Abstract
Background
Individuals with mental disorders face substantially reduced life expectancy, with cardiovascular disease (CVD) as the leading cause of premature death, likely due to a complex interplay of multiple factors. Prior studies examining whether patients with comorbid mental illness receive guideline-concordant pharmacological secondary prevention of ischemic heart disease (IHD) and cerebrovascular disease (CeVD) at rates comparable to those without psychiatric diagnoses are few and yielded inconsistent findings.
Methods
We conducted a retrospective cross-sectional analysis of statutory health insurance data for 2019–2022 (n = 238,616 adults with IHD or CeVD). Secondary prevention was defined as concurrent prescription of a lipid-modifying agent and an antiplatelet therapy or anticoagulant. Propensity score matching combined with logistic regression was used to estimate adjusted odds ratios (ORs) across all ICD-10 F-chapters (F0–F9) and for severe mental illness (SMI).
Results
Patients with any mental disorder had significantly higher odds of receiving secondary prevention for IHD (OR 1.18) and CeVD (OR 1.17) than those without. SMI was associated with even higher odds (IHD: OR 1.36; CeVD: OR 1.32). Diagnosis-specific subgroup analysis yielded no substantial differences between different mental disorders.
Conclusion
In general, prescription rates of pharmacological secondary prevention were higher in patients with comorbid mental disorders compared to patients without mental comorbidity. Factors other than pharmacological undertreatment – such as lifestyle-related prevention and physical health monitoring – may play a more central role in addressing the excess cardiovascular mortality burden in individuals with mental illness.
Keywords: Cardiovascular disease, Ischemic heart disease, Cerebrovascular disease, Mental disorder, Secondary prevention, Pharmacoepidemiology
Graphical abstract
1. Background
Individuals with mental disorders have a markedly reduced life expectancy compared to the general population, with an estimated 14.66 years of potential life lost (YPLL) across all mental disorders [1]. Among people with severe mental illness (SMI) such as schizophrenia and bipolar disorder, life expectancy reductions of up to 20 years relative to the general population have been reported [2,3]. Although suicide and poisoning (accidental overdose of drugs) account for part of this excess mortality, most of the life expectancy gap is attributable to somatic illnesses, with cardiovascular disease (CVD) constituting the leading cause of death [[4], [5], [6]]. People with SMI are not only more likely to develop CVD, but they also experience higher CVD-related mortality than those without mental disorders [7,8]. Data from the United States indicate that, over the past two decades, the gap in cardiovascular mortality between individuals with and without mental disorders has widened: cardiovascular death rates declined in the general population, but they increased among those with mental and behavioral disorders [9]. Similar patterns were observed in Danish registry data, where mortality rate ratios for external causes decreased, but those for natural causes, particularly cancer and circulatory diseases, increased in people with mental disorders compared to the general population [10,11]. These findings underscore the need for improved preventive strategies in patients with mental disorders and co-occurring CVD.
The reasons for these disparities in mortality are likely multifactorial, including biological factors, patient-related factors and health care system factors [12,13]. Individuals with SMI were shown to have a significantly lower physical activity level [14] and are at higher risk for metabolic syndrome [15]. Also, patients with schizophrenia and stroke had lower adherence rates to secondary prevention medication, including antihypertensive, lipid-lowering and anticoagulant therapy [16]. Furthermore, treatment with antipsychotic drugs, and to a lesser extent with mood stabilizers and antidepressants, was associated with an increased risk of metabolic risk factors like obesity and dyslipidemia [17]. However, treatment with antipsychotic drugs itself was not associated with an increased risk of stroke or myocardial infarction [18]. In addition to these patient-related factors, health care system factors such as negative attitudes towards patients with mental illness and disparities in access to health care may also play a role [3]. Invasive procedures after acute coronary artery events are less likely in patients with some mental disorders [19]. A large study from Hong Kong showed how the lack of revascularization in acute coronary states and the lack of cardioprotective pharmacotherapy were associated with mortality in patients with psychotic disorders [20]. A nationwide cohort study from Denmark showed that people with atrial fibrillation and bipolar disorder or schizophrenia were less likely to receive oral anticoagulation than those without a mental disorder [21]. A cross-sectional study from Scotland, UK showed that people with bipolar disorder and comorbid coronary heart disease or hypertension were less likely to receive antihypertensive or cholesterol-lowering medication [22]. Patients with schizophrenia and stroke also showed deficits in multiple aspects of vascular risk management, including less frequent lipid screening, lower rates of antihypertensive prescribing, and reduced attainment of guideline-recommended lipid targets [23]. With regard to ischemic heart disease (IHD), a Swedish cohort study found that individuals with schizophrenia had no increased prevalence of IHD. However, individuals with schizophrenia experienced elevated IHD-related mortality despite markedly higher rates of health care utilization compared to the general population [24].
