Life course socioeconomic position and the risk of cardiovascular events and all-cause mortality in a Norwegian twin study

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Gjerde, Øyvind Naess This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4993031/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background Socioeconomic disadvantage throughout life is associated with an increased risk of cardiovascular diseases (CVD) and all-cause mortality. However, the extent to which common familial genetic and environmental factors explain this association is unclear. This study investigates family- and individual-specific effects on this association using twin data. Methods Data from 21,051 twins were linked to census and registry data on 20 indicators of socioeconomic position (SEP) from 1960 to 1990, with follow-up on hospital discharges and causes of death until 2018. A graded item response model was used to aggregate SEP indicators into a linear scale, divided into quintiles. Hazard ratios were estimated using a mixed-effects Weibull proportional hazard regression model. Results At the familial level, higher SEP quintiles were associated with a lower risk of CVD events (HR = 0.51 for monozygotic [MZ] twins, HR = 0.56 for dizygotic [DZ] twins). Individual-specific effects showed hazard ratios of 0.80 for MZ twins and 0.73 for DZ twins. For all-cause mortality, familial risks for the top two quintiles were HR = 0.64 in MZ twins and HR = 0.61 in DZ twins, with individual-specific effects at HR = 0.71 in MZ twins and HR = 0.74 in DZ twins. Conclusion The associations between life course SEP and both CVD and all-cause mortality are partly explained by shared familial factors from early life, with shared genetic factors having minimal additional influence. Health sciences/Diseases/Cardiovascular diseases Biological sciences/Genetics Earth and environmental sciences/Environmental social sciences Health sciences/Risk factors Figures Figure 1 Figure 2 Introduction Studies have shown that individuals who experience socioeconomic disadvantage at multiple time points through their life have elevated risk of cardiovascular diseases (CVD) and premature mortality 1 – 4 . This risk appears to increase in an accumulative manner, suggesting that a comprehensive assessment of socioeconomic indicators throughout an individual’s life is necessary to fully capture the impact of socially patterned environmental factors on chronic disease risk. For instance, Degerud et al. found a linear dose-response relationship between incremental increases in life course socioeconomic position (SEP) (range 0–20) and CVD 4 . Compared to individuals with high SEP, risk ratios for risk of CVD mortality were 1.16 (1.11–1.22) for middle SEP and 1.50 (1.41–1.59) for low SEP. The model of risk accumulation for many non-communicable diseases is biologically supported, as impaired organ function can accumulate over time due to increased environmental exposures, such as atherosclerotic changes to blood vessels or increased resistance to glucose levels in blood 2 , 5 . However, limited research has tested the accumulation model of social causation for CVD or mortality 3 . A linear gradient of association for life course SEP is consistent with both social causation and social selection. It is essential to consider underlying non-causal factors, such as familial factors (including genetics and family environment), which may be associated with both disadvantaged socioeconomic trajectories and increased disease risk. These factors can create spurious associations between life course SEP and CVD 3 , 6 , 7 . Genetically informed studies may help elucidate whether the dose-response association between life course SEP and disease risk can be attributed to familial factors. Twin studies assume that monozygotic (MZ) 'identical' twins, originating from one egg cell, and dizygotic (DZ) 'fraternal' twins, originating from two different egg cells, share all their family environments. MZ twins share all their genome, whereas DZ twins share on average 50% of their segregating genes. If there is a difference in CVD mortality among MZ twins discordant in SEP, the association cannot be due to genetic or familial environmental effects, suggesting a causal relationship. The accumulation model of social causation has not been tested with family-based designs. Our primary objective was to determine whether the risk of cardiovascular disease (CVD) and all-cause mortality exhibits a linear relationship with life course socioeconomic position (SEP). Our secondary objective was to examine the associations of life course SEP with CVD and all-cause mortality, considering both family-specific factors (shared genetic and shared environment) and individual-specific factors. Methods Participants We included Norwegian twins born between 1915 and 1960 who participated in the Norwegian Twin Registry 8 . They did not emigrate or die until after September 20, 1990, and they completed the mandatory censuses in Norway from 1960 through 1990. The sample comprised 21,051 complete same-sex twin pairs (4,909 monozygotic male, 6,233 dizygotic male, 4,062 monozygotic female, and 6,247 dizygotic female). Zygosity was determined by questionnaire and subsequently by genetic marker analysis for a subsample 8 . The twins were sent health questionnaires in 1978–1982 and again in 1990–1998. Data were obtained by linking census data from 1960–1990, the national educational database, and population and household income data with hospitalization records and the Cause of Death Registry. Life course SEP We collected life course SEP indicators on household conditions from population censuses in 1960, 1970, and 1980. This included type of dwelling (apartment block, row or detached house), ownership status, rooms per household capita, telephone ownership, access to water closet, and bath inside the dwelling. Household income data were used in 1990 because data on housing conditions were collected on only 10%. The highest level of education obtained was in the National Educational Database. Educational attainment was categorized as “higher than high school” versus “high school or less,” based on the highest completed level of education recorded in national education registers. The “higher than high school” category includes individuals who completed any form of post-secondary education, including vocational or technical training, as well as those who attended but did not complete a university degree. Information on the role of household indicators is described elsewhere 4 , 9 – 11 . This provided a total of 20 indicators, which were then reduced to 17 indicators and scored (0 or 1) (Supplementary table 1 ). Due to high tetrachoric correlation, we grouped the '1960s bathroom' and 'water closet (WC)' indicators into one ('BADWC 60'), signifying access to either facility in the 1960s (Supplementary Fig. 1–2). This approach was similarly applied for the 1970s and 1980s indicators. To combine all the indicators into a SEP continuum score, we subjected all the items to a graded response Item Response Theory (IRT) analysis, see Supplementary tables for further information. We adjusted the estimated results by the influence of two different birth cohorts born 1915–1945 and 1946–1960. Outcome data and follow–up The Cause of Death Registry provided mortality data using the ninth and tenth revisions of the International Classification of Diseases (ICD). The CVD outcomes included fatal and non-fatal first events of coronary heart disease or stroke from 1994 (1990–1995: ICD–9: 390–459; 1996–2014: ICD–10: I00–I99). The information was primarily based on certificates filled out by on-site medical doctors in the death certificate or at hospital discharge. Our sample was followed up from 1994 until the first event (fatal or non-fatal) of CVD or death from any cause, or until December 31, 2018. The time scale was from the date of birth. Statistical methods We used the Generalized Partial Credit Model (GRM) within Item Response Theory (IRT) (GRM-IRT) 12 , 13 . The GRM-IRT is a logistic model with two parameters for each observed variable: ´difficulty´ and ´discriminability´. The difficulty parameter localizes each item on the standard normal continuum, while the discriminability parameter represents how well an item can reliably differentiate individuals on this continuum. We stored empirical Bayes means of this linear continuum as IRT-scores and divided these into quintiles. By genetic theory we constrained the between-monozygotic twin effect to be all additive genetic, non-additive genetic, or familial/environmental effects. We implemented logistic generalized structural equation models with a frailty parameter, estimating the individual-specific environmental effect by subtracting the between twin effect from the total observed variance. The association between SEP and CVD events was calculated using a mixed-effects Weibull proportional hazard regression model with individuals on level 1, zygotes on level 2, and pregnancies on level 3 14 . Participants were followed from the last questionnaire sent to twins in 1990 until the date of death or at the end of the study. Since monozygotic pairs share 100% of their genome, we assumed that the cluster mean fixed effect represented 100% of the genetic covariance 14 . We centered the SEP score on the cluster mean and assumed that the within pair fixed effects for monozygotic twins represented environmental effects. Additionally, the within pair fixed effect for dizygotic pairs was assumed to reflect the within monozygotic pair effect plus half of the genetic effect. Since dizygotic pairs share 50% of their genome, we assumed that the group mean fixed effect for dizygotic pairs represented 50% of the genetic covariance. We also simulated a classical twin design to decompose phenotypic variance into additive genetic (A), shared environment (C), and unique environment (E) components. While we conduct an ACE analysis on the full dataset, the simulation study complements and contextualizes the findings by assessing model performance under controlled conditions and highlighting potential biases, such as limited power to detect shared environmental effects. This strengthens confidence in the empirical results by validating the ACE model under study-relevant conditions. Our statistical analyses were implemented in STATA 15.1. Results All-cause and cardiovascular mortality In the final sample of 21,051 complete twin pairs (42,102 individuals), 6,266 participants died prematurely before the age of 65 from any cause, and 2,770 individuals experienced a fatal or non-fatal cardiovascular disease (CVD) event. Analysis revealed that both CVD events and all-cause mortality rates were lower among the middle and high socioeconomic position (SEP) groups compared to the low SEP group (Table 1). The study population consisted of slightly more women than men (51%), with an average birth year of 1942. Notably, the lowest and highest SEP quintiles had slightly younger average birth years compared to the other SEP groups. Item Response Theory modelling of SEP Detailed results from the GRM-IRT analyses are presented in online supplement 1.2. These analyses demonstrated that the 17 selected SEP indicators, scored as 0 or 1, exhibited strong discriminative power across distinct levels of the SEP continuum. The SEP continuum score had substantial reliability ranging from -1.5 to 4 standard deviations (SD). Factor scores, calculated using empirical Bayesian methods, were stratified into five quintiles of SEP. The mean ± SD of the SEP scores across the quintiles were −1.09 ± 0.35, −0.39 ± 0.12, −0.01 ± 0.11, 0.39 ± 0.13, and 1.00 ± 0.29, with corresponding reliabilities (α) of 0.73, 0.79, 0.78, 0.76, and 0.68, respectively (Table 1). Quintile one represented the lowest SEP category, indicating the most deprived socioeconomic circumstances. SEP and CVD events: familial and individual risk factors In the absence of competing risks, 75% of the population experienced a fatal or non-fatal CVD event by the age of 100. Kaplan-Meier failure estimates (Figure 1) illustrate survival probabilities for CVD events (Part A) and all-cause mortality (Part B) across five SEP categories. The results for CVD risk revealed a pronounced disparity in survival probability between the lowest and highest SEP categories, significantly exceeding the differences observed among the three middle SEP quintiles. This pattern was consistent for both monozygotic and dizygotic twins (Supplementary figures 3–4). This disparity was even more striking and statistically significant when examining all-cause mortality, highlighting the substantial impact of SEP on overall survival. Table 2 presents the analysis examining the between-individual (traditional cohort analysis), familial, and individual-specific effects of SEP on CVD events. In the cohort analysis, individuals in the top two quintiles of SEP had a 58% lower risk of experiencing a CVD event compared to those in the low to moderate SEP group (HR = 0.42; 95% CI 0.37 - 0.47). Familial risks revealed a 49% reduction in risk for CVD events in monozygotic twin pairs from the top two quintiles of SEP (HR = 0.51; 95% CI 0.43 - 0.59) and a 44% reduction for dizygotic twin pairs (HR = 0.56; 95% CI 0.49 - 0.64). The individual-specific effects analysis showed that individuals in the top two quintiles of SEP within monozygotic twin pairs had a 20% decreased risk of CVD compared to those in the low SEP group (HR = 0.80; 95% CI 0.66 - 0.97), while the reduction was 27% for dizygotic twin pairs (HR = 0.73; 95% CI 0.61 - 0.87). Table 3 presents the association between SEP and all-cause mortality. The cohort analysis showed a significant protective effect of high SEP on all-cause mortality (HR = 0.48; 95% CI 0.44 - 0.52). Familial analyses indicated an inverse relationship between increasing levels of SEP and all-cause mortality, with a reduced risk observed for both monozygotic twin pairs (HR = 0.64; 95% CI 0.58 - 0.70) and dizygotic twin pairs (HR = 0.61; 95% CI 0.56 - 0.66). The individual-specific effects analysis showed a protective effect against all-cause mortality for individuals in the top two quintiles of SEP, both for monozygotic twin pairs (HR = 0.71; 95% CI 0.64 - 0.80) and dizygotic twin pairs (HR = 0.74; 95% CI 0.67 - 0.82). The observed pattern appeared to be linear across all SEP quintiles in the cohort for both CVD and all-cause mortality risk (Figure 2 and Table 3). Heritability of SEP and CVD events In a simulation study with 1,000 pairs of twins (half monozygotic and half dizygotic) was conducted, simulating additive genetic, shared environment, and non-shared environment variables. The simulation indicated significant genetic and environmental effects on the phenotype. Life course SEP in the general population was estimated to be influenced by 41% (95% CI 25-56%) additive genetic factors, 24% (95% CI 10-37%) shared environmental factors, and 35% (95 % CI 31-40%) individual-specific environmental factors. For CVD events, additive genetic, non-additive genetic, and individual-specific risk factors explained 41% (95% CI 26-58%), 23% (95% CI 12-40 %) and 36 % (95 % CI 31-41 %) of variance, respectively. Discussion We investigated the extent to which life course socioeconomic position (SEP) was associated with fatal and non-fatal events of cardiovascular disease (CVD) and all-cause mortality in twins. We identified a graded dose-response relationship for these outcomes. Additionally, we observed more pronounced family-specific effects of life course SEP compared to individual-specific effects in both dizygotic and monozygotic twins. Minor differences were noted in the family-specific effects between dizygotic and monozygotic twins. These findings suggest that the associations observed for life course SEP on CVD events and all-cause mortality may partly be explained by shared environmental factors, with shared genetic factors playing a minor role. Previous literature We are not aware of similar studies investigating family- and individual-specific effects of life course SEP using indicators at multiple time points. One study on Swedish twins, which examined social mobility and preventable mortality, found that familial or genetic confounding could not explain adult socioeconomic inequalities or lower risk for those being upwardly mobile 1 . Our study differs as it focuses on cumulative exposure through adulthood rather than two time points. Our finding of a linear relationship between life course SEP and CVD events and mortality aligns with other observational studies that provide evidence of accumulation over the life course 4 . There are no sibling comparison studies investigating life course SEP on CVD and all-cause mortality. Some twin and sibling comparison studies have explored the relationship using education or other SEP indicators in adulthood, finding some attenuation that suggests early life family factors may play a role 7 , 15 – 21 . Our study extends this by examining the role of early life family factors on accumulation. Heritability estimates showed that the relative magnitude of additive genetic, shared environment and unique individual effects of life course SEP are similar to other studies, with additive genetic and individual-specific components being larger than shared environment for most behavioural and social factors. The life course SEP measure showed a larger estimate for the shared environment component than other studies of related SEP measures. The broad-sense heritability of CVD mortality has been estimated to be 47% 22 . Previous estimates of heritability for death from coronary heart disease in a Swedish twin sample found heritability of 0.57 (95% CI, 0.45–0.69) among male twins and 0.38 (95% CI: 0.26–0.50) among female twins 23 , 24 . In the simulated dataset used to illustrate the GRM-IRT method, the heritability of fatal and non-fatal CVD events was estimated at 0.64 (not tabulated), reflecting the predefined parameters of the simulation rather than empirical data. Interestingly, even with large heritability estimates for both exposure and outcome, most of the family-specific effect was still not genetic, as we observed little difference between DZ and MZ twins in either outcome 25 – 30 . This suggests a pronounced influence of the shared environment. This difference between high heritability estimates within traits and lack of genetic effects in bivariate analysis may seem counterintuitive but is consistent with other studies in cardiovascular epidemiology and behavioural genetics 31 . For outcomes like CVD, it is well established that inter-individual differences, such as genetic factors, account for little of the variation at the population level 32 . Elevated levels of risk factors in some individuals have much less impact on disease incidence in the population compared to moderately increased levels in the majority. Hence, population-wide strategies have more impact on reducing disease incidence compared to a high-risk strategy focusing on individuals, such as treating elevated levels of risk factors like blood pressure and lipids with medication. Twin models that decompose variance into the ACE model (additive genetic [A], shared environment [C], and unique individual [E]) typically match twin pairs on year and place of birth, excluding factors that determine the distribution of risk factor levels in the population but may have a small influence on individuals 31 . Within twin pairs, most variation, even for social indicators, is due to genetic and non-shared factors. However, at the population level, a large share of cases can be attributed to modifiable environmental factors from the shared environment in childhood 15 , 17 , 20 . Strengths and limitation An important strength of this study is the combination of a comprehensive number of life course indicators with hospital discharge and mortality data in twins. Several limitations should be mentioned. Incorporating multiple indicators of SEP for both type of twins resulted in instances of missing data. SEP levels were determined based on five quintiles using empirical Bayes means as Item Response Theory scores, which may not fully capture SEP disparities among twins. Additionally, the cohort included twins born from 1915 to 1960, resulting in different ages of follow-up (from 1990 to 2018) throughout their life course. Most endpoints came from older participants since CVD is more common in older groups and because premature CVD has declined over this period. Moreover, the absence of information on CVD risk factors and treatment means we were unable to adjust for these. However, we have no reason to believe this would significantly alter the influence of family factors. Furthermore, sibling