Weighting Health-Related Estimates in the GCAT Cohort and the General Population of Catalonia

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However, it is known that cohorts are affected by the "healthy volunteer bias" where participants are generally healthier than the broader population, compromising its representativeness. Here, we assess the healthy bias, identifying bias key indicators for representativeness of the GCAT cohort, encompassing 20 000 adult participants of Catalonia, and generating survey raked survey weights to enhance the cohort’s comparability. To assess and correct the bias, we compare multiple variables across sociodemographic, lifestyle, diseases and medication domains. Electronic health records of Catalonia (SIDIAP), the Health Survey of Catalonia (ESCA) and registers from the statistics institute of Catalonia (IDESCAT) and Spain (INE) were used to make the comparisons. We observed that the GCAT cohort is enriched in women and younger individuals, with higher socioeconomic status, more health conscious and healthier in terms of mortality and chronic disease prevalence. Raked survey weighting identified sex, birth year, rurality, education level, civil status, occupation status, smoking habit, household size, self-perceived health status and number of primary care visits as key weight variables. On average, raked weights reduced the differences by 70% for compared variables, and by 26% in disease prevalence estimates. We can conclude that the application of raked weights has enhanced the cohort's representativeness, improved comparability, and yielded more precise estimates when analysing GCAT data. Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors GCAT Cohort Bias Raked Weights Representativeness Population health Figures Figure 1 Figure 2 Figure 3 Key Message Population-based cohorts usually encompass a healthy volunteer bias, including healthier individuals with higher socioeconomic status. This bias challenges the generalisation of the results to the entire population. Detection and quantification of this bias is important for results interpretation. Raked weights can be used to mitigate the impact of the healthy volunteer bias. Introduction Diseases are caused by a combination of multiple genetic factors, environmental exposures and lifestyle habits, and they represent the majority of diseases [ 1 , 2 ]. Some of these factors have not yet been identified due to its complex interplay, which increases with multimorbidity [ 3 ]. To this purpose, longitudinal population-based cohorts play an important role, as they usually involve a large number of well characterised individuals, representative of the population. These characteristics are not only important for risk factors detection, but to develop general prevention strategies and treatments [ 1 ]. GCAT is an adult population-based cohort from Catalonia, Spain, consisting of volunteer residents aged between 40–65 years at the time of recruitment (2014–2017) with a total of 19,390 participants. We collected longitudinal health-related data, at recruitment, and follow-up in 2018, 2020, 2021 and 2023. Yearly follow up is done by access their electronic health record (EHR) from public health system data and residence for environmental exposure mapping [ 4 ]. EHR-linked population-based cohorts offer an extremely rich longitudinal characterization of individual-level health factors [ 2 , 5 ], however, population-based cohorts commonly exhibit biases due to the healthy volunteer effect, which is a form of selection bias wherein volunteer participants tend to possess better health compared to the general population [ 6 , 7 ]. Even if this does not invalidate the research analysis itself, it can challenge the generalization of the results obtained, as some population groups, such as individuals with low socioeconomic status or poor health, are not well represented. This type of bias can be mitigated using weighting approaches, such as raked weights, which improve the accuracy of prevalence estimates for health conditions and risk factors [ 8 , 9 ]. In this study, we compare the GCAT cohort with their age-matching general population of Catalonia, stratifying by age and sex, in order to assess their differences in sociodemographic, lifestyle, and health-related factors. In addition, we identify bias key indicators and create raked weights to mitigate the bias and enhance cohort’s representativeness. Methods Design, setting and study population This is an observational longitudinal study in which we have compared the GCAT cohort with four databases(SIDIAP, ESCA, IDESCAT and INE, described below) representative of the general population of Catalonia (Spain), a region with 7,675,217 inhabitants in 2019 according to the official population figures of the Municipal Register [ 10 ]. GCAT cohort. Is a population-based cohort from Catalonia, with active follow-up, who enrolled voluntarily in the study (2014–2018) with the unique restriction of being between 40 and 65 years old. Participants were recruited through the national wide blood and tissue donation centres of the Blood and Tissue Bank of Catalonia [ 11 ], but no restricted to usual donors. Specifically, informed participants agreed to participate in the study, provided informed consent and allowed access to the EHR from the public healthcare system for passive follow-up and to be contacted regularly to collect follow-up information on lifestyle and additional information. Multiple individual variables are available: Anthropometric measurements were taken, blood samples were provided, and participants completed a comprehensive self-reported questionnaire about sociodemographic factors, lifestyle habits and diseases. Multiomics data has been generated including genotypes (SNP-Array), sequencing data (WGS), metabolome, proteome, and epigenetic data for a subset of individuals in the cohort. Participants can opt-out or withdraw their consent for specific areas of research. See cohort protocol description in Obón-Santacana et al 2018 [ 4 ]. The GCAT study was approved by the Germans Trias I Pujol University Hospital Ethical Committee (PI-13-020) and all research was performed in accordance with relevant guidelines and regulations following the Declaration of Helsinki. As general population, for comparison purposes we used: SIDIAP , the Information System for Performing Primary Care Research (SIDIAP) database collects pseudo-anonymized EHRs from 328 primary care centres in Catalonia managed by the Catalan Health Institute since 2005, and it currently has data on almost 80% of the Catalan population and is a reliable representation of the region in terms of age, sex and geographic distribution [ 12 ]. ESCA , Health Survey of Catalonia (2015) is the official survey of the Department of Health and includes information about health status, lifestyle and use of health services for the whole population resident in Catalonia [ 13 ]. IDESCAT , the Statistical Institute of Catalonia, that since 1989 provide official statistics of Catalonia, including social, demographic and economic data [ 10 ]. INE , the National Statistics Institute, that since 1945 provides official statistics of Spain, mainly on sociodemographic and economic characteristics [ 14 ]. Inclusion and exclusion criteria The participants included in the analysis met the following inclusion criteria: alive and living in Catalonia on the GCAT recruitment start date (2014) and born between 1951 and 1970 to maximise the overlap between databases. Individuals from the GCAT with no EHR link, detected to be duplicated, that opted out, or with discordant sex or birthdate were excluded. Finally, a total number of 13,434 GCAT, 1,625,075 SIDIAP, 1,459 ESCA, 1,930,005 IDESCAT and 2,068,697 INE individuals were included in the comparison study (Supplementary Fig. 1). Domains and variables Data collected included 17 variables in four main domains (i) sociodemographic: sex, age, deprivation index of baseline residence [ 15 ], rurality, educational attainment, employment status, civil status, household size; (ii) lifestyle habits: smoking habit and alcohol consumption, (iii) health-related factors: mortality, body mass index (BMI), self-perceived health status, number of primary care visits, Elixhauser Comorbidity Index (calculated using comorbidity R package v1.0.7 [ 16 ]), primary care diagnosis; and (iv) medication dispensed in pharmacies: type of medication and quantity. To minimize attrition bias, longitudinal comparisons were made until the end of 2019, or in the case of those variables measured at one-timepoint, the closest available register for the general population was obtained. Detailed information about all variables and missing rates can be found in Supplementary Table S1 . For this study, we grouped all primary care diagnosis into three-digits ICD-10 codes [ 17 ] and retained 20 diagnosis groups according to their chronicity (CCI Version 2021.1 [ 18 ]), prevalence (with at least 5%) and relevance in terms of morbidity; as well as the 20 more prevalent types of cancer (see Supplementary Table S1 ). For the medicament use, we selected all those dispensed at the pharmacy grouped by second level ATC code [ 19 ] with at least 5% of prevalence in the general population. A total of 37 drug groups of 11 categories (first level ATC code) were retained for the comparison. The final list of selected diseases include: Type 2 diabetes mellitus (E11), Overweight and obesity (E66), Disorders of lipoprotein metabolism and other lipidemias (E78), Alcohol related disorders (F10), Nicotine dependence (F17), Major depressive disorder, single episode (F32), Other anxiety disorders (F41), Migraine (G43), Essential (primary) hypertension (I10), Angina pectoris (I20), Chronic ischemic heart disease (I25), Atrial fibrillation and flutter (I48), Heart failure (I50), Cerebral infarction (I63), Atherosclerosis (I70), Vasomotor and allergic rhinitis (J30), Other chronic obstructive pulmonary disease (J44), Asthma (J45), Psoriasis (L40), Osteoporosis without current pathological fracture (M81). Statistical analysis For categorical variables, statistical differences were computed with Fisher’s exact test , and Odds Ratio (OR), 95% confidence interval (CI) and p-value were calculated. For continuous variables, we used t-test to calculate the p-value. The threshold for statistical significance in both tests was p-value < 0.05. Lifetime prevalence, by sex, was computed as the number of individuals diagnosed at least once in a lifetime for a specific disease by the total number of individuals in the cohort. Mortality rates and cancer incidence were calculated as the number of deaths or cases by the total number of person-years at risk, and compared using the Exact Rate Ratio Test, calculating the OR, 95% CI and p-value. Individuals with missing data in any variable were excluded for that particular comparison. Comparisons were done both aggregated, and stratified by age and sex to account for potential confounders. In addition, we performed a sensitivity analysis comparing the GCAT individuals included in the analysis and the ones excluded due to the unavailability of EHR, as these individuals could have a different profile and introduce a selection bias. The comparison was performed using the gtsummary package in R. Weighting We computed raked weights, an iterative proportional fitting procedure that adjusts