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Showing content from https://pmc.ncbi.nlm.nih.gov/articles/PMC4274941/ below:

Patterns of seeking medical care among Egyptian breast cancer patients: Relationship to late-stage presentation

. Author manuscript; available in PMC: 2014 Dec 23.

Abstract

Breast cancer is the most common cancer among Egyptian women, accounting for 37.6% of female tumors, and is often diagnosed at later stages. The objective of this study was to investigate breast cancer patient navigation through the health care system in the Nile Delta. Interviews were conducted with 163 newly diagnosed breast cancer patients at the Tanta Cancer Center (TCC), the major cancer center of the region. Patients described their medical care pathway from the initial symptom experienced until their arrival at TCC. Patients whose initial contact was with a general surgeon (OR: 7.6, 95% CI: 2.1, 27.6), primary care provider (OR: 12.2, 95% CI: 2.9, 51.0), or gynecologist (OR: 8.6, 95% CI: 1.4, 53.4) were significantly more likely to experience a delay in reaching the TCC as compared to those visiting a surgical oncologist. Overcoming health care system and patient navigation barriers in developing countries may reduce the time for breast cancer patients to reach a cancer center for early management.

Keywords: Breast cancer, Medical care, Referral, Patient navigation, Egypt, Developing countries

Introduction

Breast cancer is the most common cause of cancer death among women worldwide, accounting for 458,000 deaths each year.1 Although the incidence of breast cancer is higher in developed countries, it is on the rise in developing countries.2,3 Women in developing countries tend to present with more advanced breast cancer upon initial diagnosis as compared to women in developed countries.4 Complications associated with late-stage diagnosis include decreased treatment options and low chances of treatment success, resulting in high mortality rates.

According to the Middle East Cancer Consortium, the incidence rate of female breast cancer in Egypt is 49.6 cases/105, approximately one half that of the United States.5 However, the majority of breast cancer patients in Egypt are diagnosed at advanced disease stages. Specifically, 45.93% and 15.95% of Egyptian breast cancer cases are diagnosed at stages III and IV, respectively.6

Advanced stage at presentation may be due to patient-mediated factors, health providers’ factors, and/or barriers in the health care system. Our recent study on patient-mediated factors linked late-stage at presentation to patients’ fear of hospitals, cultural taboos associated with cancer centers, time constraints, and lack of knowledge regarding risk factors and specific symptoms of breast cancer.7 Other factors included patients’ lack of knowledge regarding screening tests such as breast-self examination, clinical examination, mammography, and mistrust of primary care physicians in the local community.7 Although factors due to health providers and health system issues are less studied, patients’ lack of access to hospitals and physicians’ lack of knowledge regarding proper breast cancer diagnosis and treatment have been linked to late-stage presentation in this population.7,8

While patient-mediated factors are important in the possible delayed diagnosis of breast cancer, health care systems’ barriers are equally important as they might constitute significant obstacles in early diagnosis, even if patients have the knowledge and attitudes to seek early diagnosis.912 Barriers in health care systems for cancer management are exceedingly important in developing countries considering the high cost of management, limited diagnostic and treatment facilities, and limited continuous medical education of oncology health professionals in developing countries including Egypt.4,8,1316 Given the scarcity of information on cancer patient navigation through the Egyptian health care system and the importance of breast cancer as the most common cancer among women in Egypt, investigating referral patterns of breast cancer patients and patient navigation systems are crucial for developing effective cancer prevention and control interventions. To investigate breast cancer patient navigation through the health care system in Egypt, we conducted this study to examine referral patterns of female breast cancer patients before their first appointment at the Tanta Cancer Center (TCC), the largest cancer center serving the population of the Nile Delta region.

