***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background There is little information on the validity of using record linkage with routinely collected data for case ascertainment of stroke in large population -based studies in the UK . We examined the accuracy of these routine record linkage approaches for identifying incident stroke cases in a large UK population -based study , the European Prospect i ve Investigation into Cancer (EPIC)-Norfolk cohort . Methods We examined a sample of hospital records of incident stroke cases identified by linkage with two routine data sources , death certificates and a national hospital record linkage system ( ENCORE )  , using predefined study criteria . Two senior Specialist Registrars with clinical experience in stroke medicine examined the hospital records and search ed for the evidence of stroke recorded in these records between 1993/97–2003 . Results Of 520 incident strokes identified between 1993/1997–2003 using record linkage systems in the EPIC-Norfolk , a sample of 250 medical case notes were examined between March and July 2004 . Using the predefined study criteria , there were 191 definite strokes ( 76 % ) , 20 probable strokes ( 8 % ) , 11 possible strokes and 11 cases of transient ischaemic attacks ( 4 % each ) i.e. 233/250 ( 93 % ) with possible or definite stroke or transient ischaemic attacks . Stroke could not be verified using hospital records in 13 cases ( 5 % ) and 4 cases ( 2 % ) had other diagnoses : 3 cases of vascular dementia and 1 case of benign intracranial hypertension . The diagnosis of stroke in 185 out of 250 cases identified in the EPIC-Norfolk ( 74.0 % ) was supported by radiological evidence using WHO criteria . Conclusion Death certificates and hospital record linkage in this British prospect i ve study have a high accuracy or positive predictive value in correctly identifying incident stroke cases BACKGROUND AND PURPOSE The Greater Cincinnati/Northern Kentucky Stroke Study was design ed to be the first large , population -based metropolitan study of temporal trends in stroke incidence rates and outcome within a biracial population . METHODS We are identifying all hospitalized and autopsied cases of stroke and transient ischemic attack ( TIA ) among the 1.3 million inhabitants of a five-county region of Greater Cincinnati/Northern Kentucky for the period 7/1/93 - 6/30/94 . We have already prospect ively monitored for out-of-hospital stroke and TIAs for this same time period at 128 screening sites , including a r and om sample of all primary care physicians and nursing homes in the region . We have already identified all hospitalized and autopsied cases of stroke and TIA among blacks for 1/1/93 - 6/30/93 and report preliminary incidence rates for this 6-month period . RESULTS The overall incidence rate for all first-ever hospitalized or autopsied stroke ( excluding TIAs ) among blacks in the Greater Cincinnati region was 288 per 100000 ( 95 % CI , 250 to 325 , age- and sex-adjusted to 1990 US population ) . The overall incidence rate for first-ever and recurrent stroke ( excluding TIAs ) was 411 per 100000 ( 95 % CI , 366 to 456 ) . By comparison , the overall incidence rate of first-ever stroke among whites in Rochester , Minn , during the period 1985 - 1989 was 179 per 100000 ( 95 % CI , 164 to 194 , age- and -sex adjusted to 1990 US population ) . The incidence rates among blacks in Greater Cincinnati were substantially greater than the rates among whites in Rochester , Minn , for all age categories except ages 75 and older , for which the rates were similar . CONCLUSIONS We conservatively estimate that 731100 first-ever or recurrent strokes occurred in the United States during 1996 . Studies of first-ever as well as total stroke among biracial and representative population s are critical for underst and ing temporal trends in the incidence rate and the burden of stroke in the US population Background and Purpose — Many studies use medical record review for ascertaining outcomes . One large , longitudinal study , the Women ’s Health Initiative ( WHI ) , ascertains strokes using participant self-report and subsequent physician review of medical records . This is re source -intensive . Herein , we assess whether Medicare data can reliably assess stroke events in the WHI . Methods — Subjects were WHI participants with fee-for-service Medicare .  Four stroke definitions were created for Medicare data using discharge diagnoses in hospitalization cl aims : definition 1 , stroke codes in any position ; definition 2 , primary position stroke codes ; and definitions 3 and 4 , hemorrhagic and ischemic stroke codes , respectively . WHI data were r and omly split into training ( 50 % ) and test sets . A concordance matrix was used to examine the agreement between WHI and Medicare stroke diagnosis . A WHI stroke and a Medicare stroke were considered a match if they occurred within ±7 days of each other . Refined analyses excluded Medicare events when medical records were unavailable for comparison . Results — Training data consisted of 24 428 r and omly selected participants . There were 577 WHI strokes and 557 Medicare strokes using definition 1 . Of these , 478 were a match . With regard to algorithm performance , specificity was 99.7 % , negative predictive value was 99.7 % , sensitivity was 82.8 % , positive predictive value was 85.8 % , and & kgr;=0.84 . Performance was similar for test data . Whereas specificity and negative predictive value exceeded 99 % , sensitivity ranged from 75 % to 88 % and positive predictive value ranged from 80 % to 90 % across stroke definitions . Conclusions — Medicare data seem useful for population -based stroke research ; however , performance characteristics depend on the definition selected BACKGROUND Surveillance for stroke/transient ischemic attack ( TIA ) using administrative data has traditionally been limited to reporting patients who had an acute event and were hospitalized . This underestimates the true prevalence because many events do not result in hospitalization . We examined whether the accuracy of administrative data for identifying prevalent stroke/TIA could be improved by using data from both inpatient and outpatient visits . METHODS An administrative data validation reference st and ard was developed through chart abstract ion of 5000 adult patients r and omly sample d from 73,014 patients of 83 family physicians who participate in the Electronic Medical Record Administrative Data Linked Data base ( EMRALD ) , in Ontario , Canada . RESULTS The prevalence of stroke/TIA in our adult population was 3.0 % . An algorithm of 1 hospital record had a sensitivity of 35.3 % ( 27.7%-43.0 % ) and specificity of 99.8 % ( 99.7%-99.9 % ) , whereas an algorithm of 2 physician billings within 1 year or 1 hospitalization had a sensitivity of 68.0 % ( 95 % confidence interval [ CI ] , 60.5%-75.5 % ) and specificity of 98.9 % ( 95 % CI , 98.6%-99.2 % ) for the identification of patients who had ever had a stroke/TIA . We found that hospitalization data underestimated the prevalence of stroke by > 50 % and TIA by > 66 % compared with using both hospitalization and physician cl aims data . CONCLUSIONS The use of outpatient physician cl aims data in addition to hospitalization data improves the sensitivity of administrative data for the identification of prevalent stroke/TIA and may be used to estimate the prevalence of cerebrovascular events in large population s and over time Background Kappa is commonly used when assessing the agreement of conditions with reference st and ard , but has been criticized for being highly dependent on the prevalence . To overcome this limitation , a prevalence-adjusted and bias-adjusted kappa ( PABAK ) has been developed . The purpose of this study is to demonstrate the performance of Kappa and PABAK , and assess the agreement between hospital discharge administrative data and chart review data conditions . Methods The agreement was compared for  r and om sampling , restricted sampling by conditions , and case-control sampling from the four teaching hospitals in Alberta , Canada from ICD10 administrative data during January 1 , 2003 and June 30 , 2003 . A total of 4,008 hospital discharge records and chart view , linked for personal unique identifier and admission date , for 32 conditions of r and om sampling were analyzed . The restricted sample for hypertension , myocardial infa rct ion and congestive heart failure , and case-control sample for those three conditions were extracted from r and om sample . The prevalence , kappa , PABAK , positive agreement , negative agreement for the condition was compared for each of three sample s. Results The prevalence of each condition was highly dependent on the sampling method , and this variation in prevalence had a significant effect on both kappa and PABAK . PABAK values were obviously high for certain conditions with low kappa values . The gap between these two statistical values for the same condition narrowed as the prevalence of the condition approached 50 % . Conclusion Kappa values varied more widely than PABAK values across the 32 conditions . PABAK values should usually not be interpreted as measuring the same agreement as kappa in administrative data , particular for the condition with low prevalence . There is no single statistic measuring agreement that captures the desired information for