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Journal of Public Health Research logoLink to Journal of Public Health Research
. 2024 Mar 22;13(1):22799036241238665. doi: 10.1177/22799036241238665

Maternal near miss hospitalizations in the Borana Zone, Ethiopia: A facility-based longitudinal cross-sectional study

Boboh Kamangira 1, Gudeta Ayele 2,, Polite Dube 1, Kaleb Melaku 1, Eubert Vushoma 1
PMCID: PMC10960350  PMID: 38525053

Abstract

Background:

Maternal near miss refers to a woman who nearly died but survived a complication that occurred during pregnancy, childbirth, or within 42 days of pregnancy termination. While there has been considerable progress in reducing maternal mortality rates, maternal near miss cases can provide valuable insights into the quality of maternal healthcare and help identify areas for improvement. However, there is limited evidence on the factors contributing to maternal near miss cases, including health system failures, delays in care, and provider-related factors. Therefore, this study aimed to assess the incidence, causes, and factors associated with maternal near misses in public Hospitals of Borena Zone.

Methods:

A facility-based longitudinal cross-sectional study design was employed at four Public Hospitals in Borena Zone from August 15, 2022, to November 15, 2022, using the WHO criteria for maternal near miss event. In total, 117 participants were included in the study. Eligibility was determined using key clinical, organ dysfunction, laboratory, and management criteria, as per the WHO guidelines for near-miss events. Underlying and contributing causes of maternal near misses were documented from each participant’s records.

Result:

There were 1421 deliveries during the study period and 117 eligible women developed potentially life-threatening conditions. Only 61 women experienced severe maternal outcomes (55 near misses and six maternal deaths). The maternal near miss incidence ratio was 38.7 per 1000 live births, with a mortality index of 9.8%. Hypertensive disorders and obstetric hemorrhage are the leading underlying causes of maternal near misses.

Conclusion:

The incidence of maternal near miss was remarkably high when compared to previous studies. Giving special emphasis to life-saving interventions, critical care, reducing delays and improving the referral system are critical to improve quality of care.

Keywords: Maternal near-miss, severe maternal outcome, WHO, Public Hospital, Borena

Introduction

Pregnancy-related morbidity and mortality continue to have a serious impact on the lives of women all over the world.1,2 Sub-Saharan Africa and Southern Asia accounted for approximately 86% of the estimated global maternal deaths.2,3 Most women who experience life-threatening complications may die, and a proportion may narrowly escape death due to a lifesaving obstetric intervention. 4 A maternal near-miss (MNM) is referred to as a woman who nearly died, but survived a complication that occurred during pregnancy, childbirth or within 42 days of termination of pregnancy.4,5 Nowadays, MNM is considered a vital indicator used to monitor the quality of obstetric care services. 4 It serves as a crucial tool for determining the most common pregnancy problems, underlying risk factors, and the availability of life-saving obstetric treatments.6,7

According to a recent systematic review and meta-analysis study, the global prevalence of MNM was 1.4%. 8 Among the studies that have been conducted in Africa, the weighted pooled prevalence of MNM was 31.9 with significant heterogeneity between studies. 6 Near miss events are common and are estimated to be around 12 times more frequent than maternal deaths.811 Studies have shown that predictors of maternal near misses are postpartum hemorrhage, pregnancy induced hypertension, sepsis, ruptured uterus, and abortion.1,4,12

Improving maternal health remains an important topic of sustainable development goals (SDG), which is to reduce the global Maternal Mortality Ratio (MMR) to less than70 per 100,000 live births by 2030 and Ethiopia also intends to achieve this target by 2030.2,13 The 2016 Ethiopian Demographic and Health Survey (EDHS) reported 412 maternal deaths per 100,000 live births. 7 Although a promising reduction had been made in past few decades, maternal near-miss, pregnancy related complications, and deaths were still exceptionally high, particularly in communities that are hard-to-reach and resources-limited areas.4,9 Thus, it is important to have up-to-date information on the magnitude and causes of MNMs to identify areas of intervention aimed at improving maternal and perinatal health.

