Lifecourse socioeconomic position and diabetes incidence in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, 2003 to 2016.

Low socioeconomic position (SEP) across the lifecourse is associated with Type 2 diabetes (T2DM). We examined whether these economic disparities differ by race and sex. We included 5448 African American (AA) and white participants aged ≥45 years from the national (United States) REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort without T2DM at baseline (2003-07). Incident T2DM was defined by fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or using T2DM medications at follow-up (2013-16). Derived SEP scores in childhood (CSEP) and adulthood (ASEP) were used to calculate a cumulative (CumSEP) score. Social mobility was defined as change in SEP. We fitted race-stratified logistic regression models to estimate the association between each lifecourse SEP indicator and T2DM, adjusting for covariates; additionally, we tested SEP-sex interactions. Over a median of 9.0 (range 7-14) years of follow-up, T2DM incidence was 167.1 per 1000 persons among AA and 89.9 per 1000 persons among white participants. Low CSEP was associated with T2DM incidence among AA (OR = 1.61; 95%CI 1.05-2.46) but not white (1.06; 0.74-2.33) participants; this was attenuated after adjustment for ASEP. In contrast, low CumSEP was associated with T2DM incidence for both racial groups. T2DM risk was similar for stable low SEP and increased for downward mobility when compared with stable high SEP in both groups, whereas upward mobility increased T2DM risk among AAs only. No differences by sex were observed. Among AAs, low CSEP was not independently associated with T2DM, but CSEP may shape later-life experiences and health risks.

Trends and socioeconomic disparities in all-cause mortality among adults with diagnosed diabetes by race/ethnicity: a population-based cohort study - USA, 1997-2015.

OBJECTIVES: By race/ethnicity and socioeconomic position (SEP), to estimate and examine changes over time in (1) mortality rate, (2) mortality disparities and (3) excess mortality risk attributed to diagnosed diabetes (DM). DESIGN: Population-based cohort study using National Health Interview Survey data linked to mortality status from the National Death Index from survey year up to 31 December 2015 with 5 years person-time. PARTICIPANTS: US adults aged ≥25 years with (31 586) and without (332 451) DM. PRIMARY OUTCOME: Age-adjusted all-cause mortality rate for US adults with DM in each subgroup of SEP (education attainment and income-to-poverty ratio (IPR)) and time (1997-2001, 2002-2006 and 2007-2011). RESULTS: Among adults with DM, mortality rates fell from 23.5/1000 person-years (p-y) in 1997-2001 to 18.1/1000 p-y in 2007-2011 with changes of -5.2/1000 p-y for non-Hispanic whites; -5.2/1000 p-y for non-Hispanic blacks; and -5.4/1000 p-y for Hispanics. Rates significantly declined within SEP groups, measured as education attainment (high school=-4.8/1000 p-y) and IPR group (poor=-7.9/1000 p-y; middle income=-4.7/1000 p-y; and high income=-6.2/1000 p-y; but not for near poor). For adults with DM, statistically significant all-cause mortality disparity showed greater mortality rates for the lowest than the highest SEP level (education attainment and IPR) in each time period. However, patterns in mortality trends and disparity varied by race/ethnicity. The excess mortality risk attributed to DM significantly decreased from 1997-2001 to 2007-2011, within SEP levels, and among Hispanics and non-Hispanic whites; but no statistically significant changes among non-Hispanic blacks. CONCLUSIONS: There were substantial improvements in all-cause mortality among US adults. However, we observed SEP disparities in mortality across race/ethnic groups or for adults with and without DM despite targeted efforts to improve access and quality of care among disproportionately affected populations.

County-Level Socioeconomic Disparities in Use of Medical Services for Management of Infections by Medicare Beneficiaries With Diabetes-United States, 2012.

OBJECTIVE: To assess county-level socioeconomic disparities in medical service usage for infections among Medicare beneficiaries with diabetes (MBWDs) who had fee-for-service health insurance claims during 2012. DESIGN: We used Medicare claims data to calculate percentage of MBWDs with infections. SETTING: Medicare beneficiaries. PARTICIPANTS: We estimated the percentage of MBWDs who used medical services for each of 3 groups of infections by sex and quintiles of the prevalence of social factors in the person's county of residence: anatomic site-specific infections; pathogen-specific infections; and HHST infections (human immunodeficiency virus/acquired immunodeficiency syndrome, viral hepatitis, sexually transmitted diseases, and tuberculosis). MAIN OUTCOME MEASURES: Using quintiles of county-specific socioeconomic determinants, we calculated absolute and relative disparities in each group of infections for male and female MBWDs. We also used regression-based summary measures to estimate the overall average absolute and relative disparities for each infection group. RESULTS: Of the 4.5 million male MBWDs, 15.8%, 25.3%, and 2.7% had 1 or more site-specific, pathogen-specific, and HHST infections, respectively. Results were similar for females (n = 5.2 million). The percentage of MBWDs with 1 or more infections in each group increased as social disadvantage in the MBWDs' county of residence increased. Absolute and relative county-level socioeconomic disparities in receipt of medical services for 1 or more infections (site- or pathogen-specific) were 12.9 or less percentage points and 65.5% or less, respectively. For HHST infections, percentage of MBWDs having 1 or more HHST infections for persons residing in the highest quintile (Q5) was 3- to 4-fold higher (P < .001) than persons residing in the lowest quintile (Q1). CONCLUSIONS: Infection burden among MBWDs is generally associated with county-level contextual socioeconomic disadvantage, and the extent of health disparities varies by infection category, socioeconomic factor, and quintiles of socioeconomic disadvantage. The findings imply ongoing need for efforts to identify effective interventions for reducing county-level social disparities in infections among patients with diabetes.

Life Course Socioeconomic Position, Allostatic Load, and Incidence of Type 2 Diabetes among African American Adults: The Jackson Heart Study, 2000-04 to 2012.

Objective: We examined whether life course socioeconomic position (SEP) was associated with incidence of type 2 diabetes (t2DM) among African Americans. Design: Secondary analysis of data from the Jackson Heart Study, 2000-04 to 2012, using Cox proportional hazard regression to estimate hazard ratios (HR) with 95% CI for t2DM incidence by measures of life course SEP. Participants: Sample of 4,012 nondiabetic adults aged 25-84 years at baseline. Outcome Measure: Incident t2DM identified by self-report, hemoglobin A1c ≥6.5%, fasting plasma glucose ≥126 mg/dL, or use of diabetes medication. Results: During 7.9 years of follow-up, 486 participants developed t2DM (incidence rate 15.2/1000 person-years, 95% CI: 13.9-16.6). Among women, but not men, childhood SEP was inversely associated with t2DM incidence (HR=.97, 95% CI: .94-.99) but was no longer associated with adjustment for adult SEP or t2DM risk factors. Upward SEP mobility increased the hazard for t2DM incidence (adjusted HR=1.52, 95% CI: 1.05-2.21) among women only. Life course allostatic load (AL) did not explain the SEP-t2DM association in either sex. Conclusions: Childhood SEP and upward social mobility may influence t2DM incidence in African American women but not in men.

The contribution of family history to the burden of diagnosed diabetes, undiagnosed diabetes, and prediabetes in the United States: analysis of the National Health and Nutrition Examination Survey, 2009-2014.

PURPOSE: Given the importance of family history in the early detection and prevention of type 2 diabetes, we quantified the public health impact of reported family health history on diagnosed diabetes (DD), undiagnosed diabetes (UD), and prediabetes (PD) in the United States. METHODS: We used population data from the National Health and Nutrition Examination Survey 2009-2014 to measure the association of reported family history of diabetes with DD, UD, and PD. RESULTS: Using polytomous logistic regression and multivariable adjustment, family history prevalence ratios were 4.27 (confidence interval (CI): 3.57, 5.12) for DD, 2.03 (CI: 1.56, 2.63) for UD, and 1.26 (CI: 1.09, 1.44) for PD. In the United States, we estimate that 10.1 million DD cases, 1.4 million UD cases, and 3.9 million PD cases can be attributed to having a family history of diabetes. CONCLUSION: These findings confirm that family history of diabetes has a major public health impact on diabetes in the United States. In spite of the recent interest and focus on genomics and precision medicine, family health history continues to be an integral component of public health campaigns to identify persons at high risk for developing type 2 diabetes and early detection of diabetes to prevent or delay complications.

County-level contextual factors associated with diabetes incidence in the United States.

PURPOSE: Health and administrative systems are facing spatial clustering in chronic diseases such as diabetes. This study explores how geographic distribution of diabetes in the United States is associated with socioeconomic and built environment characteristics and health-relevant policies. METHODS: We compiled nationally representative county-level data from multiple data sources. We standardized characteristics to a mean = 0 and a SD = 1 and modeled county-level age-adjusted diagnosed diabetes incidence in 2013 using 2-level hierarchical linear regression. RESULTS: Incidence of age-standardized diagnosed diabetes in 2013 varied across U.S. counties (n = 3109), ranging from 310 to 2190 new cases/100,000, with an average of 856.4/100,000. Socioeconomic and health-related characteristics explained ∼42% of the variation in diabetes incidence across counties. After accounting for other characteristics, counties with higher unemployment, higher poverty, and longer commutes had higher incidence rates than counties with lower levels. Counties with more exercise opportunities, access to healthy food, and primary care physicians had fewer diabetes cases. CONCLUSIONS: Features of the socioeconomic and built environment were associated with diabetes incidence; identifying the salient modifiable features of counties can inform targeted policies to reduce diabetes incidence.

Disparities in the Prevalence of Diagnosed Diabetes - United States, 1999-2002 and 2011-2014.

The prevalence of diabetes mellitus has increased rapidly in the United States since the mid-1990s. By 2014, an estimated 29.1 million persons, or 9.3% of the total population, had received a diagnosis of diabetes (1). Recent evidence indicates that the prevalence of diagnosed diabetes among non-Hispanic black (black), Hispanic, and poorly educated adults continues to increase but has leveled off among non-Hispanic whites (whites) and persons with higher education (2). During 2004-2010, CDC reported marked racial/ethnic and socioeconomic position disparities in diabetes prevalence and increases in the magnitude of these disparities over time (3). However, the magnitude and extent of temporal change in socioeconomic position disparities in diagnosed diabetes among racial/ethnic populations are unknown. CDC used data from the National Health Interview Survey (NHIS) for the periods 1999-2002 and 2011-2014 to assess the magnitude of and change in socioeconomic position disparities in the age-standardized prevalence of diagnosed diabetes in the overall population and among blacks, whites, and Hispanics. During each period, significant socioeconomic position disparities existed in the overall population and among the assessed racial/ethnic populations. Disparities in prevalence increased with increasing socioeconomic disadvantage and widened over time among Hispanics and whites but not among blacks. The persistent widening of the socioeconomic position gap in prevalence suggests that interventions to reduce the risk for diabetes might have a different impact according to socioeconomic position.

Association Between County-Level Characteristics and Eye Care Use by US Adults in 22 States After Accounting for Individual-Level Characteristics Using a Conceptual Framework.

