The Gender health paradox in European countries after the COVID-19 breakdown

The Gender health paradox in European countries after the COVID-19 breakdown | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Gender health paradox in European countries after the COVID-19 breakdown Simone Sarti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3960867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction There are well-established facts regarding the gender health gap. While women tend to live longer than men, they often experience poorer health conditions, even when controlling for age and other socio-economic factors. This counterintuitive relationship between longevity and psycho-physical vulnerability is known as the ‘gender health paradox’, prevalent not only in Europe. Bambra and colleagues (2021) have argued that the COVID-19 pandemic might have exacerbated this paradox, citing two main causes: higher mortality rates for men due to COVID-19, but a greater likelihood for women being diagnosed with the virus, implying a potential worsening of their health. Methods Utilizing Eurostat statistics on mortality and individual data from the European Social Survey (ESS) collected between 2018 and 2021, this study conducts an analysis of gender-based health inequalities. Data from both pre and post-pandemic periods are available for 17 European countries, allowing for an assessment of changes in gender health inequalities using multivariate regression analysis and indicators such as perceived health and health problems. Results Results indicate a slight increase in the gender difference in life expectancy, favouring women by 0.143 years. Simultaneously, both poor perceived health and health problems (resulting in difficulties in daily activities) have slightly increased for highly educated women in the COVID-19 era compared to their male counterparts in the pre-COVID-19 period. However, the variation among countries warrants further investigation. Conclusions These findings support Bambra and colleagues' concerns regarding a potential exacerbation of the ‘gender health paradox’ after COVID-19, with the resulting socio-health implications. gender health gap health inequalities gender health paradox gender studies gender/sex ESS Comparative studies COVID-19 gender disparities gender gap Figures Figure 1 Figure 2 Introduction Gender health disparity is well-documented evidence (Bambra et al. 2021 , Doyal 1995 ), with women consistently exhibiting higher life expectancies than men. Eurostat reports that the gender difference in life expectancy in 2019 in the Euro area (19 countries) was 5.1 years, respectively 84.9 for women and 79.8 years for men. However, the aftermath of the COVID-19 pandemic in 2021 witnessed a decrease in life expectancy to 84.2 years for women and 79.0 years for men, with a consequential increase in the gender difference by 0.1 years (Eurostat 2023 ). The advantage in survivorship for women is paradoxically associated with poorer health conditions, leading scholars to refer to this phenomenon as the ‘gender health paradox’. Numerous studies indicate that women exhibit a higher prevalence of mental health disorders, an increased risk of obesity, and, due to their longer life expectancy, a greater likelihood of living for many years with cancer or cardiovascular diseases (Oksuzyam et al. 2010, Afifi 2007 , Arber et al. 1999). However, despite controlling for age and other socio-economic factors, the higher prevalence and incidence of psycho-physical problems persist (Gómez-Costilla et al. 2021 ). Even when considering variables such as a low level of education, women not only show a higher likelihood of survival but also an increased probability of experiencing worse health conditions (Bingpong et al. 2022 ). Some scholars suggest that the pandemic may exacerbate these pre-existing gender differences, considering its direct effects, such as higher mortality among men and an increased likelihood of illness among women (Bambra et al. 2021 ). This study contributes to the discourse by conducting a pre/post COVID-19 analysis of gender health inequalities. The article unfolds in three sections: 1-A theoretical framework exploring the gender health paradox ‘s theoretical underpinnings and potential implications of COVID-19 based on the research of Bambra and other scholars (Bambra et al. 2021 ). Hypotheses aligned with their work are introduced. 2- The second section describes data and methods used for hypothesis testing, introducing the European Social Survey (ESS) - Round 9 and 10. 3-The results are presented in the third section: A brief contextual analysis of mortality trends in the selected countries is provided (Eurostat 2023 ), followed by a multivariate analysis on individual data to investigate changes in gender health inequalities. This analysis focuses on two key health indicators: self-rated health (SRH) and the presence of health problems (experiencing limitations in daily activities due to health issues). I-Effects of COVID-19 on the gender health differences Various factors have been proposed and examined to explain variations in gender health disparities, encompassing distinct biological risks, acquired risks, reporting biases, and healthcare experiences (Macintyre et al. 1996 ). The extent and direction of these disparities fluctuate based on specific symptoms or conditions and the life cycle phase. Female prevalence is consistently observed only in the case of psychological distress throughout the lifespan, whereas for several physical symptoms and conditions, it is less evident or even reversed. Despite the complexity and challenges in outlining a common path of explanation for these differences, different possible explanations have been suggested. Generally, the paradox is explained through multiple causes, employing various frameworks: it is possible to distinguish between social or biological differences between sexes and genders, or to define biological, behavioural, and social mechanisms. More exhaustively Bambra and colleagues identify four main generative factors: biological, social, economic, and public policy (Bambra et al. 2021 ), in particular: Biological perspectives explore genetic and physiological differences between males and females, potentially influencing susceptibility to certain health outcomes. For instance, immune system variations in sexual chromosomal expressions may impact autoimmunity diseases like Systemic Lupus Erythematosus (SLE) (Wilkinson et al. 2022 ). Social explanations focus on gendered behaviours tied to social rules and roles. Traditional masculinity habits lead to health-damaging behaviours in men, while women may experience mental strain due to the imbalance resulting from juggling dual roles in both work and family (Wang et al. 2008 ). Other health risk factors, such as homicides, are more frequent among men (Lu et al. 2023 ), as is the consumption of drugs or alcohol (Fonseca et al. 2021 , Kossova et al. 2020 ). Economic factors, such as low rates of female occupation, the gender pay gap, and labour market segregation, contribute to women's higher rates of poverty and morbidity. Women are also more prone to precarious employment or working in low-wage sectors of the economy (AUTHORS). Overall, female workers tend to have a better health condition than housewives, although this pattern was stronger for low educated women (Bambra et al. 2021 , Artazcoz et al. 2004 ). Public policy explanations emphasize macro-level determinants shaping gender inequalities, with mixed effects on health outcomes. European family policies, including childcare and parental leave, aim to address gendered care burdens, but their impact on health inequalities remains heterogeneous, crossing with other factors, amplifying them or reducing their effects (Gómez-Costilla et al. 2021 ). According to Bambra and colleagues, these four generative factors in shaping the ‘gender health paradox’ could interact with the COVID-19 pandemic effects (Bambra et al. 2021 ). Recently, scholars highlighted various points of attention, including sex differences in the severity of symptoms and mortality due to COVID-19. Males were more likely than females to require intensive care unit admission, likely due to biochemical sex differences in specific immune factors (ACE2 enzyme, T cell response, and others) (Wilkinson et al. 2022 ). Other scholars emphasize higher psychological distress in women during the more dramatic periods of the COVID-19 outbreak, perhaps explained by a greater negative reaction to adverse events tied to the pandemic (Szabo et al. 2020 , AUTHORS). A particular concern is the augmented risk for women for intimate partner violence. A literature review by Kourti et al. shows that lockdown led to constant contact between perpetrators and victims, resulting in increased episodes of domestic violence suffered by women (Kourti et al. 2023 ). Examining the occupational structure, women are more present in healthcare and assistance services, directly experiencing a higher risk of exposure to COVID-19 (Bandyopadhyay et al. 2020 ). Moreover, according to Utzet and colleagues (Utzet et al. 2022 ), the suspension of activities during lockdown also had indirect gender impacts, particularly affecting women in roles such as healthcare, cleaning, geriatric care, and food