Risk factors for Self-Reported Diagnosed Cataract among older adults in Poland. Findings from PolSenior2 Study.

Risk factors for Self-Reported Diagnosed Cataract among older adults in Poland. 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Findings from PolSenior2 Study. Natalia Lange, Kacper Jagiełło, Piotr Bandosz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308277/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose : The aim of our study was to investigate sociodemographic factors, comorbidities and health behaviors associated with self-reported diagnosed cataract in a large, nationally representative population of older adults in Poland, aged 60 and above. Patient and Methods : An analysis was conducted on a survey among 5956 participants of the nationally representative PolSenior2 study conducted between 2018 and 2019. Multiple logistic regression analysis was employed to ascertain the association between self-reported diagnosed cataract and sociodemographic factors, health behaviors, and comorbidities. Results : In the final multivariate model, the odds radio (OR) of self-reported diagnosed cataract were 1.71 times higher among women compared to men. Additionally, the odds increased significantly with age, with 70-79-year-olds having 3.38 times higher odds, 80-89-year-olds having 8.08 times higher odds, and those aged 90 years and older having 10.76 times higher odds compared to the reference group (60-69 years old). The prevalence of self-reported diagnosed cataract was found to be 1.47 times higher among individuals with diabetes, 1.20 times higher among those with hypertension, and 1.25 times higher among tobacco users compared to their respective counterparts. Additionally, rural dwellers exhibited a lower risk for self-reported cataract (OR = 0.63). Conclusion : Our study revealed a positive correlation between several demographic and health factors—namely, older age, female gender, urban residence, hypertension, diabetes, and smoking—and an elevated risk of cataract. 1. Introduction Cataract is the leading cause of reversible blindness and visual impairment worldwide [ 1 ], including Poland [ 2 , 3 ]. Epidemiological studies have shown that cataract results from multiple contributing factors [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Risk factors for, and causes of, cataract include ageing, genetics, lifestyle exposure, behaviors, infections, various health conditions, and socioeconomic factors [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Several cross-sectional studies worldwide have examined the prevalence of self-reported diagnosed cataract and their associated risk factors [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. However, there is a notable absence of data on this topic in Poland [ 14 ]. Due to the significant influence of cataract on public health and its widespread repercussions, cataract has consistently been the subject of ongoing epidemiological research. Understanding the underlying causes that can be addressed through intervention helps lessen the impact of the disease [ 15 ]. Therefore, the aim of our study was to investigate sociodemographic factors, comorbidities and health behaviors, associated with self-reported diagnosed cataract in a large, nationally representative population of older adults in Poland, aged 60 and above. 2. Materials and Methods 2.1 Data source The data used in this analysis were derived from the PolSenior2 study, which was carried out between 2018 and 2019. This study comprised a cross-sectional survey involving a representative sample of 5987 individuals aged 60 years and above. Subjects were randomly selected from all regions of Poland. We used a three-stage stratified draw. The first stage draw identified local administrative units, including urban, rural, and urban-rural municipalities in each of 16 voivodships (regions). Towns and cities were divided into five groups, depending on size: ≤ 20,000 residents, 20,000–50,000 residents, 50,000–200,000 residents, 200,000–500,000 residents, and > 500,000 residents. The final number of territorial strata was 78. The number of respondents to be drawn in each stratum was proportional to its population size and was based on the population structure in December 2016. Then, 137 municipalities were drawn in previously defined strata, with the probability proportional to the size of the population aged 60 + years in a particular municipality [ 16 ]. The study protocol included three paper-based questionnaires, specialized geriatric assessments, anthropometric measurements, blood pressure evaluations, and analyses of blood and urine samples [ 16 ]. Trained nurses conducted the medical and socioeconomic sections of the questionnaire through face-to-face interviews during three home visits with the participants. Additionally, certain data were gathered from the self-administered portion of the questionnaire, completed individually by the respondents [ 16 ]. Each participant involved in the study provided written informed consent before their inclusion. The study protocol received approval from the Bioethics Committee of the Medical University of Gdansk (NKBBN/257/2017). Initial ophthalmology screenings were carried out during the first visit. Data from 5956 subjects were collected and analyzed. A small fraction of respondents (0.5%) were excluded due to either non-response regarding cataract diagnosis or responses indicating uncertainty ("I don't remember/I don't know"). However, the absence of this information appeared to be random. 2.2 Self-reported cataract Information regarding the presence of cataract was obtained through questions in the Medical Questionnaire, specifically: "Has your doctor diagnosed you with cataract in either eye?" Respondents were presented with the options "Yes/No/I don't remember, I don't know." 2.3 Sociodemographic variables Data on patients' gender, age, and place of residence were obtained from the Ministry of Digitization. To determine the subjects' education level, the respondents were presented with the question “What is your current education level?”. The possible answers were (1) no education (2) incomplete primary, (3) primary, (4) middle school (5) vocational high school, (6) high school, (7) 2-year college, (8) bachelor’s, engineer’s degree, and (9) master's degree. To simplify the analysis, education levels (1)-(3) were classified as "Primary or incomplete primary", (4)-(6) were classified as “Middle” and (7)-(9) were classified as "Higher". The financial situation of the household was assessed with the question: “Which of the following sentences best describes the financial situation of your household?” The possible answers were (1) “We live comfortably without having to save for special purchases.”, (2) “We live frugally and we have enough to cover all of our expenses;”, (3) “We need to put money aside to save for special purchases.”, (4) “We have enough money for basic needs such as food and clothing.”, (5) “We only have enough money for food.”, (6) “We don’t have enough money even to meet our basic needs.” For the consistency of the results, the responses were grouped into respective categories: Can easily afford everything (1), Can afford everything but only when saving (2), Self-reported poverty(3)(4)(5)(6). Quality of life (QoL „How would you rank your overall quality of life? The possible choices were: „high”, „ normal”, „low”. 2.4 Health status variables Diabetes mellitus was noted when a patient declared that it was previously diagnosed, or if fasting glucose was ≥ 126 mg/dL, or using hypoglycemic drugs was reported. Hypertension was diagnosed if average blood pressure values from two measurements during each visit were equal to or higher than 140mmHg (Systolic Blood Pressure) and/or 90mmHg (Diastolic Blood Pressure), or if the patient was taking hypotensive drugs over the past 2 weeks because of an earlier diagnosis of hypertension. Participants' nutritional risk was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF), which has a maximum score of 14 points. Those scoring less than 12 points were grouped into a category termed "poor nutritional status" (PNS) [ 17 ]. Obesity was defined as the Body Mass Index (BMI) ≥ 30.0 kg/m2, using an approved Tanita BC-545N portable electronic scale, with an accuracy of 0.1 kg [ 18 ]. Hypercholesterolemia was defined as total cholesterol level ≥ 190 mg/dl (≥ 5.0 mmol/l) or taking statins/fibrates. We assessed the risk of depression with the short version of the Geriatric Depression Scale (GDS), composed of 15 questions. Subjects who received at least 6 points in the GDS were classified as having depression symptoms. The GDS was omitted in participants with the Mini–Mental State Examination (MMSE) score of fewer than 19 points to rule out unreliable answers given by subjects suspected of moderate/severe dementia. Information about stroke was determined by the questions “Have you ever been diagnosed with stroke?” and the possible answers were “Yes” or “No”. Two health behavioral variables were assessed by providing answers to the following questions: „Have you ever regularly smoked tobacco (cigarettes, pipe)?” and „How often did you drink vodka or other alcoholic beverages (wine, beer) in the last 12 months?” To simplify the analysis, we devised the following classification system: respondents indicating "twice a month and more" were categorized as moderate to heavy drinkers, those reporting „once a month or less” were classified as light drinkers, and those indicating "none" were categorized as non-drinkers. 