eHealth inequalities in a universal healthcare system: a cross-sectional study on the role of social determinants in knowledge and use

eHealth inequalities in a universal healthcare system: a cross-sectional study on the role of social determinants in knowledge and use | 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 eHealth inequalities in a universal healthcare system: a cross-sectional study on the role of social determinants in knowledge and use Amaia Ayala-Garcia, Garazi Monasterio Gangoiti, Unai Martin Roncero This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8500757/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objectives This study examines social inequalities in knowledge and use of eHealth tools in a region of Spain. Methods A cross-sectional analysis was conducted using data from the 2023 Basque Health Survey (n = 5,683). Prevalence of knowledge and use of digital tools was described stratified by sex and social determinants. Poisson regression models with robust variance were used to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for not knowing or not using each tool. Models were progressively adjusted for age, healthcare utilisation, and knowledge in use models. Results Knowledge and use of eHealth tools varied substantially by age, education, and occupational class. The Web Appointment tool showed the highest levels of both knowledge and use. For women aged 80+, the probability of not knowing the tool was 5.09 times higher than for women aged 18–35 (95% CI: 3.54–7.32), and 4.34 (95% CI: 2.92–6.45) in men. Education and class gradients were strong, particularly in knowledge. For example, women with primary education had higher probability of not knowing the Web Appointment tool than university-educated peers (fully adjusted PR = 3.28, 95% CI: 2.39–4.51). Use of digital tools showed similar but smaller gradients. Differences by deprivation and health limitations were modest and less consistent. Conclusions Digital health inequalities in the usage of eHealth tools are strongly patterned by age and socioeconomic status, especially for knowledge; importantly, gaps in use remain even when tools are known, highlighting the need for targeted equity-focused strategies. Digital health eHealth health inequalities social determinants of health healthcare access digital divide primary care health equity Figures Figure 1 Figure 2 KEY POINTS Marked social inequalities persist in both knowledge and use of eHealth tools within a universal public healthcare system. Age, education, and occupational class are the strongest and most consistent determinants of digital exclusion. Inequalities in use remain even after accounting for knowledge, indicating that awareness alone is insufficient to ensure equitable uptake. BACKGROUND The digitalisation of healthcare systems has become increasingly important worldwide. In Europe, the European Commission actively promotes the development and integration of eHealth tools to improve access to care [ 1 ]. These technologies encompass various aspects of care, including those that connect individuals directly to the health system, such as online appointment booking, teleconsultations, and patient-accessible digital records [ 2 ]. Beyond service delivery, they also promote patient engagement in health monitoring and self-management [ 3 , 4 ] transforming how people interact with healthcare. Despite the growing presence of eHealth, discussions on equity remain limited, and much of the literature has primarily focused on the benefits of these technologies, which have even been proposed as tools to improve outcomes related to the social determinants of health. However, the persistence of the digital divide raises concerns that unequal access to these tools may reinforce or even worsen existing disparities in healthcare access [ 5 , 6 ]. This is especially concerning given evidence that technology is not socially neutral and often reflects the structural inequities of the systems in which it is developed [ 7 , 8 ]. Recent frameworks have introduced the concept of digital determinants of health, highlighting how digitalization can reinforce existing social inequalities. Paradoxically, those most in need of healthcare—often the most vulnerable groups—may also the least likely to use digital health technologies [ 9 , 10 ]. In this context, digitalisation has become a determinant of health, as it can influence how social inequalities affect access to and delivery of healthcare [ 9 , 10 ]. While qualitative studies have explored barriers and facilitators to digital health access [ 11 , 12 , 13 ], quantitative evidence remains scarce. Existing research often examines specific tools (e.g., online consultations [ 14 ], patient portals [ 15 ]), healthcare levels other than primary care [ 16 ], or populations with chronic conditions (e.g., diabetes) [ 17 , 18 ]. European studies indicate higher adoption among women [ 19 ], younger individuals, and urban residents [ 20 ], though findings on factors like education are mixed [ 14 , 19 ]. Given the risk that eHealth interventions could unintentionally exacerbate inequities if their design does not consider systemic barriers [ 21 ], it is essential to identify groups at greater risk of exclusion and the factors driving these disparities. In this context, adopting an intersectional approach is particularly relevant, as the interplay between social determinants—such as gender, age, education, or class—may shape unequal access in ways that are not captured by isolated analyses. Disaggregated and interaction-based analyses are therefore necessary to uncover hidden disparities and better inform equity-oriented digital health policies [ 22 ]. This study aims to analyse inequalities in the knowledge and use of eHealth tools in the Basque Health Service–Osakidetza (e.g., online appointment booking, and patient accessible electronic health records) and to examine how social determinants contribute to these inequalities, using data from the Basque Health Survey (ESCAV). METHODS Data source, design, setting and participants of the study A cross-sectional study was conducted using data from the 2023 Basque Health Survey (ESCAV), a population-representative survey of adults (aged 18 +) living in private households in the Basque Country (Spain). ESCAV is conducted every five years by the Basque Government’s Department of Health and collects comprehensive data on individuals’ and households’ health status, clinical conditions, risk factors, social and environmental determinants of health, and the use, access, and perception of health services. The theoretical ESCAV‑2023 household sample comprised 5,790 households; in the actual fieldwork 5,688 household questionnaires were completed. The final sample included 5,683 individuals of ≥ 18 of age and with digital health data. In the Basque Country, digital health initiatives have expanded steadily since 2008, including the launch of Osabide (the unified electronic health record), the digital health folder, online appointment systems, and telephone consultations. The digital health folder (2013) allows patients to access their clinical records, prescriptions, test results, and vaccinations online [ 23 ], while the Osakidetza mobile app (2017) enables mobile access to many of these features [ 24 ]. In 2022, the online appointment booking system was updated to allow direct scheduling, modification, or cancellation of in-person visits via the Osakidetza website [ 25 ]. Since the 2018 edition, a sub-section has been included with specific questions on the use and knowledge of the new information technologies implemented in the health system, including the online appointment booking (officially named web appointment), the electronic health records (EHRs) (officially named health folder) and the eHealth portal (officially named Osakidetza App). The detailed methodology of the survey can be consulted in the methodological publication of the Department of Health [ 26 ]. Variables As dependent variables, knowledge and use of the following digital tools were used: online appointment booking, EHRs, and eHealth portal. The ESCAV collects information on these variables through the dichotomous questions: “Indicate whether you are aware of these services” and “Have you used it?” (the latter only asked of those who responded affirmatively to the first). As independent variables the analysis focused on several social determinants of health hypothesised to influence inequalities in the knowledge and use of digital health tools. We included age group (18–35, 36–50, 51–65, 66–79, ≥ 80 years), conceptualised both as a marker of social position and a key factor shaping digital competence. Educational level (primary, lower secondary, upper secondary, university), occupational social class—classified according to the Spanish Society of Epidemiology (SEE) [ 27 ]—and the area-level deprivation index [ 28 ], categorised into quintiles with the first quintile representing the least deprived areas, were also included as indicators of socioeconomic position. In addition, self-reported health-related limitations (yes/no) were considered as a potential axis of inequality, as functional limitations may affect access to and use of digital tools. We treated chronic conditions (yes/no) and healthcare use—categorised into tertiles (low, medium, high) based on the number of general practitioner or paediatric visits in the last 12 months—as adjustment covariates in the regression models, given their potential influence on eHealth tool usage. Sex was used as a stratification variable in the main analysis, included as an interaction term in pooled models to assess effect modification, and also analysed as a main independent variable to estimate overall differences between women and men. Statistical analysis Firstly, the prevalence of knowledge and use of digital services for accessing the health system was calculated according to social determinants of health and other covariables (Supplementary Table 1). To analyse the association between knowledge and use of the online appointment system, the personal health folder, and the Osakidetza app with social determinants of health, Poisson regression models with robust variance were applied and prevalence ratios of not knowing and not using eHealth tools were calculated with their 95% confidence intervals. The models were adjusted for potential confounders identified from the literature and the research team's prior knowledge (age, chronic disease, healthcare use and knowledge of the eHealth tool). For knowledge outcomes, we progressively adjusted the models: first estimating crude associations (Model 1), then adjusting for age (Model 2), and finally for age, chronic conditions, and healthcare utilisation (Model 3). For use outcomes, an additional step was included (Model 4), which further adjusted for knowledge of the corresponding digital tool. Analyses were performed separately for each of the following determinants: age group, educational level, social class, and health-related limitations. Models including occupational social class or educational level as variables of interest or adjustment were restricted to individuals older than 25 years, since these indicators are less stable among younger adults who may still be studying or entering the labor market. Sex was used as a stratification variable in the main analysis, with all models run separately for women and men. This allowed us to examine inequalities within each sex and avoid diluting gender-specific disparities that might be masked in pooled analyses. To formally assess whether associations differed by sex, we additionally fitted pooled models including interaction terms between sex and each main exposure. These models reproduced the estimates from the stratified analyses while enabling formal testing of effect modification. Finally, we conducted an additional set of models treating sex as the main independent variable, to quantify overall differences between women and men in knowledge and use of each digital health tool, adjusted for the same covariates. Models 1 and 2 are presented in tabular format (Tables 1 and 2 ), while