High maternal mortality in rural south-west Ethiopia: Estimate by using the sisterhood method

BMC Pregnancy and Childbirth, Volume 12, Year 2012

Interpretação

Background: Estimation of maternal mortality is difficult in developing countries without complete vital registration. The indirect sisterhood method represents an alternative in places where there is high fertility and mortality rates. The objective of the current study was to estimate maternal mortality indices using the sisterhood method in a rural district in south-west Ethiopia.Method: We interviewed 8,870 adults, 15-49 years age, in 15 randomly selected rural villages of Bonke in Gamo Gofa. By constructing a retrospective cohort of women of reproductive age, we obtained sister units of risk exposure to maternal mortality, and calculated the lifetime risk of maternal mortality. Based on the total fertility for the rural Ethiopian population, the maternal mortality ratio was approximated.Results: We analyzed 8503 of 8870 (96%) respondents (5262 [62%] men and 3241 ([38%] women). The 8503 respondents reported 22,473 sisters (average = 2.6 sisters for each respondent) who survived to reproductive age. Of the 2552 (11.4%) sisters who had died, 819 (32%) occurred during pregnancy and childbirth. This provided a lifetime risk of 10.2% from pregnancy and childbirth with a corresponding maternal mortality ratio of 1667 (95% CI: 1564-1769) per 100,000 live births. The time period for this estimate was in 1998. Separate analysis for male and female respondents provided similar estimates.Conclusion: The impoverished rural area of Gamo Gofa had very high maternal mortality in 1998. This highlights the need for strengthening emergency obstetric care for the Bonke population and similar rural populations in Ethiopia. © 2012 Yaya and Lindtjørn; licensee BioMed Central Ltd. We conducted this study in 15 of 30 randomly selected rural kebeles (lowest administrative units) in the Bonke woreda (district) of the Gamo Gofa zone in south-west Ethiopia. Bonke is one of 15 woredas in the Gamo Gofa zone and had a population of 173,240 in 2010 [16]. The woreda consists of 31 kebeles; 1 of these kebeles is a town. Geresse, the administrative centre of Bonke, is 618 km from Addis Ababa and 68 km from the zonal town, Arba Minch. However, greater than two-thirds of the people in Bonke live in highlands, which are far from roads. The only road to the woreda is the road from Arba Minch to Kamba. The road is often interrupted because of overflowing rivers during the rainy season and most of the population lives in remote villages far from the road. The district is divided into the cold and mountainous highlands, and hot lowlands with malaria endemic to the lowland area. Healthcare is provided by a health centre at the town, and three other rural health centres. There are no medical doctors working in the district, and the health institutions are staffed by a few health officers and nurses. In the woreda, there is no access to comprehensive emergency obstetric care providing caesarean deliveries and blood transfusions. There are villages that are as far as a 14-h walk (approximately 72 km) from a road and a 20-h walk (100 km) from the nearest comprehensive emergency obstetric care at Arba Minch Hospital. We conducted this study as part of an intervention project to reduce maternal mortality in Gamo Gofa. The work also included studies on the estimation of maternal mortality through a community-based birth registry, a retrospective 5-year recall period household survey, and a health facilities obstetric care quality study. In the sisterhood method, adult men and women report the proportion of their adult sisters (born to the same mother) dying during pregnancy, childbirth, or within 6 weeks following pregnancy [17]. The main objective of this method is to create a retrospective cohort of women at risk of pregnancy-related death, and to estimate the lifetime risk (LTR; the chance of a woman dying from pregnancy-related causes during her entire reproductive period). Then, the LTR is translated into the more conventional MMR. The MMR estimate obtained through the indirect sisterhood method using respondents 15–49 years of age refers to events approximately 10–12 years before the collection of data. The time of estimation for the MMR extends up to 35 years from the time of data collection, when the respondents are older (if included, > 50 years of age). Therefore, the information obtained from such surveys is used as a quick reference of past mortality rather than of recent events. This method is not recommended for overseeing the trend over the long period of maternal mortality or for geographic comparisons [18]. To translate the lifetime risk into the MMR, the method recommends that the total fertility rate (TFR; the average number of children that would be born to a woman over her lifetime) should be ≥ 5. In 2000, the TFR for the rural Ethiopian population was 6.4 [19]. Because this rural area has a high illiteracy rate, and is a densely-populated, subsistent-farming community, we assumed the population to have similar fertility with other rural areas in Ethiopia. Therefore we used a TFR of 6.4 in the current study. We recruited data collectors who had completed the 12th grade, lived in the area, and were familiar with the local language and culture. Five diploma graduates who also had a thorough knowledge of the culture and language of the area supervised the data collectors. Each enumerator was trained for 2 days. The training included pre-test field interviews, translation of the questions, and understanding the different interpretations of the questions by the respondents. We asked men and women 15 – 49 years of age the following standard questions using the sisterhood method [17]: 1. How many sisters (born to the same mother) have you had who survived to reproductive age (15 years of age)? 