comparison studies rely on a comparison between the full cohort and the within-pair difference in exposure, which may introduce selection bias if the full cohort differs from discordant siblings 31 , 33 . This bias may be more problematic for categorical or dichotomous exposures when large shares of the population may be concordant. A limitation of this study is that the ACE model was not applied to the same empirical dataset. Another limitation is that although sex was included as a covariate in all models, we did not perform sex-stratified analyses, which may have masked potential sex-specific differences in the associations; this remains a direction for future research. We adjusted for birth cohort as a covariate in all models. We did not stratify analyses by birth cohort, which may have masked cohort-specific variation in the contributions of genetic and shared environmental factors. Lastly, we acknowledge the heterogeneity within the “higher than high school” category as a limitation in interpreting socioeconomic patterns. Interpretation Our results indicate that both familial and individual effects are important in the accumulation of socioeconomic risk on outcomes like CVD and all-cause mortality. This supports the notion that both childhood SEP and adult socioeconomic environment have a causal role, with shared genetic factors having little influence. This is not surprising given what we know about the role of individual and population level determinants on chronic diseases. However, recent literature on health inequalities has raised questions about the role of personal factors in advanced welfare states like the Nordic countries 34 . Comprehensive welfare states persist in demonstrating large and comparable inequalities in mortality, a phenomenon known as the Nordic paradox. It has been suggested that the determinants of these inequalities lie deeper than what policies can achieve. Indirect selection of underlying personal factors may be particularly important in societies with extensive inter-generational social mobility, with most disease burden related to non-communicable diseases and health behaviours playing a significant role. The observed attenuation of association between SEP and CVD in within-pair analyses, especially among MZ twins, supports the role of shared familial factors and suggests that part of the association may be due to shared environmental confounding. Our study highlights the potential role of early life in preventing future disease risk and points away from associations being genetically determined. Future research should collate family data across diverse populations and birth cohorts to study the impact of life course SEP on CVD and other non-communicable diseases more extensively. The higher HRs observed when SEP quintiles collapsed into a binary high vs. low measure in the within-pair analyses may seem counterintuitive but likely reflect several factors. First, broader categories increase within-group heterogeneity e.g., quintile 3 may dilute contrasts when grouped with lower SEP levels, shifting the average HR toward the null. Second, the association between SEP and cardiovascular risk appears to be non-linear in the within-pair analyses, with the most substantial drop in risk occurring between quintiles 1 and 2. This nuance is lost when using a binary SEP measure. Lastly, the two approaches differ conceptually: quintile models estimate risks relative to the lowest SEP group, while binary models compare average risks across broader, more heterogeneous groups, inherently affecting HR magnitude. Conclusion Moderate to high life course SEP was associated with a lower risk of fatal and non-fatal CVD as well as all-cause mortality in both family- and individual-specific analyses. Observed associations between life course SEP and CVD and all-cause mortality are to some extent explained by shared family factors in early life, with shared genetic factors having little additional influence. Declarations Contributions ØN conceived the idea, ØN and HN designed the study, EY and HN analysed the data, HN drafted the initial manuscript, EY, LCG and ØN interpreted the data, critically appraised the manuscript, and approved the last version. Competing interest The authors declare no conflicts of interest. Funding The study was funded by The Research Council of Norway (2137788) through the programme Better Health and Quality of Life. The funder did not take part in the planning, conduction, or reporting of the study. EY is funded by the Research Council of Norway (288083, 336078, and 174842) and the European Union (101045526). Ethics approval This project obtained ethical approval (REK 2012/827) from the Regional Committees for Medical and Health Research Ethics of Norway. Information about the study was given, and possible consequences of the study were explained to all participants before written informed consent was obtained. It carried out in accordance with the principles embodied in the Declaration of Helsinki. Data availability The consent given by the participants does not open for storage of data on an individual level in repositories or journals. The datasets analysed during the current study are not publicly available due to data protection regulations. The individual-level data that support the findings of this study are not publicly available due to legal restrictions in Norway. The analytical code can be accessed by contacting the corresponding author. Acknowledgements We would like to thank the participants of the Twin studies for their valuable efforts, as well as the study personnel. We thank The Research Council of Norway for funding the project (Grant number 287347). References Ericsson M, Pedersen NL, Johansson ALV, Fors S, Dahl Aslan AK. Life-course socioeconomic differences and social mobility in preventable and non-preventable mortality: a study of Swedish twins. International Journal of Epidemiology. 2019; 48:1701-9. Pedamallu H, Zmora R, Perak AM, Allen NB. Life Course Cardiovascular Health: Risk Factors, Outcomes, and Interventions. Circulation Research. 2023; 132:1570-83. Bann D, Wright L, Hughes A, Chaturvedi N. Socioeconomic inequalities in cardiovascular disease: a causal perspective. Nature Reviews Cardiology. 2023. Degerud E, Ariansen I, Ystrom E, Graff-Iversen S, Høiseth G, Mørland J, et al. Life course socioeconomic position, alcohol drinking patterns in midlife, and cardiovascular mortality: Analysis of Norwegian population-based health surveys. PLOS Medicine. 2018; 15:e1002476. Wagner C, Carmeli C, Jackisch J, Kivimäki M, van der Linden BWA, Cullati S, et al. Life course epidemiology and public health. The Lancet Public Health. 2024; 9:e261-e9. Ariansen I, Mortensen L, Igland J, Tell GS, Tambs K, Graff-Iversen S, et al. The educational gradient in coronary heart disease: the association with cognition in a cohort of 57 279 male conscripts. Journal of Epidemiology and Community Health (1979-). 2015; 69:322-9. Næss Ø, Hoff DA, Lawlor D, Mortensen LH. Education and adult cause-specific mortality—examining the impact of family factors shared by 871 367 Norwegian siblings. International Journal of Epidemiology. 2012; 41:1683-91. Nilsen TS, Brandt I, Magnus P, Harris JR. The Norwegian Twin Registry. Twin Research and Human Genetics. 2012; 15:775-80. Fiskå BS, Ariansen I, Graff-Iversen S, Tell GS, Egeland GM, Næss Ø. Family history of premature myocardial infarction, life course socioeconomic position and coronary heart disease mortality — A Cohort of Norway (CONOR) study. International Journal of Cardiology. 2015; 190:302-7. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1). Journal of Epidemiology and Community Health. 2006; 60:7. Næss Ø, Claussen B, Thelle DS, Davey Smith G. Cumulative deprivation and cause specific mortality. A census based study of life course influences over three decades. Journal of Epidemiology and Community Health. 2004; 58:599. Samejima F. ESTIMATION OF LATENT ABILITY USING A RESPONSE PATTERN OF GRADED SCORES1. ETS Research Bulletin Series. 1968; 1968:i-169. Thissen D, Steinberg L. A taxonomy of item response models. Psychometrika. 1986; 51:567-77. Rabe-Hesketh S, Skrondal A, Gjessing HK. Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software. Biometrics. 2008; 64:280-8. Tarkiainen L, Martikainen P, Laaksonen M, Aaltonen M. Childhood family background and mortality differences by income in adulthood: fixed-effects analysis of Finnish siblings. European Journal of Public Health. 2015; 25:305-10. Søndergaard G, Mortensen LH, Nybo Andersen A-M, Andersen PK, Dalton SO, Madsen M, et al. Does Shared Family Background Influence the Impact of Educational Differences on Early Mortality? American Journal of Epidemiology. 2012; 176:675-83. Ariansen I, Mortensen LH, Graff-Iversen S, Stigum H, Kjøllesdal MKR, Næss Ø. The educational gradient in cardiovascular risk factors: impact of shared family factors in 228,346 Norwegian siblings. BMC Public Health. 2017; 17:281. Lundborg P, Lyttkens CH, Nystedt P. The Effect of Schooling on Mortality: New Evidence From 50,000 Swedish Twins. Demography. 2016; 53:1135-68. KjØLlesdal MKR, Ariansen I, Mortensen LH, Smith GD, NÆSs Ø. Educational differences in cardiovascular mortality The role of shared family factors and cardiovascular risk factors. Scandinavian Journal of Public Health. 2016; 44:744-50. Madsen M, Andersen PK, Gerster M, Andersen A-MN, Christensen K, Osler M. Are the educational differences in incidence of cardiovascular disease explained by underlying familial factors? A twin study. Social Science & Medicine. 2014; 118:182-90. Gilman SE, Loucks EB. Another casualty of sibling fixed-effects analysis of education and health: An informative null, or null information? Social Science & Medicine. 2014; 118:191-3. Ystrom E, Degerud E, Tesli M, Høye A, Reichborn-Kjennerud T, Næss Ø. Alcohol consumption and lower risk of cardiovascular and all-cause mortality: the impact of accounting for familial factors in twins. Psychological Medicine. 2023; 53:4130-8. Zdravkovic S, Wienke A, Pedersen NL, Marenberg ME, Yashin AI, De Faire U. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. Journal of Internal Medicine. 2002; 252:247-54. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics. 2015; 47:702-9. Baier T, Eilertsen EM, Ystrøm E, Zambrana IM, Lyngstad TH. An anatomy of the intergenerational correlation of educational attainment – Learning from the educational attainments of Norwegian twins and their children. Research in Social Stratification and Mobility. 2022; 79:100691. Davies G, Marioni RE, Liewald DC, Hill WD, Hagenaars SP, Harris SE, et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Molecular Psychiatry. 2016; 21:758-67. Branigan AR, McCallum KJ, Freese J. Variation in the Heritability of Educational Attainment: An International Meta-Analysis. Social Forces. 2013; 92:109-40. Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics. 2022; 54:437-49. Hyytinen A, Ilmakunnas P, Johansson E, Toivanen O. Heritability of lifetime earnings. The Journal of Economic Inequality. 2019; 17:319-35. Liu H. Genetic architecture of socioeconomic outcomes: Educational attainment, occupational status, and wealth. Social Science Research. 2019; 82:137-47. Smith GD. Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice. International Journal of Epidemiology. 2011; 40:537-62. Rose G. Sick individuals and sick populations. International Journal of Epidemiology. 2001; 30:427-32. Frisell T, Öberg S, Kuja-Halkola R, Sjölander A. Sibling Comparison Designs: Bias From Non-Shared Confounders and Measurement Error. Epidemiology. 2012; 23. Mackenbach JP. Health inequalities: Persistence and change in European welfare states: Oxford University Press; 2019. Available from: https://doi.org/10.1093/oso/9780198831419.001.0001. Tables Table 1. Descriptive characteristics of the study population at baseline according to categories of life course SEP. SEP groups 1 2 3 4 5 All individuals Characteristics 4216 (20.03) 4206(19.98) 4209(19.99) 4212(20.00) 4208(20.00) 21051(100.00) Sex (male, %) 2078 (49.29) 2008 (47.74) 2051(48.73) 2053(48.74) 2119 (50.36) 10309 (48.97) Year of birth 1940.06 (13.86) 1943.93 (12.01) 1943.47 (12.14) 1943.04 (12.36) 1938(13.29) 1942 (12.95) Monozygotic (%) 1723 (40.87) 1678 (39.90) 1799 (42.74) 1727 (41.00) 1644 (39.07) 8571 (40.71) CVD events (%) 736 (17.46) 490 (11.65) 456 (10.83) 466 (11.06) 622 (14.78) 2770 (13.16) All-cause deaths (%) 1783 (42.29) 1033 (24.56) 1013 (24.07) 1029 (24.43) 1408 (33.46) 6266 (29.76) Birth cohort 1915-1945 All (%) 2339 (21.26) 1911 (17.37) 1970 (17.91) 2032 (18.47) 2747 (24.97) 21051(100.00) Sex (male, %) 1221 (52.20) 918 (48.04) 950 (48.22) 957 (47.10) 1340 (48.78) 5386 (48.97) Year of birth 1930 (9.54) 1933(2.01) 1933 (8.69) 1932 (8.93) 1930 (8.86) 1931 (8.85) Monozygotic (%) 966 (41.30) 750 (39.25) 888 (45.08) 817 (40.21) 1089 (39.64) 4510 (41.00) CVD events (%) 736 (0.17) 490 (0.12) 456 (0.11) 466 (0.11) 622 (0.15) 2474 (22.49) All-cause deaths (%) 1550 (66.27) 871 (45.58) 875 (44.42) 883 (43.45) 1337 (48.67) 5516 (50.15) Birth cohort 1946-1960 All (%) 1877 (18.67) 2295 (22.83) 2239 (22.27) 2180 (21.69) 1461 (14.53) 21051(100.00) Sex (male, %) 857 (45.66) 1090 (47.49) 1101 (49.17) 1096 (50.28) 779 (53.32) 4923 (48.97) Year of birth 1952.96 (4.35) 1953.04 (4.31) 1952.87 (4.29) 1952.96 (4.25) 1952.99 (4.14) 1953 (4.27) Monozygotic (%) 757 (40.33) 928 (40.44) 911 (40.69) 910 (41.74) 555 (37.99) 4061 (40.40) CVD events (%) 69 (3.68) 70 (3.05) 45 (2.01) 76 (3.49) 36 (2.46) 296 (2.94) All-cause deaths (%) 233 (12.41) 162 (7.06) 138 (6.16) 146 (6.70) 71 (4.86) 750 (7.46) Note: Presented as count (percentages), except for “Year of birth,” which is presented as mean (standard deviation). Table 2 . Hazard ratios and their 95%CI for fatal and non-fatal cardiovascular events by categories of life-course SEP. Table 3 . Hazard ratios and their 95%CI for all-cause mortality by categories of life-course SEP. Additional Declarations No competing interests reported. Supplementary Files Twinsupplementary06.07.2025.docx Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 20 Aug, 2025 Reviews received at journal 25 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 24 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4993031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503044106,"identity":"87b4e89a-9b86-4b6d-8e15-25ea39e7f7ac","order_by":0,"name":"Huong Nguyen Thu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACgwNAgvEfkJRIAAvIsc9gbCCsBUzKQLQY89wgWoscREtizw1CDruRfOzhF4M7iQ1y6dekeSrupPdINzcwF1Tg1mJ/Iy3dWMbgWWKDdE6ZNM+ZZ7k9MgcbmGecwWdLjpm0hMFhkJY0ad62w7n7JRIbmHnbiNEi+Qao5d/hdB5itEh+AGmReH5MmrfhcAJhLWeepUkzGBw2bpN4w2w559hhwx6glsM8+PxyPPmY5A+Dw7L9EukPb7ypOSzPA2Q85sETYiDAzAMk2Bh4DJh4oCIH8GsAppcfYIr9AZQxCkbBKBgFowAVAADPsFlkm0yR5AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Oslo","correspondingAuthor":true,"prefix":"","firstName":"Huong","middleName":"Nguyen","lastName":"Thu","suffix":""},{"id":503044107,"identity":"a33b96f9-4afa-409c-81d0-d5efdba26654","order_by":1,"name":"Eivind Ystrom","email":"","orcid":"","institution":"Norwegian Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Eivind","middleName":"","lastName":"Ystrom","suffix":""},{"id":503044108,"identity":"c6432f1d-96da-4cca-867d-224a113ac105","order_by":2,"name":"Line C. Gjerde","email":"","orcid":"","institution":"Norwegian Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Line","middleName":"C.","lastName":"Gjerde","suffix":""},{"id":503044109,"identity":"c59efcf1-b41d-473a-8edf-8e0c03694130","order_by":3,"name":"Øyvind Naess","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Øyvind","middleName":"","lastName":"Naess","suffix":""}],"badges":[],"createdAt":"2024-08-28 18:27:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4993031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4993031/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-16975-6","type":"published","date":"2025-10-02T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90043443,"identity":"409b8265-6d4b-4e61-bb9b-45f21abbee9c","added_by":"auto","created_at":"2025-08-27 17:36:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186157,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier failure estimates for CVD events (A) and for all-cause mortality (B) according to five categories of life-course SEP.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4993031/v1/b67c3977a26499da23455efd.png"},{"id":90043002,"identity":"55d9bce4-aa87-44c3-bdfa-832ae0e750c0","added_by":"auto","created_at":"2025-08-27 17:28:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94072,"visible":true,"origin":"","legend":"\u003cp\u003eA. Histogram of SEP as continuous variable. B Spline visualisation of the association between SEP as continuous variable and CVD events risk. SEP as continuous variable was fitted as a continuous variable using penalised smoothed splines in a Cox model adjusted for birth cohort and sex. The vertical axis shows the log hazard ratio (the exponential value is equivalent to the hazard ratio). The solid line depicts the spline and the dashed curves depict twice the standard error (as plotted by the termplot function in R statistical software, which was used for visualisation). Twice the standard error is a close approximation of a 95% confidence interval, as 95% of the values fall within 1.96 × standard error of the mean if the values are normally distributed. The \u003cem\u003ey\u003c/em\u003e-axis is fixed between −0.5 and 1.0.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4993031/v1/7d7c4fd3dc765a5eb679c5f9.png"},{"id":92883641,"identity":"2ec67f0c-17d5-45b2-97f6-30e98cf4bc5b","added_by":"auto","created_at":"2025-10-06 16:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981291,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4993031/v1/d31bf3b6-9360-4883-afc4-efa291586c27.pdf"},{"id":90043005,"identity":"6d9f1370-b190-4c0d-b61d-34ec0364197e","added_by":"auto","created_at":"2025-08-27 17:28:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":211320,"visible":true,"origin":"","legend":"","description":"","filename":"Twinsupplementary06.07.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-4993031/v1/3bca9826a5e99580fb6760e1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Life course socioeconomic position and the risk of cardiovascular events and all-cause mortality in a Norwegian twin study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStudies have shown that individuals who experience socioeconomic disadvantage at multiple time points through their life have elevated risk of cardiovascular diseases (CVD) and premature mortality \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This risk appears to increase in an accumulative manner, suggesting that a comprehensive assessment of socioeconomic indicators throughout an individual’s life is necessary to fully capture the impact of socially patterned environmental factors on chronic disease risk. For instance, Degerud et al. found a linear dose-response relationship between incremental increases in life course socioeconomic position (SEP) (range 0–20) and CVD \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Compared to individuals with high SEP, risk ratios for risk of CVD mortality were 1.16 (1.11–1.22) for middle SEP and 1.50 (1.41–1.59) for low SEP.\u003c/p\u003e\u003cp\u003eThe model of risk accumulation for many non-communicable diseases is biologically supported, as impaired organ function can accumulate over time due to increased environmental exposures, such as atherosclerotic changes to blood vessels or increased resistance to glucose levels in blood \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, limited research has tested the accumulation model of social causation for CVD or mortality \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A linear gradient of association for life course SEP is consistent with both social causation and social selection. It is essential to consider underlying non-causal factors, such as familial factors (including genetics and family environment), which may be associated with both disadvantaged socioeconomic trajectories and increased disease risk. These factors can create spurious associations between life course SEP and CVD \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGenetically informed studies may help elucidate whether the dose-response association between life course SEP and disease risk can be attributed to familial factors. Twin studies assume that monozygotic (MZ) 'identical' twins, originating from one egg cell, and dizygotic (DZ) 'fraternal' twins, originating from two different egg cells, share all their family environments. MZ twins share all their genome, whereas DZ twins share on average 50% of their segregating genes. If there is a difference in CVD mortality among MZ twins discordant in SEP, the association cannot be due to genetic or familial environmental effects, suggesting a causal relationship. The accumulation model of social causation has not been tested with family-based designs.