---
title: "MEWS in Ethiopia: Predicting and Reducing Severe Maternal Outcomes"
id: "plos-one-17-maternal-early-warning-system-mews-model-for-predicting-and-reducing-severe"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-17-maternal-early-warning-system-mews-model-for-predicting-and-reducing-severe"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0356105"
published_at: "2026-08-14T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# MEWS in Ethiopia: Predicting and Reducing Severe Maternal Outcomes
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-17-maternal-early-warning-system-mews-model-for-predicting-and-reducing-severe
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0356105)
- **Published At:** 2026-08-14T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This quasi-experimental study evaluated a validated **Maternal Early Warning System (MEWS)** chart for bedside monitoring among 1,138 obstetric inpatients in four public hospitals in North Shewa Zone, Ethiopia, recruited from 05/05/2025–31/08/2025. - The sample was evenly split: intervention (n = 569) monitored with the MEWS chart; control (n = 569) received standard clinical monitoring. - MEWS recorded twelve clinical parameters including vital signs, oxygen saturation, urine output, consciousness, pain, and postpartum-specific items (vaginal bleeding, uterine contraction, perineal tear), with color-coded escalation: **Green**, **Yellow**, **Red**. - Key process improvements with MEWS: shorter time from admission to first trigger (mean 5.61 vs 10.27 hours), faster physician evaluation after trigger (mean 49.3 vs 71.9 minutes), and reduced time from trigger to intervention (mean 14.6 vs 25.9 minutes) compared with control. - Clinical resource use decreased: fewer ultrasound scans per patient (1.32 vs 2.30) and shorter hospital stay (4.83 vs 5.29 days) in the MEWS group. - MEWS monitoring was associated with a statistically significant reduction in risk of **severe maternal outcomes** (adjusted risk ratio aRR = 0.85, 95% CI: 0.73–0.99) and increased likelihood of triggering timely clinical response (aRR = 1.15, 95% CI: 1.03–1.28). - The study used multivariate generalized estimating equation (GEE) models with Poisson regression to estimate adjusted risk ratios and 95% confidence intervals. - Authors conclude MEWS implementation improved early detection, shortened response times, reduced investigations and length of stay, and lowered severe maternal outcomes in this setting. - Limitations and next steps reported: need for studies with larger numbers of clusters and assessment across different risk groups and settings; implementation fidelity in low-resource contexts may vary. - Trial registration: Pan African Clinical Trial Registry PACTR202506739780428. Funding provided by International Institute for Primary Health Care–Ethiopia; funder had no role in design, conduct, or reporting.
## Clinical Analysis & Structured Key Points
Maternal early warning system (MEWS) model for predicting and reducing severe maternal outcomes in Ethiopia | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Background Maternal mortality in Ethiopia remains high, while most of these deaths are preventable. Early detection of deterioration and prompt response are essential to reduce these preventable deaths. The Maternal Early Warning System (MEWS) is a reliable clinical tool for this purpose. However, its effectiveness is underexplored and its bedside use is inconsistent. This study evaluated the MEWS model for predicting and reducing severe maternal outcomes. Method A parallel, quasi-experimental study design was conducted among 1138 obstetric inpatients at four public hospitals of North Shewa Zone, Ethiopia. The recruitment period was from 05/05/2025–31/8/2025. The intervention group (n = 569) was monitored using the MEWS chart, which included vital signs, oxygen saturation, urine output, consciousness, and pain, and for the postpartum women; vaginal bleeding, uterine contraction, and perineal tear, were categorized as Green, Yellow, or Red. The control group (n = 569) received the standard clinical monitoring. A multivariate generalized estimating equation (GEE) model with Poisson regression was used to compare the outcomes and estimate adjusted risk ratios (aRR) with 95% confidence intervals. Result The mean duration from admission to the first trigger was shorter by 4.7 hours (5.61 vs. 10.27 hours), trigger to physician evaluation by 22.6 minutes (49.3 vs. 71.9 minutes), and trigger to clinical intervention by 11.3 minutes (14.6 vs. 25.9 minutes) among women in the intervention group. Women also underwent fewer ultrasound scans (1.32 vs. 2.30) and had a shorter hospital stay by about 0.5 days (4.83 vs. 5.29 days). Women monitored with the MEWS chart had a 20% lower risk of severe maternal outcomes (aRR = 0.85, 95% CI: 0.73–0.99). Additionally, MEWS-monitored women were 9% more likely to be triggered for timely clinical response (aRR = 1.15, 95% CI: 1.03–1.28). Conclusion Implementation of the MEWS was associated with earlier detection of maternal deterioration, shorter clinical response, fewer ultrasound investigations, shorter hospital stays, and lower severe maternal outcomes. Further studies with larger number of clusters are needed to evaluate the effectiveness of MEWS across different risk groups and settings. Trial registration Pan African Clinical Trial Registry (PACTR), PACTR202506739780428, https://pactr.samrc.ac.za Citation: Tessema SD, Tadese M, Hailemeskel S, Mule CT, Tiche GD, Mekonnen LA, et al. (2026) Maternal early warning system (MEWS) model for predicting and reducing severe maternal outcomes in Ethiopia. PLoS One 21(8): e0356105. https://doi.org/10.1371/journal.pone.0356105 Editor: James Colborn, Clinton Health Access Initiative, UNITED STATES OF AMERICA Received: December 9, 2025; Accepted: July 27, 2026; Published: August 14, 2026 Copyright: © 2026 Tessema et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are incorporated within the paper and the original raw data are submitted as supplementary file. Funding: This study was funded by the International Institute for Primary Health Care-Ethiopia (IPHC-E). Every phase of the study was evaluated and monitored by Debre Berhan University, Asrat Woldeyes Health Science Campus, and IPHC-E. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: DBU, Debre Berhan University; EDHS, Ethiopian Demographic and Health Survey; IPHC-E, International Institute for Primary Health Care-Ethiopia; MEWS, Maternal Early Warning System; VS, Vital sign Introduction Despite a 34% global decline in maternal mortality from 2000 to 2020, an estimated 287,000 women still died in 2020, with 95% of these deaths occurring in low-income countries, including Ethiopia [ 1 ]. In Ethiopia, the maternal mortality ratio declined from 871 per 100,000 live births in 2000–267 in 2020 [ 2 ], but this remains far above the Sustainable Development Goal target of 70 per 100,000 [ 3 ]. Beyond mortality, the World Health Organization (WHO) estimates that for every maternal death, 20–30 women experience maternal morbidity, underscoring the magnitude of the problem [ 4 ]. Nearly three-quarters of maternal deaths are attributable to hemorrhage, infections, hypertensive disorders, delivery complications, and unsafe abortion, conditions that are largely preventable with timely detection and intervention [ 4 , 5 ]. Delays in recognizing and managing complications remain a critical barrier to improving outcomes. Evidence from high-income countries demonstrates that the Maternal Early Warning System (MEWS), a structured bedside tool for monitoring physiological parameters, enhances early detection of deterioration and facilitates timely intervention, thereby reducing severe maternal complications and deaths [ 6 ]. MEWS systematically tracks indicators such as blood pressure, heart rate, respiratory rate, temperature, oxygen saturation, urine output, consciousness, pain, and postpartum-specific parameters, including vaginal bleeding, uterine contraction, and perineal status [ 7 ]. These are categorized as “Green,” “Yellow,” or “Red,” providing clear escalation triggers for clinical response [ 8 ]. Globally, studies provide strong evidence of MEWS effectiveness. In Finland, the system showed high sensitivity in detecting leading causes of maternal mortality, such as preeclampsia, sepsis, and postpartum hemorrhage [ 7 ]. In Indonesia, it improved the frequency of patient monitoring and facilitated timely interventions [ 9 ], while in Nigeria, it enhanced early detection of deterioration and supported healthcare providers in managing workload [ 6 ]. Studies in Spain [ 10 ], China [ 11 ], and the UK [ 12 ] similarly demonstrated that MEWS trigger/activation was strongly associated with severe maternal complications. A systematic review concluded that MEWS improves routine monitoring, reduces delays in response to abnormal findings, and can lower the severity of maternal morbidity [ 13 ]. However, evidence from low-resource settings remains limited, and findings from some contexts highlight challenges in implementation fidelity [ 14 ]. Despite Ethiopia’s progress in reducing maternal mortality, preventable maternal morbidity and mortality remain high, compounded by suboptimal quality of care and reliance on reactive monitoring systems [ 15 ]. MEWS offers a proactive, evidence-based approach that can strengthen clinical decision-making, standardize monitoring, and facilitate timely intervention. Yet, its effectiveness in the Ethiopian context has not been assessed. This study, therefore, seeks to evaluate the impact of MEWS on predicting and reducing severe maternal outcomes in Ethiopian hospitals, generating context-specific evidence to inform, scale-up, and integrate into national maternal health strategies. Materials and methods Study design, setting, and population A parallel, quasi-experimental study design was implemented in North Shewa Zone, Amhara Region, Ethiopia. The recruitment period was from 05/05/2025–31/8/2025. The Zone is bordered by the Oromia Region to the south and west, South Wollo to the north, and the Afar Region to the east. Debre Berhan is the capital city of the North Shewa Zone and is located about 130 kilometers northeast of Addis Ababa on the Ethiopian highway. The total population of the zone is estimated at 2,429,108, of which 1,203,366 are females [ 16 ]. There are eleven public hospitals in the zone: 3 General hospitals, 7 primary hospitals, and 1 comprehensive Specialized hospital. Population and eligibility criteria The study included pregnant and postpartum women admitted with antepartum, intrapartum, or postpartum complications, as well as those with abortion-related cases, who had undergone obstetric or gynecologic surgery, and high-risk conditions. Women in normal labor who were planned for discharge within 24 hours after birth, those who died from accidental causes, and those transferred directly to the intensive care unit (ICU) without inpatient admission were excluded. Intervention The