Geospatial analysis of Covid-19 mortality linked to environmental risk factors in Iran- 2019-2021 (preprint)

Objectives This study aims to investigate the impact of various demographic, environmental, and topographical factors on COVID-19 mortality rates in different geographical provinces of Iran.Methods The research utilized data from DATASUS (Ministry of Health), International Classification of Diseases (ICD-10), WorldClimV1, Sentinel-5P TROPOMI-based datasets, Open Street Map (OSM), and the Shuttle Radar Topography Mission satellite (SRTM) to gather mortality, demographic, environmental, and topographical data, evaluating them by sex, age group, and province. The analysis employed Geographic Information Systems methodology and logistic regression.Results Higher mortality rates were observed in the central and southern regions, with West Azerbaijan and Sistan-Baluchestan provinces showing elevated rates compared to their population sizes. Additionally, South Khorasan, Sistan-Baluchestan, Semnan, Bushehr, and Ilam provinces exhibited higher mortality ratios relative to mean temperature. The central and southern provinces displayed a higher ratio of air pollution concerning Covid-19 mortality, notably around Uremia Lake, showing a significant correlation. Logistic regression analysis revealed positive correlations of NO2 and O3 with Covid-19 mortality, while CO2 and SO2 showed negative correlations. Furthermore, population, population density, and area emerged as the most influential factors affecting the Covid-19 mortality rate.Conclusions The findings of this study offer valuable insights for policymakers and public health officials to develop targeted interventions for reducing the virus's impact in high-risk areas and enhancing healthcare resources and infrastructure in urban settings.

Characterization of Long COVID Definitions and Clinical Coding Practices (preprint)

Background: Long COVID characterized as post-acute sequelae of SARS-CoV-2 (PASC) has no universal clinical case definition. Recent efforts have focused on understanding long COVID symptoms and electronic health records (EHR) data provides a unique resource for understanding this condition. The introduction of the International Classification of Diseases (ICD)-10 code U09.9 for - Post COVID-19 condition, unspecified to identify patients with long COVID has provided a method of evaluating this condition in EHRs, however, the accuracy of this code is unclear. Objective: Our study aimed to characterize the utility and accuracy of the U09.9 code across three healthcare systems - The Veterans Health Administration (VHA), Beth Israel Deaconess Medical Center (BIDMC) and The University of Pittsburgh Medical Center (UPMC) against patients identified with long COVID via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control (CDC) definitions. Methods: COVID positive patients with either a U07.1 ICD code or positive polymerase chain reaction (PCR) test within these healthcare systems were identified for chart review. Among this cohort we sampled patients based on two approaches i) with a U09.9 code and ii) without a U09.9 code but with a new onset PASC related ICD code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID definition based on health agency guidelines, we grouped symptoms into a core cluster of 11 commonly reported symptoms among long COVID patients and an extended cluster, that captured all other symptoms by disease domain. Patients having at least 2 symptoms persisting for >=60 days that were new onset after their COVID infection, with at least one symptom in the core cluster, were labeled as having long COVID per chart review. We compared the performance of the code across three health systems and across different time periods of the pandemic. Results: A total of 900 patient charts were reviewed across 3 healthcare systems. The prevalence of long COVID among the cohort with the U09.9 ICD code, based on the operationalized WHO definition was between 23.2%-62.4% across these healthcare systems. We also evaluated a less stringent version of the WHO definition and the Centers for Disease Control (CDC) definition and observed an increase in the prevalence of long COVID at all three healthcare systems. Conclusions: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple healthcare systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Offspring cardiometabolic outcomes and postnatal growth trajectories after exposure to maternal SARS-CoV-2 infection (preprint)