Materials and methods Research setting

Located in the capital of the Gharbiah province, the Tanta Cancer Center (TCC) is the largest cancer center in the Nile delta region of Egypt. It services approximately 350–400 breast cancer cases per year from the Gharbiah province as well as neighboring provinces. TCC patients are either referred by physicians or self-referred and treated free through a government health plan of the Ministry of Health. The TCC is a member of the Middle East Cancer Consortium (MECC), a partnership between the United States and the Egyptian, Cyprus, Israeli, Jordanian, Palestinian, and Turkish ministries of health.17 Established in 1996, the overarching aim of the MECC collaboration is to increase cancer knowledge and reduce the cancer burden in the region through the creation of population-based cancer registries, joint scientific research collaborations, and palliative care services.17

Data collection

Data were collected beginning December 2009 through November 2010. Interviews were conducted in Arabic with newly diagnosed breast cancer patients as part of routine medical care by one of the co-authors (EE). The interviewer collected data on the following socio-demographic indicators: age, residential status (urban/rural), occupation, and education. Patients were asked to describe their medical care pathway: the initial symptom, the cause for seeking medical care, and the chronological order of facilities/providers visited until arrival at the TCC. Each facility/provider visited during the patient’s pathway is referred to as a “node” of care, and the chronological order of the nodes is called the patient’s “navigation chain.”18 The time elapsed between each event was recorded. The mean age, residential status distribution, and breast cancer tumor stage distribution of the study sample were compared to the TCC’s overall breast cancer patient population, and no significant differences were found. Further, the interviewer validated the information reported by the patients by verifying the patient-reported number and types of facilities visited against the documentation reported in medical records. This study was approved by the University of Michigan Institutional Review Board and the Gharbiah Cancer Society ethical committee for medical research.

Data management

Mean and median ages at diagnosis were calculated. Age was further categorized into 2 categories: <50 years and 50+ years. Breast cancer tumor stage was also further categorized into early stage (stages 1 and 2) and late stage (stages 3 and 4) breast cancer. Frequency distributions were calculated for the categorized age variable, residential status, educational level, occupation, breast cancer tumor stage, family history of cancer, type of first symptom, and cause for seeking medical care. Patient delay was transformed to log-scale because of the lack of normal distribution but the results presented in the analysis are for the non-transformed data.

To assess the most commonly visited nodes of care at each step during the navigation chain, patients were categorized based on their first, second, third, fourth, and fifth nodes of care. To assess the relationship between non-TCC nodes of entry and the patient’s consequent arrival at TCC, frequencies were calculated for each non-TCC node of entry based on the patients’ TCC node number. Patient navigation chains were categorized based on each unique pathway of facilities. Each unique chain consisted of the patient’s node of entry, or the initial facility visited, followed by the second, third, etc. facilities visited until arrival at the TCC. Patients were categorized into each type of navigation chain. The mean number of total nodes visited before arrival at TCC was calculated. The mean and median time elapsed was calculated for the following event intervals: between the patients’ first symptom and the type of node of entry, and between the type of node of entry and the arrival at TCC.

Data analysis

The main variables of interest were type of first symptom, node of entry, and patient navigation chain. Potential risk factors were age, residential status, education, occupation, breast cancer tumor stage, and family history of cancer. Chi-square tests of association were conducted between the risk factors and outcomes. Each risk factor was considered in the model if it changed the main exposure estimate by at least 10% or if previous research suggested a potential confounding effect.

The first outcome of interest, patient delay, was defined as the time elapsed between the first symptom and the first medical consultation. Based on previous studies, patient delay was dichotomized into a wait time greater than 3 months and a wait time less than or equal to 3 months.19,20 The second variable, system delay, was defined as the time elapsed between the first medical consultation and arrival at TCC. Based on previous studies, this study defined system delay as a time of more than 2 weeks and a time of less than 2 weeks.10

Crude and adjusted logistic regression models were constructed to estimate the odds ratios (OR) and 95% confidence intervals (CI) for the association between patient delay and type of first symptom, as well as system delay and type of first facility visited and patient navigation pathway. Log-transformed patient delay was also included as continuous variable in a multiple regression model in addition to its analysis as a dichotomous variable. Data were analyzed using SAS version 9.2 (SAS Institute Inc., Cary, NC).

Results

Data were collected from 163 new breast cancer patients at the TCC. The mean age was 51.6± 11.52 years, and the median age was 53 years. As shown by the descriptive information in Table 1, approximately two-thirds of the women lived in rural regions, and nearly all of the women were housewives. About 60% of the participants presented with late-stage breast cancer. Breast mass was the most commonly reported first symptom. Furthermore, the development or increase in breast mass size was the main cause for seeking medical care in three-quarters of the patients (Table 1).

