Estimates of county-level disease prevalence have a variety of applications. Such estimation is often done via model-based small-area estimation using survey data. However, for conditions with low prevalence (i.e., rare diseases or newly diagnosed diseases), counties with a high fraction of zero counts in surveys are common. They are often more common than the model used would lead one to expect; such zeros are called 'excess zeros'. The excess zeros can be structural (there are no cases to find) or sampling (there are cases, but none were selected for sampling). These issues are often addressed by combining multiple years of data. However, this approach can obscure trends in annual estimates and prevent estimates from being timely. Using single-year survey data, we proposed a Bayesian weighted Binomial Zero-inflated (BBZ) model to estimate county-level rare diseases prevalence. The BBZ model accounts for excess zero counts, the sampling weights and uses a power prior. We evaluated BBZ with American Community Survey results and simulated data. We showed that BBZ yielded less bias and smaller variance than estimates based on the binomial distribution, a common approach to this problem. Since BBZ uses only a single year of survey data, BBZ produces more timely county-level incidence estimates. These timely estimates help pinpoint the special areas of county-level needs and help medical researchers and public health practitioners promptly evaluate rare diseases trends and associations with other health conditions. © 2023 The Author(s).

Bayesian hierarchical regression (BHR) is often used in small area estimation (SAE). BHR conditions on the samples. Therefore, when data are from a complex sample survey, neither survey sampling design nor survey weights are used. This can introduce bias and/or cause large variance. Further, if non-informative priors are used, BHR often requires the combination of multiple years of data to produce sample sizes that yield adequate precision; this can result in poor timeliness and can obscure trends. To address bias and variance, we propose a design assisted model-based approach for SAE by integrating adjusted sample weights. To address timeliness, we use historical data to define informative priors (power prior); this allows estimates to be derived from a single year of data. Using American Community Survey data for validation, we applied the proposed method to Behavioral Risk Factor Surveillance System data. We estimated the prevalence of disability for all U.S. counties. We show that our method can produce estimates that are both more timely than those arising from widely-used alternatives and are closer to ACS' direct estimates, particularly for low-data counties. Our method can be generalized to estimate the county-level prevalence of other health related measurements.

INTRODUCTION: Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory factor analysis at the county level across the United States. METHODS: Ninety-nine variables drawn from public use data sets from 2010 to 2016 were used to create a multifactor index - the burden index. We applied principal components analysis with promax rotation to allow the factors to correlate. Correlation coefficients for each factor and the outcome of interest, age-adjusted cancer death rate, were calculated. We used both unadjusted and adjusted linear regression techniques. RESULTS: The final additive county-level index included 9 factors that explained 68.3% of the variance in the counties and county equivalents. The burden index had a moderate association with the age-adjusted cancer death rates (r =.48, P <.001), and adjusted linear regression with all 9 factors explained 34% of the variance in the age-adjusted cancer death rate. Results were mapped, and the geographic distribution of both the burden index and age-adjusted cancer mortality were assessed. There are distinct geospatial patterns for both. CONCLUSIONS: Results from this study show potential areas of need, as well as the importance of including environmental variables in the study of cancer etiology. Future studies can aim to validate these findings by quantifying burden as it relates to overall cancer mortality by using epidemiologic measures, along with other confirmatory statistical methods.

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 beta-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.

The residuals of a least squares regression model are defined as the observations minus the modeled values. For least squares regression to produce valid CIs and P values, the residuals must be independent, be normally distributed, and have a constant variance. If these assumptions are not satisfied, estimates can be biased and power can be reduced. However, there are ways to assess these assumptions and steps one can take if the assumptions are violated. Here, we discuss both assessment and appropriate responses to violation of assumptions.

