The Use of Animal Models to Study Bacterial Translocation During Acute Pancreatitis

Infection of pancreatic necrosis with intestinal flora is accepted to be a main predictor of outcome during severe acute pancreatitis. Bacterial translocation is the process whereby luminal bacteria migrate to extraintestinal sites. Animal models were proven indispensable in detecting three major aspects of bacterial translocation: small bowel bacterial overgrowth, mucosal barrier failure, and disturbed immune responses. Despite the progress made in the knowledge of bacterial translocation, the exact mechanism, origin and route of bacteria, and the optimal prophylactic and treatment strategies remain unclear. Methodological restrictions of animal models are likely to be the cause of this uncertainty. A literature review of animal models used to study bacterial translocation during acute pancreatitis demonstrates that many experimental techniques per se interfere with intestinal flora, mucosal barrier function, or immune response. Interference with these major aspects of bacterial translocation complicates interpretation of study results. This paper addresses these and other issues of animal models most frequently used to study bacterial translocation during acute pancreatitis.
Introduction
Experimental models of acute pancreatitis exist for almost 150 years, with Claude Bernard first describing experimental pancreatitis by injection of bile and olive oil into the pancreatic duct of a rabbit.1 Ever since, animal experiments were indispensable in providing insight in pathophysiology and treatment of acute pancreatitis. Experimental studies have major advantages over clinical studies, such as the availability of study subjects, standardization of disease severity, ability to perform invasive tests, extensive tissue sampling, and the possibility to test prophylactic treatment strategies.2 Despite these advantages, some major aspects of the pathophysiology of acute pancreatitis remain unclear, mortality in severe acute pancreatitis is still as high as 5–28%, and optimal treatment strategies remain a topic of debate.3,4
In 1986, Beger et al. demonstrated a link between the intestinal flora, infection of pancreatic necrosis, and clinical outcome in patients with severe acute pancreatitis.5 At the present time, infection of pancreatic necrosis is still regarded to be a main predictor of outcome during severe acute pancreatitis, and bacterial translocation of intestinal flora is considered to be the cause.4
Changes in intestinal motility and the associated shift of intestinal flora, mucosal barrier function, and the immune system were identified as pivotal aspects of bacterial translocation during acute pancreatitis.6–11 This has greatly increased the understanding of bacterial translocation, but better insight into the exact mechanism of bacterial translocation and subsequent infection of pancreatic necrosis is needed to develop adequate prophylaxis and treatment strategies for patients with severe acute pancreatitis.
A multitude of animal models were used to study the mechanism of bacterial translocation, including radiolabeling, plasmid-labeled bacteria, or fluorescent beads.12–15 Despite all these efforts, however, the exact origin, route, and mechanism of bacterial translocation causing infection of pancreatic necrosis are still unclear. The main reason for this uncertainty is the lack of an “ideal” animal model of acute pancreatitis to study pathophysiology of bacterial translocation and its treatment. The ideal model should be minimally invasive, standardized, reproducible, and resemble etiology, pathophysiology, disease course, and outcome of clinical acute pancreatitis, including response to treatment.2 Experimental models used to study bacterial translocation in acute pancreatitis and its treatment all seem to have methodological restrictions that complicate the interpretation of study results. In 2000, Foitzik et al. reviewed the use of animal models of acute pancreatitis and their suitability for evaluating therapy and concluded that animal models should be designed to mimic etiology and clinical course of human pancreatitis to increase their value.2 In addition, we would like to discuss the value animals studies and experimental models of acute pancreatitis have in face of their interference with one or more of the known aspects of bacterial translocation: intestinal motility and flora, mucosal barrier function, or the immune system.
The aim of this paper is to provide useful insights into the use of animal models to study bacterial translocation during acute pancreatitis, in the light of current knowledge of pathophysiology.
Animal Species and Housing Conditions
Before the late 1970s, larger laboratory animals such as dogs were predominantly used to study acute pancreatitis. But since the introduction of models of acute pancreatitis in small laboratory animals, mice or rats are generally favored for financial and ethical or practical reasons. Because of physiological and anatomical differences between species, choice of laboratory animal has important implications on the study results and extrapolation to the human situation.
Intestinal flora differs between animal species, largely depending on dietary demands and anatomical differences of the gastrointestinal tract and habits.16–18 The protein-rich diet of dogs or cats results in lower counts of endogenous lactobacilli and higher counts of potential pathogens (e.g., clostridia species), compared to rats or mice with fiber-rich diets. Coprophagy, demonstrated by most rodents, also influences intestinal flora, resulting in higher counts of gram-negative bacteria in the proximal gastrointestinal tract.19,20 Also, rats and mice are often bred and kept under specific pathogen-free conditions, introducing modifications of intestinal flora.
Intestinal barrier function also differs between species. In an experiment comparing small intestinal permeability between humans and rats, significant interspecies variation in urinary recovery of orally delivered mannitol was observed.21
Anatomical differences between species should also be considered. The relative size of the jejunum, ileum, cecum, and colon of different laboratory animals can influence origin and route of bacterial translocation during acute pancreatitis. In humans, retroperitoneal connections between the intestines and pancreas can greatly affect the clinical course of the disease.22 Similar to humans, the dog pancreas is situated retroperitoneally. Rat and mouse pancreata, however, are almost fully enveloped by peritoneum, resembling a more intraperitoneal localization. Variation in retroperitoneal connections between intestines and the pancreas offers different routes for bacteria to translocate without being exposed to intraperitoneal immune cells.23
Experiments using small animals (e.g., mouse or rat) usually incorporate a larger number of animals compared to experiments with large laboratory animals (e.g., cat or dog). The use of a larger number of small laboratory animals improves statistical power of an experiment. On the other hand, the use of larger animals could resemble human pathophysiology better, but a smaller number of animals means lower statistical power and increased potential false negative or false positive results.
Models of Acute Pancreatitis
An abundance of animal models of acute pancreatitis is used to investigate bacterial translocation. Only models most frequently used for this purpose will be discussed. Baseline characteristics of the discussed models and their potential effects on intestinal flora, mucosal barrier, and immune function are summarized in Tables 1 and 2.
