atrial fibrillation ( af ) is the most common cardiac arrhythmia , affecting about 46.1 million people globally , including 350 000 canadians . af is associated with significant morbidity , including a 3 to 5 times increase in risk of stroke and a 3 times increase in risk of congestive heart failure . the prevalence of af doubles with each decade of life beyond age 50 , affecting almost 10% of the population by age 80 . alarmingly , the overall burden , incidence , prevalence , and mortality of af are increasing , rendering this disease a public health crisis . af is associated with substantial healthcare costs , estimated at $ 6.65 billion ( usd ) annually in the united states . limitations of the current literature include the heterogeneity between studies in their use of economic models , surveys , administrative data , and patientreported data , which has produced inconsistencies in cost estimates . moreover , most of the literature has focused on hospitalization costs , despite most of the treatment for af actually occurring in the outpatient setting . for example , outpatient and medication costs are estimated to be $ 1.77 billion ( usd ) in the united states . other healthcare sectors such as home care and longterm care may be important components of cumulative cost , given the advanced age of onset of af and its associated comorbidities . few studies have examined overall healthcare costs longitudinally from initial af diagnosis , and these studies have had limited followup , from 0.5 to 3 years . these studies have simply explored annual costs or shortterm costs , which does not take into consideration the natural history of af . studies in cancer and heart failure suggest that there are distinct phases of cumulative cost accumulation that parallel the natural history of the disease , such as that immediately after diagnosis and that before death . it is important to study these phases , because each may have different patient or systemlevel predictors of costs . such information is valuable for decision makers when targeting how to improve the efficiency of care delivery for those with a particular health condition . accordingly , we sought to address this gap in knowledge by estimating per patient longitudinal cumulative costs over time from initial emergency department ( ed ) diagnosis to death across all healthcare sectors . ontario is canada 's largest province , with a population of 13 million , all of whom have universal healthcare coverage provided by a single thirdparty payer , the ontario ministry of health and long term care ( mohltc ) . our objectives were to determine if there are clinically relevant phases of cumulative cost accumulation after an index diagnosis of af and to determine the predictors of healthcare cost in these phases . this study was approved by the institutional research ethics board at sunnybrook health sciences centre , university of toronto . under ontario 's personal health information protection act ( phipa ) , these datasets were linked using unique , encoded identifiers and analyzed at the institute for clinical evaluative sciences ( ices ) . the canadian institute for health information discharge abstract database ( cihidad ) was used to identify all acute and chronic care hospitalizations in our cohort . the national ambulatory care reporting system database ( nacrs ) was used for hospitalbased ambulatory care , including ed visits , outpatient surgical procedures , medical day and night care , and highcost ambulatory clinics data . we obtained postal codes for socioeconomic status and rurality index through statistics canada . the ontario health insurance program ( ohip ) comprehensive drug information was obtained from the ontario drug benefit database ( odb ) for all patients aged 65 years , because the mohltc provides full drug coverage only for these patients . the canadian institute for health information same day surgery ( cihisds ) database was used to obtain patient day surgery information . longterm care data were from the continuing care reporting system ( ccrs ) database , which contains demographic , clinical , functional , and resource utilization information on patients in longterm care facilities . home care information was obtained from the home care database ( hcd ) , which contains administrative data regarding care delivery . data from the national rehab system ( cihinrs ) provided information on inpatient rehabilitation programs . our cohort consisted of newly diagnosed af patients based on the international classification of diseases version 10 main diagnosis code i48 in the nacrs database , with their first ed visit between april 1 , 2005 , and march 31 , 2010 . we restricted the cohort to patients who were residents of ontario , who were 20 years old , who had valid ohip identification numbers , who did not have a previous diagnosis of af within 3 years prior to the index visit , and who survived the index ed visit . we only included patients who were discharged home from the ed after their index presentation , as our algorithm for identification was validated in this population , with a positive predictive value of 93.0% ( 95% ci 91.6% to 94.2% ) and a sensitivity of 96.6% ( 95% ci 94.1% to 98.2% ) . for patients with multiple visits during the study timeframe , the first episode was selected as the index event . the primary outcome was per patient longitudinal cumulative healthcare costs , from the index ed visit to a maximum followup until death or march 31 , 2012 . we used a bottomup approach , whereby episodes of care for each sector per patient were identified in administrative databases during the followup period . perepisode cost for hospitalizations , ed visits , sameday surgeries , complex continuing care services , home care services , and longterm care were determined by using resource intensity weights ( riws ) and casemix methodology . we multiplied the riw associated with the casemix group for each episode of care by the mean provincial cost per weighted case ( cpwc ) for that fiscal year . costs for physician visits and laboratory tests were obtained directly from the ohip claims history database , while medication costs were obtained directly from the odb database . we only included medication costs for patients 65 years of age , because mohltc coverage was limited to this population . costs per episode were adjusted to 2013 canadian dollars using the consumer price index , ( http://www.bankofcanada.ca/en/cpi.html ) and then summed over the entire followperiod . previous studies by our team and others on heart failure and cancer care have shown that healthrelated costs are not constant over patient life span but rather have distinct periods of rapid cost accumulation , interspaced by periods of lower cost . we hypothesized that there may be a similar pattern over the lifetime of af patients , with 3 phases of cost : ( 1 ) a post  index ed visit phase of high cost , ( 2 ) a stable / chronic phase of relatively constant cost , and ( 3 ) a predeath phase of increasing cost . we first conducted exploratory analyses in patient subgroups who died 9 to 12 months postindex , 33 to 36 months postindex , or 57 to 60 months postindex and patients who were alive during the entire followup period . we examined their cumulative cost profiles to determine if there were clinically relevant phases of cost for post  index ed visit or predeath . if so , joinpoint analyses ( joinpoint regression software version 4.1.1 , statistical methodology and applications branch , surveillance research program , national cancer institute ) were conducted on each patient subgroup ( death at 9 to 12 , 33 to 36 , and 57 to 60 months postindex ) to determine the inflection points that separated cost phases , by using a monte carlo permutation method . based on the inflections in these subgroups , we approximated the inflections for the overall cohort . this is similar to the methodology used in previous publications in studies on heart failure , prostate cancer , and hepatitis c virus . after determining the presence and duration of each phase of cost , the total cost for each patient was allocated to each phase in a hierarchical fashion , where predeath phase cost was assigned first , then to the post  index ed visit , and , finally , all remaining cost to the stable phase . individual patient costs were assigned to each block within each phase hierarchically , first to the predeath phase , then to the post  index ed visit phase , and finally to the stable / chronic phase . for example , for a patient who died 13 months post  index ed visit , the mean cost for the last 12 months was assigned to the 30 patientday blocks of the predeath phase , and then the mean cost for the first month was assigned to a 30 patientday block in the post  index ed visit phase . for a patient who died 34 months post  index ed visit , there were 20 months of cost assigned to the stable / chronic phase after assigning the last 12 months ' cost to predeath and the first 2 months to the post costs for each of the 20 stable / chronic phase months were then averaged to provide a single 30 patientday block estimate . importantly , the cost for any particular episode for each individual patient was assigned to only 1 phase . once assigned , 30 patientday mean cost was determined for each phase . for the predeath and postindex phases , a separate 30 patientday mean for the stable phase , all remaining costs were averaged to produce a single 30day mean cost estimate . we used multivariable hierarchical generalized linear models with a logarithmic link and gamma distribution to determine predictors of