Objective: Education often reflects socioeconomic status. Research indicates that lower socioeconomic status may increase the risk of diverticulosis, and according to data from the USA, diverticular disease is a significant and costly health problem. Our study explores the link between educational level and colonic diverticula occurrence. Subject and Methods: We conducted a cohort study on 5,532 asymptomatic Austrian patients who underwent colonoscopy, categorizing them by education level using the updated Generalized International Standard Classification of Education (GISCED). Logistic regression models, adjusting for age, gender, metabolic syndrome, diet, and activity, were used to determine the association between education and diverticulosis. Results: Overall, 39% of the patients had low educational status, while 53% had medium, and 8% had high educational status. Colon diverticula were less frequent in patients with medium (OR 0.73) and high (aOR 0.62) educational status. Medium educational level remained associated with lower rates of diverticulosis after adjustment for age and sex (aOR 0.85) and further metabolic syndrome, dietary habits, and physical activity (aOR 0.84). In higher education status, this phenomenon was only seen by trend. Conclusion: Low education correlated with higher colon diverticula risk, while medium education showed lower rates even after adjustments. This trend persisted at higher education levels, highlighting the potential for strategies for cost reduction tailored to socioeconomic conditions.

Highlights of the Study

  • This study on 5,532 Austrian patients who underwent colonoscopy found a link between education level (classified by the Generalized International Standard Classification of Education) and occurrence of colonic diverticula.

  • Higher education correlated with lower diverticulosis rates, even after adjusting for factors like age, gender, and lifestyle.

  • Tailored cost-reduction strategies could be developed based on socioeconomic status.

Higher levels of education are linked to better health outcomes [1]. This correlation is observed even in countries with high levels of development, such as the USA and Sweden [2]. Despite the robust relationship between education and health, the causality of this connection is still under debate [3]. Improved education, and therefore a better socioeconomic status, results from four different factors: economic conditions, health behaviors, socio-psychological factors, and access to healthcare. Education can lead to better job opportunities, which often provide higher incomes, and this can create more financial opportunities for maintaining and improving one’s health [4]. In general, achieving higher levels of education is associated with improved nutrition, enhanced health literacy, and increased access to healthcare services [1, 4]. Furthermore, people with higher levels of education are less likely to be obese and consume less alcohol [5, 6]. People with lower levels of education were found to be prone to limited physical activity and inadequate sleep. As education levels increase, there is a corresponding decrease in the percentage of individuals who smoke [5]. In higher education, there are also more social resources available to provide support in special situations such as stress and everyday problems, which effects mental hygiene [4]. Individuals with a higher level of education are more inclined to utilize preventive health measures, which is a favorable factor in the prevention of diseases [6].

In this context, our study aimed to investigate the prevalence of diverticulosis among a cohort of Austrian individuals undergoing screening for colorectal cancer, stratified by educational level. Colonic diverticulosis is a commonly occurring condition, particularly among elderly individuals, where protrusions known as diverticula develop within the intestinal wall. In most cases, this condition remains asymptomatic [7]. About 30% of patients under 60 and approximately 70% of those over 80 are affected by diverticulosis [8]. The incidence of diverticulosis has increased to 50% in individuals older than 60 years and a significant rise of incidence has been seen in younger age-groups [9]. Besides genetic causes and advancing age, there are links between cardiometabolic risk factors such as hypertension, obesity, increased accumulation of abdominal visceral and subcutaneous fat, and fatty liver disease [10]. Dietary habits such as alcohol consumption and high-fat diets have also been associated with the development of diverticula [11]. Recent data contradict the notion that a high-fiber diet is a protective factor against the development of diverticula [12]. It is controversial whether asymptomatic diverticulosis can be considered a disease in its own right [13]. While diverticulosis typically does not cause any symptoms, diverticular disease can be classified into two types based on severity, symptomatic uncomplicated diverticular disease, and symptomatic complicated disease, including acute diverticulitis (with or without complications) or diverticular hemorrhage [14]. However, 10–25% of patients with diverticulosis may become symptomatic and may develop symptomatic uncomplicated diverticular disease or at worst diverticulitis (4%), perforation, and bleeding [15]. Lifestyle habits have also been linked to diverticulitis [16]. Studies have shown that individuals with diverticulosis had lower levels of education compared to healthy controls, but the findings did not reach statistical significance [17].

