OBJECTIVE

To estimate the impact of diabetes on mortality in patients after first stroke event.

RESEARCH DESIGN AND METHODS

Using claims data from a nationwide statutory health insurance fund (Gmünder ErsatzKasse), we assessed all deaths in a cohort of 5,757 patients with a first stroke between 2005 and 2007 (69.3% male, mean age 68.1 years, 32.2% with diabetes) up to 2009. By use of Cox regression, we estimated time-dependent hazard ratios (HRs) to compare patients with and without diabetes stratified by sex.

RESULTS

The cumulative 5-year mortality was 40.0 and 54.2% in diabetic men and women, and 32.3 and 38.1% in their nondiabetic counterparts, respectively. In males, mortality was significantly lower in diabetic compared with nondiabetic patients in the first 30 days (multiple-adjusted HR 0.67 [95% CI 0.53–0.84]). After approximately a quarter of a year, the diabetes risk increased, yielding crossed survival curves. Later on, mortality risk tended to be similar in diabetic and nondiabetic men (1–2 years: 1.42 [1.09–1.85]; 3–5 years: 1.00 [0.67–1.41]; time dependency of diabetes, P = 0.008). In women, the pattern was similar; however, time dependency was not statistically significant (P = 0.89). Increasing age, hemorrhagic stroke, renal failure (only in men), levels of care dependency, and number of prescribed medications were significantly associated with mortality.

CONCLUSIONS

We found a time-dependent mortality risk of diabetes after first stroke in men. Possible explanations may be type of stroke or earlier and more intensive treatment of risk factors in diabetic patients.

Cardiovascular events, such as strokes, are significant sources of morbidity in the diabetic population. Although the reduction of stroke in diabetes has frequently been cited as a primary objective by health systems and organizations (1), diabetic individuals still have an approximately twofold stroke risk compared with nondiabetic patients (26). Avoidance of stroke should be targeted for both the associated economic consequences (high costs due to repeated hospitalizations, rehabilitation, home care, and social service support) and quality-of-life issues.

Diabetes has been considered a risk factor for higher mortality in patients after stroke (79). However, to the best of our knowledge, there are only a few population- or insurance-based studies that investigate mortality after stroke in the diabetic population compared with the nondiabetic population (1012), and these studies analyze only single subtypes of stroke or shorter periods of follow-up. The studies find a higher mortality in the diabetic population for the 28-day fatality as well as for periods up to 1 year. The latter finding is in contrast to the short-term mortality after beginning renal replacement therapy and after amputation, where a time-dependent impact of diabetes for mortality has been found, with lower or virtually the same mortality in diabetic patients during the first period (1315). However, thereafter, diabetes became a risk factor. For mortality after beginning renal replacement therapy, differences between men and women have been observed (1315).

Hence, the aim of our study was to evaluate the mortality risk in diabetic and nondiabetic individuals after a first stroke up to 5 years of follow-up in Germany, using claims data from a nationwide statutory health insurance fund. We further focused on differences between men and women.

Database and identification of patients

We used data of a cohort of patients with incident stroke, for which analyses on incidence and attributable risks have been published elsewhere (2). In brief, these patients were derived from a statutory health insurance company, the Gmünder ErsatzKasse (GEK), which insures ∼1.6 million people located in all regions of Germany (1.9% of the German population). We included only first strokes between 2005 and 2007 in persons with a period free from stroke of at least 1 year. Strokes (cerebral ischemia, intracerebral hemorrhage, subarachnoid hemorrhage, and stroke of uncertain cause, but no transient ischemic attacks) were defined following the World Health Organization definition (16), using specific ICD-10 codes of hospital admissions (I60–I61, I63–I64, and including I62 to avoid missing unspecific cases). Diabetes status was assessed according to an established algorithm that has been used in several studies analyzing claims data of German statutory health insurance funds (13,17). A person was identified as having diabetes if at least one of the following characteristics was fulfilled within 12 months in the observation period between 2004 and 2007: 1) diabetes diagnosis (ICD E10–E14) in at least three of four consecutive quarters in outpatient care, 2) at least two prescriptions of antihyperglycemic medication (Anatomical Therapeutic Chemical code A10) within 12 months, or 3) at least one prescription of an antihyperglycemic medication and one diabetes diagnosis or one measurement of blood glucose or HbA1c within 12 months.

