Black and Latino individuals are underrepresented in COVID-19 treatment and vaccine clinical trials, calling for an examination of factors that may predict willingness to participate in trials.

We administered the Common Survey 2.0 developed by the Community Engagement Alliance (CEAL) Against COVID-19 Disparities to 600 Black and Latino adults in Baltimore City, Prince George’s County, Maryland, Montgomery County, Maryland, and Washington, DC, between October and December 2021. We examined the relationship between awareness of clinical trials, social determinants of health challenges, trust in COVID-19 clinical trial information sources, and willingness to participate in COVID-19 treatment and vaccine trials using multinomial regression analysis.

Approximately half of Black and Latino respondents were unwilling to participate in COVID-19 treatment or vaccine clinical trials. Results showed that increased trust in COVID-19 clinical trial information sources and trial awareness were associated with greater willingness to participate in COVID-19 treatment and vaccine trials among Black and Latino individuals. For Latino respondents, having recently experienced more challenges related to social determinants of health was associated with a decreased likelihood of willingness to participate in COVID-19 vaccine trials.

The willingness of Black and Latino adults to participate in COVID-19 treatment and vaccine clinical trials is influenced by trial awareness and trust in trial information sources. Ensuring the inclusion of these communities in clinical trials will require approaches that build greater awareness and trust.

Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed.

The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105.

The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.