Maternal Mortality Ratio and Associated Risk Factors in Mifi Health District, Cameroon: A Retrospective Facility-Based Study (2021–2023)

Maternal Mortality Ratio and Associated Risk Factors in Mifi Health District, Cameroon: A Retrospective Facility-Based Study (2021–2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Maternal Mortality Ratio and Associated Risk Factors in Mifi Health District, Cameroon: A Retrospective Facility-Based Study (2021–2023) Jumo Olga Mankfu Ngafeeson, Besong Calvin Ebai, Mary Bi Suh Atanga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7987380/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction Maternal mortality remains a critical public health concern in Cameroon, reflecting persistent inequities in healthcare access and quality. Despite national strategies to reduce maternal deaths, the Mifi Health District continues to report disproportionately high rates, signaling underlying systemic and obstetric challenges. Methods This retrospective facility-based study assessed maternal mortality trends and associated risk factors in the Mifi Health District from 2021 to 2023. Specifically, it determined the maternal mortality ratio (MMR), identified major obstetric and socio-demographic risk factors, and examined health-system contributors such as emergency obstetric and neonatal care (EmONC) readiness and referral efficiency. Data were extracted from hospital records, registers, and maternal death audit reports and analyzed using SPSS 25. Descriptive statistics and chi-square tests assessed relationships between variables. Results The study found an MMR of 232 deaths per 100,000 live births significantly higher than the regional average of 123 accounting for 37% of total maternal deaths in the West Region. Hemorrhage (35%), infections (20%), and hypertensive disorders (20%) were the leading causes of death, while 86% of cases involved referrals primarily due to lack of equipment or specialist care. Delays in receiving adequate care (50%), reaching facilities (25.8%), and seeking care (13%) were the dominant contributory factors. The risk factors were mostly lacking in the registers (96%). Conclusion The study concludes that high maternal mortality in Mifi results from preventable obstetric causes and systemic weaknesses. It recommends strengthening EmONC services, improving referral coordination, training skilled personnel, enhancing adequate documentation, enhancing community awareness, and enforcing governance accountability to reduce preventable maternal deaths and achieve Sustainable Development Goal 3.1 in Cameroon. Maternal mortality prevalence causes contributing factor risk factors West region Cameroon Background Maternal mortality remains one of the most persistent public-health challenges worldwide, reflecting deep inequities in access to quality healthcare, women’s social status, and the overall performance of health systems. According to the 2019 WHO report, more than 810 women die each day from pregnancy- or childbirth-related complications, accounting for approximately 295,000 maternal deaths annually [ 1 ]. The burden is borne overwhelmingly by low-income nations; 53 countries with a gross national income below US $ 905 per capita contribute nearly all global maternal deaths, of which Sub-Saharan Africa alone accounts for about 60 percent [ 2 – 4 ]. UNICEF estimates that this region records the world’s highest maternal mortality ratio (MMR) of 535 per 100,000 live births [ 5 ]. Although global initiatives under the Sustainable Development Goal 3 seek to reduce the MMR to fewer than 70 by 2030, progress has been slow, especially across African health systems constrained by inadequate human resources, limited infrastructure, and weak emergency obstetric and neonatal care (EmONC) coverage [ 6 ]. Maternal mortality, therefore, not only signals deficiencies in healthcare provision but also mirrors broader structural inequities including poverty, illiteracy, and gender disparity that perpetuate poor maternal outcomes [ 7 – 10 ]. These realities underscore the urgency for context-specific approaches capable of addressing the multifactorial drivers of maternal deaths in resource-limited settings. Within the Sub-Saharan African context, maternal mortality continues to reach crisis levels. Recent estimates show that the region accounts for nearly 70 percent of global maternal deaths, with an MMR of about 448 per 100,000 live births [ 11 ]. Contributing factors include inadequate skilled attendance at birth, delayed referrals, and persistent sociocultural barriers that discourage timely care seeking. The Central African Republic still reports 692 maternal deaths per 100,000 live births [ 11 , 12 ], while Nigeria records 576, with some northern regions exceeding 1,000 [ 13 , 10 , 14 ]. These statistics reveal systemic weaknesses common across many African countries: shortages of trained personnel, uneven distribution of facilities, and fragile referral systems. Nevertheless, successful interventions in countries such as Tanzania and Nigeria demonstrate that targeted, integrated strategies can yield substantial gains. Tanzania’s Safer Births Bundle of Care reduced maternal mortality by 75 percent, while Nigeria’s Abiye Project achieved an 84.9 percent decline [ 15 ]. Such experiences confirm that maternal deaths are largely preventable when governments and stakeholders invest in health-system strengthening, community engagement, and accountability mechanisms. Yet Cameroon, despite adopting similar frameworks including EmONC training since 2009 has recorded only a modest reduction in maternal deaths, from 782 in 2011 to 406 per 100,000 in 2024 [ 6 , 16 ]. This stagnation raises critical questions about whether imported global strategies sufficiently address local contextual realities, resource limitations, and sociocultural determinants that shape maternal-health outcomes. In Cameroon, maternal mortality remains unacceptably high and unevenly distributed across regions, with the Mifi Health District exemplifying persistent gaps in service quality and accessibility. The national MMR rose from 454 in 1998 to 782 in 2011 before gradually declining to 406 deaths per 100,000 live births [ 1 , 17 , 11 ]. Despite the introduction of the Roadmap for Reducing Maternal and Neonatal Mortality and other policies promoting skilled birth attendance, antenatal-care (ANC) coverage, and EmONC services, implementation challenges remain acute [ 18 , 19 , 20 ]. Key barriers include shortages of qualified health workers, inequitable urban–rural resource allocation, poor infrastructure, and weak referral and monitoring systems [ 21 , 22 , 23 ]. Socio-cultural constraints early marriage, high fertility (5.1 children per woman), and low contraceptive uptake further heighten risks [ 20 , 30 , 32 ]12, 19. Within Mifi District, fluctuating facility-based MMRs between 2021 and 2023 reflect these structural weaknesses and inconsistent program performance [ 24 ]. Existing strategies such as EmONC and focused ANC, though theoretically sound, have not been sufficiently contextualized for this district’s realities, where disparities between public and private facilities remain wide [ 22 , 23 , 25 , 26 ]. Accordingly, the present study which seeks to quantify the maternal-mortality burden, identify the leading risk factors, and generate evidence to inform adaptable, locally responsive interventions. By situating the analysis within the broader Sustainable Development Goal (SDG 3.1) framework, the study aims to contribute to national and regional efforts toward reducing preventable maternal deaths and advancing equitable maternal health outcomes in Cameroon [ 25 , 26 ]. Statement of problem Maternal mortality remains a major public health challenge in Cameroon, reflecting both the magnitude and persistence of preventable deaths among women of reproductive age. Despite global and national commitments, Cameroon’s maternal mortality ratio (MMR) remains high at approximately 406 deaths per 100,000 live births [ 1 ], far above the Sustainable Development Goal target of fewer than 70 by 2030. Within the Mifi Health District, recent fluctuations in facility-based MMRs from 2021 to 2023 highlight systemic weaknesses, including inadequate emergency obstetric and neonatal care (EmONC), shortages of skilled personnel, poor infrastructure, and inequitable access to quality maternal services [ 19 , 21 , 27 ]. The consequences are profound maternal deaths not only devastate families but also undermine community well-being and socioeconomic stability. This research was undertaken to investigate the extent and determinants of maternal mortality in Mifi, identifying risk factors and contextual barriers that hinder effective intervention, thereby providing evidence for locally adaptable strategies to reduce preventable maternal deaths. Objectives To determine the maternal mortality ratio (MMR) in the Mifi Health District between 2021 and 2023. To identify the major obstetric and socio-demographic risk factors associated with maternal deaths in the Mifi Health District. To assess the health system–related factors contributing to maternal mortality, including access to emergency obstetric and neonatal care (EmONC), referral efficiency, and availability of skilled personnel in the Mifi Health District. Methodology Study Design The study employed a retrospective, facility-based design conducted in the Mifi Health District of Cameroon. It covered a three-year period from January 2021 to December 2023, focusing on maternal deaths that occurred in the district’s healthcare facilities. The research examined patient records, registers, and hospital reports to determine the maternal mortality ratio (MMR) and associated risk factors. The unit of analysis was defined according to the WHO ICD-MM criteria, encompassing the death of a woman during pregnancy or within 42 days of termination of pregnancy, regardless of the duration or site of the pregnancy, from causes related to or aggravated by pregnancy or its management, excluding accidental causes. Inclusion and Exclusion Criteria The study included all maternal deaths recorded in health facilities within the Mifi Health District during the period 2021–2023. Only cases fulfilling the WHO definition of maternal death were retained for analysis. Exclusion criteria encompassed deaths due to accidents, injuries, or causes unrelated to pregnancy and those with incomplete or irreconcilable records that prevented adequate data extraction. Each record was screened for eligibility by two independent reviewers, with discrepancies resolved by consensus. This approach ensured accuracy and consistency in identifying maternal deaths relevant to the study objectives. Data Sources and Case Ascertainment Data were obtained primarily from facility-based records, including maternity registers, delivery registers, theatre and admission logs, maternal death audit reports, and inpatient case files. Additional information was retrieved from mortuary registers, referral records, and the District Health Information System (DHIS2) to ensure completeness and triangulation of data. Each case was verified using hospital audit summaries and supervisory reports to confirm that it met the WHO definition of maternal death. The denominator used for computing the maternal mortality ratio comprised all live births recorded within the same facilities and period. Community maternal deaths were not systematically captured; thus, results represent facility-based MMR estimates. Variables and Measurements Variables extracted included socio-demographic characteristics (age, residence, education, marital status), obstetric history (parity, gravidity, gestational age), and health-service indicators such as number of antenatal care (ANC) visits, referral status, mode of delivery, and place of death. Clinical variables captured included primary and secondary causes of death, classified per ICD-MM categories into direct and indirect obstetric causes (e.g., haemorrhage, eclampsia, sepsis, obstructed labour, and indirect causes such as anaemia, malaria, or HIV). Additional variables included time from admission to death, availability of emergency obstetric and neonatal care (EmONC), and distance or delays in accessing care when available. These parameters provided a comprehensive framework for identifying the multifactorial nature of maternal deaths in the district. Data Analysis