Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Policy and Programmes Division, World Vision UK, Milton Keynes, UK.
BMJ Glob Health. 2021 Jun;6(6). doi: 10.1136/bmjgh-2020-004223.
Routine health information system(s) (RHIS) facilitate the collection of health data at all levels of the health system allowing estimates of disease prevalence, treatment and preventive intervention coverage, and risk factors to guide disease control strategies. This core health system pillar remains underdeveloped in many low-income and middle-income countries. Efforts to improve RHIS data coverage, quality and timeliness were launched over 10 years ago.
A systematic review was performed across 12 databases and literature search engines for both peer-reviewed articles and grey literature reports on RHIS interventions. Studies were analysed in three stages: (1) categorisation of RHIS intervention components and processes; (2) comparison of intervention component effectiveness and (3) whether the post-intervention outcome improved above the WHO integrated disease surveillance response framework data quality standard of 80% or above.
5294 references were screened, resulting in 56 studies. Three key performance determinants-technical, organisational and behavioural-were proposed as critical to RHIS strengthening. Seventy-seven per cent [77%] of studies identified addressed all three determinants. The most frequently implemented intervention components were 'providing training' and 'using an electronic health management information systems'. Ninety-three per cent [93%] of pre-post or controlled trial studies showed improvements in one or more data quality outputs, but after applying a standard threshold of >80% post-intervention, this number reduced to 68%. There was an observed benefit of multi-component interventions that either conducted data quality training or that addressed improvement across multiple processes and determinants of RHIS.
Holistic data quality interventions that address multiple determinants should be continuously practised for strengthening RHIS. Studies with clearly defined and pragmatic outcomes are required for future RHIS improvement interventions. These should be accompanied by qualitative studies and cost analyses to understand which investments are needed to sustain high-quality RHIS in low-income and middle-income countries.
常规健康信息系统(RHIS)有助于在卫生系统的各个层面收集健康数据,从而能够估算疾病的流行率、治疗和预防干预措施的覆盖面以及风险因素,为疾病控制策略提供指导。这一核心卫生系统支柱在许多低收入和中等收入国家仍不够发达。十多年前,就已经启动了旨在提高 RHIS 数据覆盖范围、质量和及时性的工作。
对 12 个数据库和文献搜索引擎进行了系统检索,以获取关于 RHIS 干预措施的同行评议文章和灰色文献报告。研究分三个阶段进行分析:(1)对 RHIS 干预措施的组成部分和流程进行分类;(2)比较干预措施的有效性;(3)干预后结果是否优于世界卫生组织综合疾病监测反应框架数据质量标准的 80%以上。
筛选了 5294 篇参考文献,最终纳入了 56 项研究。提出了三个关键绩效决定因素——技术、组织和行为,认为这些因素对于加强 RHIS 至关重要。77%的研究确定了所有三个决定因素。实施频率最高的干预措施包括“提供培训”和“使用电子健康管理信息系统”。93%的前后或对照试验研究显示,在一个或多个数据质量产出方面有所改善,但在应用>80%的干预后标准阈值后,这一数字减少到 68%。多因素干预措施具有明显的优势,这些干预措施既开展了数据质量培训,又针对 RHIS 的多个流程和决定因素进行了改进。
应持续实施针对多个决定因素的全面数据质量干预措施,以加强 RHIS。未来的 RHIS 改进干预措施需要有明确和务实的结果。这些措施应伴随着定性研究和成本分析,以了解在低收入和中等收入国家需要哪些投资来维持高质量的 RHIS。