Progression of the first stage of spontaneous labour: A prospective cohort study in two sub-Saharan African countries

PLoS Medicine, Volume 15, No. 1, Year 2018

Ukuhunyushwa

Background: Escalation in the global rates of labour interventions, particularly cesarean section and oxytocin augmentation, has renewed interest in a better understanding of natural labour progression. Methodological advancements in statistical and computational techniques addressing the limitations of pioneer studies have led to novel findings and triggered a re-evaluation of current labour practices. As part of the World Health Organization’s Better Outcomes in Labour Difficulty (BOLD) project, which aimed to develop a new labour monitoring-to-action tool, we examined the patterns of labour progression as depicted by cervical dilatation over time in a cohort of women in Nigeria and Uganda who gave birth vaginally following a spontaneous labour onset. Methods and findings: This was a prospective, multicentre, cohort study of 5,606 women with singleton, vertex, term gestation who presented at ≤ 6 cm of cervical dilatation following a spontaneous labour onset that resulted in a vaginal birth with no adverse birth outcomes in 13 hospitals across Nigeria and Uganda. We independently applied survival analysis and multistate Markov models to estimate the duration of labour centimetre by centimetre until 10 cm and the cumulative duration of labour from the cervical dilatation at admission through 10 cm. Multistate Markov and nonlinear mixed models were separately used to construct average labour curves. All analyses were conducted according to three parity groups: parity = 0 (n = 2,166), parity = 1 (n = 1,488), and parity = 2+ (n = 1,952). We performed sensitivity analyses to assess the impact of oxytocin augmentation on labour progression by re-examining the progression patterns after excluding women with augmented labours. Labour was augmented with oxytocin in 40% of nulliparous and 28% of multiparous women. The median time to advance by 1 cm exceeded 1 hour until 5 cm was reached in both nulliparous and multiparous women. Based on a 95th percentile threshold, nulliparous women may take up to 7 hours to progress from 4 to 5 cm and over 3 hours to progress from 5 to 6 cm. Median cumulative duration of labour indicates that nulliparous women admitted at 4 cm, 5 cm, and 6 cm reached 10 cm within an expected time frame if the dilatation rate was ≥ 1 cm/hour, but their corresponding 95th percentiles show that labour could last up to 14, 11, and 9 hours, respectively. Substantial differences exist between actual plots of labour progression of individual women and the ‘average labour curves’ derived from study population-level data. Exclusion of women with augmented labours from the study population resulted in slightly faster labour progression patterns. Conclusions: Cervical dilatation during labour in the slowest-yet-normal women can progress more slowly than the widely accepted benchmark of 1 cm/hour, irrespective of parity. Interventions to expedite labour to conform to a cervical dilatation threshold of 1 cm/hour may be inappropriate, especially when applied before 5 cm in nulliparous and multiparous women. Averaged labour curves may not truly reflect the variability associated with labour progression, and their use for decision-making in labour management should be de-emphasized. Scientific and technical approval for this study was obtained from the Review Panel on Research Projects (RP2) of the UNDP/UNFPA/UNICEF/WHO/World Bank Special Program of Research, Development and Research Training in Human Reproduction (HRP), Department of Reproductive Health and Research, WHO. Ethical approval was obtained from the WHO Ethical Review Committee (protocol A65879), the Makerere University School of Health Sciences Research and Ethics Committee, Uganda (protocol #SHSREC REF 2014–058), University of Ibadan/University College Hospital Ethics Committee (UI/EC/14/0223), Federal Capital Territory Health Research Ethics Committee, Nigeria (protocol FHREC/2014/01/42/27-08-14), and Ondo State Government Ministry of Health Research Ethics Review Committee, Nigeria (AD 4693/160). The study was conducted according to the Declaration of Helsinki of the World Medical Association. The WHO BOLD research project was primarily designed to identify the essential elements of labour monitoring that trigger the decision to use interventions aimed at preventing poor labour outcomes, with the aim of developing a new labour monitoring-to-action tool. The study protocol and detailed methodological considerations have been published elsewhere [23]. In brief, this was a prospective, multicentre, cohort study of women admitted for vaginal birth with single live fetuses during early first stage of labour across 13 hospitals in Nigeria and Uganda. This included women undergoing induction of labour and those with spontaneous labour onset who presented at cervical dilatation of ≤ 6 cm. Women with multiple pregnancies, gestational age less than 34 weeks, elective cesarean section, and those who were unwilling to participate or incapable of giving consent due to obstetric emergencies were excluded. 