2. How many sisters who reached reproductive age (15 years of age) are alive now? 3. How many sisters died? 4. How many sisters died during pregnancy, childbirth, or 6 weeks after delivery or termination of pregnancy In addition, we collected data on the age, gender, and education of each respondent. Fifteen years of age was considered the common age at which women are expected to undergo menarche. Therefore, we used 15 years as the proxy age for reaching reproductive age with additional probing of a reproductive age phrase itself. Data collectors were carefully trained not to include the responding woman in the reported number of sisters born to her mother. The questions were translated to Amharic (Ethiopian official state language), and the enumerators administered Amharic using the local Gamotho language. The enumerators visited each household in the selected communities that had at least one pregnancy during the 5 years prior to the study. The enumerators asked the four questions (vide supra) to the husband and wife, and to the children, if any, who were 15–49 years of age. Other extended adult family members in the household were also interviewed. If an adult person was not present during the first visit, the data collectors re-visited the household the following morning. The sample size recommended by Graham and colleagues was 3000–6000 adult respondents [17]. A more precise recommendation of the sample size estimation, which considers the margin of error, confidence level, power of the estimate, and the required number of maternal deaths of sisters, suggests a more detailed sample size determination [20]. The formula which calculates the number of maternal deaths required for reporting by respondents was determined as follows: r ≥ [Zα/2]2 * [100÷% ME]2, where r is the number of sister deaths due to maternal causes that were required, Zα/2 is the standard normal deviate at a two-sided confidence level of 100[1-α], and the% ME is the percentage margin of error tolerated by the investigators. We used a tolerable margin of error of 10%, and an α value of 5% (two-sided 95% CI). From the formula we calculated [1.96]2 * [100/10]2 = 384 sister deaths due to pregnancy, childbirth, or 6 weeks after the pregnancy terminated. Hanely and colleagues [20] have suggested that with 80% statistical power for a community with a MMR > 750 per 100,000 live births, a report of ≥ 384 maternal deaths is expected from interviewing 8000 adult siblings. In 2000, the MMR estimate was 937 for Ethiopia [6]. To account for non-responses and missed information, we decided to interview 9000 respondents. We grouped the 30 kebeles of Bonke Woreda into three climatic zones (hot, temperate, and cold). To ensure fair representation of all three climatic conditions, we selected one-half of the kebeles in each climatic zone using a lottery method. Thus, we selected 8 of 16 Dega (cold weather), 4 of 8 Woinadega (moderate temperature), and 3 of 6 Kolla (hot temperature) kebeles. Then, the 9000 respondents were distributed to the study kebeles proportionate to the population size. SPSS 16 (SPSS, Inc., Chicago, IL, USA) was used for data entry and analysis [21]. We used an inflation adjustment to determine the final number of surviving adult sisters for the younger respondents (15–24 years of age. This was done by multiplying the number of respondents in the young age groups by the average number of sisters among the older respondents (25–49 years of age), which was 2.65 in this data. For example, 2.65* 2443= 6471 adjusted sisters for the 15–19 year old respondents [17]. This factor was used with the assumption that the younger respondents had sisters who had yet to reach reproductive age. Using standard adjustment factors [17], we adjusted for the expected proportion of sisters that would have finished their reproductive age for respondents in each age category. Thus, 90% of the sisters of respondents 45–49 years of age are expected to have passed through their reproductive life, but only 10.7% of the sisters of 15–19 year old respondents. The adjustment was implemented so as to determine the number of sister units exposed to maternal death. This retrospective cohort analysis provided 8,068 sister units exposed to the risk of maternal death that served as the denominator for calculating the lifetime risk of maternal death. The lifetime risk (Q) of maternal death was calculated by Q=r/ β, where r is the number of maternal deaths and β is the sister units exposed to the risk of maternal death. We calculated the MMR as MMR =1-(P) 1/TFR, where P is the probability of surviving, which equals (1-Q), and TFR is the total fertility rate [20]. This study was approved by the Ethical Review Committee for Health Research of the Southern Nations Nationalities and the Peoples’ Regional State (SNNPRS) Health Bureau in Ethiopia, and the Regional Committee for Medical and Health Research Ethics of North Norway (REK Nord). We obtained informed oral consent from all of the respondents.