In the previously published analysis, data of 6,160 patients (n = 1,932 had diabetes) with an incident stroke between 2005 and 2007 were available. For this study, only persons aged ≥30 years were included (n = 6,100). We further excluded all persons coinsured as a dependent and members who left the GEK for reasons other than death within the study period (n = 343). Both criteria were applied to avoid informative censoring in the survival analysis (e.g., an insurance period ends because of death, but this reason might not be documented in these cases). Our final cohort, therefore, consisted of 5,757 patients with a first stroke during 2005 to 2007 and follow-up until the end of 2009.

Covariates

From the claims data, we assessed reimbursed medications as well as services of the long-term care insurance for the year preceding the index event (the first stroke). Treatment with cardiovascular drugs (β-blockers, ACE inhibitors/sartans, and calcium antagonists) and antihyperglycemic drugs (insulin and oral antihyperglycemic agents) was assessed. We determined the number of distinct medications prescribed within this period as a comorbidity measure because it has been shown to be a good predictor of mortality (18). Services from the German long-term care insurance are provided to those who require support in the activities of daily living, including personal hygiene, eating, mobility, and—separate from personal care—housekeeping. There are three levels of care dependency related to the estimated time required for assistance and indicating considerable (level 1), severe (level 2), and extreme (level 3) care dependency (19). The highest level of dependency within the year before the index date was included as a proxy for functional and cognitive impairments.

Furthermore, we assessed the following outpatient diagnoses: hypertension (ICD-10: I10–I15), chronic ischemic heart diseases (ICD-10: I20–I21, I25), and renal failure (ICD-10: N18–N19) coded according to a previous study using German claims data (20). At least one of these diagnoses had to be recorded in a 1-year period (including the quarter of the index date and the preceding three quarters). Quarters had to be chosen because this is the basic time period for coding diagnoses in outpatient care in Germany.

Statistical analysis

The main analyses were performed stratified for men and women. The outcome of interest was the time from the first stroke up to death or the end of the study period (31 December 2009), whichever came first. We assessed crude survival with the Kaplan-Meier estimator, stratified for diabetes as well as for sex. The appropriateness of the Cox proportional hazards assumption was further visualized using log-log survival plots, that is, plotting log (-log(S(t)) against log(t). If the assumption is fulfilled, the curves should be parallel. Furthermore, we tested the proportional hazards assumption via the test proposed by Grambsch and Therneau (21). Because we expected that the interaction between diabetes and time was statistically significant, which means that the proportional hazards assumption was violated, we performed Cox regression using discrete time intervals to model the time dependency of diabetes (22). We estimated time-dependent hazard ratios (HRs) and 95% CIs in multivariate analyses. As predictors, we included diabetes, interaction of diabetes with the discrete time intervals (30 days and 6, 12, 24, 36, and 60 months), and age (as continuous variable). We chose the time intervals in line with previous studies to be able to compare our results and on the basis of clinical experience. In a second model, type of stroke (ischemic, hemorrhagic, and not specified), number of prescribed medications (as continuous variable), level of care dependency (four categories), and the above-stated outpatient diagnoses for hypertension, coronary heart diseases, and renal failure were added as further independent variables.

All analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC). The results of the Cox models were verified using R (A Language and Environment for Statistical Computing, Release 2.12.1, R Foundation for Statistical Computing, http://www.R-project.org).

The study was conducted according to the principles expressed in the Declaration of Helsinki. We considered the STROBE statement (Strengthening the Reporting of Observational Studies in Epidemiology) and the criteria of a national good practice guideline (23,24). The use of health insurance claims data for scientific research is regulated by the German Code of Social Law (SGB X). Because our study was based on pseudonymous data, we did not have to obtain informed consent. According to the Good Practice of Secondary Data Analysis, a national guideline for the use of administrative databases, no approval of an ethical committee was required (24).

Baseline characteristics of the study population

Table 1 shows the characteristics of the 5,757 individuals with a first stroke between 2005 and 2007 in total as well as stratified for diabetes and sex. Cerebral infarction was by far the most common type of stroke, followed by cerebral hemorrhage and subarachnoid hemorrhage. Comorbidities, such as hypertension and coronary heart disease, were predominant among these individuals. Approximately 13% were at least considerable (level 1) care dependent. On average, 7.2 distinct medications were prescribed.

