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Open Access

Peer-reviewed

Research Article

Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation

  • Neamin Tesfay ,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    neamintesfaye2123@gmail.com

    Affiliation Ethiopian Public Health Institute, Centre of Public Health Emergency Management, Addis Ababa, Ethiopia

    ⨯
  • Alemu Zenebe,

    Roles Conceptualization

    Affiliation Ethiopian Public Health Institute, Centre of Public Health Emergency Management, Addis Ababa, Ethiopia

    ⨯
  • Zewdnesh Dejene,

    Roles Conceptualization

    Affiliation Ethiopian Public Health Institute, Centre of Public Health Emergency Management, Addis Ababa, Ethiopia

    ⨯
  • Henok Tadesse,

    Roles Writing – review & editing

    Affiliation Ethiopian Public Health Institute, Centre of Public Health Emergency Management, Addis Ababa, Ethiopia

    ⨯
  • Fitsum Woldeyohannes,

    Roles Writing – review & editing

    Affiliation Health Financing Program, Clinton Health Access Initiative, Addis Ababa, Ethiopia

    ⨯
  • Araya Gebreyesus,

    Roles Writing – review & editing

    Affiliation Department of Medical Microbiology and Immunology, College of Health Sciences, Mekelle University, Tigray, Ethiopia

    ⨯
  • Amit Arora

    Roles Writing – review & editing

    Affiliations School of Health Sciences, Western Sydney University, Penrith, NSW, Australia, Health Equity Laboratory, Campbelltown, NSW, Australia, Translational Health Research Institute, Western Sydney University, Campbelltown, NSW, Australia, Faculty of Medicine and Health, Discipline of Child and Adolescent Health, The Children’s Hospital at Westmead Clinical School, The University of Sydney, Westmead, NSW, Australia, Oral Health Services, Sydney Local Health District and Sydney Dental Hospital, NSW Health, Surry Hills, NSW, Australia

    ⨯

Abstract

Background

In Ethiopia, Maternal Death Surveillance and Response (MDSR) was integrated into the existing Integrated Disease Surveillance and Response (IDSR) system in 2014. Despite providing valuable evidence to inform policies and actions, system implementation has not been evaluated. Thus, a national-level evaluation was conducted to assess the level and status of system implementation.

Methods

A national cross-sectional study was conducted using a multi-stage sampling approach in 2020. A total of 629 health facilities were included in the study. A modified tool, adapted from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), was employed to assess each functional component of the system, encompassing structure, core, supportive, and system attributes. The score for each component was based on Ethiopian Public Health Institute’s mid-term evaluation metrics. To objectively evaluate the implementation status, a composite score of the Maternal Death Surveillance and Response Performance Index (MDSRPI) was calculated based on five performance indicators. Descriptive statistics, independent t-tests, and one-way analysis of variance (ANOVA) with Bonferroni correction were used to examine the variations in scores among the different characteristics.

Results

Of the total sample size, 82.5% (519/629) of health facilities were assessed. Among the assessed health facilities, 77.0% (400/519) fulfilled the criteria for final analysis. Accordingly, the overall readiness score was 44.9% (95% CI: 43.9% to 45.9%), which is rated as less functional. The structures of the system were rated at 51.7% (95% CI: 49.9% to 53.4%), and the system attributes were rated at 69.6% (95% CI: 68.0% to 71.2%), which were considered fairly functional. In contrast, the core functions were rated at 20.0% (95% CI: 18.9% to 21.1%), and the supportive functions were rated at 38.4% (95% CI: 36.4% to 40.4%), which were categorized as not functioning and less functional, respectively. Regionally, Tigray’s overall readiness score (54.8%, 95% CI: 50.4–59.1%) was significantly higher than Oromia (41.6%, 95% CI: 40.2–43.0%, P = 0.0001), Amhara (47.7%, 95% CI: 43.9–45.9%, P = 0.05), and SNNPR (42.3%, 95% CI: 39.3–45.3, P = 0.0001). Additionally, Amhara’s score was significantly higher than Oromia and SNNPR. Secondary-level healthcare facilities (49.6%, 95% CI: 45.7–53.7, P = 0.029) had a significantly higher readiness score compared to primary health facilities (44.6%, 95% CI: 43.5–45.6). The overall score for the Maternal Death Surveillance and Response Performance Index (MDSPI) was 33.9%.

Conclusion

Despite the noticeable regional variation, the overall system readiness and status to implement MDSR were suboptimal, characterized by low representativeness, completeness, and community engagement. Efforts should be directed toward improving community surveillance and enhancing all components of the system to address regional variations and improve overall performance through triangulation and integration with various data sources.

Citation: Tesfay N, Zenebe A, Dejene Z, Tadesse H, Woldeyohannes F, Gebreyesus A, et al. (2024) Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation. PLoS ONE 19(12): e0312958. https://doi.org/10.1371/journal.pone.0312958

Editor: Eric H. Y. Lau, The University of Hong Kong, CHINA

Received: May 1, 2024; Accepted: October 15, 2024; Published: December 3, 2024

Copyright: © 2024 Tesfay 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: Data cannot be shared publicly because it contains sensitive patient information. Data can be availed by the EPHI data management center after approval by the Research Ethics Committee and Public Health Emergency Management unit. Interested researchers can send an official request via email address: (info@ndmc.ephi.gov.et); or telephone number: (+251 (0)1 12 30 50 79).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Public health surveillance is a tool used to estimate and monitor the health status and behavior of a population by implementing mechanisms for collecting, analysing, and interpreting data continuously, as well as disseminating information to those who need to take necessary actions [1–3]. Considering the paramount importance of surveillance in managing and containing large emerging and reemerging outbreaks, the WHO African Region designed and subsequently customized a strategy called Integrated Disease Surveillance and Response (IDSR) in 2000 [4]. IDSR is an all-rounded and evidence-based approach used to maximize national public health surveillance and response, commencing from the community to the national level [5].

In its inception, IDSR exclusively focused on infectious diseases, excluding other vital health events [6]. As the strategy evolved, significant experience was gained in implementing and effectively realizing it. Both essential human and financial resources have been dedicated to successfully integrating them into a country’s health system [7]. These tangible strides have created opportunities to consider other public health priorities, such as maternal death, to be included as mandatory notifiable events, which will be reported to subnational and national health authorities [8].

Considering the presence of an established surveillance platform and the limited availability of reliable mortality data sources (which are critical elements for the accurate estimation of mortality rates, including the maternal mortality rate [MMR]), such as vital event registration, which is capable of accurately quantifying and determining the causes of death, the importance of mortality surveillance has become increasingly prominent [9,10]. In addition, to accelerate the pace of reducing preventable maternal deaths globally, the United Nations Commission passed a resolution on the status of women in 2012 [11]. This resolution calls for the elimination of preventable maternal mortality through effective real-time measurements and tracking of death [12]. Thus, maternal death surveillance and response (MDSR) has become a model that works per ratified resolution [13].

MDSR is a continuous cycle of notification, review, and analysis of maternal deaths that could provide amicable solutions to prevent similar deaths in the future [14]. The system is expected to generate accurate and timely maternal mortality data (MMR tracking) and aims to identify major causes of maternal death, formulate appropriate interventions, and institute service delivery improvements. The response component is designed to act on quality of care improvement measures, community mobilization, and awareness [15–17].

At the national level, Ethiopia has accepted the initiative promoted by the WHO to bolster and redouble efforts to improve maternal health outcomes over the last three decades [18]. This is evidenced by a remarkable reduction in maternal mortality rates (MMR) from 871 per 100,000 live births (95% CI, 705–1039) in 2000 to 267 per 100,000 live births (95% CI, 189–427) in 2020, according to estimates made by the Ethiopian Demographic and Health Survey (EDHS), the World Health Organization (WHO), the United Nations Children’s Fund (UNICEF), the United Nations Population Fund (UNFPA), the World Bank Group, and the United Nations Department of Economic and Social Affairs (UNDESA) [19,20]. Accordingly, the system was piloted in 2013 in four larger agrarian regions and three city administrations, before being formally included in Ethiopia’s integrated disease surveillance structure, known as Public Health Emergency Management (PHEM), in 2014 [21,22]. In 2017, the perinatal death component was included, and the name of the system changed to Maternal and Perinatal Death Surveillance and Response (MPDSR) [23,24].

In Ethiopia, the system operates at two levels, a health facility, and a community, using the PHEM platform, which encompasses both indicator and event-based surveillance approaches [25]. The indicator-based approach involves the formal notification and reporting of maternal deaths that occur within the health tier system using a defined case definition through existing means of communication, extending from reporting health facilities to the national level. In contrast, event-based surveillance, typically seen as complementary to indicator-based surveillance, provides information on possible maternal deaths from various sources that often extend beyond the health system platform. Subsequently, the received information is scrutinized to determine whether it meets the criteria for reporting to the next level [26]. Notified deaths were investigated using verbal autopsy (VA) and Facility-Based Maternal Death Abstraction Format (FBMDAF) to facilitate the review. Subsequently, the investigated deaths were reviewed by the MPDSR committee, which is positioned at each reporting health facility, to determine the cause and contributing factor responsible for women’s deaths. Based on this review, an action plan will be developed to address the quality of care at health facilities, and the report will be sent to the national data hub using a structured format called the maternal death reporting format (MDRF) [27].

