ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The coronavirus disease 2019 (COVID-19) is an infectious disease with high transmissibility and acquired through the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Scientists, physicians, and health officials are seeking innovative approaches to understand the complex COVID-19 pandemic pathway and decrease its morbidity and mortality. Incorporating artificial intelligence and data science techniques across the health science domain could improve disease surveillance, intervention planning, and policymaking. In this paper, we report our effort on the deployment of multimodal big data analytics to improve pandemic surveillance and preparedness. A common challenge for conducting multimodal big data analytics in clinical and public health settings is the issue of the integration of multidimensional heterogeneous data sources. Additional challenges for developers are explaining decisions and actions made by intelligent systems to human users, maintaining interpretability between different data sources, and privacy of health information. We present Urban Population Health Observatory (UPHO), an explainable knowledge-based multimodal data analytics platform to facilitate CoVID-19 surveillance by integrating a large volume of multimodal multidimensional, heterogenous data including social determinants of health indicators, clinical and population health data. © 2021 IEEE.
ABSTRACT
Background: Cancer outcomes in the U.S. Mid-South (West Tennessee, Mississippi Delta, Eastern Arkansas) are poor, and have potentially been exacerbated by the COVID-19 pandemic. Unplanned interruption of daily radiation therapy (RT) is associated with socially vulnerable populations and inferior survival outcomes. Radiation treatment interruption (RTI) rates during the pandemic remain unreported. The purpose of this work was to quantify our local RTI rates before and after the onset of the pandemic, and to characterize social risk factors predictive for interruption during COVID-19. Methods: Demographic, clinical and treatment information were retrospectively analyzed for patients receiving RT with curative or palliative intent at a single academic center between January 2015 and December 2020. Minor RTI was defined as a delay in 2 or more scheduled radiation treatments. Major RTI was defined as greater than or equal to 5 (i.e. one week or greater) unplanned RT appointment cancellations. Patient insurance status was considered “At-Risk” if they had Medicaid or no insurance. Patient predicted income (PPI) was categorized as low, middle or high using 2020 US Census data for patient's home address zip code. RTI was compared across insurance type, race, PPI and whether they started RT before or after the onset of COVID-19 (March 15, 2020). Results: 2176 out of a total 2731 patients treated at our academic center were analyzable;1913 were treated before and 263 were treated after COVID-19 onset. On-treatment patient census fell by >50% following onset of COVID-19, with protracted, incomplete recovery through 2020. 829 (38.3%) patients experienced minor RTI, while 381 (17.5%) of patients experienced major RTI. All RTI rates increased following onset of COVID-19 relative to pre-pandemic (43.0% vs. 14.0%, P <0.001 and 74.1% vs. 33.1%, P < 0.001, for major and minor RTI, respectively). Compared to baseline disparities, increased major, but not minor, RTI rates were seen in African American compared to White patients during the pandemic (48.4% vs. 38.4%, P<0.05). Additionally, patients with Medicaid or no insurance experienced increased rate in major RTI compared to patients with commercial insurance in contrast to pre-pandemic differences (56.1% vs. 32.0%, P<0.05) Conclusion: We have previously shown minority and low socioeconomic patient populations to be at risk for RT quality shortfalls. The COVID-19 pandemic exacerbated pre-existing RTI rates at our academic center, and disproportionately impacted socially vulnerable groups. Our findings are limited to a single institution which saw protracted reductions in patient referrals during the early pandemic. This may represent a consequence of upstream barriers to care, the most severe form of treatment “interruption”. To improve generalizability and robustness of our findings, this study should be reproduced broadly at other centers. Future directions will focus on identification of candidate mechanisms responsible for elevated RTI observed in vulnerable populations during the pandemic and beyond.