Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics
Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics
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DOI：
10.1186/1741-7015-5-37
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发表时间：
2007-12-11
期刊：
影响因子：
9.3
通讯作者：
Snow, Robert W.
中科院分区：
文献类型：
--
作者：
Gething, Peter W.;Noor, Abdisalan M.;Snow, Robert W.
背景资料：非洲大多数国家的卫生部都投入大量资源，建立某种形式的卫生管理信息系统，以协调全国各地卫生设施每月治疗和护理记录的日常获取和汇编工作。尽管这些系统的成本很高，但数据覆盖率低意味着它们很少（如果有的话）用于为决策者提供可靠的证据。整个非洲的一个关键弱点是，目前缺乏有效监测长期服务使用模式的能力，因此无法评估政策或服务提供变化的影响。在这里，我们提出了一种新的方法，第一次，允许国家在卫生服务使用的重大卫生政策变化的时间可靠地跟踪使用不完善的数据从一个国家HMIS.Methods：每月的出勤记录从肯尼亚HMIS获得1 271政府运行和402信仰为基础的门诊设施在1996年和2004年之间。一个空间-时间的地统计模型被用来补偿大比例的缺失记录所造成的非报告的卫生设施，允许强大的估计每月和每年使用的服务门诊病人在此期间。结果：我们能够重建强大的时间序列的平均水平的门诊利用卫生设施在国家一级，并为所有六个主要省份在肯尼亚。这些图首次可靠地显示，1996年至2002年期间，肯尼亚保健设施的使用率在全国范围内稳步下降，随后从2003年开始急剧上升。这种模式是一致的，在不同的原因出席，并独立观察到在每个provinces.Conclusion：所提出的方法可以弥补缺失的记录在卫生信息系统提供强大的估计门诊服务使用的国家模式。这是首次使用HMIS数据，有助于恢复这些昂贵但未得到充分利用的系统作为国家监测工具。在肯尼亚采用这一方法所取得的成果有可能立即提高决策者监测全国服务使用模式和评估卫生政策和服务提供变化的影响的能力。
Background: Most Ministries of Health across Africa invest substantial resources in some form of health management information system (HMIS) to coordinate the routine acquisition and compilation of monthly treatment and attendance records from health facilities nationwide. Despite the expense of these systems, poor data coverage means they are rarely, if ever, used to generate reliable evidence for decision makers. One critical weakness across Africa is the current lack of capacity to effectively monitor patterns of service use through time so that the impacts of changes in policy or service delivery can be evaluated. Here, we present a new approach that, for the first time, allows national changes in health service use during a time of major health policy change to be tracked reliably using imperfect data from a national HMIS.Methods: Monthly attendance records were obtained from the Kenyan HMIS for 1 271 government-run and 402 faith-based outpatient facilities nationwide between 1996 and 2004. A space-time geostatistical model was used to compensate for the large proportion of missing records caused by non-reporting health facilities, allowing robust estimation of monthly and annual use of services by outpatients during this period.Results: We were able to reconstruct robust time series of mean levels of outpatient utilisation of health facilities at the national level and for all six major provinces in Kenya. These plots revealed reliably for the first time a period of steady nationwide decline in the use of health facilities in Kenya between 1996 and 2002, followed by a dramatic increase from 2003. This pattern was consistent across different causes of attendance and was observed independently in each province.Conclusion: The methodological approach presented can compensate for missing records in health information systems to provide robust estimates of national patterns of outpatient service use. This represents the first such use of HMIS data and contributes to the resurrection of these hugely expensive but underused systems as national monitoring tools. Applying this approach to Kenya has yielded output with immediate potential to enhance the capacity of decision makers in monitoring nationwide patterns of service use and assessing the impact of changes in health policy and service delivery.