Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

BACKGROUND: Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales. OBJECTIVE: To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches. METHODS: We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches. RESULTS: We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models. SIGNIFICANCE: The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

The future of oncology policy.

To ensure the previous progress seen in cancer survival rates continues as we move through the 21st Century it is important to determine future effective policy related to oncology healthcare delivery and funding. Recent successes with, for example, the COVID vaccine response, the decision-making agility exhibited by governments and healthcare systems and the effective use of telehealth and real-world evidence highlight the progress that can be made with pooled efforts and innovative thinking. This shared approach is the basis for the European Beating Cancer Plan which outlines action points for governments and health systems for the period 2021-2025. It focuses on a whole government approach, centred on patients, maximising the potential of new technologies and insights across policy areas including employment, education, transport and taxation, enabling the tackling of cancer drivers in schools, workplaces, research labs, towns and cities and rural communities. Despite the plan there are still concerns that oncology policy has not adequately responded to the pace of innovation and the unique challenges generated by innovative oncological technologies. There needs to be focus on: gaining consensus on the most appropriate methods to assess and price combination therapies and cell and gene therapies, developing effective outcome-based payment models for personalised medicine and developing consensus on the ideal approach for multiple indication pricing. Finally, future policy needs to ensure pharmaceutical companies and other research organisations are adequately rewarded for innovation to ensure continued R&D and the development of innovative oncological products.

Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey.

COVID-19 created an unprecedented global public health crisis during 2020-2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.

The impact of the COVID-19 outbreak on surgical site infections in elective colorectal cancer surgery: One potential benefit of the pandemic?

Background: The COVID-19 pandemic, also known as the coronavirus pandemic, has affected either directly or directly all medical fields. It caused a major reduction of elective surgical operations as well as overall admissions to surgical departments because of the widespread hospital fear and anxiety experienced by most patients during the peak of this outbreak. However, colorectal cancer operations were performed in large numbers also during the pandemic. In order to protect patients and health workers, hygiene and public health measures were intensified when the coronavirus pandemic began. The aim of the present study was to evaluate the rate of surgical site infections (SSIs) after the beginning of COVID-19 hygiene measures, which was in March 2020 in Greece. Methods: A total of 173 patients who underwent elective colorectal cancer surgery were enrolled retrospectively. Patients were divided into two groups. Group A included 98 patients undergoing colorectal cancer surgery between January 2019-December 2019 (pre-COVID-19 era), whereas 75 patients (group B) underwent colorectal cancer procedures between April 2020-March 2021 (after the beginning of COVID-19 hygiene measures). Statistical analyses were done using Stata13. The student’s t-test was used to compare results between groups. Results: SSI developed in 35 of the 173 patients (20.2%). According to the results of our study, there was a statistically significant difference between the total numbers of SSIs between the 2 examined periods. 25 (25.5%) wound infections occurred in group A-patients postoperatively, whereas only 10 (13.3%) SSIs were developed in patients undergoing colorectal cancer surgery after the beginning of COVID-19 measures (P=0.048). Conclusions: The current study demonstrates that COVID-19 hygiene and public health measures affect the rate of SSI after elective colorectal cancer surgery. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.