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Article

Sex and Gender Disparities in Missed Acute Ischemic Stroke: A Nested Case-Control Study

by
Cécile Barras
1,*,
Michael Amiguet
1,
Joëlle Schwarz
1,
Patrik Michel
2,† and
Carole Clair
1,†
1
Health and Gender Unit, Unisanté, Center for Primary Care and Public Health, University of Lausanne, 1010 Lausanne, Switzerland
2
Stroke Center, Neurology Service, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, 1011 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Clin. Transl. Neurosci. 2025, 9(2), 22; https://doi.org/10.3390/ctn9020022
Submission received: 20 December 2024 / Revised: 26 March 2025 / Accepted: 26 March 2025 / Published: 2 April 2025
(This article belongs to the Special Issue Brain Health)
Browse Figures
Versions Notes

Abstract

:
Backround: The aim is to analyze whether sex and gender are associated with missed acute ischemic stroke (M-AIS). Methods: We performed a nested case-control study, using data collected from March 2003 to December 2020 from ASTRAL (Acute STroke Registry and Analysis of Lausanne). M-AIS were compared with a randomly selected control sample of acute ischmemic stroke (AIS). We extracted six gender-related socioeconomic variables. A gendered socioeconomic position (SEP) score was constructed reflecting the level of correspondence with feminine characteristics. Associations between M-AIS and the gender-related socioeconomic variables and the gendered SEP score were investigated using logistic regression. Results: Of the 6007 AIS, 182 (3%) were M-AIS. 80 (44%) were women. No association between administrative sex and M-AIS was found (OR 1.25, 95% CI 0.78–2.00). Differences were found for gender-related socioeconomic variables, women in the middle professional category had higher odds of an M-AIS compared to women in the lower professional category (OR 3.93, 95% CI 1.19–13.03). Men with higher education had lower odds of an M-AIS compared to men with lower education (OR 0.31, 95% CI 0.10–0.92). For women, a 20-unit increase in the gendered SEP score was associated with lower odds of an M-AIS (OR 0.66, 95% CI 0.46–0.94). For men, the same increase in the score tended to higher the odds of an M-AIS (OR 1.52, 95% CI 1.00–2.32). Conclusions: The interaction of administrative sex with gender-related socioeconomic variables revealed different associations with M-AIS for women and men. Correspondence to expected gender norms may have a protective effect against M-AIS.

1. Introduction

Rapidly and correctly identifying a stroke in the emergency department (ED) is a challenge for health care providers. Missed acute ischemic stroke (M-AIS) is defined as acute ischemic stroke (AIS) that is not initially suspected or ruled out after initial investigations such as neuroimaging [1,2,3]. Patients with young age, low stroke severity and non-traditional stroke symptoms are associated with M-AIS [4,5,6,7]. In a 2017 review of US studies, M-AIS (of any type) accounted for 8.7% of all strokes in the ED [4]. Missed stroke is associated with poorer functional outcomes and higher mortality rates [6].
Women, but also people with low socioeconomic status and ethnic minorities, are more likely to be missed, suggesting an influence of presenting symptoms and possible biases in management among health care providers [8,9,10]. Women appear to be at a disadvantage compared to men, although no clear biological differences have been found to explain these differences.
To understand the differences between men and women, we need to take gender into account. Distinguishing between sex and gender is necessary to avoid attributing differences to biology when they result from gendered social determinants [11]. In line with SAGER guidelines and health research, we explain the definitions of sex and gender and our terminology choices in Box 1 [12,13].
Box 1. Definition of sex, gender, and socioeconomic position.
Sex: Sex is defined as the biological differences between women and men such as differences in gene expression, hormonal levels and anatomy [14]. Sex is not a fixed, binary category but a fluid continuum of biological variations (e.g., expressed in variation in sexual development) [15].
Gender: Gender reflects the hierarchical organization of the social world that shapes the lives of women and men. Gender includes multiple dimensions, usually organized as four dimensions: identity, role (e.g., occupation), relation (e.g., marital status) and the institutionalized dimension (e.g., education, income) [15,16]. Gender is constantly performed and reproduced in society according to socially valued and expected roles and norms. Colineaux defined, on the one hand, gender performance as the social characteristics that are expected and reproduced (performed) by an individual according to social norms, and on the other hand, gender pressure as the effect of the sex assigned at birth on future social characteristics [17]. For example, the expected social role of women in some cultures to care for children or to work part-time.
Gender interacts with multiple social determinants including age, ethnicity, socioeconomic position (SEP) [18].
Socioeconomic position: SEP represents the social and economic characteristics of individuals that determine their hierarchical position in society. It can be measured by various available indicators, some of which may affect health. It depends on history and cultural context. Here we have focused on socioeconomic characteristics influenced by gender [19].
In research, the only variable generally available to categorize women and men is the administrative sex (i.e., the category indicated on identity documents), based on the observed or assigned sex at birth. It is widely used as a proxy for biological sex or social gender, but provides limited evidence on specific biological or social mechanisms that determine health outcomes [17].
In this paper, we seek to explore the complex interplay between administrative sex and socioeconomic variables related to gender (gender-related variables).
The influence of sex and gender and SEP on the management and prognosis of M-AIS remains unclear. This nested case-control study aims to fill this gap by exploring how the accuracy of AIS diagnosis may differ in men and women due to their gendered SEP, using an intersectional approach.

