Skip to main content
Download PDF

Explaining time elapsed prior to cancer diagnosis: patients’ perspectives

BMC Health Services Research volume 17, Article number: 448 (2017) Cite this article

Abstract

Background

Cancer is the leading cause of death in Canada. Early cancer diagnosis could improve patients’ prognosis and quality of life. This study aimed to analyze the factors influencing elapsed time between the first help-seeking trigger and cancer diagnosis with respect to the three most common and deadliest cancer types: lung, breast, and colorectal.

Methods

This paper presents the qualitative component of a larger project based on a sequential explanatory design. Twenty-two patients diagnosed were interviewed, between 2011 to 2013, in oncology clinics of four hospitals in the two most populous regions in Quebec (Canada). Transcripts were analyzed using the Model of Pathways to Treatment.

Results

Pre-diagnosis elapsed time and phases are difficult to appraise precisely and vary according to cancer sites and symptoms specificity. This observation makes the Model of Pathways to Treatment challenging to use to analyze patients’ experiences. Analyses identified factors contributing to elapsed time that are linked to type of cancer, to patients, and to health system organization.

Conclusions

This research allowed us to identify avenues for reducing the intervals between first symptoms and cancer diagnosis. The existence of inequities in access to diagnostic services, even in a universal healthcare system, was highlighted.

Peer Review reports

Background

Cancer is the primary cause of death in Canada (30% of deaths), ahead of cardiovascular and respiratory diseases [1]. Two out of four Canadians will develop cancer, and one Canadian in four will die from it. More than half of newly-diagnosed cancers are lung, breast, colorectal, or prostate cancers. The most fatal cancers are also the most common: lung cancer accounts for around 27% of cancer deaths, colorectal cancer causes 11.5% of cancer deaths in women and nearly 13% in men, and 14% of cancer deaths among women are due to breast cancer [1].

The precise impact on cancer progression or survival of the interval between first symptoms and cancer diagnosis is difficult to estimate [2,3,4,5]. However, shortening this interval could lead to diagnosis at earlier stages of illness, thereby improving patient prognosis [2, 6, 7] and quality of life post-treatment [8], especially in cases of breast and colorectal cancer [2]. Furthermore, the period from first signs to diagnosis appears to be a key determinant of cancer outcomes [7]. Gaining a better understanding of what happens in the time preceding cancer diagnosis will provide a clearer picture of the current situation and help identify levers for improving patients’ pre-diagnostic pathways [9]. Writings suggest that delays before diagnosis are related to various factors that can refer to the characteristics of cancer, to the patient, and to the healthcare system [7, 10,11,12]. In this study’s context, in which patients experience recurrent problems in accessing primary care and diagnostic services [13], it is important to document and understand the factors explaining such delays.

The objective of this article is to analyze the factors influencing time elapsed between the first help-seeking trigger—such as the first appearance of symptoms or a letter of invitation to take part in a provincial cancer screening program—and cancer diagnosis. The study looked at the three cancer types that are most common and have the highest mortality rates: lung, breast, and colorectal. This was a qualitative analysis based on patients’ experiences and perspectives. This study was conducted in Quebec, which has a free universal healthcare system in which primary care, diagnostic, and specialized services are mainly covered by public funding. Patients may also, if they wish and can afford it, use certain diagnostic services provided in private clinics. The results of this study will be of interest to health services researchers, policy-makers, managers, and practitioners involved in optimizing health system organization, particularly with respect to cancer, primary care, and diagnostic services.

New contribution

The contribution of this paper is threefold. First, it revisits the Model of Pathways to Treatment, the current reference for analyzing intervals in early cancer diagnosis. In particular, the blurred boundaries between the first stages of appraisal and help-seeking, the non-linear patient pathways in these stages, and the fact that phases vary depending on the type of cancer make time intervals as identified in the Model of Pathways to Treatment difficult to operationalize. Other authors have successfully used this model to estimate time intervals [14]. However, these studies do not analyze first stages, which are usually not covered by administrative data sets, which might explain why the Model of Pathways to Treatment was considered to be really helpful. Second, the present paper highlights the importance of primary care organization and coordination for shortening time elapsed before diagnosis. Third, it reveals the existence of significant access inequities even in a universal and public healthcare system.

Methods

Conceptual framework

The model around which there appears to be consensus on the conceptual representation of intervals and their determinants, of processes, and of events related to care access is the Model of Pathways to Treatment, referred to above [7, 10]. This model, a refinement of Andersen’s model [8, 15], identifies four time intervals: 1) The “appraisal interval” is the period in which the person notices somatic changes, solicits others’ opinions, and looks for ways to minimize symptoms, such as through self-medication or lifestyle modifications. 2) The “help-seeking interval” stretches from the moment when the person decides on the need to consult up to the first visit with a physician. 3) The “diagnostic interval” is the period of investigation, during which the physician documents symptoms and prescribes various tests and examinations. If the physician’s diagnosis is inconclusive, the patient reverts back to the “appraisal interval” [10]. 4) Once a diagnosis has been made, there is a “pre-treatment interval”, during which the treatment is planned. Our study covers the first three stages, as it is focused on time elapsed to diagnosis. The conceptual framework is presented in Fig. 1. It is an adaptation of the Model of Pathways to Treatment [7, 10], as our study does not cover the post cancer diagnosis period.

Fig. 1
figure 1

The Model of Pathways to Treatment

Full size image

The time elapsed before diagnosis varies depending on factors related to the characteristics of cancer, to the patient, and to the healthcare system [7, 10,11,12]. Among the factors related to cancer characteristics are symptom non-specificity [16, 17], symptom acuity [11], and presence of pain [11].

Among the personal factors related to patients are: advanced age [18,19,20]; family history [18, 21]; sex [11]; low education level and social environment [18, 21, 22]; social isolation [11, 18, 23]; presence of comorbidities [11, 24, 25]; attitude toward symptoms [11, 26], such as minimization [27], fear of diagnosis [11, 28, 29], and feelings about diagnostic testing (embarrassment, fear of pain) [29,30,31]; patient’s psychological status [32, 33]; postponement of appointments [34]; low frequency of medical visits [17, 21, 35, 36]; ethnicity [11, 37]; and socioeconomically disadvantaged status [11].

Factors related to health system organization include difficult access to primary care, diagnostic, and specialized services [38,39,40]; lack of medical insurance [41, 42]; lack of a family physician [43]; size of wait lists [32]; physician behaviour and practice routines [44]; physician–patient communication [45, 46]; discriminatory practices [11, 23]; symptom-recognition skills; ability to interpret tests; quality of referrals; systematic screening [11, 47, 48]; constraints related to the institution [31, 34, 49]; availability of materials and personnel; and the organization’s curative vs. preventive priorities [50].

Study design and collection and analysis of qualitative data

The analyses presented here were nested in a larger project based on a sequential explanatory design [51] that was aimed at understanding the role of primary care clinic affiliation in early cancer diagnosis. That project involved two complementary data collection strategies. First, a quantitative study was conducted among 438 adults with cancer (breast, lung, or colorectal) enrolled for less than three months in an oncology clinic in one of the four participating hospitals located in Quebec’s two most populated regions. If eligible for the study, patients were invited by nurses to participate in the study (methodological details regarding the survey are available in Provost et al., 2015) [9]. Time to cancer diagnosis was estimated based on dates provided by the patient: “date when the patient began to have unusual symptoms or signs that could now be attributed to the cancer; date when the patient made an appointment with a physician for these unusual signs or symptoms; date when the physician prescribed tests to diagnose the cancer; and date when the patient received the cancer diagnosis” [9]. When patients were unable to provide an exact date, they were asked for information on the month and year of the event. We used oncology clinics’ cancer registries to estimate residual missing data. Second, a qualitative study was conducted to explain variations in time elapsed between first symptoms and diagnosis, in order to better understand, from the patients’ perspective, how these intervals were influenced by personal, cancer-related, and primary care characteristics. We conducted 22 in-depth interviews, between the summer of 2011 and the summer of 2012, with patients who had agreed to answer the questionnaire. These patients were purposefully selected to obtain contrasting cases [52] in terms of time elapsed between first symptoms and cancer diagnosis. We identified patients for whom that time was either particularly short or long. We also took into account sex, presence or non-presence of symptoms, and affiliation or non-affiliation with a usual source of care. As the recruitment of survey participants took longer than expected, we began selecting patients for interviews when around two-thirds of our targeted sample had been recruited. We regularly monitored new questionnaires in order to select new potential participants for the qualitative phase of the study. We tried to keep the time between the questionnaire and the interview as short as possible because of the patient’s illness and to minimize memory bias. The participants in our study had been diagnosed with breast (n = 7), lung (n = 8), or colorectal (n = 7) cancer. Our sample consisted of five males and 17 females aged between 46 and 76 years (mean = 60.2; SD = 7.8). Participants were contacted through a letter of invitation. A phone contact was proposed, and the consent form was sent in advance. None of the persons contacted refused to participate. Participants were met in person at the time and place of their convenience, and some chose to be accompanied by a loved one. The consent form was signed before interviews, but the interviewer discussed the form again at the time of the interview to obtain oral consent from participants before proceeding.

Semi-structured interviews lasting approximately one hour were conducted by the team’s research assistants. Open-ended questions probed respondents’ medical consultation habits, their care pathway prior to diagnosis, their attitudes and those of their loved ones, as well as key markers in their care experience over that period. All interviews were audio-recorded with the participants’ consent and subsequently transcribed. All participants signed an information and consent form before the interview in which they were informed that the data collected would be used for scientific purposes in scientific publications.

The project received ethical approval from the four hospitals and from the ethics committee of the research centre where the principal investigator was primarily affiliated.

Two researchers double-coded each interview. All divergences were discussed by the team to obtain a consensual reading. The analysis by cancer type was carried out in three stages. First, data on participants’ age and sex, presence or non-presence of symptoms, affiliation or non-affiliation with a usual source of primary care, and diagnostic tests prescribed were compiled in a table for each type of cancer. A histogram was created showing the intervals of elapsed time experienced by the patients. All intervals were coded separately by the two researchers. Any divergences were discussed in team meetings to reach agreement. Second, each patient’s pathway was summarized. The summaries were then ordered by intervals, from shortest to longest. For each cancer type, we identified the factors that explained the short or long intervals experienced by the patients. Third, the results of the analyses for each of the three cancers were compared to uncover the explanatory factors they had in common and those that differed by cancer type.

It is difficult to say anything conclusive about information saturation. In the analyses, the same types of explanatory factors emerged from one case to the next. In that respect, it could be said we achieved a level of saturation in the analysis. However, because the stories were all very different from each other, we cannot say we explored all possible examples.

Results

Very early on, it became clear that dividing patients’ experience into time intervals as proposed in the Model of Pathways to Treatment was a challenging methodology to apply to analyzing the interviews. In fact, these intervals represent psychological and behavioural stages, from the standpoint of personal experience. As such, it becomes difficult to draw precise boundaries between the stages and to interpret the care pathways. This model assumes that symptoms and events suggestive of cancer are easily recognizable, which is not always the case. We saw in the transcripts that, when patients recounted their stories, it was sometimes difficult to attribute their symptoms to cancer, and the transition from appraisal to help-seeking, and even sometimes to diagnosis, could be vague and non-linear. Even though Walter et al. (2012) recognize that patients may not experience “a linear passage through these intervals” ((7), p.116) and use double arrows to represent the complexity of pathways, nevertheless the various stages, even if they are real, are sometimes difficult to operationalize in order to analyze time intervals and their determinants in the cancer pre-diagnosis period. Furthermore, as noted by Walter et al. [7], how stages are experienced varies according to cancer sites. Considering all these difficulties, in this study, we ultimately decided to treat the intervals in different ways based on cancer type.