Models 3 and 4 are shown graphically, one figure for knowledge (Fig. 1 - Model 3 by eHealth tool) and another for use (Fig. 2 - Models 3 and 4 by eHealth tool). Numeric data from Figs. 1 and 2 are available in Supplementary Tables 2 and 3, respectively. Table 1 Crude and age-adjusted prevalence ratios of not knowing the digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, including stratified and pooled analyses by sex (ESCAV 2023). Online appointment booking Electronic health records eHealth Portal Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Social determinants of health PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) Sex Men 1 1 1 1 1 1 Women 1.04 (0.95–1.14) 0.97 (0.88–1.06) 0.93 (0.89–0.98)* 0.90 (0.86–0.94)* 1.01 (0.96–1.06) 0.98 (0.93–1.02) M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) Age 18–35 1 1 1 1 1 1 1 1 1 36–50 0.88 (0.57–1.37) 0.71 (0.46–1.09) 0.483 0.93 (0.79–1.10) 0.86 (0.71–1.05) 0.556 1.22 (1.00–1.51) 1.02 (0.84–1.25) 0.223 51–65 1.55 (1.04–2.32)* 1.02 (0.69–1.52) 0.144 1.03 (0.89–1.21) 1.03 (0.86–1.23) 0.950 1.34 (1.10–1.64)* 1.33 (1.10–1.60)* 0.930 66–79 2.7 (1.83–3.99)* 3.23 (2.24–4.65)* 0.512 1.23 (1.05–1.43)* 1.79 (1.51–2.13)* 0.001* 1.91 (1.58–2.32)* 2.03 (1.70–2.43)* 0.651 80 and more 4.34 (2.92–6.45)* 5.09 (3.54–7.32)* 0.563 1.72 (1.48–2.00)* 2.28 (1.93–2.70)* 0.015* 2.47 (2.04–2.99)* 2.43 (2.03–2.90)* 0.897 Educational level University 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Higher secondary 1.45 (1.11–1.89)* 1.74 (1.32–2.30)* 0.075 1.43 (1.10–1.87)* 1.58 (1.20–2.06)* 0.465 1.32 (1.17–1.48)* 1.65 (1.44–1.89)* 0.000* 1.32 (1.17–1.49)* 1.58 (1.38–1.81)* 0.000* 1.22 (1.09–1.37)* 1.18 (1.06–1.32)* 0.236 1.21 (1.08–1.35)* 1.12 (1.01–1.25)* 0.903 Lower secondary 2.56 (1.96–3.34)* 3.44 (2.65–4.48)* 0.143 2.08 (1.59–2.73)* 2.29 (1.74–3.01)* 0.407 1.73 (1.53–1.95)* 2.45 (2.15–2.8)* 0.000* 1.63 (1.44–1.84)* 2.02 (1.76–2.31)* 0.001* 1.44 (1.27–1.62)* 1.56 (1.41–1.74)* 0.337 1.28 (1.13–1.44)* 1.27 (1.14–1.42)* 0.790 Primary and lower 4.62 (3.59–5.95)* 6.40 (5.00–8.20)* 0.412 3.39 (2.60–4.40)* 3.39 (2.58–4.47)* 0.692 1.98 (1.76–2.23)* 3.05 (2.69–3.46)* 0.025* 1.80 (1.59–2.03)* 2.20 (1.92–2.52)* 0.050 1.96 (1.75–2.18)* 2.14 (1.94–2.35)* 0.674 1.62 (1.45–1.82)* 1.55 (1.39–1.72)* 0.365 Occupational social class I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 II 1.68 (1.13–2.50)* 1.55 (0.97–2.46) 0.684 1.76 (1.20–2.59)* 1.33 (0.86–2.06) 0.291 1.20 (1.00–1.45) 1.22 (0.96–1.56) 0.742 1.22 (1.02–1.47)* 1.14 (0.91–1.44) 0.890 1.04 (0.88–1.24) 1.06 (0.88–1.27) 0.989 1.07 (0.90–1.26) 0.98 (0.83–1.17) 0.516 III 1.95 (1.40–2.72)* 2.02 (1.38–2.95)* 0.973 1.89 (1.37–2.62)* 1.58 (1.09–2.27)* 0.422 1.36 (1.17–1.58)* 1.5 (1.24–1.81)* 0.358 1.35 (1.16–1.57)* 1.34 (1.11–1.62)* 0.731 1.18 (1.03–1.36)* 1.21 (1.05–1.39)* 0.982 1.16 (1.02–1.33)* 1.08 (0.94–1.24) 0.370 IV 2.32 (1.71–3.15)* 3.47 (2.44–4.95)* 0.115 2.24 (1.66–3.01)* 2.23 (1.58–3.16)* 0.910 1.61 (1.40–1.84)* 2.2 (1.84–2.62)* 0.003* 1.59 (1.39–1.81)* 1.78 (1.50–2.13)* 0.087 1.28 (1.14–1.45)* 1.42 (1.24–1.62)* 0.349 1.26 (1.12–1.41)* 1.16 (1.02–1.32)* 0.401 V 3.13 (2.25–4.35)* 3.86 (2.69–5.53)* 0.508 3.04 (2.20–4.19)* 2.44 (1.71–3.47)* 0.383 1.59 (1.36–1.86)* 2.48 (2.07–2.96)* 0.000* 1.57 (1.35–1.84)* 1.99 (1.67–2.38)* 0.012* 1.4 (1.22–1.62)* 1.53 (1.34–1.76)* 0.462 1.38 (1.21–1.59)* 1.24 (1.08–1.42)* 0.315 Health limitation No 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.000 Yes 1.41 (1.00–1.98) 2.27 (1.74–2.94)* 0.031* 1.11 (0.80–1.55) 1.42 (1.10–1.84)* 0.119 1.07 (0.88–1.30) 1.37 (1.16–1.62)* 0.060 0.98 (0.81–1.19) 1.04 (0.90–1.20) 0.195 1.18 (0.99–1.40) 1.37 (1.19–1.59)* 0.191 1.04 (0.88–1.24) 1.09 (0.94–1.25) 0.513 Deprivation index 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.000 2 1.07 (0.84–1.36) 0.85 (0.68–1.07) 0.179 1.05 (0.83–1.32) 0.86 (0.69–1.06) 0.228 1.09 (0.98–1.22) 0.99 (0.88–1.13) 0.260 1.09 (0.97–1.21) 1.00 (0.89–1.12) 0.316 1.06 (0.95–1.19) 0.96 (0.86–1.06) 0.178 1.05 (0.95–1.17) 0.96 (0.87–1.07) 0.255 3 1.26 (1.00–1.59) 1.17 (0.96–1.44) 0.649 1.30 (1.04–1.62)* 1.12 (0.93–1.36) 0.312 1.06 (0.95–1.19) 1.14 (1.02–1.29)* 0.370 1.07 (0.96–1.20) 1.12 (1.00–1.25) 0.665 1.05 (0.94–1.17) 1.03 (0.92–1.14) 0.754 1.07 (0.96–1.19) 1.01 (0.91–1.11) 0.465 4 1.04 (0.81–1.33) 1.02 (0.83–1.27) 0.932 1.11 (0.88–1.41) 0.97 (0.80–1.18) 0.370 1.03 (0.92–1.16) 1.13 (1.01–1.27)* 0.283 1.06 (0.94–1.18) 1.10 (0.99–1.23) 0.704 1.00 (0.89–1.12) 1.02 (0.92–1.13) 0.764 1.03 (0.93–1.15) 1.00 (0.91–1.1) 0.675 5 1.43 (1.13–1.80)* 1.22 (1.00–1.50) 0.329 1.44 (1.16–1.80)* 1.23 (1.02–1.48)* 0.305 1.11 (0.99–1.25) 1.19 (1.06–1.34)* 0.408 1.12 (1.00–1.25) 1.2 (1.08–1.33)* 0.413 0.99 (0.88–1.11) 1.05 (0.95–1.16) 0.473 1.00 (0.89–1.12) 1.06 (0.97–1.17) 0.388 Model 1 : crude analysis; Model 2 : analysis adjusted for age; M : men; W : women; PR : prevalence ratio; CI : confidence interval; * indicates statistical significance. Table 2 Crude and age-adjusted prevalence ratios of not using digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, including stratified and pooled analyses by sex (ESCAV 2023). Online appointment booking Electronic health records eHealth Portal Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Social determinants of health PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) Sex Men 1 1 1 1 1 1 Women 1.01 (0.96–1.07) 0.98 (0.93–1.04) 0.97 (0.94–1.00) 0.96 (0.93–0.99)* 0.99 (0.96–1.02) 0.98 (0.95–1.00) M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction M W p value for sex interaction PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) PR (CI95%) Age 18–35 1 1 1 1 1 1 1 1 1 36–50 0.93 (0.73–1.17) 0.71 (0.57–0.90) 0.122 1.00 (0.90–1.12) 0.92 (0.82–1.04) 0.325 0.99 (0.89–1.09) 0.94 (0.84–1.05) 0.524 51–65 1.32 (1.06–1.64)* 1.08 (0.88–1.33) 0.186 1.06 (0.96–1.18) 1.07 (0.96–1.20) 0.906 1.05 (0.96–1.16) 1.14 (1.03–1.26)* 0.265 66–79 1.76 (1.42–2.19)* 2.06 (1.69–2.5)* 0.300 1.22 (1.10–1.35)* 1.43 (1.29–1.58)* 0.03* 1.24 (1.13–1.36)* 1.40 (1.27–1.54)* 0.064 80 and more 2.23 (1.79–2.78)* 2.45 (2.01–2.97)* 0.537 1.41 (1.28–1.56)* 1.53 (1.39–1.70)* 0.243 1.34 (1.23–1.47)* 1.47 (1.34–1.62)* 0.178 Educational level University 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Higher secondary 1.24 (1.08–1.44)* 1.37 (1.18–1.58)* 0.026 1.24 (1.07–1.43)* 1.27 (1.10–1.47)* 0.324 1.20 (1.11–1.29)* 1.24 (1.15–1.34)* 0.004* 1.20 (1.11–1.29)* 1.21 (1.12–1.31)* 0.048 1.09 (1.02–1.16)* 1.03 (0.97–1.10) 0.608 1.08 (1.02–1.15)* 1.00 (0.94–1.06) 0.455 Lower secondary 1.87 (1.62–2.16)* 2.37 (2.07–2.72)* 0.025* 1.66 (1.43–1.92)* 1.8 (1.57–2.07)* 0.140 1.41 (1.31–1.52)* 1.59 (1.48–1.71)* 0.027* 1.36 (1.26–1.47)* 1.42 (1.32–1.53)* 0.087 1.22 (1.14–1.30)* 1.25 (1.18–1.32)* 0.532 1.16 (1.09–1.24)* 1.11 (1.04–1.18)* 0.941 Primary and lower 2.19 (1.90–2.53)* 2.75 (2.41–3.14)* 0.427 1.83 (1.57–2.12)* 1.8 (1.56–2.07)* 0.805 1.48 (1.37–1.59)* 1.71 (1.60–1.83)* 0.528 1.39 (1.29–1.50)* 1.43 (1.33–1.54)* 0.754 1.31 (1.23–1.40)* 1.34 (1.27–1.41)* 0.278 1.22 (1.14–1.3)* 1.11 (1.05–1.18)* 0.113 Occupational social class I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 II 1.18 (0.94–1.47) 1.24 (0.97–1.58) 0.827 1.21 (0.98–1.50) 1.13 (0.89–1.42) 0.631 1.04 (0.93–1.17) 1.17 (1.02–1.34)* 0.143 1.05 (0.94–1.18) 1.13 (0.99–1.29) 0.302 0.96 (0.87–1.07) 1.03 (0.93–1.13) 0.457 0.97 (0.88–1.07) 0.99 (0.90–1.09) 0.856 III 1.23 (1.03–1.48)* 1.35 (1.11–1.65)* 0.655 1.21 (1.01–1.45)* 1.17 (0.96–1.41) 0.711 1.19 (1.09–1.3)* 1.24 (1.11–1.38)* 0.441 1.18 (1.08–1.29)* 1.17 (1.05–1.3)* 0.832 1.06 (0.98–1.14) 1.05 (0.97–1.13) 0.848 1.05 (0.98–1.14) 0.99 (0.91–1.07) 0.347 IV 1.55 (1.32–1.82)* 1.98 (1.65–2.38)* 0.062 1.52 (1.30–1.78)* 1.53 (1.28–1.83)* 0.817 1.25 (1.15–1.35)* 1.5 (1.36–1.66)* 0.003* 1.24 (1.14–1.34)* 1.35 (1.22–1.49)* 0.067 1.09 (1.02–1.17)* 1.14 (1.06–1.22)* 0.458 1.08 (1.02–1.16)* 1.03 (0.96–1.11)* 0.428 V 1.85 (1.55–2.21)* 2.22 (1.84–2.67)* 0.235 1.82 (1.53–2.16)* 1.68 (1.40–2.02)* 0.631 1.29 (1.18–1.42)* 1.58 (1.43–1.75)* 0.003* 1.29 (1.17–1.41)* 1.41 (1.28–1.56)* 0.069 1.14 (1.05–1.23)* 1.20 (1.11–1.29)* 0.368 1.13 (1.05–1.22)* 1.07 (0.99–1.16)* 0.554 Health limitation No 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Yes 1.35 (1.11–1.64)* 1.83 (1.59–2.10)* 0.013* 1.17 (0.97–1.41) 1.33 (1.16–1.53)* 0.069 1.08 (0.97–1.21) 1.22 (1.11–1.32) 0.096 1.03 (0.92–1.15) 1.05 (0.96–1.15) 0.295 1.06 (0.96–1.18) 1.11 (1.01–1.22)* 0.547 1.01 (0.91–1.11) 0.98 (0.89–1.08) 0.918 Deprivation index 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1.03 (0.89–1.19) 0.96 (0.83–1.1) 0.485 1.02 (0.89–1.17) 0.96 (0.85–1.10) 0.600 1.03 (0.96–1.11) 1.06 (0.99–1.14) 0.587 1.03 (0.96–1.10) 1.07 (0.99–1.14) 0.458 1.02 (0.95–1.08) 0.99 (0.93–1.06) 0.647 1.01 (0.95–1.08) 1.00 (0.94–1.06) 0.777 3 1.11 (0.97–1.28) 1.18 (1.04–1.34)* 0.537 1.13 (0.99–1.29) 1.15 (1.02–1.29)* 0.845 1.05 (0.98–1.12) 1.06 (0.98–1.14) 0.816 1.05 (0.98–1.13) 1.05 (0.98–1.12) 0.896 1.04 (0.97–1.10) 1.01 (0.95–1.07) 0.562 1.04 (0.98–1.11) 1.00 (0.94–1.06) 0.351 4 1.01 (0.88–1.17) 1.11 (0.98–1.27) 0.344 1.05 (0.92–1.21) 1.08 (0.96–1.22) 0.820 1.02 (0.95–1.1) 1.10 (1.03–1.18)* 0.131 1.03 (0.96–1.11) 1.09 (1.02–1.16)* 0.341 0.99 (0.93–1.06) 1.02 (0.96–1.08) 0.626 1.01 (0.95–1.07) 1.01 (0.95–1.07) 0.932 5 1.24 (1.08–1.42)* 1.16 (1.02–1.32)* 0.505 1.25 (1.09–1.42)* 1.17 (1.04–1.32)* 0.522 1.09 (1.02–1.17)* 1.09 (1.01–1.17)* 0.920 1.10 (1.02–1.17)* 1.09 (1.02–1.17)* 0.937 1.02 (0.95–1.09) 1.04 (0.98–1.11) 0.637 1.02 (0.96–1.09) 1.05 (0.99–1.11) 0.572 Model 1 : crude analysis; Model 2 : analysis adjusted for age; M : men; W : women; PR : prevalence ratio; CI : confidence interval; * indicates statistical significance. RESULTS The final sample included 5,683 individuals (54.1% women). The age distribution was skewed toward older groups, with the largest proportion aged 51–65 years (≈ 15% in both sexes). Regarding socioeconomic status, most participants belonged to occupational social classes IV and III. Educational attainment was concentrated at the secondary and university levels. Women were more likely than men to live in the most deprived areas and belong to lower occupational classes (e.g., Class V: 10.4% vs. 5.1%). In terms of education, women were more likely to have only primary education (10.2% vs. 6.5%) or university-level education (13.1% vs. 9.7%). Regarding healthcare use, 41.7% of participants fell into the low or medium use tertiles, but women were more frequently in the highest tertile (11.6% vs. 7.5%). Approximately one quarter reported having a chronic condition, and 2.5% reported limitations in daily activities due to health problems (Supplementary Table 1). Overall, both knowledge and use of eHealth tools varied by tool, sex, and sociodemographic characteristics. Online appointment booking showed the highest levels of knowledge and use, followed by electronic health records (EHRs). In contrast, eHealth portal showed the lowest levels of both knowledge and use (Supplementary Table 1). At the descriptive level, older adults reported markedly lower knowledge and use across all tools. For example, use of EHRs and eHealth portal was below 4% among individuals aged 80 or older. Clear social gradients were observed across age, education, and occupational social class. Among women in occupational class I, knowledge of online appointment booking was 91.4%, but only 66.8% in class V. Usage followed a similar pattern. In university-educated women, 91.5% knew the online appointment booking and 74.2% used it, whereas both figures were substantially lower in lower educational groups (Supplementary Table 1). Regression models confirmed strong associations between age and sociodemographic variables with both knowledge and use. Age: Age-related inequalities were consistently observed across tools. For online appointment booking, compared to those aged 18–35, the probability of not knowing the tool among women aged 80 + was significantly higher (Model 1: PR = 5.09, 95% CI: 3.54–7.32), and remained elevated after adjustment for healthcare use and chronic disease (Model 3: PR = 5.49, 95% CI: 3.64–8.28). In men, the corresponding estimate was also elevated (Model 3: PR = 4.13, 95% CI: 2.53–6.72). For EHRs, women aged 80 + also had a higher probability of