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.

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.

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.

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.

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

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.

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%).

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.

Age at diagnosis of diabetes in Appalachia.

BACKGROUND: Appalachia is a region of the United States noted for the poverty and poor health outcomes of its residents. Residents of the poorest Appalachian counties have a high prevalence of diabetes and risk factors (obesity, low income, low education, etc.) for type 2 diabetes. However, diabetes prevalence exceeds what these risk factors alone explain. Based on this, the history of poor health outcomes in Appalachia, and personally observed high rates of childhood obesity and lack of concern about prediabetes, we speculated that people in Appalachia with diagnosed diabetes might tend to be diagnosed younger than their non-Appalachian counterparts. METHODS: We used data from the Behavioral Risk Factor Surveillance System (2006-2008). We compared age at diagnosis among counties by Appalachian Regional Commission-defined level of economic development. To account for risk differences, we constructed a model for average age at diagnosis of diabetes, adjusting for county economic development, obesity, income, sedentary lifestyle, and other covariates. FINDINGS: After adjustment for risk factors for diabetes, people in distressed or at-risk counties (the least economically developed) had their diabetes diagnosed two to three years younger than comparable people in non-Appalachian counties. No significant differences between non-Appalachian counties and Appalachian counties at higher levels of economic development remained after adjusting. CONCLUSIONS: People in distressed and at-risk counties have poor access to care, and are unlikely to develop diabetes at the same age as their non-Appalachian counterparts but be diagnosed sooner. Therefore, people in distressed and at-risk counties are likely developing diabetes at younger ages. We recommend that steps to reduce health disparities between the poorest Appalachian counties and non-Appalachian counties be considered.

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.

Obesity prevention and diabetes screening at local health departments.

OBJECTIVES: We assessed whether local health departments (LHDs) were conducting obesity prevention programs and diabetes screening programs, and we examined associations between LHD characteristics and whether they conducted these programs. METHODS: We used the 2005 National Profile of Local Health Departments to conduct a cross-sectional analysis of 2300 LHDs nationwide. We used multivariate logistic regressions to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Approximately 56% of LHDs had obesity prevention programs, 51% had diabetes screening programs, and 34% had both. After controlling for other factors, we found that employing health educators was significantly associated with LHDs conducting obesity prevention programs (OR = 2.08; 95% CI = 1.54, 2.81) and diabetes screening programs (OR = 1.63; 95% CI = 1.23, 2.17). We also found that conducting chronic disease surveillance was significantly associated with LHDs conducting obesity prevention programs (OR = 1.66; 95% CI = 1.26, 2.20) and diabetes screening programs (OR = 2.44; 95% CI = 1.90, 3.15). LHDs with a higher burden of diabetes prevalence were more likely to conduct diabetes screening programs (OR = 1.20; 95% CI = 1.11, 1.31) but not obesity prevention programs. CONCLUSIONS: The presence of obesity prevention and diabetes screening programs was significantly associated with LHD structural capacity and general performance. However, the effectiveness and cost-effectiveness of both types of programs remain unknown.

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.

Impact of state mandatory insurance coverage on the use of diabetes preventive care.

BACKGROUND: 46 U.S. states and the District of Columbia have passed laws and regulations mandating that health insurance plans cover diabetes treatment and preventive care. Previous research on state mandates suggested that these policies had little impact, since many health plans already covered the benefits. Here, we analyze the contents of and model the effect of state mandates. We examined how state mandates impacted the likelihood of using three types of diabetes preventive care: annual eye exams, annual foot exams, and performing daily self-monitoring of blood glucose (SMBG). METHODS: We collected information on diabetes benefits specified in state mandates and time the mandates were enacted. To assess impact, we used data that the Behavioral Risk Factor Surveillance System gathered between 1996 and 2000. 4,797 individuals with self-reported diabetes and covered by private insurance were included; 3,195 of these resided in the 16 states that passed state mandates between 1997 and 1999; 1,602 resided in the 8 states or the District of Columbia without state mandates by 2000. Multivariate logistic regression models (with state fixed effect, controlling for patient demographic characteristics and socio-economic status, state characteristics, and time trend) were used to model the association between passing state mandates and the usage of the forms of diabetes preventive care, both individually and collectively. RESULTS: All 16 states that passed mandates between 1997 and 1999 required coverage of diabetic monitors and strips, while 15 states required coverage of diabetes self management education. Only 1 state required coverage of periodic eye and foot exams. State mandates were positively associated with a 6.3 (P = 0.04) and a 5.8 (P = 0.03) percentage point increase in the probability of privately insured diabetic patient's performing SMBG and simultaneous receiving all three preventive care, respectively; state mandates were not significantly associated with receiving annual diabetic eye (0.05 percentage points decrease, P = 0.92) or foot exams (2.3 percentage points increase, P = 0.45). CONCLUSIONS: Effects of state mandates varied by preventive care type, with state mandates being associated with a small increase in SMBG. We found no evidence that state mandates were effective in increasing receipt of annual eye or foot exams. The small or non-significant effects might be attributed to small numbers of insured people not having the benefits prior to the mandates' passage. If state mandates' purpose is to provide improved benefits to many persons, policy makers should consider determining the number of people who might benefit prior to passing the mandate.