Context. Prior birth cohorts have suggested an association between maternal infection in pregnancy and offspring risk for childhood obesity. Whether maternal SARS-CoV-2 infection is similarly associated with increased cardiometabolic risk for offspring is not known. Objective. To determine whether in utero exposure to SARS-CoV-2 is associated with increased risk for cardiometabolic diagnoses by 18 months after birth, compared with unexposed offspring born during the COVID-19 pandemic. Design. This retrospective cohort study included the live offspring of all individuals who delivered during the COVID-19 pandemic (April 1, 2020 - December 31, 2021) at 8 hospitals within 2 health systems in Massachusetts. Exposure. SARS-CoV-2 positivity on polymerase chain reaction (PCR) test during pregnancy. Main Outcome Measures. Electronic health record documentation of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic codes corresponding to cardiometabolic disorders. Offspring weight-for-age, length-for-age, and body mass index (BMI)-for-age z-scores at birth, 6 months, 12 months, and 18 months of age. Results. The full study cohort includes 29,510 live born offspring (1,599 exposed and 27,911 unexposed offspring). 6.7% of exposed and 4.4% of unexposed offspring had received a cardiometabolic diagnosis by 18 months of age (crude OR 1.47 [95% CI: 1.10-1.94], p=0.007; adjusted OR 1.37 [1.01-1.83]; p=0.04). These diagnoses were preceded by significantly greater mean BMI-for-age z-scores in exposed versus unexposed offspring at 6 months (mean z-score difference 0.19, 95% CI: 0.10, 0.29, p<0.001), and a greater proportion of offspring at risk of, or meeting criteria for, overweight/obesity (16.5% vs. 12.2%, p=0.006). Conclusions. Exposure to maternal SARS-CoV-2 infection was associated with an increased risk of receiving a cardiometabolic diagnosis by 18 months and greater BMI-for-age at 6 months.

Upper airway dimensions and the skeletal parameters in orthodontic patients who developed moderate-severe COVID-19 symptoms during the pandemic.

BACKGROUND: Large airway dimensions are associated with a rapid decline in the lung function and a higher risk of hospitalization. Therefore, the airway dimensions of healthy subjects who tested positive for coronavirus disease 2019 (COVID-19) may be associated with the severity of COVID-19 symptoms. OBJECTIVES: The objectives of this study were to measure the upper airway dimensions and the craniofacial skeletal parameters in patients who tested positive for COVID-19, to compare the upper airway dimensions and the craniofacial skeletal parameters between patients who developed no/mild symptoms and those with moderate-severe COVID-19 symptoms, and to assess any association of the skeletal relationships (anteroposterior (AP) and vertical) and the upper airway dimensions with the severity of COVID-19 symptoms in adult subjects. MATERIAL AND METHODS: A total of 204 orthodontic patients who tested positive for COVID-19 were evaluated. Of these, only 137 met the inclusion criteria. The sample was further subdivided into 2 groups based on the severity of symptoms: cases (moderate-severe symptoms; n = 56); and controls (asymptomatic/ mild symptoms; n = 81). The upper airway dimensions and the skeletal parameters were measured on lateral cephalograms. The nonparametric Mann-Whitney U test was used to detect differences between the cases and the controls. Binary logistic regression analysis was used to evaluate the association between the studied variables and the severity of symptoms. RESULTS: The cases had a reduced lower face height (LFH) and a reduced perpendicular distance from the hyoid bone to the line connecting the anteroinferior limit of the 3rd cervical vertebra (C3) and the retrognathion point (RGN) (HH1) as compared to the controls. Regression analysis revealed a significant association of LFH (p = 0.013), the vertical airway length (VAL) (p = 0.002) and HH1 (p = 0.021) with the severity of COVID-19 symptoms. CONCLUSIONS: The types of malocclusion were similar in the cases and the controls. Patients with reduced LFH and VAL, and a superiorly positioned hyoid bone in relation to the mandible developed more severe COVID-19 symptoms.

Occupational differences in the prevalence and severity of long-COVID: Analysis of the ONS Coronavirus (COVID-19) Infection Survey (preprint)

Objectives: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. Methods: We utilised ONS Coronavirus Infection Survey (CIS) data (February 2021-April 2022) of working-age participants (16-65 years). Exposures were industrial sector, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms; and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR) and prevalence (marginal means) for all exposures. Results: Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations had highest odds ratios for long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, CIs: 1.38-1.52) had substantially elevated odds than average. For almost all exposures, the pattern of odds ratios for long-COVID symptoms followed that for SARS-CoV-2 infections, except for professional occupations (OR<1 for infection; OR>1 for long-COVID). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by a lot ranged from 17.1% (arts, entertainment and recreation) to 22-23% (teaching and education and armed forces ) and to 27% (those not working). Conclusions: The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID.

