DEVELOPMENT OF A PRACTICE-BASED COMMUNITY OUTREACH INTERVENTION TO ADDRESS INEQUITIES IN COVID19 VACCINATIONS

STATEMENT OF PROBLEM/QUESTION: In early 2021, only 14% of vaccine eligible patients of two academic outpatient practices had received at least one COVID-19 vaccine dose;Black patients comprised 16% of all vaccine eligible patients, but only 10% of those vaccinated, while Hispanic patients comprised 13% of all vaccine eligible patients, but only 7% of those vaccinated. DESCRIPTION OF PROGRAM/INTERVENTION: The objective of this study was to rapidly develop and implement an equity-focused outreach intervention that facilitated COVID-19 vaccine appointments in patients who qualified for a vaccine. The intervention aimed to contact Black and Latino adults ≥65 years old, or ≥18 years old with qualifying chronic conditions, who had at least one visit at the practices within the past three years and who had not received or scheduled a COVID-19 vaccination per electronic health records. Using the Plan-Do-Study-Act model, we developed and executed a multipronged outreach intervention consisting of (1) a mailed letter from the patient's physician endorsing the COVID-19 vaccine, (2) a phone call/recall system to remind patients about their eligibility to receive the vaccine, (3) discussions with a vaccine ambassador to answer questions or address hesitancies regarding the vaccines, and (4) real-time facilitation with scheduling a vaccine appointment and information about where in the community to receive or schedule a vaccine. Volunteer callers were recruited and trained in executing all components of the intervention. MEASURES OF SUCCESS: Bi-weekly focus groups were held with callers to identify challenges with implementation. The number of calls made, patients reached and vaccinations scheduled were analyzed weekly. FINDINGS TO DATE: From March 15 to May 28, 2021, 38 callers were recruited and 5058 calls were made as part of our outreach intervention. Of 2794 total patients attempted, 1519 were successfully reached, 746 of whom had already scheduled or received a vaccine (without documentation in the electronic health record). Of the 750 patients who were reached and eligible for the intervention, 129 (17.2%) had a vaccine scheduled by the caller, 72 (9.6%) planned to self- schedule a vaccine and were instructed on how to do so by the callers, and 549 (73.2%) did not want nor planned to have the vaccine scheduled. The weekly proportion of eligible patients scheduled for vaccine decreased over time, starting at 30% and ending at 0% in the 11th week of the intervention, at which point the intervention was stopped. “Low confidence in the vaccine” and “Still contemplating whether to receive the vaccine” were the two reasons most cited for declining or being unsure about receiving the vaccine. KEY LESSONS FOR DISSEMINATION: An equity-focused outreach program to facilitate vaccination scheduling can be rapidly developed and implemented in outpatient primary care practices, but may need to further consider inaccuracies in vaccination status as documented in the electronic records and increase access to accessible vaccinations sites.

Savable but Lost Lives when ICU Is Overloaded: a Model from 733 Patients in Epicenter Wuhan, China

Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted into the ICU will be valuable to optimize the management and assignment of ICU. Retrospective, 733 in-patients diagnosed with COVID-19 at a local hospital (Wuhan, China), as of March 18, 2020. Demographic, clinical and laboratory results were collected and analyzed using machine learning to build a predictive model. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing ICU care yet they did not as Missing-ICU (MI)-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care. Its predictive accuracy was 0.8288, with an AUC of 0.9119. On our cohort of 733 patients, 25 in-patients who have been predicted by our model that they should need ICU, yet they did not enter ICU due to lack of shorting ICU wards. Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions.

Machine Learning to Predict ICU Admission, ICU Mortality and Survivors' Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC.

Investigation and research on the state of clinical trials during epidemic period of COVID-19

With coronavirus disease 2019 (COVID-19) spreading, the rhythm of clinical trials has been challenged. Based on the guidelines and relevant regulations of clinical trials during the epidemic period, the Drum Tower Hospital Affiliated to Nanjing University School of medicine has designed a survey to investigate the current situation of clinical trials. Association, CRA, clinical research coordinator (CRC), and test managers were involved in the research. According to the survey results, most researchers reflected that the progress of clinical trials was affected (68.24%), and the clinical trials of non-oral drugs were greatly affected (69.96%), which was mainly manifested in the malignant tumor project (88.2%), during the trial, the most obvious influence is 92.7% for out of visit window, and the subject worrying about the deterioration of disease (78.97%), the risk of drug overtemperature exists in mailing drugs (60.09%), and the number of trials timeliness (89.09%) and integrity (79.61%) were the most affected. Therefore, it is a new working mode for our clinical trial institutions to improve the emergency mechanism of clinical trials, strengthen the supervision of trials, and realize the sharing of clinical trial data and the whole process information management. © 2021, Chinese Journal of New Drugs Co. Ltd. All right reserved.