A genetic link for diabetes - Essay Example

Mortality from Ischemic Heart Disease and Diabetes Mellitus (Type 2) in Four U.S. Wheat-Producing s: A Hypothesis-Generating Study. Publication Information: Article Title: Mortality from Ischemic Heart Disease and Diabetes Mellitus (Type 2) in Four U.S. Wheat-Producing States: A Hypothesis-Generating Study. Contributors: Dina M. Schreinemachers - author. Journal Title: Environmental Health Perspectives. Volume: 114. Issue: 2. Publication Year: 2006. Page Number: 186+. COPYRIGHT 2006 National Institute of Environmental Health Sciences; by Dina M. Schreinemachers In this ecologic study I examined ischemic heart disease (IHD) and diabetes mortality in rural agricultural counties of Minnesota, Montana, North Dakota, and South Dakota, in association with environmental exposure to chlorophenoxy herbicides, using wheat acreage as a surrogate exposure. I collected data on agricultural land use and 1979-1998 mortality from the U.S. Department of Agriculture and the Centers for Disease Control and Prevention websites, respectively. Counties were grouped based on percentage of land area dedicated to wheat farming. Poisson relative risks (RR) and 95% confidence intervals (CIs), comparing high- and medium- with low-wheat counties, were obtained for IHD, the subcategories acute myocardial infarction (AMl) and coronary atherosclerosis (CAS), and diabetes, adjusting for sex, age, mortality cohort, and poverty index. Mortality from IHD was modestly increased (RR : 1.08; 95% CI, 1.04-1.12). Analyses of its two major forms were more revealing. Compared with low-wheat counties, mortality in high-wheat counties 2from AMI increased (RR = 1.20; 95% CI, 1.14-1.26), and mortality from CAS decreased (RR = 0.89; 95% CI, 0.83-0.96). Mortality from AMI was more pronounced for those < 65 years of age (RR = 1.31; 95% CI 1.22-1.39). Mortality from type 2 diabetes increased (RR = 1.16; 95% CI, 1.08-1.24). These results suggest that the underlying cause of mortality from AMI and type 2 diabetes increased and the underlying cause of mortality from CAS decreased in counties where a large proportion of the land area is dedicated to spring and durum wheat farming. Firm conclusions on causal inference cannot be reached until more definitive studies have been conducted. Key words: chlorophenoxy herbicides, clofibrate, coronary atherosclerosis, C-reactive protein, diabetes, ischemic heart disease, myocardial infarction. doi:10.1289/ehp.8352 available via http://dx.doi.org/[Online 6 October 2005] A series of International Agency for Research on Cancer multinational studies of workers involved in the production of chlorophenoxy herbicides and chlorophenols indicated excess mortality from cancer, ischemic heart disease (IHD), and possibly diabetes in association with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure (Flesch-Janys et al. 1995; Hooiveld et al. 1998; Kogevinas et al. 1997; Vena et al. 1998). Chemical production workers exposed to TCDD are simultarteously exposed to much higher levels of the commercial chemicals being produced, such as chlorophenoxy herbicides and chlorophenols (Remillard and Bunce 2002). Results from the cancer mortality study among chlorophenoxy herbicide production workers, some of whom were exposed to TCDD or higher chlorinated dioxins (Kogevinas et al. 1997), were similar to those reported in an ecologic mortality study of cancer among residents in rural, agricultural counties of Minnesota, Montana, North Dakota, and South Dakota potentially exposed to chlorophenoxy herbicides and/or contaminants (Schreinemachers 2000). This similarity of results led to the question of whether increased mortality from IHD and diabetes observed among the chlorophenoxy herbicide production workers (Vena et al. 1998) might also be observed among residents of agricultural counties of Minnesota, Montana, North Dakota, and South Dakota, where the major field crops spring and durum wheat have been treated predominantly and long term with chlorophenoxy herbicides (Lin et al. 1995). The chlorophenoxy herbicides 2,4-dichlorophenoxyacetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA) have been widely applied in the United States since World War II and are used for broadleaf weed control in wheat farming and maintenance of home lawns and parks, rights-of-way, and road sides (Short and Colborn 1999). 2,4-D used for home lawn maintenance is likely to be found in residential carpet dust up to 1 year after application (Nishioka et al. 1996). Chlorophenoxy herbicides are present in agricultural and urban streams and in the atmosphere [U.S. Geological Survey (USGS) 1995, 2003]. 2,4-D and MCPA are transported over short and long distances attached to air particles, with concentrations highest in regions where they are applied at the time of application (Waite et al. 2002, 2005). Contaminants present in technical-grade 2,4-D and MCPA include polychlorinated dibenzodioxins and dibenzofurans and 2,4-dichlorophenol [U.S. Department of Agriculture (USDA) 1998]. More recently, aryl hydrocarbon receptor-based assays for dioxin-like activity of chlorinated herbicides applied in the Minnesota Red River Valley showed that most of these commercial-grade mixtures had measurable dioxin-like activity (Huwe et al. 2003). Confirmatory analytic chemical studies showed that some of the 2,4-D ester formulations were contaminated with dioxins/furans, including trace amounts of TCDD. Residents of agricultural counties of Minnesota, Montana, North Dakota, and South Dakota may be environmentally and/or occupationally exposed to 2,4-D and MCPA. Wheat acreage per county or percentage of the county's land area dedicated to wheat farming was used in two previous ecologic studies as a surrogate measure of exposure to chlorophenoxy herbicides and/or contaminants, because information on herbicide use by county was not available (Schreinemachers 2000, 2003). Use of this surrogate exposure measure was a reasonable choice because chlorophenoxy herbicides are the predominant herbicides applied to wheat and because other major field crops in these four states, corn and soybeans, are mostly treated with other herbicides, based on information on herbicide use by crop, state, and year, available since 1991 (USDA 1991). Before 1991, information on herbicide use was available only for groups of states. In the present ecologic study I investigated the possible links between environmental exposures to chlorophenoxy herbicides and mortality during 1979-1988 and 1989-1998 from IHD and type 2 diabetes. Counties with different levels of wheat farming provided a gradient of exposures, thereby overcoming the lack of a null referent. This hazard identification study can be one of the initial steps in defining an association between environmental exposures to chlorophenoxy herbicides and mortality from IHD and diabetes. Materials and Methods Mortality. I obtained data on underlying cause of death based on the International Classification of Diseases, 9th Revision (ICD-9) (U.S. Department of Health and Human Services 1989) by state, county, sex, and age group for 1979-1988 and 1989-1998 for Minnesota, Montana, North Dakota, and South Dakota [Centers for Disease Control and Prevention (CDC) 2004a]. Deaths were distributed by 5-year age groups for mortality < 25 years of age and by 10-year age groups for mortality [greater than or equal to] 25 years of age. Mortality data for the following diseases were extracted: IHD (ICD-9 410-414.9), acute myocardial infarction (AMI; ICD-9 410), coronary