IMPORTANCE: Individual-level characteristics are associated with eye care use. The influence of contextual factors on vision and eye health, as well as health behavior, is unknown. OBJECTIVE: To examine the association between county-level characteristics and eye care use after accounting for individual-level characteristics using a conceptual framework. DESIGN, SETTING, AND PARTICIPANTS: This investigation was a cross-sectional study of respondents 40 years and older participating in the Behavioral Risk Factor Surveillance System surveys between 2006 and 2010 from 22 states that used the Visual Impairment and Access to Eye Care module. Multilevel regressions were used to examine the association between county-level characteristics and eye care use after adjusting for individual-level characteristics (age, sex, race/ethnicity, educational attainment, annual household income, employment status, health care insurance coverage, eye care insurance coverage, personal established physician, poor vision or eye health, and diabetes status). Data analysis was performed from March 23, 2014, to June 7, 2016. MAIN OUTCOMES AND MEASURES: Eye care visit and receipt of a dilated eye examination in the past year. RESULTS: Among 117 295 respondents who resided in 828 counties, individual-level data were obtained from the Behavioral Risk Factor Surveillance System surveys. All county-level variables were aggregated at the county level from the Behavioral Risk Factor Surveillance System surveys except for a high geographic density of eye care professionals, which was obtained from the 2010 Area Health Resource File. After controlling for individual-level characteristics, the odds of reporting an eye care visit in the past year were significantly higher among people living in counties with high percentages of black individuals (adjusted odds ratio [aOR], 1.12; 95% CI, 1.01-1.24; P = .04) or low-income households (aOR, 1.12; 95% CI, 1.00-1.25; P = .045) or with a high density of eye care professionals (aOR, 1.18; 95% CI, 1.07-1.29; P < .001) than among those living in counties with the lowest tertile of each county-level characteristic. The odds of reporting receipt of a dilated eye examination in the past year were also higher among people living in counties with the highest percentages of black individuals (aOR, 1.20; 95% CI, 1.07-1.34; P = .002) or low-income households (aOR, 1.17; 95% CI, 1.04-1.32; P = .01). However, the odds of reported receipt of a dilated eye examination in the past year were lower in counties with the highest percentages of people with poor vision and eye health compared with counties with lower percentages (aOR, 0.85; 95% CI, 0.77-0.94; P = .002). CONCLUSIONS AND RELEVANCE: Contextual factors, measured at the county level, were associated with eye care use independent of individual-level characteristics. The findings suggest that, while individual characteristics influence health care use, it is also important to address contextual factors to improve eye care use and ultimately vision health.

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.

Measurement of Health Disparities, Health Inequities, and Social Determinants of Health to Support the Advancement of Health Equity.

Reduction of health disparities and advancement of health equity in the United States require high-quality data indicative of where the nation stands vis-à-vis health equity, as well as proper analytic tools to facilitate accurate interpretation of these data. This article opens with an overview of health equity and social determinants of health. It then proposes a set of recommended practices in measurement of health disparities, health inequities, and social determinants of health at the national level to support the advancement of health equity, highlighting that (1) differences in health and its determinants that are associated with social position are important to assess; (2) social and structural determinants of health should be assessed and multiple levels of measurement should be considered; (3) the rationale for methodological choices made and measures chosen should be made explicit; (4) groups to be compared should be simultaneously classified by multiple social statuses; and (5) stakeholders and their communication needs can often be considered in the selection of analytic methods. Although much is understood about the role of social determinants of health in shaping the health of populations, researchers should continue to advance understanding of the pathways through which they operate on particular health outcomes. There is still much to learn and implement about how to measure health disparities, health inequities, and social determinants of health at the national level, and the challenges of health equity persist. We anticipate that the present discussion will contribute to the laying of a foundation for standard practice in the monitoring of national progress toward achievement of health equity.

Association of Socioeconomic Status with Eye Health Among Women With and Without Diabetes.

OBJECTIVE: To investigate the association between socioeconomic position (SEP) and poor eye health among women. MATERIALS AND METHODS: We included the 7,708 women aged ≥ 40 years who participated in the 2008 National Health Interview Survey. We defined poor eye health as self-reported age-related eye diseases (AREDs; cataract, glaucoma, macular degeneration, or diabetic retinopathy) or visual impairment (VI). We identified diagnosed diabetes by self-report. We measured SEP by education attained and annual household income. We conducted logistic regression analyses while controlling for demographic, clinical, behavioral, and healthcare access variables. RESULTS: The age-standardized prevalence of VI and ARED was significantly higher among women with diagnosed diabetes than among those without diagnosed diabetes, 29.8% versus 14.4% and 34.1% versus 20.8%, respectively (p < 0.05 for both). The prevalence of VI and ARED increased with decreasing SEP, but the trends were only significant among women without diabetes. After multivariable adjustment, education and income were significantly associated with VI but not with ARED. We found no interaction with diagnosed diabetes. CONCLUSIONS: SEP was inversely associated with VI but not with ARED. We found no interaction with diagnosed diabetes.