We classified the educational background of patients into three categories: low, medium, and high using a standardized questionnaire. This was based on the Generalized International Standard Classification of Education (GISCED) as reported by Schneider et al. [18].

We hypothesized that there might be an independent correlation between educational level and the prevalence of diverticulosis, considering the mentioned association between education and health behavior. Our findings could provide further insights into the relationship between education and health outcomes and may help identify strategies for preventive interventions in individuals at risk for diverticulosis.

Subjects

We analyzed participants from the Salzburg Colon Cancer Prevention Initiative (Sakkopi), a cohort of asymptomatic patients who underwent screening for colorectal cancer at a single center in Austria between January 2007 and March 2020. We included 5,532 patients with complete data on education and the presence of diverticulosis. All participants completed a medical history questionnaire and underwent clinical and laboratory assessments. Using current guidelines, we calculated body mass index (BMI), arterial hypertension, smoking status, dyslipidemia, and metabolic syndrome [19]. Based on the Generalized International Standard Classification of Education (GISCED) as reported by Schneider et al. [18], we categorized patients educational status into three groups: low (GISCED 1–2), medium (GISCED 3–4), and high (GISCED 5–6).

Statistical Analysis

Continuous data are presented as median ± interquartile range and compared using Mann’s Whitney U test or mean ± standard deviation and compared using Student’s T test accordingly. Categorical data are given as numbers (percentage) and compared using the χ2 test. All tests were two sided, and a p value of <0.05 was considered statistically significant. The endpoint (dependent variables in the regression models) was the presence of diverticulosis, in screening colonoscopy. The primary exposure (independent variable) was the educational status as categorical fixed effect with lower education as the reference category. We fitted models with the occurrence of the binary endpoint as dependent variables using multilevel logistic regressions with robust standard errors with the year of inclusion as a random effect and as a fixed effect the educational status as categorical variable (model-1). We further fitted multivariable multilevel logistic regression models using the year of inclusion as a random effect, the educational status as a categorical variable, and the covariables age, sex (model-2) and additionally metabolic syndrome, dietary habits like fast food, vegetables, fruits, alcohol consumption, and amount of physical activity (model-3). We obtained odds ratios (OR) and respective 95% CI for the binary dependent variables. Stata/IC 17 was used for all statistical analyses.

Overall, 2,153 of the 5,532 patients studied had low levels of education, while 2,925 had medium, and 454 had high levels of education (Fig. 1). The mean ages in the group with medium education level (56; 51–63) and high education level (54; 51–60) were significantly lower than in the group with low education level (60; 53–68) (Table 1). In terms of gender distribution, there was a statistically significant difference between educational groups; a higher proportion of women were in the lower education group (56%), while a higher proportion of men were in the middle (57%) and high education (58%) groups.

Fig. 1.

Percentage representation of included patients assigned to respective education categories.

Fig. 1.

Percentage representation of included patients assigned to respective education categories.

Close modal
Table 1.