In light of these persistent disparities, we aimed to further elucidate the reasons for the reduced life expectancy in patients with mental illness, especially those with SMI. We hypothesized that patients with IHD and cerebrovascular disease (CeVD) with comorbid mental disorders – especially schizophrenia, severe depressive disorder, and bipolar disorder – would be less likely to receive prescriptions for secondary prevention of myocardial infarction or stroke by means of lipid-modifying agents and antiplatelet therapy or anticoagulants. We hypothesized that individuals with SMI are less likely to be prescribed secondary prevention drugs, potentially attributable to health care system factors. To our knowledge, this is the first study to investigate health care system-related factors in the secondary prevention of CVD among individuals with mental illness in Germany.
2. Methods
2.1. Data sources, study design and patient population
Health insurance is compulsory for all citizens and permanent residents in Germany. Employees above an opt-out threshold of income and certain professional groups can choose between private and statutory health insurance (SHI) services, with SHIs covering approximately 90% of the German population [25]. For this study, we analyzed routine data from Germany's largest SHI fund, the Techniker Krankenkasse (TK), from the state of North Rhine-Westphalia (NRW), which is the most populous federal state. Approximately 2.8 million residents are insured with TK in NRW.
The study population comprised adults (≥18 years) with the diagnosis of IHD defined as International Statistical Classification of Disease and Related Health Problems (ICD-10) I20-I25 or CeVD defined as ICD-10 I60-I64. These inclusion criteria yielded a study population of 238,616 patients, representing approximately 8.5% of all individuals insured with TK in NRW. Patients with and without comorbid mental disorders (ICD-10 F0-F9) were compared using both outpatient and inpatient claims data from 2019 to 2022. We analyzed the use of any antiplatelet therapy or oral anticoagulants from the Anatomical Therapeutic Chemical classification system (ATC) chapter B01A and any lipid-modifying agents, such as statins, from the ATC chapter C10. In accordance with the guideline recommendations of the European Society of Cardiology and the German Society of Neurology, we defined pharmacological secondary prevention of IHD and CeVD as treatment with both a lipid-modifying agent for plaque stabilization and any antiplatelet therapy or oral anticoagulant [26,27]. The category of CeVD (ICD-10: I60–I64) includes ischemic strokes, subarachnoid hemorrhages, and intracerebral hemorrhages. Since 87% of all strokes are ischemic in origin [28], we chose to analyze guideline-concordant secondary prevention of ischemic stroke.
This analysis is part of the project The Somatic Care of Patients with Comorbid Mental Disorder (SoKo). Further details regarding the overall study design can be found in the study protocol [29]. The reporting of the results is in accordance with the STROBE checklist for observational cross-sectional studies [30].
2.2. Statistical analysis
We aimed to estimate the effect of health care system factors and clinical characteristics on the likelihood of receiving secondary prevention in individuals with IHD or CeVD and co-occurring mental disorders. In order to control for sociodemographic characteristics and severity of somatic comorbidity we calculated propensity scores using logistic regression based on the individuals' gender, age, employment status, somatic comorbidities (type 2 diabetes, arterial hypertension, obesity), and the cumulative duration of inpatient stays over the observation period of four years for IHD and CeVD treatment. In this study, gender denotes the legal gender marker as registered in official records, not self-identified gender. The only variable with missing data was employment status (n = 424). Given the relatively small number of cases with missing values, these observations were subsumed under the category ‘Other’.