intervention group was monitored using a statistically developed and validated Maternal Early Warning System (MEWS) chart ( S1 File ), which replaced the standard vital signs sheet for all enrolled participants at the intervention sites. The MEWS chart, previously validated and described in detail elsewhere [ 10 , 17 ], is a simple, observation-based tool designed to facilitate the early detection of maternal clinical deterioration. It categorizes parameters into three color-coded zones: Green (normal; no concern), Yellow (moderate abnormality; requires closer observation), and Red (severe derangement; demands immediate attention). The chart incorporates twelve key maternal clinical parameters ( Table 1 ). Monitoring follows an escalation protocol. If all parameters remained within the normal range (green), routine monitoring continued as per standard practice. A single yellow alert prompted repeat observations within 30 minutes while maintaining standard monitoring. A patient was considered triggered for further assessment if they received one red score or two yellow scores, in which case repeat observations were carried out within 30 minutes, followed by confirmation of findings through history and examination. Monitoring frequency was increased, and corrective measures such as administration of intravenous fluids, oxygen at 10 L/min if required, antihypertensives, review of charts, or appropriate maternal positioning (e.g., left tilt for pregnant women) were initiated. If the patient’s condition stabilized, routine monitoring was resumed; however, persistent or worsening red alerts required immediate review by a senior obstetrician within 60 minutes, which could lead to emergency intervention, urgent referral, or transfer to the intensive care unit (ICU). In cases where three or more yellow alerts or at least two red alerts were identified, immediate obstetrician review was mandatory, reassessment was conducted within 15 minutes, and continuous monitoring was started. If the situation remained unresolved, escalation of care to an anesthesiologist, critical care, and pain medicine specialist was required. This structured response system was designed to ensure timely recognition and rapid escalation of care, ultimately aiming to prevent progression to severe maternal morbidity or mortality ( S3 File ). Download: PNG larger image TIFF original image Table 1. Components of maternal early warning scores (MEWS) criteria for prediction of Severe Maternal Morbidity, Ethiopia, 2025. https://doi.org/10.1371/journal.pone.0356105.t001 Control group The control hospitals were continuing with their existing/standard clinical monitoring practices. This involved monitoring and recording temperature, pulse, blood pressure, and respiratory rate on vital sign sheets. Clinical outcome The primary clinical outcome was the number of patients identified by MEWS as at risk for severe maternal outcomes and the number who subsequently developed such outcomes. Secondary outcomes included intensive care unit (ICU) admissions, emergency surgical interventions, length of hospital stay, time to diagnose severe maternal morbidity (SMM), time to intervention, frequency of MEWS recording, and adherence to maternal monitoring through the use of the MEWS chart. Operational definition Triggering on MEWS chart: A trigger is defined as a single markedly abnormal observation (red trigger) or a combination of two simultaneous mildly abnormal observations (two yellow triggers) [ 8 ]. Severe maternal morbidity (SMM): is a clinical condition or disease that can threaten a woman’s life during pregnancy and labor and after termination of pregnancy [ 18 ]. It was assessed using the Centers for Disease Control and Prevention (CDC) International Disease Classification indicators [ 19 , 20 ]. It includes hemorrhage, sepsis, pre-eclampsia, eclampsia, shock, acute renal failure, cardiovascular disorder (stroke), heart failure, severe anemia, pulmonary edema, hysterectomy, and thrombotic embolism. Maternal death: death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes [ 18 ]. Severe maternal outcome: Severe maternal morbidity and maternal death [ 18 ]. Composite maternal morbidity: refers to the occurrence of one or more severe complications during pregnancy, childbirth, or within 42 days postpartum, including secondary outcomes [ 20 ]. Emergency surgical interventions: include emergency cesarean section, surgical repair of uterine rupture, hysterectomy, laparotomy, uterine artery ligation , compression sutures (e.g., b-lynch sutures), repair of perineal or vaginal tears, drainage of pelvic abscess or puerperal sepsis, and Wound Re-exploration for surgical site infection (SSI). Sample size There was no comparable baseline study in Ethiopia. However, the incidence of severe maternal morbidity (SMM) in the study setting was 14.3% [ 21 ]. We hypothesize that the MEWS intervention would increase patients triggered for potential SMM by 21% [ 12 ]. To detect this difference at a 0.05 significance level with 80% power, and allowing for a 5% loss to follow-up, the study required a total of 1138 samples, 569 in the intervention and 569 in the control group. Study participants’ selection procedure The study was conducted in four purposively selected hospitals. Debre Berhan University Hakim Gizaw Hospital and Debre Berhan Comprehensive Specialized Hospital (CSH) were designated as intervention sites, while Enat Hospital and Mehalmeda Hospital served as control sites. These hospitals were chosen because they