Abstract
Objective:
Maternal near-miss refers to a woman who nearly died but survived complications in pregnancy, childbirth, or within 42 days of termination of pregnancy. The study of maternal near-miss has become essential for improving the quality of obstetric care. The objective of this study was to identify the determinants of maternal near-miss among women admitted to major private hospitals in eastern Ethiopia.
Method:
An unmatched nested case–control study was conducted in major private hospitals in eastern Ethiopia from 5 March to 31 March 2020. Cases were women who fulfilled the sub-Saharan African maternal near-miss criteria and those admitted to the same hospitals but discharged without any complications under the sub-Saharan African maternal near-miss tool were controls. For each case, three corresponding women were randomly selected as controls. Factors associated with maternal near-misses were analyzed using binary and multiple logistic regressions with an adjusted odds ratio along with a 95% confidence interval. Finally, p-value < 0.05 was considered as a cut-off point for the significant association.
Results:
A total of 432 women (108 cases and 324 controls) participated in the study. History of prior cesarean section (AOR = 4.33; 95% CI = 2.36–7.94), anemia in index pregnancy (AOR = 4.38; 95% CI = 2.43–7.91), being ⩾ 35 years of age (AOR = 2.94; 95% CI = 1.37–6.24), not attending antenatal care (AOR = 3.11; 95% CI = 1.43–6.78), and history of chronic medical disorders (AOR = 2.18; 95% CI = 1.03–4.59) were independently associated with maternal near-miss.
Conclusion:
Maternal age ⩾ 35 years, had no antenatal care, had prior cesarean section, being anemic in index pregnancy, and have history of chronic medical disorders were the determinants of maternal near-miss. Improving maternal near-misses requires strengthening antenatal care (including supplementation of iron and folic acid to reduce anemia) and prioritizing women with a history of chronic medical illnesses. Interventions for preventing primary cesarean sections are crucial in this era of the cesarean epidemic to minimize its effect on maternal near-miss.
Keywords: determinants, Ethiopia, maternal near-miss, private hospitals
Introduction
With the overall reduction of maternal deaths in high resource settings and the need to improve quality of care in low-resource settings, the study of women who survived complications (maternal near-miss, MNM) has become common since the 1990s. 1 MNM refers to a woman who nearly died but survived severe complications during pregnancy, childbirth, or within 42 days of termination of pregnancy. 1 It serves as a proxy for better knowledge about a set of conditions and preventable factors of maternal death. 2
Studying MNM addresses the limitation of mortality only: under-reporting 3 and rare in absolute number. 4 In addition to having similar characteristics with maternal deaths, MNM is less threatening to report and less likely to blame because the women survived.4,5 Therefore, studying MNM for countries, such as Ethiopia—with high maternal mortality 6 but significant under-reporting 3 —is not only for increasing the number of cases to be included and to address problems of under-reporting but also to create a conducive environment in the no mother should die era. 7
Until 2009, when the World Health Organization (WHO) proposed the MNM criteria, studies on MNM used different identification criteria. However, those studies refer to similar concepts: women surviving severe complications. Although frequently used in MNM studies, including in low-resource settings, the WHO MNM tool underestimates the burden of MNM in low-resource settings.8–10 Hence, a modified MNM criterion for use in low-resource settings of sub-Saharan Africa was proposed in 2017. 11 The modified MNM criteria called the sub-Saharan African MNM criteria contain 27 indicators grouped into the clinical-, laboratory-, and management-based approaches following the 2009 WHO MNM approach. 11 The tool has already been tested in three studies in Ethiopia, 12 Namibia, 13 and Suriname 14 and found that effective for MNM studies in low-resource settings.
