Ambient Air Pollution and Dysanapsis: Associations with Lung Function and Chronic Obstructive Pulmonary Disease in the Canadian Cohort Obstructive Lung Disease Study

Rationale: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known about how airway abnormalities and lung growth might modify this relationship. Objectives: To evaluate the associations of ambient air pollution exposure with lung function and COPD and examine possible interactions with dysanapsis. Methods: We made use of cross-sectional postbronchodilator spirometry data from 1,452 individuals enrolled in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study with linked ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution estimates. Dysanapsis, or the ratio of the airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions. Measurements and Main Results: In adjusted models, 101.7 ml (95% confidence interval [CI], -166.2 to -37.2) and 115.0 ml (95% CI, -196.5 to -33.4) lower FEV1 were demonstrated per increase of 2.4 ug/m3 PM2.5 and 9.2 ppb NO2, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modeling the airway-to-lung ratio as a continuous variable. However, a 109.8 ml (95% CI, -209.0 to -10.5] lower FEV1 and an 87% (95% CI, 12% to 213%) higher odds of COPD were observed among individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m3 increment of PM2.5. Conclusions: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis. Keywords: air quality; chronic airflow obstruction; chronic obstructive pulmonary disease; computed tomography; pulmonary function test

Social media and internet search data to inform drug utilization: A systematic scoping review

INTRODUCTION Drug utilization is currently assessed through traditional data sources such as big electronic medical records (EMRs) databases, surveys, and medication sales. Social media and internet data have been reported to provide more accessible and more timely access to medications' utilization. OBJECTIVE This review aims at providing evidence comparing web data on drug utilization to other sources before the COVID-19 pandemic. METHODS We searched Medline, EMBASE, Web of Science, and Scopus until November 25th, 2019, using a predefined search strategy. Two independent reviewers conducted screening and data extraction. RESULTS Of 6,563 (64%) deduplicated publications retrieved, 14 (0.2%) were included. All studies showed positive associations between drug utilization information from web and comparison data using very different methods. A total of nine (64%) studies found positive linear correlations in drug utilization between web and comparison data. Five studies reported association using other methods: One study reported similar drug popularity rankings using both data sources. Two studies developed prediction models for future drug consumption, including both web and comparison data, and two studies conducted ecological analyses but did not quantitatively compare data sources. According to the STROBE, RECORD, and RECORD-PE checklists, overall reporting quality was mediocre. Many items were left blank as they were out of scope for the type of study investigated. CONCLUSION Our results demonstrate the potential of web data for assessing drug utilization, although the field is still in a nascent period of investigation. Ultimately, social media and internet search data could be used to get a quick preliminary quantification of drug use in real time. Additional studies on the topic should use more standardized methodologies on different sets of drugs in order to confirm these findings. In addition, currently available checklists for study quality of reporting would need to be adapted to these new sources of scientific information

SARS-CoV-2 Quarantine Mandated by Contact Tracing: Burden and Infection Rate Among Close Contacts in Zurich, Switzerland, 2020-2021

OBJECTIVES: Before vaccines and effective treatments were available, quarantine of close contacts was important to limit the spread of SARS-CoV-2. To evaluate potential benefits and harms of quarantine, we aimed to estimate infection rates and describe experiences and mental health among persons in mandated quarantine during the early SARS-CoV-2 pandemic. METHODS: We invited adults in mandated quarantine after an exposure to SARS-CoV-2 identified through contact tracing of the Canton of Zurich, Switzerland, between August 2020 and January 2021. Participants completed two questionnaires and received up to two SARS-CoV-2 polymerase chain reaction tests, during and at the end of quarantine. RESULTS: Among 395 participants, quarantine duration ranged from 2 to 20 days. By day 11 since the last contact, 11.1% [95% CI 8.4%-14.7%] were infected with SARS-CoV-2. The proportion of participants with symptoms of depression doubled from 9.3% before quarantine to 18.9% during quarantine, and 12.1% reported quarantine was very or extremely difficult. CONCLUSION: Although quarantine was only moderately burdensome for most participants, some experienced significant difficulties and burden. Policymakers need to balance infection control with potential harms placed on individuals

