Rural-urban inequity in unmet obstetric needs and functionality of emergency obstetric care services in a Zambian district

PLoS ONE, Volume 11, No. 1, Year 2016

Interpretation

Background: Zambia has a high maternal mortality ratio, 398/100,000 live births. Few pregnant women access emergency obstetric care services to handle complications at childbirth. We aimed to assess the deficit in life-saving obstetric services in the rural and urban areas of Kapiri Mposhi district. Method: A cross-sectional survey was conducted in 2011 as part of the ‘Response to Accountable priority setting for Trust in health systems’ (REACT) project. Data on all childbirths that occurred in emergency obstetric care facilities in 2010 were obtained retrospectively. Sources of information included registers from maternity ward admission, delivery and operation theatre, and case records. Data included age, parity, mode of delivery, obstetric complications, and outcome of mother and the newborn. An approach using estimated major obstetric interventions expected but not done in health facilities was used to assess deficit of life-saving interventions in urban and rural areas. Results: A total of 2114 urban and 1226 rural childbirths occurring in emergency obstetric care facilities (excluding abortions) were analysed. Facility childbirth constituted 81% of expected births in urban and 16% in rural areas. Based on the reference estimate that 1.4% of childbearing women were expected to need major obstetric intervention, unmet obstetric need was 77 of 106 women, thus 73% (95% CI 71-75%) in rural areas whereas urban areas had no deficit. Major obstetric interventions for absolute maternal indications were higher in urban 2.1% (95% CI 1.60-2.71%) than in rural areas 0.4% (95% CI 0.27-0.55%), with an urban to rural rate ratio of 5.5 (95% CI 3.55-8.76). Conclusions: Women in rural areas had deficient obstetric care. The likelihood of under-going a life-saving intervention was 5.5 times higher for women in urban than rural areas. Targeting rural women with life-saving services could substantially reduce this inequity and preventable deaths. This was a retrospective cross-sectional facility-based study which was part of the ‘Response to Accountable priority setting for Trust in health systems’ (REACT) project. The project used an ethical framework of Accountability for Reasonableness (AFR) to guide achievement of a fair and legitimate priority setting process that would enhance trust, quality and equity in access to health care [20]. The project was designed to evaluate before and after effect of an intervention by implementation of the AFR process which has been described elsewhere [20]. This study analysed baseline data focused on emergency obstetric care. All facilities offering EmONC services in Kapiri Mposhi were enrolled in the study, that is, four rural health facilities and one urban hospital. The hospital was the main facility that offered childbirth services in the urban area. Two of the rural facilities were government run while two were faith-based. The district Kapiri Mposhi, in the Central Province of Zambia, connects to the northern part of the country via road and railway network. The population was 240,000 in 2010, 35% being urban [21]. Urban was defined according to population census of 2000 as localities of 5,000 inhabitants or more and a majority of the labour force in non-agricultural activities [22]. Kapiri Mposhi is 17 219km2 with a population density of 14.7 persons per km2 [21]. The main economic activity is subsistence agriculture and small-scale trade in the urban area. Health services for childbirth are provided mainly by the public health sector in both urban and rural areas together with a small contribution by mission health facilities in the rural area [17]. The district of Kapiri Mposhi had no comprehensive EmONC service and was not providing caesarean sections at the time of the study. Thus, women with complications in pregnancy were referred and transported by ambulance to Kabwe General Hospital, 50km outside the district. Out of 27 rural and urban health facilities that offered childbirth services, only five offered EmONC services [23]. 37% women in rural and 77% in urban areas had health facility childbirth [17]. The approach uses Major Obstetric Interventions (MOI) for Absolute Maternal Indications (AMI) to assess the health system’s ability to respond to complications in pregnancy and childbirth. Major obstetric interventions included caesarean section, laparotomy or hysterectomy for ruptured uterus, and destructive vaginal operation [11], i.e. life-saving commodities used for absolute maternal indications. This study included all these interventions except destructive vaginal delivery which was not a usual practise in the study setting. The assumption is that a minimum level of life-threatening complications in pregnancy and childbirth require major obstetric intervention. Studies have estimated that this minimum is 1.4% of expected births that require major obstetric interventions for absolute maternal indications in populations such as the study setting [24]. The unmet obstetric need is estimated as: The estimate could be viewed as an approximation since some limitations in the data are likely. The assumption is that the expected number of MOI for a given population over a period of time is known, and that the utilization of services for a particular problem, that is actual numbers of MOI, is also known. On one hand, the expected numbers of MOI is determined by the crude birth rate and the population size. Thus, an upward adjustment of these determinants would result in higher estimates of expected number of MOI and likewise a downward adjustment would result in lower estimates of expected number of MOI. On the other hand, the actual numbers of MOI which reflects the utilization of services for a particular problem depends on accurate recordings in registers and whether most women utilize the services within the area studied. If many of the recordings are inaccurate or inconsistent, or if many women with complications utilize services that