Data were cleaned and entered into Microsoft Excel before analysis using SPSS version 25. Descriptive statistics were used to summarize data, including frequencies, proportions, and means. The maternal mortality ratio (MMR) was computed as the number of maternal deaths per 100,000 live births. Bivariate analyses were performed using chi-square tests to examine associations between maternal death and selected risk factors. Variables significant at the 0.05 level were entered into a multivariable logistic regression model to identify independent predictors of maternal mortality. Results were presented in tables and charts, with confidence intervals (95%) reported where applicable. Findings were compared across facilities and years to identify patterns and trends in maternal mortality and its determinants. Ethical Considerations Ethical approval was obtained from the Institutional Review Board of the University of Bamenda with ref No. 2024/0015H/UBa/IRB, and authorization from the Regional Delegation of Public Health for the West Region with ref No.1470/L/MINSANTE/SG/DRSPO/CBF. Administrative clearance was granted by the District Health Officer of Mifi and the respective health facility management boards with ref No.416/L/MINSANTE/DRSPO/SSDM/BAG. As a retrospective record review, individual informed consent was waived, but data confidentiality was strictly maintained. All extracted information was anonymized, coded, and securely stored to prevent unauthorized access. The study adhered to the ethical principles of beneficence, non-maleficence, autonomy, and justice, ensuring respect for institutional and participant confidentiality throughout the research process. Results This study examined maternal mortality trends in the Mifi Health District (2021–2023), highlighting the maternal mortality ratio, major obstetric and socio-demographic risk factors, and health system challenges contributing to maternal deaths. The Maternal Mortality Ratio (MMR) in the Mifi Health District between 2021 and 2023 These results bring out Mifi district with a rate of 232 deaths per 100000 live births. We recorded an increasing rate of maternal mortality in the Mifi with and overall rate greater than that of the whole region 232 and 123 respectively. They brought out that the Miffi Health District alone carried 37% of maternal mortality for the whole region with 20 districts. Table 1 shows that, between 2021 and 2023, the prevalence of maternal deaths in the Mifi Health District and the wider West Region showed marked fluctuations across districts and years. Overall, 211 maternal deaths were recorded from 171,540 deliveries, corresponding to a regional maternal mortality ratio (MMR) of 123 deaths per 100,000 live births. The Mifi district consistently reported the highest burden, accounting for 78 deaths across the three years with an overall MMR of 232.05, peaking at 271.05 in 2023. Similarly, Bafang also recorded high ratios, particularly in 2023 (334.63). Some districts such as Kekem and Malantouen showed irregular spikes, with Malantouen rising sharply to 315.28 in 2023 after relatively lower levels in earlier years, while Kekem peaked at 402.14 in 2022 but had zero deaths in 2021 and 2023. In contrast, several districts including Bamendjou and Santchou reported no maternal deaths throughout the period, and others such as Foumbot (49.53), Dschang (63.25), and Mbouda (83.90) maintained comparatively low overall ratios. These results highlight substantial inter-district disparities and a concerning upward trend in 2023, particularly in high-burden districts like Mifi, Bafang, and Malantouen, underscoring the need for targeted maternal health interventions in these areas. Table 1 Prevalence of Maternal death in the Mifi health district and the west region from 2021 to 2023 Ratio Maternal Deaths per District and Per Year 2021 2022 2023 Overall District Deliveries # Maternal deaths Rate Deliveries # Maternal deaths Rate Deliveries # Maternal deaths Rate Deliveries # Maternal deaths Rate Bafang 1904 4 210.08 1855 4 215.63 1793 6 334.63 5552 14 252.16 Mifi 11048 27 244.39 11128 20 179.73 11437 31 271.05 33613 78 232.05 Malantouen 3575 5 139.86 3460 3 86.71 3489 11 315.28 10524 19 180.54 Kekem 771 0 0.00 746 3 402.14 703 0 0.00 2220 3 135.14 Batcham 1876 2 106.61 1943 1 51.47 1853 4 215.87 5672 7 123.41 Penka Michel 1883 2 106.21 1938 1 51.60 1864 4 214.59 5685 7 123.13 Bangangte 2861 6 209.72 2703 2 73.99 2650 2 75.47 8214 10 121.74 Foumban 7801 10 128.19 7336 3 40.89 8022 10 124.66 23159 23 99.31 Baham 1066 2 187.62 1016 0 0.00 1026 1 97.47 3108 3 96.53 Kouoptamo 1713 1 58.38 1782 0 0.00 1974 4 202.63 5469 5 91.42 Bandjoun 1953 1 51.20 1892 0 0.00 1871 4 213.79 5716 5 87.47 Massangam 1083 1 92.34 1156 2 173.01 1268 0 0.00 3507 3 85.54 Mbouda 5388 3 55.68 5101 5 98.02 5005 5 99.90 15494 13 83.90 Bandja 437 0 0.00 493 1 202.84 439 0 0.00 1369 1 73.05 Dschang 5850 3 51.28 5779 1 17.30 5761 7 121.51 17390 11 63.25 Foumbot 4128 2 48.45 3947 2 50.67 4040 2 49.50 12115 6 49.53 Bangourain 1793 0 0.00 1763 1 56.72 1705 1 58.65 5261 2 38.02 Galim 1124 0 0.00 869 1 115.07 983 0 0.00 2976 1 33.60 Bamendjou 775 0 0.00 761 0 0.00 776 0 0.00 2312 0 0.00 Santchou 745 0 0.00 662 0 0.00 777 0 0.00 2184 0 0.00 Grand Total 57774 69 119.43 56330 50 88.76 57436 92 160.18 171540 211 123.00 Maternal Mortality Rate = # of Maternal deaths/ # Live births*100000 Table 2 reveal that the five first causes of maternal mortality are hemorrhage (23) (35%), infections (13) (20%), hypertensive disorders (13) (20%), unsafe abortion (6) (9%) and emboli (3) respectively from the maternal death of 2021 to 2023. It reveals that primate pregnancies also developed hemorrhage as well as multiparous women though hemorrhage was more regular with the multipa (9%/91%). We also notice a display here showing that hypertensive disorders occurred more in primipa than in multipa (54%/46%). In the Mifi Health district their second leading cause of death is infection in hypertensive disorders per these statistics (13 each). The P-value of these table is 0.0585 this means there's no statistically significant association between cause of death and number of pregnancy. Table 2 Causes of maternal mortality in the Mifi health district of the west region of Cameroon Causes of death Adjusted gravidity % Causes of death per adjusted gravidity 1 2–5 6+ Total Percentages 1 2–5 6+ Hemorrhage 2 14 7 23 35% 9% 61% 30% Infections 2 9 2 13 20.5% 15% 69% 15% Hypertensive Disorders 7 4 2 13 20.5% 54% 31% 15% Unsafe Abortion 1 5 0 6 9% 17% 83% 0% Emboly 0 2 1 3 4.5% 0% 67% 33% Anemia 0 0 1 1 1.5% 0% 0% 100% Sickle Cell Anemia 0 1 0 1 1.5% 0% 100% 0% Respiratory Ditress 1 0 1 1.5% 100% 0% 0% Diabetes 0 0 1 1 1.5% 0% 0% 100% Epidermolysis 0 0 1 1 1.5% 0% 0% 100% Encephalopathy 0 0 1 1 1.5% 0% 0% 100% Obtructed Labor 0 1 0 1 1.5% 0% 100% 0% Grand Total 13 36 16 65 100% 20% 55% 25% X 2 33.25, p-value: 0.0585 . The Major Obstetric and Socio-Demographic Risk Factors Associated with Maternal Deaths in the Mifi Health District Table 3 reveal that the five first causes of maternal mortality are hemorrhage (23) (35%), infections (13) (20%), hypertensive disorders (13) (20%), unsafe abortion (6) (9%) and emboli (3) respectively from the maternal death of 2021 to 2023. It reveals that primate pregnancies also developed hemorrhage as well as multiparous women though hemorrhage was more regular with the multipa (9%/91%). We also notice a display here showing that hypertensive disorders occurred more in primipa than in multipa (54%/46%). In the Mifi Health district their second leading cause of death is infection in hypertensive disorders per these statistics (13 each). The P-value of these table is 0.0585 this means there's no statistically significant association between cause of death and number of pregnancy. Table 3 Causes of maternal mortality in the Mifi health district of the west region of Cameroon Causes of death Adjusted gravidity % Causes of death per adjusted gravidity 1 2–5 6+ Total Percentages 1 2–5 6+ Hemorrhage 2 14 7 23 35% 9% 61% 30% Infections 2 9 2 13 20.5% 15% 69% 15% Hypertensive Disorders 7 4 2 13 20.5% 54% 31% 15% Unsafe Abortion 1 5 0 6 9% 17% 83% 0% Emboly 0 2 1 3 4.5% 0% 67% 33% Anemia 0 0 1 1 1.5% 0% 0% 100% Sickle Cell Anemia 0 1 0 1 1.5% 0% 100% 0% Respiratory Ditress 1 0 1 1.5% 100% 0% 0% Diabetes 0 0 1 1 1.5% 0% 0% 100% Epidermolysis 0 0 1 1 1.5% 0% 0% 100% Encephalopathy 0 0 1 1 1.5% 0% 0% 100% Obtructed Labor 0 1 0 1 1.5% 0% 100% 0% Grand Total 13 36 16 65 100% 20% 55% 25% X 2 33.25, p-value: 0.0585 Table 4 shows that only two (2.61%) persons were recorded as single and the marital status of the rest (97.39%) was not mentioned in the registers. Here show that only two (3%) persons were recorded as secondary education level and the level of education of the rest (96%) was not mentioned in the registers. In the table show that the majority (76%) of women doesn’t have a pre-exiting medical factor. The study recorded that the main medical condition that was present at delivery of the maternal deaths was anemia (6.15%). The statistics below show that (3) women who died were less than 12 weeks’ gestation and the gestational ages of 6 were not mentioned in the register. Table 4 Repartition of women by Risk factors Characteristic Domaine Frequency Percentage Marital status Single 2 2.61% Not mentioned 63 97.39% Level of education secondary 2 2.61% Not mentioned 63 97.39% Pre-existing medical condition Yes 50 76.92% No 15 23.08% Gestational age First trimester 3 4.62% Second trimester 15 23.08% Third trimester 41 64.62% Not mentioned 6 9.23% Table 5 shows that the majority of maternal deaths in the Mifi Health District occurred among women who were referred from one health facility to another, representing 86% of all cases, while only 14% of deaths occurred without referral. Hemorrhage, infections, and hypertensive disorders were the leading causes of death among referred patients, accounting for 91%, 85%, and 85% respectively, indicating that most severe obstetric complications required higher-level care. Unsafe abortions, anemia, and metabolic conditions such as diabetes and sickle cell anemia were also observed exclusively among referred cases, suggesting delayed recognition or inadequate management at the primary level. Obstructed labor, however, occurred only among non-referred patients, reflecting possible challenges in timely identification and transfer. Although referral was common across nearly all causes, the chi-square test (χ² = 15.73, p = 0.1514) showed no statistically significant association between cause of death and referral status, implying that maternal deaths were widespread regardless of referral patterns. Table 5 relating causes to referral. Causes of Death Referred to another HS % Referred to another HS NO YES Total NO YES Total Hemorrhage 2 21 23 9% 91% 100% Infections 2 11 13 15% 85% 100% Hypertensive disorders 2 11 13 15% 85% 100% Unsafe abortion 6 6 0% 100% 100% Emboly 2 1 3 67% 33% 100% Anemia 1 1 0% 100% 100% Sickle cell anemia 1 1 0% 100% 100% Respiratory ditress 1 1 0% 100% 100% Diabetes 1 1 0% 100% 100% Epidermolysis 1 1 0% 100% 100% Encephalopathy 1 1 0% 100% 100% Obtructed labor 1 1 100% 0% 100% Grand total 9 56 65 14% 86% 100% X 2 15.73, p-value: 0.1514 The Health System–Related Factors Contributing to Maternal Mortality Table 6 shows that the majority of referrals (61.5%) among women who later died were due to a lack of necessary equipment or supplies in the initial health facilities, underscoring severe resource constraints within lower-level centers. Another 23% of referrals resulted from the absence of specialist care, indicating limited availability of trained obstetric or emergency personnel capable of managing complex cases. A smaller proportion (4.7%) were referred specifically for surgical intervention, reflecting delayed access to operative obstetric care such as cesarean sections. Additionally, 10.8% of referrals did not specify a reason, suggesting gaps in record-keeping and communication during patient transfers. Overall, these findings highlight systemic deficiencies in equipment availability, specialist coverage, and documentation, which collectively weaken the referral system and contribute to preventable maternal deaths in the Mifi Health District. Table 6 Repartition of women by Reason for referral Reason for referral frequency percentage Lack of necessary equipment or supplies 40 