For a more fine-grained analysis, we also investigated patients with mental disorders from different diagnostic groups based on the ICD-10 chapters on mental disorders (F0-F9) separately. Patients with a mental disorder from different diagnostic groups were included for each subgroup analysis. As patients with SMI have a significant lower life expectancy than patients with any mental disorder [1], we aimed to investigate this subgroup separately. Although there is no uniformly accepted definition for SMI, people with a diagnosis of schizophrenia and other psychotic disorders (ICD-10: F20-F29), bipolar affective disorders (ICD-10: F30, F31) or (recurrent) severe depressive disorders (ICD-10: F32.2, F32.3, F33.2, F33.3) with a significant functional impairment or disability that typically lasts two years or more are considered severely mental ill [31]. We therefore selected all patients in whom at least one of the aforementioned diagnoses was registered for at least eight consecutive quarters. In addition, we analyzed patients with a bipolar disorder or severe depressive disorder separately from patients with other affective disorders from the ICD-10 F3 chapter. As patients with tobacco use disorder (ICD-10 F17) usually do not seek specialized psychiatric treatment and the tobacco use itself is considered a risk factor for patients with IHD and CeVD [32], we chose to report results for patients with a substance use disorder other than tobacco use as a separate subgroup.
Propensity score matching was performed using generalized full matching, with model fit evaluated by standardized mean difference (SMD < 0.1). Due to the limited number of control cases available, we opted for full matching, in which weighted samples ensure that each individual has one or more matched controls [33]. Generalized full matching was explicitly designed for population-representative causal effect estimates [34] and has shown to be effective at reducing bias due to observed confounding variables [35].
For each analysis, a separate propensity score matching was performed to obtain a comparable subsample for all analyses. Most matchings reproduced well-matched samples with all 15 matching parameters being adequately matched (SMD < 0.1). In three subsamples, one of 15 matching parameters was not ideally balanced.
Odds ratios were calculated using logistic regression, implemented as a binomial generalized linear model based on the weighted propensity score-matched samples. Statistical significance was assessed at p < 0.05. For the descriptive characteristics, the mean and median cumulative duration of inpatient stays were calculated only for patients with at least one day of inpatient treatment for CVD during the observation period.
Generalized full matching was performed using the MatchIt package in R [36], which calls functions from the quickmatch package [34]. Tables were generated using the R packages finalfit [37] and gt [38]. Graphics were created with the ggplot2 package [39]. All analyses were conducted using R version 4.5.0 [40].
3. Results
3.1. Study population
The sociodemographic and clinical characteristics of the study population are shown in Table 1.
Table 1.
Sociodemographic and clinical characteristics of the study population– Column percentages, interquartile range (IQR) and standard deviations (SD) are depicted in brackets. Outpatient treatment continuity was defined as the number of quarters with at least one outpatient appointment during the observation period. Low outpatient contact density was defined as mean appointments per quarter = 1, high contact density as mean appointments per quarter > 1.
