Projection of the future diabetes burden in the United States through 2060.

BACKGROUND: In the United States, diabetes has increased rapidly, exceeding prior predictions. Projections of the future diabetes burden need to reflect changes in incidence, mortality, and demographics. We applied the most recent data available to develop an updated projection through 2060. METHODS: A dynamic Markov model was used to project prevalence of diagnosed diabetes among US adults by age, sex, and race (white, black, other). Incidence and current prevalence were from the National Health Interview Survey (NHIS) 1985-2014. Relative mortality was from NHIS 2000-2011 follow-up data linked to the National Death Index. Future population estimates including birth, death, and migration were from the 2014 Census projection. RESULTS: The projected number and percent of adults with diagnosed diabetes would increase from 22.3 million (9.1%) in 2014 to 39.7 million (13.9%) in 2030, and to 60.6 million (17.9%) in 2060. The number of people with diabetes aged 65 years or older would increase from 9.2 million in 2014 to 21.0 million in 2030, and to 35.2 million in 2060. The percent prevalence would increase in all race-sex groups, with black women and men continuing to have the highest diabetes percent prevalence, and black women and women of other race having the largest relative increases. CONCLUSIONS: By 2060, the number of US adults with diagnosed diabetes is projected to nearly triple, and the percent prevalence double. Our estimates are essential to predict health services needs and plan public health programs aimed to reduce the future burden of diabetes.

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%, 34.0%, and 24.3% were accounted for by prescription drugs, outpatient, and inpatient care, respectively. For disability-based Medicaid enrollees, the estimated annual diabetes-related total medical expenditure was $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.

Using multi-year national survey cohorts for period estimates: an application of weighted discrete Poisson regression for assessing annual national mortality in US adults with and without diabetes, 2000-2006.

BACKGROUND: Monitoring national mortality among persons with a disease is important to guide and evaluate progress in disease control and prevention. However, a method to estimate nationally representative annual mortality among persons with and without diabetes in the United States does not currently exist. The aim of this study is to demonstrate use of weighted discrete Poisson regression on national survey mortality follow-up data to estimate annual mortality rates among adults with diabetes. METHODS: To estimate mortality among US adults with diabetes, we applied a weighted discrete time-to-event Poisson regression approach with post-stratification adjustment to national survey data. Adult participants aged 18 or older with and without diabetes in the National Health Interview Survey 1997-2004 were followed up through 2006 for mortality status. We estimated mortality among all US adults, and by self-reported diabetes status at baseline. The time-varying covariates used were age and calendar year. Mortality among all US adults was validated using direct estimates from the National Vital Statistics System (NVSS). RESULTS: Using our approach, annual all-cause mortality among all US adults ranged from 8.8 deaths per 1,000 person-years (95% confidence interval [CI]: 8.0, 9.6) in year 2000 to 7.9 (95% CI: 7.6, 8.3) in year 2006. By comparison, the NVSS estimates ranged from 8.6 to 7.9 (correlation = 0.94). All-cause mortality among persons with diabetes decreased from 35.7 (95% CI: 28.4, 42.9) in 2000 to 31.8 (95% CI: 28.5, 35.1) in 2006. After adjusting for age, sex, and race/ethnicity, persons with diabetes had 2.1 (95% CI: 2.01, 2.26) times the risk of death of those without diabetes. CONCLUSION: Period-specific national mortality can be estimated for people with and without a chronic condition using national surveys with mortality follow-up and a discrete time-to-event Poisson regression approach with post-stratification adjustment.

Changes in Disparity in County-Level Diagnosed Diabetes Prevalence and Incidence in the United States, between 2004 and 2012.