Baseline characteristics

Lower education (N = 2,153)Medium education (N = 2,925)High education (N = 454)p value
Age 60 (53–68) 56 (51–63) 54 (51–60) <0.001 
 <45 years, n (%) 5 (102) 6 (178) 8 (38)  
 45–54 years, n (%) 25 (548) 35 (1,035) 44 (202)  
 55–64 years, n (%) 34 (737) 36 (1,064) 34 (154)  
 65–74 years, n (%) 27 (583) 19 (548) 12 (53)  
 ≥75 years, n (%) 8 (183) 3 (100) 2 (7)  
Sex    <0.001 
 Female, n (%) 56 (1,204) 43 (1,245) 42 (190)  
 Male, n (%) 44 (949) 57 (1,680) 58 (264)  
BMI 27 (24–30) 26 (24–29) 25 (23–28) <0.001 
Hypertension (yes/no), n (%) 63 (1,347) 55 (1,613) 43 (193) <0.001 
Diabetes (yes/no), n (%) 17 (364) 16 (474) 14 (63) 0.28 
HbA1c per cent 5.6 (5.4–5.9) 5.4 (5.2–5.7) 5.4 (5.1–5.6) <0.001 
Metabolic syndrome (yes/no), n (%) 73 (1,579) 79 (2,317) 75 (339) <0.001 
Creatinine 0.8 (0.8–0.9) 0.9 (0.8–1.0) 0.9 (0.8–1.0) 0.002 
HGB 14.5 (13.7–15.3) 14.7 (13.9–15.5) 14.7 (13.7–15.3) <0.001 
MCV 87 (84–89) 87 (84–90) 88 (85–90) 0.070 
Thrombocytes 229 (195–269) 229 (198–266) 234 (203–271) 0.080 
Leukocytes 6.0 (5.1–7.4) 5.8 (4.8–6.9) 5.5 (4.7–6.5) <0.001 
C-reactive protein 0.2 (0.1–0.4) 0.2 (0.1–0.3) 0.1 (0.1–0.2) <0.001 
Smoking status    <0.001 
 Never smoker, n (%) 26 (374) 44 (1,179) 54 (240)  
 Ex-smoker, n (%) 45 (641) 36 (979) 30 (135)  
 Active smoker, n (%) 29 (418) 20 (541) 16 (72)  
Alcohol use    <0.001 
 No alcohol, n (%) 23 (447) 10 (289) 4 (16)  
 <2 drinks/day, n (%) 56 (1,107) 83 (2,354) 92 (411)  
 ≥2 drinks/day, n (%) 21 (413) 6 (182) 4 (19)  
Red meat servings per week 2 (1–3) 2 (1–3) 2 (1–3) <0.001 
Fast food, n (%)    0.25 
 Fast food <weekly 84 (1,817) 83 (2,441) 86 (392)  
 Fast food ≥weekly 16 (336) 17 (484) 14 (62)  
Vegetables, n (%)    0.35 
 Vegetables ≥daily 46 (983) 47 (1,388) 48 (220)  
 Vegetables <daily 54 (1,170) 53 (1,537) 52 (234)  
Fruits, n (%)    0.013 
 Fruits ≥daily 43 (935) 40 (1,178) 37 (168)  
 Fruits <daily 57 (1,218) 60 (1,747) 63 (286)  
Physical activity, n (%)    <0.001 
 <1 h 25 (442) 10 (232) 2 (8)  
 <2 h 51 (925) 40 (897) 39 (129)  
 <3 h 19 (343) 36 (818) 45 (152)  
 ≥3 h 5 (89) 13 (298) 14 (46)  