In addition, to focus on determining the prevalence and associated factors of MNM,12,15–19 existing MNM studies in Ethiopia are limited to public facilities. Given significant demographic, obstetrics, and medical characteristic differences between women in public and private hospitals, such studies failed to identify peculiar factors of private facilities. In this study, we report the determinants of MNM nested in a larger retrospective cohort conducted in major private hospitals in eastern Ethiopia. 20
Methods
Study settings
This study was an unmatched case–control study nested in a large cohort of women admitted to two major private hospitals in eastern Ethiopia (Harar General Hospital and Bilal General Hospital). The hospitals were selected because of having a high number of annual deliveries, having qualified consultants for the care of women with life-threatening complications, such as emergency cesarean section (CS), and having an established intensive care unit. There were 1167 live births and 1214 maternity admissions during the 1-year study period in both hospitals. Details of the baseline study have been described elsewhere. 20 In brief, through a review of all maternity admissions during the study period, women who developed MNM per the sub-Saharan African MNM tool were identified. The data collection period was from 5 March to 31 March 2020.
Populations
The source populations were women who were admitted to private hospitals in eastern Ethiopia during pregnancy, childbirth, or within 42 days of termination of pregnancy. The study populations were women who were admitted from 9 January 2019 to 8 January 2020 in selected hospitals in eastern Ethiopia during pregnancy, childbirth, or within 42 days of termination of pregnancy.
Inclusion and exclusion criteria for cases and controls
Cases
Women who were admitted in the selected hospitals during the study period during pregnancy, childbirth, or within 42 days of termination of pregnancy and fulfilled at least one of the MNM conditions as per the sub-Saharan African MNM criteria (Table 1). 11 Women whose medical records missed important variables were excluded.
Table 1.
Sub-Saharan African MNM criteria. 1
| Clinical-based criteria | Laboratory-based criteria | Management-based criteria |
|---|---|---|
| Acute cyanosis
a
Gasping b Respiratory rate > 40 or < 6/min Shock c Oliguria nonresponsive to fluids or diuretics d Failure to form clots e Loss of consciousness lasting ⩾ 12 h f Cardiac arrest Stroke g Uncontrollable fit/total paralysis h Jaundice in the presence of pre-eclampsia i Eclampsia j Uterine rupture k Sepsis or severe systemic infection l Pulmonary edema m Severe abortion complications n Severe malaria o Severe pre-eclampsia with ICU admission |
Oxygen saturation < 90% for > 60 min Creatinine ⩾ 300 μmol/L or ⩾ 3.5 mg/dL Acute thrombocytopenia (<50,000 platelets/mL) Loss of consciousness and ketoacids in urine |
Hysterectomy following infection or hemorrhage Transfusion of ⩾ 2 units of red blood cells Intubation and ventilation for 60 min not related to anesthesia Cardiopulmonary resuscitation Laparotomy other than for cesarean section |
Acute cyanosis is blue or purple coloration of the skin or mucous membranes due to low oxygen saturation.
Gasping is a terminal respiratory pattern, and the breath is convulsively and audibly caught.
Shock is persistent severe hypotension, defined as a systolic BP < 90 mmHg for ⩾ 60 min with a pulse rate of at least 120 despite aggressive fluid replacement (>2L).
Oliguria is defined as a urinary output < 30 mL/h for 4 h or < 400 mL/24 h.
Failure to form clots can be assessed by the bedside clotting test or absence of clotting from the IV site after 7–10 min.
Loss of consciousness lasting > 12 h is a profound alteration of mental state that involves complete or near-complete lack of responsiveness to external stimuli. It is defined as a Glasgow Coma Scale < 10 (moderate or severe coma).
Stroke is a neurological deficit of cerebrovascular cause that persists beyond 24 h or is interrupted by death within 24 h.
Uncontrolled fits/total paralysis is refractory, persistent convulsions or status epilepticus.
Pre-eclampsia is defined as the presence of hypertension associated with proteinuria. Hypertension is defined as a BP of at least 140/90 mmHg on at least two occasions and at least 4–6 h apart after the 20th week of gestation in women known to be normotensive beforehand. Proteinuria is defined as excretion of 300 mg or more of protein every 24 h. If 24-h urine samples are not available, proteinuria is defined as a protein concentration of 300 mg/L or more (⩾ 1 on dipstick) in at least two random urine samples taken at least 4–6 h apart.
Eclampsia is diastolic BP ⩾ 90 mmHg or proteinuria + 3 and convulsion or coma.
Uterine rupture is a complete rupture of the uterus during labor and/or confirmed later by laparotomy.