BACKGROUND: In recent decades, the United States experienced increasing prevalence and incidence of diabetes, accompanied by large disparities in county-level diabetes prevalence and incidence. However, whether these disparities are widening, narrowing, or staying the same has not been studied. We examined changes in disparity among U.S. counties in diagnosed diabetes prevalence and incidence between 2004 and 2012. METHODS: We used 2004 and 2012 county-level diabetes (type 1 and type 2) prevalence and incidence data, along with demographic, socio-economic, and risk factor data from various sources. To determine whether disparities widened or narrowed over the time period, we used a regression-based ß-convergence approach, accounting for spatial autocorrelation. We calculated diabetes prevalence/incidence percentage point (ppt) changes between 2004 and 2012 and modeled these changes as a function of baseline diabetes prevalence/incidence in 2004. Covariates included county-level demographic and, socio-economic data, and known type 2 diabetes risk factors (obesity and leisure-time physical inactivity). RESULTS: For each county-level ppt increase in diabetes prevalence in 2004 there was an annual average increase of 0.02 ppt (p<0.001) in diabetes prevalence between 2004 and 2012, indicating a widening of disparities. However, after accounting for covariates, diabetes prevalence decreased by an annual average of 0.04 ppt (p<0.001). In contrast, changes in diabetes incidence decreased by an average of 0.04 ppt (unadjusted) and 0.09 ppt (adjusted) for each ppt increase in diabetes incidence in 2004, indicating a narrowing of county-level disparities. CONCLUSIONS: County-level disparities in diagnosed diabetes prevalence in the United States widened between 2004 and 2012, while disparities in incidence narrowed. Accounting for demographic and, socio-economic characteristics and risk factors for type 2 diabetes narrowed the disparities, suggesting that these factors are strongly associated with changes in disparities. Public health interventions that target modifiable risk factors, such as obesity and physical inactivity, in high burden counties might further reduce disparities in incidence and, over time, in prevalence.

Compression of disability between two birth cohorts of US adults with diabetes, 1992-2012: a prospective longitudinal analysis.

BACKGROUND: The life expectancy of the average American with diabetes has increased, but the quality of health and functioning during those extra years are unknown. We aimed to investigate the net effect of recent trends in diabetes incidence, disability, and mortality on the average age of disability onset and the number of healthy and disabled years lived by adults with and without diabetes in the USA. We assessed whether disability expanded or was compressed in the population with diabetes and compared the findings with those for the population without diabetes in two consecutive US birth cohorts aged 50-70 years. METHODS: In this prospective longitudinal analysis, we analysed data for two cohorts of US adults aged 50-70 years from the Health and Retirement Study, including 1367 people with diabetes and 11 414 without diabetes. We assessed incident disability, remission from disability, and mortality between population-based cohort 1 (born 1931-41, follow-up 1992-2002) and cohort 2 (born 1942-47, follow up 2002-12). Disability was defined by mobility loss, difficulty with one or more instrumental activities of daily living, and difficulty with one or more activities of daily living. We entered age-specific probabilities representing the two birth cohorts into a five-state Markov model to estimate the number of years of disabled and disability-free life and life-years lost by age 70 years. FINDINGS: In people with diabetes, compared with cohort 1 (n=1067), cohort 2 (n=300) had more disability-free and total years of life, later onset of disability, and fewer disabled years. Simulations of the Markov models suggest that in men with diabetes aged 50 years, this difference between cohorts amounted to a 0·8-2·3 year delay in disability across the three metrics (mobility, 63·0 [95% CI 62·3-63·6] to 64·8 [63·6-65·7], p=0·01; instrumental activities of daily living, 63·5 [63·0-64·0] to 64·3 [63·0-65·3], p=0·24; activities of daily living, 62·7 [62·1-63·3] to 65·0 [63·5-65·9], p<0·0001) and 1·3 fewer life-years lost (ie, fewer remaining life-years up to age 70 years; from 2·8 [2·5-3·2] to 1·5 [1·3-1·9]; p<0·0001 for all three measures of disability). Among women with diabetes aged 50 years, this difference between cohorts amounted to a 1·1-2·3 year delay in disability across the three metrics (mobility, 61·3 [95% CI 60·5-62·1] to 63·2 [61·5-64·5], p=0·0416; instrumental activities of daily living, 63·0 [62·4-63·7] to 64·1 [62·7-65·2], p=0·16; activities of daily living, 62·3 [61·6-63·0] to 64·6 [63·1-65·6], p<0·0001) and 0·8 fewer life-years lost by age 70 years (1·9 [1·7-2·2] to 1·1 [0·9-1·5]; p<0·0001 for all three measures of disability). Parallel improvements were gained between cohorts of adults without diabetes (cohort 1, n=8687; cohort 2, n=2727); within both cohorts, those without diabetes had significantly more disability-free years than those with diabetes (p<0·0001 for all comparisons). INTERPRETATION: Irrespective of diabetes status, US adults saw a compression of disability and gains in disability-free life-years. The decrease in disability onset due to primary prevention of diabetes could play an important part in achieving longer disability-free life-years. FUNDING: US Department of Health & Human Services and the US Centers for Disease Control and Prevention.