Lower education (N = 2,153)Medium education (N = 2,925)High education (N = 454)p value
Age 60 (53–68) 56 (51–63) 54 (51–60) <0.001 
 <45 years, n (%) 5 (102) 6 (178) 8 (38)  
 45–54 years, n (%) 25 (548) 35 (1,035) 44 (202)  
 55–64 years, n (%) 34 (737) 36 (1,064) 34 (154)  
 65–74 years, n (%) 27 (583) 19 (548) 12 (53)  
 ≥75 years, n (%) 8 (183) 3 (100) 2 (7)  
Sex    <0.001 
 Female, n (%) 56 (1,204) 43 (1,245) 42 (190)  
 Male, n (%) 44 (949) 57 (1,680) 58 (264)  
BMI 27 (24–30) 26 (24–29) 25 (23–28) <0.001 
Hypertension (yes/no), n (%) 63 (1,347) 55 (1,613) 43 (193) <0.001 
Diabetes (yes/no), n (%) 17 (364) 16 (474) 14 (63) 0.28 
HbA1c per cent 5.6 (5.4–5.9) 5.4 (5.2–5.7) 5.4 (5.1–5.6) <0.001 
Metabolic syndrome (yes/no), n (%) 73 (1,579) 79 (2,317) 75 (339) <0.001 
Creatinine 0.8 (0.8–0.9) 0.9 (0.8–1.0) 0.9 (0.8–1.0) 0.002 
HGB 14.5 (13.7–15.3) 14.7 (13.9–15.5) 14.7 (13.7–15.3) <0.001 
MCV 87 (84–89) 87 (84–90) 88 (85–90) 0.070 
Thrombocytes 229 (195–269) 229 (198–266) 234 (203–271) 0.080 
Leukocytes 6.0 (5.1–7.4) 5.8 (4.8–6.9) 5.5 (4.7–6.5) <0.001 
C-reactive protein 0.2 (0.1–0.4) 0.2 (0.1–0.3) 0.1 (0.1–0.2) <0.001 
Smoking status    <0.001 
 Never smoker, n (%) 26 (374) 44 (1,179) 54 (240)  
 Ex-smoker, n (%) 45 (641) 36 (979) 30 (135)  
 Active smoker, n (%) 29 (418) 20 (541) 16 (72)  
Alcohol use    <0.001 
 No alcohol, n (%) 23 (447) 10 (289) 4 (16)  
 <2 drinks/day, n (%) 56 (1,107) 83 (2,354) 92 (411)  
 ≥2 drinks/day, n (%) 21 (413) 6 (182) 4 (19)  
Red meat servings per week 2 (1–3) 2 (1–3) 2 (1–3) <0.001 
Fast food, n (%)    0.25 
 Fast food <weekly 84 (1,817) 83 (2,441) 86 (392)  
 Fast food ≥weekly 16 (336) 17 (484) 14 (62)  
Vegetables, n (%)    0.35 
 Vegetables ≥daily 46 (983) 47 (1,388) 48 (220)  
 Vegetables <daily 54 (1,170) 53 (1,537) 52 (234)  
Fruits, n (%)    0.013 
 Fruits ≥daily 43 (935) 40 (1,178) 37 (168)  
 Fruits <daily 57 (1,218) 60 (1,747) 63 (286)  
Physical activity, n (%)    <0.001 
 <1 h 25 (442) 10 (232) 2 (8)  
 <2 h 51 (925) 40 (897) 39 (129)  
 <3 h 19 (343) 36 (818) 45 (152)  
 ≥3 h 5 (89) 13 (298) 14 (46)  

While the prevalence of diabetes did not differ significantly between education groups, HbA1c was highest in the low education group (5.6%) and lowest in the high education group (5.4%) (p < 0.001). Mean BMI was highest in the low education group (27) and lowest in the high education group (25) (p < 0.001). The prevalence of hypertension was significantly (p < 0.001) higher in the low education group (63%) compared to the medium (55%) and high education group (43%). While the number of active smokers was highest among those with a low level of education (29%), the largest number of those who never smoked were in the group with a higher level of education (54%).

Interestingly, an increased prevalence of metabolic syndrome was observed among higher education groups. There was a statistically significant difference in physical activity between the groups (p < 0.001), with a trend indicating higher levels of physical activity among those with higher education. The number of servings of red meat and fast food per week, as well as the amount of vegetables per day, was similar across all educational groups. The consumption of fruits decreased with higher educational levels (p = 0.013).

Patients with medium (34%) and high (30%) educational status had lower rates of colon diverticula compared to patients with lower (41%) educational status (Table 2). On univariable analysis (model 1), medium (aOR 0.73; 95% CI: 0.65–0.82; p < 0.001) and high educational levels (aOR 0.62; 95% CI: 0.50–0.77; p < 0.001) were associated with a lower rate of diverticulosis (Table 3). After adjusting for age and sex (model 2), medium educational level was associated with a lower rate of diverticulosis (aOR 0.85; 95% CI: 0.76–0.97; p = 0.012). This association remained significant after additional adjustment for metabolic syndrome, physical activity, and dietary habits like fast food, vegetables, fruits, and alcohol consumption (model 3) (aOR 0.84; 95% CI: 0.72–0.98; p = 0.024). After adjusting for these factors, this significant association was not found to be consistently present in patients with high levels of education, although it was observed as a general trend. In the sex-specific univariate sensitivity analysis, the odds of diverticulosis remained lower in females with medium education (OR 0.65; 95% CI: 0.55–0.67, p < 0.001) and high education (OR 0.50; 95% CI: 0.35–0.71, p < 0.001), as well as in males (medium education: OR 0.77; 95% CI: 0.65–0.91, p = 0.002; high education: OR 0.69; 95% CI: 0.52–0.91, p = 0.01). The distribution of diverticula in the different segments of the colon did not exhibit any specific pattern based on educational status (Table 2).

Table 2.

Education and diverticulosis

Lower education (N = 2,153), n (%)Medium education (N = 2,925), n (%)High education (N = 454), n (%)p value
Diverticulosis    <0.001 
No diverticulosis 59 (1,271) 66 (1,938) 70 (317)  
Left-sided diverticulosis 25 (529) 23 (662) 20 (92)  
Right-sided diverticulosis 6 (131) 3 (97) 4 (16)  
Pandiverticulosis 10 (222) 8 (228) 6 (29)  
Lower education (N = 2,153), n (%)Medium education (N = 2,925), n (%)High education (N = 454), n (%)p value
Diverticulosis    <0.001 
No diverticulosis 59 (1,271) 66 (1,938) 70 (317)  
Left-sided diverticulosis 25 (529) 23 (662) 20 (92)  
Right-sided diverticulosis 6 (131) 3 (97) 4 (16)  
Pandiverticulosis 10 (222) 8 (228) 6 (29)  
Table 3.

Education and diverticulosis, multivariable multilevel logistic regression

Model 1Model 2Model 3
lower educationmedium educationhigher educationlower educationmedium educationhigher educationlower educationmedium educationhigher education
Diverticulosis Ref aOR 0.73; 95% CI: 0.65–0.82; p< 0.001 aOR 0.62; 95% CI: 0.50–0.77; p< 0.001 Ref aOR 0.85; 95% CI: 0.76–0.97; p= 0.012 aOR 0.83; 95% CI: 0.66–1.05; p= 0.119 Ref aOR 0.84; 95% CI: 0.72–0.98; p= 0.024 aOR 0.80; 95% CI: 0.61–1.05; p= 0.107 
Model 1Model 2Model 3
lower educationmedium educationhigher educationlower educationmedium educationhigher educationlower educationmedium educationhigher education
Diverticulosis Ref aOR 0.73; 95% CI: 0.65–0.82; p< 0.001 aOR 0.62; 95% CI: 0.50–0.77; p< 0.001 Ref aOR 0.85; 95% CI: 0.76–0.97; p= 0.012 aOR 0.83; 95% CI: 0.66–1.05; p= 0.119 Ref aOR 0.84; 95% CI: 0.72–0.98; p= 0.024 aOR 0.80; 95% CI: 0.61–1.05; p= 0.107 

