diabetes mellitus is one of the most challenging chronic diseases ( king et al 1998 ; lipscombe and hux 2007 ) associated with significant morbidity and mortality ( baker et al 2008 ; pinto et al 2008 ) . in order to avoid or delay microvascular complications , a strict metabolic control of blood glucose is recommended ( ukpds 1998 ; leroith and smith 2005 ; vermeire et al 2005 ) . measure of glycosylated hemoglobin a ( hba1c ) is used to evaluate glycemia control . for the majority of diabetics , glycemia control is considered attained when hba1c levels are less than 7% ( harris and lank 2004 ; anonymous 2008 ) . many patients do not achieve optimal glycemia control ( saaddine et al 2002 ; putzer et al 2004 ) . longer diabetes duration and presence of complications are both associated with poor glycemia control ( druss et al 2001 ; putzer et al 2004 ; weiner and long 2004 ; egede 2005 ; kerr et al 2007 ; kivimaki et al 2007 ; parchman et al 2007 ; suh et al 2008 ) . the majority of diabetic patients have at least one comorbid chronic condition ( druss et al 2001 ; egede 2005 ; kivimaki et al 2007 ) , a problem that may also have an impact on glycemia control . indeed , competing situations may occur in the treatment of patients ( parchman et al 2007 ) , and the presence of several symptomatic comorbidities may influence patients self management ( kerr et al 2007 ) . however , reports on the relationship between comorbidity and glycemia control are conflicting . in some cases , evidence suggests that comorbidity is associated with lower glycemia control ( zhang et al 2000 ; weiner and long 2004 ; suh et al 2008 ) , whereas other data argue against any association ( el - kebbi et al 2001 ) . these conflicting results may be explained by the use of different methods to measure comorbidity . in two studies , the charlson comorbidity index was used ( zhang et al 2000 ; weiner and long 2004 ) . however , this index was originally developed to predict mortality risk and includes a limited list of diseases . another study used the chronic disease score ( cds ) ( el - kebbi et al 2001 ) , a comorbidity measure based on sex , age , and drugs prescribed to the patient . the remaining study took into account a limited number of conditions traditionally known as diabetes complications ( suh et al 2008 ) . uncontrolled diabetes may result in complications such as nephropathy , renal failure , hypertension , congestive heart failure and coronary disease ( ada 2008 ) . therefore , any study on a possible association between glycemia control and comorbidity , in which the measure of comorbidity would only include such chronic conditions would be biased towards finding an association . furthermore , in addition to the type of comorbidity , its severity may either cause poor glycemia control or be a consequence of it . since many diabetic patients have comorbidity ( druss et al 2001 ; egede 2005 ; kivimaki et al 2007 ) , it is important to determine if comorbidity increases the difficulty to manage the disease and to achieve good glycemia control . therefore , the aim of the present study was to evaluate the relationship between diabetes control and comorbidity using a comprehensive comorbidity index in a primary care setting . we performed a retrospective descriptive study based on chart review at the family medicine unit ( fmu ) of the centre de sant et de services sociaux de chicoutimi , quebec , canada . the study was approved by the institutional ethics committee . using the electronic laboratory database available , we performed a random selection of patients with type 2 diabetes who had at least two measurements of hba1c levels between january 1st , 2004 and september 1st , 2006 . patients diagnosed with diabetes less than a year before the last measurement of hba1c and those under the care of the researchers involved in this study were excluded . data extracted from the charts included sex , age , the last two measures of hba1c , the presence or absence of diabetes - related complications , time elapsed since the diagnosis of diabetes ( diabetes duration at the moment of the second measurement of hba1c ) as well as information about the patient s chronic conditions necessary to score the cumulative illness rating scale ( cirs ) ( linn et al 1968 ; fortin et al 2005b , 2006a , 2006b ) . the three nurses , blinded to the objectives of the study , had received an half - day training in cirs scoring prior to the study . the cirs is a measure of comorbidity that takes into account all medical conditions and includes the notion of severity ( linn et al 1968 ; fortin et al 2005b , 2006a , 2006b ) . it has been validated in primary care as a tool for quantifying comorbidity ( hudon et al 2005 , 2007 ) . the cirs uses a scoring system that encompasses 14 anatomical domains ( cardiac , vascular , hematological , respiratory , ophthalmologic  otorhinolaryngologic , upper gastrointestinal , lower gastrointestinal , hepatic  pancreatic , renal , genitourinary , musculoskeletal tegument , neurological , endocrine  metabolic  breast , and psychiatric ) and assigns a value from 0 ( no condition in this domain ) to 4 ( extremely severe problem ) to determine a severity score for each domain . in the case of multiple conditions affecting a particular domain , the global score is the sum of each domain s score . to use the cirs as a measure of comorbidity for diabetes , diabetes was considered as controlled if the mean value of the two measurements of hba1c levels was less than 7% ; mean values equal or greater than 7% were considered as uncontrolled diabetes ( harris and lank 2004 ; anonymous 2008 ) . the differences between patients with controlled and uncontrolled diabetes were tested using the t - test for continuous variables such as cirs score , age and diabetes duration , and the chi - square test for categorical variables like sex and diabetes - related complications . then , variables with p < 0.1 were used as covariables in a multivariable logistic regression model to measure the association between diabetes control ( dependant variable ) and the cirs score ( independent variable ) . according to various studies , the percentage of patients controlling their diabetes varies from 33% to 55% ( weiner and long 2004 ; meduru et al 2007 ; suh et al 2008 ) . when we estimated the sample size , we used the worst case scenario and anticipated a 2-to-1 ratio when comparing subjects with uncontrolled diabetes to subjects with controlled diabetes . a total sample size of 75 subjects was needed in order to achieve 80% statistical power , to detect a difference of 4 points on the cirs with a standard deviation of 5.64 ( based on a previous study ) ( fortin et al 2005a ) , with a two - sided t - test at the 5% significance level ( nquery advisor 6.01 ) . we increased this sample size by 30% to account for possible missing data in the charts and to allow controlling for 2 to 3 variables in the logistic regression model ( requiring at least 30 subjects in the smallest group ) . analyses were made with spss software ( version 13.0 ; spss inc . , chicago , il ) and a p value < 0.05 was considered statistically significant . the differences between patients with controlled and uncontrolled diabetes were tested using the t - test for continuous variables such as cirs score , age and diabetes duration , and the chi - square test for categorical variables like sex and diabetes - related complications . then , variables with p < 0.1 were used as covariables in a multivariable logistic regression model to measure the association between diabetes control ( dependant variable ) and the cirs score ( independent variable ) . according to various studies , the percentage of patients controlling their diabetes varies from 33% to 55% ( weiner and long 2004 ; meduru et al 2007 ; suh et al 2008 ) . when we estimated the sample size , we used the worst case scenario and anticipated a 2-to-1 ratio when comparing subjects with uncontrolled diabetes to subjects with controlled diabetes . a total sample size of 75 subjects was needed in order to achieve 80% statistical power , to detect a difference of 4 points on the cirs with a standard deviation of 5.64 ( based on a previous study ) ( fortin et al 2005a ) , with a two - sided t - test at the 5% significance level ( nquery advisor 6.01 ) . we increased this sample size by 30% to account for possible missing data in the charts and to allow controlling for 2 to 3 variables in the logistic regression model ( requiring at least 30 subjects in the smallest group ) . analyses were made with spss software ( version 13.0 ; spss inc . , chicago , il ) and a p value < 0.05 was considered statistically significant . statistical results from the univariate analyses performed to measure the associations between diabetes control and the patients characteristics are indicated in table 1 . diabetes control was not significantly related with the cirs score , age , sex , or diabetes - related complications . a logistic regression model was thus used to measure the association between diabetes control and the cirs , by including the diabetes duration as a covariable . the relation was still not significant ( adjusted odds ratio = 0.889 ; 95% confidence interval : 0.7361.074 ; p = 0.223 ) . the results of this study suggest that in patients with type 2 diabetes mellitus , the presence of comorbidity measured with the cirs , is not related to glycemia control defined by hba1c levels . the finding that glycemia control is associated with diabetes duration is similar to that of previous reports ( leelawattana et al 2006 ; shim et al 2006 ; tascona et al 2006 ; heisler et al 2007 ; lopez stewart et al 2007 ; tien et al 2008 ; tikellis et al 2008 ) . to our knowledge , this is the first study that used the cirs to measure comorbidity in order to assess the relationship between comorbidity and glycemia control in type 2 diabetic patients . the cirs score takes into account all comorbidities as well as their severity , and provides a good assessment of other chronic diseases that may coexist with diabetes mellitus , being or not etiologically associated with it . compared to our comorbidity measuring method , previous studies on a possible association between comorbidity condition and glycemia control included a limited number of diseases ( zhang et al 2000 ; weiner and long 2004 ; suh et al 2008 ) or diseases associated to diabetes exclusively ( suh et al 2008 ) . in these studies , our finding that the presence of comorbidities is not associated with glycemia control supports the conclusion of another study which also used a more comprehensive measurement of comorbidity , the chronic disease score ( cds ) ( el - kebbi et al 2001 ) . it has been reported that depending on the type of comorbid condition they have , diabetic patients are more or less likely to achieve hba1c levels under 7% ( meduru et al 2007 ) . therefore , when hba1c less than 7% threshold is used as a performance measure for diabetes quality of care , assessment of an association between glycemia control and comorbidity may be influenced by the type of comorbid conditions . in fact , the likelihood of finding an association between poor glycemia control and comorbidity in patients with diabetes - related complications would be higher than in patients without this type of complications ( meduru et al 2007 ) . however , our study suggests that the presence of multiple chronic conditions is not a factor limiting the achievement of a good control of glycemia , even after taking into account the severity of the coexistent diseases . it has been reported that services received by diabetic patients do not differ based on comorbid illness burden ( halanych et al 2007 ) , however