Age-Related Changes in Clinical Presentation of Covid-19: the EPICOVID19 Web-Based Survey

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

BACKGROUND

Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges.

OBJECTIVE

This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used.

METHODS

We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies.

RESULTS

Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions.

CONCLUSIONS

Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

Illness Experiences of Brazilian People Who Were Hospitalized Due to COVID-19 and Faced Long COVID Repercussions in Their Daily Life: A Constructivist Grounded Theory Study

Long COVID is a multisystem condition that has multiple consequences for the physical, mental, and social health of COVID-19 survivors. The impact of the long COVID condition remains unclear, particularly among middle-aged and older adults, who are at greater risk than younger people of persisting symptoms associated with COVID-19. Therefore, we aimed to understand the experiences of middle-aged and older people who had been hospitalized for COVID-19 and the repercussions of long-term COVID symptoms in their daily lives. A qualitative study was carried out, adopting the framework of the constructivist grounded theory (CGT) proposed by Kathy Charmaz. Fifty-six middle-aged and older adult participants from the southern region of Brazil were recruited. Data were gathered from semi-structured telephone interviews. Concomitantly a comparative analysis was performed to identify categories and codes using the MaxQDA® software (version 2022). Three subcategories were identified: (1) experiencing COVID-19 in the acute phase; (2) oscillating between 'good days' and 'bad days' in long COVID; and (3) (re)constructing identity. These concepts interact with each other and converge upon the central category of this study: recasting oneself to the uniqueness of the illness experience of long COVID. Our findings provided insights related to the disruption in the lives of long COVID-19 sufferers who still live with persistent symptoms of the disease, including physical, social, family, emotional and spiritual repercussions. Likewise, this study may aid in developing friendly and welcoming social environments, lowering stigma and prejudice towards patients with long COVID, and fostering prompt and suitable policy support and mental health care for these individuals.

Associations of COVID-19 symptoms with omicron subvariants BA.2 and BA.5, host status, and clinical outcomes in Japan: a registry-based observational study

BACKGROUND

Previous SARS-CoV-2 infection and vaccination, coupled with the rapid evolution of SARS-CoV-2 variants, have modified COVID-19 clinical manifestations. We aimed to characterise the clinical symptoms of COVID-19 individuals in omicron BA.2 and BA.5 Japanese pandemic periods to identify omicron and subvariant associations between symptoms, immune status, and clinical outcomes.

METHODS

In this registry-based observational study, individuals registered in Sapporo's web-based COVID-19 information system entered 12 pre-selected symptoms, days since symptom onset, vaccination history, SARS-CoV-2 infection history, and background. Eligibility criteria included symptomatic individuals who tested positive for SARS-CoV-2 (PCR or antigen test), and individuals who were not tested for SARS-CoV-2 but developed new symptoms after a household member tested positive for SARS-CoV-2. Symptom prevalence, variables associated with symptoms, and symptoms associated with progression to severe disease were analysed.

FINDINGS

Data were collected and analysed between April 25 and Sept 25, 2022. For 157 861 omicron-infected symptomatic individuals, cough was the most common symptom (99 032 [62·7%] patients), followed by sore throat (95 838 [60·7%] patients), nasal discharge (69 968 [44·3%] patients), and fever (61 218 [38·8%] patients). Omicron BA.5 infection was associated with a higher prevalence of systemic symptoms than BA.2 in vaccinated and unvaccinated individuals (adjusted odds ratio [OR] for fever: 2·18 [95% CI 2·12-2·25]). Omicron breakthrough-infected individuals with three or more vaccinations or previous infection were less likely to exhibit systemic symptoms (fever 0·50 [0·49-0·51]), but more likely to exhibit upper respiratory symptoms (sore throat 1·33 [1·29-1·36]; nasal discharge 1·84 [1·80-1·89]). Infected older individuals (≥65 years) had lower odds for all symptoms. However, when symptoms were manifest, systemic symptoms were associated with increased odds for severe disease (dyspnoea 3·01 [1·84-4·91]; fever 2·93 [1·89-4·52]), whereas upper respiratory symptoms were associated with decreased odds (sore throat 0·38 [0·24-0·63]; nasal discharge 0·48 [0·28-0·81]).

INTERPRETATION

Host immunological status, omicron subvariant, and age were associated with a spectrum of COVID-19 symptoms and outcomes. BA.5 produced a higher systemic symptom prevalence than BA.2. Vaccination and previous infection reduced systemic symptom prevalence and improved outcomes but increased upper respiratory tract symptom prevalence. Systemic, but not upper respiratory, symptoms in older people heralded severe disease. Our findings could serve as a practical guide to use COVID-19 symptoms to appropriately modify health-care strategies and predict clinical outcomes for older patients with omicron infections.