9,995 women (56.1%) out of 17,810 women who were screened in all hospitals during the study period met these inclusion criteria and participated in the study. Participating hospitals had a minimum of 1,000 deliveries per year with stable access to cesarean section, augmentation of labour, and instrumental vaginal birth. Estimation of gestational age at birth was in accordance with individual institutional practices, which relied upon the woman’s first date of the last menstrual period in the majority of cases. Labour was managed by midwives or obstetric residents and/or obstetricians. Doppler fetal monitor was used to assess fetal vital status at hospital admission and for intermittent monitoring throughout labour. Labour management protocol, as well as the number and timing of pelvic examinations, were not standardized across participating institutions. None of the institutions subscribed to the ‘Active Management of Labour’ protocol during the study period. Although the partograph was a standard element in all labour protocols, adherence to its application for labour management during the study period varied widely across hospitals. Eligible women were recruited into the study between December 2014 and November 2015. From the medical record, trained research nurses prospectively extracted detailed information on sociodemographic, anthropometric, obstetric, and medical characteristics of study participants at hospital admission, multiple assessments for labour monitoring and interventions performed throughout the first and second stages of labour, and maternal and neonatal outcomes following labour. Attending staff were approached to complement medical records data when needed. Data collection was limited to the hospital stay of the mother and baby, and there was no follow-up after hospital discharge. The current study used information on maternal baseline and admission characteristics, repeated assessments of cervical dilatation over time, maternal and neonatal characteristics throughout labour, and perinatal outcome data. This analysis was focused on describing the labour patterns of women without adverse birth outcomes and not on determining correlation to clinical outcomes (See S1 STROBE Checklist). From a total of 8,957 singleton births with consistent time records in the database, we restricted our analysis to examine labour progression to 5,606 women on the basis of the following inclusion criteria (Fig 1): term births (between 37 weeks and 0 days and 41 weeks and 6 days) with vertex presentation and spontaneous labour onset. We excluded women who had labour induction, previous uterine scar, or intrapartum cesarean section. To examine the labour patterns in women with normal perinatal outcomes, we excluded women whose labour resulted in severe adverse outcomes, which was defined as occurrence of any of the following: stillbirth, early neonatal death, neonatal use of anticonvulsant, neonatal cardiopulmonary resuscitation, 5-minute Apgar score < 6, maternal death or organ dysfunction associated with labour dystocia, or uterine rupture. Furthermore, we excluded women who gave birth to neonates with severe congenital malformation and those with fewer than two cervical dilatation assessments during the first stage of labour (since a single data point cannot be used to generate a labour pattern for the individual woman). * Excluding significant outliers due to unusual rapidity, regression, or inconsistencies with time. BOLD, Better Outcomes in Labour Difficulty. We grouped women in the selected sample into three parity groups (0, 1, and 2+) to explore any differences in labour patterns according to parity. We used two independent approaches to analyse labour progression patterns and construct average labour curves for the selected sample. In the first approach, we performed survival analyses to estimate the time it took to progress from one level of cervical dilatation to the next (called ‘sojourn time’) (i.e., from 3 to 4 cm, 4 to 5 cm, 5 to 6 cm, until full dilatation [10 cm]). We used both complete (where available) and interval-censored times to estimate the distribution of times for progression from one integer centimetre of dilatation to the next, with an assumption that the labour data are log-normally distributed. Based on this model, the median, 5th, and 95th percentiles were calculated. We used the same approach to derive the cumulative duration of labour for women presenting at different cervical dilatations (3 cm, 4 cm, 5 cm, and 6 cm) to evaluate any potential differences in the patterns of labour progression. To illustrate the ‘slowest-yet-normal’ labour patterns, we plotted the 95th percentiles for the cumulative duration of labour based on the cervical dilatation at admission. To construct average labour curves, we applied a nonlinear mixed model that best fit our data instead of polynomial models used by previous authors [12–16, 24]. We expressed cervical dilatation for subject i in time j (yij) as a function of time (tij) according to the following three-parameter logistic growth model: in which β0 is dilatation value when tij → −∞, β1 is the asymptotic curve height, and β2 is the inflection point and at this time value when the dilatation reaches half of its height. For simplicity, we estimated β0, β1 as fixed effects and included the random term bi in the inflection point and assumed that this term follows a normal distribution, i.e., bi∼N(0,σb2). Given that women in this analysis entered the cervical dilatation time curve at different dilatations but all ended at full dilatation (10 cm), the starting point (time = 0) on the x-axis was set at full dilatation (10 cm), which was reached by all women in the sample and then calculated backwards (e.g., 1 hour before 10 cm becomes −1 hour and so on). This x-axis (time) was then reverted to a positive value. For example, instead of −12 → 0 hours, it became 0 → 12 hours. We used R-Cran version 3.2 for these statistical analyses [25]. In the second approach, we applied a multistate Markov modelling technique to examine the labour progression patterns in the same sample. This mathematical modelling technique from matrix algebra describes the transitions that a cohort of individuals make among a number of mutually exclusive and exhaustive health states during a series of short time intervals [26]. As cervical dilatation progression is a state- and time-related phenomenon during a period ranging from labour onset through to full cervical dilatation and birth of the baby (i.e., there is a finite set of states), the labour process can be considered a mathematical model that is suitable for the application of multistate Markov modelling. We therefore represented the sequence of labour progress as states based on every observed centimetre from 2 to 10 cm until birth of the baby—the ‘absorbing state’, as illustrated in S1 Fig. At a time t, the woman is in state S(t). The model was designed as a progressive unidirectional model, which only allows a choice of a way out of a particular state, but once a woman has left a state she cannot return. The next state to which a woman moves and the time of the change are governed by a set of transition intensities for each pair of states r and s. The transition intensity represents the instantaneous likelihood of moving from state r to state s. The full set of intensities for the system form the matrix Q. A Markov process is based on the transition matrix with a probability structure P(u, t + u). The (r, s) entry (the elements of entire matrix) of P(u, t + u), is the probability of being in state s at a time t + u, given the state at time u is r. P(u, t + u) is calculated in terms of Q. Assuming that the transition intensity matrix Q is constant over the interval (u, t + u), as in a time-homogeneous process, P(u, t + u) = P(t) and the equations are solved by the matrix exponential of Q scaled by the time interval, P(t) = Exp(tQ) (S1 Fig). We used msm package for R Project programming environment to fit the multistate Markov model [26]. We generated random observations of cervical dilatation based on the transition matrix P(t) for the entire duration of labour (S2 Fig) to derive average labour curves according to parity and calculated the median, 5th, and 95th percentiles of sojourn times and cumulative duration of labour according to cervical dilatation at admission. In order to assess the influence of oxytocin augmentation on the described labour patterns, we applied the survival analyses and multistate Markov models to perform sensitivity analyses comparing labour progression patterns of all women with that of a population excluding women with oxytocin augmentation (i.e., our entire study population versus study population excluding women with augmented labours). The plan for the above survival analyses was first presented at an expert meeting convened by the WHO in November 2016, following which the analyses were started. In February 2017, after a review of the preliminary results of these analyses, the WHO study-coordinating unit requested an independent application of multistate Markov models to the same sample of women in order to determine whether the findings are consistent between the two analytical approaches. From June to July 2017, sensitivity analyses were conducted using the two analytical approaches to assess the influence of oxytocin augmentation on the described labour patterns for the study population, following the suggestions of the BOLD project technical advisory group and study co-authors.