AI Digest

Study Justification:

– Estimation of maternal mortality is difficult in developing countries without complete vital registration.
– The sisterhood method provides an alternative in places with high fertility and mortality rates.
– The objective of the study was to estimate maternal mortality indices using the sisterhood method in a rural district in south-west Ethiopia.

Highlights:

– The study was conducted in 15 randomly selected rural villages in Bonke, Gamo Gofa, south-west Ethiopia.
– The analysis included 8,503 respondents (62% men and 38% women) who reported on 22,473 sisters.
– Of the sisters who had died, 32% occurred during pregnancy and childbirth.
– The lifetime risk of maternal mortality was estimated to be 10.2% with a corresponding maternal mortality ratio of 1667 per 100,000 live births.
– The study highlights the need for strengthening emergency obstetric care for the Bonke population and similar rural populations in Ethiopia.

Recommendations:

– Strengthen emergency obstetric care in the Bonke population and similar rural populations in Ethiopia.
– Improve access to comprehensive emergency obstetric care, including caesarean deliveries and blood transfusions.
– Increase the number of healthcare providers, including medical doctors, in the rural area.
– Enhance transportation infrastructure to improve access to healthcare facilities.

Key Role Players:

– Local government authorities
– Health ministry officials
– Healthcare providers (doctors, nurses, midwives)
– Community health workers
– Non-governmental organizations (NGOs) working in maternal health

Cost Items for Planning Recommendations:

– Construction and equipping of emergency obstetric care facilities
– Recruitment and training of healthcare providers
– Development of transportation infrastructure
– Health education and awareness campaigns
– Monitoring and evaluation of maternal health programs