are not part of the studied area, there could be an under-estimate of the actual numbers of MOI performed. Data were collected in 2011. All recorded institutional deliveries from 1st January to 31st December 2010 in the EmONC rural health facilities and the urban hospital were obtained. Data on admissions, subsequent deliveries and referrals to the comprehensive EmONC service at Kabwe General Hospital were collected retrospectively and extracted by an obstetrician and two nurse midwives using a data abstraction form. Information on catchment area of origin, mode of delivery, obstetric complications, and outcome for both the mother and the newborn was also obtained. Sources of information were registers of maternity admission and delivery wards, and follow-up from maternity ward and operation theatre in the comprehensive EmONC facility at the general hospital. Further information was obtained from case records when register information was incomplete. Reliability of the data depends on reproducibility, which implies the ability of health care providers to notify the same complications in the same way. This includes whether the same health care provider (test-retest intra-rater reliability), and other health care providers (inter-rater concurrent reliability) notify complications in the same way. Validity of the data depended upon how reliable the recorded diagnoses were made, such as cephalo-pelvic disproportion which could have low specificity. Cephalo-pelvic disproportion is a common and accepted diagnosis and may be used by default in less clear conditions. However, a gauge of reliability and validity of data poses challenges when dealing with women who pass quickly through the system of care providers. On data completeness, it was possible that some women could have utilized other hospitals neighbouring Kapiri Mposhi district, although the geographical distribution of other hospitals made self-referrals less likely. Whereas Kabwe General Hospital was about 50km from Kapiri Mposhi, Ibenga Mission Hospital and Mpongwe Mission Hospital were 94km and 99km away, respectively, and public motor transport was not easily available for the local community (Fig 2). Data obtained from registers mainly agreed with those recorded in case records, such that there was no reason to query the quality of information in the registers. Information on functionality of basic and comprehensive EmONC facilities was also obtained. The rationale for this was based on knowledge that to reduce maternal deaths, certain obstetric functions in facilities must be available. Certain health facilities may be categorised as EmONC by the health system, yet if conditions are not met this may hide inadequacy in coverage of services. Fig 1 shows signal functions that describe basic and comprehensive EmONC facilities. A standard tool was used to obtain information on functions performed at least once in the previous three months prior to the survey by interviewing the nurse-in-charge of the maternity ward [8]. Reasons for not performing any of the signal functions were recorded. Inspection for availability of equipment and drugs in maternity wards was done. The WHO defines a basic EmONC facility as one that performed all seven signal functions in the last three months prior to a survey [8]. A comprehensive EmONC facility performs caesarean section and blood transfusion plus the seven functions in basic EmONC in the last three months. However, if a function such as assisted vaginal delivery is systematically absent in an area due to policy, the functioning of the facilities is indicated as “basic EmONC minus 1” or “comprehensive EmONC minus 1” [8]. Data analysis was done using SPSS for Windows version 20, SPSS Inc. Chicago Illinois. Absolute maternal indications included uterine rupture; obstructed labour due to mal-presentation (transverse lie, oblique lie, shoulder presentation) and cephalo-pelvic disproportion; antepartum and severe postpartum haemorrhage; and abdominal pregnancy. For women who had more than one diagnosis, the main indication that led to intervention was used in the analysis. Maternal complications that could result in a vaginal delivery without causing a maternal death such as eclampsia and foetal indications such as foetal distress, breech presentation and cord presentation/prolapse were not included as absolute maternal indications. Major obstetric interventions included caesarean section, hysterectomy or repair for ruptured uterus and laparotomy for abdominal pregnancy. Abortions were excluded. We compared age and parity between the rural and urban population by using independent t-test; origin of women, absolute maternal and foetal indications for interventions, and outcome of new-borns by using Pearson’s chi square test; and major obstetric interventions by using Fischer’s exact test. Origin of women meant residence either within the catchment area, from 12km to 35km radius of a health facility, or from outside the catchment area. Frequencies and proportions of complications were estimated for rural and urban health facilities. We also calculated the major obstetric interventions for absolute maternal indications as a percentage of population expected births (estimated by multiplying the crude birth rate with population size) [21], and calculated urban rural rate ratio. Based on the reference of 1.4% (95% CI 1.27% to 1.52%), the variance estimate for unmet obstetric need for the total study population was 40.8% (95% CI 34.9% to 45.8%). The variance estimate for the urban area was -48.6% (95% CI -66.7% to -37.5%), and the rural area was 72.6% (95% CI 69.8% to 74.8%). We assessed the functioning of the facilities as EmONC by finding out whether defining procedures were performed in the three months prior to the study. Ethical clearance was obtained from University of Zambia Research Ethics Committee. Permission was also granted by Kapiri Mposhi District Health Management office. This study was specifically approved by the Steering Committee of REACT project, which is also the project review board. Confidentiality and anonymity of study candidates was maintained.

