Abstract：
COVID-19 quickly overwhelmed the world, but disproportionately affects certain communities, particularly minority groups. Despite overrepresentation among COVID-19 cases and death, minority groups were underrepresented in some of the early COVID-19 clinical trials.
To assess and compare the demographic characteristics of COVID-19 clinical trial participants to national COVID-19 data.
PubMed was searched from December 1, 2019 to November 24, 2020, for randomized controlled trials evaluating a pharmacologic treatment for COVID-19 patients from one or more U.S. sites written in the English language following the PRISMA checklist. Descriptive statistics were calculated to characterize patient demographics enrolled in the included clinical trials, as well as for comparison with national COVID-19 data.
A total of 4472 records were identified, of which 16 studies were included. The median number of participants was higher in studies of nonhospitalized patients compared to those of hospitalized patients (n = 452 [range 20-1062] vs n = 243 [152-2795]). Ten (63%) studies reported mean or median ages of 50 years or older among all study arms. Males comprised more than half of the study cohort in ten (63%) studies. Race and ethnicity were reported separately in four (25%) studies but were combined when reported in five (31%) studies, while six (38%) reported only race or ethnicity. Proportional representation based on age, sex, race, and ethnicity was evident in some trials, but not in others, when compared to national data.
Overall, participants often did not reflect the actual population with COVID-19 and demographic characteristics were inconsistently reported.
To assess and compare the demographic characteristics of COVID-19 clinical trial participants to national COVID-19 data.
PubMed was searched from December 1, 2019 to November 24, 2020, for randomized controlled trials evaluating a pharmacologic treatment for COVID-19 patients from one or more U.S. sites written in the English language following the PRISMA checklist. Descriptive statistics were calculated to characterize patient demographics enrolled in the included clinical trials, as well as for comparison with national COVID-19 data.
A total of 4472 records were identified, of which 16 studies were included. The median number of participants was higher in studies of nonhospitalized patients compared to those of hospitalized patients (n = 452 [range 20-1062] vs n = 243 [152-2795]). Ten (63%) studies reported mean or median ages of 50 years or older among all study arms. Males comprised more than half of the study cohort in ten (63%) studies. Race and ethnicity were reported separately in four (25%) studies but were combined when reported in five (31%) studies, while six (38%) reported only race or ethnicity. Proportional representation based on age, sex, race, and ethnicity was evident in some trials, but not in others, when compared to national data.
Overall, participants often did not reflect the actual population with COVID-19 and demographic characteristics were inconsistently reported.
摘要：
背景：COVID-19迅速淹没了世界，但对某些社区的影响不成比例，尤其是少数群体。尽管COVID-19病例和死亡人数过多，在一些早期的COVID-19临床试验中，少数群体的代表性不足。
目的：评估和比较COVID-19临床试验参与者的人口统计学特征与国家COVID-19数据。
方法：从2019年12月1日至2020年11月24日搜索PubMed，以评估来自一个或多个美国网站的COVID-19患者的药物治疗的随机对照试验。计算描述性统计数据以表征纳入临床试验的患者人口统计学特征，以及与国家COVID-19数据的比较。
结果：总共确定了4472条记录，其中包括16项研究。与住院患者相比，非住院患者的研究参与者的中位数更高（n=452[范围20-1062]vsn=243[152-2795]）。10项（63％）研究报告了所有研究组中50岁或以上的平均年龄。在10项(63%)研究中，男性占研究队列的一半以上。在四项（25％）研究中分别报告了种族和种族，但在五项（31％）研究中报告时将其合并。而6人(38%)只报告了种族或族裔。基于年龄的比例代表性，性别,种族,种族在一些试验中很明显，但不是在别人，与国家数据相比。
结论：总体而言，参与者通常无法反映COVID-19的实际人群，并且人口统计学特征的报告不一致。
目的：评估和比较COVID-19临床试验参与者的人口统计学特征与国家COVID-19数据。
方法：从2019年12月1日至2020年11月24日搜索PubMed，以评估来自一个或多个美国网站的COVID-19患者的药物治疗的随机对照试验。计算描述性统计数据以表征纳入临床试验的患者人口统计学特征，以及与国家COVID-19数据的比较。
结果：总共确定了4472条记录，其中包括16项研究。与住院患者相比，非住院患者的研究参与者的中位数更高（n=452[范围20-1062]vsn=243[152-2795]）。10项（63％）研究报告了所有研究组中50岁或以上的平均年龄。在10项(63%)研究中，男性占研究队列的一半以上。在四项（25％）研究中分别报告了种族和种族，但在五项（31％）研究中报告时将其合并。而6人(38%)只报告了种族或族裔。基于年龄的比例代表性，性别,种族,种族在一些试验中很明显，但不是在别人，与国家数据相比。
结论：总体而言，参与者通常无法反映COVID-19的实际人群，并且人口统计学特征的报告不一致。