2. Materials and Methods

2.1. Design

We conducted a retrospective nested case-control study of data in the Acute STroke Registry and Analysis of Lausanne (ASTRAL) cohort, whose detailed characteristics have been described in the baseline publication [20]. ASTRAL is a single-center cohort study that, since 2003, has enrolled all adult patients (≥16 years) admitted to the stroke unit or intensive care unit of the University Hospital of Lausanne in Switzerland, with AIS within 24 h of the last proof of good health (LPGH). The cohort also includes in-hospital AIS discovered within 24 h of LPGH. Exclusion criteria comprise intracerebral hemorrhage, subarachnoid hemorrhage, cerebral sinus venous thrombosis and late admission > 24 h after stroke onset. During the data collection phase, study investigators had unblinded access to the electronic database and electronic medical records of cases and controls. Data analysis was performed after anonymization of data as described below. Efforts to limit potential sources of bias were made by gathering as complete data as possible from multiple sources in the medical records, and by using a nested case-control study design.

2.2. Case and Control Ascertainment

We identified all M-AIS in ASTRAL that occurred between March 2003 and December 2020 as cases (n = 182). In our center, patients who have prehospital criteria for potential acute revascularization treatment are announced by paramedics using “stroke codes” and are simultaneously seen by an ED physician and the on-call neurology consultant on arrival. Other patients with acute neurologic symptoms suggestive of stroke are evaluated first by ED physicians. If they suspect a stroke, they call the neurology consultant for an evaluation. M-AIS is defined for both these situations as failure to suspect stroke by the ED physician or an incorrect exclusion of a stroke diagnosis after the neurologic consultation [6]. A final diagnosis of stroke is made by academic stroke neurologists who review all available clinical and paraclinical information on patients seen by the neurology consultant. To differentiate between ischemic stroke and TIA, we used the ICD-10 definition until 2018; thereafter we considered TIA with a corresponding acute lesion on imaging as a stroke according to the ICD-11. Neuroradiological stroke diagnosis was facilitated by the routine use in our institution of perfusion CT since the start of this cohort, and an MRI as the preferred first imaging modality since 5/2018.
Due to the need to review medical records to extract gender-related socioeconomic variables not recorded in ASTRAL (see below) and the large number of AIS in the registry (n = 6007), we decided to randomly select as many controls as M-AIS (1:1 ratio). We did not need to match cases and controls, as cases and controls were both drawn from the same cohort by design (nested case-control study).

