Impact of Pre-existing Type 2 Diabetes Mellitus and Cardiovascular Disease on Healthcare Resource Utilization and Costs in Patients With COVID-19.

Background: The economic burden associated with type 2 diabetes mellitus (T2DM) and concurrent cardiovascular disease (CVD) among patients with COVID-19 is unclear. Objective: We compared healthcare resource utilization (HCRU) and costs in patients with COVID-19 and T2DM and CVD (T2DM + CVD), T2DM only, or neither T2DM nor CVD (T2DM/CVD). Methods: A retrospective observational study in COVID-19 patients using data from the Healthcare Integrated Research Database (HIRD®) was conducted. Patients with COVID-19 were identified between March 1, 2020, and May 31, 2021, and followed from first diagnosis or positive lab test to the end of health plan enrollment, end of study period, or death. Patients were assigned one of 3 cohorts: pre-existing T2DM+CVD, T2DM only, or neither T2DM/CVD. Propensity score matching and multivariable analyses were performed to control for differences in baseline characteristics. Study outcomes included all-cause and COVID-19-related HCRU and costs. Results: In all, 321 232 COVID-19 patients were identified (21 651 with T2DM + CVD, 28 184 with T2DM only, and 271 397 with neither T2DM/CVD). After matching, 6967 patients were in each group. Before matching, 46.0% of patients in the T2DM + CVD cohort were hospitalized for any cause, compared with 18.0% in the T2DM-only cohort and 6.3% in the neither T2DM/CVD cohort; the corresponding values after matching were 34.2%, 26.0%, and 21.2%. The proportion of patients with emergency department visits, telehealth visits, or use of skilled nursing facilities was higher in patients with COVID-19 and T2DM + CVD compared with the other cohorts. Average all-cause costs during follow-up were 12 324,7882, and $7277 per-patient-per-month after matching for patients with T2DM + CVD, T2DM-only, and neither T2DM/CVD, respectively. COVID-19-related costs contributed to 78%, 75%, and 64% of the overall costs, respectively. The multivariable model showed that per-patient-per-month all-cause costs for T2DM + CVD and T2DM-only were 54% and 21% higher, respectively, than those with neither T2DM/CVD after adjusting for residual confounding. Conclusion: HCRU and costs in patients were incrementally higher with COVID-19 and pre-existing T2DM + CVD compared with those with T2DM-only and neither T2DM/CVD, even after accounting for baseline differences between groups, confirming that pre-existing T2DM + CVD is associated with increased HCRU and costs in COVID-19 patients, highlighting the importance of proactive management.

Impact of pre-existing type 2 diabetes with and without cardiovascular disease on patients with COVID-19.

AIM: To compare adverse outcomes among COVID-19 patients with pre-existing type 2 diabetes (T2D) only, T2D and cardiovascular disease (CVD), or neither. METHODS: This retrospective cohort study used administrative claims, laboratory and mortality data from the HealthCore Integrated Research Database. Patients with COVID-19 were identified from 3 January 2020 to 31 May 2021 and stratified by the presence of T2D and CVD. Outcomes included hospitalization, intensive care unit (ICU) admission, mortality and complications following COVID-19 infection. Propensity score matching and multivariable analyses were performed. RESULTS: A total of 321 232 COVID-19 patients were identified (21 651 T2D + CVD, 28 184 T2D only, and 271 397 neither) with a mean (SD) follow-up of 5.4 (3.0) months. After matching, 6 967 patients were identified for each group, and residual baseline differences remained. Adjusted analyses showed that COVID-19 patients with T2D + CVD were 59% more probable to be hospitalized, 74% more probable to be admitted to the ICU, and had a 26% higher mortality risk than those with neither. COVID-19 patients with T2D only were 28% and 32% more probable to be admitted to the hospital and ICU than those with neither, respectively. Among all T2D + CVD patients, acute respiratory distress syndrome (31%) and acute kidney disease (24%) were observed. CONCLUSION: Our study highlights the incrementally poorer outcomes associated with pre-existing T2D + CVD in COVID-19 patients compared with those without T2D/CVD and suggests consideration of a more optimal management approach in these patients.

Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices.