\u003c/p\u003e\u003cp\u003eOur primary objective was to determine whether the risk of cardiovascular disease (CVD) and all-cause mortality exhibits a linear relationship with life course socioeconomic position (SEP). Our secondary objective was to examine the associations of life course SEP with CVD and all-cause mortality, considering both family-specific factors (shared genetic and shared environment) and individual-specific factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe included Norwegian twins born between 1915 and 1960 who participated in the Norwegian Twin Registry \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. They did not emigrate or die until after September 20, 1990, and they completed the mandatory censuses in Norway from 1960 through 1990. The sample comprised 21,051 complete same-sex twin pairs (4,909 monozygotic male, 6,233 dizygotic male, 4,062 monozygotic female, and 6,247 dizygotic female). Zygosity was determined by questionnaire and subsequently by genetic marker analysis for a subsample \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The twins were sent health questionnaires in 1978–1982 and again in 1990–1998. Data were obtained by linking census data from 1960–1990, the national educational database, and population and household income data with hospitalization records and the Cause of Death Registry.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLife course SEP\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe collected life course SEP indicators on household conditions from population censuses in 1960, 1970, and 1980. This included type of dwelling (apartment block, row or detached house), ownership status, rooms per household capita, telephone ownership, access to water closet, and bath inside the dwelling. Household income data were used in 1990 because data on housing conditions were collected on only 10%. The highest level of education obtained was in the National Educational Database. Educational attainment was categorized as “higher than high school” versus “high school or less,” based on the highest completed level of education recorded in national education registers. The “higher than high school” category includes individuals who completed any form of post-secondary education, including vocational or technical training, as well as those who attended but did not complete a university degree. Information on the role of household indicators is described elsewhere \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This provided a total of 20 indicators, which were then reduced to 17 indicators and scored (0 or 1) (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to high tetrachoric correlation, we grouped the '1960s bathroom' and 'water closet (WC)' indicators into one ('BADWC 60'), signifying access to either facility in the 1960s (Supplementary Fig.\u0026nbsp;1–2). This approach was similarly applied for the 1970s and 1980s indicators. To combine all the indicators into a SEP continuum score, we subjected all the items to a graded response Item Response Theory (IRT) analysis, see Supplementary tables for further information. We adjusted the estimated results by the influence of two different birth cohorts born 1915–1945 and 1946–1960.\u003c/p\u003e\u003cp\u003e\u003cem\u003eOutcome data and follow–up\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe Cause of Death Registry provided mortality data using the ninth and tenth revisions of the International Classification of Diseases (ICD). The CVD outcomes included fatal and non-fatal first events of coronary heart disease or stroke from 1994 (1990–1995: ICD–9: 390–459; 1996–2014: ICD–10: I00–I99). The information was primarily based on certificates filled out by on-site medical doctors in the death certificate or at hospital discharge. Our sample was followed up from 1994 until the first event (fatal or non-fatal) of CVD or death from any cause, or until December 31, 2018. The time scale was from the date of birth.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical methods\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe used the Generalized Partial Credit Model (GRM) within Item Response Theory (IRT) (GRM-IRT) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The GRM-IRT is a logistic model with two parameters for each observed variable: ´difficulty´ and ´discriminability´. The difficulty parameter localizes each item on the standard normal continuum, while the discriminability parameter represents how well an item can reliably differentiate individuals on this continuum. We stored empirical Bayes means of this linear continuum as IRT-scores and divided these into quintiles.\u003c/p\u003e\u003cp\u003eBy genetic theory we constrained the between-monozygotic twin effect to be all additive genetic, non-additive genetic, or familial/environmental effects. We implemented logistic generalized structural equation models with a frailty parameter, estimating the individual-specific environmental effect by subtracting the between twin effect from the total observed variance.\u003c/p\u003e\u003cp\u003eThe association between SEP and CVD events was calculated using a mixed-effects Weibull proportional hazard regression model with individuals on level 1, zygotes on level 2, and pregnancies on level 3 \u003csup\u003e14\u003c/sup\u003e. Participants were followed from the last questionnaire sent to twins in 1990 until the date of death or at the end of the study. Since monozygotic pairs share 100% of their genome, we assumed that the cluster mean fixed effect represented 100% of the genetic covariance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. We centered the SEP score on the cluster mean and assumed that the within pair fixed effects for monozygotic twins represented environmental effects. Additionally, the within pair fixed effect for dizygotic pairs was assumed to reflect the within monozygotic pair effect plus half of the genetic effect. Since dizygotic pairs share 50% of their genome, we assumed that the group mean fixed effect for dizygotic pairs represented 50% of the genetic covariance. We also simulated a classical twin design to decompose phenotypic variance into additive genetic (A), shared environment (C), and unique environment (E) components. While we conduct an ACE analysis on the full dataset, the simulation study complements and contextualizes the findings by assessing model performance under controlled conditions and highlighting potential biases, such as limited power to detect shared environmental effects. This strengthens confidence in the empirical results by validating the ACE model under study-relevant conditions. Our statistical analyses were implemented in STATA 15.1.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eAll-cause and cardiovascular mortality\u003c/h2\u003e\n\u003cp\u003eIn the final sample of 21,051 complete twin pairs (42,102 individuals), 6,266 participants died prematurely before the age of 65 from any cause, and 2,770 individuals experienced a fatal or non-fatal cardiovascular disease (CVD) event. Analysis revealed that both CVD events and all-cause mortality rates were lower among the middle and high socioeconomic position (SEP) groups compared to the low SEP group (Table 1). The study population consisted of slightly more women than men (51%), with an average birth year of 1942. Notably, the lowest and highest SEP quintiles had slightly younger average birth years compared to the other SEP groups.\u003c/p\u003e\n\u003ch2\u003eItem Response Theory modelling of SEP\u003c/h2\u003e\n\u003cp\u003eDetailed results from the GRM-IRT analyses are presented in online supplement 1.2. These analyses demonstrated that the 17 selected SEP indicators, scored as 0 or 1, exhibited strong discriminative power across distinct levels of the SEP continuum. The SEP continuum score had substantial reliability ranging from -1.5 to 4 standard deviations (SD). Factor scores, calculated using empirical Bayesian methods, were stratified into five quintiles of SEP. The mean \u0026plusmn; SD of the SEP scores across the quintiles were \u0026minus;1.09 \u0026plusmn; 0.35, \u0026minus;0.39 \u0026plusmn; 0.12, \u0026minus;0.01 \u0026plusmn; 0.11, 0.39 \u0026plusmn; 0.13, and 1.00 \u0026plusmn; 0.29, with corresponding reliabilities (\u0026alpha;) of 0.73, 0.79, 0.78, 0.76, and 0.68, respectively (Table 1). Quintile one represented the lowest SEP category, indicating the most deprived socioeconomic circumstances.\u003c/p\u003e\n\u003ch2\u003eSEP and CVD events: familial and individual risk factors\u003c/h2\u003e\n\u003cp\u003eIn the absence of competing risks, 75% of the population experienced a fatal or non-fatal CVD event by the age of 100. Kaplan-Meier failure estimates (Figure 1) illustrate survival probabilities for CVD events (Part A) and all-cause mortality (Part B) across five SEP categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results for CVD risk revealed a pronounced disparity in survival probability between the lowest and highest SEP categories, significantly exceeding the differences observed among the three middle SEP quintiles. This pattern was consistent for both monozygotic and dizygotic twins (Supplementary figures 3\u0026ndash;4). This disparity was even more striking and statistically significant when examining all-cause mortality, highlighting the substantial impact of SEP on overall survival.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the analysis examining the between-individual (traditional cohort analysis), familial, and individual-specific effects of SEP on CVD events. In the cohort analysis, individuals in the top two quintiles of SEP had a 58% lower risk of experiencing a CVD event compared to those in the low to moderate SEP group (HR = 0.42; 95% CI 0.37 - 0.47). Familial risks revealed a 49% reduction in risk for CVD events in monozygotic twin pairs from the top two quintiles of SEP (HR = 0.51; 95% CI 0.43 - 0.59) and a 44% reduction for dizygotic twin pairs (HR = 0.56; 95% CI 0.49 - 0.64). The individual-specific effects analysis showed that individuals in the top two quintiles of SEP within monozygotic twin pairs had a 20% decreased risk of CVD compared to those in the low SEP group (HR = 0.80; 95% CI 0.66 - 0.97), while the reduction was 27% for dizygotic twin pairs (HR = 0.73; 95% CI 0.61 - 0.87).\u003c/p\u003e\n\u003cp\u003eTable 3 presents the association between SEP and all-cause mortality. The cohort analysis showed a significant protective effect of high SEP on all-cause mortality (HR = 0.48; 95% CI 0.44 - 0.52). Familial analyses indicated an inverse relationship between increasing levels of SEP and all-cause mortality, with a reduced risk observed for both monozygotic twin pairs (HR = 0.64; 95% CI 0.58 - 0.70) and dizygotic twin pairs (HR = 0.61; 95% CI 0.56 - 0.66). The individual-specific effects analysis showed a protective effect against all-cause mortality for individuals in the top two quintiles of SEP, both for monozygotic twin pairs (HR = 0.71; 95% CI 0.64 - 0.80) and dizygotic twin pairs (HR = 0.74; 95% CI 0.67 - 0.82). The observed pattern appeared to be linear across all SEP quintiles in the cohort for both CVD and all-cause mortality risk (Figure 2 and Table 3).\u003c/p\u003e\n\u003ch2\u003eHeritability of SEP and CVD events\u003c/h2\u003e\n\u003cp\u003eIn a simulation study with 1,000 pairs of twins (half monozygotic and half dizygotic) was conducted, simulating additive genetic, shared environment, and non-shared environment variables. The simulation indicated significant genetic and environmental effects on the phenotype. Life course SEP in the general population was estimated to be influenced by 41% (95% CI 25-56%) additive genetic factors, 24% (95% CI 10-37%) shared environmental factors, and 35% (95 % CI 31-40%) individual-specific environmental factors. For CVD events, additive genetic, non-additive genetic, and individual-specific risk factors explained 41% (95% CI 26-58%), 23% (95% CI 12-40 %) and 36 % (95 % CI 31-41 %) of variance, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the extent to which life course socioeconomic position (SEP) was associated with fatal and non-fatal events of cardiovascular disease (CVD) and all-cause mortality in twins. We identified a graded dose-response relationship for these outcomes. Additionally, we observed more pronounced family-specific effects of life course SEP compared to individual-specific effects in both dizygotic and monozygotic twins. Minor differences were noted in the family-specific effects between dizygotic and monozygotic twins. These findings suggest that the associations observed for life course SEP on CVD events and all-cause mortality may partly be explained by shared environmental factors, with shared genetic factors playing a minor role.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePrevious literature\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe are not aware of similar studies investigating family- and individual-specific effects of life course SEP using indicators at multiple time points. One study on Swedish twins, which examined social mobility and preventable mortality, found that familial or genetic confounding could not explain adult socioeconomic inequalities or lower risk for those being upwardly mobile \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Our study differs as it focuses on cumulative exposure through adulthood rather than two time points. Our finding of a linear relationship between life course SEP and CVD events and mortality aligns with other observational studies that provide evidence of accumulation over the life course \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are no sibling comparison studies investigating life course SEP on CVD and all-cause mortality. Some twin and sibling comparison studies have explored the relationship using education or other SEP indicators in adulthood, finding some attenuation that suggests early life family factors may play a role \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our study extends this by examining the role of early life family factors on accumulation.\u003c/p\u003e\u003cp\u003eHeritability estimates showed that the relative magnitude of additive genetic, shared environment and unique individual effects of life course SEP are similar to other studies, with additive genetic and individual-specific components being larger than shared environment for most behavioural and social factors. The life course SEP measure showed a larger estimate for the shared environment component than other studies of related SEP measures. The broad-sense heritability of CVD mortality has been estimated to be 47% \u003csup\u003e22\u003c/sup\u003e. Previous estimates of heritability for death from coronary heart disease in a Swedish twin sample found heritability of 0.57 (95% CI, 0.45\u0026ndash;0.69) among male twins and 0.38 (95% CI: 0.26\u0026ndash;0.50) among female twins \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In the simulated dataset used to illustrate the GRM-IRT method, the heritability of fatal and non-fatal CVD events was estimated at 0.64 (not tabulated), reflecting the predefined parameters of the simulation rather than empirical data.\u003c/p\u003e\u003cp\u003eInterestingly, even with large heritability estimates for both exposure and outcome, most of the family-specific effect was still not genetic, as we observed little difference between DZ and MZ twins in either outcome \u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This suggests a pronounced influence of the shared environment. This difference between high heritability estimates within traits and lack of genetic effects in bivariate analysis may seem counterintuitive but is consistent with other studies in cardiovascular epidemiology and behavioural genetics \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor outcomes like CVD, it is well established that inter-individual differences, such as genetic factors, account for little of the variation at the population level \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Elevated levels of risk factors in some individuals have much less impact on disease incidence in the population compared to moderately increased levels in the majority. Hence, population-wide strategies have more impact on reducing disease incidence compared to a high-risk strategy focusing on individuals, such as treating elevated levels of risk factors like blood pressure and lipids with medication. Twin models that decompose variance into the ACE model (additive genetic [A], shared environment [C], and unique individual [E]) typically match twin pairs on year and place of birth, excluding factors that determine the distribution of risk factor levels in the population but may have a small influence on individuals \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Within twin pairs, most variation, even for social indicators, is due to genetic and non-shared factors. However, at the population level, a large share of cases can be attributed to modifiable environmental factors from the shared environment in childhood \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStrengths and limitation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAn important strength of this study is the combination of a comprehensive number of life course indicators with hospital discharge and mortality data in twins. Several limitations should be mentioned. Incorporating multiple indicators of SEP for both type of twins resulted in instances of missing data. SEP levels were determined based on five quintiles using empirical Bayes means as Item Response Theory scores, which may not fully capture SEP disparities among twins. Additionally, the cohort included twins born from 1915 to 1960, resulting in different ages of follow-up (from 1990 to 2018) throughout their life course. Most endpoints came from older participants since CVD is more common in older groups and because premature CVD has declined over this period. Moreover, the absence of information on CVD risk factors and treatment means we were unable to adjust for these. However, we have no reason to believe this would significantly alter the influence of family factors. Furthermore, sibling comparison studies rely on a comparison between the full cohort and the within-pair difference in exposure, which may introduce selection bias if the full cohort differs from discordant siblings \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This bias may be more problematic for categorical or dichotomous exposures when large shares of the population may be concordant. A limitation of this study is that the ACE model was not applied to the same empirical dataset. Another limitation is that although sex was included as a covariate in all models, we did not perform sex-stratified analyses, which may have masked potential sex-specific differences in the associations; this remains a direction for future research. We adjusted for birth cohort as a covariate in all models. We did not stratify analyses by birth cohort, which may have masked cohort-specific variation in the contributions of genetic and shared environmental factors. Lastly, we acknowledge the heterogeneity within the \u0026ldquo;higher than high school\u0026rdquo; category as a limitation in interpreting socioeconomic patterns.