Trends, causes, and delays associated with maternal mortality in public hospitals of Hawassa City, Southern Ethiopia: a five-year retrospective study

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Although reductions have been reported nationally, recent facility-based evidence from Sidama Region is limited. This study aimed to assess trends, causes, and contributing factors of maternal deaths in public hospitals of Hawassa City Administration. Methods: A five-year retrospective review was conducted on maternal deaths that occurred between January 2020 and December 2024 in three public hospitals in Hawassa City. The data were extracted from health facility maternal medical records. Descriptive statistics were used to present background information, causes of death, and delays based on the World Health Organization three-delay model. Maternal mortality ratios were calculated per 100,000 live births. Results: A total of 94 maternal deaths and 44,086 live births were documented. resulting in an overall Maternal Mortality Ratio of 213.2 per 100,000 live births. Most of deaths (86.2%) were due to direct obstetric causes, with hypertensive disorders of pregnancy (30.9%) and obstetric haemorrhage (27.7%) being the leading causes. Indirect causes accounted for 13.8% of deaths, primarily malaria and anaemia. The majority of maternal deaths (74.5%) occurred postpartum. Delay in reaching a health facility (63%) was the most common contributing factor, followed by delay in seeking care (32.1%). Conclusion: Maternal mortality in Hawassa City is higher than the Sustainable Development Goal target even though the number is lower than the national rate and the results of previous studies. Strengthening referral systems, increasing availability of emergency obstetric services and addressing delays in seeking and reaching care are critical to further reduce maternal deaths. Maternal mortality Direct obstetric causes Indirect obstetric causes Three-delay model Ethiopia Figures Figure 1 Introduction World Health Organization (WHO) defines maternal death as “a death in a woman from any cause related to or aggravated by the pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy”( 1 , 2 ). Maternal mortality remains a major public health issue, with deaths in low- and middle-income countries accounting for 86% of the total. According to the 2030 Sustainable Development Goals (SDGs), all countries are expected to reduce maternal mortality by at least two-thirds from their 2010 baseline levels. In addition, the global target is to achieve an average maternal mortality ratio (MMR) of fewer than 70 maternal deaths per 100,000 live births by 2030. Despite global progress, Ethiopia remains among the countries with the highest maternal mortality burden, with an estimated MMR of 267 maternal deaths per 100,000 live births in 2020 ( 3 ). Despite persistently high maternal mortality in developing regions, notable reductions were achieved between 1990 and 2013. Africa experienced a 47% decline in MMR, while East African countries achieved a 57% reduction. Similarly, Ethiopia recorded an estimated 60.5% decline in maternal mortality between 2011 and 2020, reflecting substantial progress over the past decade ( 3 – 5 ). A retrospective analysis conducted in Addis Ababa from 2017 to 2021 revealed that the MMR was 74/100,000. Similarly, retrospective review of maternal deaths in Hawassa University Comprehensive Specialised Hospital (HUCSH) from 2016 to 2017 showed that the overall facility-based MMR was 910/100,000. Another study, which was conducted in Eastern Ethiopia, reported that the overall estimated MMR was 324 per 100,000 live births between 2008 to 2014( 6 – 8 ). In developing countries, maternal mortality is primarily attributed to obstetric haemorrhage, hypertensive disorders of pregnancy, obstructed labour, unsafe abortion, and embolism. Similarly, evidence from Ethiopia indicates that hypertensive disorders of pregnancy, obstetric haemorrhage, sepsis, and embolism continue to be the major causes of maternal mortality( 9 – 12 ). Several global and national initiatives have been implemented to reduce maternal mortality. The World Health Organization initiated the Maternal Death Surveillance and Response (MDSR) system to enable systematic maternal death reviews at community and health-facility levels in 2013. Ethiopia integrated this system into its national public health emergency management framework, enabling continuous data collection and informing interventions to address the leading causes of maternal mortality( 3 , 6 , 13 ). An institution-based study assessing maternal mortality was previously conducted at Hawassa Comprehensive Specialized Hospital (HUCSH) in 2017 ( 7 ). However, to more accurately reflect the burden and distribution of maternal mortality in the region, it is necessary to incorporate data from other public hospitals within Hawassa City, Sidama Region. Moreover, recent evidence on the MMR in the area is limited, underscoring the need for updated analyses. Therefore, this study aims to describe the temporal trends in maternal mortality from 2020 to 2024 and to identify the leading causes and contributing factors of maternal deaths across three public hospitals in Hawassa City. By providing an updated and broader institutional perspective, the findings are expected to inform the development and periodic evaluation of targeted strategies for the prevention of maternal deaths in the region. Methods Study design and setting A retrospective facility-based observational study was conducted from secondary data of patient records on maternal deaths that occurred during January 2020 to December 2024. The study was conducted in three public hospitals: HUCSH, Adare general hospital, and Hawela Tula general hospital, located in Hawassa City, Sidama Zone. Hawassa city is located 273 km south of Addis Ababa. Sidama Regional State is one of Ethiopia's twelve regional states. The region had a population of 4.3 million people in 2020 and administratively divided into 30 rural districts. The region has 18 hospitals (1 tertiary, 4 general and 13 primary), 137 health centres and 553 health posts operated by the government. Health canters provide basic emergency obstetric and newborn care, while hospitals deliver comprehensive care in addition to these basic services ( 14 ). Source population All pregnant mothers who were admitted and receiving obstetrics and gynaecologic care at the selected hospitals. Study population The study population was composed of patients who died in these hospitals between January 2020 and December 2024 after being admitted and receiving obstetric and gynaecologic care. The study included all deceased mothers with a complete chart record and have received obstetric services, including termination