Table 1

Description of the study population: GEK insurants with first incident stroke during 2005 to 2007, stratified for diabetes and sex

Description of the study population: GEK insurants with first incident stroke during 2005 to 2007, stratified for diabetes and sex
Description of the study population: GEK insurants with first incident stroke during 2005 to 2007, stratified for diabetes and sex

For both sexes, diabetic patients were older than nondiabetic individuals and had more ischemic strokes compared with nondiabetic patients. The latter was particularly true for women. Persons with diabetes were more likely to have a diagnosis of hypertension, coronary heart disease, or renal failure, with similar differences among men and women. They also had a higher level of care dependency, which was particularly pronounced in the female population. Likewise, diabetic patients had an almost twofold higher intake of drug prescriptions for both sexes, which was also true for cardiovascular medications, such as β-blockers, ACE inhibitors/sartans, and calcium antagonists.

The mean observation time was 2.66 years (25 and 75% quartiles 1.98 and 3.83, respectively). Overall, 1.828 individuals died within the study period of up to 5 years, including 470 and 698 men as well as 264 and 396 women with and without diabetes, respectively. The cumulative mortalities, including the population at risk, are presented in Table 2.

Table 2

Crude mortality estimates after first incident stroke: GEK insurants during 2005–2007, stratified for sex and diabetes

Crude mortality estimates after first incident stroke: GEK insurants during 2005–2007, stratified for sex and diabetes
Crude mortality estimates after first incident stroke: GEK insurants during 2005–2007, stratified for sex and diabetes

Impact of diabetes on survival and mortality

Figure 1 shows the Kaplan-Meier curves and the log-log survival plots. We present both since in the log-log plots, the early period after stroke event can be seen, whereas the Kaplan-Meier curves give a better picture of the later period. In men, the crude relative mortality risk as a result of diabetes was significantly time dependent (P = 0.002): in the first month after stroke, diabetic individuals had better survival, but thereafter, mortality risk in diabetic men increased, resulting in a higher survival in nondiabetic men. The curves cross each other after approximately a quarter of a year. After ∼3 years, the curves seem to become more convergent again, which means that the difference between diabetic and nondiabetic men is no longer visible. A significant time dependency of diabetes on mortality could also be seen in multivariate analysis (P = 0.008). Adjusted for age, it yields a significant decreased mortality risk in diabetes within the first month and an increased mortality risk in diabetes between 1 month and 3 years of follow-up, which was no longer the case after 3 to 5 years (model 1, Table 3). After further adjustment for comorbidities, level of care dependency, number of prescribed medications, and subtype of stroke, relative risks decreased somewhat but remained significantly increased between 1 and 3 years (model 2, Table 3).

Figure 1

A: Kaplan-Meier estimates of crude survival after first incident stroke for male GEK insurants, Germany, 2005–2007. B: Kaplan-Meier estimates of crude survival after first incident stroke for female GEK insurants, Germany, 2005–2007. C: Crude log-log survival curves after first incident stroke for male GEK insurants, Germany, 2005–2007. D: Crude log-log survival curves after first incident stroke for female GEK insurants, Germany, 2005–2007. SDF, survival distribution function.

Figure 1

A: Kaplan-Meier estimates of crude survival after first incident stroke for male GEK insurants, Germany, 2005–2007. B: Kaplan-Meier estimates of crude survival after first incident stroke for female GEK insurants, Germany, 2005–2007. C: Crude log-log survival curves after first incident stroke for male GEK insurants, Germany, 2005–2007. D: Crude log-log survival curves after first incident stroke for female GEK insurants, Germany, 2005–2007. SDF, survival distribution function.

