concerned bodies. For studies that are not clinical trials that involve invasive procedures, taking verbal consent is the standard requirement of the Institutional Review Board of Addis Ababa University. Hence, verbal consent was taken to abstract pertinent information from the participant’s record. The anonymity of the participants was respected via the use of codes rather than the name of the participant. The names of the participants were not reported in the findings of the study to ensure confidentiality.

Materiais

https://efashare.b-cdn.net/materials/e96f295c32cd2d0eb738bc01d1b68ff4929245c924c5fb4f130111b597a4e460/pone.0179013.s001.docx

Inovações

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

1. Telemedicine: Implementing telemedicine services can allow healthcare providers to remotely monitor and provide consultations to pregnant women, especially those in remote areas, reducing the need for them to travel long distances for check-ups.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and reminders about prenatal care, nutrition, and warning signs during pregnancy can help improve maternal health literacy and empower women to take better care of themselves.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and referrals to pregnant women in underserved areas can help increase access to maternal health services.

4. Transportation services: Establishing transportation services, such as ambulances or community transport systems, specifically for pregnant women can ensure timely access to healthcare facilities during emergencies or for routine check-ups.

5. Maternal waiting homes: Creating safe and comfortable accommodation facilities near healthcare facilities where pregnant women can stay before and after delivery can help reduce delays in accessing care, especially for those who live far away.

6. Task-shifting: Training and empowering non-specialist healthcare providers, such as midwives or nurses, to perform certain tasks traditionally done by doctors can help alleviate the shortage of skilled healthcare professionals and increase access to maternal health services.

7. Health information systems: Implementing robust health information systems that can track and monitor maternal health indicators can help identify areas with high maternal near-miss rates and guide targeted interventions to improve access and quality of care.

8. Public-private partnerships: Collaborating with private healthcare providers to expand access to maternal health services can help bridge gaps in service delivery, especially in areas where public facilities are limited.

9. Financial incentives: Providing financial incentives, such as conditional cash transfers or health insurance schemes, to pregnant women who seek antenatal care and deliver in healthcare facilities can help overcome financial barriers and increase utilization of maternal health services.

10. Quality improvement initiatives: Implementing quality improvement initiatives, such as regular training and mentoring of healthcare providers, establishing clinical guidelines, and conducting audits, can help improve the overall quality of maternal healthcare services and reduce maternal near-miss cases.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health is to focus on improving pre-hospital barriers. The study found that the majority of maternal near-miss cases occurred before the women’s arrival at the participating hospitals. This suggests that efforts should be made to address the factors that prevent women from accessing timely and appropriate care.

To develop this recommendation into an innovation, here are some potential strategies:

1. Mobile health clinics: Implementing mobile health clinics that can reach remote areas and provide essential maternal health services, including antenatal care, skilled birth attendance, and emergency obstetric care. These clinics can travel to communities with limited access to healthcare facilities, ensuring that pregnant women receive the necessary care closer to their homes.

2. Community health workers: Train and deploy community health workers who can provide education, counseling, and basic maternal health services within their communities. These workers can identify high-risk pregnancies, provide antenatal care, and refer women to appropriate healthcare facilities when necessary.

3. Telemedicine: Establish telemedicine networks to connect healthcare providers in remote areas with specialists in urban centers. This would enable healthcare providers in underserved areas to consult with specialists and receive guidance on managing complicated cases, improving the quality of care provided.

4. Transportation support: Develop transportation systems or programs that provide affordable and reliable transportation for pregnant women to reach healthcare facilities. This could include initiatives such as subsidized transportation vouchers, community-based transportation services, or partnerships with existing transportation providers.

5. Community awareness campaigns: Conduct community awareness campaigns to educate women and their families about the importance of seeking timely and appropriate maternal healthcare. These campaigns can address cultural beliefs, myths, and misconceptions that may hinder women from accessing healthcare services.

6. Strengthening referral systems: Improve the coordination and communication between primary healthcare centers and higher-level facilities to ensure timely referrals and transfers of pregnant women with complications. This can involve training healthcare providers on referral protocols, establishing clear communication channels, and providing necessary resources for transportation and emergency transfers.

By implementing these innovative strategies, it is possible to improve access to maternal health services and reduce the occurrence of maternal near-miss cases that occur before women’s arrival at healthcare facilities.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthen pre-hospital care: Since a majority of maternal near-miss cases occurred before women’s arrival at the participating hospitals, efforts should be made to improve pre-hospital barriers. This can include increasing awareness about the importance of early antenatal care, providing transportation services for pregnant women in remote areas, and training community health workers to identify and refer high-risk pregnancies.

2. Enhance emergency obstetric care: Improving the management of life-threatening obstetric complications can help reduce the occurrence of maternal near-miss cases during hospitalization. This can be achieved by ensuring the availability of skilled healthcare providers, essential medical supplies, and functioning facilities for emergency obstetric care, including blood transfusion services and facilities for cesarean sections.

3. Implement telemedicine and teleconsultation services: Utilizing technology to provide remote consultations and support can help overcome geographical barriers and improve access to maternal health services. Telemedicine can enable healthcare providers to remotely assess and manage high-risk pregnancies, provide guidance to healthcare workers in remote areas, and facilitate timely referrals.

4. Strengthen data collection and monitoring systems: Establishing robust data collection and monitoring systems can help identify gaps in maternal health services and track progress in improving access. This can involve implementing electronic health records, conducting regular audits of maternal near-miss cases, and using data to inform evidence-based decision-making and resource allocation.

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

1. Define the baseline: Collect data on the current incidence of maternal near-miss cases, the causes of near-miss events, and the existing barriers to accessing maternal health services. This will serve as the baseline against which the impact of the recommendations will be measured.

2. Develop a simulation model: Create a simulation model that incorporates the key variables and factors influencing access to maternal health, such as distance to healthcare facilities, availability of transportation, availability of skilled healthcare providers, and availability of essential medical supplies. The model should also consider the potential impact of the recommendations on these variables.

3. Input data and parameters: Input the baseline data and parameters into the simulation model. This includes information on the current incidence of maternal near-miss cases, the causes of near-miss events, and the existing barriers to accessing maternal health services. Additionally, input data on the potential impact of the recommendations, such as the expected reduction in near-miss cases due to improved pre-hospital care or the expected increase in access to emergency obstetric care.

4. Run simulations: Run the simulation model multiple times, varying the input parameters to simulate different scenarios. For example, simulate the impact of improving pre-hospital care by increasing the percentage of women receiving early antenatal care or simulating the impact of implementing telemedicine services by increasing the availability of remote consultations.

5. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommendations on improving access to maternal health. This can include quantifying the expected reduction in maternal near-miss cases, estimating the increase in access to emergency obstetric care, or identifying the potential improvements in maternal health outcomes.

6. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data. Refine the model based on feedback and further insights gained from the analysis.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on implementing interventions.

Autor

Dima Health

Statistics:

Citations: 36

Identifiers:

DOI: 10.1371/journal.pone.0179013

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury, Workforce

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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