Faculty of Health Sciences, School of Nursing, University of Ottawa, 451 Smyth Rd, Ottawa, ON, K1H 8M5, Canada.
Department of Medicine, School of Population and Global Health, McGill University, 5252 Boulevard de Maisonneuve W., Montréal, QC, H4A 3S5, Canada.
BMC Public Health. 2024 Feb 15;24(1):485. doi: 10.1186/s12889-024-17783-9.
In Ontario, Canada we developed and implemented an online screening algorithm for the distribution of HIV self-tests, known as GetaKit. During the COVID pandemic, we adapted the GetaKit algorithm to screen for COVID based on population and infection data and distributed COVID rt-LAMP self-tests (using the Lucira Check-It®) to eligible participants.
GetaKit/COVID was a prospective observational study that occurred over a 7-month period from September 2021 to April 2022. All potential participants completed an online registration and risk assessment, including demographic information, COVID symptoms and risk factors, and vaccination status. Bivariate comparisons were performed for three outcomes: results reporting status, vaccination status, and COVID diagnosis status. Data were analysed using Chi-Square for categorial covariates and Independent Samples T-Test and Mann-Whitney U test for continuous covariates. Bivariate logistic regression models were applied to examine associations between the covariates and outcomes.
During the study period, we distributed 6469 COVID self-tests to 4160 eligible participants; 46% identified as Black, Indigenous or a Person of Colour (BIPOC). Nearly 70% of participants reported their COVID self-test results; 304 of which were positive. Overall, 91% also reported being vaccinated against COVID. Statistical analysis found living with five or fewer people, having tested for COVID previously, and being fully vaccinated were positive factors in results reporting. For COVID vaccination, people from large urban centers, who identified their ethnicity as white, and who reported previous COVID testing were more likely to be fully vaccinated. Finally, being identified as a contact of someone who had tested positive for COVID and the presence of COVID-related symptoms were found to be positive factors in diagnosis.
While most participants who accessed this service were vaccinated against COVID and the majority of diagnoses were identified in participants who had symptoms of, or an exposure to, COVID, our program was able to appropriately link participants to recommended follow-up based on reported risks and results. These findings highlight the utility of online screening algorithms to provide health services, particularly for persons with historical barriers to healthcare access, such as BIPOC or lower-income groups.
在加拿大安大略省，我们开发并实施了一种用于 HIV 自检分发的在线筛选算法，称为 GetaKit。在 COVID 大流行期间，我们根据人口和感染数据改编了 GetaKit 算法，以筛查 COVID，并向符合条件的参与者分发 COVID rt-LAMP 自检（使用 Lucira Check-It®）。
GetaKit/COVID 是一项前瞻性观察研究，从 2021 年 9 月至 2022 年 4 月持续了 7 个月。所有潜在参与者都完成了在线注册和风险评估，包括人口统计信息、COVID 症状和危险因素以及疫苗接种状况。对于三个结果：报告结果状态、疫苗接种状态和 COVID 诊断状态，进行了双变量比较。使用卡方检验进行分类协变量的比较，使用独立样本 T 检验和曼-惠特尼 U 检验进行连续协变量的比较。应用双变量逻辑回归模型检验协变量与结果之间的关系。
在研究期间，我们向 4160 名符合条件的参与者分发了 6469 份 COVID 自检，其中 46%为黑人、原住民或有色人种（BIPOC）。近 70%的参与者报告了他们的 COVID 自检结果；其中 304 份结果为阳性。总体而言，91%的人还报告接种了 COVID 疫苗。统计分析发现，与 5 人或更少的人同住、以前接受过 COVID 检测以及完全接种疫苗是报告结果的积极因素。对于 COVID 疫苗接种，来自大城市中心的人、自认为是白人的人以及报告以前有过 COVID 检测的人更有可能完全接种疫苗。最后，被确定为 COVID 检测呈阳性的人的接触者以及出现 COVID 相关症状是诊断为阳性的因素。
虽然大多数使用该服务的参与者都接种了 COVID 疫苗，而且大多数诊断结果都是在有 COVID 症状或接触 COVID 的参与者中发现的，但我们的计划能够根据报告的风险和结果，为参与者提供适当的后续建议。这些发现强调了在线筛选算法提供卫生服务的实用性，特别是对于那些存在获取医疗保健历史障碍的人，例如 BIPOC 或低收入群体。