We categorized a group of asymptomatic patients who underwent screening for colorectal cancer via colonoscopy, based on their level of education. Initially, we observed several differences in the baseline characteristics of patients across different educational categories and attempted to address these disparities by performing multivariable adjustments. We observed that patients with intermediate and high levels of education had lower rates of colonic diverticula compared to those with lower levels of education. This finding remained significant after adjustment for age and sex and also for metabolic syndrome, physical activity, and dietary habits. After adjusting for these factors, this significant association was not found to be consistently present in patients with high levels of education, although it was observed as a general trend. As is typical in Western populations, diverticula were mainly observed in the left colon among our study participants [20]. No association was found between diverticulum distribution and level of education. A clear correlation between increasing body weight and diverticular formation has been demonstrated in several previous studies, with more pronounced findings in those with increasing body weight [21]. Consistent with these studies, lower BMI values were evident in the group with higher levels of education. Furthermore, our data showed a noticeable and significant contrast in the amount of physical activity between the groups, with a pattern suggesting that individuals with higher education tended to engage in more physical activity. The mean age was significantly lower with increasing education level. Given that we are dealing with a group of asymptomatic patients undergoing colorectal cancer screening, we can assume that participation in the screening process may occur earlier due to higher educational attainment.

One of the biggest public health challenges is the disparity in health outcomes between different socioeconomic groups [22]. Physical health is significantly influenced by socioeconomic status, with all of its defining components contributing decisively to overall health. Higher occupation, higher salaries, and better education lead to better financial opportunities, a more pleasant working environment and housing conditions, as well as better knowledge and awareness of health [23, 24]. Inequalities in access to quality healthcare also contribute to inequalities in mortality [25]. As these factors are largely determined by the level of education, the use of GISCED subdivision has considerable justification. The socioeconomic factors mentioned earlier also play a significant role in determining life expectancy; just over one-third of premature deaths can be attributed to socioeconomic inequality, with the primary causes being coronary heart disease, lung cancer, and COPD [24]. Apart from these socioeconomic factors, health behavior also significantly differs among different educational levels. Unhealthy behaviors such as smoking, alcohol consumption, and obesity are less frequently observed in individuals with higher educational levels. Furthermore, individuals with lower educational levels pay less attention to sufficient physical activity and healthy sleep habits [5, 6].

The prevention of diverticulosis has become an important focus due to its increasing prevalence worldwide and the associated economic impact [26]. According to data from the US Centers for Disease Control and Prevention, diverticular disease is a growing health problem with significant costs. In 2010, healthcare facilities in the USA received 2,734,119 presentations due to diverticulitis. The resulting 216,560 hospitalizations caused a total of 2,181,992 hospital days and total costs of $2,178,031,586. Compared to 2006, emergency department visits have increased 31% and inpatient admissions have increased 21% since 2003 [27]. Between 1996 and 2006, the UK experienced a similar situation, where the rate of national admissions increased from 0.56 to 1.20 per 1,000 per year [28]. For perforated sigmoid diverticulitis, data from northern Finland showed an increase in annual prevalence, rising from 2.4 per 100,000 in 1986 to 3.8 per 100,000 in 2000 [29]. Not surprisingly, this trend was also observed in data from Italy, where the hospitalization rate increased significantly from 39 to 48 per 100,000 between 2008 and 2015 [30]. These results suggest that there is a need for increased attention and enhanced clinical expertise in managing this common condition. It is important to take into account the demographic trends and comparatively greater life expectancy in Western countries, resulting in more patients with diverticulosis, its complications, and associated costs. Subsequently, the introduction of new guidelines for the management of acute, uncomplicated diverticulosis in the USA has led to a reduction in emergency room visits, hospitalizations, and associated costs. This could already be considered as a potential strategy for cost reduction [31].

A study conducted by Lukosiene et al. [17] in Germany and Lithuania suggested that patients with diverticulosis had lower educational levels when compared to a control group, but the results did not reach statistical significance. This study in contrast to ours did not differentiate based on a standardized classification such as GISCED; they only distinguished between higher or lower educational levels without providing a precise definition. Better information on the work environment, as indicated by the collected night work data, would be interesting. Additionally, it is noteworthy that the sample size was significantly smaller. Furthermore, they discovered that having a higher level of education was linked to an increased probability of developing diverticulitis (2.453; 95% CI: 1.31–4.59) possibly caused by a more sedentary lifestyle [17], which possibly cannot be compensated even by the slightly increased physical activity we detected.