of pregnancy, delivery, or treatment for postnatal complications between January 2020 and December 2024, and excluded pregnant women who died from accidental or intentional injuries. Data collection and procedures All maternal deaths in the hospitals from January 2020 to December 2024 were identified by reviewing all registers at the emergency department, labour ward, operating theatres, antenatal and post-natal wards, intensive care unit, and from the health facility Health Information Management System (HIMS). The hospital numbers were used to retrieve the case file from the Record Department. Every hospital implemented Maternal Death Surveillance and Response (MDSR) system. There was a committee established for reviewing maternal deaths in all health facilities, and every facility appointed a specific MDSR coordinator. The total number of admission, deliveries, and live births under the period of review was obtained. Data was collected by using a semi-structured checklist, which was adopted from a previous study, and assesses variables such as the WHO delay modalities, sociodemographic characteristics, obstetric history, diagnosis, and cause of death ( 7 ). Four data collectors were trained for two days, and close supervision was maintained during data collection. All checklists were double-checked for consistency and completeness. The data collectors were instructed to write card numbers on the checklist during data collection. So that any identified error was traced back using the card numbers. Operational Definition Maternal mortality: Any death that occurred during pregnancy or within 42 days of postpartum (irrespective of the duration and site of the pregnancy) from any cause related to, or aggravated by, the pregnancy or its management but not from accidental or incidental causes( 15 ). Direct obstetric death: Death that resulted from complications of the pregnancy, labor, or puerperium, as well as maternal deaths arising from interventions, omissions, or incorrect treatments. Indirect obstetric death: Death that resulted from either pre-existing or new-onset medical conditions aggravated by the physiologic process of pregnancy. Delay 1- traditional practice, lack of the decision to go to health facility, family poverty, and delayed referral from home. Delay 2- delayed arrival to referred facility, lack of transportation, lack of road, no facility within reasonable distance and lack of money for transport. Delay 3- delayed arrival to next facility, delayed or lacking of supplies and equipment, delayed management after admission and human error or mismanagement( 16 ). Data analysis Data were entered and analysed using the Statistical Software for Social Sciences (SPSS) Version 26. Data were assessed for normality of distribution prior to analysis. Descriptive statistics were used to summarize participant characteristics, including socio-demographic variables, obstetric history, and causes of maternal death. Categorical variables are presented as frequencies and percentages, while continuous variables are summarized using means and standard deviations (SD). Descriptive analyses were also performed to examine the types of investigations, procedures, and interventions provided. Causes of maternal death were stratified according to the three delays framework and described under the respective categories of delay, in accordance with the WHO delay classification. In addition, trend analysis was conducted to illustrate patterns in the maternal mortality ratio (MMR) across the study period. Ethical Considerations Prior to data collection, ethical clearance was obtained from the Institutional Review Board of Hawassa University College of Medicine and Health Sciences (Ref.No:IRB/322/16). The Ethical Review Committee of College of Health Sciences and HUCSH permitted us to collect the data from patient records without the need for patient consent. To establish anonymous linkage only the card number, and not the names of the participant from the chart, were registered on the checklist. Result Socio-demographic and obstetric characteristics During the study period, 94 maternal deaths met the inclusion criteria. The mean (SD) age of the deceased women was 26.6 (4.2) years, with 80.8% aged 20–29 years. The majority of women, 85.1%, were married, and 77.7% lived in rural areas. Regarding educational status, 36.2% had completed primary (elementary) education, and 33.0% had no formal education. Most women were multiparous (74.5%), and 92.6% had received antenatal care. In terms of mode of delivery, 57.4% delivered vaginally, while 33.0% underwent caesarean section. Most deliveries occurred in health institutions (89.4%), whereas 8.5% took place at home. Nearly 90% of women were referral cases, and 42.6% required intensive care unit (ICU) admission. Overall, 69.1% of women stayed in the hospital for more than 24 hours, and 74.5% of maternal deaths occurred during the postpartum period ( Table 1 ). Table 1 Socio-demographic and obstetric characteristics of deceased women at public hospitals in Hawassa City Administration, Ethiopia, 2020–2024 (N = 94) Characteristics Count (%) Maternal age (years) < 20 3 (3.2) 20–24 22 (23.4) 25–29 54 (57.4) 30–34 10 (10.6) ≥ 35 5 (5.3) Marital status Single 4 (4.3) Married 80 (85.1) Divorced 3 (3.2) Widowed 2 (2.1) Unknown 5 (5.3) Educational level No formal education 31 (33.0) Can read and write 25 (26.6) Elementary school 34 (36.2) College and above 4 (4.3) Residence Urban 21 (22.3) Rural 73 (77.7) Parity P0 18 (19.2) P1 21 (22.3) P2-4 30 (31.9) P5+ 19 (20.2) Unknown 6 (6.4) Antenatal care Yes 87 (92.6) No 6 (6.4) Unknown 1 (1.0) Mode of delivery Vaginal 54 (57.4) Caesarean 31 (33.0) Instrumental 6 (6.4) Laparotomy 3 (3.2) Place of delivery Hospital 59 (62.8) Health center 25 (26.6) Home 8 (8.5) Unknown 2 (2.1) Outcome of newborn Dead 41 (43.6) Alive 38 (40.4) Unknown 15 (16.0) Referral status Referred 76 (80.9) Not referred 11 (11.7) Unknown 7 (7.4) ICU admission Yes 40 (42.6) No 54 (57.4) Gestational age Less than 37 32 (34.0) 37–42 48 (51.0) Unknown 14 (15.0) Total duration of hospital stays Less than 24 hrs 29 (30.9) More than 24 hrs 65 (69.1) Time of death Before delivery 17 (18.1) During delivery 7 (7.4) After delivery 70 (74.5) Causes of maternal death The analysis demonstrated that direct and indirect causes were responsible for 86.2% and 13.8% of maternal deaths, respectively. The most common direct causes were hypertensive disorder in pregnancy (30.9%), obstetric haemorrhage (27.7%), puerperal sepsis (9.6%), and obstructed Labor (6.4%), whereas the most common indirect causes were malaria (4.3%), anaemia (4.25%), and tuberculosis (1.1%) (Table 2 ). Table 2 Clinical Causes of Maternal Death in Public Hospitals of Hawassa City Administration, 2020–2024 Cause of Death 2020 2021 2022 2023 2024 Total Direct Causes Haemorrhage 5 6 5 3 7 26 (27.7%) Obstructed Labor 2 0 2 0 2 6 (6.4%) Hypertensive disorder 7 7 5 7 3 29 (30.9%) Abortion 0 0 2 1 0 3 (3.2%) Sepsis 2 2 1 2 2 9 (9.6%) Others (Direct) 3 4 1 0 0 8 (8.5%) Subtotal Direct 19 19 16 13 14 81 (86.2%) Indirect Causes Malaria 2 0 2 0 0 4 (4.3%) Anaemia 2 0 0 1 1 4(4.25%) Tuberculosis 0 1 0 0 0 1 (1.1%) Others (Indirect) 2 0 0 1 1 4 (4.25%) Subtotal Indirect 6 1 2 2 2 13 (13.8%) Total 25 20 18 15 16 94 (100.0%) Factors contributing to maternal deaths Table 3 illustrates the delays and contributing factors of maternal mortality among the 81 women who died due to direct obstetric causes. The most frequently mentioned delay was a delay in reaching health care (delay type 2), which was a contributing factor in 51 (63%) of the deaths. The main causes for the delay in reaching the health facility were identified as delayed arrival to the referred facility, 43(84.3%), lack of transportation,1(1.9%), and absence of a facility within a reasonable distance, 7(13.7%). The second most frequently reported delay was a delay in decision-making whether to seek care (delay type 1), which was a contributing factor in 26 (32.1%) of the fatalities. The main factors leading to delays in deciding to seek care included failure to recognize the problem, 30 (38.5%), lack of decision to go to a health facility, 7 (26.9%), delayed referral from home, 6 (23.1%), traditional practices, 2 (7.7%), and family poverty, 1 (3.8%). (Table 3 ) The third cause of delay noted was a delayed appropriate care and intervention at the health facilities (delay type 3), which accounted for 21 (25.9%) deaths. The primary reasons for the delay in receiving appropriate care were identified as delayed referrals 9 (43%), post-admission case management delays, 4 (19%), shortages of basic supplies and equipment, 4 (19%), and incorrect risk assessments and treatments, 4 (19%). (Table 3 ) Table 3 Delays and Contributing Factors to Direct Maternal Death in Hawassa City, 2020–2024(n = 81) Contributory Factor Frequency (%) Delay One: Seeking Care; n (%) = 26(32.1) Traditional practice 2 (7.7) Failure of recognition of the problem 10 (38.5) Lack of decision to go to the health facility 7 (26.9) Family poverty 1 (3.8) Delayed referral from home 6 (23.1) Delay Two: Reaching Care; n (%) = 51(63) Delayed arrival to referred facility 43 (84.3) Lack of transportation 1 (1.9) No facility with reasonable distance 7 (13.7) Delay Three: Receiving Care; n(%) = 21(25.9%) Delayed arrival to next facility from another facility 9 (43.0) Delayed or lacking supplies and equipment 4 (19.0) Delayed management after admission 4 (19.0) Incorrect risk assessment and treatment 4 (19.0) There are repeated responses, so the sum may exceed the total frequency and percentage. Maternal Mortality Trends A total of 94 maternal deaths and 44086 live births occurred from 2020 to 2024, giving a maternal mortality ratio (MMR) of 213.2 per 100,000 live births. Among these 94 deaths 80 (85.1%) of them occurred at HUCSH,13 (13.8%) at Adare generalized hospital, and 1 (1.06%) in Hawela Tula general hospital. The highest death rate occurred in 2020 with an MMR of 293/100,000 followed by MMR of 222/100,000 in 2021. The lowest death rate occurred in 2022 with an MMR of 179/100,000. The maternal mortality ratio declined from 2020 (293/100000) to 2023 (180.7/100000), followed by a slight increase in 2024 (195/100000) (Fig. 1 ) . Discussion The estimated maternal mortality ratio (MMR) in the present study over the five-year period was 213 maternal deaths per 100,000 live births, which is substantially lower than estimates reported in previous studies conducted in Sidama Region in 2019 (419 per 100,000 live births) and Jimma in 2017 (857 per 100,000 live births). It is also lower than the MMR reported for low-income countries by the WHO 2023 (346 per 100,000 live births) and the estimate from the 2016 Ethiopia Demographic and Health Survey (EDHS) (412 per 100,000 live births( 1 , 14 , 17 , 18 ). The results were also lower when compared to findings from other studies conducted in Africa, such as a 2019 study from Nigeria (644/100,000) and a 2015 study from Gambia (1461/100,000)( 19 , 20 ). This lower maternal mortality ratio may be attributed to improved access to healthcare services, better health-seeking practices, and the more effective implementation of interventions in the study area. This study reported that over 86.2% of maternal deaths were caused by direct obstetric causes, which is consistent with the findings of other studies in Somalia, Pakistan, Indonesia, and including a global estimate from the WHO( 3 , 12 , 21 , 22 ). The most common direct causes in this study was hypertensive disorder in pregnancy (30.9%), which align with results from other studies conducted in Addis Ababa, and a multi centre study conducted in Nigeria( 9 , 23 , 24 ). This differs from most local, sub-Saharan Africa, and global findings where haemorrhage is the most common cause( 5 , 6 , 20 , 25 ). The observed discrepancy may be due to differences in referral patterns, and improved prevention and management of obstetric haemorrhage at the study facility. This study demonstrated that 13.8% maternal death is due to indirect causes. The most common indirect causes in this study was malaria (4.3%), which is consistent with the results of other studies in Rwanda, and Nigeria( 25 , 26 ). In contrast to this study, reports from Tanzania, and Somalia revealed that anaemia is the commonest cause of indirect maternal death( 12 , 27 ). It is important to emphasize that study conducted in developed nations indicates that indirect factors contributing to maternal mortality, such as cardiac conditions, are more prevalent( 28 , 29 ). The discrepancy may be explained by differences in study settings. An important issue for planning of interventions is an understanding of the timing of maternal deaths with respect to labour and delivery. In this study, it was found that 74.5% of maternal deaths occur postpartum, which is consistent with finding from other study in Addis Ababa, Rwanda, and systematic analysis for the Global Burden of Disease Study, underscoring the need for strengthened postnatal surveillance, timely recognition of complications( 5 , 6 , 25 ). Our study indicated that patients whose admission ended in maternal mortality faced various forms of delay. However, the most observed delay was delay in reaching health facility (delay two), followed by the delay in seeking care (delay one). This result align with results from other studies conducted in Ethiopia and African countries( 7 , 20 , 30 , 31 ). In contrary to this finding, reports from Addis Ababa, Rwanda, Indonesia, and study from South-American upper-middle income country showed that delay in receiving care (delay 3) is the commonest delay( 6 , 25 , 32 , 33 ). The observed discrepancy may be attributed to differences in geographic accessibility, and socioeconomic contexts across study settings. Strength and Limitation Strengths: The study included three public hospitals and covered a five-year timeframe, allowing assessment of maternal mortality trends using standardized WHO definitions and delay models. Limitations: The retrospective, facility-based approach relied on the completeness of records and did not account for community deaths, which restricts generalizability and may underestimate the actual incidence of maternal mortality. Conclusion The maternal mortality ratio in public hospitals of Hawassa City from 2020–2024 was 213 per 100,000 live births which is higher than the Sustainable Development Goal target even though the number is lower than the national rate and the results of previous studies. The majority of maternal deaths were due to direct obstetric causes, particularly hypertensive disorders of pregnancy and obstetric haemorrhage, while malaria and anaemia were the leading indirect causes. Delays in reaching health facilities and delays in seeking care were the most common contributors, highlighting the need to strengthen referral systems, transportation, and early recognition of obstetric complications. Abbreviations HIMS Health Information Management System HUCSH Hawassa University Comprehensive Specialized Hospital ICU Intensive Care Unit MDSR Maternal Death Surveillance and Response MMR Maternal Mortality Ratio SDGs Sustainable Development Goals WHO World Health Organization Declarations Ethical approval Prior to data collection, ethical clearance was obtained from the Institutional Review Board of Hawassa University College of Medicine and Health Sciences (Ref.No:IRB/322/16). The Ethical Review Committee of College of Health Sciences and HUCSH permitted us to collect the data from patient records without the need for patient consent. To establish anonymous linkage only the card number, and not the names of the participant from the chart, were registered on the checklist. Consent for publication: Not applicable Availability of data and materials: The datasets used during the current study are available from the corresponding author on reasonable request Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article. Funding: The authors did not receive support from any organisation for the submitted work. Authors' contributions: Mihiretu Tesfamariam, and Abdulmalik Usman contributed to the study conception and design. Material preparation and data collection were performed by Mihiretu Tesfamariam, and Temesgen Teklu. The first draft of the manuscript was written by Abdulmalik Usman. Mihiretu Tesfamariam supervised the study, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: The authors are grateful to the data collectors who mediated data collection in health facility. References WHO. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Editor invited by journal 20 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 19 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8609187","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592407510,"identity":"993a0ba8-238a-486f-b331-50b40204d801","order_by":0,"name":"Abdulmalik Usman","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Abdulmalik","middleName":"","lastName":"Usman","suffix":""},{"id":592407516,"identity":"a9d2cff6-e0db-49fa-9415-6e4622a19aad","order_by":1,"name":"Temesgen Teklu","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Temesgen","middleName":"","lastName":"Teklu","suffix":""},{"id":592407523,"identity":"92061ab8-446b-4077-a3e7-e80a2987df97","order_by":2,"name":"Mihiretu Tesfamariam Goshu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACZgY2MM3ewNj4AEjz8BGnJQGo9ABzswFICxsR9sC0sLdJQLn4gW4787MHH3/Y2PNIJLZVfs2xk2FjYH746AYeLWaH2cwNZySkJfYAtdyW3ZYMdBibsXEOXi08bNI8CYcT7EFaJLcxA7UARYjQ8h/ssGLJbfVEaznACHIY48dth4nRwmYmOSMtObGH52GzNOO24zxszIT8cv7wM4kPNnb2POzpDz/+3FZtz8/e/PAxPi0ogJkHTBKrHAQYf5CiehSMglEwCkYMAACy+UBn0jqp4AAAAABJRU5ErkJggg==","orcid":"","institution":"Besheno Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mihiretu","middleName":"Tesfamariam","lastName":"Goshu","suffix":""}],"badges":[],"createdAt":"2026-01-15 09:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8609187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8609187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102913583,"identity":"f9c520e2-e169-4df3-8ed0-1d484639239b","added_by":"auto","created_at":"2026-02-18 10:43:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77999,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of maternal mortality ratio in public hospitals of Hawassa City Administration, Ethiopia, 2020–2024.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8609187/v1/aa57c8f9652cc6b434642cee.jpeg"},{"id":102963877,"identity":"5b9460b5-9563-43ef-bc2c-774ceef9bbd6","added_by":"auto","created_at":"2026-02-19 04:20:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8609187/v1/f4cd7938-b64b-4571-9db2-6be947b7163c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends, causes, and delays associated with maternal mortality in public hospitals of Hawassa City, Southern Ethiopia: a five-year retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWorld Health Organization (WHO) defines maternal death as \u0026ldquo;a death in a woman from any cause related to or aggravated by the pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy\u0026rdquo;(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Maternal mortality remains a major public health issue, with deaths in low- and middle-income countries accounting for 86% of the total. According to the 2030 Sustainable Development Goals (SDGs), all countries are expected to reduce maternal mortality by at least two-thirds from their 2010 baseline levels. In addition, the global target is to achieve an average maternal mortality ratio (MMR) of fewer than 70 maternal deaths per 100,000 live births by 2030. Despite global progress, Ethiopia remains among the countries with the highest maternal mortality burden, with an estimated MMR of 267 maternal deaths per 100,000 live births in 2020 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite persistently high maternal mortality in developing regions, notable reductions were achieved between 1990 and 2013. Africa experienced a 47% decline in MMR, while East African countries achieved a 57% reduction. Similarly, Ethiopia recorded an estimated 60.5% decline in maternal mortality between 2011 and 2020, reflecting substantial progress over the past decade (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). A retrospective analysis conducted in Addis Ababa from 2017 to 2021 revealed that the