The Ethiopian government has disseminated the information generated from the system through policy briefs and the publication of annual performance reports, which are prepared with the involvement of the national technical working group for MPDSR [28]. In this regard, the system has made an indispensable contribution by providing ample evidence that plays a seminal role in promoting the use of non-pneumatic anti-shock garments (NASGs) for managing bleeding in women during referrals from health posts to higher facilities [29,30]. Furthermore, the evidence generated from the system has played a pivotal role in developing a national obstetric hemorrhage response plan, resulting in tangible progress such as the establishment of mini blood banks at Comprehensive Emergency Obstetric and Newborn Care (CEmONC) facilities [31,32]. Despite the system guiding evidence-based decisions, its implementation to the fullest extent was hampered by individual (lack of training, blame-shifting, and defensive attitudes), interpersonal (underreporting, lack of an effective feedback loop, and irregular supportive supervision), and organizational (limited engagement of the community, lack of reporting formats and guidelines, and inadequate available essential medical commodities) factors [33–39]. In addition to the aforementioned gaps in the system, there are limitations in working closely with Civil Registration and Vital Statistics systems (CRVS) in death identification and cause of death assignment, despite the CRVS system not being fully institutionalized [40–42]. Additionally, the slow pace of digitalization of the reporting mechanism and inadequate contextualization of the system in humanitarian settings were also identified as hindrances to advancing the system to the next level [43,44]. Thus, periodic evaluation is required to determine how well the system operates to meet its stated purposes and objectives. Accordingly, a national-level system evaluation was conducted to assess the level and extent of the MDSR system implementation.

Methods and materials

Study setting and period

Ethiopia is the second most populous country after Nigeria, with a population of 117 million in 2020 [45]. Furthermore, Ethiopia has nine regions and two city administrations, namely, Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, the Southern Nations Nationalities and Peoples Region (SNNPR), Gambella, Harari, the Addis Ababa city administration and the Dire Dawa city administration, before further balkanization of the SNNPR into four separate regions [46]. The country has 17187 health posts, 3724 health centres, 302 public hospitals, and 5401 private health facilities [47]. The evaluation was conducted at the national level from September 2019 to April 2020, after six years of system implementation.

Study design, scope, and data collection tool

This nationwide cross-sectional study was conducted at selected health facilities, which included both hospitals and health centers. A modified version of the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) evaluation tools was utilized [48,49]. The scope of the evaluation emphasized four components of surveillance systems: the structure of the system, core of function, supportive function, and system attributes. The structure of the system component primarily aimed to assess the presence of mandatory notifications, coordinating bodies, strengthening strategies, and networks and partnerships. On the other hand, the component included under the core function of the system examines the process and output of the system, which extends from case detection to feedback. Furthermore, the supportive function component comprehensively overlooks the availability of a conducive environment for the smooth implementation of the system. This includes focusing on the availability of resources, means of communication, training, supportive supervision, standards, and guidelines. Finally, the quality of surveillance was evaluated using both qualitative and quantitative indicators of system attributes. Simplicity, flexibility, acceptability, usefulness, and stability were qualitative indicators of system quality, while review rate and community engagement were quantity indicators used to objectively assess the quality of the surveillance. A diagrammatic representation of each component and their definitions are provided in the S1 and S2 Appendices in S1 File. Furthermore, the modified data collection tool and its respective scores are provided in S3 Appendix in S1 File.

Sample size determination, sampling strategy, and sample allocation

Multistage sampling techniques were used to select the health facilities for the study. The study population (health centers and hospitals) was nested within districts, with districts further nested within zones (provinces) and provinces nested into regions. The design effect was included in the sample size estimation to accommodate for the clustering effect and gain power. Accordingly, the sample size was determined first, and all regions were selected as primary sampling units because of their small size. Second, a random sample of health facilities and hospitals was selected using stratified random sampling techniques, based on the type of facilities as stratifying variables. To determine the sample size N = N1+N2, where N1 is the total number of health centres and N2 is the total number of hospitals under investigation in Ethiopia. The critical value of the standard normal distribution (Zα/2), the pooled variance of system performance based on the indicators to measure the system (σ2), must be less than a given accuracy or marginal error (ϵ), i.e., . Furthermore, the design effect (deff) was computed using this formula Deff = 1+(m−1)*ρ [50], where ρ is intercorrelated with the cluster due to the similarity of observation within the region rather than between regions—a value typically between 0 and 1 (common conservative choice is 0.33), with m being the average number of observations within clusters [regions] consisting of health centres or hospitals. The computed deff value of 1.2 aligns with the number of health studies [51,52]. The required sample size, N = N1 [sample of health center] + N2 [sample of hospitals], was computed based on the relevant parameters.

Thus, the final sample size for the study based on the parameters was computed as:

Or it can be simplified as

is the initial population size, but the study population size is known; then, the required sample size for ith strata was computed using n0i and Ni as follows:

denoting the sample of health center size or hospital size determined. Thus, the sample size is computed based on the available parameters using the above formula:

The total sample size was computed as , and other detailed explanations of the sample size determination are provided in the S4 Appendix in S1 File. Moreover, sample allocation was based on the probability proportional to their size [PPS] for each region, specifically using . Then, the sample allocated for each region was computed with , where nr is the sample size allocated for each region, Nr is the total number of health centers or hospitals for rth r = 1,2,…,11, and N is the total number of facility types under investigation.

Data sources and survey participants

Facility-level information was obtained from MDSR focal points and medical directors of the health facilities. The evaluation was conducted in public health facilities, which account for 13% of the total public facilities (i.e., only including health centers and hospitals), by excluding private, non-governmental organizations (NGOs), and special clinics.

Data collection tools and quality management

The data collection tool was tested with 19 selected health facilities (seven hospitals and 12 health centers), and necessary corrections were made to the tool before it was used for full-scale data collection. Subsequently, the data collectors underwent a three-day training session and were provided with a field guide and a nearby field supervisor. The entire data collection process was conducted using an open data kit (ODK), and the quality of the data was checked and corrected with the help of the central teams.

Data management and analysis

The readiness of the facilities to fully implement the system was measured using four dimensions (structure, core, support, and attributes) of the surveillance component. Before calculating the single-weight average readiness score, the scores for each dimension of the system were independently computed. Subsequently, by equally weighting the dimensions, a single weighted measure of health facility readiness was computed, as shown in S5 Appendix in S1 File.

Dimension score measurement

  1. System structure: This component was measured using seven variables with yes/no responses. The overall score for the structural system (ST) was computed and weighted accordingly to produce the final weighted score (SW).
  2. Core function: This component was assessed using 11 variables with yes or no responses. The overall score for core function (CT) was calculated, and the weight score (CW) was computed.
  3. Supportive function: This dimension was gauged using ten variables with a yes/no response. The total supportive function (SUT) score was calculated based on the total score and the weighted score (SUW) was calculated.
  4. System attributes: Five subcategories with 26 items (simplicity (7), flexibility (2), acceptability (5), usefulness (8), and stability (4)) with yes/no responses were used to compute the overall score (AT). The weight score (AW) was then calculated.
  5. Overall score: To compute the overall score (OT), the weighted score of each dimension was summed to produce the final score. Subsequently, all scores in each dimension, including the overall score, are presented using the mean () and standard deviation (SD) after confirming the skewness levels of the observations. Furthermore, after assessing normality using the Shapiro-Wilk test, independent sample t-tests (for two-group variables) and one-way analysis of variance (ANOVA) were employed to examine the variation in the scores across various features. In cases where ANOVAs were significant, Bonferroni post-hoc analyses were used to identify further mean differences. The level of statistical significance was set at a two-sided p-value of < 0.05. Additionally, the overall score, including the score for each component, was rated according to the guidelines of the midterm evaluation of the Ethiopian Public Health Institutes (EPHI). The ratings are as follows: ’91 to 100% = Very effectively functioning = fully achieved, very few or no shortcomings’, ’76 to 90% = effectively functioning = largely achieved, despite a few shortcomings’, ’51 to 75% = Fairly functioning = only partially achieved’, ’21 to 50% = Less functioning = very limited achievement, extensive shortcomings, and ’20% or less = Not functioning = not achieved’ [53].

Measurement of maternal death surveillance and response performance index.

To objectively assess the status of system implementation, five parameters were used to provide a comprehensive picture and gauge the system over the years of implementation [28]. Accordingly, notification rate (Rn), notification coverage rate (CRn), review rate (Rr), review coverage rate (CRr), and proportion of reviewed community deaths (CRDp) were computed to derive the Maternal Death Surveillance and Response Performance Index (MDSRPI). All these parameters were calculated by summing each numerator and denominator over a seven-year timeframe from 2014 to 2020.

  1. A. Maternal death notification rate

The notification rate (Rn) was computed to assess how well the system is capturing and notifying deaths using potential data sources at both the health facility and community levels. In Ethiopia, every death should be reported to the next level on a weekly basis through the Public Health Emergency Management (PHEM) system. A score below 80% indicates a gap in the effective notification of deaths. To calculate the Rn score, the number of weekly notified deaths (dn) from 2014 to 2020 was taken as the numerator, and the identified deaths (di) from potential data sources, such as the District Health Information System (DHIS_2) and performance reports, from 2014 to 2020 were considered as the denominator. The details are depicted in Table 1.

  1. B. Maternal death notification coverage rate

The maternal death notification coverage rate (CRn) was calculated to assess how well the system captures estimated(excepted) deaths, which is foundational for measuring the representativeness of the system. Accordingly, the number of notified deaths (dn) reported from 2014 to 2020 was taken as the numerator, and the estimated deaths (E(d)) were taken as the denominator. The E(d) was measured using the UN Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) estimation and the Ethiopian population projections from 2014 to 2020 based on the 2007 census [54,55]. The details are depicted in Table 1.

  1. C. Maternal death review coverage rate

The maternal death review coverage rate (CRr) is a parameter used to assess the status of the Maternal Death Surveillance and Response (MDSR) system. It reflects the capacity to review deaths that occurred at both community and facility levels, in comparison to the expected deaths (E(d)). CRr is computed using the number of deaths reviewed (dr) that are reported to the central data hub using the Maternal Death Reporting Format (MDRF) during the years 2014 to 2020. These deaths are investigated and extracted using Verbal Autopsy (VA) for community-level deaths and the Facility-Based Maternal Death Abstraction Format (FBMDAF) for facility deaths before being reported using MDRF. The denominator is the expected deaths (E(d)), and the details are depicted in Table 1.

