breast cancer is the most common cancer and leading cause of cancer death among women worldwide.1 among danish women , breast cancer accounted for 29% of all incident cancers , and was the cause of 16% of all cancer deaths among women during the period 19992006.2 to improve survival of danish patients with breast cancer , nationwide biannual mammographic screening was introduced in 2007 and offered to women between 50 and 69 years of age.3 cancer stage is an important predictor of prognosis , with 5-year survival of 98% for localized breast cancer decreasing to 27% for metastatic disease.4 therefore , analyses of temporal changes in stage - specific incidence and mortality from breast cancer are important for evaluating the effectiveness of early detection and treatment programs . cancer registries are valuable resources for monitoring cancer incidence and survival . the danish cancer registry ( dcr ) has recorded incident cases of cancer on a nationwide basis since 1943 and has been shown to have accurate and almost complete ascertainment of cancer cases . the international classification of disease revision 7 ( icd-7 ) was used until 2003 to categorize cancer sites , and has been converted to the international classification of disease revision 10 ( icd-10).5,6 the dcr includes information on diagnosis , cancer type , topography , morphology , and stage according to the tumor , node , metastasis ( tnm ) classification.7 information on tnm classification may be prone to underreporting and misclassification , eg , of tumor size ( t ) and lymph node ( n ) status.8 missing information may potentially lead to biased results , if the missing information is not random . thus , to draw valid inferences from stage - specific analysis , completeness needs to be quantified . currently , there is a lack of studies on completeness of tnm classification in the dcr . therefore , we conducted this study to evaluate completeness of tnm classification for breast cancer . in addition , we aimed to determine whether the completeness varied by gender , patient age , calendar period , cancer stage , or level of comorbidity . all residents are provided with free , tax - supported medical care . since 1968 , the danish civil registration system has assigned a unique 10-digit personal identification number to all danish residents,9 encoding date of birth and gender . this number is used in all danish registers , allowing unambiguous individual - level data linkage . from the dcr , we identified all patients with a primary diagnosis of invasive breast cancer ( icd-10 code c50 ) between 2004 and 2009.5,6 tumor stage was recorded as local , regional , or distant in the dcr until 2003.5 since 2004 , stage has been recorded using the tnm classification.7 from the dcr , we also obtained information on date of diagnosis , age , and gender . data on the presence of comorbidity were obtained from the danish national patient registry ( dnpr).10 this registry contains data on all admissions to nonpsychiatric hospitals in denmark since 1977 and outpatient contacts since 1995 , including the personal identification number , date of admission / contact and discharge , and diagnosis codes ( icd-10 from 1994 and onwards ) . we defined pre - existing comorbidity using the charlson comorbidity index ( cci ) , based on hospital diagnoses within 10 years preceding the date of breast cancer diagnosis . the cci is based on disease categories which are each weighted according to the adjusted risk of one - year mortality.11 excluding breast cancer , we defined the level of comorbidity as low ( cci score 0 ) , medium ( cci score 12 ) , and high ( ccc score 3 ) . we calculated the completeness and corresponding 95% confidence intervals , of the tnm registration overall , by each component individually ( ie , t , n , and m ) , and according to the stage categories ( see appendix ) . to reduce the prevalence of patients with breast cancer of unknown stage , we converted the tnm classification into summary staging using an algorithm that allowed categorization of tumors with certain missing tnm stage components into localized , regional , distant , and unknown stage ( see appendix ) . missing data were allowed if the information available on other t , n , or m components provided sufficient and meaningful information to stage the tumors . unknown  category represented tumors of high t class , which are known to have considerable risk of lymph node or distant metastasis , but lacked information of either or both . completeness was defined as the number of individuals with tnm recordings , and defined stage categories , divided by the total number of patients . we stratified completeness by gender , age ( 039 years , 4059 years , 6079 years , and 80 years ) , year of cancer diagnosis , and cci score ( low , medium , high ) . analyses were performed using sas ( version 9.2 ; sas institute inc , cary , nc ) . from the dcr , we identified all patients with a primary diagnosis of invasive breast cancer ( icd-10 code c50 ) between 2004 and 2009.5,6 tumor stage was recorded as local , regional , or distant in the dcr until 2003.5 since 2004 , stage has been recorded using the tnm classification.7 from the dcr , we also obtained information on date of diagnosis , age , and gender . data on the presence of comorbidity were obtained from the danish national patient registry ( dnpr).10 this registry contains data on all admissions to nonpsychiatric hospitals in denmark since 1977 and outpatient contacts since 1995 , including the personal identification number , date of admission / contact and discharge , and diagnosis codes ( icd-10 from 1994 and onwards ) . we defined pre - existing comorbidity using the charlson comorbidity index ( cci ) , based on hospital diagnoses within 10 years preceding the date of breast cancer diagnosis . the cci is based on disease categories which are each weighted according to the adjusted risk of one - year mortality.11 excluding breast cancer , we defined the level of comorbidity as low ( cci score 0 ) , medium ( cci score 12 ) , and high ( ccc score 3 ) . we calculated the completeness and corresponding 95% confidence intervals , of the tnm registration overall , by each component individually ( ie , t , n , and m ) , and according to the stage categories ( see appendix ) . to reduce the prevalence of patients with breast cancer of unknown stage , we converted the tnm classification into summary staging using an algorithm that allowed categorization of tumors with certain missing tnm stage components into localized , regional , distant , and unknown stage ( see appendix ) . missing data were allowed if the information available on other t , n , or m components provided sufficient and meaningful information to stage the tumors . unknown  category represented tumors of high t class , which are known to have considerable risk of lymph node or distant metastasis , but lacked information of either or both . completeness was defined as the number of individuals with tnm recordings , and defined stage categories , divided by the total number of patients . we stratified completeness by gender , age ( 039 years , 4059 years , 6079 years , and 80 years ) , year of cancer diagnosis , and cci score ( low , medium , high ) . analyses were performed using sas ( version 9.2 ; sas institute inc , cary , nc ) . a total of 26,350 ( 99.5% ) breast cancer patients were women , and 138 ( 0.5% ) were men . there was a slightly better registration for women than for men ( 85.4% versus 81.2% ) . there were no major changes in tnm completeness during the six - year study period , though completeness was slightly lower in 2009 compared with previous years . completeness declined markedly with advancing age , from 91.3% among patients aged 039 years to 57.0% among patients aged 80 years and older . completeness also declined with increasing cci score , from 87.9% among patients with a low level of comorbidity to 69.7% among patients with high levels of comorbidity . similar patterns were found for the individual components of the tnm classification , although the n and m recordings had somewhat more missing values than t. these data are presented in table 1 . using the algorithm allowing inclusion of some tx , nx , and mx codes in the classification of tumor stage ( appendix ) decreased the number of unknown tumors from 3867 when not allowing any x s in the definite stage categories to 2551 using the algorithm allowing some x s in these categories ( data not shown ) . thus , with our algorithm , the proportion of tumors with unknown stage was reduced from 14.6% to 9.6% . similar to the primary analyses of tnm completeness , the proportion of tumors of unknown stage was highest among older patients and those with high cci scores . the proportion of patients with localized breast cancer at the time of diagnosis increased from 43.2% in 2004 to 49.8% in 2009 . in this nationwide study , we found reasonable completeness of tnm registration in the dcr . the completeness was relatively constant during the study period , except for a slight decline from 2008 through 2009 , which was due to a delay in the recording of tnm . however , we found that completeness varied substantially with patient age and level of comorbidity . when we applied a stage algorithm allowing some missing tnm components in the various categories , the proportion of cases with definite staging increased . the main strengths of our study included the population - based design and use of nationwide data from the dcr , which has virtually complete ascertainment of breast cancer.12 however , we only examined completeness of tnm registration , and our study did not allow for estimation of the accuracy of tnm registration . furthermore , the impact of classifying some unknown stage breast tumors into categories with known tumors may not be appropriate for all purposes . moreover , we may have misclassified comorbidity due to inaccuracy of hospital and outpatient diagnoses as well as lack of information on comorbidities diagnosed in primary care . even so , a recent study reported consistently high positive predictive values for all of the cci diseases based on diagnoses recorded in the dnpr.13 compared with other cancer sites , the proportion of unstaged breast cancer patients is typically relatively low,8 though other studies , in agreement with our findings , have shown declining completeness of tnm registration with increasing age and comorbidity score.8,14 in particular , yancik et al15 found that women older than 70 years underwent fewer lymph node dissections than younger women . other studies have also shown that elderly breast cancer patients and patients with comorbidity are less likely to receive breast cancer treatment according to treatment guidelines and have a poorer prognosis than younger patients with no severe comorbid conditions.16,17 the reasons are likely multifactorial , and may include less complete diagnostic assessment related to age , comorbidity and disabilities , patient preferences , apparent signs of distant metastases obviating the need for staging in treatment decisions , or competing medical conditions requiring treatment prior to cancer therapy . however , cautious interpretation of our results is necessary , as age and comorbidity are usually correlated . for example , a breast cancer case who for various reasons was not treated surgically might be coded nx , although axillary lymph node biopsy revealed positive lymph node metastases . as a second example , for breast cancer treated with curative intent surgery , no other diagnostic work - up other than a lung x - ray is performed . these cases are likely registered as m0 by most surgeons , although some might prefer to denote them as mx . because completeness was lower among the elderly and those with a high level of comorbidity , the data on tnm stage was not missing at random . consequently , study results may potentially be biased when staging constitutes the exposure or a confounding factor , and this may lead to incorrect conclusions . thus , patients with missing data on tnm stage should be carefully handled in the statistical analyses , eg , by applying missing imputation methods,18 or bias analysis.19 for instance , population - based cancer registry data is important for monitoring the effectiveness of mammography screening programs . stage - specific analyses are especially crucial , and stage - recording practices may affect the evaluation of screening program and trends in stage - specific incidence and prognosis . however , most patients with screening mammography - detected breast cancer undergo surgery , and consequently have high tnm completeness . in conclusion , our study showed that completeness of tnm registration for breast cancer varied differentially with age and level of comorbidity . therefore , careful consideration should be given to the methodological implications in studies of cancer - related outcomes using data from the dcr .
backgroundthe purpose of this study was to investigate the completeness of tnm ( tumor , node , metastasis ) staging for breast cancer in the danish cancer registry.methodswe identified 26,488 patients with a first diagnosis of breast cancer between 2004 and 2009 from the danish cancer registry . 
 we obtained information on comorbidity through the danish national patient registry . 
 we estimated the completeness of tnm registration in the danish cancer registry and stratified the analysis by gender , age , year of cancer diagnosis , and comorbidity . 
 we designed an algorithm categorizing breast cancer into localized , regional , distant , or unknown stage based on tnm codes.resultsthe overall completeness of tnm registration was 85.4% . 
 the completeness varied little by gender and study year , but decreased from 91.3% in patients aged 039 years to 57.0% in patients aged 80 years or more , and from 87.9% among patients with a low level of comorbidity to 69.7% among patients with a high level of comorbidity.conclusionthe completeness of the tnm registration varied substantially by age and level of comorbidity . 
 thus , depending on the outcome under study , stage - specific analyses may yield biased results . 
 the completeness of tnm should be considered in study designs using tnm information .