Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil.

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Antecedent and persistent symptoms in COVID-19 and other respiratory illnesses: Insights from prospectively collected data in the BRACE trial.

BACKGROUND: Some individuals have a persistence of symptoms following both COVID-19 (post-acute COVID-19 syndrome; PACS) and other viral infections. This study used prospectively collected data from an international trial to compare symptoms following COVID-19 and non-COVID-19 respiratory illness, to identify factors associated with the risk of PACS, and to explore symptom patterns before and after COVID-19 and non-COVID-19 respiratory illnesses. METHODS: Data from a multicentre randomised controlled trial (BRACE trial) involving healthcare workers across four countries were analysed. Symptom data were prospectively collected over 12 months, allowing detailed characterisation of symptom patterns. Participants with COVID-19 and non-COVID-19 respiratory illness episodes were compared, focussing on symptom severity, duration (including PACS using NICE and WHO definitions), and pre-existing symptoms. FINDINGS: Compared to those with a non-COVID-19 illness, participants with COVID-19 had significantly more severe illness (OR 7·4, 95%CI 5·6-9·7). Symptom duration meeting PACS definitions occurred in a higher proportion of COVID-19 cases than non-COVID-19 respiratory controls using both the NICE definition (2·5% vs 0·5%, OR 6·6, 95%CI 2·4-18·3) and the WHO definition (8·8% vs 3·7%, OR 2·5, 95%CI 1·4-4·3). When considering only participants with COVID-19, age 40-59 years (aOR 2·8, 95%CI 1·3-6·2), chronic respiratory disease (aOR 5·5, 95%CI 1·3-23·1), and pre-existing symptoms (aOR 3·0, 95%CI 1·4-6·3) were associated with an increased risk of developing PACS. Symptoms associated with PACS were also reported by participants in the months preceding their COVID-19 or non-COVID-19 respiratory illnesses (32% fatigue and muscle ache, 11% intermittent cough and shortness of breath). INTERPRETATION: Healthcare workers with COVID-19 were more likely to have severe and longer-lasting symptoms than those with a non-COVID-19 respiratory illness, with a higher proportion meeting the WHO or NICE definitions of PACS. Age, chronic respiratory disease, and pre-existing symptoms increased the risk of developing PACS following COVID-19.

Symptom patterns and life with post-acute COVID-19 in children aged 8-17 years: a mixed-methods study protocol.

BACKGROUND: While there is a substantial body of knowledge about acute COVID-19, less is known about long-COVID, where symptoms continue beyond 4 weeks. AIM: To describe longer-term effects of COVID-19 infection in children and young people (CYP) and identify their needs in relation to long-COVID. DESIGN & SETTING: This study comprises an observational prospective cohort study and a linked qualitative study, identifying participants aged 8-17 years in the West Midlands of England. METHOD: CYP will be invited to complete online questionnaires to monitor incidences and symptoms of COVID-19 over a 12-month period. CYP who have experienced long-term effects of COVID will be invited to interview, and those currently experiencing symptoms will be asked to document their experiences in a diary. Professionals who work with CYP will be invited to explore the impact of long-COVID on the wider experiences of CYP, in a focus group. Descriptive statistics will be used to describe the incidence and rates of resolution of symptoms, and comparisons will be made between exposed and non-exposed groups. Logistic regression models will be used to estimate associations between candidate predictors and the development of long-COVID, and linear regression will be used to estimate associations between candidate predictors. Qualitative data will be analysed thematically using the constant comparison method. CONCLUSION: This study will describe features and symptoms of long-COVID and explore the impact of long-COVID within the lives of CYP and their families, to provide better understanding of long-COVID and inform clinical practice.

Characterising patterns of COVID-19 and long COVID symptoms: evidence from nine UK longitudinal studies.