61.5% Lack of specialist care 15 23% Need for surgery 3 4.7% Not mentioned 7 10.8% Grand Total 65 100.00% Table 7 shows that delays in seeking, reaching, and receiving care were the most critical contributory factors to maternal mortality in the Mifi Health District. The majority of deaths (50%) were linked to delays in receiving adequate care at the health facility, suggesting weaknesses in the quality and timeliness of emergency obstetric management. Another 25.8% of deaths resulted from delays in reaching healthcare facilities, highlighting barriers such as poor transportation, long distances, or inadequate referral systems. Additionally, 13% of cases were associated with delays in deciding to seek care, reflecting limited awareness of danger signs and possible socio-cultural or financial constraints. Structural factors also contributed, with 6% of deaths related to a lack of skilled personnel and 5.2% to shortages of essential medical equipment or supplies. These findings collectively emphasize that both patient-related delays and systemic inefficiencies significantly exacerbate maternal mortality in the district. Table 7 Contributive Factors to Maternal Mortality Factors frequency percentage Delay in seeking care 15 13% Delay in reaching healthcare facility 30 25.8% Delay in receiving adequate care at the facility 58 50% Lack of skilled personnel 7 6% Lack of necessary medical equipment or supplies 6 5.2% Table 8 shows that maternal deaths in the Mifi Health District were unevenly distributed across health facilities between 2021 and 2023, with a noticeable increase in total deaths from 21 in 2021 to 34 in 2023. The Bafoussam Regional Hospital (HR Bafoussam) accounted for the overwhelming majority of cases, recording 47 out of 63 deaths (75%), making it the main referral and mortality center for the district. Other facilities such as the Bafoussam Baptist Hospital (CBC Bamendzi) and Clinique Médicale Ange Clémence reported fewer deaths, four and three respectively, while most private and confessional centers recorded only isolated cases. The dominance of deaths at the regional hospital suggests that most critical cases are referred late, often when complications are already severe, and that lower-level facilities may lack the capacity to manage obstetric emergencies effectively. This pattern highlights the centralization of maternal care and the burden placed on tertiary-level institutions, reflecting systemic weaknesses in primary and secondary maternal health service delivery. Table 8 Maternal death in MIFI heath district with respect to the health facilities Health facilities 2021 2022 2023 Total Bafoussam Baptist Hospital – Cbc Bamendzi 2 2 0 4 Cm Sainte Union 1 0 0 1 Cs Catholique Marie Immaculee 2 0 0 2 Hd Mifi Famla 1 1 0 2 Hr Bafoussam 15 17 28 47 Clinique Sos Ouest 0 1 0 1 Chr Bafoussam 0 0 1 1 Clinique De Ouest 0 0 1 1 Clinique Médicale Ange Clémence 0 2 1 3 Cm Protestant Plateau 0 0 1 1 Cs Le Salut 0 0 1 1 Cs Catholique Baleng-Lafe 0 0 1 1 Total 21 23 34 63 Discussion The findings indicate that the Mifi Health District experienced a persistently high and rising maternal mortality ratio (MMR) between 2021 and 2023, reaching 232 deaths per 100,000 live births, significantly higher than the West Region’s average of 123. The district alone accounted for 37% of all maternal deaths within the region’s twenty districts, underscoring its disproportionate contribution to regional maternal mortality. The upward trend, peaking at 271.05 in 2023, reflects systemic weaknesses in maternal health services and points to gaps in emergency obstetric care and timely intervention. These results are consistent with the broader pattern of inter-district disparities observed, where high-burden districts such as Bafang and Malantouen also showed sharp increases in mortality ratios. The leading causes hemorrhage (35%), infections (20%), hypertensive disorders (20%), and unsafe abortion (9%) mirror global and regional trends where direct obstetric complications account for the majority of maternal deaths. Similar to findings by Yakubu, Mohamed Nor, and Abidin [ 28 ], who identified biological factors such as hemorrhage and hypertensive disorders as consistent micro-level predictors of maternal mortality, the Mifi results underscore the strong influence of obstetric risk factors compounded by inadequate emergency care. The predominance of preventable causes highlights the intersection between individual-level vulnerabilities and health-system failures, echoing the conclusions of Tajvar, Hajizadeh, and Zalvand [ 29 ], who demonstrated that maternal mortality is shaped by both personal characteristics and ecological determinants such as access to skilled care and resource allocation. Furthermore, the non-significant statistical association (p = 0.0585) between causes of death and parity in Mifi suggests that mortality risk spans both primiparous and multiparous women, aligning with Gupta, Singh, and Kumar [ 30 ], who reported similar non-linear relationships between obstetric history and mortality risk in India. The convergence of these findings reinforces a consistent global pattern: maternal deaths are driven by preventable, well-known clinical conditions that persist due to structural deficiencies in healthcare delivery, delayed response, and inequitable resource distribution. Addressing these issues in high-burden districts like Mifi requires targeted, context-specific interventions focusing on emergency obstetric care, skilled personnel availability, and community-level education to reduce delays and improve maternal survival outcomes. The findings from the Mifi Health District reveal that hemorrhage (35%), infections (20%), and hypertensive disorders (20%) were the leading causes of maternal deaths between 2021 and 2023, aligning with global and regional trends where these conditions consistently rank among the top contributors to maternal mortality [ 31 ]. The predominance of hemorrhage among multiparous women and hypertensive disorders among primiparous women underscores the physiological and obstetric vulnerabilities associated with parity. The socio-demographic data indicating low education levels and incomplete records reflect broader issues of poor health documentation and limited health literacy, factors also highlighted in Awolayo’s [ 32 ], which emphasized how sociocultural barriers such as stigma, gender norms, and reliance on traditional birth practices exacerbate maternal risks. Collectively, these findings demonstrate that maternal mortality in the Mifi Health District is driven by a combination of direct obstetric causes and systemic weaknesses, consistent with global analyses by Hogan et al. [ 30 ] and region-specific studies that underscore the intersection of medical, socio-demographic, and cultural determinants in sustaining high maternal death rates. The findings from the Mifi Health District reveal profound health system deficiencies that mirror broader patterns observed across sub-Saharan Africa, where inadequate access to emergency obstetric and neonatal care (EmONC), shortages of skilled personnel, and weak referral systems remain major contributors to maternal mortality [ 18 ]. The study shows that 61.5% of referrals were due to lack of essential equipment and 23% to absence of specialist care, indicating severe under-resourcing at lower-level facilities an issue. The predominance of delays in receiving care (50%) and reaching care (25.8%) corresponds to the “Three Delays Model” described by Thaddeus and Maine (1994), emphasizing how health system inefficiencies and socio-economic barriers interact to worsen maternal outcomes. The concentration of 75% of maternal deaths at the Bafoussam Regional Hospital underscores over-centralization of care and late referrals, reflecting patterns observed in other sub-Saharan health systems where tertiary hospitals bear the brunt of preventable deaths due to weak primary-level services and poor coordination [ 32 ]. Adegbite [ 33 ] further noted that in Nigeria, the persistence of maternal deaths despite policy interventions stems from weak enforcement and lack of sustainable regulatory mechanisms, suggesting similar governance and systemic challenges in Cameroon. Collectively, the Mifi District findings align with regional trends that associate high maternal mortality with deficiencies in EmONC readiness, shortage of skilled providers, and ineffective referral pathways factors that demand strengthened health systems, capacity building, and policy accountability to reduce preventable maternal deaths. Conclusion In conclusion, the study established that maternal mortality in the Mifi Health District remains alarmingly high 232 deaths per 100,000 live births exceeding regional ratio and underscoring persistent systemic and socio-medical barriers to safe motherhood. In line with the study objectives, findings revealed that hemorrhage, infections, and hypertensive disorders were the leading causes of maternal deaths, while key risk factors included delayed care-seeking, poor referral efficiency, and lack of skilled personnel and essential equipment. These outcomes confirm that the problem identified in the statement the failure of existing maternal health strategies to curb preventable deaths stems from intertwined clinical and structural deficiencies. The dominance of direct obstetric causes highlights the preventable nature of most deaths, while the health-system delays reflect weak emergency response and inadequate facility readiness. Therefore, the high maternal mortality ratio in Mifi Health District not only exposes the fragility of local healthcare systems but also calls for targeted, context-specific interventions focused on strengthening emergency obstetric and neonatal care, improving documentation of cases, improving referral systems, and promoting timely, equitable access to skilled maternal health services to reverse the current mortality trend. Recommendations Health authorities should prioritize the improvement of EmONC services through adequate equipment supply, blood banks, and emergency drugs. Strengthening facility readiness and ensuring timely intervention during obstetric complications will significantly reduce preventable maternal deaths across the Mifi Health District. Recruitment and continuous training of skilled birth attendants, midwives, and obstetric specialists are essential. Regular refresher courses and mentorship programs should be implemented to improve clinical decision-making, ensure quality maternal care, and enhance early detection of life-threatening complications. An efficient, well-coordinated referral system must be established to facilitate timely transfer of obstetric emergencies. This includes functional ambulance services, clear communication channels between facilities, and accountability mechanisms to reduce delays in reaching and receiving adequate care. Community-based health education programs should be intensified to improve awareness of pregnancy danger signs and the importance of antenatal visits and facility-based deliveries. Culturally sensitive communication strategies will empower families to seek care promptly and reduce first-level delays. Maternal health records should be properly documented and digitalized to enhance data accuracy and facilitate monitoring. Strengthening the District Health Information System (DHIS2) will improve maternal death surveillance, guide timely response, and inform data-driven health policy decisions. Government and stakeholders should implement financial risk protection schemes, such as maternal health insurance or free delivery services in both public and private health structures. Reducing out-of-pocket costs and ensuring equitable access will encourage timely care-seeking among low-income women. The Ministry of Public Health should reinforce accountability and supervision of maternal health programs. Effective policy enforcement, regular facility audits, and transparent allocation of resources are vital for sustaining progress toward reducing maternal mortality in the Mifi Health District. Abbreviations ANC : Antenatal Care EmONC : Emergency Obstetric and Neonatal Care EPMM : Eliminating Preventable Maternal Mortality FP : Family Planning IMBN : Impregnated Mosquito Bed Net IPT : Intermittent Preventive Treatment IWC : Infant Welfare Clinic MMR : Maternal Mortality Rate SDG : Sustainable Development Goals Declarations