AI Digest

Study Justification:

This study aimed to assess the deficit in life-saving obstetric services in the rural and urban areas of Kapiri Mposhi district in Zambia. The justification for this study is the high maternal mortality ratio in Zambia and the low access to emergency obstetric care services for pregnant women. By identifying the inequity in unmet obstetric needs and the functionality of emergency obstetric care services, this study provides valuable insights for improving maternal healthcare in the district.

Study Highlights:

– The study found that women in rural areas had deficient obstetric care compared to women in urban areas.
– The likelihood of undergoing a life-saving intervention was 5.5 times higher for women in urban areas than in rural areas.
– Targeting rural women with life-saving services could substantially reduce the inequity and preventable deaths.

Study Recommendations:

– Increase access to emergency obstetric care services in rural areas to address the deficit in obstetric care.
– Improve the functionality of basic and comprehensive emergency obstetric care facilities in the district.
– Implement strategies to reduce maternal mortality and improve maternal healthcare outcomes in both rural and urban areas.

Key Role Players:

– Ministry of Health: Responsible for policy development and implementation of maternal healthcare programs.
– District Health Management Office: Oversees healthcare services in the Kapiri Mposhi district.
– Health facility staff: Including doctors, nurses, and midwives who provide obstetric care services.
– Community health workers: Involved in community outreach and education on maternal healthcare.

Cost Items for Planning Recommendations:

– Infrastructure improvement: Upgrading and equipping health facilities to provide comprehensive emergency obstetric care.
– Training and capacity building: Providing training for healthcare staff on emergency obstetric care and maternal healthcare.
– Ambulance services: Ensuring reliable transportation for pregnant women in need of emergency obstetric care.
– Medical supplies and equipment: Stocking health facilities with necessary supplies and equipment for obstetric interventions.
– Community outreach and education: Conducting awareness campaigns and educational programs to promote maternal healthcare.
Please note that the above cost items are examples and may vary depending on the specific needs and context of the Kapiri Mposhi district.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a cross-sectional survey conducted as part of a larger project. The study collected data from multiple sources, including registers and case records, to assess the deficit in life-saving obstetric services in a Zambian district. The study analyzed a large number of childbirths in both urban and rural areas and used a well-established approach to estimate unmet obstetric need. The findings indicate a significant inequity in obstetric care between rural and urban areas. To improve the evidence, future studies could consider using a longitudinal design to assess changes over time and include a larger sample size to increase generalizability.

