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1.
Journal of forestry ; 2021.
Article in English | EuropePMC | ID: covidwho-1602033

ABSTRACT

The COVID-19 pandemic has created unprecedented challenges in the way the USDA Forest Service conducts business. Standard data collection methods were immediately challenged due to travel restrictions and due to uncertainty regarding when it would be safe to return to a “business as usual” approach. These challenges were met with an inspiring collaboration between forest health specialists directly involved in the annual Aerial Detection Survey (ADS) program and remote sensing specialists from the Forest Service and academia. This group worked together to generate informative training materials, new workflows, and weekly help sessions to directly address problems that arose during this capacity building exercise. Small ad hoc teams were created to identify regionally specific program resources to enhance remote sensing utilization while supplementing information gaps where aerial detection surveys were either limited or not possible. The lessons learned from this challenge provide an opportunity to continue the exploration of combining ADS, remote sensing, and field data to deliver comprehensive information for managing the nation’s forests, while applying what is working and learning and growing from both successes and limitations. Study Implications: The 2020 USDA Forest Service’s (USFS) Aerial Detection Survey (ADS) program faced unprecedented challenges resulting from the COVID-19 pandemic, which limited surveys across nearly all USFS regions. However, this pandemic created an unexpected positive outcome through an ongoing and wide-reaching collaboration between federal, state, academic, and private sectors that has allowed for a strong and lasting collaboration across USFS regions. Moreover, this collaboration has provided a unique opportunity to optimize a combination of ADS, remote sensing, and field visits to deliver a comprehensive, robust, and near-real-time assessment of the health of our nation’s forests.

2.
Future Healthc J ; 8(3): e638-e643, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1566800

ABSTRACT

Introduction: In April 2020, a new workforce of clinical assistants (CAs), comprising predominantly of medical students, began work at Northampton General Hospital. Clinical-years students had a role similar to final-year student assistants; pre-clinical students were offered a healthcare assistant role. This research aimed to evaluate both CAs' and clinicians' perceptions of this programme. Methods: Separate questionnaires were developed for CAs and clinicians, assessing the scheme's successes and failures. Data analysis was carried out using MS Excel and SPSS. Results and discussion: Forty-nine CAs and 60 clinicians responded. CAs of all years were completing the higher-level role. They were perceived to improve continuity of care (74% CA agreement; 88% clinician agreement), reduce clinician workload (90% clinician agreement) and felt significantly more confident with practical and administrative tasks. Sixty-eight per cent of CAs and 72% of clinicians believed the role should be available to students before their final year.

3.
Contemp Clin Trials ; 108: 106482, 2021 09.
Article in English | MEDLINE | ID: covidwho-1427719

ABSTRACT

BACKGROUND: 20-60% of patients with initially locally advanced Renal Cell Carcinoma (RCC) develop metastatic disease despite optimal surgical excision. Adjuvant strategies have been tested in RCC including cytokines, radiotherapy, hormones and oral tyrosine-kinase inhibitors (TKIs), with limited success. The predominant global standard-of-care after nephrectomy remains active monitoring. Immune checkpoint inhibitors (ICIs) are effective in the treatment of metastatic RCC; RAMPART will investigate these agents in the adjuvant setting. METHODS/DESIGN: RAMPART is an international, UK-led trial investigating the addition of ICIs after nephrectomy in patients with resected locally advanced RCC. RAMPART is a multi-arm multi-stage (MAMS) platform trial, upon which additional research questions may be addressed over time. The target population is patients with histologically proven resected locally advanced RCC (clear cell and non-clear cell histological subtypes), with no residual macroscopic disease, who are at high or intermediate risk of relapse (Leibovich score 3-11). Patients with fully resected synchronous ipsilateral adrenal metastases are included. Participants are randomly assigned (3,2:2) to Arm A - active monitoring (no placebo) for one year, Arm B - durvalumab (PD-L1 inhibitor) 4-weekly for one year; or Arm C - combination therapy with durvalumab 4-weekly for one year plus two doses of tremelimumab (CTLA-4 inhibitor) at day 1 of the first two 4-weekly cycles. The co-primary outcomes are disease-free-survival (DFS) and overall survival (OS). Secondary outcomes include safety, metastasis-free survival, RCC specific survival, quality of life, and patient and clinician preferences. Tumour tissue, plasma and urine are collected for molecular analysis (TransRAMPART). TRIAL REGISTRATION: ISRCTN #: ISRCTN53348826, NCT #: NCT03288532, EUDRACT #: 2017-002329-39, CTA #: 20363/0380/001-0001, MREC #: 17/LO/1875, ClinicalTrials.gov Identifier: NCT03288532, RAMPART grant number: MC_UU_12023/25, TransRAMPART grant number: A28690 Cancer Research UK, RAMPART Protocol version 5.0.