Disability-Free Life-Years Lost Among Adults Aged ≥50 Years With and Without Diabetes.

OBJECTIVE: To quantify the impact of diabetes status on healthy and disabled years of life for older adults in the U.S. and provide a baseline from which to evaluate ongoing national public health efforts to prevent and control diabetes and disability. RESEARCH DESIGN AND METHODS: Adults (n = 20,008) aged 50 years and older were followed from 1998 to 2012 in the Health and Retirement Study, a prospective biannual survey of a nationally representative sample of adults. Diabetes and disability status (defined by mobility loss, difficulty with instrumental activities of daily living [IADL], and/or difficulty with activities of daily living [ADL]) were self-reported. We estimated incidence of disability, remission to nondisability, and mortality. We developed a discrete-time Markov simulation model with a 1-year transition cycle to predict and compare lifetime disability-related outcomes between people with and without diabetes. Data represent the U.S. population in 1998. RESULTS: From age 50 years, adults with diabetes died 4.6 years earlier, developed disability 6-7 years earlier, and spent about 1-2 more years in a disabled state than adults without diabetes. With increasing baseline age, diabetes was associated with significant (P < 0.05) reductions in the number of total and disability-free life-years, but the absolute difference in years between those with and without diabetes was less than at younger baseline age. Men with diabetes spent about twice as many of their remaining years disabled (20-24% of remaining life across the three disability definitions) as men without diabetes (12-16% of remaining life across the three disability definitions). Similar associations between diabetes status and disability-free and disabled years were observed among women. CONCLUSIONS: Diabetes is associated with a substantial reduction in nondisabled years, to a greater extent than the reduction of longevity.

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.

Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012.

IMPORTANCE: Although the prevalence and incidence of diabetes have increased in the United States in recent decades, no studies have systematically examined long-term, national trends in the prevalence and incidence of diagnosed diabetes. OBJECTIVE: To examine long-term trends in the prevalence and incidence of diagnosed diabetes to determine whether there have been periods of acceleration or deceleration in rates. DESIGN, SETTING, AND PARTICIPANTS: We analyzed 1980-2012 data for 664,969 adults aged 20 to 79 years from the National Health Interview Survey (NHIS) to estimate incidence and prevalence rates for the overall civilian, noninstitutionalized, US population and by demographic subgroups (age group, sex, race/ethnicity, and educational level). MAIN OUTCOMES AND MEASURES: The annual percentage change (APC) in rates of the prevalence and incidence of diagnosed diabetes (type 1 and type 2 combined). RESULTS: The APC for age-adjusted prevalence and incidence of diagnosed diabetes did not change significantly during the 1980s (for prevalence, 0.2% [95% CI, -0.9% to 1.4%], P = .69; for incidence, -0.1% [95% CI, -2.5% to 2.4%], P = .93), but each increased sharply during 1990-2008 (for prevalence, 4.5% [95% CI, 4.1% to 4.9%], P < .001; for incidence, 4.7% [95% CI, 3.8% to 5.6%], P < .001) before leveling off with no significant change during 2008-2012 (for prevalence, 0.6% [95% CI, -1.9% to 3.0%], P = .64; for incidence, -5.4% [95% CI, -11.3% to 0.9%], P = .09). The prevalence per 100 persons was 3.5 (95% CI, 3.2 to 3.9) in 1990, 7.9 (95% CI, 7.4 to 8.3) in 2008, and 8.3 (95% CI, 7.9 to 8.7) in 2012. The incidence per 1000 persons was 3.2 (95% CI, 2.2 to 4.1) in 1990, 8.8 (95% CI, 7.4 to 10.3) in 2008, and 7.1 (95% CI, 6.1 to 8.2) in 2012. Trends in many demographic subpopulations were similar to these overall trends. However, incidence rates among non-Hispanic black and Hispanic adults continued to increase (for interaction, P = .03 for non-Hispanic black adults and P = .01 for Hispanic adults) at rates significantly greater than for non-Hispanic white adults. In addition, the rate of increase in prevalence was higher for adults who had a high school education or less compared with those who had more than a high school education (for interaction, P = .006 for

