Abstract
Background
Maternal near-miss (MNM) is an event in which a woman who nearly died but survived a complication that occurred during pregnancy, childbirth, or with 42 days of termination of pregnancy (Pattinson et al., 2009). Improving maternal health is one of the world’s most challenging problems (Abdollahpour et al., 2019). Existing evidence indicates that every 24 hr, around 1,000 mothers are losing their lives due to problems related to complications during their pregnancy globally, most of whom are from low-income countries (Abdel-Raheem et al., 2017). Maternal mortality is one of the important indicators used for the measurement of maternal health. Although the maternal mortality ratio remains high, maternal deaths in absolute numbers are rare in a community. To overcome this challenge, maternal near-miss has been recommended as a complement to maternal death (Pragti Chhabra, P.C, 2014). Nevertheless, a recent systematic review evidenced that the maternal near-miss tool is not uniformly applied in Sub-Saharan African countries (Tura et al., 2019).
In spite of the efforts of international developments and the widespread diffusion of obstetric management guidelines, globally, the prevalence of maternal near-misses is very high. A systematic review indicated that the global prevalence of maternal near-misses was 18.67/1,000 (Abdollahpour et al., 2019). MNM has a higher incidence rate than maternal mortality. MNM is 15 to 26 times more common in low-resource settings than maternal death, according to other studies (Filippi et al., 2018; Gedefaw et al., 2014; Goldenberg et al., 2017). Reports indicated that the median maternal near-miss ratio in middle-income countries was 9.6 per 1,000 live births. In lower-middle-income countries, the median maternal near-miss ratio was 15.9 per 1,000 live births. Whereas, for upper-middle-income countries, the median maternal near-miss ratio was 7.8 per 1,000 live births (Heitkamp et al., 2021). The rates are disproportionately higher in low- and middle-income countries in Asia and Africa (Tunçalp et al., 2012).
Previous studies found that previous cesarean sections, preexisting medical disorders, induction of labor, and a lack of antenatal care are all predictors of maternal near misses (Mekango et al., 2017; Ngoma-Hazemba et al., 2019). Similarly, delays at home, on the way to health facilities, and at the health facility significantly contribute to the occurrence of MNM (Abdel-Raheem et al., 2016; Pacagnella et al., 2014).
Maternal near-misses are associated with severe maternal consequences. For instance, women with maternal near misses were found to have twice the increased risk of postpartum depression (Abdollahpour et al., 2022). Maternal near-misses affect the occurrence of neonatal mortality too (Aliyi et al., 2021). It results in a fivefold increase in perinatal outcomes (Liyew et al., 2018). A recent study in Ethiopia indicated that among mothers who sustained MNM, 32% of perinatal deaths occurred, of which 88.6% were stillbirths and 11.4% were early neonatal deaths (Tura et al., 2020). Additionally, even though there is no association between MNM and nutritional disorders in children, it was documented that a 12% decrease in overall maternal breastfeeding was associated with near misses (Zanardi et al., 2016). Furthermore, the cost of treating maternal near misses is high and leads to catastrophic spending through out-of-pocket payments (Juma et al., 2021).
In Ethiopia, a low-income country, the Federal Ministry of Health of Ethiopia (FMOH) is striving to reduce the rate of maternal mortality in the country. So far, FMOH has taken various moves, such as organizing and mobilizing the Health Development Army at all levels to endorse behavioral change, supplying all districts in the country with ambulances, providing free obstetric care, ensuring the continuous development of health professionals, and providing adequate drugs, medical supplies, and equipment (Elias & Accorsi, 2014). Furthermore, a maternal death review committee was established in 64% of health facilities (Hadush et al., 2020). Despite all efforts made, the maternal mortality rate of the country remains unacceptably high; the pooled burden of MNM in the country was 12.57%, with the highest burden in the Amhara region (26.75%) and the lowest in Addis Ababa (0.8%) (Mengist et al., 2021).
Various studies have been conducted in Ethiopia to deal with maternal near-miss problems. However, the studies conducted so far included tertiary hospitals where advanced care is provided. Additionally, the magnitude of the near-miss varies from place to place, and the factors associated with it also vary. In addition, according to the local reports from the West Shewa Zonal health department, the level of maternal near-miss is disproportionately high in the study area (Gedo General Hospital); one-sixth of the near-miss cases in the zone were documented in the hospital. In consideration of the burden of MNM in the study area and its severe consequences, it is very crucial that studies be conducted on determinants of maternal near-miss to reduce the burden. Therefore, this study was conducted to identify the determinants of maternal near-miss among women admitted to Gedo General Hospital.