atherosclerosis (CAS; ICD-9 414.0), and diabetes mellitus (all types, ICD-9 250.0-250.9). Population data were obtained from the Population Census (CDC 2003). Only white subjects were included because the percentage of nonwhites in these states ( < 10%) was too small to support reliable analyses. This study analyzed publicly available data sets and was exempt from institutional review board approval. Agricultural data. From the combined 262 counties of the four states, agricultural counties with a mostly rural population were selected based on the criteria of [greater than or equal to] 20% of county's land area dedicated to cropland and [greater than or equal to] 50% of population defined as rural (U.S. Census Bureau 1980a; USDA 1982). Rural populations living in agricultural counties are more likely to be exposed to agricultural chemicals than are urban populations. Farming communities tend to be more residentially stable than urban communities (Blair and Zahm 1995). Although farmers and their families may have been exposed to higher levels of pesticides than the general rural population, it was not possible to distinguish between farmers and nonfarmers. Averages of total wheat acreage per county were determined for 1970-1979, 1980-1989, and 1990-1999, based on annual estimates of total wheat acreages per county (USDA 2004). Wheat acreage for 1964 was obtained from the 1964 USDA Agricultural Census (USDA 1964). To further define wheat acreage in rural, agricultural counties of Minnesota, Montana, North Dakota, and South Dakota as a surrogate for chlorophenoxy herbicide use, I determined acreage for the different classes of wheat in the selected counties. The percentage of herbicide-treated acreage for spring and durum wheat is larger than for winter wheat, which is a better competitor with weeds (Lin et al. 1995). For counties with a large winter wheat acreage, I applied a correction factor to the winter wheat acreage before combining it with spring and durum wheat acreage to obtain a value for total wheat acreage. This correction factor was based on 1991-1998 herbicide treatment for the different classes of wheat (USDA 1991). This information was not available for years before 1991. Statistical methods. I used Spearman correlations to determine the associations among 1964 and average 1970-1979, 1980-1989, and 1990-1999 levels of wheat acreage. Depending on the number of deaths available for the specific underlying cause under study, counties were grouped based on the median or tertiles of the percentage of land area dedicated to wheat. For diabetes mortality, I compared high-wheat counties with low-wheat counties. For mortality from IHD, including AMI and CAS, I compared high- and medium-wheat counties with low-wheat counties. For the univariate analyses by mortality cohort, sex, and age groups, I calculated standardized rate ratios (SRRs) and 95% confidence interval (CI) using direct age standardization based on the 1970 U.S. population, according to established methods (Greenland and Rothman 1998). For the multivariate analyses I used Poisson regressions, adjusting for mortality cohort (1989-1998 vs. 1979-1988), sex (male vs. female), age ([greater than or equal to] 65 vs. < 65 years of age), and county's poverty level (based on median percentage of families with income below poverty level in 1979, [greater than or equal to] 13.15 vs. < 13.15 [U.S. Census Bureau 1980b]). Analyses for IHD mortality excluded subjects < 25 years of age. Analyses for diabetes mortality included subjects [greater than or equal to] 45 years of age so that the obtained results refer mostly to type 2 diabetes (Calle et al. 1998). The high number of deaths from AMI made it possible to include additional analyses. Separate Poisson models were run for subjects < 65 years and [greater than or equal to] 65 years of age. I estimated the wheat effect on AMI mortality for individual states by comparing high- with low-wheat counties based on the median wheat percentage of each state. Statistical analyses and creation of figures were performed using SAS software (SAS Institute Inc. 2001). Results Wheat and chlorophenoxy herbicide use. Although the average percentage of a county's land area dedicated to wheat increased from 6.9% in 1964 to 12.1% during 1990-1999, Spearman correlations for the average 1970-1979 wheat acreage with the 1964, average 1980-1989, and average 1990-1999 wheat acreages were 0.98 (p < 0.0001), 0.95 (p < 0.0001), and 0.94 (p < 0.0001), respectively. This implied that a county with a high percentage of its land area dedicated to wheat during 1970-1979 was most likely also a high-wheat county during 1964, 1980-1989, and 1990-1999. The average total wheat acreage for 1970-1979 expressed as a percentage of a county's land area was used as a surrogate measure of exposure to chlorophenoxy herbicides. Wheat acreage during the other time periods could probably also have been used given the high correlations. A USDA report showed that in 1976, > 90% of the herbicides applied to wheat in the United States consisted of chlorophenoxy herbicides (Eichers et al. 1978). The percentage of chlorophenoxy herbicides applied overall to wheat decreased to 67% in 1992 because of increased use of other herbicides (Lin et al. 1995). I determined acreage for the different classes of wheat in the selected counties of the four states for 1970-1979. The estimated acreage of winter wheat during 1970-1979 was < 2% in the selected counties of Minnesota and North Dakota, so total wheat acreage was practically synonymous with the combined durum and other spring wheat acreage. However, the average acreage of winter wheat in the selected Montana and South Dakota counties contributed 36% and 20%, respectively, to the total wheat acreage. Given these data, it seemed appropriate to apply a correction factor to winter wheat acreage in Montana and South Dakota to account for the less intensive herbicide treatment. The amount of herbicides used on different classes of wheat has been made available for individual states since 1991. The 1991-1998 average percentage of durum and other spring wheat acreage treated with any herbicides in Minnesota, Montana, North Dakota, and South Dakota was 91%, whereas the average percentages of winter wheat in Montana and South Dakota treated with any herbicide were 86% and 67%, respectively. Assuming that the percentage of herbicide treated winter wheat acreage in Montana and South Dakota during 1991-1998 was similar to the percentage of herbicide-treated acreage during 1970-1979, a correction was applied to the 1970-1979 winter wheat acreage in counties of Montana and South Dakota, by multiplying the winter wheat acreage by 0.95 (86/91) and 0.74 (67/91), respectively, before combining winter wheat acreage with durum and other spring wheat acreage by county to obtain a value for adjusted total wheat acreage. County characteristics. Characteristics of the 152 selected counties distributed over three groups based on tertiles of the counties' wheat percentage are presented in Table 1. Most counties in the low-wheat group were located in Minnesota, whereas counties in the high-wheat group were mostly located in North Dakota. Medium- and high-wheat counties grew less corn and soybeans than did low-wheat counties and had a larger rural and farm population size. With increasing wheat percentage, the total population size decreased, whereas the percentage of subjects [greater than or equal to] 65 years of age increased. Cardiovascular disease and diabetes mortality. Table 2 presents crude and age-adjusted mortality rates for all ages by sex and mortality cohort. Age-adjusted U.S. rates are also presented for comparison (CDC 2004a). U.S. rates for mortality from IHD and diabetes were slightly higher than rates in the combined selected counties. Comparison of the two cohorts showed that 1989-1998 mortality from diabetes (all types) increased > 20%, whereas mortality from IHD decreased > 30%, which is consistent with the overall decline of mortality from IHD (including AMI and CAS) due to improved dietary patterns and treatment, decreased smoking, and increased physical activity (CDC 1992, 1997). Analysis of the two main subcategories of IHD revealed that age-adjusted rates for AMI among men were approximately 10% higher, and rates of CAS for both men and women were > 20% lower, in selected counties compared with the U.S. population. Age-standardized mortality rates by age group, sex, and mortality cohort are presented for IHD, AMI, and CAS, in grouped low-, medium-, and high-wheat counties, and for diabetes in grouped low- and high-wheat counties (Table 3, Figures 1-4). Wheat effects (surrogate for chlorophenoxy herbicide exposure) on IHD were slightly higher for the 1989-1998 cohort (Figure 1). These effects were more pronounced for AMI, especially for men and women < 65 years of age (Figure 2). Contrary to the increasing trend of mortality from AMI with increased wheat acreage, the trend for mortality from CAS was reversed, showing a decrease in high-wheat counties. This downward trend in deaths was observed for both mortality cohorts, both sexes, and both age groups (Figure 3), as indicated by SRR values of < 1 and 95% CI values excluding 1. Generally, diabetes mortality was higher in high-wheat counties (Table 3, Figure 4). [FIGURES 1-4 OMITTED] The results from Poisson models adjusting simultaneously for mortality cohort, sex, age, and poverty are presented in Table 4. In high-wheat counties, mortality from IHD, AMI, and diabetes (mostly type 2 because of excluding deaths of subjects < 45 years of age) was significantly increased by 8, 20, and 16%, respectively, whereas mortality from CAS was significantly decreased by 11% (models 1-4). The mortality increase from AMI in high-wheat counties was more pronounced among subjects < 65 years (31%) than for those [greater than or equal to] 65 years of age (16%) (models 5-6). Given that chlorophenoxy herbicides use started around 1950, those that died < 65 years of age may have been exposed prenatally and/or as a child. Mortality from AMI for individual states (models 7-10) was significantly increased in Minnesota (12%), North Dakota (16%), and South Dakota (7%). The effect for Montana (6%) was not significant, which was likely due to the low number of deaths from AMI and the few and narrow range of counties. To test this possibility, I ran a separate model for Minnesota, restricted to counties in the same wheat percentage range as Montana, which resulted in a nonsignificant wheat effect of 1.05 (0.91-1.15), similar to the results obtained for Montana. I repeated the Poisson models after applying a correction factor based on the winter wheat acreage in Montana and South Dakota. The adjusted results were very similar to the nonadjusted results (data not shown). Discussion The observed increases in mortality from IHD and diabetes in the general rural population of agricultural counties in Minnesota, Montana, North Dakota, and South Dakota are consistent with results from studies on effects from exposure to dioxin and dioxin-like compounds. Chlorophenoxy herbicides and chlorophenol production workers exposed to dioxin, based on estimates from job records and company exposure questionnaires, showed an increase in mortality from IHD and possibly diabetes (Vena et al. 1998). Likewise, studies of Operation Ranch Hand veterans showed an increase of mortality from circulatory system diseases (Michalek et al. 1998) and an increase in diabetes prevalence (Henriksen et al. 1997). This association between diabetes and serum dioxin levels in the Vietnam veterans has been defined as "limited and suggestive" (Brown 2000). A dose response of IHD mortality in association with exposure to polychlorinated dibenzo-p-dioxins and dibenzofurans was observed in a German herbicide-producing plant (Flesch-Janys et al. 1995). Widespread exposure to dioxin from the Seveso, Italy, accident was associated with increased mortality from chronic IHD, cancer, and diabetes (Bertazzi et al. 2001). Increased hospitalization rates for IHD were observed among New York State residents living near sites contaminated with persistent organic pollutants (Sergeev and Carpenter 2005). Increased mortality from diabetes and several cancers has been observed for pulp and paper mill workers (Axelson et al. 1998; Henneberger et al. 1989; Schwartz 1988; Wingren et al. 1991). Environmental factors are thought to play a role (Carpenter et al. 2002; Longnecker and Daniels 2001; Remillard and Bunce 2002). The increase in mortality from type 2 diabetes with increasing wheat acreage should be considered with caution. Estimated rates of diabetes mortality may be unreliable because of severe underreporting of the disease (CDC 2004b), in addition to usually not being listed as underlying cause of death (Geiss et al. 1995). To support the diabetes results, mortality in high- and low-wheat counties were compared for two additional diseases known to be associated with diabetes, namely, renal disease (ICD-9 580-589) and cerebrovascular disease (ICD-9 430-438) (CDC 2004b; Harris 1995). Mortality from renal disease increased by 21% in high-wheat counties [risk ratio (RR) = 1.21; 95% CI, 1.12-1.31], whereas mortality from cerebrovascular disease did not show an effect (RR = 0.98; 95% CI = 0.94-1.03). These two disease groups were not further investigated. The seemingly contradictory outcomes of increasing mortality from AMI and decreasing mortality from CAS in association with wheat acreage agree with results of a study on cardiovascular mortality during 1987-1997 in Minnesota (Morrison et al. 2000). This study showed that in the northwest region of Minnesota, where wheat is one of the major field crops, mortality from AMI was higher and mortality from CAS was lower compared with the other Minnesota regions, both in younger and older men and women. Assuming that CAS is a major risk factor for AMI, one could argue that among subjects susceptible to IHD, those more highly exposed died from AMI as underlying cause, resulting in fewer deaths being available for CAS, a case of competing mortality. Other interpretations are also worth considering. AMI often occurs in the absence of hyperlipidemia. Recent studies have shown that high levels of C-reactive protein (C-RP), an indicator of systemic inflammation, are associated with increased risk of AMI (as well as metabolic syndrome and type 2 diabetes), independent of the level of CAS (Pradhan et al. 2001; Ridker et al. 2004). Although increased levels of C-RP and atherosclerosis may indicate distinct risk groups (Theuma and Fonseca 2003), elevated C-RP levels are thought to be the stronger predictor of future cardiovascular events and may be associated with plaque fragility and rupture (Aronow 2003; Hansson 2005; Ridker et al. 2002, 2004). If the increased mortality observed for AMI in the present study is indeed associated with increased C-RP levels, the observed decrease in mortality from CAS may now have an alternative interpretation. Considering that 2,4-D and MCPA have similar