  1. D. Maternal death review rate

The maternal death review rate (Rr) is an indicator used to provide insight into how capable the system is at reviewing deaths after notification using the PHEM weekly reporting system. A score above 100% indicates that deaths are not being notified in consideration of potential data sources (i.e., various points of care in health facilities). To compute the Rr score, dn was taken as the denominator for deaths notified from 2014 to 2020, and dr as the numerator for deaths reviewed within the same period.

  1. E. Proportion of reviewed community deaths

The proportion of reviewed community deaths (CRDP) is used as a resolution to effectively monitor the community loop of the surveillance. To calculate CRDP, deaths reported through MDRF, investigated, and extracted using VA were declared as community deaths (drc) and served as the numerator, while dr was used as the denominator to compute the proportion. The details are depicted in Table 1. In addition, the Mann–Kendall trend test and Sen’s slope were used to assess the trend in the utilization of various data sources for maternal death reviews over the year [56].

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Table 1. Key performance indicators to measure the status of maternal death surveillance in Ethiopia from 2014 to 2020.

https://doi.org/10.1371/journal.pone.0312958.t001

After computing each of the five parameters, the composite score of the MDSPI was calculated to provide a judgment on the overall status of the system. The weighting of the aforementioned parameters was conducted in consideration of their role in defining the system, in consultation with the national technical working group for MPDSR. CRn and CRr were weighted 0.3 each out of 1 due to their role in defining the representativeness of the system. Similarly, Rr was weighted 0.2 due to its indication of the capacity of the review committee to review and set actions to mitigate similar future deaths. Rn and CRDP were weighted 0.1 each because they show the level of completeness and community loop of the surveillance. After computing each score per the defined weighting criteria, the MDSRPI was produced by summing each weighted indicator. Subsequently, the MDSRPI was further classified into three groups: a score below 0.4 was considered “low performing,” a score between 0.4 and 0.6 was declared “moderate performing,” and a score above 0.6 was declared “good performing” [57].

Furthermore, health facilities were classified into three categories, primary, secondary, and tertiary, based on their human resources and the types of services they provide. Additionally, the health facilities were classified into five altitude groups: desert areas (below 500 meters), lowlands (500–1500 meters), midlands (1500–2300 meters), highlands (2300–3200 meters), and upper highlands (3200 meters and above) [58]. Moreover, the regions were classified into three contextual categories—pastoralist, agrarian, and city administration–based on the cultural and socioeconomic backgrounds of their populations [59].

Overall, the data were exported from ODK to STATA version 17 for recoding, cleaning, and statistical analysis. According to the preliminary analysis, data were collected from 519 health facilities (445 health centers and 74 hospitals). Among them, 400 health facilities were categorized as MDSR-implementing facilities because they had notified and reviewed at least one maternal death a year before the survey [60]. This study was exclusively focused on these facilities.

Ethical statement

The assessment protocol received ethical clearance from the Ethiopian Public Health Institute Institutional Review Board (EPHI-IRB) with reference no. EPHI 5.3_1/36. A support letter from the EPHI was also written to all selected woredas, regional health bureaus, and selected health facilities, indicating the objective and final aim of the evaluation. Informed verbal consent was also obtained from the study participants by informing them about the evaluation and purpose of the study. Each respondent was allowed to provide consent or refuse to participate after receiving complete information regarding the study. The data collection period spanned from 20/02/2020 to 20/06/2020, and all records, including medical records, were fully anonymized before being accessed for this study. This process was aligned with IRB requirements, which mandate full anonymity.

Results

Characteristics of the MDSR implementing facilities

Of the 400 MDSR implementing facilities, primary healthcare facilities accounted for 89.3%, while secondary health facilities contributed 7.3% to the total. Furthermore, 78.5% of the facilities had implemented the system for more than two years, and 59.5% of them were in rural areas. Regionally, 70.5% of the health facilities were located in the Oromia and Amhara regions. According to the agroecological zone classification, more than half (51.8%) were located in midland areas (Table 2).

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Table 2. Characteristics of MDSR implementing health facilities in Ethiopia, 2020 (N = 400).

https://doi.org/10.1371/journal.pone.0312958.t002

Health facility readiness for the implementation of maternal death surveillance and response

The national mean score for the system structure was 51.7% (95% CI: 49.9% to 53.4%). The mean score for secondary health facilities was 63.1% (95% CI: 57.1% to 69.1%), which was significantly higher than the mean score of primary health facilities at 50.4% (95% CI: 48.6% to 52.2%, P ≤ 0.0001). Regionally, the mean score for the Tigray region was 66.3% (95% CI: 58.7% to 73.9%), which is noticeably higher than the mean scores for Oromia at 46.7% (95% CI: 44.2% to 49.9%, P ≤ 0.001), SNNPR at 51.6% (95% CI: 46.5% to 56.8%, P = 0.02), and Amhara at 52.2% (95% CI: 49.2% to 55.1%, P = 0.007). Urban health facilities scored higher at 55.3% (95% CI: 52.8%–57.8%) than rural facilities at 49.3% (95% CI: 47.0%–51.6%, P = 0.0007) (Table 3).

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Table 3. Mean () and standard error (SE) scores for the structure of the system among MDSR implementing health facilities in Ethiopia, 2020 (n = 400).

https://doi.org/10.1371/journal.pone.0312958.t003

The national mean score for the core function was 20.0% (95% CI: 18.9% to 22.1%). The Tigray region scored significantly better at 29.8% (95% CI: 25.0% to 34.6%) compared to Oromia at 16.7% (95% CI: 15.4% to 18.1%, P ≤ 0.0001), SNNPR at 14.5% (95% CI: 12.8% to 16.2%, P ≤ 0.0001), and Addis Ababa at 17.1% (95% CI: 11.8% to 22.4%, P = 0.004). Furthermore, the score for the core function in the Amhara region was 25.2% (95% CI: 23.1% to 27.3%), which was significantly higher than that of Oromia and SNNPR. Similarly, the Benishangul Gumuz region had a significantly greater score of 26.4% (95% CI: 18.6% to 34.1%) compared to the SNNPR region (P = 0.04) (Table 4).

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Table 4. Mean () and standard error (SE)scores core function of the system among MDSR implementing health facilities in Ethiopia, 2020 (n = 400).

https://doi.org/10.1371/journal.pone.0312958.t004

On the other hand, the national mean score for supportive functions was 38.4% (95% CI: 36.4% to 40.4%). The mean score for secondary health facilities was 50.2% (95% CI: 41.0% to 59.4%), which was significantly greater than that for primary health facilities at 37.3% (95% CI: 35.2% to 39.3%, P = 0.003). Regionally, the mean score for the Tigray region was 54.2% (95% CI: 45.6% to 62.8%), which was significantly greater than that for Oromia at 35.7% (95% CI: 33.0% to 38.5%, P = 0.001) and SNNPR at 31.3% (95% CI: 24.9% to 37.7%, P ≤ 0.0001) (Table 5).

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Table 5. Mean () and standard error (SE) supportive function of the system among MDSR implementing health facilities in Ethiopia, 2020 (n = 400).

https://doi.org/10.1371/journal.pone.0312958.t005

Furthermore, the national mean score for system attributes was 69.6% (95% CI: 68.0% to 71.2%). The mean score for the Amhara region was 73.7% (95% CI: 70.8% to 76.5%), which was significantly greater than that for the Oromia region at 67.1% (95% CI: 64.6% to 69.6%, P = 0.04) (Table 6). Overall, the national readiness score for MDSR implementation was 44.9% (95% CI: 43.9% to 45.9%). The mean score for secondary health facilities was 49.7% (95% CI: 45.7% to 53.7%), which was significantly greater than that for primary health facilities at 44.6% (95% CI: 43.5% to 45.6%, P = 0.03). Regionally, the mean score for the Tigray region was 54.8% (95% CI: 50.4% to 59.1%), which was significantly greater than that for Oromia at 41.6% (95% CI: 40.2% to 43.0%, P ≤ 0.0001), Amhara at 47.7% (95% CI: 46.0% to 49.4%, P = 0.05), and SNNPR at 42.3% (95% CI: 39.3% to 45.0%, P ≤ 0.0001). Similarly, the mean score for the Amhara region was significantly higher than that for the Oromia region (P ≤ 0.0001) and SNNPR (P = 0.04) (Table 7).

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Table 6. Mean () and standard error (SE) score for the attribute of the system among MDSR implementing health facilities in Ethiopia, 2020 (n = 400).

https://doi.org/10.1371/journal.pone.0312958.t006

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Table 7. Mean () and standard error (SE) scores for overall health facility readiness among MDSR implementing health facilities in Ethiopia, 2020 (n = 400).

https://doi.org/10.1371/journal.pone.0312958.t007

Maternal death surveillance and response system performance indictors

The notification rate was 76.3%, with a minimum of 44.3% from the Tigray region and a maximum of 85.3% from the Amhara region. Since the score was below 80%, it indicates a gap in completeness and the need for exhaustively searching for potential data sources before notifying the next level. Furthermore, both the coverage rate for notification and review were below 10%, clearly demonstrating that the system has issues depicting the real picture and is not in a position to estimate the magnitude, which is one of its objectives. However, Harir and Dire Dawa have better notification and review coverage rates due to their proximity to the Oromia and Somalia regions and their tertiary facilities serving as catchment areas. This results in a case overload, potentially inflating performance metrics. However, the proportion of reviewed community deaths, typically tracked and reviewed for their permanent residents, is below 20%, indicating that most reviewed deaths are from nearby regions (Table 8).