OBJECTIVE: We examine barriers to receiving recommended eye care among people aged ≥40 years with diagnosed diabetes. RESEARCH DESIGN AND METHODS: We analyzed 2006-2010 Behavioral Risk Factor Surveillance System data from 22 states (n = 27,699). Respondents who had not sought eye care in the preceding 12 months were asked the main reason why. We categorized the reasons as cost/lack of insurance, no need, no eye doctor/travel/appointment, and other (meaning everything else). We used multinomial logistic regression to control for race/ethnicity, education, income, and other selected covariates. RESULTS: Among adults with diagnosed diabetes, nonadherence to the recommended annual eye examinations was 23.5%. The most commonly reported reasons for not receiving eye care in the preceding 12 months were "no need" and "cost or lack of insurance" (39.7 and 32.3%, respectively). Other reasons were "no eye doctor," "no transportation" or "could not get appointment" (6.4%), and "other" (21.5%). After controlling for covariates, adults aged 40-64 years were more likely than those aged ≥65 years (relative risk ratio [RRR] = 2.79; 95% CI 2.01-3.89) and women were more likely than men (RRR = 2.33; 95% CI 1.75-3.14) to report "cost or lack of insurance" as their main reason. However, people aged 40-64 years were less likely than those aged ≥65 years to report "no need" (RRR = 0.51; 95% CI 0.39-0.67) as their main reason. CONCLUSIONS: Addressing concerns about "cost or lack of insurance" for adults under 65 years and "no perceived need" among those 65 years and older could help improve eye care service utilization among people with diabetes.

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.

BACKGROUND: Medicare Part D, implemented in 2006, provided coverage for prescription drugs to all Medicare beneficiaries. OBJECTIVE:: To examine the effect of Part D on the financial burden of persons with diagnosed diabetes. RESEARCH DESIGN, SUBJECTS, AND OUTCOME MEASURES: We conducted an interrupted time-series analysis using data from the 1996 to 2008 Medical Expenditure Panel Survey (11,178 persons with diabetes who were covered by Medicare, and 8953 persons aged 45-64 y with diabetes who were not eligible for Medicare coverage). We then compared changes in 4 outcomes: (1) annual individual out-of-pocket expenditure (OOPE) for prescription drugs; (2) annual individual total OOPE for all health care services; (3) annual total family OOPE for all health care services; and (4) percentage of persons with high family financial burden (OOPE ≥10% of income). RESULTS: For Medicare beneficiaries with diabetes, Part D was associated with a 28% ($530) decrease in individual annual OOPE for prescription drugs, a 23% ($560) reduction in individual OOPE for all health care, a 23% ($863) reduction in family OOPE for all health care, and a 24% reduction in the percentage of families with high financial burden in 2006. There were similar reductions in 2007 and 2008. By 2008, the percentage of Medicare beneficiaries with diabetes living in high financial burden families was 37% lower than it would have been had Part D not been in place. CONCLUSIONS: Introduction of Part D coverage was associated with a substantial reduction in the financial burden of Medicare beneficiaries with diabetes and their families.

BACKGROUND: Adiposity predicts health outcomes, but this relationship could depend on population characteristics and adiposity indicator employed. In a representative sample of 11,437 US adults (National Health and Nutrition Examination Survey, 1988-1994, ages 18-64) we estimated associations with all-cause mortality for body mass index (BMI) and four abdominal adiposity indicators (waist circumference [WC], waist-to-height ratio [WHtR], waist-to-hip ratio [WHR], and waist-to-thigh ratio [WTR]). In a fasting subsample we considered the lipid accumulation product (LAP; [WC enlargement*triglycerides]). METHODS AND FINDINGS: For each adiposity indicator we estimated linear and categorical mortality risks using sex-specific, proportional-hazards models adjusted for age, black ancestry, tobacco exposure, and socioeconomic position. There were 1,081 deaths through 2006. Using linear models we found little difference among indicators (adjusted hazard ratios [aHRs] per SD increase 1.2-1.4 for men, 1.3-1.5 for women). Using categorical models, men in adiposity midrange (quartiles 2+3; compared to quartile 1) were not at significantly increased risk (aHRs<1.1) unless assessed by WTR (aHR 1.4 [95%CI 1.0-1.9]). Women in adiposity midrange, however, tended toward elevated risk (aHRs 1.2-1.5), except for black women assessed by BMI, WC or WHtR (aHRs 0.7-0.8). Men or women in adiposity quartile 4 (compared to midrange) were generally at risk (aHRs>1.1), especially black men assessed by WTR (aHR 1.9 [1.4-2.6]) and black women by LAP (aHR 2.2 [1.4-3.5]). Quartile 4 of WC or WHtR carried no significant risk for diabetic persons (aHRs 0.7-1.1), but elevated risks for those without diabetes (aHRs>1.5). For both sexes, quartile 4 of LAP carried increased risks for tobacco-exposed persons (aHRs>1.6) but not for non-exposed (aHRs<1.0). CONCLUSIONS: Predictions of mortality risk associated with top-quartile adiposity vary with the indicator used, sex, ancestry, and other characteristics. Interpretations of adiposity should consider how variation in the physiology and expandability of regional adipose-tissue depots impacts health.