FUNDING

Japan Agency for Medical Research and Development.

Impact of Changes in Human Airway Epithelial Cellular Composition and Differentiation on SARS-CoV-2 Infection Biology

The consequences of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can range from asymptomatic to fatal disease. Variations in epithelial susceptibility to SARS-CoV-2 infection depend on the anatomical location from the proximal to distal respiratory tract. However, the cellular biology underlying these variations is not completely understood. Thus, air-liquid interface cultures of well-differentiated primary human tracheal and bronchial epithelial cells were employed to study the impact of epithelial cellular composition and differentiation on SARS-CoV-2 infection by transcriptional (RNA sequencing) and immunofluorescent analyses. Changes of cellular composition were investigated by varying time of differentiation or by using specific compounds. We found that SARS-CoV-2 primarily infected not only ciliated cells but also goblet cells and transient secretory cells. Viral replication was impacted by differences in cellular composition, which depended on culturing time and anatomical origin. A higher percentage of ciliated cells correlated with a higher viral load. However, DAPT treatment, which increased the number of ciliated cells and reduced goblet cells, decreased viral load, indicating the contribution of goblet cells to infection. Cell entry factors, especially cathepsin L and transmembrane protease serine 2, were also affected by differentiation time. In conclusion, our study demonstrates that viral replication is affected by changes in cellular composition, especially in cells related to the mucociliary system. This could explain in part the variable susceptibility to SARS-CoV-2 infection between individuals and between anatomical locations in the respiratory tract.

Associations of COVID-19 Symptoms with Omicron Subvariants BA.2 and BA.5, Host Status, and Clinical Outcomes: A Registry-Based Observational Study in Sapporo, Japan

Background

Previous SARS-CoV-2 infection and vaccination, coupled to rapid evolution of SARS-CoV-2 variants, have modified COVID-19 clinical manifestations. We characterized clinical symptoms of COVID-19 individuals in omicron BA.2 and BA.5 Japanese pandemic periods to identify omicron and subvariant associations between symptoms, immune status, and clinical outcomes.

Methods

Individuals registered in Sapporo's web-based COVID-19 information system entered 12 pre-selected symptoms, days since symptom onset, vaccination history, SARS-CoV-2 infection history, and background. Symptom frequencies, variables associated with symptoms, and symptoms associated with progression to severe disease were analysed.

Results

For all omicron-infected individuals, cough was the most common symptom (62.7%), followed by sore throat (60.7%), nasal discharge (44.3%), and fever (38.8%). Omicron BA.5 infection was associated with a higher symptom burden than BA.2 in vaccinated and unvaccinated individuals. Omicron breakthrough-infected individuals with ≥ 3 vaccinations or previous infection were less likely to exhibit systemic symptoms, but more likely to exhibit upper respiratory symptoms. Infected elderly individuals had lower odds for all symptoms, but, when symptoms were manifest, systemic symptoms were associated with an increased risk, whereas upper respiratory symptoms with a decreased risk, of severe disease.

Conclusion

Host immunological status, omicron subvariant, and age were associated with a spectrum of COVID-19 symptoms and outcomes. BA.5 produced a greater symptom burden than BA.2. Vaccination and prior infection mitigated systemic symptoms and improved outcomes, but increased upper respiratory tract symptom burden. Systemic, but not upper respiratory, symptoms in the elderly heralded severe disease.

The Role of Symptom Clusters in Triage of COVID-19 Patients

BACKGROUND AND OBJECTIVE

COVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19.

METHODS

We surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs).

RESULTS

In 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters.

CONCLUSIONS

Using clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.

Differential diagnosis of COVID-19 and influenza

This study uses two existing data sources to examine how patients' symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

BACKGROUND

COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020.

OBJECTIVES

To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19.

SEARCH METHODS

We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions.

SELECTION CRITERIA

Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable.

MAIN RESULTS

We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%.

AUTHORS' CONCLUSIONS

Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil-lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.