Subject(s)
Carcinoma, Renal Cell , Kidney Neoplasms , Carcinoma, Renal Cell/surgery , Chronic Disease , Humans , Kidney Neoplasms/surgery , Quality of Life , Recurrence
4.
Epidemics ; 35: 100457, 2021 06.
Article in English | MEDLINE | ID: covidwho-1291790

ABSTRACT

BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally. Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning. METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard's model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model. Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis. RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1, 2020 and May 1, 2020 would be 11,156 (90 % prediction interval: 9,156-13,905) and 54,745 (90 % prediction interval: 54,252-55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation. Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances. The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed. CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada. Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures. Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Models, Statistical , COVID-19/prevention & control , Canada/epidemiology , Humans , Incidence , Pandemics , Public Health , SARS-CoV-2
5.
Can J Public Health ; 111(6): 926-938, 2020 12.
Article in English | MEDLINE | ID: covidwho-1081437

ABSTRACT

OBJECTIVES: To compare a mathematical tool and time-dependent reproduction number (Rt) estimates to assess the COVID-19 pandemic progression in a Canadian context. METHODS: Total number of reported cases were plotted against total number of tests for COVID-19 performed over time, with and without smoothing, for Canada and some Canadian provinces individually. Changes in curvature profile were identified as either convex or concave as indicators of pandemic acceleration or deceleration, respectively. Rt estimates were calculated on an exponential growth rate. RESULTS: For Canada as a whole, the testing graphs had a slightly concave profile and a coincident decrease in Rt estimates. Saskatchewan more recently had a convex profile with a gradual shift to a concave profile and also demonstrated a gradual decline in Rt estimates. Curves and Rt estimates for Alberta, British Columbia, Manitoba, Nova Scotia, Ontario and Quebec displayed a gradual shift towards concavity over time and an overall decrease in Rt estimates, which is suggestive of a positive impact of public health interventions implemented federally and provincially. CONCLUSION: The present analyses compared a mathematical tool to Rt estimates to ascertain the status of the pandemic in Canada. Caution should be taken when interpreting results due to factors such as varying testing protocols, available testing data unique to each province and limitations inherent to each method, which may generate different results using the two approaches. Analysis of testing data may complement metrics obtained from surveillance data to allow for a weight-of-evidence approach to assess the status of the COVID-19 pandemic.


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , Models, Theoretical , Pandemics , Canada/epidemiology , Humans
6.
Infect Dis Model ; 6: 123-132, 2021.
Article in English | MEDLINE | ID: covidwho-957111

ABSTRACT

While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

7.
Public Health Res Pract ; 30(2)2020 Jun 30.
Article in English | MEDLINE | ID: covidwho-890804

ABSTRACT

The effects of the coronavirus disease 2019 (COVID-19) pandemic upon human health, economic activity and social engagement have been swift and far reaching. Emerging evidence shows that the pandemic has had dramatic mental health impacts, bringing about increased anxiety and greater social isolation due to the physical distancing policies introduced to control the disease. In this context, it is possible to more deeply appreciate the health consequences of loneliness and social isolation, which researchers have argued are enduring experiences for many people and under-recognised contributors to public health. In this paper, we examine the social and psychological consequences of the COVID-19 pandemic, with a focus on what this has revealed about the need to better understand and respond to social isolation and loneliness as public health priorities. Social isolation and loneliness are understood to be distinct conditions, yet each has been found to predict premature mortality, depression, cardiovascular disease and cognitive decline. Estimates of the prevalence and distribution of social isolation and loneliness vary, possibly ranging from one-in-six to one-in-four people, and the lack of knowledge about the extent of these conditions indicates the need for population monitoring using standardised methods and validated measures. Reviews of the evidence relating to social isolation and loneliness interventions have found that befriending schemes, individual and group therapies, various shared activity programs, social prescription by healthcare providers, and diverse strategies using information and communication technologies have been tried. There remains uncertainty about what is effective for different population groups, particularly for prevention and for addressing the more complex condition of loneliness. In Australia, a national coalition - Ending Loneliness Together - has been established to bring together researchers and service providers to facilitate evidence gathering and the mobilisation of knowledge into practice. Research-practice partnerships and cross-disciplinary collaborations of this sort are essential for overcoming the public health problems of loneliness and social isolation that have pre-existed and will endure beyond the COVID-19 pandemic.