Table 1Characteristics of Several Animal Models of Acute PancreatitisModelAnimal SpeciesPancreatic NecrosisPancreatic InfectionMortalityInvasivenessDuodenal loop24,25RatNoConsiderableHighLaparotomyCholine-deficient diet30–32MouseYesLittleHighMinimalDuct ligation34–37Rat/opossumNo/YesLittleLowLaparotomyCerulein44Mouse/ratYes/NoLittleLowMinimalDuct perfusion48Rat/dog/pigYesConsiderableModerate to highLaparotomyDuct perfusion + cerulein52RatYesConsiderableModerateLaparotomyTable 2Aspects of Bacterial Translocation and Potential Confounding Factors of Animal ModelsAspectConfounding FactorModelIntestinal motility and floraAnimal speciesPotentially all modelsHousing conditions (SPF)Potentially all modelsDietCDE dietAnalgesicsInvasive modelsLaparotomyInvasive modelsBile flowDuct ligationCeruleinCerulein modelsIntestinal manipulationInvasive modelsMucosal barrier functionStressPotentially all models DietCDE dietAnestheticsInvasive modelsPancreatic proteasesDuct ligationIntestinal manipulation/punctureDuct perfusionImmune systemStressPotentially all modelsDietCDE dietDisease course/severitySpecies-dependentObstructive jaundiceDuct ligation, duodenal loopIntestinal manipulationInvasive models
Duodenal Loop
Closing the duodenal lumen proximally and distally to the papilla of Vater results in reflux of the duodenal contents enclosed in the loop, including bile and pancreatic secretions, into the biliopancreatic duct.24 In rats, this leads to acute pancreatitis of varying severity.25 Discontinuation of the gastrointestinal tract leads to mucosal atrophy and functional changes to the mucosal barrier.26 Furthermore, obstruction of bile flow into the intestine was shown to reduce intestinal motility, causing small bowel bacterial overgrowth and increased bacterial translocation.27–29 Another major downside is the occurrence of reflux of duodenal contents, including bacteria, into the biliopancreatic duct. These obvious drawbacks of this model in experiments concerning bacterial translocation are the cause of its limited popularity.
Ethionine-supplemented Choline Deficiency
Lombardi et al.30 described severe acute pancreatitis in young female mice after feeding a choline-deficient, ethionine-supplemented (CDE) diet.31 Acute hemorrhagic pancreatitis ensues, as well as diffuse intraperitoneal fat necrosis and several systemic effects such as acidosis, hypoxia, and hypovolemia. In this model, mortality ranges from 0 to 100% after 4 days and can be controlled by varying the duration of the choline-deficient diet.32 To ensure homogeneity and reproducibility, sex, age, and weight of the mice have to be closely matched, as well as food intake of all animals.32
Apart from these practical downsides of the model, systemic complications unrelated to pancreatitis (e.g., parotitis and fatty liver disease) render the model less useful for investigating systemic events (e.g., immune response) of acute pancreatitis.31 Little is known of the effect of ethionine suppletion or choline deficiency on intestinal flora or mucosal barrier function. But the most important drawback of this model to study bacterial translocation is the low incidence of pancreatic infection (3–8%), even in severe necrotizing pancreatitis.33
Biliopancreatic Duct Ligation
In the duct ligation model, the common biliopancreatic duct is surgically clipped or tied at the sphincter of Oddi complex. The resulting obstruction of pancreatic secretions and potential biliary reflux into the pancreatic duct produce moderate pancreatitis, characterized by edema, moderate inflammation and hemorrhage, fat necrosis, and minimal acinar cell necrosis. Only in the American opossum does biliopancreatic duct ligation leads to severe acute pancreatitis with considerable necrosis.34–37
This model of acute pancreatitis greatly interferes with the pathophysiology of bacterial translocation. Obstruction of bile flow into the intestine causes small bowel bacterial overgrowth and bacterial translocation.28 Also, exclusion of pancreatic proteases in the gut lumen alters intestinal permeability.38,39 Apart from effects on the intestinal flora and mucosal barrier function, obstruction-induced jaundice also causes impairment of the immunesystem.40–42 These effects complicate the interpretation of bacteriological results to study bacterial translocation.
Cerulein Infusion
Infusion of low doses of cerulein, a cholecystokinin analog, enhances production of pancreatic exocrine cell secretions without cell necrosis. In most species, infusion of supramaximal doses results in a decrease of secretion and acute pancreatitis with interstitial edema and inflammatory cell infiltration.43 In mice, cerulein causes severe acute pancreatitis with necrosis of 40% of acinar cells.44 In rats and other animals, however, cerulein-induced pancreatitis is usually mild and generally self-limiting. Moreover, pigs are reported to be insensitive to cerulein hyperstimulation.45 It should be noted that cerulein is known to affect intestinal motility. Studies investigating the use of cerulein in man have shown absence of recognizable migrating motor complexes with decreased colonic transit time.46 In general, experimental acute pancreatitis is associated with reduced small bowel motility, resulting in small bowel bacterial overgrowth and increased bacterial translocation to extraintestinal sites.6,47 Thus, cerulein may interfere with intestinal flora by altering intestinal motility. Investigators should keep this in mind when designing a study and interpreting study results.
Biliopancreatic Duct Perfusion
Duct perfusion models are currently the most popular models of acute pancreatitis. Induction of acute pancreatitis involves infusion of bile, bile salts with or without bacteria, or activated pancreatic enzymes into the (bilio-)pancreatic duct. Early experiments mainly involved dogs, but currently, rats are used most frequently. Severity and reproducibility of acute pancreatitis and ensuing bacteriological results strongly depend on infusate, infusion pressure, volume, and time.48
The most commonly used infusates are solutions containing various concentrations of bile salts of varying hydrophobicity. Both chemical and pressure effects of infusion were suggested to play a major role in the pathogenesis of pancreatitis in perfusion models.48,49 In both chemical- and pressure-induced pancreatitis, destruction of the pancreatic duct mucosal barrier is the key event. This is followed by pancreatic edema, autolysis, reduction of pancreatic blood flow, and, in severe cases, destruction of pancreatic parenchyma and formation of pancreatic necrosis.50 Uncontrolled pressure-related damage causes variation in severity of the induced acute pancreatitis between study subjects, and thus should be avoided. Several experiments were performed to assess maximal pancreatic duct pressure before rupture of the duct epithelium causing increased and uncontrolled severity of acute pancreatitis. Data are conflicting, with rupture pressures varying from 15 to 82 mmHg.48,49,51,52 A maximum infusion pressure of 30 to 50 mmHg is currently accepted for rat models.
Perfusion is usually performed by puncturing the duodenum and cannulating the papilla of Vater. The introduction of duodenal bacteria, through the papilla of Vater into the biliopancreatic duct could potentially be a confounding factor in transduodenal duct perfusion models. It was demonstrated, however, that significant bacterial infection of the pancreas (>1 × 102 colony forming units per gram) because of the surgical procedure does not occur.53
Advantages of this model are the quick procedure of acute pancreatitis induction and the reproducibility of results. Other than duodenal puncturing and intestinal handling during surgery, both potentially affecting mucosal barrier function, no direct effects on intestinal flora or immune function are expected in this model.