one variable at a time [ 20 , 21 ] for the GCAT cohort to be representative for the whole population. Calculation of weights was performed in R [ 22 ] using the anesrake package v0.80. The package adjusts survey weights to enhance representativeness by aligning sample characteristics with known population benchmarks, utilizing an iterative proportional fitting (raking) methodology based on demographic variables. To identify the best model, we compared the performance of all possible variable combinations (6,911 iterations, Supplementary Table S2 ), forcing sex and birth year into the model and allowing only one source at a time for each variable. Only individuals with complete data for these variables were included in this calculation. The best performance was determined by computing disease prevalence of the selected individuals after weighting and selected the one more similar to the general population (Supplementary Fig. 2). The model with the best performance included 10 variables; sex, birth year, rurality, education level, civil status, occupation status, smoking habit, household size, self-perceived health status and number of primary care visits, with a maximum weight of 10 per individual (equivalent to 10 individuals), and no convergence issues. The number of individuals with complete data for theses variables was 13,018 (97%). Results For this analysis, 69.3% of the GCAT participants met the inclusion criteria, including 13,434 individuals, who were then compared with age matching SIDIAP, ESCA, IDESCAT or INE subjects. Representativeness assessment in the GCAT cohort To determine the extent of the healthy volunteer bias in the GCAT cohort, we compare a set of variables available for the general population, divided in four domains. Sociodemographic characteristics The comparison of sociodemographic variables is detailed for all individuals in Table 1 , and in Supplementary Table S3 for sex and age-stratified analysis. The analysis shows the following results: Age and gender GCAT participants are younger with a higher female-to-male ratio. Residence Cohort participants reside in more urban and less deprived areas, with a markedly overrepresentation when compared at the extreme quintile of the distribution (less deprived). Educational attainment As expected, a higher proportion subjects have a higher education level. Employment status Employment rates were higher among GCAT participants, as well as the ones of retired individuals, whilst the proportion of unemployed, incapacity and home duties were lower. Civil status The proportion of single and widowed individuals is lower than the general population, whereas the ones of married and separated/divorced individuals is higher. Household size Household sizes of 2 and more than 4 are less prevalent among GCAT participants. Table 1 Comparison of sociodemographic characteristics between the general population of Catalonia and the GCAT cohort before and after applying raked weights. SIDIAP IDESCAT INE ESCA GCAT GCAT weighted % % Diff % % Diff N 1,625,075 1,930,005/ 2,068,697 1,459 13,434 13,018 Gender* # Female (%) 49.85 51.21/50.54 49.42 58.57 8.72 49.85 0.00 Age/Birth year* # Mean (SD) 57.61 (5.72) -/58.13 (5.98) 57.81 (5.59) 57.18 (5.4) -0.43 57.59 -0.02 1951–1955 20.1 16.25 -3.85 20.1 0.00 1956–1960 23.31 22.73 -0.58 23.31 0.00 1961–1965 26.49 30.56 4.07 26.49 0.00 1966–1970 30.11 30.46 0.35 30.11 0.00 Deprivation index (%) Inferior [-2.58,-0.87] 14.14 47.32 33.18 41.25 27.11 Intermediate low [-0.86,-0.28] 31.11 27.64 -3.47 30.18 -0.93 Intermediate [-0.27,0.21] 31.76 13.71 -18.05 14.70 -17.06 Intermediate high [0.22,0.82] 18.50 8.95 -9.55 10.78 -7.72 Superior [0.83,4.88] 4.50 2.38 -2.12 3.08 -1.42 Rurality* Urban 93.32 98.63 5.31 93.32 0.00 Rural 6.68 1.37 -5.31 6.68 0.00 Education level (%)* # Primary or less 8.84 22.18 13.24 22.18 0.00 Secondary 1 34.79 21.99 11.00 21.99 0.00 Secondary 2 25.33 24.04 27.37 24.04 0.00 Higher 31.03 31.80 48.40 31.80 0.00 Employment status Employed 64.08 71.47 7.39 61.63 -2.45 Inactive 23.86 18.16 -5.70 26.12 2.26 Unemployed 12.06 10.37 -1.69 12.26 0.20 Occupation status* Working 61.63 71.47 9.84 61.63 0.00 Retired 4.59 7.08 2.49 4.59 0.00 Home duties 11.21 6.54 -4.67 11.21 0.00 Incapacity 7.85 2.40 -5.45 7.85 0.00 Unemployed 14.71 12.50 -2.21 14.71 0.00 Civil status* Single 13.54 9.29 -4.25 13.54 0.00 Married 71.26 73.50 2.24 71.26 0.00 Separated/Divorced 10.85 13.82 2.97 10.85 0.00 Widowed 4.36 3.38 -0.98 4.36 0.00 Household size* 1 8.51 10.09 1.58 8.51 0.00 2 32.66 28.46 -4.20 32.66 0.00 3 25.35 26.80 1.45 25.35 0.00 4 25.47 28.32 2.85 25.47 0.00 5+ 8.01 6.33 -1.68 8.01 0.00 * Bias key indicators, used to generate the raked weights # Source for the comparison and to generate the raked weights if applies Lifestyle habits Smoking and alcohol consumption were compared. See detailed results for all individuals in Table 2 , and in Supplementary Table S4 for sex and age-stratified analysis. The analysis shows the following results: Smoking habits The proportion of current smokers is lower among GCAT participants compared to the general population, while the proportion of ex-smokers is higher. Alcohol Consumption In the GCAT cohort, alcohol consumption exhibits a U-shaped distribution, with higher proportions of both high- and low-risk drinkers compared to the general population. The disparity is more pronounced when comparing GCAT to SIDIAP (medical records) than to ESCA (health survey). Table 2 Comparison of lifestyle habits characteristics between the general population of Catalonia and the GCAT cohort before and after applying raked weights. SIDIAP ESCA GCAT GCAT weighted % % Diff % % Diff N 1,625,075 1,459 13,434 13,018 Smoking habit* # Smoker 28.21 29.30 19.80 -9.50 29.30 0.00 Ex-smoker 26.22 21.77 42.62 20.85 21.77 0.00 Non-smoker 45.57 48.92 37.58 -11.34 48.92 0.00 Alcohol # High-risk 2.53 3.18 3.96 0.78 4.50 1.32 Low risk 47.95 67.17 75.24 8.07 71.24 4.07 Non-drinker 49.53 29.65 20.79 -8.86 23.88 -5.77 * Bias key indicators, used to generate the raked weights # Source for the comparison and to generate the raked weights if applies Health-related factors Five health-related variables (Table 3 and Supplementary Table S5 for sex and age-stratified analysis), 20 common chronic conditions and the 20 most common cancer types. were analysed. Mortality Mortality rates are lower in the GCAT cohort compared with the general population across all sexes and age ranges considered (Fig. 1 a). BMI The GCAT cohort has a higher proportion of individuals who are overweight or obese compared to the general population. Self-perceived health status The proportion of individuals in the cohort who perceive their health status as "good" is significantly higher, while those describing their health as "regular," "bad," or "very bad" are lower. However, the proportion of individuals rating their health as "very good" is similar to that of the general population. Healthcare services use Healthcare service usage by the targeted population may affect prevalence estimates. A comparison of diagnosis-associated primary care visits reveals that GCAT younger participants, born between 1961 and 1970, have more primary care visits than the general population. In contrast, older individuals have a similar number of primary care visits to that of the general population. Elixhauser Comorbidity Index GCAT cohort participants exhibit a U-shaped distribution in comorbidity scores, with lower overall scores indicating fewer comorbidities, but with more pronounced differences at the extremes (0 and 4+). Table 3 Comparison of health-related factors between the general population of Catalonia and the GCAT cohort before and after applying raked weights. SIDIAP ESCA GCAT GCAT weighted % % Diff % % Diff N 1,625,075 1,459 13,434 13,018 Mortality Mortality rate per 1,000 person-years 3.76 1.02 -2.74 1.35 -2.41 Body mass index Underweight 0.84 0.20 -0.64 0.18 -0.66 Normal weight 38.86 29.84 -9.02 26.66 -12.20 Overweight 42.95 43.34 0.39 43.18 0.23 Obesity 17.34 26.62 9.28 30.02 12.68 Self-perceived health status* Very good 15.25 15.42 0.17 15.25 0.00 Good 59.76 72.56 12.80 59.76 0.00 Regular 18.95 11.13 -7.82 18.95 0.00 Bad or very bad 6.04 0.89 -5.15 6.04 0.00 Primary care visits per year* Mean (SD) 1.19 (1.02) 1.31 (1.09) 0.12 1.26 0.07 0–4 25.79 24.23 -1.56 25.79 0.00 5–9 23.08 19.16 -3.92 23.08 0.00 10–14 18.85 18.35 -0.50 18.85 0.00 15+ 32.27 38.26 5.99 32.27 0.00 Elixhauser Comorbidity Index Mean (SD) 1.61 (1.67) 1.30 (1.41) -0.31 1.38 -0.23 0 32.12 37.72 5.60 37.96 5.84 1 24.69 25.58 0.89 24.07 -0.62 2 18.34 17.92 -0.42 17.18 -1.16 3 11.78 10.73 -1.05 10.98 -0.80 4+ 13.07 8.05 -5.02 9.80 -3.27 * Bias key indicators, used to generate the raked weights Lifetime Prevalence : Differences were observed in 15 out of 20 common chronic diseases when comparing the GCAT cohort to the general population; with a lower prevalence of type 2 diabetes (E11), alcohol related disorders (F10), nicotine dependence (F17), essential hypertension (I10), angina pectoris (I20), chronic ischemic heart disease (I25), atrial fibrillation and flutter (I48), heart failure (I50), cerebral infarction (I63) and COPD (J44). On the other hand, among GCAT participants, a higher prevalence of migraine (G43) and vasomotor and allergic rhinitis (J30) was observed. Some diseases exhibited significant gender bias; lower prevalence for overweight and obesity (E66) and disorders of lipid metabolism (E78) among females compared with the general population, and higher prevalence of asthma (J45) among men. In some conditions, no significant differences were observed for major depressive disorder (F32), anxiety (F41), atherosclerosis (I70), Psoriasis (L40) and osteoporosis (M81). (Fig. 1 c, Supplementary Table S5). Cancer The overall lifetime prevalence of any cancer was lower among GCAT participants, with their risk of having any type of cancer being half of that from the general population, being remarkable the lower prevalence in; secondary neoplasms (C78, C79), bronchus and lung cancer (C34), ovarian (C56), uterus (C54), female breast cancer (C50), bladder cancer (C67) in men and colon cancer (C18) in women, and the absence of any liver cancer (C22) case in men. The only exception was non-melanoma skin cancer (C44) cases with a higher prevalence among GCAT participants (Fig. 1 d, Supplementary Table S5). Regarding cancer incidence, the rate for any cancer is lower in the GCAT cohort compared to the general population, except in older age groups (> 60 years), where no significant difference is observed. (Fig. 1 b, Supplementary Table S5). Medication use The data shows that, overall, medication usage patterns in the GCAT cohort indicates a similar to or slightly higher use compared to the general population, though there are notable exceptions: the GCAT cohort uses cardiovascular medications (C codes), diabetes medications (A10), and antidiarrheals, intestinal anti-inflammatory/anti-infective agents (A07) less frequently (Fig. 2 a, Supplementary Table S6). Additionally, while the mean number of prescriptions per person is generally similar or slightly lower in the GCAT cohort, there are notable sex-specific differences: GCAT women use psychoanaleptics (N06), thyroid therapy (H03), and nasal preparations (R01) more frequently, whereas GCAT men use agents acting on the renin-angiotensin system (C09), anti-inflammatory and antirheumatic products (M01), and antihistamines for systemic use (R06) more frequently compared to the general population (Fig. 2 b, Supplementary Table S6). In the sensitivity analysis, we didn’t observe overall differences in the compared variables between GCAT included individuals and excluded individuals not linked to EHR (Supplementary Table S7). Raked Weighting After assessing and determining the presence of a healthy volunteer bias, we computed raked weights to improve the cohort representativeness. Most of the bias key indicators belongs to the sociodemographic domain (sex, birthday, rurality, educational attainment, employment status, civil status, household size), one to the lifestyle domain (smoking), and two to the health-related domain (self-perceived health status, number of primary care visits). After applying raked weights, the GCAT profile aligns with the general population on the variables used for weighting, such as age, gender, and education, indicating that the sample has been effectively adjusted to reflect the broader population. Beyond matching these specific variables, the raked weights also improve the estimates of other variables not directly used in the weighting process (Deprivation index, employment status, alcohol use), suggesting that the weighted GCAT profile provides more representative and generalizable results for most variables (Table 1 – 3 ). In the case of lifetime disease prevalence of the 20 selected diseases (Supplementary Table S8) we observed that the prevalence estimate improves, being similar to the general population in 19 of them, however overweight and obesity weighted prevalence is overestimated using the selected weights (Fig. 3 ). Estimations for certain diseases, such as asthma, psoriasis, and osteoporosis, remain similar, demonstrating robust values from the cohort, while previously underestimated conditions—like T2 diabetes, disorders of lipoprotein metabolism, essential hypertension, and other chronic obstructive pulmonary diseases—now have increased estimates that align more closely with the general population. In contrast, conditions related to toxic habits, such as alcohol-related disorders and nicotine dependence, show increased but still underestimated estimates compared to public data. For mental health conditions, survey weights correct initial overestimates observed in the cohort, particularly for major depression, other anxiety disorders, and migraine. Additionally, for less frequent (< 5%) cardiovascular diseases (I code), weights also improve estimates for angina pectoris, ischemic heart disease, atrial fibrillation and flutter, heart failure, cerebral infarction, and atherosclerosis. Discussion In this study, we analysed a range of measures across sociodemographic, lifestyle, disease, and medication categories, using several public health and statistical databases from the Catalan Health Service (CatSalut) and the Spanish National Institute of Statistics (INE) and identified key metrics to assess the representativeness of the GCAT cohort as a population-based sample of Catalonia. Our analysis uncovered key differences between the GCAT cohort and the general population, shedding light on the cohort's representativeness and potential sources of bias. These findings are essential for accurately interpreting results and effectively applying them to primary prevention efforts and broader public health strategies. Women and younger individuals seem to be overrepresented in the GCAT cohort, and volunteers generally have a higher level of education and tend to reside in less deprived and more urban areas compared to the broader population, both factors often associated with healthier lifestyles and better access to healthcare. Concordantly, lower mortality rate and reduced prevalence estimates for common disease conditions (such as cancer, endocrine, and cardiovascular diseases) are observed from the GCAT cohort. Higher socioeconomic status (SES) correlates with a higher education attainment and being employed. Participants with higher SES might have a greater interest in contributing to scientific studies, motivated and health-conscious, despite being associated with time constraints, they are aware of the importance of these type of studies [ 23 ], introducing a healthy volunteer bias. Regarding civil status, higher divorce rates observed among female participant can be explained by higher SES, and an increased economic independence leading to greater autonomy [ 24 ], whilst lower prevalence of widowed females in the GCAT cohort due to healthier partners is in line with the overall better health status of the participants. Higher prevalence of marriages among male participants could be explained by the well-documented relationship between marriage and good health [ 25 ]. Household size differences reflect the differences in civil status as well as in socioeconomic status, as higher economic independence allows fewer cohabitants. Gender bias has been explained by the reverse gender gap in volunteer activities is associated with cultural gender roles and inequality [ 26 ]. Regarding age bias, there is variability in the literature. In our case, the overrepresentation of younger individuals may be linked to the fact that 97% of the cohort consists of current blood donors. Blood donors in the 45–54 age range make up 24% of the donor population, but this proportion declines significantly in older age groups (18.15% in the 55–59 age group and 10.52% in the 60–69 age group) [ 27 ]. Despite the decrease in blood donations among older individuals, the decline in GCAT participation was not as pronounced for this age range. This resulted in an overrepresentation of older women in the GCAT cohort compared to blood donors, likely due to fewer time constraints. Smoking prevalence is lower in the cohort, but unexpectedly, alcohol users are overrepresented. Higher education and SES are typically associated with lower smoking rates; however, higher SES also correlates with increased alcohol consumption, potentially due to social use and cultural factors. Methodology (quantitative in GCAT vs qualitative by the interviewer) differences and social stigma associated with alcohol abuse might influence self-reported data, being people tending to report more desirable answers in the presence of an interviewer [ 28 ], an idea which is reinforced by the observation that alcohol-related disorder diagnoses (ICD-10 code F10) show a lower prevalence among GCAT participants. A similar trend was observed in smoking habit, were both sources are available (Supplementary Table S9). This result outlines the urgent need for enhanced and comprehensive data collection practices during routine healthcare visits; effective personalized support tools that consider individual risk behaviours, integrating real-world data solutions into current healthcare practices is imperative Healthy volunteer bias has been already observed in other population-based cohorts [ 29 – 31 ]. Overall, a lower prevalence of compared illnesses was observed among unweighted cohort participants, particularly with regard to cardiovascular and endocrine diseases, which are known morbid risk factors for cardiovascular conditions [ 32 ]. These diseases are profoundly influenced by unhealthy lifestyle habits, such as poor diet, lack of physical activity, inadequate sleep, and smoking [ 33 ], as well as psychosocial risks stemming from poor health during childhood in families with low socioeconomic status [ 34 , 35 ]. A similar healthy trend is observed in cancer prevalence, probably as indicative of healthier lifestyle habits, a more health-consciousness and proactive health behaviours, as observed in other general-population studies [ 31 ], but keeping in mind the blood donors profile of the cohort with few exceptions [ 36 ]. Intriguingly, in contrast non-melanoma skin cancer (NMSC) is more prevalent among GCAT participants. An association with higher socioeconomic status was already observed [ 37 ], in particular in basal cell carcinoma, a type of non-melanoma skin cancer, associated with intermittent sun exposure and sunburn, probably due to more leisure time, leading to outdoor activities and holidays in the sun, as well as for the socially favoured perception of tanning [ 38 ]. Healthcare access or awareness among GCAT participants, coupled with an expected higher health literacy, may contribute to these higher estimates. This increased access and awareness could result in better disease management and prevention, potentially explaining the observed trends [ 39 ]. Weight restrictions in blood donation could have impacted the representation of overweight and obese individuals in the GCAT cohort. However, interestingly, this does not translate to a higher prevalence of overweight and obesity diagnoses (E66), which remains similar to that of the general population. This reinforces the notion that BMI measures may have limitations in accurately determining body composition and obesity [ 40 ]. Furthermore, although GCAT participants generally use fewer medications overall, they exhibit a wider variety of medication use. This may be attributed to higher observed SES and better access to healthcare, which can facilitate access to a broader range of medications. This trend could also reflect more personalized and comprehensive healthcare, potentially linked to observed educational attainment and thus a higher health literacy. These factors may influence estimates in follow-up periods and should be considered carefully in the future. After applying raked weights, we found that chronic conditions, even those with very low frequency, such as certain cardiovascular diseases, show improved estimates, highlighting the effectiveness of the weighting process. Overestimated mental health conditions, are also successfully corrected using the multidomain raked approach. High frequent common diseases, such as type 2 diabetes, hypertension, and lipidemias, also show improved estimates after applying the weights. However, disorders related to toxic habits, like alcohol-related disorders and nicotine dependence, remain underestimated in the cohort, suggesting that the current survey on toxic habits may not adequately capture or identify the true prevalence. Multidomain raked weights calculated using mostly demographic variables, reduced participation bias and incorporated low SES and low educational attainment groups who are less represented in population-based surveys. The inclusion of these groups in the weighting formula has significantly enhanced the accuracy of estimates within the cohort, benefiting not only disease prevalence but also key variables like the deprivation index and comorbidity. These indicators are crucial for developing effective public health strategies and interventions, ultimately leading to better health outcomes for under-represented populations. In the same way, the inclusion of complementary general population databases has allowed a better representativeness, as the best model included variables from ESCA, SIDIAP and IDESCAT (INE was only used for Employment status). Interestingly, when more than one source was available, the best option for the model was ESCA. This is probably because is a survey-based database, as the GCAT. Our study is unique in its application of raked weights using multiple population benchmarks (from SIDIAP, ESCA, IDESCAT, and INE) to adjust the GCAT