Trends in type 2 diabetes detection among adults in the USA, 1999-2014

Objective To examine recent trends in type 2 diabetes detection among adults in the USA. Research design and methods We used data from the 1999–2014 National Health and Nutrition Examination Surveys on non-pregnant adults (aged ≥18 years) not reporting a diagnosis of diabetes (n=16 644 participants, averaging about 2000 for each 2-year cycle). We defined undiagnosed diabetes as a fasting plasma glucose ≥126 mg/dL or a hemoglobin A1c ≥6.5% (48 mmol/mol). We measured case detection as the probability of finding undiagnosed type 2 diabetes among the population without diagnosed diabetes. Linear regression models were used to examine trends overall and by sociodemographic characteristics (ie, age, gender, race/ethnicity, education, poverty-income ratio (PIR)). Results Age-standardized probability of finding undiagnosed type 2 diabetes was 3.0% (95% CI 2.1% to 4.2%) during 1999–2000 and 2.8% (2.2%–3.6%) during 2013–2014 (P for trend=0.52). Probability increased among Mexican-Americans (P for trend=0.01) but decreased among adults aged 65 years or older (P for trend=0.04), non-Hispanic (NH) white (P for trend=0.02), and adults in the highest PIR tertile (P for trend=0.047). For all other sociodemographic groups, no significant trends were detected. Conclusions We found little evidence of increased detection of undiagnosed type 2 diabetes among adults in the USA during the past 15 years. Although improvements were seen among NH white, older, and wealthy adults, these improvements were not large. As the scope of primary prevention efforts increases, case detection may improve

The impact of repeat hospitalizations on hospitalization rates for selected conditions among adults with and without diabetes, 12 US states, 2011

Introduction Hospitalization data typically cannot be used to estimate the number of individuals hospitalized annually because individuals are not tracked over time and may be hospitalized multiple times annually. We examined the impact of repeat hospitalizations on hospitalization rates for various conditions and on comparison of rates by diabetes status. Methods We analyzed hospitalization data for which repeat hospitalizations could be distinguished among adults aged 18 or older from 12 states using the 2011 Agency for Healthcare Research and Quality’s State Inpatient Databases. The Behavioral Risk Factor Surveillance System was used to estimate the number of adults with and without diagnosed diabetes in each state (denominator). We calculated percentage increases due to repeat hospitalizations in rates and compared the ratio of diabetes with non-diabetes rates while excluding and including repeat hospitalizations. Results Regardless of diabetes status, hospitalization rates were considerably higher when repeat hospitalizations within a calendar year were included. The magnitude of the differences varied by condition. Among adults with diabetes, rates ranged from 13.0% higher for stroke to 41.6% higher for heart failure; for adults without diabetes, these rates ranged from 9.5% higher for stroke to 25.2% higher for heart failure. Ratios of diabetes versus non-diabetes rates were similar with and without repeat hospitalizations. Conclusion Hospitalization rates that include repeat hospitalizations overestimate rates in individuals, and this overestimation is especially pronounced for some causes. However, the inclusion of repeat hospitalizations for common diabetes-related causes had little impact on rates by diabetes status

Modeling the impact of prevention policies on future diabetes prevalence in the United States: 2010-2030

Background Although diabetes is one of the most costly and rapidly increasing serious chronic diseases worldwide, the optimal mix of strategies to reduce diabetes prevalence has not been determined. Methods Using a dynamic model that incorporates national data on diabetes prevalence and incidence, migration, mortality rates, and intervention effectiveness, we project the effect of five hypothetical prevention policies on future US diabetes rates through 2030: 1) no diabetes prevention strategy; 2) a “high-risk” strategy, wherein adults with both impaired fasting glucose (IFG) (fasting plasma glucose of 100–124 mg/dl) and impaired glucose tolerance (IGT) (2-hour post-load glucose of 141–199 mg/dl) receive structured lifestyle intervention; 3) a “moderate-risk” strategy, wherein only adults with IFG are offered structured lifestyle intervention; 4) a “population-wide” strategy, in which the entire population is exposed to broad risk reduction policies; and 5) a “combined” strategy, involving both the moderate-risk and population-wide strategies. We assumed that the moderate- and high-risk strategies reduce the annual diabetes incidence rate in the targeted subpopulations by 12.5% through 2030 and that the population-wide approach would reduce the projected annual diabetes incidence rate by 2% in the entire US population. Results We project that by the year 2030, the combined strategy would prevent 4.6 million incident cases and 3.6 million prevalent cases, attenuating the increase in diabetes prevalence by 14%. The moderate-risk approach is projected to prevent 4.0 million incident cases, 3.1 million prevalent cases, attenuating the increase in prevalence by 12%. The high-risk and population approaches attenuate the projected prevalence increases by 5% and 3%, respectively. Even if the most effective strategy is implemented (the combined strategy), our projections indicate that the diabetes prevalence rate would increase by about 65% over the 23 years (i.e., from 12.9% in 2010 to 21.3% in 2030). Conclusions While implementation of appropriate diabetes prevention strategies may slow the rate of increase of the prevalence of diabetes among US adults through 2030, the US diabetes prevalence rate is likely to increase dramatically over the next 20 years. Demand for health care services for people with diabetes complications and diabetes-related disability will continue to grow, and these services will need to be strengthened along with primary diabetes prevention efforts

