Long COVID is the persistence of one or more COVID-19 symptoms after the initial viral infection, and there is evidence supporting its association with lung damage. In this systematic review, we provide an overview of lung imaging and its findings in long COVID patients. A PubMed search was performed on 29 September 2021, for English language studies in which lung imaging was performed in adults suffering from long COVID. Two independent researchers extracted the data. Our search identified 3130 articles, of which 31, representing the imaging findings of 342 long COVID patients, were retained. The most common imaging modality used was computed tomography (CT) (N = 249). A total of 29 different imaging findings were reported, which were broadly categorized into interstitial (fibrotic), pleural, airway, and other parenchymal abnormalities. A direct comparison between cases, in terms of residual lesions, was available for 148 patients, of whom 66 (44.6%) had normal CT findings. Although respiratory symptoms belong to the most common symptoms in long COVID patients, this is not necessarily linked to radiologically detectable lung damage. Therefore, more research is needed on the role of the various types of lung (and other organ) damage which may or may not occur in long COVID.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40–65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3–6 and 12–18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient’s home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3–6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes. On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality. A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM_2.5. The meta-analyses revealed that a 1 μg/m³ increase in PM_2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO₂), ozone (O₃), sulphur dioxide (SO₂), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality. This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Background: Although the coronavirus disease 2019 (COVID-19) pandemic is no longer a public health emergency of international concern, 30% of COVID-19 patients still have long-term complaints. A better understanding of the progression of symptoms after COVID-19 is needed to reduce the burden of the post COVID-19 condition. Objective: This study aims to investigate the progression of symptoms, identify patterns of symptom progression, and assess their associations with patient characteristics. Methods: Within the P4O2 COVID-19 study, patients aged 40–65 years were recruited from five Dutch hospitals. At 3–6 and 12–18 months post COVID-19, medical data were collected, and pulmonary function tests were performed. In between, symptoms were assessed monthly with a questionnaire. Latent class mixed modelling was used to identify symptom progression patterns over time, with multinomial logistic regression to examine associations with patient characteristics. Results: Eighty-eight patients (aged 54.4 years, 48.9% males) were included. Three trajectories were identified for fatigue and dyspnoea: decreasing, high persistent, and low persistent. The odds of “decreasing fatigue” was higher for never smokers and participants in the lifestyle intervention and lower for those having a comorbidity. The odds of “decreasing dyspnoea” was higher for moderate COVID-19 patients and lifestyle intervention participants and lower for males, mild COVID-19 patients, and those with a higher age. Conclusions: Three distinct trajectories were identified for fatigue and dyspnoea, delineating patterns of symptom persistence following COVID-19. Sex, age, smoking status, participation in lifestyle interventions and COVID-19 severity were associated with the likelihood of belonging to different trajectories. These findings highlight the heterogeneity of the long-term symptoms experienced by post COVID-19 patients and emphasise the importance of personalised treatment strategies.

Accurate residential air quality assessment is crucial for studying health risks, evaluating local mitigation measures, and empowering citizens. Low-cost-sensors (LCS) have gained popularity for enhancing monitoring coverage and providing individuals with air quality measurement tools. This study examines the added value of a low-cost sensor in estimating residential fine particulate matter (PM _2.5 ) concentrations in the Netherlands. We employed a real-time Sensirion SPS30 dust sensor to monitor residential PM _2.5 concentrations. 73 sensors were deployed outdoors at participants’ residences with an average measurement time of 131 days. Data from LCS were compared with that of regulatory stations, using hourly and daily averages for comparison. Spatial variability of sensor measurements was assessed, and determinants were explored that explain potential differences between PM _2.5 concentrations from regulatory stations and LCS measurements. After data cleaning, 95.7% of measurements were retained. Meteorological factors did not impact sensor performance. The mean Pearson temporal correlation between the LCS and regulatory network was 0.75 for hourly and 0.88 for daily PM _2.5 averages. The average difference ranged from − 0.17 to 0.63 µg/m ³ , and the average absolute difference ranged from 2.42 to 4.50 µg/m ³ . Spatial variability of LCS-based average concentrations at similar locations was larger than that of regulatory stations. LCS measuring direction, traffic intensity, humidity, LCS readings, and distance to nearest background station had a significant effect on the difference between sensor and regulatory station concentrations. This study demonstrates that PM _2.5 can be accurately measured over extended periods using LCS, offering a dynamic, high-quality perspective on air quality, recording variations that regulatory stations and predictive air quality models may overlook.