Material and methods

Setting

Borena Zone is one of the 21 zonal administrative divisions in the Southern Oromia region in Ethiopia. According to the 2007 Census, the zone had a female population of 475,465 representing the 49.8% of the total population of 962,489 inhabitants. It is served by five public hospitals and 44 health centers. All public hospitals in Borena Zones have been providing comprehensive emergency obstetrics care services. The study was conducted as part of a project aimed at complimenting the efforts of performance-based financing (PBF) for health project in Borena zone.

Study design

This was a facility-based longitudinal cross-sectional study at four 4 public hospitals in Borena Zone from August 15 to November 15, 2022. Participants’ medical records and charts were reviewed until participants were discharged from the hospital.

Participants

This study included all women who were pregnant, in labor, or who delivered or aborted in the past 42 days arriving at the facility with complications or who developed those complications during their stay at the health facilities were included in the study. Women who developed complications after 42 days of termination of pregnancy were excluded from the study.

Variables

The main outcomes variables were MNM ratio, MNM mortality ratio and Mortality index. Additional studied variables were patients’ demographics, severe complications with related underlying/contributing causes, organ dysfunction and intervention types.

Data sources/measurement

Data was collected using the WHO near-miss criteria. 4 Collected patients’ demographic and prehospitalization characteristics were Age (15–24, 25–34, and ≥35), Residence (Urban, Rural), Marital status (Married, Single/separated/divorced), Educational status (No formal education, with formal education), Age at first marriage (<18 years, ≥18 years), Antenatal care (ANC: Yes, Not), Referral status (from other facility, Not referred).

Severe complications were Severe postpartum hemorrhage, Severe preeclampsia, Eclampsia, Sepsis or severe systemic infection, Ruptured uterus. Critical (intensive care unit) interventions were Use of blood products, Interventional radiology, Laparotomy, Admission to Intensive Care Unit. Organ dysfunction/life-threatening conditions were Cardiovascular dysfunction, Respiratory dysfunction, Renal dysfunction, hematologic dysfunction, Hepatic dysfunction, Neurologic dysfunction, Uterine dysfunction.

Underlying Causes of Death/Near Miss were Pregnancy with abortive outcome, Obstetric hemorrhage, Hypertensive disorders, Pregnancy-related infection. Contributing causes were Anemia, Previous cesarean section, Prolonged / obstructed labor. Treatments were Oxytocin, Ergometrine, Magnesium sulfate, Parenteral therapeutic antibiotics.

Additional outcome variables were Proportion of severe maternal outcomes occurring within 12 h of admission (SMO12) cases among all severe maternal outcomes (SMO) cases, SMO12 mortality index, Intrahospital SMO rate (per 1000 live births), Intrahospital mortality index, ICU admission rate, ICU admission rate among women with SMO, Proportion of maternal deaths occurring without ICU admission. Hospital access related variables were Delay in deciding to seek health facility (Delay one), Time to reach the hospital (Delay two), Delay at treating hospital (Delay three). All relevant dates were collected along with the referral process followed.

Data processing and Analysis

According to formulas reported in, 4 the number of women giving birth within the study period served as the denominator for the SMO ratio and the maternal near-miss ratio (MNMR). As the numerator, each of the two outcomes had the corresponding number of events. The MNM mortality ratio was calculated as the ratio between MNM cases and maternal deaths. The SMO12 proportion was calculated as the proportion of SMO12 cases among all SMO cases. Hospital access indicators were calculated. Intra-hospital care indicators were also calculated.

Ethical consideration

Prior to data collection ethical clearance was obtained from Oromia Health Bureau, Health Research Ethical Review Committee. The anonymity of the participants was respected via the use of medical record number (MRN) rather than the name of the participant. The names of the participants were not reported in the findings of the study to ensure confidentiality.

Results

Socio-demographic characteristics

A total of 1421 live births were recorded from all participating hospitals during the study period. A total 117 of women developed potentially life-threatening conditions. The average age of women who developed potentially life-threatening diseases was around 25.5 years old, with a standard deviation of 5.5 and most participant’s age fall under the age category of 25–34. More than half (55.7%) of the cases were illiterate (had no formal education). About 23.9% of cases did not attend ANC (Antenatal Care) service. The majority (64.1%) of the cases were referred from lower health facilities (Table 1).