Força da prova

N/A

Resumo

We conducted this study in 15 of 30 randomly selected rural kebeles (lowest administrative units) in the Bonke woreda (district) of the Gamo Gofa zone in south-west Ethiopia. Bonke is one of 15 woredas in the Gamo Gofa zone and had a population of 173,240 in 2010 [16]. The woreda consists of 31 kebeles; 1 of these kebeles is a town. Geresse, the administrative centre of Bonke, is 618 km from Addis Ababa and 68 km from the zonal town, Arba Minch. However, greater than two-thirds of the people in Bonke live in highlands, which are far from roads. The only road to the woreda is the road from Arba Minch to Kamba. The road is often interrupted because of overflowing rivers during the rainy season and most of the population lives in remote villages far from the road. The district is divided into the cold and mountainous highlands, and hot lowlands with malaria endemic to the lowland area. Healthcare is provided by a health centre at the town, and three other rural health centres. There are no medical doctors working in the district, and the health institutions are staffed by a few health officers and nurses. In the woreda, there is no access to comprehensive emergency obstetric care providing caesarean deliveries and blood transfusions. There are villages that are as far as a 14-h walk (approximately 72 km) from a road and a 20-h walk (100 km) from the nearest comprehensive emergency obstetric care at Arba Minch Hospital. We conducted this study as part of an intervention project to reduce maternal mortality in Gamo Gofa. The work also included studies on the estimation of maternal mortality through a community-based birth registry, a retrospective 5-year recall period household survey, and a health facilities obstetric care quality study. In the sisterhood method, adult men and women report the proportion of their adult sisters (born to the same mother) dying during pregnancy, childbirth, or within 6 weeks following pregnancy [17]. The main objective of this method is to create a retrospective cohort of women at risk of pregnancy-related death, and to estimate the lifetime risk (LTR; the chance of a woman dying from pregnancy-related causes during her entire reproductive period). Then, the LTR is translated into the more conventional MMR. The MMR estimate obtained through the indirect sisterhood method using respondents 15–49 years of age refers to events approximately 10–12 years before the collection of data. The time of estimation for the MMR extends up to 35 years from the time of data collection, when the respondents are older (if included, > 50 years of age). Therefore, the information obtained from such surveys is used as a quick reference of past mortality rather than of recent events. This method is not recommended for overseeing the trend over the long period of maternal mortality or for geographic comparisons [18]. To translate the lifetime risk into the MMR, the method recommends that the total fertility rate (TFR; the average number of children that would be born to a woman over her lifetime) should be ≥ 5. In 2000, the TFR for the rural Ethiopian population was 6.4 [19]. Because this rural area has a high illiteracy rate, and is a densely-populated, subsistent-farming community, we assumed the population to have similar fertility with other rural areas in Ethiopia. Therefore we used a TFR of 6.4 in the current study. We recruited data collectors who had completed the 12th grade, lived in the area, and were familiar with the local language and culture. Five diploma graduates who also had a thorough knowledge of the culture and language of the area supervised the data collectors. Each enumerator was trained for 2 days. The training included pre-test field interviews, translation of the questions, and understanding the different interpretations of the questions by the respondents. We asked men and women 15 – 49 years of age the following standard questions using the sisterhood method [17]: 1. How many sisters (born to the same mother) have you had who survived to reproductive age (15 years of age)? 2. How many sisters who reached reproductive age (15 years of age) are alive now? 3. How many sisters died? 4. How many sisters died during pregnancy, childbirth, or 6 weeks after delivery or termination of pregnancy In addition, we collected data on the age, gender, and education of each respondent. Fifteen years of age was considered the common age at which women are expected to undergo menarche. Therefore, we used 15 years as the proxy age for reaching reproductive age with additional probing of a reproductive age phrase itself. Data collectors were carefully trained not to include the responding woman in the reported number of sisters born to her mother. The questions were translated to Amharic (Ethiopian official state language), and the enumerators administered Amharic using the local Gamotho language. The enumerators visited each household in the selected communities that had at least one pregnancy during the 5 years prior to the study. The enumerators asked the four questions (vide supra) to the husband and wife, and to the children, if any, who were 15–49 years of age. Other extended adult family members in the household were also interviewed. If an adult person was not present during the first visit, the data collectors re-visited the household the following morning. The sample size recommended by Graham and colleagues was 3000–6000 adult respondents [17]. A more precise recommendation of the sample size estimation, which considers the margin of error, confidence level, power of the estimate, and the required number of maternal deaths of sisters, suggests a more detailed sample size determination [20]. The formula which calculates the number of maternal deaths required for reporting by respondents was determined as follows: r ≥ [Zα/2]2 * [100÷% ME]2, where r is the number of sister deaths due to maternal causes that were required, Zα/2 is the standard normal deviate at a two-sided confidence level of 100[1-α], and the% ME is the percentage margin of error tolerated by the investigators. We used a tolerable margin of error of 10%, and an α value of 5% (two-sided 95% CI). From the formula we calculated [1.96]2 * [100/10]2 = 384 sister deaths due to pregnancy, childbirth, or 6 weeks after the pregnancy terminated. Hanely and colleagues [20] have suggested that with 80% statistical power for a community with a MMR > 750 per 100,000 live births, a report of ≥ 384 maternal deaths is expected from interviewing 8000 adult siblings. In 2000, the MMR estimate was 937 for Ethiopia [6]. To account for non-responses and missed information, we decided to interview 9000 respondents. We grouped the 30 kebeles of Bonke Woreda into three climatic zones (hot, temperate, and cold). To ensure fair representation of all three climatic conditions, we selected one-half of the kebeles in each climatic zone using a lottery method. Thus, we selected 8 of 16 Dega (cold weather), 4 of 8 Woinadega (moderate temperature), and 3 of 6 Kolla (hot temperature) kebeles. Then, the 9000 respondents were distributed to the study kebeles proportionate to the population size. SPSS 16 (SPSS, Inc., Chicago, IL, USA) was used for data entry and analysis [21]. We used an inflation adjustment to determine the final number of surviving adult sisters for the younger respondents (15–24 years of age. This was done by multiplying the number of respondents in the young age groups by the average number of sisters among the older respondents (25–49 years of age), which was 2.65 in this data. For example, 2.65* 2443= 6471 adjusted sisters for the 15–19 year old respondents [17]. This factor was used with the assumption that the younger respondents had sisters who had yet to reach reproductive age. Using standard adjustment factors [17], we adjusted for the expected proportion of sisters that would have finished their reproductive age for respondents in each age category. Thus, 90% of the sisters of respondents 45–49 years of age are expected to have passed through their reproductive life, but only 10.7% of the sisters of 15–19 year old respondents. The adjustment was implemented so as to determine the number of sister units exposed to maternal death. This retrospective cohort analysis provided 8,068 sister units exposed to the risk of maternal death that served as the denominator for calculating the lifetime risk of maternal death. The lifetime risk (Q) of maternal death was calculated by Q=r/ β, where r is the number of maternal deaths and β is the sister units exposed to the risk of maternal death. We calculated the MMR as MMR =1-(P) 1/TFR, where P is the probability of surviving, which equals (1-Q), and TFR is the total fertility rate [20]. This study was approved by the Ethical Review Committee for Health Research of the Southern Nations Nationalities and the Peoples’ Regional State (SNNPRS) Health Bureau in Ethiopia, and the Regional Committee for Medical and Health Research Ethics of North Norway (REK Nord). We obtained informed oral consent from all of the respondents.