retail. These sectors, already marked by precarious conditions and indicators of poor health before the pandemic, faced intensified challenges. Women, whether essential or non-essential, engaged in telework, took on the majority of household caregiving responsibilities, potentially contributing to declining mental health during lockdown, especially for the most vulnerable women. Collectively, these factors could accelerate the ‘gender health paradox’, increasing mortality in men and worsening health in women. II-Data and methods Two primary dimensions of change in the ‘gender health paradox’ are analysed. Firstly, we examine overall changes in life expectancy, aiming to assess if the higher mortality risk associated with COVID-19 for men (Bambra et al. 2021) significantly altered the gender difference in the likelihood of survival. Secondly, we conduct more detailed analyses involving individual data, focusing on changes in health conditions pre and post COVID-19. Using individual data allows for more specific insights, particularly concerning social health inequalities. To address this second research question, we utilize data from the European Social Survey (ESS). The ninth round of the survey was conducted in the 2018/2019 period, and the tenth round in the 2020/21 period (see the detailed interview schedule shown in Table 1). The research design enables a direct comparison between pre and post COVID-19 breakdowns across 17 countries (as some countries are present in only one of the two rounds), representing approximately 218 million European individuals. While the data are not panel due to independent samples in ESS rounds, methodological precautions allow for a longitudinal approach. Specifically, the analysis considers groups based on the following characteristics: Only subjects aged at least 30 years old and less than 70. Gender (considered stable on average in the two rounds). Level of education is considered for individuals aged 30 years or older who have completed their formal education. We distinguish between low and high educated people, defining low education as the lowest ISCED level if that level represents more than 5% of cases per nation; otherwise, it also includes the penultimate ISCED level. These groups are considered homogeneous with respect to these characteristics. Conditions such as mortality rates high enough to generate significant selection effects in this population segment (30-70 years old) or substantial migratory processes that could alter group composition are excluded. Additionally, two critical control variables are considered: country and the age of interviewees. Age is a fundamental determinant of health, just as the role of the national context shapes population health conditions in the framework of gender health disparities (Gómez-Costilla et al. 2021, Bingpong et al. 2022). Multivariate regression models (OLS and MLE) are applied to estimate associations between socio-demographic groups and health indicators (dependent variables). Health indicators in ESSs useful for answering the research questions include self-rated health (SRH), a subjective general health measure with five categories (1 Very good, 2 Good, 3 Fair, 4 Bad, 5 Very bad), and the presence of health problems ("Hampered in daily activities by illness/disability/infirmity/mental problem"). Although SRH is considered a reliable predictor of interviewees' actual health conditions (Gunasekara et al. 2021, Idler et al. 1997, Jylhä 2009), the "health problems" variable is employed as an additional control, specifically targeting a more detailed physical dimension of the health status. To conduct robustness checks, perceived health is considered differently in three models (Models 1-A; 1-B; 1-C): i) as a metric, from 1 to 5, with OLS estimation technique of regression coefficients; ii) as binary, where ‘Very Good’ and ‘Good’ are equal to zero, and ‘Fair’, ‘Bad’, and ‘Very bad’ are equal to one, also with OLS estimation; iii) the previous variable as dichotomous, using MLE estimation of odds ratio technique. The model referring to Health problems (Model 2) considers this outcome as dichotomous, also applying MLE estimation (Hellevik 2009, Wooldridge 2009, King et al. 2001). In all analyses, a combination of post-stratification weights, including design weight and the Population size weight, is applied, being interested in an overall estimate of the health gender gap. The ‘gender health paradox’ results from two aspects: a contemporaneous condition where women live longer but with worse health status. Therefore, if Bambra and colleagues' concerns are valid, we would anticipate a rise in gender differences in life expectancy, disadvantaging men, and a simultaneous deterioration in health conditions for women after the pandemic outbreak (Bambra et al. 2021). Tab.1. Countries and interview periods. Year 2018 2018 2019 2019 2019 2019 2020 2021 2021 2021 2021 Months 7-9 10-12 1-3 4-6 7-9 10-12 9-10 1-3 4-6 7-9 10-12 Total Bulgaria 0 1413 0 0 0 0 0 601 1140 0 0 3154 Switzerland 403 514 36 0 0 0 0 322 255 205 182 1917 Czechia 0 794 828 0 0 0 0 780 864 0 0 3266 Estonia 0 1084 127 0 0 0 0 210 626 181 0 2228 Finland 205 690 170 0 0 0 0 0 617 301 0 1983 France 0 719 562 1 0 0 0 0 900 381 0 2563 Croatia 0 0 0 0 79 1047 0 335 646 5 0 2112 Hungary 0 0 1004 62 0 0 0 750 417 0 0 2233 Iceland 0 0 0 0 0 503 0 2 446 104 5 1060 Italy 0 60 1565 0 0 0 0 0 15 520 1053 3213 Lithuania 0 0 0 0 15 1135 0 133 503 443 0 2229 Montenegro 0 0 0 210 480 122 0 0 0 549 258 1619 Netherlands 227 739 72 0 0 0 0 0 178 454 283 1953 Norway 0 357 400 112 0 0 0 72 472 216 151 1780 Portugal 0 58 244 164 37 141 0 0 431 594 143 1812 Slovenia 19 804 30 0 0 0 393 367 17 0 0 1630 Slovakia 0 0 0 11 548 169 0 561 417 0 0 1706 Total 854 7232 5038 560 1159 3117 393 4133 7944 3953 2075 36458 III-Gender gap in SRH and Health problems pre and post COVID-19 The table 2 displays the difference in life expectancy in the countries of interest, revealing two significant findings. First, the overall (weighted) gender gap in life expectancy slightly increased by 0.143 years from 2019 to 2021 after the pandemic outbreak. Second, this increase is not uniform across countries. Some countries experienced a reduction in the gap (Norway, Lithuania, Finland, Iceland, and Portugal), while others witnessed an increase (Montenegro, Czechia, Hungary, Switzerland, and Slovenia), varying by two or more decimals. Therefore, the initial evidence indicates a slight augmentation of the gender gap in life expectancy, with diversified impacts on the examined European countries. Tab.2. Life expectancy by gender in 2019 and 2021, and gender gap differences (Source: Eurostat, online data code: demo_mlexpec). 2019 Women 2019 Men 2021 Women 2021 Men Gender gap in 2019 Gender gap in 2021 D ifference in Gender gap 2021-2019 Population (thousands) in 2020 5 Weight Weighted difference in gender gap Bulgaria 78.8 71.6 75.1 68.0 7.2 7.1 -0.1 6,934 0.032 -0.003 Switzerland 85.8 82.1 85.8 81.8 3.7 4.0 0.3 8,638 0.040 0.012 Czechia 1 82.2 76.4 80.5 74.1 5.8 6.4 0.6 10,697 0.049 0.029 Estonia 83.0 74.5 81.4 72.7 8.5 8.7 0.2 1,329 0.006 0.001 Finland 84.8 79.3 84.6 79.3 5.5 5.3 -0.2 5,529 0.025 -0.005 France 2 85.9 79.9 85.5 79.3 6.0 6.2 0.2 67,571 0.309 0.062 Croatia 1 81.6 75.3 79.8 73.6 6.3 6.2 -0.1 4,047 0.019 -0.002 Hungary 79.7 73.1 77.8 70.7 6.6 7.1 0.5 9,750 0.045 0.022 Iceland 84.7 81.7 84.6 81.8 3.0 2.8 -0.2 366 0.002 0.000 Italy 3 85.7 81.4 84.9 80.5 4.3 4.4 0.1 59,438 0.272 0.027 Lithuania 81.2 71.6 78.8 69.5 9.6 9.3 -0.3 2,794 0.013 -0.004 Montenegro 79.5 74.0 77.0 70.8 5.5 6.2 0.7 621 0.003 0.002 Netherlands 83.7 80.6 83.0 79.7 3.1 3.3 0.2 17,441 0.080 0.016 Norway 84.7 81.3 84.7 81.7 3.4 3.0 -0.4 5,379 0.025 -0.010 Portugal 4 84.8 78.7 84.4 78.5 6.1 5.9 -0.2 10,297 0.047 -0.009 Slovenia 84.5 78.7 83.8 77.7 5.8 6.1 0.3 2,102 0.010 0.003 Slovakia 81.2 74.3 78.2 71.2 6.9 7.0 0.1 5,458 0.025 0.002 Total 218,391 1.000 0.143 1 2021: Break in series. 2 2019 and 2021: Provisional. 3 2019: Break in series. 4 2021: Break in series, provisional. 