Joys or Sorrows of Parenting During the COVID-19 Lockdown: A Scoping Review

Objectives: The aim of this scoping review was to map out the existing evidence of the impact of the COVID-19 lockdown on parents of children and adolescents. We sought to: 1) identify parenting domains that were particularly affected by lockdown measures, 2) describe the challenges and opportunities of lockdown measures in these domains, and 3) define protective and exacerbating factors modulating the effect of lockdown measures on parents. Methods: We identified five main domains investigated in the context of parenting during the early COVID-19 lockdown derived from 84 studies: health and wellbeing, parental role, couple functioning, family and social relationships, and paid and unpaid work. For each domain, we listed challenges and opportunities, as well as discriminant factors. Results: The lockdown impacted all five different but interconnected domains, introduced new roles in parents' lives, and particularly affected women and vulnerable populations. Conclusion: This scoping review highlights the importance of approaching public health policymaking from a social justice perspective. Such an approach argues for social and public health policies to promote health accounting for its social, economic, political, and commercial determinants

Longitudinal humoral and cell-mediated immune responses in a population-based cohort in Zurich, Switzerland between March and June 2022 - evidence for protection against Omicron SARS-CoV-2 infection by neutralizing antibodies and spike-specific T-cell responses

OBJECTIVES: The correlate(s) of protection against SARS-CoV-2 remain incompletely defined. Additional information regarding the combinations of antibody and T cell-mediated immunity which can protect against (re)infection is needed. METHODS: We conducted a population-based, longitudinal cohort study including 1044 individuals of varying SARS-CoV-2 vaccination and infection statuses. We assessed spike (S)- and nucleocapsid (N)-immunoglobulin(Ig)G and wildtype, Delta, and Omicron-neutralizing antibody (N-Ab) activity. In a subset of 328 individuals, we evaluated S, membrane (M), and N-specific T cells. Three months later, we reassessed Ab (n = 964) and T cell (n = 141) responses and evaluated factors associated with protection from (re)infection. RESULTS: At the study start, >98% of participants were S-IgG seropositive. N-IgG and M/N-T-cell responses increased over time, indicating viral (re)exposure, despite existing S-IgG. Compared to N-IgG, M/N-T cells were a more sensitive measure of viral exposure. High N-IgG titers, Omicron-N-Ab activity, and S-specific-T-cell responses were all associated with a reduced likelihood of (re)infection over time. CONCLUSION: Population-level SARS-CoV-2 immunity is S-IgG-dominated, but heterogeneous. M/N-T-cell responses can distinguish previous infection from vaccination, and monitoring a combination of N-IgG, Omicron-N-Ab, and S-T-cell responses may help estimate protection against SARS-CoV-2 (re)infection

A global systematic analysis of the occurrence, severity, and recovery pattern of long COVID in 2020 and 2021

IMPORTANCE While much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID. OBJECTIVE To estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recovery. DESIGN We jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study. RESULTS Analyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8-312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38-7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9-92.4), 60.4% (18.9-89.1), and 35.4% (9.4-75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84-4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10-9.78]). At twelve months, 15.1% (10.3-21.1) continued to experience long COVID symptoms. CONCLUSIONS AND RELEVANCE The occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane. KEY POINTS Question: What are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021?Findings: Globally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered.Meaning: The substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane

Efficacy of a digital lifestyle intervention on health-related QUAlity of life in non-small cell LUng CAncer survivors following inpatient rehabilitation: protocol of the QUALUCA Swiss multicentre randomised controlled trial

Introduction: Non-small cell lung cancer (NSCLC) survivors suffer from impaired physical and psychological functioning and reduced health-related quality of life (HRQoL) that persist after active treatment ends. Sustaining rehabilitation benefits, promoting a healthy lifestyle and facilitating self-management at home require a multifaceted aftercare programme. We aim to investigate the effect of a 12-week digital lifestyle intervention on HRQoL and lifestyle-related outcomes in NSCLC survivors after completion of inpatient rehabilitation. Methods and analysis: QUAlity of life in LUng CAncer Survivors (QUALUCA) is a multicentre randomised controlled trial that follows a hybrid type 1 design. We randomly allocate participants in a 1:1 ratio to the intervention group (digital lifestyle intervention) or the control group (standard care) using block randomisation stratified by tumour stage and study site. Four accredited Swiss inpatient rehabilitation centres recruit participants. Key inclusion criteria are a diagnosis of NSCLC, an estimated life expectancy of ≥6 months and access to a smartphone or tablet. The 12-week intervention comprises physical activity, nutrition and breathing/relaxation, delivered through a mobile application (app). The primary outcome is the change in HRQoL from baseline (1 week after rehabilitation) to follow-up (3 months after baseline), assessed by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30). Secondary outcomes include body mass index, self-reported physical activity, exercise capacity, risk of low protein intake, appetite, psychological distress, cancer-related fatigue, enablement and self-rated health. Explanatory outcomes in the intervention group include app usability, acceptability, appropriateness, and feasibility of the intervention, experiences and satisfaction with the intervention, and app usage data. We aim to enrol 88 participants. For the main statistical analysis, we will use analysis of covariance, adjusted for baseline measures, stratification variables, age and sex. Ethics and dissemination: The Ethics Committees of the Canton of Zurich (lead), the Canton of Bern and Northwest and Central Switzerland approved the study (2023-00245). We will disseminate study results to researchers, health professionals, study participants and relevant organisations, and through publications in international peer-reviewed journals. Trial registration number NCT05819346

Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging

Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner

Comparison of three different methods for risk adjustment in neonatal medicine

Abstract Background Quality improvement in health care requires identification of areas in need of improvement by comparing processes and patient outcomes within and between health care providers. It is critical to adjust for different case-mix and outcome risks of patient populations but it is currently unclear which approach has higher validity and how limitations need to be dealt with. Our aim was to compare 3 approaches towards risk adjustment for 7 different major quality indicators in neonatal intensive care (21 models). Methods We compared an indirect standardization, logistic regression and multilevel approach. Parameters for risk adjustment were chosen according to literature and the condition that they may not depend on processes performed by treating clinics. Predictive validity was tested using the mean Brier Score and by comparing area under curve (AUC) using high quality population based data separated into training and validation sets. Changes in attributional validity were analysed by comparing the effect of the models on the observed-to-expected ratios of the clinics in standardized mortality/morbidity ratio charts. Results Risk adjustment based on indirect standardization revealed inferior c-statistics but superior Brier scores for 3 of 7 outcomes. Logistic regression and multilevel modelling were equivalent to one another. C-statistics revealed that predictive validity was high for 8 and acceptable for 11 of the 21 models. Yet, the effect of all forms of risk adjustment on any clinic’s comparison with the standard was small, even though there was clear risk heterogeneity between clinics. Conclusions All three approaches to risk adjustment revealed comparable results. The limited effect of risk adjustment on clinic comparisons indicates a small case-mix influence on observed outcomes, but also a limited ability to isolate quality improvement potential based on risk-adjustment models. Rather than relying on methodological approaches, we instead recommend that clinics build small collaboratives and compare their indicators both in risk-adjusted and unadjusted form together. This allows qualitatively investigating and discussing the residual risk-differences within networks. The predictive validity should be quantified and reported and stratification into risk groups should be more widely used to correct for confounding

Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis.

The emergence of new digital technologies has enabled a new way of doing research, including active collaboration with the public ('citizen science'). Innovation in machine learning (ML) and natural language processing (NLP) has made automatic analysis of large-scale text data accessible to study individual perspectives in a convenient and efficient fashion. Here we blend citizen science with innovation in NLP and ML to examine (1) which categories of life events persons with multiple sclerosis (MS) perceived as central for their MS; and (2) associated emotions. We subsequently relate our results to standardized individual-level measures. Participants (n = 1039) took part in the 'My Life with MS' study of the Swiss MS Registry which involved telling their story through self-selected life events using text descriptions and a semi-structured questionnaire. We performed topic modeling ('latent Dirichlet allocation') to identify high-level topics underlying the text descriptions. Using a pre-trained language model, we performed a fine-grained emotion analysis of the text descriptions. A topic modeling analysis of totally 4293 descriptions revealed eight underlying topics. Five topics are common in clinical research: 'diagnosis', 'medication/treatment', 'relapse/child', 'rehabilitation/wheelchair', and 'injection/symptoms'. However, three topics, 'work', 'birth/health', and 'partnership/MS' represent domains that are of great relevance for participants but are generally understudied in MS research. While emotions were predominantly negative (sadness, anxiety), emotions linked to the topics 'birth/health' and 'partnership/MS' was also positive (joy). Designed in close collaboration with persons with MS, the 'My Life with MS' project explores the experience of living with the chronic disease of MS using NLP and ML. Our study thus contributes to the body of research demonstrating the potential of integrating citizen science with ML-driven NLP methods to explore the experience of living with a chronic condition