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埃塞俄比亚所有育龄妇女计划生育需求未满足情况的空间分布及决定因素:多层次逻辑回归建模方法

Spatial distribution and determinant factors of unmet need for family planning among all reproductive-age women in Ethiopia: a multi-level logistic regression modelling approach.

作者信息

Azanaw Melkalem Mamuye, Fentie Dawit Tefera, Bukayaw Yeaynmarnesh Asmare, Lakew Ayenew Molla, Sisay Malede Mequanent

机构信息

Department of Public Health, College of Medicine and Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.

Amhara Regional Health Bureaus, Zonal Health Office, Gondar, Ethiopia.

出版信息

Contracept Reprod Med. 2022 Aug 1;7(1):13. doi: 10.1186/s40834-022-00178-9.

Abstract

BACKGROUND

Unmet need for family planning has been remaining high in developing countries than developed countries, notably in sub-Saharan Africa. Data on unmet needs can help countries set service priorities. This study aimed to explore the geographical disparities of unmet need among reproductive-age women in Ethiopia using a 2016 national population-based survey.

METHODS

This study was based on the nationally representative 2016 Ethiopian Demographic and Health Survey data. We used a total weighted sample of 15,683 reproductive-aged women. A multi-level logistic regression analysis was used to account for the Demographic Health Survey data's hierarchal nature. In the multivariable multi-level analysis, those variables with a p-value < 0.05 were significantly associated with unmet needs. Spatial autocorrelation techniques were used to explore the clustering tendencies of unmet needss using Getis-Ord Gi* statistics.

RESULTS

Overall, 15.2% (95% Confidence Interval (CI): 14.63, 15.76) of women of the reproductive age group in Ethiopia had an unmet need for family planning in 2016. In multivariable multilevel logistic regression analysis; individual-level variables such as being married (Adjusted odds ratio (AOR) = 25.7,95% CI: 11.50,60.42), lowest wealth status (AOR = 1.43,95% CI:1.14,1.79), having five or more children (AOR = 1.98, 95% CI:1.62,2.41), being a follower of Muslim religion (AOR = 1.35,95% CI:1.03,1.76) and protestant religion (AOR = 0.73,95% CI: 0.53,0.99) than orthodox Christian followers were statistically associated factors with unmet need. Among community-level variables; being in rural residency (AOR = 1.37, 95% CI: 1.01, 1.93), belong to the Oromia region (AOR = 1.53, 95%CI: 1.10, 2.11) and Somali region (AOR = 0.37, 95% CI: 0.22, 0.61) were significantly associated unmet need. The spatial analysis of unmet need among all women revealed that Oromia, Southern Nations, and Nationality of People and Gambela regions had high hotspots than other parts of the country.

CONCLUSIONS

In this study, the prevalence of unmet needs was high. Significant regional unmet need variation was indicated among reproductive-age women in Ethiopia, specifically in western parts of the country. Wealth status, number of children, marital status, residence, and religion were the most important associated factors with unmet needs. Addressing unmet needs targeted rural residents with low socioeconomic status, and western regions should be given top priority.

摘要

背景

发展中国家计划生育需求未得到满足的情况一直高于发达国家,尤其是在撒哈拉以南非洲地区。未满足需求的数据有助于各国确定服务重点。本研究旨在利用2016年全国人口调查探讨埃塞俄比亚育龄妇女未满足需求的地理差异。

方法

本研究基于具有全国代表性的2016年埃塞俄比亚人口与健康调查数据。我们使用了15683名育龄妇女的总加权样本。采用多层次逻辑回归分析来考虑人口与健康调查数据的层次性质。在多变量多层次分析中,p值<0.05的变量与未满足需求显著相关。使用空间自相关技术,通过Getis-Ord Gi*统计量来探索未满足需求的聚集趋势。

结果

总体而言,2016年埃塞俄比亚育龄妇女中有15.2%(95%置信区间(CI):14.63,15.76)的计划生育需求未得到满足。在多变量多层次逻辑回归分析中;个体层面的变量,如已婚(调整优势比(AOR)=25.7,95%CI:11.50,60.42)、最低财富状况(AOR=1.43,95%CI:1.14,1.79)、有五个或更多孩子(AOR=1.98,95%CI:1.62,2.41)、是穆斯林宗教信徒(AOR=1.35,95%CI:1.03,1.76)和新教宗教信徒(AOR=0.73,95%CI:0.53,0.99)相比东正教基督教信徒是与未满足需求有统计学关联的因素。在社区层面的变量中;居住在农村(AOR=1.37,95%CI:1.01,1.93)、属于奥罗米亚地区(AOR=1.53,95%CI:1.10,2.11)和索马里地区(AOR=0.37,95%CI:0.22,0.61)与未满足需求显著相关。对所有妇女未满足需求的空间分析表明,奥罗米亚地区、南方各族人民地区和甘贝拉地区比该国其他地区有更高的热点。

结论

在本研究中,未满足需求的患病率很高。埃塞俄比亚育龄妇女中存在显著的地区未满足需求差异,特别是在该国西部地区。财富状况、子女数量、婚姻状况、居住地和宗教是与未满足需求最重要的相关因素。解决未满足需求应优先针对社会经济地位较低的农村居民和西部地区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac89/9341114/3e62a016d443/40834_2022_178_Fig1_HTML.jpg

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