背景:在安大略省,加拿大我们开发并实施了一种用于HIV自检分布的在线筛查算法,被称为GetaKit。在COVID大流行期间,我们调整了GetaKit算法以根据人群和感染数据筛选COVID,并向符合条件的参与者分发了COVIDrt-LAMP自检(使用LuciraCheck-It®).
方法:GetaKit/COVID是一项前瞻性观察性研究,发生在2021年9月至2022年4月的7个月内。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,和疫苗接种状况。对三个结果进行了双变量比较:结果报告状态,疫苗接种状况,和COVID诊断状态。使用卡方分析分类协变量,使用独立样本T检验和Mann-WhitneyU检验分析连续协变量。双变量逻辑回归模型用于检查协变量和结果之间的关联。
结果: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;46%被确定为黑人,土著或有色人种(BIPOC)。近70%的参与者报告了他们的COVID自检结果;其中304例为阳性。总的来说,91%的人还报告接种了COVID疫苗。统计分析发现与五个或更少的人生活在一起,之前测试过COVID,和完全接种疫苗是结果报告中的积极因素.对于COVID疫苗接种,来自大城市中心的人,他们认定他们的种族是白人,并且报告以前进行COVID检测的人更有可能完全接种疫苗。最后,被确定为COVID检测呈阳性的人的联系人,并且发现存在COVID相关症状是诊断的积极因素。
结论:虽然大多数使用这项服务的参与者都接种了COVID疫苗,大多数诊断是在有症状的参与者中发现的,或者暴露于,COVID,我们的项目能够根据报告的风险和结果将参与者与推荐的随访适当联系起来.这些发现突出了在线筛查算法提供健康服务的实用性,特别是对于那些在获得医疗保健方面存在历史障碍的人,例如BIPOC或低收入群体。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.