Using routine health information systems for well-designed health evaluations in low- and middle-income countries
Using routine health information systems for well-designed health evaluations in low- and middle-income countries
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DOI：
10.1093/heapol/czv029
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发表时间：
2016-02-01
影响因子：
3.2
通讯作者：
Wagenaar, Alexander C.
中科院分区：
文献类型：
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作者：
Wagenaar, Bradley H.;Sherr, Kenneth;Wagenaar, Alexander C.
几乎每个国家都建立了常规卫生信息系统 (RHIS)，并提供定期收集的各级卫生系统服务提供的全覆盖记录。然而，由于对完整性、及时性、代表性和准确性的担忧，这些丰富的数据来源在评估健康计划的因果效应时经常被忽视。以莫桑比克国家 RHIS（Modulo Basico）为例，我们敦促重新关注使用 RHIS 数据进行健康评估。提高数据质量的干预措施已经存在，并已在低收入和中等收入国家 (LMIC) 进行了测试。 RHIS 数据的内在特征（长时间内的大量重复观察、卫生设施的全面覆盖以及服务覆盖率和利用率的大量实时指标）提供了非常稳健的准实验设计，例如受控中断时间序列（cITS），这是间歇性社区抽样调查不可能实现的。此外，cITS 分析非常适合中低收入国家不断变化的发展环境，因为：(1) 允许在干预措施实施之前、期间和之后测量和控制趋势和其他模式； (2) 促进在多个嵌套级别使用大量同时控制组和非等价因变量，以提高因果推理的有效性和强度； (3) 允许整合连续的“收到的有效剂量”实施措施。随着 RHIS 数据在卫生规划评估中的广泛使用，对数据系统的投资、卫生工作者对 RHIS 数据的兴趣和利用以及数据质量将随着时间的推移进一步提高。由于 RHIS 数据由部委拥有和运营，随着时间的推移，依赖这些数据将有助于可持续的国家能力。
Routine health information systems (RHISs) are in place in nearly every country and provide routinely collected full-coverage records on all levels of health system service delivery. However, these rich sources of data are regularly overlooked for evaluating causal effects of health programmes due to concerns regarding completeness, timeliness, representativeness and accuracy. Using Mozambique's national RHIS (Modulo Basico) as an illustrative example, we urge renewed attention to the use of RHIS data for health evaluations. Interventions to improve data quality exist and have been tested in low- and middle-income countries (LMICs). Intrinsic features of RHIS data (numerous repeated observations over extended periods of time, full coverage of health facilities, and numerous real-time indicators of service coverage and utilization) provide for very robust quasi-experimental designs, such as controlled interrupted time-series (cITS), which are not possible with intermittent community sample surveys. In addition, cITS analyses are well suited for continuously evolving development contexts in LMICs by: (1) allowing for measurement and controlling for trends and other patterns before, during and after intervention implementation; (2) facilitating the use of numerous simultaneous control groups and non-equivalent dependent variables at multiple nested levels to increase validity and strength of causal inference; and (3) allowing the integration of continuous 'effective dose received' implementation measures. With expanded use of RHIS data for the evaluation of health programmes, investments in data systems, health worker interest in and utilization of RHIS data, as well as data quality will further increase over time. Because RHIS data are ministry-owned and operated, relying upon these data will contribute to sustainable national capacity over time.