2.3. Patient and Stroke Variables

Data routinely collected include demographics (age, administrative sex) as presented by the identity documents, ethnicity (White, African-black, African-Maghreb, East-Asian, Middle-East, South-Asian/Indian, Latino-American), cerebrovascular risk factors (CVRFs) such as hypertension, diabetes, hypercholesterolemia, smoking, alcohol abuse, migraine, atrial fibrillation, cancer, previous ischemic or hemorrhagic event.
The pre-stroke modified Rankin Scale (mRS) was estimated by mRS-certified stroke physicians for M-AIS and AIS [21]. The admission National Institutes of Health Stroke Scale (NIHSS) score was performed by NIHSS-certified stroke physicians or supervised by such [22,23]. In patients with an M-AIS, the NIHSS score was reconstructed from the clinical notes, a method shown to be reliable in previous publications [22]. The following symptoms and signs were also registered on history taking or admission exam: paresis, sensory deficit, visual field deficit, eye deviation, cerebellar deficit, dysarthria, aphasia, vigilance deficit, neglect. Finally, the arterial territory of the stroke (anterior, posterior, both or undetermined) was identified using all available clinical and imaging information.

2.4. Gender-Related Socioeconomic Variables

We extracted six socioeconomic variables from the patients’ medical charts (civil status, living situation, education level, professional categories, being professionally active and having children). We could thus cover several aspects of the four dimensions of gender: role dimension (professional categories, having children), the relation dimension (civil status, living situation) and the institutionalized dimension (education level) [15]. There was no information on the gender identity dimension. There is good evidence that these variables interact with gender and can thus be used as indicators of gendered SEP [24].

2.4.1. Civil Status and Living Situation

We found 4 categories of civil status: widow, married, single, divorced. Living situation was classified as living alone or in a joint household, regardless of civil status.

2.4.2. Education Level

We classified the level of education according to the categories recognized by the Federal Office of Public Health (FOPH) in Switzerland: no education, minimal compulsory education, upper secondary school (corresponding to professional and general education) and tertiary education (corresponding to university or professional schools) [25]. We dichotomized the categories into low (no, compulsory or secondary school) or high (university) levels of education.

2.4.3. Professional Activity and Categories

We dichotomized participants into professionally active or inactive, the latter category including retired, unemployed, student, or beneficiary of disability insurance. To define the socio-professional categories, we used the FOPH classification. The categories are grouped according to organizational skills (e.g., being able to plan one’s work, manage teams), level of training (not just education but also work experience), and type of activity. We obtained 13 categories: managers, liberal profession, self-employed, intellectual and managerial professions, intermediaries, skilled employees, skilled workers, unskilled workers, students/apprentices, unemployed, in training, retired, housekeeper [26]. When information about former occupation of retirees could be retrieved, it we used it to define the professional category. When not retrievable, we reclassified it as missing. We merged the categories into high (managers and liberal profession), middle (self-employed, intellectual and managerial professions, intermediaries), low (skilled employees, skilled workers, unskilled workers) or no professional category (student/apprentice, unemployed, in training, housekeeper), as done in previous studies [27].

2.4.4. Having Children

We grouped patients who reported having children, and those without; we did not know if the children still lived with the patients.

2.5. Gendered Socioeconomic Position Score

In order to measure the possible effect of an accumulation of gender-related variables on M-AIS, we constructed a score of gendered SEP based on previously published methods [28,29]. This approach allowed us to create a score that indicates the level of correspondence to feminine or masculine characteristics, using all dimensions of gender. The gendered SEP score was built by estimating a logistic regression model with administrative sex as outcome and all gender-related socioeconomic variables as explanatory variables. A number of variables were found to be associated with woman administrative sex, which we called “feminine characteristics” (see results for these variables). For each individual, the score was defined as the predicted probability of being a woman, given the individual’s observed gender-related socioeconomic variables. Thus, the minimum and maximum possible values for the score were 0% (absence of feminine characteristics as defined by the model) and 100% (maximal presence of feminine characteristics). This allowed us to explore the influence of the degree of femininity of the different gender profiles on the chances of an M-AIS, for women and men separately.