Table 1.

Socio-demographic and socioeconomic characteristics of mothers with severe maternal outcome at public Hospitals in Borena Zone, Ethiopia, 2022 (n = 117).

Characteristics Categories Frequency Percent (%)
Age 15–24 46 39.3
25–34 57 48.8
≥35 14 11.9
Residence Urban 80 68.4
Rural 37 31.6
Marital status Married 114 97.4
Single/separated/divorced 3 2.6
Educational status No formal education 55 47.1
Has formal education 62 52.9
Age at first marriage Below 18 years 42 35.9
18 years and above 75 64.1
ANC Yes 89 76.1
No 28 23.9
Referral status Referred from other facility 75 64.1
Not referred 42 35.9

Morbidity condition among women with potentially life-threatening conditions

Among women presented potentially life-threatening conditions, severe pre-eclampsia (48.7%) and severe postpartum hemorrhage (22.2%) were the leading severe maternal complications for hospitalization. A substantial number of women underwent critical interventions of which the use of blood products (29.5%) and admission to the intensive care unit (ICU) (27.4%) were the common interventions. The number of major organ dysfunctions seen in most cases were respiratory dysfunction and cardiovascular dysfunction at 53.6% and 33.3% respectively (Table 2).

Table 2.

Morbidity conditions in the audited sample of women with potentially life-threatening conditions at public health facilities in Borena Zone, Ethiopia, 2022. (n = 117).

Severe complications Frequency Percentage (%)
Severe postpartum hemorrhage 26 22.2
Severe pre-eclampsia 57 48.7
Eclampsia 21 17.9
Sepsis or severe systemic infection 14 12.0
Ruptured uterus 2 1.7
Critical interventions
Use of blood products 35 29.9
Interventional radiology 2 1.7
Laparotomy 10 8.5
Admission to intensive care unit 32 27.4
Organ dysfunction
Cardiovascular dysfunction 39 33.3
Respiratory dysfunction 51 53.6
Renal dysfunction 9 7.7
Coagulation/hematologic dysfunction 6 5.1
Hepatic dysfunction 8 6.8
Neurologic dysfunction 8 6.8
Multiple organ dysfunction 43 36.7

Incidence of maternal near miss

From 117 women who developed potentially life-threatening conditions, 55 women experienced near misses and 6 maternal deaths. The MRM and SMO incidence ratios were 38.7 and 42.9 per 1000 live births, respectively. The MNM mortality ratio was 9.17–1 with a mortality index of 9.8% (Table 3).

Table 3.

Near miss incidence and outcome indicators at public Hospitals in Borena Zone, Ethiopia, 2022 (n = 117).

Outcomes Near-miss indicators
All live births in the population under surveillance 1421
Women with potentially life-threatening conditions 117
Severe maternal outcomes (SMO) cases (number) 61
Maternal deaths (n) 6
Maternal near-miss cases (n) 55
Overall near-miss indicators
Severe maternal outcome ratio (per 1000 live births) 42.93
Maternal near-miss ratio (per 1000 live births) 38.71
Maternal near-miss mortality ratio 9.17: 1
Mortality index 9.8%

Underlying and contributory causes

In this study, the leading causes of MNM were Hypertensive disorders (severe pre-eclampsia and eclampsia) 61.8% (CI; 48%−74%) and obstetric hemorrhage at 29.1% (CI; 18%− 42%). Sepsis or pregnancy related infection (66.7%) was the leading cause of maternal death. The majors contributing cause of maternal near miss (43.6%) (CI; 30%−57%) reported was anemia (Table 4).

Table 4.

Underlying and contributory causes of maternal near miss at public Hospitals in Borena Zone, Ethiopia, 2022 (n = 55).