Biliopancreatic Duct Injection and Cerulein Hyperstimulation
The combination of retrograde infusion of bile salts with superimposed cerulein hyperstimulation in rats was introduced by Schmidt et al. and was advocated as “a better model for evaluating therapy.”52 Although the disadvantages described for biliopancreatic duct injection and cerulein hyperstimulation all apply to this model, it was proven a very valuable model to examine bacterial translocation and treatment strategies. The major advantages are that histological and qualitative bacteriological results as well as reaction to treatment and disease course resemble human acute pancreatitis more closely than other models.2,52 Although proven a very valuable model, potential model-related confounding factors as described above should always be kept in mind when interpreting results.
Disease Course
Especially in the severe form of acute pancreatitis, systemic events can be divided into two phases: early proinflammatory and late immunosuppressive.54 In severe acute pancreatitis, the early phase is associated with a systemic inflammatory response syndrome (SIRS), potentially leading to multiple organ failure and early mortality. The late phase is characterized by immunosuppression, providing opportunity for infectious complications (e.g., infection of pancreatic necrosis) associated with sepsis and late mortality.2,55 Laboratory animal species and experimental models, however, each show their own disease course of acute pancreatitis.
Animal models were mainly used to investigate the early phase of acute pancreatitis.56 However, the model described by Schmidt et al. seems the most appropriate to investigate early and late systemic complications, considering that both phases can be discerned.52,57 In this model, infection of pancreatic necrosis progresses at least until 96 h. When taking into account that disease course is more rapid in small rodents, timing could well correlate with data on the course of severe acute pancreatitis in humans, as described by Foitzik et al.2, Beger et al.,4 and Lankisch et al.58
Severity
Pancreatic necrosis is produced in several animal models of acute pancreatitis (Table 1). On the other hand, only duct perfusion, with or without superimposed cerulein hyperstimulation, and murine CDE models demonstrate mortality comparable to human necrotizing acute pancreatitis.32,52,59 Models with high early mortality may be useful to investigate early phase systemic inflammatory response and organ failure, but are less adequate to investigate late infectious complications and associated (multiple) organ failure.
In most models, necrosis needs to be present for pancreatic infection to occur. It needs to be noted that this does not apply for the duodenal loop model in which reflux of duodenal contents into the biliopancreatic duct occurs.60 In contrast, the murine CDE model produces elaborate necrosis, but is associated with very low rates of pancreatic infection.33
Culturing, Controls, and Route of Bacterial Translocation
In all animal models, factors such as analgesia, anesthesia, or surgical techniques can influence bacteriological results. Morphine-like analgesics have a significant effect on bowel motility and cause bacterial overgrowth and translocation to extraintestinal sites.61 The anesthetic pentobarbital was suspected to be a factor in promoting bacterial translocation in a model of hemorrhagic shock.62
Also, stress causes mucosal barrier failure and bacterial translocation.63 Surgical procedures are stressful events, but animal transport or handling alone could potentially cause stress-induced bacterial translocation. The influence of stress on adrenaline and corticosteroid levels could have its own effect on the function of the immune system, potentially influencing the systemic reaction to acute pancreatitis and bacterial translocation.
Proper sterile surgical techniques are very important when investigating bacterial translocation. If abdominal surgery is involved, control cultures of the peritoneal cavity to trace surgical contamination are of special importance. If peritoneal cultures are found to be positive, extra caution should be taken with interpretation of bacteriological analysis of abdominal organs. In case of surgical contamination or transperitoneal bacterial translocation, the peritoneal covering of the organ samples might be the cause of positive organ cultures, not the bacterial colonization in the organ itself (false positive culture).
Puncturing the duodenum in duct infusion models hypothetically causes spillage of duodenal contents onto the peritoneum, covering all abdominal organs. In rats, however, duodenal contents usually have low bacterial counts, mainly consisting of nonpathogenic lactobacilli only. On the other hand, a duct infusion study by Cicalese et al. reported positive peritoneal cultures at time of induction of pancreatitis of 16.6 to 33.3% of the studied rats.15 Literature review of different animal models fairly frequently shows positive peritoneal cultures at the time of termination and organ sample collection of rats with acute pancreatitis. Positive peritoneal cultures are observed varying from 0–10% in minimally invasive models of acute pancreatitis (cerulein injection, CDE diet) to 8–100% in more invasive models (duct perfusion with or without cerulein hyperstimulation).6,14,15,64–66
Discussion
Changes in intestinal motility and flora, mucosal barrier function, and immune response were established as pivotal aspects in the process of bacterial translocation during acute pancreatitis. Early after the onset of acute pancreatitis, neurohormonal effects result in reduced small bowel motility.6 This causes stasis of luminal contents and small bowel bacterial overgrowth with potential pathogens, including Escherichia coli and Enterococcus species. The abundant presence of luminal pathogens forms a challenge for the mucosal barrier. Furthermore, pancreatitis-associated reduced intestinal blood flow results in mucosal ischemia and reperfusion damage.67–69 Luminal bacteria, normally held at bay by the mucosal barrier, now have opportunity to penetrate into the intestinal epithelium. Local intestinal inflammation follows, further compromising mucosal barrier function. Pancreatitis and ensuing intestinal inflammation both contribute to a systemic proinflammatory response (SIRS), with damaging effects on distant organs.70,71 If the systemic response is severe, multiple organ dysfunction syndrome (MODS) might follow.72,73 If the patient survives the early phase, counterregulatory immunological pathways releasing anti-inflammatory cytokines result in a refractory state characterized by immunosuppression.74,75 Persistent immunosuppression will render the patient liable for infection of pancreatic necrosis. Multiple organ dysfunction syndrome caused by infectious complications is considered accountable for so-called late mortality or “late septic death.”74,76
Although animal models were proven indispensable in acute pancreatitis research, model-related problems are most likely the reason for important questions on pathophysiology and treatment strategies to remain unanswered. Current topics of debate include the route and origin of bacterial translocation and optimal prophylaxis and treatment strategies.