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Table 8. Summary of key maternal death surveillance and response performance indicators in Ethiopia from 2014 to 2020.

https://doi.org/10.1371/journal.pone.0312958.t008

The review rate was 79.5%, and regions like Tigray (213.6%), SNNPR (126.9%), and Dire Dawa (136.4%) scored above 100%, indicating a gap in notifying deaths based on available data sources. On the other hand, this shows the functionality of the review committee to review deaths irrespective of their notification status. Moreover, the proportion of reviewed community deaths was 53.5%, with the highest proportion observed in Tigray (78.4%) and Amhara (66.9%), indicating that these regions have better capacity and performance in conducting community surveillance (Table 8). Furthermore, the proportion of community-level reviewed maternal deaths declined from 100% in 2013 to 41.7% in 2020, with a Sen’s slope of -0.5 (95% CI: -0.3 to -0.8, p = 0.003). In contrast, facility-level maternal deaths increased from 0% in 2013 to 58.3% in 2020, also showing a statistically significant increase with a Sen’s slope of 0.5 (95% CI: 0.3 to 0.8, p = 0.003) (Fig 1).

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Fig 1. Proportion of reviewed maternal deaths by the source of data extraction from 2013 to 2020 in Ethiopia: FBMDAF (Facility-Based Maternal Death Abstraction Format) and VA (Verbal Autopsy).

Case-based reporting using the MDRF (Maternal Death Reporting Format) was initiated in 2013.

https://doi.org/10.1371/journal.pone.0312958.g001

Overall, the MDSRPI was 33.9%, which was declared as low-performing, characterized by low representativeness, completeness, and community engagement. However, only Tigray was identified as a good-performing region, while Amhara, Harari, and Dire Dawa were identified as moderate-performing compared to the remaining regions of Ethiopia (Table 8). Additionally, the detailed notified, reviewed, estimated, and identified maternal deaths from 2014 to 2020 are annexed in S6-S10 Appendices in S1 File.

Discussion

The implementation status of the Maternal Death Surveillance and Response (MDSR) was evaluated using a modified standard tool among 400 health facilities, and the system’s performance was measured using two key performance indicators based on nationally collected data to provide a comprehensive overview of the system. The overall system implementation was deemed suboptimal, with limited capacity to review notified deaths and capture and review community maternal deaths. Additionally, among the surveillance components, the core function had the lowest score compared with the other components of the system. Furthermore, significant variations in the implementation of the system were observed based on region and type of health facility.

Based on the evaluation findings, the score index for the structure of the system was rated as functioning fairly and only partially achieved its expectations of having a well-developed structure. This was because of limitations in the functionality of the review committee, partner mapping, and clear, well-defined strategies. These findings are consistent with those of previous studies [61,62]. The functionality of the review committee was compromised because of a lack of supportive supervision and staff. This has far-reaching repercussions in accurately determining the causes of death and identifying the contributors to maternal deaths [63,64]. Furthermore, engaging partners is considered a parameter of strong leadership for MDSR implementation [65], although it was identified as a weak point in this evaluation. Henceforth, the quality of leadership plays a pivotal role in mobilizing resources from various actors to address critical gaps in supplies and to support community outreach activities related to safe motherhood. In this way, the engagement of external actors generally plays a role in solidifying the system structure [66,67]. In addition, the lack of well-defined strategies or plans to bolster the system has affected its structure, which has implications for the Woreda Health Sector plan. The plan calls for the establishment of a culture of performance management and accountability to include maternal death as one of the main performance indicators [68,69]. Accordingly, the MDSR system is expected to improve the quality of care and support civil registration and vital statistics, which are the top priorities of the country [14,70]. Considering this, the progress made in Ethiopia to expand its usefulness is not yet adequate [71–73]. Overall, the structure of the system varies based on the region, type of health facility, and location (urban vs. rural). These issues should be considered while planning system improvements, along with enhancing political will and commitment, which are of paramount importance in prioritizing the system as a national agenda and fostering the pace and effort needed to meet national targets (i.e., to reduce the maternal mortality ratio (MMR) to 199 per 100,000 live births by 2025). All these efforts are paving the way to achieve global targets set under the Sustainable Development Goals (SDGs) (i.e., reducing the global MMR to less than 70 per 100,000 live births by 2030) [74].

According to the evaluation findings, the core function of the system was rated as ineffective, and the system was unable to function or perform its essential duties. This colossal deficiency in the system has emanated from poor practices in routine data analysis, developing action plans for reviewed deaths, preparing emergency response plans for known major causes of death, assigning causes of death, tracking developed action plans, and providing feedback to lower levels and receiving feedback from higher levels. A similar finding was also revealed in evaluations conducted in Nigeria and Tanzania [75,76]. Routine analysis of MDSR, which includes simple line listing, descriptive statistics (people, place, and time of maternal deaths), and case reviews [77], is not a common practice in Ethiopia, particularly at a lower level of support for routine activities [78,79]. In light of this gap, Ethiopia has made significant investments in establishing a Performance Monitoring Team (PMT) at the facility level as part of its information revolution strategy. These teams aim to encourage routine data analysis and utilization for decision-making, including planning, monitoring, quality improvement, budget allocation, supervision, and feedback [80–82]. However, despite these efforts, the desired outcome has not been achieved in terms of effective utilization of data at the subnational level (i.e., health facilities, districts, and provinces). This is primarily due to infrastructure limitations (e.g., computer and Internet access), negative attitudes toward data, inadequate data quality, an absence of recognition for outstanding performance, insufficient training, and a lack of guidelines and formats [83–86]. Furthermore, at the national level, gaps are observed in conducting in-depth analyses and data triangulation, which are major obstacles to improving the culture of data use [87,88]. To expand on this, in Ethiopia, among the reviewed deaths, 8% had no assigned cause of death [28], indicating the need for computerized analysis of verbal autopsies to be in place [89]. Embedding the Maternal and Perinatal Death Surveillance and Response (MDSR) system within existing health systems is recommended, with strategies that include enhancing Program Management Teams (PMTs), conducting regular review meetings, strengthening routine joint steering committee meetings among regions and partners, improving plan alignment forums, and providing mentoring on data analysis. Additionally, sharing and incentivizing data utilization through regular training, organizing recognition programs, and arranging peer learning sessions are recommended to enhance the utilization of MDSR data [90,91]. To this end, these efforts will have far-reaching implications, from routinely analysing MDSR data to tracking action plans developed based on review findings.

In this evaluation, the score for supporting function was graded as less functional and imbued with extensive shortcomings. These shortcomings include the unavailability of financial resources, computers, implementation manuals, reporting formats (i.e., VA, FBMDAF, MDSR, and identification and notification formats), training and means of communication tools (such as monthly bulletins and annual reports), and regular supportive supervision for catchment areas that were mentioned upfront. This finding concurred with those reported by Cameroon, Kenya, Malawi, and Nigeria, indicating that such challenges fully hinder system implementation [9]. Recognizing the limitations in the capacity of the system, a capacity-building and mentorship program (CBMP) focusing on clinical and public health was established as an advanced version of supportive supervision. This initiative has resulted in improvements in the quality of care and data utilization [92–95]. However, due to the lack of an adequate dedicated budget for these initiatives, full implementation at the national level has not materialized [96]. As a way forward, it is recommended to create a facade of academic-government collaboration, along with building local ownership of the program. These approaches have the potential to be useful in sustaining systems [97]. To address this gap in the availability of reporting formats, there has been an initiative to transform paper-based reporting modalities into electronic reporting. District health information systems (DHIS-2) and community health information systems (CHIS) were introduced to the health system for this purpose [98]. However, these initiatives were not fully optimized due to gaps in leaders’ commitment, resource availability, institutionalization, continued capacity building, communication technology infrastructure, and business continuity plans. Hence, further investment is needed to achieve an acceptable level of implementation and transform the data management process [99–102]. Training, including the Maternal Death Surveillance and Response (MDSR), in preservice and in-service training could ensure its sustainability in the long run [103]. Although training is mandatory for effective implementation, it has not yielded the desired outcomes due to limitations in comprehensive planning, effective post-training supervision, and an implementation training curriculum (lacking elements such as individual learning through problem-solving and case analysis as part of project work and portfolios) [104–106]. To address these gaps, accreditation of training materials and broadening alternative training methods such as e-learning have been implemented [107]. With regard to producing and sharing annual reports, they were not regularly produced and made available publicly to a wider audience, which is also a critical gap at the national level [60]. In summary, strengthening the electronic reporting system, sustaining capacity building and mentorship programs, producing regular annual reports, and implementing a comprehensive training curriculum could be taken as effective measures to enhance the supportive function of the system.

According to the evaluation, the system attribute was rated as functioning fairly well, but it has some limitations that hinder the maintenance of the overall quality of surveillance. These limitations revolve around the attributes of simplicity and acceptability, which are expressed in terms of the limited involvement of health professionals in notifying and reporting deaths, the need for higher-level training and follow-up, and a shortage of computer and reporting tools. Similar findings were reported in evaluations conducted in Zimbabwe and Jordan [108,109]. Concerted efforts are needed to institutionalize Maternal Death Surveillance and Response (MDSR) as a routine practice through engagement with all health professionals using advocacy and considering it a performance indicator. This will facilitate the involvement of all health professionals in the system. As mentioned earlier, digitalizing the system with a business continuity plan and maintaining the Capacity Building and Mentorship Program (CBMP) would play a role in stabilizing and enhancing the system’s simplicity.

The system’s overall readiness to implement MDSR was rated as limited functioning with extensive shortcomings that require further improvement. To objectively assess the system’s status, review rates, and community engagement were used as performance indicators. Nationally, only 79.5% of deaths were reviewed after notification, and among those reviewed, 53.5% were community-level deaths. Accordingly, the Tigray region demonstrated superior performance across all system components and performance indicators, despite the health system being languished in the doldrum by the war in 2020 [110]. Before the conflict, the region had a well-established community health structure that supported community surveillance in identifying and reporting deaths to the next level [111–113]. Moreover, the war also highlighted and evidenced the system’s inadequacies during humanitarian emergencies, necessitating further reconsideration and readjustment to address such crises [114]. On the other hand, the findings highlight that the review process heavily relied on facility deaths, although more than 70% of deaths, including maternal deaths, occur at the community level [115,116]. The findings suggest that having a relatively strong system at the health facility level makes the evidence generated from such a system more applicable to improving the quality of care within health facilities while neglecting community-level factors that are commonly related to health-seeking behavior and access. In this regard, improving the engagement of community health structures led by health extension workers (HEWs) and ancillary structures such as the Women’s Development Army (WDA) could improve community surveillance, which has already proven their method of enhancing health service utilization [117]. However, these programs are facing challenges due to gaps in skills and motivation [118,119]. Addressing these gaps and integrating them well into their routine functions could strengthen the community loop of surveillance and could provide ample evidence to prevent community deaths. Furthermore, there were disparities in death reviews among regions, with the Tigray region outperforming others in terms of reviewed deaths. Nevertheless, gaps were observed, as not all reviewed deaths were reported using the weekly reporting system. In summary, system performance varied significantly by region and type of health facility. Efforts should prioritize improving community surveillance and enhancing each system component to optimize performance.

Generally, according to this evaluation finding combined with the Maternal Death Surveillance and Response Performance Index (MDSRPI), the current MDSR system is not in a position to yield the expected outcomes due to limitations in representativeness, completeness, and community engagement. There is ample evidence indicating that the system is not moving forward to meet its objectives, this indicates that a periodic evaluation of the functionality of the system is required to make necessary adjustments. Furthermore, the findings from the current evaluation clearly indicate the paramount importance of having complementary and supplementary systems that enable fine-tuned recommendations, which can be translated into policy and improved practices. In this regard, the institutionalization of facility-based maternal death audits and reviews, along with a confidential inquiry approach (a detailed review of every single death in comparison to clinical standards and current practices), as well as the enhancement of community-based maternal death reviews with the inclusion of social autopsy, has the potential to influence policy and practice [120–122]. Moreover, the establishment and integration of severe maternal morbidity and emerging pathogen surveillance into routine MDSR will play a seminal role in providing more reliable information, which could shape practice in the continuum of care [123–125]. Additionally, the adaptation of tools and practices of After Action Reviews (AAR) could also play a role in identifying best practices, gaps, and lessons learned, thereby ameliorating subsequent measures [126]. By including the aforementioned reformative measures, the MDSR could be recalibrated to align with the fundamental principles of collaborative surveillance, which call for the triangulation of a panoply of sources of information to facilitate better decision-making [127].

The evaluation has limitations that need to be acknowledged during the interpretation of the findings. (1) An economic evaluation was not included to assess how costly the system is. (2) The evaluation did not consider the perinatal death component of the system, which has peculiar features; thus, not all findings could apply to this component. (3) inability to assess all selected health facilities for various reasons, including inaccessibility due to security.

Conclusion

Overall, the readiness to implement the MDSR system was suboptimal and not in a position to meet its objectives, compounded by noticeable regional variations. Additionally, not all notified deaths were reviewed, particularly those that occurred at the community level. Furthermore, not all reviewed deaths were notified through the weekly reporting system. Additionally, the system has limitations in terms of representativeness, completeness, and community engagement

Therefore, efforts should focus on engaging political leaders to enhance resource mobilization and prioritize this agenda. Moreover, improving data utilization by strengthening both existing and new monitoring mechanisms, as well as optimizing digitalization efforts, is crucial. Furthermore, implementing algorithm-based cause-of-death assignments, enhancing community surveillance, bolstering the Capacity Building and Mentorship Program (CBMP), contextualizing to humanitarian settings, organizing effective training approaches—including pre-service and in-service training—and conducting periodic evaluations are strongly recommended to reduce variations among regions and types of health facilities and to upgrade the system to the next level.

Alongside the aforementioned recommendations, both local and global actors in MDSR should consider calibrating the system within the framework of collaborative surveillance, which necessitates strengthening the inclusion and integration of severe morbidity and selected pathogens surveillance alongside the confidence-inquiry system. This should be followed by a robust monitoring and learning system by adapting tools and practices from After Action Reviews (AAR) and collaborating with the CRVS system, which could open new doors for the development and transformation of the MDSR system and other event-based surveillance systems.

Acknowledgments

We appreciate the support of all the regional health offices EPHI staff and deputy generals for their support in the successful completion of the evaluation.