not using the tool (Model 3: PR = 1.53, 95% CI: 1.37–1.72), which decreased after adjusting for knowledge in Model 4 (PR = 1.11, 95% CI: 1.01–1.22). For eHealth tool, women aged 80 + had a significantly higher probability of not knowing (Model 3: PR = 2.60, 95% CI: 2.14–3.17) and not using the tool (Model 3: PR = 1.53, 95% CI: 1.36–1.71), which remained significant in Model 4 (PR = 1.11, 95% CI: 1.01–1.21). Education: Strong educational inequalities were observed across all tools. For women with primary education, the probability of not knowing the online appointment booking system was substantially higher compared to university-educated women (Model 3: PR = 3.26, 95% CI: 2.37–4.48), and the corresponding estimate in men was PR = 4.03 (95% CI: 2.79–5.80). For EHRs, women with primary education also showed higher probability of not having knowledge (Model 3: PR = 2.22, 95% CI: 1.90–2.59) and not using the tool (Model 3: PR = 1.49, 95% CI: 1.37–1.62). For eHealth portal, the probability of not knowing the tool was also higher among primary-educated women (Model 3: PR = 1.57, 95% CI: 1.39–1.77), and the probability of not using the tool was elevated (Model 3: PR = 1.17, 95% CI: 1.09–1.25). Use estimates were slightly reduced after adjusting for knowledge in Model 4. Occupational class: Gradients were evident across all tools. For online appointment booking, women in class V had a higher probability of not knowing the tool than those in class I (Model 3: PR = 3.13, 95% CI: 2.00–4.91). For use, the fully adjusted Model 4 still showed a higher probability of non-use among class V women (PR = 1.64, 95% CI: 1.32–2.05). Similar patterns were observed for EHRs (Model 3: PR = 1.49, 95% CI: 1.32–1.68; Model 4: PR = 1.21, 95% CI: 1.09–1.34 in women) and eHealth portal (Model 3: PR = 1.12, 95% CI: 1.03–1.23). Health limitations: Women with health limitations had a significantly higher probability of not knowing the online appointment booking tool (Model 3: PR = 1.37, 95% CI: 1.02–1.85). In terms of use, the fully adjusted Model 4 showed persistent inequalities for online appointment booking (PR = 1.29, 95% CI: 1.11–1.50), though no significant associations were observed for the other tools or in men. Deprivation index: Differences by area-level deprivation were modest but showed some significant patterns. For online appointment booking, women in the most deprived quintile (Q5) had a higher probability of not using the tool compared to those in the least deprived quintile (Model 4: PR = 1.19, 95% CI: 1.03–1.37). Similar trends were seen for EHRs (Model 3: PR = 1.11, 95% CI: 1.03–1.20), though they attenuated after adjustment in Model 4. No consistent patterns were found for eHealth portal, and most differences by deprivation were not statistically significant. When sex was included as the main independent variable in pooled models, differences between men and women were generally small. However, women were slightly less likely than men to report not knowing about the EHR system (Model 3 PR = 0.91; 95% CI: 0.87–0.97). For EHR use and for other digital tools, sex differences were not statistically significant. Interaction terms between sex and age, education, and occupational class were included in pooled models to assess effect modification. While some statistically significant interactions were observed, particularly for knowledge of electronic health records (EHRs) by educational level, these findings were not consistently replicated across other tools, outcomes, or adjustment levels. Overall, sex did not appear to meaningfully modify the associations between social determinants and digital health tool use or knowledge. DISCUSSION This study examined social inequalities in the knowledge and use of eHealth tools within a universal healthcare system, focusing on online appointment booking, patient-accessible EHRs, and eHealth portal, and explored how social determinants shape these disparities. The results identified persistent inequalities in knowledge and use of eHealth tools across key social determinants, even within a universal healthcare system. Age, education, and occupational class showed the most consistent and significant gradients. Importantly, while inequalities in knowledge remained largely unchanged after adjusting for age, healthcare use, and chronic illness (Model 3), inequalities in use persisted even after further adjustment for knowledge (Model 4). This suggests that lack of knowledge is a major barrier, but not the only one, pointing to deeper structural or contextual factors limiting the uptake of eHealth services. Although interaction models suggested some statistically significant sex-based differences, particularly in EHR knowledge by education, these patterns were isolated to specific models and not consistent across digital tools. Overall, our findings do not support sex as a strong modifier of social inequalities in digital health tool knowledge or use. Still, in the case of electronic health records, we observed modest but statistically significant lower probabilities of lacking knowledge among women. This aligns with prior research showing higher female engagement with EHRs and other digital health tools [ 29 ]. Studies suggest that this trend may reflect traditional gender roles, in which women are more often responsible for caregiving tasks, leading to greater familiarity with health systems and more proactive health-seeking behaviours. However, in our data, these sex differences were not consistent across tools or robust across models, indicating that broader structural determinants may play a more central role in shaping digital inequalities. Age was the most prominent determinant across tools, with consistently higher probabilities of non-knowledge and non-use among the oldest age groups. These differences remained significant even after adjusting for healthcare use and knowledge, suggesting that digital health tools may offer limited benefit to some older adults. Beyond digital competence, this may reflect a mismatch between digital tools and the physical, cognitive, and sensory changes that accompany aging [ 30 ]. The fast-paced, hyperconnected logic of digital services may not be adapted to those aging outside digital ecosystems. This raises broader questions about whether we are adapting healthcare models to match older populations’ needs—or asking older people to adapt to a system designed for efficiency rather than inclusion. Socioeconomic inequalities were particularly marked in education and occupational class. Lower educational attainment and class position were strongly associated with both lack of knowledge and use of eHealth tools, suggesting that digital exclusion reflects broader social disadvantage. These results confirm findings from previous literature on the digital divide and inequalities in eHealth [ 31 ]. Importantly, some of these differences persisted even after adjusting for knowledge, indicating that additional barriers—such as digital literacy, confidence, or structural accessibility—may also be potential mediators of inequality [ 32 ]. Experiences from other European context reinforce this view, where digital tools are widely used, inequalities still persist.Universal access by itself does not ensure equity; implementation strategies and social context are equally decisive. These results support recent [ 33 ] and not so recent [ 21 ] critiques of intervention-generated inequalities in digital health. eHealth is not socially neutral—it is embedded in systems that reproduce structural exclusion. Shifting to a digital-first model with the goal of improving sustainability may inadvertently marginalize those least equipped to navigate it [ 3 ]. Addressing these disparities requires shifting from framing digital inclusion as an individual issue (e.g., skills training) toward a systems-level approach. The persistence of digital inequalities despite growing technological access suggests that the problem lies more in structural barriers than in proximal causes [ 34 ]. That includes co-designing tools with excluded groups, investing in accessible alternatives, and placing equity at the centre of digital health strategies [ 35 , 36 ]. This study contributes to the growing body of evidence from public health systems, contrasting with prior research mostly based in private-sector contexts. It highlights the need for Europe’s universal health systems to critically evaluate the digital transition and its equity implications. This study is among the first to examine inequalities in knowledge and use of eHealth tools within the Basque Health Service using a large, population-representative sample. Its contribution is particularly relevant as it provides empirical evidence from a universal public health system, in contrast to most existing research, which has been conducted in private or mixed healthcare settings. The stratified analysis by sex, combined with pooled models including interaction terms, allowed for an intersectional approach that captured potential sex-based effect modification and avoided masking group-specific disparities. The use of progressively adjusted Poisson models allowed us to explore the role of social determinants and the mediating effect of knowledge on eHealth use. Analysing multiple tools separately also provided insight into tool-specific adoption patterns. However, the cross-sectional design limits causal inference, and the reliance on self-reported measures may introduce recall or social desirability bias. Although we adjusted for age, healthcare utilisation, and health-related limitations, residual confounding from unmeasured factors such as digital literacy, device access, or trust in eHealth cannot be ruled out. Future research should include specific indicators such as these to better understand the mechanisms behind non-use, since qualitative research has already shed light on some of these barriers in other settings. Finally, as the study focused on three tools within one regional health system, findings may not generalize to other contexts. CONCLUSIONS Significant inequalities persist in both knowledge and use of eHealth tools within the Basque Health Service, particularly among older adults and those in disadvantaged socioeconomic positions. While gaps in use are partly reduced once individuals are informed, inequalities in use still remain even when the tools are known, underscoring the role of broader structural barriers. Targeted strategies are needed to improve awareness and ensure equitable digital transitions in healthcare. Abbreviations EHRs Electronic Health Records WHO World Health Organisation PR Prevalence Ratio 95%CI 95% Confidence Interval Declarations Ethics approval and consent to participate This study was conducted using anonymised secondary data from the 2023 Basque Health Survey (ESCAV), carried out by the Basque Government Department of Health. According to Spanish regulations on biomedical research and data protection, analyses of fully anonymised survey data do not require approval from a research ethics committee. All procedures were performed in accordance with the principles of the Declaration of Helsinki. All participants in the Basque Health Survey provided informed consent prior to participation. The authors had access only to anonymised data. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the “Instituto de Salud Carlos II” I and co-funded by the European Union (Grant No. PI23/01063), within the framework of the 2023 call for Health R&D Projects. Author Contribution UMR conceived the study and contributed to its design and writing key conceptual and methodological decisions. AAG developed the study design further and conducted the analysis in close collaboration with UMR. AAG drafted the manuscript, with collaboration of GMG, and all authors revised the text comprehensively before submission.All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The data supporting this study are derived from the “Encuesta de Salud de la CAPV (ESCAV)”—the Basque Country Health Survey—conducted by the Basque Government. These data are subject to access restrictions and used under license. 