Abstract：
BACKGROUND: Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings, where the heterogeneity of transmission is often pronounced. Novel mobile applications offer new opportunities for disease mapping. We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba, Tanzania.
METHODS: A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba. For the survey, 1400 housing structures were prospectively and randomly selected from shapefile data. To identify pre-selected structures and collect survey-related data, field enumerators searched for the houses\' geolocation using the mobile applications Open Data Kit (ODK) and MAPS.ME. The number of inhabited and uninhabited structures, the median distance between the pre-selected and recorded locations, and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed.
RESULTS: Among the 1400 randomly selected housing structures, 1396 (99.7%) were identified by the enumerators. The median distance between the pre-selected and recorded structures was 5.4 m. A total of 1098 (78.7%) were residential houses. Among them, 99 (9.0%) were dropped due to continuous absence of residents and 40 (3.6%) households refused to participate. In 797 (83.1%) among the 959 participating households, all eligible household members or all but one provided a urine sample of sufficient volume.
CONCLUSIONS: The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures. Dropouts due to non-residential housing structures, absence, non-participation and lack of urine need to be considered in survey designs. Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas. Trial registration ISRCTN, ISCRCTN91431493. Registered 11 February 2020, https://www.isrctn.com/ISRCTN91431493.
METHODS: A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba. For the survey, 1400 housing structures were prospectively and randomly selected from shapefile data. To identify pre-selected structures and collect survey-related data, field enumerators searched for the houses\' geolocation using the mobile applications Open Data Kit (ODK) and MAPS.ME. The number of inhabited and uninhabited structures, the median distance between the pre-selected and recorded locations, and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed.
RESULTS: Among the 1400 randomly selected housing structures, 1396 (99.7%) were identified by the enumerators. The median distance between the pre-selected and recorded structures was 5.4 m. A total of 1098 (78.7%) were residential houses. Among them, 99 (9.0%) were dropped due to continuous absence of residents and 40 (3.6%) households refused to participate. In 797 (83.1%) among the 959 participating households, all eligible household members or all but one provided a urine sample of sufficient volume.
CONCLUSIONS: The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures. Dropouts due to non-residential housing structures, absence, non-participation and lack of urine need to be considered in survey designs. Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas. Trial registration ISRCTN, ISCRCTN91431493. Registered 11 February 2020, https://www.isrctn.com/ISRCTN91431493.
摘要：
背景：血吸虫病的精细比例尺图以指导干预措施的微靶向将在消除设置中变得重要，其中传播的异质性通常很明显。新型移动应用程序为疾病测绘提供了新的机会。我们提供了实用的介绍和文档，说明了基于GPS的家庭识别和参与者招募的优点和缺点，使用基于平板电脑的应用程序在奔巴偏远地区的分区级别进行精细规模的血吸虫病测绘，坦桑尼亚。
方法：从2020年11月至2021年2月，在奔巴的20个小行政区进行了基于社区的泌尿生殖道血吸虫病评估家庭调查。对于调查，从shapefile数据中前瞻性地随机选择了1400个房屋结构。为了识别预选结构并收集与调查相关的数据，字段枚举器使用移动应用程序OpenDataKit(ODK)和MAPS搜索房屋\'地理位置。我。有人居住和无人居住的建筑物的数量，预选位置和记录位置之间的中位数距离，并评估了由于未参与或未提交足够量的尿样进行血吸虫病检测而导致的辍学率.
结果：在1400个随机选择的房屋结构中，1396人（99.7%）被鉴定。预选结构和记录结构之间的中值距离为5.4m。共有1098套(78.7%)住宅。其中,由于居民持续缺席，下降了99个（9.0％），而40个（3.6％）家庭拒绝参加。在959个参与家庭中的797个(83.1%)，所有符合条件的家庭成员或除一名以外的所有人都提供了足够量的尿液样本。
结论：使用ODK和安装在平板电脑上的离线导航应用程序的组合的精细比例映射方法允许非常精确地识别外壳结构。由于非住宅住房结构而导致的辍学，缺席,在调查设计中需要考虑不参与和缺乏尿液。我们的发现可以指导未来以家庭为基础的制图或纵向调查的规划和实施，从而支持偏远地区血吸虫病控制和消除干预措施的微观目标和后续行动。ISRCTN试用注册，ISCRCTN91431493。2020年2月11日注册，https://www。isrctn.com/ISRCTN91431493.
方法：从2020年11月至2021年2月，在奔巴的20个小行政区进行了基于社区的泌尿生殖道血吸虫病评估家庭调查。对于调查，从shapefile数据中前瞻性地随机选择了1400个房屋结构。为了识别预选结构并收集与调查相关的数据，字段枚举器使用移动应用程序OpenDataKit(ODK)和MAPS搜索房屋\'地理位置。我。有人居住和无人居住的建筑物的数量，预选位置和记录位置之间的中位数距离，并评估了由于未参与或未提交足够量的尿样进行血吸虫病检测而导致的辍学率.
结果：在1400个随机选择的房屋结构中，1396人（99.7%）被鉴定。预选结构和记录结构之间的中值距离为5.4m。共有1098套(78.7%)住宅。其中,由于居民持续缺席，下降了99个（9.0％），而40个（3.6％）家庭拒绝参加。在959个参与家庭中的797个(83.1%)，所有符合条件的家庭成员或除一名以外的所有人都提供了足够量的尿液样本。
结论：使用ODK和安装在平板电脑上的离线导航应用程序的组合的精细比例映射方法允许非常精确地识别外壳结构。由于非住宅住房结构而导致的辍学，缺席,在调查设计中需要考虑不参与和缺乏尿液。我们的发现可以指导未来以家庭为基础的制图或纵向调查的规划和实施，从而支持偏远地区血吸虫病控制和消除干预措施的微观目标和后续行动。ISRCTN试用注册，ISCRCTN91431493。2020年2月11日注册，https://www。isrctn.com/ISRCTN91431493.