| Variable | Levels | Comorbid Mental Disorder | No Mental Disorder | Total |
|---|---|---|---|---|
| Total N (%) | 143,606 (60.18) | 95,010 (39.82) | 238,616 | |
| Gender | Male | 84,912 (59.13) | 64,046 (67.41) | 148,958 (62.43) |
| Female | 58,694 (40.87) | 30,964 (32.59) | 89,658 (37.57) | |
| Age | Mean (SD) | 69.98 (13.26) | 69.89 (14.56) | 69.95 (13.79) |
| Median (IQR) | 70 (61 to 81) | 72 (61 to 81) | 71 (61 to 81) | |
| Employment | Employee | 42,834 (29.83) | 27,987 (29.46) | 70,821 (29.68) |
| Self-employed | 3377 (2.35) | 3388 (3.57) | 6765 (2.84) | |
| Unemployed | 6764 (4.71) | 2229 (2.35) | 8993 (3.77) | |
| Student | 149 (0.10) | 316 (0.33) | 465 (0.19) | |
| Retiree | 81,315 (56.62) | 55,072 (57.96) | 136,387 (57.16) | |
| Not self-insured (spouse) | 6686 (4.66) | 4023 (4.23) | 10,709 (4.49) | |
| Not self-insured (child) | 173 (0.12) | 453 (0.48) | 626 (0.26) | |
| Other | 2308 (1.61) | 1542 (1.62) | 3850 (1.61) | |
| Ischemic heart disease (IHD) | Yes | 117,874 (82.08) | 79,674 (83.86) | 197,548 (82.79) |
| Cerebrovascular disease (CeVD) | Yes | 41,818 (29.12) | 21,939 (23.09) | 63,757 (26.72) |
| Arterial hypertension | Yes | 126,543 (88.12) | 75,437 (79.40) | 201,980 (84.65) |
| Diabetes (type 2) | Yes | 46,005 (32.04) | 23,672 (24.92) | 69,677 (29.20) |
| Obesity | Yes | 44,706 (31.13) | 20,215 (21.28) | 64,921 (27.21) |
| Outpatient treatment continuity in quarters | Mean (SD) | 13.42 (4.20) | 11.82 (5.55) | 12.78 (4.84) |
| Median (IQR) | 16 (12 to 16) | 15 (8 to 16) | 16 (11 to 16) | |
| Outpatient contact density | Low | 4092 (2.85) | 11,424 (12.02) | 15,516 (6.50) |
| High | 139,514 (97.15) | 83,586 (87.98) | 223,100 (93.50) | |
| Cumulative inpatient duration for IHD in days | Mean (SD) | 9.05 (10.72) | 7.70 (8.39) | 8.56 (9.96) |
| Median (IQR) | 6 (3 to 10) | 5 (3 to 9) | 5 (3 to 10) | |
| Cumulative inpatient duration for CeVD in days | Mean (SD) | 20.03 (27.64) | 13.10 (17.40) | 17.72 (24.92) |
| Median (IQR) | 11 (6 to 22) | 8 (5 to 14) | 9 (6 to 19) |
During the observation period, 60% of the patients with IHD or CeVD were diagnosed with at least one mental illness diagnosis (ICD-10: F0-F9). Overall, 62% of patients were male; the proportion of female patients was higher among those with a mental disorder than without (41% vs 33%). The mean and median age was approximately 70 years in both groups, and the majority (57%) were retired, followed by employees (30%). In the German SHI system, certain individuals are not self-insured but hold their insurance status through their family members. In our dataset, this was the case for less than 5% of the patients (spouses and adult children). 83% of the patients were diagnosed with IHD and 27% with CeVD. While IHD was distributed almost equally between patients with and without a mental disorder, CeVD was more prevalent among individuals with mental illness (29% vs 23%). The mean and median outpatient treatment continuity during the observation period were higher in patients with mental illness. Similarly, the proportion of patients with a high outpatient contact density was higher among patients with mental illness. Regarding hospitalizations, among patients with at least one day of inpatient treatment for IHD during the observation period, the mean cumulative inpatient duration was 9.1 days in patients with mental illness and 7.7 days in those without; the respective median values were 6 and 5 days. In patients with at least one day of inpatient treatment for CeVD during the observation period, the mean cumulative inpatient duration was 20 days in patients with a comorbid mental disorder and 13.1 in those without; the respective median values were 11 and 8 days.
The prevalence of the various comorbid mental disorders in the population is presented in Table 2. Approximately half of the patients with a mental disorder (48.5%) received diagnoses spanning more than one ICD-10 chapter.
Table 2.