2.6. Statistical Analysis

Differences in baseline characteristics between cases and controls in women and men were assessed using a t-test for continuous variables and Pearson’s chi squared test for categorical variables.
The unadjusted association between administrative sex and an M-AIS was assessed using logistic regression. This association was then adjusted for a selection of known risk factors for an M-AIS.
Then, we used logistic regressions to investigate the administrative sex adjusted associations of gender-related socioeconomic variables with the odds of M-AIS. In subsequent analyses, interactions between these variables and administrative sex were used to investigate sex and gender differences in the corresponding associations. Due to the limited sample size, interactions were introduced one at a time and a separate model was estimated for each to ensure a minimum of 10 cases per explanatory variable.
The association between the score and the odds of an M-AIS was assessed using logistic regression, and administrative sex differences in this association were estimated in an interaction analysis.
Apart from the first unadjusted analysis, all analyses were adjusted for a selection of known risk factors for an M-AIS [6]. The selection was done from a broader list of potential confounders via a stepwise procedure, starting with a logistic regression model with M-AIS as outcome and gender-related socioeconomic variables plus administrative sex as regressors. The potential confounders were then added to the model one at a time, and the confounder inducing the largest mean relative change in the gender-related socioeconomic variables ORs was kept. This process was repeated until no additional confounder induced a mean relative change exceeding 10%. The following confounders were retained: age, pre-stroke mRS, eye deviation, any paresis and posterior circulation. The list of potential confounders also contained the following (not retained) variables: cerebellar deficit (yes/no), onset type (awake known, during night sleep, during day sleep, unwitnessed), migraine (yes/no), ethnicity (see above for groups), NIHSS at admission, time since LPGH, number of cerebrovascular risk factors among hypertension, atrial fibrillation, smoking, diabetes, body mass index, and hypercholesterolemia.
In order to optimize the adjustment, fractional polynomials were used to model the relationship between continuous confounders (e.g., age) and the outcome as accurately as possible. Rates of missing data ranged from 0% to 2.47% in confounders and from 2.2% to 42.6% in gender-related variables. Given the high missing rate in some gender-related variables, missing values were imputed using multiple imputation by chained equations.
General significance level was set at 0.05. Statistical analyses were conducted using R statistical software version 4.1.1 [30].
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) method was applied to report results [31].

2.7. Ethical Considerations

ASTRAL is registered with our institution as a clinical and research database. Patients received written information from the hospital that their routinely collected clinical data may be used for quality and scientific purposes. However, a patient’s decision to opt out of data analysis did not need to be considered because this was a quality assurance project of the diagnostic practice in our institution, falling outside of the Swiss Human Research Act; therefore, both an approval by the institutional ethical commission and a patient consent procedure were not applicable. The data were anonymized before analysis by institutional medical personnel not involved in the clinical care or the analysis of the data. For anonymization, we followed the principles of the Swiss Human Research Ordinance from 2013 (HRO, Art. 25), i.e., all items which when combined would enable the subject to be identified were irreversibly deleted from the datafile. Specifically, no unique identifiers were contained in the anonymized dataset.

3. Results

We identified 182 M-AIS, representing 3% of all AIS between March 2003 and December 2020.