Residence in a distressed county in Appalachia as a risk factor for diabetes, Behavioral Risk Factor Surveillance System, 2006-2007.

INTRODUCTION: We compared the risk of diabetes for residents of Appalachian counties to that of residents of non-Appalachian counties after controlling for selected risk factors in states containing at least 1 Appalachian county. METHODS: We combined Behavioral Risk Factor Surveillance System data from 2006 and 2007 and conducted a logistic regression analysis, with self-reported diabetes as the dependent variable. We considered county of residence (5 classifications for Appalachian counties, based on economic development, and 1 for non-Appalachian counties), age, sex, race/ethnicity, education, household income, smoking status, physical activity level, and obesity to be independent variables. The classification "distressed" refers to counties in the worst 10%, compared with the nation as a whole, in terms of 3-year unemployment rate, per capita income, and poverty. RESULTS: Controlling for covariates, residents in distressed Appalachian counties had 33% higher odds (95% confidence interval, 1.10-1.60) of reporting diabetes than residents of non-Appalachian counties. We found no significant differences between other classifications of Appalachian counties and non-Appalachian counties. CONCLUSION: Residents of distressed Appalachian counties are at higher risk of diabetes than are residents of other counties. States with distressed Appalachian counties should implement culturally sensitive programs to prevent diabetes.

Access to health care and undiagnosed diabetes along the United States-Mexico border.

OBJECTIVE: To examine the relationship between access to health care and undiagnosed diabetes among the high-risk, vulnerable population in the border region between the United States of America and Mexico. METHODS: Using survey and fasting plasma glucose data from Phase I of the U.S.-Mexico Border Diabetes Prevention and Control Project (February 2001 to October 2002), this epidemiological study identified 178 adults 18-64 years old with undiagnosed diabetes, 326 with diagnosed diabetes, and 2 966 without diabetes. Access to health care among that sample (n = 3,470), was assessed by type of health insurance coverage (including "none"), number of health care visits over the past year, routine pattern of health care utilization, and country of residence. RESULTS: People with diabetes who had no insurance and no place to go for routine health care were more likely to be undiagnosed than those with insurance and a place for routine health care (odds ratio [OR] 2.6, 95% confidence interval [CI] 1.0-6.6, and OR 4.5, 95% CI 1.4-14.1, respectively). When stratified by country, the survey data showed that on the U.S. side of the border there were more people with undiagnosed diabetes if they were 1) uninsured versus the insured (28.9%, 95% CI 11.5%-46.3%, versus 9.1%, 95% CI 1.5%-16.7%, respectively) and if they 2) had made no visits or 1-3 visits to a health care facility in the past year versus had made ≥ 4 visits (40.8%, 95% CI 19.6%-62.0%, and 23.4%, 95% CI 9.9%-36.9%, respectively, versus 2.4%, 95% CI -0.9%-5.7%) (all, P < 0.05). No similar pattern was found in Mexico. CONCLUSIONS: Limited access to health care--especially not having health insurance and/or not having a place to receive routine health services--was significantly associated with undiagnosed diabetes in the U.S.-Mexico border region.

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.

Gender-specific mental and behavioral outcomes among physically abused high-risk seventh-grade youths.

