Estimating abortion incidence and unintended pregnancy among adolescents in Zimbabwe, 2016: A cross-sectional study

BMJ Open, Volume 10, No. 4, Year 2020

Interpretation

Objective To estimate age-specific abortion incidence and unintended pregnancy in Zimbabwe, and to examine differences among adolescents by marital status and residence. Design We used a variant of the Abortion Incidence Complications Methodology, an indirect estimation approach, to estimate age-specific abortion incidence. We used three surveys: the Health Facility Survey, a census of 227 facilities that provide postabortion care (PAC); the Health Professional Survey, a purposive sample of key informants knowledgeable about abortion (n=118) and the Prospective Morbidity Survey of PAC patients (n=1002). Setting PAC-providing health facilities in Zimbabwe. Participants Healthcare providers in PAC-providing facilities and women presenting to facilities with postabortion complications. Primary and secondary outcome measures The primary outcome measure was abortion incidence (in rates and ratios). The secondary outcome measure was the proportion of unintended pregnancies that end in abortion. Results Adolescent women aged 15-19 years had the lowest abortion rate at five abortions per 1000 women aged 15-19 years compared with other age groups. Adolescents living in urban areas had a higher abortion ratio compared with adolescents in rural areas, and unmarried adolescent women had a higher abortion ratio compared with married adolescents. Unintended pregnancy levels were similar across age groups, and adolescent women had the lowest proportion of unintended pregnancies that ended in induced abortion (9%) compared with other age groups. Conclusions This paper provides the first estimates of age-specific abortion and unintended pregnancy in Zimbabwe. Despite similar levels of unintended pregnancy across age groups, these findings suggest that adolescent women have abortions at lower rates and carry a higher proportion of unintended pregnancies to term than older women. Adolescent women are also not a homogeneous group, and youth-focused reproductive health programmes should consider the differences in experiences and barriers to care among young people that affect their ability to decide whether and when to parent. We used an age-specific variant of the Abortion Incidence Complications Methodology (AICM), an indirect estimation approach, to estimate age-specific abortion rates.5 6 The AICM has been used in over 20 countries with restrictive abortion laws to indirectly measure the incidence of abortion.12 22–35 This indirect method obtains the national number of facility-based PAC cases and estimates the proportion of abortions that would result in women having complications and receiving PAC. An age-specific variant of the AICM was employed in Ethiopia and Uganda, and we followed the approach in those studies.5 6 We calculated abortion incidence by age groups (15–19, 20–24, 25–29, 30–34 and 35–49). We also calculated abortion incidence by marital status and residence subgroups within two age groups: adolescent women (aged 15–19 years) and all women of reproductive age (15–49 years). We defined the marital status subgroup dichotomously based on PAC patients’ self-report of being currently married/in union or not currently married. We categorised the dichotomous residence subgroup based on PAC patients’ self-report of living in urban or rural areas. The AICM approach relies on three key data inputs: the number of PAC cases, the proportion of all abortions that result in treated complications, and the age distribution (as well as marital status and place of residence) of PAC patients. These data inputs come from three surveys that were conducted in Zimbabwe in August to November 2016 as part of a larger study that indirectly estimated the incidence of abortion.12 The data on the estimated annual number of PAC cases came from a Health Facility Survey (HFS), which interviewed PAC providers in a census of 227 facilities that provide PAC in Zimbabwe. This was combined with estimates of the proportion of abortions that resulted in complications that received treatment from a Health Professional Survey (HPS), which was a purposive sample of 118 key informants knowledgeable about abortion provision in Zimbabwe. The data on the characteristics of women receiving PAC came from the Prospective Morbidity Survey (PMS), which was conducted in a nationally representative sample of facilities with PAC capacity, using the HFS universe as the sampling frame. Data were collected on all women presenting to the 127 participating facilities for PAC during the 28-day study period (unweighted n=1002). Sociodemographic characteristics of PAC patients from the PMS can be found in online supplementary file 1. The HFS and PMS collected information on women who received PAC in health facilities for either induced abortions or miscarriages. Further details on the study design, sampling and informed consent processes for the HFS and HPS can be found in Sully et al 12 and in Madziyire et al for the PMS.15 bmjopen-2019-034736supp001.pdf We used age-specific fertility rates from the 2015 Zimbabwe Demographic and Health Survey (ZDHS)13 and age-specific population numbers of women of reproductive age from the Zimbabwe National Statistic Agency’s (ZNSA) Population Projections Report to calculate the age-specific number of births.36 This first step of the age-specific variant of the AICM estimated the number of PAC cases by age group. This was done by multiplying the national number of PAC cases in Zimbabwe12 by the weighted age distribution of PAC patients in the PMS.15 To calculate the number of urban and rural PAC patients within the 15–19 and 15–49 age groups, we multiplied this age-specific number of PAC cases times the proportion of PAC patients within that age group who self-reported living in rural or urban areas. We did the same for PAC patient’s self-reported marital status. Second, we estimated the age-specific number of PAC cases due to induced abortion. To do this, we first estimated the proportion of second trimester