Multiple studies across global populations have established the primary symptoms characterising Coronavirus Disease 2019 (COVID-19) and long COVID. However, as symptoms may also occur in the absence of COVID-19, a lack of appropriate controls has often meant that specificity of symptoms to acute COVID-19 or long COVID, and the extent and length of time for which they are elevated after COVID-19, could not be examined. We analysed individual symptom prevalences and characterised patterns of COVID-19 and long COVID symptoms across nine UK longitudinal studies, totalling over 42,000 participants. Conducting latent class analyses separately in three groups ('no COVID-19', 'COVID-19 in last 12 weeks', 'COVID-19 > 12 weeks ago'), the data did not support the presence of more than two distinct symptom patterns, representing high and low symptom burden, in each group. Comparing the high symptom burden classes between the 'COVID-19 in last 12 weeks' and 'no COVID-19' groups we identified symptoms characteristic of acute COVID-19, including loss of taste and smell, fatigue, cough, shortness of breath and muscle pains or aches. Comparing the high symptom burden classes between the 'COVID-19 > 12 weeks ago' and 'no COVID-19' groups we identified symptoms characteristic of long COVID, including fatigue, shortness of breath, muscle pain or aches, difficulty concentrating and chest tightness. The identified symptom patterns among individuals with COVID-19 > 12 weeks ago were strongly associated with self-reported length of time unable to function as normal due to COVID-19 symptoms, suggesting that the symptom pattern identified corresponds to long COVID. Building the evidence base regarding typical long COVID symptoms will improve diagnosis of this condition and the ability to elicit underlying biological mechanisms, leading to better patient access to treatment and services.

Symptom Patterns of the Occurrence of Depression and Anxiety in a Japanese General Adult Population Sample: A Latent Class Analysis.

BACKGROUND: Given the high comorbidity and shared risk factors between depression and anxiety, whether they represent theoretically distinct disease entities or are just characteristics of a common negative affect dimension remains debated. Employing a data-driven and person-centered approach, the present study aims to identify meaningful and discrete symptom patterns of the occurrence of depression and anxiety. METHODS: Using data from an adult sample from the Japanese general population (n = 403, including 184 females, age = 42.28 ± 11.87 years), we applied latent class analysis to identify distinct symptom patterns of depression (PHQ-9) and anxiety (STAI Y1). To empirically validate the derived class memberships, we tested the association between the derived classes and personal profiles including childhood experiences, life events, and personality traits. RESULTS: The best-fitting solution had four distinct symptom patterns or classes. Whereas both Class 1 and 2 had high depression, Class 1 showed high anxiety due to high anxiety-present symptoms (e.g., "I feel nervous") while Class 2 showed moderate anxiety due to few anxiety-absent symptoms (e.g., "I feel calm"). Class 3 manifested mild anxiety symptoms due to lacking responses on anxiety-absent items. Class 4 manifested the least depressive and anxiety-present symptoms as well as the most anxiety-absent symptoms. Importantly, whereas both Class 1 and 2 had higher childhood neglect and reduced reward responsiveness, etc. compared to Class 4 (i.e., the most healthy class), only Class 1 had greater negative affect and reported more negative life events. CONCLUSIONS: To our knowledge, this is the first latent class analysis that examined the symptom patterns of depression and anxiety in Asian subjects. The classes we identified have distinct features that confirm their unique patterns of symptom endorsement. Our findings may provide insights into the etiology of depression, anxiety, and their comorbidity.

Gaming and Social Media Addiction in University Students: Sex Differences, Suitability of Symptoms, and Association With Psychosocial Difficulties.

Background: Previous research has shown that addictions to digital media can have negative impact on psychosocial health. Although Internet Gaming Disorder (IGD) has received most scholarly recognition, the potential negative consequences of Social Media Disorder (SMD) have also been found. However, few studies have assessed the symptoms of these two digital media addictions in the same way, making comparisons difficult. The present study aims to fill this gap by investigating differences and similarities regarding how common the symptoms are, sex differences, the suitability of the symptoms, and their association with psychosocial difficulties. Method: A total of 688 university students (63.2% women, Mean age = 25.98) completed a questionnaire measuring symptoms of IGD and SMD, as well as psychosocial difficulties (i.e., psychosomatic symptoms, low self-concept, and social problems). Results: Results showed that 1.2% of the men and 0.9% of the women met the symptom criteria for IGD (non-significant difference), whereas 3.2% men and 2.8% women met the symptom criteria for SMD (non-significant difference). Dimensional analyses indicated that men had higher IGD scores compared to women, whereas the opposite was found for SMD. Symptoms of heavy involvement in digital media (i.e., Preoccupation, Tolerance, Withdrawal, Unsuccessful attempts to control, and Escape) had high sensitivity, but low positive predictive value (PPV). However, symptoms associated with negative consequences of digital media use (i.e., Loss of interest, Continued excessive use, Deception, and Jeopardizing career/relationships) had low sensitivity, but high PPV. These symptom patterns were similar for IGD and SMD. Meeting the criteria for IGD or SMD as well as being at risk of these disorders were significantly associated with psychosocial difficulties. Symptoms of SMD generally had stronger associations with psychosomatic symptoms compared to symptoms of IGD. Conclusions: We conclude that heavy involvement in digital media seems common among individuals with IGD or SMD, but also among those not meeting the symptom criteria, whereas negative consequences are less common but highly predictive of digital media addictions once present. Further attention to SMD is warranted, as it seems more common than IGD and also seems to be equally or more strongly associated with psychosocial difficulties.