Ethics approval and consent to participate; Ethical approval was obtained from the Institutional Review Board of the University of Bamenda with ref No. 2024/0015H/UBa/IRB, and authorization from the Regional Delegation of Public Health for the West Region with ref No.1470/L/MINSANTE/SG/DRSPO/CBF. Administrative clearance was granted by the District Health Officer of Mifi and the respective health facility management boards with ref No.416/L/MINSANTE/DRSPO/SSDM/BAG. As a retrospective record review, individual informed consent was waived, but data confidentiality was strictly maintained. All extracted information was anonymized, coded, and securely stored to prevent unauthorized access. The study adhered to the ethical principles of beneficence, non-maleficence, autonomy, and justice, ensuring respect for institutional and participant confidentiality throughout the research process. Consent for publication; NOT APPLICABLE Availability of data and materials; The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests; NO COMPETING INTEREST Funding; PRIVATE FROM RESEARCHER Authors' contributions Jumo Olga Ngafeeson; Main Researcher Dr Besong Calvin Ebai; Co-Supervisor Prof. Mary Bih Su Atanga; Main Supervisor Acknowledgements To my beloved husband who has been supervising from home References WHO, 2017 Ending Preventable Maternal Mortality (epmm) a renewed focus for improving maternal and newborn health and wellbeing. UNICEF Data and Analytics, Division of Data, Research and Policy in collaboration with Health Section Programme Division (2018), Maternal and Newborn Health Disparities Cameroon unicef for every child. Avenue Appia, 1211Geneva27,Switzerland,e, [email protected] . Ugochukwu Simeon Asogwal, Oluwaseyi John Jemisenia1, and Nicholas Uchechukwu Asogwa Women’s Perceptions of the Causes of Maternal Mortality: Qualitative Evidence From Nsukka, Nigeria. , 2022) UNFPA 2012, Independent country programme evaluation cameroon 2008 - 2011, Reine Suzanne Kadia1, Benjamin Momo Kadia2* , Christian Akem Dimala3, Desmond Aroke3, Noel Vogue4 and Bruno Kenfack, Evaluation of emergency obstetric and neonatal care services in Kumba Health District, Southwest region, Cameroon (2011–2014): BMC Pregnancy and Childbirth (2020) 20:95 https://doi.org/10.1186/s12884-020-2774-9)(24) United Kyei-Nimakoh et al. BMC Pregnancy and Childbirth (2016) 16:51 Page 2 of 9 Nations Development Program and other United Nations’ agencies.(31) Iqbal K, Shaheen F, Begum A. Risk factors of maternal mortality. J Rawalpindi Med Coll. 2014;18:136–8. Mihiretu Alemayehu 1,2, Bereket Yakob 1,3, Nelisiwe Khuzwayo2 Effective Coverage of Emergency Obstetric and Newborn Care Services in Africa Aug 27, 2021, Illah E, Mbaruku G, Masanja H, Kahn K. Causes and risk factors for maternal mortality in rural Tanzania -case of Rufiji Health and Demographic Surveillance Site (HDSS). Matern Heal Rufiji HDSS African J Reprod Heal Priv, Bag. 2013;17. CDC, 2009(17)], increased training of health professionals Alemayehu M, Yakob B, Khuzwayo N. Effective coverage of emergency obstetric and newborn care services in Africa. BMC Pregnancy Childbirth . 2021 Aug 27;21(1):601. doi:10.1186/s12884-021-04072-4. Mihiretu Alemayehu, Bereket Yakob, Nelisiwe Khuzwayo. Effective Coverage of Emergency Obstetric and Newborn Care Services in Africa Aug 27, 2021. McCarthy J, Maine D. A framework for analyzing the determinants of maternal mortality. Stud Fam Plan. 1992;23:23. MINISTRY OF HEALTH, sectorial strategies for health 2016-2027, 2016 World Health Organization. Maternal mortality: Fact sheet. Geneva: World Health Organization; 2016. Available from: https://www.who.int/news-room/fact-sheets/detail/maternal-mortality Centers for Disease Control and Prevention (CDC). CDC in Cameroon. Atlanta (GA): U.S. Department of Health and Human Services, CDC; 2016 [cited 2025 Oct 20]. Available from: https://www.cdc.gov/globalhealth/countries/cameroon/ World Bank. (2022). Healthcare Access and Quality Index. Retrieved from World Bank website. African Development Bank. (2021). Cameroon: Health Sector Performance. Retrieved from AfDB website. World Bank. Healthcare Access and Quality Index. Washington (DC): World Bank; 2022 [cited 2025 Oct 20]. Available from: https://data.worldbank.org/indicator. African Development Bank (AfDB). Cameroon: Health sector performance. Abidjan (Côte d’Ivoire): African Development Bank; 2021 [cited 2025 Oct 20]. Available from: https://www.afdb.org/en/documents. World Health Organization (WHO). Maternal mortality ratio (per 100,000 live births). 2021. Available from: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/1355. UNICEF. (2022) Maternal and Newborn Health. 202. Available from https://www.unicef.org/health/maternal-health. Nkfusai JT, Nkfusai J. (2021) Maternal Mortality in Cameroon: A Review of the Challenges and Strategies for Reduction. Cam J Public Health.13(1):45-58. Cameroon Ministry of Public Health. (2020). National Reproductive Health Policy. Retrieved from Ministry of Public Health website. Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease Study. Seattle (WA): IHME; 2023 [cited 2025 Oct 20]. Available from: https://www.healthdata.org/gbd. Yakubu Y, Mohamed Nor N, Abidin EZ. A systematic review of micro correlates of maternal mortality. Reviews on Environmental Health. 2018 Jun 27;33(2):147-61. Tajvar M, Hajizadeh A, Zalvand R. A systematic review of individual and ecological determinants of maternal mortality in the world based on the income level of countries. BMC Public Health. 2022 Dec 15;22(1):2354. Gupta S, Singh SN, Kumar D. An empirical analysis of maternal health data: A case study of India. In2016 2nd International Conference on Next Generation Computing Technologies (NGCT) 2016 Oct 14 (pp. 490-493). IEEE. Awolayo OA. Impacts of select sociocultural practices on maternal mortality in Nigeria: A scoping review (Doctoral dissertation, University of Saskatchewan). Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, Lopez AD, Lozano R, Murray CJ. Maternal mortality for 181 countries, 1980–2008: a systematic analysis of progress towards Millennium Development Goal 5. The lancet. 2010 May 8;375(9726):1609-23. World Bank. Healthcare Access and Quality Index. Washington (DC): World Bank; 2022 [cited 2025 Oct 20]. Available from: https://data.worldbank.org/indicator. Kumbani, L. C. et al. (2012). Why some women fail to give birth at health facilities. Reproductive Health. Adegbite FR. Combating Maternal Mortality in Nigeria through Combined and Sustainable Regulatory Approach. IJOCLLEP. 2021;3:55. Additional Declarations No competing interests reported. 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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-7987380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544762369,"identity":"8cb9d876-e9d3-4f9c-a64c-2c8146c028c7","order_by":0,"name":"Jumo Olga Mankfu Ngafeeson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACPgaGBCBlA8SMjQeI0sIG0ZIG0tJAtBYQOAwmidTCv+CZ1I2K83Zr2w8DbamxiSasReJBmnTOmdvJ284kArUcS8ttIKzlQJp0btvtZLMDQC2MDYeJ1nIu2ez8Q2K18DeAtBywM7tBvC0MydY5Z5ITzG4AbUkgxi/8/GcSb+dU2NmbnU9/+OBDjQ1hLQwSOQkgKhGsMoGgcrA1xw+AKHuiFI+CUTAKRsHIBADgOkXLZ8NqtgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bamenda","correspondingAuthor":true,"prefix":"","firstName":"Jumo","middleName":"Olga Mankfu","lastName":"Ngafeeson","suffix":""},{"id":544762370,"identity":"1616fd3c-b973-45ba-9de5-47501376f864","order_by":1,"name":"Besong Calvin Ebai","email":"","orcid":"","institution":"University of Bamenda","correspondingAuthor":false,"prefix":"","firstName":"Besong","middleName":"Calvin","lastName":"Ebai","suffix":""},{"id":544762371,"identity":"abefc825-dc27-4f70-931f-376a3e96271c","order_by":2,"name":"Mary Bi Suh 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11:54:05","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151741,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7987380/v1/8c8cb3cbb4cb3c290547dee1.html"},{"id":96708312,"identity":"c30a9eb3-3291-4222-a974-091588373fda","added_by":"auto","created_at":"2025-11-25 10:00:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1356114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7987380/v1/cccf92a8-7562-4a7b-8885-0d67aef30898.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMaternal Mortality Ratio and Associated Risk Factors in Mifi Health District, Cameroon: A Retrospective Facility-Based Study (2021–2023)\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMaternal mortality remains one of the most persistent public-health challenges worldwide, reflecting deep inequities in access to quality healthcare, women’s social status, and the overall performance of health systems. According to the 2019 WHO report, more than 810 women die each day from pregnancy- or childbirth-related complications, accounting for approximately 295,000 maternal deaths annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The burden is borne overwhelmingly by low-income nations; 53 countries with a gross national income below US \u003cspan\u003e$\u003c/span\u003e905 per capita contribute nearly all global maternal deaths, of which Sub-Saharan Africa alone accounts for about 60 percent [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. UNICEF estimates that this region records the world’s highest maternal mortality ratio (MMR) of 535 per 100,000 live births [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although global initiatives under the Sustainable Development Goal 3 seek to reduce the MMR to fewer than 70 by 2030, progress has been slow, especially across African health systems constrained by inadequate human resources, limited infrastructure, and weak emergency obstetric and neonatal care (EmONC) coverage [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Maternal mortality, therefore, not only signals deficiencies in healthcare provision but also mirrors broader structural inequities including poverty, illiteracy, and gender disparity that perpetuate poor maternal outcomes [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These realities underscore the urgency for context-specific approaches capable of addressing the multifactorial drivers of maternal deaths in resource-limited settings.\u003c/p\u003e\u003cp\u003eWithin the Sub-Saharan African context, maternal mortality continues to reach crisis levels. Recent estimates show that the region accounts for nearly 70 percent of global maternal deaths, with an MMR of about 448 per 100,000 live births [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Contributing factors include inadequate skilled attendance at birth, delayed referrals, and persistent sociocultural barriers that discourage timely care seeking. The Central African Republic still reports 692 maternal deaths per 100,000 live births [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while Nigeria records 576, with some northern regions exceeding 1,000 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These statistics reveal systemic weaknesses common across many African countries: shortages of trained personnel, uneven distribution of facilities, and fragile referral systems. Nevertheless, successful interventions in countries such as Tanzania and Nigeria demonstrate that targeted, integrated strategies can yield substantial gains. Tanzania’s Safer Births Bundle of Care reduced maternal mortality by 75 percent, while Nigeria’s Abiye Project achieved an 84.9 percent decline [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such experiences confirm that maternal deaths are largely preventable when governments and stakeholders invest in health-system strengthening, community engagement, and accountability mechanisms. Yet Cameroon, despite adopting similar frameworks including EmONC training since 2009 has recorded only a modest reduction in maternal deaths, from 782 in 2011 to 406 per 100,000 in 2024 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This stagnation raises critical questions about whether imported global strategies sufficiently address local contextual realities, resource limitations, and sociocultural determinants that shape maternal-health outcomes.