Integrating evidence, models and maps to enhance Chagas disease vector surveillance.
Integrating evidence, models and maps to enhance Chagas disease vector surveillance.
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DOI：
10.1371/journal.pntd.0006883
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发表时间：
2018-11
影响因子：
3.8
通讯作者：
Levy MZ
中科院分区：
文献类型：
--
作者：
Gutfraind A;Peterson JK;Billig Rose E;Arevalo-Nieto C;Sheen J;Condori-Luna GF;Tankasala N;Castillo-Neyra R;Condori-Pino C;Anand P;Naquira-Velarde C;Levy MZ
直到最近，恰加斯病媒介——致病锥蝽还在秘鲁阿雷基帕广泛传播，但经过长达数十年的活动，对 70,000 多所房屋进行了杀虫剂处理，感染流行率现已大大降低。为了监测马铃薯晚疫病的死灰复燃，该市目前正处于监测阶段，每年都会抽取一些房屋样本进行检查。尽管控制活动中的大量数据可用于为监视提供信息，但检查房屋的选择往往是随意或出于方便而进行的。因此，我们想知道，如何通过创造数据为病媒监测提供机会，加强预防马铃薯卷土病卷土重来的努力？为此，我们开发了一款移动应用程序，可提供利用预测模型中运行的控制活动的数据生成的病媒感染风险地图。该应用程序旨在通过让检查人员有机会将感染风险信息纳入其监视活动来加强病媒监视活动，但它并不规定要监视哪些房屋。因此，一个关键问题就变成了，检查人员会使用风险信息吗？为了回答这个问题，我们进行了一项试点研究，将使用该应用程序的监视与当前的做法（纸质地图）进行了比较。我们假设检查员会使用应用程序提供的风险信息，这些信息是通过访问高风险房屋的频率以及检查员在现场移动模式的定性分析来衡量的。我们还比较了两种媒介的效率，以确定可能阻碍风险信息使用的因素。在十天的时间里（每种媒介各五天），使用纸质地图走访了 1,081 所房屋，其中 366 所 (34%) 接受了检查，而使用应用程序走访了 1,038 所房屋，其中 401 所 (39%) 接受了检查。八名检查员中有五名 (62.5%) 在使用该应用程序时参观了风险较高的房屋（Fisher 精确检验，p < 0.001）。在所有检查员中，使用该应用程序时，访问高风险房屋的比例出现上升（Mantel-Haenszel 检验，共同比值比 (OR) = 2.42，95% CI 2.00–2.92），并且在使用广义线性混合模型的第二次分析中，使用应用程序将访问高风险房屋的几率增加了 2.73 倍（95% CI 2.24–3.32），这表明大多数检查员都使用该应用程序。对检查员流动情况的定性分析揭示了八分之七（87.5%）检查员使用风险信息的迹象。应用程序和纸质地图在访问（配对 t 检验，p = 0.67）或检查（p = 0.17）的房屋数量方面没有差异，这表明应用程序的使用不会降低监视效率。如果在病媒控制活动后不对残留和重新出现的病媒疫源地保持警惕，疾病传播最终会卷土重来，所取得的进展也会发生逆转。我们的结果表明，只要有机会，大多数检查员都会使用风险信息来指导他们的监视活动，至少在短期内如此。这项研究是迈向基于证据的病媒监测的第一步，也是关键的一步。查加斯病是一种严重的感染，由一种被称为“接吻虫”的吸血昆虫传播。这些虫子生活在人类住宅内和周围，直到最近，它们还侵扰了秘鲁第二大城市阿雷基帕的数千个人类住宅。然而，长达数十年的控制活动大大减少了受感染房屋的数量，该市目前正处于卫生人员每年检查全市房屋样本是否有接吻虫再次感染的阶段。在控制活动期间收集了大量信息，这些信息可用于帮助识别再次感染风险最高的房屋，因此我们开发了一款手机应用程序，以交互式、用户友好的风险地图的形式向卫生人员提供这些信息。我们进行了一项试点研究，看看卫生人员是否会使用这些地图来选择房屋来检查是否再次感染，我们发现大多数检查人员确实使用了这些信息。我们还观察到，使用该应用程序并没有减慢检查员的速度，这在引入新技术时可能会成为一个问题。我们的结果表明，该应用程序可能成为监测城市昆虫传播疾病的有用工具。
Until recently, the Chagas disease vector, Triatoma infestans, was widespread in Arequipa, Perú, but as a result of a decades-long campaign in which over 70,000 houses were treated with insecticides, infestation prevalence is now greatly reduced. To monitor for T. infestans resurgence, the city is currently in a surveillance phase in which a sample of houses is selected for inspection each year. Despite extensive data from the control campaign that could be used to inform surveillance, the selection of houses to inspect is often carried out haphazardly or by convenience. Therefore, we asked, how can we enhance efforts toward preventing T. infestans resurgence by creating the opportunity for vector surveillance to be informed by data? To this end, we developed a mobile app that provides vector infestation risk maps generated with data from the control campaign run in a predictive model. The app is intended to enhance vector surveillance activities by giving inspectors the opportunity to incorporate the infestation risk information into their surveillance activities, but it does not dictate which houses to surveil. Therefore, a critical question becomes, will inspectors use the risk information? To answer this question, we ran a pilot study in which we compared surveillance using the app to the current practice (paper