Mental (co-)morbidity of the study population– The percentage indicates the proportion of patients who were diagnosed with any disorder from one of the ICD-10 F-chapters (Mental, behavioral and neurodevelopmental disorders).
| Diagnosis | Number of patients | Percentage (%) |
|---|---|---|
| F0 - Organic mental disorder | 26,467 | 18.43 |
| F1 - Substance use disorder | 48,004 | 33.43 |
| F1 - Substance use disorder (excl. tobacco) | 10,109 | 7.04 |
| F2 - Psychotic disorder | 2831 | 1.97 |
| F30/31 + F32/3.2/3 - Bipolar & severe depressive disorder | 11,033 | 7.68 |
| F3 - Affective disorder (excl. bipolar & severe depressive disorder) | 51,262 | 35.70 |
| F4 - Neurotic and stress-related disorder | 78,323 | 54.54 |
| F5 - Eating and other behavioral syndromes | 20,390 | 14.20 |
| F6 - Personality disorder | 6814 | 4.74 |
| F7 - Intellectual disability | 409 | 0.28 |
| F8 - Developmental disorder | 2357 | 1.64 |
| F9 - Childhood-onset behavioral and emotional disorder | 4000 | 2.79 |
The most frequently diagnosed mental disorders were neurotic and stress-related disorders such as anxiety disorder, acute stress reaction and post-traumatic stress disorder (PTSD) followed by affective disorders such as depressive and bipolar disorder. Substance use disorders were also very prevalent in the study population, but mostly due to tobacco use (ICD-10: F17).
3.2. Overall analysis
Table 3 shows the results for patients with either any mental disorder, SMI or no mental disorder at all.
Table 3.
Use of medication according to guideline for IHD and CeVD patients– IHD: ischemic heart disease; CeVD: cerebrovascular disease; SMI: severe mental illness (patients with a psychotic disorder, bipolar disorder or severe depressive disorder and the diagnosis of a mental illness for at least eight subsequent quarters during the observation period); OR: odds ratio; CI: confidence interval.
| Variable | Group | n | Yes | Yes [%] | OR (95% CI) | p |
|---|---|---|---|---|---|---|
| IHD medication according to guideline | Any mental disorder | 117,874 | 61,719 | 52.36 | 1.18 (1.16–1.21) | <0.001 |
| SMI | 54,865 | 30,918 | 56.35 | 1.36 (1.33–1.39) | <0.001 | |
| No mental disorder | 79,674 | 36,324 | 45.59 | |||
| CeVD medication according to guideline | Any mental disorder | 41,818 | 23,029 | 55.07 | 1.17 (1.14–1.21) | <0.001 |
| SMI | 19,603 | 11,664 | 59.50 | 1.32 (1.27–1.37) | <0.001 | |
| No mental disorder | 21,939 | 10,889 | 49.63 |
Patients with any mental disorder had significantly higher odds of receiving guideline-concordant IHD and CeVD secondary prevention medication compared to patients without any mental disorder. Patients with SMI also had significantly higher odds for receiving IHD or CeVD secondary prevention medication compared to those without any mental disorder. Odds ratios were higher in patients with SMI than in patients with any mental disorder.
3.2.1. IHD: analysis for specific comorbid mental disorders
In patients with IHD, most groups with comorbid mental disorders showed significantly higher odds of receiving secondary prevention medication compared to people without mental disorders (Fig. 1).
Fig. 1.
Use of IHD medication according to guideline in different diagnostic groups of mental disorders– An odds ratio above 1 indicates a higher proportion of patients with a mental disorder receiving IHD medication according to guideline than without a mental disorder. Significant odds ratios are indicated in magenta.
The highest OR was found in patients with substance use disorder, including tobacco use disorder. The second highest OR was observed in patients with an organic mental disorder. Bipolar and severe depressive disorder, intellectual disability and childhood-onset behavioral and emotional disorder did not reach statistical significance.
3.2.2. CeVD: analysis for specific comorbid mental disorders
In all patients with CeVD, most mental disorder groups showed significantly higher odds of receiving secondary prevention medication compared to CeVD patients without mental disorders (Fig. 2).
Fig. 2.
Use of CeVD medication according to guideline in different diagnostic groups of mental disorders– An odds ratio above 1 indicates a higher proportion of patients with a mental disorder receiving CeVD medication according to guideline than without a mental disorder. Significant odds ratios are indicated in magenta.
Substance use disorder showed the highest OR, followed by developmental disorder and childhood-onset behavioral and emotional disorder, but again, the OR for substance use disorder was higher in the sample that included patients with tobacco use disorder. Organic mental disorder, psychotic disorder and intellectual disability showed non-significant results. Mental disorders from other F-chapters showed marginally elevated, yet statistically significant associations with CeVD preventive medication prescription.