News Headlines Classification for Disease Outbreak Detection using Modified Term Weighting approach (preprint)

The Covid-19 pandemic exposed weaknesses in healthcare systems in the world and revealed the importance of efficient Bio surveillance systems that can monitor disease outbreaks on a real-time basis. Event-based health surveillance systems are popular due to their ability to utilize health information from internet sources such as digital newspapers and social networking sites for early detection of outbreaks. Studies claim that all deadly outbreaks declared by WHO are first detected through these informal online sources. Unfortunately, existing systems are not providing actionable data for outbreak prevention. Action plans for handling outbreaks can be developed only if regional-specific data is available. The proposed study is intended to detect local or regional level outbreaks happening in the health domain of Kerala, in particular, by automatic extraction and examination of internet media reports covering Kerala news. In this paper, various methods for retrieving outbreak news from news portals are studied and a novel method is proposed for retrieving disease-related news items using ML techniques by implementing various text classification algorithms. Implementation of a modified term weighting approach to augment classification accuracy is a major contribution of the proposed work. Traditional TF-IDF term weighting algorithm do not consider the significance of a term in a particular domain. The Random Forest classifier gave maximum accuracy of 94.48% by the TF-IDF approach which improved to 100% by our modified term weighting scheme where the significance of the term with respect to a particular domain is also considered while determining the weight of the term during vectorization.

What the analysis of causes of death in France in 2020 reveals about the impact of the Covid-19 epidemic (preprint)

Context: In 2020, the French population lived under the threat of the Covid-19 epidemic, which would allegedly cause an exceptional excess mortality rate. Mortality data by cause of death for the year 2020 are now available. These data allow us to quantify and qualify the impact of the epidemic. This analysis presents the evolution of the main mortality indicators by cause and by age group. It is intended to demonstrate how 2020 was an exceptional year. Materials and methods: In France, causes of death are labeled according to the International Classification of Diseases (ICD-10) based on medical death certificates. The study focuses on aggregated data for the year 2020 as well as complete data from 1979 to 2017 available online. To estimate excess mortality by cause of death, mortality data were standardized to 2020 to take into account changes in the age structure of the population. Results: The year 2020 is marked, on the one hand, by the introduction of "Covid-19" as a cause of death, which accounts for 10.4% of deaths; and, on the other hand, by a strong downward trend in most other causes of death. Discussion: In 2020, we show that the overall number of additional deaths due to the Covid-19 epidemic is paradoxically lower than the number of deaths caused by this disease. According to official sources, the number of Covid-19-labeled deaths is nearly 50% higher than the number of additional deaths. Besides excess mortality in 2020 being modest compared to other years that saw health events and it affecting only individuals over 65 years of age, what could explain that this disease has caused more deaths than additional deaths? This analysis shows that the emergency implementation of the "Covid-19" classification led to many biases. A significant number of deaths, usually labeled for other major causes (e.g., neoplasm, circulatory system diseases) were, in a way, transferred to this new label. This analysis of mortality by cause of death provides quantitative answers to the overestimation of the impact of the Covid-19 epidemic in France.

CHARACTERISTICS OF ACUTE PSYCHIATRIC DISORDERS IN PATIENTS WITH COVID-19 IN A THIRD-LEVEL HOSPITAL IN PERU (preprint)