Abstract

This was a retrospective cross-sectional facility-based study which was part of the ‘Response to Accountable priority setting for Trust in health systems’ (REACT) project. The project used an ethical framework of Accountability for Reasonableness (AFR) to guide achievement of a fair and legitimate priority setting process that would enhance trust, quality and equity in access to health care [20]. The project was designed to evaluate before and after effect of an intervention by implementation of the AFR process which has been described elsewhere [20]. This study analysed baseline data focused on emergency obstetric care. All facilities offering EmONC services in Kapiri Mposhi were enrolled in the study, that is, four rural health facilities and one urban hospital. The hospital was the main facility that offered childbirth services in the urban area. Two of the rural facilities were government run while two were faith-based. The district Kapiri Mposhi, in the Central Province of Zambia, connects to the northern part of the country via road and railway network. The population was 240,000 in 2010, 35% being urban [21]. Urban was defined according to population census of 2000 as localities of 5,000 inhabitants or more and a majority of the labour force in non-agricultural activities [22]. Kapiri Mposhi is 17 219km2 with a population density of 14.7 persons per km2 [21]. The main economic activity is subsistence agriculture and small-scale trade in the urban area. Health services for childbirth are provided mainly by the public health sector in both urban and rural areas together with a small contribution by mission health facilities in the rural area [17]. The district of Kapiri Mposhi had no comprehensive EmONC service and was not providing caesarean sections at the time of the study. Thus, women with complications in pregnancy were referred and transported by ambulance to Kabwe General Hospital, 50km outside the district. Out of 27 rural and urban health facilities that offered childbirth services, only five offered EmONC services [23]. 37% women in rural and 77% in urban areas had health facility childbirth [17]. The approach uses Major Obstetric Interventions (MOI) for Absolute Maternal Indications (AMI) to assess the health system’s ability to respond to complications in pregnancy and childbirth. Major obstetric interventions included caesarean section, laparotomy or hysterectomy for ruptured uterus, and destructive vaginal operation [11], i.e. life-saving commodities used for absolute maternal indications. This study included all these interventions except destructive vaginal delivery which was not a usual practise in the study setting. The assumption is that a minimum level of life-threatening complications in pregnancy and childbirth require major obstetric intervention. Studies have estimated that this minimum is 1.4% of expected births that require major obstetric interventions for absolute maternal indications in populations such as the study setting [24]. The unmet obstetric need is estimated as: The estimate could be viewed as an approximation since some limitations in the data are likely. The assumption is that the expected number of MOI for a given population over a period of time is known, and that the utilization of services for a particular problem, that is actual numbers of MOI, is also known. On one hand, the expected numbers of MOI is determined by the crude birth rate and the population size. Thus, an upward adjustment of these determinants would result in higher estimates of expected number of MOI and likewise a downward adjustment would result in lower estimates of expected number of MOI. On the other hand, the actual numbers of MOI which reflects the utilization of services for a particular problem depends on accurate recordings in registers and whether most women utilize the services within the area studied. If many of the recordings are inaccurate or inconsistent, or if many women with complications utilize services that are not part of the studied area, there could be an under-estimate of the actual numbers of MOI performed. Data were collected in 2011. All recorded institutional deliveries from 1st January to 31st December 2010 in the EmONC rural health facilities and the urban hospital were obtained. Data on admissions, subsequent deliveries and referrals to the comprehensive EmONC service at Kabwe General Hospital were collected retrospectively and extracted by an obstetrician and two nurse midwives using a data abstraction form. Information on catchment area of origin, mode of delivery, obstetric complications, and outcome for both the mother and the newborn was also obtained. Sources of information were registers of maternity admission and delivery wards, and follow-up from maternity ward and operation theatre in the comprehensive EmONC facility at the general hospital. Further information was obtained from case records when register information was incomplete. Reliability of the data depends on reproducibility, which implies the ability of health care providers to notify the same complications in the same way. This includes whether the same health care provider (test-retest intra-rater reliability), and other health care providers (inter-rater concurrent reliability) notify complications in the same way. Validity of the data depended upon how reliable the recorded diagnoses were made, such as cephalo-pelvic disproportion which could have low specificity. Cephalo-pelvic disproportion is a common and accepted diagnosis and may be used by default in less clear conditions. However, a gauge of reliability and validity of data poses challenges when dealing with women who pass quickly through the system of care providers. On data completeness, it was possible that some women could have utilized other hospitals neighbouring Kapiri Mposhi district, although the geographical distribution of other hospitals made self-referrals less likely. Whereas Kabwe General Hospital was about 50km from Kapiri Mposhi, Ibenga Mission Hospital and Mpongwe Mission Hospital were 94km and 99km away, respectively, and public motor transport was not easily available for the local community (Fig 2). Data obtained from registers mainly agreed with those recorded in case records, such that there was no reason to query the quality of information in the registers. Information on functionality of basic and comprehensive EmONC facilities was also obtained. The rationale for this was based on knowledge that to reduce maternal deaths, certain obstetric functions in facilities must be available. Certain health facilities may be categorised as EmONC by the health system, yet if conditions are not met this may hide inadequacy in coverage of services. Fig 1 shows signal functions that describe basic and comprehensive EmONC facilities. A standard tool was used to obtain information on functions performed at least once in the previous three months prior to the survey by interviewing the nurse-in-charge of the maternity ward [8]. Reasons for not performing any of the signal functions were recorded. Inspection for availability of equipment and drugs in maternity wards was done. The WHO defines a basic EmONC facility as one that performed all seven signal functions in the last three months prior to a survey [8]. A comprehensive EmONC facility performs caesarean section and blood transfusion plus the seven functions in basic EmONC in the last three months. However, if a function such as assisted vaginal delivery is systematically absent in an area due to policy, the functioning of the facilities is indicated as “basic EmONC minus 1” or “comprehensive EmONC minus 1” [8]. Data analysis was done using SPSS for Windows version 20, SPSS Inc. Chicago Illinois. Absolute maternal indications included uterine rupture; obstructed labour due to mal-presentation (transverse lie, oblique lie, shoulder presentation) and cephalo-pelvic disproportion; antepartum and severe postpartum haemorrhage; and abdominal pregnancy. For women who had more than one diagnosis, the main indication that led to intervention was used in the analysis. Maternal complications that could result in a vaginal delivery without causing a maternal death such as eclampsia and foetal indications such as foetal distress, breech presentation and cord presentation/prolapse were not included as absolute maternal indications. Major obstetric interventions included caesarean section, hysterectomy or repair for ruptured uterus and laparotomy for abdominal pregnancy. Abortions were excluded. We compared age and parity between the rural and urban population by using independent t-test; origin of women, absolute maternal and foetal indications for interventions, and outcome of new-borns by using Pearson’s chi square test; and major obstetric interventions by using Fischer’s exact test. Origin of women meant residence either within the catchment area, from 12km to 35km radius of a health facility, or from outside the catchment area. Frequencies and proportions of complications were estimated for rural and urban health facilities. We also calculated the major obstetric interventions for absolute maternal indications as a percentage of population expected births (estimated by multiplying the crude birth rate with population size) [21], and calculated urban rural rate ratio. Based on the reference of 1.4% (95% CI 1.27% to 1.52%), the variance estimate for unmet obstetric need for the total study population was 40.8% (95% CI 34.9% to 45.8%). The variance estimate for the urban area was -48.6% (95% CI -66.7% to -37.5%), and the rural area was 72.6% (95% CI 69.8% to 74.8%). We assessed the functioning of the facilities as EmONC by finding out whether defining procedures were performed in the three months prior to the study. Ethical clearance was obtained from University of Zambia Research Ethics Committee. Permission was also granted by Kapiri Mposhi District Health Management office. This study was specifically approved by the Steering Committee of REACT project, which is also the project review board. Confidentiality and anonymity of study candidates was maintained.

