Comparison of Active and Passive Surveillance for Cerebrovascular Disease
The Brain Attack Surveillance in Corpus Christi (BASIC) Project
Paisith Piriyawat1,
Miriam
majsová1,
Melinda A. Smith1,
Sanjay Pallegar1,
Areej Al-Wabil1,
Nelda M. Garcia1,
Jan M. Risser2,
Lemuel A. Moyé3 and
Lewis B. Morgenstern1,2,
1 Stroke Program, Department of Neurology, University of Texas at Houston, Houston, TX.
2 Department of Epidemiology, School of Public Health, University of Texas at Houston, Houston, TX.
3 Department of Biometry, School of Public Health, University of Texas at Houston, Houston, TX.
Received for publication January 22, 2002; accepted for publication July 25, 2002.
 |
ABSTRACT
|
---|
To provide a scientific rationale for choosing an optimal stroke surveillance method, the authors compared active surveillance with passive surveillance. The methods involved ascertaining cerebrovascular events that occurred in Nueces County, Texas, during calendar year 2000. Active methods utilized screening of hospital and emergency department logs and routine visiting of hospital wards and out-of-hospital sources. Passive means relied on International Classification of Diseases, Ninth Revision (ICD-9), discharge codes for case ascertainment. Cases were validated by fellowship-trained stroke neurologists on the basis of published criteria. The results showed that, of the 6,236 events identified through both active and passive surveillance, 802 were validated to be cerebrovascular events. When passive surveillance alone was used, 209 (26.1%) cases were missed, including 73 (9.1%) cases involving hospital admission and 136 (17.0%) out-of-hospital strokes. Through active surveillance alone, 57 (7.1%) cases were missed. The positive predictive value of active surveillance was 12.2%. Among the 2,099 patients admitted to a hospital, passive surveillance using ICD-9 codes missed 73 cases of cerebrovascular disease and mistakenly included 222 noncases. There were 57 admitted hospital cases missed by active surveillance, including 13 not recognized because of human error. This study provided a quantitative means of assessing the utility of active and passive surveillance for cerebrovascular disease. More uniform surveillance methods would allow comparisons across studies and communities.
cerebral hemorrhage; cerebrovascular accident; cerebrovascular disorders; epidemiologic methods; population surveillance
Abbreviations:
Abbreviations: BASIC, Brain Attack Surveillance in Corpus Christi; ICD-9, International Classification of Diseases, Ninth Revision; ICD-10, International Classification of Diseases, Tenth Revision.
 |
INTRODUCTION
|
---|
In population-based studies of stroke, two key methods may be used for case identification: active surveillance and passive surveillance. Passive surveillance (or "cold pursuit" (1)) ascertains cases by searching hospital discharge diagnoses. Discharge codes from the International Classification of Diseases, Ninth Revision (ICD-9), or its clinical modification are generally utilized for passive surveillance (24). Cases are identified as stroke if they have hospital diagnoses with ICD-9 codes 430438 (table 1). Those patients seen only as outpatients or in the hospitals emergency department are not captured by this method. ICD-9 codes 430438, especially codes 433 and 437, have been shown to have limited validity because of their relatively low accuracy (57). Passive surveillance requires that the patients chart be complete and the ICD-9 code be assigned prior to case ascertainment, therefore adding delay from hospitalization to case abstraction.
Active surveillance (or "hot pursuit" (1)) identifies cases through screening of hospital admission records, emergency department logs, medical wards, and intensive care units and out-of-hospital facilities, including nursing homes, radiology centers, and physicians offices. Case identification using this method is typified by active searching for all potential events. Active surveillance occurs in a more timely fashion than passive surveillance. Use of other sources besides hospital discharge logs allows the capture of community cases of cerebrovascular disease. Screening is the essential step in active surveillance. Appropriate screening terms and rigorous standardized procedures are necessary to minimize the number of missing cases.
We compared active and passive surveillance in a population-based cerebrovascular epidemiology project. We considered the possibility of combining the strengths of both methods in order to maximize case capture and improve efficiency for epidemiologic research on cerebrovascular disease.
 |
MATERIALS AND METHODS
|
---|
The Brain Attack Surveillance in Corpus Christi (BASIC) Project is an ongoing population-based cerebrovascular disease surveillance study in Nueces County, Texas. In Nueces County, 95 percent of the population resides within the city of Corpus Christi. Acute cerebrovascular events (completed ischemic stroke, transient ischemic attack, spontaneous intracerebral hemorrhage, and subarachnoid hemorrhage) are sought among patients admitted to the six hospitals in the area, as well as out-of-hospital sources. These out-of-hospital locations include six emergency departments, 11 nursing homes, three radiology centers, and the office of the medical examiner. Neurology, internal medicine, and family practice offices were also targeted through a series of medical staff and county medical society meetings, newsletters, and faxes. One full-time employee and one half-time employee routinely visit medical offices in the community to encourage nurses and doctors to report all out-of-hospital cerebrovascular disease cases.
The data presented in this paper are for the full calendar year 2000. Exclusion criteria were: age less than 45 years, non-Nueces-County residence (as defined by ZIP code based on US Census data), and cerebrovascular events resulting from trauma.
Active surveillance involved screening of both in-hospital and out-of-hospital events. The "hot pursuit" method was similar to that used in the World Health Organizations MONICA [Monitoring of Trends and Determinants in Cardiovascular Disease] Project (1) for admitted patients. Abstractors received formal training in case identification and had to pass a certifying examination. Routine quality assurance measures included interabstractor reliability measures and comparisons with abstractions performed by the study investigators. Active surveillance occurred the next day for patients seen or admitted on Sunday through Thursday. Patients who requested health care over the weekend were not identified until early in the following week. We did not have data on the proportion of patients identified after discharge, but we suspect that the number was low.
The screening process for hospital admission logs and emergency department visit logs included the use of previously validated cerebrovascular screening diagnostic terms (8). These terms included "altered mental status," "confusion," "dizziness," "falling," "imbalance," "loss of speech," "numbness," "slurred speech," "syncope/fainting/trouble walking," "vertigo," "visual disturbance," "weakness," "cerebrovascular accident," "intracranial hemorrhage," "subarachnoid hemorrhage," "stroke," and "transient ischemic attack." Patients who had health events coded with any of these terms were selected for chart abstraction. In addition to screening of admission and emergency department logs, abstractors routinely visited nursing units where stroke patients were cared for to detect cases not included in admission logs and cases arising during hospitalization.
Case ascertainment by passive surveillance was undertaken using primary and secondary ICD-9 discharge codes for acute cerebrovascular eventsnamely, codes 430438, except those with a fifth digit specification of 0 (xxx.x0), which means "not an acute stroke." In addition, we excluded ICD-9 codes 437.0, 437.2, 437.3, 437.4, 437.5, 437.7, 437.8, and 438, since prior research demonstrated their very poor positive predictive value for acute stroke cases (5) (table 1). Monthly lists of ICD-9 discharge codes of all admissions were obtained from each study hospital. The list of patients with any of these ICD-9 codes was compared with the BASIC active surveillance database. Data on events not identified through active surveillance were screened and abstracted. For this paper, we also obtained discharge ICD-9 codes on all events identified by active surveillance in which patients were admitted to hospitals to determine whether passive surveillance would also have detected these cases.
The medical record of each event identified by in-hospital and out-of-hospital active surveillance was immediately reviewed, and an abstraction form was completed for persons who met eligibility criteria. A verification phase then ensued in which abstractors read key facts from the source documents (emergency department physician and nursing notes, radiology and laboratory notes, and neurology consultation if available) to the study investigators. This included a standardized set of patient clinical facts. On the basis of published cerebrovascular criteria (9), the events were classified as "yes, potential cerebrovascular event" or "no, not a potential cerebrovascular event." Source documents for events verified as potential cerebrovascular events were sent to the coordinating center for a final validation decision by fellowship-trained stroke neurologists based on published criteria (9). In the current paper, "validated" is defined as a cases meeting published criteria for a cerebrovascular event, as determined by stroke neurologists utilizing source documentation. So that bias could be avoided, demographic information, including identifiers and ages, was intentionally made unavailable at the time of verification and validation. The physicians who validated cases were also blinded as to whether the case had been identified through active or passive surveillance.
Positive predictive values were calculated for both screening diagnostic terms and ICD-9 codes. The positive predictive value was calculated as the ratio of true positives to the true positives plus the false positives. The stroke neurologists validation was considered the "gold standard" for comparisons. The accuracy of the ICD-9 codes was further evaluated by their sensitivity and specificity. This project was approved by the University of Texas at Houston Committee for the Protection of Human Subjects, the Corpus Christi/Nueces County Health District, and all participating hospitals.
 |
RESULTS
|
---|
Figure 1 demonstrates the relative contributions of active and passive surveillance to the construction of the study cohort. During calendar year 2000, 6,236 eligible events were identified by active or passive surveillance and had relevant data abstracted. A total of 802 of these events were verified and validated to be acute cerebrovascular disease events. The overall positive predictive value for the method that includes both active and passive surveillance was 13 percent (802/6,236). Of the 6,236 eligible events, 6,095 were identified through active surveillance, and 745 of these were validated as acute cases of cerebrovascular disease (figure 2). These 745 cases included 136 validated cases identified from emergency departments and other out-of-hospital sources. Finally, 141 of the eligible events were identified and abstracted through passive surveillance of ICD-9 codes (figure 3). Of these, 57 were verified and validated as acute cerebrovascular disease events.

View larger version (16K):
[in this window]
[in a new window]
|
FIGURE 1. Contributions of active and passive surveillance in the Brain Attack Surveillance in Corpus Christi (BASIC) Project, Nueces County, Texas, 2000. OOH, out-of-hospital; ED, emergency department.
|
|

View larger version (15K):
[in this window]
[in a new window]
|
FIGURE 2. Active surveillance in the Brain Attack Surveillance in Corpus Christi (BASIC) Project, Nueces County, Texas, 2000. ED, emergency department.
|
|

View larger version (12K):
[in this window]
[in a new window]
|
FIGURE 3. Passive surveillance in the Brain Attack Surveillance in Corpus Christi (BASIC) Project, Nueces County, Texas, 2000. Only case subjects not identified by active surveillance were included in passive surveillance. ICD-9, International Classification of Diseases, Ninth Revision.
|
|
Active surveillance
The overall positive predictive value for active surveillance was 12 percent (745/6,095). Among the 6,095 events identified by active surveillance, 5,578 had a single screening term and 482 had more than one screening term. The positive predictive values for the screening terms are shown in table 2. Two screening terms had a positive predictive value less than 1.0 percent: "imbalance" and "falling." If these two screening terms are removed from the analysis, the overall positive predictive value of active surveillance using a single screening term increases to 16.1 percent (612/3,797), and only eight validated events are lost (1.3 percent or eight of 620). Analysis of the 482 events identified by more than one screening term revealed 14 combinations that appeared more than 10 times. These combinations added 45 validated cases (5.6 percent, 45/802) but did not improve the overall positive predictive value of the screening terms (from 11.1 percent to 11.3 percent).
View this table:
[in this window]
[in a new window]
|
TABLE 2. Single screening diagnostic terms used in surveillance for cerebrovascular disease and their positive predictive values, Nueces County, Texas, 2000
|
|
A total of 2,099 cases with hospital admission were identified, 141 from passive surveillance and 1,958 from active surveillance. We wished to consider what would have occurred if only passive surveillance had been used to screen these admitted hospital patients. While 593 of the 815 cases that the ICD-9 codes identified as cerebrovascular disease cases were later validated as such, 73 (11.0 percent, 73/666) of the admitted cerebrovascular disease cases would have been missed if only passive surveillance had been used. Of the 73 cases missed by passive surveillance, 20 (27 percent) had ICD-9 codes between 430 and 438 but met our exclusion criteria. There were 38 primary ICD-9 codes that identified this diverse group of 73 patients. Table 3 shows the final diagnoses of these 73 admitted hospital patients identified through active surveillance but missed by the ICD-9 codes.
View this table:
[in this window]
[in a new window]
|
TABLE 3. Final diagnoses for hospitalized cerebrovascular disease patients whose cases were missed by passive surveillance, Nueces County, Texas, 2000
|
|
Figure 4 shows the overall results of active surveillance compared with passive surveillance or of both used together. In addition to the 73 admitted hospital cases (26 percent transient ischemic attacks) that would not have been captured if only passive surveillance had been used, ICD-9 discharge codes alone would not have identified the 119 emergency department cases (56 percent transient ischemic attacks) and 17 out-of-hospital cases (53 percent transient ischemic attacks). Therefore, a total of 209 cases (26.1 percent; 46 percent of these transient ischemic attacks) were captured only because active surveillance was utilized in this population-based project. If passive surveillance had been combined with emergency department screening, 90 cases would have been missed (11 percent).

View larger version (24K):
[in this window]
[in a new window]
|
FIGURE 4. Allocation of all validated cases according to their sources of case identification, Brain Attack Surveillance in Corpus Christi (BASIC) Project, Nueces County, Texas, 2000.
|
|
On the other hand, 57 cases (7.1 percent, 57/802) would have been missed if active surveillance had been used exclusively. Of these, 13 were missed because abstractors failed to recognize one of the screening diagnostic terms while they were screening events involving hospital admission. This human error was exceptionally rare (0.7 percent, 13/1,958). The remaining 44 patients were admitted to a hospital without the use of a diagnostic term recognized in the BASIC Project. Table 4 lists the symptoms/diagnoses of these cases.
View this table:
[in this window]
[in a new window]
|
TABLE 4. Admission diagnoses of hospitalized cerebrovascular disease patients whose cases were missed by active surveillance, Nueces County, Texas, 2000
|
|
Passive surveillance
Of the 2,099 screened hospital admissions, 815 events (787 primary diagnoses and 28 secondary diagnoses) had been assigned an ICD-9 discharge diagnosis of 430438 by the hospital. Of these, 666 acute cases of cerebrovascular disease were validated by study neurologists. Only 15 of these validated cases had stroke listed as a secondary diagnosis. There were 222 events incorrectly identified as acute cerebrovascular events. Therefore, the sensitivity and positive predictive value of discharge ICD-9 codes for admitted hospital events were 89.0 percent (593/666) and 72.8 percent (593/815), respectively. We could not calculate the specificity or negative predictive value, since we did not know the total number of patients admitted to all of the hospitals during the study period. When data were restricted to admission events, the sensitivity and positive predictive value of active surveillance were 91.5 percent (609/666) and 31.1 percent (609/1,958), respectively (figure 2).
The ICD-9 codes of patients identified by passive surveillance but missed by active surveillance are shown in table 5. This represents a diverse group of codes. Although ICD-9 codes detected some cases not identified by active surveillance, information on stroke type conveyed by ICD-9 codes was inaccurate in 15.5 percent (92/593) of cases.
View this table:
[in this window]
[in a new window]
|
TABLE 5. International Classification of Diseases, Ninth Revision, codes for 44 cases of cerebrovascular disease missed* by active surveillance, Nueces County, Texas, 2000
|
|
 |
DISCUSSION
|
---|
This study found that active surveillance provides better case identification than passive surveillance alone for population-based surveillance of cerebrovascular disease. The percentage of cases missed by passive surveillance alone was 26.1 percent, as compared with a combination of active and passive surveillance. The major component missing from passive surveillance was cases of patients who were treated in the emergency department and not hospitalized (n = 119), 56 percent of which were transient ischemic attacks. If passive surveillance is combined with emergency department screening, the percentage of missed cases drops to 11 percent. This compromise may allow the efficiency of passive surveillance with high case capture rates for cerebrovascular disease.
Active surveillance missed 7.1 percent of cases in comparison with the combination of active and passive surveillance. Active surveillance also allows rapid identification of cerebrovascular disease patients for timely chart abstraction, interviews, blood drawing, and case validation. The entire process of surveillance is greatly accelerated through the use of active surveillance methods. The ability to ascertain the time of symptom onset from patients and their caregivers for calculation of delay to treatment is also enhanced through this method. If a 93 percent case ascertainment rate is sufficient, resources for screening are available, and speed is an issue, then active surveillance for cerebrovascular disease is the method of choice. However, active surveillance alone was not sufficient to include more than 95 percent of cases, a percentage some have proposed as a minimum cutoff for population-based surveillance (1).
Our inclusion of transient ischemic attack as a cerebrovascular event was purposeful, because recognition of the importance of transient ischemic attack on outcome is growing (10). Transient ischemic attacks are more difficult to diagnose, and such patients are less likely to be admitted to a hospital. This explains, in part, the relative success of active surveillance efforts compared with passive surveillance in this project.
The screening diagnostic terms used in active surveillance in this project were shown to be useful, capturing 91.5 percent (609/666) of admitted hospital cases. The few cases that were missed (table 5) do not lend themselves to additional screening terms without remarkable reduction in the efficiency of active surveillance. Although it is highly informative, active surveillance is associated with extensive screening and is therefore considered a low-efficiency procedure. We used previously validated cerebrovascular screening diagnostic terms (8). To increase the efficiency of active surveillance, removal of the terms "falling" and "imbalance" should be considered. Both of these terms had a positive predictive value less than 1 percent, requiring 1,781 screenings.
Despite exhaustive efforts to identify out-of-hospital cases, we found only 17 validated events. This suggests that the financial resources utilized for the screening of non-emergency-department out-of-hospital cases may be better directed elsewhere. The exception to this conclusion may be found in projects such as BASIC, where racial/ethnic or socioeconomic comparisons may suggest that certain groups are more or less likely to seek treatment at outpatient facilities for cerebrovascular care (11).
