Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.
Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.
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DOI：
10.1371/journal.pdig.0000025
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发表时间：
2022-05
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公共卫生干预措施需要循证决策，以最大限度地扩大影响。空间决策支持系统（SDSS）旨在收集、存储、处理和分析数据，以生成知识并为决策提供信息。本文讨论了如何使用SDSS，运动信息管理系统（CIMS），以支持在比奥科岛的疟疾控制业务的影响室内滞留喷洒（IRS）的关键过程指标：覆盖率，业务效率和生产力。我们使用了过去五轮年度IRS（2017年至2021年）的数据来估计这些指标。室内滞留喷雾杀虫剂的覆盖率是按每单位面积喷洒的房屋所占百分比计算的，以100 x100米的地图扇区表示。最佳覆盖率被定义为80%和85%之间，和覆盖率低于80%和高于85%，分别为不足和过度喷雾。运营效率被定义为实现最佳覆盖的地图部门的比例。日生产率表示为每台喷雾器每天喷洒的房屋数量（h/s/d）。这些指标在五轮中进行了比较。总体IRS覆盖率（即按轮次喷洒的房屋总数对总体分母的百分比）在2017年最高（80.2%），但这一轮显示过度喷洒的地图部门比例最大（36.0%）。相反，尽管总体覆盖率较低（77.5%），但2021年一轮显示出最高的运营效率（37.7%）和最低的过度喷洒地图部门比例（18.7%）。于二零二一年，营运效率提高亦伴随生产力轻微上升。生产力介乎2020年的3. 3小时╱秒╱日至2021年的3. 9小时╱秒╱日（中位数为3. 6小时╱秒╱日）。我们的研究结果表明，CIMS提出的数据收集和处理的新方法显着提高了比奥科IRS的运营效率。规划和部署期间的高空间粒度，加上实地工作队使用实时数据进行更密切的后续行动，有助于更均匀地提供最佳覆盖面，同时保持高生产率。有效的公共卫生干预措施依赖于高覆盖率，以提供社区保护。覆盖面取决于接受干预的特定目标人口的比例。所需的覆盖水平因环境和健康问题而异。如何以公平的方式实现高覆盖率的问题在操作上具有挑战性。在这里，我们描述了使用数字化工具来支持和优化比奥科岛上一种关键的、经过验证的疟疾控制干预措施--室内滞留喷洒（IRS）的实施。我们表明，在计划交付和计算覆盖范围的规模是至关重要的，以保证整个目标人口得到平等的服务。我们还表明，在IRS实施过程中实现足够的高覆盖率是具有挑战性的，但可以通过将目标区域细分为多个小区域单元并使用空间决策支持来指导部署来提供极大的支持。我们将IRS作为一个具体的例子，但同样的数字化工具也可用于其他公共卫生干预措施，其方法是在实施过程中促进决策，并允许更好地监测干预措施的覆盖范围，从而提高交付效率。
Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity. Effective public health interventions rely on high coverage to provide community protection. Coverage is determined by the proportion of a given target population that receives the intervention. The level of coverage required varies across settings and health problems. The question about how one achieves high coverage in an equitable manner is operationally challenging. Here, we describe the use of digital tools to support and optimize the delivery of a crucial and proven malaria control intervention, indoor residual spraying (IRS), on Bioko Island. We demonstrate that the scale at which one plans delivery and calculates coverage is critical for guaranteeing that the whole target population is served equally. We also show that achieving adequate high coverage during IRS implementation is challenging, but can be greatly supported by subdividing the target area into multiple, small area units and by using spatial decision support to guide deployment. We focused on IRS as a specific example, but the same digital tools can be used for other public health interventions, with an approach that promotes decision-making during implementation and allows better monitoring of intervention coverage, resulting in more efficient delivery.