diabetic patients with elevated hba1c receive a closer attention in primary care encounters ( parchman et al 2007 ) . therefore , in the presence of multiple comorbid conditions , diabetic patients may receive better care . as a matter of fact , it has been reported that vulnerable elders with multiple chronic conditions , including diabetes , receive better overall quality of care ( min et al 2007 ) , and that the quality of care improves as a patient s number of chronic conditions increases ( higashi et al 2007 ) . it could have been preferable to use a mean value of all hba1c over two years rather than using only the most recent two hba1c measures . however , given data abstraction techniques , it was not feasible to utilize additional values . since we performed a retrospective study , one limitation of our method is that different variables such as the time elapsed between the two measurements of hba1c could not be controlled . furthermore , in our sample , 69% of the patients had a good glycemia control ( table 1 ) , a higher proportion compared to that in other studies ( 33%55% ) ( weiner and long 2004 ; meduru et al 2007 ; suh et al 2008 ) . the fact that our institution is a teaching hospital with a fmu may have influenced the quality of care provided , and biased the results towards a better glycemia control . there is also the possibility that a number of patients with more severe diabetes are followed up in specialized clinics and were therefore underrepresented in our sample . another limitation of the study is that the patients were recruited from only one setting and therefore extrapolation of our results to other settings is more difficult to make . lack of statistical power could be an issue in the assessment of the relationship between diabetes - related complications and glycemia control . given that the proportion of complications is around 50% when diabetes is not controlled , the two group chi - square test at the 0.05 level had a 34% statistical power to detect an odds ratio of 0.5 with sample sizes of 30 ( uncontrolled diabetes ) and 66 ( controlled diabetes ) ( nquery advisor 6.01 ) . despite these limitations , our study is strengthened by the use of a comprehensive comorbidity index to measure comorbidity that takes into account disease severity , a random patients selection and the mean of two hba1c measurements instead of a single measurement . it could have been preferable to use a mean value of all hba1c over two years rather than using only the most recent two hba1c measures . however , given data abstraction techniques , it was not feasible to utilize additional values . since we performed a retrospective study , one limitation of our method is that different variables such as the time elapsed between the two measurements of hba1c could not be controlled . furthermore , in our sample , 69% of the patients had a good glycemia control ( table 1 ) , a higher proportion compared to that in other studies ( 33%55% ) ( weiner and long 2004 ; meduru et al 2007 ; suh et al 2008 ) . the fact that our institution is a teaching hospital with a fmu may have influenced the quality of care provided , and biased the results towards a better glycemia control . there is also the possibility that a number of patients with more severe diabetes are followed up in specialized clinics and were therefore underrepresented in our sample . another limitation of the study is that the patients were recruited from only one setting and therefore extrapolation of our results to other settings is more difficult to make . lack of statistical power could be an issue in the assessment of the relationship between diabetes - related complications and glycemia control . given that the proportion of complications is around 50% when diabetes is not controlled , the two group chi - square test at the 0.05 level had a 34% statistical power to detect an odds ratio of 0.5 with sample sizes of 30 ( uncontrolled diabetes ) and 66 ( controlled diabetes ) ( nquery advisor 6.01 ) . despite these limitations , our study is strengthened by the use of a comprehensive comorbidity index to measure comorbidity that takes into account disease severity , a random patients selection and the mean of two hba1c measurements instead of a single measurement . our study suggests that comorbidity , measured with an exhaustive index , is not a factor that prevents the achievement of a good glycemia control in patients with type 2 diabetes . further studies are needed to extend these results to other settings and to evaluate the impact of different types of medical conditions on glycemia control .
reports on the relationship between comorbidity and glycemia control in diabetic patients are conflicting and the method of measuring comorbidity varies widely among studies . the aim of the present study was to evaluate the relationship between diabetes control and comorbidity , taking into account all comorbidities and their severity , in a primary care setting . 
 we performed a retrospective descriptive study based on chart review of 96 randomly selected type 2 diabetic patients . 
 comorbidity was measured with the cumulative illness rating scale ( cirs ) , an exhaustive comorbidity index . 
 diabetes was considered as controlled if the mean value of two measurements of glycosylated hemoglobin a ( hba1c ) was less than 7% . 
 taking diabetes control as the dependent variable , its relationship with the cirs score , age , sex , diabetes duration , and diabetes - related complications was explored . 
 diabetes control was not significantly related with the cirs score , age , sex or diabetes severity . 
 diabetes duration was the only variable significantly related to diabetes control . 
 our study suggests that comorbidity measured with the cirs in patients with type 2 diabetes is not a factor that prevents the achievement of a good glycemia control .