The Effect and Feasibility of mHealth-Supported Surgical Site Infection Diagnosis by Community Health Workers After Cesarean Section in Rural Rwanda: Randomized Controlled Trial.
The Effect and Feasibility of mHealth-Supported Surgical Site Infection Diagnosis by Community Health Workers After Cesarean Section in Rural Rwanda: Randomized Controlled Trial.
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DOI：
10.2196/35155
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发表时间：
2022-06-08
影响因子：
5
通讯作者：
Hedt-Gauthier, Bethany
中科院分区：
文献类型：
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作者：
Kateera, Fredrick;Riviello, Robert;Goodman, Andrea;Nkurunziza, Theoneste;Cherian, Teena;Bikorimana, Laban;Nkurunziza, Jonathan;Nahimana, Evrard;Habiyakare, Caste;Ntakiyiruta, Georges;Matousek, Alexi;Gaju, Erick;Gruendl, Magdalena;Powell, Brittany;Sonderman, Kristin;Koch, Rachel;Hedt-Gauthier, Bethany
关键词：
剖宫产（剖腹产）后手术部位感染（SSI）的发展是包括卢旺达在内的中低收入国家发病率和死亡率的重要原因。卢旺达依靠一个强有力的社区卫生工作者领导的、以家庭为基础的模式，为分娩后的妇女提供后续护理。然而，该计划目前不包括剖腹产后妇女的术后护理，如SSI筛查。本试验评估CHW使用移动的健康（mHealth）便利的检查表亲自或通过电话管理是否提高了在卢旺达农村地区医院剖腹产后发生SSI的妇女的护理回报率。第二个目标是评估在这个农村地区实施CHW领导的移动健康干预措施的可行性。2017年11月至2018年9月期间在Kirehe地区医院接受剖腹产手术的1025名年龄≥18岁的女性被随机分为以下三个术后护理组：（1）家访干预（n=335，32.7%），（2）电话干预（n=334，32.6%）和（3）标准治疗（n=356，34.7%）。在两个干预组中提供了CHW领导的、mHealth支持的SSI诊断方案，而标准护理组的患者则被指示遵守常规的健康中心随访。我们评估了每个干预组的干预完成情况，并使用逻辑回归来评估返回护理的几率。第1组（n=295，88.1%）和第2组（n=226，67.7%）中的大多数女性返回护理，并在当地诊所接受SSI评估。30天内返回诊所的比率无显著差异（P= 0.21），所有三个组的比率均较高（第1组：99.7%，第2组：98.4%，第3组：99.7%）。在非洲农村，由移动健康支持的社区卫生工作者进行家庭剖腹产后随访是可行的。在这项研究中，我们发现干预组和标准治疗组之间的回归率没有差异。然而，考虑到我们之前的研究结果描述了前往健康中心造成的重大患者经济负担，我们认为这种干预措施有可能通过限制患者在家中排除SSI时前往健康中心来减轻这种负担。需要进一步研究（1）确定CHW和患者对这种干预的可接受性作为剖腹产后的新护理标准，以及（2）评估应用程序是否可以通过基于图像的机器学习来补充mHealth筛查清单，以提高CHW诊断的准确性。ClinicalTrials.gov NCT03311399; https://clinicaltrials.gov/ct2/show/NCT03311399
The development of a surgical site infection (SSI) after cesarean section (c-section) is a significant cause of morbidity and mortality in low- and middle-income countries, including Rwanda. Rwanda relies on a robust community health worker (CHW)–led, home-based paradigm for delivering follow-up care for women after childbirth. However, this program does not currently include postoperative care for women after c-section, such as SSI screenings. This trial assesses whether CHW’s use of a mobile health (mHealth)–facilitated checklist administered in person or via phone call improved rates of return to care among women who develop an SSI following c-section at a rural Rwandan district hospital. A secondary objective was to assess the feasibility of implementing the CHW-led mHealth intervention in this rural district. A total of 1025 women aged ≥18 years who underwent a c-section between November 2017 and September 2018 at Kirehe District Hospital were randomized into the three following postoperative care arms: (1) home visit intervention (n=335, 32.7%), (2) phone call intervention (n=334, 32.6%), and (3) standard of care (n=356, 34.7%). A CHW-led, mHealth-supported SSI diagnostic protocol was delivered in the two intervention arms, while patients in the standard of care arm were instructed to adhere to routine health center follow-up. We assessed intervention completion in each intervention arm and used logistic regression to assess the odds of returning to care. The majority of women in Arm 1 (n=295, 88.1%) and Arm 2 (n=226, 67.7%) returned to care and were assessed for an SSI at their local health clinic. There were no significant differences in the rates of returning to clinic within 30 days (P=.21), with high rates found consistently across all three arms (Arm 1: 99.7%, Arm 2: 98.4%, and Arm 3: 99.7%, respectively). Home-based post–c-section follow-up is feasible in rural Africa when performed by mHealth-supported CHWs. In this study, we found no difference in return to care rates between the intervention arms and standard of care. However, given our previous study findings describing the significant patient-incurred financial burden posed by traveling to a health center, we believe this intervention has the potential to reduce this burden by limiting patient travel to the health center when an SSI is ruled out at home. Further studies are needed (1) to determine the acceptability of this intervention by CHWs and patients as a new standard of care after c-section and (2) to assess whether an app supplementing the mHealth screening checklist with image-based machine learning could improve CHW diagnostic accuracy. ClinicalTrials.gov NCT03311399; https://clinicaltrials.gov/ct2/show/NCT03311399