cumulative healthcare costs ; methods that are consistent with those used in previous studies examining end of life and phases of costs to account for the skewed positive nature of the data . we built separate models for the postindex phase and the predeath phase and tested the appropriateness of a log link and gamma distribution for each model by using the pregibon link test and modified hosmer for the postindex phase of cost , the pregibon link test indicated that the logarithmic link was appropriate for the generalized linear model ( p=0.19 ) , and the modified hosmer for the predeath phase , the pregibon link test supported the logarithmic link ( p=0.394 ) and the modified hosmer the advantage of this type of model is that it accommodates the skewed nature of cost data and produces coefficients ( rate ratio [ rr ] ) that are interpreted as the relative increase in mean cost for each increment in the covariate . covariates included in the 2 models were demographics ( age , sex ) and comorbidities , including hypertension , diabetes , and kidney / liver dysfunction . we then reran the 2 models using only cha2ds2vasc score as the covariate of interest . the cha2ds2vasc score consists of congestive heart failure / left ventricular dysfunction , hypertension , age 75 years ( doubled ) , diabetes , stroke ( doubled)vascular disease , age 65 to 74 years , and sex category ( female ) . we chose the cha2ds2vasc score because it is inclusive of common clinically relevant major and nonmajor risk factors for stroke , and it has been validated in multiple cohort studies . we used sas version 9.3 ( sas institute inc ) for statistical analyses and stata version 13.1 for goodnessoffit and goodnessoflink tests . these datasets were linked using unique , encoded identifiers and analyzed at the institute for clinical evaluative sciences ( ices ) . the canadian institute for health information discharge abstract database ( cihidad ) was used to identify all acute and chronic care hospitalizations in our cohort . the national ambulatory care reporting system database ( nacrs ) was used for hospitalbased ambulatory care , including ed visits , outpatient surgical procedures , medical day and night care , and highcost ambulatory clinics data . we obtained postal codes for socioeconomic status and rurality index through statistics canada . the ontario health insurance program ( ohip ) feeforservice claims history was accessed for data on physician services . comprehensive drug information was obtained from the ontario drug benefit database ( odb ) for all patients aged 65 years , because the mohltc provides full drug coverage only for these patients . the canadian institute for health information same day surgery ( cihisds ) database was used to obtain patient day surgery information . longterm care data were from the continuing care reporting system ( ccrs ) database , which contains demographic , clinical , functional , and resource utilization information on patients in longterm care facilities . home care information was obtained from the home care database ( hcd ) , which contains administrative data regarding care delivery . data from the national rehab system ( cihinrs ) provided information on inpatient rehabilitation programs . our cohort consisted of newly diagnosed af patients based on the international classification of diseases version 10 main diagnosis code i48 in the nacrs database , with their first ed visit between april 1 , 2005 , and march 31 , 2010 . we restricted the cohort to patients who were residents of ontario , who were 20 years old , who had valid ohip identification numbers , who did not have a previous diagnosis of af within 3 years prior to the index visit , and who survived the index ed visit . we only included patients who were discharged home from the ed after their index presentation , as our algorithm for identification was validated in this population , with a positive predictive value of 93.0% ( 95% ci 91.6% to 94.2% ) and a sensitivity of 96.6% ( 95% ci 94.1% to 98.2% ) . for patients with multiple visits during the study timeframe , the first episode was selected as the index event . the primary outcome was per patient longitudinal cumulative healthcare costs , from the index ed visit to a maximum followup until death or march 31 , 2012 . we used a bottomup approach , whereby episodes of care for each sector per patient were identified in administrative databases during the followup period . perepisode cost for hospitalizations , ed visits , sameday surgeries , complex continuing care services , home care services , and longterm care were determined by using resource intensity weights ( riws ) and casemix methodology . we multiplied the riw associated with the casemix group for each episode of care by the mean provincial cost per weighted case ( cpwc ) for that fiscal year . costs for physician visits and laboratory tests were obtained directly from the ohip claims history database , while medication costs were obtained directly from the odb database . we only included medication costs for patients 65 years of age , because mohltc coverage was limited to this population . costs per episode were adjusted to 2013 canadian dollars using the consumer price index , ( http://www.bankofcanada.ca/en/cpi.html ) and then summed over the entire followperiod . previous studies by our team and others on heart failure and cancer care have shown that healthrelated costs are not constant over patient life span but rather have distinct periods of rapid cost accumulation , interspaced by periods of lower cost . we hypothesized that there may be a similar pattern over the lifetime of af patients , with 3 phases of cost : ( 1 ) a post  index ed visit phase of high cost , ( 2 ) a stable / chronic phase of relatively constant cost , and ( 3 ) a predeath phase of increasing cost . we first conducted exploratory analyses in patient subgroups who died 9 to 12 months postindex , 33 to 36 months postindex , or 57 to 60 months postindex and patients who were alive during the entire followup period . we examined their cumulative cost profiles to determine if there were clinically relevant phases of cost for post  index ed visit or predeath . if so , joinpoint analyses ( joinpoint regression software version 4.1.1 , statistical methodology and applications branch , surveillance research program , national cancer institute ) were conducted on each patient subgroup ( death at 9 to 12 , 33 to 36 , and 57 to 60 months postindex ) to determine the inflection points that separated cost phases , by using a monte carlo permutation method . based on the inflections in these subgroups , we approximated the inflections for the overall cohort . this is similar to the methodology used in previous publications in studies on heart failure , prostate cancer , and hepatitis c virus . after determining the presence and duration of each phase of cost , the total cost for each patient was allocated to each phase in a hierarchical fashion , where predeath phase cost was assigned first , then to the post  index ed visit , and , finally , all remaining cost to the stable phase . individual patient costs were assigned to each block within each phase hierarchically , first to the predeath phase , then to the post for example , for a patient who died 13 months post  index ed visit , the mean cost for the last 12 months was assigned to the 30 patientday blocks of the predeath phase , and then the mean cost for the first month was assigned to a 30 patientday block in the post  index ed visit phase . for a patient who died 34 months post  index ed visit , there were 20 months of cost assigned to the stable / chronic phase after assigning the last 12 months ' cost to predeath and the first 2 months to the post costs for each of the 20 stable / chronic phase months were then averaged to provide a single 30 patientday block estimate . importantly , the cost for any particular episode for each individual patient was assigned to only 1 phase . once assigned , 30 patientday mean cost was determined for each phase . for the predeath and postindex phases , a separate 30 patientday mean for the stable phase , all remaining costs were averaged to produce a single 30day mean cost estimate . we used multivariable hierarchical generalized linear models with a logarithmic link and gamma distribution to determine predictors of cumulative healthcare costs ; methods that are consistent with those used in previous studies examining end of life and phases of costs to account for the skewed positive nature of the data . we built separate models for the postindex phase and the predeath phase and tested the appropriateness of a log link and gamma distribution for each model by using the pregibon link test and modified hosmer for the postindex phase of cost , the pregibon link test indicated that the logarithmic link was appropriate for the generalized linear model ( p=0.19 ) , and the modified hosmer for the predeath phase , the pregibon link test supported the logarithmic link ( p=0.394 ) and the modified hosmer the advantage of this type of model is that it accommodates the skewed nature of cost data and produces coefficients ( rate ratio [ rr ] ) that are interpreted as the relative increase in mean cost for each increment in the covariate . covariates included in the 2 models were demographics ( age , sex ) and comorbidities , including hypertension , diabetes , and kidney / liver dysfunction . we then reran the 2 models using only cha2ds2vasc score as the covariate of interest . the cha2ds2vasc score consists of congestive heart failure / left ventricular dysfunction , hypertension , age 75 years ( doubled ) , diabetes , stroke ( doubled)vascular disease , age 65 to 74 years , and sex category ( female ) . we chose the cha2ds2vasc score because it is inclusive of common clinically relevant major and nonmajor risk factors for stroke , and it has been validated in multiple cohort studies . we used sas version 9.3 ( sas institute inc ) for statistical analyses and stata version 13.1 for goodnessoffit and goodnessoflink tests . there was a total of 61 112 ed visits for af in ontario , canada , between april 1 , 2005 , and march 31 , 2010 ( figure 1 ) . the majority of patients had hypertension ( 60.7% ) , while only 18.8% had diabetes mellitus . to stroke risk , 35.8% of patients had low cha2ds2vasc scores ( scores 0 to 1 ) , and the majority of patients had scores < 5 ( 90.2% ) . a total of 1055 patients ( 5.9% ) died within 1 year of their index ed presentation . baseline characteristics except for age , all values are n ( % ) . costing profile for incident emergency department atrial fibrillation . , we confirmed that cumulative cost curves for af were defined by discrete cost phases ( figure 2 ) . index ed visit and stable / chronic phases at 2 months postindex , and the stable / chronic and predeath phases at 12 months predeath . there were two 30 patientday blocks of consecutive cost for the postindex phase , and 12 blocks for the predeath phase . figure 3 shows the mean cost per patient for each 30day block of each phase , and table 2 shows the costs divided into components of cost . of the 3 phases , total cost was highest in the predeath phase . in the predeath phase , total cost increased by 75% across 30 patientday blocks , from 12 months predeath ( $ 1984 , 95% ci $ 1816 to $ 2152 ) to 1 month predeath ( $ 8050 , 95% ci $ 7666 to $ 8434 ) . for the postindex phase , the mean total cost per patient was highest at 1 month postindex ( $ 1876 , 95% ci $ 1822 to $ 1931 ) and decreased substantially by 46% at 2 months postindex ( $ 872 , 95% ci $ 828 to $ 916 ) . for the stable / chronic phase , mean total cost was the lowest of the 3 phases , at $ 640 ( 95% ci $ 624 to $ 655 ) per month . acute hospitalization cost was the predominant driver of cost in the predeath phase ( 72% of all costs for 1 month predeath ) . in the postindex phase , cost was more evenly distributed among physician services , acute hospitalizations , and ed visits ( 32% , 31% , and 24% of all costs for 1 month postindex , respectively ) . for the stable / chronic phase , acute hospitalizations were the largest contributor to total costs ( 29% ) , but physician services ( 23% of all costs ) and medications ( 16% ) also remained high . several patient characteristics were significant predictors of total per patient cost . in the postindex phase , per patient cost was more likely to be higher for patients who were older ( rr 1.019 , 95% ci 1.018 to 1.020 , p<0.0001 ) or who had a history of diabetes mellitus , congestive heart failure , hypertension , or renal or liver dysfunction ( table 3 ) . in contrast , we found that for the predeath phase , perpatient cost was actually lower for patients who were older ( rr 0.996 , 95% ci 0.992 to 0.999 , p=0.0156 ) . however , comorbidities such as a history of diabetes mellitus ( rr 1.09 , 95% ci 1.01 to 1.19 , p=0.0323 ) or renal dysfunction ( rr 1.46 , 95% ci 1.21 to 1.77 , p=0.0001 ; table 3 ) were associated with increased costs . predictors of cumulative cost for the 2 months post  index ed visit and the 12 months before death ed indicates emergency department . rate ratio = the % increase in mean cost for each unit increase in the covariate . when our models were repeated with only the cha2ds2vasc score , we found that the score was a strong predictor of perpatient cost for both postindex and predeath analyses . there was a general gradient of cost with increasing comorbidity , as reflected by a higher cha2ds2vasc score ( rr 5.08 and 1.91 for a score of 7 compared with a score of 0 , respectively ) . there was a total of 61 112 ed visits for af in ontario , canada , between april 1 , 2005 , and march 31 , 2010 ( figure 1 ) . the majority of patients had hypertension ( 60.7% ) , while only 18.8% had diabetes mellitus . to stroke risk , 35.8% of patients had low cha2ds2vasc scores ( scores 0 to 1 ) , and the majority of patients had scores < 5 ( 90.2% ) . a total of 1055 patients ( 5.9% ) died within 1 year of their index ed presentation . baseline characteristics except for age , all values are n ( % ) . costing profile for incident emergency department atrial fibrillation . in our exploratory analyses , we confirmed that cumulative cost curves for af were defined by discrete cost phases ( figure 2 ) . index ed visit and stable / chronic phases at 2 months postindex , and the stable / chronic and predeath phases at 12 months predeath . there were two 30 patientday blocks of consecutive cost for the postindex phase , and 12 blocks for the predeath phase . figure 3 shows the mean cost per patient for each 30day block of each phase , and table 2 shows the costs divided into components of cost . of the 3 phases , total cost was highest in the predeath phase . in the predeath phase , total cost increased by 75% across 30 patientday blocks , from 12 months predeath ( $ 1984 , 95% ci $ 1816 to $ 2152 ) to 1 month predeath ( $ 8050 , 95% ci $ 7666 to $ 8434 ) . for the postindex phase , the mean total cost per patient was highest at 1 month postindex ( $ 1876 , 95% ci $ 1822 to $ 1931 ) and decreased substantially by 46% at 2 months postindex ( $ 872 , 95% ci $ 828 to $ 916 ) . for the stable / chronic phase , mean total cost was the lowest of the 3 phases , at $ 640 ( 95% ci $ 624 to $ 655 ) per month . acute hospitalization cost was the predominant driver of cost in the predeath phase ( 72% of all costs for 1 month predeath ) . in the postindex phase , cost was more evenly distributed among physician services , acute hospitalizations , and ed visits ( 32% , 31% , and 24% of all costs for 1 month postindex , respectively ) . for the stable / chronic phase , acute hospitalizations were the largest contributor to total costs ( 29% ) , but physician services ( 23% of all costs ) and medications ( 16% ) also remained high . several patient characteristics were significant predictors of total per patient cost . in the postindex phase , per patient cost was more likely to be higher for patients who were older ( rr 1.019 , 95% ci 1.018 to 1.020 , p<0.0001 ) or who had a history of diabetes mellitus , congestive heart failure , hypertension , or renal or liver dysfunction ( table 3 ) . in contrast , we found that for the predeath phase , perpatient cost was actually lower for patients who were older ( rr 0.996 , 95% ci 0.992 to 0.999 , p=0.0156 ) . however , comorbidities such as a history of diabetes mellitus ( rr 1.09 , 95% ci 1.01 to 1.19 , p=0.0323 ) or renal dysfunction ( rr 1.46 , 95% ci 1.21 to 1.77 , p=0.0001 ; table 3 ) were associated with increased costs . predictors of cumulative cost for the 2 months post  index ed visit and the 12 months before death ed indicates emergency department . rate ratio = the % increase in mean cost for each unit increase in the covariate . when our models were repeated with only the cha2ds2vasc score , we found that the score was a strong predictor of perpatient cost for both postindex and predeath analyses . there was a general gradient of cost with increasing comorbidity , as reflected by a higher cha2ds2vasc score ( rr 5.08 and 1.91 for a score of 7 compared with a score of 0 , respectively ) . the authors examined perpatient longitudinal cumulative cost from initial ed visit diagnosis across all healthcare sectors , by determining clinically relevant phases of total cost and the predictors of cost in these phases . we found that cost was highest in the predeath phase , due mainly to acute hospitalization cost , and increased as patients approached death . cost was next highest in the postindex phase , with physician services , acute hospitalizations , and ed visits as the highest costs , and cost was greater during the first month postindex . cost was lowest in the stable / chronic phase and was mainly attributable to acute hospitalizations , physician services , and medications . comorbidities contributed to higher healthcare cost , and cha2ds2vasc score was a particularly strong predictor . previous studies have determined that afrelated medical costs are high . a recent systematic review estimated a mean overall annual perpatient cost of af at $ 2000 to $ 14 200 usd in the united states , using both prevalent and incident af cohorts . our study provides a more complete picture of individual patient cumulative costs , as we examined longitudinal cost from index af diagnosis to death and incorporated the impact of the natural history of af by examining phases of cost . of the studies in the literature that examined longitudinal costs from af diagnosis , we are only aware of studies where followup was short term ( 0.5 to 3 years ) , which prevents direct comparisons to our results . our study highlights that simply examining annual costs is potentially misleading , as costs are highly dependent on the phase of the disease , with a 10fold difference in monthly costs between the stable phase and predeath phase . in terms of contributors of cost in af , systematic reviews have determined that hospitalization is the largest cost , accounting for 50% to 70% of mean total cost . our phasebased af costing method provides additional insights , as we found that contributors of cost were also highly dependent on the disease phase . while hospitalization was a large contributor of cost in all phases in our study , it was only within the same range of total cost of previous studies during the predeath phase , where hospitalizations contributed to 51% of total cost . other cost components were also predominant contributors of cost during the stable / chronic and postindex phases . our phasebased costing approach is the first of its kind for af costing , and to our knowledge this method has only been previously applied to cancer and heart failure . cost per phase and the contributors of cost differ depending on the diseases studied thus far . a previous study by our research team found that mean total cost per phase was highest in the predeath phase for heart failure clinic patients ; this is similar with our results in af . in contrast to our af study , hospitalization was overwhelmingly the largest contributor of cost for heart failure clinic patients , representing > 80% of total healthcare costs for patients in the predeath and post  hospital discharge phases and 50% of total costs in the stable / chronic phase . a study by brown and colleagues found that for colorectal cancer , overall total cost was highest for the predeath phase , as is consistent with these 2 studies , although monthly mean cost was highest for the postindex phase , rather than for predeath as in the current study . unfortunately , cost was not broken down into its components , so no comparison could be made to af costing components . it is important to note that our costing approach is not specific to the cost of treating af but is instead closely tied with other comorbidities , and so it can be thought of as the cost of patients with af . it is well known that comorbidities are commonly associated with af , especially cardiovascular conditions such as ischemic heart disease , heart failure , and hypertension . a recent retrospective observational study reported that 98% of the af population had at least 1 other comorbidity , and 63% had 4 comorbidities , 45% and 21% had a moderate or high comorbidity burden , and 45% had a moderate risk for stroke ( chads2 scores of 2 ) . the large influence of comorbidities is reflected in our af costing results , as we found that comorbidities were the largest predictors of afrelated cost . the cha2ds2vasc score was a particularly important predictor , a result that is consistent with another study that examined predictors of af cost . first , we did not take on a societal perspective but rather that of the thirdparty payer for the province . for example , we could not include medication costs for patients < 65 years old , as ontario only provides comprehensive coverage for patients 65 years old . moreover , we did not include patient outofpocket costs or loss of productivity related to missed work . however , given the elderly age of most af patients , we do not believe this would have made a large difference in our conclusions . second , we limited our analyses to 3 phases and did not look at other potentially important clinical events such as the occurrence of a major stroke or major bleed . third , we did not include location of death ( ie , hospital , home ) in our analyses . patients who die in hospital would incur higher immediate predeath costs than those who die at home . if characteristics of patients who die at home differ from those of other patients in the cohort , then location of death can be an important predictor of healthcare costs . unfortunately , our available databases do not provide the location of death , so we could not include this covariate . fourth , there are limitations to existing methods for estimating healthcare costs and implications for the choice of method of analysis . we used generalized linear models , which are generally an improvement to the transformed linear models that were previously commonly used . a criticism of generalized linear models is that they can be imprecise and lack robustness , consistency , and efficient methods . however , there are issues with the diagnostic tests used to determine the optimal model . lemeshow test is prone to influential outliers , the value of the statistic depends on the choice of cutpoint that defines the groups , and the test can have low power for detecting lackoffit . the pregibon test and other link tests are more parsimonious tests , but mildly influential observations can result in falsepositive and negative results . new alternative methods for estimating costing have been developed , but further research is needed to improve these methods . finally , endogeneity in costs is a wellrecognized phenomenon , in particular for costs at the end of life , where costs are likely to be impacted by unmeasured confounders and other selection biases . as such , in light of these features of cost data , compounded by the observational nature of our study , we can not assume causality between predictors of cost and cost ; therefore , our findings should be considered hypothesis generating rather than conclusive . first , we did not take on a societal perspective but rather that of the thirdparty payer for the province . for example , we could not include medication costs for patients < 65 years old , as ontario only provides comprehensive coverage for patients 65 years old . moreover , we did not include patient outofpocket costs or loss of productivity related to missed work . however , given the elderly age of most af patients , we do not believe this would have made a large difference in our conclusions . second , we limited our analyses to 3 phases and did not look at other potentially important clinical events such as the occurrence of a major stroke or major bleed . third , we did not include location of death ( ie , hospital , home ) in our analyses . patients who die in hospital would incur higher immediate predeath costs than those who die at home . if characteristics of patients who die at home differ from those of other patients in the cohort , then location of death can be an important predictor of healthcare costs . unfortunately , our available databases do not provide the location of death , so we could not include this covariate . fourth , there are limitations to existing methods for estimating healthcare costs and implications for the choice of method of analysis . we used generalized linear models , which are generally an improvement to the transformed linear models that were previously commonly used . a criticism of generalized linear models is that they can be imprecise and lack robustness , consistency , and efficient methods . however , there are issues with the diagnostic tests used to determine the optimal model . lemeshow test is prone to influential outliers , the value of the statistic depends on the choice of cutpoint that defines the groups , and the test can have low power for detecting lackoffit . the pregibon test and other link tests are more parsimonious tests , but mildly influential observations can result in falsepositive and negative results . new alternative methods for estimating costing have been developed , but further research is needed to improve these methods . finally , endogeneity in costs is a wellrecognized phenomenon , in particular for costs at the end of life , where costs are likely to be impacted by unmeasured confounders and other selection biases . as such , in light of these features of cost data , compounded by the observational nature of our study , we can not assume causality between predictors of cost and cost ; therefore , our findings should be considered hypothesis generating rather than conclusive . in our phasebased longitudinal costing approach to afrelated costs in patients from af diagnosis to death , we have identified 3 distinct phases of costs and found that costs were highest in the predeath phase , where hospitalization cost was a far greater contributor to total cost than in other phases . further research is needed on methods to incorporate these phase based costs into costeffectiveness analyses for af therapies .
backgroundatrial fibrillation ( af ) has substantial impacts on healthcare resource utilization . our objective was to understand the pattern and predictors of cumulative healthcare costs in af patients after incident diagnosis in an emergency department ( ed).methods and resultspatients discharged after a first presentation of af to an ed in ontario , canada , 
 were identified from april 1 , 2005 , through march 31 , 2010 . 
 perpatient cumulative healthcare costs were determined until death or march 31 , 2012 . 
 joinpoint analyses identified clinically relevant cost phases . hierarchical generalized linear models with a logarithmic link and gamma distribution determined predictors of cost per phase . 
 our cohort was 17 980 patients . during a mean followup of 3.9 years 
 , 17.1% of patients died . 
 three distinct cost phases were identified : 2month post  index ed visit phase , 12month predeath phase , and a stable / chronic phase . the mean cost per patient in the first month post 
 index ed visit was $ 1876 ( 95% ci $ 1822 to $ 1931 ) , $ 8050 ( 95% ci $ 7666 to $ 8434 ) in the month before death , and $ 640 ( 95% ci $ 624 to $ 655 ) per month for the stable / chronic phase . 
 the main cost component in the postindex phase was physician services ( 32% of all costs ) and hospitalizations for the predeath phase ( 72% of all costs ) . 
 the cha2ds2vasc clinical risk score was a strong predictor of costs ( rate ratio 1.91 and 5.08 for score of 7 versus score of 0 in predeath phase and postindex phase , respectively).conclusionsthere are distinct phases of resource utilization in af , with highest costs in the predeath phase .