Materiais

N/A

Inovações

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

1. Mobile clinics: Implementing mobile clinics that can travel to remote villages and provide essential maternal health services such as prenatal care, postnatal care, and emergency obstetric care.

2. Telemedicine: Utilizing telemedicine technology to connect rural healthcare providers with specialists in urban areas. This would allow for remote consultations, diagnosis, and treatment recommendations for pregnant women in rural areas.

3. Community health workers: Training and deploying community health workers in rural areas to provide basic maternal health services, educate pregnant women on healthy practices, and facilitate referrals to higher-level healthcare facilities when necessary.

4. Transportation solutions: Developing transportation solutions such as ambulances or emergency transport systems to ensure that pregnant women in remote areas can access healthcare facilities in a timely manner during emergencies.

5. Maternal waiting homes: Establishing maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes would provide a safe place for women to stay during the final weeks of pregnancy, ensuring they are close to the facility when labor begins.

6. Health education programs: Implementing comprehensive health education programs that focus on maternal health, including prenatal care, nutrition, family planning, and birth preparedness. These programs would aim to increase awareness and knowledge among women and their families.

7. Strengthening healthcare infrastructure: Investing in the improvement and expansion of healthcare facilities in rural areas, ensuring they have the necessary equipment, supplies, and skilled healthcare providers to provide quality maternal health services.

8. Financial incentives: Introducing financial incentives for healthcare providers to work in rural areas, as well as for pregnant women to seek antenatal and postnatal care. This could help attract and retain healthcare professionals and encourage women to access necessary care.

9. Partnerships with non-governmental organizations (NGOs): Collaborating with NGOs that specialize in maternal health to provide additional resources, funding, and expertise to improve access to maternal healthcare in rural areas.

10. Data collection and monitoring systems: Implementing robust data collection and monitoring systems to track maternal health indicators in rural areas. This would help identify gaps in access and quality of care, allowing for targeted interventions and improvements.

It is important to note that the specific context and needs of the rural area in question should be considered when implementing any of these innovations.