The use of wearables is increasing and data from these devices could improve the prediction of changes in glycemic control. We conducted a randomized trial with adults with prediabetes who were given either a waist-worn or wrist-worn wearable to track activity patterns. We collected baseline information on demographics, medical history, and laboratory testing. We tested three models that predicted changes in hemoglobin A1c that were continuous, improved glycemic control by 5% or worsened glycemic control by 5%. Consistently in all three models, prediction improved when (a) machine learning was used vs. traditional regression, with ensemble methods performing the best; (b) baseline information with wearable data was used vs. baseline information alone; and (c) wrist-worn wearables were used vs. waist-worn wearables. These findings indicate that models can accurately identify changes in glycemic control among prediabetic adults, and this could be used to better allocate resources and target interventions to prevent progression to diabetes.

Effect of Gamification With and Without Financial Incentives to Increase Physical Activity Among Veterans Classified as Having Obesity or Overweight: A Randomized Clinical Trial.

Importance: Gamification is increasingly being used for health promotion but has not been well tested with financial incentives or among veterans. Objective: To test the effectiveness of gamification with social support, with and without a loss-framed financial incentive, to increase physical activity among veterans classified as having overweight and obesity. Design, Setting, and Participants: This 3-group randomized clinical trial had a 12-week intervention period and an 8-week follow-up period. Participants included veterans with a body mass index greater than or equal to 25 who were receiving care from a single site in Philadelphia, Pennsylvania. Participants underwent a remotely monitored intervention from March 19, 2019, to August 9, 2020. Data analyses were conducted between October 1, 2020, and November 14, 2020. Interventions: All participants received a wearable device to track step counts and selected a step goal. The control group received feedback from their devices only. Participants in the 2 gamification groups were entered into a 12-week game with points and levels designed using behavioral economic principles and selected a support partner to receive weekly updates. Participants in the loss-framed financial incentive group had $120 allocated to a virtual account and lost $10 if weekly goals were not achieved. Main Outcomes and Measures: The primary outcome was the change in mean daily steps from baseline during the intervention. Secondary outcomes include proportion of days goals were achieved and changes during follow-up. Results: A total of 180 participants were randomized, 60 to the gamification with social support group, 60 to the gamification with social support and loss-framed financial incentives group, and 60 to the control group. The participants had a mean (SD) age of 56.5 (12.9) years and a mean (SD) body mass index of 33.0 (5.6); 71 participants (39.4%) were women, 90 (50.0%) were White, and 67 (37.2%) were Black. During the intervention period, compared with control group participants, participants in the gamification with financial incentives group had a significant increase in mean daily steps from baseline (adjusted difference, 1224 steps; 95% CI, 451 to 1996 steps; P = .005), but participants in the gamification without financial incentives group did not (adjusted difference, 433 steps; 95% CI, -337 to 1203 steps; P = .81). The increase for the gamification with financial incentives group was not sustained during the follow-up period, and the step count was not significantly different than that of the control group (adjusted difference, 564 steps; 95% CI, -261 to 1389 steps; P = .37). Compared with the control group, participants in the intervention groups had a significantly higher adjusted proportion of days meeting their step goal during the main intervention and follow-up period (gamification with social support group, adjusted difference from control, 0.21 participant-day; 95% CI, 0.18-0.24 participant-day; P < .001; gamification with social support and loss-framed financial incentive group, adjusted difference from control, 0.34 participant-day; 95% CI, 0.31-0.37 participant-day; P < .001). Conclusions and Relevance: Among veterans classified as having overweight and obesity, gamification with social support combined with loss-framed financial incentives was associated with a modest increase in physical activity during the intervention period, but the increase was not sustained during follow-up. Gamification without incentives did not significantly change physical activity. Trial Registration: ClinicalTrials.gov Identifier: NCT03563027.

Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): Design and rationale of a randomized trial of patients discharged from the hospital to home.

BACKGROUND: Hospital readmission prediction models often perform poorly. A critical limitation is that they use data collected up until the time of discharge but do not leverage information on patient behaviors at home after discharge. METHODS: PREDICT is a two-arm, randomized trial comparing ways to use remotely-monitored patient activity levels after hospital discharge to improve hospital readmission prediction models. Patients are randomly assigned to use a wearable device or smartphone application to track physical activity data. The study collects also validated assessments on patient characteristics as well as disparate data on credit scores and medication adherence. Patients are followed for 6 months. We evaluate whether these data sources can improve prediction compared to standard modelling approaches. CONCLUSION: The PREDICT Trial tests a novel method of remotely-monitoring patient behaviors after hospital discharge. Findings from the trial could inform new ways to improve the identification of patients at high-risk for hospital readmission. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT02983812.