4. Discussion
The present study analyzed data from 238,616 patients with IHD or CeVD insured with Germany's largest SHI fund. Overall and contrary to our hypothesis, patients with mental disorders were more likely to receive guideline-concordant pharmacological secondary prevention for both IHD and CeVD. This pattern was also observed among patients with SMI (defined as patients with a diagnosis of a bipolar, severe depressive or psychotic disorder in at least eight subsequent quarters), who showed even higher odds than the broader psychiatric population. This finding is contrary to findings from a recent systematic review and meta-analysis, which showed a significantly lower prescription rate of statins and no significant difference in the prescription of aspirin and clopidogrel in patients with SMI [41]. The findings do not support our hypothesis that pharmacological underprescription is a systematic driver of excess cardiovascular mortality in individuals with SMI in the German SHI context.
Several mechanisms may contribute to the overall higher likelihood of patients with comorbid mental disorders to receive guideline-concordant pharmacological secondary prevention for both IHD and CeVD. First, patients with mental disorders had substantially higher outpatient contact density and treatment continuity in the current study than those without. For certain conditions, such as somatoform and anxiety disorders, this pattern has been described in the literature and is therefore not entirely unexpected [42,43]. More frequent healthcare contacts may create additional opportunities for somatic prescribing and medication review. This mechanism may, in part, also account for the higher odds ratios observed in patients with SMI, given that we operationalized a two-year persistence criterion based on documented diagnoses in eight consecutive quarters. Second, in European health care systems, cross-disciplinary integration may be more prevalent than in other countries: a recent systematic review found that 80% of European cardiovascular guidelines include psychiatric contents, compared with 13% of American guidelines [44], which may translate into higher rates of pharmacological secondary prevention in mental disorder patients in Germany. However, a previous meta-analysis showed no differences in mortality risks in people with mental disorders based on the geographic location of the study [45]. Third, the current analysis measured prevalent prescription rather than incident initiation of a medication following a cardiovascular event. Several studies suggesting underprescription specifically assessed the acute post-event window [41]. Patients who have survived this period and remained in active care – the population captured in our analysis – may represent a selected group with higher overall medication engagement and better prognosis compared to those who might have lost contact with the health care system.
Factors besides secondary prevention medication may play a more central role in the excess mortality of patients with CVD and mental illness. In particular, lifestyle-related components of secondary prevention – smoking cessation, regular physical activity, dietary modification, and weight management – may be substantially more difficult to implement and maintain in individuals with SMI. A global meta-analysis found that patients with SMI spent almost 8 h per day in sedentary behavior, with 54.7% failing to meet physical activity guidelines [14] – a gap that pharmacological treatment alone cannot address. Similarly, a systematic review and meta-analysis found that people with SMI have significantly higher dietary energy and sodium intake, indicative of poorer overall diet quality [46]. With regard to smoking cessation, data from UK primary care indicate that cessation advice was provided at similar or higher rates in patients with SMI than in those without, but quit rates did not differ significantly between groups [47], suggesting a persistent effectiveness gap rather than an access gap. These behavioral dimensions of secondary prevention are not captured in prescription data and may account for a substantial proportion of the persistent cardiovascular mortality gap.
4.1. Strengths and limitations
The dataset was drawn from Germany's largest SHI fund, covering approximately 2.8 million insured individuals. The outcome definition was based on established European Society of Cardiology and German Neurological Society guidelines, ensuring clinical validity. The systematic analysis across all ICD-10 mental disorder chapters and the separate analysis for SMI provide comprehensive diagnostic coverage. Furthermore, because the dataset consists of health insurance claims – rather than data from a population registry – individuals who had no contact with the health care system during the 2019–2022 observation period were not captured. This may introduce selection bias toward more treatment-engaged individuals. In addition, the claims data contained diagnostic information only in a clustered structure, such as CeVD defined to include ischemic stroke but also subarachnoid and intracerebral hemorrhages. This may have introduced misclassification bias in the CeVD group by including some patients for whom antiplatelet therapy might have been inappropriate. Additionally, the observation period encompassed the COVID-19 pandemic. During this time, a reduction in the duration of inpatient treatment was observed, which was incorporated as a surrogate matching parameter for somatic illness severity. A residual impact on the results cannot be entirely ruled out. Furthermore, prescription claims data reflect only medications that were dispensed from the pharmacy. Therefore, non-adherence on the patient's part, rather than underprescription, cannot be entirely ruled out. We were not able to assess mortality rates, so we cannot be certain that prescribing secondary prevention medication led to decreased cardiovascular and cerebrovascular mortality in our study cohort. Finally, in the propensity score matching, not all variables were ideally balanced across all subgroup analyses, potentially introducing residual confounding.