Several different routes of bacterial translocation were described and have directed efforts for many prophylactic and therapeutic strategies. Webster et al. showed bacteremia to occur early after induction of acute pancreatitis in CDE-induced acute pancreatitis, suggesting a hematogenous route.77 Likewise, rapid passage of bacteria into the blood was found in other models of acute pancreatitis.78 On the other hand, Runkel et al. found bacteria migrating to lymph nodes before their translocation to distant sites in a duct ligation model, suggesting a lymphogenous route.79 Widdison et al. suggested transperitoneal translocation of bacteria originating from the colon in a feline model of severe necrotizing pancreatitis.80 Other study groups, including our own, have provided proof of the role of the small bowel in the pathophysiology of bacterial translocation in acute pancreatitis or after morphine administration.6,61,81
The model of duct perfusion and cerulein hyperstimulation described by Schmidt et al. was proven very useful because it resembles human disease quite well, considering its biphasic disease course, pancreatic histology, “moderate” mortality, and the bacterial spectrum in pancreatic necrosis.52 However, whether a confounder is introduced by puncturing the duodenum and cannulating the biliopancreatic duct is unknown. Therefore, to ensure quality of the presented study results, control cultures of the peritoneal cavity should be done when organ samples are analyzed bacteriologically. Peritoneal bacteria can potentially affect bacteriological analysis of all abdominal tissues. Widdison et al. washed abdominal samples before analysis, but this is not commonly performed.80 A pilot study by Arendt et al. showed that washing removed 94–97% of intraperitoneally injected bacteria.23 Immunohistologically localizing bacteria can help clarify if positive cultures of abdominal tissues are because of peritoneally located bacteria or actual bacterial colonization in the underlying organ tissue.
When experimentally evaluating therapy, treatment often starts before induction of acute pancreatitis. Obviously, this is an important reason why results cannot directly be translated to the clinical situation. On the other hand, these experimental studies provide proof of principle concerning the tested therapy. If prophylactically successful, the tested treatment strategy might be beneficial when started after the onset of acute pancreatitis and should therefore be further investigated. On the other hand, the faster course of acute pancreatitis in rodent models provides only a very short treatment window between the onset of the disease and early or late phase complications. This may lead to false negative effects of the therapy tested.
In conclusion, animal models of acute pancreatitis are indispensable tools, but model-related drawbacks often interfere with one or more pathophysiological aspects of bacterial translocation, complicating interpretation of results. When the ideal model of acute pancreatitis is not at hand, it is of major value that numerous alternatives are available. But with each experimental hypothesis, special care should be taken to select the most suitable model. Despite all the experimental work done, the route by which pancreatic infection occurs and gives rise to septic complications and mortality has not yet fully been elucidated. Optimal prophylactic and treatment strategies are also still widely debated. In the future, animal models will undoubtedly provide increasing understanding of these subjects, but model-related drawbacks should always be kept in mind when designing a study or when interpreting results.

animal
model
bacterial translocation
pancreatitis


Characterizing Internet Searchers of Smoking Cessation Information

Background The Internet is a viable channel to deliver evidence-based smoking cessation treatment that has the potential to make a large population impact on reducing smoking prevalence. There is high demand for smoking cessation information and support on the Internet. Approximately 7% (10.2 million) of adult American Internet users have searched for information on quitting smoking. Little is known about these individuals, their smoking status, what type of cessation services they are seeking on the Internet, or how frequently these searches for cessation information are conducted.
Introduction
The Internet has become the first source of health information for many people, primarily due to the ease of finding information [1]. In particular, there appears to be great demand for online information and services related to smoking cessation. In a random-digit dial survey conducted in 2004, 7% of Internet users in the United States reported using the Web to search for information on “how to quit smoking” [2]; more women reported to have looked then men (10% vs 7%), and unlike other health-related information seekers, they tended to be younger. At the time, this represented approximately 10.2 million people who had ever turned to the Internet for smoking cessation–related information or services. Little is known about these individuals, including their basic demographic characteristics, smoking status (eg, current smokers seeking cessation treatment, recent quitters seeking support to maintain abstinence), readiness to quit, quitting history, and treatment preferences. With the proliferation of antismoking sentiments and restrictive smoking policies, a diverse group of individuals may be turning to the Internet for assistance. In order to provide individually tailored and effective cessation treatment services via the Internet, it is necessary to better understand the characteristics and needs of this population.
The Internet is a powerful delivery channel that has the potential to deliver behavior change interventions on a population-wide basis to help people modify risk factors such as smoking [3]. There are limited, but encouraging, data to indicate that Web-based cessation interventions are effective in controlled trials [4-6]. However, it is not known if these approaches are appealing to or appropriate for the broader population of Internet users seeking cessation assistance. For example, approximately 30% of visitors to a widely utilized smoking cessation website indicated that they had quit smoking within the past week [7]. These individuals would be excluded from most randomized clinical trials of smoking cessation treatment, but they may represent a sizable population in need of assistance to remain abstinent. Information and services may need to be specially tailored to address the unique needs of individuals searching for cessation information based on their smoking status, demographic characteristics, and quitting history.
The incidence of cessation-related Internet searches may provide an effective proxy for consumer demand for cessation services. To date, there is little information about the rate at which searches for smoking cessation information occur. Several different techniques have been used to estimate the frequency of general health-related Internet searches [8-10], with widely varying results. Analyzing the first 300 search terms of the Wordtracker Top 500 keyword list, Phillipov and Phillips found less than 1% to be health-related terms [10]. Eysenbach took repeated snapshots of current search terms used on a search engine over a 15-month period, analyzed a random subset of queries, and found that 3.6-5.3% could be classified as health related [8]. Fox found that 79% of surveyed individuals had ever searched for health or medical information, while 7% had searched for smoking cessation information [2].
The primary purpose of this study was to characterize individuals who search for smoking cessation information. Specifically, we sought to gather information about sociodemographic and smoking history variables, search patterns (eg, time of day, search terms used), and perceptions about specific types of cessation services. Additionally, we used publicly available data to estimate the incidence of these searches. This information will be critical to develop appropriate and effective online cessation treatment programs, to triage patients as part of a stepped-care treatment model, or to successfully recruit smokers into treatment via the Internet.
Methods
Recruitment and Eligibility
Our recruitment strategy leveraged the prominent position of QuitNet (www.quitnet.com) on three of the largest Internet search engines. QuitNet is an established smoking cessation website [7] that is highly utilized, with over 600000 visitors and 97000 new registrants in 2004 from the United States alone. During the period of this study, it was listed in the top results for queries using “quit smoking” or “stop smoking” on three large search engines: Google, Yahoo!, and MSN (Appendix 1). In 2003, approximately 210000 (globally) and 110000 US individuals looking for information on quitting smoking arrived at QuitNet via these search engines. It has been estimated that 80% or more of Web users seeking health information start from search engines [11,12]. Research shows that Internet users read search engine results linearly, pay the most attention to the top three to five results, and click on the first promising link they find in the results [11,13]. Therefore, individuals who click on the link to QuitNet from a search engine results page are likely to be a representative sample of those individuals looking for cessation information on the Internet.
