IMPORTANCE Treatment advances in diabetes can meaningfully improve outcomes only if they effectively reach the populations at risk. OBJECTIVES To evaluate whether the cascade of US diabetes care, defined as diabetes diagnosis, linkage to care, and achievement of individual and combined treatment targets, improved from 2005 to 2016 and to investigate potential disparities in US diabetes care. DESIGN, SETTING, AND PARTICIPANTS Nationally representative, serial cross-sectional studies included in the 2005-2016 National Health and Nutrition Examination Survey were evaluated. Data on nonpregnant US adults (age Ն18 years) with diabetes who had reported fasting for 9 or more hours (n = 1742 diagnosed and 746 undiagnosed) were included. Data analysis was performed from August 1, 2018, to May 10, 2019.

Background Health-care resource constraints in low-income and middle-income countries necessitate the identification of cost-effective public health interventions to address COVID-19. We aimed to develop a dynamic COVID-19 microsimulation model to assess clinical and economic outcomes and cost-effectiveness of epidemic control strategies in KwaZulu-Natal province, South Africa. Methods We compared different combinations of five public health interventions: health-care testing alone, where diagnostic testing is done only for individuals presenting to health-care centres; contact tracing in households of cases; isolation centres, for cases not requiring hospital admission; mass symptom screening and molecular testing for symptomatic individuals by community health-care workers; and quarantine centres, for household contacts who test negative. We calibrated infection transmission rates to match effective reproduction number ( R e ) estimates reported in South Africa. We assessed two main epidemic scenarios for a period of 360 days, with an R e of 1·5 and 1·2. Strategies with incremental cost-effectiveness ratio (ICER) of less than US$3250 per year of life saved were considered cost-effective. We also did sensitivity analyses by varying key parameters ( R e values, molecular testing sensitivity, and efficacies and costs of interventions) to determine the effect on clinical and cost projections. Findings When R e was 1·5, health-care testing alone resulted in the highest number of COVID-19 deaths during the 360-day period. Compared with health-care testing alone, a combination of health-care testing, contact tracing, use of isolation centres, mass symptom screening, and use of quarantine centres reduced mortality by 94%, increased health-care costs by 33%, and was cost-effective (ICER $340 per year of life saved). In settings where quarantine centres were not feasible, a combination of health-care testing, contact tracing, use of isolation centres, and mass symptom screening was cost-effective compared with health-care testing alone (ICER $590 per year of life saved). When R e was 1·2, health-care testing, contact tracing, use of isolation centres, and use of quarantine centres was the least costly strategy, and no other strategies were cost-effective. In sensitivity analyses, a combination of health-care testing, contact tracing, use of isolation centres, mass symptom screening, and use of quarantine centres was generally cost-effective, with the exception of scenarios in which R e was 2·6 and when efficacies of isolation centres and quarantine centres for transmission reduction were reduced. Interpretation In South Africa, strategies involving household contact trac...

Many colleges and universities in the United States are attempting to continue undergraduate onsite learning and residential living during the COVID-19 pandemic. Modeling the outcomes, cost, and cost-effectiveness of mitigation strategies, such as social distancing, masking, and laboratory testing, can inform these efforts.

Key Points Question What are the projected clinical outcomes and costs associated with strategies for reducing severe acute respiratory syndrome coronavirus 2 infections among people experiencing sheltered homelessness? Findings In this decision analytic model, daily symptom screening with polymerase chain reaction (PCR) testing of individuals who had positive symptom screening paired with nonhospital care site management of people with mild to moderate coronavirus disease 2019 (COVID-19) was associated with a substantial decrease in infections and lowered costs over 4 months compared with no intervention across a wide range of epidemic scenarios. In a surging epidemic, adding periodic universal PCR testing to symptom screening and nonhospital care site management was associated with improved clinical outcomes at modestly increased costs. Meaning In this study, daily symptom screening with PCR testing of individuals who had positive symptom screening and use of alternative care sites for COVID-19 management among individuals experiencing sheltered homelessness were associated with substantially reduced new cases and costs compared with other strategies.