Underlying causes and associated conditions Maternal near-miss cases Mortality Ratio Mortality Index
Number Percent (%) Ratio Percent (%)
Underlying causes
Abortion 9 16.4 9.0: 1 10.0
Obstetric hemorrhage 16 29.1 16.0: 1 5.9
Hypertensive disorders 34 61.8 11.3: 1 8.1
Pregnancy-related infection 16 29.1 4.0: 1 20.0
Contributory causes
Anemia 24 43.6 24.0: 1 4.0
Previous cesarean section 2 3.6 - 0.0
Prolonged/obstructed labor 2 3.6  - 0.0

Process or facility related indicators

Out of the total 61 SMO cases, 23 (37.7%) of women presented the life-threatening conditions on arrival or within the first 12 h of hospital admission. The mortality index (MI) of women with SMO on arrival or within the first 12 h of hospital admission was 21.7%. The intrahospital SMO12 rate was 26.7 per 1000 live births and the SMO12 mortality index was 2.6%. The ICU admission rate was 2.3%, while the ICU admission rate among women with SMO was 24.6%. Half of the maternal deaths occurred without ICU admission. All of the women with severe post-partum hemorrhage (PPH) received uterotonics where oxytocin was the most common uterotonics used. Of the 21 cases with eclampsia 20 (95.2%) received anticonvulsants in which most of the anticonvulsants given was magnesium sulfate (Table 5).

Table 5.

Process or facility related indicators in public Hospitals of Borena Zone, Ethiopia.

Use of intervention and treatment Percentage (%)
Treatment of severe postpartum hemorrhage
Oxytocin 100.0
Ergometrine 11.5
Anticonvulsants for eclampsia
Magnesium sulfate 95.2
Treatment for sepsis
Parenteral therapeutic antibiotics 85.7
Hospital access indicators
Proportion of SMO12 cases among all SMO cases 37.7
SMO12 mortality index 21.7
Intrahospital care
Intrahospital SMO rate (per 1000 live births) 26.74
Intrahospital mortality index 2.6
Intensive care use
ICU admission rate 2.3
ICU admission rate among women with SMO 24.6
Proportion of maternal deaths assisted without ICU admission 50

Access to health facility

A considerable number of cases encountered some form of delay in the continuum of care. This is categorized as delay one, delay two, and delay three. A total of 49 (41.88%) women out of the 117 women experiencing potentially life threatening conditions experienced delay one. The dominant reason for the first delay was lack of support from family followed by underestimating the severity of the conditions, use of herbal medication (traditional medications), and terrible experience with health systems. Regarding delay two, 63 (53%) of the cases took more than2 h to reach the nearest health facility. Sixty (51.3%) cases encountered delay three at the referring or first health facility visited. Delay two and delay three highlight the influence of access and the referral system on women with SMO in low-resourced settings in which maternal mortality remains unacceptably high (Table 6).

Table 6.

Delays on access to reproductive health services among the study participants.

Access to reproductive health services Categories Frequency (%)
Delay in deciding to seek health facility (delay one) Yes 49 (41.9)
No 68 (58.1)
Time to reach the hospital (delay two) <2 h 55 (47.0)
>2 h 62 (53.0)
Delay at treating hospital (delay three) <15 min 60 (51.3)
>15 min 57 (48.7)

Factors associated with to severe maternal outcome

The findings revealed several significant associations. Firstly, mothers from higher-income households demonstrated a lower likelihood of developing severe maternal outcomes. Additionally, those who entered their first marriage before the age of 18 were found to be at a higher risk for SMO. Anemia was identified as a risk factor for SMO. Time to reach nearby health facility markedly associated with SMO as mother who took more than 2 h to reach facility had more chance to develop SMO. Moreover, prolonged waiting time at the hospital (waiting over 15 min after arrival) was identified as a risk factor for SMO. Admission to the intensive care unit was strongly linked to SMO cases, and a notable correlation was observed between the number of antenatal care (ANC) visits and severe maternal outcomes (Table 7).

Table 7.

Factors associated with severe maternal outcome in public hospitals of Borena Zone. Ethiopia.

