PDF(Pubmed)
Abstract：
BACKGROUND: National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques.
METHODS: Essential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively.
RESULTS: GAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria.
CONCLUSIONS: High-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.
METHODS: Essential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively.
RESULTS: GAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria.
CONCLUSIONS: High-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.
摘要：
背景：全国范围内的母婴健康（MCH）服务覆盖率估计掩盖了地区级和社区级的地理不平等现象。这项研究的目的是使用机器学习技术估算尼日利亚基本MCH服务的网格级别覆盖率。
方法：本研究中的基本MCH服务包括产前护理，基于设施的交付，儿童疫苗接种和儿童疾病的治疗。我们使用2003年至2018年尼日利亚五次国家代表性横断面调查的数据和地理空间社会经济数据，估计了每个基本MCH服务的广义累加模型（GAM）和梯度增强回归（GB）。环境和物理特征。使用每个服务的最佳性能模型，我们绘制了城市和农村地区1平方公里和5平方公里空间分辨率的预测覆盖率，分别。
结果：在一系列基本MCH服务中，GAM的表现始终优于GB模型，显示较低的系统预测误差。高分辨率地图显示了MCH服务覆盖范围的明显地理差异，特别是在农村和城市地区之间以及不同的州和服务类型之间。时间趋势表明，从2003年到2018年，MCH服务覆盖范围总体增加，尽管服务类型和位置有所不同。确定了孕产妇和疫苗接种服务覆盖率较低的优先领域，大部分位于尼日利亚北部。
结论：高分辨率空间估计可以指导地理优先级划分，并有助于为实施计划制定更好的策略，允许将有限的资源用于基本妇幼保健服务覆盖率较低的地区。
方法：本研究中的基本MCH服务包括产前护理，基于设施的交付，儿童疫苗接种和儿童疾病的治疗。我们使用2003年至2018年尼日利亚五次国家代表性横断面调查的数据和地理空间社会经济数据，估计了每个基本MCH服务的广义累加模型（GAM）和梯度增强回归（GB）。环境和物理特征。使用每个服务的最佳性能模型，我们绘制了城市和农村地区1平方公里和5平方公里空间分辨率的预测覆盖率，分别。
结果：在一系列基本MCH服务中，GAM的表现始终优于GB模型，显示较低的系统预测误差。高分辨率地图显示了MCH服务覆盖范围的明显地理差异，特别是在农村和城市地区之间以及不同的州和服务类型之间。时间趋势表明，从2003年到2018年，MCH服务覆盖范围总体增加，尽管服务类型和位置有所不同。确定了孕产妇和疫苗接种服务覆盖率较低的优先领域，大部分位于尼日利亚北部。
结论：高分辨率空间估计可以指导地理优先级划分，并有助于为实施计划制定更好的策略，允许将有限的资源用于基本妇幼保健服务覆盖率较低的地区。