5. Conclusions
Overall, patients with mental disorders were more likely to receive guideline-concordant pharmacological secondary prevention for both IHD and CeVD compared with patients without mental illness. This pattern held even for patients with SMI. These findings suggest that factors other than pharmacological undertreatment – such as lifestyle-related prevention and physical health monitoring – may play a more central role in addressing the excess cardiovascular mortality burden in individuals with mental illness.
List of Abbreviations
| ATC | Anatomical Therapeutic Chemical classification system |
| CVD | Cardiovascular disease |
| CeVD | Cerebrovascular disease |
| ICD | International Statistical Classification of Disease and Related Health Problems |
| IHD | Ischemic heart disease |
| NRW | North Rhine-Westphalia |
| PTSD | Post-traumatic stress disorder |
| SHI | Statutory health insurance |
| SMI | Severe mental illness |
| SMD | Standardized mean difference |
| TK | Techniker Krankenkasse |
| YPLL | Years-of-potential-life-lost |
Consent to publish
NA.
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the North Rhine Medical Association (no. 2020343). Data collection and analysis followed the Declaration of Helsinki. The trial was registered with the German Clinical Trials Register on February 3, 2023 (no. DRKS00030513).
Availability of data and material
The dataset used and analyzed in this study is not publicly available. German statutory health insurance funds may only provide claims data for research under specific legal provisions that strictly regulate access, purposes, and technical safeguards. These provisions do not allow unrestricted data sharing with third parties or public repositories.
Declaration of generative AI use
During the preparation of this work the authors used Perplexity in order to improve language and readability. After using this service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
Funding
This project was funded by the Innovation Committee of the Federal Joint Committee (G-BA), Germany (funding no. 01VSF19052). The funding organization had no influence on the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
CRediT authorship contribution statement
Sönke Johann Peters: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing. Lara Schlomann: Conceptualization, Data curation, Methodology, Writing – review & editing. Michaela Jänner: Conceptualization, Methodology, Writing – review & editing. Jürgen Medenbach: Conceptualization, Data curation, Writing – review & editing. Jürgen Zielasek: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Sophie E. Groß: Conceptualization, Data curation, Methodology, Project administration, Writing – original draft, Writing – review & editing. Euphrosyne Gouzoulis-Mayfrank: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would like to thank all those who contributed data and advice to this study. Specifically, we thank J. Mader-Qian, I. Schellartz, I. Klee, H. Büscher, D. Reske and the members of the scientific advisory board of the SoKo project.
Contributor Information
Sönke Johann Peters, Email: SoenkeJohann.Peters@lvr.de.
Lara Schlomann, Email: Lara.Schlomann@lvr.de.
Michaela Jänner, Email: Michaela.Jaenner@lvr.de.
Jürgen Medenbach, Email: Juergen.Medenbach@tk.de.
Jürgen Zielasek, Email: Juergen.Zielasek@lvr.de.
Sophie E. Groß, Email: Sophie.Gross@lvr.de.
Euphrosyne Gouzoulis-Mayfrank, Email: Euphrosyne.Gouzoulis-Mayfrank@lvr.de.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset used and analyzed in this study is not publicly available. German statutory health insurance funds may only provide claims data for research under specific legal provisions that strictly regulate access, purposes, and technical safeguards. These provisions do not allow unrestricted data sharing with third parties or public repositories.