The lifetime cost of diabetes and its implications for diabetes prevention.

OBJECTIVE: To assess the cost implications of diabetes prevention, it is important to know the lifetime medical cost of people with diabetes relative to those without. We derived such estimates using data representative of the U.S. national population. RESEARCH DESIGN AND METHODS: We aggregated annual medical expenditures from the age of diabetes diagnosis to death to determine lifetime medical expenditure. Annual medical expenditures were estimated by sex, age at diagnosis, and diabetes duration using data from 2006-2009 Medical Expenditure Panel Surveys, which were linked to data from 2005-2008 National Health Interview Surveys. We combined survival data from published studies with the estimated annual expenditures to calculate lifetime spending. We then compared lifetime spending for people with diabetes with that for those without diabetes. Future spending was discounted at 3% annually. RESULTS: The discounted excess lifetime medical spending for people with diabetes was $124,600 ($211,400 if not discounted), $91,200 ($135,600), $53,800 ($70,200), and $35,900 ($43,900) when diagnosed with diabetes at ages 40, 50, 60, and 65 years, respectively. Younger age at diagnosis and female sex were associated with higher levels of lifetime excess medical spending attributed to diabetes. CONCLUSIONS: Having diabetes is associated with substantially higher lifetime medical expenditures despite being associated with reduced life expectancy. If prevention costs can be kept sufficiently low, diabetes prevention may lead to a reduction in long-term medical costs.

Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985-2011: a modelling study.

BACKGROUND: Diabetes incidence has increased and mortality has decreased greatly in the USA, potentially leading to substantial changes in the lifetime risk of diabetes. We aimed to provide updated estimates for the lifetime risk of development of diabetes and to assess the effect of changes in incidence and mortality on lifetime risk and life-years lost to diabetes in the USA. METHODS: We incorporated data about diabetes incidence from the National Health Interview Survey, and linked data about mortality from 1985 to 2011 for 598 216 adults, into a Markov chain model to estimate remaining lifetime diabetes risk, years spent with and without diagnosed diabetes, and life-years lost due to diabetes in three cohorts: 1985-89, 1990-99, and 2000-11. Diabetes was determined by self-report and was classified as any diabetes, excluding gestational diabetes. We used logistic regression to estimate the incidence of diabetes and Poisson regression to estimate mortality. FINDINGS: On the basis of 2000-11 data, lifetime risk of diagnosed diabetes from age 20 years was 40·2% (95% CI 39·2-41·3) for men and 39·6% (38·6-40·5) for women, representing increases of 20 percentage points and 13 percentage points, respectively, since 1985-89. The highest lifetime risks were in Hispanic men and women, and non-Hispanic black women, for whom lifetime risk now exceeds 50%. The number of life-years lost to diabetes when diagnosed at age 40 years decreased from 7·7 years (95% CI 6·5-9·0) in 1990-99 to 5·8 years (4·6-7·1) in 2000-11 in men, and from 8·7 years (8·4-8·9) to 6·8 years (6·7-7·0) in women over the same period. Because of the increasing diabetes prevalence, the average number of years lost due to diabetes for the population as a whole increased by 46% in men and 44% in women. Years spent with diabetes increased by 156% in men and 70% in women. INTERPRETATION: Continued increases in the incidence of diagnosed diabetes combined with declining mortality have led to an acceleration of lifetime risk and more years spent with diabetes, but fewer years lost to the disease for the average individual with diabetes. These findings mean that there will be a continued need for health services and extensive costs to manage the disease, and emphasise the need for effective interventions to reduce incidence. FUNDING: None.