We recruited individuals based on four inclusion criteria: (1) use of the terms “quit smoking,” “quitting smoking,” “stop smoking,” or “stopping smoking” in a search engine query; (2) use of one of three major search engines (Google, Yahoo!, or MSN) to conduct these queries; (3) no prior visit to the QuitNet website (defined as www.quitnet.com or www.quitnet.org) as determined by the absence of a persistent (long-term) tracking cookie; and (4) location within the United States as determined by reverse lookup of IP (Internet protocol) addresses. When eligible Internet users clicked on the QuitNet link in the results of a search engine query, they were “intercepted” and recruited to participate in the study. The recruitment screen contained links to the survey and to the QuitNet website (Appendix 2). Those who accepted the invitation were directed to the QuitNet website following completion of the survey. Those who declined the survey invitation went directly to the QuitNet website. Recruitment for the survey was conducted for a total of 10 days: it began December 30, 2003, was suspended January 1 through January 3 due to technical concerns, and was completed January 12, 2004.
Generalizabilty was established from the complete panel of respondents, while we restricted further analysis to the respondents that reported any history of smoking and were seeking assistance for themselves.
Measures
The survey consisted of 10 questions that included basic demographic information (age, gender), reasons for searching for cessation information, current smoking status, readiness to quit, quitting history (number of past quit attempts, length of quit, quit methods used), information desired, and ratings of perceived helpfulness of various online cessation features (eg, bulletin board, assistance in setting a quit date). The survey questions were administered on three separate screens, with no more than three questions per screen. Date and time of survey completion were automatically logged to the database.
Data on utilization of QuitNet after survey administration were extracted, including registration and total time online. Time online was defined as the time between the first page view after completion of the survey through the time of the last page view.
Statistical Analyses
To determine the generalizability of our final sample, we compared survey respondents to nonrespondents who went on to register and use the QuitNet website on the demographic, website utilization, and search pattern variables obtained from the QuitNet database. In addition, we sought to determine the generalizability of our sample to the broader population of individuals who search for online smoking cessation information throughout the year. To do this, we examined the percentage of participants referred from each search engine as well as the total volume of cessation search terms used in Internet search engine queries, using publicly available data from Nielsen/NetRatings [14], Overture, and Wordtracker. Chi-square analyses were used to compare our sample to these national datasets.
For the 10-item survey, frequency tables were used to summarize the categorical data, and nonparametric tests were used to determine the statistical significance level. We used t tests for normally distributed continuous and ordinal variables.
Finally, to estimate the incidence of cessation-related Internet searches each year, we replicated the technique used by Eysenbach and Kohler [8]. MetaSpy was queried several times per day over the course of 9 months and the active queries were logged. Duplicate results (defined as the same set of 10 search terms being returned in succession) were removed. Searches containing the key words “quit[ing] smoking” or “stop[ing] smoking” were classified as cessation related.
Results
Recruitment Outcomes
During the 10-day study period, 2265 eligible US residents were intercepted. Of those, 35.8% (N = 811) clicked on the “survey” link, 48% (N = 1088) clicked on the link to take them directly to the site (“declined”), and 16.2% (N = 366) did neither (“abandoned”). Of the 811 individuals who clicked through to the survey, 87.2% (N = 655) completed the full survey, yielding an overall response rate of 29% (Figure 1). Of the survey completers, 29 individuals reported having never smoked, leaving a final sample of 626 respondents.
Figure 1
Eligibility and Recruitment Results
Generalizability
To assess generalizability, we compared all survey participants (N = 655) with nonrespondents who proceeded to register with QuitNet (N = 243). Overall, nonrespondents (N = 1454, abandoned and declined) were significantly less likely than survey respondents to register on QuitNet (16.7 vs 51.4%, Χ2
2 = 303.7, P < .001). Compared to survey respondents, nonrespondents spent less time on QuitNet (4.5 vs 12.0 minutes, t = 13.4, P < .001) and viewed fewer pages (5.9 vs 15.3 pages, t = 16.0, P < .001) on the website. Nonrespondents were more likely to be female (59.4 vs 51.9%, Χ2
2 = 4.2, P = .02) but did not differ by age, smoking status, time of survey invitation, or specific search engine used.
As shown in Table 1, the relative volume of participants referred from each search engine was consistent with national usage patterns (Χ2
2 = 1.06, P = .59). In this study, 57% of participants were referred from Google, 29% from Yahoo!, and 14% from MSN. At the time of this study, 60% of all Internet search queries were estimated to be conducted using Google, 23% with Yahoo!, and 17% with MSN [14].
Table 1
Comparison of search engine usage to Nielsen/NetRatings statistics
Relative Reach of Search Engines
 Search Engine
Survey Recruitment (%)
National Usage (%)
Google
57
60
Yahoo!
29
23
MSN
14
17
Total
100
100
The use of key search terms (“quit smoking,” “quitting smoking,” “stop smoking,” or “stopping smoking”) by survey respondents was also consistent with search patterns captured by Overture and Wordtracker. As shown in Table 2, the most commonly used search term was “quit smoking,” which constituted 52.9% of study queries, 59.1% of Overture queries, and 47.8% of Wordtracker queries. “Stop smoking” was the second most frequently used search term, which constituted 24.9% of study queries, 31.1% of Overture queries, and 36.5% of Wordtracker queries.
Table 2
Frequency of smoking-related search terms in search engine queries
Search Term
Searches (%)
Survey Participants (%)(Χ24 = 3.35, P = .80)
Overture (%)(Χ24 = 152, P < .001)
Wordtracker (%)(Χ24 = 138, P < .001)
quit smoking
52.9
55.4
59.1
47.8
stop smoking
24.9
23.9
31.1
36.5
quitting smoking
21.9
20.4
9.0
13.4
stopping smoking
0.3
0.4
0.6
1.8
giving up smoking
0.00
0.00
0.2
0.6
Participant Characteristics
As shown in Table 3, the majority of study participants were female (61.2%, n = 383) and between the ages of 26 and 44 years (62.7%, n = 393); 18.7% (n = 117) were aged 18-25 years, 17.1% (n = 107) were aged 45-64, and less than 1% were 65 or older (n = 4) or under age 18 (n = 5). Adjusted to local time of the participant, more than half (53.4%) of search engine queries for cessation information occurred during work hours (8 am-5 pm), 26.6% occurred between 5-9 pm, and 20% occurred at night (9 pm-6 am).