Structure and clinical correlates of obsessive-compulsive symptoms in a large sample of children and adolescents: a factor analytic study across five nations.

The underlying structure of obsessive-compulsive disorder (OCD) remains to be confirmed in child and adolescent populations. In this paper we report the first factor analytic study of individual OCD items from Children's Yale-Brown Obsessive Compulsive Scale (CY-BOCS). OCD symptoms were assessed using the CY-BOCS symptom checklist in a sample of 854 patients with OCD (7-18 years of age) recruited from clinics in five countries. Pooled data were subjected to exploratory and confirmatory factor analysis (CFA) to identify the optimal factor structure. Various models were tested for age and gender subgroups. Also, the invariance of the solution across age and gender was tested and associations with demographic and clinical factors were explored. A three-factor model provided the best-fit solution. It consisted of the following factors: (1) harm/sexual, (2) symmetry/hoarding, (3) contamination/cleaning. The factor structure was invariant for age and gender across subgroups. Factor one was significantly correlated with anxiety, and factor two with depression and anxiety. Factor three was negatively correlated with tic disorder and attention-deficit/hyperactivity disorder (ADHD). Females had higher scores on factor two than males. The OCD symptom structure in children and adolescents is consistent across age and gender and similar to results from recent child and adolescents although hoarding may not be a separate factor. Our three-factor structure is almost identical to that seen in early studies on adults. Common mental disorders had specific patterns of associations with the different factors.

Navigating the Transition From Cancer Care to Primary Care: Assistance of a Survivorship Care Plan.

PURPOSE/OBJECTIVES: To examine symptom and quality-of-life (QOL) trajectories in breast cancer and lymphoma survivors enrolled in a survivorship navigation intervention and to explore patient, caregiver, and primary care provider (PCP) satisfaction with receipt of a survivorship care plan (SCP). 
. DESIGN: Prospective, cohort, longitudinal.
. SETTING: The Billings Clinic, an integrated cancer center in Montana. 
. SAMPLE: 67 patients with breast cancer or lymphoma who recently completed cancer treatment, along with 39 of their caregivers and 23 PCPs. 
. METHODS: Data collection at one, three, and six months by the Functional Assessment of Cancer Therapy-General and satisfaction surveys.
. MAIN RESEARCH VARIABLES: Symptoms, QOL, and satisfaction with the survivorship navigator and the SCP.
. FINDINGS: Symptoms persisted six months following treatment. Symptoms and QOL indicators with worst intensity were energy, sleep, coping, and satisfaction with sex life. Patients with more comorbidities reported worse QOL, telephoned the survivorship navigator more often, and were more satisfied with the SCP. Patients with lymphoma reported higher QOL, but it was not significantly different from patients with breast cancer. Patients were significantly more satisfied than caregivers with the SCP at time 1. PCPs were highly satisfied with the SCP.
. CONCLUSIONS: Some symptoms persist, even when cancer treatment has ended. Patients with comorbidities are at higher risk for more severe symptoms and worse QOL and may benefit from ongoing support. SCPs can facilitate patients' transition to primary care following cancer treatment. 
. IMPLICATIONS FOR NURSING: Healthcare professionals who care for breast cancer survivors need to routinely assess them for the presence of comorbid conditions. Obese breast cancer survivors may benefit from weight reduction interventions to possibly decrease their risk of developing lymphedema and improve their overall health status.

Somatic symptom reports in the general population: Application of a bi-factor model to the analysis of change.