Table 1.

Descriptive information on the 163 breast cancer cases.

Variable Distribution No. % Age (years)163   <50 67 41.1   50+ 96 58.9 Residential status163   Urban 60 36.8   Rural 103 63.2 Education level160,b   <Bachelor 94 58.8   Bachelor+ 66 41.2 Occupation156,b   Housewife 145 93.0   Secretary 5 3.2   Teacher 3 1.9   Othera 3 1.9 Stage156,c   Early (stage 1 or 2) 61 39.1   Late (stage 3 or 4) 95 60.9   Family history of breast cancer154,b   No 144 93.5   Yes 10 6.5 First symptom159,b   Breast mass 123 77.4   Breast pain 12 7.6   Nipple discharge 5 3.1   Increased breast size 4 2.5   Axillary mass 4 2.5   Otherd 11 6.9 Cause for seeking medical care107,b   Persistent/increased breast mass size 67 62.6   Breast mass development 11 10.3   Persistent/increased breast pain 7 6.5   Breast pain development 7 6.5   Breast skin discoloration 5 4.7   Othere 10 9.4

The majority of the participants had reached the TCC by the third node of care, with the average total number of facilities visited equaling 2.5. Over one third (38.8%) of patients’ initial contact with the health care system was through a general surgeon, followed by 19.4% who visited primary care providers and 13.8% who went directly to the TCC. Other initial providers included surgical (9.4%) and medical oncologists (6.3%) as well as gynecologists (5.6%). Among the 143 patients who did not go directly to the TCC, the most common types of second nodes visited were: TCC (46.9%), surgical oncologists (26.6%), general surgeons (10.5%), medical oncologists (9.8%), and primary care providers (4.2%). Among the 80 patients who had not arrived at the TCC by the second node of care, the types of third nodes visited were: TCC (68.8%), surgical oncologists (16.3%), medical oncologists (8.8%), general surgeons (5.0%), and primary care providers (1.3%). Over 90% of the 24 fourth node visits were to the TCC. No patient took longer than five nodes to arrive at the TCC.

As shown in Table 2, of the patients who visited general surgeons, primary care providers, and gynecologists initially, only one half, one third, and nearly one quarter of the three groups, respectively, reached the TCC by the second facility visit. Among those who visited surgical and medical oncologists, however, the majority (80%) reached the TCC by the second facility visit (Table 2).

Table 2.

Distribution of patients’ arrival to Tanta Cancer Center (TCC) by type of node of entry (1st node).a

Node of care General surgeon
– 1st node Primary care – 1st
node Surgical
oncologist – 1st
node Medical
oncologist – 1st
node Gynecologist –
1st node Othersb – 1st
node No. % No. % No. % No. % No. % No. % TCC-2nd node 31 50 9 29.03 12 80 8 80 2 22.2 0 0 TCC-3rd node 27 43.5 12 38.71 2 13.3 2 20 5 55.6 3 30 TCC-4th node 4 6.5 9 29.03 1 6.7 – – 2 22.2 6 60 TCC-5th node – – 1 3.23 – – – – – – 1 10 Total 62 100% 31 100% 15 100% 10 100% 9 100% 10 100%

Table 3 presents information on the overall types of navigation pathways taken by patients to reach the TCC. The most common type of pathway was followed by about 20% of the study population and involved an initial visit to a general surgeon followed by arrival at the TCC. The next most common pathway also involved an initial visit to a general surgeon, but then included a trip to a surgical oncologist before arrival at the TCC. Other commonly-followed pathways involved two nodes of care: an initial visit to either a surgical oncologist, a primary care provider, or a medical oncologist, followed by the patient’s arrival at the TCC.

Table 3.

Navigation chains and referral patterns of the study population.