Sepsis or severe systemic infection is defined as a clinical sign of infection and three of the following: temperature > 38°C or < 36°C, respiration rate > 20/min, pulse rate > 90/min, WBC > 12,000.
Pulmonary edema is the accumulation of fluids in the air spaces and parenchyma of the lungs.
Severe abortion complications are defined as septic in incomplete abortion, a complicated gestational trophoblastic disease with anemia.
Severe malaria is defined as major signs of organ dysfunction and/or high-level parasitemia or cerebral malaria.
Controls
Women admitted to the same hospitals and discharged without any complications under the sub-Saharan African MNM tool were controls. Similarly, women whose medical records missed important variables were excluded.
Sample size determination
The sample size was estimated using Epi Info 7 Statcalc software for an unmatched case–control study with the assumption of a 95% confidence interval, power of 80%, and case to the control ratio of 1:3. The proportion of case and control with exposure (prior history of CS) was 22.5% and 8.7%, respectively, and AOR of 3.53 from a previous study in Ethiopia. 18 The minimum sample size with 10% non-response was 332 (83 cases and 249 controls). We included all women with near-miss cases during the study period to increase the power of the study.
Sampling technique and procedure
Maternal admissions during pregnancy, childbirth, or within 42 days of termination of pregnancy during the study period were 1214 (cases = 108 and controls = 1106) in both hospitals. All cases (n = 108) admitted during the study period were included in the study. For the selection of controls, we prepared a sampling frame using their unique medical registration number. Then, a computer-generated random sampling technique was applied to select controls. For each case, three corresponding women were selected randomly as controls.
Data collection
Data were collected using the validated sub-Saharan African MNM criteria by trained research assistants.11–13 Detailed socio-demographic characteristics, obstetrics history, preexisting medical conditions, MNM events, and underlying complications were collected by reviewing the medical records of women. The dependent variable was MNM, defined as the presence of any of the sub-Saharan African MNM criteria. 11 Independent variables were demographic characteristics, such as residence, age, referral status, and marital status; obstetrics histories, such as parity, history of prior CS, history of abortion, history of stillbirth, antenatal care (ANC) utilization, anemia in index pregnancy, and history of chronic medical disorders.
Data management and analysis
Data were cleaned and entered into EpiData 3.1 and then exported to SPSS 20 for analysis. Frequency tables and mean were used to describe the characteristics of the study participants for categorical and continuous variables, respectively. Bivariate analysis was used to identify potential variables for the multivariable logistic regression model. Independent variables with a p-value of ⩽ 0.25 were entered into a multiple logistic regression model. The goodness of model fitness was checked using the Hosmer–Lemeshow statistic (0.456). Adjusted odds ratio along with 95% CI was used to describe the association in the multiple logistic regressions. Finally, p-value < 0.05 was considered as a cut-off point for the statistically significant association.
Ethical considerations
The Institutional Health Research Ethics Review Committee of the College of Health and Medical Sciences, Haramaya University in Ethiopia, approved this study (Ref No: IHRERC/045/2020). As the study was retrospective, the need for individual informed consent was waived. We submitted a support letter to participating hospitals and got permission. Data collection was anonymous to maintain the confidentially of participants.
Results
Socio-demographic characteristics of participants
A total of 432 women (108 cases and 324 controls) participated in the study. The mean age of respondents was 28.9 (± 6.2) and 26.4 (± 4.9) years among cases and controls, respectively. The majority of the respondents were 20–34 years of age (68.5% cases and 87.7% controls) and married (97.5% cases and 94.4% controls). Referral from other health facilities was significantly higher among cases than controls (p < 0.0001). However, there was no significant difference in place of residence and marital status (p > 0.05) (Table 2).
Table 2.