5 Source: World Bank. The second aspect assessed to identify changes in the ‘gender health paradox’ is the deterioration in women's conditions. Subsequently, a multivariate analysis was conducted, incorporating social health inequalities by using education as a proxy for socio-economic status and distinguishing between low and higher educated individuals. Detailed results are presented in Table 3, and Figures 1 and 2 highlight the crucial coefficients from the regression analyses, with "Men with high education Pre COVID-19" as the reference category. For simplicity, comments refer to Model 1-C (see Figure 1), where odds ratios (OR) represent the likelihood of perceiving poor health compared to good health. The OR reveals that less educated individuals have worse perceived health compared to more educated ones (2.114; 2.516; 1.662; 1.921 vs. 1.000; 1.130; 0.931; 1.246). This data confirms what is widely known in the literature on social health inequalities: A low educational level is often associated with social, material, cultural, and relational disadvantages, leading to poorer health conditions (Bingpong et al. 2022, Marmot 2020, Raghupathi et al. 2020). This inequality persists from the pre-pandemic period, although differences in SRH between less and more educated individuals seem less intense. A subtle effect is observable in the OR of "Women with high education post COVID-19," significantly higher than the reference category (1.246 with a 95% Confidence Interval from 1.025 to 1.385). Similar results are observed when considering SRH as a metric, dichotomous, and using different estimation procedures (OLS or MLE). Regarding the health problems indicator, the results appear consistent. Less educated individuals present health problems, and a more significant worsening of women's health between the two periods is evident in more educated women compared to more educated men (see Figure 2 – Model 2). Women with higher education post COVID-19 have an OR of 1.192 (CI 95% from 1.018 to 1.349) compared to the reference category (men with high education pre COVID-19). Moreover, men with higher education present an OR of 0.833 (CI 95% from 0.707 to 0.981), suggesting a slight improve with respect to the category of reference. In all models, the control variables confirm the obvious role of age in the aggravation of health conditions (SHR and health problems), along with significant country-level heterogeneity. Tab.3. Coefficients and Odds ratios of gender inequalities on SRH (metric or dichotomous) and Health problems, interactions with pre-pandemic (2018/19) and post-pandemic (2020/2021) period, controlled for age and nation (valid cases 36588, weighted). Model 1-A Model 1-B Model 1-C Model 2 SHR (1-5) Beta (OLS) C.I. 95% Beta SHR Good vs Not Beta (OLS) C.I. 95% Beta SHR Good vs Not O.R. (MLE) C.I. 95% Exp(Beta) Health problems Yes vs Not O.R. (MLE) C.I. 95% Exp(Beta) Intercept 1.000 (0.931;1.067) -0.265 (-0.303;-0.227) - - Age 0.020 (0.018;0.020) 0.009 (0.008;0.009) 1.050 (1.047;1.051) 1.040 (1.037;1.042) Pre covid-19 Men high educ. 0 a 0 a 1 a 1 a Men low educ. 0.287 (0.25;0.324) 0.135 (0.114;0.155) 2.114 (1.878;2.378) 1.651 (1.449;1.882) Women low educ. 0.324 (0.287;0.361) 0.174 (0.153;0.194) 2.516 (2.237;2.829) 1.809 (1.588;2.059) Women high educ. 0.050 (0.006;0.092) 0.020 (-0.003;0.044) 1.130 (0.981;1.299) 0.986 (0.842;1.153) Post covid-19 Men low educ. 0.171 (0.133;0.207) 0.087 (0.066;0.107) 1.662 (1.475;1.872) 1.645 (1.443;1.875) Men high educ. -0.061 (-0.104;-0.016) -0.009 (-0.033;0.015) 0.931 (0.803;1.079) 0.833 (0.707;0.981) W omen low educ. 0.244 (0.207;0.281) 0.117 (0.096;0.137) 1.921 (1.706;2.163) 1.830 (1.605;2.084) W omen high educ. 0.053 (0.010;0.094) 0.036 (0.012;0.059) 1.246 (1.086;1.428) 1.192 (1.025;1.385) Nation Slovakia 0 a 0 a 1 a 1 a Bulgaria -0.031 (-0.097;0.035) -0.021 (-0.057;0.016) 0.901 (0.741;1.095) 0.321 (0.252;0.407) Switzerland -0.336 (-0.399;-0.272) -0.140 (-0.175;-0.104) 0.387 (0.313;0.477) 0.657 (0.536;0.805) Czechia 0.009 (-0.051;0.068) 0.016 (-0.018;0.049) 1.090 (0.915;1.299) 1.255 (1.049;1.50) Estonia 0.267 (0.154;0.379) 0.147 (0.083;0.209) 2.063 (1.512;2.813) 1.055 (0.75;1.484) Finland 0.005 (-0.066;0.077) 0.009 (-0.031;0.049) 1.053 (0.851;1.302) 1.735 (1.407;2.137) France 0.151 (0.099;0.202) 0.085 (0.056;0.114) 1.534 (1.319;1.782) 1.001 (0.857;1.169) C roatia -0.081 (-0.157;-0.004) 0.037 (-0.006;0.079) 1.195 (0.96;1.485) 0.753 (0.594;0.954) Hungary 0.026 (-0.036;0.087) -0.002 (-0.036;0.032) 0.998 (0.832;1.196) 0.588 (0.482;0.716) Iceland -0.154 (-0.366;0.057) -0.038 (-0.156;0.081) 0.804 (0.409;1.577) 1.199 (0.634;2.265) Italy -0.119 (-0.170;-0.067) -0.066 (-0.094;-0.036) 0.714 (0.612;0.830) 0.300 (0.255;0.353) Lithuania 0.280 (0.195;0.364) 0.158 (0.110;0.205) 2.124 (1.678;2.687) 2.073 (1.634;2.628) Montenegro -0.102 (-0.254;0.049) -0.009 (-0.093;0.076) 0.968 (0.614;1.526) 0.781 (0.476;1.280) Netherlands -0.009 (-0.066;0.048) -0.029 (-0.061;0.002) 0.854 (0.72;1.011) 1.189 (1.002;1.411) Norway -0.097 (-0.169;-0.023) -0.033 (-0.073;0.007) 0.833 (0.669;1.037) 1.240 (0.998;1.538) Portugal 0.250 (0.189;0.311) 0.143 (0.108;0.177) 1.966 (1.650;2.340) 0.620 (0.511;0.751) Slovenia 0.021 (-0.037;0.068) 0.023 (-0.009;0.048) 1.132 (0.952;1.301) 1.119 (0.847;1.476) a Categories of reference. Conclusion “Men die, women suffer.” (Oksuzyam et al. 2010): ‘gender health paradox’ is the resultant of the combination of a higher chance to die for men, but worst health living condition for women: Women tend to survive men in bad health condition as cancer, mental illness as depressive and anxiety disorders (Afifi 2007 , Wilkinson et al. 2022 ]. They tend also to declare more pain problems than men (Macintyre et al. 1996 ). Bambra and colleagues highlighted the risk of reinforcing the ‘gender health paradox’ in last years, considering the higher severity of COVID-19 for men and various other factors affecting gender disparities during the pandemic, both biological, social and economic (Bambra et al. 2021 , Wilkinson et al. 2022 , Bandyopadhyay et al. 2020 ). The most important factors mentioned above are: an augmented risk of domestic violence for women (Lu et al. 2023 ), a more pervasive socio-economic vulnerability for women (AUTHORS, Artazcoz et al. 2004 ), a higher exposition to the virus for women (Bandyopadhyay et al. 2020 ), and social policy interactions between welfare and healthcare needs (Bambra et al. 2021 ). Considering 17 European countries, which collectively represent approximately 218 million Europeans, general data on life expectancy (at birth) provided by Eurostat in December 2023 show that the gender gap in mortality has slightly increased by 0.143 years from 2019 to 2021. Meanwhile, an analysis here conducted on individual data stemming from ESS surveys between the 2018-19 and 2020-21 reveals a relative worsening of health among women. Perceived health among highly educated women after COVID-19 outbreak is 1.246 times worse compared to highly educated men in pre-pandemic era. Post-COVID-19 women with a high level of education are 1.192 times more likely to be hindered in daily activities due to health problems compared to more educated men in the pre-pandemic years. Despite the limitations of the data used (some Eurostat estimates are provisional or declare breaks in the historical series), and in the models applied, the hypothesis of Bambra and colleagues seems confirmed, the gender health paradox would appear to have increased slightly with COVID-19. It is not possible to attribute a causal meaning to the relationship between COVID-19 and ‘gender health paradox’, but what we can highlight is a relative worsening of the conditions for more educated women compared to more educated men in the pandemic period. This could constitute an alarm signal to be taken into account in the coming years, also suggesting a further unequal consequence of the pandemic outbreak, with consequences on public demand for health services, with an increase in the number of women, especially elderly, with severe health problems (McGowan et al. 2022). While awaiting further confirmation, at least two points remain to be explored. The European country heterogeneity, and the possibility of examining the processes that more specifically led to this slight increase. Declarations Declaration of conflicting interests The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article. Funding The authors received no financial support for the research, authorship and/or publication of this article. Data availability statement The datasets generated during the current study are available from the corresponding author. Original datasets are taken from the public databases. ESS surveys are available at: https://www.europeansocialsurvey.org/data-portal; EUROSTAT data are available at:https://ec.europa.eu/eurostat/databrowser/view/demo_mlexpec/default/table?lang=en. Author Contribution There is only one author. References Afifi, M. (2007). Gender differences in mental health. Singapore medical journal , 48 (5), 385. Arber, S., & Cooper, H. (1999). Gender differences in health in later life: the new paradox?. Social science & medicine , 48 (1), 61-76. Artazcoz, L., Borrell, C., Benach, J., Cortès, I., & Rohlfs, I. (2004). Women, family demands and health: the importance of employment status and socio-economic position. Social science & medicine , 59 (2), 263-274. AUTHORS, Applied Research in Quality of Life AUTHORS, Social Indicators Research . Bambra, C., Albani, V., & Franklin, P. (2021). COVID-19 and the gender health paradox. Scandinavian Journal of Public Health , 49(1), 17-26. 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Logistic regression in rare events data. Political Analysis, 9 (2), 137–163. https:// doi. org/ 10. 1093/ oxfor djour nals. pan. a0048 68 Kossova, T., Kossova, E., & Sheluntcova, M. (2020). Gender gap in life expectancy in Russia: The role of alcohol consumption. Social Policy and Society , 19 (1), 37-53. Kourti, A., Stavridou, A., Panagouli, E., Psaltopoulou, T., Spiliopoulou, C., Tsolia, M., ... & Tsitsika, A. (2023). Domestic violence during the COVID-19 pandemic: a systematic review. Trauma, violence, & abuse , 24 (2), 719-745. Lu, Y., Santos, M. R., & Zhang, Z. (2023). Social Change, Gender Stratification and the Sex Gap of Homicide Victimization in 76 Countries, 1975–2017. The British Journal of Criminology , 63 (4), 1058-1079. Macintyre, S., Hunt, K., & Sweeting, H. (1996). Gender differences in health: are things really as simple as they seem?. Social science & medicine , 42(4), 617-624. Marmot, M. (2020). Health equity in England. BMJ: British Medical Journal , 368 , 1-4. McGowan, V. J., & Bambra, C. (2022). COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities. The Lancet Public Health , 7(11), e966-e975. Oksuzyan, A., Brønnum-Hansen, H., & Jeune, B. (2010). Gender gap in health expectancy. European Journal of Ageing , 7 , 213-218. Raghupathi, V., & Raghupathi, W. (2020). The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives of Public Health , 78 (1), 1-18. Szabo, A., Ábel, K., & Boros, S. (2020). Attitudes toward COVID-19 and stress levels in Hungary: Effects of age, perceived health status, and gender. Psychological Trauma: Theory, Research, Practice, and Policy , 12(6), 572. Utzet, M., Bacigalupe, A., & Navarro, A. (2022). Occupational health, frontline workers and COVID-19 lockdown: new gender-related inequalities?. J Epidemiol Community Health , 76 (6), 537-543. Wang, J., Lesage, A., Schmitz, N., & Drapeau, A. (2008). The relationship between work stress and mental disorders in men and women: findings from a population-based study. Journal of Epidemiology & Community Health , 62 (1), 42-47. Wilkinson, N. M., Chen, H. C., Lechner, M. G., & Su, M. A. (2022). Sex differences in immunity. Annual review of immunology , 40, 75-94. Wooldridge, J. (2009). Introductory Econometrics: A Modern Approach. Mason, OH: South-Western. R. Desbordes and V. Verardi, 181. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3960867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282523212,"identity":"9e73a624-d093-4c10-8511-bd40cef997f4","order_by":0,"name":"Simone Sarti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACZiT2gQ8MDDwgkQNEamFmODgDpgW/HiQtzDxw+/Cok2/nTnxcUMFgzz/7/MHDtm13ZHTbGRgPf8CjxeAw72bjGWcYEmecS2Y4nNv2jMfsMAGHGTDzbpPmbWNIYDjDDNJymLAW+WaQln8M9vIgLZbEaGE4DNLSwMC4AaSFkRgtYL/wHJNI3HiG2eBgzzmQFsaGA2fwOaz/7MbHPDU29nJnGB9/+FF22N7s/OHDHyrwOQwCJJA5jA2ENYyCUTAKRsEowAsAz79MREXp2SEAAAAASUVORK5CYII=","orcid":"","institution":"University of Milan","correspondingAuthor":true,"prefix":"","firstName":"Simone","middleName":"","lastName":"Sarti","suffix":""}],"badges":[],"createdAt":"2024-02-16 09:48:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3960867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3960867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53580359,"identity":"76a58093-736e-4964-8f0b-93146b193254","added_by":"auto","created_at":"2024-03-27 17:27:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209555,"visible":true,"origin":"","legend":"\u003cp\u003eModel 1-C: Odds ratios of gender inequalities pre and post-pandemic on SHR as dichotomous (MLE), Good health vs Not good, and confidence intervals (95%). Weighted.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3960867/v1/9b73a19d22466542d144bf31.png"},{"id":53581529,"identity":"e1c646bc-180f-4997-aeef-d96540131e3b","added_by":"auto","created_at":"2024-03-27 17:35:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267053,"visible":true,"origin":"","legend":"\u003cp\u003eModel 2: Odds ratios of gender inequalities pre and post-pandemic on Health problems as dichotomous (MLE), Yes vs No, and confidence intervals (95%). Weighted.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3960867/v1/47a17fb55bbb979f12acd91f.png"},{"id":57473416,"identity":"05146ff8-451c-4a9c-8bec-241a56723c53","added_by":"auto","created_at":"2024-05-31 07:25:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1596995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3960867/v1/1658484f-efb5-471a-88ba-88c33829197b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Gender health paradox in European countries after the COVID-19 breakdown","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGender health disparity is well-documented evidence (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Doyal \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), with women consistently exhibiting higher life expectancies than men. Eurostat reports that the gender difference in life expectancy in 2019 in the Euro area (19 countries) was 5.1 years, respectively 84.9 for women and 79.8 years for men. However, the aftermath of the COVID-19 pandemic in 2021 witnessed a decrease in life expectancy to 84.2 years for women and 79.0 years for men, with a consequential increase in the gender difference by 0.1 years (Eurostat \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe advantage in survivorship for women is paradoxically associated with poorer health conditions, leading scholars to refer to this phenomenon as the \u0026lsquo;gender health paradox\u0026rsquo;. Numerous studies indicate that women exhibit a higher prevalence of mental health disorders, an increased risk of obesity, and, due to their longer life expectancy, a greater likelihood of living for many years with cancer or cardiovascular diseases (Oksuzyam et al. 2010, Afifi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Arber et al. 1999). However, despite controlling for age and other socio-economic factors, the higher prevalence and incidence of psycho-physical problems persist (G\u0026oacute;mez-Costilla et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even when considering variables such as a low level of education, women not only show a higher likelihood of survival but also an increased probability of experiencing worse health conditions (Bingpong et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome scholars suggest that the pandemic may exacerbate these pre-existing gender differences, considering its direct effects, such as higher mortality among men and an increased likelihood of illness among women (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study contributes to the discourse by conducting a pre/post COVID-19 analysis of gender health inequalities. The article unfolds in three sections:\u003c/p\u003e \u003cp\u003e1-A theoretical framework exploring the gender health paradox \u0026lsquo;s theoretical underpinnings and potential implications of COVID-19 based on the research of Bambra and other scholars (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hypotheses aligned with their work are introduced.\u003c/p\u003e \u003cp\u003e2- The second section describes data and methods used for hypothesis testing, introducing the European Social Survey (ESS) - Round 9 and 10.\u003c/p\u003e \u003cp\u003e3-The results are presented in the third section: A brief contextual analysis of mortality trends in the selected countries is provided (Eurostat \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), followed by a multivariate analysis on individual data to investigate changes in gender health inequalities. This analysis focuses on two key health indicators: self-rated health (SRH) and the presence of health problems (experiencing limitations in daily activities due to health issues).\u003c/p\u003e"},{"header":"I-Effects of COVID-19 on the gender health differences","content":"\u003cp\u003eVarious factors have been proposed and examined to explain variations in gender health disparities, encompassing distinct biological risks, acquired risks, reporting biases, and healthcare experiences (Macintyre et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The extent and direction of these disparities fluctuate based on specific symptoms or conditions and the life cycle phase. Female prevalence is consistently observed only in the case of psychological distress throughout the lifespan, whereas for several physical symptoms and conditions, it is less evident or even reversed. Despite the complexity and challenges in outlining a common path of explanation for these differences, different possible explanations have been suggested. Generally, the paradox is explained through multiple causes, employing various frameworks: it is possible to distinguish between social or biological differences between sexes and genders, or to define biological, behavioural, and social mechanisms. More exhaustively Bambra and colleagues identify four main generative factors: biological, social, economic, and public policy (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in particular:\u003c/p\u003e \u003cp\u003eBiological perspectives explore genetic and physiological differences between males and females, potentially influencing susceptibility to certain health outcomes. For instance, immune system variations in sexual chromosomal expressions may impact autoimmunity diseases like Systemic Lupus Erythematosus (SLE) (Wilkinson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial explanations focus on gendered behaviours tied to social rules and roles. Traditional masculinity habits lead to health-damaging behaviours in men, while women may experience mental strain due to the imbalance resulting from juggling dual roles in both work and family (Wang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Other health risk factors, such as homicides, are more frequent among men (Lu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as is the consumption of drugs or alcohol (Fonseca et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kossova et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEconomic factors, such as low rates of female occupation, the gender pay gap, and labour market segregation, contribute to women's higher rates of poverty and morbidity. Women are also more prone to precarious employment or working in low-wage sectors of the economy (AUTHORS). Overall, female workers tend to have a better health condition than housewives, although this pattern was stronger for low educated women (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Artazcoz et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePublic policy explanations emphasize macro-level determinants shaping gender inequalities, with mixed effects on health outcomes. European family policies, including childcare and parental leave, aim to address gendered care burdens, but their impact on health inequalities remains heterogeneous, crossing with other factors, amplifying them or reducing their effects (G\u0026oacute;mez-Costilla et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Bambra and colleagues, these four generative factors in shaping the \u0026lsquo;gender health paradox\u0026rsquo; could interact with the COVID-19 pandemic effects (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, scholars highlighted various points of attention, including sex differences in the severity of symptoms and mortality due to COVID-19. Males were more likely than females to require intensive care unit admission, likely due to biochemical sex differences in specific immune factors (ACE2 enzyme, T cell response, and others) (Wilkinson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other scholars emphasize higher psychological distress in women during the more dramatic periods of the COVID-19 outbreak, perhaps explained by a greater negative reaction to adverse events tied to the pandemic (Szabo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, AUTHORS). A particular concern is the augmented risk for women for intimate partner violence. A literature review by Kourti et al. shows that lockdown led to constant contact between perpetrators and victims, resulting in increased episodes of domestic violence suffered by women (Kourti et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Examining the occupational structure, women are more present in healthcare and assistance services, directly experiencing a higher risk of exposure to COVID-19 (Bandyopadhyay et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, according to Utzet and colleagues (Utzet et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the suspension of activities during lockdown also had indirect gender impacts, particularly affecting women in roles such as healthcare, cleaning, geriatric care, and food retail. These sectors, already marked by precarious conditions and indicators of poor health before the pandemic, faced intensified challenges. Women, whether essential or non-essential, engaged in telework, took on the majority of household caregiving responsibilities, potentially contributing to declining mental health during lockdown, especially for the most vulnerable women.\u003c/p\u003e \u003cp\u003eCollectively, these factors could accelerate the \u0026lsquo;gender health paradox\u0026rsquo;, increasing mortality in men and worsening health in women.\u003c/p\u003e"},{"header":"II-Data and methods","content":"\u003cp\u003eTwo primary dimensions of change in the \u0026lsquo;gender health paradox\u0026rsquo; are analysed. Firstly, we examine overall changes in life expectancy, aiming to assess if the higher mortality risk associated with COVID-19 for men (Bambra et al. 2021) significantly altered the gender difference in the likelihood of survival. Secondly, we conduct more detailed analyses involving individual data, focusing on changes in health conditions pre and post COVID-19. Using individual data allows for more specific insights, particularly concerning social health inequalities. To address this second research question, we utilize data from the European Social Survey (ESS). The ninth round of the survey was conducted in the 2018/2019 period, and the tenth round in the 2020/21 period (see the detailed interview schedule shown in Table 1).\u003c/p\u003e\n\u003cp\u003eThe research design enables a direct comparison between pre and post COVID-19 breakdowns across 17 countries (as some countries are present in only one of the two rounds), representing approximately 218 million European individuals. While the data are not panel due to independent samples in ESS rounds, methodological precautions allow for a longitudinal approach. Specifically, the analysis considers groups based on the following characteristics:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOnly subjects aged at least 30 years old and less than 70.\u003c/li\u003e\n \u003cli\u003eGender (considered stable on average in the two rounds).\u003c/li\u003e\n \u003cli\u003eLevel of education is considered for individuals aged 30 years or older who have completed their formal education. We distinguish between low and high educated people, defining low education as the lowest ISCED level if that level represents more than 5% of cases per nation; otherwise, it also includes the penultimate ISCED level.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese groups are considered homogeneous with respect to these characteristics. Conditions such as mortality rates high enough to generate significant selection effects in this population segment (30-70 years old) or substantial migratory processes that could alter group composition are excluded. Additionally, two critical control variables are considered: country and the age of interviewees. Age is a fundamental determinant of health, just as the role of the national context shapes population health conditions in the framework of gender health disparities (G\u0026oacute;mez-Costilla et al. 2021, Bingpong et al. 2022).\u003c/p\u003e\n\u003cp\u003eMultivariate regression models (OLS and MLE) are applied to estimate associations between socio-demographic groups and health indicators (dependent variables). Health indicators in ESSs useful for answering the research questions include self-rated health (SRH), a subjective general health measure with five categories (1 Very good, 2 Good, 3 Fair, 4 Bad, 5 Very bad), and the presence of health problems (\u0026quot;Hampered in daily activities by illness/disability/infirmity/mental problem\u0026quot;).\u003c/p\u003e\n\u003cp\u003eAlthough SRH is considered a reliable predictor of interviewees\u0026apos; actual health conditions (Gunasekara et al. 2021, Idler et al. 1997, Jylh\u0026auml; 2009), the \u0026quot;health problems\u0026quot; variable is employed as an additional control, specifically targeting a more detailed physical dimension of the health status. To conduct robustness checks, perceived health is considered differently in three models (Models 1-A; 1-B; 1-C): i) as a metric, from 1 to 5, with OLS estimation technique of regression coefficients; ii) as binary, where \u0026lsquo;Very Good\u0026rsquo; and \u0026lsquo;Good\u0026rsquo; are equal to zero, and \u0026lsquo;Fair\u0026rsquo;, \u0026lsquo;Bad\u0026rsquo;, and \u0026lsquo;Very bad\u0026rsquo; are equal to one, also with OLS estimation; iii) the previous variable as dichotomous, using MLE estimation of odds ratio technique. The model referring to Health problems (Model 2) considers this outcome as dichotomous, also applying MLE estimation (Hellevik 2009, Wooldridge 2009, King et al. 2001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn all analyses, a combination of post-stratification weights, including design weight and the Population size weight, is applied, being interested in an overall estimate of the health gender gap.\u003c/p\u003e\n\u003cp\u003eThe \u0026lsquo;gender health paradox\u0026rsquo; results from two aspects: a contemporaneous condition where women live longer but with worse health status. Therefore, if Bambra and colleagues\u0026apos; concerns are valid, we would anticipate a rise in gender differences in life expectancy, disadvantaging men, and a simultaneous deterioration in health conditions for women after the pandemic outbreak (Bambra et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTab.1. Countries and interview periods.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e1983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n 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width=\"7.216494845360825%\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e2112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHungary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e1004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n 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width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n 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width=\"7.216494845360825%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e3213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLithuania\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n 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width=\"8.24742268041237%\"\u003e\n \u003cp\u003e1619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetherlands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n 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width=\"7.216494845360825%\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e1630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlovakia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e1706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e7232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e5038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e3117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e4133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e7944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e3953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e2075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e36458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"III-Gender gap in SRH and Health problems pre and post COVID-19","content":"\u003cp\u003eThe table 2 displays the difference in life expectancy in the countries of interest, revealing two significant findings. First, the overall (weighted) gender gap in life expectancy slightly increased by 0.143 years from 2019 to 2021 after the pandemic outbreak. Second, this increase is not uniform across countries. Some countries experienced a reduction in the gap (Norway, Lithuania, Finland, Iceland, and Portugal), while others witnessed an increase (Montenegro, Czechia, Hungary, Switzerland, and Slovenia), varying by two or more decimals.\u003c/p\u003e\n\u003cp\u003eTherefore, the initial evidence indicates a slight augmentation of the gender gap in life expectancy, with diversified impacts on the examined European countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTab.2. Life expectancy by gender in 2019 and 2021, and gender gap differences (Source: Eurostat, online data code: demo_mlexpec).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender gap\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ein 2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender gap\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ein 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eifference in Gender gap\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2021-2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation (thousands) in 2020\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted difference in gender gap\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBulgaria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e71.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e75.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e6,934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSwitzerland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e8,638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCzechia\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e82.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e74.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e10,697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstonia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e1,329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e5,529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrance\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e85.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e67,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCroatia\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e75.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e4,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHungary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e77.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e9,750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIceland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eItaly\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e59,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLithuania\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e71.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e2,794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMontenegro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e70.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetherlands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e80.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e17,441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e5,379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePortugal\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e10,297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlovenia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e83.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e2,102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlovakia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\" valign=\"top\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e5,458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.827956989247312%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e218,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.143\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e2021: Break in series.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e2019 and 2021: Provisional.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e2019: Break in series.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003e2021: Break in series, provisional.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eSource: World Bank.\u003c/p\u003e\n\u003cp\u003eThe second aspect assessed to identify changes in the \u0026lsquo;gender health paradox\u0026rsquo; is the deterioration in women\u0026apos;s conditions. Subsequently, a multivariate analysis was conducted, incorporating social health inequalities by using education as a proxy for socio-economic status and distinguishing between low and higher educated individuals. Detailed results are presented in Table 3, and Figures 1 and 2 highlight the crucial coefficients from the regression analyses, with \u0026quot;Men with high education Pre COVID-19\u0026quot; as the reference category.\u003c/p\u003e\n\u003cp\u003eFor simplicity, comments refer to Model 1-C (see Figure 1), where odds ratios (OR) represent the likelihood of perceiving poor health compared to good health. The OR reveals that less educated individuals have worse perceived health compared to more educated ones (2.114; 2.516; 1.662; 1.921 vs. 1.000; 1.130; 0.931; 1.246). This data confirms what is widely known in the literature on social health inequalities: A low educational level is often associated with social, material, cultural, and relational disadvantages, leading to poorer health conditions (Bingpong et al. 2022, Marmot 2020, Raghupathi et al. 2020). This inequality persists from the pre-pandemic period, although differences in SRH between less and more educated individuals seem less intense. A subtle effect is observable in the OR of \u0026quot;Women with high education post COVID-19,\u0026quot; significantly higher than the reference category (1.246 with a 95% Confidence Interval from 1.025 to 1.385). Similar results are observed when considering SRH as a metric, dichotomous, and using different estimation procedures (OLS or MLE).\u003c/p\u003e\n\u003cp\u003eRegarding the health problems indicator, the results appear consistent. Less educated individuals present health problems, and a more significant worsening of women\u0026apos;s health between the two periods is evident in more educated women compared to more educated men (see Figure 2 \u0026ndash; Model 2). Women with higher education post COVID-19 have an OR of 1.192 (CI 95% from 1.018 to 1.349) compared to the reference category (men with high education pre COVID-19). Moreover, men with higher education present an OR of 0.833 (CI 95% from 0.707 to 0.981), suggesting a slight improve with respect to the category of reference.\u003c/p\u003e\n\u003cp\u003eIn all models, the control variables confirm the obvious role of age in the aggravation of health conditions (SHR and health problems), along with significant country-level heterogeneity.\u003c/p\u003e\n\u003cp\u003eTab.3. Coefficients and Odds ratios of gender inequalities on SRH (metric or dichotomous) and Health problems, interactions with pre-pandemic (2018/19) and post-pandemic (2020/2021) period, controlled for age and nation (valid cases 36588, weighted).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1-A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1-B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eSHR (1-5)\u003c/p\u003e\n \u003cp\u003eBeta (OLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eC.I. 95%\u003c/p\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eSHR\u003c/p\u003e\n \u003cp\u003eGood vs Not\u003c/p\u003e\n \u003cp\u003eBeta (OLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eC.I. 95%\u003c/p\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eSHR\u003c/p\u003e\n \u003cp\u003eGood vs Not\u003c/p\u003e\n \u003cp\u003eO.R. (MLE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eC.I. 95%\u003c/p\u003e\n \u003cp\u003eExp(Beta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eHealth problems\u003c/p\u003e\n \u003cp\u003eYes vs Not O.R. (MLE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\"\u003e\n \u003cp\u003eC.I. 95%\u003c/p\u003e\n \u003cp\u003eExp(Beta)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.931;1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.265\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.303;-0.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.018;0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.008;0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.047;1.