chemical structures as the hypolipidemic drug ctofibrate (2-[4-chlorophenoxy]-2-methylpropionic acid ethyl ester) (Axelson et al. 1980) and that 2,4-D and MCPA, as well as clofibrate, have lipid-lowering effects in rats (Vainio et al. 1983), one might ask if the observed decrease in mortality from CAS could be caused by a lipid-lowering effect from environmental exposures to chlorophenoxy herbicides. Further studies will have to investigate this. Finally, differences among counties in determination of underlying cause of death as well as chance may have contributed to these findings. Conducting investigations of associations between environmental exposures and adverse human health effects is difficult for several reasons. Exposures are most likely widespread and low dose. A null comparison group may be unavailable (damage to the environment is global). Long lag periods between time of exposure and time of diagnosis of the chronic disease under study may present problems (McMichael 2002). Use of a multilevel approach may be warranted (Pekkanen and Pearce 2001; Susser 1998). Evidence from studies at the molecular, individual, population, and/or ecosystem level needs to be combined to completely define the link between environmental exposure and health effects. This requires multidisciplinary studies and interdisciplinary collaborations. Population (or ecologic) studies are fundamental as a first step in identifying a potential hazard and defining the key public health problem, because effects from exposures common throughout a study population can be uncovered only by comparison of populations (Pekkanen and Pearce 2001). Results from a single ecologic study can be easily misinterpreted and cannot establish causal inference (Morgenstern 1995). Individual risk factors cannot be accounted for in an ecologic study, because the focus is on the environment in which people live rather than on their personal lifestyles. Therefore, an ecologic study needs to be followed by an individual risk factor study based on hypotheses generated by the ecologic results, with adjustment for individual risk factors and confounders. An example in this study would be the potential confounding effect from smoking. Controlling for smoking usually has only a modest effect if risk estimates are high (Axelson and Steenland 1988). Although in the present study risk estimates for AMI are relatively low, there is no reason to assume that smoking rates per county increase with intensity of wheat farming. The fact that mortality from AMI increases with wheat percentage for both sexes, both younger and older subjects, and both 1979-1988 and 1989-1998 mortality cohorts suggests that the effects are associated with wheat agriculture. The present ecologic study as well as several previous studies used existing databases (Garry et al. 1996; Schreinemachers 2000, 2003; Schreinemachers et al. 1999). Comparison of regions at different levels of wheat farming provided the opportunity to observe adverse health effects in association with environmental exposures to chlorophenoxy herbicides and/or contaminants. Individual risk factor analysis could not have uncovered these associations easily. Results from the present study have generated hypotheses concerning the increase of AMI and the decrease of CAS in association with environmental, repeated (annually), low-dose exposures to chlorophenoxy herbicides and/or contaminants. Future, more definitive studies should include biomarkers such as serum levels of dibenzodioxins and dibenzofutans, levels of glycosylated hemoglobin, lipid levels, white blood cell counts and determination of C-RP. Subject-based studies should adjust for known individual risk factors for the diseases under study, such as obesity, smoking, socioeconomic factors, and access to medical care. Molecular studies could investigate whether chlorophenoxy herbicides are synthetic ligands for peroxisome proliferator activated receptors (PPARs). Activation of PPARs is the mechanism by which hypolipidemic fibrates induce hypolipidemia in humans (Clay et al. 2000; Vamecq and Latruffe 1999). In summary, this ecologic study is an example of how population studies can make valuable contributions to public health by identifying potential environmental hazards. ADDENDUM The population estimates used for the analyses were obtained from CDC WONDER and consist of Census intercensal estimates for 1971-1979 and 1981-1989, Census postcensal estimates for 1991-1999, and modified age-race-sex Census counts for the census years (1970, 1980, 1990, and 2000). 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Pesticides in the Atmosphere. Washington, DC:U.S. Geological Survey. Available: http://ca.water.usgs. gov/pnsp/atmos [accessed 25 August 2004]. Vainio H, Linnainmaa K, KiihSnen M, Nickels J, Hietanen E, Marniemi J. 1983. Hypolipidemia and peroxisome proliferation induced by phenoxyacetic acid herbicides in rats. Riochem Pharmacol 32:2775-2779. Vamecq J, Latruffe N. 1999. Medical significance of peroxisome proliferetor-activated receptors. Lancet 354:141-148. Vena J, Boffetta P, Becher H, Benn T, Bueno-de-Mesquita HB, Coggen D, et al. 1998. Exposure to dioxin and nonneoplastic mortality in the expanded IABC international cohort study of phenoxy herbicide and chlorophenol production workers and sprayers. Environ Health Perspect 106(suppl 2):645-653. Waite DT, Bailey P, Sproull JF, Quiring DV, Chau DF, Bailey J, et al. 2005. Atmospheric concentrations and dry and wet deposits of some herbicides currently used on the Canadian prairies. Chemosphere 58:693-703. Waite DT, Cessna AJ, Grover B, Kerr LA, Snihura AD. 2002. Environmental concentrations of agricultural herbicides: 2,4-D and triallate. J Environ Qual 31:129-144. Wingren G, Persson B, Thoren K, Axelson O. 1991. Mortality pattern among pulp and paper mill workers in Sweden: a case-referent study. Am J Ind Med 20:769-774. Address correspondence to D.M. Schreinemachers, Epidemiology and Biomarkers Branch, Human Studies Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, MD 58A, Research Triangle Park, NC 27711 USA. Telephone: (919) 966-5875. Fax: (919) 966-7584. E-mail: schreinemachers.dina@epa.gov I am grateful to the late O. Axelson for his support and for sharing his insight into the clofibrate-chlorophenoxy herbicide connection. I thank L. Birnbaum, J. Creason, B. Hilborn, P. Mendola, and especially V. Garry for their review of the manuscript. I also thank the anonymous EHP reviewers for their useful comments. The research described in this article has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency. The author declares she has no competing financial interests. Received 24 May 2005; accepted 6 October 2005. Table 1. Characteristics [median (range)] of three groups of counties based on tertiles of 1970-1979 percentage wheat acreage/county. Average 1970-1979 percent wheat acreage per growth Characteristics Low of county groups 0.95 (0.09-4.00) No. of selected counties Minnesota 32 Montana 1 North Dakota 0 South Dakota 18 Total 51 County land area, acres x [10.sup.6] 0.37 (0.22-1.92) County cropland, acres x [10.sup.6] 0.24 (0.07-0.51) 1970-1979 percent dedicated land Corn 21.74 (0.00-39.89) Soybeans 4.16 (0.00-38.98) Farm size, 1982 (acres) 303 (147-4,726) Percent rural population, 1982 79.4 (53.1-100) Percent farm population, 1982 25.1 (6.2-48.6) Percent families below poverty level, 1979 12.0 (4.7-38.0) Average annual population at risk (percent [greater than or equal to] 65 years) 1979-1988 Male 447,124 (12.5) Female 452,703 (16.5) 1989-1998 Male 479,003 (12.5) Female 482,825 (16.6) Average 1970-1979 percent wheat acreage per growth Characteristics Medium of county groups 8.43 (4.04-14.32) No. of selected counties Minnesota 14 Montana 8 North Dakota 10 South Dakota 19 Total 51 County land area, acres x [10.sup.6] 0.71 (0.33-3.16) County cropland, acres x [10.sup.6] 0.34 (0.15-0.71) 1970-1979 percent dedicated land Corn 0.93 (0.00-31.98) Soybeans 0.00 (0.00-28.87) Farm size, 1982 (acres) 1,276 (233-5,154) Percent rural population, 1982 100 (52.2-100) Percent farm population, 1982 30.5 (11.9-64.8) Percent families below poverty level, 1979 14.9 (7.5-34.3) Average annual population at risk (percent [greater than or equal to] 65 years) 1979-1988 Male 220,924 (15.1) Female 220,884 (18.8) 1989-1998 Male 209,192 (16.3) Female 210,840 (20.6) Average 1970-1979 percent wheat acreage per growth Characteristics High of county groups 22.91 (14.52-35.47) No. of selected counties Minnesota 10 Montana 6 North Dakota 30 South Dakota 4 Total 50 County land area, acres x [10.sup.6] 0.71 (0.28-2.55) County cropland, acres x [10.sup.6] 0.50 (0.18-1.24) 1970-1979 percent dedicated land Corn 0.06 (0.00-21.59) Soybeans 0.01 (0.00-10.25) Farm size, 1982 (acres) 984 (453-3,661) Percent rural population, 1982 100 (50.7-100) Percent farm population, 1982 30.2 (13.9-51.1) Percent families below poverty level, 1979 12.7 (6.6-25.7) Average annual population at risk (percent [greater than or equal to] 65 years) 1979-1988 Male 172,776 (16.2) Female 171,621 (20.0) 1989-1998 Male 149,245 (18.1) Female 150,107 (22.9) Table 2. Mortality rates of IHD, AMI, CAS, and diabetes mellitus for all ages in rural, agricultural counties of Minnesota, Montana, North Dakota, and South Dakots, compared with U.S. rates. Selected counties (n = 152) Rate/ 100,000 Underlying cause (ICD-9) Year of death No. Crude IHD (410-414.9) Male 1979-1988 28,599 340.1 1989-1998 21,763 259.9 Female 1979-1988 19,651 232.5 1989-1998 16,821 199.4 AMI (410) Male 1979-1988 18,338 218.1 1989-1998 12,493 149.2 Female 1979-1988 10,996 130.1 1989-1998 8,663 102.7 CAS (414.0) Male 1979-1988 7,318 87.0 1989-1998 5,032 60.1 Female 1979-1988 6,831 80.8 1989-1998 4,821 57.1 Diabetes mellitus (250) Male 1979-1988 1,235 14.7 1989-1998 1,888 22.5 Female 1979-1988 1,453 17.2 1989-1998 2,148 25.5 Person-years at risk Male 1979-1988 8,408,234 1989-1998 8,374,404 Female 1979-1988 8,452,082 1989-1998 8,437,721 Selected counties (n = 152) Rate/ 100,000 U.S. (white) Age rate/100,000 Underlying cause (ICD-9) Year of death adjusted age adjusted IHD (410-414.9) Male 1979-1988 250.7 272.2 1989-1998 176.4 189.1 Female 1979-1988 110.6 138.2 1989-1998 79.8 100.9 AMI (410) Male 1979-1988 165.0 150.6 1989-1998 104.0 92.5 Female 1979-1988 66.9 68.8 1989-1998 44.3 47.3 CAS (414.0) Male 1979-1988 59.9 88.1 1989-1998 38.9 50.1 Female 1979-1988 33.5 54.8 1989-1998 20.2 31.6 Diabetes mellitus (250) Male 1979-1988 10.9 12.7 1989-1998 15.4 17.0 Female 1979-1988 9.3 11.6 1989-1998 12.0 14.0 Person-years at risk Male 1979-1988 1989-1998 Female 1979-1988 1989-1998 Table 3. Age-standardized mortality rates/100,000, ratios, and 95% CIs for IHD, AMI, CAS, and diabetes mellitus, in low-, medium-, and high-wheat counties. Underlying cause of death (ICD-9) Age (years) Sex Year of death IHD (410-414.9) 25- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 25-64 Male 1979-1988 1989-1998 Female 1979-1988 1989-1998 65- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 AMI (410) 25- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1975-1988 1989-1998 25-64 Male 1979-1988 1989-1998 Female 1975-1988 1989-1998 65- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 CAS (414.0) 25- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 25-64 Male 1979-1988 1989-1998 Female 1979-1988 1989-1998 65- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 Diabetes mellitus (250.0-250.9) 45- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 45-64 Male 1979-1988 1989-1998 Female 1979-1988 1989-1998 65- [greater than or Male 1979-1988 equal to] 85 1989-1998 Female 1979-1988 1989-1998 Underlying cause Low wheat of death (ICD-9) Age (years) Sex rate IHD (410-414.9) 25- [greater than or Male 458.00 equal to] 85 312.03 Female 205.03 143.93 25-64 Male 148.30 89.26 Female 36.89 22.27 65- [greater than or Male 1844.07 equal to] 85 1309.05 Female 957.57 688.44 AMI (410) 25- [greater than or Male 285.31 equal to] 85 173.68 Female 117.25 75.24 25-64 Male 104.21 53.11 Female 25.54 13.46 65- [greater than or Male 1095.81 equal to] 85 713.32 Female 527.69 351.77 CAS (414.0) 25- [greater than or Male 122.72 equal to] 85 77.44 Female 67.54 39.18 25-64 Male 28.64 22.92 Female 7.55 4.76 65- [greater than or Male 543.74 equal to] 85 321.46 Female 335.99 193.20 Diabetes mellitus (250.0-250.9) 45- [greater than or Male 32.60 equal to] 85 44.79 Female 28.75 35.83 45-64 Male 13.15 15.00 Female 8.85 9.87 65- [greater than or Male 73.13 equal to] 85 106.86 Female 70.22 89.94 Medium wheat Underlying cause of death (ICD-9) Age (years) Sex Rate IHD (410-414.9) 25- [greater than or Male 463.55 equal to] 85 331.23 Female 196.47 148.76 25-64 Male 158.30 99.75 Female 34.30 26.91 65- [greater than or Male 1829.70 equal to] 85 1367.23 Female 922.24 694.10 AMI (410) 25- [greater than or Male 315.87 equal to] 85 202.03 Female 123.06 85.27 25-64 Male 121.89 71.20 Female 26.06 18.13 65- [greater than or Male 1184.03 equal to] 85 787.56 Female 557.16 385.76 CAS (414.0) 25- [greater than or Male 102.34 equal to] 85 69.49 Female 56.16 37.56 25-64 Male 20.52 14.87 Female 4.80 4.53 65- [greater than or Male 468.55 equal to] 85 313.94 Female 286.01 185.38 Diabetes mellitus (250.0-250.9) 45- [greater than or Male NA equal to] 85 NA Female NA NA 45-64 Male NA NA Female NA NA 65- [greater than or Male NA equal to] 85 NA Female NA NA Medium wheat Underlying cause of death (ICD-9) Age (years) Sex SRR (95% CI) IHD (410-414.9) 25- [greater than or Male 1.01 (0.98-1.04) equal to] 85 1.06 (1.03-1.10) Female 0.96 (0.92-0.99) 1.03 (0.99-1.08) 25-64 Male 1.07 (1.00-1.14) 1.18 (1.03-1.21) Female 0.93 (0.82-1.06) 1.21 (1.04-1.41) 65- [greater than or Male 0.99 (0.96-1.02) equal to] 85 1.04 (1.01-1.08) Female 0.96 (0.93-1.00) 1.01 (0.97-1.05) AMI (410) 25- [greater than or Male 1.11 (1.07-1.15) equal to] 85 1.16 (1.11-1.21) Female 1.05 (1.00-1.10) 1.13 (1.07-1.20) 25-64 Male 1.17 (1.09-1.26) 1.34 (1.22-1.48) Female 1.02 (0.88-1.19) 1.35 (1.12-1.63) 65- [greater than or Male 1.08 (1.04-1.12) equal to] 85 1.10 (1.05-1.16) Female 1.06 (1.01-1.11) 1.10 (1.04-1.16) CAS (414.0) 25- [greater than or Male 0.83 (0.79-0.88) equal to] 85 0.90 (0.84-0.96) Female 0.83 (0.78-0.88) 0.96 (0.89-1.03) 25-64 Male 0.72 (0.61-0.84) 0.65 (0.54-0.78) Female 0.64 (0.46-0.87) 0.95 (0.67-1.36) 65- [greater than or Male 0.86 (0.81-0.91) equal to] 85 0.98 (0.91-1.05) Female 0.85 (0.80-0.91) 0.96 (0.89-1.03) Diabetes mellitus (250.0-250.9) 45- [greater than or Male NA equal to] 85 NA Female NA NA 45-64 Male NA NA Female NA NA 65- [greater than or Male NA equal to] 85 NA Female NA NA High wheat Underlying cause of death (ICD-9) Age (years) Sex Rate IHD (410-414.9) 25- [greater than or Male 475.52 equal to] 85 353.28 Female 213.27 154.43 25-64 Male 155.32 109.90 Female 35.56 29.56 65- [greater than or Male 1908.59 equal to] 85 1442.56 Female 1008.60 713.32 AMI (410) 25- [greater than or Male 333.99 equal to] 85 225.77 Female 138.32 93.91 25-64 Male 126.04 82.16 Female 28.58 22.08 65- [greater than or Male 1264.69 equal to] 85 868.54 Female 629.51 415.35 CAS (414.0) 25- [greater