2.5 Statistical analysis The data management and the statistical analyses were performed with R version 3.6.3 R (R Core Team, version 3.6.3) and SAS 9.4 TS Level 1M5 (SAS Institute Inc., Cary, NC, USA). The data was presented as percentages with 95% confidence intervals. A univariate and multivariable logistic regression models were created to determine the association between binary dependent variable and covariates. The multiple logistic regression model contains independent variables which were Age, Sex, Education Level, Financial situation of the household, Quality of Life, Place of residence, Stroke, Hypertension, Diabetes, Obesity, Tobacco, Alcohol, Hypercholesterolemia, and Poor Nutritional Status. Sampling weights were included in statistical calculations to account for the complex survey design using R survey package. The post-stratification procedure was used to match age–sex sample distribution to the population of Poland. The level of significance was set at p < 0.05. 3. Results 3.1 Prevalence of Self-Reported Diagnosed Cataract The overall occurrence of cataract among older adults in Poland was found to be 22.6% (95% CI: 21.2–24.1). Notably, the prevalence was significantly higher among women, at 27.4%, compared to men, at 16.5%. Moreover, the prevalence of cataract showed an increase with age and was more pronounced among individuals with primary education (29.9%), those reporting financial hardship (27.5%), and individuals with medium (28.3%) to low (29.3%) QOL. Urban residents also exhibited a higher prevalence, at 25.0% (Table 1 ). Table 1 Self-reported cataract in the population of Polish seniors by sociodemographic factors, comorbidities, health behaviors. Variables Cataract % (95% CI) Sociodemographic factors Age 60–69 70–79 80–89 90+ 11.4(9.7–13.1) 27.6(25.0-30.2) 50.6(47.0-54.3) 60.7(55.1–66.2) Gender Female Male 27.4(25.3–29.5) 16.5(14.8–18.3) Education level Higher Middle Primary and incomplete primary 19.7(16.0-23.4) 20.7(18.6–22.7) 29.9(26.4–33.4) Place of residence Urban Rural areas 25.0(23.2–26.7) 19.6(17.5–21.8) Financial situation of the household Can easily afford everything Can afford everything but only when saving Self-reported poverty 17.7(14.9–20.5) 22.0(20.1–23.8) 27.5(24.1–30.9) Quality of life High Medium Low 20.8(19.1–22.4) 28.3(24.9–31.6) 29.3(22.6–36.0) Comorbidities Hypertension Yes No 24.4(22.8–26.0) 18.6(15.6–21.6) Diabetes mellitus Yes No 30.8(27.2–34.4) 20.3(18.8–21.9) Obesity Yes No 24.2(21.7–26.6) 21.9(20.1–23.6) Poor Nutritional Status Yes No 29.3(26.6–31.9) 20.4(18.9–22.0) Hypercholesterolemia Yes No 22.1(20.5–23.7) 25.1(22.1–28.0) Depression Yes No 30.9(27.8–34.1) 19.1(17.5–20.6) Stroke Yes No 34.9(28.1–41.7) 21.8(20.2–23.3) Health behaviors Tobacco Yes No 20.8(18.7–23.0) 25.1(23.0-27.1) Alcohol Moderate to heavy drinkers Light drinkers Non-drinkers 16.7(14.5–18.9) 20.2(17.8–22.6) 32.3(29.3–35.4) Additionally, the prevalence of cataract was elevated among those with comorbidities including stroke (34.9%), depression (30.9%), diabetes (30.8%), malnutrition (29.3%), and arterial hypertension (24.4%), in contrast to those without these conditions, except for obesity, hypercholesterolemia, and smoking. Non-drinkers had a significantly higher prevalence of cataract (32.3%) compared to light and moderate to heavy drinkers (Table 1 ). 3.2. Univariate logistic regression analysis The presence of self-reported diagnosed cataract in univariate logistic regression analysis (Table 2 ) was associated with increasing age (70–79, 80–89, ≤ 90 versus age 60–69, OR = 3.26, 7.41, 9.85 respectively), female gender (OR = 1.44), primary education (OR = 1.45), self-reported poverty (OR = 1.33), medium and low QoL (OR = 1.5; 1.84), diabetes (OR = 1.57), arterial hypertension (OR = 1.52), stroke (OR = 1.59), malnutrition (OR = 1.62). There was an inverse association between self-reported cataract and rural residence (OR = 0.68), hypercholesterolemia (OR = 0.86), nicotinism (OR = 0.85), any alcohol use (OR = 0.61 for light drinkers and OR = 0.49 for moderate to heavy drinkers). Table 2 Factors associated with prevalence of self-reported cataract. Univariate logistic regression. Variables OR lower.CI upper.CI P-value Sociodemographic factors Age 60–69 (ref) - - - - 70–79 3.26 2.76 3.85 < 0.001 80–89 7.41 6.25 8.80 < 0.001 90+ 9.85 7.86 12.33 < 0.001 Gender Men (ref) - - - - Women 1.44 1.28 1.61 < 0.001 Education level Higher (ref) - - - - Middle 0.90 0.76 1.05 0.167 Primary and incomplete primary 1.45 1.23 1.72 < 0.001 Place of residence Urban (ref) - - - - Rural areas 0.68 0.61 0.77 < 0.001 Financial situation of the household Can easily afford everything (ref) - - - - Can afford everything but only when saving 1.10 0.95 1.29 0.208 Self-reported poverty 1.33 1.12 1.57 0.001 Quality of Life High (ref) - - - - Medium 1.50 1.32 1.70 < 0.001 Low 1.84 1.40 2.43 < 0.001 Comorbidities Hypertension No (ref) - - - - Yes 1.52 1.32 1.75 < 0.001 Diabetes (ref) No - - - - Yes 1.57 1.39 1.77 < 0.001 Poor Nutritional Status (PNS) No (ref) - - - - Yes 1.62 1.44 1.83 < 0.001 Obesity No (ref) - - - - Yes 0.99 0.88 1.12 0.931 Hypercholesterolemia No (ref) - - - - Yes 0.86 0.76 0.98 0.027 Stroke No (ref) - - - - Yes 1.59 1.32 1.91 < 0.001 Health behaviors Tabacco No (ref) - - - - Yes 0.85 0.76 0.95 0.004 Alcohol Non-drinkers (ref) - - - - Light drinkers 0.61 0.54 0.69 < 0.001 Moderate to heavy drinkers 0.49 0.42 0.58 < 0.001 3.3 Multivariate logistic regression analysis Multivariate logistic regression analysis revealed that increasing age, female gender, hypertension, diabetes, and smoking were factors significantly associated with self-reported diagnosed cataract among older adults in Poland (Table 3 ). Specifically, the odds of self-reported diagnosed cataract were 1.71 times higher among women compared to men. Additionally, the odds increased significantly with age, with 70-79-year-olds having 3.38 times higher odds, 80-89-year-olds having 8.08 times higher odds, and those aged 90 years and older having 10.76 times higher odds compared to the reference group (60–69 years old). Moreover, individuals with diabetes had 1.47 times higher odds of self-reported diagnosed cataract, while those with hypertension had 1.20 times higher odds, and tobacco users had 1.25 times higher odds compared to their counterparts. Interestingly, rural residents exhibited a lower risk for self-reported diagnosed cataract, with an odds ratio of 0.63 (Table 3 ). Table 3 Factors associated with prevalence of self-reported cataract -multiple logistic regression, The multiple logistic regression model contains independent variables which are Age, Sex, Education Level, Financial situation of the household, Quality of Life, Place of residence, Stroke, Hypertension, Diabetes, Obesity, Tobacco, Alcohol, Hypercholesterolemia, and Poor Nutritional Status. Variables OR Lower 95% CI Upper 95% CI p-value Sociodemographic factors Age 60–69 (ref) - - - - 70–79 3.38 2.83 4.05 < 0.001 80–89 8.08 6.63 9.85 < 0.001 90+ 10.76 8.13 14.24 < 0.001 Gender Men (ref) - - - - Women 1.71 1.48 1.99 < 0.001 Educational level Higher (ref) - - - - Middle 0.99 0.83 1.19 0.944 Primary 0.95 0.77 1.17 0.616 Place of residence Urban - - - - Rural areas 0.63 0.54 0.73 < 0.001 Financial situation of the household Can easily afford everything (ref) - - - - Can afford everything but only when saving 1.08 0.90 1.29 0.392 Self-reported poverty 1.22 0.99 1.50 0.061 Quality of Life High (ref) - - - - Medium 1.11 0.95 1.30 0.188 Low 1.18 0.83 1.69 0.353 Comorbidities Hypertension No (ref) - - - - Yes 1.20 1.01 1.42 0.034 Diabetes No (ref) - - - - Yes 1.47 1.27 1.70 < 0.001 Poor Nutritional Status No (ref) - - - - Yes 1.01 0.86 1.17 0.923 Obesity No (ref) - - - - Yes 0.99 0.86 1.14 0.860 Hypercholesterolemia No (ref) - - - - Yes 0.98 0.84 1.14 0.776 Stroke No (ref) - - - - Yes 1.16 0.93 1.45 0.196 Health behaviors Tobacco No (ref) - - - - Yes 1.25 1.08 1.44 0.003 Alcohol Non-drinkers - - - - Light drinkers 0.86 0.74 1.00 0.056 Moderate to heavy drinkers 0.86 0.70 1.05 0.129 OR - odds ratio, Lower 95% CI – lower bound of the 95% confidence interval, Upper 95% CI – upper bound of the 95% confidence interval 4. Discussion 4.1 Socioeconomic factor and cataract Consistent with prior studies [ 6 , 7 , 8 , 9 , 10 , 13 , 19 , 20 , 21 ], the multivariate analysis revealed a strong association between increasing age and the prevalence of cataract. For self-reported diagnosed cataract, prevalence rates ranged from 11.4% (95% CI = 9.7–13.1) in adults aged 60–69 years to 60.7% (95% CI = 55.1–66.2) in those aged 90 and above. In comparison to research conducted in China, where the prevalence of cataract based on ophthalmological examination, rather than self-reported data, ranged from 24% in those aged 60–65 years to 75% in individuals aged 85–89 years [ 22 ]. According to GUS, a Polish Census Bureau, Poland's population continues to age [ 23 ], and therefore there might be an expected increase in the number of individuals affected by cataract [ 1 ]. Our research, along with other relevant studies, indicated a higher prevalence of cataract diagnosis among women compared to men [9,11,21,24,25,]. Some studies suggest that women, on average, have longer lifespans than men, consequently placing them at a higher risk of developing age-related eye conditions [ 1 ]. Moreover, analysis of population-based surveys conducted in low- and middle-income countries consistently reveals that women are significantly less likely than men to undergo cataract surgery [ 26 , 27 ]. We have previously addressed this gender disparity in our published work [ 27 ]. Our research showed that there was a correlation between living in rural areas and diminished risk of self-reported cataract. This was also evidenced in other studies that assessed cataract prevalence through surveys [ 6 , 8 ]. This correlation may indicate that factors associated with rural living, such as reduced exposure to environmental pollutants, less screen time, or potentially even dietary differences, could have a protective effect against cataract development. However, we couldn't establish causation using these cross-sectional data. In our view, this correlation could stem from individuals residing in rural areas having limited access to healthcare, resulting in lower awareness of the disease compared to those in urban areas. Studies employing slit lamp examination found no discrepancy in cataract prevalence between rural and urban dwellings [ 19 , 28 ]. The prevalence of self-reported cataract in univariate logistic regression analysis was also found to be associated with primary education, self-reported poverty, medium, and low QoL. However, these factors did not demonstrate significance in the multivariate regression analysis, as reported in another study [ 7 , 13 ]. 4.2 Comorbidities and cataract Our study corroborated the conclusions of prior research, indicating a higher prevalence of cataract among individuals with hypertension [ 6 , 7 , 8 , 9 , 25 , 29 , 30 ] and diabetes mellitus [ 6 , 7 , 8 , 9 , 10 , 11 , 20 , 21 , 31 , 32 , 33 , 34 ]. Multivariate logistic regression analysis indicated that the prevalence of cataract was 1.47 times higher for individuals with diabetes and 1.20 with hypertension. Cataract formation in individuals with diabetes seemed to be related to hyperglycemia or to increased senile lens opacity [ 31 , 34 ]. Previous studies show that diabetic patients are 2–5 times more at risk for cataract formation and are more likely to get it at an earlier age [ 33 , 34 ]. Hypertension can cause cataract by inducing intense systemic inflammation [ 35 ]. Although several plausible mechanisms have been proposed based on laboratory results, the conclusions from epidemiologic studies remain inconsistent [ 30 ]. Our analysis revealed a relatively higher prevalence of self-reported diagnosed cataract among elderly patients with PNS, a finding confirmed by univariate regression analysis. However, there was no significant difference observed in multivariate logistic regression analysis. Also, our study found no difference between individuals with obesity and those with normal BMI. Other studies done in the areas of obesity, malnutrition and cataract show similar results. For example, in older Australian population there was no causal association between obesity and cataract [ 36 ] and obesity wasn’t associated with cataract in other studies [ 6 , 8 , 12 ]. Another study that showed similar results to ours, suggested that there was an association between protein undernutrition and an increased risk of cataract [ 37 ]. The study indicated that low protein intake may induce deficiencies of specific amino acids that are needed to maintain the health of the lens [ 37 ]. Low BMI may be associated with an increased risk for cataract [ 21 ]. In our univariable analysis, we observed an association between hypercholesterolemia and a decreased prevalence of self-reported cataract; however, this did not maintain significance in the multivariate logistic regression analysis. The oldest individuals, who are more likely to have cataract, tend to have lower cholesterol levels [ 38 ], thereby contributing to the observed association of low cholesterol with the higher prevalence of cataract. This suggests that the association in the univariate analysis is likely confounded by age. Interestingly, our findings contrast with those of other studies. For instance, research conducted among the Chinese population suggested significantly higher total cholesterol concentration in age-related cataract (ARC) patients compared to those without ARC [ 25 ]. However, hypercholesterolemia in this study was defined as a total cholesterol serum level of ≥ 5.20 mmol/L [ 25 ]. Hence, disparities between our study and theirs may arise from differences in these criteria. Notably, another study has reported no association between dyslipidemia and cataract development [ 39 ]. Our research also examined stroke as another comorbidity. Although our study showed a higher prevalence of cataract among individuals with prior stroke, this association did not emerge as a significant independent factor in multiple analyses, which is concurrent with findings from other studies [ 7 , 8 ]. However, based on some studies the association between cataract and individuals with a previous history of stroke remains unclear [ 7 , 8 ]. Our study also revealed a higher prevalence of self-reported diagnosed cataract among individuals with symptoms of depression compared to those without, which is consistent with findings from prior research [ 8 , 10 ]. Another study, however, explored the possibility of reverse causation [ 40 ]. It found that cataract may be a risk factor for major depressive disorder in the elderly, especially among the male population [ 40 ]. Research is also investigating the impact of antidepressants on the formation of cataract [ 41 , 42 ]. 4.3 Health behavior and cataract Smoking is a risk factor with robust evidence regarding the higher prevalence of cataract [ 6 , 20 , 43 ], which was also found in our study. The odds of self-reported diagnosed cataract were 1.25 for tobacco users compared to their counterparts (95% CI: 1.08–1.44, p = 0.003). Interestingly, other studies revealed that while there was a significant positive correlation between smoking and cataract, the cataractogenic impact was lower among former smokers compared to current smokers. This finding highlights the potential for reversibility in this context [ 20 , 44 ]. Furthermore, in our study nondrinkers reported significantly higher rates of cataract (32.3%) compared to light and moderate to heavy drinkers. According to a meta-analysis of one study, heavy alcohol consumption significantly elevates the risk of ARC, while moderate consumption may offer protection against cataract [ 45 ]. This meta-analysis specifically included studies that assessed cataract through lens photographs or diagnosis by ophthalmologists, excluding those reliant on self-reported questionnaires for cataract measurement. Additionally, another study indicates that individuals with low to moderate alcohol consumption have a reduced likelihood of needing cataract surgery [ 46 ]. 4.4 Strengths and limitations of study Our study has several notable strengths. Firstly, we gathered data from a large, representative, community-dwelling population aged 60 and over, with a high proportion of older individuals. Secondly, our study not only examined conventional health hazards like diabetes and smoking but also delved into less commonly explored factors such as PNS and hypercholesterolemia. Furthermore, we employed rigorous diagnostic criteria, diagnosing hypercholesterolemia and diabetes through laboratory tests, evaluating hypertension via blood pressure measurements, and assessing PNS and depression using specialized scales. Additionally, we utilized an approved Tanita BC-545N portable electronic scale to determine obesity. Perhaps most importantly, the findings we obtained might fill a crucial gap in cataract epidemiology within Poland and enhance understanding of the risk factors associated with cataract in the Polish population. Consequently, our study holds the potential to aid in the prevention of cataract. However, our study comes with several limitations. Firstly, the prevalence of cataract was determined solely based on participants' self-reported diagnoses by a doctor, potentially leading to an underestimation of the true prevalence. Similarly, data on the prevalence of stroke, sociodemographic factors, and health behaviors also relied on self-reported survey responses. Additionally, symptoms of depression were excluded from the regression analysis due to the limited sample size of individuals assessed with the GDS scale. The size of the sample was impacted by the exclusion of individuals with MMSE scores below 19 from the analysis. Lastly, it's important to note that our analysis is cross-sectional in nature, which means it only examines correlations between variables and doesn't establish causation. This design limitation precludes the assessment of causal relationships between variables within the study. 5. Conclusion Our study revealed a positive correlation between several demographic and health factors—namely, older age, female gender, urban residence, hypertension, diabetes, and smoking—and an elevated risk of cataract. Abbreviations ARC age-related cataract BMI Body Mass Index GDS Geriatric Depression Scale MMSE Mini–Mental State Examination MNA-SF Mini Nutritional Assessment-Short Form (MNA-SF) OR odds radio PNS poor nutritional status QOL quality of life Declarations Author Contributions NL conceived the project. KJ performed the statistical analysis. NL, KJ, PB analysed results. NL wrote the manuscript. PB reviewed the paper.. All authors read and approved the final manuscript Funding This paper was implemented under the contract No. 6/5/4.2/NPZ/2017/1203/1257 for the implementation of the task in the field of public health of the Operational Objective No. 5 point 4.2. of the National Health Program for years 2016-2020, entitled "Health Status and Its Socioeconomic Covariates of the Older Population in Poland - the Nationwide PolSenior2 Survey" (PolSenior2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments The acknowledgments to all the persons supporting and involved in the implementation of the PolSenior2 program in 2018-2019, especially: head of the research team-Professor Tomasz Zdrojewski, experts of each branch of medical research, rector of the Medical University of Gdansk- Professor Marcin Gruchała. Acknowledgments to Dana Guest for language correction. Availability of data and materials All data generated or analysed during this study are included in this published article. Consent for publication Not applicable. Ethics approval and consent to participate Each participant involved in the study provided written informed consent before their inclusion. The study protocol received approval from the Bioethics Committee of the Medical University of Gdansk (NKBBN/257/2017). Competing interests The authors declare that they have no competing interests. References World Health Organization. World report on vision. World Health Organ. (2019) https://apps.who.int/iris/handle/10665/328717 . Partyka O, Wysocki MJ. Epidemiology of eye diseases and infrastructure of ophthalmology in Poland. Przegl Epidemiol. 2015;69(4):773. Nowak MS, Smigielski J. The prevalence and causes of visual impairment and blindness among older adults in the city of Lodz, Poland. Medicine. 2015;94:e505. Shiels A, Hejtmancik JF. Molecular Genetics of Cataract. 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Wystepowanie zaćmy i czynniki jej ryzyka w północno-wschodnim regionie Polski [Incidence of cataract and risk factors in Northeastern Poland]. Klin Oczna. 1997;99(6):385–91. Garg P, Mullick R, Bharti Nigam B, Raj P. Risk factors associated with development of senile cataract. Ophthalmol J. 2020;5:17–24. Wierucki Ł, Kujawska-Danecka H, Mossakowska M, et al. Health status and its socio-economic covariates in the older population in Poland – the assumptions and methods of the nationwide, cross-sectional PolSenior2 survey. Archives Med Sci. 2022;18(1):92–102. Krzymińska-Siemaszko R, Deskur-Śmielecka E, Kaluźniak-Szymanowska A, Kaczmarek B, Kujawska-Danecka H, Klich-Rączka A, Mossakowska M, Małgorzewicz S, Dworak LB, Kostka T, et al. Socioeconomic Risk Factors of Poor Nutritional Status in Polish Elderly Population: The Results of PolSenior2 Study. Nutrients. 2021;13:4388. https://doi.org/10.3390/nu13124388 . Puzianowska-Kuznicka M, Kurylowicz A, Wierucki, et al. Obesity in Caucasian Seniors on the Rise: Is It Truly Harmful? Results of the PolSenior2 Study. Nutrients. 2022;14:4621. Du YF, Liu HR, Zhang Y, et al. Prevalence of cataract and cataract surgery in urban and rural Chinese populations over 50 years old: a systematic review and Meta-analysis. Int J Ophthalmol. 2022;15(1):141–9. Robman L, Taylor H. External factors in the development of cataract. Eye (Lond). 2005;19(10):1074–82. Leske MC, Wu SY, Nemesure B, Hennis A. Barbados Eye Studies Group. Risk factors for incident nuclear opacities. Ophthalmology. 2002;109(7):1303–8. Song P, Wang H, Theodoratou E, Chan KY, Rudan I. The national and subnational prevalence of cataract and cataract blindness in China: a systematic review and meta-analysis. J Glob Health. 2018;8(1):010804. GUS. 2023. National population and housing census 2021. Final results of the national population and housing census 2021. Mukesh BN, Le A, Dimitrov PN, Ahmed S, Taylor HR, McCarty CA. Development of cataract and associated risk factors: the Visual Impairment Project. Arch Ophthalmol. 2006;124(1):79–85. Li S, Li D, Zhang Y, Teng J, Shao M, Cao W. Association between serum lipids concentration and patients with age-related cataract in China: a cross-sectional, case-control study. BMJ Open. 2018;8(4):e021496. Ramke J, Gilbert CE, Lee AC, Ackland P, Limburg H, Foster A. Effective cataract surgical coverage: An indicator for measuring quality-of-care in the context of Universal Health Coverage. PLoS ONE. 2017;12(3):e0172342. Lange N, Kujawska-Danecka H, Wyszomirski A, et al. Significant improvements in cataract treatment and persistent inequalities in access to cataract surgery among older Poles from 2009 to 2019: results of the PolSenior and PolSenior2 surveys. Front Public Health. 2023;11:1201689. McElroy JA, Klein BE, Lee KE, Howard KP, Klein R. Place-based exposure and cataract risk in the Beaver Dam cohort. J Environ Health. 2014 Jan-Feb;76(6):34–40. Erratum in: J Environ Health. 2014;76(8):4. Mylona I, Dermenoudi M, Ziakas N, Tsinopoulos I. Hypertension is the Prominent Risk Factor in Cataract Patients. Med (Kaunas). 2019;55(8):430. Yu X, Lyu D, Dong X, He J, Yao K. Hypertension and risk of cataract: A meta-analysis. PLoS ONE. 2014;9:e114012. Drinkwater JJ, Davis WA, Davis TME. A systematic review of risk factors for cataract in type 2 diabetes. Diabetes Metab Res Rev. 2019;35(1):e3073. Saxena S, Mitchell P, Rochtchina E. Five-year incidence of cataract in older persons with diabetes and pre-diabetes. Ophthalmic Epidemiol. 2004;11:271–7. Klein BE, Klein R, Wang Q, Moss SE. Older-onset diabetes and lens opacities. The Beaver Dam Eye Study. Ophthalmic Epidemiol. 1995;2:49–55. Sayin N, Kara N, Pekel G. Ocular complications of diabetes mellitus. World J Diabetes. 2015;6(1):92–108. Schaumberg DA, Ridker PM, Glynn RJ, Christen WG, Dana MR, Hennekens CH. High levels of plasma C-reactive protein and future risk of age-related cataract. Ann Epidemiol. 1999;9(3):166–71. Tan AG, Kifley A, Flood VM, Holliday EG, Scott RJ, Cumming RG, Mitchell P. Jie Jin Wang, Evaluating the associations between obesity and age-related cataract: a Mendelian randomization study,The American. J Clin Nutr Volume 110, Issue 4,2019. Delcourt C, Dupuy A, Carriere I, Lacroux A, Cristol J. Pathologies Oculaires Liées à l'Age (POLA) Study Group. Albumin and Transthyretin as Risk Factors for Cataract: The POLA Study. Arch Ophthalmol. 2005;123(2):225–32. Ferrara A, Barrett-Connor E, Shan J. Total, LDL, and HDL cholesterol decrease with age in older men and women. The Rancho Bernardo Study 1984–1994. Circulation. 1997;96(1):37–43. 10.1161/01.cir.96.1.37 . PMID: 9236414. Wang S, Xu L, Jonas JB, You QS, Wang YX, Yang H. Dyslipidemia and eye diseases in the adult Chinese population: the Beijing eye study. PLoS ONE. 2012;7(3):e26871. Kang MJ, Do KY, Park N, Kang MW, Jeong KS. The Risk of Major Depressive Disorder Due to Cataracts among the Korean Elderly Population: Results from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2016 and 2018. Int J Environ Res Public Health. 2023;20(2):1547. Etminam M, Mikelberg FS, Brophy JM. Selective Serotonin Reuptake Inhibitors and the risk of cataracts: A nested case-control study. Am Acad Ophthalmol. 2010;117:1251–5. Fu Y, Dai Q, Zhu L, Wu S. Antidepressants use and risk of cataract development: a systematic review and meta-analysis. BMC Ophthalmol. 2018;18(1):31. DeBlack SS. Cigarette smoking as a risk factor for cataract and age-related macular degeneration: a review of the literature. Optometry. 2003;74(2):99–110. Christen WG, Glynn RJ, Ajani UA, Schaumberg DA, Buring JE, Hennekens CH, Manson JE. Smoking cessation and risk of age-related cataract in men. JAMA. 2000;284(6):713–6. Gong Y, Feng K, Yan N, Xu Y, Pan CW. Different amounts of alcohol consumption and cataract: a meta-analysis. Optom Vis Sci. 2015;92(4):471–9. Chua SYL, Luben RN, Hayat S, et al. Alcohol Consumption and Incident Cataract Surgery in Two Large UK Cohorts. Ophthalmology. 2021;128(6):837–47. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 19 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 01 Aug, 2024 Reviewers agreed at journal 01 Aug, 2024 Reviewers invited by journal 25 Jul, 2024 Editor invited by journal 10 May, 2024 Submission checks completed at journal 07 May, 2024 Editor assigned by journal 07 May, 2024 First submitted to journal 22 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4308277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300978348,"identity":"6c7d91d8-8b0d-48dc-a191-9922f1128c9f","order_by":0,"name":"Natalia Lange","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACCQYehgNAyMAP4iUUkKJFsgGkxYBILQwgLQYHQFxitEjOyD144MeZO3nG51cnfnhgwCDPL3YAvxZpibyEgz03nhWb3Xi7WQLoMMOZsxPwa5GTyDE4wPPhcOK2G2c3gLQkGNwmQsvBP0Atm2ec3fyDKC3SQC2HeW4cTtzA37uNOFske94lHJY5czhxxg3ebRYJBhKE/SJxPPfwxzfHDif295/dfPNHhY08vzQBLUiawSoliFUOAvwHSFE9CkbBKBgFIwkAAPT+TkltFW+OAAAAAElFTkSuQmCC","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":true,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Lange","suffix":""},{"id":300978349,"identity":"871a967d-0b18-456f-99ab-055e865911e4","order_by":1,"name":"Kacper Jagiełło","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Kacper","middleName":"","lastName":"Jagiełło","suffix":""},{"id":300978351,"identity":"7a62b9c8-2d99-4d56-b382-07c2573d62b0","order_by":2,"name":"Piotr Bandosz","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Piotr","middleName":"","lastName":"Bandosz","suffix":""}],"badges":[],"createdAt":"2024-04-22 22:13:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4308277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308277/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21713-8","type":"published","date":"2025-03-17T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79121266,"identity":"390af9a5-6c7b-475b-9066-27737f9ec236","added_by":"auto","created_at":"2025-03-24 16:11:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308277/v1/04c05c82-ba6b-4ea9-b80b-7062c9033a1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors for Self-Reported Diagnosed Cataract among older adults in Poland. Findings from PolSenior2 Study.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCataract is the leading cause of reversible blindness and visual impairment worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], including Poland [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological studies have shown that cataract results from multiple contributing factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Risk factors for, and causes of, cataract include ageing, genetics, lifestyle exposure, behaviors, infections, various health conditions, and socioeconomic factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral cross-sectional studies worldwide have examined the prevalence of self-reported diagnosed cataract and their associated risk factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there is a notable absence of data on this topic in Poland [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the significant influence of cataract on public health and its widespread repercussions, cataract has consistently been the subject of ongoing epidemiological research. Understanding the underlying causes that can be addressed through intervention helps lessen the impact of the disease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, the aim of our study was to investigate sociodemographic factors, comorbidities and health behaviors, associated with self-reported diagnosed cataract in a large, nationally representative population of older adults in Poland, aged 60 and above.