I-AI Digest

Study Justification:

– The study aimed to better understand natural labour progression in order to address the increasing rates of labour interventions, such as cesarean sections and oxytocin augmentation.
– Methodological advancements in statistical and computational techniques allowed for more accurate findings and a re-evaluation of current labour practices.
– The study was part of the World Health Organization’s Better Outcomes in Labour Difficulty (BOLD) project, which aimed to develop a new labour monitoring-to-action tool.

Study Highlights:

– The study included 5,606 women in Nigeria and Uganda who gave birth vaginally following a spontaneous labour onset.
– Survival analysis and multistate Markov models were used to estimate the duration of labour centimeter by centimeter and the cumulative duration of labour from admission through 10 cm.
– The study found that cervical dilatation during labour can progress more slowly than the widely accepted benchmark of 1 cm/hour, regardless of parity.
– Interventions to expedite labour to conform to the 1 cm/hour threshold may be inappropriate, especially before 5 cm in nulliparous and multiparous women.
– Averaged labour curves may not accurately reflect the variability associated with labour progression, and their use for decision-making in labour management should be de-emphasized.

Recommendations:

– The findings suggest that the current practice of using a fixed benchmark for labour progression may need to be re-evaluated.
– Interventions to expedite labour should be carefully considered, especially before 5 cm in nulliparous and multiparous women.
– Decision-making in labour management should take into account the individual variability in labour progression rather than relying solely on averaged labour curves.

Key Role Players:

– Obstetricians and midwives: Responsible for managing labour and making decisions regarding interventions.
– Hospital administrators: Involved in implementing changes in labour management practices.
– Policy makers: Responsible for developing guidelines and policies based on the study findings.

Cost Items for Planning Recommendations:

– Training and education: Costs associated with providing training and education to healthcare providers on the new labour management practices.
– Equipment and technology: Costs for acquiring or upgrading equipment and technology needed for improved labour monitoring.
– Research and data collection: Costs for conducting further research and collecting data to validate the study findings.
– Implementation and monitoring: Costs for implementing the new labour management practices and monitoring their effectiveness.

Amandla Obufakazi

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 prospective cohort study of over 5,000 women in two sub-Saharan African countries. The study used advanced statistical and computational techniques to analyze the patterns of labour progression. The study also conducted sensitivity analyses to assess the impact of oxytocin augmentation on labour progression. However, to improve the evidence, the abstract could provide more details on the specific statistical methods used and the results of the sensitivity analyses.