Methods
Study Area and Period
This study was conducted at Gedo General Hospital. The hospital is found in Gedo town, Chelia district, West Shoa zone, and Oromia regional state. It is located 179 km in the west direction from Addis Ababa, the capital city of Ethiopia. The hospital provides comprehensive obstetric care in addition to other different medical, surgical, and maternal and child health care services for more than 1.2 million people in the catchment area and surrounding areas. The gynecology and obstetrics department of the hospital was led by a team of health professionals comprising gynecologists, IEOs (Integrated Emergency Obstetrics), and midwives (Gedo General Hospital Annual Report, 2021). This study was conducted from August to October 2021.
Study Design
A hospital-based, unmatched case-control study was conducted.
Population
All women who came to Gedo General Hospital during pregnancy, labor, and the postnatal period for antenatal care, delivery, and postnatal care services were the source population.
Cases were women admitted during pregnancy and postpartum periods whose reason for admission fulfilled at least one of the Sub-Saharan Africa MNM tool criteria (which is adapted from the WHO MNM tool criteria, which has 27 criteria categorized into clinical criteria, laboratory-based criteria, and management-based criteria) (Tura et al., 2019), while controls were those women admitted due to pregnancy complications, labor, or postnatal care services not fulfilling the Sub-Saharan Africa MNM criteria’s for cases.
Inclusion and Exclusion Criteria
Inclusion Criteria
All women admitted to obstetric care services during pregnancy, labor, and the postpartum period were included in the study.
Exclusion Criteria
Those women readmitted for obstetric services during the data collection period were excluded.
Sample Size
The sample size was determined by using the STAT CALC application of EPI Info version 7.2.3.1 software for an unmatched case-control study with the assumptions of 90% power, 95% confidence level, and the ratio of control to case of two (2:1). Taking 7.8% of controls and 24.59% of cases with gravidity ≥ 5 from a previous study (Kumela et al., 2020) and adding 5% of the non-response rate, the final minimum sample size required was 251 (84 cases and 167 controls).
Sampling Procedure
Patient cards, admissions, and operation theater logbooks were used to identify cases based on Sub-Saharan MNM tool criteria (Tura et al., 2018). Cases were identified by trained data collectors, and two controls were selected for each identified case using simple random sampling. A lottery method was used based on patient medical records among eligible women (women who did not meet the criteria for cases) admitted within 24 hr of their admission. Cases were sequentially included in the study as they were presented. A remark was given on patient medical records to avoid the inclusion of readmitted women during the data collection period in the study by data collectors.
Variables of the Study
Dependent Variable
Maternal near-miss.
Independent Variables
Socio-demographic Factors
Residence, age, monthly income, marital status, mother’s education, mother’s occupation, husband’s education, husband’s occupation, religion.
The Delay Factors
Decision-maker for the place of delivery, delay in deciding to come to the health facility, exposure to media, not going to the health facility after deciding to go, the time it takes to reach the health facility on foot, means of transportation, problems encountered after reaching the health facility
Clinical Factors
Previous chronic hypertension, previous anemia, history of maternal cardiac diseases, history of diabetes mellitus, duration of hospital stays, presence of blood product for blood transfusion.
Reproductive Factors
History of abortion, number of abortions, contraceptive use before current pregnancy, history of female genital cutting, age at first pregnancy, age at marriage, number of pregnancies, birth preparedness, ANC follow up for last pregnancy, the timing of ANC booking, number of ANC visits, prior history of C/S, birth attendant preferences, induction of labor, number of deliveries, previous place of delivery, mode of current delivery, history of stillbirth, duration of labor, place of current delivery.
Operational Definition
Maternal near miss: women admitted due to maternal health problems identified who fulfilled at least one of the Sub-Saharan Africa MNM tool criteria’s (which adapted from the WHO MNM tool criteria, which has 27 criteria (19 criteria out of 25 original WHO criteria and 8 newly suggested criteria, which are categorized into clinical criteria, laboratory-based criteria, and management-based criteria) during pregnancy, labor, or after giving birth (Aliyi et al., 2021).
The first maternal delay occurred between the identification of health problems and the decision to pursue maternal health care. If it took more than 24 hr to decide to seek treatment, there was a delay; otherwise, there was no delay. The second maternal delay was a time after decision-making to reach health facilities. The time has been estimated at more than 1 hr to reach the existing health facility and otherwise not (Mekango et al., 2017).
Data Collection Tool and Procedure
Data were collected by using a pretested interviewer-administered questionnaire adapted from different literatures (Firdawek, 2018; Kasahun & Wako, 2018; Kumela et al., 2020; Mekango et al., 2017). The questionnaire comprised four parts, which include socio-demographic factors, delay factors, clinical factors, and reproductive factors. Maternal near-miss was assessed based on the Sub-Saharan Africa MNM tool, adapted from WHO MNM tool criteria. The tool has 27 criteria (19 criteria out of 25 original WHO criteria and 8 newly suggested criteria) categorized into clinical criteria, laboratory-based criteria, and management-based criteria during pregnancy, labor, or after giving birth (Tura et al., 2020).