Epidemiology of SARS-CoV-2 Infection in Italy Using Real-World Data: Methodology and Cohort Description of the Second Phase of Web-Based EPICOVID19 Study

Digital technologies have been extensively employed in response to the SARS-CoV-2 pandemic worldwide. This study describes the methodology of the two-phase internet-based EPICOVID19 survey, and the characteristics of the adult volunteer respondents who lived in Italy during the first (April-May 2020) and the second wave (January-February 2021) of the epidemic. Validated scales and ad hoc questionnaires were used to collect socio-demographic, medical and behavioural characteristics, as well as information on COVID-19. Among those who provided email addresses during phase I (105,355), 41,473 participated in phase II (mean age 50.7 years ± 13.5 SD, 60.6% females). After a median follow-up of ten months, 52.8% had undergone nasopharyngeal swab (NPS) testing and 13.2% had a positive result. More than 40% had undergone serological test (ST) and 11.9% were positive. Out of the 2073 participants with at least one positive ST, 72.8% had only negative results from NPS or never performed it. These results indicate that a large fraction of individuals remained undiagnosed, possibly contributing to the spread of the virus in the community. Participatory online surveys offer a unique opportunity to collect relevant data at individual level from large samples during confinement.

Fear and Attitude towards SARS-CoV-2 (COVID-19) Infection in Spanish Population during the Period of Confinement

In January 2020, the WHO classified SARS-CoV-2 infection as a public health emergency and it was declared a pandemic on 11 March 2020. The media warned about the danger of infection, fuelling the population’s fear of the new situation and increasing the perception of risk. This fear can cause behaviour that will determine the course of the pandemic and, therefore, the purpose of this study was to analyse the fear of infection from COVID-19 among the Spanish population during the state of emergency. A cross-sectional, descriptive observational study was conducted with 16,372 participants. Data on sociodemographic factors, health factors, risk perception and fear were collected through an online survey. Level of fear is associated with older age, a lower level of education, having a person infected with SARS-CoV-2 in the immediate surroundings and living with and belonging to the most socioeconomically vulnerable group of people. Risk perception is associated with increased preventive behaviour. This paper provides relevant information for the public health sector since it contributes first-hand knowledge of population data that is highly useful in terms of prevention. Understanding the experiences of people in this pandemic helps to create more effective future intervention strategies in terms of planning and management for crisis situations.

Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study

BACKGROUND

The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes.

OBJECTIVE

This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization.

METHODS

A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed.

RESULTS

The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept -0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients.

CONCLUSIONS

A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

How did COVID-19 affect medical and cardiology journals? A pandemic in literature

BACKGROUND AND AIMS

The spreading speed of the COVID-19 pandemic forced the medical community to produce efforts in updating and sharing the evidence about this new disease, trying to preserve the accuracy of the data but at the same time avoiding the potentially harmful delay from discovery to implementation. The aim of our analysis was to assess the impact of the COVID-19 pandemic on medical literature in terms of proportion of COVID-19-related published papers and temporal patterns of publications within a sample of general/internal medicine and cardiology journals.

METHODS

We searched through PubMed scientific papers published from 1 January 2020 to 31 January 2021 about COVID-19 in ten major medical journals, of which five were in general/internal medicine and five in the cardiology field. We analyzed the proportion of COVID-19-related papers, and we examined temporal trends in the number of published papers.

RESULTS

Overall, the proportion of COVID-19-related papers was 18.5% (1986/10 756). This proportion was higher among the five selected general/internal medicine journals, compared with cardiology journals (23.8% vs 9.5%). The vast majority of papers were not original articles; in particular, in cardiology journals, there were 28% 'original articles', 17% 'review articles' and 55.1% 'miscellaneous', compared with 20.2%, 5.1% and 74.7% in general/internal medicine journals, respectively.

CONCLUSIONS

Our analysis highlights the big impact of the COVID-19 pandemic on international scientific literature. General and internal medicine journals were mainly involved, with cardiology journals only at a later time.

Differences in the Prevalence of SARS-CoV-2 Infection and Access to Care between Italians and Non-Italians in a Social-Housing Neighbourhood of Milan, Italy

The northern Italian region of Lombardy has been severely affected by the COVID-19 pandemic since its arrival in Europe. However, there are only a few published studies of the possible influence of social and cultural factors on its prevalence in the general population. This cross-sectional study of the San Siro social-housing neighbourhood of Milan, which was carried about between 23 December 2020 and 19 February 2021, found that the prevalence of anti-SARS-CoV-2 nucleocapsid antibodies in the population as a whole was 12.4% (253/2044 inhabitants), but there was a more than two-fold difference between non-Italians and Italians (23.3% vs. 9.1%). Multivariable analyses showed that being more than 50 years old, living in crowded accommodation, being a non-Italian, and having a low educational level were associated with higher odds of a positive SARS-CoV-2 test, whereas a higher level of education, retirement, and being a former or current cigarette smoker were inversely associated with SARS-CoV-2 infection. Our findings are in line with previous observations indicating that a lower socio-economic status may be a risk factor for COVID-19 and show that non-Italians are disproportionately affected by SARS-CoV-2 infection. This suggests that public health policies should focus more on disadvantaged populations.