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Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. Int J Med Inf. 2023;177:105124. 10.1016/j.ijmedinf.2023.105124 . Epub 2023 Jun 10. PMID:37329766. Coetzer JA, Loukili I, Goedhart NS, Ket JCF, Schuitmaker-Warnaar TJ, Zuiderent-Jerak T, Dedding C. The potential and paradoxes of eHealth research for digitally marginalised groups: a qualitative meta-review. Soc Sci Med. 2024;350:116895. 10.1016/j.socscimed.2024.116895 . Epub 2024 Apr 18. PMID:38710135. Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. J Health Soc Behav. 2010;51(Suppl):S28–40. 10.1177/0022146510383498 . Goedhart NS, Verdonk P, Dedding C. Never good enough. A situated understanding of the impact of digitalization on citizens living in a low socioeconomic position. Public Policy Adm. 2022 Jul;18. 10.1002/poi3.315 . Groom LL, Schoenthaler AM, Mann DM, Brody AA. Construction of the Digital Health Equity-Focused Implementation Research Conceptual Model - Bridging the Divide Between Equity-focused Digital Health and Implementation Research. PLOS Digit Health. 2024;3(5):e0000509. 10.1371/journal.pdig.0000509 . Additional Declarations No competing interests reported. Supplementary Files BMCPHSupplementary2511.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Editor invited by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 07 Jan, 2026 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. 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1","display":"","copyAsset":false,"role":"figure","size":107103,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence ratios for not knowing the digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, adjusted for use of the health system+chronic disease (Model 3), and stratified by sex.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8500757/v1/5eedb0a0374e1f744dd48adf.png"},{"id":100055880,"identity":"656c2390-ecd6-4154-933e-a7cfc60d6f7a","added_by":"auto","created_at":"2026-01-12 13:55:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140098,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence ratios for not using the digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, adjusted for use of the health system+chronic disease (Model 3) and for use of the system+chronic disease +knowledge of the digital tool (Model 4), and stratified by sex.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8500757/v1/66962132f5a7367bcbadcbb7.png"},{"id":100381749,"identity":"b12dbfcc-0dff-48d9-a7fc-562117340a8f","added_by":"auto","created_at":"2026-01-16 10:39:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1692514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8500757/v1/6fcdd928-3dfc-4640-83c5-3c345016f632.pdf"},{"id":100055873,"identity":"4e77bf62-94ba-4530-b874-8fbaf12e5455","added_by":"auto","created_at":"2026-01-12 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insufficient to ensure equitable uptake.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003eThe digitalisation of healthcare systems has become increasingly important worldwide. In Europe, the European Commission actively promotes the development and integration of eHealth tools to improve access to care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These technologies encompass various aspects of care, including those that connect individuals directly to the health system, such as online appointment booking, teleconsultations, and patient-accessible digital records [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond service delivery, they also promote patient engagement in health monitoring and self-management [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] transforming how people interact with healthcare.\u003c/p\u003e \u003cp\u003eDespite the growing presence of eHealth, discussions on equity remain limited, and much of the literature has primarily focused on the benefits of these technologies, which have even been proposed as tools to improve outcomes related to the social determinants of health. However, the persistence of the digital divide raises concerns that unequal access to these tools may reinforce or even worsen existing disparities in healthcare access [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This is especially concerning given evidence that technology is not socially neutral and often reflects the structural inequities of the systems in which it is developed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent frameworks have introduced the concept of digital determinants of health, highlighting how digitalization can reinforce existing social inequalities. Paradoxically, those most in need of healthcare\u0026mdash;often the most vulnerable groups\u0026mdash;may also the least likely to use digital health technologies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, digitalisation has become a determinant of health, as it can influence how social inequalities affect access to and delivery of healthcare [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While qualitative studies have explored barriers and facilitators to digital health access [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], quantitative evidence remains scarce. Existing research often examines specific tools (e.g., online consultations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], patient portals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]), healthcare levels other than primary care [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], or populations with chronic conditions (e.g., diabetes) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. European studies indicate higher adoption among women [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], younger individuals, and urban residents [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], though findings on factors like education are mixed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the risk that eHealth interventions could unintentionally exacerbate inequities if their design does not consider systemic barriers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], it is essential to identify groups at greater risk of exclusion and the factors driving these disparities. In this context, adopting an intersectional approach is particularly relevant, as the interplay between social determinants\u0026mdash;such as gender, age, education, or class\u0026mdash;may shape unequal access in ways that are not captured by isolated analyses. Disaggregated and interaction-based analyses are therefore necessary to uncover hidden disparities and better inform equity-oriented digital health policies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to analyse inequalities in the knowledge and use of eHealth tools in the Basque Health Service\u0026ndash;Osakidetza (e.g., online appointment booking, and patient accessible electronic health records) and to examine how social determinants contribute to these inequalities, using data from the Basque Health Survey (ESCAV).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, design, setting and participants of the study\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted using data from the 2023 Basque Health Survey (ESCAV), a population-representative survey of adults (aged 18 +) living in private households in the Basque Country (Spain). ESCAV is conducted every five years by the Basque Government\u0026rsquo;s Department of Health and collects comprehensive data on individuals\u0026rsquo; and households\u0026rsquo; health status, clinical conditions, risk factors, social and environmental determinants of health, and the use, access, and perception of health services. The theoretical ESCAV‑2023 household sample comprised 5,790 households; in the actual fieldwork 5,688 household questionnaires were completed. The final sample included 5,683 individuals of \u0026ge;\u0026thinsp;18 of age and with digital health data.\u003c/p\u003e \u003cp\u003eIn the Basque Country, digital health initiatives have expanded steadily since 2008, including the launch of Osabide (the unified electronic health record), the digital health folder, online appointment systems, and telephone consultations. The digital health folder (2013) allows patients to access their clinical records, prescriptions, test results, and vaccinations online [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while the Osakidetza mobile app (2017) enables mobile access to many of these features [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In 2022, the online appointment booking system was updated to allow direct scheduling, modification, or cancellation of in-person visits via the Osakidetza website [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the 2018 edition, a sub-section has been included with specific questions on the use and knowledge of the new information technologies implemented in the health system, including the online appointment booking (officially named web appointment), the electronic health records (EHRs) (officially named health folder) and the eHealth portal (officially named Osakidetza App). The detailed methodology of the survey can be consulted in the methodological publication of the Department of Health [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eAs dependent variables, knowledge and use of the following digital tools were used: online appointment booking, EHRs, and eHealth portal. The ESCAV collects information on these variables through the dichotomous questions: \u0026ldquo;Indicate whether you are aware of these services\u0026rdquo; and \u0026ldquo;Have you used it?\u0026rdquo; (the latter only asked of those who responded affirmatively to the first).\u003c/p\u003e \u003cp\u003eAs independent variables the analysis focused on several social determinants of health hypothesised to influence inequalities in the knowledge and use of digital health tools. We included age group (18\u0026ndash;35, 36\u0026ndash;50, 51\u0026ndash;65, 66\u0026ndash;79, \u0026ge;\u0026thinsp;80 years), conceptualised both as a marker of social position and a key factor shaping digital competence. Educational level (primary, lower secondary, upper secondary, university), occupational social class\u0026mdash;classified according to the Spanish Society of Epidemiology (SEE) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u0026mdash;and the area-level deprivation index [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], categorised into quintiles with the first quintile representing the least deprived areas, were also included as indicators of socioeconomic position. In addition, self-reported health-related limitations (yes/no) were considered as a potential axis of inequality, as functional limitations may affect access to and use of digital tools.\u003c/p\u003e \u003cp\u003eWe treated chronic conditions (yes/no) and healthcare use\u0026mdash;categorised into tertiles (low, medium, high) based on the number of general practitioner or paediatric visits in the last 12 months\u0026mdash;as adjustment covariates in the regression models, given their potential influence on eHealth tool usage.\u003c/p\u003e \u003cp\u003eSex was used as a stratification variable in the main analysis, included as an interaction term in pooled models to assess effect modification, and also analysed as a main independent variable to estimate overall differences between women and men.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFirstly, the prevalence of knowledge and use of digital services for accessing the health system was calculated according to social determinants of health and other covariables (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eTo analyse the association between knowledge and use of the online appointment system, the personal health folder, and the Osakidetza app with social determinants of health, Poisson regression models with robust variance were applied and prevalence ratios of not knowing and not using eHealth tools were calculated with their 95% confidence intervals. The models were adjusted for potential confounders identified from the literature and the research team's prior knowledge (age, chronic disease, healthcare use and knowledge of the eHealth tool). For knowledge outcomes, we progressively adjusted the models: first estimating crude associations (Model 1), then adjusting for age (Model 2), and finally for age, chronic conditions, and healthcare utilisation (Model 3). For use outcomes, an additional step was included (Model 4), which further adjusted for knowledge of the corresponding digital tool. Analyses were performed separately for each of the following determinants: age group, educational level, social class, and health-related limitations. Models including occupational social class or educational level as variables of interest or adjustment were restricted to individuals older than 25 years, since these indicators are less stable among younger adults who may still be studying or entering the labor market.\u003c/p\u003e \u003cp\u003eSex was used as a stratification variable in the main analysis, with all models run separately for women and men. This allowed us to examine inequalities within each sex and avoid diluting gender-specific disparities that might be masked in pooled analyses. To formally assess whether associations differed by sex, we additionally fitted pooled models including interaction terms between sex and each main exposure. These models reproduced the estimates from the stratified analyses while enabling formal testing of effect modification. Finally, we conducted an additional set of models treating sex as the main independent variable, to quantify overall differences between women and men in knowledge and use of each digital health tool, adjusted for the same covariates.\u003c/p\u003e \u003cp\u003eModels 1 and 2 are presented in tabular format (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while Models 3 and 4 are shown graphically, one figure for knowledge (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e - Model 3 by eHealth tool) and another for use (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - Models 3 and 4 by eHealth tool). Numeric data from Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are available in Supplementary Tables\u0026nbsp;2 and 3, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude and age-adjusted prevalence ratios of not knowing the digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, including stratified and pooled analyses by sex (ESCAV 2023).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eOnline appointment booking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eElectronic health records\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e \u003cp\u003eeHealth Portal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial determinants of health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.04 (0.95\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.97 (0.88\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.98)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e0.90 (0.86\u0026ndash;0.94)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.57\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.46\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93 (0.79\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86 (0.71\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.22 (1.00\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.02 (0.84\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.04\u0026ndash;2.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.69\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03 (0.89\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03 (0.86\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.34 (1.10\u0026ndash;1.64)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.33 (1.10\u0026ndash;1.60)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7 (1.83\u0026ndash;3.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23 (2.24\u0026ndash;4.65)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23 (1.05\u0026ndash;1.43)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.79 (1.51\u0026ndash;2.13)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.91 (1.58\u0026ndash;2.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.03 (1.70\u0026ndash;2.43)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 and more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34 (2.92\u0026ndash;6.45)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09 (3.54\u0026ndash;7.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72 (1.48\u0026ndash;2.00)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.28 (1.93\u0026ndash;2.70)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.47 (2.04\u0026ndash;2.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.43 (2.03\u0026ndash;2.90)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.11\u0026ndash;1.89)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74 (1.32\u0026ndash;2.30)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43 (1.10\u0026ndash;1.87)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58 (1.20\u0026ndash;2.06)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32 (1.17\u0026ndash;1.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.65 (1.44\u0026ndash;1.89)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.32 (1.17\u0026ndash;1.49)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.58 (1.38\u0026ndash;1.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.22 (1.09\u0026ndash;1.37)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.18 (1.06\u0026ndash;1.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.21 (1.08\u0026ndash;1.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.12 (1.01\u0026ndash;1.25)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56 (1.96\u0026ndash;3.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44 (2.65\u0026ndash;4.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08 (1.59\u0026ndash;2.73)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.29 (1.74\u0026ndash;3.01)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.73 (1.53\u0026ndash;1.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.45 (2.15\u0026ndash;2.8)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.63 (1.44\u0026ndash;1.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.02 (1.76\u0026ndash;2.31)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.44 (1.27\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.56 (1.41\u0026ndash;1.74)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.28 (1.13\u0026ndash;1.44)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.27 (1.14\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary and lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.62 (3.59\u0026ndash;5.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.40 (5.00\u0026ndash;8.20)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.39 (2.60\u0026ndash;4.40)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39 (2.58\u0026ndash;4.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98 (1.76\u0026ndash;2.23)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.05 (2.69\u0026ndash;3.46)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.80 (1.59\u0026ndash;2.03)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.20 (1.92\u0026ndash;2.52)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.96 (1.75\u0026ndash;2.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.14 (1.94\u0026ndash;2.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.62 (1.45\u0026ndash;1.82)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.55 (1.39\u0026ndash;1.72)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational social class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 (1.13\u0026ndash;2.50)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 (0.97\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76 (1.20\u0026ndash;2.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33 (0.86\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20 (1.00\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.22 (0.96\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.22 (1.02\u0026ndash;1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.14 (0.91\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.04 (0.88\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.06 (0.88\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.07 (0.90\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.98 (0.83\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.40\u0026ndash;2.72)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02 (1.38\u0026ndash;2.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89 (1.37\u0026ndash;2.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58 (1.09\u0026ndash;2.27)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36 (1.17\u0026ndash;1.58)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5 (1.24\u0026ndash;1.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.35 (1.16\u0026ndash;1.57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.34 (1.11\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.18 (1.03\u0026ndash;1.36)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.21 (1.05\u0026ndash;1.39)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.16 (1.02\u0026ndash;1.33)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.08 (0.94\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32 (1.71\u0026ndash;3.15)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47 (2.44\u0026ndash;4.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24 (1.66\u0026ndash;3.01)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.23 (1.58\u0026ndash;3.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.61 (1.40\u0026ndash;1.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.2 (1.84\u0026ndash;2.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.59 (1.39\u0026ndash;1.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.78 (1.50\u0026ndash;2.13)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.28 (1.14\u0026ndash;1.45)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.42 (1.24\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.26 (1.12\u0026ndash;1.41)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.16 (1.02\u0026ndash;1.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13 (2.25\u0026ndash;4.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86 (2.69\u0026ndash;5.53)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04 (2.20\u0026ndash;4.19)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44 (1.71\u0026ndash;3.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59 (1.36\u0026ndash;1.86)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.48 (2.07\u0026ndash;2.96)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.57 (1.35\u0026ndash;1.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.99 (1.67\u0026ndash;2.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.4 (1.22\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.53 (1.34\u0026ndash;1.76)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.38 (1.21\u0026ndash;1.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.24 (1.08\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth limitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.00\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27 (1.74\u0026ndash;2.94)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.80\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (1.10\u0026ndash;1.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07 (0.88\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37 (1.16\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98 (0.81\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.04 (0.90\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.18 (0.99\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.37 (1.19\u0026ndash;1.