3.1. Univariate Analysis: Sociodemographic Characteristics of Patients

Only significant differences are described below for patient characteristics (Table 1). Mean age was higher among AIS than M-AIS (71.1 years versus 66.6 years). AIS had more hypertension than M-AIS (74.7% vs. 62%), this difference was seen in women but not in men. Similarly, NIHSS was higher in AIS than in M-AIS, but this difference was not seen among men (Please refer to the Supplementary Material for a complete list of patient characteristics).
Differences in focal neurological symptoms were found in women and men, with AIS having globally more paresis, sensory deficit, visual field deficit, eye deviation and neglect than M-AIS. M-AIS have more cerebellar deficit. The most commonly affected arterial territory was the anterior territory in both men and women. However, the proportion of posterior territory was greater in M-AIS than in AIS (44.9% for both territories in M-AIS compared with 69.3% anterior and 25% posterior in AIS).
Looking at gender-related socioeconomic variables, M-AIS contained more single status than AIS (20.9% versus 9.8%). There were more widows in AIS than in M-AIS. There were no other significant socioeconomic differences between M-AIS and AIS.
In particular, the proportion of women did not differ between M-AIS and AIS (44% vs. 43%, OR 1.02, CI 0.68–1.55). After adjustment for confounders, there was still no significant association between administrative sex and M-AIS (OR 1.25, 95% CI 0.78–2.00).

3.2. Multivariate Analysis: Sex and Gender Differences in M-AIS

In the adjusted multivariable logistic regression analysis without interactions, an association was found between civil status and M-AIS. Patients who were single had greater odds of an M-AIS than widowed patients (OR 4.51, 95% CI 1.47–13.89). In the interaction analysis between gender-related variables and administrative sex, this association was significant in women (OR = 4.71, 95% CI 1.07–20.68), but not in men. Women in the middle professional category had greater odds of an M-AIS than women in the low professional category (OR = 3.93, 95% CI 1.19–13.03). Men with higher education had lower odds of an M-AIS than men with lower education (OR = 0.31, 95% CI 0.10–0.92). However, there were no statistically significant differences between the associations in men and in women (no significant interaction terms) (Figure 1).

3.3. Gendered SEP Score in M-AIS

The following characteristics contributed to a higher predicted probability of being in the woman category (i.e., a higher SEP score), reported as “feminine characteristics”: being widowed, living alone, having a higher level of education for the same professional category or having a lower professional category for the same level of education (being overqualified for the job), having no professional category and being non-active professionally (Figure 2). Figure 3 shows the distribution of the score for women and men calculated via kernel density estimation. Score values were overall higher for women than men, but also more spread, ranging from about 20 to 90, whereas men’s scores were typically concentrated in the 20 to 40 range.
In the overall adjusted model, the gendered SEP score was not significantly associated with an M-AIS. However, analyses including interaction with administrative sex revealed statistically significant differences between women and men. For women, having a higher gendered SEP score was associated with lower odds of an M-AIS (OR 0.66, 95% CI 0.46–0.94). For men, the association was inversed, i.e., having a higher gendered SEP score was associated with higher odds of an M-AIS (OR 1.52, 95% CI 1.00–2.32, p-value = 0.051; interaction coefficient 0.43, 95% CI 0.25–0.74) (Figure 4). Another way to look at these results is to compute the OR of administrative sex for predicting M-AIS, at different score values: being in the woman category increased the odds of an M-AIS for small values of the score and lowered these odds for high score values (Figure 5).

4. Discussion

Our analysis of consecutive M-AIS shows that while the risk of an M-AIS did not globally differ by administrative sex, it did so by interacting with gender-related socioeconomic variables. Using a gendered SEP score suggested that correspondence to expected gender positions protected both men and women, while non-correspondence increased the likelihood of M-AIS.

4.1. Differences in Stroke Characteristics Between Women and Men

Our sample showed differences in stroke characteristics between women and men. M-AIS was more prevalent in younger men [6], while no difference was observed in women. Looking at symptoms, women with M-AIS had more cerebellar symptoms than controls while no significative differences were observed in men. This difference in symptoms between women and men cannot be explained by a difference in arterial territory, as women and men showed the same proportion of posterior territory among M-AIS. Women with M-AIS had milder symptoms compared to women AIS, reflected by a lower NIHSS score. These results support the finding that cerebellar symptoms and milder stroke severity are more prevalent in M-AIS, particularly in women [4]. This difference in clinical presentation between women and men may be due to biological factors but may also be due to gendered SEP that leads to different expression of symptoms, which may affect recognition and grading by health care providers. As an example, the influence of sex and gender on the assessment of pain has been demonstrated in previous studies [32,33]. The neural circuits of pain differ according to biological variations, but gender also modulates pain perception and expression in patients, as well as the interpretation of symptoms by health care providers.

4.2. Gender-Related Socioeconomic Variables Associated with M-AIS

Our aim was to analyze the effects of gender-related socioeconomic variables on M-AIS beyond a binary administrative sex analysis by using an intersectional approach. We found that in unstratified administrative sex analyses, being single was associated with increased chances of an M-AIS compared to being widowed. We can interpret these results in the light of Swiss epidemiological data [34]. Single people receive lower pensions than widows. This contributes to a lower SEP, putting them at a higher risk of being in a more vulnerable state of health. This finding supports other studies showing that populations with a lower SEP have poorer pre-hospital recognition of stroke symptoms, increasing their risk of having an M-AIS [9,10].
For men, a higher level of education had a protective effect. This finding is reinforced by a study that showed that lower levels of education were associated with misdiagnosis of hemorrhagic stroke [35]. A sociological study showed that a patient’s cultural capital (defined in terms of educational level and first language) can compensate for certain deficits or, on the contrary, make them more visible [36]. Thus, language disorders could be minimized in patients who express themselves less fluently in the examiner’s language and might influence recognition of stroke by health care providers.
Women in the middle professional category were disadvantaged compared to women in the low professional category. This difference was not found in men. It has been shown that a high level of job stress seems to affect women more than men in terms of stroke risk [37,38]. In our population sample, we did not have a measure of psychosocial job-related stress, but we hypothesize that the middle category had more responsibility than the lower category and less authority than the higher category (high job demands and low job control), so they might experience a higher effect of stress. However, the relationship between work stress and the risk of misdiagnosis of stroke remains to be proven. Without establishing a direct causal relationship between these gender-related socioeconomic variables and M-AIS, our study underlines the need for health care providers to be aware of the contribution of these social risk factors to the diagnosis and prognosis of stroke.

4.3. Gendered SEP Score and M-AIS

The use of a gendered SEP score highlights the effect of the accumulation of gender-related socioeconomic variables on the probability of an M-AIS. Our score distribution for women and men is similar to another study by Pelletier et al. in young patients with recurrent acute coronary syndrome [28]. Both show that women have a more widespread distribution than men, who have a more concentrated profile, suggesting greater variability in gender characteristics for women than men. In the Pelletier study, however, having a feminine gender score was associated with a higher cardiovascular risk, regardless of administrative sex [28]. Another study showed that a high masculine gender score was protective for women and men in chronic health diseases [39]. In our study, a high level of adherence to feminine characteristics was protective for women while it showed an inverse trend in men, suggesting that correspondence to the expectations of a social position may have a protective effect on misdiagnosis. Failure to conform to expected social positions in the context of a medical emergency could lead to implicit bias among health care providers about how to care for women and men due to internalized gender stereotypes [40].

4.4. Limitations of the Study

One of the main limitations of this study is the small sample of M-AIS in our database. Furthermore, we had a high rate of missing values related to the difficulty of extracting gender-related socio-economic variables, which was partially corrected by multiple imputation. This study was done in a single institution in Switzerland and may not be applicable to other institutions or populations, especially to non-white elderly populations. Our database included only AIS and M-AIS that were recognized as such; it is possible that some M-AIS were never identified either before or after hospital discharge and were therefore not included. The gender-related socioeconomic variables did not include the gender identity dimension as this information was not recorded in the medical records or in ASTRAL. However, two other studies have suggested that the use of role-related gender variables is more representative of gender differences than the use of a gender identity index or related variables [24,41]. In fact, we have explored the intersectional aspects of gender using the SEP in an attempt to scrutinize the complex dimensions of sex and gender, a measure of gender-related variables that goes beyond the “administrative sex”. Finally, the gender of the health care provider was missing, and thereby, some stereotypes and beliefs influencing the perception of the patient’s symptoms and complaints could not be assessed.
In conclusion, gender-related socioeconomic variables play a role in the identification of AIS. Furthermore, when combined into a gendered SEP score, gender-related socioeconomic variables show different effects on M-AIS in women and men. While these findings are hypothesis-generating, a prospective qualitative study is needed to better explore the differences in diagnosis and care within the field. We believe that our study contributes to raising awareness of health care providers in contact with acute stroke patients on implicit bias and on reducing the negative impact of stereotypes on health, thereby improving health equity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ctn9020022/s1, Table S1: Patient characteristics by administrative sex.

Author Contributions

Conceptualization, C.B., P.M. and C.C.; Methodology, M.A., J.S., P.M. and C.C. Software, M.A.; Formal analysis, M.A.; Investigation, C.B.; Resources, J.S.; Data curation, P.M.; Writing—original draft, C.B.; Writing—review & editing, C.B., M.A., J.S., P.M. and C.C.; Supervision, P.M. and C.C.; Project administration, C.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent for participation was not required because this was a quality assurance project of the diagnostic practice in our institution, falling outside of the Swiss Human Research Act; therefore, both an approval by the institutional ethical commission and a patient consent procedure were not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

Glossary

AISAcute ischemic stroke
M-AISMissed acute ischemic stroke
CVRFCerebrovascular risk factor
EDEmergency department
mRSmodified Rankin Scale
NIHSSNational Institutes of Health stroke scale
OROdds ratio
SEPSocioeconomic position
LPGHLast proof of good health

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Figure 1. Logistic regression analysis of the probability of an M-AIS on gender-related socioeconomic variables. Adjusted for age, pre-stroke mRS, eye deviation, any paresis and posterior circulation. Global model also adjusted for administrative sex. Abbreviations: G = Global (no interactions), W = Women (model with interactions between gender-related variables and administrative sex), M = Men (model with interactions between gender-related variables and administrative sex).
Figure 1. Logistic regression analysis of the probability of an M-AIS on gender-related socioeconomic variables. Adjusted for age, pre-stroke mRS, eye deviation, any paresis and posterior circulation. Global model also adjusted for administrative sex. Abbreviations: G = Global (no interactions), W = Women (model with interactions between gender-related variables and administrative sex), M = Men (model with interactions between gender-related variables and administrative sex).
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Figure 2. Logistic regression analysis of administrative sex on gender-related socioeconomic variables, used for the construction of the gendered socioeconomic position score.
Figure 2. Logistic regression analysis of administrative sex on gender-related socioeconomic variables, used for the construction of the gendered socioeconomic position score.
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Figure 3. Distribution of the gendered socioeconomic position score for women and men calculated via kernel density estimation.
Figure 3. Distribution of the gendered socioeconomic position score for women and men calculated via kernel density estimation.
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Figure 4. Logistic regression analysis of the probability of an M-AIS on the gendered socioeconomic position score; The reported odds ratios refer to a 20-unit increase in the score. Abbreviations: G = Global (no interactions), W = Women (model with interactions between the gendered SEP score and administrative sex), M = Men (model with interactions between the gendered SEP score and administrative sex).
Figure 4. Logistic regression analysis of the probability of an M-AIS on the gendered socioeconomic position score; The reported odds ratios refer to a 20-unit increase in the score. Abbreviations: G = Global (no interactions), W = Women (model with interactions between the gendered SEP score and administrative sex), M = Men (model with interactions between the gendered SEP score and administrative sex).