PURPOSE: To estimate the prevalence of annual eye care among visually impaired United States residents aged 40 years or older, by state, race/ethnicity, education, and annual income. DESIGN: Cross-sectional study. METHODS: In analyses of 2006-2009 Behavioral Risk Factor Surveillance System data from 21 states, we used multivariate regression to estimate the state-level prevalence of yearly eye doctor visit in the study population by race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, and other), annual income (≥$35,000 and <$35,000), and education (< high school, high school, and > high school). RESULTS: The age-adjusted state-level prevalence of yearly eye doctor visits ranged from 48% (Missouri) to 69% (Maryland). In Alabama, Colorado, Indiana, Iowa, New Mexico, and North Carolina, the prevalence was significantly higher among respondents with more than a high school education than among those with a high school education or less (P < .05). The prevalence was positively associated with annual income levels in Alabama, Georgia, New Mexico, New York, Texas, and West Virginia and negatively associated with annual income levels in Massachusetts. After controlling for age, sex, race/ethnicity, education, and income, we also found significant disparities in the prevalence of yearly eye doctor visits among states. CONCLUSION: Among visually impaired US residents aged 40 or older, the prevalence of yearly eye examinations varied significantly by race/ethnicity, income, and education, both overall and within states. Continued and possibly enhanced collection of eye care utilization data, such as we analyzed here, may help states address disparities in vision health and identify population groups most in need of intervention programs.

PURPOSE: To assess vision health disparities in the United States by race/ethnicity, education, and economic status. DESIGN: Cross-sectional, nationally representative samples. METHODS: We used national survey data from the National Health and Nutrition Examination Survey (NHANES) and the National Health Interview Survey (NHIS). Main outcome measures included, from NHANES, age-related eye diseases (ie, age-related macular degeneration [AMD], cataract, diabetic retinopathy [DR], glaucoma) and from NHIS, eye care use (ie, eye doctor visits and cannot afford eyeglasses when needed) among those with self-reported visual impairment. The estimates were age- and sex-standardized to the 2000 US Census population. Linear trends in the estimates were assessed by weighted least squares regression. RESULTS: Non-Hispanic whites had a higher prevalence of AMD and cataract surgery than non-Hispanic blacks, but a lower prevalence of DR and glaucoma (all P < .001 in NHANES 2005-2008). From 1999 to 2008, individuals with less education (ie, <high school vs >high school) and lower income (poverty income ratio [PIR] <1.00 vs ≥4.00) were consistently less likely to have had an eye care visit in the past 12 months compared with their counterparts (all P < .05). During this period, inability to afford needed eyeglasses increased among non-Hispanic whites and Hispanics (trend P = .004 and P = .007; respectively), those with high school education (trend P = .036), and those with PIR 1.00-1.99 (trend P < .001). CONCLUSIONS: Observed vision health disparities suggest a need for educational and innovative interventions among socioeconomically disadvantaged groups.

OBJECTIVE: To examine the impact of the geographic density of eye care professionals (ECPs) on the receipt of annual dilated eye examinations among adults in the United States with diabetes. METHOD: Data from the 2006 Behavioral Risk Factor Surveillance System were linked to the 2007 Area Resource File to examine the association between the density of ECPs (ophthalmologists and optometrists) per 100,000 people and self-reports of having had a dilated eye examination in the last year. The sample included adults aged 18+ years with diabetes (N = 29,495). Multivariate logistic regression was conducted to estimate adjusted odds ratios of annual dilated eye examinations, while controlling for age, sex, marital status and education, and stratifying by health insurance. RESULTS: Approximately 10% of respondents with diabetes lived in counties with no ECPs. Prevalence of being uninsured was 7.4% and 15.5% in those with and without dilated eye exams, respectively. After controlling for covariates and stratifying by health insurance, diabetic adults with health insurance cover residing in areas with no ECPs were less likely to report having had a dilated eye examination in the past year than those with 20 or more ECPs/100,000 people (odds ratio 0.72, 95% confidence interval 0.58-0.91). CONCLUSION: Residence in a county with a low density of ECPs reduced the likelihood of receiving annual dilated eye examinations among insured adults with diabetes. Enhancing the ability of ECPs to reach and care for those in need might better protect vision in people with diabetes. More research is needed to determine the mix of services that produces the best patient outcome.

OBJECTIVE: To examine the relationship between access to health care and diabetes control. RESEARCH DESIGN AND METHODS: Using data from the National Health and Nutrition Examination Survey, 1999-2008, we identified 1,221 U.S. adults (age 18-64 years) with self-reported diabetes. Access was measured by current health insurance coverage, number of times health care was received over the past year, and routine place to go for health care. Diabetes control measures included the proportion of people with A1C >9%, blood pressure ≥140/90 mmHg, and non-HDL cholesterol ≥130 mg/dL. RESULTS: An estimated 16.0% of known diabetic adults were uninsured. Diabetes control profiles were worse among uninsured than among insured persons (A1C >9% [34.1 vs. 16.5%, P = 0.002], blood pressure ≥140/90 mmHg [31.8 vs. 22.8%, P < 0.05], and non-HDL cholesterol ≥130 mg/dL [67.1 vs. 65.4%, P = 0.7]). Compared with insured persons, uninsured persons were more likely to have A1C >9% (multivariate-adjusted odds ratio 2.4 [95% CI 1.2-4.7]). Compared with those who reported four or more health care visits in the past year, those who reported no health care visits were more likely to have A1C >9% (5.5 [1.2-26.3]) and blood pressure ≥140/90 mmHg (1.9 [1.1-3.4]). CONCLUSIONS: In people with diabetes, lack of health care coverage is associated with poor glycemic control. In addition, low use of health care service is associated with poor glucose and blood pressure control.

BACKGROUND: There is substantial evidence of a disparity in access to eye care services among adults in the U.S.; however, little is known about health disparities for children's eye care. PURPOSE: The goal of the study was to assess the prevalence of and risk factors for 5th-grade students' unmet eye care needs. METHODS: Data were collected from 5147 5th-grade students (aged 10-11 years) and their parents and primary caregivers (hereafter "parents") participating in the Healthy Passages study between fall 2004 and summer 2006 (analyzed in 2011). Logistic regression estimated the probability of inability to afford needed eyeglasses and absence of vision insurance coverage. RESULTS: 1794 5th-grade students wore eyeglasses or were told that they need eyeglasses; 13.7% of their parents were unable to afford needed eyeglasses (new prescription or replacement) for their children; 27.4% of their parents reported no vision insurance coverage for eye examinations and eyeglasses. After controlling for confounders, parents without general children's health insurance were more likely to report being unable to afford eyeglasses than those with health insurance (Medicaid, SCHIP, private/other insurance; adjusted percentages: 22.5% vs 10.9%, 9.6%, 12.5%; all p<0.05). Parents with lower income were more likely to report being unable to afford children's eyeglasses even after controlling for all other factors (17.6% with income <$15,000 vs 2.7% with income ≥$70,000; p<0.001). CONCLUSIONS: SES and health insurance status are strongly associated with 5th-grade students' unmet eye care needs. Policies targeting socioeconomically disadvantaged groups and those without insurance may be needed to reduce disparities in access to appropriate eye care.

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.

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.

We examined ranges of A1C useful for identifying persons at high risk for diabetes prior to preventive intervention by conducting a systematic review. From 16 included studies, we found that annualized diabetes incidence ranged from 0.1% at A1C <5.0% to 54.1% at A1C >or=6.1%. Findings from 7 studies that examined incident diabetes across a broad range of A1C categories showed 1) risk of incident diabetes increased steeply with A1C across the range of 5.0 to 6.5%; 2) the A1C range of 6.0 to 6.5% was associated with a highly increased risk of incident diabetes, 25 to 50% incidence over 5 years; 3) the A1C range of 5.5 to 6.0% was associated with a moderately increased relative risk, 9 to 25% incidence over 5 years; and 4) the A1C range of 5.0 to 5.5% was associated with an increased incidence relative to those with A1C <5%, but the absolute incidence of diabetes was less than 9% over 5 years. Our systematic review demonstrated that A1C values between 5.5 and 6.5% were associated with a substantially increased risk for developing diabetes.

OBJECTIVE: To synthesize the cost-effectiveness (CE) of interventions to prevent and control diabetes, its complications, and comorbidities. RESEARCH DESIGN AND METHODS: We conducted a systematic review of literature on the CE of diabetes interventions recommended by the American Diabetes Association (ADA) and published between January 1985 and May 2008. We categorized the strength of evidence about the CE of an intervention as strong, supportive, or uncertain. CEs were classified as cost saving (more health benefit at a lower cost), very cost-effective (<or=$25,000 per life year gained [LYG] or quality-adjusted life year [QALY]), cost-effective ($25,001 to $50,000 per LYG or QALY), marginally cost-effective ($50,001 to $100,000 per LYG or QALY), or not cost-effective (>$100,000 per LYG or QALY). The CE classification of an intervention was reported separately by country setting (U.S. or other developed countries) if CE varied by where the intervention was implemented. Costs were measured in 2007 U.S. dollars. RESULTS: Fifty-six studies from 20 countries met the inclusion criteria. A large majority of the ADA recommended interventions are cost-effective. We found strong evidence to classify the following interventions as cost saving or very cost-effective: (I) Cost saving- 1) ACE inhibitor (ACEI) therapy for intensive hypertension control compared with standard hypertension control; 2) ACEI or angiotensin receptor blocker (ARB) therapy to prevent end-stage renal disease (ESRD) compared with no ACEI or ARB treatment; 3) early irbesartan therapy (at the microalbuminuria stage) to prevent ESRD compared with later treatment (at the macroalbuminuria stage); 4) comprehensive foot care to prevent ulcers compared with usual care; 5) multi-component interventions for diabetic risk factor control and early detection of complications compared with conventional insulin therapy for persons with type 1 diabetes; and 6) multi-component interventions for diabetic risk factor control and early detection of complications compared with standard glycemic control for persons with type 2 diabetes. (II) Very cost-effective- 1) intensive lifestyle interventions to prevent type 2 diabetes among persons with impaired glucose tolerance compared with standard lifestyle recommendations; 2) universal opportunistic screening for undiagnosed type 2 diabetes in African Americans between 45 and 54 years old; 3) intensive glycemic control as implemented in the UK Prospective Diabetes Study in persons with newly diagnosed type 2 diabetes compared with conventional glycemic control; 4) statin therapy for secondary prevention of cardiovascular disease compared with no statin therapy; 5) counseling and treatment for smoking cessation compared with no counseling and treatment; 6) annual screening for diabetic retinopathy and ensuing treatment in persons with type 1 diabetes compared with no screening; 7) annual screening for diabetic retinopathy and ensuing treatment in persons with type 2 diabetes compared with no screening; and 8) immediate vitrectomy to treat diabetic retinopathy compared with deferred vitrectomy. CONCLUSIONS: Many interventions intended to prevent/control diabetes are cost saving or very cost-effective and supported by strong evidence. Policy makers should consider giving these interventions a higher priority.