Close modal
Table 3

Predictors for mortality after first incident stroke, Cox regression GEK insurants during 2005–2007, stratified for sex

Predictors for mortality after first incident stroke, Cox regression GEK insurants during 2005–2007, stratified for sex
Predictors for mortality after first incident stroke, Cox regression GEK insurants during 2005–2007, stratified for sex

In women, there is a quite similar pattern. However, time dependency was not statistically significant in crude (P = 0.08) or multivariate analysis (P = 0.89). The curves do cross, albeit only slightly, in the first week of follow-up, and the Cox model shows no significant decreased HR in the first months after stroke. Nevertheless, the relative risk of mortality in the fully adjusted model was significantly increased for diabetic women between 6 months and 1 year as well as between 2 and 3 years, with an almost twofold increased risk of diabetic women for the first time interval. Again, we found no significant differences between 3 and 5 years of follow-up.

Increasing age, renal failure (only in men), levels of care dependency, number of prescribed medications, and hemorrhage stroke were positively associated with mortality in the fully adjusted model (model 2, Table 3). In contrast, mortality was significantly lower in patients with a diagnosis of hypertension for both sexes.

Study findings and implications

In this study based on data of a nationwide health insurance fund, we analyzed survival in patients with incident stroke in Germany during a period of up to 5 years (2005–2009), with a focus on diabetes as a predictor. As expected, we found a high mortality in this population. After 5 years of follow-up, more than one-third of the patients in our cohort had died. It is interesting that the influence of diabetes in our study was significantly time dependent in men: in the first 30 days after incident stroke, mortality was lower in diabetic than in nondiabetic individuals. Thereafter, there was an increasing trend of diabetes risk during observation time, and after approximately a quarter of a year, diabetic individuals had a higher mortality than nondiabetic individuals. After 3 years, the mortality risk tended to become equal. In women, the pattern was similar; however, there was no statistically significant time dependency. Age, renal failure (only in men), level of care dependency, number of prescribed drugs, and hemorrhagic stroke were significantly associated with mortality; however, they did not alter the association between diabetes and mortality. Our results remained almost unchanged in several sensitivity analyses, for example, using logistic regression models with the variable log(time) as well as time as a linear predictor (data not shown).

Looking for an explanation for our finding that mortality in the first 30 days after stroke was lower in diabetic men, one may find several possible hypotheses. First, one could argue that diabetic patients are more closely monitored by several specialists because of their chronic disease. They have more comorbidities, as indicated by medications and outpatient diagnoses; however, if problems arise, they might be identified and treated earlier. In Germany, nationwide disease management programs for diabetes have been implemented since 2003. These programs define contents and time frames for the treatment of diabetes and its complications, as well as the associated cardiovascular risk factors. In this context, the observed larger number of prescribed medications in diabetic patients also might hint at more aggressive management of cardiovascular risk factors. The question remains, however, as to why diabetic men should have a higher benefit than diabetic women. Women with a stroke event are older than men and more likely to be in long-term care; hence, they might be less likely to be included in a disease management program (25). Furthermore, it might be that cardiovascular diseases in particular are treated earlier in younger patients and in men. Increasing age and female sex have been found to be related to a prolonged delay of emergency care in acute stroke events (26,27). This hypothesis is further supported by our observation that the impact of diabetes seems to differ between younger and older individuals. In stratified models, there was a more pronounced time dependency in individuals aged ≤70 compared with those >70 years. In individuals aged ≤70 years, mortality during the first 30 days was lower in diabetic compared with nondiabetic individuals in both men and women (even though the time dependency was significant only in men), while this was not the case in patients >70 (data not shown). Second, the type of stroke may play a role. Diabetic individuals were more likely to have an ischemic stroke. In previously published series, patients with ischemic strokes had a lower case fatality (mortality during the first 28 or 30 days after stroke) than patients with a hemorrhage stroke, whereas in the period after 30 days, mortality was higher after ischemic strokes (28). Third, hypertension was more prevalent in diabetic persons. Previously known hypertension at the time of the stroke event has been reported to be significantly associated with a decreased mortality for both sexes, possibly explained by a better tolerance toward higher admission blood pressure in those individuals, which might be clinically more relevant for the early outcome of hemorrhagic than ischemic strokes (29,30). Fourth, other factors may play a role, such as a higher prevalence of obesity among diabetic individuals undergoing stroke compared with their nondiabetic counterparts (31). It is known that obesity and overweight have a potential protective effect in elderly stroke patients (32,33). These phenomena should have larger effects in men since the degree of obesity is commonly higher in men than in women. However, in our data, we have no information about detailed clinical or lifestyle variables and only limited information about history of coronary events, chronic heart failure, and renal function. Also, exact causes of deaths cannot be determined by our data.