than or Male 96.19 equal to] 85 61.05 Female 56.89 32.72 25-64 Male 16.00 12.27 Female 4.44 3.99 65- [greater than or Male 455.07 equal to] 85 279.38 Female 291.62 161.30 Diabetes mellitus (250.0-250.9) 45- [greater than or Male 36.28 equal to] 85 55.73 Female 30.19 42.03 45-64 Male 13.35 18.77 Female 8.70 11.95 65- [greater than or Male 84.07 equal to] 85 132.72 Female 74.98 104.72 High wheat Underlying cause of death (ICD-9) Age (years) Sex SRR (95% CI) IHD (410-414.9) 25- [greater than or Male 1.04 (1.01-1.07) equal to] 85 1.13 (1.09-1.17) Female 1.04 (1.00-1.08) 1.07 (1.03-1.12) 25-64 Male 1.05 (0.98-1.12) 1.23 (1.13-1.34) Female 0.96 (0.84-1.10) 1.33 (1.13-1.56) 65- [greater than or Male 1.03 (1.00-1.07) equal to] 85 1.10 (1.06-1.14) Female 1.05 (1.01-1.09) 1.04 (0 99-1.08) AMI (410) 25- [greater than or Male 1.17 (1.13-1.21) equal to] 85 1.30 (1.24-1.36) Female 1.18 (1.12-1.24) 1.25 (1.18-1.32) 25-64 Male 1.21 (1.12-1.31) 1.55 (1.40-1.71) Female 1.12 (0.96-1.31) 1.64 (1.35-1.99) 65- [greater than or Male 1.15 (1.11-1.20) equal to] 85 1.22 (1.16-1.28) Female 1.19 (1.13-1.25) 1.18 (1.11,1.25) CAS (414.0) 25- [greater than or Male 0.78 (0.74-0.83) equal to] 85 0.79 (0.73-0.85) Female 0.84 (0.79-0.90) 0.84 (0.77-0.91) 25-64 Male 0.56 (0.46-0.68) 0.54 (0.43-0.67) Female 0.59 (0.41-0.84) 0.84 (0.55-1.27) 65- [greater than or Male 0.84 (0.79-0.89) equal to] 85 0.87 (0.80-0.94) Female 0.87 (0.81-0.93) 0.83 (0.77-0.91) Diabetes mellitus (250.0-250.9) 45- [greater than or Male 1.11 (0.98-1.26) equal to] 85 1.24 (1.13-1.37) Female 1.05 (0.93-1.18) 1.17 (1.06-1.30) 45-64 Male 1.01 (0.76-1.35) 1.25 (0.97-1.62) Female 0.98 (0.70-1.39) 1.21 (0.88-1.66) 65- [greater than or Male 1.15 (1.01-1.31) equal to] 85 1.24 (1.12-1.38) Female 1.07 (0.95-1.21) 1.16 (1.05-1.29) NA, not applicable. Table 4. Poisson regression comparing high-wheat (HW) and medium-wheat (MW) counties with low-wheat counties (LW), with adjustment for mortality cohort, sex, age, and poverty index. Model no., disease (ICD-9) Age (years) RR (95% CI) Combined states, combined age groups 1. IHD (410-414.9) 25- [greater than or LW equal to] 85 1.00 MW 1.01 (0.98-1.04) HW 1.08 (1.04-1.12) 2. AMI (410) 25- [greater than or LW equal to] 85 1.00 MW 1.09 (1.04-1.14) HW 1.20 (1.14-1.26) 3. CAS (414.0) 25- [greater than or LW equal to] 85 1.00 MW 0.90 (0.85-0.96) HW 0.89 (0.83-0.96) 4. Diabetes mellitus 45- [greater than or (250.0-250.9) equal to] 85 LW 1.00 HW 1.16 (1.08-1.24) Combined states, separate age groups 5. AMI (410) 25-64 LW 1.00 MW 1.21 (1.14-1.29) HW 1.31 (1.22-1.39) 6. AMI (410) 65- [greater than or LW equal to] 85 1.00 MW 1.05 (1.00-1.11) HW 1.16 (1.10-1.22) Separate states, combined age groups AMI (410) 25- [greater than or 7. Minnesota, LW equal to] 85 1.00 Minnesota, HW 1.12 (1.06-1.19) 8. Montana, LW 1.00 Montana, HW 1.06 (0.91-1.23) 9. North Dakota, LW 1.00 North Dakota, HW 1.16 (1.08-1.24) 10. South Dakota, LW 1.00 South Dakota, HW 1.07 (1.00-1.15) Model no., disease (ICD-9) No. (Male/female) Combined states, combined age groups 1. IHD (410-414.9) LW 42,444 (24,139/18,305) MW 23,780 (14,112/9,668) HW 20,592 (12,099/8,493) 2. AMI (410) LW 23,359 (14,016/9,343) MW 14,293 (8,925/5,368) HW 12,828 (7,884/4,944) 3. CAS (414.0) LW 12,732 (6,535/6,197) MW 6,300 (3,271/3,029) HW 4,967 (2,541/2,426) 4. Diabetes mellitus (250.0-250.9) LW 4,251 (1,920/2,331) HW 2,271 (1,083/1,188) Combined states, separate age groups 5. AMI (410) LW 3,705 (2,952/753) MW 2,378 (1,924/454) HW 2,018 (1,612/406) 6. AMI (410) LW 19,654 (11,064/8,590) MW 11,915 (7,001/4,914) HW 10,810 (6,272/4,538) Separate states, combined age groups AMI (410) 7. Minnesota, LW 16,337 (9,813/6,524) Minnesota, HW 14,586 (8,978/5,608) 8. Montana, LW 939 (616/323) Montana, HW 1,013 (616/397) 9. North Dakota, LW 3,282 (2,055/1,227) North Dakota, HW 4,947 (3,162/1,785) 10. South Dakota, LW 4,757 (2,797/1,960) South Dakota, HW 4,619 (2,788/1,831) RR values are adjusted for sex (male vs. female), age ([greater than or equal to] 65 vs. < 65), mortality cohort (1989-1998 vs. 1979-1988), and poverty index (1980 median, [greater than or equal to] 13.15 vs. < 13.15). The exposure variable, percentage of a county's land area dedicated to wheat farming, is based on tertiles with cut-points 4.04 and 14.50 (models 1-3, 5, 6) or the median with cut-point 8.43 (model 4). Median wheat percentage values for the individual states are as follows: Minnesota, 3.19 (model 7); Montana, 9.87 (model 8); North Dakota, 19.93 (model 9); and South Dakota, 5.29 (model 10). Median for individual states' poverty index are Minnesota, 11.00; Montana, 11.40; North Dakota, 13.20; and South Dakota, 19.30. ARTICLE 2 Growing Diabetes Epidemic: Patient/Physician Disconnect on Disease Management. PR Newswire; 5/31/2006 Diabetes Experts Share Nationwide Survey Data, Emphasize Team Approach WASHINGTON, May 31 /PRNewswire/ -- Limited understanding of disease progression and frustration with disease management contribute to the clinical challenge of meeting the rising type 2 diabetes epidemic in America, according to the Diabetes Roundtable. The Roundtable, a multidisciplinary group of diabetes experts convened by the American Association of Diabetes Educators (AADE) and the American Association of Clinical Endocrinologists (AACE), with support from Merck & Co., Inc., calls for the medical community to take a more collaborative approach to caring for people with type 2 diabetes as a way to improve both disease management and outcomes. A Harris Interactive(R) survey commissioned by AADE suggests a disconnect between what patients with type 2 diabetes and primary care physicians who treat the disease believe is the state of diabetes management. For example, two-thirds of patients (69 percent) say they feel very knowledgeable or knowledgeable about managing their condition. At the same time, 81 percent of physicians surveyed say they are frustrated with the number of their type 2 diabetes patients who do not follow their treatment regimen exactly as prescribed. The survey also shows gaps in understanding of the disease itself. Half of patients surveyed say they have little or no understanding of their A1C level or in the past six months have not had it checked or are unsure if they have had it checked. A1C is basic lab test for evaluating glucose control, an important aspect of diabetes management. "We are dealing with some critical information gaps," said S. Sethu K. Reddy, M.D., M.B.A., F.A.C.E., F.A.C.P., chairman and program director of the Department of Endocrinology, Diabetes and Metabolism at The Cleveland Clinic and a member of the Diabetes Roundtable. "Type 2 diabetes is a chronic and complex disease, and for patients to self-manage their condition, it is useful for them to fully understand the basics of the disease and its progression- such as the role of declining pancreatic beta cell function. Yet, the majority (78 percent) of the primary care physicians surveyed say insulin resistance is the most important contributor to, and is primarily responsible for, the progression of type 2 diabetes in the majority of their patient population, with only 20 percent saying it is beta cell dysfunction. This suggests that primary care physicians do not consistently focus on how beta cells in the pancreas work, including as they relate to the incretin system. I also think most physicians don't clearly realize that beta cell function may play a role in determining how well patients respond to oral agents in diabetes." The Roundtable advocates for improvements in the current diabetes care