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On April 26, 2009, the United States declared a public health emergency in response to the 2009 pandemic influenza A(H1N1)pdm09 virus (1). The Centers for Disease Control and Prevention (CDC) [...]

Objective: We examined changes in glucose-lowering medication spending and quantified the magnitude of factors that are contributing to these changes.Research Design and Methods: Using the Medical Expenditure Panel Survey, we estimated the change in spending on glucose-lowering medications during 2005-2007 and 2015-2017 among adults aged ≥18 years with diabetes. We decomposed the increase in total spending by medication groups: for insulin, by human and analog; and for noninsulin, by metformin, older, newer, and combination medications. For each group, we quantified the contributions by the number of users and cost-per-user. Costs were in 2017 U.S. dollars.Results: National spending on glucose-lowering medications increased by $40.6 billion (240%), of which insulin and noninsulin medications contributed $28.6 billion (169%) and $12.0 billion (71%), respectively. For insulin, the increase was mainly associated with higher expenditures from analogs (156%). For noninsulin, the increase was a net effect of higher cost for newer medications (+88%) and decreased cost for older medications (-34%). Most of the increase in insulin spending came from the increase in cost-per-user. However, the increase in the number of users contributed more than cost-per-user in the rise of most noninsulin groups.Conclusions: The increase in national spending on glucose-lowering medications during the past decade was mostly associated with the increased costs for insulin, analogs in particular, and newer noninsulin medicines, and cost-per-user had a larger effect than the number of users. Understanding the factors contributing to the increase helps identify ways to curb the growth in costs. [ABSTRACT FROM AUTHOR]

Objective: To examine changes in diabetes-related preventable hospitalization costs and to determine the contribution of each underlying factor to these changes.Research Design and Methods: We used data from the 2001-2014 U.S. National (Nationwide) Inpatient Sample (NIS) for adults (≥18 years old) to estimate the trends in hospitalization costs (2014 USD) in total and by condition (short-term complications, long-term complications, uncontrolled diabetes, and lower-extremity amputation). Using regression and growth models, we estimated the relative contribution of following underlying factors: total number of hospitalizations, rate of hospitalization, the number of people with diabetes, mean cost per admission, length of stay, and cost per day.Results: During 2001-2014, the estimated total cost of diabetes-related preventable hospitalizations increased annually by 1.6% (92.9 million USD; P < 0.001). Of this 1.6% increase, 75% (1.2%) was due to the increase in the number of hospitalizations, which is a result of a 3.8% increase in diabetes population and a 2.6% decrease in the hospitalization rate, and 25% (0.4%) was due to the increase in cost per admission, for a net result of a 1.6% increase in cost per day and a 1.3% decline in mean length of stay. By component, the cost of short-term complications, lower-extremity amputations, and long-term complications increased annually by 4.2, 1.9, and 1.5%, respectively, while the cost of uncontrolled diabetes declined annually by 2.6%.Conclusions: The total cost of diabetes-related preventable hospitalizations had been increasing during 2001-2014, mainly resulting from increases in number of people with diabetes and cost per hospitalization day. The underlying factors identified in our study could lead to efforts that may lower future hospitalization costs. [ABSTRACT FROM AUTHOR]

Objective: We assessed the excess medical expenditures for adults newly diagnosed with diabetes, for up to 10 years before and after diabetes diagnosis.Research Design and Methods: Using the 2001-2013 MarketScan data, we identified people with newly diagnosed diabetes among adults aged 25-64 years (diabetes cohort) and matched them with people who did not have diagnosed diabetes (control cohort) using 1:1 propensity score matching. We followed these two cohorts up to ±10 years from the index date, with annual matched cohort sizes ranging from 3,922 to 39,726 individuals. We estimated the yearly and cumulative excess medical expenditures of the diabetes cohorts before and after the diagnosis of diabetes.Results: The per capita annual total excess medical expenditure for the diabetes cohort was higher for the entire 10 years prior to their index date, ranging between $1,043 in year -10 and $4,492 in year -1. Excess expenditure spiked in year 1 ($8,109), declined in year 2, and then increased steadily, ranging from $4,261 to $6,162 in years 2-10. The cumulative excess expenditure for the diabetes cohort during the entire 20 years of follow-up was $69,177 ($18,732 before and $50,445 after diagnosis).Conclusions: People diagnosed with diabetes had higher medical expenditures compared with their counterparts, not only after diagnosis but also up to 10 years prior to diagnosis. Managing risk factors for type 2 diabetes and cardiovascular disease before diagnosis, and for diabetes-related complications after diagnosis, could alleviate medical expenditure in people with diabetes. [ABSTRACT FROM AUTHOR]