The advantage of passive surveillance is its efficiency. As table 3 illustrates, 73 percent of cases identified by means of ICD-9 codes were truly cases of cerebrovascular disease, as compared with 12 percent of cases identified through screening diagnostic terms that are truly cerebrovascular (figure 2). Passive surveillance also allows review of a completed chart and can be carried out at any point in the future, commencing a few months after the discharge date. Passive surveillance with ICD-9 codes misses all out-of-hospital cases, including nonadmitted emergency department patients. In this study, 14.8 percent (119/802) of patients were seen and discharged from emergency departments. Passive surveillance also missed a substantial number of admitted cerebrovascular disease patients (11 percent). The accuracy of ICD-9 diagnosis has been challenged by investigators in several previous studies (57). The results of this study echo this observation. ICD-9 codes also falsely identified noncerebrovascular cases as cases of stroke and misclassified stroke type (table 2). The recently issued International Classification of Diseases, Tenth Revision (ICD-10) (12), consists of a few changes with respect to cerebrovascular diseases. Transient ischemic attack, previously listed under "cerebrovascular diseases" (ICD-9 code 435), was moved to the section "diseases of the nervous system" (ICD-10 code G45). Cerebrovascular disorders remain part of "diseases of the circulatory system" (ICD-10 codes I60I69). In order to use the ICD-10 for surveillance, we need to include codes I60I69 and G45. No data suggest that the use of the ICD-10 will improve case identification.
The combination of active and passive surveillance offers the highest sensitivity, which is often the most critical goal of surveillance efforts. Combining both strategies also combines their weaknesses: inefficiency and time delay. Ideally, we could identify a few specific ICD-9 codes with which to supplement active surveillance efforts. Unfortunately, table 5 shows that a diverse group of ICD-9 codes would be needed. Similarly, there does not appear to be an easy addition to the active surveillance efforts to improve case capture as shown in table 4. Adding screening terms such as "chest pain" and "hypertension" would prohibitively reduce efficiency while adding few extra cases.
There are several limitations to this studys methodology. While we assertively searched for nonadmitted community cases, we cannot be absolutely sure that all cases were captured. The out-of-hospital component relied on physician reports stimulated by frequent reminders. While we were pleased by the response we received, we may have missed non-emergency-department out-of-hospital cases. This study would have benefited from a cost-effectiveness analysis. While it is certain that more personnel are required for active surveillance than for passive surveillance, the true cost difference is not known.
Of all of the cerebrovascular disease cases in this study, 14.8 percent were identified in the emergency department and the patients were discharged from the emergency department without hospital admission. Of these cases, 43 percent were completed strokes. Studies have shown that community emergency department practice frequently deviates from published guidelines (13). While these patients probably had minimal deficits, maximization of secondary stroke prevention would suggest the need for hospital admission and expeditious evaluation of these patients.
A variety of methods for surveillance of cerebrovascular disease have been used throughout the world (table 6). Since methods of case ascertainment vary so widely, it is difficult to compare rates, even within a single country. The current study provides a scientific rationale for the selection of active surveillance, passive surveillance, or a combination of these methods. Standardization of cerebrovascular surveillance methods would allow a more accurate comparison of international rates across studies and foster collaborative public health efforts whose results could be easily tracked.
 |
ACKNOWLEDGMENTS
|
---|
This study was supported by grant RO1 NS38916 from the National Institute of Neurological Disorders and Stroke.
 |
NOTES
|
---|
Reprint requests to Dr. Lewis B. Morgenstern, University of Michigan Health System, TC 1920/0316, 1500 East Medical Center Drive, Ann Arbor, MI 48109-0316 (e-mail: lmorgens{at}umich.edu). 
 |
REFERENCES
|
---|
- WHO MONICA Project. MONICA manual. Part IV: event registration. Section 3: event registration quality assurance methods (November 1990). [Accessed online December 26, 2001]. Geneva, Switzerland: World Health Organization, 1990. (World Wide Web URL: www.ktl.fi/publications/monica/manual/part4/iv-3.htm).
- Sacco RL, Boden-Albala B, Gan R, et al. Stroke incidence among white, black, and Hispanic residents of an urban community: The Northern Manhattan Stroke Study. Am J Epidemiol 1998;147:25968.[Abstract]
- Ellekjær H, Holmen J, Indredavik B, et al. Epidemiology of stroke in Innherred, Norway, 1994 to 1996: incidence and 30-day case fatality rate. Stroke 1997;28:21804.[Abstract/Free Full Text]
- Broderick J, Brott T, Kothari R, et al. The Greater Cincinnati/Northern Kentucky Stroke Study: preliminary first-ever and total incidence rates of stroke among blacks. Stroke 1998;29:41521.[Abstract/Free Full Text]
- Morgenstern LB, Pandey DK, Smith MA, et al. Greater stroke rate during hospitalization for acute heart disease among Mexican Americans than non-Hispanic whites. Neuroepidemiology 1999;18:2417.[ISI][Medline]
- Derby CA, Lapane KL, Feldman HA, et al. Possible effect of DRGs on the classification of stroke: implications for epidemiological surveillance. Stroke 2001;32:148791.[Abstract/Free Full Text]
- Benesch C, Witter DM Jr, Wilder AL, et al. Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease. Neurology 1997;49:6604.[Abstract]
- Morgenstern LB, Wein TH, Smith MA, et al. Comparison of stroke hospitalization rates among Mexican Americans and non-Hispanic whites. Neurology 2000;54:20002.[Abstract/Free Full Text]
- Asplund K, Tuomilehto J, Stegmayr B, et al. Diagnostic criteria and quality control of the registration of stroke events in the MONICA project. Acta Med Scand Suppl 1988;728:2639.[Medline]
- Johnston SC, Gress DR, Browner WS, et al. Short-term prognosis after emergency department diagnosis of TIA. JAMA 2000;284:29016.[Abstract/Free Full Text]
- Wein TH, Smith MA, Morgenstern LB. Race/ethnicity and location of stroke mortality: implications for population based studies. Stroke 1999;30:15015.[Abstract/Free Full Text]
- World Health Organization. ICD-10: international statistical classification of diseases and related health problems. Geneva, Switzerland: World Health Organization, 1992.
- Burgin WS, Staub L, Chan W, et al. Acute stroke care in non-urban emergency departments: The TLL Temple Foundation Stroke Project. Neurology 2001;57:200612.[Abstract/Free Full Text]
- Boysen G, Nyboe J, Appleyard M, et al. Stroke incidence and risk factors for stroke in Copenhagen, Denmark. Stroke 1988;19:134553.[Abstract]
- Petty GW, Brown RD, Whisnant JP, et al. Ischemic stroke subtypes: a population-based study of incidence and risk factors. Stroke 1999;30:251316.[Abstract/Free Full Text]
- Bamford J, Sandercock P, Dennis M, et al. A prospective study of acute cerebrovascular disease in the community: the Oxfordshire Community Stroke Project 198186. 1. Methodology, demography and incident cases of first-ever stroke. J Neurol Neurosurg Psychiatry 1988;51:137380.[Abstract]
- Williams GR. Incidence and characteristics of total stroke in the United States. BMC Neurol 2001;1:2.[Medline]
- Smadja D, Cabre P, May F, et al. ERMANCIA: epidemiology of stroke in Martinique, French West Indies. Part I: methodology, incidence, and 30-day case fatality rate. Stroke 2001;32:27417.[Abstract/Free Full Text]
- Feigin VL, Wiebers DO, Whisnant JP, et al. Stroke incidence and 30-day case fatality rates in Novosibirsk, Russia, 1982 through 1992. Stroke 1995;26:9249.[Abstract/Free Full Text]
- Thorvaldsen P, Asplund K, Kuulasmaa K, et al. Stroke incidence, case fatality, and mortality in the WHO MONICA project. Stroke 1995;26:3617.[Abstract/Free Full Text]
- Thrift AG, Dewey HM, Macdonell RA, et al. Stroke incidence on the East Coast of Australia. The North East Melbourne Stroke Incidence Study (NEMESIS). Stroke 2000;31:208792.[Abstract/Free Full Text]
- Kramer S, Diamond EL, Lilienfeld AM. Patterns of incidence and trends in diagnostic classification of cerebrovascular disease in Washington County, Maryland, 19691971 to 19741976. Am J Epidemiol 1982;115:398411.[Abstract]
- Rosamond WD, Folsom AR, Chambless LE, et al. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke 1999;30:73643.[Abstract/Free Full Text]
- Kittner SJ, White LR, Losonczy KG, et al. Black-white differences in stroke incidence in a national sample: the contribution of hypertension and diabetes mellitus. JAMA 1990;264:126770.[Abstract]