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.037;1.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePre covid-19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n 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width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen low educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.287\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.25;0.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.135\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.114;0.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.114\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.878;2.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.651\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.449;1.882)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen low educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.324\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.287;0.361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.174\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.153;0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.516\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(2.237;2.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.809\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.588;2.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen high educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.006;0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.003;0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.130\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.981;1.299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.842;1.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePost covid-19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen low educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.171\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.133;0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.087\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.066;0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.662\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.475;1.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.645\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.443;1.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen high educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.061\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.104;-0.016)\u003c/p\u003e\n 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width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.830\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.605;2.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003c/strong\u003e\u003cstrong\u003eomen high educ.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.053\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.010;0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.097;0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.057;0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.901\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.741;1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.321\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.252;0.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSwitzerland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.336\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.399;-0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.175;-0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.387\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.313;0.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.657\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.536;0.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCzechia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.051;0.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.018;0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.090\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.915;1.299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.255\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.049;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstonia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.267\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.154;0.379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.147\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.083;0.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.512;2.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.75;1.484)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.066;0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.031;0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.053\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.851;1.302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.735\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.407;2.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.151\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.099;0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.085\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.056;0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.534\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.319;1.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.857;1.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eroatia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e(0.832;1.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.588\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.482;0.716)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIceland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.154\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.366;0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.156;0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.804\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.409;1.577)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.199\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.634;2.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItaly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.119\u003c/strong\u003e\u003c/p\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e(-0.066;0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.061;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.854\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.72;1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.189\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.002;1.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.097\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.169;-0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(-0.073;0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.833\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.669;1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.998;1.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePortugal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.250\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.189;0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.143\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.108;0.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.650;2.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.620\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.511;0.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.978494623655914%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlovenia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.75268817204301%\" valign=\"top\"\u003e\n \u003cp\u003e(-0.037;0.068)\u003c/p\u003e\n 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reference.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u0026ldquo;Men die, women suffer.\u0026rdquo; (Oksuzyam et al. 2010): \u0026lsquo;gender health paradox\u0026rsquo; is the resultant of the combination of a higher chance to die for men, but worst health living condition for women: Women tend to survive men in bad health condition as cancer, mental illness as depressive and anxiety disorders (Afifi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Wilkinson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. They tend also to declare more pain problems than men (Macintyre et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBambra and colleagues highlighted the risk of reinforcing the \u0026lsquo;gender health paradox\u0026rsquo; in last years, considering the higher severity of COVID-19 for men and various other factors affecting gender disparities during the pandemic, both biological, social and economic (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Wilkinson et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Bandyopadhyay et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The most important factors mentioned above are: an augmented risk of domestic violence for women (Lu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a more pervasive socio-economic vulnerability for women (AUTHORS, Artazcoz et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), a higher exposition to the virus for women (Bandyopadhyay et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and social policy interactions between welfare and healthcare needs (Bambra et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering 17 European countries, which collectively represent approximately 218\u0026nbsp;million Europeans, general data on life expectancy (at birth) provided by Eurostat in December 2023 show that the gender gap in mortality has slightly increased by 0.143 years from 2019 to 2021. Meanwhile, an analysis here conducted on individual data stemming from ESS surveys between the 2018-19 and 2020-21 reveals a relative worsening of health among women. Perceived health among highly educated women after COVID-19 outbreak is 1.246 times worse compared to highly educated men in pre-pandemic era. Post-COVID-19 women with a high level of education are 1.192 times more likely to be hindered in daily activities due to health problems compared to more educated men in the pre-pandemic years.