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.04 (0.88\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.09 (0.94\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeprivation index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.84\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.68\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (0.83\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86 (0.69\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 (0.98\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99 (0.88\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09 (0.97\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.06 (0.95\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.96 (0.86\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.05 (0.95\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.96 (0.87\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.00\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.96\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30 (1.04\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12 (0.93\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.95\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.14 (1.02\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07 (0.96\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.12 (1.00\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.05 (0.94\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.03 (0.92\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.07 (0.96\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.01 (0.91\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.81\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.83\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.88\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.80\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03 (0.92\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.13 (1.01\u0026ndash;1.27)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.06 (0.94\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.10 (0.99\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.02 (0.92\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.03 (0.93\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.00 (0.91\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.13\u0026ndash;1.80)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.00\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44 (1.16\u0026ndash;1.80)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23 (1.02\u0026ndash;1.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11 (0.99\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19 (1.06\u0026ndash;1.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.12 (1.00\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.2 (1.08\u0026ndash;1.33)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.99 (0.88\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.05 (0.95\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.06 (0.97\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003e\u003cb\u003eModel 1\u003c/b\u003e: crude analysis; \u003cb\u003eModel 2\u003c/b\u003e: analysis adjusted for age; \u003cb\u003eM\u003c/b\u003e: men; \u003cb\u003eW\u003c/b\u003e: women; \u003cb\u003ePR\u003c/b\u003e: prevalence ratio; \u003cb\u003eCI\u003c/b\u003e: confidence interval; \u003cb\u003e*\u003c/b\u003e indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude and age-adjusted prevalence ratios of not using digital tools for accessing the national health system (web appointment, health folder, and the Osakidetza app) according to social determinants of health, including stratified and pooled analyses by sex (ESCAV 2023).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eOnline appointment booking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eElectronic health records\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e \u003cp\u003eeHealth Portal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial determinants of health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003ePR (CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.97 (0.94\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e0.96 (0.93\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003eW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep value for sex interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003ePR (CI95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.73\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.57\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.90\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92 (0.82\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.99 (0.89\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.94 (0.84\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (1.06\u0026ndash;1.64)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.88\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.96\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07 (0.96\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.05 (0.96\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.14 (1.03\u0026ndash;1.26)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76 (1.42\u0026ndash;2.19)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06 (1.69\u0026ndash;2.5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22 (1.10\u0026ndash;1.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43 (1.29\u0026ndash;1.58)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.24 (1.13\u0026ndash;1.36)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.40 (1.27\u0026ndash;1.54)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 and more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 (1.79\u0026ndash;2.78)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45 (2.01\u0026ndash;2.97)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41 (1.28\u0026ndash;1.56)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.53 (1.39\u0026ndash;1.70)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.34 (1.23\u0026ndash;1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.47 (1.34\u0026ndash;1.62)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.08\u0026ndash;1.44)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.18\u0026ndash;1.58)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24 (1.07\u0026ndash;1.43)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27 (1.10\u0026ndash;1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20 (1.11\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24 (1.15\u0026ndash;1.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20 (1.11\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.21 (1.12\u0026ndash;1.31)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.03 (0.97\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.08 (1.02\u0026ndash;1.15)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.00 (0.94\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87 (1.62\u0026ndash;2.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.37 (2.07\u0026ndash;2.72)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66 (1.43\u0026ndash;1.92)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8 (1.57\u0026ndash;2.07)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41 (1.31\u0026ndash;1.52)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.59 (1.48\u0026ndash;1.71)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.36 (1.26\u0026ndash;1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.42 (1.32\u0026ndash;1.53)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.22 (1.14\u0026ndash;1.30)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.25 (1.18\u0026ndash;1.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.16 (1.09\u0026ndash;1.24)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary and lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19 (1.90\u0026ndash;2.53)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75 (2.41\u0026ndash;3.14)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83 (1.57\u0026ndash;2.12)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8 (1.56\u0026ndash;2.07)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48 (1.37\u0026ndash;1.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71 (1.60\u0026ndash;1.83)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.39 (1.29\u0026ndash;1.50)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.43 (1.33\u0026ndash;1.54)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.31 (1.23\u0026ndash;1.40)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.34 (1.27\u0026ndash;1.41)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.22 (1.14\u0026ndash;1.3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.11 (1.05\u0026ndash;1.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational social class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.94\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (0.97\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (0.98\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.89\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04 (0.93\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.17 (1.02\u0026ndash;1.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05 (0.94\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.13 (0.99\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.96 (0.87\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.03 (0.93\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.97 (0.88\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.99 (0.90\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.03\u0026ndash;1.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.11\u0026ndash;1.65)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (1.01\u0026ndash;1.45)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (0.96\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19 (1.09\u0026ndash;1.3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24 (1.11\u0026ndash;1.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.18 (1.08\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.17 (1.05\u0026ndash;1.3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.06 (0.98\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.05 (0.97\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.05 (0.98\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.99 (0.91\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.32\u0026ndash;1.82)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98 (1.65\u0026ndash;2.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52 (1.30\u0026ndash;1.78)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53 (1.28\u0026ndash;1.83)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25 (1.15\u0026ndash;1.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5 (1.36\u0026ndash;1.66)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.24 (1.14\u0026ndash;1.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.35 (1.22\u0026ndash;1.49)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.08 (1.02\u0026ndash;1.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.03 (0.96\u0026ndash;1.11)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.55\u0026ndash;2.21)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22 (1.84\u0026ndash;2.67)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82 (1.53\u0026ndash;2.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68 (1.40\u0026ndash;2.02)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29 (1.18\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.58 (1.43\u0026ndash;1.75)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.29 (1.17\u0026ndash;1.41)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.41 (1.28\u0026ndash;1.56)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.14 (1.05\u0026ndash;1.23)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.20 (1.11\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.13 (1.05\u0026ndash;1.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.07 (0.99\u0026ndash;1.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth limitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.11\u0026ndash;1.64)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 (1.59\u0026ndash;2.10)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.97\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33 (1.16\u0026ndash;1.53)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08 (0.97\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.22 (1.11\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (0.92\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05 (0.96\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.06 (0.96\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.11 (1.01\u0026ndash;1.