are comparable in terms of service provision, the presence of obstetricians, availability of intensive care units, and their status as government facilities. The calculated sample was proportionally allocated to each hospital based on the six-month caseload of high-risk and postnatal care admissions. Study participants were then selected using a systematic random sampling method with a sampling interval of two, after the first participant was chosen by lottery in each hospital. Recruitment continued until the required sample size was achieved, including only women who met the eligibility criteria and consented to participate. Geographical separation between the hospitals helped minimize the risk of information contamination. At each hospital, all potentially eligible women were approached and informed about the study and care procedures before enrollment. Data collection tool and procedure Midwives, interns, and general practitioners (GPs) were trained on the importance of accurately charting patient parameters and the mandatory requirement to call and involve an obstetrician whenever a trigger occurred. Compliance and completeness of MEWS documentation were regularly audited. To minimize observer bias, however, these staff members were not informed about the specific study objectives. Women were followed from enrollment until hospital discharge. In both intervention and control hospitals, monitoring of vital parameters was performed every six hours, or more frequently if indicated by the managing clinician. The frequency of observations was determined by the woman’s risk status, diagnosis, reason for admission, and initial assessment at admission. Individualized care plans specifying observation schedules were decided by the attending obstetrician. After childbirth or cesarean delivery, midwives used the MEWS chart to monitor women for two hours in the delivery room or post-anesthesia care unit (PACU) and continued monitoring in the postnatal or post-cesarean ward as per physician order or protocol. The data collection tool was developed based on instruments applied in previous studies [ 6 , 10 , 17 , 20 , 22 ] ( S2 File ). Midwives conducted all MEWS assessments during inpatient care, identifying complications, detecting triggers, and ensuring timely clinical responses, with complete documentation in the medical record. Maternal outcome data and MEWS-related information were obtained through medical record reviews and face-to-face interviews. An independent data collector, blinded to group allocation, extracted outcome data from medical records within 24 hours of discharge. Data quality control The questionnaire was translated from English to Amharic and back-translated to ensure consistency and preservation of meaning. A pretest involving 5% of the sample size (28 women from the intervention group and 28 from the control group) was conducted in a comparable facility to assess the validity and reliability of the tool, and necessary modifications were made based on the findings. Twelve BSc midwives (data collectors) and six MSc midwives (supervisors) were trained on the proper use of the MEWS chart, standardized clinical definitions, the data collection tool, and ethical considerations. Recruitment was conducted using neutral framing, and maternal outcome data were kept blinded from healthcare providers to reduce the Hawthorne effect. Both data collectors and providers were unaware of intervention and group assignments, and data collectors further minimized bias by building strong rapport with participants and spending adequate time with them. During data collection, supervisors and investigators provided continuous on-site supervision, closely monitoring the completeness, accuracy, and clarity of the collected data. All parameters were measured using calibrated and validated equipment following standardized protocols to ensure accuracy and consistency. Data verification was performed through cross-checking with patient charts and periodic inter-observer validation. Monthly meetings were held to review adherence, address challenges, and implement corrective measures. In addition, women were anonymously surveyed to confirm adherence to key components, including pain management, vaginal bleeding, uterine contractions, and perineal tear monitoring. Data processing and analysis plan The data were collected using the Kobo toolbox, exported to SPSS version 26, and cleaned for completeness, consistency, including identification and correction of missing and outlier values before analysis. Categorical variables were summarized using frequencies and percentages, while continuous variables were summarized using means with standard deviations and median with interquartile ranges. Proportions were compared using Pearson’s Chi-Square (χ2) test for categorical variables and the independent t-test for continuous variables. A generalized estimating equation (GEE) model was employed to investigate the difference in changes between the intervention and control groups. The effect of the MEWS intervention on binary outcomes, e.g., ICU admission, triggered cases, and SMM triggers, was estimated using the modified Poisson regression with a log link and robust (sandwich) standard errors, controlling for maternal and facility-level covariates ( S1 Data ). Results were reported as adjusted risk ratios with 95% confidence intervals, with a two-sided significance level of 0.05 ( S1 Checklist ). Ethical approval The project was
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