Sociodemographic characteristics SMO N (%) Non-SMO N (%) p-Value
Attended formal education Yes 27 (44.3) 35 (62.5) 0.048
No 34 (55.7) 21 (37.5)
Admitted to ICU Yes 15 (24.6) 17 (30.4) 0.485
No 46 (75.4) 39 (69.6)
Delivery or abortion occurred before reaching HF Yes 11 (18.0) 5 (8.9) 0.152
No 50 (82.0) 51 (91.1)
Age at first marriage Below 18 years 55 (90.2) 20 (35.7) <0.001
18 years and above 6 (9.8) 36 (64.3)
Women delayed in seeking help (delay 1) Yes 24 (39.3) 25 (44.6) 0.562
No 37 (60.7) 31 (55.4)
Time to reach HF (delay 2)  More than 2 h 23 (37.7) 1 (1.8) <0.001
Less than 2 h 38 (62.3) 55 (98.2)
Waiting time (delay 3) More than 15 min 44 (72.1) 13 (23.2) <0.001
Less than 15 min 17 (27.9) 43 (76.8)
Household income Below median 42 (68.9) 6 (10.7) <0.001
Above median 19 (31.1) 50 (89.3)
Anemic Yes 25 (41.0) 8 (14.3) 0.001
No 36 (59.0) 48 (85.7)
Cesarean section Previous history 2 (3.3) 5 (8.9) 0.198
No previous history 59 (96.7) 51 (91.1)
Age category 15–24 26 (42.62) 20 (35.71) 0.135
25–34 25 (40.98) 32 (57.14)
35 and above 10 (16.39) 4 (7.14)
Number of ANC visit None 20 (32.79) 8 (14.29) 0.001
One visit 12 (19.67) 5 (8.93)
Two visits 23 (37.70) 19 (33.93)
Three visits 5 (8.20) 18 (38.14)
Four visits 1 (1.64) 6 (10.71)
Parity Nully 18 (29.5) 18 (32.1) 0.805
Primi 10 (16.4) 11 (19.6)
Multi 33 (54.1) 27 (48.2)
Duration of hospital stay ≤7 days 51 (83.6) 47 (83.9) 0.962
≥8 days 10 (16.4) 9 (16.1)

Discussion

The magnitude of MNM among hospital deliveries was 38.7 per 1000 live births within the surveyed hospitals. The incidence of SMO was 42.9 per 1000 live births. Most women with MNM developed organ dysfunction before or within the first 12 h of hospital arrival. Moreover, ICU admission of women with SMO was 13%. According to a recent WHO report, an MNM or SMO ratio higher than 10 per 1000 live births indicates that a substantial percentage of women will need lifesaving interventions to survive their complications. 4

The MNMR found in this study is higher than in previous studies conducted in various parts of Ethiopia: 8.01 in Addis Ababa, 14 16.1 in Hawassa, 15 24.3 in South Region, 9 and 28.5 in Tigray Ethiopia. 3 It also higher than in the studies conducted in Tanzania, 16 Sudan, 17 Uganda. 18 The observed discrepancy might be due to differences in case definition and types of health facility included. Another explanation for the higher magnitude of MNM presented in this study could be attributed to the presence of severe drought in the studied area. Conversely, the magnitude of MNM is this study is lower than prior studies conducted in east Ethiopia (80 per 1000 live births), 19 and south-west Ethiopia (50.4 per 1000 live births). 20 The prior studies used disease-based criteria, which were less stringent than the WHO criteria for identifying maternal near-miss cases.

The current study showed that the leading causes of MNM were pregnancy induced hypertension and severe postpartum hemorrhage, which are comparable to the findings of studies conducted elsewhere in Ethiopia8,14,15,2022 and other African countries.16,17 Pregnancy related infection (sepsis) was associated with the highest case fatality; like the findings of earlier studies done in other parts of the country22,23 and other developing countries.16,17 Near-miss cases that develop during hospitalization can help to measure the quality of obstetric care provided within the health facilities.4,22 In line with previous findings, this study showed that most women experienced SMO on admission or within 12 h of arrival at hospitals.10,14,22 This suggests the existence of substandard obstetrics care and a weak referral system at lower facilities. According to the WHO, the presence of substantial proportion of women arrive at health facilities with SMO suggests a delay in seeking either care (first delay) or delay in reaching a health facility (second delay). 4