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThe data used in this analysis were derived from the PolSenior2 study, which was carried out between 2018 and 2019. This study comprised a cross-sectional survey involving a representative sample of 5987 individuals aged 60 years and above.\u003c/p\u003e \u003cp\u003eSubjects were randomly selected from all regions of Poland. We used a three-stage stratified draw. The first stage draw identified local administrative units, including urban, rural, and urban-rural municipalities in each of 16 voivodships (regions). Towns and cities were divided into five groups, depending on size: \u0026le; 20,000 residents, 20,000\u0026ndash;50,000 residents, 50,000\u0026ndash;200,000 residents, 200,000\u0026ndash;500,000 residents, and \u0026gt;\u0026thinsp;500,000 residents. The final number of territorial strata was 78. The number of respondents to be drawn in each stratum was proportional to its population size and was based on the population structure in December 2016. Then, 137 municipalities were drawn in previously defined strata, with the probability proportional to the size of the population aged 60\u0026thinsp;+\u0026thinsp;years in a particular municipality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study protocol included three paper-based questionnaires, specialized geriatric assessments, anthropometric measurements, blood pressure evaluations, and analyses of blood and urine samples [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTrained nurses conducted the medical and socioeconomic sections of the questionnaire through face-to-face interviews during three home visits with the participants. Additionally, certain data were gathered from the self-administered portion of the questionnaire, completed individually by the respondents [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Each participant involved in the study provided written informed consent before their inclusion. The study protocol received approval from the Bioethics Committee of the Medical University of Gdansk (NKBBN/257/2017). Initial ophthalmology screenings were carried out during the first visit. Data from 5956 subjects were collected and analyzed. A small fraction of respondents (0.5%) were excluded due to either non-response regarding cataract diagnosis or responses indicating uncertainty (\"I don't remember/I don't know\"). However, the absence of this information appeared to be random.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Self-reported cataract\u003c/h2\u003e \u003cp\u003eInformation regarding the presence of cataract was obtained through questions in the Medical Questionnaire, specifically: \"Has your doctor diagnosed you with cataract in either eye?\" Respondents were presented with the options \"Yes/No/I don't remember, I don't know.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sociodemographic variables\u003c/h2\u003e \u003cp\u003eData on patients' gender, age, and place of residence were obtained from the Ministry of Digitization. To determine the subjects' education level, the respondents were presented with the question \u0026ldquo;What is your current education level?\u0026rdquo;. The possible answers were (1) no education (2) incomplete primary, (3) primary, (4) middle school (5) vocational high school, (6) high school, (7) 2-year college, (8) bachelor\u0026rsquo;s, engineer\u0026rsquo;s degree, and (9) master's degree. To simplify the analysis, education levels (1)-(3) were classified as \"Primary or incomplete primary\", (4)-(6) were classified as \u0026ldquo;Middle\u0026rdquo; and (7)-(9) were classified as \"Higher\".\u003c/p\u003e \u003cp\u003eThe financial situation of the household was assessed with the question: \u0026ldquo;Which of the following sentences best describes the financial situation of your household?\u0026rdquo; The possible answers were (1) \u0026ldquo;We live comfortably without having to save for special purchases.\u0026rdquo;, (2) \u0026ldquo;We live frugally and we have enough to cover all of our expenses;\u0026rdquo;, (3) \u0026ldquo;We need to put money aside to save for special purchases.\u0026rdquo;, (4) \u0026ldquo;We have enough money for basic needs such as food and clothing.\u0026rdquo;, (5) \u0026ldquo;We only have enough money for food.\u0026rdquo;, (6) \u0026ldquo;We don\u0026rsquo;t have enough money even to meet our basic needs.\u0026rdquo; For the consistency of the results, the responses were grouped into respective categories: Can easily afford everything (1), Can afford everything but only when saving (2), Self-reported poverty(3)(4)(5)(6).\u003c/p\u003e \u003cp\u003eQuality of life (QoL \u0026bdquo;How would you rank your overall quality of life? The possible choices were: \u0026bdquo;high\u0026rdquo;, \u0026bdquo; normal\u0026rdquo;, \u0026bdquo;low\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Health status variables\u003c/h2\u003e \u003cp\u003eDiabetes mellitus was noted when a patient declared that it was previously diagnosed, or if fasting glucose was \u0026ge;\u0026thinsp;126 mg/dL, or using hypoglycemic drugs was reported.\u003c/p\u003e \u003cp\u003eHypertension was diagnosed if average blood pressure values from two measurements during each visit were equal to or higher than 140mmHg (Systolic Blood Pressure) and/or 90mmHg (Diastolic Blood Pressure), or if the patient was taking hypotensive drugs over the past 2 weeks because of an earlier diagnosis of hypertension.\u003c/p\u003e \u003cp\u003eParticipants' nutritional risk was evaluated using the Mini Nutritional Assessment-Short Form (MNA-SF), which has a maximum score of 14 points. Those scoring less than 12 points were grouped into a category termed \"poor nutritional status\" (PNS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity was defined as the Body Mass Index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m2, using an approved Tanita BC-545N portable electronic scale, with an accuracy of 0.1 kg [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypercholesterolemia was defined as total cholesterol level\u0026thinsp;\u0026ge;\u0026thinsp;190 mg/dl (\u0026ge;\u0026thinsp;5.0 mmol/l) or taking statins/fibrates.\u003c/p\u003e \u003cp\u003eWe assessed the risk of depression with the short version of the Geriatric Depression Scale (GDS), composed of 15 questions. Subjects who received at least 6 points in the GDS were classified as having depression symptoms. The GDS was omitted in participants with the Mini\u0026ndash;Mental State Examination (MMSE) score of fewer than 19 points to rule out unreliable answers given by subjects suspected of moderate/severe dementia.\u003c/p\u003e \u003cp\u003eInformation about stroke was determined by the questions \u0026ldquo;Have you ever been diagnosed with stroke?\u0026rdquo; and the possible answers were \u0026ldquo;Yes\u0026rdquo; or \u0026ldquo;No\u0026rdquo;.\u003c/p\u003e \u003cp\u003eTwo health behavioral variables were assessed by providing answers to the following questions: \u0026bdquo;Have you ever regularly smoked tobacco (cigarettes, pipe)?\u0026rdquo; and \u0026bdquo;How often did you drink vodka or other alcoholic beverages (wine, beer) in the last 12 months?\u0026rdquo; To simplify the analysis, we devised the following classification system: respondents indicating \"twice a month and more\" were categorized as moderate to heavy drinkers, those reporting \u0026bdquo;once a month or less\u0026rdquo; were classified as light drinkers, and those indicating \"none\" were categorized as non-drinkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data management and the statistical analyses were performed with R version 3.6.3 R (R Core Team, version 3.6.3) and SAS 9.4 TS Level 1M5 (SAS Institute Inc., Cary, NC, USA). The data was presented as percentages with 95% confidence intervals. A univariate and multivariable logistic regression models were created to determine the association between binary dependent variable and covariates. The multiple logistic regression model contains independent variables which were Age, Sex, Education Level, Financial situation of the household, Quality of Life, Place of residence, Stroke, Hypertension, Diabetes, Obesity, Tobacco, Alcohol, Hypercholesterolemia, and Poor Nutritional Status. Sampling weights were included in statistical calculations to account for the complex survey design using R survey package. The post-stratification procedure was used to match age\u0026ndash;sex sample distribution to the population of Poland. The level of significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Prevalence of Self-Reported Diagnosed Cataract\u003c/h2\u003e \u003cp\u003eThe overall occurrence of cataract among older adults in Poland was found to be 22.6% (95% CI: 21.2\u0026ndash;24.1). Notably, the prevalence was significantly higher among women, at 27.4%, compared to men, at 16.5%. Moreover, the prevalence of cataract showed an increase with age and was more pronounced among individuals with primary education (29.9%), those reporting financial hardship (27.5%), and individuals with medium (28.3%) to low (29.3%) QOL. Urban residents also exhibited a higher prevalence, at 