cohort, which is a methodological approach not previously used in this context. The study's strengths include the use of weights that increase the representativeness of the cohort, a consistent variable selection from shared EHR data sources (GCAT and SIDIAP), and gender and age-range stratification to uncover health-related differences. However, limitations exist, such as the non-representation of individuals excluded in the analysis (not included in the birth year range or with missing data), the dependence of the availability of reliable data to make the comparisons, and potential data gaps before 2010, which could underrepresent healthier individuals or those with complex needs treated by specialists. Estimating early-onset disease diagnosis is challenging with this data, though it remains reliable for late-onset diseases. There is sample overlap between GCAT and general population sources, but as it is minimal (< 1%), it has a meaningless impact on test performance [ 41 ]. Although exposure-disease associations remain reliable as long as relevant exposures are well-represented [ 42 ], we recommend other study authors to assess the representativeness of their samples and, if they are biased, select the best weighting model to account for it, enhancing the utility of the data in shaping public health strategies, particularly in the area of prevention. They also must be aware that the performance of this methodology depends on the variables included in the model, and the representativeness should be checked after applying it. In conclusion, the integration of multidomain weights—covering sociodemographic traits, lifestyle choices, and health-related indicators—has effectively addressed the inherent biases in disease prevalence estimates within the GCAT cohort. This adjustment has improved the alignment of the cohort's characteristics with those of the general Catalan population, thereby enhancing its representativeness and overall applicability. Additionally, attrition and changes in healthcare access during the follow-up period warrant further calibration to ensure the accuracy and reliability of future analyses. Declarations Ethics approval The GCAT study was approved by the Germans Trias I Pujol University Hospital Ethical Committee (CEIC-HGTP) on April 26 th , 2013 (PI-13-020). SIDIAP Data Use was approved by the Scientific and Ethical Committees on December 18 th , 2019 (19/518-P). Data availability Original data from GCAT cohort is available upon request to the authors (corresponding author: [email protected] ). All the data generated during this study (results) is available as supplementary data. Supplementary data Supplementary data are available online. Author contributions NB, LACR, CV and RdC contributed to the study conceptualization and design. RdC and CV participated in the funding acquisition. XF, SIG, CV and RdC participated in data acquisition. NB and LACR performed data collection and curation. NB performed formal analysis, interpretation and visualization. NB and RdC drafted the manuscript, and ALL authors participated in the review and editing of the final manuscript. Funding GCAT was funded by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; and have additional support by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) [SGR 01537], Spanish National Grant [PI18/01512]. The SIDIAP project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in 2019 under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Research National Plan 2013-2016 [PI19/00535], and the PFIS Grant [FI20/00040], co-funded with European Union European Regional Development Fund funds. Acknowledgements This study makes use of data generated by the GCAT Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació IGTP. IGTP is part of the CERCA Program / Generalitat de Catalunya. This study was carried out using anonymized data provided by the Catalan Agency for Quality and Health Assessment, within the framework of the PADRIS Program. The authors of this study would like to acknowledge all GCAT project investigators who contributed to the generation of the GCAT data. A full list of the investigators is available from www.genomesforlife.com/. We thank the Blood and Tissue Bank from Catalonia (BST) and all the GCAT volunteers that participated in the study. We gratefully acknowledge Beatriz Cortés (2019-2022) and Anna Carreras (2012-2022), former GCAT workers for their contributions to this study. Conflict of interest None declared. References Hunter, D. J. Gene-environment interactions in human diseases. Nat. Rev. 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The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog Cardiovasc. Dis. 70 , 8–15. https://doi.org:10.1016/j.pcad.2021.12.001 (2022). Clark, A. M., DesMeules, M., Luo, W., Duncan, A. S. & Wielgosz, A. Socioeconomic status and cardiovascular disease: risks and implications for care. Nat. Rev. Cardiol. 6 , 712–722. https://doi.org:10.1038/nrcardio.2009.163 (2009). Schultz, W. M. et al. Socioeconomic Status and Cardiovascular Outcomes: Challenges and Interventions. Circulation 137 , 2166–2178. https://doi.org:10.1161/CIRCULATIONAHA.117.029652 (2018). Banc de Sang i Teixits. Blood donation , https://donarsang.gencat.cat/ (. Farre, X. et al. Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort. Genes (Basel) . 14. https://doi.org:10.3390/genes14010149 (2023). Steding-Jessen, M. et al. Socioeconomic status and non-melanoma skin cancer: a nationwide cohort study of incidence and survival in Denmark. Cancer Epidemiol. 34 , 689–695. https://doi.org:10.1016/j.canep.2010.06.011 (2010). Beaulieu, D., Gao, D. X., Swetter, S. M., Hawryluk, E. B. & Geller, A. C. Association between income and suspected nonmelanoma and melanoma skin cancers among participants of the American Academy of Dermatology's SPOT Skin Cancer screening program: A cross-sectional analysis. J. Am. Acad. Dermatol. 86 , 1401–1403. https://doi.org:10.1016/j.jaad.2021.05.048 (2022). Wu, Y., Li, D. & Vermund, S. H. Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity. Int. J. Environ. Res. Public. Health . 21 https://doi.org:10.3390/ijerph21060757 (2024). Hayes, L. J. & Berry, G. Comparing the part with the whole: should overlap be ignored in public health measures? J. Public. Health (Oxf) . 28 , 278–282. https://doi.org:10.1093/pubmed/fdl038 (2006). Rothman, K. J., Gallacher, J. E. & Hatch, E. E. Why representativeness should be avoided. Int. J. Epidemiol. 42 , 1012–1014. https://doi.org:10.1093/ije/dys223 (2013). Additional Declarations No competing interests reported. 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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-5219180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431653883,"identity":"6d188586-0818-4cad-b0e2-4327d17ea02e","order_by":0,"name":"Rafael de Cid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFCCBCBis+EhWUsaDwMbmGdApBYGtsMMxGsxb08++OFB2XkZ+fjeAww/av4Q1iJz5lmyRMK52zyGx/gSGHuOEWGLhESOGUNiG1BLG48BMwMbUVryvwG1nINq+UecLWxALQd45NmAWhjbiNHC88wY6JdkHgO2vISDvX3GRGhhT3748UeZnb1889mDD358kyOsBQ4MDvAwHCBBPRDIN5CUZkbBKBgFo2AkAQDaLjLtGD4KjgAAAABJRU5ErkJggg==","orcid":"","institution":"Genomes for Life-GCAT lab. 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(\u003cstrong\u003ea\u003c/strong\u003e) Mortality rate by sex. In the x-axis the age range and in the y-axis the mortality rate during the follow-up period. (\u003cstrong\u003eb\u003c/strong\u003e) Age at first cancer, including all malignant cancer types (ICD-10 codes C00-C99). In the x-axis the age range and in the y-axis the cancer incidence. (\u003cstrong\u003ec\u003c/strong\u003e) Disease prevalence. In the y-axis, the selected diseases (ICD-10 code) and in the x-axis the lifetime prevalence of each disease. (\u003cstrong\u003ed\u003c/strong\u003e) Cancer prevalence. In the y-axis, the different cancers (ICD-10 code) and in the x-axis the lifetime prevalence of each one. * stands for P\u0026lt;0.05, ** stands for P\u0026lt;0.01, *** stands for P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/0072576ccb05c313125d36f7.png"},{"id":79085981,"identity":"4de5a990-04b3-4c36-8ce5-a704e3dd40c2","added_by":"auto","created_at":"2025-03-24 09:11:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1512997,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of medication use between GCAT Cohort and general Catalan population. (a) Drug use prevalence by sex. In the y-axis, the different groups of ATC codes and in the x-axis the prevalence of use for each group of drugs. (b) Number of mean prescriptions per person. In the y-axis, the different groups of ATC codes and in the x-axis the mean of prescriptions per person. * stands for P\u0026lt;0.05, ** stands for P\u0026lt;0.01, *** stands for P\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/dbdb99509f5e8ea62242f310.png"},{"id":79084701,"identity":"cd00da8c-fd8b-43f8-b01d-753f9b474b63","added_by":"auto","created_at":"2025-03-24 09:03:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115510,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot of the lifetime disease prevalence between GCAT and the general Catalan population, before (yellow) and after weighting (brown) in a selection of diseases. In the x-axis the disease prevalence and in the y-axis the different diseases (ICD-10 code and description).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/95c818d1d740d012c5acfadd.png"},{"id":83068038,"identity":"f65c01e5-e02b-49db-af9c-4499884bb3e6","added_by":"auto","created_at":"2025-05-19 16:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3073218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/b5518a7d-e57b-44ac-8bcd-cd8c790a52bf.pdf"},{"id":79084119,"identity":"477bb426-637f-4d2c-86ed-4121c468c8f8","added_by":"auto","created_at":"2025-03-24 08:55:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":157779,"visible":true,"origin":"","legend":"","description":"","filename":"CohortcomparisonSupFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/981bba1e0bc2b1479006f1c8.docx"},{"id":79084703,"identity":"9717f4f2-a622-45b2-bfbd-43a3b1ee3ee9","added_by":"auto","created_at":"2025-03-24 09:03:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":314229,"visible":true,"origin":"","legend":"","description":"","filename":"Cohortcomparisonsuptables1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5219180/v1/a7107bafde15c7679175c775.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weighting Health-Related Estimates in the GCAT Cohort and the General Population of Catalonia","fulltext":[{"header":"Key Message","content":"\u003cp\u003ePopulation-based cohorts usually encompass a healthy volunteer bias, including healthier individuals with higher socioeconomic status. This bias challenges the generalisation of the results to the entire population. Detection and quantification of this bias is important for results interpretation. Raked weights can be used to mitigate the impact of the healthy volunteer bias.