miscarriages by age group6 37 and then adjusted this by the proportion of those miscarriages likely to receive treatment. In the absence of data on access to PAC, we assumed the proportion of women receiving care was equal to the age and subgroup specific proportion of women who give birth in a facility from the ZDHS.13 The result was subtracted from the total number of PAC cases to obtain the number that was due to induced abortion. The third step was estimating abortions that do not result in facility-based care. This could be due to two reasons: (1) the person having the abortion did not have complications and therefore did not need facility-based treatment or (2) the person having the abortion had complications but did not receive facility-based treatment. In Zimbabwe in 2016, it was estimated that for every one woman receiving PAC, there were 4.7 women who had abortions that did not result in facility-based care (see online supplementary file 2). This estimate, referred to as the multiplier, was applied to all age groups and marital status subgroups in the absence of age-specific and marital status-specific multipliers. We calculated new multipliers for rural women and urban women separately, using the underlying data from the HPS, which was collected separately by residence. Since the HPS is a purposive sample, we could not estimate 95% CI around the multiplier. We therefore conducted a bootstrapping simulation of 10 000 draws with replacement from the HPS respondents and calculated a multiplier with each draw.12 The upper and lower bounds presented contain 95% of the multiplier values from the bootstrapping. Further details on the multiplier calculations are in online supplementary file 2. bmjopen-2019-034736supp002.pdf Fourth, to estimate the total number of induced abortions by age and subgroup, we multiplied the number of induced abortions that received PAC times the respective multiplier for that age and subgroup. We then adjusted all estimates to account for abortions occurring outside of Zimbabwe, estimated from the HPS as 12% of all abortions, which we assumed was the same across all age and subgroups due to lack of data availability.12 Online supplementary file 2 provides more details on the steps of the AICM, data sources and assumptions, and adjustments made to align all summed age groups and subgroups to national totals. We calculated age-specific abortion rates per 1000 women by dividing the total number of induced abortions by the age-specific population size, which was taken from the ZNSA Population Projections Report.36 We also wanted to account for risk of pregnancy given the lower levels of reported recent sexual activity, defined as sex in the past 12 months, among adolescents (29%) compared with women aged 20–49 years (84%).13 Therefore, we also calculated separate estimates of age-specific abortion rates among women who reported having sex in the previous 12 months. We estimated age and subgroup specific abortion ratios per 100 live births by dividing the number of induced abortions by the age and subgroup specific number of births.13 36 To calculate the number of births by marital status and residence among 15–19 and 15–49 age groups, we multiplied the age-specific number of births by the age-specific proportion of births to married or unmarried women (or the proportion of births among women who lived in urban or rural areas) from the ZDHS.13 Lastly, we estimated unintended pregnancy by age and subgroup using the age and subgroup specific abortion estimates, age and subgroup specific data on births by intention status from the ZDHS,13 and estimated miscarriages, which were estimated to be 20% of births and 10% of abortions and applied uniformly across age groups.38 Using the national multiplier for all age and marital status groups assumes that complication rates and treatment seeking do not differ by age or marital status. We tested the validity of these assumptions using PAC patient data, the only available source of representative data on abortion complications and treatment among women in Zimbabwe. The first assumption we tested was whether experiencing complications differed between adolescent and non-adolescent PAC patients, and if this differed by marital status among adolescents. We operationalised complications using the severity classifications from Madziyire et al 15 and considered moderate and severe complications, maternal near-miss and maternal death as having a complication (coded as 1) and a mild complication (defined as no infection, no organ failure and no blood transfusion needed) as no complication (coded as 0). Using this complication variable as the dependent variable, we ran multivariable logistic regression models comparing adolescents versus non-adolescents, and then included an interaction term between adolescents and marital status. The second assumption we tested was whether adolescent and non-adolescent PAC patients differed in treatment seeking. PAC patients provided information on their delays in seeking care from the (1) time (in hours) it took from realising they had a complication to deciding to seek treatment and (2) time (in hours) it took from them deciding to seek care to the time they arrived at the facility. These two delays are related to user-related health seeking behaviours (delay 1) as well as access to facilities (delay 2), which when combined capture treatment seeking experiences.39–41 We ran a Cox proportional hazards model to estimate differences in hazard ratios between adolescents and non-adolescents, and we included an interaction term between adolescents and marital status. All models controlled for residence, secondary education, wealth, previous pregnancy, whether the pregnancy was reported as unintended and being in the second trimester. This research involved interviews with postabortion care patients. Patients were not invited to comment on the study design or involved in the writing or editing of this document. We sought guidance for study planning and dissemination from our Technical Advisory Committee, which included community representatives and technical experts.