\u003c/p\u003e \u003cp\u003eDespite the limitations of the data used (some Eurostat estimates are provisional or declare breaks in the historical series), and in the models applied, the hypothesis of Bambra and colleagues seems confirmed, the gender health paradox would appear to have increased slightly with COVID-19. It is not possible to attribute a causal meaning to the relationship between COVID-19 and \u0026lsquo;gender health paradox\u0026rsquo;, but what we can highlight is a relative worsening of the conditions for more educated women compared to more educated men in the pandemic period. This could constitute an alarm signal to be taken into account in the coming years, also suggesting a further unequal consequence of the pandemic outbreak, with consequences on public demand for health services, with an increase in the number of women, especially elderly, with severe health problems (McGowan et al. 2022). While awaiting further confirmation, at least two points remain to be explored. The European country heterogeneity, and the possibility of examining the processes that more specifically led to this slight increase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author. Original datasets are taken from the public databases. ESS surveys are available at: \u0026nbsp;https://www.europeansocialsurvey.org/data-portal; EUROSTAT data are available at:https://ec.europa.eu/eurostat/databrowser/view/demo_mlexpec/default/table?lang=en.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThere is only one author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAfifi, M. (2007). Gender differences in mental health. \u003cem\u003eSingapore medical journal\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(5), 385.\u003c/li\u003e\n \u003cli\u003eArber, S., \u0026amp; Cooper, H. (1999). Gender differences in health in later life: the new paradox?. \u003cem\u003eSocial science \u0026amp; medicine\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(1), 61-76.\u003c/li\u003e\n \u003cli\u003eArtazcoz, L., Borrell, C., Benach, J., Cort\u0026egrave;s, I., \u0026amp; Rohlfs, I. (2004). 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Logistic regression in rare events data. \u003cem\u003ePolitical Analysis, 9\u003c/em\u003e(2), 137\u0026ndash;163. https:// doi. org/ 10. 1093/ oxfor djour nals. pan. a0048 68\u003c/li\u003e\n \u003cli\u003eKossova, T., Kossova, E., \u0026amp; Sheluntcova, M. (2020). Gender gap in life expectancy in Russia: The role of alcohol consumption. \u003cem\u003eSocial Policy and Society\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 37-53.\u003c/li\u003e\n \u003cli\u003eKourti, A., Stavridou, A., Panagouli, E., Psaltopoulou, T., Spiliopoulou, C., Tsolia, M., ... \u0026amp; Tsitsika, A. (2023). Domestic violence during the COVID-19 pandemic: a systematic review. \u003cem\u003eTrauma, violence, \u0026amp; abuse\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 719-745.\u003c/li\u003e\n \u003cli\u003eLu, Y., Santos, M. R., \u0026amp; Zhang, Z. (2023). Social Change, Gender Stratification and the Sex Gap of Homicide Victimization in 76 Countries, 1975\u0026ndash;2017. \u003cem\u003eThe British Journal of Criminology\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(4), 1058-1079.\u003c/li\u003e\n \u003cli\u003eMacintyre, S., Hunt, K., \u0026amp; Sweeting, H. (1996). Gender differences in health: are things really as simple as they seem?. \u003cem\u003eSocial science \u0026amp; medicine\u003c/em\u003e, 42(4), 617-624.\u003c/li\u003e\n \u003cli\u003eMarmot, M. (2020). Health equity in England. \u003cem\u003eBMJ: British Medical Journal\u003c/em\u003e, \u003cem\u003e368\u003c/em\u003e, 1-4.\u003c/li\u003e\n \u003cli\u003eMcGowan, V. J., \u0026amp; Bambra, C. (2022). COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities. \u003cem\u003eThe Lancet Public Health\u003c/em\u003e, 7(11), e966-e975.\u003c/li\u003e\n \u003cli\u003eOksuzyan, A., Br\u0026oslash;nnum-Hansen, H., \u0026amp; Jeune, B. (2010). Gender gap in health expectancy. \u003cem\u003eEuropean Journal of Ageing\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 213-218.\u003c/li\u003e\n \u003cli\u003eRaghupathi, V., \u0026amp; Raghupathi, W. (2020). The influence of education on health: an empirical assessment of OECD countries for the period 1995\u0026ndash;2015. \u003cem\u003eArchives of Public Health\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(1), 1-18.\u003c/li\u003e\n \u003cli\u003eSzabo, A., \u0026Aacute;bel, K., \u0026amp; Boros, S. (2020). Attitudes toward COVID-19 and stress levels in Hungary: Effects of age, perceived health status, and gender. \u003cem\u003ePsychological Trauma: Theory, Research, Practice, and Policy\u003c/em\u003e, 12(6), 572.\u003c/li\u003e\n \u003cli\u003eUtzet, M., Bacigalupe, A., \u0026amp; Navarro, A. (2022). Occupational health, frontline workers and COVID-19 lockdown: new gender-related inequalities?. \u003cem\u003eJ Epidemiol Community Health\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(6), 537-543.\u003c/li\u003e\n \u003cli\u003eWang, J., Lesage, A., Schmitz, N., \u0026amp; Drapeau, A. (2008). The relationship between work stress and mental disorders in men and women: findings from a population-based study. \u003cem\u003eJournal of Epidemiology \u0026amp; Community Health\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(1), 42-47.\u003c/li\u003e\n \u003cli\u003eWilkinson, N. M., Chen, H. C., Lechner, M. G., \u0026amp; Su, M. A. (2022). Sex differences in immunity. \u003cem\u003eAnnual review of immunology\u003c/em\u003e, 40, 75-94.\u003c/li\u003e\n \u003cli\u003eWooldridge, J. (2009). Introductory Econometrics: A Modern Approach. Mason, OH: South-Western. R. Desbordes and V. Verardi, 181.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gender health gap, health inequalities, gender health paradox, gender studies, gender/sex, ESS, Comparative studies, COVID-19, gender disparities, gender gap","lastPublishedDoi":"10.21203/rs.3.rs-3960867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3960867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\n\u003cp\u003eThere are well-established facts regarding the gender health gap. While women tend to live longer than men, they often experience poorer health conditions, even when controlling for age and other socio-economic factors. This counterintuitive relationship between longevity and psycho-physical vulnerability is known as the ‘gender health paradox’, prevalent not only in Europe. Bambra and colleagues (2021) have argued that the COVID-19 pandemic might have exacerbated this paradox, citing two main causes: higher mortality rates for men due to COVID-19, but a greater likelihood for women being diagnosed with the virus, implying a potential worsening of their health.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eUtilizing Eurostat statistics on mortality and individual data from the European Social Survey (ESS) collected between 2018 and 2021, this study conducts an analysis of gender-based health inequalities. Data from both pre and post-pandemic periods are available for 17 European countries, allowing for an assessment of changes in gender health inequalities using multivariate regression analysis and indicators such as perceived health and health problems.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eResults indicate a slight increase in the gender difference in life expectancy, favouring women by 0.143 years. Simultaneously, both poor perceived health and health problems (resulting in difficulties in daily activities) have slightly increased for highly educated women in the COVID-19 era compared to their male counterparts in the pre-COVID-19 period. However, the variation among countries warrants further investigation.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThese findings support Bambra and colleagues' concerns regarding a potential exacerbation of the ‘gender health paradox’ after COVID-19, with the resulting socio-health implications.\u003c/p\u003e","manuscriptTitle":"The Gender health paradox in European countries after the COVID-19 breakdown","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:27:38","doi":"10.21203/rs.3.rs-3960867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb7445f4-ae29-4a71-8137-813fe57cb646","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-31T07:17:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 17:27:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3960867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3960867","identity":"rs-3960867","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}