ABSTRACT
BACKGROUND: Early ecological studies have suggested links between air pollution and risk of coronavirus disease 2019 (COVID-19), but evidence from individual-level cohort studies is still sparse. We examined whether long-term exposure to air pollution is associated with risk of COVID-19 and who is most susceptible. METHODS: We followed 3 721 810 Danish residents aged ≥30â years on 1 March 2020 in the National COVID-19 Surveillance System until the date of first positive test (incidence), COVID-19 hospitalisation or death until 26 April 2021. We estimated residential annual mean particulate matter with diameter ≤2.5â µm (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) in 2019 by the Danish DEHM/UBM model, and used Cox proportional hazards regression models to estimate the associations of air pollutants with COVID-19 outcomes, adjusting for age, sex, individual- and area-level socioeconomic status, and population density. RESULTS: 138 742 individuals were infected, 11 270 were hospitalised and 2557 died from COVID-19 during 14â months. We detected associations of PM2.5 (per 0.53â µg·m-3) and NO2 (per 3.59â µg·m-3) with COVID-19 incidence (hazard ratio (HR) 1.10 (95% CI 1.05-1.14) and HR 1.18 (95% CI 1.14-1.23), respectively), hospitalisations (HR 1.09 (95% CI 1.01-1.17) and HR 1.19 (95% CI 1.12-1.27), respectively) and death (HR 1.23 (95% CI 1.04-1.44) and HR 1.18 (95% CI 1.03-1.34), respectively), which were strongest in the lowest socioeconomic groups and among patients with chronic respiratory, cardiometabolic and neurodegenerative diseases. We found positive associations with BC and negative associations with O3. CONCLUSION: Long-term exposure to air pollution may contribute to increased risk of contracting severe acute respiratory syndrome coronavirus 2 infection as well as developing severe COVID-19 disease requiring hospitalisation or resulting in death.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Humans , Cohort Studies , Nitrogen Dioxide/adverse effects , Nitrogen Dioxide/analysis , Environmental Exposure/adverse effects , Environmental Exposure/analysis , SARS-CoV-2 , Air Pollution/adverse effects , Air Pollution/analysis , Air Pollutants/adverse effects , Air Pollutants/analysis , Particulate Matter/adverse effects , Particulate Matter/analysis , Hospitalization , Soot , Denmark/epidemiologyABSTRACT
Closed incision negative pressure wound therapy (CINPWT) has been shown to be clinically effective compared with the traditional gauze dressing, reducing surgical site infections and wound complications. We evaluated the effect of CINPWT compared with gauze dressing on the need for revision surgery and survival after non-traumatic major lower amputation. We included 309 patients undergoing 403 major lower amputations in a retrospective study from January 1, 2010 to November 23, 2017. A total of 139 patients received CINPWT, and 170 patients received stump bandage. There was no statistically significant difference between the two groups regarding the need for revision surgery (P = .45). Fourteen stump bandage patients and 15 CINPWT patients died in hospital (P = .57). One year after amputation, 55 CINPWT patients and 66 stump bandage patients had died (P = .82). Survival probabilities adjusted for age and gender 2 years after amputation were .52 (.43-.61) and .49 (.42-.58), respectively, and 3 years after amputation were .36 (.25-.50) and .39 (.32-.47), respectively. We also found no significant difference in the need for revision surgery in survival probabilities up till 3 years after amputation between patients treated with CINPWT and patients treated with gauze bandage postoperatively.
Subject(s)
Amputation, Surgical/adverse effects , Lower Extremity/surgery , Negative-Pressure Wound Therapy/methods , Reoperation/methods , Wound Healing/physiology , Aged , Amputation, Surgical/methods , Amputation, Surgical/mortality , Amputation Stumps/pathology , Amputation Stumps/surgery , Bandages , Cohort Studies , Denmark , Female , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment , Survival Analysis , Treatment OutcomeABSTRACT
BACKGROUND: Although breast cancer screening reduces breast cancer mortality at the population level, subgroups of women may benefit differently. We investigated the impact of health status on the effect of breast cancer screening. METHODS: The study included 181 299 women invited in two population-based screening programs in Denmark and 1 526 446 control subjects, followed from April 1981 to December 2014. Poisson regressions were used to compare the observed breast cancer mortality rate in women invited to screening with the expected rate in the absence of screening among women with and without chronic diseases. Chronic diseases were defined as any diagnosis in the Charlson Comorbidity Index during 4 years before the first invitation to screening. RESULTS: Almost 10% of women had chronic diseases before first invitation to screening. Whereas we observed a reduction in breast cancer mortality following invitation to screening of 28% (95% CI, 20% to 35%) among women without chronic diseases, only a 7% (95% CI, -39% to 37%) reduction was seen for women with chronic diseases (P-value for interaction = .22). For participants, the reduction, corrected for selection bias, was 35% (95% CI 16% to 49%) for women without, and 4% (95% CI -146% to 62%) for women with chronic diseases (P-value for interaction = .43). CONCLUSION: Our data indicate a marginal effect of mammography screening on breast cancer mortality in women with chronic diseases. If our results are confirmed in other populations, the presence of chronic diseases will be an important factor to take into consideration in personalized screening.
Subject(s)
Breast Neoplasms/diagnosis , Early Detection of Cancer/methods , Health Status , Mammography/methods , Aged , Case-Control Studies , Chronic Disease , Female , Follow-Up Studies , Humans , Middle Aged , Prognosis , Survival RateABSTRACT
We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons.