this study was approved by the university of wisconsin - madison health sciences institutional review board .
the methods used in this study were adapted from methods described previously ( 6 ) . in brief , patients included were 20 years of age on 1 january 2005 and had at least one family practice or internal medicine visit to the physician group in each of the 3 study years , 2005 , 2006 , and 2007 , in addition to a yearly visit in each of the 2 preceding years , 2003 and 2004 . a 3-year period from 2005 to 2007 was chosen based on ada recommendations to screen every 3 years ( 5 ) .
data from years 2003 and 2004 were used to determine prior diagnosis of diabetes , prediabetes , preexisting comorbidities , and pregnancy and to determine visit eligibility .
patients with any visit for pregnancy in the years 2003 to 2007 were excluded , as were patients who died during the 3-year study period .
patients with two or more outpatient encounters with a diagnosis of diabetes in the years 2003 to 2004 were excluded , as were patients without health care insurance .
patients clinical , laboratory , encounter , and demographic information were obtained from the electronic health record of a large , midwestern , academic physician group as described previously ( 6 ) .
for all patients , data were extracted on age , sex , ethnicity , bmi , evaluation and management ( e / m ) outpatient encounter data , provider specialty , and laboratory data .
in addition , we abstracted fasting plasma glucose ( fpg ) , random glucose ( rg ) , 2-h glucose tolerance test ( gtt ) , and hemoglobin a1c ( hba1c ) .
glucose laboratory tests were classified as fpg if they were labeled as fasting or were drawn at the same time as a fasting ldl or triglyceride level per institution protocol .
these fasting tests , in addition to gtts and hba1c , were considered the screening tests for this analysis .
although hba1c was not an accepted test for diabetes screening during the study years , it was included in our screening profile and used in the sensitivity test since it is known that providers used hba1c for this purpose because of its recent incorporation into guidelines ( 5 ) .
eight high - risk variables were defined based on the ada - designated risk factors for screening ( 5 ) .
these factors include 45 years of age , ethnicity , hypertension , hypercholesterolemia , polycystic ovarian syndrome ( pcos ) , vascular disease , overweight ( bmi 25 kg / m ) , and history of prediabetes .
the ada risk factors of first - degree family history , physical inactivity , and other clinical conditions associated with insulin resistance could not be obtained .
definitions for high - risk variables were determined based on a combination of one or more factors , including validated international classification of diseases , 9th revision ( icd-9 ) codes ( 8) , laboratory data , and clinical information , with detailed criteria for each high - risk factor listed previously ( 6 ) . in brief , diagnosis of hypertension , pcos , prediabetes , and vascular disease was determined by presence of two validated diagnosis codes on two separate occasions within 2 years by previously established criteria ( 6 ) .
age was determined as the age as of 1 january 2005 ; overweight was determined by having two icd-9 codes by the same criteria above or if last listed bmi was 25 kg / m ; and hypercholesterolemia was also defined by the presence of two icd-9 codes or by the presence of abnormal laboratory tests ( 6 ) .
patients with a listed ethnicity of african american , latino , native american , asian american , or pacific islander were designated as high - risk minorities . using the defined high - risk factors , the population meeting ada screening criteria was determined .
this included any patient 45 years of age or any overweight patient < 45 years with at least one additional high - risk factor ( 5 ) .
the number of eligible patients screened was determined . because screening for patients < 45 years should be triggered by the presence of at least one high - risk factor in addition to obesity , a separate analysis of patients in this category was conducted .
multivariable logistic regression was used to estimate adjusted odds ratios ( ors ) , predicted probabilities , and 95% ci for relationships between diabetes screening and patient and visit characteristics .
predicted probabilities were calculated using the margins command in stata and calculating the effects at the average of the covariates .
predicted probabilities were produced from a fitted logistic regression model where the outcome was whether a subject would receive diabetes screening .
therefore , from this model , one can calculate the probability of receiving diabetes screening at any given level of covariates .
additional models predicted fpg screening for patients with high - risk and non  high - risk ethnic status and for the subsample of obese patients < 45 years with an additional risk factor . as a sensitivity analysis
the presence of prediabetes and pcos was given in low numbers in the sample and was not used in analyses .