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDiseases are caused by a combination of multiple genetic factors, environmental exposures and lifestyle habits, and they represent the majority of diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Some of these factors have not yet been identified due to its complex interplay, which increases with multimorbidity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To this purpose, longitudinal population-based cohorts play an important role, as they usually involve a large number of well characterised individuals, representative of the population. These characteristics are not only important for risk factors detection, but to develop general prevention strategies and treatments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGCAT is an adult population-based cohort from Catalonia, Spain, consisting of volunteer residents aged between 40\u0026ndash;65 years at the time of recruitment (2014\u0026ndash;2017) with a total of 19,390 participants. We collected longitudinal health-related data, at recruitment, and follow-up in 2018, 2020, 2021 and 2023. Yearly follow up is done by access their electronic health record (EHR) from public health system data and residence for environmental exposure mapping [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEHR-linked population-based cohorts offer an extremely rich longitudinal characterization of individual-level health factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], however, population-based cohorts commonly exhibit biases due to the healthy volunteer effect, which is a form of selection bias wherein volunteer participants tend to possess better health compared to the general population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Even if this does not invalidate the research analysis itself, it can challenge the generalization of the results obtained, as some population groups, such as individuals with low socioeconomic status or poor health, are not well represented. This type of bias can be mitigated using weighting approaches, such as raked weights, which improve the accuracy of prevalence estimates for health conditions and risk factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we compare the GCAT cohort with their age-matching general population of Catalonia, stratifying by age and sex, in order to assess their differences in sociodemographic, lifestyle, and health-related factors. In addition, we identify bias key indicators and create raked weights to mitigate the bias and enhance cohort\u0026rsquo;s representativeness.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign, setting and study population\u003c/h2\u003e \u003cp\u003eThis is an observational longitudinal study in which we have compared the GCAT cohort with four databases(SIDIAP, ESCA, IDESCAT and INE, described below) representative of the general population of Catalonia (Spain), a region with 7,675,217 inhabitants in 2019 according to the official population figures of the Municipal Register [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e\u003cb\u003eGCAT cohort.\u003c/b\u003e Is a population-based cohort from Catalonia, with active follow-up, who enrolled voluntarily in the study (2014\u0026ndash;2018) with the unique restriction of being between 40 and 65 years old. Participants were recruited through the national wide blood and tissue donation centres of the Blood and Tissue Bank of Catalonia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], but no restricted to usual donors. Specifically, informed participants agreed to participate in the study, provided informed consent and allowed access to the EHR from the public healthcare system for passive follow-up and to be contacted regularly to collect follow-up information on lifestyle and additional information. Multiple individual variables are available: Anthropometric measurements were taken, blood samples were provided, and participants completed a comprehensive self-reported questionnaire about sociodemographic factors, lifestyle habits and diseases. Multiomics data has been generated including genotypes (SNP-Array), sequencing data (WGS), metabolome, proteome, and epigenetic data for a subset of individuals in the cohort. Participants can opt-out or withdraw their consent for specific areas of research. See cohort protocol description in Ob\u0026oacute;n-Santacana et al 2018 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The GCAT study was approved by the Germans Trias I Pujol University Hospital Ethical Committee (PI-13-020) and all research was performed in accordance with relevant guidelines and regulations following the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eAs general population, for comparison purposes we used: \u003cb\u003eSIDIAP\u003c/b\u003e, the Information System for Performing Primary Care Research (SIDIAP) database collects pseudo-anonymized EHRs from 328 primary care centres in Catalonia managed by the Catalan Health Institute since 2005, and it currently has data on almost 80% of the Catalan population and is a reliable representation of the region in terms of age, sex and geographic distribution [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cb\u003eESCA\u003c/b\u003e, Health Survey of Catalonia (2015) is the official survey of the Department of Health and includes information about health status, lifestyle and use of health services for the whole population resident in Catalonia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cb\u003eIDESCAT\u003c/b\u003e, the Statistical Institute of Catalonia, that since 1989 provide official statistics of Catalonia, including social, demographic and economic data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cb\u003eINE\u003c/b\u003e, the National Statistics Institute, that since 1945 provides official statistics of Spain, mainly on sociodemographic and economic characteristics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe participants included in the analysis met the following inclusion criteria: alive and living in Catalonia on the GCAT recruitment start date (2014) and born between 1951 and 1970 to maximise the overlap between databases. Individuals from the GCAT with no EHR link, detected to be duplicated, that opted out, or with discordant sex or birthdate were excluded. Finally, a total number of 13,434 GCAT, 1,625,075 SIDIAP, 1,459 ESCA, 1,930,005 IDESCAT and 2,068,697 INE individuals were included in the comparison study (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eDomains and variables\u003c/h3\u003e\n\u003cp\u003eData collected included 17 variables in four main domains (i) sociodemographic: sex, age, deprivation index of baseline residence [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], rurality, educational attainment, employment status, civil status, household size; (ii) lifestyle habits: smoking habit and alcohol consumption, (iii) health-related factors: mortality, body mass index (BMI), self-perceived health status, number of primary care visits, Elixhauser Comorbidity Index (calculated using comorbidity R package v1.0.7 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]), primary care diagnosis; and (iv) medication dispensed in pharmacies: type of medication and quantity. To minimize attrition bias, longitudinal comparisons were made until the end of 2019, or in the case of those variables measured at one-timepoint, the closest available register for the general population was obtained. Detailed information about all variables and missing rates can be found in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor this study, we grouped all primary care diagnosis into three-digits ICD-10 codes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and retained 20 diagnosis groups according to their chronicity (CCI Version 2021.1 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), prevalence (with at least 5%) and relevance in terms of morbidity; as well as the 20 more prevalent types of cancer (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For the medicament use, we selected all those dispensed at the pharmacy grouped by second level ATC code [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] with at least 5% of prevalence in the general population. A total of 37 drug groups of 11 categories (first level ATC code) were retained for the comparison.\u003c/p\u003e \u003cp\u003eThe final list of selected diseases include: Type 2 diabetes mellitus (E11), Overweight and obesity (E66), Disorders of lipoprotein metabolism and other lipidemias (E78), Alcohol related disorders (F10), Nicotine dependence (F17), Major depressive disorder, single episode (F32), Other anxiety disorders (F41), Migraine (G43), Essential (primary) hypertension (I10), Angina pectoris (I20), Chronic ischemic heart disease (I25), Atrial fibrillation and flutter (I48), Heart failure (I50), Cerebral infarction (I63), Atherosclerosis (I70), Vasomotor and allergic rhinitis (J30), Other chronic obstructive pulmonary disease (J44), Asthma (J45), Psoriasis (L40), Osteoporosis without current pathological fracture (M81).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor categorical variables, statistical differences were computed with \u003cem\u003eFisher\u0026rsquo;s exact test\u003c/em\u003e, and Odds Ratio (OR), 95% confidence interval (CI) and p-value were calculated. For continuous variables, we used \u003cem\u003et-test\u003c/em\u003e to calculate the p-value. The threshold for statistical significance in both tests was p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Lifetime prevalence, by sex, was computed as the number of individuals diagnosed at least once in a lifetime for a specific disease by the total number of individuals in the cohort. Mortality rates and cancer incidence were calculated as the number of deaths or cases by the total number of person-years at risk, and compared using the Exact Rate Ratio Test, calculating the OR, 95% CI and p-value. Individuals with missing data in any variable were excluded for that particular comparison. Comparisons were done both aggregated, and stratified by age and sex to account for potential confounders. In addition, we performed a sensitivity analysis comparing the GCAT individuals included in the analysis and the ones excluded due to the unavailability of EHR, as these individuals could have a different profile and introduce a selection bias. The comparison was performed using the gtsummary package in R.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWeighting\u003c/h3\u003e\n\u003cp\u003eWe computed raked weights, an iterative proportional fitting procedure that adjusts one variable at a time [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] for the GCAT cohort to be representative for the whole population. Calculation of weights was performed in R [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] using the anesrake package v0.80. The package adjusts survey weights to enhance representativeness by aligning sample characteristics with known population benchmarks, utilizing an iterative proportional fitting (raking) methodology based on demographic variables. To identify the best model, we compared the performance of all possible variable combinations (6,911 iterations, Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), forcing sex and birth year into the model and allowing only one source at a time for each variable. Only individuals with complete data for these variables were included in this calculation. The best performance was determined by computing disease prevalence of the selected individuals after weighting and selected the one more similar to the general population (Supplementary Fig.