Rationale & objective: As the prevalence of obesity continues to rise in the United States, it is important to understand its impact on the lifetime risk of chronic kidney disease (CKD). Study design: The CKD Health Policy Model was used to simulate the lifetime risk of CKD for those with and without obesity at baseline. Model structure was updated for glomerular filtration rate (GFR) decline to incorporate new longitudinal data from the Chronic Renal Insufficiency Cohort (CRIC) study. Setting and population: The updated model was populated with a nationally representative cohort from National Health and Nutrition Examination Survey (NHANES). Outcomes: Lifetime risk of CKD, highest stage and any stage. Model, perspective, & timeframe: Simulation model following up individuals from current age through death or age 90 years. Results: Lifetime risk of any CKD stage was 32.5% (95% CI 28.6%–36.3%) for persons with normal weight, 37.6% (95% CI 33.5%–41.7%) for persons who were overweight, and 41.0% (95% CI 36.7%–45.3%) for persons with obesity at baseline. The difference between persons with normal weight and persons with obesity at baseline was statistically significant (p<0.01). Lifetime risk of CKD stages 4 and 5 was higher for persons with obesity at baseline (Stage 4: 2.1%, 95% CI 0.9%–3.3%; stage 5: 0.6%, 95% CI 0.0%–1.1%), but the differences were not statistically significant (stage 4: p = 0.08; stage 5: p = 0.23). Limitations: Due to limited data, our simulation model estimates are based on assumptions about the causal pathways from obesity to CKD, diabetes, and hypertension. Conclusions: The results of this study indicate that obesity may have a large impact on the lifetime risk of CKD. This is important information for policymakers seeking to set priorities and targets for CKD prevention and treatment. [ABSTRACT FROM AUTHOR]

Background: Better treatment during early stages of chronic kidney disease (CKD) may slow progression to end-stage renal disease and decrease associated complications and medical costs. Achieving early treatment of CKD is challenging, however, because a large fraction of persons with CKD are unaware of having this disease. Screening for CKD is one important method for increasing awareness. We examined the cost-effectiveness of identifying persons for early-stage CKD screening (i.e., screening for moderate albuminuria) using published CKD risk scores.Methods: We used the CKD Health Policy Model, a micro-simulation model, to simulate the cost-effectiveness of using CKD two published risk scores by Bang et al. and Kshirsagar et al. to identify persons in the US for CKD screening with testing for albuminuria. Alternative risk score thresholds were tested (0.20, 0.15, 0.10, 0.05, and 0.02) above which persons were assigned to receive screening at alternative intervals (1-, 2-, and 5-year) for follow-up screening if the first screening was negative. We examined incremental cost-effectiveness ratios (ICERs), incremental lifetime costs divided by incremental lifetime QALYs, relative to the next higher screening threshold to assess cost-effectiveness. Cost-effective scenarios were determined as those with ICERs less than $50,000 per QALY. Among the cost-effective scenarios, the optimal scenario was determined as the one that resulted in the highest lifetime QALYs.Results: ICERs ranged from $8,823 per QALY to $124,626 per QALY for the Bang et al. risk score and $6,342 per QALY to $405,861 per QALY for the Kshirsagar et al. risk score. The Bang et al. risk score with a threshold of 0.02 and 2-year follow-up screening was found to be optimal because it had an ICER less than $50,000 per QALY and resulted in the highest lifetime QALYs.Conclusions: This study indicates that using these CKD risk scores may allow clinicians to cost-effectively identify a broader population for CKD screening with testing for albuminuria and potentially detect people with CKD at earlier stages of the disease than current approaches of screening only persons with diabetes or hypertension. [ABSTRACT FROM AUTHOR]