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Figure 5. Variation of the odds ratio of administrative sex in predicting M-AIS with the gendered socioeconomic position score; Abbreviation: OR = Odds Ratio.
Figure 5. Variation of the odds ratio of administrative sex in predicting M-AIS with the gendered socioeconomic position score; Abbreviation: OR = Odds Ratio.
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Table 1. Sociodemographic characteristics of patients divided into case and control groups and by administrative sex.
Table 1. Sociodemographic characteristics of patients divided into case and control groups and by administrative sex.
Patient Characteristics by Administrative SexWomenMen
cases n = 80controls n = 79 cases n = 102controls n = 103
Administrative sex ref:women, n (%)
Age, mean (SD)71.7(18.5)71.7(18.5) 62.6(19.3)67.4(13.8)*
Stroke symptoms at admission, n (%)
Paresis48(60.0)67(85.9)**57(56.4)79(76.7)**
Sensory deficit25(31.3)37(48.1)*29(28.7)53(51.5)**
Visual fields deficit18(22.5)35(46.1)**30(29.7)41(39.8)
Eye deviation7(8.8)28(35.9)**11(10.9)23(22.3)*
Cerebellar deficit24(30.4)10(13.2)**38(38.4)33(32.7)
Dysarthria38(47.5)43(55.8) 41(41.0)49(48.0)
Aphasia23(29.1)30(38.5) 25(25.0)26(25.2)
Vigilance deficit12(15.0)16(20.3) 28(27.5)10(9.7)**
Neglect14(17.5)29(37.7) 11(11.0)26(25.2)**
Arterial territory, n (%) ** **
Anterior37(48.1)57(73.1) 42(42.4)65(66.3)
Posterior31(40.3)18(23.1) 48(44.5)26(26.5)
Both8(10.4)0(0.0) 7(7.0)4(4.1)
Undetermined1(1.3)3(3.9) 2(2.0)3(3.1)
Gender-related socioeconomic variables, n (%)
Civil status *
Widow22(27.5)30(40.0) 8(7.8)10(10.1)
Single12(15.0)6(8.0) 26(25.5)11(11.1)
Married32(40.0)27(36.0) 50(49.0)63(63.6)
Divorced14(14.0)12(16.0) 18(17.7)15(15.2)
Living situation
Living alone39(48.8)25(47.2) 31(30.7)19(24.1)
Living in a household41(51.3)28(52.8) 70(69.3)60(76.0)
Education level
Low37(78.2)32(78.1) 51(78.5)37(66.1)
High10(21.3)9(22.0) 14(21.5)19(33.9)
Professional categories
High4(6.8)3(6.4) 7(8.9)14(22.2)
Middle16(27.1)5(10.6) 13(16.5)12(19.1)
Low35(59.3)32(68.1) 54(68.4)34(54.0)
None4(6.8)7(14.9) 5(6.3)3(4.8)
Professionally active22(28.2)15(19.0) 37(38.5)34(33.3)
Having children50(72.5)43(75.4) 51(60.0)39(69.6)
Statistically significant in univariate comparisons: p value ≤ 0.05 *, p-value ≤ 0.01 **.
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Barras, C.; Amiguet, M.; Schwarz, J.; Michel, P.; Clair, C. Sex and Gender Disparities in Missed Acute Ischemic Stroke: A Nested Case-Control Study. Clin. Transl. Neurosci. 2025, 9, 22. https://doi.org/10.3390/ctn9020022

AMA Style

Barras C, Amiguet M, Schwarz J, Michel P, Clair C. Sex and Gender Disparities in Missed Acute Ischemic Stroke: A Nested Case-Control Study. Clinical and Translational Neuroscience. 2025; 9(2):22. https://doi.org/10.3390/ctn9020022

Chicago/Turabian Style

Barras, Cécile, Michael Amiguet, Joëlle Schwarz, Patrik Michel, and Carole Clair. 2025. "Sex and Gender Disparities in Missed Acute Ischemic Stroke: A Nested Case-Control Study" Clinical and Translational Neuroscience 9, no. 2: 22. https://doi.org/10.3390/ctn9020022

APA Style

Barras, C., Amiguet, M., Schwarz, J., Michel, P., & Clair, C. (2025). Sex and Gender Disparities in Missed Acute Ischemic Stroke: A Nested Case-Control Study. Clinical and Translational Neuroscience, 9(2), 22. https://doi.org/10.3390/ctn9020022

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