this study was approved by the university of wisconsin - madison health sciences institutional review board .
the methods used in this study were adapted from methods described previously ( 6 ) . in brief , patients included were 20 years of age on 1 january 2005 and had at least one family practice or internal medicine visit to the physician group in each of the 3 study years , 2005 , 2006 , and 2007 , in addition to a yearly visit in each of the 2 preceding years , 2003 and 2004 . a 3-year period from 2005 to 2007 was chosen based on ada recommendations to screen every 3 years ( 5 ) .
data from years 2003 and 2004 were used to determine prior diagnosis of diabetes , prediabetes , preexisting comorbidities , and pregnancy and to determine visit eligibility .
patients with any visit for pregnancy in the years 2003 to 2007 were excluded , as were patients who died during the 3-year study period .
patients with two or more outpatient encounters with a diagnosis of diabetes in the years 2003 to 2004 were excluded , as were patients without health care insurance .
patients clinical , laboratory , encounter , and demographic information were obtained from the electronic health record of a large , midwestern , academic physician group as described previously ( 6 ) .
for all patients , data were extracted on age , sex , ethnicity , bmi , evaluation and management ( e / m ) outpatient encounter data , provider specialty , and laboratory data .
in addition , we abstracted fasting plasma glucose ( fpg ) , random glucose ( rg ) , 2-h glucose tolerance test ( gtt ) , and hemoglobin a1c ( hba1c ) . glucose laboratory tests were classified as fpg if they were labeled as fasting or were drawn at the same time as a fasting ldl or triglyceride level per institution protocol .
these fasting tests , in addition to gtts and hba1c , were considered the screening tests for this analysis . although hba1c was not an accepted test for diabetes screening during the study years ,
it was included in our screening profile and used in the sensitivity test since it is known that providers used hba1c for this purpose because of its recent incorporation into guidelines ( 5 ) .
eight high - risk variables were defined based on the ada - designated risk factors for screening ( 5 ) .
these factors include 45 years of age , ethnicity , hypertension , hypercholesterolemia , polycystic ovarian syndrome ( pcos ) , vascular disease , overweight ( bmi 25 kg / m ) , and history of prediabetes .
the ada risk factors of first - degree family history , physical inactivity , and other clinical conditions associated with insulin resistance could not be obtained .
definitions for high - risk variables were determined based on a combination of one or more factors , including validated international classification of diseases , 9th revision ( icd-9 ) codes ( 8) , laboratory data , and clinical information , with detailed criteria for each high - risk factor listed previously ( 6 ) . in brief , diagnosis of hypertension , pcos , prediabetes , and vascular disease was determined by presence of two validated diagnosis codes on two separate occasions within 2 years by previously established criteria ( 6 ) .
age was determined as the age as of 1 january 2005 ; overweight was determined by having two icd-9 codes by the same criteria above or if last listed bmi was 25 kg / m ; and hypercholesterolemia was also defined by the presence of two icd-9 codes or by the presence of abnormal laboratory tests ( 6 ) .
patients with a listed ethnicity of african american , latino , native american , asian american , or pacific islander were designated as high - risk minorities . using the defined high - risk factors , the population meeting ada screening criteria was determined .
this included any patient 45 years of age or any overweight patient < 45 years with at least one additional high - risk factor ( 5 ) .
the number of eligible patients screened was determined . because screening for patients < 45 years should be triggered by the presence of at least one high - risk factor in addition to obesity , a separate analysis of patients in this category was conducted .
multivariable logistic regression was used to estimate adjusted odds ratios ( ors ) , predicted probabilities , and 95% ci for relationships between diabetes screening and patient and visit characteristics . predicted probabilities were calculated using the margins command in stata and calculating the effects at the average of the covariates .
predicted probabilities were produced from a fitted logistic regression model where the outcome was whether a subject would receive diabetes screening .
therefore , from this model , one can calculate the probability of receiving diabetes screening at any given level of covariates .