Objective: To describe the sociodemographic and clinical characteristics of acute psychiatric disorders in COVID-19 patients in an emergency department at a national reference psychiatry and mental health hospital. Methods: A descriptive observational study was performed. Data were collected from medical records of patients admitted by emergency according to the International Classification of Diseases (ICD-11). The group of patients with a first acute psychiatric episode vs. patients with more than one acute psychiatric episode were compared. Results: 110 patients were included; 61.8% corresponded to the female sex and the mean age was 36 years. 49.1% corresponded to schizophrenia, followed by acute polymorphic psychotic disorder (13.6%), bipolar disorder (10%), and depressive episodes (7.3%). Psychotic disorders and depressive episodes occurred in a higher percentage in the group with a first episode, 42.4% (p< 0.001), and 15.2% (p< 0.001), respectively. The episodes of schizophrenia were higher in the group of patients with previous episodes (63.6%). Conclusions: A higher frequency of cases of acute psychotic disorder and depressive disorders was found as the first episode in patients with COVID-19 infection; however, within the group with previous episodes, greater predominance of patients with acute disorders due to schizophrenia was found.

Characterizing the use of the ICD-10 Code for Long COVID in 3 US Healthcare Systems (preprint)

The International Classification of Diseases (ICD)-10 code (U09.9) for post-acute sequelae of COVID-19 (PASC) was introduced in October of 2021. As researchers seek to leverage this billing code for research purposes in large scale real-world studies of PASC, it is of utmost importance to understand the functional use of the code by healthcare providers and the clinical characteristics of patients who have been assigned this code. To this end, we operationalized clinical case definitions of PASC using World Health Organization and Centers for Disease Control guidelines. We then chart reviewed 300 patients with COVID-19 from three participating healthcare systems of the 4CE Consortium who were assigned the U09.9 code. Chart review results showed the average positive predictive value (PPV) of the U09.9 code ranged from 40.2% to 65.4% depending on which definition of PASC was used in the evaluation. The PPV of the U09.9 code also fluctuated significantly between calendar time periods. We demonstrated the potential utility of textual data extracted from natural language processing techniques to more comprehensively capture symptoms associated with PASC from electronic health records data. Finally, we investigated the utilization of long COVID clinics in the cohort of patients. We observed that only an average of 24.0% of patients with the U09.9 code visited a long COVID clinic. Among patients who met the WHO PASC definition, only an average of 35.6% visited a long COVID clinic.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Artificial Intelligence for Automatic Detection and Classification Disease on the X-Ray Images (preprint)

Detecting and classifying diseases using X-ray images is one of the more challenging core tasks in the medical and research world. Due to the recent high interest in radiological images and AI, early detection of diseases in X-ray images has become notably more essential to prevent further spreading and flatten the curve. Innovations and revolutions of Computer Vision with Deep learning methods offer great promise for fast and accurate diagnosis of screening and detection from chest X-ray images (CXR). This work presents rapid detection of diseases in the lung using the efficient Deep learning pre-trained RepVGG algorithm for deep feature extraction and classification. We used X-ray images as an example to show the model's efficiency. To perform this task, we classify X-Ray images into Covid-19, Pneumonia, and Normal X-Ray images. Employ ROI object to improve the detection accuracy for lung extraction, followed by data pre-processing and augmentation. We are applying Artificial Intelligence technology for automatic highlighted detection of affected areas of people's lungs. Based on the X-Ray images, an algorithm was developed that classifies X-Ray images with height accuracy and power faster thanks to the architecture transformation of the model. We compared deep learning frameworks' accuracy and detection of disease. The study shows the high power of deep learning methods for X-ray images based on COVID-19 detection utilizing chest X-rays. The proposed framework offers better diagnostic accuracy by comparing popular deep learning models, i.e., VGG, ResNet50, inceptionV3, DenseNet, and InceptionResnetV2.

Pneumonia Classification: A limited data approach for global understanding (preprint)

