The COVID-19 pandemic has had heterogeneous impacts, with certain populations being disproportionately affected. The literature on COVID-19 indicates that socioeconomically disadvantaged groups face higher risks of contracting the virus and experiencing severe outcomes such as hospitalization and mortality [1–5]. This risk is linked to socioeconomic determinants, where limited income and education create conditions that elevate exposure risk and susceptibility to infection [1]. In many countries, studies have highlighted socioeconomic disparities in the COVID-19 cascade, both nationally [5–8] and regionally [4, 9], indicating that neighborhood-level socioeconomic vulnerability shapes these disparities [10]. Additionally, substantial gender differences, particularly in labor and family domains, impact individuals throughout their lifespan, contributing to gendered socioeconomic inequalities that affect health [11].

Globally, men were more likely to develop severe forms of COVID-19, resulting in higher hospitalizations and mortality rates compared to women [12–18]. The origins and pathways of these disparities are rooted in a complex interplay of gender-specific social processes and sex-related biological attributes. Gender, as a major social determinant of health, shapes life experiences and health outcomes through systematic differences in roles, responsibilities, access to power, and opportunities between women and men [19, 20]. Gender inequalities in health arise from the intricate interaction of multiple factors, including differential exposure to health risks, health-related behaviors, access to healthcare, and gender biases in healthcare and research [19, 21]. Exploring gender and sex reveals nuanced pathways impacting the COVID-19 progression from testing rates to mortality. Gender affects individuals’ health behaviors, access to healthcare, occupational exposures, and adherence to public health measures, thereby potentially affecting COVID-19 exposure, testing rates, positivity, and the burden of disease [15, 22–25]. Meanwhile, sex-related differences, such as immune responses and hormone levels, primarily influence susceptibility, severity, and mortality rates of COVID-19 [13, 26, 27]. Studies suggest that hormones like oestrogens and progesterone, typically higher in women, offer protection against viral infections, whereas testosterone, predominant in men, may have the opposite effect [28, 29]. Additionally, men often exhibit higher ACE-2 receptor levels, used by SARS-CoV-2 to enter cells, potentially explaining the more severe infection cases [26, 28, 30].

There are significant variations in the influence of gender, sex, and socioeconomic conditions on health throughout the lifespan, shaped by factors including evolving gender norms, fluctuating economic resources, and sex-specific developmental stages such as puberty or menopause. These variations underscore the importance of intersectional approaches to deepen our understanding of COVID-19 epidemiology [28, 31]. Building on Kimberlé Crenshaw’s work, the concept of intersectionality emerges as a critical framework that recognizes that women and men do not constitute homogenous groups [32]. It argues that their health should be investigated by simultaneously considering other major determinants of health, such as age and socioeconomic status [19, 33]. An intersectional perspective highlights the intricate ways in which socially constructed categories—rooted in structural power relations—intersect at various levels to produce nuanced layers of advantage or disadvantage [19, 34, 35]. This approach moves beyond the consideration of isolated risk factors [36] and focuses on identifying modifiable causes for health inequalities, offering major insights for formulating equitable public health policies and interventions [37].

This study analyzes surveillance data from the canton of Vaud, located in the southwestern part of Switzerland within the French-speaking region. As one of Switzerland’s larger cantons by population and area, it encompasses diverse urban and rural settings, representing approximately one-tenth of the national population. The primary objective is to explore gender and sex disparities in the COVID-19 epidemiology cascade, from testing to mortality, including test positivity, hospitalization, and intensive care unit (ICU) admission. We examined these disparities in the context of key social determinants of health, focusing on neighborhood-based socioeconomic position (SEP) and age, through an intersectional analytical approach. We aim to uncover the complex dynamics underlying these disparities and enhance our understanding of COVID-19’s broader epidemiology. Such knowledge, by considering gender and sex influences, vulnerabilities, and diverse social determinants of health is essential for developing targeted and effective public health strategies to address COVID-19 and guide responses to future pandemics [28].

By the end of 2020, Vaud population was 815,300, comprising 412,599 women (50.6%) and 402,701 men (49.4%) (Table 1). From March 2020 to June 2021, a total of 885,925 SARS-CoV-2 tests, 96,963 positive tests, 6,356 hospitalizations, 1,134 ICU admissions and 1,175 deaths (before excluding non-geocoded death notifications) were notified and met eligibility criteria (see Supplementary Figure S5). Although women had more tests and positive results, the majority of hospitalizations, ICU admissions, and deaths occurred among men.