This study noted that ICU admission rate among women with SMO is low and about half of maternal deaths occurred without ICU admission. This low percentage of women with SMO admitted to the ICU and the high percentage of maternal deaths that occurred without ICU admission suggest a shortage of ICU beds.1,4,22 Therefore, it is necessary to set up ICUs dedicated solely to the obstetric population. Appropriate use of effective interventions results in improved health outcomes.1,2,4

This study has added to the literature by assessing interventions provided for women with maternal near miss and hospital access indicators, which are essential components of quality of care. The use of prospective data to identify women with SMO and the quality of care provided to women with life-threatening conditions using a validated WHO tool add valuable information that can used to design programs and interventions aimed at improving maternal health in resource limited and remote settings.

However, because of logistic and feasibility concerns, our follow-up time was limited to only the length of the hospital stay instead of up to 42 days of postpartum as recommended by WHO. The short and long-term maternal consequences of near-miss events were also not addressed in the current study. The focus on women experiencing SMOs limited the data available to perform advanced statistical analysis. A sample size of 117 is considered too small to produce robust and reliable results in a logistic regression model, with 500 being the recommended minimum sample size. 24

Conclusion

During the study period, the incidence of maternal near miss was 38.87 per 1000 live births in all participating hospitals, which is higher than the previous studies. The common underlying causes of most maternal near miss cases were hypertensive disorders and obstetric hemorrhage. Anemia was the major contributing cause reported for maternal near miss. Giving special emphasis to life-saving interventions, critical care and implementation of prenatal risk identification and prompt referral to the nearby hospitals are essential for reducing delays in provision of appropriate care.

Acknowledgments

We would also like to extend our gratitude to the hospital heads, the study participants, and the data collectors for their due cooperation and involvement.

Footnotes

Authors contributions: All authors have made a substantial contribution to the conception and design, acquisition, analysis, and interpretation of data, drafted and wrote the manuscript from its initial state to the last version and have given the final approval for publication. BK has been involved in the conception of the study and was the principal investigator. BK, GA, and KM have been involved in the design of the study, protocol development, the analysis and drafting the manuscript. PD and EV contributed to study design and reviewed the manuscript and made substantial revisions and finally has given the final approval for publication.

Availability of data and materials: All the data of this study are available from the corresponding author upon reasonable request.

Consent for publication: Not applicable

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by Catholic Organization for Relief and Development Aid (CORDAID) as part of the “Complimenting Borena PBF program to further improve MCH services” project.

Ethical clearance: Ethical clearance was obtained from Oromia Health Bureau, Health Research Ethical Review Committee and it was in accordance with the principles of the Helsinki II declaration. Ref number (BFO/HBTFH/1-16/416)