Influence of diabetes complications on HbA(1c) treatment goals among older US adults: A cost-effectiveness analysis

OBJECTIVE Guidelines on the standard care of diabetes recommend that glycemic treatment goals for older adults consider the patient’s complications and life expectancy. In this study, we examined the influence of diabetes complications and associated life expectancies on the cost-effectiveness (CE) of HbA1c treatment goals. RESEARCH DESIGN AND METHODS We used data from the 2011–2016 National Health and Nutrition Examination Survey (NHANES) to generate nationally representative subgroups of older individuals with diabetes with various health states. We used the Centers for Disease Control and Prevention–RTI International diabetes CE model to estimate the long-term consequences of two treatment goals—a stringent control goal (HbA1c $82,413 per QALY). Further, a stringent goal was not cost-effective when an individual had less than 7 years of life remaining. CONCLUSIONS Our findings support the guideline recommendation that glycemic goals for older adults should consider the complexity of their complications and their life expectancy from a CE perspective

Medical expenditures associated with diabetes among youth with medicaid coverage

Background: Information on diabetes-related excess medical expenditures for youth is important to understand the magnitude of financial burden and to plan the health care resources needed for managing diabetes. However, diabetes-related excess medical expenditures for youth covered by Medicaid program have not been investigated recently. Objective: To estimate excess diabetes-related medical expenditures among youth aged below 20 years enrolled in Medicaid programs in the United States. Methods: We analyzed data from 2008 to 2012 MarketScan multistate Medicaid database for 6502 youths with diagnosed diabetes and 6502 propensity score matched youths without diabetes, enrolled in fee-for-service payment plans. We stratified analysis by Medicaid eligibility criteria (poverty or disability). We used 2-part regression models to estimate diabetes-related excess medical expenditures, adjusted for age, sex, race/ethnicity, year of claims, depression status, asthma status, and interaction terms. Results: For poverty-based Medicaid enrollees, estimated annual diabetes-related total medical expenditure was $9046 per person [$3681 (no diabetes) vs. $12,727 (diabetes); P<0001], of which 41.7$9944 per person ($14,149 vs.$24,093; P<0001), of which 41.5% was accounted for by prescription drugs, 31.3% by inpatient, and 27.3% by outpatient care. Conclusions: The per capita annual diabetes-related medical expenditures in youth covered by publicly financed Medicaid programs are substantial, which is larger among those with disabilities than without disabilities. Identifying cost-effective ways of managing diabetes in this vulnerable segment of the youth population is needed

Development and demonstration of a state model for the estimation of incidence of partly undetected chronic diseases

Background Estimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection. Methods We developed a four-part compartment model for undiagnosed cases of irreversible chronic diseases with a preclinical state that precedes the diagnosis. Applicability of the model is tested in a simulation study of a hypothetical chronic disease and using diabetes data from the Health and Retirement Study (HRS). Results A two dimensional system of partial differential equations forms the basis for estimating incidence of the undiagnosed and diagnosed disease states from the prevalence of the associated states. In the simulation study we reach very good agreement between the estimates and the true values. Application to the HRS data demonstrates practical relevance of the methods. Discussion We have demonstrated the applicability of the modeling framework in a simulation study and in the analysis of the Health and Retirement Study. The model provides insight into the epidemiology of undiagnosed chronic diseases