\u0026nbsp;2). The model with the best performance included 10 variables; sex, birth year, rurality, education level, civil status, occupation status, smoking habit, household size, self-perceived health status and number of primary care visits, with a maximum weight of 10 per individual (equivalent to 10 individuals), and no convergence issues. The number of individuals with complete data for theses variables was 13,018 (97%).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor this analysis, 69.3% of the GCAT participants met the inclusion criteria, including 13,434 individuals, who were then compared with age matching SIDIAP, ESCA, IDESCAT or INE subjects.\u003c/p\u003e\n\u003ch3\u003eRepresentativeness assessment in the GCAT cohort\u003c/h3\u003e\n\u003cp\u003eTo determine the extent of the healthy volunteer bias in the GCAT cohort, we compare a set of variables available for the general population, divided in four domains.\u003c/p\u003e\n\u003ch3\u003eSociodemographic characteristics\u003c/h3\u003e\n\u003cp\u003eThe comparison of sociodemographic variables is detailed for all individuals in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and in Supplementary Table S3 for sex and age-stratified analysis. The analysis shows the following results:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAge and gender\u003c/strong\u003e \u003cp\u003eGCAT participants are younger with a higher female-to-male ratio.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResidence\u003c/strong\u003e \u003cp\u003eCohort participants reside in more urban and less deprived areas, with a markedly overrepresentation when compared at the extreme quintile of the distribution (less deprived).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEducational attainment\u003c/strong\u003e \u003cp\u003eAs expected, a higher proportion subjects have a higher education level.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEmployment status\u003c/strong\u003e \u003cp\u003eEmployment rates were higher among GCAT participants, as well as the ones of retired individuals, whilst the proportion of unemployed, incapacity and home duties were lower.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCivil status\u003c/strong\u003e \u003cp\u003eThe proportion of single and widowed individuals is lower than the general population, whereas the ones of married and separated/divorced individuals is higher.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHousehold size\u003c/strong\u003e \u003cp\u003eHousehold sizes of 2 and more than 4 are less prevalent among GCAT participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of sociodemographic characteristics between the general population of Catalonia and the GCAT cohort before and after applying raked weights.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSIDIAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIDESCAT INE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGCAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eGCAT weighted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,625,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,930,005/ 2,068,697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13,018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.21/50.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge/Birth year*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.61 (5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-/58.13 (5.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.81 (5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.18 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1951\u0026ndash;1955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1956\u0026ndash;1960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1961\u0026ndash;1965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1966\u0026ndash;1970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeprivation index (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior [-2.58,-0.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate low [-0.86,-0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate [-0.27,0.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-17.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate high [0.22,0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-7.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior [0.83,4.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRurality*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level (%)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation status*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncapacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCivil status*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* Bias key indicators, used to generate the raked weights\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e# Source for the comparison and to generate the raked weights if applies\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle habits\u003c/h2\u003e \u003cp\u003eSmoking and alcohol consumption were compared. See detailed results for all individuals in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and in Supplementary Table S4 for sex and age-stratified analysis. The analysis shows the following results:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSmoking habits\u003c/strong\u003e \u003cp\u003eThe proportion of current smokers is lower among GCAT participants compared to the general population, while the proportion of ex-smokers is higher.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e \u003cp\u003eIn the GCAT cohort, alcohol consumption exhibits a U-shaped distribution, with higher proportions of both high- and low-risk drinkers compared to the general population. The disparity is more pronounced when comparing GCAT to SIDIAP (medical records) than to ESCA (health survey).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lifestyle habits characteristics between the general population of Catalonia and the GCAT cohort before and after applying raked weights.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSIDIAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGCAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGCAT weighted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,625,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13,018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking habit*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Bias key indicators, used to generate the raked weights\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e# Source for the comparison and to generate the raked weights if applies \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHealth-related factors\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFive health-related variables (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table S5 for sex and age-stratified analysis), 20 common chronic conditions and the 20 most common cancer types. were analysed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMortality\u003c/strong\u003e \u003cp\u003eMortality rates are lower in the GCAT cohort compared with the general population across all sexes and age ranges considered (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBMI\u003c/strong\u003e \u003cp\u003eThe GCAT cohort has a higher proportion of individuals who are overweight or obese compared to the general population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSelf-perceived health status\u003c/strong\u003e \u003cp\u003eThe proportion of individuals in the cohort who perceive their health status as \"good\" is significantly higher, while those describing their health as \"regular,\" \"bad,\" or \"very bad\" are lower. However, the proportion of individuals rating their health as \"very good\" is similar to that of the general population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHealthcare services use\u003c/strong\u003e \u003cp\u003eHealthcare service usage by the targeted population may affect prevalence estimates. A comparison of diagnosis-associated primary care visits reveals that GCAT younger participants, born between 1961 and 1970, have more primary care visits than the general population. In contrast, older individuals have a similar number of primary care visits to that of the general population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eElixhauser Comorbidity Index\u003c/strong\u003e \u003cp\u003eGCAT cohort participants exhibit a U-shaped distribution in comorbidity scores, with lower overall scores indicating fewer comorbidities, but with more pronounced differences at the extremes (0 and 4+).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of health-related factors between the general population of Catalonia and the GCAT cohort before and after applying raked weights.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSIDIAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eESCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGCAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGCAT weighted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% Diff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,625,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13,018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate per 1,000 person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-perceived health status*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad or very bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary care visits per year*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElixhauser Comorbidity Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Bias key indicators, used to generate the raked weights \u003cb\u003eLifetime Prevalence\u003c/b\u003e: Differences were observed in 15 out of 20 common chronic diseases when comparing the GCAT cohort to the general population; with a lower prevalence of type 2 diabetes (E11), alcohol related disorders (F10), nicotine dependence (F17), essential hypertension (I10), angina pectoris (I20), chronic ischemic heart disease (I25), atrial fibrillation and flutter (I48), heart failure (I50), cerebral infarction (I63) and COPD (J44). On the other hand, among GCAT participants, a higher prevalence of migraine (G43) and vasomotor and allergic rhinitis (J30) was observed. Some diseases exhibited significant gender bias; lower prevalence for overweight and obesity (E66) and disorders of lipid metabolism (E78) among females compared with the general population, and higher prevalence of asthma (J45) among men. In some conditions, no significant differences were observed for major depressive disorder (F32), anxiety (F41), atherosclerosis (I70), Psoriasis (L40) and osteoporosis (M81). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Supplementary Table S5).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCancer\u003c/strong\u003e \u003cp\u003eThe overall lifetime prevalence of any cancer was lower among GCAT participants, with their risk of having any type of cancer being half of that from the general population, being remarkable the lower prevalence in; secondary neoplasms (C78, C79), bronchus and lung cancer (C34), ovarian (C56), uterus (C54), female breast cancer (C50), bladder cancer (C67) in men and colon cancer (C18) in women, and the absence of any liver cancer (C22) case in men. The only exception was non-melanoma skin cancer (C44) cases with a higher prevalence among GCAT participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Supplementary Table S5). Regarding cancer incidence, the rate for any cancer is lower in the GCAT cohort compared to the general population, except in older age groups (\u0026gt;\u0026thinsp;60 years), where no significant difference is observed. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Supplementary Table S5).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMedication use\u003c/h2\u003e \u003cp\u003eThe data shows that, overall, medication usage patterns in the GCAT cohort indicates a similar to or slightly higher use compared to the general population, though there are notable exceptions: the GCAT cohort uses cardiovascular medications (C codes), diabetes medications (A10), and antidiarrheals, intestinal anti-inflammatory/anti-infective agents (A07) less frequently (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Table S6). Additionally, while the mean number of prescriptions per person is generally similar or slightly lower in the GCAT cohort, there are notable sex-specific differences: GCAT women use psychoanaleptics (N06), thyroid therapy (H03), and nasal preparations (R01) more frequently, whereas GCAT men use agents acting on the renin-angiotensin system (C09), anti-inflammatory and antirheumatic products (M01), and antihistamines for systemic use (R06) more frequently compared to the general population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, we didn\u0026rsquo;t observe overall differences in the compared variables between GCAT included individuals and excluded individuals not linked to EHR (Supplementary Table S7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRaked Weighting\u003c/h2\u003e \u003cp\u003eAfter assessing and determining the presence of a healthy volunteer bias, we computed raked weights to improve the cohort representativeness. Most of the bias key indicators belongs to the sociodemographic domain (sex, birthday, rurality, educational attainment, employment status, civil status, household size), one to the lifestyle domain (smoking), and two to the health-related domain (self-perceived health status, number of primary care visits).\u003c/p\u003e \u003cp\u003eAfter applying raked weights, the GCAT profile aligns with the general population on the variables used for weighting, such as age, gender, and education, indicating that the sample has been effectively adjusted to reflect the broader population. Beyond matching these specific variables, the raked weights also improve the estimates of other variables not directly used in the weighting process (Deprivation index, employment status, alcohol use), suggesting that the weighted GCAT profile provides more representative and generalizable results for most variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the case of lifetime disease prevalence of the 20 selected diseases (Supplementary Table S8) we observed that the prevalence estimate improves, being similar to the general population in 19 of them, however overweight and obesity weighted prevalence is overestimated using the selected weights (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Estimations for certain diseases, such as asthma, psoriasis, and osteoporosis, remain similar, demonstrating robust values from the cohort, while previously underestimated conditions\u0026mdash;like T2 diabetes, disorders of lipoprotein metabolism, essential hypertension, and other chronic obstructive pulmonary diseases\u0026mdash;now have increased estimates that align more closely with the general population. In contrast, conditions related to toxic habits, such as alcohol-related disorders and nicotine dependence, show increased but still underestimated estimates compared to public data. For mental health conditions, survey weights correct initial overestimates observed in the cohort, particularly for major depression, other anxiety disorders, and migraine. Additionally, for less frequent (\u0026lt;\u0026thinsp;5%) cardiovascular diseases (I code), weights also improve estimates for angina pectoris, ischemic heart disease, atrial fibrillation and flutter, heart failure, cerebral infarction, and atherosclerosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analysed a range of measures across sociodemographic, lifestyle, disease, and medication categories, using several public health and statistical databases from the Catalan Health Service (CatSalut) and the Spanish National Institute of Statistics (INE) and identified key metrics to assess the representativeness of the GCAT cohort as a population-based sample of Catalonia. Our analysis uncovered key differences between the GCAT cohort and the general population, shedding light on the cohort's representativeness and potential sources of bias. These findings are essential for accurately interpreting results and effectively applying them to primary prevention efforts and broader public health strategies.\u003c/p\u003e \u003cp\u003eWomen and younger individuals seem to be overrepresented in the GCAT cohort, and volunteers generally have a higher level of education and tend to reside in less deprived and more urban areas compared to the broader population, both factors often associated with healthier lifestyles and better access to healthcare. Concordantly, lower mortality rate and reduced prevalence estimates for common disease conditions (such as cancer, endocrine, and cardiovascular diseases) are observed from the GCAT cohort.\u003c/p\u003e \u003cp\u003eHigher socioeconomic status (SES) correlates with a higher education attainment and being employed. Participants with higher SES might have a greater interest in contributing to scientific studies, motivated and health-conscious, despite being associated with time constraints, they are aware of the importance of these type of studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], introducing a healthy volunteer bias. Regarding civil status, higher divorce rates observed among female participant can be explained by higher SES, and an increased economic independence leading to greater autonomy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], whilst lower prevalence of widowed females in the GCAT cohort due to healthier partners is in line with the overall better health status of the participants. Higher prevalence of marriages among male participants could be explained by the well-documented relationship between marriage and good health [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Household size differences reflect the differences in civil status as well as in socioeconomic status, as higher economic independence allows fewer cohabitants.\u003c/p\u003e \u003cp\u003eGender bias has been explained by the reverse gender gap in volunteer activities is associated with cultural gender roles and inequality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Regarding age bias, there is variability in the literature. In our case, the overrepresentation of younger individuals may be linked to the fact that 97% of the cohort consists of current blood donors. Blood donors in the 45\u0026ndash;54 age range make up 24% of the donor population, but this proportion declines significantly in older age groups (18.15% in the 55\u0026ndash;59 age group and 10.52% in the 60\u0026ndash;69 age group) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite the decrease in blood donations among older individuals, the decline in GCAT participation was not as pronounced for this age range. This resulted in an overrepresentation of older women in the GCAT cohort compared to blood donors, likely due to fewer time constraints.\u003c/p\u003e \u003cp\u003eSmoking prevalence is lower in the cohort, but unexpectedly, alcohol users are overrepresented. Higher education and SES are typically associated with lower smoking rates; however, higher SES also correlates with increased alcohol consumption, potentially due to social use and cultural factors. Methodology (quantitative in GCAT vs qualitative by the interviewer) differences and social stigma associated with alcohol abuse might influence self-reported data, being people tending to report more desirable answers in the presence of an interviewer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], an idea which is reinforced by the observation that alcohol-related disorder diagnoses (ICD-10 code F10) show a lower prevalence among GCAT participants. A similar trend was observed in smoking habit, were both sources are available (Supplementary Table S9). This result outlines the urgent need for enhanced and comprehensive data collection practices during routine healthcare visits; effective personalized support tools that consider individual risk behaviours, integrating real-world data solutions into current healthcare practices is imperative\u003c/p\u003e \u003cp\u003eHealthy volunteer bias has been already observed in other population-based cohorts [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Overall, a lower prevalence of compared illnesses was observed among unweighted cohort participants, particularly with regard to cardiovascular and endocrine diseases, which are known morbid risk factors for cardiovascular conditions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These diseases are profoundly influenced by unhealthy lifestyle habits, such as poor diet, lack of physical activity, inadequate sleep, and smoking [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], as well as psychosocial risks stemming from poor health during childhood in families with low socioeconomic status [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A similar healthy trend is observed in cancer prevalence, probably as indicative of healthier lifestyle habits, a more health-consciousness and proactive health behaviours, as observed in other general-population studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but keeping in mind the blood donors profile of the cohort with few exceptions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Intriguingly, in contrast non-melanoma skin cancer (NMSC) is more prevalent among GCAT participants. An association with higher socioeconomic status was already observed [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], in particular in basal cell carcinoma, a type of non-melanoma skin cancer, associated with intermittent sun exposure and sunburn, probably due to more leisure time, leading to outdoor activities and holidays in the sun, as well as for the socially favoured perception of tanning [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Healthcare access or awareness among GCAT participants, coupled with an expected higher health literacy, may contribute to these higher estimates. This increased access and awareness could result in better disease management and prevention, potentially explaining the observed trends [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Weight restrictions in blood donation could have impacted the representation of overweight and obese individuals in the GCAT cohort. However, interestingly, this does not translate to a higher prevalence of overweight and obesity diagnoses (E66), which remains similar to that of the general population. This reinforces the notion that BMI measures may have limitations in accurately determining body composition and obesity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, although GCAT participants generally use fewer medications overall, they exhibit a wider variety of medication use. This may be attributed to higher observed SES and better access to healthcare, which can facilitate access to a broader range of medications. This trend could also reflect more personalized and comprehensive healthcare, potentially linked to observed educational attainment and thus a higher health literacy. These factors may influence estimates in follow-up periods and should be considered carefully in the future.