Data collectors were five midwives nurses (all of them were Bachelor of Sciences). The overall study was supervised by one master’s-holder Integrated Emergency Obstetrics and Surgery (IEOS) professional.
Data Quality Control
The English version tool was translated to the regional language (Afan Oromo) and translated back to English to ensure its consistency. Training was also provided to the data collectors. Each completed questionnaire was checked for completeness and consistency at the site of data collection. Moreover, the questionnaire was pretested on 5% (13) of respondents outside the study area, and necessary modifications were made accordingly.
Data Analysis Procedure
Data were checked for completeness and consistency, entered into Epidata version 3.1, and exported to SPSS version 25 for analysis. Descriptive statistics were used to describe the background variables. To compare the proportion of cases and controls, a chi-square test was used. Binary logistic regression was undertaken to assess the association of each independent variable with the maternal near-miss. Multivariable logistic regression analysis was done by entering all variables with a
Results
Two hundred forty-eight (248) study participants (83 cases and 165 controls) were included in this study, yielding a response rate of 98.80%. About 71.1% of cases and 81.2% of controls were in the age group 20 to 34. Regarding the residence of study participants, about 79.5% of cases and 45.5% of controls resided in rural areas. Regarding the occupational status of the study participants, 60.2% of cases and 58.8% of controls were housewives.
The majority of the cases (97.6%) and controls (98.8%) were married. About 25.9% of cases and almost nine in ten of controls, the husbands had no formal education. Concerning study participants’ family monthly income, 24.1% of the cases and 47.9% of the controls had a monthly income of more than 2001 Ethiopian birr (ETB) (Table 1).
Socio-demographic Characteristics of Pregnant and Postpartum Women Admitted to Gedo General Hospital, Central Ethiopia, 2021.
Delay to Seek Care and Accessibility of Service
Among study participants, 34.9% and 10.3% of the cases and controls, respectively, did not decide to go to a health facility immediately when the problem was identified. About 27.7% of cases and 15.8% of controls did not go to a health facility after identifying their current problem. This study revealed that to reach a nearby health facility on foot for 41% of cases and 9.7% of controls, it took more than 1 hr. Regarding means of transportation, 85.5% of cases and 63.3% of controls come to the hospital by ambulance. One-fourth of cases and almost 8 out of 10 controls reported that they faced problems related to service after arriving at the study hospital (Table 2).
Delay and Accessibility Related Factors Among Pregnant and Postpartum Women Admitted to Gedo General Hospital, Central Ethiopia, 2021.
Clinical Factors
Among study participants, 38.6% and 2.4% of cases and controls, respectively, had a history of hypertension. More than one in four cases and 1 in 10 controls had previous anemia during their current pregnancy. During their visit, 38.6% of respondents among the cases were told that blood was needed for their current problem. About 61.4% of cases and 3.6% of controls had a history of chronic diseases during their current pregnancy (Table 3).
Clinical Factors Among Pregnant and Postpartum Women Admitted to Gedo General Hospital, Central Ethiopia, 2021.
Reproductive Factors
Regarding the history of pregnancy of study participants, about 44.6% and 5.5% of cases and controls, respectively, had a history of five or more pregnancies. Nearly one-third of cases and 9.1% of controls had a history of previous cesarian sections (C/S). Regarding their previous abortions, 23.5% and 10.5% of cases and controls, respectively, had a history of two or more abortions. Moreover, about two-thirds (66.3%) of the cases and 77.6% of the controls decide the place of delivery jointly with their husbands. About 62.7% of cases and 64.2% of controls had used family planning before the current pregnancy. About 33.7% of cases and 4.8% of controls had a total of five or more live births in their lives. Regarding their antenatal care follow-up, 57.8% and 42.2% of cases and controls, respectively, had a follow-up of antenatal care (ANC) during their current pregnancy (Table 4).
Reproductive Health Factors Among Pregnant and Postpartum Women Admitted to Gedo General Hospital, Central Ethiopia, 2021.
Determinants of Maternal Near-Miss
Binary logistic regression was run for each independent variable and dependent variable. Variables with
After controlling for the possible effect of confounders, monthly income, the decision to instantly seek service, the time it takes to reach the nearby health facility, birth preparedness, and complication readiness were found to be determinants of the maternal near miss.