Socio-demographic characteristics of women admitted in private hospitals in eastern Ethiopia, 2020 (n = 432).
| Variable | Category | Total | Cases (n = 108) | controls (n = 324) | p-value | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Age in years | <20 | 28 | 6.5 | 9 | 8.3 | 19 | 5.9 | 0.000 |
| 20–34 | 358 | 82.9 | 74 | 68.5 | 284 | 87.7 | ||
| ⩾35 | 46 | 10.6 | 25 | 23.2 | 21 | 6.4 | ||
| Residence | Urban | 284 | 65.7 | 65 | 60.2 | 219 | 67.6 | 0.100 |
| Rural | 148 | 34.3 | 43 | 39.8 | 105 | 32.4 | ||
| Marital status | Single | 14 | 3.2 | 6 | 5.6 | 8 | 2.5 | 0.108 |
| Married | 418 | 96.8 | 102 | 94.4 | 316 | 97.5 | ||
| Referral status | Self-referral | 410 | 94.9 | 87 | 80.6 | 323 | 99.7 | 0.000 |
| Referred | 22 | 5.1 | 21 | 17.4 | 1 | 0.3 | ||
Obstetrics and medical conditions of participants
The majority of respondents were multiparous (68.5% of cases and 59.3% of controls). Compared to controls, women with MNM were more likely to have a history of previous CS, abortion, and stillbirth (p < 0.05). In addition, CS was more likely among cases than controls (63% versus 22.5%, p < 0.0001). Anemia in index pregnancy and history of chronic medical disorders were higher among MNM cases than controls (p < 0.05). But no significant differences exist about parity (p > 0.05) (Table 3).
Table 3.
Obstetrics and medical conditions of women with and without MNM admitted in private hospitals in Eastern Ethiopia, 2020 (n = 432).
| Variable | Category | Total | Cases (n = 108) | Controls (n = 324) | p-value | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Parity | Primiparous | 166 | 38.4 | 34 | 31.5 | 132 | 40.7 | 0.054 |
| Multiparous | 266 | 61.6 | 74 | 68.5 | 192 | 59.3 | ||
| Received ANC | Yes | 393 | 91.0 | 92 | 85.2 | 301 | 92.9 | 0.016 |
| No | 39 | 9.0 | 16 | 14.8 | 23 | 7.1 | ||
| History of still birth | Yes | 35 | 8.1 | 14 | 13 | 21 | 6.5 | 0.030 |
| No | 397 | 91.9 | 94 | 87 | 303 | 93.5 | ||
| History of abortion | Yes | 95 | 22 | 31 | 28.7 | 64 | 19.8 | 0.037 |
| No | 337 | 78 | 77 | 71.3 | 260 | 80.2 | ||
| Previous CS | Yes | 76 | 17.6 | 37 | 34.3 | 39 | 12 | 0.000 |
| No | 356 | 82.4 | 71 | 65.7 | 285 | 88 | ||
| Gestational age at delivery (weeks) | <37 | 24 | 5.6 | 20 | 18.5 | 4 | 1.2 | 0.000 |
| 37–42 | 403 | 93.3 | 86 | 79.6 | 317 | 97.8 | ||
| >42 | 5 | 1.2 | 2 | 1.9 | 3 | 1.0 | ||
| Mode of delivery | Vaginal | 258 | 59.7 | 32 | 29.6 | 226 | 69.8 | 0.000 |
| CS b | 164 | 40.3 | 76 | 70.4 | 98 | 30.2 | ||
| Anemia in the index pregnancy | Yes | 77 | 17.8 | 42 | 38.9 | 35 | 10.8 | 0.000 |
| No | 355 | 81.2 | 66 | 61.1 | 289 | 89.2 | ||
| History of chronic medical disorders a | Yes | 42 | 9.7 | 16 | 14.8 | 26 | 8.0 | 0.034 |
| No | 390 | 90.3 | 92 | 85.2 | 298 | 92.0 | ||
| Chronic hypertension | Yes | 34 | 7.9 | 14 | 13.0 | 20 | 6.2 | 0.030 |
| No | 398 | 92.1 | 94 | 87.0 | 304 | 93.8 | ||
| Preexisting diabetes mellitus | Yes | 5 | 1.2 | 2 | 1.9 | 3 | 0.9 | 0.367 |
| No | 427 | 98.8 | 106 | 98.2 | 321 | 99.2 | ||
| Cardiovascular disease | Yes | 3 | 0.7 | 1 | 0.9 | 2 | 0.6 | 0.579 |
| No | 429 | 99.3 | 107 | 99.1 | 322 | 99.4 | ||
N: number; ANC: antenatal care; CS: cesarean section.