\u003c/p\u003e \u003cp\u003eAfter applying raked weights, we found that chronic conditions, even those with very low frequency, such as certain cardiovascular diseases, show improved estimates, highlighting the effectiveness of the weighting process. Overestimated mental health conditions, are also successfully corrected using the multidomain raked approach. High frequent common diseases, such as type 2 diabetes, hypertension, and lipidemias, also show improved estimates after applying the weights. However, disorders related to toxic habits, like alcohol-related disorders and nicotine dependence, remain underestimated in the cohort, suggesting that the current survey on toxic habits may not adequately capture or identify the true prevalence. Multidomain raked weights calculated using mostly demographic variables, reduced participation bias and incorporated low SES and low educational attainment groups who are less represented in population-based surveys. The inclusion of these groups in the weighting formula has significantly enhanced the accuracy of estimates within the cohort, benefiting not only disease prevalence but also key variables like the deprivation index and comorbidity. These indicators are crucial for developing effective public health strategies and interventions, ultimately leading to better health outcomes for under-represented populations. In the same way, the inclusion of complementary general population databases has allowed a better representativeness, as the best model included variables from ESCA, SIDIAP and IDESCAT (INE was only used for Employment status). Interestingly, when more than one source was available, the best option for the model was ESCA. This is probably because is a survey-based database, as the GCAT.\u003c/p\u003e \u003cp\u003eOur study is unique in its application of raked weights using multiple population benchmarks (from SIDIAP, ESCA, IDESCAT, and INE) to adjust the GCAT cohort, which is a methodological approach not previously used in this context. The study's strengths include the use of weights that increase the representativeness of the cohort, a consistent variable selection from shared EHR data sources (GCAT and SIDIAP), and gender and age-range stratification to uncover health-related differences. However, limitations exist, such as the non-representation of individuals excluded in the analysis (not included in the birth year range or with missing data), the dependence of the availability of reliable data to make the comparisons, and potential data gaps before 2010, which could underrepresent healthier individuals or those with complex needs treated by specialists. Estimating early-onset disease diagnosis is challenging with this data, though it remains reliable for late-onset diseases. There is sample overlap between GCAT and general population sources, but as it is minimal (\u0026lt;\u0026thinsp;1%), it has a meaningless impact on test performance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Although exposure-disease associations remain reliable as long as relevant exposures are well-represented [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], we recommend other study authors to assess the representativeness of their samples and, if they are biased, select the best weighting model to account for it, enhancing the utility of the data in shaping public health strategies, particularly in the area of prevention. They also must be aware that the performance of this methodology depends on the variables included in the model, and the representativeness should be checked after applying it.\u003c/p\u003e \u003cp\u003eIn conclusion, the integration of multidomain weights\u0026mdash;covering sociodemographic traits, lifestyle choices, and health-related indicators\u0026mdash;has effectively addressed the inherent biases in disease prevalence estimates within the GCAT cohort. This adjustment has improved the alignment of the cohort's characteristics with those of the general Catalan population, thereby enhancing its representativeness and overall applicability. Additionally, attrition and changes in healthcare access during the follow-up period warrant further calibration to ensure the accuracy and reliability of future analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe GCAT study was approved by the Germans Trias I Pujol University Hospital Ethical Committee (CEIC-HGTP) on April 26\u003csup\u003eth\u003c/sup\u003e, 2013 (PI-13-020). SIDIAP Data Use was approved by the Scientific and Ethical Committees on December 18\u003csup\u003eth\u003c/sup\u003e, 2019 (19/518-P).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOriginal data from GCAT cohort is available upon request to the authors (corresponding author: [email protected]). All the data generated during this study (results) is available as supplementary data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupplementary data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data are available online.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNB, LACR, CV and RdC contributed to the study conceptualization and design. RdC and CV participated in the funding acquisition. XF, SIG, CV and RdC participated in data acquisition. NB and LACR performed data collection and curation. NB performed formal analysis, interpretation and visualization. NB and RdC drafted the manuscript, and ALL authors participated in the review and editing of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGCAT was funded by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya [ADE 10/00026]; and have additional support by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) [SGR 01537], Spanish National Grant [PI18/01512]. The SIDIAP project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in 2019 under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Research National Plan 2013-2016 [PI19/00535], and the PFIS Grant [FI20/00040], co-funded with European Union European Regional Development Fund funds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study makes use of data generated by the GCAT Genomes for Life, a cohort study of the Genomes of Catalonia, Fundació IGTP. IGTP is part of the CERCA Program / Generalitat de Catalunya. This study was carried out using anonymized data provided by the Catalan Agency for Quality and Health Assessment, within the framework of the PADRIS Program. The authors of this study would like to acknowledge all GCAT project investigators who contributed to the generation of the GCAT data. A full list of the investigators is available from www.genomesforlife.com/. We thank the Blood and Tissue Bank from Catalonia (BST) and all the GCAT volunteers that participated in the study. We gratefully acknowledge Beatriz Cortés (2019-2022) and Anna Carreras (2012-2022), former GCAT workers for their contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHunter, D. J. Gene-environment interactions in human diseases. \u003cem\u003eNat. Rev. 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Epidemiol.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 1012\u0026ndash;1014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/ije/dys223\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/ije/dys223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"GCAT, Cohort, Bias, Raked Weights, Representativeness, Population health","lastPublishedDoi":"10.21203/rs.3.rs-5219180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5219180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePopulation-based cohorts play a key role in personalized medicine. However, it is known that cohorts are affected by the \"healthy volunteer bias\" where participants are generally healthier than the broader population, compromising its representativeness. Here, we assess the healthy bias, identifying bias key indicators for representativeness of the GCAT cohort, encompassing 20 000 adult participants of Catalonia, and generating survey raked survey weights to enhance the cohort\u0026rsquo;s comparability. To assess and correct the bias, we compare multiple variables across sociodemographic, lifestyle, diseases and medication domains. Electronic health records of Catalonia (SIDIAP), the Health Survey of Catalonia (ESCA) and registers from the statistics institute of Catalonia (IDESCAT) and Spain (INE) were used to make the comparisons. We observed that the GCAT cohort is enriched in women and younger individuals, with higher socioeconomic status, more health conscious and healthier in terms of mortality and chronic disease prevalence. Raked survey weighting identified sex, birth year, rurality, education level, civil status, occupation status, smoking habit, household size, self-perceived health status and number of primary care visits as key weight variables. On average, raked weights reduced the differences by 70% for compared variables, and by 26% in disease prevalence estimates. We can conclude that the application of raked weights has enhanced the cohort's representativeness, improved comparability, and yielded more precise estimates when analysing GCAT data.\u003c/p\u003e","manuscriptTitle":"Weighting Health-Related Estimates in the GCAT Cohort and the General Population of Catalonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 08:55:50","doi":"10.21203/rs.3.rs-5219180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-11T13:59:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T13:54:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T11:04:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T10:54:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45149668506850193149609639758557172534","date":"2025-03-22T15:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28679928657904839358011330049201668433","date":"2025-03-21T10:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22585467690788327653598060888240948260","date":"2025-03-20T14:38:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T14:28:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T05:01:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-10T15:50:29+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":"e3e71235-8dd7-49ac-ad81-d609989d121d","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45977007,"name":"Health sciences/Diseases"},{"id":45977008,"name":"Health sciences/Medical research"},{"id":45977009,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-19T16:06:07+00:00","versionOfRecord":{"articleIdentity":"rs-5219180","link":"https://doi.org/10.1038/s41598-025-01284-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-16 15:57:31","publishedOnDateReadable":"May 16th, 2025"},"versionCreatedAt":"2025-03-24 08:55:50","video":"","vorDoi":"10.1038/s41598-025-01284-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01284-9","workflowStages":[]},"version":"v1","identity":"rs-5219180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5219180","identity":"rs-5219180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}