Improving imperfect data from health management information systems in Africa using space-time geostatistics.
Improving imperfect data from health management information systems in Africa using space-time geostatistics.
复制标题
使用时空地统计学改善非洲健康管理信息系统的不完美数据。
DOI：
10.1371/journal.pmed.0030271
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发表时间：
2006-06
期刊：
影响因子：
15.8
通讯作者：
Atkinson, Peter M.
中科院分区：
文献类型：
--
作者：
Gething, Peter W.;Noor, Abdisalan M.;Gikandi, Priscilla W.;Ogara, Esther A. A.;Hay, Simon I.;Nixon, Mark S.;Snow, Robert W.;Atkinson, Peter M.
卫生系统内有关特定疾病治疗负担的可靠且及时的信息对于服务提供的规划和监测至关重要。卫生管理信息系统（HMIS）的存在是为了在非洲全国范围内满足这一需求，但由于卫生机构普遍报告不足，未能提供足够的数据。面对这种不足，重要的公共卫生决策往往依赖于对区域和国家治疗负担粗略调整后的估计值。
本研究以肯尼亚基本不完整的卫生管理信息系统数据库中的门诊疑似疟疾为例，定义了一个地统计建模框架，该框架能够预测所有时空缺失数据的值。由此得到的完整数据集可用于确定任何时空聚合水平下的疑似疟疾治疗负担。对模型的验证表明，这些负担在地区、省和国家层面都能以可接受的准确度进行量化。
据我们所知，这里提出的建模框架首次从不完善的卫生管理信息系统数据中提供了可靠信息，以支持国家和国家以下各级基于证据的决策。
当应用于卫生系统报告时，时空克里金法能够将大量缺失数据估计到可接受的准确度水平，从而实现基于证据的决策和资源分配。
Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens. This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale. The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels. When adopted to health systems reporting, space-time kriging can estimate large numbers of missing data to acceptable levels of accuracy, and thus allow evidence-based decision making and allocation of resources.
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DOI：
10.1016/s0035-9203(98)90936-1
发表时间：
1998-01-01
影响因子：
2.2
作者：
Hay, SI;Snow, RW;Rogers, DJ
通讯作者：
Rogers, DJ
影响因子：
2.7
作者：
Noor, AM;Gikandi, PW;Hay, SI;Muga, RO;Snow, RW
通讯作者：
Snow, RW
影响因子：
3.3
作者：
Noor, AM;Amin, AA;Snow, RW
通讯作者：
Snow, RW
影响因子：
3.3
作者：
Noor, AM;Zurovac, D;Snow, RW
通讯作者：
Snow, RW
影响因子：
4.4
作者：
De Cesare, L;Myers, DE;Posa, D
通讯作者：
Posa, D