validity of administrative data . Research ers should report kappa , the prevalence , positive agreement , negative agreement , and the relative frequency in each cell ( i.e. a , b , c and d ) to enable the reader to judge the validity of administrative data from multiple aspects Background and Purpose — Because acute ischemic strokes ( ISs ) are mainly hospitalized , hospital discharge data could be used to routinely follow their incidence management . We aim ed to assess sensitivity and positive predictive value of the  French hospital discharge data base ( HDD )  to identify patients with acute IS using a prospect i ve and exhaustive cohort ( AVC69 ) of acute IS cases . Methods — A selection algorithm based on IS diagnosis coded with the International Classification of Diseases ( ICD-10 ) and cerebral imaging codes was used to identify all hospital stays with the primary diagnosis of IS in the HDD of the university hospitals of the Rhône area . Cases identified through HDD search were compared with IS cases identified through an exhaustive cohort study conducted in the Rhône district and confirmed on medical records review . Results — There were 465 confirmed cases of IS hospitalized in 1 of the 4 university hospitals during the study period . The HDD search identified 313 among those ( true-positive cases ) but missed 152 cases ( false-negative cases ) . The sensitivity of the HDD search was 67.3 % ( 95 % confidence interval , 63.1–71.5 ) , and the positive predictive value was 95.1 % ( 95 % confidence interval , 92.8–97.4 ) . Additionally , HDD search retrieved 16 cases , which were not eventually IS ( false positives ) . Sensitivity was better when patients were hospitalized in neurological departments . Conclusions — The lack of sensitivity to identify acute IS patients  through HDD search does not seem to be accurate enough to vali date the use of these data for incidence estimates . Efforts have to be made to improve the coding quality Background and Purpose — Surveillance is necessary to underst and and meet the future dem and s stroke will place on health care . Administrative data are the most accessible data source for stroke surveillance in Canada . The International Classification of Diseases , 10th revision ( ICD-10 ) coding system has potential improvements over ICD-9 for stroke classification . Our purpose was to compare hospital discharge abstract coding using ICD-9 and ICD-10 for stroke and its risk factors . Methods — We took advantage of a switch in coding systems from ICD-9 to ICD-10 to independently review stroke patient charts . From time periods April 2000 to March 2001 , 717 charts , and from April 2002 to March 2003 , 249 charts were r and omly selected for review . Using a before- and -after time period design , the accuracy of hospital coding of stroke ( part I ) and stroke risk factors ( part II ) using ICD-9 and ICD-10 was compared . We used careful definitions of stroke and its types based on ICD-9 using the fourth and fifth digit modifier codes . Results — Stroke coding was equally good with ICD-9 ( 90 % [ CI95 86 to 93 ] correct ) and ICD-10 [ 92 % ( CI95 88 to 95 correct ) with ICD-10 . There were some differences in coding by stroke type , notably with transient ischemic attack , but these differences were not statistically significant . Atrial fibrillation , coronary artery disease/ischemic heart disease , diabetes mellitus , and hypertension were coded with high sensitivity ( 81 % to 91 % ) and specificity ( 83 % to 100 % ) .  ICD-10  was as good as ICD-9 for stroke risk factor coding . Conclusions — Passive surveillance using administrative data are a useful tool for identifying stroke and its risk factors using both ICD-9 and ICD-10 Background The burden of stroke is high and increasing in China . We modelled variations in , and predictors of , the costs of hospital care for patients with acute stroke in China . Methods and Findings Baseline characteristics and hospital costs for 5,255 patients were collected using the prospect i ve register-based ChinaQUEST study , conducted in 48 Level 3 and 14 Level 2 hospitals in China during 2006–2007 . Ordinary least squares estimation was used to determine factors associated with hospital costs . Overall mean cost of hospitalisation was 11,216 Chinese Yuan Renminbi ( CNY ) ( 
    Output:
    
While most cases of prevalent cerebrovascular disease can be detected using 430 - 438/I60-I69 collectively , acute stroke must be defined using more specific codes .
Most in-hospital deaths and death certificates with stroke as a cause-of-death correspond to true stroke deaths .
Linking vital statistics and hospitalization data may improve the ascertainment of fatal stroke