Measuring health disparities: a comparison of absolute and relative disparities.

Monitoring national trends in disparities in different diseases could provide measures to evaluate the impact of intervention programs designed to reduce health disparities. In the US, most of the reports that track health disparities provided either relative or absolute disparities or both. However, these two measures of disparities are not only different in scale and magnitude but also the temporal changes in the magnitudes of these measures can occur in opposite directions. The trends for absolute disparity and relative disparity could move in opposite directions when the prevalence of disease in the two populations being compared either increase or decline simultaneously. If the absolute disparity increases but relative disparity declines for consecutive time periods, the absolute disparity increases but relative disparity declines for the combined time periods even with a larger increase in absolute disparity during the combined time periods. Based on random increases or decreases in prevalence of disease for two population groups, there is a higher chance the trends of these two measures could move in opposite directions when the prevalence of disease for the more advantaged group is very small relative to the prevalence of disease for the more disadvantaged group. When prevalence of disease increase or decrease simultaneously for two populations, the increase or decrease in absolute disparity has to be sufficiently large enough to warrant a corresponding increase or decrease in relative disparity. When absolute disparity declines but relative disparity increases, there is some progress in reducing disparities, but the reduction in absolute disparity is not large enough to also reduce relative disparity. When evaluating interventions to reduce health disparities using these two measures, it is important to consider both absolute and relative disparities and consider all the scenarios discussed in this paper to assess the progress towards reducing or eliminating health disparities.

Association of Socioeconomic Position With Sensory Impairment Among US Working-Aged Adults.

OBJECTIVES: We examined the relationship between socioeconomic position (SEP) and sensory impairment. METHODS: We used data from the 2007 to 2010 National Health Interview Surveys (n = 69 845 adults). Multivariable logistic regressions estimated odds ratios (ORs) for associations of educational attainment, occupational class, and poverty-income ratio with impaired vision or hearing. RESULTS: Nearly 20% of respondents reported sensory impairment. Each SEP indicator was negatively associated with sensory impairment. Adjusted odds of vision impairment were significantly higher for farm workers (OR = 1.41; 95% confidence interval [CI] = 1.01, 2.02), people with some college (OR = 1.29; 95% CI = 1.16, 1.44) or less than a high school diploma (OR = 1.36; 95% CI = 1.19, 1.55), and people from poor (OR = 1.35; 95% CI = 1.20, 1.52), low-income (OR = 1.28; 95% CI = 1.14, 1.43), or middle-income (OR = 1.19; 95% CI = 1.07, 1.31) families than for the highest-SEP group. Odds of hearing impairment were significantly higher for people with some college or less education than for those with a college degree or more; for service groups, farmers, and blue-collar workers than for white-collar workers; and for people in poor families. CONCLUSIONS: More research is needed to understand the SEP-sensory impairment association.

Secular changes in prediabetes indicators among older-adult Americans, 1999-2010.

