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1.
BMJ Open ; 11(3): e044888, 2021 03 15.
Article in English | MEDLINE | ID: covidwho-1455712

ABSTRACT

INTRODUCTION: Type 2 diabetes is a global health priority. People with diabetes are more likely to experience mental health problems relative to people without diabetes. Diabetes guidelines recommend assessment of depression and diabetes distress during diabetes care. This systematic review will examine the effect of routinely assessing and addressing depression and diabetes distress using patient-reported outcome measures in improving outcomes among adults with type 2 diabetes. METHODS AND ANALYSIS: MEDLINE, Embase, CINAHL Complete, PsycInfo, The Cochrane Library and Cochrane Central Register of Controlled Trials will be searched using a prespecified strategy using a prespecified Population, Intervention, Comparator, Outcomes, Setting and study design strategy. The date range of the search of all databases will be from inception to 3 August 2020. Randomised controlled trials, interrupted time-series studies, prospective and retrospective cohort studies, case-control studies and analytical cross-sectional studies published in peer-reviewed journals in the English language will be included. Two review authors will independently screen abstracts and full texts with disagreements resolved by a third reviewer, if required, using Covidence software. Two reviewers will undertake risk of bias assessment using checklists appropriate to study design. Data will be extracted using prespecified template. A narrative synthesis will be conducted, with a meta-analysis, if appropriate. ETHICS AND DISSEMINATION: Ethics approval is not required for this review of published studies. Presentation of results will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidance. Findings will be disseminated via peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020200246.


Subject(s)
Diabetes Mellitus, Type 2 , Adult , Cross-Sectional Studies , Depression/diagnosis , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/therapy , Humans , Meta-Analysis as Topic , Patient Reported Outcome Measures , Prospective Studies , Research Design , Retrospective Studies , Systematic Reviews as Topic
2.
Br J Gen Pract ; 71(706): 196-197, 2021 05.
Article in English | MEDLINE | ID: covidwho-1448956
3.
Trials ; 22(1): 605, 2021 Sep 08.
Article in English | MEDLINE | ID: covidwho-1403256

ABSTRACT

BACKGROUND: Many non-COVID-19 trials were disrupted in 2020 and either struggled to recruit participants or stopped recruiting altogether. In December 2019, just before the pandemic, we were awarded a grant to conduct a randomised controlled trial, the Should I Take Aspirin? (SITA) trial, in Victoria, the Australian state most heavily affected by COVID-19 during 2020. MAIN BODY: We originally modelled the SITA trial recruitment method on previous trials where participants were approached and recruited in general practice waiting rooms. COVID-19 changed the way general practices worked, with a significant increase in telehealth consultations and restrictions on in person waiting room attendance. This prompted us to adapt our recruitment methods to this new environment to reduce potential risk to participants and staff, whilst minimising any recruitment bias. We designed a novel teletrial model, which involved calling participants prior to their general practitioner appointments to check their eligibility. We delivered the trial both virtually and face-to-face with similar overall recruitment rates to our previous studies. CONCLUSION: We developed an effective teletrial model which allowed us to complete recruitment at a high rate. The teletrial model is now being used in our other primary care trials as we continue to face the impacts of the COVID-19 pandemic.


Subject(s)
COVID-19 , Pandemics , Australia , Humans , Pandemics/prevention & control , Primary Health Care , SARS-CoV-2
4.
Asia-Pacific Journal of Clinical Oncology ; n/a(n/a), 2021.
Article in English | Wiley | ID: covidwho-1077200