AI Digest

Study Justification:

– The study aims to estimate age-specific abortion incidence and unintended pregnancy among adolescents in Zimbabwe.
– This information is crucial for understanding the reproductive health needs of adolescents and informing policies and programs to address these needs.
– The study fills a gap in knowledge by providing the first estimates of age-specific abortion and unintended pregnancy in Zimbabwe.

Highlights:

– Adolescent women aged 15-19 years had the lowest abortion rate compared to other age groups.
– Adolescents living in urban areas had a higher abortion ratio compared to those in rural areas.
– Unmarried adolescent women had a higher abortion ratio compared to married adolescents.
– Unintended pregnancy levels were similar across age groups, but adolescent women had the lowest proportion of unintended pregnancies that ended in induced abortion (9%).

Recommendations:

– Youth-focused reproductive health programs should consider the differences in experiences and barriers to care among young people.
– Policies and programs should address the higher abortion ratio among unmarried adolescent women and those living in urban areas.
– Efforts should be made to reduce unintended pregnancies among adolescents and ensure access to comprehensive reproductive health services.

Key Role Players:

– Ministry of Health: Responsible for implementing policies and programs related to reproductive health.
– Healthcare providers: Involved in delivering reproductive health services, including counseling, contraception, and postabortion care.
– Community leaders and organizations: Play a role in raising awareness, reducing stigma, and promoting reproductive health education.
– NGOs and international organizations: Provide support, resources, and technical expertise in implementing reproductive health programs.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers on adolescent-friendly reproductive health services.
– Development and dissemination of educational materials and campaigns targeting adolescents.
– Strengthening health facilities to provide comprehensive reproductive health services, including contraception and postabortion care.
– Monitoring and evaluation of program implementation to assess effectiveness and make necessary adjustments.
– Research and data collection to track progress and inform evidence-based decision making.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a cross-sectional study using a variant of the Abortion Incidence Complications Methodology. However, there are some actionable steps to improve it. Firstly, providing more information on the sample size and representativeness of the surveys used would enhance the credibility of the findings. Additionally, including information on the data collection methods and any potential limitations would provide a more comprehensive understanding of the study. Lastly, sharing the statistical analyses conducted and the significance of the findings would further strengthen the evidence.

Abstract

We used an age-specific variant of the Abortion Incidence Complications Methodology (AICM), an indirect estimation approach, to estimate age-specific abortion rates.5 6 The AICM has been used in over 20 countries with restrictive abortion laws to indirectly measure the incidence of abortion.12 22–35 This indirect method obtains the national number of facility-based PAC cases and estimates the proportion of abortions that would result in women having complications and receiving PAC. An age-specific variant of the AICM was employed in Ethiopia and Uganda, and we followed the approach in those studies.5 6 We calculated abortion incidence by age groups (15–19, 20–24, 25–29, 30–34 and 35–49). We also calculated abortion incidence by marital status and residence subgroups within two age groups: adolescent women (aged 15–19 years) and all women of reproductive age (15–49 years). We defined the marital status subgroup dichotomously based on PAC patients’ self-report of being currently married/in union or not currently married. We categorised the dichotomous residence subgroup based on PAC patients’ self-report of living in urban or rural areas. The AICM approach relies on three key data inputs: the number of PAC cases, the proportion of all abortions that result in treated complications, and the age distribution (as well as marital status and place of residence) of PAC patients. These data inputs come from three surveys that were conducted in Zimbabwe in August to November 2016 as part of a larger study that indirectly estimated the incidence of abortion.12 The data on the estimated annual number of PAC cases came from a Health Facility Survey (HFS), which interviewed PAC providers in a census of 227 facilities that provide PAC in Zimbabwe. This was combined with estimates of the proportion of abortions that resulted in complications that received treatment from a Health Professional Survey (HPS), which was a purposive sample of 118 key informants knowledgeable about abortion provision in Zimbabwe. The data on the characteristics of women receiving PAC came from the Prospective Morbidity Survey (PMS), which was conducted in a nationally representative sample of facilities with PAC capacity, using the HFS universe as the sampling frame. Data were collected on all women presenting to the 127 participating facilities for PAC during the 28-day study period (unweighted n=1002). Sociodemographic characteristics of PAC patients from the PMS can be found in online supplementary file 1. The HFS and PMS collected information on women who received PAC in health facilities for either induced abortions or miscarriages. Further details on the study design, sampling and informed consent processes for the HFS and HPS can be found in Sully et al 12 and in Madziyire et al for the PMS.15 bmjopen-2019-034736supp001.pdf We used age-specific fertility rates from the 2015 Zimbabwe Demographic and Health Survey (ZDHS)13 and age-specific population numbers of women of reproductive age from the Zimbabwe National Statistic Agency’s (ZNSA) Population Projections Report to calculate the age-specific number of births.36 This first step of the age-specific variant of the AICM estimated the number of PAC cases by age group. This was done by multiplying the national number of PAC cases in Zimbabwe12 by the weighted age distribution of PAC patients in the PMS.15 To calculate the number of urban and rural PAC patients within the 15–19 and 15–49 age groups, we multiplied this age-specific number of PAC cases times the proportion of PAC patients within that age group who self-reported living in rural or urban areas. We did the same for PAC patient’s self-reported marital status. Second, we estimated the age-specific number of PAC cases due to induced abortion. To do this, we first estimated the proportion of second trimester miscarriages by age group6 37 and then adjusted this by the proportion of those miscarriages likely to receive treatment. In the absence of data on access to PAC, we assumed the proportion of women receiving care was equal to the age and subgroup specific proportion of women who give birth in a facility from the ZDHS.13 The result was subtracted from the total number of PAC cases to obtain the number that was due to induced abortion. The third step was estimating abortions that do not result in facility-based care. This could be due to two reasons: (1) the person having the abortion did not have complications and therefore did not need facility-based treatment or (2) the person having the abortion had complications but did not receive facility-based treatment. In Zimbabwe in 2016, it was estimated that for every one woman receiving PAC, there were 4.7 women who had abortions that did not result in facility-based care (see online supplementary file 2). This estimate, referred to as the multiplier, was applied to all age groups and marital status subgroups in the absence of age-specific and marital status-specific multipliers. We calculated new multipliers for rural women and urban women separately, using the underlying data from the HPS, which was collected separately by residence. Since the HPS is a purposive sample, we could not estimate 95% CI around the multiplier. We therefore conducted a bootstrapping simulation of 10 000 draws with replacement from the HPS respondents and calculated a multiplier with each draw.12 The upper and lower bounds presented contain 95% of the multiplier values from the bootstrapping. Further details on the multiplier calculations are in online supplementary file 2. bmjopen-2019-034736supp002.pdf Fourth, to estimate the total number of induced abortions by age and subgroup, we multiplied the number of induced abortions that received PAC times the respective multiplier for that age and subgroup. We then adjusted all estimates to account for abortions occurring outside of Zimbabwe, estimated from the HPS as 12% of all abortions, which we assumed was the same across all age and subgroups due to lack of data availability.12 Online supplementary file 2 provides more details on the steps of the AICM, data sources and assumptions, and adjustments made to align all summed age groups and subgroups to national totals. We calculated age-specific abortion rates per 1000 women by dividing the total number of induced abortions by the age-specific population size, which was taken from the ZNSA Population Projections Report.36 We also wanted to account for risk of pregnancy given the lower levels of reported recent sexual activity, defined as sex in the past 12 months, among adolescents (29%) compared with women aged 20–49 years (84%).13 Therefore, we also calculated separate estimates of age-specific abortion rates among women who reported having sex in the previous 12 months. We estimated age and subgroup specific abortion ratios per 100 live births by dividing the number of induced abortions by the age and subgroup specific number of births.13 36 To calculate the number of births by marital status and residence among 15–19 and 15–49 age groups, we multiplied the age-specific number of births by the age-specific proportion of births to married or unmarried women (or the proportion of births among women who lived in urban or rural areas) from the ZDHS.13 Lastly, we estimated unintended pregnancy by age and subgroup using the age and subgroup specific abortion estimates, age and subgroup specific data on births by intention status from the ZDHS,13 and estimated miscarriages, which were estimated to be 20% of births and 10% of abortions and applied uniformly across age groups.38 Using the national multiplier for all age and marital status groups assumes that complication rates and treatment seeking do not differ by age or marital status. We tested the validity of these assumptions using PAC patient data, the only available source of representative data on abortion complications and treatment among women in Zimbabwe. The first assumption we tested was whether experiencing complications differed between adolescent and non-adolescent PAC patients, and if this differed by marital status among adolescents. We operationalised complications using the severity classifications from Madziyire et al 15 and considered moderate and severe complications, maternal near-miss and maternal death as having a complication (coded as 1) and a mild complication (defined as no infection, no organ failure and no blood transfusion needed) as no complication (coded as 0). Using this complication variable as the dependent variable, we ran multivariable logistic regression models comparing adolescents versus non-adolescents, and then included an interaction term between adolescents and marital status. The second assumption we tested was whether adolescent and non-adolescent PAC patients differed in treatment seeking. PAC patients provided information on their delays in seeking care from the (1) time (in hours) it took from realising they had a complication to deciding to seek treatment and (2) time (in hours) it took from them deciding to seek care to the time they arrived at the facility. These two delays are related to user-related health seeking behaviours (delay 1) as well as access to facilities (delay 2), which when combined capture treatment seeking experiences.39–41 We ran a Cox proportional hazards model to estimate differences in hazard ratios between adolescents and non-adolescents, and we included an interaction term between adolescents and marital status. All models controlled for residence, secondary education, wealth, previous pregnancy, whether the pregnancy was reported as unintended and being in the second trimester. This research involved interviews with postabortion care patients. Patients were not invited to comment on the study design or involved in the writing or editing of this document. We sought guidance for study planning and dissemination from our Technical Advisory Committee, which included community representatives and technical experts.