Participants were asked the reason they were searching for smoking cessation information. The majority of survey respondents (90.1%, n = 590) indicated that they were looking for help or support for themselves; 5.6% (n = 37) were looking for general information; 3.4% (n = 22) were looking for help for someone else; and 1% (n = 6) were health professionals or researchers looking for information. Further analyses were limited to individuals looking for cessation help or support for themselves or for general cessation information (N = 626). Among these individuals, 75.4% (n = 472) were current smokers, 17.4% (n = 109) had quit within 7 days (“recent quitters”), and 7.2% (n = 45) had quit more than 7 days ago (“longer-term quitters”).
Table 3
Demographic and smoking characteristics of study participants (N = 626)
Characteristic
Number of Participants (%)
Age
< 18
5 (0.8)
18-25
117 (18.7)
26-34
232 (37.0)
35-44
161 (25.7)
45-54
87 (13.9)
55-64
20 (3.2)
65 or older
4 (0.6)
Gender
Male
243 (38.8)
Female
383 (61.2)
Smoking Status
Current smoker
472 (75.4)
Not thinking of quitting
1 (0.2)
Thinking of quitting in 6 months
222 (35.5)
Thinking of quitting in 30 days
249 (39.8)
Quit ≤ 1 week
109 (17.4)
Quit > 1 week, ≤ 1 month
43 (6.9)
Quit > 1 month
2 (0.3)
The majority of current smokers (52.8%, n = 249) planned to quit in the next 30 days, 47.0% (n = 222) planned to quit in the next 6 months, and one person (0.2%) was not thinking about quitting. Smokers had made an average of 5.1 quit attempts (SD = 14.7; median = 1) during the past year.
Information Preferences
As shown in Table 4, information preferences varied by smoking status. Current smokers were more likely than recent quitters and longer-term quitters to be interested in information about how to quit smoking (88.1%, 54.1%, and 40.0%, respectively; Χ2
2 = 104.7, P < .001) and medication usage (30.7%, 5.5%, and 4.4%, respectively; Χ2
2 = 41.0, P < .001). Not surprisingly, both recent quitters and longer-term quitters were more interested than current smokers in information about withdrawal (77.1%, 66.7%, and 59.7%, respectively; Χ2
2 = 11.7, P = .003).
Table 4
Information sought by smoking status (N = 626)
Information
Current Smoker (%)
(n = 474)
Quit ≤ 1 Week (%)
(n = 109)
Quit > 1 Week (%)
(n = 45)
Χ22
P value*
How to quit
88.1
54.1
40.0
104.7
< .001
Medications
30.7
5.5
4.4
41.0
< .001
Alternative methods
57.6
16.5
17.8
77.3
< .001
Withdrawal
59.7
77.1
66.7
11.7
.003
*Current smokers are the reference group.
Note: Multiple responses were allowed, so total percentages within smoking category exceed 100%.
Perceived Helpfulness of Cessation Services
Participants were also asked to rate the perceived helpfulness of various smoking cessation treatment interventions on a scale from 1 to 5, with 1 representing “very helpful” and 5 representing “not helpful at all.” As shown in Table 5, the three features that were rated most highly by all participants were (1) individually tailored information (mean = 1.90, SD = 1.18); (2) information on withdrawal (mean = 1.84, SD = 1.15); and (3) a meter that keeps track of personal data (mean = 2.14, SD = 1.37). The three features rated the lowest by all participants were (1) support from a telephone counselor (mean = 3.21, SD = 1.35); (2) email support (mean = 2.95, SD = 1.40); and (3) support from others (mean = 2.90, SD = 1.38). Ratings of perceived helpfulness varied according to smoking status. Current smokers rated information about medications, assistance in setting a quit date, and assistance in choosing a medication as more helpful than did recent quitters and ex-smokers. Support from others and information about withdrawal received higher ratings of perceived helpfulness from recent quitters and ex-smokers than from current smokers. As detailed in Table 6, information of withdrawal, individually tailored information, and tracking meters were rated as “helpful” or “very helpful” by over half of the participants, while telephone counseling was thought to be helpful by less than 30% of participants.
Table 5
Perceived helpfulness of Internet features by smoking status
Feature
All Participants, Mean (SD)
(N = 626)
Current Smokers, Mean (SD)
(n = 472)
Quit ≤ 1 Week
(n = 109)
Quit > 1 Week
(n = 45)
Mean (SD)
P value*
Mean (SD)
P value*
Information on withdrawal
1.84 (1.15)
1.90 (1.17)
1.67 (1.08)
.06
1.51 (0.75)
.04
Individually tailored information
1.90 (1.18)
1.88 (1.18)
2.00 (1.25)
.36
1.79 (0.95)
.62
A meter that keeps track of personal data
2.14 (1.37)
2.14 (1.37)
2.14 (1.42)
1.0
2.15 (1.31)
.97
Information on medication side effects
2.59 (1.38)
2.55 (1.38)
2.79 (1.34)
.11
2.54 (1.43)
.97
Assistance in choosing a medication product
2.72 (1.37)
2.61 (1.36)
2.97 (1.38)
.02
3.24 (1.24)
.007
Information on medications
2.72 (1.36)
2.62 (1.36)
2.97 (1.37)
.02
3.23 (1.23)
.007
Online, personal help from a professional
2.81 (1.38)
2.79 (1.40)
2.86 (1.29)
.67
2.88 (1.39)
.70
Ability to find buddies
2.82 (1.37)
2.87 (1.39)
2.74 (1.29)
.40
2.59 (1.34)
.22
Assistance in setting a quit date
2.83 (1.39)
2.69 (1.37)
3.25 (1.32)
< .001
3.39 (1.37)
.003
Support via chat, forums, or email
2.90 (1.38)
2.98 (1.39)
2.67 (1.35)
.04
2.57 (1.30)
.07
Additional information that arrives by email
2.95 (1.40)
2.91 (1.43)
3.06 (1.30)
.34
3.08 (1.26)
.49
Talking by phone with a professional counselor
3.21 (1.35)
3.17 (1.39)
3.32 (1.22)
.32
3.46 (1.29)
.20
*
P values compared to current smokers; P = ns for all comparisons between recent and long-term quitters.