AI Innovations Description

Based on the study conducted in rural south-west Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening emergency obstetric care: The study highlights the need for improving emergency obstetric care in the Bonke population and similar rural populations in Ethiopia. This can be achieved by providing comprehensive emergency obstetric care services, including caesarean deliveries and blood transfusions, in rural health centers. This would ensure that women have access to life-saving interventions during pregnancy and childbirth.

2. Improving transportation infrastructure: The study mentions that many villages in the Bonke district are far from roads, making it difficult for pregnant women to access healthcare facilities. Developing better transportation infrastructure, such as roads and bridges, can significantly improve access to maternal health services. This would enable pregnant women to reach health centers in a timely manner, especially during emergencies.

3. Increasing healthcare workforce: The study mentions that there are no medical doctors working in the Bonke district, and the health institutions are staffed by a few health officers and nurses. Increasing the number of healthcare professionals, particularly skilled birth attendants, in rural areas can improve access to quality maternal health services. This can be achieved through recruitment and training programs targeted at rural areas.

4. Community-based interventions: Implementing community-based interventions can help raise awareness about maternal health and promote healthy behaviors among pregnant women. This can include educating communities about the importance of antenatal care, skilled birth attendance, and postnatal care. Community health workers can play a crucial role in delivering these interventions and providing support to pregnant women and their families.

5. Mobile health (mHealth) solutions: Utilizing mobile health technologies can help overcome geographical barriers and improve access to maternal health information and services. Mobile applications can provide pregnant women with information on antenatal care, nutrition, and danger signs during pregnancy. Additionally, telemedicine platforms can enable remote consultations with healthcare providers, reducing the need for women to travel long distances for routine check-ups.

By implementing these recommendations, it is possible to develop innovative solutions that can improve access to maternal health in rural areas, ultimately reducing maternal mortality rates.

AI Innovations Methodology

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

1. Strengthen emergency obstetric care: Improve access to comprehensive emergency obstetric care, including caesarean deliveries and blood transfusions, in rural areas like Bonke in Gamo Gofa, Ethiopia. This can be achieved by establishing well-equipped health centers and training healthcare professionals to provide emergency obstetric care.

2. Improve transportation infrastructure: Address the issue of limited access to healthcare facilities by improving transportation infrastructure, especially roads, in rural areas. This will ensure that pregnant women can reach healthcare facilities in a timely manner, reducing delays in receiving necessary care.

3. Increase availability of skilled healthcare providers: Address the shortage of medical doctors and healthcare professionals in rural areas by implementing strategies to attract and retain skilled healthcare providers. This can include offering incentives such as higher salaries, better working conditions, and career development opportunities.

4. Enhance community-based interventions: Implement community-based interventions to raise awareness about maternal health and promote healthy practices during pregnancy and childbirth. This can involve training community health workers to provide education, counseling, and support to pregnant women and their families.

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

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of maternal deaths, maternal mortality ratio, percentage of women receiving timely obstetric care, and distance to the nearest healthcare facility.

2. Collect baseline data: Gather baseline data on the current status of maternal health in the target area, including maternal mortality rates, healthcare infrastructure, availability of skilled healthcare providers, and transportation accessibility.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, healthcare resources, and socio-economic factors.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Adjust the parameters of the model based on the expected outcomes of the recommendations.

5. Analyze results: Analyze the results of the simulations to determine the projected impact of the recommendations on improving access to maternal health. This can include assessing changes in maternal mortality rates, improvements in healthcare infrastructure, and increased availability of skilled healthcare providers.

6. Validate the model: Validate the simulation model by comparing the projected results with real-world data and observations. This will help ensure the accuracy and reliability of the model.

7. Refine and iterate: Based on the analysis and validation, refine the simulation model and repeat the simulations to further assess the impact of the recommendations. Iterate this process until satisfactory results are achieved.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health. This can inform decision-making and resource allocation to effectively address the challenges faced in rural areas like Bonke in Gamo Gofa, Ethiopia.

Autores & Coautores

Statistics:

Citations: 9

Authors: 2

Identifiers:

DOI: 10.1186/1471-2393-12-136

Research Areas:

Community Interventions, Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Workforce

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female

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