Epidemiological Principles Research Articles

Epidemiological Studies on COVID-19 and Mental Health in Mexico: Better Methodology for Better results: a Literature Review

Background. Updated information is required on studies conducted in Mexico on the mental health consequences of COVID-19. Objective. In comparison with previous efforts, we increased the timeframe of the literature search to February 2023 and used a larger list of potential mental health outcomes. Method. We used common international literature databases and more local databases, such as SciELO, and the Biblioteca Virtual en Salud (BVS). We focussed on the methodological issues of these studies, their prevalence estimates and suspected risk factors. Results. We reported on ninety-five studies that included Mexican data on symptoms of alcohol use, anxiety, depression, gaming, sleep problems and suicidal thoughts and behaviours. In most cases, we found a cross-sectional survey with self-reported information on mental health symptoms usually although not always with validated screening instruments and scales. These studies were implemented on websites, and sometimes over the phone, on self-selected volunteers. We found a small subset of fourteen longitudinal datasets (for alcohol use, anxiety, depression, and suicidal thoughts and behaviours) that suggested a more cautious interpretation of the effects of the COVID-19 pandemic on the mental health of Mexicans. Discussion and conclusion. Although the initial rapid studies during the early phases of the pandemic yielded warning signs, they did not necessarily follow standard epidemiological principles. Methodological improvements are required for future studies on the mental health consequences of natural, man-made disasters and epidemic contingencies.

Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study

BackgroundInvasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.ObjectiveThis study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma.MethodsA total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients.ResultsCox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863.ConclusionsA machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.

Social Determinants of COVID-19 Pandemic Control: Participatory Learnings From Everyday Experiences in Cape Town, South Africa.

As countries adapted their disaster responses to the COVID-19 pandemic, South Africa responded with an alert-level risk approach based on epidemiological principles that impacted all societal levels. We explored the everyday experiences of people in Cape Town whose basic needs were met and who could report on the realities of the COVID-19 pandemic control. Gaining insight into their perspectives contributes to knowledge that can inform policies and practices for managing future public health crises. Community-Based Participatory Research principles guided the design and a series of facilitated dialogues with 18 research participants. The thematic analysis was deepened through two colloquiums with members of an overarching research consortium and a participant reflection workshop. The pandemic impacted individuals, their interpersonal relationships, workplaces and communities, leading to societal processes such as stigma, virtue signalling and the subversion of mandates. The public health response had mixed reactions, with useful information about preventive measures being diluted by COVID-19 denialism and fake news. Health and other authorities were frequently perceived as out of touch with, and unresponsive to, the everyday realities of local communities. Our study demonstrates the connectedness of people and the need to maintain and re-establish severed connections. A holistic approach to health care and promotion from social determinants of health and a community-engaged perspective may significantly increase the outcomes of public health responses. People with everyday experience of the COVID-19 pandemic-including community members, healthcare workers, case managers, carers and researchers-collaborated on the study design, interview schedule, data interpretation, analysis and refinement of this article.

To secure an e‐commerce system using epidemic mathematical modeling with neural network

SummarySecuring an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.

Deep Learning - Methods to Amplify Epidemiological Data Collection and Analyses.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

Application of virtual simulation technology in epidemiology education: a systematic review

Objective: To systematically review the progress, advantages, disadvantages, precautions and future trends of virtual simulation technology used in epidemiology teaching. Methods: A systematical literature retrieval was conducted by using PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and VIP Paper Check System with key words of epidemiology, teaching and virtual simulation, and the literatures included were screened and classified with narrative integration method. Chinese virtual simulation teaching platforms were used to select the literatures about existing epidemiology virtual simulation teaching projects for integration and analysis. Results: A total of 22 articles were included (7 in Chinese and 15 in English), most of which were teaching projects for students majoring in Public Health. We also found 24 national first-class courses and 21 provincial first-class courses in virtual simulation of epidemiology in China. The application of virtual simulation technology in epidemiology education is still in its infancy, and the interaction degree is mostly moderate. It is mainly used in three scenarios: improving the visualization degree of complex concepts and structures, training the operational skills through low-risk and low-cost virtual environment, serving as an effective supplement to the teaching of epidemiological field investigation and response to public health emergencies. In terms of effect, it is conducive to students' understanding of epidemiology related phenomena and principles, and has the advantages of breaking through time and space constraints, reducing teaching costs and risks, improving students' attention and satisfaction and so on. However, it also faces the lack of foundation in the initial stage, and some students have problems such as psychological and physiological discomfort. In the future, we need to optimize the development process, program design and so on. At the same time, we should conduct more in-depth research on cost calculation, effect evaluation and curriculum integration. Conclusions: The application of virtual simulation technology in epidemiology education is an important part of training high-level applied public health talents. We encourage to actively carry out virtual simulation teaching in epidemiology, and train public health talents with Chinese characteristics.