In the period after 30 days, the mortality risk in individuals with diabetes compared with those without diabetes increased. The observation that in the 3 to 5 years after stroke there was no longer a difference in mortality between individuals with and without diabetes in both men and women may be due to a lack of power resulting from lower case numbers. However, it might be explained by the fact that individuals who survive 3 years are healthier, independent from their diabetes status.

Comparison with other studies

The mortality after 30 days (case fatality, 10.5%) as well as the 1-, 2-, and 5-year mortalities in our study (19.5, 25.1, and 37.3%, respectively) were well in line with the findings of other more recent studies and, as expected, lower than earlier studies. Case fatalities in the literature ranged between 10 and 22% (7,9,34), and 1- and 5-year mortalities were 27 and 53%, respectively (10). Only a few studies investigated mortality after stroke in diabetic and nondiabetic patients (1012,35,36); however, these studies were in part clinic based and analyzed only single subtypes of stroke or shorter periods of follow-up. In the study by Rautio et al. (10), only case fatality was investigated. During the period from 1985 to 1987, case fatality was 18% in diabetic and 15% in nondiabetic patients, with higher mortality in women than in men. However, in the period from 2000 to 2003, case fatality was ∼15% in diabetic women, whereas in diabetic men as well as in nondiabetic men and women, it was ∼10%. In our study, case fatality was ∼9% in diabetic as well as nondiabetic men, and 16 and 13% in diabetic and nondiabetic women, respectively. Thus, we are in line with the Rautio et al. (10) study that finds diabetic women have a higher excess risk to die within the first course after stroke; on the other hand, Rautio et al. (10) did not find lower risks for diabetic men. A further study analyzes the 3-month mortality in a clinic-based sample and finds diabetes to be a significant predictor of mortality (35).

To the best of our knowledge, there is no population- or insurance-based study that analyzes diabetic and nondiabetic patients for longer periods. Several studies evaluate diabetes as a predictor of mortality after incident stroke, but with conflicting results. Although Hart et al. (8) found diabetes to be associated with mortality after stroke, Benatru et al. (7) and Petty et al. (9) did not find an association. Kamalesh et al. (11) analyzed the mortality after discharge from the hospital for a longer period, but only ischemic strokes were included in their study. Kaplan-Meier survival plots did not show a difference between individuals with and without diabetes during the first 60 days after discharge but did reflect lower survival in individuals with diabetes compared with those without diabetes after 1 year (11). The latter is in line with our results. Likewise, Gunarathne et al. (36) analyzed the 5-year mortality in individuals with and without diabetes after ischemic strokes. The 5-year mortality was significantly increased 1.6-fold in individuals with diabetes compared with those without diabetes; however, the study subjects were a clinic-based sample of migrant South Asian patients (36). Winell et al. (12) analyzed the 28-day and 1-year fatality after stroke, yet they included only ischemic strokes in their study. Both were significantly increased in individuals with diabetes compared with those without diabetes, without differences between men and women (12). Thus, study results remain conflicting, and further studies are warranted to confirm and explain our findings.

Study limitations and strengths

Several limitations have to be considered. First, in particular during the last years of observation and especially in women, the case numbers are low, leading to a lack of power to detect statistically significant differences between patients with and without diabetes. Second, we cannot exclude misclassification when we define patients with diabetes because our identification criteria had to be fulfilled within 12 months in the observation period between 2004 and 2007 and not solely before the first stroke. On the other hand, diabetes is often identified for the first time in hospital stays as a result of typical complications, such as strokes, and these patients would not be classified as patients with diabetes if we used only the period before the event. However, we performed a sensitivity analysis, defining a person as having diabetes when our criteria were fulfilled within the 12 months before the first stroke. We found that approximately 9 of 10 diabetic patients already fulfilled our criteria before their index stroke. Furthermore, results of the mortality analysis remained unchanged. Third, we studied stroke survivors, and the number of fatal strokes may differ among those with and without diabetes. This may be an explanation for the reduced mortality seen within the first 30 days among patients with diabetes. However, on the basis of data from the German stroke registry as well as from several other countries, it can be assumed that the number of fatal strokes and strokes that are treated outside the hospital are small. Approximately 95% of stroke patients are hospitalized in clinics and, thus, identified by our data (40). Fourth, information about clinical variables (e.g., blood glucose and diabetes duration) and patient lifestyle (e.g., smoking and physical activities) is not available in the database. However, we included number of prescribed drugs as well as outpatient diagnoses of relevant comorbidities and level of care dependency. Fifth, a translation of our results to other populations should be performed with caution since it is known that differences in morbidity as well as demographic and socioeconomic variables exist between health insurance funds (37,38). However, the incidence of stroke in our population was well in line with the incidence of stroke in a well-designed regional register-based study (2,39,40). Furthermore, the population has been used for several analyses regarding comorbidities in diabetes (2,13,14).