Subject(s)
Community Networks , Coronavirus Infections/psychology , Loneliness/psychology , Pneumonia, Viral/psychology , Quarantine/psychology , Social Isolation/psychology , Australia , COVID-19 , Humans , Mental Health , Pandemics , Self Efficacy , Social Support
8.
Infect Dis Model ; 5: 346-356, 2020.
Article in English | MEDLINE | ID: covidwho-436787

ABSTRACT

The SARS-CoV-2 virus causes the disease COVID-19, and has caused high morbidity and mortality worldwide. Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term. A modified Incidence Decay and Exponential Adjustment (m-IDEA) model was developed to predict the progression of infectious disease outbreaks. The modification allows for the production of precise daily estimates, which are critical during a pandemic of this scale for planning purposes. The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19. Both deterministic and stochastic approaches were applied. Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts, and performance was retrospectively assessed. The performance of the m-IDEA for projection cases in the near-term was improved using shorter serial intervals (1-4 days) at early stages of the pandemic, and longer serial intervals at mid- to late-stages (5-9 days) thus far. This, coupled with epidemiological reports, suggests that the serial interval of COVID-19 might increase as the pandemic progresses, which is rather intuitive: Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures, public caution and social distancing, thus increasing the time between transmission events. In most cases, the stochastic approach captured the majority of future reported incidence data, because it accounts for the uncertainty around the serial interval of COVID-19. As such, it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.

9.
JAMA Netw Open ; 3(3): e200802, 2020 03 02.
Article in English | MEDLINE | ID: covidwho-99094

ABSTRACT

Importance: Opioid prescriptions for treatment of pain in emergency departments (EDs) are associated with long-term opioid use. The temporal pattern of opioid prescribing in the context of the opioid epidemic remains unknown. Objective: To examine the temporal pattern of opioid prescribing within an ED for varying pain conditions between 2009 and 2018. Design, Setting, and Participants: A population-based, cross-sectional study was conducted at the ED of an urban academic medical center. All patients treated within that ED between January 1, 2009, and December 31, 2018, were included. Main Outcomes and Measures: The proportion of patients prescribed an opioid for treatment of pain in the ED temporally by condition, condition type, patient demographics, and physician prescriber. Results: Between 2009 and 2018, 556 176 patient encounters took place in the ED, with 70 218 unique opioid prescriptions ordered. A total of 316 632 patients (55.9%) were female, 45 070 (42.6%) were of white race, and 43 412 (40.6%) were privately insured; the median age group was 41 to 45 years. Yearly opioid prescriptions decreased by 66.3% (from 16.3 to 5.5 opioids per 100 encounters) between 2013 and 2018, with a yearly adjusted odds ratio (aOR) of 0.808 (95% CI, 0.802-0.814) compared with the prior year. In patients with musculoskeletal pain (back, joint, limb, and neck pain), opioid prescribing decreased by 71.1% (from 36.7 to 10.6 opioids per 100 encounters between 2013 and 2018; aOR, 0.758; 95% CI, 0.744-0.773). In patients with musculoskeletal trauma (fracture, sprain, contusion, and injury), opioid prescribing decreased by 58.0% (from 34.2 to 14.8 opioids per 100 encounters; aOR, 0.811; 95% CI, 0.797-0.824). In patients with nonmusculoskeletal pain (abdominal pain, kidney stone, respiratory distress, and pharyngitis) opioid prescribing decreased by 53.7% (from 20.1 to 9.3 opioids per 100 encounters; aOR, 0.850; 95% CI, 0.834-0.868). Between 2009 and 2018, patients who were black (aOR, 0.760; 95% CI, 0.741-0.779) and those who were Asian (aOR, 0.714; 95% CI, 0.665-0.764) had the lowest odds of receiving an opioid compared with other racial/ethnic groups. Conclusions and Relevance: There was a substantial temporal decrease in the number of opioid prescriptions within this ED during the study period. This decrease was associated with substantial relative reductions in opioid prescribing for treatment of musculoskeletal pain compared with fractures and kidney stones.


Subject(s)
Analgesics, Opioid/pharmacology , Drug Prescriptions/statistics & numerical data , Emergency Service, Hospital/statistics & numerical data , Hospitals, Urban , Pain Management/methods , Pain/drug therapy , Practice Patterns, Physicians' , Adult , Cross-Sectional Studies , Female , Humans , Incidence , Male , Middle Aged , Pain/ethnology , United States/epidemiology
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