In the canton of Vaud, 38% of women and 34% of men were aged 50 and above. Among women, this age group accounted for 33% of all tests and 38% of positive tests, but represented 80% of hospitalizations, 89% of ICU admissions, and 99.8% of deaths. Likewise, men aged 50 and older accounted for 32% of tests, 39% of positive tests, 86% of hospitalizations, 91% of ICU admissions, and 99.4% of deaths.

For total tests, 17% of women and 18% of men were in the lowest socioeconomic quintile (Q1), while 21% of tests for both women and men occurred in the highest quintile (Q5). Regarding positive tests, 21% for women and 20% for men were recorded in Q1, with 18% in Q5 for both. In terms of mortality, 18% of men who died were in Q1, and 14% in Q5. Among women, 12% of deaths occurred in Q1 and 15% in Q5.

The weekly incidence of outcomes per 100,000 showed distinct patterns between women and men throughout the study period (Figure 1). Women had higher incidence rate of tests and positive tests compared to men, especially during the second wave of the pandemic. In contrast, men had higher incidence rates of hospitalizations and ICU admissions throughout the study period, though the disparity in mortality rates was less pronounced. During the third wave, while testing rates peaked, both severe outcomes and positivity rates were comparatively lower.

The cumulative incidences of outcomes across age groups, gender/sex categories, and SEP quintiles revealed distinct patterns (Figure 2). Individuals aged 20 to 39 were the most tested group, whereas children under 10 were the least tested. Testing rates were higher for people aged 80 and above compared to those aged 60–69 and 70–79. Similar patterns emerged across age groups concerning positivity. For severe outcomes, prominent age-related trends were observed, with older age groups experiencing higher incidence rates, though ICU admission were less frequent among those aged 80 and above. Men experienced higher incidence rates of hospitalization, ICU admission, and death than women.

In terms of SEP quintiles (Figure 2B), the cumulative incidence of testing progressively increased from Q1 to Q5. For the cumulative incidence of positive tests, the three lowest SEP quintiles (Q1–Q3) showed similar rates, with lower rates observed in the two highest quintiles (Q4–Q5). A consistent trend was observed, with women having higher cumulative incidence of both tests and positive tests across all quintiles, except in Q5 where the positivity incidence was comparable between women and men. For death notifications, men’s cumulative mortality rate appeared to decrease from Q1 to Q5, whereas for women, the mortality rate was lowest in Q1 and Q4. Men displayed higher mortality rates across all SEP quintiles, except in Q2.

Regression analyses demonstrated distinct testing patterns between women and men across age groups. Notably, women aged 20–29 (IRR 1.14, CI 1.07–1.22) and 30–39 (IRR 1.16, CI 1.09–1.24) displayed a significantly higher likelihood of undergoing testing compared to men in the same age groups (Figure 3, left panel). Conversely, girls under 10 (IRR 0.91, CI 0.85–0.97) and women aged 60–69 (IRR 0.92, CI 0.86–0.98) and 70–79 (IRR 0.85, CI 0.80–0.91) were less likely to get tested compared to their male counterparts. For incidence of positive tests per population, similar gender/sex trends were observed across age groups (Figure 3, center panel). However, these differences were no longer significant when adjusting for the initial gender/sex differences in testing, as indicated by the regression results of positive tests per test (Figure 3, right panel). An exception was observed among individuals aged 60 and older, where women were less likely to test positive per test compared to men. Specifically, women had an IRR of 0.92 in the 60–69 age group (CI 0.86–0.98), 0.89 in the 70–79 age group (CI 0.82–0.95), and 0.83 among those aged 80 and older (CI 0.86–1.00), indicating a lower likelihood of a positive result when tested. Moreover, when comparing women and men in similar SEP quintiles (Figure 3, red coefficients), no statistically significant differences in testing and positive testing rates were found, except for women in Q3 who presented a slightly reduced probability of testing positive per test compared to their male counterparts.

In regression models without an interaction term for gender/sex categories (Supplementary Table S6), individuals in Q5 were notably more likely to undergo testing (IRR 1.25, CI 1.19–1.30) compared to those in Q1. Conversely, individuals in Q5 showed a decreased likelihood of testing positive per person (IRR 0.89, CI 0.85–0.95) and testing positive per test (IRR 0.71, CI 0.68–0.74).