BACKGROUND: Sex-specific prediabetes estimates are not available for older-adult Americans. PURPOSE: To estimate prediabetes prevalence, using nationally representative data, in civilian, non-institutionalized, older U.S. adults. METHODS: Data from 7,995 participants aged ≥50 years from the 1999-2010 National Health and Nutrition Examination Surveys were analyzed in 2013. Prediabetes was defined as hemoglobin A1c=5.7%-6.4% (39-47 mmol/mol [HbA1c5.7]), fasting plasma glucose of 100-125 mg/dL (impaired fasting glucose [IFG]), or both. Crude and age-adjusted prevalences for prediabetes, HbA1c5.7, and IFG by sex and three age groups were calculated, with additional adjustment for sex, age, race/ethnicity, poverty status, education, living alone, and BMI. RESULTS: From 1999 to 2005 and 2006 to 2010, prediabetes increased for adults aged 50-64 years (38.5% [95% CI=35.3, 41.8] to 45.9% [42.3, 49.5], p=0.003) and 65-74 years (41.3% [37.2, 45.5] to 47.9% [44.5, 51.3]; p=0.016), but not significantly for adults aged ≥75 years (45.1% [95% CI=41.1, 49.1] to 48.9% [95% CI=45.2, 52.6]; p>0.05). Prediabetes increased significantly for women in the two youngest age groups, and HbA1c5.7 for both sexes (except men aged ≥75 years), but IFG remained stable for both sexes. Men had higher prevalences than women for prediabetes and IFG among adults aged 50-64 years, and for IFG among adults aged ≥75 years. Across demographic subgroups, adjusted prevalence gains for both sexes were similar and most pronounced for HbA1c5.7, virtually absent for IFG, but greater for women than men for prediabetes. CONCLUSIONS: Given the large, growing prediabetes prevalence and its anticipated burden, older adults, especially women, are likely intervention targets.

The relationship between county-level contextual characteristics and use of diabetes care services.

OBJECTIVES: To examine the relationship between county-level measures of social determinants and use of preventive care among US adults with diagnosed diabetes. To inform future diabetes prevention strategies. METHODS: Data are from the Behavioral Risk Factor Surveillance System (BRFSS) 2004 and 2005 surveys, the National Diabetes Surveillance System, and the Area Resource File. Use of diabetes care services was defined by self-reported receipt of 7 preventive care services. Our study sample included 46 806 respondents with self-reported diagnosed diabetes. Multilevel models were run to assess the association between county-level characteristics and receipt of each of the 7 preventive diabetes care service after controlling for characteristics of individuals. Results were considered significant if P < .05. RESULTS: Controlling for individual-level characteristics, our analyses showed that 7 of the 8 county-level factors examined were significantly associated with use of 1 or more preventive diabetes care services. For example, people with diabetes living in a county with a high uninsurance rate were less likely to have an influenza vaccination, visit a doctor for diabetes care, have an A1c test, or a foot examination; people with diabetes living in a county with a high physician density were more likely to have an A1c test, foot examination, or an eye examination; and people with diabetes living in a county with more people with less than high-school education were less likely to have influenza vaccination, pneumococcal vaccination, or self-care education (all P < .05). CONCLUSIONS: Many of the county-level factors examined in this study were found to be significantly associated with use of preventive diabetes care services. County policy makers may need to consider local circumstances to address the disparities in use of these services.

Education and income - United States, 2009 and 2011.

The factors that influence the socioeconomic position of individuals and groups within industrial societies also influence their health. Socioeconomic position has continuous and graded effects on health that are cumulative over a lifetime. The socioeconomic conditions of the places where persons live and work have an even more substantial influence on health than personal socioeconomic position. In the United States, educational attainment and income are the indicators that are most commonly used to measure the effect of socioeconomic position on health. Research indicates that substantial educational and income disparities exist across many measures of health. A previous report described the magnitude and patterns of absolute and relative measures of disparity in noncompletion of high school and poverty in 2005 and 2009. Notable disparities defined by race/ethnicity, socioeconomic factors, disability status, and geographic location were identified for 2005 and 2009, with no evidence of a temporal decrease in racial/ethnic disparities, whereas socioeconomic and disability disparities increased from 2005 to 2009.

Diabetes - United States, 2006 and 2010.

In 2011, an estimated 26 million persons aged ≥20 years (11.3% of the U.S. population) had diabetes. Both the prevalence and incidence of diabetes have increased rapidly since the mid-1990s, with minority racial/ethnic groups and socioeconomically disadvantaged groups experiencing the steepest increases and most substantial effects from the disease.

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%).