References

  • 1. Geller SE, Koch AR, Garland CE, et al. A global view of severe maternal morbidity: Moving beyond maternal mortality. Reprod Health 2018; 15: 98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. World Health Organization. Trends in maternal mortality 2000 to 2017 estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. World Health Organization, 2019. [Google Scholar]
  • 3. Teka H, Yemane A, Berhe Zelelow Y, et al. Maternal near-miss and mortality in a teaching hospital in Tigray region, Northern Ethiopia. Womens Health 2022; 18: 17455057221078739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Evaluating the quality of care for severe pregnancy complications The WHO near-miss approach for maternal health [Internet], www.who.int/reproductivehealthISBN9789241502221; https://www.who.int/publications/i/item/9789241502221
  • 5. Sara J, Haji Y, Gebretsadik A. Determinants of maternal death in a pastoralist area of Borena Zone, Oromia Region, Ethiopia: unmatched case-control study. Obstet Gynecol Int 2019; 2019: 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Abdollahpour S, Miri HH, Khadivzadeh T. The global prevalence of maternal near miss: a systematic review and meta-analysis. Health promotion perspectives, Vol. 9. Tabriz, Iran: Tabriz University of Medical Sciences, 2019, pp.255–262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Ethiopia demographic and health survey. Ethiopia Demographic and Health Survey 2016 Final Report, 2016. https://dhsprogram.com/publications/publication-FR328-DHS-Final-Reports.cfm [Google Scholar]
  • 8. Mengist H, Gedfew M, Tadese M. Determinants of maternal near-miss among women admitted to public hospitals in North Shewa Zone, Ethiopia: a case-control study. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Yemane Y, Tiruneh F. Incidence-proportion of maternal near-misses and associated factors in southwest Ethiopia: a prospective cross-sectional study. Int J Womens Health 2020; 12: 1125–1134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Chhabra P. Maternal near miss: an indicator for maternal health and maternal care. Indian J Commun Med 2014; 39(3): 132–137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Mekango DE, Alemayehu M, Gebregergs GB, et al. Determinants of maternal near miss among women in public hospital maternity wards in Northern Ethiopia: a facility based case-control study. PLoS One 2017; 12(9): e0183886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Gebrehiwot Y, Tewolde BT. Improving maternity care in Ethiopia through facility based review of maternal deaths and near misses Int J Gynecol Obstet 2014; 127(S1): S29–S34. [DOI] [PubMed] [Google Scholar]
  • 13. The Federal Democratic Republic of Ethiopia Ministry of Health HSTP health sector transformation plan. https://www.cmpethiopia.org/content/download/2268/9612/file/HSTP%20Final%202015-10-19.pdf [Google Scholar]
  • 14. Liyew EF, Yalew AW, Afework MF, et al. Incidence and causes of maternal near-miss in selected hospitals of Addis Ababa, Ethiopia. PloS One 2017; 12(6): e0179013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Tolesa D, Abera N, Worku M, et al. Prevalence and associated factors with maternal near-miss among pregnant women at Hawassa University Comprehensive Specialized Hospital, Sidama Region, Ethiopia. Int J Women’s Health Wellness 2021; 7(2): 132–176. [Google Scholar]
  • 16. Nelissen EJ, Mduma E, Ersdal HL, et al. Maternal near miss and mortality in a rural referral hospital in northern Tanzania: a cross-sectional study. BMC Pregnancy Childbirth 2013; 13: 160–245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Ali AA, Khojali A, Okud A, et al. Maternal near-miss in a rural hospital in Sudan. BMC Pregnancy Childbirth 2011; 11: 105–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Ns S, Saha R, Ns S, et al. Near miss maternal morbidity and maternal mortality at Kathmandu Medical College Teaching Hospital. Kathmandu Univ Med J 2010; 8: 109–133. [DOI] [PubMed] [Google Scholar]
  • 19. Tura AK, Zwart J, van Roosmalen J, et al. Severe maternal outcomes in eastern Ethiopia: application of the adapted maternal near miss tool. PloS One 2018; 13(11): 101–122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Woldeyes WS, Asefa D, Muleta G. Incidence, and determinants of severe maternal outcome in Jimma University Teaching Hospital, South-West Ethiopia: a prospective cross-sectional study. BMC Pregnancy Childbirth 2018; 18(1): 255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Habte A, Wondimu M. Determinants of maternal near miss among women admitted to maternity wards of tertiary hospitals in Southern Ethiopia, 2020: a hospital-based case-control study. PloS One 2021; 16: e0251826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Beyene T, Chojenta C, Smith R, et al. Severe maternal outcomes and quality of maternal health care in South Ethiopia. Int J Womens Health 2022; 14: 119–130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Berhan Y, Berhan A. Causes of maternal mortality in Ethiopia: a significant decline in abortion related death, http://dx.doi.org/10.4314/ejhs.v24i1.3S [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Bujang MA, Sa’at N, Sidik TMITAB, et al. Sample size guidelines for logistic regression from observational studies with large population: emphasis on the accuracy between statistics and parameters based on real life clinical data. Malays J Med Sci 2018; 25(4): 122–130. [DOI] [PMC free article] [PubMed] [Google Scholar]

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