Age was the strongest predictor for hospitalisations, ICU admissions and deaths, increasing age being associated with higher likelihoods of these events, as shown by the regression analysis in models without an interaction term by gender/sex (Supplementary Table S6). Analyzing the interaction of gender/sex with age (Figure 4A), women exhibited lower probabilities of COVID-19 hospitalization than men across all age cohorts, although the differences were not statistically significant. The IRR for women up to 59 was 0.80 (CI 0.52–1.22), 0.49 (CI 0.18–1.36) for those 60–69, 0.50 (CI 0.18–1.38) for the 70–79 age group, and 0.58 (CI 0.21–1.61) for those 80 and above. Statistical significance was achieved for ICU admissions in the under-60 cohort, where women had a 55% decreased risk (IRR 0.45, CI 0.23–0.86). Women consistently showed a lower risk of ICU admissions compared to men when hospitalized (Figure 4B). Women under 60 presented an IRR of 0.59 (CI 0.44–0.78), denoting a 41% lower risk. In the 60–69 age group, the IRR was 0.71 (CI 0.49–1.01), with the risk reduction becoming more pronounced with advancing age. Women aged 70–79 had an IRR of 0.62 (CI 0.43–0.88), while those aged 80 and over had an IRR of 0.56 (CI 0.37–0.85), mirroring the risk reduction observed in the youngest age group.

Regarding mortality, individuals in Q5 had a lower likelihood of death (IRR 0.71, CI 0.54–0.95) compared to those in Q1, as indicated by the regression models without a gender/sex interaction term (Supplementary Table S6). This association between SEP and death persisted in the sensitivity analysis that included nursing home residents and remained robust when extended to include non-precisely geocoded death notifications (Supplementary Material S7). Lower mortality rates among women were noted across all age groups (Figure 5). Women demonstrated a reduced mortality risk compared to men of 55% at ages 60–69 (IRR 0.45, CI 0.23–0.88), 58% at ages 70–79 (IRR 0.42, CI 0.30–0.59), and 45% for those aged 80 and above (IRR 0.55, CI 0.46–0.66). Regarding SEP, the gender/sex disparities in mortality were more pronounced in Q1, with women having a 68% reduction in mortality risk (IRR 0.32, CI 0.20–0.52); and these disparities were not statistically significant in Q2 and Q5. Exploring the combined effects of gender/sex, age, and SEP on mortality among older groups (70–79 and 80+), a triple interaction term was employed (Supplementary Figure S8). Our findings indicate a reduction in gender/sex mortality disparities with increasing SEP, as the IRR tends toward unity from the lowest to highest quintiles.

In the resident population of the Canton of Vaud, Switzerland, women contributed to a higher number of COVID-19 tests and positive tests than men, whereas more hospitalizations, ICU admissions, and deaths occurred among men. This finding underscores a pronounced gender/sex disparity in the pandemic’s health impact, highlighting the need to explore underlying causes, such as potential biological differences, gender-specific behavioral patterns, and occupational exposures.

Individuals residing in the highest SEP neighborhoods underwent more COVID-19 testing than those in the lowest SEP areas, accompanied by a lower likelihood of testing positive and a reduced risk of mortality. These observations suggest significant socio-economic influences on health-related behaviors and resource accessibility and utilization. Notably, our intersectional analysis revealed that these disparities in testing and positivity rates are consistent across women and men within similar SEP quintiles. Moreover, the gender/sex disparities in mortality across SEP quintiles highlight the intricate interplay between socioeconomic factors and gender/sex, reinforcing the value of an intersectional approach in uncovering nuanced aspects of COVID-19 epidemiology.

Moreover, age-related variations in SARS-CoV-2 testing rates between women and men were evident in our data. Women aged 20–29 and 30–39 had higher testing rates than men in corresponding age groups, whereas this trend reversed in the younger (<20) and older age groups (60–69 and 70–79), echoing trends observed in other European countries [48]. These variations illustrate a complex relationship between age, gender/sex, and health-seeking behavior, calling for further investigations.

Age is a key factor in understanding COVID-19 gender/sex disparities. The influence of gender norms on health outcomes varies across the life course [19, 28, 31], and the disparities in testing rates between men and women across different age groups likely reflect the evolving societal roles and responsibilities [49]. In the 20 to 40 age range, where gender differences in testing were most pronounced, marked distinctions in family and employment domains are generally observed. Women are more likely to work in essential service sectors involving close contacts and limited telecommuting options, such as in service and healthcare jobs [49–52], which may account for their higher testing rates. Yet, this potential increased exposure did not translate into higher positivity rates when accounting for initial differences in testing, possibly attributable to greater adherence to health recommendations and protective measures among women compared to men [23, 26, 29, 51, 53]. Additionally, women in this age group often bear a disproportionate burden of unpaid care responsibilities, likely influencing their decisions regarding COVID-19 testing [29, 54]. The observed higher testing rates in men aged 60 and above may be attributed to the preferential ascertainment of severe cases [55]. Individuals who are perceived as more likely to suffer from severe forms of infection–such as men, due to early reports of higher mortality rates–, are therefore tested more frequently. Although the ratios of positive tests were generally similar, women aged 60 and above were significantly less likely to test positive per test conducted compared to their male counterparts, highlighting possible differences in exposure.