Materials

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide information and support to pregnant women and new mothers. These platforms can offer guidance on prenatal care, nutrition, and postpartum care, as well as reminders for appointments and medication.

2. Telemedicine: Implement telemedicine services to enable remote consultations between healthcare providers and pregnant women, especially those in rural or underserved areas. This can help address the shortage of healthcare professionals and improve access to specialized care.

3. Community Health Workers: Train and deploy community health workers to provide education, counseling, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, ensuring that women receive the necessary care and support.

4. Transportation Solutions: Develop innovative transportation solutions, such as mobile clinics or ambulance services, to transport pregnant women to healthcare facilities in a timely and safe manner. This can be particularly beneficial for women living in remote or hard-to-reach areas.

5. Financial Incentives: Implement financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek antenatal care and deliver in healthcare facilities. This can help reduce financial barriers and increase utilization of maternal health services.

6. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, pharmacies, and technology companies to expand service delivery and reach more women.

7. Health Information Systems: Strengthen health information systems to collect, analyze, and utilize data on maternal health. This can help identify gaps in service delivery, monitor progress, and inform evidence-based decision-making for targeted interventions.

8. Maternal Health Education: Develop comprehensive and culturally sensitive educational programs on maternal health, targeting both women and their families. These programs can raise awareness about the importance of antenatal care, safe delivery practices, and postpartum care, empowering women to make informed decisions about their health.

9. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that maternal health services are delivered in a safe, respectful, and effective manner. This can involve training healthcare providers, improving infrastructure, and strengthening infection prevention and control measures.