COVID-19 in persons aged 70+ in an early affected German district: Risk factors, mortality and post-COVID care needs-A retrospective observational study of hospitalized and non-hospitalized patients

BACKGROUND

Cohorts of hospitalized COVID-19 patients have been studied in several countries since the beginning of the pandemic. So far, there is no complete survey of older patients in a German district that includes both outpatients and inpatients. In this retrospective observational cohort study, we aimed to investigate risk factors, mortality, and functional outcomes of all patients with COVID-19 aged 70 and older living in the district of Tübingen in the southwest of Germany.

METHODS

We retrospectively analysed all 256 patients who tested positive for SARS-CoV-2 in one of the earliest affected German districts during the first wave of the disease from February to April 2020. To ensure inclusion of all infected patients, we analysed reported data from the public health department as well as the results of a comprehensive screening intervention in all nursing homes of the district (n = 1169). Furthermore, we examined clinical data of all hospitalized patients with COVID-19 (n = 109).

RESULTS

The all-cause mortality was 18%. Screening in nursing homes showed a point-prevalence of 4.6%. 39% of residents showed no COVID-specific symptoms according to the official definition at that time. The most important predictors of mortality were the need for inpatient treatment (odds ratio (OR): 3.95 [95%-confidence interval (CI): 2.00-7.86], p<0.001) and care needs before infection (non-hospitalized patients: OR: 3.79 [95%-CI: 1.01-14.27], p = 0.037, hospitalized patients: OR: 2.89 [95%-CI 1.21-6.92], p = 0.015). Newly emerged care needs were a relevant complication of COVID-19: 27% of previously self-sufficient patients who survived the disease were not able to return to their home environment after discharge from the hospital.

CONCLUSION

Our findings demonstrate the importance of a differentiated view of risk groups and long-term effects within the older population. These findings should be included in the political and social debate during the ongoing pandemic to evaluate the true effect of COVID-19 on healthcare systems and individual functional status.

Cross sectional study of the clinical characteristics of French primary care patients with COVID-19

The early identification of patients suffering from SARS-CoV-2 infection in primary care is of outmost importance in the current pandemic. The objective of this study was to describe the clinical characteristics of primary care patients who tested positive for SARS-CoV-2. We conducted a cross-sectional study between March 24 and May 7, 2020, involving consecutive patients undergoing RT-PCR testing in two community-based laboratories in Lyon (France) for a suspicion of COVID-19. We examined the association between symptoms and a positive test using univariable and multivariable logistic regression, adjusted for clustering within laboratories, and calculated the diagnostic performance of these symptoms. Of the 1561 patients tested, 1543 patients (99%) agreed to participate. Among them, 253 were positive for SARS-CoV-2 (16%). The three most frequently reported 'ear-nose-throat' and non-'ear-nose-throat' symptoms in patients who tested positive were dry throat (42%), loss of smell (36%) and loss of taste (31%), respectively fever (58%), cough (52%) and headache (45%). In multivariable analyses, loss of taste (OR 3.8 [95% CI 3.3-4.4], p-value < 0.001), loss of smell (OR 3.0 [95% CI 1.9-4.8], p < 0.001), muscle pain (OR 1.6 [95% CI 1.2-2.0], p = 0.001) and dry nose (OR 1.3 [95% CI 1.1-1.6], p = 0.01) were significantly associated with a positive result. In contrast, sore throat (OR 0.6 [95% CI 0.4-0.8], p = 0.003), stuffy nose (OR 0.6 [95% CI 0.6-0.7], p < 0.001), diarrhea (OR 0.6 [95% CI 0.5-0.6], p < 0.001) and dyspnea (OR 0.5 [95% CI 0.3-0.7], p < 0.001) were inversely associated with a positive test. The combination of loss of taste or smell had the highest diagnostic performance (OR 6.7 [95% CI 5.9-7.5], sensitivity 44.7% [95% CI 38.4-51.0], specificity 90.8% [95% CI 89.1-92.3]). No other combination of symptoms had a higher performance. Our data could contribute to the triage and early identification of new clusters of cases.