OBJECTIVE: To investigate the latent structure of somatic symptom reports in the general population with a bi-factor model and apply the structure to the analysis of change in reported symptoms after the emergence of an uncertain environmental health risk. METHODS: Somatic symptoms were assessed in two general population environmental health cohorts (AMIGO, n=14,829 & POWER, n=951) using the somatization scale of the four-dimensional symptom questionnaire (4DSQ-S). Exploratory bi-factor analysis was used to determine the factor structure in the AMIGO cohort. Multi-group and longitudinal models were applied to assess measurement invariance. For a subsample of residents living close to a newly introduced power line (n=224), we compared a uni- and multidimensional method for the analysis of change in reported symptoms after the power line was put into operation. RESULTS: We found a good fit (RMSEA=0.03, CFI=0.98) for a bi-factor model with one general and three symptom specific factors (musculoskeletal, gastrointestinal, cardiopulmonary). The latent structure was found to be invariant between cohorts and over time. A significant increase (p<.05) was found only for musculoskeletal and gastrointestinal symptoms after the power line was put into operation. CONCLUSIONS: In our study we found that a bi-factor structure of somatic symptoms reports was equivalent between cohorts and over time. Our findings suggest that taking this structure into account can lead to a more informative interpretation of a change in symptom reports compared to a unidimensional approach.

Trajectories of Evening Fatigue in Oncology Outpatients Receiving Chemotherapy.

CONTEXT: Fatigue is a distressing persistent sense of physical tiredness that is not proportional to a person's recent activity. Fatigue impacts patients' treatment decisions and can limit their self-care activities. Although significant interindividual variability in fatigue severity has been noted, little is known about predictors of interindividual variability in initial levels and trajectories of evening fatigue severity in oncology patients receiving chemotherapy. OBJECTIVES: To determine whether demographic, clinical, and symptom characteristics were associated with initial levels and the trajectories of evening fatigue. METHODS: A sample of outpatients with breast, gastrointestinal, gynecological, and lung cancer (N = 586) completed demographic and symptom questionnaires a total of six times over two cycles of chemotherapy. Fatigue severity was evaluated using the Lee Fatigue Scale. Hierarchical linear modeling was used to answer the study objectives. RESULTS: A large amount of interindividual variability was found in the evening fatigue trajectories. A piecewise model fit the data best. Patients who were white, diagnosed with breast, gynecological, or lung cancer, and who had more years of education, childcare responsibilities, lower functional status, and higher levels of sleep disturbance and depression reported higher levels of evening fatigue at enrollment. CONCLUSION: This study identified both nonmodifiable (e.g., ethnicity) and modifiable (e.g., childcare responsibilities, depressive symptoms, sleep disturbance) risk factors for more severe evening fatigue. Using this information, clinicians can identify patients at higher risk for more severe evening fatigue, provide individualized patient education, and tailor interventions to address the modifiable risk factors.

Specific and unspecific gynecological alarm symptoms--prevalence estimates in different age groups: a population-based study.

OBJECTIVE: To determine prevalence estimates of gynecological alarm symptoms in different age groups and to describe common patterns of gynecological symptoms. DESIGN: Web-based cross-sectional survey study. SETTING: Nationwide in Denmark. POPULATION: A random sample of 51,090 women aged 20 years or above from the general population. METHODS: An internet-based questionnaire study regarding the prevalence estimates of symptom experiences. A total of 18 symptoms of cervical, endometrial and ovarian cancer were selected through an extensive literature search, which included national and international guidelines. MAIN OUTCOME MEASURES: Prevalence estimates of self-reported experience of gynecological alarm symptoms within the preceding 4 weeks. RESULTS: A total of 26,466 women (54.5%) participated in the study. Some 80.3% had experienced at least one of the alarm symptoms within the preceding 4 weeks, and the median number of experienced symptoms was 2 (interquartile range 1-4). The most common symptoms were tiredness (53.0%) and abdominal bloating (36.7%); postmenopausal bleeding (2.3%) and involuntary weight loss (2.8%) were least frequent. Most of the symptoms were more prevalent among younger women, whereas only dyspnea and increased urgency of urination were more frequent among older women. Among younger women, multiple abdominal symptoms often occurred simultaneously and frequently in combination with pelvic pain, whereas older women were more likely to report single symptoms. CONCLUSIONS: Gynecological alarm symptoms are frequent in the general population, mostly among younger women. Older women reported fewer symptoms, and these often appeared as single symptoms.