Chronic hypertension, preexisting diabetes mellitus, and cardiovascular disease.
Includes laparotomy (n = 4).
Underlying complications among cases
Obstetric hemorrhage (50%) was the leading underlying complication followed by hypertensive disorders of pregnancy (27.8%) among cases (Table 4).
Table 4.
Underling complications of MNM among women admitted in private hospitals in eastern Ethiopia, 2020 (n = 108).
| Underlying complication | N | % |
|---|---|---|
| Obstetric hemorrhage | 54 | 50 |
| Abortion related | 7 | 6.5 |
| Ectopic pregnancy | 4 | 3.7 |
| Abruptio placenta | 18 | 16.7 |
| Placenta previa | 7 | 6.5 |
| Uterine rupture | 8 | 7.4 |
| Severe postpartum hemorrhage | 12 | 11.1 |
| Hypertensive disorders | 30 | 27.8 |
| Eclampsia | 18 | 16.7 |
| Pre-eclampsia | 12 | 11.1 |
| Sepsis/severe systemic infection | 26 | 24.2 |
Determinants of MNM
In the adjusted analysis, age, ANC, prior history, anemia in the index pregnancy, and history of chronic medical disorders independently associated with MNM. The odds of having a previous CS were 4.33 times higher among cases than controls (AOR = 4.33; 95% CI = 2.36–7.94). Similarly, the odds of having anemia in index pregnancy and being ⩾ 35 years of age were 4.38 (AOR = 4.38; 95% CI = 2.43–7.91) and 2.94 (AOR = 2.94; 95% CI = 1.37–6.24) times higher among cases than controls, respectively. The odds of not attending ANC were also three (AOR = 3.11; 95% CI = 1.43–6.78) times higher among near-miss cases than controls. In addition, a history of chronic medical disorders was 2.18 times more likely among near-misses than controls (AOR 2.18; 95% CI = 1.03–4.59) (Table 5).
Table 5.
Determinants of MNM among women admitted in private hospitals of eastern Ethiopia, 2020.
| Variable | Category | MNM status | COR (95% CI) | AOR (95% CI) | |
|---|---|---|---|---|---|
| Cases, n (%) (n = 108) |
Controls, n (%) (n = 324) |
||||
| Age in years | 20–34 | 74 (68.5) | 284 (87.7) | 1.0 | 1.0 |
| <20 | 9 (8.30 | 19 (5.9) | 1.82 (0.79–4.18) | 2.86 (1.11–7.42) *** | |
| ⩾35 | 25 (23.2) | 21 (6.5) | 4.57 (2.42–8.61) | 2.92 (1.37–6.24) *** | |
| Residence | Urban | 65(60.2) | 219 (67.6) | 1.0 | 1.0 |
| Rural | 43 (39.8) | 105 (32.4) | 1.38 (0.88–2.16) | 1.47 (0.87–2.48) | |
| History of stillbirth | No | 94 (87.0) | 303 (93.5) | 1.0 | 1.0 |
| Yes | 14 (83.0) | 21 (6.5) | 2.15 (1.05–4.39) | 1.69 (0.75–3.86) | |
| History of Abortion | No | 77 (71.3) | 260 (80.2) | 1.0 | 1.0 |
| Yes | 31 (28.77) | 24 (19.8) | 1.64 (0.99–2.69) | 1.53 (0.86–2.74) | |
| ANC utilization | Yes | 92 (85.2) | 301 (92.9) | 1.0 | 1.0 |
| No | 16 (14.8) | 23 (7.1) | 2.27 (1.15–4.49) | 3.11 (1.43–6.78) ** | |
| Parity | Primiparous | 34 (31.5) | 132 (40.7) | 1.0 | 1.0 |
| Multiparous | 74 (68.5) | 192 (59.3) | 1.40 (0.94–2.38) | 0.92 (0.52–1.64) | |
| Previous CS | No | 71 (65.7) | 285 (88) | 1.0 | 1.0 |
| Yes | 37 (34.3) | 39 (12) | 3.81 (2.27–6.40) | 4.33 (2.36–7.94) **** | |
| Anemia in the index pregnancy | No | 66 (61.1) | 289 (89.2) | 1.0 | 1.0 |
| Yes | 42 (38.9) | 35 (10.8) | 5.26 (3.12–8.86) | 4.38 (2.43–7.91) **** | |
| History of chronic medical disorders a | No | 92 (85.2) | 298 (92.0) | 1.0 | 1.0 |
| Yes | 16 (14.8) | 26 (8.0) | 1.99 (1.03–3.88) | 2.18 (1.03–4.59) * | |
MNM: maternal near-miss; ANC: antenatal care; CS: cesarean section; COR: crude odds ratio; AOR: adjusted odds ratio.
