Accessibility and quality of medical care for patients with chronic noncommunicable diseases during COVID-19 pandemic.

The purpose of this study is to conduct a comparative analysis of the impact of the accessibility and quality of medical care provided to patients with chronic noncommunicable diseases (CNCDs) during COVID-19 pandemic on the course and outcome of COVID-19 infection. The study included 132 patients hospitalized with a diagnosis of COVID-19 and having one or more concomitant CNCDs. The patients were divided into two groups based on the quality of the initial CNCD therapy they received. Group 1 involved 58 patients (42%) who received treatment according to clinical guidelines and had a compensated CNCD. Group 2 consisted of 76 patients (58%) who received treatment that was not in line with modern clinical guidelines and/or had a decompensated CNCD. All 'red zone' hospitalized patients were surveyed. In particular, they were asked questions related to the quality and accessibility of medical care during COVID-19 pandemic and their satisfaction with the medical care received during the pandemic. Reduced access to medical care (the failure to have the therapy received timely evaluated and adjusted) during COVID-19 pandemic affects the quality of the therapy received by patients with CNCDs. Generally, an unfavorable course and outcome of COVID-19 infection are typical for patients receiving a non-optimal CNCD therapy as compared to patients receiving a therapy that meets current clinical guidelines.

Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation.

BACKGROUND: Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. METHODS: For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. RESULTS: Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. CONCLUSIONS: The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.