The main strength of our study is that we were able to analyze a large dataset without selection with respect to diabetes complications that could be followed up to 5 years.

In conclusion, in our German study, based on data from a nationwide health insurance fund, we found a high mortality in patients with a first stroke. It is interesting that the influence of diabetes was time dependent in men: in approximately the first quarter of a year after incident stroke, mortality was lower in diabetic than in nondiabetic individuals. Thereafter, diabetic patients had a higher mortality than nondiabetic patients, and after ∼3 years, there was a convergence. In women, the pattern seems to be similar: no significant time dependency was found. Our observation is in line with findings for mortality in diabetic compared with nondiabetic patients after beginning renal replacement therapy and amputation. Possible explanations may be differences in the type of stroke or in earlier and more intensive treatment of distinct cardiovascular risk factors in diabetic patients, in particular men. Patients that survive up to 3 years after stroke might be healthier, independent of their diabetes status. However, results remain conflicting, and further studies are warranted to confirm and explain the results.

This project was supported by a grant from the German Ministry of Health.

The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

No potential conflicts of interest relevant to this article were reported.

A.I. initiated the study, developed the protocol, and wrote the manuscript. H.C. coordinated the data analysis and performed the statistical analysis. S.M. provided clinical expertise. G.G. contributed to the data management and the discussion. F.H. developed the study protocol and coordinated the data analysis. All authors commented on drafts of the manuscript. A.I. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
American Diabetes Association
.
Standards of medical care in diabetes—2011
.
Diabetes Care
2011
;
34
(
Suppl. 1
):
11
61
2.
Icks
A
,
Scheer
M
,
Genz
J
,
Giani
G
,
Glaeske
G
,
Hoffmann
F
.
Stroke in the diabetic and non-diabetic population in Germany: relative and attributable risks, 2005-2007
.
J Diabetes Complications
2011
;
25
:
90
96
[PubMed]
3.
Almdal
T
,
Scharling
H
,
Jensen
JS
,
Vestergaard
H
.
The independent effect of type 2 diabetes mellitus on ischemic heart disease, stroke, and death: a population-based study of 13,000 men and women with 20 years of follow-up
.
Arch Intern Med
2004
;
164
:
1422
1426
[PubMed]
4.
Donnan
GA
,
Fisher
M
,
Macleod
M
,
Davis
SM
.
Stroke
.
Lancet
2008
;
371
:
1612
1623
[PubMed]
5.
Mulnier
HE
,
Seaman
HE
,
Raleigh
VS
, et al
.
Risk of stroke in people with type 2 diabetes in the UK: a study using the General Practice Research Database
.
Diabetologia
2006
;
49
:
2859
2865
[PubMed]
6.
Zhang
Y
,
Galloway
JM
,
Welty
TK
, et al
.
Incidence and risk factors for stroke in American Indians: the Strong Heart Study
.
Circulation
2008
;
118
:
1577
1584
[PubMed]
7.
Benatru
I
,
Rouaud
O
,
Durier
J
, et al
.
Stable stroke incidence rates but improved case-fatality in Dijon, France, from 1985 to 2004
.
Stroke
2006
;
37
:
1674
1679
[PubMed]
8.
Hart
CL
,
Hole
DJ
,
Smith
GD
.