Abstract

Scientific and technical approval for this study was obtained from the Review Panel on Research Projects (RP2) of the UNDP/UNFPA/UNICEF/WHO/World Bank Special Program of Research, Development and Research Training in Human Reproduction (HRP), Department of Reproductive Health and Research, WHO. Ethical approval was obtained from the WHO Ethical Review Committee (protocol A65879), the Makerere University School of Health Sciences Research and Ethics Committee, Uganda (protocol #SHSREC REF 2014–058), University of Ibadan/University College Hospital Ethics Committee (UI/EC/14/0223), Federal Capital Territory Health Research Ethics Committee, Nigeria (protocol FHREC/2014/01/42/27-08-14), and Ondo State Government Ministry of Health Research Ethics Review Committee, Nigeria (AD 4693/160). The study was conducted according to the Declaration of Helsinki of the World Medical Association. The WHO BOLD research project was primarily designed to identify the essential elements of labour monitoring that trigger the decision to use interventions aimed at preventing poor labour outcomes, with the aim of developing a new labour monitoring-to-action tool. The study protocol and detailed methodological considerations have been published elsewhere [23]. In brief, this was a prospective, multicentre, cohort study of women admitted for vaginal birth with single live fetuses during early first stage of labour across 13 hospitals in Nigeria and Uganda. This included women undergoing induction of labour and those with spontaneous labour onset who presented at cervical dilatation of ≤ 6 cm. Women with multiple pregnancies, gestational age less than 34 weeks, elective cesarean section, and those who were unwilling to participate or incapable of giving consent due to obstetric emergencies were excluded. 9,995 women (56.1%) out of 17,810 women who were screened in all hospitals during the study period met these inclusion criteria and participated in the study. Participating hospitals had a minimum of 1,000 deliveries per year with stable access to cesarean section, augmentation of labour, and instrumental vaginal birth. Estimation of gestational age at birth was in accordance with individual institutional practices, which relied upon the woman’s first date of the last menstrual period in the majority of cases. Labour was managed by midwives or obstetric residents and/or obstetricians. Doppler fetal monitor was used to assess fetal vital status at hospital admission and for intermittent monitoring throughout labour. Labour management protocol, as well as the number and timing of pelvic examinations, were not standardized across participating institutions. None of the institutions subscribed to the ‘Active Management of Labour’ protocol during the study period. Although the partograph was a standard element in all labour protocols, adherence to its application for labour management during the study period varied widely across hospitals. Eligible women were recruited into the study between December 2014 and November 2015. From the medical record, trained research nurses prospectively extracted detailed information on sociodemographic, anthropometric, obstetric, and medical characteristics of study participants at hospital admission, multiple assessments for labour monitoring and interventions performed throughout the first and second stages of labour, and maternal and neonatal outcomes following labour. Attending staff were approached to complement medical records data when needed. Data collection was limited to the hospital stay of the mother and baby, and there was no follow-up after hospital discharge. The current study used information on maternal baseline and admission characteristics, repeated assessments of cervical dilatation over time, maternal and neonatal characteristics throughout labour, and perinatal outcome data. This analysis was focused on describing the labour patterns of women without adverse birth outcomes and not on determining correlation to clinical outcomes (See S1 STROBE Checklist). From a total of 8,957 singleton births with consistent time records in the database, we restricted our analysis to examine labour progression to 5,606 women on the basis of the following inclusion criteria (Fig 1): term births (between 37 weeks and 0 days and 41 weeks and 6 days) with vertex presentation and spontaneous labour onset. We excluded women who had labour induction, previous uterine scar, or intrapartum cesarean section. To examine the labour patterns in women with normal perinatal outcomes, we excluded women whose labour resulted in severe adverse outcomes, which was defined as occurrence of any of the following: stillbirth, early neonatal death, neonatal use of anticonvulsant, neonatal cardiopulmonary resuscitation, 5-minute Apgar score < 6, maternal death or organ dysfunction associated with labour dystocia, or uterine rupture. Furthermore, we excluded women who gave birth to neonates with severe congenital malformation and those with fewer than two cervical dilatation assessments during the first stage of labour (since a single data point cannot be used to generate a labour pattern for the individual woman). * Excluding significant outliers due to unusual rapidity, regression, or inconsistencies with time. BOLD, Better Outcomes in Labour Difficulty. We grouped women in the selected sample into three parity groups (0, 1, and 2+) to explore any differences in labour patterns according to parity. We used two independent approaches to analyse labour progression patterns and construct average labour curves for the selected sample. In the first approach, we performed survival analyses to estimate the time