25.0% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eSelf-reported cataract in the population of Polish seniors by sociodemographic factors, comorbidities, health behaviors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCataract\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003cp\u003e90+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4(9.7\u0026ndash;13.1)\u003c/p\u003e \u003cp\u003e27.6(25.0-30.2)\u003c/p\u003e \u003cp\u003e50.6(47.0-54.3)\u003c/p\u003e \u003cp\u003e60.7(55.1\u0026ndash;66.2)\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 \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.4(25.3\u0026ndash;29.5)\u003c/p\u003e \u003cp\u003e16.5(14.8\u0026ndash;18.3)\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 \u003cp\u003eHigher\u003c/p\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003cp\u003ePrimary and incomplete primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7(16.0-23.4)\u003c/p\u003e \u003cp\u003e20.7(18.6\u0026ndash;22.7)\u003c/p\u003e \u003cp\u003e29.9(26.4\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUrban\u003c/p\u003e \u003cp\u003eRural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0(23.2\u0026ndash;26.7)\u003c/p\u003e \u003cp\u003e19.6(17.5\u0026ndash;21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinancial situation of the household\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCan easily afford everything\u003c/p\u003e \u003cp\u003eCan afford everything but only when saving\u003c/p\u003e \u003cp\u003eSelf-reported poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.7(14.9\u0026ndash;20.5)\u003c/p\u003e \u003cp\u003e22.0(20.1\u0026ndash;23.8)\u003c/p\u003e \u003cp\u003e27.5(24.1\u0026ndash;30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuality of life\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8(19.1\u0026ndash;22.4)\u003c/p\u003e \u003cp\u003e28.3(24.9\u0026ndash;31.6)\u003c/p\u003e \u003cp\u003e29.3(22.6\u0026ndash;36.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4(22.8\u0026ndash;26.0)\u003c/p\u003e \u003cp\u003e18.6(15.6\u0026ndash;21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.8(27.2\u0026ndash;34.4)\u003c/p\u003e \u003cp\u003e20.3(18.8\u0026ndash;21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2(21.7\u0026ndash;26.6)\u003c/p\u003e \u003cp\u003e21.9(20.1\u0026ndash;23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoor Nutritional Status\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.3(26.6\u0026ndash;31.9)\u003c/p\u003e \u003cp\u003e20.4(18.9\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1(20.5\u0026ndash;23.7)\u003c/p\u003e \u003cp\u003e25.1(22.1\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.9(27.8\u0026ndash;34.1)\u003c/p\u003e \u003cp\u003e19.1(17.5\u0026ndash;20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.9(28.1\u0026ndash;41.7)\u003c/p\u003e \u003cp\u003e21.8(20.2\u0026ndash;23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth behaviors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8(18.7\u0026ndash;23.0)\u003c/p\u003e \u003cp\u003e25.1(23.0-27.1)\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 \u003cp\u003eModerate to heavy drinkers\u003c/p\u003e \u003cp\u003eLight drinkers\u003c/p\u003e \u003cp\u003eNon-drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7(14.5\u0026ndash;18.9)\u003c/p\u003e \u003cp\u003e20.2(17.8\u0026ndash;22.6)\u003c/p\u003e \u003cp\u003e32.3(29.3\u0026ndash;35.4)\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\u003eAdditionally, the prevalence of cataract was elevated among those with comorbidities including stroke (34.9%), depression (30.9%), diabetes (30.8%), malnutrition (29.3%), and arterial hypertension (24.4%), in contrast to those without these conditions, except for obesity, hypercholesterolemia, and smoking. Non-drinkers had a significantly higher prevalence of cataract (32.3%) compared to light and moderate to heavy drinkers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Univariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eThe presence of self-reported diagnosed cataract in univariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was associated with increasing age (70\u0026ndash;79, 80\u0026ndash;89, \u0026le; 90 versus age 60\u0026ndash;69, OR\u0026thinsp;=\u0026thinsp;3.26, 7.41, 9.85 respectively), female gender (OR\u0026thinsp;=\u0026thinsp;1.44), primary education (OR\u0026thinsp;=\u0026thinsp;1.45), self-reported poverty (OR\u0026thinsp;=\u0026thinsp;1.33), medium and low QoL (OR\u0026thinsp;=\u0026thinsp;1.5; 1.84), diabetes (OR\u0026thinsp;=\u0026thinsp;1.57), arterial hypertension (OR\u0026thinsp;=\u0026thinsp;1.52), stroke (OR\u0026thinsp;=\u0026thinsp;1.59), malnutrition (OR\u0026thinsp;=\u0026thinsp;1.62). There was an inverse association between self-reported cataract and rural residence (OR\u0026thinsp;=\u0026thinsp;0.68), hypercholesterolemia (OR\u0026thinsp;=\u0026thinsp;0.86), nicotinism (OR\u0026thinsp;=\u0026thinsp;0.85), any alcohol use (OR\u0026thinsp;=\u0026thinsp;0.61 for light drinkers and OR\u0026thinsp;=\u0026thinsp;0.49 for moderate to heavy drinkers).\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\u003eFactors associated with prevalence of self-reported cataract. Univariate logistic regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elower.CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eupper.CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\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\u003eSociodemographic factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e60\u0026ndash;69 (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eMen (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003cp\u003eHigher (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary and incomplete primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003cp\u003eUrban (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial situation of the household\u003c/p\u003e \u003cp\u003eCan easily afford everything (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCan afford everything but only when saving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003cp\u003eHigh (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (ref)\u003c/p\u003e \u003cp\u003eNo\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Nutritional Status (PNS)\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth behaviors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTabacco\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003cp\u003eNon-drinkers (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to heavy drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis revealed that increasing age, female gender, hypertension, diabetes, and smoking were factors significantly associated with self-reported diagnosed cataract among older adults in Poland (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the odds of self-reported diagnosed cataract were 1.71 times higher among women compared to men. Additionally, the odds increased significantly with age, with 70-79-year-olds having 3.38 times higher odds, 80-89-year-olds having 8.08 times higher odds, and those aged 90 years and older having 10.76 times higher odds compared to the reference group (60\u0026ndash;69 years old). Moreover, individuals with diabetes had 1.47 times higher odds of self-reported diagnosed cataract, while those with hypertension had 1.20 times higher odds, and tobacco users had 1.25 times higher odds compared to their counterparts. Interestingly, rural residents exhibited a lower risk for self-reported diagnosed cataract, with an odds ratio of 0.63 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eFactors associated with prevalence of self-reported cataract -multiple logistic regression, The multiple logistic regression model contains independent variables which are Age, Sex, Education Level, Financial situation of the household, Quality of Life, Place of residence, Stroke, Hypertension, Diabetes, Obesity, Tobacco, Alcohol, Hypercholesterolemia, and Poor Nutritional Status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower \u003c/p\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper \u003c/p\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\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\u003eSociodemographic factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e60\u0026ndash;69 (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eMen (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003cp\u003eHigher (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003cp\u003eUrban\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial situation of the household\u003c/p\u003e \u003cp\u003eCan easily afford everything (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCan afford everything but only when saving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003cp\u003eHigh (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Nutritional Status\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \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\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth behaviors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco\u003c/p\u003e \u003cp\u003eNo (ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003cp\u003eNon-drinkers\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to heavy drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e- odds ratio, \u003cb\u003eLower 95% CI\u003c/b\u003e \u0026ndash; lower bound of the 95% confidence interval, \u003cb\u003eUpper 95% CI\u003c/b\u003e \u0026ndash; upper bound of the 95% confidence interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Socioeconomic factor and cataract\u003c/h2\u003e \u003cp\u003eConsistent with prior studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the