ABSTRACT
ImportanceSocial determinants of health (SDOH) play an important role in COVID-19 outcomes. More research is needed to quantify this relationship and understand the underlying mechanisms. ObjectivesTo examine differential patterns in COVID-19-related mortality by area-level SDOH accounting for confounders; and to compare these patterns to those for non-COVID-19 mortality, and COVID-19 case fatality (COVID-19-related death among those diagnosed). Design, setting, and participantsPopulation-based retrospective cohort study including all community living individuals aged 20 years or older residing in Ontario, Canada, as of March 1, 2020 who were followed through to March 2, 2021. ExposureSDOH variables derived from the 2016 Canada Census at the dissemination area-level including: median household income; educational attainment; proportion of essential workers, racialized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. Main outcomes and measuresCOVID-19-related death was defined as death within 30 days following, or 7 days prior to a positive SARS-CoV-2 test. Cause-specific hazard models were employed to examine the associations between SDOH and COVID-19-related mortality, treating non-COVID-19 mortality as a competing risk. ResultsOf 11,810,255 individuals included, 3,880 (0.03%) died related to COVID-19 and 88,107 (0.75%) died without a positive test. After accounting for demographics, baseline health, and other SDOH, the following SDOH were associated with increased hazard of COVID-19-related death (hazard ratios [95% confidence intervals]) comparing the most to least vulnerable group): lower income (1.30[1.09-1.54]), lower educational attainment (1.27[1.10-1.47]), higher proportion essential workers (1.28[1.10-1.50]), higher proportion racialized groups (1.42[1.16-1.73]), higher proportion apartment buildings (1.25[1.11-1.41]), and larger vs. medium household size (1.30[1.13-1.48]). In comparison, areas with higher proportion racialized groups were associated with a lower hazard of non-COVID-19 mortality (0.88[0.85-0.92]). With the exception of income, SDOH were not independently associated with COVID-19 case fatality. Conclusions and relevanceArea-level social and structural inequalities determine COVID-19-related mortality after accounting for individual demographic and clinical factors. COVID-19 has reversed the pattern of lower non-COVID-19 mortality by racialized groups. Pandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin disproportionate acquisition and transmission risks and shape barriers to the reach of, and access to prevention interventions. Key pointsO_ST_ABSQuestionC_ST_ABSAre area-level social determinants of health factors independently associated with coronavirus disease 2019 (COVID-19)-related mortality after accounting for demographics and clinical factors? FindingsIn this population-based cohort study including 11.8 million adults residing in Ontario, Canada and 3,880 COVID-19-related death occurred between Mar 1, 2020 and Mar 2, 2021, we found that areas characterized by lower SES (including lower income, lower educational attainment, and higher proportion essential workers), greater ethnic diversity, more apartment buildings, and larger vs. medium household size were associated with increased hazard of COVID-19-related mortality compared to their counterparts, even after accounting for individual-level demographics, baseline health, and other area-level SDOH. MeaningPandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin inequalities in acquisition and transmission risks, and in the reach of, and access to prevention interventions.
ABSTRACT
BackgroundWe compared the risk of, testing for, and death following COVID-19 infection across three settings (long-term care homes (LTCH), shelters, the rest of the population) in the Greater Toronto Area (GTA), Canada. MethodsWe sourced person-level data from COVID-19 surveillance and reporting systems in Ontario, and examined settings with population-specific denominators (LTCH residents, shelters, and the rest of the population). We calculated cumulatively, the diagnosed cases per capita, proportion tested for COVID-19, daily and cumulative positivity, and case fatality proportion. We estimated the age- and sex-adjusted relative rate ratios for test positivity and case fatality using quasi-Poisson regression. ResultsBetween 01/23/2020-05/25/2020, we observed a shift in the proportion of cases: from travel-related and into LTCH and shelters. Cumulatively, compared to the rest of the population, the number of diagnosed cases per 100,000 was 59-fold and 18-fold higher among LTCH and shelter residents, respectively. By 05/25/2020, 77.2% of LTCH residents compared to 2.4% of the rest of the population had been tested. After adjusting for age and sex, LTCH residents were 2.5 times (95% confidence interval (CI): 2.3-2.8) more likely to test positive. Case fatality was 26.3% (915/3485), 0.7% (3/402), and 3.6% (506/14133) among LTCH residents, shelter population, and others in the GTA, respectively. After adjusting for age and sex, case fatality was 1.4-fold (95%CI: 1.1-1.9) higher among LTCH residents than the rest of the population. InterpretationHeterogeneity across micro-epidemics among specific populations in specific settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.