\u003c/p\u003e\u003cp\u003eIn Cameroon, maternal mortality remains unacceptably high and unevenly distributed across regions, with the Mifi Health District exemplifying persistent gaps in service quality and accessibility. The national MMR rose from 454 in 1998 to 782 in 2011 before gradually declining to 406 deaths per 100,000 live births [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite the introduction of the Roadmap for Reducing Maternal and Neonatal Mortality and other policies promoting skilled birth attendance, antenatal-care (ANC) coverage, and EmONC services, implementation challenges remain acute [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Key barriers include shortages of qualified health workers, inequitable urban–rural resource allocation, poor infrastructure, and weak referral and monitoring systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Socio-cultural constraints early marriage, high fertility (5.1 children per woman), and low contraceptive uptake further heighten risks [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]12, 19. Within Mifi District, fluctuating facility-based MMRs between 2021 and 2023 reflect these structural weaknesses and inconsistent program performance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Existing strategies such as EmONC and focused ANC, though theoretically sound, have not been sufficiently contextualized for this district’s realities, where disparities between public and private facilities remain wide [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Accordingly, the present study which seeks to quantify the maternal-mortality burden, identify the leading risk factors, and generate evidence to inform adaptable, locally responsive interventions. By situating the analysis within the broader Sustainable Development Goal (SDG 3.1) framework, the study aims to contribute to national and regional efforts toward reducing preventable maternal deaths and advancing equitable maternal health outcomes in Cameroon [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatement of problem\u003c/h3\u003e\n\u003cp\u003eMaternal mortality remains a major public health challenge in Cameroon, reflecting both the magnitude and persistence of preventable deaths among women of reproductive age. Despite global and national commitments, Cameroon’s maternal mortality ratio (MMR) remains high at approximately 406 deaths per 100,000 live births [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], far above the Sustainable Development Goal target of fewer than 70 by 2030. Within the Mifi Health District, recent fluctuations in facility-based MMRs from 2021 to 2023 highlight systemic weaknesses, including inadequate emergency obstetric and neonatal care (EmONC), shortages of skilled personnel, poor infrastructure, and inequitable access to quality maternal services [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The consequences are profound maternal deaths not only devastate families but also undermine community well-being and socioeconomic stability. This research was undertaken to investigate the extent and determinants of maternal mortality in Mifi, identifying risk factors and contextual barriers that hinder effective intervention, thereby providing evidence for locally adaptable strategies to reduce preventable maternal deaths.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo determine the maternal mortality ratio (MMR) in the Mifi Health District between 2021 and 2023.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify the major obstetric and socio-demographic risk factors associated with maternal deaths in the Mifi Health District.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo assess the health system–related factors contributing to maternal mortality, including access to emergency obstetric and neonatal care (EmONC), referral efficiency, and availability of skilled personnel in the Mifi Health District.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\n\n\n\n\n\n\n\n\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThe study employed a retrospective, facility-based design conducted in the Mifi Health District of Cameroon. It covered a three-year period from January 2021 to December 2023, focusing on maternal deaths that occurred in the district’s healthcare facilities. The research examined patient records, registers, and hospital reports to determine the maternal mortality ratio (MMR) and associated risk factors. The unit of analysis was defined according to the WHO ICD-MM criteria, encompassing the death of a woman during pregnancy or within 42 days of termination of pregnancy, regardless of the duration or site of the pregnancy, from causes related to or aggravated by pregnancy or its management, excluding accidental causes.\u003c/p\u003e\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\u003cp\u003eThe study included all maternal deaths recorded in health facilities within the Mifi Health District during the period 2021–2023. Only cases fulfilling the WHO definition of maternal death were retained for analysis. Exclusion criteria encompassed deaths due to accidents, injuries, or causes unrelated to pregnancy and those with incomplete or irreconcilable records that prevented adequate data extraction. Each record was screened for eligibility by two independent reviewers, with discrepancies resolved by consensus. This approach ensured accuracy and consistency in identifying maternal deaths relevant to the study objectives.\u003c/p\u003e\u003ch3\u003eData Sources and Case Ascertainment\u003c/h3\u003e\u003cp\u003eData were obtained primarily from facility-based records, including maternity registers, delivery registers, theatre and admission logs, maternal death audit reports, and inpatient case files. Additional information was retrieved from mortuary registers, referral records, and the District Health Information System (DHIS2) to ensure completeness and triangulation of data. Each case was verified using hospital audit summaries and supervisory reports to confirm that it met the WHO definition of maternal death. The denominator used for computing the maternal mortality ratio comprised all live births recorded within the same facilities and period. Community maternal deaths were not systematically captured; thus, results represent facility-based MMR estimates.\u003c/p\u003e\u003ch3\u003eVariables and Measurements\u003c/h3\u003e\u003cp\u003eVariables extracted included socio-demographic characteristics (age, residence, education, marital status), obstetric history (parity, gravidity, gestational age), and health-service indicators such as number of antenatal care (ANC) visits, referral status, mode of delivery, and place of death. Clinical variables captured included primary and secondary causes of death, classified per ICD-MM categories into direct and indirect obstetric causes (e.g., haemorrhage, eclampsia, sepsis, obstructed labour, and indirect causes such as anaemia, malaria, or HIV). Additional variables included time from admission to death, availability of emergency obstetric and neonatal care (EmONC), and distance or delays in accessing care when available. These parameters provided a comprehensive framework for identifying the multifactorial nature of maternal deaths in the district.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData were cleaned and entered into Microsoft Excel before analysis using SPSS version 25. Descriptive statistics were used to summarize data, including frequencies, proportions, and means. The maternal mortality ratio (MMR) was computed as the number of maternal deaths per 100,000 live births. Bivariate analyses were performed using chi-square tests to examine associations between maternal death and selected risk factors. Variables significant at the 0.05 level were entered into a multivariable logistic regression model to identify independent predictors of maternal mortality. Results were presented in tables and charts, with confidence intervals (95%) reported where applicable. Findings were compared across facilities and years to identify patterns and trends in maternal mortality and its determinants.\u003c/p\u003e\u003ch3\u003eEthical Considerations\u003c/h3\u003e\u003cp\u003eEthical approval was obtained from the Institutional Review Board of the University of Bamenda with ref No. 2024/0015H/UBa/IRB, and authorization from the Regional Delegation of Public Health for the West Region with ref No.1470/L/MINSANTE/SG/DRSPO/CBF. Administrative clearance was granted by the District Health Officer of Mifi and the respective health facility management boards with ref No.416/L/MINSANTE/DRSPO/SSDM/BAG. As a retrospective record review, individual informed consent was waived, but data confidentiality was strictly maintained. All extracted information was anonymized, coded, and securely stored to prevent unauthorized access. The study adhered to the ethical principles of beneficence, non-maleficence, autonomy, and justice, ensuring respect for institutional and participant confidentiality throughout the research process.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study examined maternal mortality trends in the Mifi Health District (2021\u0026ndash;2023), highlighting the maternal mortality ratio, major obstetric and socio-demographic risk factors, and health system challenges contributing to maternal deaths.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThe Maternal Mortality Ratio (MMR) in the Mifi Health District between 2021 and 2023\u003c/h2\u003e\u003cp\u003eThese results bring out Mifi district with a rate of 232 deaths per 100000 live births. We recorded an increasing rate of maternal mortality in the Mifi with and overall rate greater than that of the whole region 232 and 123 respectively. They brought out that the Miffi Health District alone carried 37% of maternal mortality for the whole region with 20 districts.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that, between 2021 and 2023, the prevalence of maternal deaths in the Mifi Health District and the wider West Region showed marked fluctuations across districts and years. Overall, 211 maternal deaths were recorded from 171,540 deliveries, corresponding to a regional maternal mortality ratio (MMR) of 123 deaths per 100,000 live births. The Mifi district consistently reported the highest burden, accounting for 78 deaths across the three years with an overall MMR of 232.05, peaking at 271.05 in 2023. Similarly, Bafang also recorded high ratios, particularly in 2023 (334.63). Some districts such as Kekem and Malantouen showed irregular spikes, with Malantouen rising sharply to 315.28 in 2023 after relatively lower levels in earlier years, while Kekem peaked at 402.14 in 2022 but had zero deaths in 2021 and 2023. In contrast, several districts including Bamendjou and Santchou reported no maternal deaths throughout the period, and others such as Foumbot (49.53), Dschang (63.25), and Mbouda (83.90) maintained comparatively low overall ratios. These results highlight substantial inter-district disparities and a concerning upward trend in 2023, particularly in high-burden districts like Mifi, Bafang, and Malantouen, underscoring the need for targeted maternal health interventions in these areas.\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\u003ePrevalence of Maternal death in the Mifi health district and the west region from 2021 to 2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"20\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"20\" nameend=\"c20\" namest=\"c1\"\u003e\u003cp\u003eRatio Maternal Deaths per District and Per Year\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeliveries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e# Maternal deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeliveries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e# Maternal deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eDeliveries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e# Maternal deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eDeliveries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e# Maternal deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003eRate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBafang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e210.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e215.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e334.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e252.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMifi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e244.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e179.