additional models predicted fpg screening for patients with high - risk and non  high - risk ethnic status and for the subsample of obese patients < 45 years with an additional risk factor . as a sensitivity analysis
the presence of prediabetes and pcos was given in low numbers in the sample and was not used in analyses .
other than age , the most common high - risk factor was hypercholesterolemia ( 70% ) .
sixty - one percent were female , and patients were seen more often by family practice providers ( 54% ) than by internists ( 46% ) .
a total of 607 ( 4% ) of patients were high - risk minorities , and minorities had significantly more primary care visits than nonminority patients ( 10.78 vs. 9.06 ; p < 0.001 ) between 2005 and 2007 .
a total of 10,586 ( 68% ) of all eligible patients and 361 ( 59% ) of eligible high - risk ethnicity patients were screened using fpg , gtt , or hba1c . of the 10,586 screened patients , 9,999 ( 94% )
had a fasting glucose identified by concurrent ldl or triglyceride draw , 58 ( 0.5% ) had independent fpg , 63 ( 0.6% ) had gtt , and 2,249 ( 21% ) had hba1c , with many patients having more than one of these tests drawn .
baseline patient characteristics of patients eligible for diabetes screening ( n = 15,557 ) * * values represent percentages unless otherwise specified , and numbers in parentheses indicate standard deviations ; high - risk factors generated from the ada screening criteria , as defined previously ( 6 ) ; primary care specialty determined for each patient by specialty in which the majority , or all , of their primary care visits occurred ;  number of primary care , specialty , and total visits is mean number of visits per patient over the time period 1 january 200531 december 2007 . of the ada high - risk factors ,
only high - risk ethnicity was not associated with higher frequency of diabetes screening after adjustment for patient and visit characteristics ( or 0.90 [ 95% ci 0.761.08 ] ) ( table 2 ) .
the predicted probability of screening was 68 and 66% for non  high - risk and high - risk ethnic groups , respectively .
the number of primary care visits ( or 0.99 [ 0.9871.000 ] ) and specialty visits ( or 1.00 [ 0.9941.005 ] ) did not correlate with screening , and patients eligible for screening who had internal medicine primary care providers were as likely to be tested ( or 1.04 [ 0.971.12 ] ) as those who had family practice providers . when comparing screening for patients in non  high - risk and high - risk ethnic groups , after adjustment , age 45 years ( or 1.55 [ 1.381.74 ] ) , overweight status ( or 1.67 [ 1.541.81 ] ) , and male sex ( or 1.33 [ 1.231.43 ] ) were significant predictors of screening for non  high - risk patients only ( table 3 ) .
unadjusted and adjusted ors , predicted probabilities , and 95% ci for the relationship between patient and visit characteristics and diabetes screening ( n = 15,557 ) adjusted for age , minority status , hypertension , cholesterol , cardiovascular disease , overweight , primary care specialty , number of primary care visits , and number of specialty visits .
* significance at p < 0.05 ; high - risk factors generated from the ada screening criteria , as defined previously ( 6 ) ; primary care specialty determined for each patient by specialty in which the majority , or all , of their primary care visits occurred ;  number of primary care , specialty , and total visits is mean number of visits per patient over the time period 1 january 200531 december 2007 .
unadjusted and adjusted ors and 95% ci for the relationship between patient and visit characteristics and diabetes screening , by high - risk ethnic group status ( n = 15,557) * significance at p < 0.05 ; high - risk ethnicities defined as african american , latino , native american , asian american , and pacific islander ; high - risk factors generated from the ada screening criteria , as defined previously ( 6 ) ;  primary care specialty determined for each patient by specialty in which the majority , or all , of their primary care visits occurred ; ||number of primary care , specialty , and total visits is mean number of visits per patient over the time period 1 january 200531 december 2007 .
after adjustment , high - risk ethnicity ( or 1.01 [ 95% ci 0.701.44 ] ) and vascular disease ( or 2.25 [ 0.865.91 ] ) were not associated with increased frequency of screening in patients < 45 years who met screening criteria by virtue of being obese and having an additional risk factor ( table 4 ) .