it took to progress from one level of cervical dilatation to the next (called ‘sojourn time’) (i.e., from 3 to 4 cm, 4 to 5 cm, 5 to 6 cm, until full dilatation [10 cm]). We used both complete (where available) and interval-censored times to estimate the distribution of times for progression from one integer centimetre of dilatation to the next, with an assumption that the labour data are log-normally distributed. Based on this model, the median, 5th, and 95th percentiles were calculated. We used the same approach to derive the cumulative duration of labour for women presenting at different cervical dilatations (3 cm, 4 cm, 5 cm, and 6 cm) to evaluate any potential differences in the patterns of labour progression. To illustrate the ‘slowest-yet-normal’ labour patterns, we plotted the 95th percentiles for the cumulative duration of labour based on the cervical dilatation at admission. To construct average labour curves, we applied a nonlinear mixed model that best fit our data instead of polynomial models used by previous authors [12–16, 24]. We expressed cervical dilatation for subject i in time j (yij) as a function of time (tij) according to the following three-parameter logistic growth model: in which β0 is dilatation value when tij → −∞, β1 is the asymptotic curve height, and β2 is the inflection point and at this time value when the dilatation reaches half of its height. For simplicity, we estimated β0, β1 as fixed effects and included the random term bi in the inflection point and assumed that this term follows a normal distribution, i.e., bi∼N(0,σb2). Given that women in this analysis entered the cervical dilatation time curve at different dilatations but all ended at full dilatation (10 cm), the starting point (time = 0) on the x-axis was set at full dilatation (10 cm), which was reached by all women in the sample and then calculated backwards (e.g., 1 hour before 10 cm becomes −1 hour and so on). This x-axis (time) was then reverted to a positive value. For example, instead of −12 → 0 hours, it became 0 → 12 hours. We used R-Cran version 3.2 for these statistical analyses [25]. In the second approach, we applied a multistate Markov modelling technique to examine the labour progression patterns in the same sample. This mathematical modelling technique from matrix algebra describes the transitions that a cohort of individuals make among a number of mutually exclusive and exhaustive health states during a series of short time intervals [26]. As cervical dilatation progression is a state- and time-related phenomenon during a period ranging from labour onset through to full cervical dilatation and birth of the baby (i.e., there is a finite set of states), the labour process can be considered a mathematical model that is suitable for the application of multistate Markov modelling. We therefore represented the sequence of labour progress as states based on every observed centimetre from 2 to 10 cm until birth of the baby—the ‘absorbing state’, as illustrated in S1 Fig. At a time t, the woman is in state S(t). The model was designed as a progressive unidirectional model, which only allows a choice of a way out of a particular state, but once a woman has left a state she cannot return. The next state to which a woman moves and the time of the change are governed by a set of transition intensities for each pair of states r and s. The transition intensity represents the instantaneous likelihood of moving from state r to state s. The full set of intensities for the system form the matrix Q. A Markov process is based on the transition matrix with a probability structure P(u, t + u). The (r, s) entry (the elements of entire matrix) of P(u, t + u), is the probability of being in state s at a time t + u, given the state at time u is r. P(u, t + u) is calculated in terms of Q. Assuming that the transition intensity matrix Q is constant over the interval (u, t + u), as in a time-homogeneous process, P(u, t + u) = P(t) and the equations are solved by the matrix exponential of Q scaled by the time interval, P(t) = Exp(tQ) (S1 Fig). We used msm package for R Project programming environment to fit the multistate Markov model [26]. We generated random observations of cervical dilatation based on the transition matrix P(t) for the entire duration of labour (S2 Fig) to derive average labour curves according to parity and calculated the median, 5th, and 95th percentiles of sojourn times and cumulative duration of labour according to cervical dilatation at admission. In order to assess the influence of oxytocin augmentation on the described labour patterns, we applied the survival analyses and multistate Markov models to perform sensitivity analyses comparing labour progression patterns of all women with that of a population excluding women with oxytocin augmentation (i.e., our entire study population versus study population excluding women with augmented labours). The plan for the above survival analyses was first presented at an expert meeting convened by the WHO in November 2016, following which the analyses were started. In February 2017, after a review of the preliminary results of these analyses, the WHO study-coordinating unit requested an independent application of multistate Markov models to the same sample of women in order to determine whether the findings are consistent between the two analytical approaches. From June to July 2017, sensitivity analyses were conducted using the two analytical approaches to assess the influence of oxytocin augmentation on the described labour patterns for the study population, following the suggestions of the BOLD project technical advisory group and study co-authors.