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.

Small area variation in diabetes prevalence in Puerto Rico.

OBJECTIVE: To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. METHODS: A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. RESULTS: The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. CONCLUSIONS: These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

Small area variation in diabetes prevalence in Puerto Rico / Variación en un área pequeña de la prevalencia de la diabetes en Puerto Rico

OBJECTIVE: To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults > 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. METHODS: A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. RESULTS: The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. CONCLUSIONS: These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

OBJETIVO: Calcular la prevalencia en el año 2009 de casos con diagnóstico de diabetes en Puerto Rico en adultos de 20 años de edad o mayores, para conocer mejor su distribución geográfica con objeto de que los responsables políticos puedan encauzar más eficientemente los programas de prevención y control. MÉTODOS: Se ajustó un modelo multinivel bayesiano a la combinación de datos del Sistema de Vigilancia de Factores de Riesgo del Comportamiento 2008-2010 y del Censo de los Estados Unidos del 2009 para calcular la prevalencia de la diabetes en cada uno de los 78 municipios de Puerto Rico. RESULTADOS: El cálculo del valor medio no ajustado para todos los municipios fue de 14,3% (intervalo por municipio de 9,9 a 18,0%). La amplitud promedio de los intervalos de confianza fue de 6,2%. Hubo poca diferencia entre los cálculos ajustados y los no ajustados. CONCLUSIONES: Los valores obtenidos mediante estos cálculos correspondientes a 78 municipios fueron por término medio más elevados y mostraron menor variabilidad (es decir, el intervalo era más pequeño) que los cálculos anteriormente publicados sobre la prevalencia de la diabetes en todos los municipios de los Estados Unidos en el 2008 (media, 9,9%; intervalo de 3,0 a 18,2%).

Medical expenditures associated with major depressive disorder among privately insured working-age adults with diagnosed diabetes in the United States, 2008.

AIM: We aimed at estimating excess medical expenditures associated with major depressive disorder (MDD) among working-age adults diagnosed with diabetes, disaggregated by treatment mode: insulin-treated diabetes (ITDM) or non-insulin-treated diabetes (NITDM). METHODS: We analyzed data for over 500,000 individuals with diagnosed diabetes from the 2008 U.S. MarketScan claims database. We grouped diabetic patients first by treatment mode (ITDM or NITDM), then by MDD status (with or without MDD), and finally by whether those with MDD used antidepressant medication. We estimated annual mean excess outpatient, inpatient, prescription drug, and total expenditures using regression models, controlling for demographics, types of health coverage, and comorbidities. RESULTS: Among persons having ITDM, the estimated annual total mean expenditure for those with no MDD (the comparison group) was $19,625. For those with MDD, the expenditures were $12,406 (63%) larger if using antidepressant medication and $7322 (37%) larger if not using antidepressant medication. Among persons having NITDM, the corresponding estimated expenditure for the comparison group was $10,746, the excess expenditures were $10,432 (97%) larger if using antidepressant medication and $5579 (52%) larger if not using antidepressant medication, respectively. Inpatient excess expenditures were the largest of total excess expenditure for those with ITDM and MDD treated with antidepressant medication; for all others with diabetes and MDD, outpatient expenditures were the largest excess expenditure. CONCLUSIONS: Among working-age adults with diabetes, MDD was associated with substantial excess medical expenditures. Implementing the effective interventions demonstrated in clinical trials and treatment guidelines recommended by professional organizations might reduce the economic burden of MDD in this population.

Bayesian Small Area Estimates of Diabetes Incidence by United States County, 2009.

In the United States, diabetes is common and costly. Programs to prevent new cases of diabetes are often carried out at the level of the county, a unit of local government. Thus, efficient targeting of such programs requires county-level estimates of diabetes incidence-the fraction of the non-diabetic population who received their diagnosis of diabetes during the past 12 months. Previously, only estimates of prevalence-the overall fraction of population who have the disease-have been available at the county level. Counties with high prevalence might or might not be the same as counties with high incidence, due to spatial variation in mortality and relocation of persons with incident diabetes to another county. Existing methods cannot be used to estimate county-level diabetes incidence, because the fraction of the population who receive a diabetes diagnosis in any year is too small. Here, we extend previously developed methods of Bayesian small-area estimation of prevalence, using diffuse priors, to estimate diabetes incidence for all U.S. counties based on data from a survey designed to yield state-level estimates. We found high incidence in the southeastern United States, the Appalachian region, and in scattered counties throughout the western U.S. Our methods might be applicable in other circumstances in which all cases of a rare condition also must be cases of a more common condition (in this analysis, "newly diagnosed cases of diabetes" and "cases of diabetes"). If appropriate data are available, our methods can be used to estimate proportion of the population with the rare condition at greater geographic specificity than the data source was designed to provide.

Projections of type 1 and type 2 diabetes burden in the U.S. population aged <20 years through 2050: dynamic modeling of incidence, mortality, and population growth.

OBJECTIVE: To forecast the number of U.S. individuals aged <20 years with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) through 2050, accounting for changing demography and diabetes incidence. RESEARCH DESIGN AND METHODS: We used Markov modeling framework to generate yearly forecasts of the number of individuals in each of three states (diabetes, no diabetes, and death). We used 2001 prevalence and 2002 incidence of T1DM and T2DM from the SEARCH for Diabetes in Youth study and U.S. Census Bureau population demographic projections. Two scenarios were considered for T1DM and T2DM incidence: 1) constant incidence over time; 2) for T1DM yearly percentage increases of 3.5, 2.2, 1.8, and 2.1% by age-groups 0-4 years, 5-9 years, 10-14 years, and 15-19 years, respectively, and for T2DM a yearly 2.3% increase across all ages. RESULTS: Under scenario 1, the projected number of youth with T1DM rises from 166,018 to 203,382 and with T2DM from 20,203 to 30,111, respectively, in 2010 and 2050. Under scenario 2, the number of youth with T1DM nearly triples from 179,388 in 2010 to 587,488 in 2050 (prevalence 2.13/1,000 and 5.20/1,000 [+144% increase]), with the greatest increase in youth of minority racial/ethnic groups. The number of youth with T2DM almost quadruples from 22,820 in 2010 to 84,131 in 2050; prevalence increases from 0.27/1,000 to 0.75/1,000 (+178% increase). CONCLUSIONS: A linear increase in diabetes incidence could result in a substantial increase in the number of youth with T1DM and T2DM over the next 40 years, especially those of minority race/ethnicity.

Smoking and visual impairment among older adults with age-related eye diseases.

INTRODUCTION: Tobacco use is the leading preventable cause of death in the United States. Visual impairment, a common cause of disability in the United States, is associated with shorter life expectancy and lower quality of life. The relationship between smoking and visual impairment is not clearly understood. We assessed the association between smoking and visual impairment among older adults with age-related eye diseases. METHODS: We analyzed Behavioral Risk Factor Surveillance System data from 2005 through 2008 on older adults with age-related eye diseases (cataract, glaucoma, age-related macular degeneration, and diabetic retinopathy; age ≥50 y, N = 36,522). Visual impairment was defined by self-reported difficulty in recognizing a friend across the street or difficulty in reading print or numbers. Current smokers were respondents who reported having smoked at least 100 cigarettes ever and still smoked at the time of interview. Former smokers were respondents who reported having ever smoked at least 100 cigarettes but currently did not smoke. We used multivariate logistic regressions to examine the association and to adjust for potential confounders. RESULTS: Among respondents with age-related eye diseases, the estimated prevalence of visual impairment was higher among current smokers (48%) than among former smokers (41%, P < .05) and respondents who had never smoked (42%, P < .05). After adjustment for age, sex, race/ethnicity, education, and general health status, current smokers with age-related eye diseases were more likely to have visual impairment than respondents with age-related eye diseases who had never smoked (odds ratio, 1.16, P < .05). Furthermore, respondents with cataract who were current smokers were more likely to have visual impairment than respondents with cataract who had never smoked (predictive margin, 44% vs 40%, P = .03), and the same was true for respondents with age-related macular degeneration (65% of current smokers vs 57% of never smokers, P = .02). This association did not hold true among respondents with glaucoma or diabetic retinopathy. CONCLUSION: Smoking is linked to self-reported visual impairment among older adults with age-related eye diseases, particularly cataract and age-related macular degeneration. Longitudinal evaluation is needed to assess smoking cessation's effect on vision preservation.

Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt.

BACKGROUND: The American "stroke belt" has contributed to the study of stroke. However, U.S. geographic patterns of diabetes have not been as specifically characterized. PURPOSE: This study identifies a geographically coherent region of the U.S. where the prevalence of diagnosed diabetes is especially high, called the "diabetes belt." METHODS: In 2010, data from the 2007 and 2008 Behavioral Risk Factor Surveillance System were combined with county-level diagnosed diabetes prevalence estimates. Counties in close proximity with an estimated prevalence of diagnosed diabetes ≥11.0% were considered to define the diabetes belt. Prevalence of risk factors in the diabetes belt was compared to that in the rest of the U.S. The fraction of the excess risk associated with living in the diabetes belt associated with selected risk factors, both modifiable (sedentary lifestyle, obesity) and nonmodifiable (age, gender, race/ethnicity, education), was calculated. RESULTS: A diabetes belt consisting of 644 counties in 15 mostly southern states was identified. People in the diabetes belt were more likely to be non-Hispanic African-American, lead a sedentary lifestyle, and be obese than in the rest of the U.S. Thirty percent of the excess risk was associated with modifiable risk factors, and 37% with nonmodifiable factors. CONCLUSIONS: Nearly one third of the difference in diabetes prevalence between the diabetes belt and the rest of the U.S. is associated with sedentary lifestyle and obesity. Culturally appropriate interventions aimed at decreasing obesity and sedentary lifestyle in counties within the diabetes belt should be considered.

Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence.

BACKGROUND: People with diabetes can suffer from diverse complications that seriously erode quality of life. Diabetes, costing the United States more than $174 billion per year in 2007, is expected to take an increasingly large financial toll in subsequent years. Accurate projections of diabetes burden are essential to policymakers planning for future health care needs and costs. METHODS: Using data on prediabetes and diabetes prevalence in the United States, forecasted incidence, and current US Census projections of mortality and migration, the authors constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. A three-state model partitions the US population into no diabetes, undiagnosed diabetes, and diagnosed diabetes. A four-state model divides the state of "no diabetes" into high-risk (prediabetes) and low-risk (normal glucose) states. A five-state model incorporates an intervention designed to prevent or delay diabetes in adults at high risk. RESULTS: The authors project that annual diagnosed diabetes incidence (new cases) will increase from about 8 cases per 1,000 in 2008 to about 15 in 2050. Assuming low incidence and relatively high diabetes mortality, total diabetes prevalence (diagnosed and undiagnosed cases) is projected to increase from 14% in 2010 to 21% of the US adult population by 2050. However, if recent increases in diabetes incidence continue and diabetes mortality is relatively low, prevalence will increase to 33% by 2050. A middle-ground scenario projects a prevalence of 25% to 28% by 2050. Intervention can reduce, but not eliminate, increases in diabetes prevalence. CONCLUSIONS: These projected increases are largely attributable to the aging of the US population, increasing numbers of members of higher-risk minority groups in the population, and people with diabetes living longer. Effective strategies will need to be undertaken to moderate the impact of these factors on national diabetes burden. Our analysis suggests that widespread implementation of reasonably effective preventive interventions focused on high-risk subgroups of the population can considerably reduce, but not eliminate, future increases in diabetes prevalence.