10. Policy and Advocacy: Advocate for policy changes and increased investment in maternal health to prioritize access and quality of care. This can involve engaging with policymakers, raising awareness among the public, and mobilizing resources to support maternal health programs.

These innovations can help address the barriers to accessing maternal health services, improve health outcomes for women and their babies, and contribute to the overall well-being of communities.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health in Zimbabwe is to develop and implement youth-focused reproductive health programs that address the specific needs and barriers faced by adolescent women.

The study findings suggest that adolescent women in Zimbabwe have lower abortion rates but a higher proportion of unintended pregnancies compared to older women. This indicates a need for targeted interventions to support adolescent women in making informed decisions about their reproductive health and accessing appropriate care.

To develop an innovation based on this recommendation, the following steps can be taken:

1. Conduct a comprehensive needs assessment: Identify the specific challenges and barriers faced by adolescent women in accessing maternal health services in Zimbabwe. This can include factors such as lack of knowledge, limited access to contraception, social stigma, and inadequate healthcare infrastructure.

2. Design tailored interventions: Develop youth-focused reproductive health programs that address the identified needs and barriers. This can include providing comprehensive sexuality education, increasing access to contraception and family planning services, promoting gender equality, and addressing social and cultural norms that hinder access to care.

3. Engage stakeholders: Collaborate with key stakeholders such as government agencies, healthcare providers, community leaders, and youth organizations to ensure the successful implementation of the interventions. Seek their input and support in designing and delivering the programs.

4. Provide accessible and youth-friendly services: Ensure that maternal health services are easily accessible to adolescent women by establishing youth-friendly clinics and facilities. These should be equipped with trained healthcare providers who are knowledgeable about the specific needs of young people and can provide non-judgmental and confidential care.

5. Empower adolescent women: Promote empowerment among adolescent women by providing them with information, skills, and resources to make informed decisions about their reproductive health. This can include promoting their rights, building their self-esteem, and fostering their ability to negotiate safe sexual practices.

6. Monitor and evaluate: Regularly monitor and evaluate the effectiveness of the implemented interventions to assess their impact on improving access to maternal health for adolescent women. Use this feedback to make necessary adjustments and improvements to the programs.

By implementing these recommendations and developing innovative youth-focused reproductive health programs, access to maternal health can be improved for adolescent women in Zimbabwe.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals in both urban and rural areas can improve access to maternal health services.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide information, reminders, and support to pregnant women and new mothers can help improve access to maternal health services, especially in remote areas.

3. Community-based interventions: Implementing community-based programs that educate and empower women, involve community health workers, and provide maternal health services at the community level can increase access to care.

4. Financial incentives: Providing financial incentives, such as cash transfers or subsidies, to pregnant women and new mothers can help reduce financial barriers and improve access to maternal health services.

5. Telemedicine: Using telemedicine technologies to provide remote consultations, monitoring, and support to pregnant women can enhance access to maternal health services, particularly for women in underserved areas.

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

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of antenatal care visits, facility-based deliveries, skilled birth attendance, and postnatal care utilization.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population, including demographic information, healthcare utilization rates, and barriers to access.

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the potential impact of the recommended interventions on the identified indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and the effectiveness of the interventions.

4. Input intervention parameters: Define the specific parameters for each intervention, such as the number of healthcare facilities to be built, the coverage of mHealth interventions, the number of community health workers to be trained, or the amount of financial incentives to be provided.

5. Run the simulation: Use the model to simulate the impact of the interventions over a specified time period. This can involve running multiple scenarios with different combinations of interventions and parameters to assess their individual and combined effects.

6. Analyze the results: Evaluate the simulation results to determine the projected changes in access to maternal health services. Assess the impact on the identified indicators and compare the outcomes of different intervention scenarios.

7. Refine and validate the model: Continuously refine the simulation model based on new data and feedback. Validate the model by comparing the simulated results with real-world data, if available.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health. This information can guide decision-making and resource allocation to prioritize the most effective strategies.

Author

Dima Health

Statistics:

Citations: 2

Identifiers:

DOI: 10.1136/bmjopen-2019-034736

Research Areas:

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

Study Countries:

Ethiopia, Multi-Countries, Uganda, Zimbabwe

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female