Auricular and periauricular pathologies in the ENT department of Sylvanus Olympio Teaching Hospital in Lome (Togo)

The aim of this study is to describe the epidemiological aspects, clinical features and principles of management of different types of auricular and peri-auricular pathologies. This is a 5-year retrospective descriptive study (from May 1, 2018 to April 30, 2023) of the records of patients who consulted for a functional or cosmetic complaint relating to the auricle or periauricular region in the ENT and cervico-facial surgery department of the Centre hospitalier universitaire Sylvanus Olympio. A total of 159 cases over 5 years, i.e. an annual frequency of 31 cases, met the study criteria. The mean age of the patients was 22.2 years. Children and students accounted for 24.5% and 23.9% of cases respectively. Auricular pathologies accounted for 64.8% of cases and peri-auricular pathologies for 36.2%.Tumors and trauma accounted for 33.3% and 29.6% of cases respectively, and congenital pathologies were found in 29.9% of cases. Among traumatic lesions, intentional assault and battery was the cause in 21.3%, followed by road accidents in 17.2%. The right ear was affected in 48% and the lobule in 40.4%. Keloids accounted for 17.6% of all cases, and 53% of tumors and pseudotumors. The left ear was involved in 50% of cases. Piercing was the cause of keloids in 10.7% of cases. Auricular and peri-auricular pathologies were dominated by benign tumors, trauma and congenital pathologies, and involved young subjects. Management is based on the type of lesion, with functional and aesthetic considerations in mind.

Epidemiology and prognostic nomogram for locally advanced gastric signet ring cell carcinoma: A population-based study.

Gastric signet ring cell carcinoma (GSRC) represents a specific subtype of gastric cancer renowned for its contentious epidemiological features, treatment principles, and prognostic factors. To investigate the epidemiology of GSRC and establish an improved model for predicting the prognosis of patients with locally advanced GSRC (LAGSRC) after surgery. The annual rates of GSRC incidence and mortality, covering the years 1975 to 2019, were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to explore the temporal trends in both disease incidence and mortality rates using Joinpoint software. The clinical data of 3793 postoperative LAGSRC patients were collected from the SEER database for the analysis of survival rates. The Cox regression model was used to explore the independent prognostic factors for overall survival (OS). The risk factors extracted were used to establish a prognostic nomogram. The overall incidence of GSRC increased dramatically between 1975 and 1998, followed by a significant downward trend in incidence after 1998. In recent years, there has been a similarly optimistic trend in GSRC mortality rates. The trend in GSRC showed discrepancies based on age and sex. Receiver operating characteristic curves, calibration curves, and decision curve analysis for 1-year, 3-year, and 5-year OS demonstrated the high discriminative ability and clinical utility of this nomogram. The area under the curve indicated that the performance of the new model outperformed that of the pathological staging system. The model we established can aid clinicians in the early prognostication of LAGSRC patients, resulting in improved clinical outcomes by modifying management strategies and patient health care.

Health Disparities: Differences in Veteran and Non-Veteran Populations using Generative AI

Introduction: This study investigated self-reported health status, health screenings, vision problems, and vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for informed health system planning for veteran populations. Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional epidemiological principles enables systematic data management, analysis, and visualization, offering a nuanced understanding of health dynamics across demographic segments and highlighting disparities essential for veteran health system planning. Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments. Conclusion: Insights from this study could inform health system planning, using epidemiological data assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of integrating Generative AI with epidemiological analysis in shaping public health policy and health planning.

