Mwaura Hellen Muringi, Kamanu Timothy Kelvin, Kulohoma Benard W
Department of Biochemistry, University of Nairobi, Nairobi, KEN.
Department of Mathematics, University of Nairobi, Nairobi, KEN.
Cureus. 2024 Oct 27;16(10):e72476. doi: 10.7759/cureus.72476. eCollection 2024 Oct.
Introduction Maternal mortality remains a critical global health issue, with ongoing efforts to reduce its incidence as part of international health priorities. Kenya, a sub-Saharan country that has a disproportionate number of maternal mortality is likely to miss this target unless evidence-based interventions are deployed. The paucity of reliable maternal health data calls for the development of alternative predictive models to complement the impaired civil registration system and the aperiodic national surveys. Methods We utilized DHS surveys from several Sub-Saharan African countries to estimate parameters for predicting Kenya's maternal mortality rate (MMR) in the absence of recent Kenya Demographic and Health Survey (KDHS) data. We developed a multiple linear regression model using supervised machine learning using the R-programming suite. Our model leverages machine learning techniques to analyze regional trends and predict sub-national MMR variations. We then applied the model to predict MMR for Kenyan counties using the data for the KDHS 2022 survey. Results Using Pearson's correlation, we observed a significant positive correlation between MMR and total fertility (r = 0.32, p = 0.025) and a significant negative correlation between MMR and maternal age at first birth (r = -0.40, p = 0.005). Additionally, a significant correlation was observed with the cumulative percentage of mothers attending post-natal clinics, the prevalence of thinness (r = 0.77, p < 0.001), HIV infection in women (r = 0.20, p = 0.164), and physical violence during pregnancy. The model estimate of national MMR in 2022 was 367 deaths per 100,000 live births, ranging from 49 deaths per 100,000 live births in Kisii County to 1794 deaths per 100,000 live births in Turkana County. Conclusion Although MMR in Kenya displayed a general downward trend, our model's estimates for DHS 2022 indicate an increase compared to the 2019 National Census and Housing Survey estimate of 355 deaths per 100,000 live births. This rise may be attributed to COVID-19-related maternal deaths during the same period. The integration of predictive models to inform interventions and resource allocation could play a crucial role in enhancing maternal healthcare outcomes in Kenya.
引言
孕产妇死亡率仍然是一个关键的全球卫生问题，作为国际卫生重点工作的一部分，人们一直在努力降低其发生率。肯尼亚是撒哈拉以南的一个国家，孕产妇死亡率过高，除非部署基于证据的干预措施，否则很可能无法实现这一目标。可靠的孕产妇健康数据匮乏，需要开发替代预测模型，以补充受损的民事登记系统和不定期的全国性调查。
方法
我们利用撒哈拉以南非洲几个国家的 DHS 调查来估计参数，以便在没有最新的肯尼亚人口与健康调查（KDHS）数据的情况下预测肯尼亚的孕产妇死亡率（MMR）。我们使用 R 编程套件，通过监督机器学习开发了一个多元线性回归模型。我们的模型利用机器学习技术来分析区域趋势，并预测国家以下层面的孕产妇死亡率变化。然后，我们应用该模型，使用 2022 年 KDHS 调查的数据来预测肯尼亚各县的孕产妇死亡率。
结果
使用皮尔逊相关性分析，我们观察到孕产妇死亡率与总生育率之间存在显著正相关（r = 0.32，p = 0.025），与首次生育时的孕产妇年龄之间存在显著负相关（r = -0.40，p = 0.005）。此外，还观察到与产后诊所就诊母亲的累积百分比、消瘦患病率（r = 0.77，p < 0.001）、女性艾滋病毒感染率（r = 0.20，p = 0.164）以及孕期身体暴力存在显著相关性。2022 年全国孕产妇死亡率的模型估计为每 10 万活产中有 367 例死亡，范围从基苏木县的每 10 万活产 49 例死亡到图尔卡纳县的每 10 万活产 1794 例死亡。
结论
尽管肯尼亚的孕产妇死亡率总体呈下降趋势，但我们对 2022 年 DHS 的模型估计表明，与 2019 年全国人口普查和住房调查估计的每 10 万活产 355 例死亡相比有所上升。这一上升可能归因于同期与 COVID - 19 相关的孕产妇死亡。整合预测模型以指导干预措施和资源分配，可能在改善肯尼亚的孕产妇保健结果方面发挥关键作用。