system, including the use of available resources to help patients best manage the disease. The survey shows 59 percent of patients surveyed have worked with a diabetes educator. Almost four in five patients surveyed (78 percent) who have not worked with a diabetes educator would like to learn something from one, including how to reduce the risk of diabetes complications (39 percent), strategies for healthy eating (38 percent), and information on new type 2 diabetes medications (33 percent). Additionally, the survey shows that diabetes educators have had a positive impact on how knowledgeable patients feel about managing their diabetes. "We are not making the best use of our resources for managing type 2 diabetes. All too often patients feel they have 'failed' and feel guilty; physicians feel frustrated; no one wins," said Diabetes Roundtable member Donna Rice, M.B.A., R.N., B.S.N., C.D.E, wellness program manager, Botsford General Hospital, Novi, MI and president-elect of AADE. "Increasingly we recognize that a team-centered approach involving the patient, primary care physician, diabetes educator, behavioral scientist and endocrinologist provides the support and resources best needed to help patients manage the disease." Members of the Roundtable believe care for people with type 2 diabetes could be enhanced by regular treatment from a team that aligns the latest in science, treatment options and education around lifestyle behavior change. In an effort to begin developing a roadmap to improve collaboration between the many areas of care directly involved in type 2 diabetes treatments, the Roundtable plans to work with other professional and patient groups to begin identifying potential solutions. For further information on the outcomes of the Roundtable meeting and survey results, visit http://www.diabetesteamsite.com/. About the Diabetes Roundtable AADE and AACE convened the Diabetes Roundtable in April 2006, to discuss ways to improve outcomes for type 2 diabetes. The multidisciplinary group of health care professionals includes experts in endocrinology, diabetes education, primary care and behavioral science. In addition to Dr. Sethu Reddy and Donna Rice, other members of the Roundtable are: Susan Cornell, Pharm.D., B.S., C.D.M., C.D.E., clinical assistant professor, Midwestern University, Downers Grove, IL; Silvio Inzucchi, M.D., professor of medicine, Section of Endocrinology, Department of Internal Medicine, Yale University, Director, Yale Diabetes Center, New Haven, CT; Edwin Fisher, Ph.D., chair, Health Behavior and Health Education, University of North Carolina, Chapel Hill, NC; and Doron Schneider, M.D., associate program director of the Internal Medicine Residency and Medical Director of the Ambulatory Services Unit, Abington Memorial Hospital, Abington, PA. The nationwide survey and the Diabetes Roundtable were supported by a grant from Merck & Co., Inc. About Type 2 Diabetes Type 2 diabetes is a condition in which the body has elevated blood sugar or glucose. With type 2 diabetes, the body may not make enough insulin (which helps the body use glucose), the insulin that the body produces may not work as well as it should, or the body may make too much glucose. Patients with diabetes can develop heart disease, kidney disease, blindness, vascular or neurological problems that can lead to amputation and they can be at risk for increased mortality. 19.3 million people in the United States have diabetes, with type 2 diabetes accounting for 90 to 95 percent of the cases(i). It is estimated that one in three Americans born in 2000 will develop diabetes sometime during their lifetime(ii). There are currently more than 194 million people with diabetes worldwide, and if nothing is done to slow the epidemic, the number will exceed 333 million by 2025(iii). About the Survey The surveys were conducted online by Harris Interactive(R) on behalf of AADE. * The patient survey was conducted between April 6 and 14, 2006, among 784 adults (aged 18 and over) diagnosed with type 2 diabetes within the United States. Figures for age, sex, race/ethnicity, education, region and household income were weighted where necessary to bring them into line with their actual proportions in the population. Propensity score weighting was also used to adjust for respondents' propensity to be online. Propensity score adjustment via weighting allows us to adjust for attitudinal and behavioral differences between those who are online versus offline, those who join online panels versus those who do not, and those who responded to this survey versus those who did not. * The primary care physicians (PCPs) survey was conducted between April 7 and 12, 2006, among 406 PCPs who see at least three type 2 diabetes patients per month. Figures for sex, years in practice, and region were weighted where necessary to bring them into line with their actual proportions in the population. These results were not propensity weighted. With pure probability samples, with 100 percent response rates, it is possible to calculate the probability that the sampling error (but not other sources of error) is not greater than some number. With a pure probability sample of 784 patients one could say with a 95 percent probability that the overall results have a sampling error of +/- 5.3 percentage points, while the error rate for 406 physicians is +/- 6.6 percentage points. Sampling error for the sub samples for each group is higher and varies. Each of the online surveys is not based on probability samples and therefore no theoretical sampling error can be calculated. About AADE Founded in 1973, the AADE is a multi-disciplinary professional membership organization dedicated to promoting the expertise of the diabetes educator, ensuring the delivery of quality diabetes self-management training to the patient and influencing and contributing to the future content and direction of the profession. The AADE mission is to drive professional practice to promote healthy living through self-management of diabetes and related conditions. About AACE AACE is a professional medical organization with more than 5,300 members in the United States and 85 other countries. Founded in 1991, AACE is dedicated to the optimal care of patients with endocrine problems. AACE initiatives inform the public about endocrine disorders. AACE also conducts continuing education programs for clinical endocrinologists, physicians whose advanced, specialized training enables them to be experts in the care of endocrine disease, such as diabetes, thyroid disorders, growth hormone deficiency, osteoporosis, cholesterol disorders, hypertension and obesity. About Merck Merck & Co., Inc. is a global research-driven pharmaceutical company dedicated to putting patients first. Established in 1891, Merck currently discovers, develops, manufactures and markets vaccines and medicines to address unmet medical needs. The Company devotes extensive efforts to increase access to medicines through far-reaching programs that not only donate Merck medicines but help deliver them to the people who need them. Merck also publishes unbiased health information as a not-for-profit service. For more information, visit http://www.merck.com/. (i) Cowie et al. Prevalence of Diabetes and Impaired Fasting Glucose in Adults in the U.S. Population. Diabetes Care June 2006: 29: 1263-1268 (ii) Centers for Disease Control and Prevention. Diabetes: Disabling, Deadly, and on the rise. http://www.cdc.gov/nccdphp/publications/aag/pdf/aag_ddt2005.pdf Accessed 1/27/06. (iii) International