Lung cancer

Table 1 summarizes the patients’ pathways to illustrate their representations of the period before their cancer diagnosis. As the trajectories are varied and the distinctions between stages can be blurred, we chose to present these in a table rather than inserting verbatim in the narration to give a better sense of each patients’ experience before their cancer diagnosis.

Table 1 Patient pathways before diagnosis of lung cancer
Full size table

Intervals

The duration of the intervals ranged from two weeks to five months (see Table 1). It was sometimes difficult for patients to quantify the pre-diagnosis elapsed time, not knowing whether certain symptoms (restless leg, back pain, chest pain and fatigue) should be associated with lung cancer or what exactly might be considered the first symptoms. For example, Respondent 8 reported a shorter elapsed time between first symptoms and diagnosis (18 months) in the survey questionnaire than in the interview, during which she said she had been noticing her symptoms for more than two years. The interviews showed how difficult it was to pinpoint when the symptoms suggestive of lung cancer actually started.

Explanatory factors related to lung cancer

We noted that all participants with lung cancer reported having had health problems in the period preceding their diagnosis. Five out of eight (respondents 1, 2, 3, 4, 5) reported having respiratory symptoms (cough, breathlessness), but none suspected cancer. Three people (respondents 3, 4, 5) were first treated for respiratory infections (pneumonia, bronchitis, asthma, acute respiratory illness) before being sent for more extensive investigation. The reported symptoms were often associated with chronic respiratory or muscular disorders, such that there was no pressure for further consultation. However, four of the eight patients interviewed (respondents 2, 3, 4, 6) started the consultation process for an acute condition when symptoms worsened. There is a certain vagueness to the cancer symptoms that blurs the boundaries between the first three stages of the Model of Pathways to Treatment. Maintaining this division leads to pathways being represented in loops rather than linearly in that model.

Factors related to patients

Certain personal factors influenced the intervals. We observed that patients with strong social networks were encouraged by their family and friends to consult, which could affect the point at which they took the decision to consult. In four cases (respondents 1, 4, 5, 6), the patient or family took a proactive role that shortened the time to first consultation or accelerated the investigation process.

Factors related to health system organization

Only two patients (respondents 3, 6) went directly to the emergency room; the others either saw their family physician or visited a walk-in clinic. All were able to see a primary care physician rapidly, once they had taken the decision to consult. The interval between this first medical contact and the first radiology exam was usually less than three weeks. The family physicians communicated the results to the patients within about two days of receiving a positive radiology result. Two people (respondents 1 and 3) experienced delays of over a month: in one case, the result was not communicated to the patient; in the other, the symptoms led to extensive investigation of another suspected illness.

Breast cancer

Table 2 summarizes the pathways of the patients in our study with breast cancer.

Table 2 Patient pathways before diagnosis of breast cancer
Full size table

The intervals

The longest interval between first symptoms and diagnosis was 13 months and the shortest, one month and a half. The interval between first symptoms and mammography ranged from 11 months to under one month. Only one woman (respondent 15) experienced short intervals for both.

Explanatory factors related to breast cancer

Either the women noticed the symptoms—typically, in the case of breast cancer, a mass—or the cancer was diagnosed through the provincial screening program. As these patients associated the breast mass with possible breast cancer, there was essentially no “appraisal” interval, in contrast to the lung cancer experience. Moreover, the investigation process for a breast mass is able to rule out cancer directly, making the linear pathway relevant (as opposed to the looped pathway for lung cancer).

Factors related to patients

Longer intervals between first symptoms and mammography were due primarily to personal attitudes. Some patients delayed the examination either because they minimized the symptoms, or they feared or had a prior negative experience of mammography, or simply because their current activities limited their availability (planned vacation, excessive workload, etc.). Some were encouraged by worried family members to go for investigation sooner. Women who were deeply concerned about the mass appeared to have been especially proactive in initiating the screening process and obtaining, in various ways, a requisition for a mammogram.

Shorter times to investigation appeared to be explained by good knowledge of the healthcare system. We had, in our sample, two expert patients who were able to navigate the system, whether public or private, and whose time to investigation was one month or less. One was a nurse (respondent 12) who had worked a long time in oncology, and the other (respondent 15) was a nurse who reported that she maintained her medical record at home.

Being proactive in managing one’s own medical record appeared to result in shorter times to investigation. Patients who were determined to get an appointment sought options that would provide the examination in the shortest time frame, even if it meant paying for it themselves. Of the seven women, four (respondents 10, 12, 13, 14) used private diagnostic services. Even though some private diagnostic tests are covered by the public health insurance plan, four women (respondents 10, 12, 13, 14) had to pay out of pocket at some point in their pathway. This raises questions regarding equity in terms of access to essential diagnostic services.

Factors related to health system organization

The intervals between mammography and transmission of results appeared quite variable. Some results were communicated on the same day (three of seven: respondents 10, 11, 13), while others took as much as seven to 10 days when transmitted by the patient’s family physician or gynaecologist. We documented two cases of error (respondents 9 and 10), in which the positive result had not been communicated to the patient. In one, the result was noted when the patient returned to the physician’s office a few months later because the mass in her breast was getting bigger and she had never heard from the doctor’s office. In the other, it was only during the patient’s routine visit almost a year later that her gynecologist saw the previous positive results and was at a loss to explain why no one had called her at the time.

The patients with the longest time to investigation were those with sequential care pathways: they made an appointment and underwent testing, the results were then sent to their physician, who prescribed another examination, and then those results were sent again to the physician, who prescribed yet another examination, etc. Delays were incurred by the friction between each of these stages. In contrast, patients who were able to obtain integrated services experienced shorter elapsed times: positive results were transmitted at once and the next examination was set in motion. These shorter intervals appeared to be due to the fact that technical and medical resources were available on the same site and that diagnostic testing was coordinated.

Colorectal cancer

Table 3 summarizes patients’ perceptions regarding the period before their cancer diagnosis.