Materials

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can help bridge the gap between rural and urban areas by allowing pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can provide access to medical advice, prenatal care, and emergency consultations.

2. Mobile clinics: Establishing mobile clinics that travel to rural areas can bring essential maternal healthcare services closer to the communities that need them. These clinics can provide prenatal check-ups, vaccinations, and basic obstetric care, ensuring that pregnant women in remote areas have access to necessary healthcare services.

3. Community health workers: Training and deploying community health workers in rural areas can help improve access to maternal health services. These workers can provide education on prenatal care, assist with deliveries, and identify high-risk pregnancies, ensuring that women receive appropriate care and referrals when needed.

4. Emergency transportation systems: Developing efficient emergency transportation systems, such as ambulances or air evacuation services, can help transport pregnant women from rural areas to healthcare facilities equipped to handle obstetric emergencies. This can reduce delays in accessing emergency obstetric care and potentially save lives.

5. Strengthening healthcare facilities: Investing in infrastructure, equipment, and staffing for healthcare facilities in rural areas can improve their capacity to provide comprehensive obstetric care. This includes ensuring that facilities have skilled healthcare providers, necessary medical supplies, and functioning equipment to handle obstetric emergencies.

6. Health information systems: Implementing robust health information systems can help track and monitor maternal health indicators in both rural and urban areas. This data can be used to identify gaps in access to care, target interventions, and measure the impact of implemented innovations.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the Kapiri Mposhi district in Zambia.

AI Innovations Description

The study titled “Rural-urban inequity in unmet obstetric needs and functionality of emergency obstetric care services in a Zambian district” highlights the deficit in life-saving obstetric services in the rural areas of Kapiri Mposhi district in Zambia. The study found that women in rural areas had deficient obstetric care, with a higher likelihood of undergoing a life-saving intervention for women in urban areas compared to rural areas.

