A Qualitative Study Exploring Motivators and Barriers to COVID-19 Vaccine Uptake among Adults in South Africa and Zimbabwe.

While vaccines are a well-established method of controlling the spread of infectious diseases, vaccine hesitancy jeopardizes curbing the spread of COVID-19. Through the Vaccine Information Network (VIN), this study explored barriers and motivators to COVID-19 vaccine uptake. We conducted 18 focus group discussions with male and female community members, stratified by country, age group, and-for Zimbabwe only-by HIV status. Participants' median age across both countries was 40 years (interquartile range of 22-40), and most (65.9%) were female. We conceptualized the key themes within the World Health Organization's Strategic Advisory Group of Experts on Immunization (SAGE) 3C (convenience, confidence, complacency) vaccine hesitancy model. Barriers to vaccine uptake-lack of convenience, low confidence, and high complacency-included inaccessibility of vaccines and vaccination sites, vaccine safety and development concerns, and disbelief in COVID-19's existence. Motivators to vaccine uptake-convenience, confidence, and low complacency-included accessibility of vaccination sites, user-friendly registration processes, trust in governments and vaccines, fear of dying from COVID-19, and knowing someone who had died from or become infected with COVID-19. Overall, vaccine hesitancy in South Africa and Zimbabwe was influenced by inconvenience, a lack of confidence, and high complacency around COVID-19 vaccines.

Investigating Attitudes, Motivations and Key Influencers for COVID-19 Vaccination Uptake among Late Adopters in Urban Zimbabwe.

The rapid development of vaccines in response to the COVID-19 pandemic has provided an effective tool for the management of COVID-19. However, in many African countries there has been a poor uptake of COVID-19 vaccines with only 32.5% first vaccine dose coverage compared to the WHO global target of 70%. As vaccine access improves, one of the important drivers of low uptake has been vaccine hesitancy, driven by levels of confidence, convenience, and complacency. Between 4 January-11 February 2022, we conducted a survey of vaccine late adopters to assess factors that influenced adults in Harare, Zimbabwe to present for their first COVID-19 vaccine dose almost 12 months after the vaccination program began. Of the 1016 adults enrolled, 50% were female and 12.4% had HIV co-infection. Binary logistic regression models were developed to understand factors associated with vaccine confidence. Women were more likely to have negative views about the COVID-19 vaccine compared to men (OR 1.51 (95%CI 1.16, 1.97, p = 0.002). Older adults (≥40 years) compared with youth (18-25 years) were more likely to have 'major concerns' about vaccines. When asked about their concerns, 602 (59.3%) considered immediate side effects as a major concern and 520 (52.1%) were concerned about long-term health effects. People living with HIV (PLWH) were more likely to perceive vaccines as safe (OR 1.71 (95%CI: 1.07, 2.74, p = 0.025) and effective (1.68 (95%CI: 1.07, 2.64, p = 0.026). Internet users were less likely to perceive vaccines as safe (OR 0.72 (95% CI: 0.55, 0.95, p = 0.021) compared to non-Internet users; and social media was a more likely source of information for youth and those with higher education. Family members were the primary key influencers for 560 (55.2%) participants. The most important reason for receiving the COVID-19 vaccine for 715 (70.4%) participants was the protection of individual health. Improving vaccine coverage will need targeted communication strategies that address negative perceptions of vaccines and associated safety and effectiveness concerns. Leveraging normative behavior as a social motivator for vaccination will be important, as close social networks are key influences of vaccination.

Vaccine Adverse Events Following COVID-19 Vaccination with Inactivated Vaccines in Zimbabwe.

Vaccination is one of the most effective methods for preventing morbidity and mortality from COVID-19. Vaccine hesitancy has led to a decrease in vaccine uptake; driven by misinformation, fear, and misperceptions of vaccine safety. Whole inactivated vaccines have been used in one-fifth of the vaccine recipients in Africa, however there are limited real-world data on their safety. We evaluated the reported adverse events and factors associated with reported adverse events following vaccination with whole inactivated COVID-19 vaccines-BBiBP-CorV (Sinopharm) and CoronaVac (Sinovac). A quantitative survey evaluating attitudes and adverse events from vaccination was administered to 1016 adults presenting at vaccination centers. Two follow-up telephone interviews were conducted to determine adverse events after the first and second vaccination dose. Overall, the vaccine was well tolerated; 26.0% and 14.4% reported adverse events after the first and second dose, respectively. The most frequent local and systemic adverse events were pain at the injection site and headaches, respectively. Most symptoms were mild, and no participants required hospitalization. Participants who perceived COVID-19 vaccines as safe or had a personal COVID-19 experience were significantly less likely to report adverse events. Our findings provide data on the safety and tolerability of whole inactivated COVID-19 vaccines in an African population, providing the necessary data to create effective strategies to increase vaccination and support vaccination campaigns.

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a "generic network" on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 85. Results Across a majority of tasks, compared with transfer learning from a nonmedical data set, SupCon reduced label requirements up to 688-fold and improved the area under the receiver operating characteristic curve (AUC) at matching data set sizes. At the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yielded an AUC of 0.95 (noninferior to radiologist performance) in classifying microbiology-confirmed tuberculosis in external validation. At a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yielded an AUC of 0.75 in predicting severe COVID-19 outcomes. Conclusion Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations. © RSNA, 2022 Online supplemental material is available for this article.

Attitudes to Vaccine Mandates among Late Adopters of COVID-19 Vaccines in Zimbabwe.

Despite sufficient supply, <25% of the population in sub-Saharan Africa has received at least one dose of COVID-19 vaccine. Vaccine mandates have previously been effective in increasing vaccine uptake. Attitudes to COVID-19 vaccine mandates and vaccines for children in African populations are not well understood. We surveyed late-adopters presenting for COVID-19 vaccination one year after program initiation in Zimbabwe. Logistic regression models were developed to evaluate factors associated with attitudes to mandates. In total, 1016 adults were enrolled; 690 (67.9%) approved of mandating vaccination for use of public spaces, 686 (67.5%) approved of employer mandates, and 796 (78.3%) approved of mandating COVID-19 vaccines for schools. Individuals of lower economic status were twice as likely as high-income individuals to approve of mandates. Further, 743 (73.1%) participants indicated that they were extremely/very likely to accept vaccines for children. Approval of vaccine mandates was strongly associated with perceptions of vaccine safety, effectiveness, and trust in regulatory processes that approved vaccines. Vaccine hesitancy is an important driver of low vaccine coverage in Africa and can be mitigated by vaccine mandates. Overall, participants favored vaccine mandates; however, attitudes to mandates were strongly associated with level of education and socioeconomic status.

Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19.

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset.