Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression.

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.

Validation of the Prevention Impacts Simulation Model (PRISM).

INTRODUCTION: Demonstrating the validity of a public health simulation model helps to establish confidence in the accuracy and usefulness of a model's results. In this study we evaluated the validity of the Prevention Impacts Simulation Model (PRISM), a system dynamics model that simulates health, mortality, and economic outcomes for the US population. PRISM primarily simulates outcomes related to cardiovascular disease but also includes outcomes related to other chronic diseases that share risk factors. PRISM is openly available through a web application. METHODS: We applied the model validation framework developed independently by the International Society of Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making modeling task force to validate PRISM. This framework included model review by external experts and quantitative data comparison by the study team. RESULTS: External expert review determined that PRISM is based on up-to-date science. One-way sensitivity analysis showed that no parameter affected results by more than 5%. Comparison with other published models, such as ModelHealth, showed that PRISM produces lower estimates of effects and cost savings. Comparison with surveillance data showed that projected model trends in risk factors and outcomes align closely with secular trends. Four measures did not align with surveillance data, and those were recalibrated. CONCLUSION: PRISM is a useful tool to simulate the potential effects and costs of public health interventions. Results of this validation should help assure health policy leaders that PRISM can help support community health program planning and evaluation efforts.

INCORPORATING DESIGN WEIGHTS AND HISTORICAL DATA INTO MODEL-BASED SMALL-AREA ESTIMATION.

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.

Using Population Health Measures to Evaluate the Environmental Burden of Cancer at the County Level.

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.

Chronic Disease Disparities by County Economic Status and Metropolitan Classification, Behavioral Risk Factor Surveillance System, 2013.

INTRODUCTION: Racial/ethnic disparities have been studied extensively. However, the combined influence of geographic location and economic status on specific health outcomes is less well studied. This study's objective was to examine 1) the disparity in chronic disease prevalence in the United States by county economic status and metropolitan classification and 2) the social gradient by economic status. The association of hypertension, arthritis, and poor health with county economic status was also explored. METHODS: We used 2013 Behavioral Risk Factor Surveillance System data. County economic status was categorized by using data on unemployment, poverty, and per capita market income. While controlling for sociodemographics and other covariates, we used multivariable logistic regression to evaluate the relationship between economic status and hypertension, arthritis, and self-rated health. RESULTS: Prevalence of hypertension, arthritis, and poor health in the poorest counties was 9%, 13%, and 15% higher, respectively, than in the most affluent counties. After we controlled for covariates, poor counties still had a higher prevalence of the studied conditions. CONCLUSION: We found that residents of poor counties had a higher prevalence of poor health outcomes than affluent counties, even after we controlled for known risk factors. Further, the prevalence of poor health outcomes decreased as county economics improved. Findings suggest that poor counties would benefit from targeted public health interventions, better access to health care services, and improved food and built environments.

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.

Summarizing health-related quality of life (HRQOL): development and testing of a one-factor model.

BACKGROUND: Health-related quality of life (HRQOL) is a multi-dimensional concept commonly used to examine the impact of health status on quality of life. HRQOL is often measured by four core questions that asked about general health status and number of unhealthy days in the Behavioral Risk Factor Surveillance System (BRFSS). Use of these measures individually, however, may not provide a cohesive picture of overall HRQOL. To address this concern, this study developed and tested a method for combining these four measures into a summary score. METHODS: Exploratory and confirmatory factor analyses were performed using BRFSS 2013 data to determine potential numerical relationships among the four HRQOL items. We also examined the stability of our proposed one-factor model over time by using BRFSS 2001-2010 and BRFSS 2011-2013 data sets. RESULTS: Both exploratory factor analysis and goodness of fit tests supported the notion that one summary factor could capture overall HRQOL. Confirmatory factor analysis indicated acceptable goodness of fit of this model. The predicted factor score showed good validity with all of the four HRQOL items. In addition, use of the one-factor model showed stability, with no changes being detected from 2001 to 2013. CONCLUSION: Instead of using four individual items to measure HRQOL, it is feasible to study overall HRQOL via factor analysis with one underlying construct. The resulting summary score of HRQOL may be used for health evaluation, subgroup comparison, trend monitoring, and risk factor identification.