Table 3 Patient pathways before diagnosis of colon cancer
Full size table

The intervals

To calculate our pathways and define our time 0, we calculated the time elapsed between the first symptoms that elicited patients’ concern and the cancer diagnosis. These intervals ranged from one week to eight months. However, this variation was even greater (sometimes over a year) when taking into account symptoms identified by patients in the interviews that were potentially related to their cancer and had negatively affected their quality of life. In fact, patients often associated the first signs of colorectal cancer with the acute symptom phase. Given the nature of the investigation, in which the biopsy is performed at the same time as the colonoscopy if a lesion is found, and given that the cancer diagnosis is normally provided two or three days after the exam, we used the date of colonoscopy as the diagnosis date in calculating intervals.

Explanatory factors related to colorectal cancer

The interviews revealed that patients sometimes had experienced signs suggestive of colorectal cancer over several years. However, these signs were interpreted in various ways (haemorrhoids, diabetes, hormonal disorders, abscess, mental illness). The symptoms’ non-specificity delayed investigation because neither the patients nor their physicians suspected colorectal cancer. In the case of colorectal cancer, the appraisal interval appeared particularly long.

Factors related to patients

Because patients attributed their bleeding, weight loss, or fatigue to other causes, the investigation could occur years after the first signs. Often the patients initiated a consultation process but their physicians did not investigate them for colorectal cancer.

Factors related to health system organization

Patients initiated consultation for signs suggestive of colorectal cancer very early, but the diagnostic process was such that colonoscopy was considered only after several other possible causes had been ruled out, thereby delaying the investigation. Often it was after symptoms had worsened that patients decided to re-consult their physician.

The patients in our sample appeared to have experienced significant delays in accessing colonoscopy, except for one who was hospitalized. Patients’ proactive attitudes helped them to surmount barriers to access to colonoscopy.

Discussion

Strengths and limitations of the study

We were able to estimate time intervals in the pre-diagnostic period, which is usually not possible for studies using cancer registries and administrative data [53]. As we selected three types of cancer, and as patients with breast cancer could be diagnosed because of symptoms or via the breast cancer screening program, we experienced a large variability in the characteristics of cancer trajectories. This variability is both a richness and a limitation. It allows us to better appraise the influence of the type of cancer on patients’ trajectories. For example, we saw that vague symptoms, such as for colorectal and lung cancer, made time intervals difficult to estimate as compared to breast cancer. Other elements, such as personal factors, appeared rather similar regardless of the type of cancer, as was also observed in the qualitative synthesis of Smith et al. (2005) [29]. Given that lung, breast, and colorectal cancers are the most commonly diagnosed, having this variety enables us to make recommendations for improving care with a potentially important impact for the population in our study context.

Time elapsed before cancer diagnosis and determining factors

The first observation of note is that the intervals estimated from the interviews were different from those reported in the questionnaires. Intervals were particularly difficult to estimate for lung and colorectal cancer. Indeed, patients often had episodes of illness or discomfort (bronchitis, haemorrhoids, etc.) for which it was difficult to know whether cancer was the cause. In this interval between first symptoms and the initiation of investigation, the elapsed time was due primarily to patients’ personal attitudes, but also to the reactions of physicians, who did not at first suspect cancer [11]. Analyzing the experience of illness and of care from the patients’ perspective, as we did here, highlighted these aspects in ways that a retrospective analysis of medical charts could not [53, 54]. Our analysis indicated that it is difficult, if not impossible, to know exactly at what point the first symptoms should be attributed to cancer. The scientific literature attempts to divide the elapsed time into phases [7, 8, 10, 53, 55], but the appropriateness of that exercise is questionable because, even though each phase is part of the consultation process, both overall elapsed time and the various phases are concepts that are difficult to operationalize with any certainty.

Factors related to type of cancer explained many of the differences in the intervals. Our analyses concurred with those of Macleod et al. [11] and Smith et al. [29] in showing a direct correlation between atypical or vague symptoms and longer pre-diagnostic intervals. For colorectal cancer, the appraisal interval [7, 8, 10] could last several months or even years [12, 55]. Clearly, for colorectal cancers, there is a need to improve pre-diagnostic medical awareness, as symptoms are often not specific [14]. A breast mass, on the other hand, leaves little doubt about the potential existence of cancer, and this interval is quite short. For breast cancer, delays occur mainly in the help-seeking and investigation phases. Because of the acute nature of respiratory problems, the help-seeking interval in lung cancer cases is generally shorter [56]. When patients are shunted back into the appraisal phase, with the consequent delays, it is often because their physician did not initially attribute the symptoms to cancer. Thus, atypical symptoms will lead patients and clinicians to treat the symptoms and rule out other possible causes before investigating for cancer [11], leading to a looped process for the first three phases of the Model of Pathways to Treatment.

In terms of personal factors, even when patients are worried, they will tend to delay the decision to consult if their lives are very busy (work, vacation, family responsibilities) and the symptoms are not acute [55]. People with strong family or social support will receive more encouragement to visit a clinician [55], which may influence their decision to consult. Patients whose close family members or friends have experienced cancer are more likely to consult earlier. Some people delay investigation because they dread the technical procedure (mammography, colonoscopy) [29].

The interval between initiating investigation and reaching a diagnosis is most often explained by health system organization, mainly with regard to difficulties in accessing medical and diagnostic services and in the sequential investigation process. We also note that time to investigation is often shorter for patients going through the emergency room because of the simultaneous accessibility of a variety of medical specialties and technical platforms. This observation creates a dilemma for health system organization: whereas current primary care accessibility problems have repercussions that include treatment delays and inappropriate use of hospital emergency services [57], should patients confronted with these accessibility problems be encouraged to go to the emergency room when they experience symptoms they suspect are suggestive of cancer, at the risk of increasing emergency consultation demand for non-cancer related reasons? Several studies [58, 59] indicate that cancer diagnosis following emergency consultation results in poorer clinical outcomes, which may be explained by a more advanced stage of cancer. Emergency presentation as related to cancer is a complex phenomenon that is the result of various causes: no easy access to a GP, patients or GPs not recognizing the symptoms, difficult access to diagnostic and specialized resources and, of course, exacerbation of health status [59]. Murchie et al. [59] underscore the fact that emergency presentation “affords individual patients the best chance of rapid treatment and cure and does not always represent failure” (p. 10). In contexts where access to care—whether primary, diagnostic or specialized—is difficult, using the emergency room becomes important and legitimate and might improve clinical outcomes for patients.