Comparison of risk factors for stroke incidence and stroke mortality in 20 years of follow-up in men and women in the Renfrew/Paisley Study in Scotland
.
Stroke
2000
;
31
:
1893
1896
[PubMed]
9.
Petty
GW
,
Brown
RD
 Jr
,
Whisnant
JP
,
Sicks
JD
,
O’Fallon
WM
,
Wiebers
DO
.
Survival and recurrence after first cerebral infarction: a population-based study in Rochester, Minnesota, 1975 through 1989
.
Neurology
1998
;
50
:
208
216
[PubMed]
10.
Rautio
A
,
Eliasson
M
,
Stegmayr
B
.
Favorable trends in the incidence and outcome in stroke in nondiabetic and diabetic subjects: findings from the Northern Sweden MONICA Stroke Registry in 1985 to 2003
.
Stroke
2008
;
39
:
3137
3144
[PubMed]
11.
Kamalesh
M
,
Shen
J
,
Eckert
GJ
.
Long term postischemic stroke mortality in diabetes: a veteran cohort analysis
.
Stroke
2008
;
39
:
2727
2731
[PubMed]
12.
Winell
K
,
Pääkkönen
R
,
Pietilä
A
,
Reunanen
A
,
Niemi
M
,
Salomaa
V
.
Prognosis of ischaemic stroke is improving similarly in patients with type 2 diabetes as in nondiabetic patients in Finland
.
Int J Stroke
2011
;
6
:
295
301
[PubMed]
13.
Hoffmann
F
,
Haastert
B
,
Koch
M
,
Giani
G
,
Glaeske
G
,
Icks
A
.
The effect of diabetes on incidence and mortality in end-stage renal disease in Germany
.
Nephrol Dial Transplant
2011
;
26
:
1634
1640
[PubMed]
14.
Icks
A
,
Scheer
M
,
Morbach
S
, et al
.
Time-dependent impact of diabetes on mortality in patients after major lower extremity amputation: survival in a population-based 5-year cohort in Germany
.
Diabetes Care
2011
;
34
:
1350
1354
[PubMed]
15.
Icks
A
,
Haastert
B
,
Genz
J
, et al
.
Time-dependent impact of diabetes on the mortality of patients on renal replacement therapy: a population-based study in Germany (2002-2009)
.
Diabetes Res Clin Pract
2011
;
92
:
380
385
[PubMed]
16.
Hatano
S
.
Experience from a multicenter stroke register: a preliminary report
.
Bull World Health Organ
1976
;
54:541–553
17.
Köster
I
,
von Ferber
L
,
Ihle
P
,
Schubert
I
,
Hauner
H
.
The cost burden of diabetes mellitus: the evidence from Germany—the CoDiM study
.
Diabetologia
2006
;
49
:
1498
1504
[PubMed]
18.
Schneeweiss
S
,
Seeger
JD
,
Maclure
M
,
Wang
PS
,
Avorn
J
,
Glynn
RJ
.
Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data
.
Am J Epidemiol
2001
;
154
:
854
864
[PubMed]
19.
Bartholomeyczik
S
,
Hunstein
D
.
Time distribution of selected care activities in home care in Germany
.
J Clin Nurs
2004
;
13
:
97
104
[PubMed]
20.
Schäfer
I
,
von Leitner
EC
,
Schön
G
, et al
.
Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions
.
PLoS ONE
2010
;
5
:
e15941
[PubMed]
21.
Grambsch
P
,
Therneau
T
.
Proportional hazards tests and diagnostics based on weighted residuals
.
Biometrika
1994
;
81
:
515
526
22.
Machin D, Cheung YB, Parmar MKB. Survival Analysis: A Practical Approach. 2nd ed. Chichester, U.K., John Wiley & Sons, 2005
23.
Vandenbroucke
JP
,
von Elm
E
,
Altman
DG
, et al
STROBE Initiative
.
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration
.
PLoS Med
2007
;
4
:
e297
[PubMed]
24.
Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten der Deutschen Gesellschaft für Sozialmedizin und Prävention; Arbeitsgruppe Epidemiologische Methoden der Deutschen Gesellschaft für Epidemiologie; Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; Deutschen Gesellschaft für Sozialmedizin und Prävention. Good practice of secondary data analysis, first revision. Gesundheitswesen 2008;70:54–60 [in German]