p = 0.041, **p = 0.004, ***p = 0.003, ****p ⩽ 0.001.
Hypertension, diabetes mellitus, and cardiovascular disease.
Discussion
In this unmatched nested case–control study, we have identified the determinants of MNM among women attending maternity units in major private hospitals in eastern Ethiopia. This study revealed that MNM cases were more likely to be older than controls. This finding is consistent with the previous studies.14,21–23 The reason might be related to a greater risk of hypertensive disorders of pregnancy, CS, or postpartum hemorrhage among older women.24,25 Those obstetrics complications lead to MNM. Compared to controls, women with MNM were less likely to receive ANC. This finding is in line with the previous study in Ethiopia,17,18 Nigeria, 26 and Brazil. 27 It indicates the importance of ANC in identifying pregnancy complications and providing early treatment for reducing the occurrence of MNM. 28
Consistent with the findings from Ethiopia,15,18,29 Tanzania, and Brazil,27,30 women with MNM were more likely to have a previous history of CS. A prior CS increases the risk of uterine rupture or placenta previa in subsequent pregnancies, which leads to MNM. 31 In addition, CS increases the risk of infection and hemorrhage, 32 thereby increasing the odds of near-miss events. This finding suggests considering potential risks of CS during assessment or decision for CS.
Women with a history of chronic medical disorders had higher odds of MNM. This finding is consistent with a study done in Ethiopia. 18 Chronic medical disorders during pregnancy, such as chronic hypertension, increased the risk of severe pregnancy complications, such as superimposed pre-eclampsia and placental abruption. 33 In addition, women with anemia in index pregnancy had higher odds of experiencing MNM. This finding is congruent with the studies in Ghana and Ethiopia.17,34 It might be because the minimum amount of bleeding in anemic patients may lead to severe postpartum hemorrhage and hypovolemic shock leading to MNM. Nutritional intervention and iron supplementation for all women during pregnancy may help to prevent and improve anemia during pregnancy. 35
This study was the first study in Ethiopia to document the determinants of MNM in private hospitals using the newly developed and validated MNM criteria. 11 The use of nested case–control within a large cohort of study 20 enabled us to draw a strong conclusion about any association. However, some socio-demographic characteristics (income, educational status, partner’s status, and occupation) were not included in our analysis since they are not routinely documented. Since data collection was retrospective from the medical records, our follow-up was limited up to the discharge of the women or 42 days of termination of pregnancy, whichever comes first. Therefore, MNM occurring among controls within 42 days might be misclassified—especially if the woman is not readmitted in those private hospitals. In addition, the lack of studies that utilized the sub-Saharan African MNM criteria made comparing our findings with others difficult.
Conclusion
Maternal age ⩾ 35 years, had no ANC, had prior CS, being anemic in index pregnancy, or having a history of chronic medical disorders were the determinants of MNM. Efforts to strengthen ANC are needed to prevent maternal near-misses. Supplementation of iron and folic acid during pregnancy is also crucial to reduce near-miss due to anemia. Interventions for preventing primary CSs are important in this era of the cesarean epidemic to minimize the burden of MNM or subsequent CSs.
Acknowledgments
The authors thank the private hospitals of eastern Ethiopia for giving necessary information, data collectors, and supervisors.