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInterpretation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOur results indicate that both familial and individual effects are important in the accumulation of socioeconomic risk on outcomes like CVD and all-cause mortality. This supports the notion that both childhood SEP and adult socioeconomic environment have a causal role, with shared genetic factors having little influence. This is not surprising given what we know about the role of individual and population level determinants on chronic diseases. However, recent literature on health inequalities has raised questions about the role of personal factors in advanced welfare states like the Nordic countries \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Comprehensive welfare states persist in demonstrating large and comparable inequalities in mortality, a phenomenon known as the Nordic paradox. It has been suggested that the determinants of these inequalities lie deeper than what policies can achieve. Indirect selection of underlying personal factors may be particularly important in societies with extensive inter-generational social mobility, with most disease burden related to non-communicable diseases and health behaviours playing a significant role. The observed attenuation of association between SEP and CVD in within-pair analyses, especially among MZ twins, supports the role of shared familial factors and suggests that part of the association may be due to shared environmental confounding. Our study highlights the potential role of early life in preventing future disease risk and points away from associations being genetically determined. Future research should collate family data across diverse populations and birth cohorts to study the impact of life course SEP on CVD and other non-communicable diseases more extensively. The higher HRs observed when SEP quintiles collapsed into a binary high vs. low measure in the within-pair analyses may seem counterintuitive but likely reflect several factors. First, broader categories increase within-group heterogeneity e.g., quintile 3 may dilute contrasts when grouped with lower SEP levels, shifting the average HR toward the null. Second, the association between SEP and cardiovascular risk appears to be non-linear in the within-pair analyses, with the most substantial drop in risk occurring between quintiles 1 and 2. This nuance is lost when using a binary SEP measure. Lastly, the two approaches differ conceptually: quintile models estimate risks relative to the lowest SEP group, while binary models compare average risks across broader, more heterogeneous groups, inherently affecting HR magnitude.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eModerate to high life course SEP was associated with a lower risk of fatal and non-fatal CVD as well as all-cause mortality in both family- and individual-specific analyses. Observed associations between life course SEP and CVD and all-cause mortality are to some extent explained by shared family factors in early life, with shared genetic factors having little additional influence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eØN conceived the idea, ØN and HN designed the study, EY and HN analysed the data, HN drafted the initial manuscript, EY, LCG and ØN interpreted the data, critically appraised the manuscript, and approved the last version.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eCompeting interest\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe study was funded by The Research Council of Norway (2137788) through the programme Better Health and Quality of Life. The funder did not take part in the planning, conduction, or reporting of the study. EY is funded by the Research Council of Norway (288083, 336078, and 174842) and the European Union (101045526).\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThis project obtained ethical approval (REK 2012/827) from the Regional Committees for Medical and Health Research Ethics of Norway. \u0026nbsp;Information about the study was given, and possible consequences of the study were explained to all participants before written informed consent was obtained. It carried out in accordance with the principles embodied in the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe consent given by the participants does not open for storage of data on an individual level in repositories or journals. The datasets analysed during the current study are not publicly available due to data protection regulations. The individual-level data that support the findings of this study are not publicly available due to legal restrictions in Norway. The analytical code can be accessed by contacting the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u003c/h1\u003e\n\u003cp\u003eWe would like to thank the participants of the Twin studies for their valuable efforts, as well as the study personnel. We thank The Research Council of Norway for funding the project (Grant number 287347).\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEricsson M, Pedersen NL, Johansson ALV, Fors S, Dahl Aslan AK. Life-course socioeconomic differences and social mobility in preventable and non-preventable mortality: a study of Swedish twins. International Journal of Epidemiology. 2019; 48:1701-9.\u003c/li\u003e\n \u003cli\u003ePedamallu H, Zmora R, Perak AM, Allen NB. Life Course Cardiovascular Health: Risk Factors, Outcomes, and Interventions. Circulation Research. 2023; 132:1570-83.\u003c/li\u003e\n \u003cli\u003eBann D, Wright L, Hughes A, Chaturvedi N. Socioeconomic inequalities in cardiovascular disease: a causal perspective. Nature Reviews Cardiology. 2023.\u003c/li\u003e\n \u003cli\u003eDegerud E, Ariansen I, Ystrom E, Graff-Iversen S, H\u0026oslash;iseth G, M\u0026oslash;rland J, et al. Life course socioeconomic position, alcohol drinking patterns in midlife, and cardiovascular mortality: Analysis of Norwegian population-based health surveys. PLOS Medicine. 2018; 15:e1002476.\u003c/li\u003e\n \u003cli\u003eWagner C, Carmeli C, Jackisch J, Kivim\u0026auml;ki M, van der Linden BWA, Cullati S, et al. Life course epidemiology and public health. The Lancet Public Health. 2024; 9:e261-e9.\u003c/li\u003e\n \u003cli\u003eAriansen I, Mortensen L, Igland J, Tell GS, Tambs K, Graff-Iversen S, et al. The educational gradient in coronary heart disease: the association with cognition in a cohort of 57 279 male conscripts. Journal of Epidemiology and Community Health (1979-). 2015; 69:322-9.\u003c/li\u003e\n \u003cli\u003eN\u0026aelig;ss \u0026Oslash;, Hoff DA, Lawlor D, Mortensen LH. Education and adult cause-specific mortality\u0026mdash;examining the impact of family factors shared by 871 367 Norwegian siblings. International Journal of Epidemiology. 2012; 41:1683-91.\u003c/li\u003e\n \u003cli\u003eNilsen TS, Brandt I, Magnus P, Harris JR. The Norwegian Twin Registry. Twin Research and Human Genetics. 2012; 15:775-80.\u003c/li\u003e\n \u003cli\u003eFisk\u0026aring; BS, Ariansen I, Graff-Iversen S, Tell GS, Egeland GM, N\u0026aelig;ss \u0026Oslash;. Family history of premature myocardial infarction, life course socioeconomic position and coronary heart disease mortality \u0026mdash; A Cohort of Norway (CONOR) study. International Journal of Cardiology. 2015; 190:302-7.\u003c/li\u003e\n \u003cli\u003eGalobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1). Journal of Epidemiology and Community Health. 2006; 60:7.\u003c/li\u003e\n \u003cli\u003eN\u0026aelig;ss \u0026Oslash;, Claussen B, Thelle DS, Davey Smith G. Cumulative deprivation and cause specific mortality. A census based study of life course influences over three decades. Journal of Epidemiology and Community Health. 2004; 58:599.\u003c/li\u003e\n \u003cli\u003eSamejima F. ESTIMATION OF LATENT ABILITY USING A RESPONSE PATTERN OF GRADED SCORES1. ETS Research Bulletin Series. 1968; 1968:i-169.\u003c/li\u003e\n \u003cli\u003eThissen D, Steinberg L. A taxonomy of item response models. Psychometrika. 1986; 51:567-77.\u003c/li\u003e\n \u003cli\u003eRabe-Hesketh S, Skrondal A, Gjessing HK. Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software. Biometrics. 2008; 64:280-8.\u003c/li\u003e\n \u003cli\u003eTarkiainen L, Martikainen P, Laaksonen M, Aaltonen M. Childhood family background and mortality differences by income in adulthood: fixed-effects analysis of Finnish siblings. European Journal of Public Health. 2015; 25:305-10.\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;ndergaard G, Mortensen LH, Nybo Andersen A-M, Andersen PK, Dalton SO, Madsen M, et al. Does Shared Family Background Influence the Impact of Educational Differences on Early Mortality? American Journal of Epidemiology. 2012; 176:675-83.\u003c/li\u003e\n \u003cli\u003eAriansen I, Mortensen LH, Graff-Iversen S, Stigum H, Kj\u0026oslash;llesdal MKR, N\u0026aelig;ss \u0026Oslash;. The educational gradient in cardiovascular risk factors: impact of shared family factors in 228,346 Norwegian siblings. BMC Public Health. 2017; 17:281.\u003c/li\u003e\n \u003cli\u003eLundborg P, Lyttkens CH, Nystedt P. The Effect of Schooling on Mortality: New Evidence From 50,000 Swedish Twins. Demography. 2016; 53:1135-68.\u003c/li\u003e\n \u003cli\u003eKj\u0026Oslash;Llesdal MKR, Ariansen I, Mortensen LH, Smith GD, N\u0026AElig;Ss \u0026Oslash;. Educational differences in cardiovascular mortality The role of shared family factors and cardiovascular risk factors. Scandinavian Journal of Public Health. 2016; 44:744-50.\u003c/li\u003e\n \u003cli\u003eMadsen M, Andersen PK, Gerster M, Andersen A-MN, Christensen K, Osler M. Are the educational differences in incidence of cardiovascular disease explained by underlying familial factors? A twin study. Social Science \u0026amp; Medicine. 2014; 118:182-90.\u003c/li\u003e\n \u003cli\u003eGilman SE, Loucks EB. Another casualty of sibling fixed-effects analysis of education and health: An informative null, or null information? Social Science \u0026amp; Medicine. 2014; 118:191-3.\u003c/li\u003e\n \u003cli\u003eYstrom E, Degerud E, Tesli M, H\u0026oslash;ye A, Reichborn-Kjennerud T, N\u0026aelig;ss \u0026Oslash;. Alcohol consumption and lower risk of cardiovascular and all-cause mortality: the impact of accounting for familial factors in twins. Psychological Medicine. 2023; 53:4130-8.\u003c/li\u003e\n \u003cli\u003eZdravkovic S, Wienke A, Pedersen NL, Marenberg ME, Yashin AI, De Faire U. Heritability of death from coronary heart disease: a 36-year follow-up of 20\u0026emsp;966 Swedish twins. Journal of Internal Medicine. 2002; 252:247-54.\u003c/li\u003e\n \u003cli\u003ePolderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics. 2015; 47:702-9.\u003c/li\u003e\n \u003cli\u003eBaier T, Eilertsen EM, Ystr\u0026oslash;m E, Zambrana IM, Lyngstad TH. An anatomy of the intergenerational correlation of educational attainment \u0026ndash; Learning from the educational attainments of Norwegian twins and their children. Research in Social Stratification and Mobility. 2022; 79:100691.\u003c/li\u003e\n \u003cli\u003eDavies G, Marioni RE, Liewald DC, Hill WD, Hagenaars SP, Harris SE, et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112\u0026thinsp;151). Molecular Psychiatry. 2016; 21:758-67.\u003c/li\u003e\n \u003cli\u003eBranigan AR, McCallum KJ, Freese J. Variation in the Heritability of Educational Attainment: An International Meta-Analysis. Social Forces. 2013; 92:109-40.\u003c/li\u003e\n \u003cli\u003eOkbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics. 2022; 54:437-49.\u003c/li\u003e\n \u003cli\u003eHyytinen A, Ilmakunnas P, Johansson E, Toivanen O. Heritability of lifetime earnings. The Journal of Economic Inequality. 2019; 17:319-35.\u003c/li\u003e\n \u003cli\u003eLiu H. Genetic architecture of socioeconomic outcomes: Educational attainment, occupational status, and wealth. Social Science Research. 2019; 82:137-47.\u003c/li\u003e\n \u003cli\u003eSmith GD. Epidemiology, epigenetics and the \u0026lsquo;Gloomy Prospect\u0026rsquo;: embracing randomness in population health research and practice. International Journal of Epidemiology. 2011; 40:537-62.\u003c/li\u003e\n \u003cli\u003eRose G. Sick individuals and sick populations. International Journal of Epidemiology. 2001; 30:427-32.\u003c/li\u003e\n \u003cli\u003eFrisell T, \u0026Ouml;berg S, Kuja-Halkola R, Sj\u0026ouml;lander A. Sibling Comparison Designs: Bias From Non-Shared Confounders and Measurement Error. Epidemiology. 2012; 23.\u003c/li\u003e\n \u003cli\u003eMackenbach JP. Health inequalities: Persistence and change in European welfare states: Oxford University Press; 2019. Available from: https://doi.org/10.1093/oso/9780198831419.001.0001.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescriptive characteristics of the study population at baseline according to categories of life course SEP.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEP groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll individuals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4216 (20.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4206(19.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4209(19.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4212(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4208(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21051(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSex (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2078 (49.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2008 (47.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2051(48.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2053(48.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2119 (50.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e10309 (48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1940.06 (13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1943.93 (12.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1943.47 (12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1943.04 (12.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1938(13.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1942 (12.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eMonozygotic (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1723 (40.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1678 (39.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1799 (42.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1727 (41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1644 (39.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8571 (40.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eCVD events (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e736 (17.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e490 (11.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e456 (10.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e466 (11.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e622 (14.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2770 (13.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAll-cause deaths (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1783 (42.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1033 (24.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1013 (24.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1029 (24.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1408 (33.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6266 (29.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth cohort\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1915-1945\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAll (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2339 (21.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1911 (17.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1970 (17.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2032 (18.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2747 (24.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21051(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSex (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1221 (52.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e918 (48.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e950 (48.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e957 (47.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1340 (48.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5386 (48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1930 (9.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1933(2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1933 (8.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1932 (8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1930 (8.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1931 (8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eMonozygotic (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e966 (41.30)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e750 (39.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e888 (45.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e817 (40.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1089 (39.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4510\u0026nbsp;(41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eCVD events (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e736 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e490 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e456 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e466 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e622 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2474 (22.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAll-cause deaths (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1550 (66.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e871 (45.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e875 (44.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e883 (43.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1337 (48.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5516 (50.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth cohort\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1946-1960\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAll (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1877 (18.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2295 (22.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2239 (22.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2180 (21.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1461 (14.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21051(100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSex (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e857 (45.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1090 (47.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1101 (49.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1096 (50.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e779 (53.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4923 (48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1952.96 (4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1953.04 (4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1952.87 (4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1952.96 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1952.99 (4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1953 (4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eMonozygotic (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e757 (40.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e928 (40.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e911 (40.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e910 (41.74) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e555 (37.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4061 (40.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eCVD events (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e69 (3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e70 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e45 (2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e76 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e36\u0026nbsp;(2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e296 (2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAll-cause deaths (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e233 (12.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e162 (7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e138 (6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e146 (6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e71 (4.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e750 (7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Note: Presented as count (percentages), except for \u0026ldquo;Year of birth,\u0026rdquo; which is presented as mean (standard deviation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Hazard ratios and their 95%CI for fatal and non-fatal cardiovascular events by categories of life-course SEP.