Note: 1 = very helpful; 2 = helpful; 3 = somewhat helpful; 4 = not very helpful; 5 = not helpful at all
Table 6
Proportion of participants (N = 626) rating Internet cessation services as helpful or very helpful
Feature Offered
Helpful or Very Helpful
n
%
Information on withdrawal
460
73.5
Individually tailored information
450
71.9
A meter that keeps track of personal data
405
64.7
Information on medication side effects
303
48.4
Information on medications
275
43.9
Assistance in choosing a medication product
273
43.6
Online, personal help from a professional
265
42.3
Ability to find buddies
250
39.9
Assistance in setting a quit date
248
39.6
Support from others, via chat, forums, or email
233
37.2
Additional information that arrives by email
223
35.6
Talking by phone with a professional counselor
184
29.4
Estimating Incidence of Cessation Queries
Over the course of 9 months, 541685 searches were extracted from MetaSpy, of which a total of 38 were smoking cessation related. Assuming a total search engine volume of 52 billion searches per year [14], this ratio yields an estimate of 3.6 million (99% CI = 2.5-4.8 million) cessation-related searches per year in the United States alone.
Discussion
The Internet holds great potential to impact population smoking prevalence by delivering evidence-based treatments to greater numbers of smokers who may never receive treatment through other modalities. This is the first study to characterize the population of individuals looking for cessation information online. Results suggest that the Internet may be an effective way to reach smokers who are younger, who search for cessation services during work hours, and who have recently quit on their own.
The relatively large proportion (17.4%) of recent quitters (within 7 days) in this study who are actively seeking assistance is of particular importance. The majority of self-quitters relapse within 8 days [15]. Over 16 million Americans try to quit on their own each year, but less than 5% maintain abstinence for 3 months [16]. Thus, more than 15 million smokers relapse. Until recently [17], this segment of the population of smokers received little attention once formal cessation treatments ended. Given the reach and 24/7 availability of the Internet, effective relapse prevention interventions can and should be delivered to the thousands of smokers trying to maintain abstinence. An effective relapse prevention service for self-quitters with intensive support around the quit date could produce a significant impact on smoking prevalence and could be used in conjunction with any other cessation treatment.
New population-based strategies to identify and reach smokers with evidence-based cessation treatment are needed [3]. Currently, telephone quit lines are the primary public health delivery channel for low cost, effective tobacco treatment. Despite the obvious advantages of convenience and cost, uptake rates in states with quit lines have remained low despite aggressive promotion, with less than 2% of smokers participating [18]. Given that Internet searchers are more likely to prefer self-help treatment with lower efficacy rates, it is important to design interventions which capture initial interest that can successfully “up-sell” more intensive and effective treatment interventions such as telephone counseling and medication use. In this manner, the Internet may be able to provide a workable model for stepped care, where participants can be further triaged to receive telephone counseling; prescription medication; in-person, group, or individual counseling; or even inpatient treatment [19].
Limitations
Several limitations should be considered when interpreting results of this study. The relatively low response rate (29%) raises concern about the generalizability of findings. Survey respondents were more likely to go on to register with the site; this likely indicates that they were in a more advanced stage of change than nonrespondents. It may, however, also indicate that the survey itself acted as an incentive to proceed to registration. Furthermore, we worked from the assumption that individuals who clicked on the link to QuitNet in search engine results were representative of the entire population of searchers. Although consistent with utilization patterns of search engines, this assumption has never been tested for searches on smoking cessation, or the QuitNet site in particular. It is possible that less motivated searchers may find the query results unappealing and not click on any link at all, thus biasing our results toward individuals closer to quitting.
A second potential limitation is the method we used to estimate the total number of people seeking smoking cessation information each year. This method does not take into account searches using other keywords or individuals using resources other than search engines to find information (eg, health Web portals, referrals from health professionals, direct-to-consumer advertising, or quit lines). In addition, individuals may search for information multiple times, making it difficult to estimate the actual number of unique individuals as opposed to the total number of searches. Finally, the dataset used to derive these estimates is of commercial nature and published online in a promotional context. It has not been peer-reviewed or made available in its raw form. The data for this study were collected from 2003-2004; it is possible that in the intervening time the demographics or search behavior of smokers has changed. However, given the limited changes in both search engine technology as well as the demographics of smokers in the United States, this seems unlikely. Despite these limitations, this study provides valuable information about people who search for smoking cessation information online, and it demonstrates a new methodology for validating this kind of survey data.
Conclusion
This study suggests that the potential public health impact that can be achieved through Internet-based smoking cessation programs is significant given the reach of the Internet—should these interventions be proven effective. Given that individuals may conduct multiple searches, our estimate of 3.6 million active searches per year for smoking cessation information is consistent with the 2004 data that showed 7% (about 10 million) of Internet users in the United States had searched for information on quitting smoking [2]. With 1.25 billion smokers throughout the world [20], there is enormous potential to globally impact smoking prevalence.
The public health community has invested heavily over the past 15 years in successfully de-normalizing smoking and encouraging cessation. However, low uptake rates seen in clinical programs and telephone quit lines call for new population-based approaches. Even if Internet-assisted tobacco interventions prove to have limited efficacy, the Web may still serve as a point of entry to multi-modality treatment programs. These programs may serve to simply link online searchers to more traditional treatment programs (such as telephone counseling or local group sessions), provide pharmaceutical products, or, in more sophisticated settings, use the Web as a platform to integrate voice counseling, local groups, mailed pharmaceutical products, and other proven modalities. We anticipate that the consumer demand demonstrated in this report will ultimately drive increasing services that will reflect a mixture of these different evidence-based treatments.

internet
smoking
cessation
search engine
query


Cancer screening in France: subjects’ and physicians’ attitudes

Objective Since screening for cancer has been advocated, funded, and promoted in France, it is important to evaluate the attitudes of subjects in the general population and general practitioners (GPs) toward cancer screening strategies.
Introduction
Early detection of cancer can theoretically prolong overall survival of the screened subjects. Reductions in cancer-related mortality have been demonstrated for mammography for breast cancer (BC) and fecal occult blood test (FOBT) for colorectal cancer (CRC) [1, 2]. For these two cancers, mass-screening programs are implemented in France, in which targeted subjects are contacted by mail. The US Preventive Services Task Force guidelines rate CRC screening as “A” (strongly recommended), BC screening as “B” (recommended), and both prostate cancer (PC) and lung cancer (LC) screenings as “I” (evidence insufficient to come down in favor or against screening) [3]. The objectives of the nationwide EDIFICE opinion poll carried out in France were to collect data at the national level (the level of funding) about (1) subjects’ individual access to cancer screening procedures (through organized programs or on subjects’ own initiative) and (2) about general practitioners’ (GPs) attitudes toward cancer screening. Four frequent cancer types were selected on the grounds of their different screening statuses: BC and CRC for evidence of efficacy of screening and existence of a national screening program and prostate and lung cancers for neither evidence of efficacy of screening nor official screening program.