Diabetes Foundation Web site, Facts and Figures. Type 2 Diabetes Nationwide Survey Summary The American Association of Diabetes Educators, supported through a grant from Merck & Co., Inc., commissioned a nationwide online survey among type 2 diabetes patients and primary care physicians who see at least three type 2 diabetes patients per month with the objective of determining attitudes toward and understanding of existing approaches to diabetes treatment. This survey was conducted by Harris Interactive(R) and the key findings are below. ******************** Overall, a majority of patients appear knowledgeable about managing their condition, but upon closer inspection the picture becomes less positive. * 69 percent of patients feel very knowledgeable or knowledgeable about managing their condition, yet 59 percent of patients believe their diabetes is somewhat or not at all well-controlled -- 76 percent of patients have reported experiencing one or more diabetes-related condition, such as fatigue (51 percent), weight gain (28 percent), blurred or impaired vision (27 percent), and hypoglycemia (26 percent) * 55 percent of patients don't know their A1C level, or in the past six months have not had it checked or are unsure if they've had it tested ******************** There is a disconnect between how well patients think they are self- managing diabetes versus how well physicians think their patients are self- managing the disease. * 83 percent of patients who say they are on a healthy, balanced diet think they follow their healthcare providers' instructions well or very well. However, in comparison, only 29 percent of physicians believe this to be the case * 77 percent of patients who engage in regular physical activity say they comply well or very well with healthcare providers' instructions for getting regular physical activity, while only 18 percent of physicians say that is the case ******************** Physician understanding of the pathophysiology of type 2 diabetes seems to be inconsistent. * The incretin system plays an important part in regulating blood sugar levels; yet 51 percent of physicians say the incretin system is somewhat important or not at all important in regulating blood sugar levels * Beta cell dysfunction plays a large role in the progression of type 2 diabetes [National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health]; yet 78 percent of physicians say insulin resistance is the most important contributor to type 2 diabetes progression in the majority of their patient population, followed by beta cell dysfunction (20 percent) ******************** Most patients on oral medications for type 2 diabetes highly rate their effectiveness; yet even among patients who tout their efficacy, many have reported having complications from diabetes or side effects from oral medication. * 77 percent of patients on oral medications say their medications are very effective (32 percent) or effective (45 percent) -- Among these patients, 74 percent experience one or more diabetes- related condition and 38 percent experience side effects from their oral medications * Patients on oral medications are most concerned about weight gain (31 percent) as a side effect of oral medication, while physicians express more concern about hypoglycemia (71 percent) ******************** Many patients have worked with diabetes educators and among most patients who haven't, there is interest to learn from them. Recommendations from a healthcare provider are a key trigger to working with diabetes educators. * 59 percent of patients have worked with a diabetes educator * Of those who have worked with a diabetes educator, 78 percent received a recommendation to do so from their healthcare provider; and of those who have not worked with a diabetes educator, 82 percent did not receive a recommendation from their healthcare provider * Patients who see an endocrinologist (80 percent) for their type 2 diabetes care are more likely than patients who see primary care physicians (56 percent) to have worked with a diabetes educator. Of patients surveyed who see an endocrinologist, 71 percent received a recommendation to see a diabetes educator, versus 52 percent of respondents who see a primary care physician(1) * 78 percent of patients who have not worked with a diabetes educator would like to learn something from them, including how to reduce the risk of diabetes complications (39 percent), strategies for healthy eating (38 percent), and information on new type 2 diabetes medications (33 percent) ******************** Patients with type 2 diabetes who have seen a diabetes educator are more positive about their knowledge of diabetes management and feel more confident about following a healthy, balanced diet than patients who have not worked with one. * 76 percent of patients who have worked with a diabetes educator feel knowledgeable about managing their type 2 diabetes, compared to only 61 percent of patients who haven't worked with a diabetes educator * 94 percent of patients with diabetes who have worked with a diabetes educator say they understand the importance of eating a healthy, balanced diet well or very well, compared to 83 percent of patients who haven't worked with a diabetes educator Survey Methodology The surveys were conducted online by Harris Interactive(R) on behalf of The American Association of Diabetes Educators. The patient survey was conducted between April 6 and 14, 2006, among 784 adults (aged 18 and over) diagnosed with type 2 diabetes within the United States. Figures for age, sex, race/ethnicity, education, region and household income were weighted where necessary to bring them into line with their actual proportions in the population. Propensity score weighting was also used to adjust for respondents' propensity to be online. Propensity score adjustment via weighting allows us to adjust for attitudinal and behavioral differences between those who are online versus offline, those who join online panels versus those who do not, and those who responded to this survey versus those who did not. The primary care physicians (PCPs) survey was conducted between April 7 and 12, 2006, among 406 PCPs who see at least three type 2 diabetes patients per month. Figures for sex, years in practice, and region were weighted where necessary to bring them into line with their actual proportions in the population. These results were not propensity weighted. With pure probability samples, with 100 percent response rates, it is possible to calculate the probability that the sampling error (but not other sources of error) is not greater than some number. With a pure probability sample of 784 patients one could say with a 95 percent probability that the overall results have a sampling error of +/- 5.3 percentage points, while the error rate for 406 physicians is +/- 6.6 percentage points. Sampling error for the sub samples for each group is higher and varies. Each of the online surveys is not based on probability samples and therefore no theoretical sampling error can be calculated. (1) Note: Caution should be exercised when interpreting these results as data of patients who have seen endocrinologists (n=83) are based on small sample size. CONTACT: Julia Gendler, +1-212-601-8188, or Kara Golub, +1-516-458-3550, both of Porter Novelli, for the American Association of Diabetes Educators and the American Association of Clinical Endocrinologists Web site: http://www.diabetesteamsite.com/ http://www.merck.com/ COPYRIGHT 2006 PR Newswire Association LLC Read More