Footnotes
Author contributions: S.G.T. and A.K.T. conceived the study and wrote the original draft of the article; S.G.T. analyzed data and its interpretation; A.K.T. and N.A. supervised the proposal development, data collection, analysis, and overall work; A.K.T., N.A., and S.G.F. reviewed the draft article for intellectual content and participated in the revision. All authors read and approved the final version of the article.
Availability of data and materials: The dataset used or analyzed during this study is available from the corresponding author on reasonable request.
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Disclaimer: The funding organization has no role in the design, execution, or decision to publish the study.
Ethical approval: The Institutional Health Research Ethics Review Committee of the College of Health and Medical Sciences, Haramaya University in Ethiopia, approved this study (Ref no: IHRERC/045/2020).
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: S.G.T. received a grant from the Ministry of Science and Higher Education (MoSHE) Ethiopia through Haramaya University for his MSc education.
ORCID iDs: Shegaw Geze Tenaw
https://orcid.org/0000-0002-7216-6283
Sagni Girma Fage
https://orcid.org/0000-0002-3858-190X
Nega Assefa
https://orcid.org/0000-0003-0341-2329
Abera Kenay Tura
https://orcid.org/0000-0002-2735-7523
References
- 1. World Health Organization. Evaluating the quality of care for severe pregnancy complications: the WHO near-miss approach for maternal health, 2011, http://apps.who.int/iris/bitstream/handle/10665/44692/9789241502221_eng.pdf;jsessionid=DA2B0A826C97C6CB21E7490C1DE748BA?sequence=1
- 2. Pattinson R, Buchmann E, Mantel G, et al. Can enquiries into severe acute maternal morbidity act as a surrogate for maternal death enquiries? BJOG 2003; 110(10): 889–893. [PubMed] [Google Scholar]
- 3. Ethiopian Public Health Institute (EPHI). National maternal death surveillance and response (MDSR) annual report, 2010 EFY. Addis Ababa, Ethiopia: Ethiopian Public Health Institute, 2017. [Google Scholar]
- 4. Say L, Souza JP, Pattinson RC, et al. Maternal near miss–towards a standard tool for monitoring quality of maternal health care. Best Pract Res Clin Obstet Gynaecol 2009; 23(3): 287–296. [DOI] [PubMed] [Google Scholar]
- 5. World Health Organization. Beyond the numbers: reviewing maternal deaths and complications to make pregnancy safer. Geneva: World Health Organization, 2004. [Google Scholar]
- 6. World Health Organization. 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. [Google Scholar]
- 7. Melberg A, Mirkuzie AH, Sisay TA, et al. ‘Maternal deaths should simply be 0’: politicization of maternal death reporting and review processes in Ethiopia. Health Policy Plan 2019; 34: 492–498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Tura AK, Trang TL, van den Akker T, et al. Applicability of the WHO maternal near miss tool in sub-Saharan Africa: a systematic review. BMC Pregnancy Childbirth 2019; 19: 79. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nelissen E, Mduma E, Broerse J, et al. Applicability of the WHO maternal near miss criteria in a low-resource setting. PLoS ONE 2013; 8(4): e61248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Van den Akker T, Beltman J, Leyten J, et al. The WHO maternal near miss approach: consequences at Malawian District level. PLoS ONE 2013; 8(1): e54805. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Tura AK, Stekelenburg J, Scherjon SA, et al. Adaptation of the WHO maternal near miss tool for use in sub-Saharan Africa: an International Delphi study. BMC Pregnancy Childbirth 2017; 17: 445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. 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): e0207350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Heemelaar S, Kabongo L, Ithindi T, et al. Measuring maternal near-miss in a middle-income country: assessing the use of WHO and sub-Saharan Africa maternal near-miss criteria in Namibia. Glob Health Action 2019; 12(1): 1646036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Verschueren KJ, Kodan LR, Paidin RR, et al. Applicability of the WHO maternal near-miss tool: a nationwide surveillance study in Suriname. J Glob Health 2020; 10(2): 020429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kasahun AW, Wako WG. Predictors of maternal near miss among women admitted in Gurage zone hospitals, South Ethiopia, 2017: a case control study. BMC Pregnancy Childbirth 2018; 18: 260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. 