Hamdan et al. [32] did not observe any socioeconomic differences in patients presenting with acute diverticulitis. Overall, these findings suggest that the risk factors for developing diverticulosis are likely distinct from those for its complications. To improve the correlation between educational status, socioeconomic factors, and the hypothesized multifactorial development of diverticula [33], as well as progression to diverticulitis [34], further studies are necessary to understand how these factors interact with each other.

The observed association between low educational status and a higher prevalence of diverticula has profound implications for public health and individual patient care. From a public health perspective, this finding enhances our understanding of the distribution of diverticula and its risk factors. It highlights the need for targeted prevention strategies tailored to specific populations at risk, particularly those with lower educational attainment. These strategies could focus on health promotion and education initiatives, emphasizing dietary modifications, physical activity, and regular healthcare visits for early detection and management.

From an individual patient standpoint, knowledge of educational status as a potential risk factor for diverticula provides physicians with a pretest probability for the disease. This information can guide clinicians in making informed decisions regarding diagnostic testing, referrals, and counseling. By understanding the patient’s educational background, physicians can offer tailored advice and promote behavioral changes that may reduce the risk of diverticula development or progression. Additionally, improved physician-patient communication surrounding risk factors and disease prognosis can enhance patient engagement and facilitate shared decision-making.

Besides the inherent limitations of a retrospective cross-sectional study, our study has a weakness in the absence of other socioeconomic data, including information on income, occupation, or the precise location of residency. Conversely, the level of education is deemed to be a dependable indicator of an individual’s socioeconomic conditions [35]. Furthermore, the observed lower prevalence of diverticulosis among individuals of higher socioeconomic status appears to be contradicted by the association with younger age. We confirm that this is a potential confounder; however, the statistically significant effect persists even after multivariable adjustment. In response to the observation that the lower prevalence of diverticulosis in higher socioeconomic status is associated with a higher metabolic syndrome rate, we acknowledge the apparent contradiction with existing literature. Upon closer examination, we posit that this discrepancy may be explained by differing lifestyle factors and occupational behaviors across socioeconomic strata. Specifically, the increased prevalence of physically demanding occupations among lower educational groups could contribute to a lower incidence of diverticulosis. Conversely, individuals in higher education strata may engage in predominantly sedentary occupations, which, coupled with a lack of compensatory physical activity during leisure time, could elevate their risk for developing diverticulosis. Another limitation is the absence of data related to the DICA classification, which would have provided valuable insights for our study. Unfortunately, such information was not available for analysis. This absence of data limits our ability to comprehensively assess certain aspects of the condition under investigation.

This study’s findings indicating a higher prevalence of diverticula among patients with low educational status carry significant implications for both public health and individual patient care. By elucidating the association between educational status and diverticula, this research contributes to a better understanding of disease distribution and risk factors. Furthermore, it emphasizes the importance of developing targeted prevention strategies and tailoring patient counseling to mitigate the impact of diverticula. Future research should focus on elucidating the underlying mechanisms linking educational status to diverticula and evaluating the effectiveness of tailored interventions in reducing the burden of this common gastrointestinal condition.

We performed the study and all procedures according to the principles of the Declaration of Helsinki. The local Ethics Committee for the province Salzburg approved the study protocol (approval no. 415-E/1262). Written informed consent was obtained from every participant.

The authors do not have any conflict of interests.

This research was not funded by any public, commercial, or not-for-profit sectors.

Andreas Völkerer and Bernhard Wernly conceived the research project. Leonora Datz and Konrad Radzikowski have structured the raw data. Andreas Völkerer wrote the first draft of the manuscript. Bernhard Wernly, Sarah Wernly, Georg Semmler, Maria Flamm, Elmar Aigner, and Christian Datz provided critical feedback and improved the manuscript. All authors read and approved the final version of the manuscript.

Additional Information

Christian Datz and Bernhard Wernly contributed equally to this work.

The data that support the findings of this study are available from the SAKOPI database upon reasonable request. Access to the data will be granted in accordance with the relevant data sharing and privacy policies.

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