MMR was 74/100,000. Similarly, retrospective review of maternal deaths in Hawassa University Comprehensive Specialised Hospital (HUCSH) from 2016 to 2017 showed that the overall facility-based MMR was 910/100,000. Another study, which was conducted in Eastern Ethiopia, reported that the overall estimated MMR was 324 per 100,000 live births between 2008 to 2014(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn developing countries, maternal mortality is primarily attributed to obstetric haemorrhage, hypertensive disorders of pregnancy, obstructed labour, unsafe abortion, and embolism. Similarly, evidence from Ethiopia indicates that hypertensive disorders of pregnancy, obstetric haemorrhage, sepsis, and embolism continue to be the major causes of maternal mortality(\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Several global and national initiatives have been implemented to reduce maternal mortality. The World Health Organization initiated the Maternal Death Surveillance and Response (MDSR) system to enable systematic maternal death reviews at community and health-facility levels in 2013. Ethiopia integrated this system into its national public health emergency management framework, enabling continuous data collection and informing interventions to address the leading causes of maternal mortality(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn institution-based study assessing maternal mortality was previously conducted at Hawassa Comprehensive Specialized Hospital (HUCSH) in 2017 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, to more accurately reflect the burden and distribution of maternal mortality in the region, it is necessary to incorporate data from other public hospitals within Hawassa City, Sidama Region. Moreover, recent evidence on the MMR in the area is limited, underscoring the need for updated analyses. Therefore, this study aims to describe the temporal trends in maternal mortality from 2020 to 2024 and to identify the leading causes and contributing factors of maternal deaths across three public hospitals in Hawassa City. By providing an updated and broader institutional perspective, the findings are expected to inform the development and periodic evaluation of targeted strategies for the prevention of maternal deaths in the region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA retrospective facility-based observational study was conducted from secondary data of patient records on maternal deaths that occurred during January 2020 to December 2024. The study was conducted in three public hospitals: HUCSH, Adare general hospital, and Hawela Tula general hospital, located in Hawassa City, Sidama Zone. Hawassa city is located 273 km south of Addis Ababa. Sidama Regional State is one of Ethiopia's twelve regional states. The region had a population of 4.3\u0026nbsp;million people in 2020 and administratively divided into 30 rural districts. The region has 18 hospitals (1 tertiary, 4 general and 13 primary), 137 health centres and 553 health posts operated by the government. Health canters provide basic emergency obstetric and newborn care, while hospitals deliver comprehensive care in addition to these basic services (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSource population\u003c/h3\u003e\n\u003cp\u003eAll pregnant mothers who were admitted and receiving obstetrics and gynaecologic care at the selected hospitals.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population was composed of patients who died in these hospitals between January 2020 and December 2024 after being admitted and receiving obstetric and gynaecologic care. The study included all deceased mothers with a complete chart record and have received obstetric services, including termination of pregnancy, delivery, or treatment for postnatal complications between January 2020 and December 2024, and excluded pregnant women who died from accidental or intentional injuries.\u003c/p\u003e\n\u003ch3\u003eData collection and procedures\u003c/h3\u003e\n\u003cp\u003eAll maternal deaths in the hospitals from January 2020 to December 2024 were identified by reviewing all registers at the emergency department, labour ward, operating theatres, antenatal and post-natal wards, intensive care unit, and from the health facility Health Information Management System (HIMS). The hospital numbers were used to retrieve the case file from the Record Department. Every hospital implemented Maternal Death Surveillance and Response (MDSR) system. There was a committee established for reviewing maternal deaths in all health facilities, and every facility appointed a specific MDSR coordinator. The total number of admission, deliveries, and live births under the period of review was obtained. Data was collected by using a semi-structured checklist, which was adopted from a previous study, and assesses variables such as the WHO delay modalities, sociodemographic characteristics, obstetric history, diagnosis, and cause of death (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFour data collectors were trained for two days, and close supervision was maintained during data collection. All checklists were double-checked for consistency and completeness. The data collectors were instructed to write card numbers on the checklist during data collection. So that any identified error was traced back using the card numbers.\u003c/p\u003e\n\u003ch3\u003eOperational Definition\u003c/h3\u003e\n\u003cp\u003eMaternal mortality: Any death that occurred during pregnancy or within 42 days of postpartum (irrespective of the duration and site of the pregnancy) from any cause related to, or aggravated by, the pregnancy or its management but not from accidental or incidental causes(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDirect obstetric death: Death that resulted from complications of the pregnancy, labor, or puerperium, as well as maternal deaths arising from interventions, omissions, or incorrect treatments.\u003c/p\u003e \u003cp\u003eIndirect obstetric death: Death that resulted from either pre-existing or new-onset medical conditions aggravated by the physiologic process of pregnancy.\u003c/p\u003e \u003cp\u003eDelay 1- traditional practice, lack of the decision to go to health facility, family poverty, and delayed referral from home.\u003c/p\u003e \u003cp\u003eDelay 2- delayed arrival to referred facility, lack of transportation, lack of road, no facility within reasonable distance and lack of money for transport.\u003c/p\u003e \u003cp\u003eDelay 3- delayed arrival to next facility, delayed or lacking of supplies and equipment, delayed management after admission and human error or mismanagement(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were entered and analysed using the Statistical Software for Social Sciences (SPSS) Version 26. Data were assessed for normality of distribution prior to analysis. Descriptive statistics were used to summarize participant characteristics, including socio-demographic variables, obstetric history, and causes of maternal death. Categorical variables are presented as frequencies and percentages, while continuous variables are summarized using means and standard deviations (SD). Descriptive analyses were also performed to examine the types of investigations, procedures, and interventions provided. Causes of maternal death were stratified according to the three delays framework and described under the respective categories of delay, in accordance with the WHO delay classification. In addition, trend analysis was conducted to illustrate patterns in the maternal mortality ratio (MMR) across the study period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e Prior to data collection, ethical clearance was obtained from the Institutional Review Board of Hawassa University College of Medicine and Health Sciences (Ref.No:IRB/322/16). The Ethical Review Committee of College of Health Sciences and HUCSH permitted us to collect the data from patient records without the need for patient consent. To establish anonymous linkage only the card number, and not the names of the participant from the chart, were registered on the checklist.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic and obstetric characteristics\u003c/h2\u003e \u003cp\u003eDuring the study period, 94 maternal deaths met the inclusion criteria. The mean (SD) age of the deceased women was 26.6 (4.2) years, with 80.8% aged 20\u0026ndash;29 years. The majority of women, 85.1%, were married, and 77.7% lived in rural areas. Regarding educational status, 36.2% had completed primary (elementary) education, and 33.0% had no formal education. Most women were multiparous (74.5%), and 92.6% had received antenatal care. In terms of mode of delivery, 57.4% delivered vaginally, while 33.0% underwent caesarean section. Most deliveries occurred in health institutions (89.4%), whereas 8.5% took place at home. Nearly 90% of women were referral cases, and 42.6% required intensive care unit (ICU) admission. Overall, 69.1% of women stayed in the hospital for more than 24 hours, and 74.5% of maternal deaths occurred during the postpartum period \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and obstetric characteristics of deceased women at public hospitals in Hawassa City Administration, Ethiopia, 2020\u0026ndash;2024 (N\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (85.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCan read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (36.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (77.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (20.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntenatal care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (92.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrumental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome of newborn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (43.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (40.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferral status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (80.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot referred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u0026ndash;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (51.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal duration of hospital stays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 24 hrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 24 hrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (69.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuring delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (74.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCauses of maternal death\u003c/h2\u003e \u003cp\u003eThe analysis demonstrated that direct and indirect causes were responsible for 86.2% and 13.8% of maternal deaths, respectively. The most common direct causes were hypertensive disorder in pregnancy (30.9%), obstetric haemorrhage (27.7%), puerperal sepsis (9.6%), and obstructed Labor (6.4%), whereas the most common indirect causes were malaria (4.3%), anaemia (4.25%), and tuberculosis (1.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Causes of Maternal Death in Public Hospitals of Hawassa City Administration, 2020\u0026ndash;2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of Death\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Causes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructed Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (Direct)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtotal Direct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Causes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4(4.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (Indirect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4 (4.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtotal Indirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e94 (100.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors contributing to maternal deaths\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the delays and contributing factors of maternal mortality among the 81 women who died due to direct obstetric causes. The most frequently mentioned delay was a delay in reaching health care (delay type 2), which was a contributing factor in 51 (63%) of the deaths. The main causes for the delay in reaching the health facility were identified as delayed arrival to the referred facility, 43(84.3%), lack of transportation,1(1.9%), and absence of a facility within a reasonable distance, 7(13.7%).\u003c/p\u003e \u003cp\u003eThe second most frequently reported delay was a delay in decision-making whether to seek care (delay type 1), which was a contributing factor in 26 (32.1%) of the fatalities. The main factors leading to delays in deciding to seek care included failure to recognize the problem, 30 (38.5%), lack of decision to go to a health facility, 7 (26.9%), delayed referral from home, 6 (23.1%), traditional practices, 2 (7.7%), and family poverty, 1 (3.8%). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe third cause of delay noted was a delayed appropriate care and intervention at the health facilities (delay type 3), which accounted for 21 (25.9%) deaths. The primary reasons for the delay in receiving appropriate care were identified as delayed referrals 9 (43%), post-admission case management delays, 4 (19%), shortages of basic supplies and equipment, 4 (19%), and incorrect risk assessments and treatments, 4 (19%). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDelays and Contributing Factors to Direct Maternal Death in Hawassa City, 2020\u0026ndash;2024(n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContributory Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay One: Seeking Care; n (%)\u0026thinsp;=\u0026thinsp;26(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure of recognition of the problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of decision to go to the health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7 (26.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed referral from home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay Two: Reaching Care; n (%)\u0026thinsp;=\u0026thinsp;51(63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed arrival to referred facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e43 (84.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo facility with reasonable distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay Three: Receiving Care; n(%)\u0026thinsp;=\u0026thinsp;21(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed arrival to next facility from another facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9 (43.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed or lacking supplies and equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed management after admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncorrect risk assessment and treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eThere are repeated responses, so the sum may exceed the total frequency and percentage.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMaternal Mortality Trends\u003c/h2\u003e \u003cp\u003eA total of 94 maternal deaths and 44086 live births occurred from 2020 to 2024, giving a maternal mortality ratio (MMR) of 213.2 per 100,000 live births. Among these 94 deaths 80 (85.1%) of them occurred at HUCSH,13 (13.8%) at Adare generalized hospital, and 1 (1.06%) in Hawela Tula general hospital. The highest death rate occurred in 2020 with an MMR of 293/100,000 followed by MMR of 222/100,000 in 2021. The lowest death rate occurred in 2022 with an MMR of 179/100,000. The maternal mortality ratio declined from 2020 (293/100000) to 2023 (180.7/100000), followed by a slight increase in 2024 (195/100000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe estimated maternal mortality ratio (MMR) in the present study over the five-year period was 213 maternal deaths per 100,000 live births, which is substantially lower than estimates reported in previous studies conducted in Sidama Region in 2019 (419 per 100,000 live births) and Jimma in 2017 (857 per 100,000 live births). It is also lower than the MMR reported for low-income countries by the WHO 2023 (346 per 100,000 live births) and the estimate from the 2016 Ethiopia Demographic and Health Survey (EDHS) (412 per 100,000 live births(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The results were also lower when compared to findings from other studies conducted in Africa, such as a 2019 study from Nigeria (644/100,000) and a 2015 study from Gambia (1461/100,000)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This lower maternal mortality ratio may be attributed to improved access to healthcare services, better health-seeking practices, and the more effective implementation of interventions in the study area.\u003c/p\u003e \u003cp\u003eThis study reported that over 86.2% of maternal deaths were caused by direct obstetric causes, which is consistent with the findings of other studies in Somalia, Pakistan, Indonesia, and including a global estimate from the WHO(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The most common direct causes in this study was hypertensive disorder in pregnancy (30.9%), which align with results from other studies conducted in Addis Ababa, and a multi centre study conducted in Nigeria(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This differs from most local, sub-Saharan Africa, and global findings where haemorrhage is the most common cause(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The observed discrepancy may be due to differences in referral patterns, and improved prevention and management of obstetric haemorrhage at the study facility.\u003c/p\u003e \u003cp\u003eThis study demonstrated that 13.8% maternal death is due to indirect causes. The most common indirect causes in this study was malaria (4.3%), which is consistent with the results of other studies in Rwanda, and Nigeria(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In contrast to this study, reports from Tanzania, and Somalia revealed that anaemia is the commonest cause of indirect maternal death(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). It is important to emphasize that study conducted in developed nations indicates that indirect factors contributing to maternal mortality, such as cardiac conditions, are more prevalent(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The discrepancy may be explained by differences in study settings.\u003c/p\u003e \u003cp\u003eAn important issue for planning of interventions is an understanding of the timing of maternal deaths with respect to labour and delivery. In this study, it was found that 74.5% of maternal deaths occur postpartum, which is consistent with finding from other study in Addis Ababa, Rwanda, and systematic analysis for the Global Burden of Disease Study, underscoring the need for strengthened postnatal surveillance, timely recognition of complications(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study indicated that patients whose admission ended in maternal mortality faced various forms of delay. However, the most observed delay was delay in reaching health facility (delay two), followed by the delay in seeking care (delay one). This result align with results from other studies conducted in Ethiopia and African countries(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In contrary to this finding, reports from Addis Ababa, Rwanda, Indonesia, and study from South-American upper-middle income country showed that delay in receiving care (delay 3) is the commonest delay(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The observed discrepancy may be attributed to differences in geographic accessibility, and socioeconomic contexts across study settings.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitation\u003c/h2\u003e \u003cp\u003eStrengths: The study included three public hospitals and covered a five-year timeframe, allowing assessment of maternal mortality trends using standardized WHO definitions and delay models.