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: 255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Liyew EF, Yalew AW, Afework MF, et al. Distant and proximate factors associated with maternal near-miss: a nested case-control study in selected public hospitals of Addis Ababa, Ethiopia. BMC Womens Health 2018; 18: 28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Dessalegn FN, Astawesegn FH, Hankalo NC. Factors associated with maternal near miss among women admitted in West Arsi zone public hospitals, Ethiopia: unmatched case-control study. J Pregnancy 2020; 2020: 6029160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Gedefaw M, Gebrehana H, Gizachew A, et al. Assessment of maternal near miss at Debre Markos referral hospital, Northwest Ethiopia: five years experience. Open J Epidemiol 2014; 4: 199–207. [Google Scholar]
- 20. Tenaw SG, Assefa N, Mulatu T, et al. Maternal near miss among women admitted in major private hospitals in eastern Ethiopia: a retrospective study. BMC Pregnancy Childbirth 2021; 21: 181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Dias MA, Domingues RM, Schilithz AO, et al. Incidence of maternal near miss in hospital childbirth and postpartum: data from the Birth in Brazil study. Cad Saude Publica 2014; 30(Suppl. 1): S1–S12. [DOI] [PubMed] [Google Scholar]
- 22. Nair M, Kurinczuk JJ, Knight M. Ethnic variations in severe maternal morbidity in the UK–a case control study. PLoS ONE 2014; 9(4): e95086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Biro MA, Davey MA, Carolan M, et al. Advanced maternal age and obstetric morbidity for women giving birth in Victoria, Australia: a population-based study. Aust N Z J Obstet Gynaecol 2012; 52(3): 229–234. [DOI] [PubMed] [Google Scholar]
- 24. Lamminpää R, Vehviläinen-Julkunen K, Gissler M, et al. Preeclampsia complicated by advanced maternal age: a registry-based study on primiparous women in Finland 1997–2008. BMC Pregnancy Childbirth 2012; 12: 47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Cleary-Goldman J, Malone FD, Vidaver J, et al. Impact of maternal age on obstetric outcome. Obstet Gynecol 2005; 105: 983–990. [DOI] [PubMed] [Google Scholar]
- 26. Adeoye IA, Ijarotimi OO, Fatusi AO. What are the factors that interplay from normal pregnancy to near miss maternal morbidity in a Nigerian tertiary health care facility? Health Care Women Int 2015; 36(1): 70–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Domingues RM, Dias MA, Schilithz AO, et al. Factors associated with maternal near miss in childbirth and the postpartum period: findings from the birth in Brazil National Survey, 2011–2012. Reprod Health 2016; 13: 115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Turi E, Fekadu G, Taye B, et al. The impact of Antenatal care on maternal near-miss events in Ethiopia: a Systematic review and meta-analysis. Int J Afr Nurs Sci 2020; 13: 100246. [Google Scholar]
- 29. 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]
- 30. Litorp H, Kidanto HL, Rööst M, et al. Maternal near-miss and death and their association with caesarean section complications: a cross-sectional study at a university hospital and a regional hospital in Tanzania. BMC Pregnancy Childbirth 2014; 14: 244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Keag OE, Norman JE, Stock SJ. Long-term risks and benefits associated with cesarean delivery for mother, baby, and subsequent pregnancies: systematic review and meta-analysis. PLoS Med 2018; 15(1): e1002494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Sway A, Nthumba P, Solomkin J, et al. Burden of surgical site infection following cesarean section in sub-Saharan Africa: a narrative review. Int J Womens Health 2019; 11: 309–318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Seely EW, Ecker J. Chronic hypertension in pregnancy. Circulation 2014; 129: 1254–1261. [DOI] [PubMed] [Google Scholar]
- 34. Peprah NY. Severe maternal morbidity and associated factors in Suntreso and Kumasi south government hospitals, Ashanti region, Ghana. Accra: University of Ghana, 2015. [Google Scholar]
- 35. World Health Organization. Recommendations on antenatal care for a positive pregnancy experience. Geneva: World Health Organization, 2016. [PubMed] [Google Scholar]