Avenues for improving early cancer diagnosis

One way to reduce delays in diagnosis is to address the interval between first symptoms and investigation. From an interventional perspective, an important question is how to make families [60], spouses [55], and clinicians [60] more alert to signs that could be suggestive of cancer. Would more awareness campaigns targeting the public and clinicians lead to more rapid reaction? (Tod and Joanne [61], in fact, developed an awareness-building tool for the NHS). Is it possible to reinforce families’ influence in triggering the consultation process? In their study, Austoker et al. [62] found that individual and community-based interventions to inform the public and encourage people to consult earlier for symptoms suggestive of cancer were not very effective. Finding the most effective way to reach the public and clinicians in order to foster early diagnosis is a major research challenge. The fact that each illness has its own natural evolution and elicits particular attitudes in patients toward signs and symptoms suggests that different strategies are needed for the different types of cancer [2].

It is also possible to change how care is organized. Several measures might be considered. First, in the 22 patients we met, we found two cases in which positive test results were not transmitted to the patients. It is true that we may have over-sampled for medical errors by purposefully selecting patients whose time to diagnosis was particularly long. Nevertheless, a mechanism should be developed to ensure this does not happen. Second, in cases of suspected cancer, it would be useful to provide integrated diagnostic services, where the technical platforms and a specialized team are together in one place, and where the patient could be seen by all relevant parties on the same day. This would, first, eliminate obstacles and shorten the wait for an appointment and, second, avoid the sequential investigation process. In fact, for breast cancer, the sequential investigation process that calls for the prescribing physician to also be the one who transmits the test results adds considerably to the investigation time. In contrast, when the investigation is concentrated in a single place and time (integrated process), the investigation process is very short. The three areas of intervention that we have highlighted—raising awareness among families and professionals and optimizing the diagnostic pathway—have also been raised by Molassiotis et al. [12] in the United Kingdom.

Inequities in early cancer diagnosis

Our results raise important questions regarding equity of access. We found that expert patients, who had a good understanding of how the health system works and were well connected with health professionals, were able to shorten investigation times considerably. Several patients also opted to consult private clinics, at their own cost, to obtain diagnostic services more rapidly. These two points raise important questions in a universal healthcare system whose objective is to provide care based on need and not according to patients’ financial capacity or personal skills. Having either financial resources or a thorough knowledge of the healthcare system allows some patients to shorten the time intervals for a same physical condition. In such cases, access is not related to gravity of illness or care need, but rather to the capacity to mobilize one’s resources. The literature on early cancer diagnosis shows that socio-economically disadvantaged patients experience longer delays in obtaining care [11]. Our results indicate that such a situation might exist in Quebec, even in the presence of public and universal health coverage. In the case of Quebec, these issues could be resolved quickly if access to professional and diagnostic services were improved. In comparison with the rest of Canada and with OECD countries, Quebec’s performance in terms of access to diagnostic and specialized services is weak. In Quebec, 61% of physicians (as opposed to 38% in Canada) report that their patients often have difficulty obtaining specialized diagnostic tests [63], and patients tend to get around access barriers by using their own means.

Conclusions

This study sheds light on why the elapsed time between first symptoms and cancer diagnosis is longer for some patients than for others. It also provides insight into the roles played by cancer type characteristics, personal attitudes, and health system organization. Numerous studies have delved into the behavioural and psychological processes associated with elapsed time that are attributable to patients [8, 29], but very few have analyzed the influence of these three factors [12]. Psychosocial and behavioural models, such as Andersen’s model and the Model of Pathways to Treatment [7, 8, 10], have introduced the influence of factors associated with cancer type and health system organization. However, our analysis showed that the links and interactions among these three types of factors are very close, making it difficult to apply such a model to represent the pathway leading to cancer diagnosis [64], especially for cancers whose signs and symptoms are vague and non-specific.

Quebec’s cancer registry has existed only since 2011. It compiles information on cancer mortality and incorporates increasingly more clinical information on stages of illness and on treatments provided. The registry does not contain information on primary care services received prior to diagnosis. We thus have very little information on the impediments to an optimal consultation and care pathway and on what happens before diagnosis. As such, this study sheds light on this poorly documented period and helps to identify measures that could be implemented for more timely diagnosis.

Our study revealed that there are inequities in access to medical and diagnostic services that could have consequences for early cancer diagnosis. This opens the way for a research agenda to document the scope of this phenomenon and identify solutions that would make universal and equitable access a reality in our healthcare system, and that would respect the core principles upon which Canada’s health system is founded.

References

  1. Canadian Cancer Society. Available at: http://www.cancer.ca/en/cancer-information/cancer-101/canadian-cancer-statistics-publication/?region=qc. Accessed June 2017.

  2. Neal RD. Do diagnostic delays in cancer matter? Br J Cancer. 2009;101(S2):S9–S12.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mountain CF. Revisions in the international system for staging lung cancer. Chest. 1997;111(6):1710–7.

    Article  CAS  PubMed  Google Scholar 

  4. Sainsbury JR, Johnston C, Haward B. Effect on survival of delays in referral of patients with breast-cancer symptoms: a retrospective analysis. Lancet. 1999;353(9159):1132–5.

