Our study found that the theoretical HBM model is consistent with CRC screening in the Spanish population. The perceived barriers stated by individuals with regard to the collection of FOBT samples appear as the most powerful cognitive concept of the Model, unlike the benefits of the screening. This has to be taken into account when planning educational interventions in the population.
Mean age of participants was around 60 years, in line with the age of similar studies, as it is the usual age range for medium-risk CRC screening (22)(23). We have found that older patients participate more in CRC screening (OR: 1.06). This coincides with other papers which also report a similar magnitude of the effect (24)(25)(26)(7). But it disagrees with other studies which do not find a correlation with age (16)(27)(28).
We have not noted a difference between genders in our sample, as other authors have also noted (28)(16)(29). On the contrary, other studies (23)(7) note that women participate less than men in CRC screening. A recent systematic review (6) (Mosquera 2020) in which the majority of studies were carried out in Western countries concluded that women participate more in the collection of FOBT samples and less in colonoscopy with regard to men; all these gender differences can be related to the conditions of gender equality in each country.
The percentage of married people (72.10%) in our study is similar to that of other papers (14)(15). Married patients in our study had increased participation in the CRC screening (OR 2.13). This connection is also found in the majority of published papers and is consistent with people who have greater social support (30)(31)(27)(28). There are, however, other series that do not find this connection to the civil status (26)(32).
Population in our sample is of urban middle / upper class extraction, partly comparable with that in a Turkish study by Ozsoy (15), as opposed to the majority of the published papers, in which the population is usually of low income and lower socio-cultural level(22)(33)(34). As in our case, several studies link a lower level of education and a lower participation in the CRC screening (32)(31)(27); on the other hand, Leung 2016 (16) and Jeihooni 2017 (28) do not find differences associated with the level of education. The relationship between the level of knowledge of the person about CRC and their participation in the screening shows differing results according to several studies (35)(34)(24). Some authors include the income level of the patients, as it is relevant in countries where patients must pay for the screening test (22)(36)(23). We do not include that level, as the Spanish Health System covers the costs of FOBT tests and colonoscopies. Rosentrock already noted in 1966 that the HBM model would be mainly applicable to the middle classes (8). This circumstance still has implications when designing the educational intervention programmes which must be adapted to the different social classes to which they are addressed.
The majority of the studies on CRC screening based on HBM are conducted in Asian countries. Following the conceptual HBM framework, we have noted in our study that BENEFITS of CRC screening are not associated to higher participation in the programme, as also noted by the majority of the authors of the published studies (24)(31)(27)(26)(35)(16). Contrary to what might be expected, we also found that patients participating in the screening do not perceive more benefits. This association is also found by Rawl in the initial validation of the questionnaire (14) and by Leung in the Hong Kong study, where it is also noted that those not participating in the screening perceive more barriers and fears, but there were no differences in the benefits (16). Kivinienmi’s systematic review (7) does not find a relationship with benefits perceived after the CRC screening in 13 out of 35 analyzed studies of faecal occult blood tests. One of the renowned authors of the HBM points out that a possible explanation for this fact is that Benefits would be more predictive to promote healthy lifestyles (non-smoking) than other preventive activities (10). Other authors have suggested as an explanation that people reduce their perception of severity after participating in the screening. All this would explain the apparent paradox of people with personal or family history of cancer not significantly increasing their participation in CRC screening programmes, as noted in our results and by other authors (31)(27)(32). However, other series do find a relationship between between a history of cancer and CRC screening (26)(29). HBM indicates that decision making in health and lifestyle is a process in different phases influenced by the social norm and group to which the individual belongs. Longitudinal surveys must be designed in order to clarify this negative association opposite to the direction of the HBM theoretical model.
Our results clearly reflect that the BARRIERS raised by the individual to take a FOBT are the factor best predicting participation in CRC screening programmes. This is consistent with the HBM postulates which state that perceiving barriers is the most powerful dimension of the Model (25)(30)(19)(37). Barriers may be different depending on the different social, cultural or ethnic groups. It is therefore advisable to study human behaviour in different populations (7).
Among the barriers raised by the patient to avoid taking a FOBT, we found that to have no time to take the test; not to know how to take it; being an unpleasant test; to have no symptoms or problems; and to be currently not a major problem are those reaching statistical significance in our study. These findings are also referred by Janz in the study conducted in the United States (31), as well as by Javadzade (38) and Jeihooni (28) in Iran. Other authors refer other statistically significant barriers in patients from Hong Kong and Iran such as fear of cancer or being ashamed about taking the test, which we did not find (24)(27).
Multivariate analysis maintains not to know how to take the test (OR 0.46) and to be currently not a major problem (OR 0.43) as statistically significant. This magnitude of the effect is similar to that found by other authors in the United States, Asia or Iran with a ranging from 0.37 to 0.42 (31)(24)(26)(34). Several authors point out the Oyster Shell Strategy as a cognitive mechanism explaining many barriers; it would involve not taking the FOBT to delay the adverse consequences of a diagnosis and the belief that taking more tests would increase the odds of contracting illnesses (24)(39)(40).
We must point out as limitations in our study that we have measured only two out of the seven HBM dimensions. This was partly due to the choice of a fast measuring instrument to adapt ourselves to the time available in the primary care centres. On the one hand, perceiving susceptibility is referenced as contributing to understand preventive habits, but carries less weight than benefits and barriers; on the other hand, there is a consensus in that perceiving severity is the dimension with the lowest significance ratios in the Model (25)(19)(13). The remaining HBM dimensions are less assessed and are most applicable to chronic illnesses (10).
We do not specifically measure one of the most important facilitators to participation, such as the physician’s recommendation or the type of health insurance (30)(24)(26)(29)(38). Other factors dependent on the healthcare provider or racial factors that we did not quantify have been referenced (41)(42). Asian doctors in Australia were noted to perceive more barriers for their patients to take the FOBT than Caucasian doctors regardless of their training, and CRC screening is less commonly recommended to immigrant patients (43). We also have to take into account the untraceable population in our study as it could introduce a bias for losses, although we did not find any differences with regard to age and gender when that population was included.
The educational interventions that proved to be most effective to increase the participation in CRC screening are focused on promoting personalized health advice, removing barriers, improving accessibility to the test (which includes providing tests outside working hours), community interventions based on health workers and dissemination of information by the media (44)(45)(32)(46). To this regard, our results support the implementation of interventions aimed at eliminating barriers and improving the accessibility of FOBT.