With the evolution of the human race, the associated diseases have also evolved. Pneumonia treated as the simple flu and allergy in the early stages of its inception is now threatening to humankind in various shapes like SARs and Covid. The advanced disease requires equal treatments and diagnosis. Our research tried to find and classify pneumonia inflammation within chest x-rays (CXR) with very limited datasets and has attempted to ensure a global perspective, i.e. one that addresses all possible inflammation regions within the CXR. In addition to having medical grade classification outputs in terms of accuracy and recall, we have also guaranteed to meet the medical requirements of classification justification with the help of modified class activation maps (mCAM). The training of a model having a global perspective understanding is carried out with the help of capsules network cluster (CsNC), which enables us to learn various geometrical, orientation, and positional views of the inflammation within the CXR. Our 16-capsules cluster helped understand different views easily within the same CXR without going through any image augmentation, as generally required by current detection models, thus reducing the overall training and evaluation time. We performed extensive experiments on the RSNA pneumonia dataset of CXR images using a set of evaluation metrics. We have been able to acquire up to 98.3% accuracy with a 99.5% recall during our final trials. We tested our final trained model over generic x-ray images acquired from clinics and found promising results over that.

Out-of-hospital Cardiac Arrest Before and During the COVID-19 Pandemic in the South Bronx (preprint)

Background: Out-of-hospital cardiac arrest (OHCA) is a major health challenge; the impact of the COVID-19 pandemic on OHCA in the South Bronx is unknown. The aim of this study was to determine differences between return of spontaneous circulation(ROSC), witnessed arrest, bystander CPR and survival to discharge, prior to and during the COVID-19 pandemic to improve ROSC and survival. Methods: Single-center retrospective study of non-traumatic OHCA adult patients admitted to Lincoln Medical Center between 8/2019 to 6/2021, 3/2020 being the first established date of the COVID-19 pandemic in New York City. International Classification of Diseases (ICD) coding was used to identify cardiac arrests and collect information. Statistical analysis was performed using IBM-SPSS. Results: ROSC time pre COVID-19 was 26 minutes, during the COVID-19 pandemic it was 25 minutes 54 seconds. A significant difference in witnessed arrests in the pre COVID-19 period compared to the COVID-19 period (86% vs 55% p = 0.03). Bystander CPR occurred 36% of the time in the pre COVID-19 period contrasting to 19% during. Prior to the COVID-19 pandemic the overall survival to discharge in OHCA ROSC cases was 28.5% comparing to 29% during the pandemic. ROSC was 18 minutes among survivors during the pandemic, compared to 21 minutes in survivors prior to COVID (p = 0.2). Conclusion: There was a non-significant difference in ROSC, bystander CPR and survival to discharge in non-traumatic OHCA prior to and during the COVID-19 pandemic in the South Bronx. There was a significant difference in witnessed vs unwitnessed OHCA prior to and during the COVID-19 pandemic.

Did the coronavirus disease 2019 pandemic affect orthodontic treatment outcomes? A clinical evaluation using the objective grading system and Peer Assessment Rating index.

INTRODUCTION: This study aimed to compare the treatment outcome of patients whose orthodontic treatment was completed before or during the coronavirus disease 2019 pandemic. METHODS: Pretreatment and posttreatment models of 100 patients treated with fixed orthodontic appliances were evaluated using the peer assessment rating (PAR) index. Posttreatment models and panoramic radiographs were measured and scored with the objective grading system (OGS). All patients had their treatment plans before the coronavirus disease 2019 pandemic, but the orthodontic treatment finishing date was before (prepandemic: group 1, n = 50) or during (pandemic: group 2, n = 50) the pandemic. Intergroup comparisons were tested with an independent samples t test or Mann-Whitney U test. Chi-square test statistics and Fisher exact test were used to compare categorical variables. RESULTS: Significant higher posttreatment weighted maxillary alignment score was found in group 2. However, no significant difference was found between the groups concerning the mean total weighted PAR reduction (29.8 ± 9.9 vs 25.6 ± 8.7) and posttreatment total weighted PAR scores (1 vs 2). The PAR index score improvement (%) was similar between the groups (93.7 ± 7.1 vs 89.9 ± 13.0). No statistically significant difference was found between the groups for the total OGS score (32 vs 33). A lower score for marginal ridge height (4 vs 3) and a higher score for buccolingual inclination (7 vs 11), and a lower score for occlusal relationship (3 vs 1) were found in group 2. Canceled appointments (1.1 ± 0.7 vs 4.8 ± 1.6) and the number of missed appointments (0.6 ± 0.5 vs 1.1 ± 0.8) were statistically higher in group 2, whereas the total number of appointments (27.3 ± 8.8 vs 21.8 ± 5.4) were statistically less. The treatment duration was comparable in both groups. CONCLUSIONS: Reduced and irregular appointments during the pandemic resulted in significantly higher posttreatment weighted maxillary alignment and worsening of the buccolingual inclination. However, the PAR score improvement, total OGS score, and treatment duration were not affected.