multivariate analysis revealed a strong association between increasing age and the prevalence of cataract. For self-reported diagnosed cataract, prevalence rates ranged from 11.4% (95% CI\u0026thinsp;=\u0026thinsp;9.7\u0026ndash;13.1) in adults aged 60\u0026ndash;69 years to 60.7% (95% CI\u0026thinsp;=\u0026thinsp;55.1\u0026ndash;66.2) in those aged 90 and above. In comparison to research conducted in China, where the prevalence of cataract based on ophthalmological examination, rather than self-reported data, ranged from 24% in those aged 60\u0026ndash;65 years to 75% in individuals aged 85\u0026ndash;89 years [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. According to GUS, a Polish Census Bureau, Poland's population continues to age [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and therefore there might be an expected increase in the number of individuals affected by cataract [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research, along with other relevant studies, indicated a higher prevalence of cataract diagnosis among women compared to men [9,11,21,24,25,]. Some studies suggest that women, on average, have longer lifespans than men, consequently placing them at a higher risk of developing age-related eye conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, analysis of population-based surveys conducted in low- and middle-income countries consistently reveals that women are significantly less likely than men to undergo cataract surgery [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We have previously addressed this gender disparity in our published work [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research showed that there was a correlation between living in rural areas and diminished risk of self-reported cataract. This was also evidenced in other studies that assessed cataract prevalence through surveys [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This correlation may indicate that factors associated with rural living, such as reduced exposure to environmental pollutants, less screen time, or potentially even dietary differences, could have a protective effect against cataract development. However, we couldn't establish causation using these cross-sectional data.\u003c/p\u003e \u003cp\u003eIn our view, this correlation could stem from individuals residing in rural areas having limited access to healthcare, resulting in lower awareness of the disease compared to those in urban areas. Studies employing slit lamp examination found no discrepancy in cataract prevalence between rural and urban dwellings [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of self-reported cataract in univariate logistic regression analysis was also found to be associated with primary education, self-reported poverty, medium, and low QoL. However, these factors did not demonstrate significance in the multivariate regression analysis, as reported in another study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comorbidities and cataract\u003c/h2\u003e \u003cp\u003eOur study corroborated the conclusions of prior research, indicating a higher prevalence of cataract among individuals with hypertension [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and diabetes mellitus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Multivariate logistic regression analysis indicated that the prevalence of cataract was 1.47 times higher for individuals with diabetes and 1.20 with hypertension. Cataract formation in individuals with diabetes seemed to be related to hyperglycemia or to increased senile lens opacity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous studies show that diabetic patients are 2\u0026ndash;5 times more at risk for cataract formation and are more likely to get it at an earlier age [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Hypertension can cause cataract by inducing intense systemic inflammation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although several plausible mechanisms have been proposed based on laboratory results, the conclusions from epidemiologic studies remain inconsistent [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Our analysis revealed a relatively higher prevalence of self-reported diagnosed cataract among elderly patients with PNS, a finding confirmed by univariate regression analysis. However, there was no significant difference observed in multivariate logistic regression analysis. Also, our study found no difference between individuals with obesity and those with normal BMI. Other studies done in the areas of obesity, malnutrition and cataract show similar results. For example, in older Australian population there was no causal association between obesity and cataract [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and obesity wasn\u0026rsquo;t associated with cataract in other studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another study that showed similar results to ours, suggested that there was an association between protein undernutrition and an increased risk of cataract [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The study indicated that low protein intake may induce deficiencies of specific amino acids that are needed to maintain the health of the lens [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Low BMI may be associated with an increased risk for cataract [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our univariable analysis, we observed an association between hypercholesterolemia and a decreased prevalence of self-reported cataract; however, this did not maintain significance in the multivariate logistic regression analysis. The oldest individuals, who are more likely to have cataract, tend to have lower cholesterol levels [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], thereby contributing to the observed association of low cholesterol with the higher prevalence of cataract. This suggests that the association in the univariate analysis is likely confounded by age. Interestingly, our findings contrast with those of other studies. For instance, research conducted among the Chinese population suggested significantly higher total cholesterol concentration in age-related cataract (ARC) patients compared to those without ARC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, hypercholesterolemia in this study was defined as a total cholesterol serum level of \u0026ge;\u0026thinsp;5.20 mmol/L [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hence, disparities between our study and theirs may arise from differences in these criteria. Notably, another study has reported no association between dyslipidemia and cataract development [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research also examined stroke as another comorbidity. Although our study showed a higher prevalence of cataract among individuals with prior stroke, this association did not emerge as a significant independent factor in multiple analyses, which is concurrent with findings from other studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, based on some studies the association between cataract and individuals with a previous history of stroke remains unclear [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study also revealed a higher prevalence of self-reported diagnosed cataract among individuals with symptoms of depression compared to those without, which is consistent with findings from prior research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another study, however, explored the possibility of reverse causation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It found that cataract may be a risk factor for major depressive disorder in the elderly, especially among the male population [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Research is also investigating the impact of antidepressants on the formation of cataract [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Health behavior and cataract\u003c/h2\u003e \u003cp\u003eSmoking is a risk factor with robust evidence regarding the higher prevalence of cataract [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which was also found in our study. The odds of self-reported diagnosed cataract were 1.25 for tobacco users compared to their counterparts (95% CI: 1.08\u0026ndash;1.44, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eInterestingly, other studies revealed that while there was a significant positive correlation between smoking and cataract, the cataractogenic impact was lower among former smokers compared to current smokers. This finding highlights the potential for reversibility in this context [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, in our study nondrinkers reported significantly higher rates of cataract (32.3%) compared to light and moderate to heavy drinkers. According to a meta-analysis of one study, heavy alcohol consumption significantly elevates the risk of ARC, while moderate consumption may offer protection against cataract [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This meta-analysis specifically included studies that assessed cataract through lens photographs or diagnosis by ophthalmologists, excluding those reliant on self-reported questionnaires for cataract measurement. Additionally, another study indicates that individuals with low to moderate alcohol consumption have a reduced likelihood of needing cataract surgery [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Strengths and limitations of study\u003c/h2\u003e \u003cp\u003eOur study has several notable strengths. Firstly, we gathered data from a large, representative, community-dwelling population aged 60 and over, with a high proportion of older individuals. Secondly, our study not only examined conventional health hazards like diabetes and smoking but also delved into less commonly explored factors such as PNS and hypercholesterolemia.