Enyew Ermias Bekele, Ayele Kokeb, Asmare Lakew, Bayou Fekade Demeke, Arefaynie Mastewal, Tsega Yawkal, Endawkie Abel, Kebede Shimelis Derso, Tareke Abiyu Abadi, Abera Kaleab Mesfine, Kebede Natnael, Feyisa Mahider Shimelis, Mihiretu Mengistu Mera
Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Department of Health Promotion, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Front Glob Womens Health. 2024 Dec 16;5:1474762. doi: 10.3389/fgwh.2024.1474762. eCollection 2024.
Home birth is described as a delivery that takes place at home without the presence of a skilled birth attendant. In 2017, nearly 295,000 mothers died from various pregnancy and childbirth-related problems, accounting for approximately 810 maternal deaths per day. Therefore, this study aims to investigate the spatial distributions of home birth and associated factors in Ethiopia using the Performance Monitoring for Action Survey (PMAS) 2019) to get information that helps to take geographic-based interventions and can assist health planners and policymakers in developing particular measures to reduce home deliveries.
In PMA-ET 2019, a community-based cross-sectional study was conducted in collaboration with Addis Ababa University, Johns Hopkins University, and the Federal Ministry of Health from September 2019 to December 2019, in Ethiopia. A multi-stage cluster sampling procedure was employed to draw from the stratified 2019 PMAS sample. A weighted total of 5,796 women were included in this study. ArcGIS version 10.7 software was used to visualize the spatial analysis. In addition, STATA version 14 of the statistical software was used for multilevel analysis The Bernoulli model was applied using Kulldorff's SaTScan version 9.6 software to identify significant purely spatial clusters for home delivery in Ethiopia. Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR) test, Median Odds Ratio (MOR), and deviance (-2LLR) values were used for model comparison and fitness. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and -value <0.05 in the multilevel logistic model were used to declare significant factors associated with home delivery.
The spatial distribution of home delivery was non-random in Ethiopia. Statistically significant high hotspots of home delivery were found in Somali, Afar, Sidama, most of South Nation Nationality and People Region (SNNP), most parts of Amhara, south west Ethiopia, and Oromia region. In the multilevel logistic regression model; Women from the lowest wealth quintile were 1.68 times [AOR = 1.68; 95% CI: 1.31, 2.15] higher odds of giving birth at home as compared to their counterparts. Regarding maternal educational status, mothers who had no education, primary education, and secondary education had 9.91 times [AOR = 9.91, 95% CI: 5.44, 18.04], 6.62 times [AOR = 6.62, 95% CI: 3.65, 12.00] and 2.99 times [AOR = 2.99, 95% CI: 1.59, 5.63] higher odds of giving birth at home compared to mothers who attained higher education, respectively. In addition, community-level factors were significantly associated with home delivery, women who had high community-level poverty were 1.76 times [AOR = 1.76; 95% CI: 1.14, 2.72] higher odds of home delivery compared to women who had low community-level poverty.
Home delivery was statistically found to be a significantly high hot spot in Somalia, Afar, Sidama, most of the South Nation Nationality and People area (SNNP), most of Amhara, southwest Ethiopia, and the Oromia region of Ethiopia. Significant factors associated with home delivery in Ethiopia were women with lower levels of education, poor wealth, living in rural areas, high levels of community poverty, divorced or separated widowed marital status, and older maternal ages. Therefore, health institutions, health professionals, National and regional policymakers health planners community leaders and all concerned should give priority to the identified hot spot clusters to design an effective intervention program to reduce home delivery.
家庭分娩被定义为在没有专业助产人员在场的情况下于家中进行的分娩。2017年,近29.5万名母亲死于各种与妊娠和分娩相关的问题,约合每天810例孕产妇死亡。因此,本研究旨在利用2019年行动绩效监测调查(PMAS)调查埃塞俄比亚家庭分娩的空间分布及相关因素,以获取有助于采取基于地理区域的干预措施的信息,并协助卫生规划者和政策制定者制定具体措施以减少家庭分娩。
在2019年埃塞俄比亚的行动绩效监测调查(PMA - ET)中,于2019年9月至12月与亚的斯亚贝巴大学、约翰霍普金斯大学以及埃塞俄比亚联邦卫生部合作开展了一项基于社区的横断面研究。采用多阶段整群抽样程序从分层的2019年PMAS样本中抽取样本。本研究共纳入5796名加权女性。使用ArcGIS 10.7版本软件进行空间分析可视化。此外,使用统计软件STATA 14版本进行多水平分析。使用Kulldorff的SaTScan 9.6版本软件应用伯努利模型来识别埃塞俄比亚家庭分娩的显著纯空间聚集区。组内相关系数(ICC)、似然比(LR)检验、中位数优势比(MOR)和偏差(-2LLR)值用于模型比较和拟合优度检验。多水平逻辑模型中调整后的优势比(AOR)及95%置信区间(CI)且p值<0.05用于确定与家庭分娩相关的显著因素。
埃塞俄比亚家庭分娩的空间分布并非随机。在索马里、阿法尔、希达莫、南苏丹民族和人民区域(SNNP)的大部分地区、阿姆哈拉的大部分地区、埃塞俄比亚西南部以及奥罗米亚地区发现了家庭分娩在统计学上具有显著意义的高热点地区。在多水平逻辑回归模型中;与处于最高财富五分位数的女性相比,最低财富五分位数的女性在家分娩的几率高出1.68倍[AOR = 1.68;95% CI:1.31,2.15]。关于孕产妇教育状况,未受过教育、接受过小学教育和中学教育的母亲在家分娩的几率分别比受过高等教育的母亲高出9.91倍[AOR = 9.91,95% CI:5.44,18.04]、6.62倍[AOR = 6.62,95% CI:3.65,12.00]和2.99倍[AOR = 2.99,95% CI:1.59,5.63]。此外,社区层面的因素与家庭分娩显著相关,社区层面贫困程度高的女性在家分娩的几率比社区层面贫困程度低的女性高出1.76倍[AOR = 1.76;95% CI:1.14,2.72]。
经统计发现,索马里、阿法尔、希达莫、南苏丹民族和人民区域(SNNP)的大部分地区、阿姆哈拉的大部分地区、埃塞俄比亚西南部以及埃塞俄比亚奥罗米亚地区的家庭分娩是显著的高热点地区。与埃塞俄比亚家庭分娩相关的显著因素包括教育水平较低、财富状况较差、居住在农村地区、社区贫困程度高、离婚或分居丧偶的婚姻状况以及较高的孕产妇年龄。因此,卫生机构、卫生专业人员、国家和地区政策制定者、卫生规划者、社区领袖及所有相关方应优先关注已确定的热点聚集区,以设计有效的干预方案来减少家庭分娩。