Accordingly, women who had a monthly income of 1,001 to 2,000 ETB had an 89% lower risk [AOR: 0.11; 95% CI: 0.02, 0.48] of maternal near-miss compared to those who had ≤1,000 ETB. The 1,000 to 2,000 ETB monthly income was lower than the average 5,000 monthly income of women Ethiopian (Asaye, 2020) . The odds of maternal near-miss were more than four times higher [AOR: 4.54; 95% CI: 1.56, 13.21] among women who didn’t decide to go to health facilities instantly as a current problem was identified compared to their counterparts. Similarly, the time it took to reach the nearby health facility was also identified as the determinant of maternal near-miss, as women for whom it took more than 60 min to reach the nearby health facility had almost seven fold [AOR: 6.95; 95% CI: 2.29, 21.01] increased odds of maternal near-miss.
The odds of maternal near-miss were more than five times [AOR: 4.1; 95% CI: 1.59, 10.58] higher among mothers who didn’t have birth preparedness and complication readiness compared to their counterparts (Table 5).
Determinants of Maternal Near-Miss Among Women Admitted at Gedo General Hospital, 2021.
Note. AOR = adjusted odds ratio; 1 = reference category; COR = crude odds ratio.
Discussion
This study was aimed at identifying the determinants of maternal near-miss among mothers admitted to Gedo General Hospital. The study revealed that monthly income, the decision to instantly seek service, the time it takes to reach the nearby health facility, birth preparedness, and complication readiness were found to be determinants of the maternal near miss.
In this study, average monthly income was found to be an independent predictor of maternal near-miss; women with a higher monthly income had 89% reduced odds of maternal near-miss. This finding is in line with other studies conducted in Ethiopia (Worke et al., 2019; Asaye, 2020). This might be because financially incompetent mothers would have difficulties affording the health care and transportation costs, resulting in delays in treatment-seeking.
Early detection and management of obstetric complications could improve maternal outcomes, and this needs timely health-seeking behaviors by clients (Vogel et al., 2014). Consistently, this study revealed that the odds of maternal near-miss were more than four times higher among women who didn’t decide to go to health facilities instantly after the current problem was identified compared to those who decided promptly. This finding is in line with studies done in West Arsi (Dessalegn et al., 2020) and southern Ethiopia (Kasahun & Wako, 2018). Another study conducted in Morocco also reported a similar finding (Assarag et al., 2015). The possible explanation for the observed association is women who don’t decide to seek health care instantly, which in turn leads to delays in seeking health care, which would result in poor health outcomes for mothers and babies (Pacagnella et al., 2014).
Again, the time it takes to reach the nearby health facility was also identified as a determinant of maternal near-miss; women who took more than 60 min to reach the nearby health facility had almost seven fold increased odds of maternal near-miss. This finding is consistent with the study done in Northern Ethiopia (Mekango et al., 2017), Western Ethiopia (Kumela et al., 2020) and Morocco (Assarag et al., 2015) where time it took to reach a health facility was found to be a to be a determinant of maternal near miss. In Ethiopia, the barriers to the second delay, that is, delay in getting access to health facilities, include lack of transport, inaccessibility of transport, long distance from functioning health facilities, and nonfunctioning health facilities in between home and the functioning health facility (Berhan & Berhan, 2014). This delay results in prolonged complications of maternal health conditions and, consequently, maternal near-misses and mortality.
The odds of a maternal near-miss were more than five times higher among mothers who didn’t have birth preparedness and complication readiness compared to their counterparts. This finding is consistent with the study conducted in southern Ethiopia (Habte & Wondimu, 2021). Other studies also revealed that birth preparedness and complication readiness are associated with good pregnancy outcomes (Gudayu & Araya, 2019). The reason behind this association is that birth preparedness is a very important obstetric plan that includes preparation for unforeseen events that are likely to occur during pregnancy. It also includes saving money for the treatment of any of the obstetric conditions, transportation, and other plans for safe delivery. This would enable the women to be ready if any complications arise. As a result, any obstetric condition will be managed early before it complicates. Conversely, women with no such plans would be at increased risk of adverse maternal and neonatal outcomes, including maternal near-misses.
Limitations of the Study
This study employed a case-control study design, a strong design, to identify the determinants of near misses. Yet, it is not devoid of limitations. First, the study might be affected by recall bias since some of the variables were measured by self-report. Again, since the study is not a longitudinal study, some of the controls might develop near-misses after they were discharged from the hospital. This would overestimate the effect of exposure variables on maternal near-misses.
Conclusions
This study revealed that monthly income, the decision to instantly seek health care, the time it takes to reach the nearby health facility, birth preparedness, and complication readiness were found to be determinants of the maternal near miss. Therefore, it is imperative for the responsible stakeholders to improve service accessibility for the improvement of maternal outcomes. In addition, it is also indispensable to counsel women regarding early health seeking, birth preparedness, and complication readiness to curb the problem. Policymakers should design strategies for preventing maternal near-misses. In addition, interventional studies should be done to improve maternal health and thus prevent maternal near-misses.
Footnotes
Declaration of Conflicting Interests
Funding
ORCID iD
