Population Vulnerability to the SARS-CoV-2 Virus Infection. A County-Level Geographical-Methodological Approach in Romania

Preparing for the next outbreak: A review of indices measuring outbreak preparedness, vulnerability, and resilience

The COVID-19 pandemic has highlighted the need for relevant metrics describing the resources and community attributes that affect the impact of communicable disease outbreaks. Such tools can help inform policy, assess change, and identify gaps to potentially reduce the negative outcomes of future outbreaks. The present review was designed to identify available indices to assess communicable disease outbreak preparedness, vulnerability, or resilience, including articles describing an index or scale developed to address disasters or emergencies which could be applied to addressing a future outbreak. This review assesses the landscape of indices available, with a particular focus on tools assessing local-level attributes. This systematic review yielded 59 unique indices applicable to assessing communicable disease outbreaks through the lens of preparedness, vulnerability, or resilience. However, despite the large number of tools identified, only 3 of these indices assessed factors at the local level and were generalizable to different types of outbreaks. Given the influence of local resources and community attributes on a wide range of communicable disease outcomes, there is a need for local-level tools that can be applied broadly to various types of outbreaks. Such tools should assess both current and long-term changes in outbreak preparedness with the intent to identify gaps, inform local-level decision makers, public policy, and future response to current and novel outbreaks.

COVID-19 and Laboratory Markers from Romanian Patients-A Narrative Review

COVID-19 has significantly impacted the whole world, and Romania was no exception. Biomarkers play a crucial role in understanding and managing the disease. However, research regarding laboratory analyses for patients with COVID-19 is fairly limited. For detection, PCR testing is still considered the golden standard, while antibodies are still useful for monitoring both patients and their vaccination status. In our country, biomarkers such as CRP, LDH, transaminases, cardiac, and iron markers have been used to assess the status of patients and even predict illness outcome. CRP, IL-6, LDH, FER, fibrinogen, creatinine, and vitamin D levels have been associated with increased severity, risk of ICU admission, and death. Cardiac markers and D-dimers are also good predictors, but their role seems more important in patients with complications. HDL cholesterol and BUN levels were also suggested as potential biomarkers. Hematological issues in SARS-CoV-2 infections include neutrophilia, lymphopenia and their ratio, while PCT, which is a marker of bacterial infections, is better to be used in patients with co- or supra-infections. The current research is a narrative review that focuses on the laboratory results of Romanian COVID-19 patients. The goal of this article is to provide an update on the research on biomarkers and other laboratory tests conducted inside the borders of Romania and identify gaps in this regard. Secondly, options for further research are discussed and encouraged.

Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania

COVID-19 revealed some major weaknesses and threats that are related to the level of territorial development. In Romania, the manifestation and the impact of the pandemic were not homogenous, which was influenced, to a large extent, by a diversity of sociodemographic, economic, and environmental/geographic factors. The paper is an exploratory analysis focused on selecting and integrating multiple indicators that could explain the spatial differentiation of COVID-19-related excess mortality (EXCMORT) in 2020 and 2021. These indicators include, among others, health infrastructure, population density and mobility, health services, education, the ageing population and distance to the closest urban center. We analyzed the data from local (LAU2) and county level (NUTS3) by applying multiple linear regression and geographically weighted regression models. The results show that mobility and lower social distancing were far more critical factors for higher mortality than the intrinsic vulnerability of the population, at least in the first two years of COVID-19. However, the highly differentiated patterns and specificities of different areas of Romania resulting from the modelling of EXCMORT factors drive to the conclusion that the decision-making approaches should be place-specific in order to have more efficiency in case of pandemics.

Community Roots of COVID-19 Infection Rates Between Population Composition and Regional Systems in Romania

This is an analysis of conditions favouring the cumulative COVID-19 infection rates between February 2020 and April 2021 in Romania, as an Eastern European society, at the local community level. What are the socio-demographic and location profiles of the local communities by considering their infection rates with SARS-COV-2 at the beginning of the pandemia as a dependent variable? This is the research question that structured the approach. The general hypothesis that is tested is that reported infections with the new coronavirus are higher in communities of higher social interactions. The theoretical model is tested by multiple regression analysis working on more than 2500 local communities, out of the 3200 local administrative units of the country. Data basis for testing the model are coming from the National Institute of Public Health and the National Institute of Statistics. Higher COVID infection rates are favoured by socio-human capital, the regional capital, migration abroad experience, and modernity at a local level. Other factors are captured by the cultural areas as subregions of historical regions of the country, formed by neighboured similar counties. Nuclei of higher infections with COVID-19 are located in developed communities around large cities, high modernity areas, and communities of high emigration abroad. Principles for health public policies are formulated at the end by considering the role of decentralisation, and better ways to do a rapid and good diagnosis at local levels. To our knowledge, this is one of the very few studies that address determinants of COVID-19 infections at the local community level for a whole country in Europe. New research questions are formulated as an outcome of conclusions. They could be answered only by supplementary multilevel research. Limitations of analysis are derived from the fact that we are using only ecological, spatially aggregated data, and not multilevel ones. Relations that were recorded to the community could not be transferred to the individual level.

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.