    Article  CAS  PubMed  Google Scholar 

  5. Allgar VL, Neal RD. Delays in the diagnosis of six cancers: analysis of data from the National Survey of NHS patients: cancer. Br J Cancer. 2005;92(11):1959–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tørring ML, Frydenberg M, Hansen RP, Olesen F, Hamilton W, Vedsted P. Time to diagnosis and mortality in colorectal cancer: a cohort study in primary care. Br J Cancer. 2011;104(6):934–40.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Walter F, Webster A, Scott S, Emery J. The Andersen model of Total patient delay: a systematic review of its application in cancer diagnosis. J Health Serv Res Policy. 2012;17(2):110–8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Andersen BL, Cacioppo JT, Roberts DC. Delay in seeking a cancer diagnosis: delay stages and psychophysiological comparison processes. Br J Soc Psychol. 1995;34(1):33–52.

    Article  PubMed  Google Scholar 

  9. Provost S, Pineault R, Tousignant P, Roberge D, Tremblay D, Breton M, et al. Does the primary care experience influence the cancer diagnostic process? Int J Family Med. 2015;2015

  10. Scott SE, Walter FM, Webster A, Sutton S, Emery J. The model of pathways to treatment: conceptualization and integration with existing theory. Br J Health Psychol. 2013;18(1):45–65.

    Article  CAS  PubMed  Google Scholar 

  11. Macleod U, Mitchell ED, Burgess C, Macdonald S, Ramirez AJ. Risk factors for delayed presentation and referral of symptomatic cancer: evidence for common cancers. Br J Cancer. 2009;101(S2):S92–S101.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Molassiotis A, Wilson B, Brunton L, Chandler C. Mapping patients’ experiences from initial change in health to cancer diagnosis: a qualitative exploration of patient and system factors mediating this process. Eur J Cancer Care. 2010;19(1):98–109.

    Article  CAS  Google Scholar 

  13. Commissaire à la santé et au bien-être. La performance du système de santé et de services sociaux québécois 2016. Québec: Government of Québec; 2017. Available at: http://www.csbe.gouv.qc.ca/fileadmin/www/2017/PerformanceGlobale/CSBE_RapportGlobal_2016_ACCESS.pdf. Accessed 27 Apr 2016.

  14. Sikdar KC, Dickinson J, Winget M. Factors associated with mode of colorectal cancer detection and time to diagnosis: a population level study. BMC Health Serv Res. 2017;17(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Andersen RS, Vedsted P, Olesen F, Bro F, Søndergaard J. Patient delay in cancer studies: a discussion of methods and measures. BMC Health Serv Res. 2009;9:189.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gonzalez-Hermoso F, Perez-Palma J, Marchena-Gomez J, Lorenzo-Rocha N, Medina-Arana V. Can early diagnosis of symptomatic colorectal cancer improve the prognosis? World J Surg. 2004;28(7):716–20.

    Article  PubMed  Google Scholar 

  17. Thorne AL, Mercer SJ, Harris GJC, Simson JNL. Reduction in late diagnosis of colorectal cancer following introduction of a specialist colorectal surgery service. Ann R Coll Surg Engl. 2006;88(6):562–5.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hoffman RM, Gilliland FD, Eley JW, Harlan LC, Stephenson RA, Stanford JL, et al. Racial and ethnic differences in advanced-stage prostate cancer: the prostate cancer outcomes study. J Natl Cancer Inst. 2001;93(5):388–95.

    Article  CAS  PubMed  Google Scholar 

  19. Silverstein MD, Nietert PJ, Ye X, Lackland DT. Access to care and stage at diagnosis for patients with lung cancer and esophageal cancer: analysis of the Savannah River region information system cancer registry data. South Med J. 2002;95(8):900–8.

    Article  PubMed  Google Scholar 

  20. Ramirez AJ, Westcombe AM, Burgess CC, Sutton S, Littlejohns P, Richards MA. Factors predicting delayed presentation of symptomatic breast cancer: a systematic review. Lancet. 1999;353(9159):1127–31.

    Article  CAS  PubMed  Google Scholar 

  21. Davis TC, Dolan NC, Ferreira MR, Tomori C, Green KW, Sipler AM, et al. The role of inadequate health literacy skills in colorectal cancer screening. Cancer Investig. 2001;19(2):193–200.

    Article  CAS  Google Scholar 

  22. Auvinen A, Karjalainen S, Pukkala E. Social class and cancer patient survival in Finland. Am J Epidemiol. 1995;142(10):1089–102.

    Article  CAS  PubMed  Google Scholar 

  23. Barry J, Breen N. The importance of place of residence in predicting late-stage diagnosis of breast or cervical cancer. Health and Place. 2005;11(1):15–29.

    Article  PubMed  Google Scholar 

  24. Klassen AC, Curriero FC, Hong JH, Williams C, Kulldorff M, Meissner HI, et al. The role of area-level influences on prostate cancer grade and stage at diagnosis. Prev Med. 2004;39(3):441–8.

    Article  PubMed  Google Scholar 

  25. Lantz PM, Mujahid M, Schwartz K, Janz NK, Fagerlin A, Salem B, et al. The influence of race, ethnicity, and individual socioeconomic factors on breast cancer stage at diagnosis. Am J Public Health. 2006;96(12):2173–8.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gould-Martin K, Paganini-Hill A, Casagrande C, Mack T, Ross RK. Behavioral and biological determinants of surgical stage of breast cancer. Prev Med. 1982;11(4):429–40.

    Article  CAS  PubMed  Google Scholar 

  27. Satariano WA, Belle SH, Swanson GM. The severity of breast cancer at diagnosis: a comparison of age and extent of disease in black and white women. Am J Public Health. 1986;76(7):779–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Worden JW, Weisman AD. Psychosocial components of lagtime in cancer diagnosis. J Psychosom Res. 1975;19(1):69–79.

    Article  CAS  PubMed  Google Scholar 

  29. Smith LK, Pope C, Botha JL. Patients’ help-seeking experiences and delay in cancer presentation: a qualitative synthesis. Lancet. 2005;366(9488):825–31.

    Article  PubMed  Google Scholar 

  30. Swanson FH, Edwards MS, Swanson MS, Lannin DR, Mathews HF, Mitchell J. Influence of socioeconomic and cultural factors on racial differences in late-stage presentation of breast cancer. JAMA. 1998;279(22):1801–7.