male sex significantly predicted screening in this group ( or 1.36 [ 1.121.65 ] ) .
unadjusted and adjusted ors , predicted probabilities , and 95% ci for the relationship between patient and visit characteristics and diabetes screening in overweight adults aged 2044 years ( n = 2,160) * significance at p < 0.05 ; overweight defined as bmi 25 kg / m or overweight or obese per elixhauser criteria ; high - risk factors generated from the ada screening criteria , as defined previously ( 6 ) ;  primary care specialty determined for each patient by specialty in which the majority , or all , of their primary care visits occurred ; ||number of primary care , specialty , and total visits is mean number of visits per patient over the time period 1 january 200531 december 2007 . in a multivariate regression model examining glucose testing by any method ( including random ) and adjusting for patient and visit characteristics ,
high - risk ethnicity similarly was the only ada - designated high - risk factor that did not lead to significantly higher odds of testing ( or 1.09 [ 95% ci 0.811.47 ] ) .
the main finding of this study is that , in an insured population presenting for yearly primary care visits , minority status is not being identified as an independent risk factor for diabetes screening as recommended by the ada guidelines ( 5 ) .
this finding is most concerning in obese patients < 45 years , because screening in this subpopulation depends on the presence and recognition of risk factors such as high - risk ethnicity .
extensive data demonstrate that minorities are more likely to have diabetes and to suffer comparatively increased morbidity and worse glycemic control ( 3,10,11 ) , so these screening inadequacies are concerning .
national statistics and numerous studies have demonstrated that health care in minority patients is inferior to care received by nonminority patients ( 3,12 ) .
although inequalities may be multifactorial in etiology , data have consistently suggested that lower rates of health care insurance and resultant decreased access to care may be the largest contributor to these disparities ( 13 ) . in 2002 ,
adults aged 1864 years , 20% of african americans and 40.7% of hispanics were uninsured compared with 12.4% of caucasians .
these inequalities in insurance coverage certainly contribute to suboptimal minority health care . because of the known effects of health care insurance on minority care , the current study includes only insured patients presenting for yearly primary care visits to determine minority - screening practices when health care access was equal .
however , although equal care is appropriate for many disease processes , the ada recommends increased screening in high - risk minority patients , particularly in younger , overweight patients ( 5 ) .
therefore , even equivalent screening means that minority patients are not being screened in accordance with the ada guidelines , despite insurance and access being equal .
our minority patients were notably seen more frequently in clinics than nonminorities , indicating that access was even more robust in this subset of patients .
first , providers may not recognize that minority status is equivalent to other stated ada risk factors for type 2 diabetes ( 3 ) .
it is notable that the majority of other ada risk factors included in this study are comorbidities typically treated with lifestyle modification and/or medication .
the presence of one of these typical cardiovascular risk factors may be more likely to prompt a provider to seek early detection of other comorbidities , such as diabetes .
second , providers may seek to treat patients of all ethnic groups equally and as a result ignore ethnicity as a risk factor for diseases , such as type 2 diabetes , where minority status should , in fact , play a role in determining care .
preventive services task force ( uspstf ) criteria , which do not factor ethnicity into screening recommendations ( 14 ) .
other reports have suggested that unconscious bias may play a role in minority inequalities ( 15 ) .
the institute of medicine s 2002 report ,  unequal treatment : confronting racial and ethnic disparities in healthcare ,  ( 16 ) highlighted the multifactorial nature of disparities , along with the reality that unconscious bias may play a role in unequal care .
although we can not entirely exclude bias as a contributing factor in this study , we saw no evidence that insured minorities who presented for care had lower rates of screening than nonminorities .
it is conceivable , however , that minorities may have more difficulty returning to clinic for what has traditionally been the test of choice to diagnose diabetes , the fasting glucose .
this possibility suggests that minority screening could be improved with the ada s recent adoption of the hba1c into diagnostic criteria ( 5 ) , because this test can be performed in a nonfasting state .
although data have demonstrated that hba1c may perform differently in certain minority populations ( 17,18 ) , access to care and return for a fasting laboratory test is also prohibitive for some patients ( 19 ) .