OBJECTIVE: Research has shown that physical abuse during childhood (early PA) is associated with various mental and behavioral problems in adolescence. However, there is little research on the differences in these associations by gender among youths residing in high-risk communities. This study investigated gender differences in the relationship between early PA and various internalizing (e.g., thoughts of suicide or victimization) and externalizing (e.g., perpetration of violence) behaviors. METHODS: A cross-sectional study was conducted using survey data (collected in 2004) provided by 1,484 seventh-grade youths residing in a high-risk community (83% participated). Students were considered victims of early PA if they reported experiencing abuse prior to age 10 years. Prevalence ratios (PRs) were calculated to estimate the association between early PA and various outcomes (e.g., suicidality, victimization, violence, and illegal drug use), adjusting for race/ethnicity and other forms of abuse. Poisson regression with robust variance estimates was used to estimate the PRs and test for early PA-gender interaction. RESULTS: Early PA was positively associated with suicidality, illegal drug use, and victimization with no significant differences by gender. The association between early PA and criminal behavior was significantly higher for females; the association between early PA and peer violence perpetration was significantly higher for males (interaction term PA*gender was significant at the p < or = 0.005 level). CONCLUSIONS: Young high-risk adolescents who experienced early PA may need counseling or other services (e.g., home visitation) to help prevent suicidality, victimization, violence perpetration, criminal behavior, and illegal drug use. Furthermore, male victims may need more attention in the area of violence prevention; female victims may need particular attention with regard to preventing criminal behavior.

Diabetic retinopathy, dilated eye examination, and eye care education among African Americans, 1997 and 2004.

OBJECTIVE: To examine diabetic retinopathy, dilated eye examination, and eye care education among African Americans before and after a community-level public health intervention. METHODS: We analyzed data from Project DIRECT (Diabetes Interventions Reaching and Educating Communities Together) participants with self-reported diabetes (617 in 1996-1997 and 672 in 2003-2004) in Raleigh (intervention community) and Greensboro (comparison community), North Carolina. All analyses were weighted to adjust for the complex sample design of pre and post cross-sectional surveys. Estimates were age standardized to the 2000 US Census population. We used multivariate logistic regression to calculate odds ratios and corresponding 95% confidence intervals. RESULTS: We found no significant difference in prevalence of diabetic retinopathy between the control and intervention communities (p > .05). However, after adjusting for other confounders, receipt of eye care education (OR, 1.59; 95% CI, 1.19-2.13) was independently associated with receipt of dilated eye examination among African Americans with diabetes. Compared with individuals without diabetic retinopathy, those with diabetic retinopathy were more likely to use eye care services (OR, 1.89; 95% CI, 1.41-2.54). CONCLUSIONS: Diabetic retinopathy is a considerable problem among African American communities. Community intervention efforts, such as comprehensive eye care education, that specifically target improvement in diabetic retinopathy and use of eye are services could help better serve this population.

Parental mental and pain-related health and pediatric ambulatory care sensitive emergency department visits and hospitalizations.

OBJECTIVE: To investigate the types of parental psychiatric and pain-related (PR) conditions that are associated with inadequate management of children's health and medical needs. DATA SOURCES: The 1997-1998 Thomson/Medstat MarketScan claims and administrative dataset. STUDY DESIGN: A cross-sectional study that assessed the associations between parents' claims for psychiatric and PR conditions, and their children's well-child care as well as emergency department (ED) visits and hospitalizations for conditions that can be treated effectively in outpatient settings (ambulatory care sensitive [ACS] conditions). DATA EXTRACTION METHODS: Claims were extracted for 258,313 children of ages 0-17 years and their parents, who had insurance coverage for a full 2-year period. PRINCIPLE FINDINGS: Multiple parental psychiatric and PR diagnoses were associated with child ACS emergency services/hospitalizations. Maternal depression was negatively associated with a child having the recommended well-child visits (odds ratio [OR]: 0.92, 95 percent confidence intervals [CI]: 0.84-0.99). The combined diagnoses of maternal depression and back pain was positively associated with a child having an ACS-ED visit (OR: 1.64, 95 percent CI: 1.33-2.03) and a child having an ACS hospitalization (OR: 2.04, 95 percent CI: 1.34-3.09). CONCLUSIONS: Pediatricians' ability to manage child health may be enhanced with coordinated management of parental psychopathology and PR health conditions.