ABSTRACT
BackgroundA hospital-level pandemic response involves anticipating local surge in healthcare needs. MethodsWe developed a mechanistic transmission model to simulate a range of scenarios of COVID-19 spread in the Greater Toronto Area. We estimated healthcare needs against 2019 daily admissions using healthcare administrative data, and applied outputs to hospital-specific data on catchment, capacity, and baseline non-COVID admissions to estimate potential surge by day 90 at two hospitals (St. Michaels Hospital [SMH] and St. Josephs Health Centre [SJHC]). We examined fast/large, default, and slow/small epidemics, wherein the default scenario (R0 2.4) resembled the early trajectory in the GTA. ResultsWithout further interventions, even a slow/small epidemic exceeded the citys daily ICU capacity for patients without COVID-19. In a pessimistic default scenario, for SMH and SJHC to remain below their non-ICU bed capacity, they would need to reduce non-COVID inpatient care by 70% and 58% respectively. SMH would need to create 86 new ICU beds, while SJHC would need to reduce its ICU beds for non-COVID care by 72%. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity. If physical distancing reduces contacts by 20%, maximizing the diagnostic capacity or syndromic diagnoses at the community-level could avoid a surge at each hospital. InterpretationAs distribution of the citys surge varies across hospitals over time, efforts are needed to plan and redistribute ICU care to where demand is expected. Hospital-level surge is based on community-level transmission, with community-level strategies key to mitigating each hospitals surge.
ABSTRACT
Target delineation is the key and difficult point in radiation oncology teaching. Combined with the teaching experience in department of cancer radio-chemotherapy, Zhongnan hospital of Wuhan university, this study focused on target delineation to explore the teaching mode and method of radiation oncology. Self-directed learning was combined with teacher's lecturing and guiding. By enhancing tumor imaging teaching and basic theory of tumor radiotherapy, students can grasp the essence and detail of target delineation and build individualized and precise radiotherapy. Finally, a new teaching mode combining students' autonomous learning with teachers' teaching and guide is established. Taking the radiation therapy of breast cancer as an example, We briefly described the concrete application of this teaching system.
ABSTRACT
Target delineation is the key and difficult point in radiation oncology teaching. Combined with the teaching experience in department of cancer radio-chemotherapy , Zhongnan hospital of Wuhan university , this study focused on target delineation to explore the teaching mode and method of radiation oncology. Self-directed learning was combined with teacher's lecturing and guiding. By enhancing tumor imaging teaching and basic theory of tumor radiotherapy, students can grasp the essence and detail of target delineation and build individualized and precise radiotherapy. Finally, a new teaching mode combining students' autonomous learning with teachers' teaching and guide is established. Taking the radiation therapy of breast cancer as an example, We briefly described the concrete application of this teaching system.
ABSTRACT
Histological classification system ( HCS) is an important system in judging and predicting ma-lignant behaviors of breast cancer .This study is aimed to analyze research progress and development direction of HCS by perusing literatures ,including the origin and development progress of HCS ,contents and deficiencies of Nottingham classification system(NCS),and introduction of novel classification system .HCS of breast cancer has a history of more than one hundred years ,among which NCS is the most widely used ,based on morphological fea-tures of cancer cells .However,there are still some shortcomings about NCS ,such as few indexes incorporation , great evaluation variation and low evaluation efficiency .Recently ,many newly evaluation systems have been devel-oped,including“nucleus+proliferation”classification system ,computer assisted classification system and compre-hensive prognostic classification model .Therefore,the future development directions of HCS on breast cancer is to use high throughput analysis technology to extract and analyze the hidden molecular information in cancer cells and the surrounding tumor microenvironment ,so as to better guide personalized therapy and predict clinical prog-nosis.