use of the hba1c may facilitate greater screening compliance in a population that may be less able to return for a follow - up fasting visit .
although presence of hypercholesterolemia was associated with significantly increased ors for diabetes screening in the sensitivity analysis , linking glucose to fasting lipid panels increased these ors , which may have introduced a selection bias .
second , although this study included 15,557 patients , only 607 ( 4% ) had high - risk minority status .
screening practices in this study may not reflect what may occur in a practice with a higher percentage of minority individuals where providers may be more aware of minority screening recommendations .
previous studies have suggested that inequalities in income and education , independent of ethnicity , may affect care ( 20 ) , although other studies such as the national health and nutrition examination survey ( nhanes ) iii demonstrated that income and education were not related to incidence of undiagnosed diabetes ( 21 ) or glycemic control . given this debate , it is less likely that income and education play a role in our results , especially as we controlled for insurance status and included those who had accessed primary care . finally , as is the case with all administrative data , we were limited in which variables we were able to include .
some data suggest that factors , such as health care literacy or medical mistrust , may contribute to minority health disparities ( 3 ) , whereas other reports have not found factors such as mistrust to play a major role ( 22 ) .
the literature has also suggested that minority patients may see less capable providers ( 23 ) , that providers may treat minorities differently than their nonminority patients , and that minorities may be seen more frequently at clinics with fewer resources and more chaos ( 24 ) .
although anecdotal evidence suggests that providers intentionally order fasting lipid panels and fasting glucoses together , we can not be entirely certain with administrative data that providers ordering a metabolic panel with a lipid panel did this intentionally to obtain a fasting , screening glucose . in summary
, this study represents a comprehensive analysis of diabetes - screening practices of insured minority patients presenting for yearly primary care visits in an ambulatory setting .
most significantly , this analysis demonstrated that high - risk ethnicity patients , despite higher frequency of clinic visits , are not more likely to be screened compared with nonminority patients with similar risk factors , which is inconsistent with current diabetes screening guidelines . because minority status confers not only increased risk of having type 2 diabetes but also risk for having increased complications once diagnosed , it is critical that these screening inequalities are identified and eliminated .
although performance of hba1c may be slightly different compared with nonminorities , use of hba1c in the minority population may increase screening compliance , as this test does not require a return fasting visit . increased provider awareness of diabetes - screening guidelines and implementation of plans to remedy these findings
 objectiveethnicity has been identified as a risk factor not only for having type 2 diabetes but for increased morbidity and mortality with the disease . 
 current american diabetes association ( ada ) guidelines advocate screening high - risk minorities for diabetes . 
 this study investigates the effect of minority status on diabetes screening practices in an ambulatory , insured population presenting for yearly health care.research design and methodsthis is a retrospective population  based study of patients in a large , midwestern , academic group practice . 
 included patients were insured , had 1 primary care visit yearly from 2003 to 2007 , and did not have diabetes but met ada criteria for screening . 
 odds ratios ( ors ) , 95% confidence intervals ( ci ) , and predicted probabilities were calculated to determine the relationship between screening with fasting glucose , glucose tolerance test , or hemoglobin a1c and patient and visit characteristics.resultsof the 15,557 eligible patients , 607 ( 4% ) were of high - risk ethnicity , 61% were female , and 86% were 45 years of age . 
 of the eight high - risk factors studied , after adjustment , ethnicity was the only factor not associated with higher diabetes screening ( or = 0.90 [ 95% ci 0.761.08 ] ) despite more primary care visits in this group . in overweight patients < 45 years , where screening eligibility is based on having an additional risk factor , high - risk ethnicity ( or 1.01 [ 0.701.44 ] ) was not associated with increased screening frequency.conclusionsin an insured population presenting for routine care , high - risk minority status did not independently lead to diabetes screening as recommended by ada guidelines . 
 factors other than insurance or access to care appear to affect minority - preventive care . 
RESEARCH DESIGN AND METHODS
Background and data
Primary data retrieval
Variable definitions
Statistical analysis
RESULTS
CONCLUSIONS