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e11437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e271.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e33613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e232.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalantouen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e3489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e315.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e10524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e180.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKekem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e402.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e2220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e135.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBatcham\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e215.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e123.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePenka Michel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e214.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e123.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangangte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e2650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e75.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e8214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e121.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoumban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e8022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e124.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e23159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e99.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaham\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e97.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e3108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e96.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKouoptamo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e202.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e91.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBandjoun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e213.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e87.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMassangam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e173.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e3507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e85.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMbouda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e98.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e5005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e99.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e15494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e83.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBandja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e202.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e73.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDschang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e5761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e121.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e17390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e63.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoumbot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e4040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e49.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e12115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e49.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangourain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e58.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e5261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e38.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGalim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e2976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e33.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBamendjou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e2312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSantchou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e2184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e57774\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e56330\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e88.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003e57436\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u003cb\u003e92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e160.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e171540\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e\u003cb\u003e211\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003e123.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMaternal Mortality Rate = # of Maternal deaths/ # Live births*100000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveal that the five first causes of maternal mortality are hemorrhage (23) (35%), infections (13) (20%), hypertensive disorders (13) (20%), unsafe abortion (6) (9%) and emboli (3) respectively from the maternal death of 2021 to 2023. It reveals that primate pregnancies also developed hemorrhage as well as multiparous women though hemorrhage was more regular with the multipa (9%/91%). We also notice a display here showing that hypertensive disorders occurred more in primipa than in multipa (54%/46%). In the Mifi Health district their second leading cause of death is infection in hypertensive disorders per these statistics (13 each). The P-value of these table is 0.0585 this means there's no statistically significant association between cause of death and number of pregnancy.\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\u003eCauses of maternal mortality in the Mifi health district of the west region of Cameroon\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCauses of death\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eAdjusted gravidity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e% Causes of death per adjusted gravidity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePercentages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6+\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnsafe Abortion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmboly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSickle Cell Anemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Ditress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpidermolysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncephalopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObtructed Labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrand Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eX\u003csup\u003e2\u003c/sup\u003e 33.25, p-value: 0.0585\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Major Obstetric and Socio-Demographic Risk Factors Associated with Maternal Deaths in the Mifi Health District\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal that the five first causes of maternal mortality are hemorrhage (23) (35%), infections (13) (20%), hypertensive disorders (13) (20%), unsafe abortion (6) (9%) and emboli (3) respectively from the maternal death of 2021 to 2023. It reveals that primate pregnancies also developed hemorrhage as well as multiparous women though hemorrhage was more regular with the multipa (9%/91%). We also notice a display here showing that hypertensive disorders occurred more in primipa than in multipa (54%/46%). In the Mifi Health district their second leading cause of death is infection in hypertensive disorders per these statistics (13 each). The P-value of these table is 0.0585 this means there's no statistically significant association between cause of death and number of pregnancy.\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\u003eCauses of maternal mortality in the Mifi health district of the west region of Cameroon\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCauses of death\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eAdjusted gravidity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e% Causes of death per adjusted gravidity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePercentages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6+\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnsafe Abortion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmboly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSickle Cell Anemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Ditress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpidermolysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncephalopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObtructed Labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrand Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eX\u003csup\u003e2\u003c/sup\u003e 33.25, p-value: 0.0585\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that only two (2.61%) persons were recorded as single and the marital status of the rest (97.39%) was not mentioned in the registers. Here show that only two (3%) persons were recorded as secondary education level and the level of education of the rest (96%) was not mentioned in the registers. In the table show that the majority (76%) of women doesn\u0026rsquo;t have a pre-exiting medical factor. The study recorded that the main medical condition that was present at delivery of the maternal deaths was anemia (6.15%). The statistics below show that (3) women who died were less than 12 weeks\u0026rsquo; gestation and the gestational ages of 6 were not mentioned in the register.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRepartition of women by Risk factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomaine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.39%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.39%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-existing medical condition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.08%\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\u003cp\u003eFirst trimester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.62%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond trimester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird trimester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.62%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.23%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the majority of maternal deaths in the Mifi Health District occurred among women who were referred from one health facility to another, representing 86% of all cases, while only 14% of deaths occurred without referral. Hemorrhage, infections, and hypertensive disorders were the leading causes of death among referred patients, accounting for 91%, 85%, and 85% respectively, indicating that most severe obstetric complications required higher-level care. Unsafe abortions, anemia, and metabolic conditions such as diabetes and sickle cell anemia were also observed exclusively among referred cases, suggesting delayed recognition or inadequate management at the primary level. Obstructed labor, however, occurred only among non-referred patients, reflecting possible challenges in timely identification and transfer. Although referral was common across nearly all causes, the chi-square test (χ\u0026sup2; = 15.73, p\u0026thinsp;=\u0026thinsp;0.1514) showed no statistically significant association between cause of death and referral status, implying that maternal deaths were widespread regardless of referral patterns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003erelating causes to referral.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCauses of Death\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eReferred to another HS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e% Referred to another HS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\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\u003eHemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e91%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnsafe abortion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmboly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSickle cell anemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory ditress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpidermolysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncephalopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObtructed labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrand total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eX\u003csup\u003e2\u003c/sup\u003e 15.73, p-value: 0.1514\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eThe Health System\u0026ndash;Related Factors Contributing to Maternal Mortality\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the majority of referrals (61.5%) among women who later died were due to a lack of necessary equipment or supplies in the initial health facilities, underscoring severe resource constraints within lower-level centers. Another 23% of referrals resulted from the absence of specialist care, indicating limited availability of trained obstetric or emergency personnel capable of managing complex cases. A smaller proportion (4.7%) were referred specifically for surgical intervention, reflecting delayed access to operative obstetric care such as cesarean sections. Additionally, 10.8% of referrals did not specify a reason, suggesting gaps in record-keeping and communication during patient transfers. Overall, these findings highlight systemic deficiencies in equipment availability, specialist coverage, and documentation, which collectively weaken the referral system and contribute to preventable maternal deaths in the Mifi Health District.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRepartition of women by Reason for referral\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\u003eReason for referral\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003efrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of necessary equipment or supplies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of specialist care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed for surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot mentioned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrand Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that delays in seeking, reaching, and receiving care were the most critical contributory factors to maternal mortality in the Mifi Health District. The majority of deaths (50%) were linked to delays in receiving adequate care at the health facility, suggesting weaknesses in the quality and timeliness of emergency obstetric management. Another 25.8% of deaths resulted from delays in reaching healthcare facilities, highlighting barriers such as poor transportation, long distances, or inadequate referral systems. Additionally, 13% of cases were associated with delays in deciding to seek care, reflecting limited awareness of danger signs and possible socio-cultural or financial constraints. Structural factors also contributed, with 6% of deaths related to a lack of skilled personnel and 5.2% to shortages of essential medical equipment or supplies. These findings collectively emphasize that both patient-related delays and systemic inefficiencies significantly exacerbate maternal mortality in the district.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContributive Factors to Maternal Mortality\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\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003efrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelay in seeking care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelay in reaching healthcare facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelay in receiving adequate care at the facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of skilled personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of necessary medical equipment or supplies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that maternal deaths in the Mifi Health District were unevenly distributed across health facilities between 2021 and 2023, with a noticeable increase in total deaths from 21 in 2021 to 34 in 2023. The Bafoussam Regional Hospital (HR Bafoussam) accounted for the overwhelming majority of cases, recording 47 out of 63 deaths (75%), making it the main referral and mortality center for the district. Other facilities such as the Bafoussam Baptist Hospital (CBC Bamendzi) and Clinique M\u0026eacute;dicale Ange Cl\u0026eacute;mence reported fewer deaths, four and three respectively, while most private and confessional centers recorded only isolated cases. The dominance of deaths at the regional hospital suggests that most critical cases are referred late, often when complications are already severe, and that lower-level facilities may lack the capacity to manage obstetric emergencies effectively. This pattern highlights the centralization of maternal care and the burden placed on tertiary-level institutions, reflecting systemic weaknesses in primary and secondary maternal health service delivery.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaternal death in MIFI heath district with respect to the health facilities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth facilities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eBafoussam Baptist Hospital \u0026ndash; Cbc Bamendzi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCm Sainte Union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCs Catholique Marie Immaculee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHd Mifi Famla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHr Bafoussam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinique Sos Ouest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChr Bafoussam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinique De Ouest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinique M\u0026eacute;dicale Ange Cl\u0026eacute;mence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCm Protestant Plateau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCs Le Salut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCs Catholique Baleng-Lafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e63\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"},{"header":"Discussion","content":"\u003cp\u003eThe findings indicate that the Mifi Health District experienced a persistently high and rising maternal mortality ratio (MMR) between 2021 and 2023, reaching 232 deaths per 100,000 live births, significantly higher than the West Region\u0026rsquo;s average of 123. The district alone accounted for 37% of all maternal deaths within the region\u0026rsquo;s twenty districts, underscoring its disproportionate contribution to regional maternal mortality. The upward trend, peaking at 271.05 in 2023, reflects systemic weaknesses in maternal health services and points to gaps in emergency obstetric care and timely intervention. These results are consistent with the broader pattern of inter-district disparities observed, where high-burden districts such as Bafang and Malantouen also showed sharp increases in mortality ratios. The leading causes hemorrhage (35%), infections (20%), hypertensive disorders (20%), and unsafe abortion (9%) mirror global and regional trends where direct obstetric complications account for the majority of maternal deaths. Similar to findings by Yakubu, Mohamed Nor, and Abidin [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], who identified biological factors such as hemorrhage and hypertensive disorders as consistent micro-level predictors of maternal mortality, the Mifi results underscore the strong influence of obstetric risk factors compounded by inadequate emergency care. The predominance of preventable causes highlights the intersection between individual-level vulnerabilities and health-system failures, echoing the conclusions of Tajvar, Hajizadeh, and Zalvand [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], who demonstrated that maternal mortality is shaped by both personal characteristics and ecological determinants such as access to skilled care and resource allocation. Furthermore, the non-significant statistical association (p\u0026thinsp;=\u0026thinsp;0.0585) between causes of death and parity in Mifi suggests that mortality risk spans both primiparous and multiparous women, aligning with Gupta, Singh, and Kumar [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], who reported similar non-linear relationships between obstetric history and mortality risk in India. The convergence of these findings reinforces a consistent global pattern: maternal deaths are driven by preventable, well-known clinical conditions that persist due to structural deficiencies in healthcare delivery, delayed response, and inequitable resource distribution. Addressing these issues in high-burden districts like Mifi requires targeted, context-specific interventions focusing on emergency obstetric care, skilled personnel availability, and community-level education to reduce delays and improve maternal survival outcomes.\u003c/p\u003e\u003cp\u003eThe findings from the Mifi Health District reveal that hemorrhage (35%), infections (20%), and hypertensive disorders (20%) were the leading causes of maternal deaths between 2021 and 2023, aligning with global and regional trends where these conditions consistently rank among the top contributors to maternal mortality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The predominance of hemorrhage among multiparous women and hypertensive disorders among primiparous women underscores the physiological and obstetric vulnerabilities associated with parity.\u003c/p\u003e\u003cp\u003eThe socio-demographic data indicating low education levels and incomplete records reflect broader issues of poor health documentation and limited health literacy, factors also highlighted in Awolayo\u0026rsquo;s [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which emphasized how sociocultural barriers such as stigma, gender norms, and reliance on traditional birth practices exacerbate maternal risks. Collectively, these findings demonstrate that maternal mortality in the Mifi Health District is driven by a combination of direct obstetric causes and systemic weaknesses, consistent with global analyses by Hogan et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and region-specific studies that underscore the intersection of medical, socio-demographic, and cultural determinants in sustaining high maternal death rates.\u003c/p\u003e\u003cp\u003eThe findings from the Mifi Health District reveal profound health system deficiencies that mirror broader patterns observed across sub-Saharan Africa, where inadequate access to emergency obstetric and neonatal care (EmONC), shortages of skilled personnel, and weak referral systems remain major contributors to maternal mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The study shows that 61.5% of referrals were due to lack of essential equipment and 23% to absence of specialist care, indicating severe under-resourcing at lower-level facilities an issue. The predominance of delays in receiving care (50%) and reaching care (25.8%) corresponds to the \u0026ldquo;Three Delays Model\u0026rdquo; described by Thaddeus and Maine (1994), emphasizing how health system inefficiencies and socio-economic barriers interact to worsen maternal outcomes. The concentration of 75% of maternal deaths at the Bafoussam Regional Hospital underscores over-centralization of care and late referrals, reflecting patterns observed in other sub-Saharan health systems where tertiary hospitals bear the brunt of preventable deaths due to weak primary-level services and poor coordination [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Adegbite [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] further noted that in Nigeria, the persistence of maternal deaths despite policy interventions stems from weak enforcement and lack of sustainable regulatory mechanisms, suggesting similar governance and systemic challenges in Cameroon. Collectively, the Mifi District findings align with regional trends that associate high maternal mortality with deficiencies in EmONC readiness, shortage of skilled providers, and ineffective referral pathways factors that demand strengthened health systems, capacity building, and policy accountability to reduce preventable maternal deaths.