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.01 (0.91\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.98 (0.89\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeprivation index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.89\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.83\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.89\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.85\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03 (0.96\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06 (0.99\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (0.96\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07 (0.99\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.02 (0.95\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.99 (0.93\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.00 (0.94\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.97\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.04\u0026ndash;1.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.99\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15 (1.02\u0026ndash;1.29)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 (0.98\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06 (0.98\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.05 (0.98\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05 (0.98\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.04 (0.97\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.04 (0.98\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.00 (0.94\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.88\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.98\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (0.92\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.96\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (0.95\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.10 (1.03\u0026ndash;1.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (0.96\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.99 (0.93\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.02 (0.96\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.08\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.02\u0026ndash;1.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (1.09\u0026ndash;1.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (1.04\u0026ndash;1.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09 (1.01\u0026ndash;1.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10 (1.02\u0026ndash;1.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.17)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.02 (0.95\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.04 (0.98\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.02 (0.96\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.05 (0.99\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003e\u003cb\u003eModel 1\u003c/b\u003e: crude analysis; \u003cb\u003eModel 2\u003c/b\u003e: analysis adjusted for age; \u003cb\u003eM\u003c/b\u003e: men; \u003cb\u003eW\u003c/b\u003e: women; \u003cb\u003ePR\u003c/b\u003e: prevalence ratio; \u003cb\u003eCI\u003c/b\u003e: confidence interval; \u003cb\u003e*\u003c/b\u003e indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe final sample included 5,683 individuals (54.1% women). The age distribution was skewed toward older groups, with the largest proportion aged 51\u0026ndash;65 years (\u0026asymp;\u0026thinsp;15% in both sexes). Regarding socioeconomic status, most participants belonged to occupational social classes IV and III. Educational attainment was concentrated at the secondary and university levels. Women were more likely than men to live in the most deprived areas and belong to lower occupational classes (e.g., Class V: 10.4% vs. 5.1%). In terms of education, women were more likely to have only primary education (10.2% vs. 6.5%) or university-level education (13.1% vs. 9.7%). Regarding healthcare use, 41.7% of participants fell into the low or medium use tertiles, but women were more frequently in the highest tertile (11.6% vs. 7.5%). Approximately one quarter reported having a chronic condition, and 2.5% reported limitations in daily activities due to health problems (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eOverall, both knowledge and use of eHealth tools varied by tool, sex, and sociodemographic characteristics. Online appointment booking showed the highest levels of knowledge and use, followed by electronic health records (EHRs). In contrast, eHealth portal showed the lowest levels of both knowledge and use (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAt the descriptive level, older adults reported markedly lower knowledge and use across all tools. For example, use of EHRs and eHealth portal was below 4% among individuals aged 80 or older. Clear social gradients were observed across age, education, and occupational social class. Among women in occupational class I, knowledge of online appointment booking was 91.4%, but only 66.8% in class V. Usage followed a similar pattern. In university-educated women, 91.5% knew the online appointment booking and 74.2% used it, whereas both figures were substantially lower in lower educational groups (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eRegression models confirmed strong associations between age and sociodemographic variables with both knowledge and use.\u003c/p\u003e \u003cp\u003eAge: Age-related inequalities were consistently observed across tools. For online appointment booking, compared to those aged 18\u0026ndash;35, the probability of not knowing the tool among women aged 80\u0026thinsp;+\u0026thinsp;was significantly higher (Model 1: PR\u0026thinsp;=\u0026thinsp;5.09, 95% CI: 3.54\u0026ndash;7.32), and remained elevated after adjustment for healthcare use and chronic disease (Model 3: PR\u0026thinsp;=\u0026thinsp;5.49, 95% CI: 3.64\u0026ndash;8.28). In men, the corresponding estimate was also elevated (Model 3: PR\u0026thinsp;=\u0026thinsp;4.13, 95% CI: 2.53\u0026ndash;6.72). For EHRs, women aged 80\u0026thinsp;+\u0026thinsp;also had a higher probability of not using the tool (Model 3: PR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.37\u0026ndash;1.72), which decreased after adjusting for knowledge in Model 4 (PR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.01\u0026ndash;1.22). For eHealth tool, women aged 80\u0026thinsp;+\u0026thinsp;had a significantly higher probability of not knowing (Model 3: PR\u0026thinsp;=\u0026thinsp;2.60, 95% CI: 2.14\u0026ndash;3.17) and not using the tool (Model 3: PR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.36\u0026ndash;1.71), which remained significant in Model 4 (PR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.01\u0026ndash;1.21).\u003c/p\u003e \u003cp\u003eEducation: Strong educational inequalities were observed across all tools. For women with primary education, the probability of not knowing the online appointment booking system was substantially higher compared to university-educated women (Model 3: PR\u0026thinsp;=\u0026thinsp;3.26, 95% CI: 2.37\u0026ndash;4.48), and the corresponding estimate in men was PR\u0026thinsp;=\u0026thinsp;4.03 (95% CI: 2.79\u0026ndash;5.80). For EHRs, women with primary education also showed higher probability of not having knowledge (Model 3: PR\u0026thinsp;=\u0026thinsp;2.22, 95% CI: 1.90\u0026ndash;2.59) and not using the tool (Model 3: PR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.37\u0026ndash;1.62). For eHealth portal, the probability of not knowing the tool was also higher among primary-educated women (Model 3: PR\u0026thinsp;=\u0026thinsp;1.57, 95% CI: 1.39\u0026ndash;1.77), and the probability of not using the tool was elevated (Model 3: PR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.09\u0026ndash;1.25). Use estimates were slightly reduced after adjusting for knowledge in Model 4.\u003c/p\u003e \u003cp\u003eOccupational class: Gradients were evident across all tools. For online appointment booking, women in class V had a higher probability of not knowing the tool than those in class I (Model 3: PR\u0026thinsp;=\u0026thinsp;3.13, 95% CI: 2.00\u0026ndash;4.91). For use, the fully adjusted Model 4 still showed a higher probability of non-use among class V women (PR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.32\u0026ndash;2.05). Similar patterns were observed for EHRs (Model 3: PR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.32\u0026ndash;1.68; Model 4: PR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.09\u0026ndash;1.34 in women) and eHealth portal (Model 3: PR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.03\u0026ndash;1.23).\u003c/p\u003e \u003cp\u003eHealth limitations: Women with health limitations had a significantly higher probability of not knowing the online appointment booking tool (Model 3: PR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.02\u0026ndash;1.85). In terms of use, the fully adjusted Model 4 showed persistent inequalities for online appointment booking (PR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.11\u0026ndash;1.50), though no significant associations were observed for the other tools or in men.\u003c/p\u003e \u003cp\u003eDeprivation index: Differences by area-level deprivation were modest but showed some significant patterns. For online appointment booking, women in the most deprived quintile (Q5) had a higher probability of not using the tool compared to those in the least deprived quintile (Model 4: PR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.03\u0026ndash;1.37). Similar trends were seen for EHRs (Model 3: PR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.03\u0026ndash;1.20), though they attenuated after adjustment in Model 4. No consistent patterns were found for eHealth portal, and most differences by deprivation were not statistically significant.\u003c/p\u003e \u003cp\u003eWhen sex was included as the main independent variable in pooled models, differences between men and women were generally small. However, women were slightly less likely than men to report not knowing about the EHR system (Model 3 PR\u0026thinsp;=\u0026thinsp;0.91; 95% CI: 0.87\u0026ndash;0.97). For EHR use and for other digital tools, sex differences were not statistically significant. Interaction terms between sex and age, education, and occupational class were included in pooled models to assess effect modification. While some statistically significant interactions were observed, particularly for knowledge of electronic health records (EHRs) by educational level, these findings were not consistently replicated across other tools, outcomes, or adjustment levels. Overall, sex did not appear to meaningfully modify the associations between social determinants and digital health tool use or knowledge.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined social inequalities in the knowledge and use of eHealth tools within a universal healthcare system, focusing on online appointment booking, patient-accessible EHRs, and eHealth portal, and explored how social determinants shape these disparities. The results identified persistent inequalities in knowledge and use of eHealth tools across key social determinants, even within a universal healthcare system. Age, education, and occupational class showed the most consistent and significant gradients. Importantly, while inequalities in knowledge remained largely unchanged after adjusting for age, healthcare use, and chronic illness (Model 3), inequalities in use persisted even after further adjustment for knowledge (Model 4). This suggests that lack of knowledge is a major barrier, but not the only one, pointing to deeper structural or contextual factors limiting the uptake of eHealth services.