25.
Schäfer I, Küver C, Gedrose B. Selection effects may account for better outcomes of the German Disease Management Program for type 2 diabetes. BMC Health Serv Res 2010;10:351
26.
Rossnagel
K
,
Jungehülsing
GJ
,
Nolte
CH
, et al
.
Out-of-hospital delays in patients with acute stroke
.
Ann Emerg Med
2004
;
44
:
476
483
[PubMed]
27.
Gargano
JW
,
Wehner
S
,
Reeves
MJ
.
Do presenting symptoms explain sex differences in emergency department delays among patients with acute stroke?
Stroke
2009
;
40
:
1114
1120
[PubMed]
28.
Andersen
KK
,
Olsen
TS
,
Dehlendorff
C
,
Kammersgaard
LP
.
Hemorrhagic and ischemic strokes compared: stroke severity, mortality, and risk factors
.
Stroke
2009
;
40
:
2068
2072
[PubMed]
29.
Asplund
K
,
Hägg
E
,
Helmers
C
,
Lithner
F
,
Strand
T
,
Wester
PO
.
The natural history of stroke in diabetic patients
.
Acta Med Scand
1980
;
207
:
417
424
[PubMed]
30.
Okumura
K
,
Ohya
Y
,
Maehara
A
,
Wakugami
K
,
Iseki
K
,
Takishita
S
.
Effects of blood pressure levels on case fatality after acute stroke
.
J Hypertens
2005
;
23
:
1217
1223
[PubMed]
31.
Yatsuya
H
,
Folsom
AR
,
Yamagishi
K
,
North
KE
,
Brancati
FL
,
Stevens
J
Atherosclerosis Risk in Communities Study Investigators
.
Race- and sex-specific associations of obesity measures with ischemic stroke incidence in the Atherosclerosis Risk in Communities (ARIC) study
.
Stroke
2010
;
41
:
417
425
[PubMed]
32.
Vemmos
K
,
Ntaios
G
,
Spengos
K
, et al
.
Association between obesity and mortality after acute first-ever stroke: the obesity-stroke paradox
.
Stroke
2011
;
42
:
30
36
[PubMed]
33.
Towfighi
A
,
Ovbiagele
B
.
The impact of body mass index on mortality after stroke
.
Stroke
2009
;
40
:
2704
2708
[PubMed]
34.
Feigin
VL
,
Lawes
CMM
,
Bennett
DA
,
Anderson
CS
.
Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century
.
Lancet Neurol
2003
;
2
:
43
53
[PubMed]
35.
Idris
I
,
Hill
R
,
Sharma
JC
.
Effects of admission serum urea, glomerular filtration rate, proteinuria and diabetes status on 3-month mortality after acute stroke
.
Diab Vasc Dis Res
2010
;
7
:
239
240
[PubMed]
36.
Gunarathne
A
,
Patel
JV
,
Potluri
R
, et al
.
Increased 5-year mortality in the migrant South Asian stroke patients with diabetes mellitus in the United Kingdom: the West Birmingham Stroke Project
.
Int J Clin Pract
2008
;
62
:
197
201
[PubMed]
37.
Hoffmann
F
,
Icks
A
. Structural differences between health insurance funds and their impact on health services research:
results of the Bertelsmann Health-Care Monitor
.
Gesundheitswesen
2012
: 
74
;
291
297
[in German
]
38.
Hoffmann
F
,
Icks
A
.
Diabetes prevalence based on health insurance claims: large differences between companies
.
Diabet Med
2011
;
28
:
919
923
[PubMed]
39.
Kolominsky-Rabas
PL
,
Sarti
C
,
Heuschmann
PU
, et al
.
A prospective community-based study of stroke in Germany—the Erlangen Stroke Project (ESPro): incidence and case fatality at 1, 3, and 12 months
.
Stroke
1998
;
29
:
2501
2506
[PubMed]
40.
Kolominsky-Rabas
PL
,
Weber
M
,
Gefeller
O
,
Neundoerfer
B
,
Heuschmann
PU
.
Epidemiology of ischemic stroke subtypes according to TOAST criteria: incidence, recurrence, and long-term survival in ischemic stroke subtypes: a population-based study
.
Stroke
2001
;
32
:
2735
2740
[PubMed]
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