Methods
General population opinion poll
The population-based EDIFICE opinion poll was carried out by telephone from 18 January to 2 February, 2005 among a representative sample of 1,504 subjects living in France and between 40 and 75 years old (1,609 subjects minus 105 who had already been affected by cancer). Sample representativeness was assessed, in relation to the statistics of the French Employment Survey in year 2002 [4], based on the following criteria: sex, age (five categories), profession (eight categories), community size (five categories), and regional distribution (nine categories).
General practitioner opinion poll
A nationwide opinion poll was carried out by telephone from 31 January to 18 February, 2005 among a representative sample of 600 GPs practicing in France. Sample representativeness was assessed based on the following criteria: age (four categories) and regional distribution (five categories).
Results
The main results of EDIFICE opinion poll are summarized in Fig. 1. Almost all (93%) interviewed women stated that they had undergone at least one mammography. In contrast, 25%, 36%, and 6% of the interviewed subjects stated that they had undergone screening tests for CRC, PC, and LC, respectively.
Fig. 1Adherence to cancer screening strategies according to the French nationwide EDIFICE opinion poll (1,504 subjects, 600 general practitioners). In France, there are organized mass-screening programs for breast cancer by mammography (100% of the population covered at the time of the opinion poll) and for colorectal cancer using Hemoccult (almost 30% of the population covered). In contrast, there is no mass-screening program for prostate cancer and lung cancer. For subjects in the general population, figures indicate the rates of subjects stating that they had undergone at least one screening test. For general practitioners, figures indicate the rates of general practitioners stating that they systematically recommended cancer screening
The corresponding percentages of GPs who stated that they recommended cancer screening tests to their patients were consistent with the proportions of subjects who had undergone screening tests for each tumor type: 68%, 18%, 58%, and 4% of the interviewed GPs stated that they systematically recommended screening for BC, CRC, PC and LC, respectively.
Reasons given to explain why screening tests had not been performed
Physicians and subjects provided contrasting answers as to why cancer-screening tests had not been performed. Physicians mainly focused on subjects’ fears while subjects denied this reason. Fear of results was cited by 44% of physicians as the main explanation why subjects did not undergo BC screening tests versus only 18% of subjects. For CRC screening, the figures were 16% and 3%, respectively. The corresponding odds ratios for fear being elicited by subjects versus GPs as the reason for not undergoing BC or CRC screening tests were 0.29 and 0.15, respectively (Table 1). Furthermore, subjects focused on a lack of advice on cancer screening from their physicians whereas GPs rarely did so. Lack of advice from the physicians was cited by 11% of subjects as the main explicative factor for subjects not undergoing BC screening tests versus only 1% of GPs. For CRC screening, the figures were 16% and 9%, respectively. The corresponding odds ratios for lack of medical advice being elicited by subjects versus GPs as the reason for subjects not undergoing BC or CRC screening tests were 11.65 and 1.93, respectively (Table 1).
Table 1Reasons cited by subjects in the general population and general practitioners why subjects did not undergo screening tests for breast cancer (BC) or colorectal cancer (CRC)Reasons citedFor not undergoing BC screening testsFor not undergoing CRC screening testsFemale subjects N = 38General practitioners N = 600Subjects N = 725General practitioners N = 600Subjects fearing the result n (%)7 (18)264 (44)20 (3)96 (16)OR (CI95%)0.29 (0.12–0.66)0.15 (0.09–0.24)Lack of physician’s advice n (%)4 (11)6 (1)116 (16)54 (9)OR (CI95%)11.65 (3.14–43.23)1.93 (1.37–2.71)OR indicates odds ratio; CI95% indicates confidence interval at the risk α = 0.05
Discussion
In the present study, we describe the adherence to different screening programs for four types of cancers (BC, CRC, PC, and LC) in France. It is important to point out that this opinion poll does not report an accurate incidence of subjects actually screened for cancer but indeed the proportion of subjects stating that they had undergone at least one screening test. It is also worth mentioning that the French national health insurance system currently makes the corresponding screening tests (FOBT, mammography, prostate specific antigen—PSA—testing and X-ray) available for free (or almost free) to all affiliates.
The major finding of this report is the obvious disagreement between evidence-based official recommendations and actual practice is both subjects in the general population and GPs in France. Despite a high level of evidence of reduction in cancer-related mortality of similar magnitude (∼15–20%) with both BC and CRC mass-screenings [1, 2], 93% of women undergo mammography screening whereas only 25% of subjects in the same age range have access to CRC screening tools. In contrast, 36% of men aged between 50 and 74 years have undergone a screening test for PC (mainly PSA testing) even though the benefit of PC screening remains unknown [5].
EDIFICE showed, on the one hand, two “rational rates” of screening—high rate of women having undergone mammography and low rate of subjects having performed LC screening—and, on the other hand, two “inadequate rates” of screening—abnormally low rate of CRC screening (nationwide coverage by the on-going program is expected by the end of 2007) and abnormally high rate of PC screening. Even more striking, the French GPs’ behavioral pattern of recommending individual cancer screening exhibited the same inconsistencies.
Three reasons can be suggested to explain the observed relationships between subjects’ and GPs’ behaviors. First, subjects in the general population may be influenced by medical counseling. Alternatively, physicians may endorse their patients’ views and agree “under pressure” [6]. Lastly, both subjects and GPs are exposed to similar not evidence-supported recommendations/information, with the “magic touch” of blood analysis for PC screening being attractive to them. Appropriate information of subjects and physicians, including the possible benefits and risks of PC screening through PSA testing, could make these erroneous behaviors less frequent [7].
Whereas GPs’ and subjects’ statements in EDIFICE appeared in rather good agreement with respect to the proportions of interviewed subjects having undergone cancer screening tests and of GPs recommending cancer screening to their patients, the reasons put forward why screening tests were not performed are different, indeed even opposite: based on their statements, GPs overestimated the negative impact of fear of the results on subjects’ participation in cancer screening and they underestimated their own role. Nevertheless, both GPs and subjects interestingly seem to attach a more dreadful meaning to the result of BC screening than to the result of CRC screening. Indeed, fear of the result acting as a check upon carrying out cancer screening tests was cited by 18% of the subjects for BC screening whereas only 3% cited this reason for not undergoing CRC screening tests. Comparatively, 44% and 16% of GPs cited fear of the result as the reason for subjects not undergoing BC and CRC screening tests, respectively.
It thus appears that there is a need for more research in social science as well as in biology and public health to improve the effectiveness of cancer screening in the framework of a national health system.

mass-screening
compliance
health care opinion poll
health services research
health services misuse