Izinto zokwakha

Emisha

Based on the provided information, it is difficult to identify specific innovations for improving access to maternal health. However, the study mentioned in the description, titled “Progression of the first stage of spontaneous labour: A prospective cohort study in two sub-Saharan African countries,” highlights the use of statistical and computational techniques to better understand natural labor progression. This research could potentially lead to innovations in labor monitoring and management, which could improve access to maternal health by providing more accurate and personalized care during childbirth.

AI Innovations Description

The study titled “Progression of the first stage of spontaneous labour: A prospective cohort study in two sub-Saharan African countries” provides valuable insights into the patterns of labor progression in women in Nigeria and Uganda. The study highlights the need for a better understanding of natural labor progression and challenges the widely accepted benchmark of 1 cm/hour for cervical dilatation.

Based on the findings of this study, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Develop evidence-based labor management guidelines: The study suggests that interventions aimed at expediting labor progression to conform to the 1 cm/hour benchmark may be inappropriate, especially before 5 cm dilatation. To improve access to maternal health, it is recommended to develop evidence-based labor management guidelines that take into account the slower progression of labor in certain women. These guidelines should be based on the specific needs and characteristics of the local population.

2. Implement training programs for healthcare providers: To ensure the effective implementation of evidence-based labor management guidelines, it is essential to provide training programs for healthcare providers. These programs should focus on updating their knowledge and skills in managing labor, including understanding the slower progression of labor in certain women. Training should also emphasize the importance of individualized care and shared decision-making with pregnant women.

3. Promote shared decision-making and informed consent: Informed consent and shared decision-making between healthcare providers and pregnant women are crucial for improving access to maternal health. Pregnant women should be provided with accurate information about the natural progression of labor and the potential risks and benefits of interventions. This will empower women to make informed decisions about their labor management and ensure that interventions are only used when necessary.

4. Strengthen antenatal care services: Early detection and management of potential risk factors during pregnancy can contribute to better maternal health outcomes. Strengthening antenatal care services, including regular check-ups and appropriate monitoring, can help identify any potential complications early on. This will enable healthcare providers to develop individualized care plans and provide necessary support during labor.

5. Improve access to skilled birth attendants: Access to skilled birth attendants is essential for ensuring safe and effective labor management. Efforts should be made to improve access to skilled birth attendants, particularly in remote and underserved areas. This can be achieved through the deployment of trained midwives and other healthcare professionals to these areas, as well as the provision of transportation and infrastructure support.

By implementing these recommendations, it is possible to improve access to maternal health and ensure that labor management practices are evidence-based, individualized, and respectful of women’s choices.

AI Innovations Methodology

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

1. Implement evidence-based labor management protocols: The study highlights the need to re-evaluate current labor practices and interventions. Implementing evidence-based labor management protocols that are tailored to the specific needs of women in sub-Saharan African countries can help improve access to maternal health by ensuring that interventions are appropriate and timely.

2. Strengthen midwifery care: The study mentions that labor was managed by midwives or obstetric residents and/or obstetricians. Strengthening midwifery care by providing adequate training, resources, and support can improve access to skilled birth attendants and ensure that women receive appropriate care during labor and childbirth.

3. Improve access to cesarean sections: The study mentions that participating hospitals had stable access to cesarean sections. However, it is important to ensure that all women who require a cesarean section have timely access to this life-saving intervention. This may involve improving infrastructure, transportation, and referral systems to ensure that women in remote areas can access cesarean sections when needed.

4. Promote community-based interventions: To improve access to maternal health, it is important to promote community-based interventions that focus on prevention, education, and early detection of complications. This can include initiatives such as antenatal care outreach programs, community health worker training, and community awareness campaigns.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed using a combination of quantitative and qualitative research methods. Here is a brief outline of a possible methodology:

1. Conduct a literature review: Start by conducting a comprehensive literature review to gather existing evidence on the impact of similar interventions on improving access to maternal health. This will help inform the design of the simulation methodology.

2. Define key indicators: Identify key indicators that can be used to measure the impact of the recommendations. This may include indicators such as cesarean section rates, maternal mortality rates, access to skilled birth attendants, and utilization of antenatal care services.

3. Collect baseline data: Collect baseline data on the identified indicators to establish a starting point for the simulation. This can be done through surveys, interviews, and analysis of existing data sources.

4. Develop a simulation model: Develop a simulation model that incorporates the identified recommendations and their potential impact on the key indicators. This may involve using statistical modeling techniques, such as regression analysis or mathematical modeling, to estimate the potential changes in the indicators based on the implementation of the recommendations.

5. Validate the simulation model: Validate the simulation model by comparing its predictions with real-world data. This can be done by comparing the simulated results with data from similar interventions implemented in other settings or by conducting pilot studies to test the feasibility and effectiveness of the recommendations.

6. Conduct sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation model and explore different scenarios. This can involve varying the parameters of the model, such as the coverage and effectiveness of the interventions, to understand their potential impact on the key indicators.

7. Communicate the findings: Communicate the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community members. This can help inform decision-making and guide the implementation of the recommendations to improve access to maternal health.

It is important to note that the methodology outlined above is a general framework and can be adapted and modified based on the specific context and resources available for the simulation study.

Umbhali