Mapping the Landscape of Continuing Professional Development: A Pilot Gap Map of Systematic Reviews

Gap maps graphically represent evidence from systematic reviews. We developed and tested a pilot gap map of systematic reviews examining the impact of continuing professional development (CPD). We conducted a mapping review and charted the content of systematic reviews in a pilot gap map. Directive content analysis helped develop categories in three dimensions: evaluation framework, study characteristics, and number of systematic reviews. Of 389 identified records, 29 systematic reviews were included in the pilot map. We found gaps in evidence evaluating the impact of e-learning and skills training programs on various outcomes (patient, care, and work environment). Most evaluations corresponded to Kirkpatrick's learning model, with few considering other outcomes or how CPD could be mediated or influenced by contextual factors. Rooted in social epidemiological principles, the pilot map framework introduced new ways of understanding evidence from systematic reviews. Although the pilot map illuminated several evidence gaps and perspectives important to future CPD, a complete map is needed to validate the findings.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

HEAD LOUSE PALEOEPIDEMIOLOGY IN THE OSMORE RIVER VALLEY, SOUTHERN PERU.

Recent studies of louse ectoparasites from mummies have developed robust data sets that allow a true epidemiological approach to the prehistory of louse parasitism. One epidemiological principle is that the binomial of overdispersion is normally negative, meaning that in a host population, parasites are aggregated in a few individuals. We demonstrate the overdispersion of lice in 3 different prehistoric communities that differ along 3 axes or variables: environmental setting, socioeconomic status, and cultural affiliation. Distinct cultural practices could have been involved in different patterns of louse infestation. Prevalence, intensity, and abundance of infestations exhibit statistically significant differences between the communities. We also find differences in prevalence between subadults and adults that contrasted by cultural affiliation and suggest conditions different from those seen today. We show that overall prevalence was affected primarily by ecological setting, not socioeconomic status nor cultural affiliation. These findings demonstrate that statistical analysis of archaeological data can reveal the states of infestation in past populations with lifestyles not seen in modern people. Our approach paves the way for future comparisons of subpopulations within archaeological communities.

Review article: Emerging and current management of acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) is a clinically and pathophysiologically distinct condition from acutely decompensated cirrhosis and is characterised by systemic inflammation, extrahepatic organ failure, and high short-term mortality. To provide a narrative review of the diagnostic criteria, prognosis, epidemiology, and general management principles of ACLF. Four specific interventions that are explored in detail are intravenous albumin, extracorporeal liver assist devices, granulocyte-colony stimulating factor, and liver transplantation. We searched PubMed and Cochrane databases for articles published up to July 2023. Approximately 35% of hospital inpatients with decompensated cirrhosis have ACLF. There is significant heterogeneity in the criteria used to diagnose ACLF; different definitions identify different phenotypes with varying mortality. Criteria established by the European Association for the Study of the Liver were developed in prospective patient cohorts and are, to-date, the most well validated internationally. Systemic haemodynamic instability, renal dysfunction, coagulopathy, neurological dysfunction, and respiratory failure are key considerations when managing ACLF in the intensive care unit. Apart from liver transplantation, there are no accepted evidence-based treatments for ACLF, but several different approaches are under investigation. The recognition of ACLF as a distinct entity from acutely decompensated cirrhosis has allowed for better patient stratification in clinical settings, facilitating earlier engagement with the intensive care unit and liver transplantation teams. Research priorities over the next decade should focus on exploring novel treatment strategies with a particular focus on which, when, and how patients with ACLF should be treated.

PURPOSE OF NURSES IN INFECTION CONTROL AND PREVENTION IN MEDICAL MANAGEMENT. A NEW APPRAISAL

Infection Control activity works in tandem with all healthcare disciplines to provide quality patient care through education and practical application of the principles of microbiology, epidemiology, and infection prevention and control.Infection prevention and control (IPC) is a practical, evidence-based approach that prevents patients and health workers from being harmed by avoidable infection as a result of antimicrobial resistance. No one should catch an infection while receiving health care, yet, these infections can spread through outbreaks and many regular care practices, affecting hundreds of millions of people across the world every year. Infection prevention and control is one of the first topics introduced in nursing programs, yet its main tenets are often lost or forgotten. Objectives: The main objective of these guidelines is to prevent the health care workers and the environment from the transmission of infections. The specific objectives of these guidelines are to provide directions and information in relation to facilities, equipment, and procedures necessary to implement standard and additional (transmission-based) precautions for control of infections Cleaning, disinfecting and reprocessing of reusable equipment, Waste management, Protection of health care workers from transmissible infections, Prevention of HAI in patients, Infection control practices in special situations.