maps). We hypothesized that inspectors would use the risk information provided by the app, as measured by the frequency of higher risk houses visited, and qualitative analyses of inspector movement patterns in the field. We also compared the efficiency of both mediums to identify factors that might discourage risk information use. Over the course of ten days (five with each medium), 1,081 houses were visited using the paper maps, of which 366 (34%) were inspected, while 1,038 houses were visited using the app, with 401 (39%) inspected. Five out of eight inspectors (62.5%) visited more higher risk houses when using the app (Fisher’s exact test, p < 0.001). Among all inspectors, there was an upward shift in proportional visits to higher risk houses when using the app (Mantel-Haenszel test, common odds ratio (OR) = 2.42, 95% CI 2.00–2.92), and in a second analysis using generalized linear mixed models, app use increased the odds of visiting a higher risk house 2.73-fold (95% CI 2.24–3.32), suggesting that the risk information provided by the app was used by most inspectors. Qualitative analyses of inspector movement revealed indications of risk information use in seven out of eight (87.5%) inspectors. There was no difference between the app and paper maps in the number of houses visited (paired t-test, p = 0.67) or inspected (p = 0.17), suggesting that app use did not reduce surveillance efficiency. Without staying vigilant to remaining and re-emerging vector foci following a vector control campaign, disease transmission eventually returns and progress achieved is reversed. Our results suggest that, when provided the opportunity, most inspectors will use risk information to direct their surveillance activities, at least over the short term. The study is an initial, but key, step toward evidence-based vector surveillance. Chagas disease is a serious infection that is spread by blood-sucking insects called ‘kissing bugs.’ These bugs live in and around human homes, and until recently, they infested thousands of human homes throughout Arequipa, the second largest city in Perú. However, a decades-long control campaign drastically reduced the number of infested houses, and the city is now in a stage where health personnel annually inspect a sample of houses throughout the city for kissing bug reinfestation. A large amount of information was collected during the control campaign that could be used to help identify the houses at highest risk for re-infestation, so we developed a cell phone app to provide this information to health personnel in the form of interactive, user-friendly risk maps. We carried out a pilot study to see if health personnel would use these maps to select houses to inspect for re-infestation, and we found that most inspectors did use the information. We also observed that using the app did not slow the inspectors down, which can be an issue when introducing new technology. Our results suggest that the app could be a useful tool for monitoring diseases spread by insects in cities.
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影响因子：
4.3
作者：
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影响因子：
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影响因子：
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