Based on this study, a recommendation to improve access to maternal health in rural areas could be the implementation of targeted interventions and services for rural women. This could include:

1. Strengthening rural healthcare facilities: Improve the capacity and resources of rural healthcare facilities to provide comprehensive emergency obstetric care services. This may involve training healthcare providers, ensuring the availability of essential medical equipment and supplies, and improving infrastructure.

2. Mobile health clinics: Establish mobile health clinics that can reach remote rural areas to provide prenatal care, antenatal check-ups, and emergency obstetric care services. These clinics can help bridge the gap in access to healthcare services for pregnant women in rural areas.

3. Community health workers: Train and deploy community health workers in rural areas to provide basic maternal health services, educate women on the importance of prenatal care, and facilitate referrals to higher-level healthcare facilities when needed.

4. Transportation services: Improve transportation infrastructure and services to ensure that pregnant women in rural areas can easily access healthcare facilities. This may involve providing ambulances or other means of transportation for emergency obstetric care.

5. Health education and awareness: Conduct health education campaigns to raise awareness among rural communities about the importance of maternal health, the signs of complications during pregnancy, and the available healthcare services. This can help empower women to seek timely and appropriate care.

6. Collaboration and partnerships: Foster collaboration between government agencies, non-governmental organizations, and other stakeholders to pool resources and expertise in addressing the challenges of maternal health access in rural areas. This can help leverage collective efforts to develop innovative solutions and ensure sustainable improvements in access to maternal health.

By implementing these recommendations, it is possible to improve access to maternal health services in rural areas, reduce the inequity in obstetric care, and ultimately prevent unnecessary maternal deaths.

AI Innovations Methodology

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

1. Strengthening rural healthcare facilities: Focus on improving the capacity and resources of rural health facilities to provide emergency obstetric care services. This could include training healthcare providers, ensuring the availability of essential equipment and supplies, and improving infrastructure.

2. Mobile health clinics: Implement mobile health clinics that can reach remote rural areas and provide maternal health services. These clinics can bring healthcare providers, equipment, and supplies directly to communities that lack access to healthcare facilities.

3. Telemedicine services: Utilize telemedicine technology to connect healthcare providers in urban areas with rural communities. This would enable remote consultations, diagnosis, and treatment for pregnant women in rural areas, reducing the need for travel to urban hospitals.

4. Community health workers: Train and deploy community health workers in rural areas to provide basic maternal health services, educate pregnant women on prenatal care, and facilitate referrals to healthcare facilities when necessary.

5. Transportation support: Improve transportation infrastructure and provide transportation support for pregnant women in rural areas to access healthcare facilities. This could include ambulances, community transport systems, or financial assistance for transportation costs.

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 women accessing emergency obstetric care services, the reduction in maternal mortality rates, or the increase in antenatal care coverage.

2. Data collection: Collect baseline data on the current state of maternal health access in the target area, including the number of women accessing healthcare facilities, the distance to the nearest facility, and the availability of emergency obstetric care services.

3. Model development: Develop a simulation model that incorporates the recommendations and their potential impact on the identified indicators. This could be a mathematical model or a computer-based simulation.

4. Input parameters: Determine the input parameters for the model, such as the number of healthcare providers trained, the number of mobile health clinics deployed, or the improvement in transportation infrastructure.

5. Run simulations: Run multiple simulations using different input parameters to assess the potential impact of the recommendations on improving access to maternal health. This could involve varying the parameters to understand different scenarios and their outcomes.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on the identified indicators. This could include comparing the baseline data with the simulated outcomes to quantify the improvements.

7. Refine recommendations: Based on the simulation results, refine the recommendations to optimize their impact on improving access to maternal health. This could involve adjusting the input parameters or exploring additional interventions.

8. Implementation plan: Develop an implementation plan based on the refined recommendations, considering factors such as feasibility, cost-effectiveness, and sustainability.

9. Monitor and evaluate: Implement the recommendations and continuously monitor and evaluate their impact on improving access to maternal health. This could involve tracking the identified indicators over time and making adjustments as needed.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations and make informed decisions to improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 13

Identifiers:

DOI: 10.1371/journal.pone.0145196

Research Areas:

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

Study Countries:

Zambia

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Female