Modern clinico-epidemiological features of the course of some communicable infectious zooanthroponous exanthemas in times of war

Objective — to study and analyze modern clinical and epidemiological features of emerging zooanthroponoses, legal and organizational measures to ensure the sanitary and epidemiological well­being of the population of Ukraine in wartime conditions and active hostilities on a large area of the south and east of the country. Materials and methods. An analytical review of a number of scientific publications devoted to the study of zoonotic diseases of the population accompanied by the appearance of exanthems on the skin was carried out. State legislative acts on issues of ensuring sanitary and epidemic welfare of citizens of Ukraine during wartime were also considered. The search for information was carried out in the medical databases PubMed, Elsevier, High Wire Press, Google Scholar for the period 2000—2022 according to the terms of the catalog of medical topics, subject headings with keywords: «dermatozoonosis», «exanthema», «epidemio­logy», «morbidity», «outbreak». Meta-analyses and reviews of relevant literature were studied. The work presents the principles of a scientific approach to the optimization of preventive and anti-epidemic measures to combat infectious zoonotic diseases, which should contribute to the maximally rational use of the laws of classical epidemiology in the conditions of war in order to increase the practical effectiveness of the anti-epidemic and dermatological services of the Ministry of Health of Ukraine.Results and discussion. The complex socio-economic situation of martial law and forced mass resettlement created quite favorable conditions for increasing the incidence of infectious zoonotic diseases in population, including their real outbreaks in certain territories near the front. The article describes methodical and diagnostic approaches, specific epidemiological principles and dermatological criteria for building effective measures to combat zoonotic diseases. A professional justification of the role and place of each of them in various infectious diseases is given, taking into account the provisions of general epidemiology and traditional dermatological semiotics. Conclusions. The principles of mandatory complexity and qualified impact on the main link of the epidemic process are of great importance in the fight against various groups of infectious diseases. Their clinically justified and timely administrative use is necessary to achieve the goals of eradication, elimination and control of infectious diseases. In Ukraine, there is a sufficient normative and legal framework for the proper provision of sanitary and epidemic welfare of the population. In order to prevent the spread of existing or potential infectious agents, in particular emerging pathogens, it is necessary to develop and improve the already existing emergency response plan, particularly, the infrastructure of the health care system, to create a multi-level network of diagnostic laboratories and an integrated system of epidemiological surveillance. It is proposed to increase the branching of the network of institutions capable of identifying pathogens of zoonoses, hospitalization of those who are already sick and persons suspected of having the disease in conditions that will ensure their isolation and proper treatment, and to develop specific recommendations for the prevention of the spread of pathogens with various transmission routes. The task of such centers should be the comprehensiveness of epidemiological, diagnostic and preventive measures which must be carried out professionally and promptly. Also, one of the priorities should be the retraining of doctors of various specialties (epidemiologists, general practitioners — family doctors, dermatologists) on topical issues of zoonoses surveillance.

Epidemiology and a Predictive Model of Prognosis Index Based on Machine Learning in Primary Breast Lymphoma: Population-Based Study.

Primary breast lymphoma (PBL) is a rare disease whose epidemiological features, treatment principles, and factors used for the patients' prognosis remain controversial. The aim of this study was to explore the epidemiology of PBL and to develop a better model based on machine learning to predict the prognosis for patients with primary breast lymphoma. The annual incidence of PBL was extracted from the surveillance, epidemiology, and end results database between 1975 and 2019 to examine disease occurrence trends using Joinpoint software (version 4.9; National Cancer Institute). We enrolled data from 1251 female patients with primary breast lymphoma from the surveillance, epidemiology, and end results database for survival analysis. Univariable and multivariable analyses were performed to explore independent prognostic factors for overall survival and disease-specific survival of patients with primary breast lymphoma. Eight machine learning algorithms were developed to predict the 5-year survival of patients with primary breast lymphoma. The overall incidence of PBL increased drastically between 1975 and 2004, followed by a significant downward trend in incidence around 2004, with an average annual percent change (AAPC) of -0.8 (95% CI -1.1 to -0.6). Disparities in trends of PBL exist by age and race. The AAPC of the 65 years or older cohort was about 1.2 higher than that for the younger than 65 years cohort. The AAPC of White patients is 0.9 (95% CI 0.0-1.8), while that of Black patients was significantly higher at 2.1 (95% CI -2.5 to 6.9). We also identified that the risk of death from PBL is multifactorial and includes patient factors and treatment factors. Survival analysis revealed that the patients diagnosed between 2007 and 2015 had a significant risk reduction of mortality compared to those diagnosed between 1983 and 1990. The gradient booster model outperforms other models, with 0.752 for sensitivity and 0.817 for area under the curve. The important features established with the gradient booster model were the year of diagnosis, age, histologic type, and primary site, which were the 4 most relevant variables to explain 5-year survival status. The incidence of PBL started demonstrating a tendency to decrease after 2004, which varied by age and race. In recent years, the prognosis of patients with primary breast lymphoma has been remarkably improved. The gradient booster model had a promising performance. This model can help clinicians identify the early prognosis of patients with primary breast lymphoma and therefore improve the clinical outcome by changing management strategies and patient health care.