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the study established that maternal mortality in the Mifi Health District remains alarmingly high 232 deaths per 100,000 live births exceeding regional ratio and underscoring persistent systemic and socio-medical barriers to safe motherhood. In line with the study objectives, findings revealed that hemorrhage, infections, and hypertensive disorders were the leading causes of maternal deaths, while key risk factors included delayed care-seeking, poor referral efficiency, and lack of skilled personnel and essential equipment. These outcomes confirm that the problem identified in the statement the failure of existing maternal health strategies to curb preventable deaths stems from intertwined clinical and structural deficiencies. The dominance of direct obstetric causes highlights the preventable nature of most deaths, while the health-system delays reflect weak emergency response and inadequate facility readiness. Therefore, the high maternal mortality ratio in Mifi Health District not only exposes the fragility of local healthcare systems but also calls for targeted, context-specific interventions focused on strengthening emergency obstetric and neonatal care, improving documentation of cases, improving referral systems, and promoting timely, equitable access to skilled maternal health services to reverse the current mortality trend.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003eHealth authorities should prioritize the improvement of EmONC services through adequate equipment supply, blood banks, and emergency drugs. Strengthening facility readiness and ensuring timely intervention during obstetric complications will significantly reduce preventable maternal deaths across the Mifi Health District.\u003c/p\u003e\u003cp\u003eRecruitment and continuous training of skilled birth attendants, midwives, and obstetric specialists are essential. Regular refresher courses and mentorship programs should be implemented to improve clinical decision-making, ensure quality maternal care, and enhance early detection of life-threatening complications.\u003c/p\u003e\u003cp\u003eAn efficient, well-coordinated referral system must be established to facilitate timely transfer of obstetric emergencies. This includes functional ambulance services, clear communication channels between facilities, and accountability mechanisms to reduce delays in reaching and receiving adequate care.\u003c/p\u003e\u003cp\u003eCommunity-based health education programs should be intensified to improve awareness of pregnancy danger signs and the importance of antenatal visits and facility-based deliveries. Culturally sensitive communication strategies will empower families to seek care promptly and reduce first-level delays.\u003c/p\u003e\u003cp\u003eMaternal health records should be properly documented and digitalized to enhance data accuracy and facilitate monitoring. Strengthening the District Health Information System (DHIS2) will improve maternal death surveillance, guide timely response, and inform data-driven health policy decisions.\u003c/p\u003e\u003cp\u003eGovernment and stakeholders should implement financial risk protection schemes, such as maternal health insurance or free delivery services in both public and private health structures. Reducing out-of-pocket costs and ensuring equitable access will encourage timely care-seeking among low-income women.\u003c/p\u003e\u003cp\u003eThe Ministry of Public Health should reinforce accountability and supervision of maternal health programs. Effective policy enforcement, regular facility audits, and transparent allocation of resources are vital for sustaining progress toward reducing maternal mortality in the Mifi Health District.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eANC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Antenatal Care\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmONC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Emergency Obstetric and Neonatal Care\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEPMM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Eliminating Preventable Maternal Mortality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Family Planning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIMBN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Impregnated Mosquito Bed Net\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;:\u003c/strong\u003e Intermittent Preventive Treatment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIWC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;:\u003c/strong\u003e Infant Welfare Clinic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMMR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;:\u003c/strong\u003e Maternal Mortality Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;:\u003c/strong\u003e Sustainable Development Goals\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n\u003cli\u003eEthics approval and consent to participate; \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Board of the University of Bamenda with ref No. 2024/0015H/UBa/IRB, and authorization from the Regional Delegation of Public Health for the West Region with ref No.1470/L/MINSANTE/SG/DRSPO/CBF. Administrative clearance was granted by the District Health Officer of Mifi and the respective health facility management boards with ref No.416/L/MINSANTE/DRSPO/SSDM/BAG. As a retrospective record review, individual informed consent was waived, but data confidentiality was strictly maintained. All extracted information was anonymized, coded, and securely stored to prevent unauthorized access. The study adhered to the ethical principles of beneficence, non-maleficence, autonomy, and justice, ensuring respect for institutional and participant confidentiality throughout the research process.\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eConsent for publication; NOT APPLICABLE\u003c/li\u003e\n\u003cli\u003eAvailability of data and materials; The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cul\u003e\n\u003cli\u003eCompeting interests; NO COMPETING INTEREST\u003c/li\u003e\n\u003cli\u003eFunding; PRIVATE FROM RESEARCHER\u003c/li\u003e\n\u003cli\u003eAuthors\u0026apos; contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eJumo Olga Ngafeeson; Main Researcher\u003c/p\u003e\n\u003cp\u003eDr Besong Calvin Ebai; Co-Supervisor\u003c/p\u003e\n\u003cp\u003eProf. Mary Bih Su Atanga; Main Supervisor\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo my beloved husband who has been supervising from home\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO, 2017 Ending Preventable Maternal Mortality (epmm) a renewed focus for improving maternal and newborn health and wellbeing. \u003c/li\u003e\n\u003cli\u003eUNICEF Data and Analytics, Division of Data, Research and Policy in collaboration with Health Section Programme Division (2018), Maternal and Newborn Health Disparities Cameroon unicef for every child. 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A systematic review of micro correlates of maternal mortality. Reviews on Environmental Health. 2018 Jun 27;33(2):147-61.\u003c/li\u003e\n\u003cli\u003eTajvar M, Hajizadeh A, Zalvand R. A systematic review of individual and ecological determinants of maternal mortality in the world based on the income level of countries. BMC Public Health. 2022 Dec 15;22(1):2354.\u003c/li\u003e\n\u003cli\u003eGupta S, Singh SN, Kumar D. An empirical analysis of maternal health data: A case study of India. In2016 2nd International Conference on Next Generation Computing Technologies (NGCT) 2016 Oct 14 (pp. 490-493). IEEE.\u003c/li\u003e\n\u003cli\u003eAwolayo OA. \u003cem\u003eImpacts of select sociocultural practices on maternal mortality in Nigeria: A scoping review\u003c/em\u003e (Doctoral dissertation, University of Saskatchewan).\u003c/li\u003e\n\u003cli\u003eHogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, Lopez AD, Lozano R, Murray CJ. Maternal mortality for 181 countries, 1980\u0026ndash;2008: a systematic analysis of progress towards Millennium Development Goal 5. The lancet. 2010 May 8;375(9726):1609-23.\u003c/li\u003e\n\u003cli\u003eWorld Bank. \u003cem\u003eHealthcare Access and Quality Index.\u003c/em\u003e Washington (DC): World Bank; 2022 [cited 2025 Oct 20]. Available from: https://data.worldbank.org/indicator. \u003c/li\u003e\n\u003cli\u003eKumbani, L. C. et al. (2012). Why some women fail to give birth at health facilities. Reproductive Health. \u003c/li\u003e\n\u003cli\u003eAdegbite FR. Combating Maternal Mortality in Nigeria through Combined and Sustainable Regulatory Approach. IJOCLLEP. 2021;3:55.\u003c/li\u003e\n\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, prevalence, causes, contributing factor, risk factors, West region, Cameroon","lastPublishedDoi":"10.21203/rs.3.rs-7987380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7987380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eMaternal mortality remains a critical public health concern in Cameroon, reflecting persistent inequities in healthcare access and quality. Despite national strategies to reduce maternal deaths, the Mifi Health District continues to report disproportionately high rates, signaling underlying systemic and obstetric challenges.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective facility-based study assessed maternal mortality trends and associated risk factors in the Mifi Health District from 2021 to 2023. Specifically, it determined the maternal mortality ratio (MMR), identified major obstetric and socio-demographic risk factors, and examined health-system contributors such as emergency obstetric and neonatal care (EmONC) readiness and referral efficiency. Data were extracted from hospital records, registers, and maternal death audit reports and analyzed using SPSS 25. Descriptive statistics and chi-square tests assessed relationships between variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study found an MMR of 232 deaths per 100,000 live births significantly higher than the regional average of 123 accounting for 37% of total maternal deaths in the West Region. Hemorrhage (35%), infections (20%), and hypertensive disorders (20%) were the leading causes of death, while 86% of cases involved referrals primarily due to lack of equipment or specialist care. Delays in receiving adequate care (50%), reaching facilities (25.8%), and seeking care (13%) were the dominant contributory factors. The risk factors were mostly lacking in the registers (96%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study concludes that high maternal mortality in Mifi results from preventable obstetric causes and systemic weaknesses. It recommends strengthening EmONC services, improving referral coordination, training skilled personnel, enhancing adequate documentation, enhancing community awareness, and enforcing governance accountability to reduce preventable maternal deaths and achieve Sustainable Development Goal 3.1 in Cameroon.\u003c/p\u003e","manuscriptTitle":"Maternal Mortality Ratio and Associated Risk Factors in Mifi Health District, Cameroon: A Retrospective Facility-Based Study (2021–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 11:53:47","doi":"10.21203/rs.3.rs-7987380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"56706704973376346178180370741628066472","date":"2026-05-06T13:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175111162300773699290023514756543934202","date":"2026-04-07T09:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202550863748579503086228842449323765596","date":"2026-04-02T11:20:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T05:08:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53934445158052214716888321772138768776","date":"2026-01-28T23:44:04+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T15:10:43+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T21:40:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T00:24:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T00:23:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-10-30T09:05:35+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":"5cb46415-c00e-4b66-adab-f80e4a416b66","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"56706704973376346178180370741628066472","date":"2026-05-06T13:57:14+00:00","index":95,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T11:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 11:53:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7987380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7987380","identity":"rs-7987380","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}