References

  1. 1. Nsubuga P, White ME, Thacker SB, Anderson MA, Blount SB, Broome CV, et al. Public health surveillance: a tool for targeting and monitoring interventions.
    • 2. Richards CL, Iademarco MF, Atkinson D, Pinner RW, Yoon P, Mac Kenzie WR, et al. Advances in public health surveillance and information dissemination at the Centers for Disease Control and Prevention. Public Health Reports. 2017 Jul;132(4):403–10. pmid:28609194
    • 3. Lee LM, Thacker SB. Public health surveillance and knowing about health in the context of growing sources of health data. American journal of preventive medicine. 2011 Dec 1;41(6):636–40. pmid:22099242
    • 4. Fall IS, Rajatonirina S, Yahaya AA, Zabulon Y, Nsubuga P, Nanyunja M, et al. Integrated Disease Surveillance and Response (IDSR) strategy: current status, challenges and perspectives for the future in Africa. BMJ global health. 2019 Jul 1;4(4):e001427. pmid:31354972
    • 5. Mremi IR, George J, Rumisha SF, Sindato C, Kimera SI, Mboera LE. Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics. One Health Outlook. 2021 Dec;3:1–5.
    • 6. Isere EE, Fatiregun AA, Ajayi IO. An overview of disease surveillance and notification system in Nigeria and the roles of clinicians in disease outbreak prevention and control. Nigerian Medical Journal. 2015 May 1;56(3):161–8. pmid:26229222
    • 7. Kihembo C, Masiira B, Nakiire L, Katushabe E, Natseri N, Nabukenya I, et al. The design and implementation of the revitalized integrated disease surveillance and response (IDSR) in Uganda, 2013–2016. BMC Public Health. 2018 Dec;18:1–1.
    • 8. World Health Organization. Maternal death surveillance and response: technical guidance information for action to prevent maternal death. World Health Organization; 2013.
      • 9. Smith H, Ameh C, Roos N, Mathai M, Broek NV. Implementing maternal death surveillance and response: a review of lessons from country case studies. BMC Pregnancy and Childbirth. 2017 Dec;17(1):1–1.
      • 10. Serbanescu F, Monet JP, Whiting-Collins L, Moran AC, Hsia J, Brun M. Maternal death surveillance efforts: notification and review coverage rates in 30 low-income and middle-income countries, 2015–2019. BMJ open. 2023 Feb 1;13(2):e066990. pmid:36806138
      • 11. Basera TJ, Schmitz K, Price J, Willcox M, Bosire EN, Ajuwon A, et al. Community surveillance and response to maternal and child deaths in low-and middle-income countries: a scoping review. PloS one. 2021 Mar 16;16(3):e0248143. pmid:33725013
      • 12. Abouchadi S, Shahabuddin AS, Zhang WH, Firoz T, Englert Y, Nejjari C, et al. Existing models of maternal death surveillance systems: protocol for a scoping review. JMIR research protocols. 2016 Oct 11;5(4):e5758. pmid:27729305
      • 13. Mathai M. Maternal Death Surveillance and Response: Looking Backwards, Going Forward. Global Health: Science and Practice. 2023 Apr 28;11(2).
      • 14. World Health Organization (WHO). Time to Respond: A Report on the Global Implementation of Maternal Death Surveillance and Response. WHO; 2016. Accessed March 29, 2023. https://www.who.int/publications/i/item/978924151123.
        • 15. Bandali S, Thomas C, Hukin E, Matthews Z, Mathai M, Dilip TR, et al. Maternal death surveillance and response systems in driving accountability and influencing change. International Journal of Gynecology & Obstetrics. 2016 Dec 1;135(3):365–71. pmid:27836470
        • 16. World Health Organization. Implementation of maternal and perinatal death surveillance and response as part of the quality-of-care efforts for maternal and newborn health: considerations for synergy and alignment: knowledge brief. Geneva; (2021).[Cited 15 March 2024] Available at https://tinyurl.com/d3yrcym4.
          • 17. Rumbeli NM, August F, Silvestri V, Sirili N. Factors influencing maternal death surveillance and review implementation in Dodoma City, Tanzania. A qualitative case study. Learning Health Systems. 2023:e10390. pmid:38633026
          • 18. Abebe B, Busza J, Hadush A, Usmael A, Zeleke AB, Sita S, et al. ‘We identify, discuss, act and promise to prevent similar deaths’: a qualitative study of Ethiopia’s Maternal Death Surveillance and Response system. BMJ Global Health. 2017 Mar 1;2(2):e000199. pmid:28589016
          • 19. World Health Organization. Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division: executive summary.
          • 20. Central Statistical Agency/CSA/Ethiopia and ICF. 2016. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF.
            • 21. Ethiopian public health institutes. Maternal and perinatal death surveillance and response technical guideline. Addis Ababa (Ethiopia): EPHI; (2017). In [Internet]. [cited 18 May 2023]. Available at: https://tinyurl.com/2p8f6rwz.
              • 22. Melberg A, Mirkuzie AH, Sisay TA, Sisay MM, Moland KM. ‘Maternal deaths should simply be 0’: the politicization of maternal death reporting and review processes in Ethiopia. Health Policy and Planning. 2019 Sep 1;34(7):492–8.
              • 23. Tesfay N, Legesse F, Kebede M, Woldeyohannes F. Determinants of stillbirth among reviewed perinatal deaths in Ethiopia. Frontiers in Pediatrics. 2022 Nov 28;10:1030981. pmid:36518781
              • 24. Tesfay N, Tariku R, Zenebe A, Dejene Z, Woldeyohannes F. Cause and risk factors of early neonatal death in Ethiopia. Plos one. 2022 Sep 29;17(9):e0275475. pmid:36174051
              • 25. Ethiopian Public Health Institutes, National Maternal Death Surveillance and Response (MDSR) System Annual Report, 2010 EFY. (2024) [Cited 2024 February 26]. Available at: http://tinyurl.com/37p74tjf.
              • 26. Tesfay N, Tariku R, Zenebe A, Woldeyohannes F. Critical factors associated with postpartum maternal death in Ethiopia. Plos one. 2022 Jun 24;17(6):e0270495. pmid:35749471
              • 27. Tesfay N, Tariku R, Zenebe A, Mohammed F, Woldeyohannes F. Area of focus to handle delays related to maternal death in Ethiopia. Plos one. 2022 Sep 19;17(9):e0274909. pmid:36121828
              • 28. Ethiopian Public Health Institutes, National Maternal and Perinatal Death Surveillance and Response (MDPSR) System Annual Report, 2013 EFY. (2020) [Cited 2023 February 26]. Available at: https://tinyurl.com/46mtt6a8.
              • 29. Tesfay N, Tariku R, Zenebe A, Firde H, Woldeyohannes F. Target areas to reduce the burden of maternal death due to obstetric hemorrhage in Ethiopia. Plos one. 2022 Sep 29;17(9):e0274866. pmid:36173995
              • 30. Jordan K, Butrick E, Yamey G, Miller S. Barriers and facilitators to scaling up the nonpneumatic anti-shock garment for treating obstetric hemorrhage: a qualitative study. PLoS One. 2016 Mar 3;11(3):e0150739. pmid:26938211
              • 31. Taye Makuria A, Gebremichael D, Demoz H, Hadush A, Abdella Y, Berhane Y, et al. Obstetric hemorrhage and safe blood for transfusion in Ethiopia: the challenges of bridging the gap. Transfusion. 2017 Oct;57(10):2526–31. pmid:28703878
              • 32. Ethiopian Ministry of Health. Annual Performance Report 2013EFY(2020/2021). (2022) [Cited 2023 June 1]. Available at: https://tinyurl.com/rdxx6jd7.
              • 33. Whiting-Collins L, Serbanescu F, Moller AB, Binzen S, Monet JP, Cresswell JA, et al. Maternal death surveillance and response system reports from 32 low-middle income countries, 2011–2020: What can we learn from the reports? PLOS Global Public Health. 2024 Mar 5;4(3):e0002153. pmid:38442110
              • 34. Ayele B, Gebretnsae H, Hadgu T, Negash D, G/silassie F, Alemu T, et al. Maternal and perinatal death surveillance and response in Ethiopia: achievements, challenges and prospects. PloS one. 2019 Oct 11;14(10):e0223540. pmid:31603937
              • 35. Alemu T, Gutema H, Legesse S, Nigussie T, Yenew Y, Gashe K. Evaluation of public health surveillance system performance in Dangila district, Northwest Ethiopia: a concurrent embedded mixed quantitative/qualitative facility-based cross-sectional study. BMC Public Health. 2019 Dec;19:1–9.
              • 36. Maphosa M, Juru TP, Masuka N, Mungati M, Gombe N, Nsubuga P, et al. Evaluation of the maternal death surveillance and response system in Hwange District, Zimbabwe, 2017. BMC pregnancy and childbirth. 2019 Dec;19:1–9.
              • 37. Cetin K, Worku D, Demtse A, Melberg A, Miljeteig I. “Death audit is a fight”–provider perspectives on the ethics of the Maternal and Perinatal Death Surveillance and Response (MPDSR) system in Ethiopia. BMC Health Services Research. 2022 Sep 29;22(1):1214. pmid:36175949
              • 38. Tura AK, Fage SG, Ibrahim AM, Mohamed A, Ahmed R, Gure T, et al. Beyond no blame: practical challenges of conducting maternal and perinatal death reviews in eastern Ethiopia. Global Health: Science and Practice. 2020 Jun 30;8(2):150–4. pmid:32461200
              • 39. Maruf F, Tappis H, Stekelenburg J, van den Akker T. Quality of maternal death documentation in Afghanistan: a retrospective health facility record review. Frontiers in global women’s health. 2021 Mar 17;2:610578. pmid:34816182
              • 40. Ye Y, Wamukoya M, Ezeh A, Emina JB, Sankoh O. Health and demographic surveillance systems: a step towards full civil registration and vital statistics system in sub-Sahara Africa?. BMC public health. 2012 Dec;12:1–1.
              • 41. Abay ST, Gebre-Egziabher AG. Status and associated factors of birth registration in selected districts of Tigray region, Ethiopia. BMC international health and human rights. 2020 Dec;20:1–0.
              • 42. Gizaw ME. The status, challenges and opportunities of civil registration and vital statistics in Ethiopia: a systematic review. International Journal of Scientific Reports. 2020 May;6(5):200.
              • 43. Mary M, Tappis H, Scudder E, Creanga AA. Complexities of implementing Maternal and Perinatal Death Surveillance and Response in crisis-affected contexts: a comparative case study. Conflict and health. 2024 Jul 16;18(1):45. pmid:39010136
              • 44. Yilma TM, Taddese A, Mamuye A, Endehabtu BF, Alemayehu Y, Senay A, et al. Maturity Assessment of District Health Information System Version 2 Implementation in Ethiopia: Current Status and Improvement Pathways. JMIR Medical Informatics. 2024 Jul 26;12:e50375. pmid:39059005
              • 45. World meter, Ethiopian population in 2020. (2020) [cited 18 March 2024].available at https://tinyurl.com/42vf26zb
              • 46. Simegn GL, Degu MZ, Gebeyehu WB, Senay AB, Krishnamoorthy J, Tegenaw GS. Spatiotemporal distribution of climate-sensitive disease incidences in Ethiopia: a longitudinal retrospective analysis of Malaria, Meningitis, Cholera, Dysentery, Leishmaniasis and Dengue fever between 2010 and 2022/2023. BMC Public Health. 2024 Mar 4;24(1):697 pmid:38439016