\u003c/p\u003e \u003cp\u003eLimitations: The retrospective, facility-based approach relied on the completeness of records and did not account for community deaths, which restricts generalizability and may underestimate the actual incidence of maternal mortality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe maternal mortality ratio in public hospitals of Hawassa City from 2020\u0026ndash;2024 was 213 per 100,000 live births which is higher than the Sustainable Development Goal target even though the number is lower than the national rate and the results of previous studies. The majority of maternal deaths were due to direct obstetric causes, particularly hypertensive disorders of pregnancy and obstetric haemorrhage, while malaria and anaemia were the leading indirect causes. Delays in reaching health facilities and delays in seeking care were the most common contributors, highlighting the need to strengthen referral systems, transportation, and early recognition of obstetric complications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIMS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Health Information Management System\u003c/p\u003e\n\u003cp\u003eHUCSH \u0026nbsp; \u0026nbsp;Hawassa University Comprehensive Specialized Hospital\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eMDSR \u0026nbsp; \u0026nbsp; \u0026nbsp; Maternal Death Surveillance and Response\u003c/p\u003e\n\u003cp\u003eMMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Maternal Mortality Ratio\u003c/p\u003e\n\u003cp\u003eSDGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to data collection, ethical clearance was obtained from the Institutional Review Board of Hawassa University College of Medicine and Health Sciences (Ref.No:IRB/322/16). The Ethical Review Committee of College of Health Sciences and HUCSH permitted us to collect the data from patient records without the need for patient consent. To establish anonymous linkage only the card number, and not the names of the participant from the chart, were registered on the checklist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors did not receive support from any organisation for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Mihiretu Tesfamariam, and Abdulmalik Usman contributed to the study conception and design. Material preparation and data collection were performed by Mihiretu Tesfamariam, and Temesgen Teklu. The first draft of the manuscript was written by Abdulmalik Usman. Mihiretu Tesfamariam supervised the study, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors are grateful to the data collectors who mediated data collection in health facility.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Maternal mortality [Internet]. [cited 2025 Dec 27]. 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Policy Pract.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKodan LR, Verschueren KJC, van Roosmalen J, Kanhai HHH, Bloemenkamp KWM. Maternal mortality audit in Suriname between 2010 and 2014, a reproductive age mortality survey. BMC Pregnancy Childbirth. 2017;17(1):275.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maternal mortality, Direct obstetric causes, Indirect obstetric causes, Three-delay model, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8609187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8609187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eDespite national efforts, maternal mortality remains a major public health challenge in Ethiopia. Although reductions have been reported nationally, recent facility-based evidence from Sidama Region is limited. This study aimed to assess trends, causes, and contributing factors of maternal deaths in public hospitals of Hawassa City Administration.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA five-year retrospective review was conducted on maternal deaths that occurred between January 2020 and December 2024 in three public hospitals in Hawassa City. The data were extracted from health facility maternal medical records. Descriptive statistics were used to present background information, causes of death, and delays based on the World Health Organization three-delay model. Maternal mortality ratios were calculated per 100,000 live births.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 94 maternal deaths and 44,086 live births were documented. resulting in an overall Maternal Mortality Ratio of 213.2 per 100,000 live births. Most of deaths (86.2%) were due to direct obstetric causes, with hypertensive disorders of pregnancy (30.9%) and obstetric haemorrhage (27.7%) being the leading causes. Indirect causes accounted for 13.8% of deaths, primarily malaria and anaemia. The majority of maternal deaths (74.5%) occurred postpartum. Delay in reaching a health facility (63%) was the most common contributing factor, followed by delay in seeking care (32.1%).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eMaternal mortality in Hawassa City is higher than the Sustainable Development Goal target even though the number is lower than the national rate and the results of previous studies. Strengthening referral systems, increasing availability of emergency obstetric services and addressing delays in seeking and reaching care are critical to further reduce maternal deaths.\u003c/p\u003e","manuscriptTitle":"Trends, causes, and delays associated with maternal mortality in public hospitals of Hawassa City, Southern Ethiopia: a five-year retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 10:43:40","doi":"10.21203/rs.3.rs-8609187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T13:09:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T18:47:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T14:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89560016465622317850606060512460230172","date":"2026-02-23T07:20:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T15:30:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71740259039393369310611205133895481966","date":"2026-02-18T12:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46723021035684166385081657216318857494","date":"2026-02-16T22:27:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268367868314221549178495919276978180419","date":"2026-02-13T09:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T21:28:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T06:38:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T11:28:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T23:31:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-01-19T23:27:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"334c6e08-dde2-4f85-8d1c-e3346ce2c9b2","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T13:40:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 10:43:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8609187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8609187","identity":"rs-8609187","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}