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INTRODUCTION: Diabetes has been identified as a high-risk comorbidity for COVID-19 hospitalization. We evaluated additional risk factors for COVID-19 hospitalization and in-hospital mortality in a nationwide US database. METHODS: This retrospective study utilized the UnitedHealth Group Clinical Discovery Database (January 1, 2019-July 15, 2020) containing de-identified nationwide administrative claims, SARS-CoV-2 laboratory test results, and COVID-19 inpatient admissions data. Logistic regression was used to understand risk factors for hospitalization and in-hospital mortality among people with type 2 diabetes (T2D) and in the overall population. Robustness of associations was further confirmed by subgroup and sensitivity analyses in the T2D population. RESULTS: A total of 36,364 people were identified who were either SARS-CoV-2+ or hospitalized for COVID-19. T2D was associated with increased COVID-19-related hospitalization and mortality. Factors associated with increased hospitalization risk were largely consistent in the overall population and the T2D subgroup, including age, male sex, and these top five comorbidities: dementia, metastatic tumor, congestive heart failure, paraplegia, and metabolic disease. Biguanides (mainly metformin) were consistently associated with lower odds of hospitalization, whereas sulfonylureas and insulins were associated with greater odds of hospitalization among people with T2D. CONCLUSION: In this nationwide US analysis, T2D was identified as an independent risk factor for COVID-19 complications. Many factors conferred similar risk of hospitalization across both populations; however, particular diabetes medications may be markers for differential risk. The insights on comorbidities and medications may inform population health initiatives, including prevention efforts for high-risk patient populations such as those with T2D.
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The focus of this work is to present the feasibility of lowering the supply and return temperatures of district heating networks in order to achieve energy savings through the implementation of feed-forward model predictive control. The current level of district heating technology dictates a need for higher supply temperatures, which is not the case when considering the future outlook. In part, this can be attributed to the fact that current networks are being controlled by operator experience and outdoor temperatures. The prospects of reducing network temperatures can be evaluated by developing a dynamic model of the process which can then be used for control purposes. Two scenarios are presented in this work, to not only evaluate a controller's performance in supplying lower network temperatures, but to also assess the boundaries of the return temperature. In Scenario 1, the historical load is used as a feed-forward signal to the controller, and in Scenario 2, a load prediction model is used as the feed-forward signal. The findings for both scenarios suggest that the new control approach can lead to a load reduction of 12.5% and 13.7% respectively for the heat being supplied to the network. With the inclusion of predictions with increased accuracy on end-user demand and feed-back, the return temperature values can be better sustained, and can lead to a decrease in supply temperatures and an increase in energy savings on the production side.