Modelling the spread and mitigation of an emerging vector-borne pathogen: Citrus greening in the U.S.

Predictive models, based upon epidemiological principles and fitted to surveillance data, play an increasingly important role in shaping regulatory and operational policies for emerging outbreaks. Data for parameterising these strategically important models are often scarce when rapid actions are required to change the course of an epidemic invading a new region. We introduce and test a flexible epidemiological framework for landscape-scale disease management of an emerging vector-borne pathogen for use with endemic and invading vector populations. We use the framework to analyse and predict the spread of Huanglongbing disease or citrus greening in the U.S. We estimate epidemiological parameters using survey data from one region (Texas) and show how to transfer and test parameters to construct predictive spatio-temporal models for another region (California). The models are used to screen effective coordinated and reactive management strategies for different regions.

Basic therapy for viral diseases of the upper respiratory tract

The high prevalence of inflammatory diseases of the upper respiratory tract brings this pathology to the forefront, occupying up to 90% of cases of contacting specialists among all infectious diseases. The main etiological factor in the occurrence of acute inflammatory diseases of the upper respiratory tract, such as rhinitis, rhinosinusitis, pharyngitis, is the defeat of a viral agent. At the same time, the damaging viral agents are extremely diverse. In turn, an incorrectly selected initial therapy can lead to a weakening of local nonspecific immunity, which in turn forms a starting point for the attachment of a bacterial infection and the development of acute bacterial rhinosinusitis. Due to the high prevalence of infections of the upper respiratory tract and unreasonable systemic antibiotic therapy, it is the main factor contributing to the growth of antibacterial resistance, and taking into account the wide range of infectious microbiota that contributes to the development of chronic pathology, the selection of adequate drugs for the treatment of inflammatory diseases of the upper respiratory tract remains relevant. If it is inappropriate to use antibacterial therapy, homeopathic preparations can be used both as monotherapy for uncomplicated forms of a viral disease, and in combination in cases where the safety of prescribed drugs is important to the patient. This is especially true in childhood, when the doctor is limited in the use of medicines. The purpose of this work is to familiarize physicians with the epidemiology, symptoms and modern principles of treatment of inflammatory diseases of the upper respiratory tract with an emphasis on risk factors. The analysis of Russian-language and English-language publications was carried out using the databases: eLibrary, PubMed, Russian Science Citation Index, Springer, Scopus, Scientific Research, Crossref.

Commentary: Establishing Causality in Quality Improvement Studies.

Commentary: Establishing Causality in Quality Improvement Studies.

International Law and Digital Disease Surveillance in Pandemics: On the Margins of Regulation

AbstractThe COVID-19 pandemic elicited a surge in the use of digital tools to replace “classic” manual disease tracking and contact tracing across individuals. The main technical reason is based on the disease surveillance needs imposed by the magnitude of the spread of the SARS-CoV-2 virus since 2020, particularly how these needs overwhelmed governments around the world. Such developments led to stark variations across countries in terms of legal approaches towards the use of digital tools, including self-reporting software and mobile phone apps, for both disease tracking and contact tracing. Against this backdrop, in this article I highlight some of the normative challenges posed by the digitalization of disease surveillance, underscoring its almost non-existent regulation under international law. I look back at the historical emergence of the epidemiological principles underlying this procedure, by referring to John Snow’s trailblazing work in cholera control. I emphasize how the COVID-19 pandemic prompted both technical and normative shifts related to the digitalization of these procedures. Furthermore, I refer to some of the overarching obstacles for deploying international law to tackle future tensions between the public health rationale for digitalized disease tracking and contact tracing, on the one hand, and normative concerns directly related to their legality, on the other hand. Lastly, I put forward conclusions in light of the current juncture of international health law reforms, and how they so far display limited potential to herald structural changes concerning the legality of the use of digital tools in disease surveillance.