    Article  PubMed  Google Scholar 

  31. Caplan LS, Helzlsouer KJ, Shapiro S, Wesley MN, Edwards BK. Reasons for delay in breast cancer diagnosis. Prev Med. 1996;25(2):218–24.

    Article  CAS  PubMed  Google Scholar 

  32. Greer S. Psychological aspects: delay in the treatment of breast cancer. Proc R Soc Med. 1974;67(6 Pt 1):470–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Cassileth BR, Lusk EJ, Miller DS, Brown LL, Miller C. Psychosocial correlates of survival in advanced malignant disease? N Engl J Med. 1985;312(24):1551–5.

    Article  CAS  PubMed  Google Scholar 

  34. Nosarti C, Crayford T, Roberts JV, Elias E, McKenzie K, David AS. Delay in presentation of symptomatic referrals to a breast clinic: patient and system factors. Br J Cancer. 2000;82(3):742–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ferrante JM, Lee J-H, McCarthy EP, Fisher KJ, Chen R, Gonzalez EC, et al. Primary care utilization and colorectal cancer incidence and mortality among Medicare beneficiaries: a population-based, case-control study. Ann Intern Med. 2013;159(7):437–46.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Love-Jackson K, Gonzalez EC, Ferrante JM, Roetzheim RG, McCarthy EP, Lee J-H, et al. Primary care utilization and colorectal cancer outcomes among Medicare beneficiaries. Arch Intern Med. 2011;171(19):1747–57.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Waller J, Robb K, Stubbings S, Ramirez A, Macleod U, Austoker J, et al. Awareness of cancer symptoms and anticipated help seeking among ethnic minority groups in England. Br J Cancer. 2009;101(S2):S24–30.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Byles JE, Redman S, Hennrikus D, Sanson-Fisher RW, Dickinson J. Delay in consulting a medical practitioner about rectal bleeding. J Epidemiol Community Health. 1992;46(3):241–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bowen EF, Rayner CFJ. Patient and GP led delays in the recognition of symptoms suggestive of lung cancer. Lung Cancer. 2002;37(2):227–8.

    Article  PubMed  Google Scholar 

  40. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance coverage and clinical outcomes among women with breast cancer. N Engl J Med. 1993;329(5):326–31.

    Article  CAS  PubMed  Google Scholar 

  41. Potosky AL, Merrill RM, Riley GF, Taplin SH, Barlow W, Fireman BH, et al. Breast cancer survival and treatment in health maintenance organization and fee-for-service settings. J Natl Cancer Inst. 1997;89(22):1683–91.

    Article  CAS  PubMed  Google Scholar 

  42. Lee-Feldstein A, Feldstein PJ, Buchmueller T, Katterhagen G. The relationship of HMOs, health insurance, and delivery systems to breast cancer outcomes. Med Care. 2000;38(7):705–18.

    Article  CAS  PubMed  Google Scholar 

  43. McCarthy EP, Burns RB, Coughlin SS, Freund KM, Rice J, Marwill SL, et al. Mammography use helps to explain differences in breast cancer stage at diagnosis between older black and white women. Ann Intern Med. 1998;128(9):729–36.

    Article  CAS  PubMed  Google Scholar 

  44. Costain SK, Hewison J. Experiencing cancer: quality of life in treatment. Buckingham; Philadelphia: Open University Press; 1999.

    Google Scholar 

  45. Burack RC, Gimotty PA, George J, Simon MS, Dews P, Moncrease A. The effect of patient and physician reminders on use of screening mammography in a health maintenance organization: results of a randomized controlled trial. Cancer. 1996;78(8):1708–21.

    Article  CAS  PubMed  Google Scholar 

  46. Taplin SH, Ichikawa L, Yood MU, Manos MM, Geiger AM, Weinmann S, et al. Reason for late-stage breast cancer: absence of screening or detection, or breakdown in follow-up? J Natl Cancer Inst. 2004;96(20):1518–27.

    Article  PubMed  Google Scholar 

  47. Goodson WH III, Moore DH II. Causes of physician delay in the diagnosis of breast cancer. Arch Intern Med. 2002;162(12):1343–8.

    Article  PubMed  Google Scholar 

  48. Barton MB, Elmore JG, Fletcher SW. Breast symptoms among women enrolled in a health maintenance organization: frequency, evaluation, and outcome. Ann Intern Med. 1999;130(8):651–7.

    Article  CAS  PubMed  Google Scholar 

  49. Hébert-Croteau N, Brisson J, Latreille J, Blanchette C, Deschênes L. Variations in the treatment of early-stage breast cancer in Quebec between 1988 and 1994. CMAJ. 1999;161(8):951–5.

    PubMed  PubMed Central  Google Scholar 

  50. McPhee SJ, Detmer WM. Office-based interventions to improve delivery of cancer prevention services by primary care physicians. Cancer. 1993;72(S3):1100–12.

    Article  CAS  PubMed  Google Scholar 

  51. Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks: Sage Publications; 2014.

    Google Scholar 

  52. Patton MQ. Qualitative research and evaluation methods. 3rd ed. Thousand Oaks: Sage Publications; 2002.

    Google Scholar 

  53. Yuan Y, Li M, Yang J, Elliot T, Dabbs K, Dickinson JA, et al. Factors related to breast cancer detection mode and time to diagnosis in Alberta, Canada: a population-based retrospective cohort study. BMC Health Serv Res. 2016;16(1):65.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Baughan P, O'Neill B, Fletcher E. Auditing the diagnosis of cancer in primary care: the experience in Scotland. Br J Cancer. 2009;101(S2):S87–91.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Oberoi DV, Jiwa M, McManus A, Hodder R. Barriers to help-seeking in men diagnosed with nenign colorectal diseases. Am J Health Behav. 2015;39(1):22–33.

    Article  PubMed  Google Scholar 

  56. Kim JOA, Davis F, Butts C, Winget M. Waiting time intervals for non-small cell lung cancer diagnosis and treatment in Alberta: quantification of intervals and identification of risk factors associated with delays. Clin Oncol. 2016;28(12):750–9.