Background We projected the clinical and economic impact of alternative testing strategies on COVID-19 incidence and mortality in Massachusetts using a microsimulation model. Methods We compared five testing strategies: 1) PCR-severe-only: PCR testing only patients with severe/critical symptoms; 2) Self-screen: PCR-severe-only plus self-assessment of COVID-19-consistent symptoms with self-isolation if positive; 3) PCR-any-symptom: PCR for any COVID-19-consistent symptoms with self-isolation if positive; 4) PCR-all: PCR-any-symptom and one-time PCR for the entire population; and, 5) PCR-all-repeat: PCR-all with monthly re-testing. We examined effective reproduction numbers (Re, 0.9-2.0) at which policy conclusions would change. We used published data on disease progression and mortality, transmission, PCR sensitivity/specificity (70/100%) and costs. Model-projected outcomes included infections, deaths, tests performed, hospital-days, and costs over 180-days, as well as incremental cost-effectiveness ratios (ICERs, $/quality-adjusted life-year [QALY]). Results In all scenarios, PCR-all-repeat would lead to the best clinical outcomes and PCR-severe-only would lead to the worst; at Re 0.9, PCR-all-repeat vs. PCR-severe-only resulted in a 63% reduction in infections and a 44% reduction in deaths, but required >65-fold more tests/day with 4-fold higher costs. PCR-all-repeat had an ICER <$100,000/QALY only when Re≥1.8. At all Re values, PCR-any-symptom was cost-saving compared to other strategies. Conclusions Testing people with any COVID-19-consistent symptoms would be cost-saving compared to restricting testing to only those with symptoms severe enough to warrant hospital care. Expanding PCR testing to asymptomatic people would decrease infections, deaths, and hospitalizations. Universal screening would be cost-effective when paired with monthly retesting in settings where the COVID-19 pandemic is surging.

To our knowledge, this is the first clinical decision-making tool that generates personalized forecasts of the trajectory of OAG progression at different target IOP levels. This approach can help clinicians determine appropriate, personalized target IOPs for patients with OAG.

Background We projected the clinical and economic impact of alternative testing strategies on COVID-19 incidence and mortality in Massachusetts using a microsimulation model. Methods We compared four testing strategies: 1) Hospitalized: PCR testing only patients with severe/critical symptoms warranting hospitalization; 2) Symptomatic: PCR for any COVID-19-consistent symptoms, with self-isolation if positive; 3) Symptomatic+asymptomatic-once: Symptomatic and one-time PCR for the entire population; and, 4) Symptomatic+asymptomatic-monthly: Symptomatic with monthly re-testing for the entire population. We examined effective reproduction numbers (Re, 0.9-2.0) at which policy conclusions would change. We assumed homogeneous mixing among the Massachusetts population (excluding those residing in long-term care facilities). We used published data on disease progression and mortality, transmission, PCR sensitivity/specificity (70/100%) and costs. Model-projected outcomes included infections, deaths, tests performed, hospital-days, and costs over 180-days, as well as incremental cost-effectiveness ratios (ICER, $/quality-adjusted life-year [QALY]). Results At Re 0.9, Symptomatic+asymptomatic-monthly vs. Hospitalized resulted in a 64% reduction in infections and a 46% reduction in deaths, but required &gt;66-fold more tests/day with 5-fold higher costs. Symptomatic+asymptomatic-monthly had an ICER &lt;$100,000/QALY only when Re ≥1.6; when test cost was ≤$3, every 14-day testing was cost-effective at all Re examined. Conclusions Testing people with any COVID-19-consistent symptoms would be cost-saving compared to testing only those whose symptoms warrant hospital care. Expanding PCR testing to asymptomatic people would decrease infections, deaths, and hospitalizations. Despite modest sensitivity, low-cost, repeat screening of the entire population could be cost-effective in all epidemic settings.

As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial as an alternative diagnostic tool. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.