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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33u2q4bmiCggyfm2s2udsG3Wl6NwItaAv49RreUNNtggcZiBVhhAG9p2lSeg5ciLb1ycY7cDhiby7wQBp1p+6Ag7beEMANx2ApqmyX9Uwg4DG6Xf1p92IrGJ7K8yduAEtYLmlIG1S69wDux0jx8/Psn3zYHjeMbnOzY8susoD18Aaf1xFMjOtzaUVde7OARocAC+2dGDhxzd0ffoA6dfsMMOJLr++D4AHzkF2nZs0EfN8AJMZxxOA5x00klJf/zhk6ZpkqOO9JyjOPK0AXvp5+WXX56PGdrR8BeRBCl8NBz9qNdZw4EZAvmiS7a9gX0CYmtJd3muFoNtG8d40OVLFixj636gyWIivDzDTnVtbYvWEVCA3jYUr9tZVGfPnBtjgC2ITvVHc54/UUYB4j0QICJJscSYgXV8EXUUCWyaqX+dJOgUcty3rxwqDp466rdp7jlpjg25j0TJUhoAFkeDIrAjEPR6rRwYDA44y84Qis6T88F4hzYBEbuKjDqDK6+3BHCgW0N231ZddVWPvSbHGawhHyY6ZsRotxkxPgAAEABJREFUiujbqWO0VaZrAyh5thXvOJH7mioHhpoD8IO/b+8YJ9kXtQYgYQnP7BOaj1abpknyRbrJuaMfvvOyEwwkA7KOnBiTHTB0H3p6luxea8vunHVpnXGMx44dmz/KRfN+7/IdmagzACyfoy6Cf/bZZ+fdQcf45OujICbAq19NM+2OgXf6Kzd2DoBfzoe+SIKjjrY4nvPWt7417yAIQHIKHAdy3A528q0jh8POeABsQQrjBeDhHR8r44VgnjqcIJFyIN4OviAHx2WDLucH8PZ+Cdj2vqBzcLwXDwRGtS9q733GTudo00kL7zMu78NP7eCpI4XGrSy+eb8j1sp4Dzymfd9C6ENNs44DMwTydY+g89pspzE0BJFg+tgFsLYgOQIAugXGuwUwHQlxHhZA1I4P3Qinez9yQZAARGdhLUTC9P3vfz//mUyLQTln1ggODxpgnDRpUv6BDMDR4vPhjn5ZvITbFhNFYAGrP5qSCDplx7nCY+MHamKMFKY5EXW3hSiKQbkEndICGPAK3yxgZ+dEDoAQ8yyC4KNo538Bc3PnvZQL3oqSeKYkKTELnrK1sEPhmk/HB4AteRQIZStxDkVNRAI8i1A6UhSRluhrvVYO9MQBRpP8iuyRHTJPJpUnd9Y+mfZhm6182+ieyb+ImIiZqBb5plsYcLpF0MCH/yKCgLMjNnSYKKIPx71T8EF73gOAO0JDN4me0W3q6Ie15xrJB2qOAugT/endPg5kSCdPnpx/3EZ9kTi6FmCwle9YkHv60TEF2+C23kXetOOIonVvHXuXIz7Kuq+pcmCoOUCG2aNIbLxgVDzHvW9TnAZgU4LGGXAPiPquyz17BlsYl7Yjiu9ZG8o4jgLw2hXwLHrOBrn3fgHJaM+Omnx1vd+9xE4C0daVaLk8SV3vikTXALRoAp6R7xrvN0ZOiTwnH5SVgG152oz+CIRpE3hWRt+Vcy9pq/3MhgPXaJJvFbTZTvQB/YNuXGy1cXnGF3bfXynzDKzrj3nwfYA8mA9vtOlZgrE4B7CXNuKZfvKsjICsOjXNOg7MMMhnXHh3PtD01TtgZusGEDfJhsDoMVxApy0wEV+RY4ZReQbZFpWdAPcMnEgbQ8mLJdg+slXH+VpnVpVjzGwZaU/ETHsEypY3g834+vKccXMG3bsAXH3Wr9GULD67FKL1knt8izFadLxtc4C3/toAQB/0I444Itlu802DRdw0Tf4LHY8++mj+60ccMtuowI7dFPznTAAV6gH4QJB5Bt5FCThdQAqHwzadOpwQkVSKCngyJ74boOgAFkd9zCeAZkeGw9ZW1tHfeq0c6MQBf94VqCdrZAc4tjaUdQWU6QoOJfDtozHGUkCBvNFPjgKoTx8B9GRam6J1gLz1JXChjjUhQIFOX9FddhDVF813BEC7nA1OgXwRdv2JBJjQc/pLT4kg6pPAiPY51vSW93m3KB7DygGxVjjB/vKFdmyJcyy0I7LG0HNWvJfD3V7z8f56rRyoHBh8DliTbKTdB04C/TD4b61vqByYyoEZBvlTq6f8J514yaL4omFAvr+OwvhFmXodXhwA+EUigRPKB1gfXj2svakcqBwYvhyoPascqBzoLwfsrvlb847MSY619LduLVc58Hw5MMfzbUB9fwJRNNbWt2itKNJojJob62hI5sa2m8igrTzPo2FcdQyVA5UDlQOVA5UDlQNDxIH62mHHgZkC8o3KWStnuHwV7miNM/nyaxp+HHB8we6LuXKe30c8w6+XtUeVA5UDlQOVA5UDlQOVA5UDM8qBmQbyZ7QDtV7lQEqpMqFyoHKgcqByoHKgcqByoHJgJnKggvyZyMzaVOVA5UDlQOXAzORAbatyoHKgcqByYEY5UEH+jHKu1qscqByoHKgcqByoHKgcqByY9Ryob+wXByrI7xebaqHKgcqByoHKgcqByoHKgcqByoGRw4EK8kfOXNWezhwO1FYqByoHKgcqByoHKgcqB0Y9ByrIH/VTXAdYOVA5UDlQOdA3B2qJyoHKgcqB0cWBCvJH13zW0VQOVA5UDlQOVA5UDlQOVA7MLA6M4HYqyB/Bk1e7XjlQOVA5UDlQOVA5UDlQOVA50IkDFeR34krNqxyYORyorVQOVA5UDlQOVA5UDlQODAkHKsgfErbXl1YOVA5UDlQOzL4cqCOvHKgcqBwYfA5UkD/4PK5vGOEc+Oc//5l+8IMfpN/85jdpwoQJacqUKSN8RIPf/UsvvTR99atfHZQX/f73v0+f+9zn0n/+858+27/sssvSb3/72z7LPd8C//73v9NFF12UnnnmmfShD30o/e1vf3u+TY7a+j/96U/Tl7/85fS///1vpo/xD3/4Qzr00EPTf//73z7bPv/889M//vGPPssNdoFHH30065f+yszhhx8+nUxbC/I7tfHtb387ff3rXx/sYcy09u+888503HHH9Wt9D/Sl9913X9p///3Tvffe22fVb33rW+nuu+/us9ysLPDHP/4xXXzxxf165VlnnZWMoSx8/fXXpy984Qvpsccem4b0i1/8IuuuaTKH4YO1TX/88pe/HJTenXrqqem73/1un20/9NBD2Q71WXCgBWZy+QryZzJDa3OjiwMU4Re/+MX0+OOPp9122y0rWHnDdZRPPPFE2mGHHdLLX/7y9NGPfnS6bj755JNpv/3266afeOKJ+V75djr66KPzmKdroJ8ZG2+8cdp66637WXpgxZZZZpnsbI0ZM6bPim984xvT8ccfnwbLIOgA4MBovupVr0rrrrtu+t73vpcefPBBpGGZ/v73v6d3v/vdHef92Wef7e7zJz7xiY5l9tprr+4yM3Kz+uqrZxmdd955Z6R6r3XMASfrRS96Ua/lEN/2trclfZkVTqD39ZQA8MmTJ6dFF120pyLT5B944IHp1a9+dXce3fT5z38+A+NOcrfJJpukrbbaqrt8bzfjxo3Lc26Op0yZMl3RCRMmdNM5U694xSvyc1t37LLLLtPVG0jGK1/5yrTnnnumMWPGDKRav8outNBCiTO08MIL91l+s802S+O6+HHbbbf1WXZWFPj5z3+ejjzyyLTpppv263Xvete7kjG0C3MC3/rWt6Ybb7wxPf30021SWnnllbODPE1mDw8cDWutPe/u99133+4aAh8rrrjidPIhb9KkSd3lBnpjbbNxr33tawdatV/lt9tuuzzvfRWeb7758pp9xzvekYbzfxXkD+fZqX0bcg4Ab8svv3zaYostcvSxvx0Cpnfdddf+Fp8Z5XIbL3zhCxOnBMD+7Gc/m+6///6cH/+I+IluH3zwwemTn/xkjjj/5Cc/Sf/617/SX/7ylwQUSxyBueeeO6olZQYSmaeIKcHuBmbizVxzzZUWWGCBNMccc/TZqnIid4A3o9NnhQEWAIqvuOKKtNRSS6U11lgjiVL3t4mnnnoqbb/99rM8mrz44ounCy+8MDG2In3mXiLj88wzTxLhBgA4A3ax0P785z+n9ddfP6200krplFNOmWaIQJDol4j0NIQeHsiVeWmapocSM55NNgC5/sjGi1/84sz7Aw44ID388MMz/tLnUZO8/OxnP8sBhDnnnLNfLS244ILJOIF7zjR+CkCQv04NGGd/1yLwNWHChOT6q1/9KrX/E0W+5pprsoNIBgQ7/vSnP3XrDn0hU+ecc067WrrjjjuyTurvzg0+APj9mcNpXtSPhxe84AUJwHftqzi+kft99tknidr2VX4w6XaczjzzzPSlL30p6Vd/3jX//PPnsnYXrelrr702LbHEEnkuOtUnR9ZOJ1qZB9h+7WtfS4sttliefzriqquuStddd11adtll8y4M2yOYgCbRNeZW8GBcl/MUbdI19BGZi7zeruSC/tDf3srNKA3f2K++6uuH8W277bbpK1/5SqLP+6ozFPS+reRQ9Kq+s3JgGHDAVi3FKuIX3QHqbrnllqxsf/zjH0d2BsEU8GmnnZaB83nnnZe3/BhDxzjQKEFtuhcJse1IsSlr+/jKK6/M7SofUbQor46IMYVIWeuDesDVAw880N0PN4C+CA5Q9oEPfEBWdxI1ZIwjY8stt8xKmcHmFDBqxxxzTKLoosxdd92V0GLbnxHXH4qboncPEADRtlF/+MMfZrBr+zf4BczYAsUPoDDadgVWvv/97+fjUNo699xzu7fJGQ15EidEeTwBNvQZgFAez370ox8lwNSciVS5V17kHz9vuukmjzM1ade48LvdsKibPnMAIl+f5V1wwQUJT22nM76SeUG7/PLLMyhyzyiqiwff/OY38+6AMaLFMzrwJU8CRsjV1VdfnRx7kKd9zp2yvSX9AfCOPfbYNLkrsrz77runpmkS8E7OAGF9b7cBfJx00kmJLBqD+Scft99+ez6+5P233npr+utf/5rM080335zQHBlgFMkxuedEKmss7fbdo5Ep71ZGNFK+I2GezzjjjOQIlzygk5y6J2f46b2O24nYkz9yiTfKSNYDHrmflQno3XvvvROH23vJivFwSK0n99YXOSc7ZNz6x2fjNBayYXz33HOPJnJSD2iKOb/hhhuS9hDx0H28SzBCfjutuuqqeUfK+9p0a3THHXfsLrrzzjsnQMv6PfvsszPYKwMBnES7hd/4xjeSedHn008/Pfcn5o8s0H/mYNKkSVn2yK9x//rXv07mMNZTzHN3J7pu6Bbza00YO1nrys6AkzwCYLGTR2d5j7bpE/d0sXfgKf1Nr6ORT+0AtMbnfigSkG6NrLLKKomzZl6NU5/w3j2eGje5NgZrjL0h8+Qd3RzgY4wBXV10eb/73e8SuXBPr5ItsqIMvSO/t0RuvvOd7ySBghNOOCE7GDvttFOuQt/ZWeYwvfOd78x58Q/ekwt1yY/3Ga93G4s8O1TkD40ddB/6nZwqS+bRo9240lt0NLtJj+EDGllgK9Qhi8rJV9b43Vtb3oV/eEuG9ce9+sq8/vWvT3hojJ6HW6ogf7jNSO3PsOEAhfeGN7whR42jU47DAB0U72GHHZa3PS1wxumlL31pcmXUmmZqlFKeYwkMEMUBgFMGjDcjTyHKp+goDlE69xQWo3PyyScnbTB2zqkySKJ2tsoZKkonwGD0Ma62Ti+55JJEecub3AXcAMD2Vv+b3vQmpCS6D6gBTG06oj4br6v+cSooRW2JqOgrsCZKIyoJhH/wgx9MFDVFPGHChGRXQz8YfeN45JFHNJ04AYwJQCmiKsp2ww03ZGfnri7nwra98TPkDAHDq23K2TcSgAZQqi8M4Ic//OFsZNDNhZeIyiy55JKJA+N5ZibOhPZEtFwl42dk9ft973tfdvbIkvPi8hhXvDYW5fFUsmsENANOjDGjhw5kMyj4CTC/5CUvSebVvMtnuLSrL3jI6OGn+ZevHaBJW30ldYAIvFpzzTVzcbw333i7yCKL5Lz4R0TZPTk2BiCUQ+gqGsvJ1JZyABpD6jhHyAy5AHTJgHXgqI32IpGF9773vYnjab3oywJp3vUAABAASURBVBFHHJE4C8ZFXgA4cqv9CV2y5niWtvQX/7Ul6gbQ6SPeW0fyJdv+ZMna9jyrkv5yjO0Ueqe1RJatUzoDcDXH+m6e9Y9D4MgGOVFHEn3FX/cHHXRQBoLq0RnWozVk/vH8/e9/f1KGo6QMOVKvncjfm9/85gxcrF80ush7yJNnabXVVksi4vivbTtmju+gRYp+kQ9yq75IrvXq+dOf/nR+D91hzrwbMLR+tbvHHnskfeb4AN70XrTtSubJ0Mc//vHsBHPkP/KRj2RHGZijE+gRelR960JZzuTEiRPzsSD6TF+At+A7eQKgvcM4red4ljcrE7knK44x4ROe06Ucv9AhwKg5paPNOz5YexwWc0B/C9zQhfrOsTKnU6ZMSR/72McSmXOMicNOJgFy645dsJbtDocsqN9TGjt2bBrXFaXXX+8SYDHXjpOh0S+p+I9sGIc5IGMcejbWXJgz82XMZN4asI6tbfLi2yzy7Xgpm2KerZv2K4466qhk3oF379Cee/LB5uIZR5BOoRv0Ef/wlFPCVtMzZEDgAD/sgpMr77Gz8rKXvSwH9zwPt9QryB9una39qRyYlRyg/C3g9jspBCCb4aHMGDeA4VOf+lSiVAEPCoIxU9e5SNFy0QvtAUnLLbdcNkIAD9C70UYbJVEGitUxGQ4BJSciCvhpl0KiKEUd11lnneTcIIC7wQYbdEe92/10r5ztRAraM4fAeV737UQhi7xQ9qIxbZp7ABYQAYYc6dhmm20SY874bLbZZjnSy2BQypShvumXus5pioS95S1vSQCXukBwRAibpkkiVGuttVY+p/2e97wnTZgwIUe6KVNGyhgcP8IvRi6iiY6dAIy2oPHC+Wq8Z1wATe/VB4mBA5Tcz8wEEDNS7Tbxy5lR/QbIAAa85xSaS9FUTtFrXvOaHCl3xhZfjYHxdvRn7NixeXdIu9rnbAI+5giYInMMFaNELrQrj2GyXY4XjJU+mDeRUm31lcwhQMCYRlmOg/6FQxj5ruTcuziGylgXZIVj8rrXvS6PD1izw0DOyYU+qWsdOCsORHmHHTORR7RIjqB4L9CJP4wxoA/4MujkRTI+fSE76i699NJpXBfYcHyEvHgHYENWGH1nzpWTGH5rkzx7nlWJPNMR8b4NN9wwA1rABg8BHuCcI2xs6623XuLgKM8hsFaBH2es6RX55AN/yQ/gpozjd2j0kHP/6ACc9qxbtDKhOZoF9KEBNPpk/XluJ06auaULzEubRh9wFs2HeaQPfSNg3egXuTCfADR5URZo0wY5fvvb356PiHFuyAdwjhbJ+OgWbdEForLaAyzpAHPvSByZBnatE3XpJesOP9TDe44eGkeTo0CPeB4zZkwChoFIz7M60YPWY/CevFoLxoOGnxxptsQ4yTodbP701dl58mBnly6QR98CvsqRN2PFQ22SO7wRoTa31ig6eVW3r0SHAO36pqzghDlhezhd8trJHArCWK9klGyyEZwaupAuIafWgbkiB2RDG9a7cvQtnhg74I4WSR3jVobse+a8sEccG/qDLjLH5C3aZj+sM3nsT9M0OXDFCf3MZz6Tv+fxDmuQPVPf83BLFeQPtxmp/Rk2HAil2u5Q0zQ5UmxhSxSPckAyoEERitoyylGvaZpE+Yj4U1hN0yTGzBYjQ0wBUYgivNpg+IB74I4ijjyRFMpWPnDg/VK8p7yiMfrea3cAEKCA2+UAG2AJUKM80RhcRtJ9p7TwwgsnhkZUg2GkPG0VGzdFy1DEe/RBXz2L0pUgQPscJ7QwABQrcKAsYM5wA4GAgfIAuyu+u1fOe+SJxnGWGCxRPnmRGMC4n1lXYy3b0i/jkW/cZITREzWNubT9K6qsTCRACVAHqIAszgKQx3j7KI6h50TarjZvAK88hli7ZE+0i/Pj/fiobfxx7U/iJOE9BwSoAQIYWqA4eOysbU9tKaOvookcV+25cl4YUrwxZ+rjHdkw//jUqZ/GgUbmtU3+jc9ckg3GViRXe9qOMWtbHW1rF1AT/eOEAHT4ak7Uk8yR66xMxlO+j6PEGeF4CyYYDwcfDzjWpcyU9dGbpsnOFR4F/5TDa+/EQ/zBF/mdknIAsSNcdlnoLUCxaZru4mQQyKJXOJ74jygS7NpTovs4fo5BmAc7XGSEHtQvukVd9/psDvW5U39jfK7ej09An/nUD04vgOa4R9M0WW9p2/i0qY4kj96gC9dee+3814gAN/mRtBn3s/LaNE3eMUmt/8wFvWCMdC/Aap7sXpKhkP1WlWlu0fEMT43LFe+apsnfOuGNMu281I//yAnZFcEnF3YX7MbQUcC8Jtg3est9p8QxJ7tsIxngGHIU2NSQBfnqWh/mj5yYU0l+O9E36hlv0zSJ04NuJ5zzwLEQhJGnnWibPcIXddE40xwA/SP3dK58CQ8l98MtVZA/3Gak9mfYcIAipaTaHbKQgWCKCnimUBlWgJIhobxEahkICgJ4FykRrQaObDtrV5Qd6KNIKB/ACsDzLudrbTlSao4riNLIZ4BcB5K8i1EFCDkaZV1RfmDdERjG1fgcFRF5aZfVRwpUBJryF5HVN1urdikoRkc6QpG36/b3PkA85Ykv+CmCTUnblsUjSrm39tAdaeEcMfBRVpRFm/E8s64ilGSBTESbwCM58Mx4iEKbS9EjecBpe6sdOGewyQQ6mrFyupwplid5FycG+CZLdnwYU84ix0AZRyHQ3Q80kUdyIHInsobntqgBacZUe97FmXMfiTy4x3tXUUJAlaEmcwC2tUJ+0AearCGygK/ewRADNoCl6C+eaJMTTH7dl8n8mH/yK0rLoQaQlMNLtDDm8mZF4sSZ+/Z8WT/yOeWOANjZc6xG/4PP0TcAzD2wTWe4n5nJLorovPn3bvoh2iffZJM8kBNzjHbrrbcmjqj7SNFPesxc0h2OWng2f2TEzmWUH+iVnFmD5IOu9T48FRjQFr1ER3OAvV9ep2Qu9MPOmGusKXISoLdTvcHOI5fWnzUQ8g1Ar7vuuvkDemAYACUD1qvdiXaf2CH6Wx5d7zoYiQ5zrM5ui+CAfomci3wLBMU77Q4ZSzzrH/niIJAJc2V8dsnJirE5oqNcjCPq9vdKHqwT5cmIgIO+OrrjOA5Hgo1o90vZdjIekX/2kQ5io9HJHXsvz/NwSxXkD7cZqf0ZNhwQ8QNWLH6dArJsOdpOpsQ233zz5PjM+PHjE0Mgb8KECQlAAuoteluDlJd7EQOGEegG6IBkhhRN1IwDQClSPgy9oy2UHCUpHwBw9EPExjENik+UTzTM2Vt9pHDsKjDMIiiiHPoJDFL+2qbUJG1ScoyDYwCeJc4Eo6a9SI5jiKaIuMnTb2CasqSQ9Ve+xPADjJwdihoIY2hFmRheUV3gl2JUPpLjJ8bsjLktaCDHeIBjfONQKGMMjLCPshhk/dCedgAiBsX7OWDyKHjOGr57npnJ+Dk4AKZ2GSLbzY4X4CU+OTJAZsiSPGeBjU1EyNVchXFRDgDVLoAFMIlMatt5ZOdG/TUHxgY4sq2uHe/QNpllfDhpjivgExlxTp/x1Q5Z5QzYhWnPO/DL6dN3soi3ZNe7tC2Rb+/TTiSRMWOTgBDRLjwnE03TJKAfAABURPnsYmjfDo2PSDkAvrdwHAkQAwKi7biK+loHEj7gG+CrLWMRGTb3ZFdkWNSezAH1jr0ZB6fI1ryrtRTAlDzqYynz8e7ButqRcHwIMGu/A3gDMPXPvKJzQpTxzEm09h05IW8cAWvN/DpvTz+QG2sm1p71wIGzDtSlR+gNekR5bUvqkxtHmjhUzjo7OmHtOJJ49tlnJ3OHj87TA8aOpJENib4TkddWJLrHjo71Sz7oCw6f8dFPjoVYP8Zol8o3O3Qj54Gesr7tbJpHfUGPtuPqw1CygT9kGm8AejLg+A2d5J6+8y46hOzhh3Fpx7pztMe7VlhhhfyXY+STIfqanvY8qxOQz/boK30Y72d7zB9HyVEqMo6n6MZCB9Cl1oP6vhWjD6xr/KJXDznkkGRt2vG1fvDJ3Jtna47cWFPmDt2a075jlGSDnJp3Ce+AdO+kA8yTebe+0COpSwa0I3HK9Nt7tI/P7I25tj6MjYNJdsiLvtE1rmTf8Rx2hu4SbKPvyIy2I9Gv5lwfrB92ht5m28m8NUAvkAW6RMBN2wIAdJO1YB2SSbsm6pIR7dN1kjF7Hm5pjuHSodqPyoHhxgGgmAELAEkROVfN8DA4Frs+i5bLEzlmIERuKSqKg+IKZUCZUCTADieAMqGgtUGRKQ/4UDSMqrORDC7jJF+E1XEYihPAdVaQ8hVZBfi0w3g6i83IA01N0+S/q+yDPu1RXECgpE0KUmTCvQQYaJtR014kRp3Cd9xBnjEzEkAmI8RIULJozoJS5JMmTUr6yFh4doQGn5y7pmwpbOUj2RHwfv1QFkhkNDxT3NoQoaeAgV2OkbFTvgCxdsxLlMcvecCPyDieep6ZyS6Ds5vGqN2mafKP7QBd+gFsMlCMljLyGD9AjozgBZCPrj4nEnAlI4692NVQFg3wJyOOUPjwiwMBGJERMqptRxPwCH/s3hgzGTF3DL12yAEZ8W7GUT0JsGL89JeTJerHeUSLRG4AZO1E4lgYm2MzTdMkoM482IVRJo5B4BUacAmAcELVY5iBMwaUPDqmol4kMs1hs7bIHH6QT30nL8AavuCdtQJUcATQjUGUzpgYeeUZbc6K6KF3cBw5hu5nZfJ+YNu42+81N9ariCaQA3g5QqIMfYJHxsf5A4SA7YkTJyYOlPWF70AQp9yaAuzsDAC41oIy1rNxc9rxU9sShw4PAUJrnFziLd3nyIX5Mnd0BCexrTvICDq51FakkEH60prnoJkDsspJodP0CTAnm2SV/JpPDoV7zgM5pw9CB0X72nAun3zou8CIfmvTGiCLxmAt0iPa4AgYg3vj0hYdpl9Aql0ia0u+tcxxdz8UyfgEiMg1MBl9cKTP/Dlqqa8AvyAKOv1JbvCPjSFTeEPu6Uy2h242Z+SJvFlHjtkZuzFbl+aITJhnbdD12ucUcyDJkzYk6408kz/6ix3jtOsXeiQBB21Eogc5FuaGHbTe8du6QHNMTZ85NeqYf/23Fsi9XSEf25MjfWZjw+YqL7HlHBp9oDutK+80ZjqBU2Q81hUe0MHatmPIrlsLdKK62vBufNc2O+d7B8caPQ+3VEH+cJuR2p9hxQFKkRKkLEWGKDlHLyQGS2ebpsl/0UIeMElJyWcYPVPSnm01ynOvDCPu6llC04Zr5KsjT/I+yty9uiKP7hlG5bTRNE3+a0DyJXnqiZY0TZPPpMrvLWlbHXUjGbf3aEde0zRJP0U0PMunkN0rG+1HHz0DNa6Ssk0z9Xyvjx5FWkTxGAftBs9e+tKX5r8u5N142W4PaNSW5P3eHfyRp2+iNkADkGhcyszM1DRNovxt5TK42vZe75fcy5MATXn6EfPrvj1e86ic8nigfJSVhz/y1Il8Y5cY9KSGAAAQAElEQVQnyQse4Jn25ONdzKl2vUN+OynvHZI21WnT4z7aUU7yrKw6nrVvbuR7Nu8x3/gR7SjjXl19xgvP8tWT7DqIpokYax9dvnHigWf1JW14ltCMx32829WzsvpENjbbbLME1JFZ7c7qJOrtg1QgRRTT+/XNWJpm6vrAl+ClvhsDOcBnz+h4Jl9S31Vqj1k5efiifVfPZMR7JXyTp2y8Ux6ad6L1laKeOpK58i7z57lpmvwneoPnrvrfNE0+ex7te5/xSej6gSY/Pfcf2SAj5MM40JHwJmRcfe83ZvfKaMsY3Ud7oVuUcc/BBoQ5CdrW7lAlffKXXuwW05P6ga/yjdUzHpl7967GJuFvPMdVvjGq7x4v8Mg9vsW9Mp7la79ppsokfkRdtEjKe78+Rb2gxVW+MpGapsl/btM71ZOvffPVNE3+RsC76Y7U9Z97bbXn0L18fdKOMXcVzf+z35wjR8zQoo/kUXltydcGWfCMH5J7NDKtzaAbg2NiHGl6X4Aj+p5fOoz+qSB/GE1G7crw4wBlY5ubAgkjPPx6OXJ7ZAsWQBYpcVaSsZ6ZoxHJFaW1czIz2223xVA44uJDrnZ+vX/+HLDtzrBysB0reP4t/n8LopOidHZI/j931t/ZxRBNj29SZn0PRuobU7K+OTIAoGM4M3MkosV2Asqo8Mx8x0Daslsr6qxfA6k3u5e1k2k3A9gvj4g+H944HkUn2b14Pu0Mdt0K8gebw7X9Ec8B5wdtAUcEYMQPaBgNoGmaJJLpnKgEMM/M7tnitXU/M9vs1JZoj92CTrSaN+MccPSNXDiS4uz/jLc0fU0fNjs6Mj1l1uYIJNAvjqfN2jeP/LeNGzcuhXz4i1Azc0S+ZRpucyIYYvdpZo5ztLflmBoZsStvrc2s8Qr8ieDPrPYGq53ZEuQPFjNru5UDlQOVA5UDlQOVA5UDlQOVA8OBAxXkD4dZqH2oHKgcGA4cqH2oHKgcqByoHKgcGDUcqCB/1ExlHUjlQOVA5UDlQOVA5cDM50BtsXJgZHKggvyROW+115UDlQOVA5UDlQOVA5UDlQOVAz1yoIL8Hlkzcwi1lcqByoHKgcqByoHKgcqByoHKgVnNgQryZzXH6/sqByoHKgdSqjyoHKgcqByoHKgcGFQOVJA/qOytjVcOVA5UDlQOVA5UDlQO9JcDtVzlwMzjQJ8g/x3veEeqqfKgykCVgSoDVQaqDFQZqDJQZaDKwMBk4Itf/OLMQ+0DbKlPkO/XvGo6I40EHtQ+1nmqMlBloMpAlYEqA1UGqgwMHxkYyh/N6hPkL7jggqmmyoMqA1UGqgyMWBmoOrzasSoDVQaqDAyRDLzoRS8aYPx95hXvE+TPvFfVlioHKgcqByoHKgcqByoHKgeGBwdqL0Y7ByrIH+0zXMdXOVA5UDlQOVA5UDlQOVA5MNtxoIL82W7KZ86AayuVA5UDlQOVA5UDlQOVA5UDw5cDFeQP37mpPascqByoHBhpHKj9rRyoHKgcqBwYJhyoIH+YTETtRuVA5UDlQOVA5UDlQOXA6ORAHdVQcKCC/KHgen1n5UDlQOVA5UDlQOVA5UDlQOXAIHKggvxBZG5teuZwoLZSOVA5UDlQOVA5UDlQOVA5MDAOVJA/MH7V0pUDlQOVA5UDw4MDtReVA5UDlQOVA71wYMhB/oUXXphOOOGEnKZMmdJLVyupcqByoHKgcqByoHKgcqByoHKgNw5UWnBgyEH+Yostln7zm9+kgw46KN1///3Rr3qtHKgcqByoHKgcqByoHKgcqByoHJhBDgw5yF9vvfXSSiutNIPdr9UqB2YuB2prlQOVA5UDlQOVA5UDlQOjgQNDDvJHAxPrGCoHKgcqByoHRjUH6uAqByoHKgdGHAcqyB9xU1Y7XDlQOVA5UDlQOVA5UDlQOTD0HBjePaggf3jPz0zv3fe+97309re/fbr0uc99Lv3vf/+b6e+rDY4MDtx5551pjz32mE4u3vGOd6Tbb799ZAyi9nKmc+D666+fTiboj2OPPTbVP5Qw09k9Ihp8+OGH8x/KIAdl+sEPfjAixlA7WTkwu3CggvzZZaafG+eb3/zm9PWvf326BODNO++8z5Wql1nBgeH0jqWWWiqdeOKJ08nFOeeck1ZYYYXh1NXal1nIAd9MddIX+++/f3rJS14yC3tSXzVcODDffPOlvffeezpdQU7e8IY3DJdu1n5UDlQOdHFgyEH+H//4x/S3v/2tqyspRwzvu+++fF//GRwOzDXXXGn++eefLs0zzzypaZrBeWltddhzYI455kgvfvGLp5MLsvKCF7xg2Pe/dnBwODDnnHN2lAkBgaap+mJwuJ6GdbNN0yT2gm4oE/syrDtfO1c5MJtxYI6hHu+kSZMSEHHwwQenH//4x+lPf/rTUHepvr9yoHKgcqByoHKgcqByoHKgcmAYcWDgXRlykP/+978/feYzn+lOtocHPoxao3KgcqByoHKgcqByoHKgcqByoHIgODDkID86Uq+VA5UDg8eB2nLlQOVA5UDlQOVA5cDsxYE+Qf7b3va2dMUVV6TLLrssbbXVVukf//hHmjhxYvr4xz+e7rnnnhT0Sy+9NI0fPz7985//TEceeWT6xCc+kf7+97/3SncW/61vfWv67ne/m77zne+krbfeOv3rX//qrt+Jfu+996YjjjgiHX300enuu+9OW265Zbryyiu766Mffvjh6Zhjjsn0LbbYIvmLMpdccknyl0Kc+S/pV111Vfr2t7+d3vnOdyb0j33sY+mTn/xk+utf/5o233zzVNIPO+yw5K9L3HXXXd30iy++OL3rXe9K//73v1NJv/rqq9O3vvWttM022+Rf9Q365MmT02abbZZK+kc/+tH0qU99KvmLJ5tuumm65ppruuv/5z//SSX9+9//fvrmN7+Z3v3ud6cHHnggHXrooem4447L9TfZZJNU0j/ykY+kT3/60+kvf/lLestb3pL8RYSLLroovec978n1O9EvvPDCtO222yZ/UePDH/5w3nn585//3F0/6A8++GAKuqNXG2+8cbr22mvTBRdckLbbbrtU0n0I/MMf/rCb/tBDD6VDDjkkffazn02+1yjp/rLDhz70oXT88cdn+pve9KbkyNf555+ftt9++1TSN9poo0w/77zz0g477JDpjoadcMIJ6Y477kgl/b///W8K+h/+8IdMv+6669I3vvGNtOOOO6aSPm7cuPSjH/2om/7II4/kX2/2Eevvf//7VNL9BaMDDzwwnXTSSQn9jW98Y/IXTHy0ttNOO+W/cNSm+5DNMbavfe1raeedd870Aw44IJ188snpd7/7XWrTd9lll/Too4+m/fffP/lrSb/97W+76V/96ldTSfdL0xtuuGH6yU9+ks4999w0YcKE9Nhjj3XX70R//PHH03777Zc+//nP529oXv/616cbbrihu35J32CDDdJPf/rTdM4556Rdd901PfHEE2nfffdNX/jCF3L9Nv29731vpn/wgx9MX/ziF9Ntt92W1l9//fSzn/0snX322amk//rXv+6mn3XWWWm33XZLTz75ZPrABz6QvvSlL6WSvvvuu6ennnqqm37rrbcmO4c33nhjOvPMM1NJv+WWW9K6666bfv7zn3fTn3766bTPPvukL3/5y6lNP+OMM9L73ve+9Mwzz+SPEk855ZRMX2edddJNN92USvpXvvKV9Ktf/Sqtvfba6Re/+EU6/fTT8183Ut/uZkk/7bTT0p577pmeffbZtNdee6VTTz013Xzzzd31O9F/+ctfprXWWiu5Kq8e8+bqWX5J1773aE+/9M97lFcv6PobdOPQX/3Wfx/xl3T88LEmOj7hB77gDz6WdPOB7/iPjt/4jv/mqU03j+bbvJvfoJtX81vSyQl5IVfki5xEffJT0skheSSX5Jccnt0lj1G/pJNz8k7uybe/TkXeyb36nejWk3Vl/UzoWofWo3VlfanfiW7dWr/WqfVu3Vu/UT/o9EDQ6Qd6gD6gV97whjekkk7P0DdBp2foG3qH/irp9Bh9Rm+h02P0Gb1Gv5V0epQ+pTfHdelPdPryoIMOSvRnm75Rl/6mh+lrepn+Dfp1XXq5pNP/9Dx9P2nSpKy/6fmoX9LZD3aGPWFX0NkR9kT9ks4+sWPsGbvFPqnPXrFbJZ39YwedVmAX2b+ozy6W9ClTpmQ7y56yy+wzOxv1O9HZcfac3Wb/2XH2POqXdDgBXoAL4AM4A16AG9QPOtwBf6DDI3AH/AHHwDMlHa65//77M85Bh2s268I3kydPznippMNV8NVdXTgKnkKHn+AoeKqkw2XwGZwGrwUdToPXSjrcB//BifAg3Kc+HAgPlnS4En6EQ+FRuBK+VB/eLOlwK/wKp8K7cC/8GvVLOt07FKlPkA/cGygwb+CLLbZYmjhxYgbar3jFKzL4R/entDBu0UUXTUcddVTC0MUXX7xX+hJLLJEuv/zyZCI4ECbu5S9/eXf9TvSXvexl2cEwoa985SuzA0JQoj46hhMYdIwmaOPHj08EZ5FFFskOSJtOkE0MwUQ3oQR6ySWXzA5ESScwFoS/SEIQ0AmOhfPSl740OxhtuoVEMC28hRdeuJs+duzYRNBKOoG1YJdeeunsYFjoUX+hhRbKDkibTlFYGBTDggsumBcUhaK+hVbSLVgK6VWvelV2ICgqC5fiUr8T3cKn+MaMGZMdCApxmWWW6a4fdH9xg8JAX3bZZbODQVFSPBRvSaeIKOqgL7DAAtkBodBf/epXZwekTfehF4XHIKBTlAwFB4ThKOkUMTrFyvCgU8gM0nLLLZdKur8cEfTXvOY1mc5QUfwMX0lnCADtoPt4lcFgEJdffvlU0l/0ohflPz+3fxcQR2eoGGqGieEt6QwhQ8+wMfzoDCJA4K/etOmAhQ8iGVyAYsUVV8wOiPoMb0lfaaWVsoMBiDDcZ599dv6gLup3os8999zZwQB4Xvva1+bvaAClqF/SAQ1AC7AArF74whdmBwQgU79NB9zQARqAbuWVV84OBKA3YcKEDLTb9Ne97nXddMAJcPThH0AFUJZ0wNOHpEFfZZVVsgMCaAJuJX3VVVfNDgagG3TfDwF8AG2bDhgCxj5gBhj36gLi6IAmIF3SAenVVlstOxBrrrlmdjAAafUB2pIOGAPSTdMkgBiQXn311bvrd6KvscYa2cFwVV49RsbVs3xA29Wz/KZpkvdoT78Abe8p6YB00I1Df/Vb/42jpOMHvqDjE37gC/7gU0nHb3wHpNHxG9/x3zy16ebRfJtX8xt08wpol3RyQl7IFSBNTqI++Snp5JA8kkvyR07JY9Qv6eScvJN78k3OyTugrX4nuvUESFs/1qH1ZF0B2up3olu3gLR1ar1btz6IjfpBpweCTj/QA/QBvUB/lHR6hr4JOj1D39A79E9Jp8foM0AanR6jz+g1+q2k06P0Jb05adKkhE5f0pv0Z5tOP9PD9DW9TP8GnV4u6fQ7sruJ7gAAEABJREFUPU/fjxs3Lutvej7ql3T2gx1hT9gVdHaEPVG/pLNP7Bh7xS6xX+qzV4B2SQek2UH2kF1k/6I+u1jS2Vd2lj1llwFpdjbqd6Kz4+w5u81+s+PsedQv6XACvAAXwAdLd+EMeAHQVj/ocAcgjQ5vwCXwBxwDj5R0uAi+CTpcA9+M7cI58FBJh5vgJzgq6PATHAVPlXS4DD6D0+C1oMNp8FpJh/vgPzgQ3oML1YcDx48fn0q6wDX8CIfCk3ClALf68GZJh1vhVzgV3oV74deoX9Lp3qFIfYL8HjtVCZUDlQOVA5UDlQOVA5UDlQOVA5UDw5IDFeQPy2mpnaocGLkcqD2vHKgcqByoHKgcqBwYeg5UkD/0c1B7UDlQOVA5UDlQOTDaOVDHVzlQOTCLOVBB/ixmeH1d5UDlQOVA5UDlQOVA5UDlQOXAYHNgZID8weZCbb9yoHKgcqByoHKgcqByoHKgcmAUcaCC/FE0mXUolQOzGwfqeCsHKgcqByoHKgcqBzpzoIL8znypuZUDlQOVA5UDlQOVAyOTA7XXlQOVA10cqCC/iwn1/8qByoHKgcqByoHKgcqByoHKgdHEgQryy9kc5Ge/eOfHZgb5NbO8eb/q50cgZvmLR9EL/djGKBpOHsozzzyTfzDNr1rmjPrPgDjw7LPPJj/SM6BKI6CwX7n044rGNwK6O+y66EeGbrvttmHXr+fbIT8e5geinm87s2t9P0o22sbuV5TJu1+2HW1jmxXjGXKQ7yeB/Uqe5JfOZsWge3uHX3rUF79e9+STT05X9NFHH01+HU8Z6Re/+EUu49cc/VqbXzzzK2s5s8M/fr3QTyEjEVxt+OU8Pz0tr1PyU89+Yc/Pewfdr7f59Ty/gKevkT+Q69///vfk/frklxV7qmuO/KKxsrHQOCp+Hc6vwannV/V+85vfJL8853m0JQ6MsRqzn67uaXx+dt0vBPo1Q3xT7vbbb8+/6uzn4T13Sn5R0C9not1zzz3JL/7ht/fK6yn5xUW/Phh0v3qonvTLX/4ysgd0JeN+oVMbfmmxrIzuFzjRI02YMCH5yXNgPvL0xS+c+pVsv1bq583LtkbSc6e+7rvvvnkNffjDH06PPfZYpyI574Ybbkg777xzvvcPHWHNvPjFL054I69Tmn/++ZO1jzZp0qT8rjXWWCP99re/ldUxCSTstdde6Uc/+lGm65d5NC/77bdfzpuRf/75z38m/V1mmWWS/vfUxn333Zd23HHH3NfQa1OmTEnWxAUXXJCrWUvkYTSCEuMaN25cHn9bZ+eBF/987nOfS+amnW2eJDqgkw1iczhI9Ix6xx13XH7X4Ycfnp5++mlZ0yT6Z4kllshlrr322mloa621Vs5fYYUVknanIfbz4YorrshtbL311unxxx/vWOucc85J7KNxHXDAAbkMPUqfynv/+9+fgLl11103XXLJJTnlQqPonzPPPDMtuOCCaZVVVkn3339/x5FdeumlmZd4Qn+25//yyy/PPFxzzTU71rXmQlf8+c9/Tm984xtzW1dddVXH8pF53nnnpU7rkL4yt1FuINeHH344sUvG8clPfnK6qujvete7cv+UkeAquoqO8CzROa7SZz/72US3TddYzeiVA0MK8h944IH07ne/O6244opp7rnnTttss03qDQj1OpKZQBRJXWCBBfICpKg7gSR9tsgoJMn9rbfemoB7oNmCAbrLcTzyyCPpve99b7LgFl988axQKWVtuFKCDz300HSjeOqppxKgp56oqALe473AION5yCGHyB5Qsli22mqrdNNNN6WLLrooUS7/+Mc/pmvj5z//eaK8LXh9ZZyBByADqDfeffbZJz3xxBPpYx/7WJp33nnTcHDWphvI88j49a9/nThkokzXX399ApTMZ7tJxo3js/HGG6ezzjorMaYMK0BMgQK/MX/teiKZ11xzTVJ2s802S+ZbO5tvvnk2el/+8peTd7brxD0Z3XPPPTPv5THSZO/uu+/Oc3DkkUfKHlDSHwBivvnmS5xLIFMf2o0wUJxh8oAP3/jGNxJ5XGihhZJ+kydGhmzccccdiRNJhr761a+2mxnx9yeeeGIG9nTCXHPNlThcnQYFzJjTO++8M5P/+Mc/JsaTTHEKb7755rwGM/G5f/CW3JC5JZdcMgFqRx11VJaJ008/PcsYMP1c8WkudIU5CHnzTK7uvffeDBJOPvnkacr354FR3mWXXZL+kG06wNyWdcn5FltskQGGMXBk8IeBNs4AoZw/6wjIo/vKdkbyszXICbYO3/KWt+R11Gk81113XVK2DeQAQfYE7y6++OJErtp1rT2ydMQRRyRrlK41J8pr5/zzz28Xz/fsB33NSeS858yuf372s58lAExd9soa7soe0P9sBrtpJ3fbbbdNBx988HT12SgA3zu8y7qhF9kNusaYyAG9qTI7Ypeik3yhD8PUZ5fMNX6TCY6dIB1A266IJ7/61a/yGscngR/zr9wxxxyT8I0Ooefb9awp885+cNrYdu+AC7RDXjphGW3QRYceemgiO54luuJb3/pWDhJoW95Akvp01Otf//oE08AG+tduA4bhnOof3XL88ccn5Y2P7ZB/0kknpQ022CDbtze84Q1JMNS42u3U+745MKQgn/E74YQTEuWzww47ZIVGafTd7ZlfgiFibBhWQJVy/vrXvz7diygioOuWW27ppi211FJpXFfkhqOy+uqrJ5EugttdoOvGFiRFx6HpekyXXXZZjna5X3755ZMIOVDluZ1EAAEBi1e+RUepU4433nijrBlKAAalvtJKK+X+isJSwu3GzI+oi4gAJQ5oWMCUFeMN1IlKABNR1zzim/babY3Ue2OjVDlxiy66aDLXHJ2S9+ZEdBOv2gaKLJ1yyik5etOJBxQg0PS6170uO0icBWCIwlN+r66I7Ne+9jW30yTycklXxItBDALg5H3kyP2rX/3qIPX7StkDcBSq6Mluu+2WHYa2POPFcsstl9skD5yg9ddfPz/POeecCZh8wQtekF7zmtdk5x1BhO6HP/xhAgI9j/TEkHJiDjvssDxG8w5Ml+PiNJlfc7HwwgtnMh2Hb/RB0zTJLhl5Mve5QNc/1v0LX/jCbrnBOzqpi5SAfoZ/8uTJHqdJ1ibZMH9B0C9RdKAQsBIpY3yD3p8rnSdybL0LUjRNkwCSdl1Ohx0dsgu4WTfoopdHH310dgQ9t5MILp2qbjt/pN4DL+YAv803x+jss8+ebjjWgfkbP358Nw0AVJa8WEPdhNbN5z//+WTHz+6YbGBx7733dpsjp8cee2y+b//DmRK88r5NNtkkk6xnTrcgDr2eM2fgH0fJyO8rXvGKvFup/+xGuyn6AcjzLvYLjT1pmiaF3iPbQD39Y13QY4Ax/av8SE7GRKfCBnQqvUg/koH2uKxTfDnjjDPa2TnI89Of/jSDYLt+5q5dwFqDR/CS/rWW5JET5bbccsscVHDfTgA+MM8hbefbfYI52Ph2fn/vvdtc0gP6I/AjqAG4Rxt0CTvqGfaCFwSD6Ms4DbDppptmJ4HzoxwZgS04AZ5r6h8HhhTkA7eMP+NDIRJKW9H96/rMLXXXXXclUcsQPECGcirfwsitttpqiXLlraI7MqP/7hlvx2hsf3qOxEgSYs9hoJdddlmPOSJDkEXmc8Zz/1jwojqbdUV4n8tKFoHFxzgc1RXZ22677YI0oKuFAnioBEyIpFjcniOJAFFKlAdQIGpsfBYxPlFU0jzzzJMAF/UWWWSRxAAxWJ5HemKUHLsxXnwCno2XMY+xAWeUGKeHs2fsnFfKK8r0dGUAgPqxY8fmIvhIwZoPGZwKRx5EzD1LlDyDSXl7jsSBdHxERI2BdB+0/l4ZVWMD5NQxJmO2bepZEl3CA/f4Q0a92zNHWcSJohY11H/55Gi99dbLOxyeR3qyVjn1MT7AhNEsxyXSSjfQG0EDYBh4bcgjA6Kd9IJnSeQNmHfPEQTQ7PZ4Vt88kUvPkUTBgAm7JpHnqi75dW8+yXG8W15/EseBTDRNkxhu8qld+ijqCxxom74wJgEC/Qx6p6v+0EP0USf6SMszbwAvx0bfOcMAmPtI9Kc1b9eP/ox888xppDd22mmnJFgQtLgCgGymZ+XIHNDoWRACH7XvORKdLfLvOEQcFfUuu+js7fbbb5/ClkWd/l45o+ZbefaT7o+AjzyJPRT8sUthl0eeY1+cAfLqmR4hk+SFfMEG9JC+o4/kxLabE2uQLqU3jEswpj0udHzCkwMPPDDF+uaMsfmcBIEkOyep9R85YHNgFtn4ho9wiWdrlW5o6xegmjNqh16ZdhIE2HDDDfNRmnZ+f++BeTaKTlSH/iNv3ulZwgO6wj3dx77QpWFH5BuX40fkxzOcyL6SOc819Y8DQwryo4s8T1Eh2/4xoUGbVVdGKYCLdwIywJv7dgKsdt9992T7TGL82nRRGGcpKbx2PpBGecmzwKUQcgufsqcM0CPZ4haloRAjTxsUM+DvmIeIokhY0Pt7teiMMcrrCx7Es6vdBpFkZ/0YbPNkB4FC1g9lmqZJ+m48qes/Y+m6JAvUdTSkcrzGGOM1PgqcwsYjUQuKmvEF2NF7S0AS2cBD5ciA9oO/lCGF3X5fGNFln3MS1ZPILwDA6Iqw6Zf8gST9IQfRH3XJSfv98iLZTaB4vVse2WSQgJjyOw9GR4RHuZGe8MOaiXGYL2sqnl1FSAFhESjPkeyg2X7GK3zjqAP0bd3HUDZNk6uQv/b7mmYq0C71k215QK7UPeZT/3JjXf/0Np9d5I7/a6OUCf1qF6aP9thjjyR6bcdAnx0bapfpdK8/1k8n2kjLA6TMafTbvbx4drXDwWGSPEeybkQvHbNwlBLwxsOgu1rTdIL7mP+QQ3xsmiYfIUOPxOFwJNTZf1F3wSvAy+4TO8Lu2ml2/DLq9PdKTqM/6nBAy3VAH3mPaD5+0E1LL710EkHmaMizRti5kDHtsDeljHnHSEt0qj7HGqTfm6aZ7vsJIB6f7NbQ+eyv8QO1eGXunNMXfBP40aZERshY8C7u45l8KKMt5SU7A3QOh9RzJLaeQxgAPfIHcjVe/Y/3G697eZ3aYSfYLf1s06+++upUHjkly+2xt8vX+84cGHKQT/gInDO+jrkMFTgUgeBNhuK0XSrCVLKtaZrUNE0SaXjzm9+cz+9HmUmTJuVn21SRF1cLvGma/Mgj5XV7nwwgDx8imitPpJZ3zqMWmXN2XsTF2cqmmdqOReF4UdtAUrryHJ8okyMB2pZEDURS3Fv8+hKRQnkSpas9i1SEHqjUd1u/4ZWrq//o6oy2ZOyijXgcygtPREdirBRY0zTd5xopSLxSN8r0dmWcg874qcd5k8eJxNsAgOQEmLJT4LyiYxCOCjnT6Ey4M7Gi6RdeeGGaMGHCNB/COfZTyoRngMzugfd5t7UQ65DBEJUBCtDbiTEXWWxHB+1gOWspGsQBsAXdroOH7eeRem9O8CZHhs8AABAASURBVCj0BfDFaLbH44gNg2yeADe7eXRD0zR5J1Bdaxywax+v0QaZMhfuAZ5FF100AT2ezYlkDXuWADc7f/SFQARdZCfFLoty+qeceiJn+u9Z8tEwOSgTI4sukQm7ee4Za32xBppmqi6Sb7eGLtBva8YakIfWVxotckH+zal1aszmhK1wH8nujnPQ+G2dSv5wQ9NM5SX+kQc8LPnSBtRkguMcwQT6nG4unYemafJRWDt7jmfS9dEX17Fjx6Z11lknCTB4ltgbwSp9LJO5V0ayq01HuecIihjTYZ4jNc3UcdFzzpnrb9M0+Rs8ukXkWUTZOmDTol459sgfaVdzZg3TF8Zk3RuDuXON1DRT+WQOHffi/CivLt42TZPWX3/9jD+02a4HX8SziDj5sxblmW+ywp57ZlvoopBBToXvPxz1UlYAgtPFuaAbOBvqSeS0lAfPEydO7JYf/WavzK06bIv+GJfndsILuxMczshXlt1wRNkaiPy44knc12vfHBhykC+C4MyyrSMePnDQd7dnfgkAl3BfeeWV+cMX/XEkpqc3UVTAXCg0W9UUozOmDKoIHmMY9XnMjGs826pV3jPPlGLmpXqW1l577XyOzkIDFAi8iEt7O0s5i3XcuHFuc2JUfbCiXpko8lyo6x+LSqTRgtdXYF0koYvU/T/nwlZhjIcisaAdPWBQgN1wOsIh0o7FXUYTuxsdYTfAlsgrBW3+HOvi4Ky66qrdI6GIAKlQhhQlUCx1F+rhRvscPopQEUbO3EfE2xnEthyaA1v6MbfO5AORIrjmEvCm3M0lpW6OtCuJ0kS99tU5T86bMgwxORHVM5feD5gCdOjtJCIJLMYaACAZbGCQPIztAg/4FnVsx1tn8TySr4wmvnBkjBsv2uvQ2ETfgs8+sATARa3QJI484KUeGZMXiUy05866cxQDne5h5Nu8FC0FHr3P0TFtfuELX8hn+r3X0Q/G066ffmtfWxKnTL0yxfltZegf4NV8km9rwE4EWiRRSGeygUByQP/pd9B7umoLKOiJPpLyHTm1HqwfgQH2xF8ZaY/htNNOS8FrkWzJjm27DF5zDuiHdj6QLgAVeeSHc++ZDWJ/3HdK+gMw6l+bTq95Dx0W+eYb8It+tq/WfJSzc+NoIAeBLReRtfaDXl71UUQ68uksAQu2j2PTNFOBrny2jMMTZUfqFeil09l5+pnjh9+Cmp3GBPjS4ejK4QuHG9AXOOBYwQtRV/sAtDmQx+4A+nHcRx1OA5pkrdkRiDmlpzh0QL85lC+QZO3SDW9729tUy4mcopcJyCdbCnFeyJhx0gHm3FFN+ejtRB/SpW1dBtPQCcZp/MpEHetCwCGe67VvDgwpyOfBiSJShL7yd44XMOm724NTQh/0RWTSYuTNWmyE31Yj48qQSbaRnH0GjkRI7UT4CzVou+66a3LmtmmmKiy9pTQD1HtmhJ3X3GyzzZIvxik+ik4UVrSDkCtXJoYfn9STtCuV5aZ7LjJE2fy1H+8FEI2VIwJc6j9QICK40UYbJSDTB5jO5AO08oBRfw3JERVKgsPjFYw7noloeR4NibLBL8cQ8MFxHEqWTOANgI43IlrmBKDDI7yisB3rohSV4Ry1eQK0k4MAcEC2Py3mo05teS95FJEF0uOoTruNuOe4AV7K2/UxL2Qq6P25MqrOzXqfo0cixHaGOG4UfPsv7XAAfdQX7TI0HAZrQd8pY1d09YFaZ409j4ZEL4iYWyecP2MVZWLwHJ2hQzqN01xbY+TC1TyFgYzywKK1GM8cL0DeO2yni3qSQVFgEfs28Is6cTWPjo+5mgO7CkHr7xVoEG0l747wcSKtcYCfrNklFLgAWJWxVugWgIM+EHhwNh1o8LFfvFd9elV7kTfSr4C3cQYf6A9n9ck+HdHT+Oh+a88c293mZFmP7fJ0rjPbkWcu2Ap1tG2NenY8h9OHt2iSXT7fcQHhjtOZT/nsDXvUdvyi/b6uAgyrrrpqonPOPffcJCoMjHL29MEasNvoPRJd510CR+wOGQEI9Q3Y8z716TntsjPyRnqy689pETjDD+uEzXQky/Ff8yRgg0dwBXBMlzdNk9gN+phswAoAepsfQLt1Jogin91RlvOkPbqEbSBTZBL4Vm6wknE6biY4SefQTWSNM+mMvcBRvNvOAB0Yz8ZGpu1M44P+s6/o6tu15pB4rql/HBhSkG8CLWZAKBIA1b+uz/xSvEkghecJ8AMtnA4f2VKulBAgLyknog2kOVPLcIuooDFiFHTTNN2dtMgp7AD6FgKFrDzlaBHyWgE5SqANzkRYOB8iO+r58EY9qe1ld7+snzcULI/duVnjo1AZgFNPPTX/2UPjt11nXN5PcWhaPnBnEYsSMPbyJXWBQv30PBqSsTj6wmjiBQBvXHgGZIlQkB0ROnPC6QN4zKeIK56IQPheo4yI4zlFaB1QZk3TJMYNSNQWZ5IjSVGLwpIB745EeZoHzxS9PtjqBD5ndC0ZC1Dm7zID9Yy/nRl/elHkzrskBl2Eyn0kThAgqe+MzJgxYzKJnAA51lDOGAX/cGLwwNrAL3xqmib/KVmG29zGMDkC5Mezs9dkwjM9QUbkt5PIF55zDuWL1pFBfOUAWot0jyixD53b0VX3HFA6RV08Nxci/ORDv+VH6u9VX+kL+g2I1ydjBk4FGsiptW+uBXAAGW3ToRwRIIT+AzTkS/SsnSl1PY+GZO7wxHoXSDEmQN+Hp3S550jmBaDxTPfbGTLHAF/bBqBLHEKBFLuvnq0vPFQHADT36tEbAKW5R5PMfeguOsZRO/n0CgdSezOSBHqsAU6/j4AFKqx1fbAGOBHeI3EIvQMQBejIE51BjuRLHD9XzkDT/L8NlTdSE5vJ+TO/vmvjtBsLgEvHmifBATxymgGmsG6UsXtj7qxftpr+kB+J3hd1F5jjILE7du/Mq/bMDxvmneyVHYKo62rN0u3uI+mvvnLeIm8gV3LAgbG7zeHVR84I2wi8R1v0FFmMZ86ecdB7+i4JaKDrD8eRjHuuqX8cGFKQD/RSAu0kr39dH5xSDKf+WCjxBnmem6ZJaBJF1jRTFRCavEho5Tiapsl/wUD0z/amtpVRR/mm+f+2vK9ppj4r1zRN/osWTTM1D109qWmm5ik30NQ0U8fTfn/TNPn8ZtNMbTf6WL6rne+9ACpFJPLs2JO80ZTa422aqbyJeW+aaZ/b/MQDz/gnNc3UsvIjAQVojq3Ja5omz7c875Xnqp2mmba+fPKgjBR9Urdppi2L3t+kTW1oL+p4f/tZmaaZ9h1NM1Wmoq7Itgiuv9wkuhNtjZYrHsRYY0zy2nyS356npmnyGsNPtE6paZrkw3tRd06bMtrwLvWaZirfvaf9rFzTNFl+mmZqGXnKqatvnmckNc3UufU+fdFG0zR5LO1n75Ga5v/f773yJH2xI8ng+46B86Ct0ZKaZipPjLVppvIAf/CtaaY+x1jxQvLsqo7kXl6ZtAMIAU5xHEPZdp2mafL8y2+aqXOG7v3RnnY8l/lBH8hVW9oxx1HPu+PZFV1qmqnjb5omy007T10Rfk6sHQLBBnmjJQWf8D3GhE+em6bJc4YfkrJRxjV46Oq5TAJ2HD/AGq1pmm7+Rluu2m6aqXOgnKQPndqdc845kzrKzEjSpvdpX/2mmdqneJYXY3cvRR31ItnVEMF3DDACB8rW1D8OzNFXMRFrnpVte1srzsqJ8PEqbZ8E3XYSj9R5XN5bf+jOdzo36mx4p/olHQiy7c+7Eynqi27bUqTN+UhAo6xf0kVPgVWetshEX3QKSfTEuXR/Hais34nuXD3jZregE93WNo816DxsUV7bq6J/fdEZT5FdwMAZzLJ+SReBdLTIdjKQ3hfduT9nNZ0RtOiivqiVhShy1aaLMgJ4zixTQGX9TnSRQhEe23wl3fnnku5YgwihyFRJFykr6SKozh46OlPS7Sh1ootOi8SVdFE4RyFKuoi/iFRJt30tGlPSRcTJdEkH9GyvOg4hMhN0/LbF26bbIhfJEa0p6b4xKekiq76DsQvVH7ropHPdIk2itWX9ki7i6OgS/jjuQWba9YPurKqtZDtavgMQ3bET0RcdyAF2fOwn8lPW70RnMIAIjnZ/6KKNjCfZFqks65d0To017kiF3Zu+6D52c9THuVN6oaxvB4DuDTp9bbvbuVXHM8r6JV2UVznl1esPXT+8T7+0X9Yv6cZpvMat/33R8ZF+Y+zJa1kfHb/xHf/Mk/kyb/jfF50ckAdyQT7K+p3o5I3ckb+STg5LOnklt+S7pFsHJd16sW7If0m3jkq69WhdWp8lna7xbRa9HXTr3brHT+u8XZ+e6ESnV+iPkk7P0Dclnd6if0o6PUeflXTrnv4r6fSoMZR0epfMkQeRZ3T6mZ6mr9Hp36hPr5d0+p8dYA/6Q2df2Bn2hl0p65d09osdY8/Ytf7Q2Um6j13sVL+ks8PssfllX/uis/PsveM98EFZP+hwAfsOR8ATcAV80BcdTrFe4RLtl/VLOhwED8FF8FF/6PAW3AV/lfXNP/m1a0V/ocNzcB18B6eV9Us62wo3wo9wYF90eBQuhU/hzLJ+SYd34V74Fw7uRNf3oUh9gnzbeLZanB9zZMDW8tixY9PYruS+pPO+nLELujrt+iW9r/ptur8zz/Nrt98b3XayIzW2BvVf/fb7O9G1bzsMKEHXfrt+SY/2bSFpvy+6LStbrtF+WZ8n236/rSzbr9q3bVe2j65/6N6vvu04ETLnfHtqH9341Ne+YyTaV9/7e6O324/6tqMd5VB/mWWWyT/Y4/3oFme7/U7123S7Aba1o367f4BBWV/7tnsZvpKuX+jaR9e/aJ8hKenqt+nR/3b7bXrUL+miUO32gy4y0qm+7VqArKQ7q0xm1HcsKvqnfdu5JV19R66CHv3TvvolPeq36foX9dt0R4V6q1/SHcFgeBwRcJyjfD+69m0Ne7/6TdMk53adN2+PrxNdfe2r7whK1I/x90SP9tv03tp3bKbdflm/TafEbVX7vqXdftQPuvGh++BWfeXVQ2+3j+44g/pBd1VePXTXvujKq6d9V8/qRX3P8oOuvd7oygfdOIxHv6N+Sfcc7Zsn68o2vPerX9Lj/eYXPdpvmql/2awvuvkkF73Vb9PJO7lpmv9vP+jksHy/9q1L8lvSyTm6+uj6H+1bPyVd/TYdf9Rvt9+mq++YJh5qP+pbt+32e6uvfXqRfoj6xh/16ZuyvvbpH+9v0+k5+ho96rfbL+n0sPrxfnTjU5+9dcww6MaIHu17f7t+Saff0en7dvtRv6Srz86wd+xKp/bbdPXZSe2zS1G/3X4nerTfrq9/Ub8Tvd1+b3R2HJ3ddiSofH+b7v1wArwAN8AH5fiCHu9XH17RPvyhffU70Tu1367fF1378f7oHzl0LNFH3UGH59DJS7v9km586PAo3Aj/aT/ql/R2fbhV+3Bu1O+Lrn2YV/tlfbpxKFKfIN+UCP6hAAAQAElEQVTZLgpYco9hmGSiCYc8NErQX20o6Y5u9ES3eNEp7E71e6JjooU8EDolon8mrFN9dFuEJd25Vv3rRKcc0Sm0gdAJDkXZU/1OdEpE/whRSacQ0Z3LK+nONZZ0gu39jM9A6YRcfYaoU/2SDuB5PyXkWEonOoXZiQ5EqF/SKUaAYSB0Ssr7KZlO9Xui+6sE3o9OyZT10Sm8TnRGvhOdYQWIBkKnZLyfEepUH92Zy5LuWxAKpxOd8uxEBxz0rxOdEUcHCPpD922C91OwdivK+r3RGUF0yrJT/Z7ogB3A0IneNE3qRGfA9K9ppqX7IN370TkqTdN/OiOoPmNU1gcoeqIDviXds/czEgOhOwrjPeqrV9bvRPce5fVb/8v66MbTid40TepENx/43jT9p+O7948ZMyZ1qt8T3TzqHzr5Keujk6dOdPLZiU7eyf1A6OTe+62TTvV7olvH1iu69VnW70S3XvXP+i/p9Aw6vdEfOj3j/fQa/VXW741OP6LTh53q90Snz+nlTnR6vhOdXta/kk7Pez86+zEQOvukPnvUqX5PdPaQXexEZyc70dlF/Svp7Dh7js4+94fOnns/+9+pfm90OAQdrmjXh2vgm5IOh+hfJzr8hA4PDYQOz8FlPdXvRIfL9A8O7IkOX5Z0OFD/1C/p8Cs6PDoQOjwMF/dUH53uHYo0x1C8tL6zcqByoHKgcqByoHKgcqByoHIgpVSZMEgcqCB/kBhbm60cqByoHKgcqByoHKgcqByoHBgqDlSQP1Scr++dORyorVQOVA5UDlQOVA5UDlQOVA5Mx4EK8qdjSc2oHKgcqByoHBjpHKj9rxyoHKgcmN05UEH+7C4BdfyVA5UDlQOVA5UDlQOVA7MHB2arUVaQP1tNdx1s5UDlQOVA5UDlQOVA5UDlwOzAgQryZ4dZrmOcORyorVQOVA5UDlQOVA5UDlQOjBAOVJA/QiaqdrNyoHKgcqByYHhyoPaqcqByoHJgOHJgyEH+U089lW677bZ0/fXXpzvvvHPIeXTHHXfkvtx9993p2Wef7bE/DzzwQPrd7343Hf3hhx9Ov/3tb9PTTz89HU3G9773vXT88ce7Tf/5z3/yu2699db06KOP5rzyn5tuuimXwZtnnnkmk//3v/8ldfDsxhtvzHkz8s/jjz+e2/7FL36RHnzwwY5NmB/v8C7vVOjJJ5/MY5f3s5/9TFb6wx/+kD772c8OiznMHZrJ//z73/9OxnrDDTekJ554osfWzZG5Ur5dCB9vvvnm9Nhjj7Wzu+/V2XPPPfOz9n/zm9/kuSnbyQW6/iGfP/nJT5JEHrqypvlfe//85z+nyevvgzH86U9/yu//y1/+0mM18h8yUPbT+9H085vf/Ga69NJLexx7jy8YAYQ2n/Ctpy4/9NBD6fe///10ZOveuiIf0xG7Mn70ox+lI444ousupSlTpuQ5+eUvf5k6zblC1jK+02PtNu+5554sK2jmRtmBJnKpPn1Ad/VU/+c//3nu5y233DJdkT//+c/p73//e9YTn/vc59If//jH6cqM9Axr3JziVU/zFGPEi1hjylrP6kUy51E2rmzMCSeckMiBPGtM+cmTJ3e0WY888kjWXcp0ai/qz6i+uP/++/N801m9rQE2xvjowZBNvGLj/vvf/xpKuu6669IXvvCFVOqTTBzh/+Cv8VujbGhPw0GzTugMZe69997MX/Mn4Re+obUTfbz99tvnLHrF+lMeVsmZxT/e8eMf/zh1ak/9kGE4oaja5yP8Qx68/69//Wuv5a0B5egomEsik/pHnqyLr3/96+kHP/hBCrnptcHhRRzy3gw5yL/66qvT+PHjk18e22WXXZKJHiqu/OpXv0qf+MQn0hVXXJGBeE+KhtB9+ctfTgcccMB0XVX/Ix/5SKJYS+Lll1+evvvd76a99torWXiM9zXXXJMIMD5Y3O06+qEOkPSpT30qKz6Lh6MAULsecsghyUJt1+vv/cknn5zOPvvsdNFFF6Vvf/vbHYEDEPO1r30t6SdlYAH++te/znXk7b777kk//CLhmmuumT7/+c8nC7S/fRgJ5QCaM888M/PovPPOS/jRU7/xa6+u+QXO2mV++MMfpre+9a2Jom/nu6fkzjnnnLTPPvtkI60s+cLfo446Kt13332KdSflL7744nTVVVelE088MV1wwQXdNDfW0Ac/+MFEdjwPNIXDpv5JJ52UHbqyDWDBe/Vx0qRJ3eOilC+77LL06U9/On3nO9/JsmBtA50cpLKdkfx8++23ZwBuTQOsk7tAVqfxMMjmd//995+O/JnPfCa9//3vT4BbSfz+97+fzjrrrPShD30oO+HHHXdcXofW6yWXXJJK46s83qPh/1133ZWbVO7aa6/Njpb5MheZMMB/Tj/99HTGGWck7Zt7oK1swto3Vu/hFNMXUQZ/dtxxxyyvfnF2rbXWSuRrRp2OaHe4Xa+88sr0la98JeuLj33sYz12z7rm2JMdhehXdgDvzCO5APDQ2on+8Wu4fqH0b3/7W/IOdQD/Trw0V2TU/B199NHtpnI/oz690p6vaQr28vDxj388XXjhhem0007r0RYBnOwi3tADHEbgb+LEiek973lPt7PnF0wXXHDBRL91WhO9dGNYk/7xj3+kL37xi9n+n3vuub3qZs7b3nvvndhZgzJ35ley9tkJGAQtkjXNltMRcATdG+tQUBHeiLKunGs2n7yhmxf5EntHTuge76S/5A8kCQTAF7DBl770pRSObNmGd3z4wx/Oeo2+In8//elP03vf+96kPt3lF2v9Cq18DmLZRn3unQNz9E4efKrFTDlst912ySSGYA/+m6d/A8DM4TjmmGOSn0VnzKYvlRIP1e5D0zTTkC1ePxs+TeZzD4ATgGjx+Jloi8pipCAnTJiQPXURkeeK54ufEz/00EMToOfnvik9nixnZNy4cUlbW221VTa6ucIA/qFIgEkg8eCDD85RPkq+bEIE1nv0E6DXZxEGSkjeBz7wgXTqqadmp0af/Aw7Y1O2M5KfKRYGiTJilPDOfHYaE4Xpp8HbNAqOkWuapp2d7zltlLifvffT15Qa5btV17zi76KLLpoNaC783D9zzTVX2nzzzZO+mDtG8zlSjnRQzKURCHpfV/0h96uvvnr65Cc/mR0TYyrriUa9853vTPrIeL/2ta/NRbwbECQXxx57bFp//fXTy172svSOd7wjjTa5sC623HLLRF+sttpqOTiQmVD8A9wCvHPMMcc0FM57T/oCWAPOgOD5558/cRoZX/wG/kTZyGS7QeXMBX2B5xGkADAAJwEI9TfddNN2tX7d03kiafpjHXBm7ViWlRltfPEezm7TTJV5kVpyTr6iznrrrZfe/e53ZzmOvNFwBU7e9773JYEZc8T56jSu888/P9HrQXv5y1+ejjzyyLymdtppp7T11lunpZdeOsj5et1112X7g2/0AGdu+eWXz3U22GCDxGnMBVv/CMAcfvjhuW12J0jmatKkSUlAwHytscYaqWmmzleU6ev6rW99Kwef9GO33XZL7EJZh13h9Ky99trZltENgBv7Ro+V5dk0zgsdU9JG6jO9KAAisGf8dLYxdhoPZ8zcBo0uNj+SNUOXLrTQQkHOtpdT+KY3vSktscQSOSjEtnOo1dGWgFB3ha6bueeeO6+9o7qCSMqxT13Z+X/rF/2jH/1olquXvOQlOb+//8AHAkRbbLFFxiirrrpq4oCU9eEhawM/9NPY6MhXvvKVaeWVV56m+FJLLZXYm06B1WkK1ofpODCt1ZmOPLgZoj4AvsVP+b/iFa9IM2KAZkYvGS3AjSAxvBRjWyHGOwBxxu5tb3vbNAqaQgLu1l133Sg6zZXHvOGGGyaKDcH2lAin+0UWWSR5v+io50jKzzPPPIlCYAAWW2yx/E4GXJSf0UejXKNOf69AB77rj/frO++7XR+4/UjXroToCscCTbmNNtooiSR5Fo3DL46LZ6AOqBUp8DzSE7k0t8sss0yiWOebb75krJ0ADmPJaVp44YW7h80pcnwBCFa3m/DcjYgWOgOLj8qLEK+44oq5hDkSDckPz/1j/pdbbrlEIQIT+++//3OUlGWlaZq07LLLducN5IYT6f2rrLJK0h8RFGNlqKMdUSFKm/I23sh31RfjF2XkuMuTOM1kWSTK80hPok62x3fYYYc8D8ZnK74cF8MuorbtttsmayfoeIzX1njkta925zh9YWA5lmH4xowZkw25HZt2nXXWWSfNO++8GQSSK+APncGlr/bbbz+PM5SAADLn3ZJ+iTy3GzMmoIFOKHkBhDDioTeiHt0C7AiaRN5IvtLHHHVOn/k2Zrq6HJPINxrwEjTzZU17FvCiM9pOgHyO8tvf/vZkLXnmUAdQVt66lN9OwD+gx+nmgKBZz+SC/ic35Ni6RhtIsquwySabZLkjn5wastJugyPBHnAy8STo5Mj8t8u6pyfHjh2bj+7Qh/JGchJwYT+BdeCZw01G6JByXPSENYQ3QaNL456+NVfx7MqZh6XYjKZp8ikBeSFbdBN9omykJZdcMklN02THkFOJxhbZ9YNx4A3rVv5Akh2+f/3rXwmGapomsROCt/oU7djRti7IMfnk0NrZQgfy1XXfTiussEIS6RcsaOfX+945MKQg32Q6+yUKwPMk+IB2710eHCoFx8mI1gG60oiKRlFqce4tyhJYBq43BwVQolDV0Q5vF1jzzDADjhaq53aixG2nUggcDMALeAKkLELeMuFv1+nPvQWljSgL6HMa4tnV/AApDIPIURkNUIYxb4MHShwgxk/0kZ6Mn4yO6QJWxsIIm6+2QwasAeK2ndGViyRSoTwDHnntq7rAoiisfFFfhoCh82yOGQmK03MkRuNd73pXPubBgFo7zjCaI/lRbqBX4zV3IatN0yRjJ6/RlijcKaeckr87sRPnGJI6AA4ZYjCACOvC+dqoB0zoazyP5Ct+tAErHllT7TE5smcXhCPWzscjQIcD1c5v33P6ATN55A9/433WGHkRoUdvp2984xs5Mgs4M7Ro9IV5VY9eMzfyB5L02TujjnboMUY38sgqp1h0166fSDUaWSXTQIjnMgGHdFuZPxKf8X3xxRfv7jogVep1a4Ce6ARkVMRX/AbSmmbayLqIsICPctYX/tPdnunzpmnyNw+eI+Gt3Rdgm53SvjmxjkWF9dn7BLW0F/X6c6VzzHuUtfNQHjEC5jgCAKTjJI7l6kPU6XQ1dgCf3Haij6Q848DX0OnWoflv84BTbmfP0bymmXbOY6zkxrpjlyPPVTtkjJ3xbD7NbTzDGewHfYQeie3ebLPNEltm51A/4Q2Ol9156xjWIRtRpz9X7dBZMI3yxqs/7JhniRw2TZN3q0Je2JK+5lvf9FUbNU3HgY4ZQwry9cjkA7GAvrNfhFz+rE48Ssdh4r2ENBZl5Dk7D7SJljhSYdHZHnW8gefrPLotdufjKGPAK+q2r4Se0QuhB/QstSGx/wAAEABJREFUDNGxdjn3ovQiLkAFAMeI2qon7LbVbJ8DmMpKlIV+2v4qE4WsjITvxuhe0hc8cB8pjId3AWthtINuN0KdEqyYR+1FuZF8bZom754Yk3FQ1ubLuD1LlI4ItaiHIy7mxzMletBBB+WPpMkGY+t8omiXep0SsGiOzSM6mQT2yIvnSCKFjIJvM7TtOAVnSzkRO7sw5MaOTdTRn1ImPDtmFuNrmiZHCeP96qJp173kPtYG5xxIIwvkC5AfN25ccsxIxNEOljqRAnjG80i94kFbxvHImmqPh46gLwQG6AUBDNFM69iZWd95iPIzyoBQu712O+Z/nq4dvaCTD7ol5qBdllEG1hhY8ogG/Lly1smj93qOpG/koEwMcZQBGNoyQXfQY0F3jffYBQIS6ETrhe4QHQTwOC9kpr1rqF1yro2RnjjH7bEAV/La48J/cyQqC/jiBV5FGeAdT0RgI6/TNeY/5MKc4HfbGVPPvOA9u+BKP3EQ7fY5XknP250ViCJb6tBpvhUoZcJze3yi0kCmOhLASPbcRyLfQD4nlfMnn+107S1xbPsCfb3VHy40gF5fzKkrHpsn69qzJNJNX9AL5MLOFpwRc6sMfR7889xTIm/whKQMcE9ftd8nXwQddnDEyokKkXbBB8FKDuhb3vKWxIFr2xBySgbKpO8xPuO1AxXvN1738rxXEhDhrJA/Ns+7yAS9ht5bUrc3eqVNy4EhB/m6w1jYghKRZszkzepE4RHSACGuPiRt9wPYPfDAA5MtdouAV+14DkH1oYh80Sr5IhptoQbg49l4bX2HsFKSlCWPu/2+uLfDYGvXQtFHC40i9w2Bc3PO8FnIyov027bVlzJ5hzKSyHJ40ACKvrS3BZVpJ/xp13d8wF8L2XnnndvFRt09fho7XuE/o0MRidDFYEXWKF9REYrTM1l2PtLHVuaBLAFKjuVQwlG3aZp8DCieGWTzDRzI4xCQqZ7WhTPhIocUq/Oe3qUPQLZ+o2lHolTRy7TyyitnR0YZ4/V9QERvgAFy225HuUjeC9QpA5SQi6AZB1o8j6ZrzD/+GBcD6XiN+0h0g+Nu+G0Oza17+Qyre2uOPJirthG2vukJbeGt3bEAV8AchwJ/0cvkPRzvppk+IkiHRbtRT9/0pUzAX5Shz+gp4IQOoi/owKaZ/h3qiMSGPhOocAxS+3QjWk99V3ckJ3qaLMRcAS6OL7XHZKfNOWj8wAdp7Nix3UXs7JlDoLg787kboOu52wTk0yuxM0MWHQkhT1GmfXX0j7yxFfRDgGjyRbdbv9azOoCXfutjmdrywzmYPHmyKvmvxdEb5ZonO7Errl36UcqVZoN/rFX6E6YI+4GHsT6wwPzTCfgpUIJmrkInCBDZzTcXyreTMvBF5OGt+TTP8tSFN7zTc5mAerLGvjlaI0ipDGfNeiUXniX91Icy0QVshzLqkS+BSc+cVvqsrU/c0ycCZurpr7bJojo1zTwODCnI95GRyeYpigBQBEMVyQfAgGbRLgZM9A1wb7MaMLYIJYaRIibsQJE8SVTTgmG0CW/Ut8hFSuKZohfF8yzCSjFbnJ7LxGgA1LzdpmnyhzY8fYvSQmEkLCz1vJOS1ZcyWbDKSKKsIooUh7O1FqE+o3VK+OFDOjQRSY7G+PHjc9TX1q8PwtD0idHRJ88jPTVNkxhSwIqyMk7RSEA6xmbuGHf8BqIoOI4WMCNPQqfsKXCGLuqaL/XJvjwRF981eI9nMggkue+UHJFRh3JmxL3Ltx74T14B+6gHmKKXya5AKFcGwxjIBmfGdwZ77LFH8o5op30F+OxaOTaGJ+SQY8PYOz+s/1FexBL4jOeRfOV0+WDMzg3d5Yw1h6s9JuspeB16wS4H0BP5eG/+GUm8j/qMvGhqPHMg8dmzCDsnEPD3XCY0Ozvmo6TtuuuuydGNdr6+bbTRRqlMbX0kkGF3yPc21j8QC4S022nf+2DfMa6mafLH19E2HQQ0tOUSqGDg2/VH6j2wRS/auTAHdlp9KN0eD0c/+CEQgBftuWQPrJMA3O26HElR38jzca+dPM8+AHee232nxP5EVJ3c2UXwQTe9JrpPZ8U7gS46K/rZvrZ1wcSJE5MPeAFJkX9/KILz0X4/ffTVr341OwH4AXDST+0y5T27ZA2EXirpI+mZjrd+rBt2xDqmW/E7xgEzWK/4TP9bexy20AnWPH3Sth1R13yYN3Mgj/2RQk7s7HKy0TolcgCHsQ/0NQxkV9YaNgfsSNQjp/pYJnjIOJXTljVud1cU35z71iDGogw55sxaJ2wF24Af7TLKlYm8cj7L/PrcMweGFOTbWjbZol3bbLNN/us6gErP3R1cirOzwK6+SASVN2yLG8jldUYPCD+hjOe4Av7yS+Vku5IxYxyVtWAd9WH0Ca4PFjkazsTZLWBM8QcdyGKc1QEu9AcoBPpt81Ou2hxosm3ufKQ/1whIUjQUkfdS+o5xeL8EsAY/9NvHL3iExlkxZu8HOhlsitzzaEicOB+3AXUUoO1247L9LZ+S8ixRtgAbXnqOZN4oJ9fIcxVdYQTJBoVIUVKIIoB4i8/WCEcKvyl7c44mmUNbrdqKpA/4T9FH3kCuDBJFLcIDXHJIRWXsVgAUInfWivdzFslDtK9/FLezvgAHQIsGsDJwxuB5NCS7j8YH1HEEOXAi3XbXODocoBgnvWAe4zmujDbAVeoLIM/uEcCuLANsixzPrTdHHAFKf6HCXDDKwCS6KLG50x91DzvssCRfCudR/kCTI2j0BGBnB49OED0GIHwo6viYd0gilkBK+Q4OBWAb+XSf6LT8yBvpV+uEk+xPHVrXxiOQ4rsm4NVzJKBJimdXcy5Q475MbANAZPcVDSAkf3huLQJfwB5HzvFKdgJNArboEfXMge83HJP1DY12ZtT2+j6HDFjf/tqXQI/dZX2wBtgTu1d0gr47vqUPeEG/kG15bJ58gSs6RrChL9Cn/EhI1oLx+MAUyHdsT7/ZXvw3554loB2w5vx7ltgYawTNczuxNcrjGf0jmGSdOq5n3ukIMsZ5ZK84e3QImmS3mK7WJp3hqJ36nDfOmfyBJvqBbmBLyCX8Y+fIX07ybmse1pGsC2PTF+9xxFGfOSlkVp7EKaBXymAKWk09c2CQQH7PL2xTgCEfA0Xqy7tv1x2se5EI/WEoGV6RSR/PxXO8d+ONN0485HiOK+MsumehRZ6rsQJvjLPIvDyKz7sAApF+eRaXCChATQmj+yBFVAVdohSd7UZzFk7ejCRAThveE0ZFP+PZQkWXJkyYkI904AcFLi+ShQjEOM9ngXJCZqQ/w7kOBe28JNAtGqmvQJ6ztfEsD2gnQ4C750iUGkexBN5AvSg4UO+v+ChPaYvQ4a/3ygMEgSzOFCWHJvkuA72dOBKMrUhqO7+/9wyJOdS+SL56DI5598yJ41igO3Nfjt8HW2j+oou6It34BqB4Hk3JmIwVoDP3TdPkPz0azzFWIEqkNZ7jKhpu/QsuRJ6r9c/4mQdgXx6g4F3O6nIu5Pmo1lww4gADOuMuAIAuea986fnoWGtcG+Y39BG5jmfOILq07777dtz9cSbcuPRLpNv3AAEw5I2GhCehnzlCxsTBt9sTz/IkfLKm3Ecyj3FfXseNG5dETelooBodSMRzf3OfPjFP+MzemG80CXgjo+pI1rH1C3QC4vJmJIlAa987tQ+Yc3TiWfDK90LKAG/xDrxwJlw+sEfm0Tyzh3Y8PI+WRB/jN+DMQTcujnn7WZ75gxPoBs8S5yBsged2YpPpWnwTpEPjxIUMAu7yzBN7RX4E95SX2Ar0SHSHfNgi+hm0/l7hH/hIO96lnt0hNjSe4Ri7BTAD3KOMBFupJ7Gl8uw20Wuwhuea+s+BIQX5/e/m6CgZHm1EgkfHqKaOQsTK9hxwY/FOza3/9ocDFDJDzQj3p/xIKiPyYrcBoGBk0kjq/BD3VfSLIzkaDZuoI30B/AW4G2J2j5jX0xWi9ZykEdPpfnbUzo7dB1F/gYV+VpvtiwnOCUT6Lmu0McPHx3Yg7ZaONsdvVsxVnyCfcba9YuuGMrbFJqrr7JYt+KADrqJJtn54ajwz22+8dcc+SjpP3xYzQMiT5ck7juJPQYkgl3RRRMcmnCmk5GxROxvtPKMIaZtO2B1xER0FoByrEO1yhMF58ZIuKgJgWSgUDMMqKgK4iiiUdFEP59N5zSIlohN+7MOWmS2qkm7b1JamjzEJqS1THrxz9qI+bTqPVQTGRzi2p0R+RbtEd3jgPt4p6SJEvHbHAUT9/ClHYNu2nLN9JV0kzdETRsKxIxFi0URRYt8jlHTb8LZTbanZhXDkQvTJ0Qy7AegiFHjo+IGIngiV7wZEpUX6HPMQmWCc2nRRCzsVjhzZTbAtaeuX8bddV9Jt+TEAjjc5UiSKLOLgGI1z6SVd9MBfPwKURMNFlUVF/KiGnYeSbtvU9rcIrXkyHtEHf/FAJLWki3SIRth9MbeOtdiSFIF1hrmkO67F2RM98ZEqQyZCoY/GU9JFeP1VBX9FRzTQVraojW9XRP7bdNvtjpeJGPm+xBEB52Md73GkpqQ7iykKJMpu217Eh0K1U2CrvaTbeRI9tPPgiJ2tdxEb0SFRu5LuWxvti+SRBUcJHDOwZSxCg44mmmfnQrTbWVVb+I68OAJiV8LRB1Ghku5omF0Hsmf3jBPheICjKo6olHS7bI4SAM8iZbavRR5FMkW7S7q/LS0qKdJkx8T3N47siXT5caE23fa73RtHrkSiGFsg1m6g71dKuu+RABqRV8ebHMURqeMU2eIOurYk0W5RXNvxfvTGkRmGz9EsTlRJ9ywS1jRNcvWM/4yKq2f5TTOVrj3tan9cV7QYXbva956Srr/6rf/Gob/6rf/GUdLxwzjwB5/ofPqcTsOnko7f+I7/+Izf+I7/5qGkm0/zan7No/k0r+bXPLfp5IS8kBvyQ07IC7khPyWdHJJHckk+ySF5JJfks6Tb5SDP5Jp8k3PyTu7Jf0m3jqwn68r6so6sJ+vK+irp+Gie6Hrryzq1Xq1b69c6tp6ta3R6gD6gF+gHeoA+oBfoh5JOz9A39A79Qs/QN/QO/VPS6TH6jF6jv4Axu9/0Gv1W0ulJ+pLepB/pSfqS3qTD2VLygU7/0sP0Mb1M/9LD9DG9XNLpd3qevqdT6Xd6nr6n90s6+0HvsifsCvuhD+wJu1LS2Sd2ir1it9gndoq9YrdKOvtH97GH7B77xw6yh+xiSWdf2Vn2ll1lX9lZ9pbdLensNzvOnluzYcdd2fU2nV0nM3ACvAAXwAdwArwAN7TpcAf8AYfAI3AH/GHNwiMl3a4i/ALnwDtwDXwD58A7JR1ugp/gKHgKboKf4CgyEHSYkC6CEfUPToM14DL4DE6D1+A2+C3ocB/8BwfCg3Af/AcHwoMlHa6EL+kleBKuhC/hTHizpJNR+BWOhVfhVn2FY/W1pNO9Q5H6BPkiLkC7M+EB2m0XA7NJ3J8AABAASURBVO22jIMO9BusQXMKDDKcAoMv6ZgYTgHmcQowE1M5BSUdE02WScM8k4SZmGry2nSTQwhMlkkz+SaJMBCKkk64CAFhI3Qmn5ARBkJR0gkXISZMhIrwEjLCTKhLusVBiC0Wi4bwWiSEmVCXdIvDIrRYLBqLzyKxGC3Kkm7xWoQWu0Vt8VnkFqNFWdItbkrEYrdoKA+LnDKhVEo65USJUFaUEuVBSYVT0KZTepQTJUhZUUqUHyVFGZZ0So9ypQQpW0qV8qNkKcOSTmlTrpQ4ZUupUt6ULGVe0iltxoESZywYBcqbkaDMSzqjwzgwQowFo8D4MBKMUUlndBg3RogxY9QYH0aOMSrpjCbjxYgydpwCxpORY0xLOqPJODOijDGjzHgy0oxpm87YM+qMMyPPGDPKjD8jXdIZe0YfuAACgAmggvEHMko6sAK0AA9ADDABVAAvQEZJB1aAFuAIiAGGgCLgBUgq6cAW0AUcAWHAEFAEfAFJJR3YArqAOyAMmAPqgC8gr6QDi0AjcAckAnNAHfAI5JV0YBFoBKqARGAUaAcegfY2HQgFdoFe4BTIBUaBUuC3pDPIwC7QC1wDucA0UM0pKOlAdIBy4BroBqaBaqC9pHsG1oFBV88MJSPj6ll+0LWn3aaZCvrRtat97ynp+qvf+m8c+qvf+m8cJR0/8AV/8Ak/8AV/8Kmk4ze+4z8+4ze+4795KOnm07yaX/NoPs2r+TXPbTo5IS/khvyQE/JCbshPSSeH5JFckk9ySB7JJfks6eScvJN78k/OyTu5J/8l3Tqynqwr68s6sp6sK+urpFun1qt1a/1ap9ardWv9lnR6gD6gF+gHeoA+oBfoh5JOz9A39A79Qs/QN/QO/VPS6TH6jF6jv+gx+oxeo99KOj1JX9Kb9CM9SV/Sm/Rnm07/0sP0Mb1M/9LD9DG9XNLpd3qevqf36Xd6nr6n90s6+8GOsCfsCvvBjrAn7EpJZ5/YKfaK3WKf2Cn2it0q6ewfO8gesovsHzvIHrKLJZ19ZWfZW3ZV0I2dZW/Z3ZLOfrPj7Dm7zX6z4+w5u17S4QM4AV6AC+ADOAFegBvadLgD/oBD4BG4A/6AQ+CRkg7XwDdwDrwD18A3cA68U9LhJvgJjoKn4Cb4CY6Cp0o6XAafwWnwGlwGn8Fp8FpJh/vgPzgQHoT74D84EB4s6XAlfAlnwpNwJXwJZ8KbJR1uFdSGY+FVuBV+hWPh2ZJO9w5F6hPkD0WnBvbOWrpyoHKgcqByoHKgcqByoHKgcqByoM2BCvLb3Kj3lQOVA6OHA3UklQOVA5UDlQOVA7MxByrIn40nvw69cqByoHKgcqByYHbjQB1v5cDswoEK8meXma7jrByoHKgcqByoHKgcqByoHJhtOFBB/oCmuhauHKgcqByoHKgcqByoHKgcqBwY/hyoIH/4z1HtYeVA5cBw50DtX+VA5UDlQOVA5cAw40AF+cNsQmp3KgcqByoHKgcqByoHRgcH6igqB4aSAxXkDyX367srByoHKgcqByoHKgcqByoHKgcGgQMV5A8CU3tr0o+X+NGT3spMpQ2ff/1giR9Q8cMsw6dXo68nfgxktI3Kjy35cRo/SjLaxjYrxoN/fkRnVrxrVr7Dj/T40Sfjm5XvHS3v8oNTd95552gZTvc4/FATfdGdUW8GxAE/CjWgCiOgsB+s8wNpfhhsBHR32HVx2IB8IMAvyPkFtqHkkl829AuOft3MrzH21Beg168ulnS/BufXDnsCxH7Fzq/GqeeXD73Lr8f55TZ5ZfKrdMr4RTrC7hco/bKevEh+XQ7/yrqdnv3CoV8fVrcnI+FX3PRJGb966Fcb/QqcX93r1ObskOfXRv2Co1+x9CuIPY3ZL3T6dT+/6hll/BK0X2Dsid/KmV8OoHu/gugXL/Hfe+WVya/3+cVQv+rpV6eD7pct1TN/fq0w8gdyJWd+4VI7fqmxp7rkXxl88SuqylHG8iRrQZ5fHfUL2H790PNoSn7p01j94qRx9jQ2v3LpV2NLul9pVN+viJc0z36V1q88ug/e7rzzzqknWdp2222T9j71qU+lkCeA+sADD8z5aNqakXT//ffnNrbeeut0ww039NgEPe49e+yxRwLi//KXv6Ttttsu15W/5ZZbph//+MfpxhtvTCeccEKP7YxUAjmnk431rrvu6nUYF1xwQWqvMffqbbHFFumWW27pWPd3v/tdOu+889KLXvSiTD/33HMzb0855ZT09NNP57z2P/QPPa5dzkGb5p6tQ7viiivyfMkbSPrJT36S33/IIYek3tbArbfemjbeeOO0yy67pIceeqj7FeSTHOgn23j++een6667rps+Wm4uu+yyPP4ddtghCZz1NC60k08+OdEZytDjO+20U+bxmWeeKatjOuKII9JLXvKSTIMHQgY50zmz+Mc76BcYo40/jjrqqPyugw46KPWEY4qmpnuke+AgctWTblOJnVIGLgrnjmyEbUFjb+keDgz8ol5N/efAsAD5DMGRRx6Zvv3tbycAo//dn7klTz311ASYiLRfeeWVidLp9AYeJWegVETGQZlqo3QQ0N73vvclC2iNNdZIf/rTn9L222+fgEF1LN5yQe25554J2ALoKFILF+B+4QtfmOupe+GFF6YHHnggLbHEEp26Ol3ehz70obTBBhuks846K02YMCHpV1kIyGSUtX/55ZenRRddNFFMxsTBKMuP9mcODjB20kknpeOOOy5xkjrxDR9+8IMfJGAODz1TSm984xuT+uX8okuU8GOPPZaBECMNEDN2+P/BD34wiW4pF4mDSQ7IKeV/zDHHZBJjvcwyy6Tvfe97iTMY+Zk4gH+MYfLkyYnRv+OOO9JFF100XW0gkxzrI+O10UYbJYbi6KOPzrJJthiPpmnSOuusk1ZdddV06aWXTtfOSM6wLidNmpSuuuqqhE/msdN4pkyZksEsnRJ08kMuPvGJT3TzK2iu6AcffHCiZ9Zff/10VxdYpD/we5999klf+tKXpgMKHMNjjz02ifwD1rfffrumEiBJHq6++uq08sorJ2AsEwbwj/5ss802uT8cSXpBn8omrr/++vSBD3wgj4k+bZom3XPPPXnd6LukD69+9avTfvvtl8jZNddcUzYzop85fGSebl5++eU76lgDND8Ar7nyzJ6YGzzaddddE30jaIAWyTywCfQCHcEukAV1OIHhbEd5V44oR8Da3LnLQZQnkYnXve51SYBBfc5X0zRI/U76s/vuu6dzzjknvepVr0rHH398x7qnn3562nDDDbONV3aBBRboLkff6R9g+NKXvjTLDyeQPukuNMJvzA09evHFFyc6+/DDD+9xRJdcckk68cQTux0hjh/cYD0B3m09Eo3Qra985SvTO9/5zgzMOU0cKvMKbMMbUdaVbl988cUTXW9etC2f7K699trJ+qbT6BP5A01sESf0+9//frrllluyjizb+P3vf58EqvSRzlpttdVyEXZypZVWyjoEbbHFFkubbLJJxjjK5UL1n35zYMhBPuD4ne98JzGW/e71IBQEmihPHuR8882XRKsA4fJVlK4o1tixY9OYMWO6ybxPylT97szWzbXXXpte8IIXJAtINmHlxbpfZZVVEgAvouE5EqW81FJLpTnmmCNZkMD9vPPOm17/+tdHkeSIB+PbndHLjUVl8Yq0AYPeS+G2q1hgjC7v2X2bJjLHMIs0tPNH8z0jdtNNN6WFFlooz8FrXvOatOyyyybGtRz3H/7wh8TIbr755t0kskS223PWTey6EfkCEl/72tcmZTm5QDwF3UVOjAEj6D6SeeMgzj333ElacsklM2mFFVZIlDzlynHkjKYB/ke+gUE7FvPPP38SAT7ttNOmcb6tWTJCPuz6xCtEnDjqninlpmmSPM9vetObMhAoZRxtJCZOGXAkYm5NirwynOVYyI+I9YILLpgYK3RzTteQJW2IruIpWiQOg3dwyOWRka222sptsnY5g2UQAkBceumlU9M02TEnBypoO0AV2Z1zzjllDyjRX3SR/rhqMyKN0RCHFsibZ555Escm8sllyKidWk4zoI/+0Y9+NNFz6noe6YlupGPpAMGRfffdN9nZK8eFDwCWgEHQgFxAh+6lbyIqG3RXIMwcsz+eATjOnXttiea6byeOpPbMO6cDjZ4R8OFACgYIJFj7aANJEydOzLZSnzj1nNKHH354miYAXAEJzgjd1qbTI2TJWohK+Oae3rR+3I/kZK2aa3P74he/ONHf9DYnrxyXPHPRthfjx4/PDpS5e/vb356UadcT5cbXVVddNc0111xZ58pbffXVczEBOs5Ffnjun426gjJsBEyiT6Gb9JHs0hdwhV2j56r0+8Lu6KNdZpiFTRJoaMsXHchWoAsCtBuHu8x7BMqChg+cnTI/6PXamQNDDvIJEY+R59a5i7Mml6dL4CwkbxQlYXzdtxPPsmmabrCORmCBO5G5hRdeWNZ0icIS3UFQHiAMxQbcicZT/OiRFllkkexpi+jpm7YBfoAhyti2tWDjubcro4zPUR9Y4LCkNG0ti5HjxWDYlQiq/lAcQEvkjfYrBQ1Mje1y6oAX/Keg77777mmGLhrHYFFC0xD6eMBr25TLLbdcLkmBUdChdIEjSh/gywW6/vF+YBlQPOCAAzKw78pOa665pktOZKy/cpErPPeP8QLu3itLPxjhdlSNQ8wIiObvtttuSRROWZF9V8m41lprrRQGm8PAwRVxQh/pCY+MgXPmam2214o8SWQbaOPkeJbUNaeAlyN7+++/fz6+ghbJGgv9gJdkUKQOfZ4uEM3BKo2jPngfsE+GYg5teeubfODO+7QzkGSugfumaRJdRBfQYYxxtEN/mXPGWzDAmkDTL1fp7LPP7pZXz+TLLiQ74HmkJ/NJ5iVjsa5LOwLkcpoccwPKlJPwjk4WpTePQDtgjhbJkRzRT88cIwA6ZNA76RIBJ/RI9IXIqh2gvffeO2eTB3NKRkSGAX55mTiAfyZNmtStd8iz5FhOuwkAn8NJ7tkUwTB0wJTdtWvsORKecAw5g2Q/8kfqNdYqBxuoNh/0Mx3eHpM1wJkv1ye9GeU4ggIo8exKv9DPoR+sQ+s81p0rjOWdykvmwy4aB9CxHYBcPifeVVKeI+B+IEkfyeDLXvayXI1cknngP2d0/eNZPkwieAhvdGXn/9kguISskJGc2fXPmDFjEqfU7kDXY/2/nxyYo5/lBq0YY8DTDIEYtBf10TAQBYhEMdE5SjSeXRk10fBYEPIkgmrrkxfsuVMC3ixuNItHstA8U+QUm4XhuZ0oBu/jXEhtGoNCwVvE7fye7i0sCyXoxivyFM+uxi0yBEAyvrZi5UeiSEpwEbTReAVigFzAyviapknmi7x4lswbQy6a63kgidyQDcBJPfPBEMQzB9D7KW30SIzpuHHjsrPpCFGpQBkWchPl+3s1XmOL96tn7O33i/w4Q2nXSiRWlMhuh7KRPDttqKzSAAAQAElEQVReEs+udqMYG/cjPeFHrF9jwSM8dx8JMAfWIwIf+daPed9rr73SYYcdljgAgDHjGGU4/RxKz+bfnMT7yAddUuonZUVUgQBOhGNb8ugQwNBRO0GBAelaDXQljl1bJugJOqyL1P0/3WA8dp84xc7QkqfuAl03vusp14mggzXQRR7x/9MF9GoMhDMsL55dAXHHUgBsz5E4TmTFXNuhBXaaZtrjM4C59adOzH/IhTlpmibR8+iRyAoHnJwJGAHWIq3kgiPhqKz5shPlnVGvP1dySkdFWfalXAeO3jgm5N3sChkU2aYLdtxxx6g6zdUuhratk2kII/AhxhDrx/ptmia1HRjyD9A6ytPTEG+77ba03nrr5Z2AdhnrkIyxS/LdwxPxTF7oq/b7lKMH7Bhbt3YU2R75kiABvcMR9TyQZLzq6oN6xutenmeJnJAJNkSgSKQftkLjcNoREhR1ZFleJGvGGojneu2bA0MK8m1rioADk3EkYd11102lh9v3MJ5/CYaG8Y2WeMdASTy72ormZVPcBJRSpJidzbVVSdnamjIugloqW21IlKI2YpwMKMVosaG3k+iZRUBBi2y0ac7xO/JDQQBdFlKZbJFGHR/wRXRNHu+fcncfqWmavOXHCBkr5RD9VIbRLpWF/NGaAC3GM44fUGDmi5KKMYvAALsirwy8yDZ5YMyjTH+v5gPPw4CTSXLF6LXb8CzyBwxQyFLQATx1nJGNPFcGtpQPzxR9KHjj5UAwFOqYb2Mnr54lZcKA2L1ytE1UCE0CINSz6+O5nYyt/TxS7/HfujNOY8AjusB9JCCXXCiLR3YxOAOMHeBF5zRNkzhrAH5PAIv8Ket92mbU6Rbr2XM70aUio47kxVp3Ntyatd7pNEe92nXoLHJQJmAsypEJ74xn+pEea5r/B6EBYsgKnRUyFXVEr8ePHz/NMUc0awpP3I/0RIfjc4xD9FpePLuSCY6OeXX8huMjT1Tc8UnOGPsR+ledTkkk1FoMBz90gDlulzcvK664Yj4vj9fkwjoE/uh5OkuAyhyEPAPhnIBSJjy3x6eMD43jfQCYXfB4duW0WBv6IRKr39aCnQVyZffceXU8Cb2nHpmN/ngeqQnPzFOMzfo1D9ZPjEnAjoNs7th8ep1eiDVPv1g/JeiN+u0rHQBPsFPy2W86yHx7joT3nAbffoj+x3rFc5F0MlMGEMmp8UjtBA/F+OSTbTrNu4xXX9gkzxLgL7l3NBTui6OddCMd4nsV+tJRZuUikd24r9e+OTCkIN/CF22UCLfu2pqiDNzPykRZUTbOWhNKQMnZtHYffJxC8UiAnA+VKFiLRJ5EQdviAqgoz6jPILeF0xZ6gHZKErhrA8eo56o/hJ1y9CxRphY+flHUzvh7f5lie1YdxzkoXH0G4njOzvihdUoAJr7w+IPunaWyCNpovFJEwDRlS4kxpK6OPcV4RRxEx/AeEKLwACuGM8r0dKX827JBQZrTOA7E8Jdy2G7LPDryEHOivAS46WsobnUAB30sk+1PRkAZ4F3UD2hniJzPN9ZYn8q0kzIMSjgUotfWEONBWdv9ivL60pOMR5mRchVRYgSvu+66BBwZZ/scrXH4QDV4LVJvrVl/5oshjTlmhO3IMWzqSdacMu4la1c59+YVH7XjuUz6Q45CX5hL82qunL9mTMlw1KOzop/tK2chyjjvaw2YawBfH8hp0MsrHWNMka9PtuRtzUdeXLUJFMTzSL6Se/NoHeAxsMQetMcksh189pGy4zkil+wBfaOsoI45a8uAfGuRjnEvcaZuvvlmt8lu4oQJE/J9p3+ALjIBuGmHzjKP7JK+Oise77fzgx79bF/bukAk3vvRvR9A1U77/XjiGxNlOD3slWMi9IE8dsj5b8d6AvhaJ3BA0/y/E9lucyTdA7dsgXVovNYf3YhXMQ4Rc2AW3QftHHF6HK/MjUAiBwl/olzUhQ3MqbrygHf62Br0DMuUukl+JACcLtGOPEFKoB9GoXPYGPkSOfWeMsFD+qYM3WRHEX4gvxwYH3gbC3qZyB9ZZwfbNP3nIHNaIt/ujvUVz/XaNweGFOQDR0CExJvTXV+SExL3szoxyqJXjqtQ0rF16kMQxrynSFt/+ulDM0owyhovw2e7yi6GBWyhAlwi9BaZL+TRfZhmAYq4Rn3lRAfjuT9XThXlKjnLzXA7g0f5cEosaGDFOyUL9+Mf/3h305SyBWvxd2cO+GbkVaCgGUfzIJIB9DtrSybwhxLuaVSUoT8JJ9pPtimpdlnKDWAUXZNP+QMFIlvmgEK3K4BOPhkAcoQm+asV5IDsiAgqy4igiQy1AYH2+0qMPNkETozVURx/0QEQE3W0e0VObO96x8SJE5MoM8cADYiznjnE6M4WeydDos1tt93W46hIxseZ8/EjoMPpZ9QYZIDF+u40UPONX+TBHDnSw8i3y9oVAn4iz1/AAIS90zwA0NpxhM9cmHPntdHJpPnadNNNc3V6FrA89NBDE5ojkmGQc4F+/AMU2pnhKHIUGHHnpgF+x8X01fx6v+QvLpHjppkK0shv0zRJv9uvsx7oHyCgnT+S7wF3c2LdCN4AzAA8HQu49DQ2QAw/8c+uMaeQs9YuL9ptLiPPfNgxVIc+ciTGmqe7lYv20J2FZ9PInnnAc99pkEEOHL3WNFPnK9rv62rHiM6wBsifcZN73yfpg3tl6Cxl9Fc0ujegxs7Sl5xoQL+vPowEukAmW+BolL+cw+4Cr47UCRJar53GIRDgjx/Y4eEkm0d/CAFfo7y1LAHV8oBp7cMRytO9dAGeWv/adIICTQLqyRpnxNrmdPo+A03qqW/e1SkJbpJ5/SVb5l5Unp3kJNiRsOYD35B164RNZe+8U2JDyEy8g5PK8dHXyKvXvjkwpCC/3T2TSSmVW33tMoN9D/QygI6+MIgWCxAmYuWDKFHX6INFC3TFc1wZY0YvIquRD6BbbBaPPMCIERClFY1h5Cl0gu59FoqjEOhoIjTqqCtZtECF+4EkdZzDlGyXqsvz95EmRcER8E7pPe95TwI2lJGcm7MzQDl5nl0S/oisMFbOUYteGTuZcP4ZMPcsMUoUm7nzzACaP46cCD9FKj+S8toAkshH0zT5ryf5FsIcMAjkEIgX3SMDohtoElll+LVD9kSMRcXQtDEjc2XO/VUQ/T7qqKOSHS7tAyEMLzlhRLyDPOGL8aAxIowSmkSG0ETyxo0blwBDz6MhAb7WMCcLeBGZa5omAVHG2dYX+BV/jo5usJ4dtTN3jmWQpTZPOP10j+995Ntpsfbw1LldzhK5U8+80BfqoJsTshaRMbqCTDKOQCCgp82BJnrKOvBu7esTeSaXdh4Yau+X0Dkh8Q6RQjxq8wTNX57BC/WTjFGQ2AZjwgOBAUOiV0PHeo5k7tkcz2QGMMY/oIiOpj/QIgFgAlB2f+VZ34JS6gCQnC9yYefHvflBkxyVInPqkRf6nz6z6yc/5AV9IAkgowOse7JojtkxfXBvPZA/ZTgV5LHdvjHQG2RZPkeILqRD1Zc30tOiiy6a/FlUOpUTaI0aE90qaMb2e5boex+dcsLGjBmTyAJHwBxK5IrDr6ykPAcNiAf+tSVQQ08oH4EY2Iqt0aYdejRJYEgfyA3Z9eeeyQQah0zfvWcgCYYhU8YJ29lZ0D4c5M/nsqnsgXeQS+/Vvr7Jk9ix9q4Qx5H+0Xdla+ofB4YNyDdxDCFB6F/XB6cUAdQPiqdpmvyRJWMez/FWSptSjue4El75pXIGjHjWDKLFqDzA5l0Ws0i9PIZRlIVyphzRLWBl0SMpx8jGc3+vgMOqq66a/1a+d6gnqkIhe9Z/75QYCXTRSQsMEGWo5c1uCcimuJwdpESNn0y0n+UxSmRI+XimqPBTKuW7aZr8N4BtY1Ou6gDUZEj5aMdcmyNzRVmjSYyEOu15lS8pjzbQZAwMrjZCBqJ9z+QkZMhWvP56B5o67QRMiOLaeWKclBtNqc0nfDM2c4cX8SzPupLvXsJDO0R4xTmQ107oDLu1H3+thAworx668o5OkC+OAz2CLnJPP6FLZI4OQ5PkzUgyz+rrk/Fog96KZyARXWLU2+MnB/qqjkSn2OECVgFieaMl4UnMRYAx4MV6jOcYKzmR2s/4Zw6t9ciPK0Bnt4weFqWXTw+pQ46apsl//YjDjd/6giaRk/ackCHrV9KGtmYkkQXte6f2JX2JZ7aQPCjDppXv0EeBBTLsSJndSfIqryw7kp/pbby2IxY2ZOzYsan9bHzWGSfJ/Fs3+NZOdI5ykfDX0To7qXYQ5eNpyGBgB/PEXmlToCjajPb0iY2LfNdO86X9vpI+0Xfa8C7lo33Pxmbc6N6BpgycJE/CA+3It1vlFEE4xPJq6h8H5uirmLNxPqRzrot3bfvdtpDkvqTbPkbzcY7I/EDotoXUp/jL+iLgPdF9dV7Sbft4v/NeA6Hb8vR+9Z0tLuuj2xrvRLed1IkuGgfYo9uCYtza9dv0qG9Lzfudxx8I3XaWoz7q2z4v63eiAxH6ZzutE91HWH7QQtSypNuCVJ8DoL5tQO8HWi3KgdBtM6vvOEin+j3R9c0xo050kY1OdMde9K+k20r0fnTR54HQnV1UXySqU/2e6BSYqDsZLuvbukb3lyja9W136l9JN1e23tEdA+kP3VET7TuO1al+b3RGGd1xhLK+vxaBf460MC6MDbCvf74pQW/XnzJlSkJ3TnggdH9G0Pnhnup3onM8vB/Y7InuLwSVdM6u/qlf0h09QQe+BkK3nS0626k+nWsLvaR79n76u1N9dPkl3VEq7+lE12/9L+neo3wnOv6gG+9A6OScXfHn+zrVN5/mpd0+vuufee6JTn5KunnWP/VLOvlDJ28DoZN3ct+pvnXQiU7uvb+kW6fWK7r11xcd+OFY+XjV+u5U33rWv050egSdXukPnZ7RP3rNvJX10em7TnTrvxOdvqaXS7qdD+AQyC/p9LL30/Od6qOzHyWdnte/TnT2CZ29GQidvWP3eqrfic4u6h872ROdfS3p7Dh7rn5JZ9+DLihgx0R99lz/2vSo/3/s3Qe8ZjXxN/A5WFBRQQRFQV2wgYqsFMXK2hALuuqrWHHtWP6IHTuo2AuCgogIiIhiAxUEC0UsgICIDVBwsSFFpNlA8b3fs2TJhufW3b33PpfZz+aeJ5kkJ5lMZn6Z5JwDh6DDFROhwyHuD/fAJ235sejwEzq805aHy0ajs4WFbs7TZXaCyAQcWNNL/XCb9rV0OND90eHLydDhV+Xh0bY8/TUaHV6u6fTwTIVxQb5BMMEFvwkGZUghlUFAE9Cl1XRCRIFOlN7W35av6ZhoktX1ozM22odelzcgBL+lMwCUAHpbXv2D6BSa+lt6qX80Oj44wlDolFxdv/KMLDpBaesn5C2dAaakAALl9U957VN/S1eeQlM/uvsrr/+l/ppOqfMC8TAMotf1ozOydfnx6Aw2JaL9yrfta+nqp7BMdory6QAAEABJREFUdEZqUP2Frn+lvMnNsCmvfcq3dPdHL+XVX5cfjU6J1PW35Vs6hUfRAgmlflvZpX2FXu6vPOXKcAMB6le+pbfl1Q9wlPKD6O5f6KX+cn/lR6MzVOh1/W15io2nkoeG8UEnT3X9o5VXP5AHkFCydf+VL/RSXv2Aofop5LHodflSf11+LDoQUdc/qHyh6z9AwvFR1y8uHb0uz5ipv9C7rgvb2eLSC139pbxrSxeXrn5X8bq8uPRCV99k6MZDu0t59ZXy+tnS1W9cjA86zx1Pb9d1YZxaeilv/Atd/aV8Szdedf3j0ck7uanrr8u3dPWbd+SXHA6qv9D1r5Q3P8xT5dWvfEvXP/RSXv11+UF0z3jxxNf1t+XN25pOr5n/bf2lfS1defoFnb4ZVH+ha38pTw/S58oPuj+6/qErTw+rX3nefF7c0ej0r/Kl/ra8/tV0+l399L36W3pbXv3sEHvIroxGL/dXPzuqfnarlB9Ed/9CL/XX5cej1/UPKo/uSB07js6u1/Wjwwd1/9DhBHgBbqjpysMf+oeuvParH15q6aV8TR9Uf01Xf3v/lu4+dsHYEPispSsP77l/TYcL4b+arn/Kw2EtHW5VHo4t9NI+9Q+iqx/mHY9OT85UGBfk2yL2AJWzVbYcneOjYGwFOSM6Gh0odCbLtoxzaG35QXSePufVHFdp6c6utnTnCW09OWta03kB5s+fHy3ddpUzjS3dGVVnwFq682gEq6UzTrbMW7qjFc4yt3RGeptttolBdEqtpfNk2MqyLdvSbVe1dGeCnXO0ldvSGYKW7oiHLTLvMW/pzoi2dCvorbfeOhwBaOm2n2u6B3pts5qUvNMToTv/50yec7mDyrf04smi0GxLjkd3bt1ZQNulzo225QfRbVs6E+uow0ToHjRydtFuh+M6bfmWbjveGU0LJ2cPx6M75uVcszPVjra15Vs6z7ljEMCUM5cToTtu4RiA7dtB5Vu6s6KO8Vi0Om88Ht3xAQsZW/e2rdvyLd3xHtu1zoA7vzke3ZED55MtKpxPb8sPojtaYKvb+d+W7kxrS3e+dN7IFjsg0tJtQ7f0ruvCOVJnXmu6s8ue7ei6ZemeBXLUoaV73sIRvZbOcPjOSEsXl97SPUytnpbufu6r/paunS1df/Sr67po6frZ0vEL3/CnpeNjSzcexgX/W7pxaunG27gb/5ZODlo6eSJX5GsidPJKbskvOWzLt3TzwbwwP8j5eHTzzbwz/8yjtnxLN5/Na/PbPJ8Ind6gP+iRQeVbOr1EP9FT9M14dHqP/qMH6bO2fEunV+lXepa+HI9Ob9Pf9Dh93pYfRGcf2Ak7HxOhsz/sEHvErrTlWzr7xs6xd+zeROjsqLPx7GZbnn1t6ew0e80ut3T2u6XDAfAAu9/S4YOWDmfAG3BFTYdL4I+WDsfAMy0d7oF/WjqcBC+1dLgKvhpEh8daOtwGv8FpLR3ea+lwIXwIB7Z0eLGlw53wJ5zZ0uHRlg7XwrdwbkuHd2s6PTxTYVyQP1MNy/smB5IDyYHkwBBwIJuYHEgOJAeSA7OSAwnyZ+WwZKOSA8mB5EByIDmQHEgODC8HsuUzz4EE+TM/BtmC5EByIDmQHEgOJAeSA8mB5MAK5UCC/BXKzqxsxXAga0kOJAeSA8mB5EByIDmQHFgeDiTIXx7uZdnkQHIgOZAcmD4O5J2SA8mB5EByYMIcSJA/YVZlxuRAciA5kBxIDiQHkgPJgdnGgWzPYA4kyB/Ml0xNDiQHkgPJgeRAciA5kBxIDgwtBxLkD+3QZcNXDAeyluRAciA5kBxIDiQHkgNzjwMzDvJ9cdAXxnyFTfA1splksy+YaYcvtQ1qhy/4oQvaXvLIL0375Snp7dWX/HyQRLqv6Smj/3VdaCWoTx7tKmnyKtOmF/pEr+pRh3uM1mb3lcdV/lK3uHRlpfuS4Ec/+tHwhbiSZy5djZW+6rP+tn2TVsbEVbzkKbxyLWntFd98fEW6svK6l/tKa0ORN3nqsVOPNOXV05abaLzU4zqojHYVfvhd8ui7+9fhwAMPDJ8eX572lPpn2xV/9NV1UNv0GV2ox0leY4iHfo8WfJ3Ux4LQlVcPHvstrQ3qk2fQ+CuDprx2tWXHiyujvHvUY16Xwwd52nuUdDTt8BXVPfbYI+Sry8+q31NsDD7pl776PVY1ZABv2jzSRuMx/u2zzz7x/e9/vy+mDvdSpk9o/shvzOTxu5BLObQ6vdAnetVOdZO50cqUPPKVduKNuFDKHnbYYbH//vuH/KPVNazpNb/1fbR+oOFRGRNxY4RP0kcrh4c+KoeuTJHB0XipLnWqW37lBPVIV158qqHUo9+j1eEe7qUNpb8lr3Zr4xVXXBH77bdf/PznP4+6nSVfXsfmwIyD/PPOOy98yc2X2QRfEhy7ySuP+oc//CF8vc6X1t72trfF5Zdffr2bASzAmC8Yfvvb3+7pl112WXzqU58KX1D0pblDDz20T2///OIXvwhfp3vrW98a//jHP+IjH/lIqMs9f/CDH1xPscnva4a+vuarg7/97W/DRDjmmGPCVxSV9TW/c845p73VhOIUqi8T+mKl3yZlXdCC5O1vf3v4sp2vW5577rk9efHixfHyl7+8T/flue9973ux8cYbxzrrrBO+aPm3v/2tzzdX/hirfffdN3xhkmzgf9u3n/3sZ2Es8MqX7n71q1/1WYCZMlbAmnhPqP5ceumlscsuu4SvOVJip5xySi8nxvcDH/hAXHnllVXuCPk/9rGP9e3x5dLPfOYzQUn+6U9/ine84x39uPha6FlnnbVMuYlGjLsvC+qrBelf/vKXZYrix1e+8pX+S6dkc/fdd+8Xd5SxLz5qt+Arh+K+3nn00UfHqaeeukw9wx7585//3I/TwoULw5dQL7jggut16Ytf/GI/x43Tl7/85aX0xYsXh68r3ve+912a1v4488wzw5c3P/ShDwVD+MlPfrKvizx95zvfidZ4yk9PPOEJT+i/snvGGWcsrfK8ET374Q9/uC9vjlo8LCVO8Icx9KVUuuDzn/9836a66Pnnnx+77rprL390oTmBftFFF4UvaZIV5fXD11N9YZfM//Wvf5VtzgS63Fd6jQNwMlrHzOtXvOIV4Su3dR78WLRoUXz2s58N+qCm+f21r30t6ABfoXX1lVnzzdd+8VqeOhx55JG9PJiHH//4x3sSm6Vtvkprnn/hC1+4nv3pM07gDzvGDvgyKhlsiwBr6tdGgS6Vhx5lL93fXGDf/Nb/gw8+uNdp8s2FAEuYf77SSzefdNJJo3YLD8nPj370oz4PeYIb8Mp10BhfeOGF/RxjC/Abb8kQfptvdHZf2bV/5N9tt91Ce4zdCSec0FPoCV+9Ng5CmcM9cRJ/2B72zld32baLL774eqXpIPpKG8kOvFMyaa/2kQtfrfVFZzrnN7/5Tckyo9dhuvmMg3zCTtDe9773hQDUzBQD3/zmNwdh+uY3vxk3v/nNgyGt20Kh3uQmNwlKUztd0aXf//73D8CHN/vrX/+65GUC4PT+978/TMI11lgjjj322DjxxBP7unbaaac44ogjogbHJoBJbvFw+OGH9+1Rr3TtYxzcf+HChaFeIG+ZG44TMVl40gAPbaYUgJa62O9+97swUd3vTne6UwD5gAVjP3/+/JC+1157BYVsgUR5AS28THU9w/7bLgXAxDjxMlE2xrP0iwL//e9/H8AunmyyySbBqFk0Ad0MqbGi8CjpUs7Vou2oo46Ku971rrHFFlv0YFn9FKwygPs3vvENWfvA6AOI8h522GH94vKggw4KY0VBA4DacPe73z0Yzb7QJP5oj3qVV4/76FddBRm8853vHO57yCGHxHHHHRfSgNy3vOUtvUxrOxCy04hsy0uRk5W6nmH/TV/d5z736efu+uuvH8ax7hO5sPDBCzw03wud0WPISry9WlhZyHEYrLnmmkFP8tyq653vfGdYWJONUs4i4Cc/+UnsvffeQV60xziiA9/m+iWXXNKPjfl+y1veEmnCgWwZP+0BGswHYKSugA549rOf3fNjs802i7PPPrsn0zVApTnxpS99KT796U+H/pPxhz3sYWER02ecI3+AXuCEvjZH6I9BXaPXa2BT8ph35nOJ11fezO9+97uxww47xKqrrtqP9yqrrNKPq4WTMarz+03O6Gi2p+hmsmBuayOn08knnzylXRUAlFzqCyfHjjvu6JbLBKAdUCM75JeDCFDVHrLA9m266aahDgBVPWwNGVumoiGOmK/0I3vLJpgH5sCgLuGLOVtoHEN77rlnWNxxHuFTobmy/V/96lfDIoDeNrbiBSOYh3S0vCXgP51M1l72spf1+gTNWCpHh7Bj9Ib0yQTyxrmhPcZ2vfXW6xesbR1kx8KUTFjQwg7ykIHTTjst4B94Q9q9733veOITnxicjuIZJs6BGQf5DA4wbcVoclP6E2/+issJtFnNbr/99nHTm940GKliJMtd7DTwejOoDLrFCRoh5rW88Y1vHJ/73OcCqJNeB30kxHe4wx36ZJOeZ0tEeUq9BvkUOI/ZrW996xAIufoBR5771VdfXdHYaqutgvIACvqECf4xmTfffPO44x3vGOuuu27vxTGx6uJA2j3ucY8e3AMF97znPXsQStEAHhY8vHG3ve1tw8pdWRMbmGHwxYc9UDg864Dq2muvHWuttVboO3ktfbvZzW4WW2+9dU/DEwtF24wAM4NcxnzbbbeN008/vQfkpazxdJSF/JA7ccZNXB4yUsth13VhHNzjRje6UWy00Uahbe5lPNEsONSFpo7JBO3mcbdoVcfjH//4/qgNz0+px3gDCBbCBx54YNi5YMS1gyzJB9itttpqseGGG4oG+WVYyH2fMOR/zDdG6qUvfWmYl+Z2a0jJhUWdLWmLep7U0m1jxYNW4u2VTFhY0w1oP/7xj8OY+E0OL7300qCzxAVyZxHJgUBP4bf7ox1//PEBkFsEkBv6Q/pkgrJApPaYA+5hXtR1kFkyB6CQI7t76PJqKyeCdtAR5hDawhEnhQXMVNqk/GwL5q7FF91J/rfZZpsA7Np20gu3u93tAritaeZH13Vh8Vinl9/sJTkq+h/4s8OI7qjGIJCPx+YqYMnrL6+5On/EUWOBwO6ps8gL+kQDJxQ51h6yRfcDlXV5tsaOoLaTCzT2gb1jf8iutgCz6GT49re/fZDbAvKUGdYAhJNxmAKP9ZldGSTznCTGy9wv/TW+eELvOkGgbKG5WkRx6JAZ4ylO15qv6GSME8/vEuhlOqLrumBf2Bk0u08bbLBBv4Akw2yM9MkE42p8janyD3rQg4L+orNKPdrIYeYUQ7tosfix+GVj6NZShs5kV+jGkpbX8TkwoyAfqOSZMHAUEOPFGz5+s1d8DoZpnXXWCWBW7RSwdvldAtBjlbxgwYKw7Ql8oxFk4I03lueGAeNxRSuBp6QAINu0gDlFi84YEGYeWnFBvLQFgOMJMz+alxgAABAASURBVAG1wSQHKKyY1WVCT1YZmoRFkVAMlCpD7N4luNe3vvWtYLyNE2WsXfjkqBKgUzy4lLNywB7lUoNC6cMa8N6Y4w8+CUBL3T98orz10TgA6oy7NIaXt8pYAd/kAuiTV8A3nj7yJm6x50qGXMkIpWf8xQUG2z39Vh7PKWxplDlwBaiLyzOZoL9kE5BTTh/UTymLC3hAVhitd73rXYHedV2/OEYXyAvj4rdAbizg9VV82IM5d5vb3Kbvu74wwMbX7xLwHz8f9ahHxbvf/e5+N67QxrsCi4CTfJwK5hpwIG5MyIA5LC7gb9EXZMwOgqMxaBwS5rBdF4abM0W70CYa6Ecy0XVdGH+6Gg/cK679Rz8w5haavHD6j8TYAyocEjx8eMGJgebKY2gBID7swSLGnNUvfaELiwNEXDB36A8gBi+lxcgf6fStxRqbMpJ0vf92Q/AXgUwATu4nTkcZd4sM8RKArqc85Snxnve8J+Qx9urXLnJrh5JO67quFJnwld3TRwWMvx2k2o5JB/IAfzJNbuksNoRc22GgG+lY7SRPXdcF5xLbxtapY5gDfQ/ol/lTZMPYlX7pN0+9BTAelnRXAN/VuFlscyyIl6AedqPks+vmHvSyPHhNp7PV4oLxJ2vsjgUILKMNdBq63+TDjpT4ZIL+ulfRX/QS2bCzWeqhGxwX0x8nAPRJ/4z3Jz7xif4YEVxU8ruqgzPtl7/8pWiGCXJglQnmWynZgADCINiypSze+973rpR7jVcpATIRSz6KyEQp8XIFvkwK2+JAC0WFBvBYmdvCZ8QoNemDAoMsAIPoRbgpQPE6oFkM8eJZJJi4u+yyS/C+2DHQBivvAgCATAsKW2BtABZL3SZZub80v/HA7zrw0lAaFJRjQfI4LygOLPAAUAa2CUs5SpuxKfFhv1KI5EE/KD+KyPiJt4FHhTKzEEQDahhjYwXszZs3L3hR0AYFMmDMjSM6mXT/QfdzHwuInXfeWdY+AHbAAsPvSFmfeO0fW+CtTIiT59K/ruvCGJf7K4rW3t8OjoUEL5AFL3Anr0DJk1lzQrwONTCt04ftN36YM6XdeGROlXi5AnHmqh0+O3PmRqFN9Gr8jUm5X5GPQfpJnXbaLODvda97iQbQ54w8YO68LbmRpyeO/DGO5KANNfB2f3I/kr3/r7/a1UeqPzyNFgQWmh/84Af7M+UWqPjgWIh70Z1FbypKXgAVv4c9WICZs6Uf+imtxF2BOXPUQgggFvDFgpk+50CxYASKjZ0yg0IZ/yIXrvRTez/A3vENwU502YGx62Lx7viIo2F0V7kPW2H3qZUJ8bp/FgfGr5TT33YeFNvEkefZDPZRmjPXgKZFjSM8dBY5K3VZRJL1Eh/Wa9ctWTwVnapPxqmePxaHdKjFDkeNcRc3pvqtrMU63omPFYy/uSrIpw76qr6fdLu0FptkwjOIfksX2HSLDYst8RKkk4E2WOyV+3Vd1zsCSlx//a7vD8cUEG+BEiP/4BZyKJ/f7mUe+D1CXvq/1ktLE+fwj+Xt2oyC/LrxJj8lZ9VXp0/Xb6AFMC3GxsqXh2m0+zPYVsH1xJDXOTcTo1aE0gk0Q+e3SQjslbI8FsAdxYdeBwINMNmqKkqdB4jCtFUK+POSUbbKmVBAue2uNriPPIIFFSXqNyWgLcVDJK0OwIHzsyaqdFv2FmXO22kLQEshoM21QOHoL15RtPjLqOF721cLO0arHONCNy74xFuG585TSy+h67ql3mBpjvbwpgBG4pScRRz5ES8BWOQFAwoYx5LuaiFqvLpuiXGRJuhDKxPilKx+yUNGySavmjgQgLbOyC6XeBt4Z+1WWKgXmvoo8UE8KnmG/apv5IBR1hegyLz3e1BwpMn4W/ANordp9AHDLB1oop+KbqJbilyi18F4AWfmZZEZC/IynsaRnileNmXJGDloQ7mfPGTM4gA4AfDpC3O+65aVMXnVrb/aLQ6sADGAnOdNbM+bK2hzLVjYsh3mp77ZDbaT4XcJ5rff+G28BDrbAgmfHFshV4BePQbKmNuugnrIHBsh7r5kjHdevA1AHZvW1unhbrqfPJcy5rMx0sY20IMln7mv7eLspwVeWVxKawPdWMrz8HMScFTYYbL7WWS+LTfMcXPZHKQjzB981k9ppV8WN+Y4ngO39D89Wmwuj7c57AH+UqZc6QJ2psTpJhjDGEorc1WaeBsWLVrUH0HVLjTySIcY27YM/dXKg7hFKzuhvLbADORBXH10AXkVb4NdBA5f5WEq/FInedZni+C2TMYnzoEZBfmUmYlu9abJFIQtRb+nOxBAwu6cGMHyQBIgP1o7nMk2CYCyOg+FB0zXnm10hhXNb8FDJIyd30A8xWxiiJdAyJ1ldEyH8NveKjTeV945htMWVkk3mXiNve2jDcBbyUeh8jDiuaMVFhA8O4XeXq2mH/KQhyxNZijch8L2dgjlEcWBybYvaMMYgF6AyZhS0sYKKGKU6v5QzB4ss/VoLHljjJ88PGQeZlq4cGEYS2klqJ/s4Zk0fHS2tXjbeD89zIhWApBn0UDGLP7sIBmfQnc99dRT+wd5/S5Bm1uZECdH7isfY2OcnTe3+OM5Ms5AO3obGCHAxHEhNEZMXH58kFaCbVYgo8SH+cow0Q/OQDNmFne22kfrE57wbjPAo+Wp03lfGdSSZofGeWdxwNHY1PNZOvn04LUFnvs4niOdAaVryLDjhI4V1nqLJ5UctKEGaxaT9DXgCXwAIS14da8SeKGdEe66JTtDdpEsEuxi4RcgUPKaT3V7SvowXvEdELOw0ecDDjggvEGk7gvPaeG1RYHgOM2iEbAlncMLb+2ilnlVyovTJyXOA2tMxT1UW+/qSauDccN7eqBOJzd0iraXdIDTEQrtaUPtiKP7PXNAHjw8qQ9sVamnvdrpro+AsD/024IFCwLfum7JopHs04sAbFvHsMXpVH0zb/HZPKY3zYXSF4szNgKvPQhr7tEvAC/9bv6yHeqyI8P+lrKAOP1tDKThP8cUmyTuQV5l/R4UzGtlOADQtcF8txvouQi6S7rAqYPeBvVrmzz0PscUG6afMBV6PeflK8EigP1jG+q5oQ3SODFLXsc98abE8zo+B2YU5AOVhNXDJhQHQQX6x2/2yslBYRJ4wk2QKF8Th1fK1rMnzrVT8NS5B4mAal51aYJXBnoYsRVoSoyAWq1qvbpNZmXUA6wDRnYzeFyABorB0++8NfIB5ZS0BcJ2220X7uPojEmuzskEIEW7AUWKGThlaIFYYATIY/zdV6A03BOId2xHHN1uQg3oKRZeCsZoMu2ZzXnxg2EUGGMPu/GS2s2gGAF6PGTAKGv8ArDIM+XGU8bQM27S6r6SH0Cd4QaQ8c6YWjCox6KXHHh4jbfNQ0eAtzlj0SAP+eS1MYbigvapp77XRH53XdcvRBhfXkNy4k0ePIs8jYw62TD+7mOuWBAXgECpAxOOl9UGmhJ3RMTbmibSjmHI481B+mXemIOAuPnNKAFbttfxSHAkwpshyviTD7zkhVWOHqz7jH/4iJ/SzSegS11eb+otJuYd3cHZ4I1XFhnHHXdc/zC2fLykyipHPsiqI1ycKuQMbaJBfTyudDb9RObopcWLFweZp688RCefQE95LZ76tU/75UczT+gdNPrCEQ9zQHwuBCDdHPUwtgdsgR4P1AJ1+lv30fwX6rSu6/oHHweNkbkIeNEVytg5BqjwlSPJgh1Alm6cLTTQBPqA/lh99dXD4gOwk06enIMGqtU5mUCmyLH5r490ogXoLrvsEnQl7zDg5j4CfWa+kHt6kRw47scWFpsJCLPD9E3Lm8m0bTblBY4trunNXXfdNWANOoMe97wMoFva23VLxp8DiAcf0MUjPMZDc7ycv1eGnrbTxpsuTmdb7LuP/HSPhb4dZc4DdZqDaIK5S17pIPU4YsrWoRkXcqveiQZyu2jRolCvXT0LRtiBruSYcCTbUTX9cg8OR/dhs+p7WDTU488mcRTRr3W+/D02B1YZm7zyqQw/D5MA6K78O459B6BVWyguQkZIgXtxhg1NsO1clCJPpzQB4Bt0B5PMAobnTz55GGW/KUeKWprJbzLy4ti6Qy+BYrYQAKoY11JGuakESkPdPPm8t+rQzhJ3dhRdABTQTWCvvZLG0yCtBOetgRd9KGlz5QpQ24lhQBlH/bLLIg6k8dzhSQk87RSxRRqwZmGmTBvwE2hi/AF9dEaQd0xd7iuNV0MegIlCRivBHPJ2DLwvaQCoclMJvEcWMuoCCtXBkOijxS5ZYVDQKW70EvTHQrcGbY4ukGnnwUu+uXIFpPCB4WK0GWYLNEaU8UITeOLMt9JvC7LCQzqDrBSaK1BscQfEA8zSAAV1MdKcENLwGnCjS5zrRi+BsZaH0XcOXrq5LW0qAaDTFh5J+kkdjDa5FbcIcA/BDhS6gC/4IV1YMOK1lQ7ocZZ45aT4XAlAjeMO+mphrl++R8IjWuLSBPOpzDFxAfDxylWgvOuWeLalCxwrFg3O7Ts2JY1X1b0cBbSwNk5eYGGxwUagCRbnxkIZIMwxDukcCNKmGugv9fAwW8QCZkB+iXseBV0g1+5jQeKNc9IshqSVwBlmAQvkl7S5cOXoAeb1zxjqk+cS6rg0+t+uPbxQdpHxqQRzRr4SzG+6BnA2p6RbUBQZLDv9xsnusjo5IUp9dL0y7JpdtZLuyragTTbAKZyI6ih635jCLeL65jQEuqM5hR/1fbwJqrTNLiCbSMfUefL3+ByYDMgfv7bMMSYHeN941AjrmBmHkAiwWAwBpQWADGE3ZqTJDD/w7v3jM9KAlXhTHkfGB/i3GFqJt5pzVXuwHf8s7uda54AND+/RFzx6c61/K7M/dkgAdoutlXmfmajbItRi1aJxEPCbiTYNwz3t/FlAtQvGYWj7eG20G2QhaheiOAjGK5P06zgwLsi36suwRX++eUXwwSvQeMJXRF2zqQ7eAt463u3Z1K5haYudImcnh6W9y7Zz7PnhXfG26b1daDLlMu8WwRtv232u8YJnm+fZBwjnWt9Wdn/sCjqH7zjMyr7XdNdvp8LOjnP6033vYb+fo7483cPej7b9dnQcdaUv7CK39GGIG5vrYPf0/hoX5NtizHBmrCge2Np2fGFF1Tdb6nFUxwPLs6U9w9gOzzsMY7vHarMjRh7sIx9j5UvaYB2DfzxZc40/jr6lvhg85hMZa8c+PAQ9kbwzmufMyfXRDo8jgMPU5tnU1q7rVhhWmS39smNF3umM2dKmybbDsd7phfbX3W1ckO8MZoYrInmQPEgZSBlIGUgZSBlIGUgZSBmYjAx4sP062D29v8YF+dPbnKV3yx/JgeRAciA5kBxIDiQHkgPJgeTAFDmQIH+KjMtiyYHkwExwIO+ZHEgOJAeSA8mB5MBEODAlkO/VTN5v63Vxgle9+ZKdVzl1XRdeobX99tuHV0N6lZL3ucco/zyUpw6vVfJ+3EHZvGHC2wSbz9RtAAAQAElEQVS8rs1rKz3c6SFFZ/fU780d6vDqMW1w9d5V56bQvVbM6zD99npA7y53xmvQvWY6zTlVD5J0XRdeMTbT7cn7JweSA8mB5EByYNZzIBuYHEgOXI8Dq1wvZQIJ3snt3bfee+p9t96H67WQ3h2vuHdA+/qd9wV7FZYP5kgfFDwxrZ5BtJLmASP5vDPaQ3wWFd4V70MxHujzWqW11lorfCjB+1R9SMNT+u7vvay+vOgrkN4Bq40+UOL9xD7CUu4xW64WKRZIs6U92Y7kQHIgOZAcSA4kB5IDyYHh48CUQP5EuukjB14X6WMcvOzK+CALL3wJ9cMI3jjjIz++CukpavlL8NVZwcd+eOcBdh+f8aGqkqdcvU/VBz+80pHXvqSXq48w+PCMt9xcu3MQFgpAv3ZZTJT4wQcfHN5sYUGD5sNGFhY+RFLiRx11VHjl1xlnnBHaL93X6bwb3JfmfBjq9NNPj3322SfQfLiGt77E9ddHSUr72us111wTdjGUFXw4wjvpS3u9MssHvNDc38crvJbutNNOa6vKeHIgOZAcSA4kB5IDyYHkwA2EA8sN8gFU77/ebLPNov7KJqANoPoi5CmnnBIAM0Drq2o+n3zYYYeFzzkXPvsEso8CAe/KlHRXX2/zsQdfYPWOYF9adUSnBvGAs2M43qd6ySWXKDYwWHg4qrPRRhtF+Yy2j2440gMse41hicujnY4G+Sqf9gPR8mqLrzr6CqiveQL6vlxnB0FbtE1egF9/HS3ytVuAH9DXX1+gs7sg/8DGjiSi+ey43RDt9RVEuxUWUerDf1/I005fVtV2H41wtGmkeP5PDiQHpsyBLJgcSA4kB5IDyYHh5cByg3yg3fEXX6nbfffdl3ICCF9nnXWWxnmgeeN9zGDDDTeMc889N4DRkuGpT31qPOABDwggGyB2Dr/QnPF3VMcXVR3XcTyIR14dJY+z+jztO+ywQ5Sdg0JzdbzH8R1AfdGiReHjBKuuuipSD/af97znhb7w0nunqS+QbrzxxuHDVRYsPt7ilUkWK3YQpPnCnA/VePZgyy23jGOOOSZOPPHE3mNvASOfG2i/36Vdng8A9C0Q7GZY3Mg3KFjwAPTqcw8LBwsMfcA/C4s11lgjLKjQLC4sitZcc81B1WVaciA5kBxIDiQHkgPLw4EsmxwYEg4sN8ifaD8BVCD78MMPD8D0EY94RDh/Pqg8UCsUmjP2QPqCBQsCyOcdRzvhhBNclgkvf/nLw0PBPNxXX331UpodAMeALDT23XffpenlBw84YH/22WeHej28C4yj24l4wQteEPe6172ifn7A8wjogsWFhcOhhx4ajhP5qIf0QeExj3lMWBAdeeSRYYeCN39QvkFpwLw+rL/++nG3u90tLErwyq6Adu6///4xf/78QUUzLTmQHEgOJAeSA8mB5EBy4AbCgWkD+TzvwC3vs88UO2pSA+bx+M17z1MPeFswzJs3LxxTGVQO6AV2LQgG0UdLe9GLXhTKnnzyybHuuuv22RxD0maf5bdA8bsnjPwBtkcu/X/Hbzxk7GjPVVddFXY21hjxsCOKW2BcddVVov0ix7MKBx10UNjxsBPSEwb8sfDwXIDgmI7dh80337zP6diSnY/zzz8/PIPgfp5ZWG+99Xp6/kkOJAeSA8mB5EByIDmQHLhhcmBKIP+kk07qj6Q4+85j7hz8jjvuGK997Wt7LgLjzpE7agLgHn/88eGhW8dtPMQKCD/jGc8Inmd51AOUe+hVBY72/OxnP/NzabBA8MDppptuGo72OPu+1VZbhQCUO7vOm41uQeHojLZ5E5DPIjtS455LKxzww3EhwfEZgFmW3XbbrT8Ks91224W60Xn6HRXyRiFtlQ/QxgPA29Ehx2ue85zn9F/Kla6vAL0HbvXDK0P1w6KC518dgrjz9X57RsDuhbft2ClwdEh79BNdeaDfQsRxIrsRCxcuRMqQHEgODBUHsrHJgeRAciA5kBxYsRyYEsgvQNdxGCD+i1/8Yuyxxx5x1llnhbP0Hmzdc889+1daehsNcA+QemAVOBYci3n9618f73vf+0I9zsFbACgPlDu7X7rq7LsHTnfaaafw1hj38QYZANdigIdbHQA3ujfdHH300bHJJpuE397cYxEgrdQ56OrsPAAPlBe65we8mtPbeNTtKNCrX/3q8PpNixOv55TXcR4P4GqPM/N2GRyb4WnXhuOOOy60w+9tt902xNV3zjnnBHCuDsHZfQsSfPjSl74U+uitOfLZDXjVq14Vq622mqz9cSdt0E8P5fLiWwz1xPyTHEgOJAeSA8mB5MBwcyBbnxxYDg5MCeRP5X6Os3j7jQ9jCd6i4wNaU6kryyQHkgPJgeRAciA5kBxIDiQHkgOjc2DaQD5A78FVr3kUPLTK+z1605KynBzI4smB5EByIDmQHEgOJAeSAzdQDkwbyPegrHPljtwIju943eMNlO/Z7eRAciA5MEMcyNsmB5IDyYHkwA2BA9MG8m8IzMw+JgeSA8mB5EByIDmQHBhKDmSj5xwHEuTPuSHNDiUHkgPJgeRAciA5kBxIDtzQOZAg/4YuASum/1lLciA5kBxIDiQHkgPJgeTALOJAgvxZNBjZlORAciA5MLc4kL1JDiQHkgPJgZniQIL8meJ83jc5kBxIDiQHkgPJgeTADZED2edp4UCC/Glhc94kOZAcSA4kB5IDyYHkQHIgOTB9HBgX5Pui6sEHHxyf//znw5dsd9lllzj55JPjpJNOil133TUG0X/yk5/EiSeeGO985zvDl2Z9ybYuX9MvvfTSOOigg+KQQw6Jiy++ON7xjnfEWPS3v/3tccopp8SPf/zjeNe73hVt+ZruS7GXXXZZfPazn40vfOELcdFFF8VodF/tvfDCC+Ntb3tbnHrqqfGjH/0o6vIt/Yc//GHstttu4Wu7Bx54YIxHP/TQQ+OCCy6It771rf1Xe9vyNf2nP/1p/OAHP4j3vOc9ccUVV8QBBxwQY9H333//8HVcXw1+y1veEnX5K6+8Mlr66aefHr7s64NkE6F/5jOfiS9/+ctx/vnnhy8Nl/K+Vvz3v/89avqb3vSm8NXf73//+/3XjGv6n//852jpvgK83377xVe+8pVo6e9///v7rwTX9J133jnOOOOMOP7446Omf/WrX40//elPsfPOOy9D/+c//xmf/vSno6b//Oc/j+OOOy4+8IEPREt/4xvfGC193333ja997Wvxxz/+MVr6v/71r2jpv/jFL+LYY4+ND37wg9HS3/CGN0Shf+hDH4p///vf8alPfSoOO+yw+MMf/hBj0X//+9/H61//+vjlL38ZxxxzTLTlW/qHP/zhuOqqq2KfffaJww8/PMaif/3rX4/zzjsvXve618WvfvWr+N73vhd1+Zr+61//Or773e/GRz7ykbj66qvjk5/8ZIxF33vvveMb3/hGLF68OF772tdGW76ln3nmmfGd73wnPvrRj8Z//vOfGI2+++67x3//+9/Ya6+94pvf/Gb4/sZrXvOaKOVb+rnnnhuvfvWr46yzzopvf/vbMR79Yx/7WFxzzTXxiU98Io444ohoy7f0c845J3baaac4++yz4+ijj46afuSRR0ZNP+qoo2KPPfaI//3vf/Hxj388avpvfvObaOk+Jvjb3/42XvWqV0VL33PPPaOli0tnSlzFS3lXcemF7n7qVX9N1z7tbOnaq90tXf/1s6XjB77UdHzFX3xq6fhd042XcTN+xmE8OnkgF+Rj9xE5qcuTk5ZO3sgN+Wnp5LDQye/iETkmz+SSfI5FNz/ME/OF3LflW7r5d97IPDSfzKux6Oa1+W2emq/mbV2+ptMX9Ab9QQ/QB2PR6SN6iX6iZ9ryLZ2+o9foL3psLDo9Sp/Sm/QjPVnK0681nX6mp+lrenk8Ov3PDtD39H5bvqWzP+wIe8Ku1HR2p6WzX+wUe1XT2Tt2r6Wzf+xkS2dP2c2Wzr6ysy2dvWZ3Wzr7zY4XOvsPB8AD7P5E6HAGvAE31OUPOOCAHn/UdPgFjoFHWvppp53W45tCh4vgIzgJ3hmPDnfBX3AYPNWWb+lwHXwHp8FrY9HhRbgR/oMD4cG6fE2HQ+FRuPTSSy/tceZYdDgX3oVb4de2PDo9O1NhlfFufMtb3jIe9rCH9eFWt7pVPOMZz4i73/3ucY973CO22267GES/293u1tOf/vSn9/StttpqmfI1fbXVVosFCxbEQx/60Lj1rW/d1z8W/ZnPfGag3/Oe9wz1t+Vr+tOe9rS4xS1u0df/kIc8pK9/NPqDH/zgWH311eNZz3pW3PWud40NN9ww6vItfaONNgof+FL/wx/+8FB/Xb6lK7/GGmv09W+wwQZ9/XX5mr7++utHKe9bAo94xCNiLPojH/nIeNCDHhTqf/aznx3qL+VvdrObRUtXvw+T+VYBuvrr8i29lL/NbW4Tz3nOc6LQn/KUp8Sqq666tH705z73ueGbCOpHV38pv+aaa0ZLV/5Rj3pUPPCBDwx09c+bNy+Uf/KTn9zXX9NL+Xvf+96h/lJ+yy23jNve9rZL6y/0m970pqE8uvqVv8td7hL3uc99Qv0tffvtt4+W/uhHPzoe8IAH9PW39Jvc5CaBrn73R7/zne8cG2+8cSxcuDBa+vOe97xAd/8nPelJPX3rrbfu619rrbViLPraa6/d0+90pzv19bflC1397o9+4xvfOB7zmMfE/e9//xiN7v5bbLFFT1+0aFGo/773vW+U8i19vfXWi0022SSe+MQnxo1udKO+/rp8TXf/bbbZJtBvd7vbxfOf//wYi+7+6667bl//tttu29dfl2/pq6yySjz2sY+NzTffPG5/+9v39dflW7oP8KHPnz8/1D8W3Re6u67r699ss836+uvyLX2dddaJF77wheFDf/e73/1iPPrjH//46LouHve4x4X6S/k73OEOoXxN33TTTUO6+l1runwtXVw6/e4qrlwpLy690NU3iN51XcjX0rVXu7vu+nT9GETHF/cvdOPWdV3PJ/WPRTdexg3/jbP66/JL6OtGoZMHdONrnMejk2f1k0/1t+ULnfyRY/JMLsm/+kejk3/zBJ3cq78u39LVb56q37xVf12+ppuXypun5qt5OxqdPqAX6Bf10wfqL+VbOn2nfvpL/ejqL+VbOn1W6qfHxqLTk/Qlval+elL9ytOfNZ1+pqfVTy8XuvqVb+n0Oz2vfnp/PDr7wY6wF/PmzevtR7m/8i2dfVI/e1Xo2sdeKd/S2T/1F3ppH3vLLrZ05dnRQi/3V379EVzQ0pVnxwud/YcDlGfXx6KX+8Mh8ALc0JaHPwrd/eEj9cMj6q/pytf0h4/gIvhIeXhqLLr7w13Kw1HwVF2+pbv/ghHcqH44Tf11+ZYOL8KN8B+cqf66fEuHR9HhSzhzPDo8rH74WP1teXp2psIq493YJDG5KSi/MZMQCn5LG0Q3EdBN4snQDaa6RyuPTsjQMb6tfxCd1EJ6NwAAEABJREFU8tB+E2ZF0ylJ9TMQg+pv6Saj9lMk2j8WneBMhm5Cq98kUX9bfjQ65an9Y9EZ4MnQKXr3Z6TU35YfjU7Zav9YdJMI3WKzrV/5lk75uj+FtLLoANJY9bd0hlb7gRDtmyidsdO/0cqjW4yjUzpt/YPolLf7U5grmt51Xb8oBOwG1d91y9IZe+0HhLS/60anU+pdN3E6ha1+jgn1t+VHozNO2r88dMCxLi/u/vSv+sUnQgfmtF/+QeVrunrb+tHdR/mWrt5BdPeRfyJ0/G3rVx6/lW/pXdf1TpWWjt/a13VTp5Prrrt+eXKo/pZO7rSvpZND7VseuvmovPk1qP6Wbj5qn/nr/iuSTk+pn94ZVH9Lp6fcn97R/rHo9Otk6PS3+ulz9bflR6PT59q/MujsnPrZrUH1t3R2TvvZLe0fi84+T4bO/qufPVd/W340Onuu/WPR4ZbJ0OEW94dz1N+WH40O52j/WHR4Dh0+a+tXvqXDc+4P/02IPuJgrOsfqzxMiw63Dqq/pcO52ge3av8gOj08U2FckD9TDcv7JgeSA8mB5EByIDmQHEgOJAeSA1PjwHKDfGfknPWbyO2dy9pxxx37c+/OJ06kjDNOyjij6HziRMpknuTACuJAVpMcSA4kB5IDyYHkQHJgKDkwZZDv4SBnujw843fbew81eAChpHtoxoMJL37xi/uz8cC7h3IL3W8PYJW4q4cwnMtyJtSDJB7IkJ4hOZAcSA4kB5IDM8eBvHNyIDmQHJj9HJgyyHfeyxP9zkUN6iaA780DhcYLD+A78+iBLU/Ve3NMocvrqewSd91hhx3Cww8eEnLeyZsYpGdIDiQHkgPJgeRAciA5kBxIDswqDsyyxkwZ5A/qR3n1k9c/eR2V15T57fVoFgUeyFLOwyzeduABJK+ukscrybzCzG/B67g8HOXqFVQe3PDwj/IZkgPJgeRAciA5kBxIDiQHkgPJgdE5sEJBvnfqekerwEvveI3fvPo8+ZrBG+89uF5xxTvvPfvyyOudsX4LysvvPa/KeDe/hYO0DMmBOciB7FJyIDmQHEgOJAeSA8mBFcaBFQryvdLqla98ZQjeU+qdxX579yqvvFY7e++jHY7h8Oh7x6w83p/s3cd+C95Di24x4GMCXifmYw3qyDA2ByySXvKSl/TvUnc8qg6OWI1dOqlzmQM+DuTjPbVMlN8+fjOX+559G58DPpJV5KG++giVjyGOX0PmWPEcyBqTA8mB5MDUOLBCQf54TeCV98U9HzzwLlSe//HKoMtrAeEqnmFsDlhQAWwedm6Dh6XHLp3UucwBC2eArZULcQvDudz37Nv4HPABKbLQBgtDRybHryFzJAeSA8mB5MC0cGACN5kyyAfQjzjiiLjsssvi1FNPjfahWR9w8NGH0oZTTjklfBlQ+rHHHht77bVXnHDCCYUcALwPbZSEUr+36hxyyCHh+I637BR6XpMDyYHkQHIgOZAcSA4kB5IDyYHBHJgyyHdO3ln7173udeHLYFddddUyd3jCE54QvjBWEi+//PLweWNf/XP+3pf2fLq70H3tjoe/xNVve1he5/kd58kHbwt38pocWC4OZOHkQHIgOZAcSA4kB+Y4B6YM8m9+85vH8573vHjFK14RixYt6oH+WLx6xCMe0eeVv4TVV1991CLq33777fsy6h/tVZ2jVpCE5EByIDmQHEgOJAcmwYHMmhxIDswlDkwZ5M8lJmRfkgPJgeRAciA5kBxIDiQHkgNziQMrDOTPJaZkX5IDyYHkQHIgOZAcSA4kB5IDw8yBBPnDPHrZ9uTA7OdAtjA5kBxIDiQHkgPJgRngQIL8GWB63jI5kBxIDiQHkgM3bA5k75MDyYGVzYEE+Subw1l/ciA5kBxIDiQHkgPJgeRAcmCaOTCUIH+aeZS3Sw4kB5IDyYHkQHIgOZAcSA4MFQcS5A/VcGVjkwPJgTE4kKTkQHIgOZAcSA4kB67lQIL8axmRl+RAciA5kBxIDiQH5iIHsk/JgRsmB8YF+X/84x/jVa96Vey0007x+9//PrbYYos49NBD4wtf+EI84AEPiEJ/9atfHeedd15svvnm8aUvfamnb7nllvGnP/0pdtxxx2jphxxySDzwgQ+MP//5z/F///d/8ZrXvCYWL14cm222WXz5y1+Olv7a1762p2+66abxla98JT7/+c/35c8///x45StfGTX9q1/9ahx88MHxoAc9KP7yl7/0H9R63eteF7/73e/ifve7X7T0l7/85fH6178+zj333Jg/f3587Wtfi8997nPx4Ac/OC644IJo6YcddlgcdNBB8ZCHPKSnv+xlL4s3vOENcc4558Qmm2wSLX2HHXaIN77xjfHb3/62px9++OHx2c9+Nh760IfGhRdeGIPoBx54YDzsYQ+Liy66KF760pfGzjvvHL/5zW/ivve9byjf0t/0pjfF2WefHRtvvHF8/etfjwMOOCC22mqruPjii/vyNf0b3/hG7L///rFgwYL461//Gi95yUsC/ayzzor73Oc+0dJf/OIXx5vf/OY488wz4973vnd885vf7Ms//OEPj0suuSRq+r3uda844ogj4jOf+Uz4ANrf/va3eNGLXhRvectb4te//nW09EsvvTRe+MIXxlvf+tb41a9+FRtttFEceeSRsd9++/XlW/qGG24Y3/rWt+LTn/50PPKRj4zLLrssXvCCF8Tb3va2vnxLv/zyy+P5z39+vP3tb49f/vKXcc973jOOOuqo2HfffeNRj3pUtPR73OMecfTRR8enPvWpePSjH93TFy1aFO94xzviF7/4RbT0K664InwUbpdddomf//znPf3b3/527LPPPrH11ltHS7/73e8e6J/85CfjMY95TFx55ZXho2+77rprnHHGGdHS//73v/f0d77znfGzn/0s7na3u8V3vvOd2HvvvWObbbaJmn766afHXe961/jud78be+21Vzz2sY+Nf/zjH/Hc5z433vWud0VLf9zjHhf//Oc/4znPeU5P/+lPfxobbLBBfO9734tPfOIT0dJPO+20WH/99eOYY47p6Y9//OPjX//6Vzz72c+Od7/73TGI/u9//zue9axnxW677RannnpqzJs3L4499tj4+Mc/Hsq39Lvc5S5x3HHHxZ577hlPeMIT4qqrropnPvOZ8Z73vKcvX9O33XbbuPrqq+MZz3hGvPe9741TTjklfDTv+OOPjz322CNa+k9+8pP+o33f//7342Mf+1g88YlP7Mtvt9128b73vS9a+pOe9KT4z3/+E09/+tPj/e9/f5x88sl9+RNOOCF23333aOknnXRSrLfeevGDH/wgPvrRj8bChQvjv//9bzztaU+LD3zgA1HTP/KRj8STn/zkuOaaa+L//b//Fx/84AfjxBNPjHXXXbcv39I/9KEPxY9//OO44x3vGD/84Q/jwx/+cDzlKU+J//3vf335li7+1Kc+tbdqruI/+tGP4g53uEO4ikuXwVV96lW/+6Brl/rdp6VrL7r264f26rf260dLxw90/MEH/MAX/Fk4wqeWjt/4jv/4jN/4fqc73akfh5ZuPI0runE0nsaVfBjnmk5OyAu5QScn5IXckJ+WTg7JI7kkf+SYPJJL8jmITt7JPfkm5+Sd3M8bkX/lW7r5Zl6ZX+aR+WRemV/K13Tz1Hw2b81f8xTdvDV/Wzo9QB/QC/QDPaA8vUA/tHR6hr6hl+gXeoa+oXfon5ZOj9Fn9Br9RY/tOqLPlKffWjo9ucuIvqQ36Ud6kr5Unn6t6fQzPUwf08vo9DB9vGjRol4/13T6nZ6n758/oveVp+fpe3q/pbMf7Ah7wq6wH+wQe8KutHT2CZ29YrcuvfTS3k6xV+xWS2f/2EH2kF1k/9hB9pBdbOnsKzvL3rKr7OvDR+wse83utnT2e8GIHUdnt9lvdpw9Z9dbOnwAJ6DDFfABnHDAAQf0uKGlwx9wyIEHHtjjDvgDDtl5BI8o39LhHzgH3tlhBPfAN3AOvAO31HS4Cb6Co+Cpww47rMdHcBQ81dLhsvnz5/c4DV6Dy5SH0+C1lu6jq/AfHAgPwn3wHxwID7Z0uBK+XLx4cY8n4Ur4Ek6FN1s63Aq/osOrcCv8qjw8W9Pp2ZkKq4x3Y0qZgqRkKUAKlPKlICnrQqekKUAKkvJGp+wpfQq0pVOQjAmjQoEyEvOuVYCMR0tnZNApQEaJAlWe0aJAazqjREEyZuuss04PSBgtCpQCbOkUIKNEQVKAjBYFyujd/va37wFTTV+4cGEPnBgtdAqQ0aJAKcCWTgEySkVBMloUIKN1u9vdLgbRKUBGa+211+4BI6NVFKjyLZ3RogApSEZt0aJFcfzxx8daa63Vl6/pjBoFeNxxx8Vtb3vbHtCiU4AUaEunIBk1CpACZdSUZ7TWXHPNHjAXOgXIKFGQjNZtbnObHpAzakUB1vQ11lijB/SMWlGAjBYFqnxLpwAZJQqSUVt99dX7BQWjpXxLv/Wtbx0UIKNFQVKAjBYFyui1dAqQUaIgGTV0CnDXEaNFgbb0W93qVkEB7jJitIqCZLQoQEarpVOQ6BQgo3XLW96yX/AxWkWB1vTVVlutpzNaRUEyahQgo1XTKUAKklGjABmtW9ziFv2ClNFq6YzSzW9+835Bi04BWugyehRgS6cAKUhGC53RutnNbtYvqBm9QfRVV121X5AzakUBMloUqPIt/bwRR8GCBQv6hT+jdtOb3rRf8DN6ytd0Ru0mN7lJ71BgtDgYOCIYLY6Fls5B8Yc//KFfPHNcWAwr/8UvfjEYrZbOKN34xjfunRqM1v3vf/9QntHaaaedeqNX04vTg9Hi1GC0bnSjG/VOD0arpjMCjNIqq6zSOzUYrS2vdYoo39IZJcaEUWHUODUYra7r+vItXZxRipF/ruKMHaPlKi59hNw7TdSnXvW7Dzqj1XVd7xRp6dqLrv36ob3azamjHy0dP9A5hfABPziD8AefWjp+cybhr3HAb3zHf+PQ0o0nMIJuHI2ncTW+xrmmkxPywlGFTk7IC7khPy2dHDLY5JL8kUPyyLCTz0F08g4YkG9yTt7JPQCgfEs3n8wr88s8Mp8AE/NL+ZpunpqvgI35a56im7fmb0unB+gDeoF+mD9/fu8UAazoh5ZOz9A3gBn9Qs/QN/QOANfS6TH6jN6i3+gx+kx5+q2l05P0Jb1JP9KT9KXy9GtNp3/pYfqYXkanh+ljerml0+/0PH1P76PT8/Q9vd/S2Q92hD1hV9gPdoQ9AcxbOoCOzl4B9uwTO8VesVstnf1DZw/ZPfaRHWQPLSxaOvu64Ygjiz1ld9lXdpa9ZXdbOvvNjqOz2+w3O86eWxi1dPgATkC3sIIP4AR4AW5o6fAHHAJvwB0WcHAIPKJ8S4d/4Bx4B66Bb+AceEf5mg43wU9wlAUn3IQOR8FTLR0ug8/gNHgMblMeToPXWrqFL/wHB8KDcB/8BwfCgy0droQv543gUHgSrrZkvHIAABAASURBVIQv4Ux4s6XDrfArOrwKt1rAKw/P1nR6dqbCuCB/phqW900OJAeSA7OYA9m05EByIDmQHEgOzGoOJMhfycNjFVk8Ziv5VtNaPS+VVe+03nQO3Mz2Oi/ZHOjKMl3gAeO5WyYxI5PmAG/jpAvN8gKOHPCq2yWY5U2d1c3j/Xf8Z1Y3cgqNs1PjVMAUis7SIjPTLLsLjhfNzN1X3l3txPH2r7w7zO2alxvkO5NlK8+WW80qZ/mc16zTnL9kxGxx2JKztVvTp/s342M7zPafLeO2vaU9zm3avrZ9I00522P64QiTLUzpdVCX7WZbqrZ5gDtbhrZ/bF2Jt/lta6nTsSdbkepwJtPWtnTB9pttJ2cZ6/Lj/cZ729PqsO1q281520HlGGNbi7YGlbPtppxg+8u2vi15W/i2egfVMYxpxsS2qjGyHU+G2344g+soAF4ItufkcfaUEbYVLj4oOB9qK9XWP746rqAO2/i2EUcbD+cObUU6CiePeypjq9K2MjkZdL/x0mzt2q61ve9IwKB6tNn2tHbaLlanc6i25/FJui1qW8B44FgEHsk3l4I+2aLWX8cKRtNd5q08gt+FB+ar7W3pjjuW9HIle44L2GI3Do46mmOOOzoOQl5K3vbKuDu+IF09jja4j+MndJX0yQbHahwfUY+tbf0fVAf97+iLfI4PlTzO0jsS4JgIHWtbH/+cAS955srV2ACqjrGYk/Rm2zd6nE41Z9g+etM4K+soLL3KPpCTtixeOv7ATjkeRPbYEDx3ZIF9asuo23lsx4QcHyi2Sz5HVkp5v6VNJtCLjktpM1kjc3V593bEw7EvbdQG464fjmVIExx1cUTE8SqLQPqtrmeu/MYPR54c0yIfbG/dNzrdkRnH0PAFXx2DdkyJ/nUMSDl8JEd1Wb/JkKMhZMvxKOfgHR9Sl6PWg+TDPekGx+zgDXNcXUW3O/4lPpVAHhzX0g/21NjX9bi3Iy6ODWqj411kinw4NiVNIB/mDDlyJJDDVNm6rvw9PgeWG+QzZINW4QwaYaubAMQCy0Cks18MWE2fzt8UIOBkIjkzaJINAusmkFWkc3EmizhlRCHrByUHhLeTD2gDnChhwuv8l7NgzowCaM5Dqq/02YQz+dVpccAz6qEcdGfZpAsmI8F3lhFtosGkAiDUARBQqiZ0W16bGOvipdd2xkU5Ab+cGXUuDk+cHXTOs61nGOPOcVKUzs06a+ysY9sPoIYxxwuB4pbHuU+Gze9BwViSfec/AR4PcZbxcL7QmHsIqi1L3ixA5UcTB8Cc1wWuGe92nsk3XlDGOWSKkwI1ls4Dt+UATDtR+uo3JctgUeDymzsWN4AHQEcunGVt6xn2OBCnj/jgfLjztK3xMp+c2ZZHcM5Vv42r3RvAXbqxll4C3eF8r8Ufp4Fz8+qnO4y18XcWteSvr2TU4r2kefbJ2LiPxShdw+gW+kSu+kVf0W3q4T12br0t6/wsh4Nz4fL5LQ8Z1VceOPIizYJBW7RJfC4F5+DpeuPKrgHA5lfdR88vFP3vvK/z/HQ+fWoM2R4gz8KoLuc3+0MfAcRd1/XPINHBeM6mmpfy1YH9cc696DMAEV1+TjnONuU5HaRPJtB1ZEKbOQgsMuvy+k5nAJ7uAbB6Lo+doCukCfQPkAnQAf/KmAt1XXPhN72hb54R4FjhHPNAaOkbm8t5Z+GHL2yvuedsPjtND8AD+M2RUsqVq+ckLQY4DzzjQpc4F68uckC/l7zlSk7pa88c0FlsE5rd2GJrxKcS2EH983wGhyDdV9dDv+CHhQj95lkLfYSNtEO7BbiK04i95FQ1x/Szrit/j8+B5QL5AD6j7kGX8W7FQ+FhE8YC4Bwv/8qma4PVMZBmsUGJmiztfQkh4fOwCRqjBeRZhYp7gwPFRHDFBQoQiC9G3oLCxLUi9yCZB294UOqtNStwypeBdD8Pn3joyYNbFLp6Bca/eFTFJxooEDsK8hsz9wBYxevA6HgYzepaOs8RHvktUFTFmItTRoCm38McADFAjuHBc8qJrLZ9YqyAKmC9pnloiBeiTqt/M+TAV5EjhtJiTR6eFIaRYhSvg/EQLzJAiduJYRjJq4Wqh8vkmUywqDVveeAZYYs5oK2ug+zzvuovQI9GPi1GeQc9FMgQMSiMF7oHlIC7QX1BH8Zgbhs/fNd+wAhAY7zFSwDyADJAu6TRFxZCgKB5ZY7X814+cxGvjYc4o0sfAfziG2+8cQBnftfB+BuLej4ynmW+2hmwYLAYrMuN91t7LFhKPWSPvNSLBf0HVnmlGWQPURY6vlgo05Nd1y29HWNu9wpgWZo4B35YgJkL5oQHDY0joFN3DR3o4dUlE+aNh149oMeG0P/ki37H+7osPQRMswfSyZOHbP0GlNpFNfkC/N3Lg8Rl4UAWLEDcW/vsvsujnskEdVuwabMHHs13+rPUwaGhD3YJpZFD9s5i0NyRZndSGV58cTwjM4MchujDHABqfKZrAVbzysO4pU8wAR0K6EvzcDFdYYHmBSbkypzkbCNH8tSB3iEfxfEH1FvEyeOBWc4kv0uwUACY4RRzldOv0Ng/D6mW+FSuFrTkU3vItvbUWINuIwv0nAUOm2L82cciH/KziXilDXjHpnAeiGeYOAemDPIpCeDCZC+34w2kQATbi96m4LdJDlTzDllt8oJ7chywKGWn+0qhFIFyb0CfIfa7DjznPOlWvNJNSIsDvwWK04SytSQuEFBKDHgTp/AAR9vW4vhGEVsciAsmL+Gn6L2ZgkKX1nVdmBDy2AamOOv7Sx8vAClABE+SvOqmdCgO8RI8+c/b4i0MJa3rrru/NBMYAPZbMP6UEsUhPqyBsjPGhbeUMcPU9geotpUJ4NjFaOmjxXlHijwAQ8ajxBl7hr8dD+BMcDyk1Nt1Xf/aR94g3i/zS/lCn+iVgvW6QfmLTJsT4iWQEaDMotA4A5RklPwCs+RaXoDPIt5vPLRAbfuCNqyBbJurjK4+4Lc5VfosTTAveC/xCfhilIyrnTGAl+fMYqg2qsoByYAR3onjnTlvnnZdF97AIY1RRheUAS7pUvESAAHOlxI3RkKJT+TKW8bwMtLy01tkFh/EBR5C+byJxiIDOJWGZufBUaHSH2kCvtGV+CM+VwIgzX7Q8fqEX9L8LoENZG/IgQUjHYIfQIt0+Sy28ZhuEC+BXTUHxdHIXZFFAFBaPcbiQCTPv7cWeX7KPLY4IFPmcnnDEblW72QCG6GtypAR+oA8igtkl1OAXRAnS3QEPdN1SxZ9Fnr6zYkkj6s2WbiKz6VQ+MWe65eFIP74LdCphSZuXpMT/MJfnnkOwq7rAviX57oQYWe1OI+MA11CHuXhKKA7pIkLwDOnnzrJh90CO8NoKyIY22LbLATNB20odeurhQSbyEmgHXAPuem6JfJh14JsS1OOfFgQto4otAxjc2BKIB8gwmxndRkyBoC32ivMDLDAYwAI+E2gABWrMYaQ14cnk7CN3byVR2V8u26JQLlL13X9O6f9FgAcfXB20XkxgVET0AWGnsfe6tdElSZQaq4llHt13XX3K7T2SjnzBNqqIug13fl+nsA6TTuNgcldB2n1IkobShu7bkk7pJW6AAgLM95+CkHcYqU+i2tFbnfChCvlTFh8YHhK2jBe8aLrlvBF+7tuWXmQJjgnTZY9p2DLnQKXPl7Ax667rv7x7of/FhI8IQw4Y8zzpR7gCY3hBhhdy/3Vy6DWslB+q6PO13XXtaek11cGgiHmDbIl73wnGeKt1ybeF2DDkR/AXtmu6/p3w5Mh8bkQ8FQ/um4Jv7puiWyUdDQBPyy68ImB5v2Wx4IKGPe8hqNZHCQ1CKQvhK5bUr8yXbfkt3q7btn7ycubCvjhs7kn0FEAvvsx8uTEeGmXegTOhSIP9VW6+8pTQtdd1wZpNd3i0/ES2+gAvXkBbHDe8MjZcQJYtUkblReATCDU77kS8KXrruNV1y0Zr7p/xoEjCLjn+fbaP+NIf3fdkrLyKKM+1xLogtFoXXf9ewHX5JBDzZUzwI4gz73fXnFNz3Nk1LrDWHFa1XLhNzugnaU92td1S9osreuWbQNwxk5Z1JJDrz51tViVX12eKwJMa9DKlgxytCkz7KHrRudX3Tf63Twyn0o6hwEMRW+Y3yW9XOn1Ih9423VddF3Xk7tuydWY9Qkjf+AKIJwed6rAsS73NP4j5FH/s/OD5AOOc99S0O+uW3JfaV23rHxIM+52Kbqu67/DpA/SBW2FL8mMhbA0wSK6dURJzzA2B6YE8iltXk4P/TlvZcJS7oD+2LdbQgUceBEJw5KU6f9LyC1Wyp39Lp5uaQSecvVQFM+6bUTK2WRDB6QsVgadMWXgBfkEyp0Qq0/cQoH3g1ITbwOj6WETW1mF5n68HDxhJc1VO50l18Y6eA9xGQ8TxYQB/EuZruv6D/uICwwDA6wOgE7cLkYBI9rOi2/RI/9cC46/AK74qW8MGyPk96AAAFvUAleD6G2ahVFRxLwTxoNylI/ypNjMCXHB4oF8GA/jC1SRRQAOXSAnjggB/+IC4MDzo1wbeJHlEbS/yIcy2iINbVCwK6fdaPKRBXPGGUreXMYCbS4G3jQGht7TP+NlTkkXb4O5zZlRdIB8ysjHG0UWzDVxASCW5rdADsx9OoNcmPs8p+QBnZwaO8c0jDGHCQAHOHZd13+UzPy1IHcWu+xOKWtXQZk2OKrmnvJor7EubdZv/eeVQxf8LnQ0+pSHkpyqS/081nZwV6SX0L1nWzBe5o95pG3GyxzxuwQOAYsuwNfxGc4hix3ea1f5eDvJlbEWL0Fa+e1exsc9pAE9dBcbIy4YG7a1jKe5KQ2oJzfykFFjVo6ISLNY59gydnXgUChjLR+9SD/6TV+SR20QL8F7w4FJYM3RDTsZZAodr4BZTj/xuR4setje4nTTf2mD+g1j0LW83zWdx9virIxfTavlg+yQs2IT2G9jU9dHFugVNl09nADSir6SNijwvHshRS0bfjvSSQ5KGfLmJIO4dG3WLnHB4t+xX84AONLzOr77UPhDtsmUXUn5MywfB6YE8jHfOWzK23kvQgnsUvITaY7VOi8UYZxI/pWRB4g28ShJgTAywOVeFClQpY8CQXT+kGeVEJqMzq3zYAJeFFopa8J0Xdd/bVYaRWr7zSSheG1rU3Cj9V/9QKA2KC8w5D624Hcd5PHQijbWgae5Nu7Ow/IEKGt3hcIXxAVKpNTjyIE4z76zguiO8uhHiUsTbDvq72h9kWcYAtm1bWjrkizwwo717APlqYzxn0j/eLwpu5KX4SMP4mTHOFJ64oJdsjLHHHNz5tLii4FFL8F2P1qJU9QUby0L5TcwXvLxrhlTxsAYUvg8zYXeXj1sXh/jwiOLfH0ADIqhAS70szYqbV3DFtc3/OLMYBzNX3PBHB3UF4v4AmIs7MwlnlRz3/MNvHTKl7Lmj3ED6qUBX+5JP4kDynSs34Ky3mhTxtV9w5WdAAAJf0lEQVQRMp5ZO0zo9IfdQKDTIkxaCTzvpVx9tXi/dqERDDLQSC6VA9b1X5vEBTKHH/JYBPD40nHOmJd6zR8yY6GhjEDm1O/3XAl4oS94YHFuoVOPFxpgY56ZH3SoMSdLHix0vEkeO6V2zYrHW5pAzswpvwV6ynz0G78t5vwuQf3GBICSZo6SOR9L4piymGDzyKNFhjyCIxMevCzjV65AnPbKIwDsnF5knG7SntqWyCN4Dm2HHXYIzggfEpMm4IN7W7CIl0Cn1o62kj7sV/PCAkf/9J3+cGRqUL/MWzLQ0pyWgJs4lloafUGPl3Q4gV0QZ8s5gvwuwQKDvvesgDS4wDOGFuviowVjCJwXuShXMsh+lXJ0kQWje8CHnj2rsQi9RJ612Xwgg+aCoA584jxrZcFiuJUZ+TOMzYEpgfyxq1xC9eok58eXxCIIEhAN3PJkUBS14ij5putKKRFAk8akALoAKKtsXjggb1BbCJ/jOYwnw6g/FDXBLfkJvIVQ8ZzamuLB8fAc8MZoO5Mm3fapc3GONlHE6vPGAXUDhurkReHF11bxqQR90kf1U9q26hgTRwu0oXhmRqubp9CYtXR9YshrANDmGZa4XSnK0eKN8rGwA7woScqXccQ/wZjbDjfW+mdxAPRaQFHS5ER6CWS/KFVpFBujqi6vGzMe5gujaRuzeELkrQNlbyyVE2zfqrvOM5HfvDv6q6zFq7ZQ/kAbZc4rbHfOPQSGifzgi50CPOJZ9hYtnqByz6Kg0UvaXLh6sLrrurALiS/GmDceD40X2WDc8MpumwUgj1bXdeGIhnli7ntITpmaJ+Y5A8vwSTc27kdnOG6hXg+/OqLj/rz08g0KZIl82B2wKKwXjoPyD0qjlwFHxlt/LOCd76Yb3Z+hN+aeCyETAAJd4vjOoPpKGh1D9wH/JW0uXDmsgHr6wNzhlTSmjleSAXOZvAB44o5feM7LYodOlYbP9IMv3bY8MZ4WRyWdvub0UQZYVBePObkDrgAh9zA+8rAzAD774e022moMPEtB55R6J3ql7y3yyTN9p830nT6q32KHM8yCkjxa5BU96R4WHR6oJPPigvJ0DLsoPpeCRRr9yqZb8NiZlcZmcACUxbxFs/ll/Er/2QljyClp/FwLrVzNQeNf4u5jninHsQPfkB8y4DgwwG3xZbdfHjqdjlHecUL4AIDnZLIYk74kTOyvPpI5OsLzBfSQRa5jQRxCdpHIBz2qvxYidKIy7kBX0iXsi7igvD7qi3iGiXNguUE+j52tx+JFKrfmCeKdKnECCqgyULYurdK67rpzWyXfdF5NNKCEV4TwMIyMM4BDUOu2ADPAOGVFeVuF8troD08MYS35ee8AQ54Zhk26cnvvvXd4wwJvibh0vAMgKV4AU322uxl17ZEHgDb5KFXxqQQTiAJVvyNHJn/XLTkPpw0WPaVeSsZDWiZpSTM5GaQSdwVKLJQAXvFhD5SP/hhXuxjGiKLxUKGxxxf8E4wt41z6TGkByBYFvGXKFpor5cyoe1OGbUnjoYy6LKAoc3IDEPL2UsTKCUCfcsaMR4h8KifYSShyIu9kgvoAD3LHG2y+8gLy3AIWzpG7h+DNWPqkjQAEHtkxqOXGYtTbhyjs4hWeTHtmc158NwfwwvlVCyLtZbCNF9nwZht0QAsoxk956AYG3dw3t+lG6SUYX+NvXHlH8Rj/PXDJG2ubXl2AmTjDXMq6agMw5bdnNHiSefrb+6BPNNDP9IT+qEv/6Ub3B960B9ijP92PLNSgzX3MIbuDfusXPUP302fS5krAC4sywMpiz7zSN4tnvDGXLZS80YQMyGcslaMHOIzw2eLZYknZOgDj5ilPpnSy5qiWMsriO28xubTgUi9dDSzJYz4aP3OSLNl55oCyOOi6ydtgesDY0nfaQW9Ko/foCfJM3oEyIFb7tLsE9oL3v8SBTG0FNMlYSZ8rV/pZfznyzBcAVt/ofwu7shC38MFPtBLsmBhDYJkzQV2FVq4Wl45OGlfecAtMdkw5cxf/YQc2hONKHRYG9Is8QHiZk+YxzOK4jMUZ+Sz3mehVXXQS+SAT5IPjAIYiN/SbxTBHqvmgj3YnS/2cqGSzxMkHG0XfkP2SnteJcWC5Qf7EbnPDy2Xxw7PLqwMQzTUOUA5W/LyL4/Ut6Us4QJl60M1RryUpc+ev4wEAHANmR2ru9Gx6esLJwKNnkTQ9d5y+u1j48jwz9LXxnr4WDPedLOQ5fQA3DoLh7s31W+9tS2ykI6UWKNfPkSljcYDziJOQfNjpHSvvMNLsQlgscyRzug1jH2ayzQnyVyL3nXEG8ueiYtYn28E8vyuRhXOuap6a2vs9VzrI28Ib7SjBXOnTdPeDrmgf2JzuNqys+9kZcCSt65bxHK+s2825enn+eTK7bu7xz7E3Xvz00k5dbB0Hcvadl37qtczOknYi7DQ7Fjc7Wzi7W5UgfyWPjyNBtutX8m2mvXrbf2WbcdpvPsQ35KnyEOMQd2Fg0223eg5lIDETJ8wBW+8TzjwkGXnfHG3jyR+SJs/KZjryOhdBnGfhHPGZlUwfokY5qgUQD1GTx2jqdSTyYbfiupT8NRkOJMifDLcyb3IgOZAcSA4kB5IDyYHkQHJgCDiQIH8IBimbOHEOZM7kQHIgOZAcSA4kB5IDyYGIBPkpBcmB5EByIDkw1zmQ/UsOJAeSAzc4DiTIv8ENeXY4OZAcSA4kB5IDyYHkQHIgYm7zIEH+3B7f7F1yIDmQHEgOJAeSA8mB5MANkAPjgnwfMMlwUCQPkgetDGQ8ZSJlIGUgZSBlIGUgZWAsGfABvJlaX4wL8rfffvvIkDxIGUgZSBlIGUgZmJAMpM1M3JAykDKwVAb222+/mcL44z9460t0Gc6J5EHyIGUgZSBlIGUgZSBlIGUgZWAyMvDe9773WpA//ZdxPfm+MpZhg0geJA9SBlIGUgZSBlIGUgZSBlIGJiMDa6+99vSj+2vvOC7IvzZfXpIDyYEZ5EDeOjmQHEgOJAeSA8mB5MBkOJAgfzLcyrzJgeRAciA5kByYPRzIliQHkgPJgVE5kCB/VNYkITmQHEgOJAeSA8mB5EByIDkwbBxY0t4E+Uv4kH+TA8mB5EByIDmQHEgOJAeSA3OGAwny58xQZkeSAyuGA1lLciA5kBxIDiQHkgPDz4EE+cM/htmD5EByIDmQHEgOrGwOZP3JgeTAkHEgQf6QDVg2NzmQHEgOJAeSA8mB5EByIDkwHgf+PwAAAP//bN6M/AAAAAZJREFUAwDGHQ0pBqYMtgAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Hazard ratios and their 95%CI for all-cause mortality by categories of life-course SEP.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4993031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4993031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSocioeconomic disadvantage throughout life is associated with an increased risk of cardiovascular diseases (CVD) and all-cause mortality. However, the extent to which common familial genetic and environmental factors explain this association is unclear. This study investigates family- and individual-specific effects on this association using twin data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from 21,051 twins were linked to census and registry data on 20 indicators of socioeconomic position (SEP) from 1960 to 1990, with follow-up on hospital discharges and causes of death until 2018. A graded item response model was used to aggregate SEP indicators into a linear scale, divided into quintiles. Hazard ratios were estimated using a mixed-effects Weibull proportional hazard regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAt the familial level, higher SEP quintiles were associated with a lower risk of CVD events (HR\u0026thinsp;=\u0026thinsp;0.51 for monozygotic [MZ] twins, HR\u0026thinsp;=\u0026thinsp;0.56 for dizygotic [DZ] twins). Individual-specific effects showed hazard ratios of 0.80 for MZ twins and 0.73 for DZ twins. For all-cause mortality, familial risks for the top two quintiles were HR\u0026thinsp;=\u0026thinsp;0.64 in MZ twins and HR\u0026thinsp;=\u0026thinsp;0.61 in DZ twins, with individual-specific effects at HR\u0026thinsp;=\u0026thinsp;0.71 in MZ twins and HR\u0026thinsp;=\u0026thinsp;0.74 in DZ twins.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe associations between life course SEP and both CVD and all-cause mortality are partly explained by shared familial factors from early life, with shared genetic factors having minimal additional influence.\u003c/p\u003e","manuscriptTitle":"Life course socioeconomic position and the risk of cardiovascular events and all-cause mortality in a Norwegian twin study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 17:28:52","doi":"10.21203/rs.3.rs-4993031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-08-20T09:55:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T14:59:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T08:57:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104532698997599002088500477645918977122","date":"2025-07-21T15:07:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113303962767810798264288689199761206639","date":"2025-07-16T17:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T11:39:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T05:22:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-24T18:45:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e77ae24-d8f4-468c-8cc6-9b82e0ad97e6","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53438760,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":53438761,"name":"Biological sciences/Genetics"},{"id":53438762,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":53438763,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-06T15:59:47+00:00","versionOfRecord":{"articleIdentity":"rs-4993031","link":"https://doi.org/10.1038/s41598-025-16975-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-02 15:57:07","publishedOnDateReadable":"October 2nd, 2025"},"versionCreatedAt":"2025-08-27 17:28:52","video":"","vorDoi":"10.1038/s41598-025-16975-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-16975-6","workflowStages":[]},"version":"v1","identity":"rs-4993031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4993031","identity":"rs-4993031","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}