ABSTRACT

Abstract Aim Decreased cancer incidence and reported changes to clinical management indicate that the COVID-19 pandemic has delayed cancer diagnosis and treatment. This study aimed to develop and apply a flexible model to estimate the impact of delayed diagnosis and treatment on survival outcomes and healthcare costs based on a shift in the disease stage at treatment initiation. Methods A model was developed and made publicly available to estimate population-level health economic outcomes by extrapolating and weighing stage-specific outcomes by the distribution of stages at treatment initiation. It was applied to estimate the impact of 3- and 6-month delays based on Australian data for stage I breast cancer, colorectal cancer, and lung cancer patients, and for T1 melanoma. Two approaches were explored to estimate stage shifts following a delay: (a) based on the relation between time to treatment initiation and overall survival (breast, colorectal, and lung cancer), and (b) based on the tumor growth rate (melanoma). Results Using a conservative once-off 3-month delay and considering only shifts from stage I/T1 to stage II/T2, 88 excess deaths and $12 million excess healthcare costs were predicted in Australia over 5 years for all patients diagnosed in 2020. For a 6-month delay, excess mortality and healthcare costs were 349 deaths and $46 million over 5 years. Conclusions The health and economic impacts of delays in treatment initiation cause an imminent policy concern. More accurate individual patient data on shifts in stage of disease during and after the COVID-19 pandemic are critical for further analyses.

5.
Asia Pac J Clin Oncol ; 17(4): 359-367, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1075759

ABSTRACT

AIM: Decreased cancer incidence and reported changes to clinical management indicate that the COVID-19 pandemic has delayed cancer diagnosis and treatment. This study aimed to develop and apply a flexible model to estimate the impact of delayed diagnosis and treatment on survival outcomes and healthcare costs based on a shift in the disease stage at treatment initiation. METHODS: A model was developed and made publicly available to estimate population-level health economic outcomes by extrapolating and weighing stage-specific outcomes by the distribution of stages at treatment initiation. It was applied to estimate the impact of 3- and 6-month delays based on Australian data for stage I breast cancer, colorectal cancer, and lung cancer patients, and for T1 melanoma. Two approaches were explored to estimate stage shifts following a delay: (a) based on the relation between time to treatment initiation and overall survival (breast, colorectal, and lung cancer), and (b) based on the tumor growth rate (melanoma). RESULTS: Using a conservative once-off 3-month delay and considering only shifts from stage I/T1 to stage II/T2, 88 excess deaths and $12 million excess healthcare costs were predicted in Australia over 5 years for all patients diagnosed in 2020. For a 6-month delay, excess mortality and healthcare costs were 349 deaths and $46 million over 5 years. CONCLUSIONS: The health and economic impacts of delays in treatment initiation cause an imminent policy concern. More accurate individual patient data on shifts in stage of disease during and after the COVID-19 pandemic are critical for further analyses.


Subject(s)
Breast Neoplasms , COVID-19 , Colorectal Neoplasms , Lung Neoplasms , Australia/epidemiology , Breast Neoplasms/mortality , Colorectal Neoplasms/mortality , Female , Humans , Lung Neoplasms/mortality , Pandemics , SARS-CoV-2
6.
Elife ; 92020 08 24.
Article in English | MEDLINE | ID: covidwho-729762

ABSTRACT

A key unknown for SARS-CoV-2 is how asymptomatic infections contribute to transmission. We used a transmission model with asymptomatic and presymptomatic states, calibrated to data on disease onset and test frequency from the Diamond Princess cruise ship outbreak, to quantify the contribution of asymptomatic infections to transmission. The model estimated that 74% (70-78%, 95% posterior interval) of infections proceeded asymptomatically. Despite intense testing, 53% (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. The data did not allow identification of the infectiousness of asymptomatic infections, however low ranges (0-25%) required a net reproduction number for individuals progressing through presymptomatic and symptomatic stages of at least 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. Control measures, and models projecting their potential impact, need to look beyond the symptomatic cases if they are to understand and address ongoing transmission.


Subject(s)
Asymptomatic Diseases , Coronavirus Infections/transmission , Pneumonia, Viral/therapy , Ships/statistics & numerical data , Betacoronavirus/isolation & purification , COVID-19 , Calibration , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Humans , Incidence , Models, Statistical , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2
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