              • 47. Federal Minister of Health of Ethiopia. Health and Health Related Indicators 2016/2017. (2017) [cited 18 March 2024]. Available at https://tinyurl.com/b5mxbrjp
              • 48. World Health Organization. Communicable disease surveillance and response systems: guide to monitoring and evaluating. World Health Organization; 2006.
                • 49. German RR, Horan JM, Lee LM, Milstein B, Pertowski CA. Updated guidelines for evaluating public health surveillance systems; recommendations from the Guidelines Working Group.
                  • 50. Alimohamadi Y, Sepandi M. Considering the design effect in cluster sampling. Journal of Cardiovascular and Thoracic Research. 2019;11(1):78. pmid:31024678
                  • 51. Saadat S, Hafezi-Nejad N, Ekhtiari YS, Rahimi-Movaghar A, Motevalian A, Amin-Esmaeili M, et al. Incidence of fall-related injuries in Iran: A population-based nationwide study. Injury. 2016 Jul 1;47(7):1404–9. pmid:27184519
                  • 52. Masood M, Reidpath DD. Intraclass correlation and design effect in BMI, physical activity and diet: a cross-sectional study of 56 countries. BMJ open. 2016 Jan 1;6(1):e008173. pmid:26743697
                  • 53. Ethiopian Public Health Institutes. Midterm evaluation of the 2nd BSC-based EPHI strategic plan (2015/16-2019/20). (2019) [Cited 2024 March 1]. Available at. https://tinyurl.com/4wdhrtb7.
                  • 54. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.
                    • 55. Ethiopian Central Statistical Agency (CSA). Population Projections for Ethiopia 2007–2037 [Internet]. Addis Ababa (Ethiopia): Central Statistical Agency; 2013 [cited 2024 Sep 26]. 173 p. Available from: https://tinyurl.com/drmwkxbs
                      • 56. Frimpong BF, Koranteng A, Molkenthin F. Analysis of temperature variability utilising Mann–Kendall and Sen’s slope estimator tests in the Accra and Kumasi Metropolises in Ghana. Environmental Systems Research. 2022 Nov 12;11(1):24.
                      • 57. Penzias RE, Bohne C, Gicheha E, Molyneux EM, Gathara D, Ngwala SK, et al. Quantifying health facility service readiness for small and sick newborn care: comparing standards-based and WHO level-2+ scoring for 64 hospitals implementing with NEST360 in Kenya, Malawi, Nigeria, and Tanzania. BMC pediatrics. 2023 Nov;23(Suppl 2):656.
                      • 58. Esayas B, Simane B, Teferi E, Ongoma V, Tefera N. Trends in extreme climate events over three agroecological zones of southern Ethiopia. Advances in Meteorology. 2018 Oct 16;2018:1–7.
                      • 59. Tesfay N, Kebede M, Asamene N, Tadesse M, Begna D, Woldeyohannes F. Factors determining antenatal care utilization among mothers of deceased perinates in Ethiopia. Frontiers in Medicine. 2023;10. pmid:38020089
                      • 60. Endris AA, Tilahun T. Health system readiness to manage maternal death data and avail evidence for decision-making through the Maternal Death Surveillance System in Ethiopia, 2020. BMC Health Services Research. 2023 Dec;23(1):1–2.
                      • 61. Hofman JJ, Mohammed H. Experiences with facility-based maternal death reviews in northern Nigeria. International Journal of Gynecology & Obstetrics. 2014 Aug 1;126(2):111–4. pmid:24834852
                      • 62. Hadush A, Dagnaw F, Getachew T, Bailey PE, Lawley R, Ruano AL. Triangulating data sources for further learning from and about the MDSR in Ethiopia: a cross-sectional review of facility-based maternal death data from EmONC assessment and MDSR system. BMC Pregnancy and Childbirth. 2020 Dec;20:1–9.
                      • 63. Yameogo WM, Nadine Ghilat Paré/Belem W, Millogo T, Kouanda S, Ouédraogo CM. Assessment of the maternal death surveillance and response implementation process in Burkina Faso. International Journal of Gynecology & Obstetrics. 2022 Aug;158:15–20. pmid:35603808
                      • 64. Bongajum AY, Foumane P, Moussi CO, Vogue N, Banseka HS, Nwobegahay JM, et al. Strengthening maternal death surveillance systems for evidence-based decision making in Sub-Saharan Africa: the case of the center region in Cameroon. International Journal of Maternal and Child Health and AIDS. 2021;10(2):258. pmid:34900394
                      • 65. Kinney MV, Walugembe DR, Wanduru P, Waiswa P, George A. Maternal and perinatal death surveillance and response in low-and middle-income countries: a scoping review of implementation factors. Health policy and planning. 2021 Jul 1;36(6):955–73. pmid:33712840
                      • 66. Serbanescu F, Goodwin MM, Binzen S, Morof D, Asiimwe AR, Kelly L, et al. Addressing the first delay in Saving Mothers, Giving Life districts in Uganda and Zambia: approaches and results for increasing demand for facility delivery services. Global Health: Science and Practice. 2019 Mar 11;7(Supplement 1):S48–67. pmid:30867209
                      • 67. Namagembe I, Beyeza-Kashesya J, Rujumba J, Kaye DK, Mukuru M, Kiwanuka N, et al. Barriers and facilitators to maternal death surveillance and response at a busy urban National Referral Hospital in Uganda. Open Research Africa. 2022;5.
                      • 68. Liu L, Desai MM, Benyam T, Fetene N, Ayehu T, Nadew K, et al. An analysis of zonal health management capacity and health system performance: Ethiopia Primary Healthcare Transformation Initiative. International Journal of Health Policy and Management. 2022 Nov;11(11):2610. pmid:35219284
                      • 69. Ethiopian Ministry of Health. Woreda-based health sector annual core plan. (2019) [Cited 2024 March 1]. Available at https://tinyurl.com/58tk9t4d
                      • 70. Scott H, Dairo A. Maternal death surveillance and response in East and Southern Africa. Journal of Obstetrics and Gynaecology Canada. 2015 Oct 1;37(10):915–21. pmid:26606709
                      • 71. Mills S, Lee JK, Rassekh BM. Benefits of linking civil registration and vital statistics with identity management systems for measuring and achieving sustainable development goal 3 indicators. Journal of Health, Population and Nutrition. 2019 Oct 18;38(Suppl 1):18. pmid:31627734
                      • 72. Biadgo A, Legesse A, Estifanos AS, Singh K, Mulissa Z, Kiflie A, et al. Quality of maternal and newborn health care in Ethiopia: a cross-sectional study. BMC Health Services Research. 2021 Dec;21:1–0.
                      • 73. Ethiopian Public Health Institute (EPHI), Ministry of Health, Ethiopia; and Countdown to 2030. 2022. Understanding the Drivers of Reduction in Maternal and Neonatal Mortality in Ethiopia—An in-depth case study of the Global Health Exemplars. Addis Ababa, Ethiopia: Federal Ministry of Health, Addis Ababa; EPHI, Ethiopia; and Countdown to 2030.
                        • 74. Ayele AA, Tefera YG, East L. Ethiopia’s commitment towards achieving sustainable development goal on reduction of maternal mortality: There is a long way to go. Women’s Health. 2021 Dec;17:17455065211067073. pmid:34913391
                        • 75. Said A, Pembe AB, Massawe S, Hanson C, Malqvist M. Maternal death surveillance and response in Tanzania: comprehensiveness of narrative summaries and action points from maternal death reviews. BMC Health Services Research. 2021 Dec;21:1–0.
                        • 76. Aikpitanyi J, Ohenhen V, Ugbodaga P, Ojemhen B, Omo-Omorodion BI, Ntoimo LF, et al. Maternal death review and surveillance: the case of central hospital, Benin City, Nigeria. PLoS One. 2019 Dec 19;14(12):e0226075. pmid:31856173
                        • 77. Hounton S, De Bernis L, Hussein J, Graham WJ, Danel I, Byass P, et al. Towards elimination of maternal deaths: maternal deaths surveillance and response. Reproductive health. 2013 Dec;10:1–8. pmid:23279882
                        • 78. Adane A, Adege TM, Ahmed MM, Anteneh HA, Ayalew ES, Berhanu D, et al. Exploring data quality and use of the routine health information system in Ethiopia: a mixed-methods study. BMJ open. 2021 Dec 1;11(12):e050356. pmid:34949613
                        • 79. Tadele MM, Yilma TM, Mekonnen ZA, Tilahun B. Routine health information use among healthcare providers in Ethiopia: a systematic review and meta-analysis. BMJ Health & Care Informatics. 2023;30(1). pmid:36997261
                        • 80. Abera A, Tolera A, Tusa BS, Weldesenbet AB, Tola A, Shiferaw T, et al. Experiences, barriers, and facilitators of health data use among performance monitoring teams (PMT) of health facilities in Eastern Ethiopia: A qualitative study. Plos one. 2023 May 11;18(5):e0285662. pmid:37167309
                        • 81. Wude H, Woldie M, Melese D, Lolaso T, Balcha B. Utilization of routine health information and associated factors among health workers in Hadiya Zone, Southern Ethiopia. Plos one. 2020 May 21;15(5):e0233092. pmid:32437466
                        • 82. Seid MA, Bayou NB, Ayele FY, Zerga AA. Utilization of routine health information from health management information system and associated factors among health workers at health centers in Oromia special zone, Ethiopia: a multilevel analysis. Risk management and healthcare policy. 2021 Mar 18:1189–98.
                        • 83. Tilahun H, Abate B, Belay H, Gebeyehu A, Ahmed M, Simanesew A, et al. Drivers and barriers to improved data quality and data-use practices: an interpretative qualitative study in Addis Ababa, Ethiopia. Global Health: Science and Practice. 2022 Sep 15;10(Supplement 1).
                        • 84. Asemahagn MA. Determinants of routine health information utilization at primary healthcare facilities in Western Amhara, Ethiopia. Cogent Medicine. 2017 Jan 1;4(1):1387971.
                        • 85. Chekol A, Ketemaw A, Endale A, Aschale A, Endalew B, Asemahagn MA. Data quality and associated factors of routine health information system among health centers of West Gojjam Zone, northwest Ethiopia, 2021. Frontiers in Health Services. 2023 Mar 24;3:1059611. pmid:37033897
                        • 86. Addise N, Tessema Aemiro M, Tariku Derseh B. Routine Health Information System Utilization and Its Associated Factors among Healthcare Professionals in Debre Berhan Town, Ethiopia. Advances in Public Health. 2023 Jun 30;2023.
                        • 87. Busza J, Lemma S, Janson A, Adem SO, Berhanu D, Defar A, et al. Strengthening routine health data analysis in Ethiopia: the Operational Research and Coaching for Analysts (ORCA) experience. Global health action. 2021 Jan 1;14(1):1901390. pmid:33789545
                        • 88. Almiñana A, Bayeh A, Girma D, Kanagat N, Oot L, Prosser W, et al. Early lessons from Ethiopia in establishing a data triangulation process to analyse immunization program and supply data for decision making. Global Health: Science and Practice. 2022 Jun 29;10(3).