Methadone overdoses increased 48% during the COVID-19 epidemic (preprint)

Background: The United States (US) is in the middle of an opioid overdose epidemic that has spanned over two decades and continues to escalate. Methadone is long-acting full opioid agonist which has been approved to treat opioid use disorder (OUD). Methadone can cause respiratory depression that may result in mortality. The restrictions on methadone availability including take-home dosing were loosened during the COVID-19 pandemic although there have been concerns about the high street value of diverted methadone. This report examined how fatal overdoses involving methadone have changed over the past two-decades including during the pandemic. Methods: The Center for Disease Control and Preventions Wide-ranging Online Data for Epidemiologic Research (WONDER) database, which draws data from death certificates, was used to find the unintentional methadone related overdose death rate from 1999-2020. Unintentional methadone deaths were defined using the International Statistical Classification of Diseases (ICD), 10th revision codes: X40-44 with only data which was coded for methadone (T40.3). Data from the Automation of Reports and Consolidated Orders System (ARCOS) on methadone overall use, narcotic treatment programs use, and pain management use was gathered for all states, including the District of Columbia, for 2020 and corrected for population. Results: There have been dynamic changes over the past two-decades in overdoses involving methadone. Overdoses increased from 1999 (0.9/million) to 2007 (15.9) and declined until 2019 (6.5). Overdoses in 2020 (9.6) were 48.1% higher than in 2019 (t(50) = 3.05, p < .005). The correlations between overall methadone use (r(49) = +0.75, p < 0.001), and narcotic treatment program use (r(49) = +0.77, p < 0.001) were positive, strong, and statistically significant. However, methadone use for pain treatment was not associated with overdoses (r(49) = -.08, p = .57). Conclusions: Overdoses involving methadone significantly increased by 48.1% in 2020 relative to 2019. This mortality increase is much larger than the 5.3% elevation in calls involving methadone reported to poison control centers in the year following the March 16, 2020 loosening of methadone take-home regulations. Policy changes that were implemented following the COVID-19 pandemic involving methadone may warrant reconsideration.

A Patient-Focused Questionnaire Designed to Support Headache Diagnosis in General Practice (preprint)

Background: There is an unmet need for the correct diagnosis of primary headache disorders, such as migraine, in primary care. Misdiagnosis is associated with suboptimal management of patients, and it is now widely accepted internationally that better diagnostic support is needed for general practitioners (GPs). In this study, we describe the development of a short, patient-directed questionnaire and supporting documents that aim to help with the diagnosis of headache disorders in primary care. We have also prepared patient feedback material and collected preliminary input from patients, but the main aim of this report is to invite comment and debate on the use of the questionnaire in real-life clinical practice. Methods: : This questionnaire was developed over 18 months using the clinical experience of the authors, current literature review and the International Classification of Headache Disorders (ICHD), 3 rd edition, for migraine, tension-type headache, cluster headache and medication-overuse headache. The questionnaire and two supporting documents will hopefully assist the GP to make a correct diagnosis. A patient survey was used to gather feedback from a small number of patients, and based on these comments, the questionnaire and the supporting documents were modified and updated. Results: : Feedback gathering was attempted in Austria, Germany, Switzerland, Ireland and Spain, but was only possible in Austria due to the COVID-19 pandemic restrictions. From the 18 patients who participated, 17 responded about how easy or difficult the questionnaire was to complete, with 14/17 (82%) being able to complete the questionnaire easily on their own. Overall, the patients found the questionnaire averagely helpful in reminding them of and communicating their headache triggers, symptoms and behaviour changes; on a scale of 1–5, with 1 being very helpful and 5 being not helpful at all, the mean scores were 2.8 and 2.7 (n=18), respectively. Conclusions: : This questionnaire and associated documents were developed with a view to helping GPs to make an accurate headache diagnosis quickly in primary care. Following feedback from patients, updates have been made, including changes to reduce the time it takes to complete the questionnaire. Next steps include wider validation and feedback from primary care physicians.