Our study corroborates the well-established correlation between age and severe COVID-19 outcomes, with older age associated with an increased risk of hospitalization, ICU admissions, and mortality. Additionally, our data confirm that men face a higher risk of severe outcomes compared to women, aligning with previous research [56–58]. Notably, women under the age of 60 and those aged 70 and above had a reduced risk of ICU admissions when hospitalized, suggesting possible variations in immune system responses, prevalence of comorbidities, health-seeking behaviors, or differences in treatment approaches between women and men.

Our findings are consistent with existing literature on the link between SEP and COVID-19 outcomes [1–5], highlighting the increased vulnerability of individuals residing in low SEP neighborhoods. This vulnerability stems from a combination of factors, including limited access to healthcare, higher exposure risks due to living and working conditions, occupational hazards, and lifestyle habits, coupled with higher comorbidities rates [59]. Previous studies show that those in lower SEP areas experienced lower testing rates–particularly pronounced in the pandemic’s early stages–and faced elevated rates of case incidence, hospitalizations, and mortality, a trend reported globally [1–5, 9, 60–62].

As extensively documented in the literature and corroborated by our findings, men experienced higher mortality rates related to COVID-19 compared to women [12, 13, 15–17, 23, 29, 63]. Our results outlined gender/sex disparities in mortality across SEP quintiles, particularly marked in the lowest SEP neighborhoods. This suggests that men from socioeconomically deprived backgrounds may encounter cumulative disadvantages that amplify their health vulnerabilities throughout their lives [64]. Biological sex-related factors are thought to play a significant role in men’s increased vulnerability to COVID-19, possibly mediated through hormonal and immune response [29, 52]. Mortality is also influenced by gendered practices and societal norms such as expressions of masculinity, which intersect with various social determinants of health [52]. These include working in hazardous industries, engaging in risky health behaviors, and maintaining lifestyles that lead to higher prevalence of chronic diseases [26, 28, 29, 65, 66], which are more common in socioeconomically disadvantaged populations [60]. Beyond age, pre-existing medical conditions such as obesity, diabetes, or chronic respiratory and cardiovascular diseases have been described as major contributors to COVID-19 severity [67]. Comparatively riskier health behaviors among men, such as smoking, excessive alcohol consumption, and unhealthy diets, combined with societal norms that valorize toughness and discourage timely medical care, are thought to contribute to their health vulnerabilities [16, 19, 26]. Our findings contribute to the growing body of intersectional studies on COVID-19 epidemiology, predominantly concentrated in the US and often emphasize ethnic/racial disparities alongside gender or sex. Notably, research within this domain showed that Black women experienced higher mortality rates than White men, while Black men had the highest mortality rates [68]. Another study exploring the combined effects of gender, SEP, and race/ethnicity revealed that, compared to high SEP White women, low SEP White men experienced a mortality rate 7.4 times higher, and 1.5 times higher compared to women in low SEP. Meanwhile, low SEP Hispanic men faced the most significant disparity, with mortality rates 27 times higher than those of high SEP White women [69].

Our study’s strengths include the use of a neighborhood-based SEP indicator to capture potential individual and local-level effects on outcomes, and the minimization of selection bias by using comprehensive surveillance data for the entire Canton of Vaud population. Although Vaud was heavily impacted during the early stages of the pandemic, its diverse rural and urban population profiles provide valuable insights that may reflect broader trends in Switzerland, despite some regional variations. However, our analysis is limited by the absence of data on key individual-level factors such as migration background status and ethnicity. Incorporating these factors could greatly enrich our understanding, especially given that approximately one-third of Vaud’s population in 2020 held non-Swiss nationality [41], and ethnic minorities faced higher exposure and vulnerability to COVID-19 [62, 68, 70]. Furthermore, disentangling the sources of disparities between gender and sex is methodologically challenging when using administrative sex to investigate women’s and men’s health outcomes. Nevertheless, our intersectional approach facilitated the development of hypotheses about gendered mechanisms, which extend beyond the traditional biological interpretations common in biomedical research. Another limitation concerns hospitalization and ICU admission data, which may be subject to underreporting due to challenges associated with identifying primary causes of hospitalization, especially among older adults with comorbidities [71]. Moreover, deaths occurring outside clinical settings frequently remain untested, complicating their classification as COVID-19 related [71, 72]. Additionally, relying on residence location for the Swiss-SEP indicator may not accurately reflect an individual’s lifelong SEP, and using a neighborhood-level indicator may also introduce inaccuracies when representing individual-level characteristics. These are common challenges associated with area-based indicators [73].