\u003c/p\u003e \u003cp\u003eFurthermore, we employed rigorous diagnostic criteria, diagnosing hypercholesterolemia and diabetes through laboratory tests, evaluating hypertension via blood pressure measurements, and assessing PNS and depression using specialized scales. Additionally, we utilized an approved Tanita BC-545N portable electronic scale to determine obesity.\u003c/p\u003e \u003cp\u003ePerhaps most importantly, the findings we obtained might fill a crucial gap in cataract epidemiology within Poland and enhance understanding of the risk factors associated with cataract in the Polish population. Consequently, our study holds the potential to aid in the prevention of cataract.\u003c/p\u003e \u003cp\u003eHowever, our study comes with several limitations. Firstly, the prevalence of cataract was determined solely based on participants' self-reported diagnoses by a doctor, potentially leading to an underestimation of the true prevalence. Similarly, data on the prevalence of stroke, sociodemographic factors, and health behaviors also relied on self-reported survey responses.\u003c/p\u003e \u003cp\u003eAdditionally, symptoms of depression were excluded from the regression analysis due to the limited sample size of individuals assessed with the GDS scale. The size of the sample was impacted by the exclusion of individuals with MMSE scores below 19 from the analysis.\u003c/p\u003e \u003cp\u003eLastly, it's important to note that our analysis is cross-sectional in nature, which means it only examines correlations between variables and doesn't establish causation. This design limitation precludes the assessment of causal relationships between variables within the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study revealed a positive correlation between several demographic and health factors\u0026mdash;namely, older age, female gender, urban residence, hypertension, diabetes, and smoking\u0026mdash;and an elevated risk of cataract.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eARC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-related cataract\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGDS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeriatric Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini\u0026ndash;Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMNA-SF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini Nutritional Assessment-Short Form (MNA-SF)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds radio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePNS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoor nutritional status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQOL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equality of life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNL conceived the project. KJ performed the statistical analysis. NL, KJ, PB analysed results. NL wrote the manuscript. PB reviewed the paper.. All authors read and approved the final manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was implemented under the contract No. 6/5/4.2/NPZ/2017/1203/1257 for the implementation of the task in the field of public health of the Operational Objective No. 5 point \u0026nbsp;4.2. of the National Health Program for years 2016-2020, entitled \u0026quot;Health Status and Its Socioeconomic Covariates of the Older Population in Poland - the Nationwide PolSenior2 Survey\u0026quot; \u0026nbsp;(PolSenior2). The funders had no role in study design, data collection and analysis, decision to \u0026nbsp;publish, or preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe acknowledgments to all the persons supporting and involved in the implementation of the PolSenior2 program in 2018-2019, especially: head of the research team-Professor Tomasz Zdrojewski, experts of each branch of medical research, rector of the Medical University of Gdansk- Professor Marcin Gruchała. Acknowledgments to Dana Guest for language correction. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant involved in the study provided written informed consent before their inclusion. The study protocol received approval from the Bioethics Committee of the Medical University of Gdansk (NKBBN/257/2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World report on vision. World Health Organ. (2019) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/328717\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/328717\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartyka O, Wysocki MJ. Epidemiology of eye diseases and infrastructure of ophthalmology in Poland. Przegl Epidemiol. 2015;69(4):773.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak MS, Smigielski J. The prevalence and causes of visual impairment and blindness among older adults in the city of Lodz, Poland. Medicine. 2015;94:e505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiels A, Hejtmancik JF. Molecular Genetics of Cataract. Prog Mol Biol Transl Sci. 2015;134:203\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest SK, Valmadrid CT. Epidemiology of risk factors for age-related cataract Surv. Ophthalmol. 1995;39:323\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRim TH, Kim DW, Kim SE, Kim SS. Factors Associated with Cataract in Korea: A Community Health Survey 2008\u0026ndash;2012. Yonsei Med J. 2015;56(6):1663\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYawson AE, Ackuaku-Dogbe EM, Seneadza NAH, et al. 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Optometry. 2003;74(2):99\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristen WG, Glynn RJ, Ajani UA, Schaumberg DA, Buring JE, Hennekens CH, Manson JE. Smoking cessation and risk of age-related cataract in men. JAMA. 2000;284(6):713\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong Y, Feng K, Yan N, Xu Y, Pan CW. Different amounts of alcohol consumption and cataract: a meta-analysis. Optom Vis Sci. 2015;92(4):471\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChua SYL, Luben RN, Hayat S, et al. Alcohol Consumption and Incident Cataract Surgery in Two Large UK Cohorts. Ophthalmology. 2021;128(6):837\u0026ndash;47.\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4308277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: The aim of our study was to investigate sociodemographic factors, comorbidities and health behaviors associated with self-reported diagnosed cataract in a large, nationally representative population of older adults in Poland, aged 60 and above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Methods\u003c/strong\u003e: An analysis was conducted on a survey among 5956 participants of the nationally representative PolSenior2 study conducted between 2018 and 2019. Multiple logistic regression analysis was employed to ascertain the association between self-reported diagnosed cataract and \u0026nbsp;sociodemographic factors, health behaviors, and comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: \u0026nbsp;In the final multivariate model, the odds radio (OR) of self-reported diagnosed cataract were 1.71 times higher among women compared to men. Additionally, the odds increased significantly with age, with 70-79-year-olds having 3.38 times higher odds, 80-89-year-olds having 8.08 times higher odds, and those aged 90 years and older having 10.76 times higher odds compared to the reference group (60-69 years old). The prevalence of self-reported diagnosed cataract was found to be 1.47 times higher among individuals with diabetes, 1.20 times higher among those with hypertension, and 1.25 times higher among tobacco users compared to their respective counterparts. Additionally, rural dwellers exhibited a lower risk for self-reported cataract (OR = 0.63).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: \u0026nbsp;Our study revealed a positive correlation between several demographic and health factors—namely, older age, female gender, urban residence, hypertension, diabetes, and smoking—and an elevated risk of cataract.\u003c/p\u003e","manuscriptTitle":"Risk factors for Self-Reported Diagnosed Cataract among older adults in Poland. Findings from PolSenior2 Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 17:21:04","doi":"10.21203/rs.3.rs-4308277/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-19T13:54:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-07T06:23:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T02:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171133066927622696833526706701625320696","date":"2024-08-01T15:25:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201036555082776337832316163117442955723","date":"2024-08-01T10:42:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-25T13:37:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-10T08:18:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-07T14:50:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-07T14:50:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-22T22:10:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f154e4c7-3f66-498d-82a5-ea9a7a8fb3f3","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:10:05+00:00","versionOfRecord":{"articleIdentity":"rs-4308277","link":"https://doi.org/10.1186/s12889-025-21713-8","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-03-17 15:57:24","publishedOnDateReadable":"March 17th, 2025"},"versionCreatedAt":"2024-05-14 17:21:04","video":"","vorDoi":"10.1186/s12889-025-21713-8","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21713-8","workflowStages":[]},"version":"v1","identity":"rs-4308277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4308277","identity":"rs-4308277","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}