Risk of Severe COVID-19 Outcomes Among Patients with Rheumatoid Arthritis in the United States (preprint)

Introduction Coronavirus disease 2019 (COVID-19) has infected over 22 million individuals worldwide. It remains unclear whether patients with COVID-19 and Rheumatoid Arthritis (RA) experience worse clinical outcomes compared to similar patients with COVID-19 without RA. Objective The aim of this study is to provide insights on how COVID-19 impacted patients with RA given the nature of the disease and medication used. Methods RA cases were identified via International Classification of Diseases (ICD) codes and COVID-19 cases by laboratory results in the U.S. based TriNetX network. Patients with COVID-19 and RA were propensity-score matched based on demographics with patients with COVID-19 without RA at a 1:3 ratio. A hospitalized sub-population was defined by procedure codes. Results We identified 1,014 COVID-19 patients with RA and 3,042 non-RA matches selected from 137,757 patients. The odds of hospitalization (non-RA:23%, RA:24.6%, OR:1.08, 95% CI: 0.88 to 1.33) or mortality (non-RA:5.4%, RA:6%, OR:0.93, 95% CI: 0.65 to 1.34) were not significantly different. The hospitalized sub-population included 249 patients with COVID-19 and RA and 745 non-RA matches selected from 21,435 patients. The risk of intensive care unit (ICU) admission (non-RA:18.8%, RA:18.1%, OR:0.94, 95% CI: 0.60 to 1.45), and inpatient mortality (non-RA:14.4%, RA:14.5%, OR:0.86, 95% CI: 0.53 to 1.40) were not significantly different. Conclusion We did not find evidence suggesting patients with COVID-19 and RA are more likely to have severe outcomes than patients with COVID-19 without RA.

Injury investigation and emergency medical care experience in mass gatherings:A case study of the 13th National Games of China. (preprint)

Objectives: To acquire the disease rules and characters of the athletes, analyze the influencing factors of traumatic injury. Methods: Five hundred ten valid cases from July 2017 to September 2017 were classified according to the International Classification of Diseases (ICD-10); a binary logistic regression model was performed to analyse the influencing factors of traumatic injury. Results: Among the 14 disease classifications, traumatic injuries accounted for the largest number, with significant differences by sex, competition venue, and competition event (P<0.05). Sex and competition events were risk factors for traumatic injury (OR>1). In particular, athletes participating in ball games were more likely to experience traumatic injury than those participating in nonball games. Conclusions: It is necessary to implement differentiated allocation of medical resources for different competition events, achieve a balance between supply and demand; take intervention measures to reduce risk factors. This can be a basis for subsequent mass gatherings in the context of the COVID-19 epidemic and disease prevention through establishing an emergency medical security system.

Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19 (preprint)

OBJECTIVE: Neurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations. METHODS: Using electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity. RESULTS: Among the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%-7.8%, pFDR

Essential Attributes of Clear Aligner Therapy in terms of Appliance Configuration, Hygiene, and Pain Levels during the Pandemic: A Brief Review.

Fixed orthodontic treatment has been compromised at many levels during the pandemic period, as clinics underwent a prolonged lockdown and patients could not be treated regularly. With the end of the pandemic nowhere in sight, may be it is time to put newer tools, such as clear aligner therapy, for better use. Fixed orthodontic appliances by nature are not always self-limiting, which, if left unmonitored over a long period may cause undesirable side effects, pain, and discomfort. The undesired tooth movements that may occur with arch wire-guided mechanics in addition to problems with cut wires or removed brackets may be minimized with the use of aligners. While the benefits of using aligners are for all to see, they do require extensive planning and careful evaluation of the progress. This article reviews the advantages of using aligners during the pandemic period and how it can be beneficial in helping orthodontists resume their practice.