Best (but oft-forgotten) practices: checking assumptions concerning regression residuals.

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.

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.

Changes over time in high out-of-pocket health care burden in U.S. adults with diabetes, 2001-2011.

OBJECTIVE: High out-of-pocket (OOP) costs can be an obstacle to health care access and treatment compliance. This study investigated trends in high OOP health care burden in people with diabetes. RESEARCH DESIGN AND METHODS: Using Medical Expenditure Panel Survey 2001-2011 data, we examined trends in the proportion of people aged 18-64 years with diabetes facing a high OOP burden. We also examined whether the trend differed by insurance status (private insurance, public insurance, or no insurance) or by income level (poor and near poor, low income, middle income, or high income). RESULTS: In 2011, 23% of people with diabetes faced high OOP burden. Between 2001-2002 and 2011, the proportion of people facing high OOP burden fell by 5 percentage points (P < 0.01). The proportion of those who were publicly insured decreased by 22 percentage points (P < 0.001) and of those who were uninsured by 12 percentage points (P = 0.01). Among people with diabetes who were poor and near poor and those with low income, the proportion facing high OOP burden decreased by 21 (P < 0.001) and 13 (P = 0.01) percentage points, respectively; no significant change occurred in the proportion with private insurance or middle and high incomes between 2001-2002 and 2011. CONCLUSIONS: The past decade has seen a narrowing of insurance coverage and income-related disparities in high OOP burden in people with diabetes; yet, almost one-fourth of all people with diabetes still face a high OOP burden.

Barriers to eye care among people aged 40 years and older with diagnosed diabetes, 2006-2010.

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.

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%).

Medicare Part D is associated with reducing the financial burden of health care services in Medicare beneficiaries with diagnosed diabetes.

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.

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%).

Association between depression and functional vision loss in persons 20 years of age or older in the United States, NHANES 2005-2008.

IMPORTANCE: This study provides further evidence from a national sample to generalize the relationship between depression and vision loss to adults across the age spectrum. Better recognition of depression among people reporting reduced ability to perform routine activities of daily living due to vision loss is warranted. OBJECTIVES: To estimate, in a national survey of US adults 20 years of age or older, the prevalence of depression among adults reporting visual function loss and among those with visual acuity impairment. The relationship between depression and vision loss has not been reported in a nationally representative sample of US adults. Previous studies have been limited to specific cohorts and predominantly focused on the older population. DESIGN: The National Health and Nutrition Examination Survey (NHANES) 2005-2008. SETTING: A cross-sectional, nationally representative sample of adults, with prevalence estimates weighted to represent the civilian, noninstitutionalized US population. PARTICIPANTS: A total of 10 480 US adults 20 years of age or older. MAIN OUTCOME MEASURES: Depression, as measured by the 9-item Patient Health Questionnaire depression scale, and vision loss, as measured by visual function using a questionnaire and by visual acuity at examination. RESULTS: In 2005-2008, the estimated crude prevalence of depression (9-item Patient Health Questionnaire score of ≥10) was 11.3% (95% CI, 9.7%-13.2%) among adults with self-reported visual function loss and 4.8% (95% CI, 4.0%-5.7%) among adults without. The estimated prevalence of depression was 10.7% (95% CI, 8.0%-14.3%) among adults with presenting visual acuity impairment (visual acuity worse than 20/40 in the better-seeing eye) compared with 6.8% (95% CI, 5.8%-7.8%) among adults with normal visual acuity. After controlling for age, sex, race/ethnicity, marital status, living alone or not, education, income, employment status, health insurance, body mass index, smoking, binge drinking, general health status, eyesight worry, and major chronic conditions, self-reported visual function loss remained significantly associated with depression (overall odds ratio, 1.9 [95% CI, 1.6-2.3]), whereas the association between presenting visual acuity impairment and depression was no longer statistically significant. CONCLUSIONS AND RELEVANCE: Self-reported visual function loss, rather than loss of visual acuity, is significantly associated with depression. Health professionals should be aware of the risk of depression among persons reporting visual function loss.

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.