\u003c/p\u003e \u003cp\u003eAlthough interaction models suggested some statistically significant sex-based differences, particularly in EHR knowledge by education, these patterns were isolated to specific models and not consistent across digital tools. Overall, our findings do not support sex as a strong modifier of social inequalities in digital health tool knowledge or use. Still, in the case of electronic health records, we observed modest but statistically significant lower probabilities of lacking knowledge among women. This aligns with prior research showing higher female engagement with EHRs and other digital health tools [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies suggest that this trend may reflect traditional gender roles, in which women are more often responsible for caregiving tasks, leading to greater familiarity with health systems and more proactive health-seeking behaviours. However, in our data, these sex differences were not consistent across tools or robust across models, indicating that broader structural determinants may play a more central role in shaping digital inequalities.\u003c/p\u003e \u003cp\u003eAge was the most prominent determinant across tools, with consistently higher probabilities of non-knowledge and non-use among the oldest age groups. These differences remained significant even after adjusting for healthcare use and knowledge, suggesting that digital health tools may offer limited benefit to some older adults. Beyond digital competence, this may reflect a mismatch between digital tools and the physical, cognitive, and sensory changes that accompany aging [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The fast-paced, hyperconnected logic of digital services may not be adapted to those aging outside digital ecosystems. This raises broader questions about whether we are adapting healthcare models to match older populations\u0026rsquo; needs\u0026mdash;or asking older people to adapt to a system designed for efficiency rather than inclusion.\u003c/p\u003e \u003cp\u003eSocioeconomic inequalities were particularly marked in education and occupational class. Lower educational attainment and class position were strongly associated with both lack of knowledge and use of eHealth tools, suggesting that digital exclusion reflects broader social disadvantage. These results confirm findings from previous literature on the digital divide and inequalities in eHealth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Importantly, some of these differences persisted even after adjusting for knowledge, indicating that additional barriers\u0026mdash;such as digital literacy, confidence, or structural accessibility\u0026mdash;may also be potential mediators of inequality [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Experiences from other European context reinforce this view, where digital tools are widely used, inequalities still persist.Universal access by itself does not ensure equity; implementation strategies and social context are equally decisive.\u003c/p\u003e \u003cp\u003eThese results support recent [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and not so recent [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] critiques of intervention-generated inequalities in digital health. eHealth is not socially neutral\u0026mdash;it is embedded in systems that reproduce structural exclusion. Shifting to a digital-first model with the goal of improving sustainability may inadvertently marginalize those least equipped to navigate it [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAddressing these disparities requires shifting from framing digital inclusion as an individual issue (e.g., skills training) toward a systems-level approach. The persistence of digital inequalities despite growing technological access suggests that the problem lies more in structural barriers than in proximal causes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. That includes co-designing tools with excluded groups, investing in accessible alternatives, and placing equity at the centre of digital health strategies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study contributes to the growing body of evidence from public health systems, contrasting with prior research mostly based in private-sector contexts. It highlights the need for Europe\u0026rsquo;s universal health systems to critically evaluate the digital transition and its equity implications.\u003c/p\u003e \u003cp\u003eThis study is among the first to examine inequalities in knowledge and use of eHealth tools within the Basque Health Service using a large, population-representative sample. Its contribution is particularly relevant as it provides empirical evidence from a universal public health system, in contrast to most existing research, which has been conducted in private or mixed healthcare settings. The stratified analysis by sex, combined with pooled models including interaction terms, allowed for an intersectional approach that captured potential sex-based effect modification and avoided masking group-specific disparities. The use of progressively adjusted Poisson models allowed us to explore the role of social determinants and the mediating effect of knowledge on eHealth use. Analysing multiple tools separately also provided insight into tool-specific adoption patterns. However, the cross-sectional design limits causal inference, and the reliance on self-reported measures may introduce recall or social desirability bias. Although we adjusted for age, healthcare utilisation, and health-related limitations, residual confounding from unmeasured factors such as digital literacy, device access, or trust in eHealth cannot be ruled out. Future research should include specific indicators such as these to better understand the mechanisms behind non-use, since qualitative research has already shed light on some of these barriers in other settings. Finally, as the study focused on three tools within one regional health system, findings may not generalize to other contexts.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eSignificant inequalities persist in both knowledge and use of eHealth tools within the Basque Health Service, particularly among older adults and those in disadvantaged socioeconomic positions. While gaps in use are partly reduced once individuals are informed, inequalities in use still remain even when the tools are known, underscoring the role of broader structural barriers. Targeted strategies are needed to improve awareness and ensure equitable digital transitions in healthcare.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Records\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrevalence Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e95%CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was conducted using anonymised secondary data from the 2023 Basque Health Survey (ESCAV), carried out by the Basque Government Department of Health. According to Spanish regulations on biomedical research and data protection, analyses of fully anonymised survey data do not require approval from a research ethics committee. All procedures were performed in accordance with the principles of the Declaration of Helsinki. All participants in the Basque Health Survey provided informed consent prior to participation. The authors had access only to anonymised data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the \u0026ldquo;Instituto de Salud Carlos II\u0026rdquo; I and co-funded by the European Union (Grant No. PI23/01063), within the framework of the 2023 call for Health R\u0026amp;D Projects.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eUMR conceived the study and contributed to its design and writing key conceptual and methodological decisions. AAG developed the study design further and conducted the analysis in close collaboration with UMR. AAG drafted the manuscript, with collaboration of GMG, and all authors revised the text comprehensively before submission.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this study are derived from the \u0026ldquo;Encuesta de Salud de la CAPV (ESCAV)\u0026rdquo;\u0026mdash;the Basque Country Health Survey\u0026mdash;conducted by the Basque Government. These data are subject to access restrictions and used under license. Although not publicly available, the data can be obtained from the authors upon reasonable request and with permission from the corresponding authority.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEuropean Court of Auditors. EU actions for cross-border healthcare: significant ambitions but improved management required. 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Construction of the Digital Health Equity-Focused Implementation Research Conceptual Model - Bridging the Divide Between Equity-focused Digital Health and Implementation Research. PLOS Digit Health. 2024;3(5):e0000509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pdig.0000509\u003c/span\u003e\u003cspan address=\"10.1371/journal.pdig.0000509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital health, eHealth, health inequalities, social determinants of health, healthcare access, digital divide, primary care, health equity","lastPublishedDoi":"10.21203/rs.3.rs-8500757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8500757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study examines social inequalities in knowledge and use of eHealth tools in a region of Spain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis was conducted using data from the 2023 Basque Health Survey (n\u0026thinsp;=\u0026thinsp;5,683). Prevalence of knowledge and use of digital tools was described stratified by sex and social determinants. Poisson regression models with robust variance were used to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for not knowing or not using each tool. Models were progressively adjusted for age, healthcare utilisation, and knowledge in use models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKnowledge and use of eHealth tools varied substantially by age, education, and occupational class. The Web Appointment tool showed the highest levels of both knowledge and use. For women aged 80+, the probability of not knowing the tool was 5.09 times higher than for women aged 18\u0026ndash;35 (95% CI: 3.54\u0026ndash;7.32), and 4.34 (95% CI: 2.92\u0026ndash;6.45) in men. Education and class gradients were strong, particularly in knowledge. For example, women with primary education had higher probability of not knowing the Web Appointment tool than university-educated peers (fully adjusted PR\u0026thinsp;=\u0026thinsp;3.28, 95% CI: 2.39\u0026ndash;4.51). Use of digital tools showed similar but smaller gradients. Differences by deprivation and health limitations were modest and less consistent.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDigital health inequalities in the usage of eHealth tools are strongly patterned by age and socioeconomic status, especially for knowledge; importantly, gaps in use remain even when tools are known, highlighting the need for targeted equity-focused strategies.\u003c/p\u003e","manuscriptTitle":"eHealth inequalities in a universal healthcare system: a cross-sectional study on the role of social determinants in knowledge and use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 13:55:08","doi":"10.21203/rs.3.rs-8500757/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-30T13:34:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232247349531721770918184680683917424373","date":"2026-03-08T17:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242488129203430028446855403368916264282","date":"2026-01-13T18:04:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T09:34:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T09:20:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T04:43:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T09:09:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-07T08:54:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"725c933e-edc1-4f1f-aedb-fc42c4679718","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T13:55:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 13:55:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8500757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8500757","identity":"rs-8500757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}