乌干达和尼日利亚丝虫鞘膜积液切除术报告进展情况

Susan F. D’Souza, J. P. Awio, Jehoshaphat Albarka, W. R. Bougma, Sarah Bartlett, Christian Nwosu, Babacar Ngom, Anthony Wani, Anita Gwom, Moses Okello, Abraham Aba, Sarah Irema, Martins Imhansoloeva, Dauda Nurudeen, Charles Wamboga, M. T. Debam, Haruna Zainab, Attahiru Aleiro, Malam H. Salah, Philip Downs
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引用次数: 0

摘要

鞘膜积液手术是世界卫生组织为淋巴丝虫病(LF)患者推荐的基本一揽子护理方案的一部分。在乌干达和尼日利亚,淋巴丝虫病计划对鞘膜积液手术的支持始于2017年。2021 年,这两项计划采用了新开发的智能手机应用程序来监测鞘膜积液手术和结果。该应用程序被称为鞘膜积液跟踪器,用于收集手术登记信息、术前信息、术中信息、术后信息和后续评估。该应用程序的开发旨在改进监督、了解手术结果和并发症的原因、改进报告并提高积极开展 LF 计划的卫生部的数据质量。2021 年 10 月至 2023 年 5 月期间在乌干达(Lango 次区域,9 个地区)和 2021 年 3 月至 2022 年 9 月期间在尼日利亚(Benue、Kaduna、Kebbi、Sokoto 和 Kogi 州)收集了鞘膜积液患者的信息。此外,还开展了患者反馈调查,以了解患者对鞘膜积液手术的体验和满意度。本文介绍了鞘膜积液跟踪系统中 2,911 份记录中的患者数据,并重点讨论了数据对 LF 计划的影响、数字化的价值以及解决数据质量问题的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advances in filarial hydrocelectomy reporting in Uganda and Nigeria
Surgery for hydrocele is part of the WHO recommended essential package of care for people affected by Lymphatic filariasis (LF). In Uganda and Nigeria, LF programme support for hydrocele surgery began in 2017. In 2021 the two programmes adopted a newly developed smartphone-based application to monitor hydrocele surgery and outcome. The application, referred to as the Hydrocele Tracker, gathers information on surgical registration, preoperative information, intraoperative information, postoperative information and follow-up evaluations. It was developed to improve supervision, understand surgical outcomes and reasons for complications, improve reporting and improve data quality for Ministries of Health with active LF programmes.Hydrocele patient information was collected between October 2021 and May 2023 in Uganda (Lango sub-region, 9 districts) and March 2021 - September 2022 in Nigeria (Benue, Kaduna, Kebbi, Sokoto and Kogi States). Additionally patient feedback surveys were undertaken to elicit patient experience and satisfaction with the hydrocele surgery.Patient data from 2,911 records in the Hydrocele Tracker are presented.The discussion highlights the implications of the data for LF programming, the value of digitisation and approaches to address data quality.
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