                        • 89. Aukes AM, Arion K, Bone JN, Li J, Vidler M, Bellad MB, et al. Causes and circumstances of maternal death: a secondary analysis of the Community-Level Interventions for Preeclampsia (CLIP) trials cohort. The Lancet Global Health. 2021 Sep 1;9(9):e1242–51. pmid:34332699
                        • 90. Ethiopia Federal Ministry of Health. Health Sector Transformation Plan II 2020/21-2024/25.(2021) [Cited 2024 March 1]. Available at. https://tinyurl.com/54aarprb
                        • 91. Gimbel S, Baynes C, Tilahun HA, Hirschhorn L, Inguane C, Kyei P, et al. Barriers and Facilitators to Data Use for Decision Making: The Experience of the African Health Initiative Partnerships in Ethiopia, Ghana, and Mozambique. GLOBAL HEALTH SCIENCE AND PRACTICE. 2022 Sep 1;10.
                        • 92. Chanyalew MA, Yitayal M, Atnafu A, Mengiste SA, Tilahun B. The Effectiveness of the Capacity Building and Mentorship Program in Improving Evidence-Based Decision-making in the Amhara Region, Northwest Ethiopia: Difference-in-Differences Study. JMIR Medical Informatics. 2022 Apr 22;10(4):e30518. pmid:35451990
                        • 93. Sultan M, Beza L, Ataro D, Hassen E, Walelign Y, Getu M, et al. The Role of locally driven Clinical Mentorship and Coaching to improve the advance Critical Care Services in Ethiopia. Ethiopian Journal of Health Development. 2023 Sep 20;37(1).
                        • 94. Taye G. Improving health care services through enhanced Health Information System: Human capacity development Model. Ethiopian Journal of Health Development. 2021 Jul 16;35(1).
                        • 95. Weldearegay HG, Kahsay AB, Godefay H, Petrucka P, Medhanyie AA. The effect of catchment-based mentorship on quality of maternal and newborn care in primary health care facilities in Tigray Region, Northern Ethiopia: A controlled quasi experimental study. Plos one. 2022 Nov 17;17(11):e0277207. pmid:36395101
                        • 96. Manzi A, Hirschhorn LR, Sherr K, Chirwa C, Baynes C, Awoonor-Williams JK. Mentorship and coaching to support strengthening healthcare systems: lessons learned across the five Population Health Implementation and Training partnership projects in sub-Saharan Africa. BMC Health Services Research. 2017 Dec;17:5–16.
                        • 97. Belay H, Mohammedsanni A, Gebeyehu A, Alemu H, Wendrad N, Abate B, et al. Lessons learned from the Capacity-Building and Mentorship Program to improve health information systems in 11 districts of Ethiopia. Global Health: Science and Practice. 2022 Sep 15;10(Supplement 1). pmid:36109060
                        • 98. Ethiopia Federal Ministry of Health. Information Revolution Roadmap. (2016) [Cited 2024 March 1]. Available at https://tinyurl.com/f7deevte.
                        • 99. Hailemariam T, Atnafu A, Gezie LD, Kaasbøll JJ, Klein J, Tilahun B. Individual and contextual level enablers and barriers determining electronic community health information system implementation in northwest Ethiopia. BMC Health Services Research. 2023 Jun 16;23(1):644. pmid:37328840
                        • 100. Biru A, Birhan D, Melkamu G, Gebeyehu A, Omer AM. Pathways to improve health information systems in Ethiopia: current maturity status and implications. Health Research Policy and Systems. 2022 Jun 29;20(1):78. pmid:35768819
                        • 101. Mekebo M, Gobena T, Hawulte B, Tamiru D, Debella A, Yadeta E, et al. Level of implementation of district health information system 2 at public health facilities in Eastern Ethiopia. Digital health. 2022 Oct;8:20552076221131151. pmid:36249476
                        • 102. Walle AD, Demsash AW, Ferede TA, Wubante SM. Healthcare professionals’ satisfaction toward the use of district health information system and its associated factors in Southwest Ethiopia: using the information system success model. Frontiers in Digital Health. 2023 Jul 17;5:1140933. pmid:37528904
                        • 103. Austin A, Gulema H, Belizan M, Colaci DS, Kendall T, Tebeka M, et al. Barriers to providing quality emergency obstetric care in Addis Ababa, Ethiopia: healthcare providers’ perspectives on training, referrals and supervision, a mixed methods study. BMC pregnancy and childbirth. 2015 Dec;15:1–0.
                        • 104. Nsibande D, Loveday M, Daniels K, Sanders D, Doherty T, Zembe W. Approaches and strategies used in the training and supervision of Health Extension Workers (HEWs) delivering integrated community case management (iCCM) of childhood illness in Ethiopia: a qualitative rapid appraisal. African health sciences. 2018 Apr 4;18(1):188–97. pmid:29977273
                        • 105. Sharma G, Molla YB, Budhathoki SS, Shibeshi M, Tariku A, Dhungana A, et al. Analysis of maternal and newborn training curricula and approaches to inform future training for routine care, basic and comprehensive emergency obstetric and newborn care in the low-and middle-income countries: Lessons from Ethiopia and Nepal. PloS one. 2021 Oct 28;16(10):e0258624.
                        • 106. Demissie M, Abera N, Gebremeskel L, Tadesse D, Dadi TL, Zebere T, et al. Ethiopian Health Extension Workers’ Training Status and Perceived Competency. Ethiopian Journal of Health Sciences. 2023 Apr 1;33(2). pmid:38362476
                        • 107. Tamiru H, Huluka SA, Negash B, Hailu K, Mekonen ZT. National Continuing Professional Development (CPD) training needs of pharmacists in Ethiopia. Human Resources for Health. 2023 Nov 7;21(1):87. pmid:37936234
                        • 108. Mutsigiri-Murewanhema F, Mafaune PT, Juru T, Gombe NT, Bangure D, Tshimanga M. Evaluation of the maternal mortality surveillance system in Mutare district, Zimbabwe, 2014–2015: a cross-sectional study. The Pan African Medical Journal. 2017;27. pmid:28904729
                        • 109. Khader Y, Alyahya M, El-Khatib Z, Batieha A, Al-Sheyab N, Shattnawi K. The Jordan stillbirth and neonatal mortality surveillance (JSANDS) system: evaluation study. Journal of Medical internet Research. 2021 Jul 21;23(7):e29143. pmid:34287214
                        • 110. Gesesew H, Berhane K, Siraj ES, Siraj D, Gebregziabher M, Gebre YG, et al. The impact of war on the health system of the Tigray region in Ethiopia: an assessment. BMJ Global Health. 2021 Nov 1;6(11):e007328. pmid:34815244
                        • 111. Jackson R, Tesfay FH, Godefay H, Gebrehiwot TG. Health extension workers and mothers’ attitudes to maternal health service utilization and acceptance in Adwa Woreda, Tigray Region, Ethiopia. PLoS One. 2016 Mar 10;11(3):e0150747. pmid:26963507
                        • 112. Gebrehiwot TG, San Sebastian M, Edin K, Goicolea I. The health extension program and its association with change in utilization of selected maternal health services in Tigray region, Ethiopia: a segmented linear regression analysis. PloS one. 2015 Jul 28;10(7):e0131195. pmid:26218074
                        • 113. Gebregizabher FA, Medhanyie AA, Bezabih AM, Persson LÅ, Abegaz DB. Is Women’s Engagement in Women’s Development Groups Associated with Enhanced Utilization of Maternal and Neonatal Health Services? A Cross-Sectional Study in Ethiopia. International journal of environmental research and public health. 2023 Jan 11;20(2):1351.
                        • 114. Russell N, Tappis H, Mwanga JP, Black B, Thapa K, Handzel E, et al. Implementation of maternal and perinatal death surveillance and response (MPDSR) in humanitarian settings: insights and experiences of humanitarian health practitioners and global technical expert meeting attendees. Conflict and Health. 2022 May 7;16(1):23. pmid:35526012
                        • 115. Anteneh A, Araya T, Misganaw A. Factors associated with place of death in Addis Ababa, Ethiopia. BMC palliative care. 2013 Dec;12:1–9.
                        • 116. Mekonnen W, Mariam DH, Meh C, Yigezu B, Assalif AT, Aimone A, et al. Child, maternal, and adult mortality in rural Ethiopia in 2019: a cross-sectional mortality survey using electronic verbal autopsies. e ClinicalMedicine. 2024 May 1;71.
                        • 117. Damtew ZA, Karim AM, Chekagn CT, Fesseha Zemichael N, Yihun B, Willey BA, et al. Correlates of the Women’s Development Army strategy implementation strength with household reproductive, maternal, newborn and child healthcare practices: a cross-sectional study in four regions of Ethiopia. BMC pregnancy and childbirth. 2018 Sep;18:5–17.
                        • 118. Ashebir F, Medhanyie AA, Mulugeta A, Persson LÅ, Berhanu D. Exploring women’s development group leaders’ support to maternal, neonatal and child health care: A qualitative study in Tigray region, Ethiopia. PloS one. 2021 Sep 23;16(9):e0257602. pmid:34555089
                        • 119. Alemayehu YK, Medhin G, Teklu AM. National assessment of the health extension program in Ethiopia: study protocol and key outputs. Ethiopian Journal of Health Sciences. 2023 Apr 1;33(2). pmid:38362474
                        • 120. Willcox ML, Price J, Scott S, Nicholson BD, Stuart B, Roberts NW, et al. Death audits and reviews for reducing maternal, perinatal and child mortality. Cochrane Database of Systematic Reviews. 2020(3). pmid:32212268
                        • 121. Biswas A, Ferdoush J, Abdullah AS, Halim A. Social autopsy for maternal and perinatal deaths in Bangladesh: a tool for community dialog and decision making. Public health reviews. 2018 Dec;39:1–7.
                        • 122. FMOH. Confidential Enquiry into Maternal, Perinatal Mortality and Morbidity (CEMPMM). Available at https://tinyurl.com/mu8u8kx9.
                          • 123. Dzakpasu S, Deb‐Rinker P, Arbour L, Darling EK, Kramer MS, Liu S, et al. Severe maternal morbidity surveillance: monitoring pregnant women at high risk for prolonged hospitalisation and death. Paediatric and Perinatal Epidemiology. 2020 Jul;34(4):427–39. pmid:31407359
                          • 124. Frey MT, Meaney-Delman D, Bowen V, Yazdy MM, Watkins SM, Thorpe PG, et al. Surveillance for emerging threats to pregnant women and infants. Journal of Women’s Health. 2019 Aug 1;28(8):1031–6. pmid:31408424
                          • 125. Tesfay N, Hailu G, Begna D, Habtetsion M, Taye F, Woldeyohannes F et al (2024) Prevalence, underlying causes, and determinants of maternal near miss in Ethiopia: a systematic review and meta-analysis. Front. Med. 11:1393118. pmid:39440038
                          • 126. The global practice of After Action Review: A Systematic Review of Literature. Geneva, Switzerland: World Health Organization; 2019 (WHO/WHE/CPI/2019.9). Licence: CC BY-NC-SA 3.0 IGO.
                            • 127. Archer BN, Abdelmalik P, Cognat S, Grand PE, Mott JA, Pavlin BI, et al. Defining collaborative surveillance to improve decision making for public health emergencies and beyond. The Lancet. 2023 Jun 3;401(10391):1831–4. pmid:37230104