    Article  CAS  Google Scholar 

  57. McCusker J, Tousignant P, Borgès Da Silva R, Ciampi A, Lévesque J-F, Vadeboncoeur A, et al. Factors predicting patient use of the emergency department: a retrospective cohort study. CMAJ. 2012;184(6):E307–16.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhou Y, Abel GA, Hamilton W, Pritchard-Jones K, Gross CP, Walter FM, et al. Diagnosis of cancer as an emergency: a critical review of current evidence. Nat Rev Clin Oncol. 2017;14(1):45–56.

    Article  PubMed  Google Scholar 

  59. Murchie P, Smith SM, Yule MS, Adam R, Turner ME, Lee AJ, et al. Does emergency presentation of cancer represent poor performance in primary care? Insights from a novel analysis of linked primary and secondary care data. Br J Cancer. 2017;116(9):1148–58.

    Article  PubMed  Google Scholar 

  60. Tod AM, Craven J, Allmark P. Diagnostic delay in lung cancer: a qualitative study. J Adv Nurs. 2008;61(3):336–43.

    Article  PubMed  Google Scholar 

  61. Tod AM, Joanne R. Overcoming delay in the diagnosis of lung cancer: a qualitative study. Nurs Stand. 2010;24(31):35–43.

    PubMed  Google Scholar 

  62. Austoker J, Bankhead C, Forbes LJL, Atkins L, Martin F, Robb K, et al. Interventions to promote cancer awareness and early presentation: systematic review. Br J Cancer. 2009;101(S2):S31–9.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Commissaire à la santé et au bien-être. Perceptions et expériences des médecins de première ligne: le Québec comparé. Résultats de l’enquête internationale sur les politiques de santé du Commonwealth Fund de 2012. Québec: Government of Québec; 2013. Available at: http://www.csbe.gouv.qc.ca/fileadmin/www/2015/CWF/CSBE_Rapport_CWF_2015.pdf. Accessed 27 Apr 2017.

  64. Weller D, Vedsted P, Rubin G, Walter FM, Emery J, Scott S, et al. The Aarhus statement: improving design and reporting of studies on early cancer diagnosis. Br J Cancer. 2012;106(7):1262–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the nurses and physicians who collaborated in this study. They played a central role in the recruitment process; this research would not have been possible without their involvement.

We would like to thank the patients for their participation in this study at a time in their lives when time itself was one of their most precious resources.

Funding

The authors are grateful to the Fonds de Recherche du Québec – Santé (FRQ-S) for its financial support to this research project. We would also like to thank the Fonds de Recherche du Québec – Santé (FRQ-S) and the Canadian Institutes of Health Research (CIHR), which fund Astrid Brousselle’s Canada Research Chair in Evaluation and Health System Improvement and Dominique Tremblay’s research fellowship.

Availability of data and materials

The datasets analysed during the current study are not publicly available due to the fact that it might create a breach in participant confidentiality but are available from the corresponding author on reasonable request.

Authors’ contributions

Analysis: AB, MB, LB, DT, DR. Interpretation and validation of the data: all authors. Writing: all authors. All authors: made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have been involved in drafting the manuscript or revising it critically for important intellectual content; have given final approval of the version to be published, and have participated sufficiently in the work to take public responsibility for appropriate portions of the content; and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Competing interests

The authors confirm that they have read BioMed Central's guidance on competing interests and none of the authors have any competing interests.

Consent for publication

All participants accepted, by signing the consent form, that data (verbatims) could be published in scientific journals at the condition that their identity could not be retrieved. All personal information has been removed from quotes.

Ethics approval and consent to participate

This research has received approval from the four ethics committees: the ethics committees of the three hospitals that participated in the data collection, as well as the ethics committee of the Charles-LeMoyne Hospital Research Centre.

All participants signed the approved consent form before the interview, in which they accepted to participate in the study.

Name of Ethics Committee (approval number)

Comité d’éthique de la recherche de l’hôpital Charles Le Moyne (AA-HCLM-10-015). Comité d’éthique de la recherche du centre universitaire de l’université de Montréal (10.093). Comité d’éthique de la recherche de l’hôpital Maisonneuve-Rosemont (10041). Comité d’éthique de la recherche et de l’évaluation des technologies de la santé de l’hôpital du Sacré-Cœur de Montréal (2010-08-63)

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. Département des Sciences de la Santé Communautaire, Centre de recherche - Hôpital Charles-Le Moyne, Université de Sherbrooke, 150 Place Charles LeMoyne bureau 200, Longueuil, Quebec, Canada

    Astrid Brousselle, Mylaine Breton & Danièle Roberge

  2. Université de Sherbrooke, Longueuil, Quebec, Canada

    Lynda Benhadj

  3. École des Sciences Infirmières, Centre de recherche - Hôpital Charles-Le Moyne, Université de Sherbrooke, Longueuil, Quebec, Canada

    Dominique Tremblay

  4. Direction de Santé Publique de Montréal, Institut de Recherche en Santé Publique de l’Université de Montréal, Montreal, Quebec, Canada

    Sylvie Provost & Raynald Pineault

  5. Direction de Santé Publique de Montréal, McGill University Health Centre, Montreal, Quebec, Canada

    Pierre Tousignant

  6. Institut National de Santé Publique, Montreal, Canada

    Raynald Pineault & Pierre Tousignant

Authors
  1. Astrid Brousselle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mylaine Breton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lynda Benhadj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dominique Tremblay
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sylvie Provost
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Danièle Roberge
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Raynald Pineault
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pierre Tousignant
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Astrid Brousselle.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brousselle, A., Breton, M., Benhadj, L. et al. Explaining time elapsed prior to cancer diagnosis: patients’ perspectives. BMC Health Serv Res 17, 448 (2017). https://doi.org/10.1186/s12913-017-2390-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12913-017-2390-1

Keywords

BMC Health Services Research

ISSN: 1472-6963

Contact us