MEASURE Evaluation, Centre for Applied Malaria Research and Evaluation, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
U.S. President's Malaria Initiative, United States Agency for International Development, Washington, D.C., USA.
J Glob Health. 2020 Dec;10(2):020413. doi: 10.7189/jogh.10.020413.
Accurate estimation of intervention coverage is a vital component of malaria program monitoring and evaluation, both for process evaluation (how well program targets are achieved), and impact evaluation (whether intervention coverage had an impact on malaria burden). There is growing interest in maximizing the utility of program data to generate interim estimates of intervention coverage in the periods between large-scale cross-sectional surveys (the gold standard). As such, this study aimed to identify relevant concepts and themes that may guide future optimization of intervention coverage estimation using routinely collected data, or data collected during and following intervention campaigns, with a particular focus on strategies to define the denominator.
We conducted a scoping review of current practices to estimate malaria intervention coverage for insecticide-treated nets (ITNs); indoor residual spray (IRS); intermittent preventive treatment in pregnancy (IPTp); mass drug administration (MDA); and seasonal malaria chemoprevention (SMC) interventions; case management was excluded. Multiple databases were searched for relevant articles published from January 1, 2015 to June 1, 2018. Additionally, we identified and included other guidance relevant to estimating population denominators, with a focus on innovative techniques.
While program data have the potential to provide intervention coverage data, there are still substantial challenges in selecting appropriate denominators. The review identified a lack of consistency in how coverage was defined and reported for each intervention type, with denominator estimation methods not clearly or consistently reported, and denominator estimates rarely triangulated with other data sources to present the feasible range of denominator values and consequently the range of likely coverage estimates.
Though household survey-based estimates of intervention coverage remain the gold standard, efforts should be made to further standardize practices for generating interim measurements of intervention coverage from program data, and for estimating and reporting population denominators. This includes fully describing any projections or adjustments made to existing census or population data, exploring opportunities to validate available data by comparing with other sources, and explaining how the denominator has been restricted (or not) to reflect exclusion criteria.
准确估计干预措施的覆盖率是疟疾规划监测和评价的重要组成部分，对于过程评估（了解规划目标的实现程度）和影响评估（干预措施的覆盖率是否对疟疾负担产生影响）都至关重要。人们越来越感兴趣的是，最大限度地利用规划数据，在大规模横断面调查（黄金标准）之间的时段内生成干预措施覆盖率的中期估计值。因此，本研究旨在确定相关概念和主题，以指导未来使用常规收集的数据或在干预活动期间和之后收集的数据优化干预措施覆盖率的估计，特别是侧重于确定分母的策略。
我们对目前使用常规收集的数据或在干预活动期间和之后收集的数据来估计驱虫蚊帐（ITN）、室内滞留喷洒（IRS）、孕妇间歇性预防治疗（IPTp）、大规模药物治疗（MDA）和季节性疟疾化学预防（SMC）干预措施的覆盖率的做法进行了范围综述。不包括病例管理。从 2015 年 1 月 1 日至 2018 年 6 月 1 日，我们在多个数据库中搜索了相关文章。此外，我们还确定并纳入了与估计人口分母有关的其他指南，重点是创新技术。
尽管规划数据有可能提供干预措施覆盖率数据，但在选择适当的分母方面仍存在很大挑战。审查发现，每种干预类型的覆盖率定义和报告方式缺乏一致性，分母估计方法未明确或一致报告，分母估计值很少与其他数据源三角测量，以呈现可行的分母值范围，从而呈现可能的覆盖率估计值范围。
尽管基于家庭调查的干预措施覆盖率估计仍然是黄金标准，但应努力进一步规范从规划数据生成干预措施覆盖率中期测量值以及估计和报告人口分母的做法。这包括充分描述对现有普查或人口数据所做的任何预测或调整，探索通过与其他来源进行比较来验证现有数据的机会，并解释分母如何被限制（或不限制）以反映排除标准。