Navigation chaina No. % General surgeon → TCC 31 19.6 TCC 22 13.9 General surgeon → surgical oncologist → TCC 15 9.5 Surgical oncologist → TCC 12 7.6 Primary care → TCC 9 5.7 Medical oncologist → TCC 8 5.1 General surgeon → medical oncologist → TCC 7 4.4 Primary care → others → TCCb 16 10.1 Otherc 38 24.1

Among all participants, the average time elapsed between the first symptom experienced and the patient’s first visit with a health care provider was 6.2 months, with a median of 2.3 months. In terms of age, residential status, education level, and family history of breast cancer there was no significant difference between patients who made contact with a health care provider within 3 months of experiencing their first system and those who took longer to make their initial visit (Table 4). In terms of breast cancer tumor stage, however, there was a significant (p = 0.04) difference between patients who made contact with a health care provider within 3 months of experiencing their first symptom and those who took longer (Table 4). Among all participants, the average time elapsed between the first facility visit and arrival at the TCC was 6.8 weeks, with a median of 2.5 weeks. In terms of age, residential status, education level, breast cancer tumor stage, and family history of breast cancer there was no significant difference between patients who arrived at the TCC within 2 weeks of their initial contact with the health care system and those who took longer to arrive at the TCC (Table 4).

Table 4.

Distribution of select descriptive information on the Tanta Cancer Center breast cancer cases by patient and system delays.

Variable Patient delaya System delayb ≤3
months >3
months ≤2 weeks >2 weeks No. % No. % No. % No. % Age (years)   <50 38 58.5 27 41.5 27 40.3 40 59.7   50+ 51 56.0 40 44.0 48 51.1 46 48.9 p = 0.76 p = 0.18 Residential status   Urban 30 52.6 27 47.4 30 50.9 29 49.1   Rural 59 59.6 40 40.4 45 44.1 57 55.9 p = 0.40 p = 0.52 Education level   <College 47 52.2 43 47.8 46 48.9 48 51.1   College+ 40 63.5 23 36.5 28 43.8 36 56.3 p = 0.17 p = 0.52 Stage   Early (stage 1 or 2) 39 65.0 21 35.0 31 50.8 30 49.2   Late (stage 3 or 4) 43 48.3 46 51.7 39 41.9 54 58.1 p = 0.04 p = 0.28 Family history of breast cancer   No 89 56.8 60 43.2 68 47.2 76 52.8   Yes 5 55.6 4 44.4 5 55.6 4 44.4 p = 0.93 p = 0.63

Table 5 reveals the crude and adjusted associations that may determine patient delay among the breast cancer patients in this study. Patient delay was not statistically associated with age, residential status, or education level. However, although statistically insignificant, the type of first symptom experienced by a breast cancer patient may be associated with patient delay, adjusted or otherwise. Patients who experienced a breast mass as their first symptom were over 2 times more likely to delay seeking care than those who experienced other symptoms. The results of the analysis using the log-transformed patient delay were in the same direction of the untransformed data. As Table 6 reveals, system delay was statistically associated with the type of provider initially visited. After adjusting for age, residential status, education level, breast cancer tumor stage, and first symptom, patients who initially visited primary care providers, gynecologists, general surgeons or medical oncologists were 12, 9, 8 and 8 times, respectively, more likely to experience a delay in arriving at the TCC than patients who went directly to the TCC. System delay was also statistically associated with certain navigation pathways, after adjusting for age, residential status, education level, breast cancer tumor stage, and first symptom (Table 7). Namely, patients who initially visited a general surgeon then went to a surgical oncologist before arriving at the TCC were 35 times more likely to experience a delay in arriving at the TCC than patients who visited the TCC directly.

Table 5.

Relationship between patient delay and age, residential status, and education level.

Factors Patient delay Crude OR (95% CI) Adjusted ORa (95% CI) Age (years)   <50 Referent Referent   50+ 1.1 (0.6, 2.1) 0.9 (0.4, 1.9) Residential status   Rural Referent Referent   Urban 1.3 (0.7, 2.6) 1.4 (0.7, 2.9) Education level   <Bachelor Referent Referent   Bachelor+ 0.6 (0.3, 1.2) 0.6 (0.3, 1.2) First symptom   Other Referent Referent   Breast mass 2.1 (0.9, 4.8) 2.1 (0.9, 4.8) Table 6.

Relationship between dystem delay and patient’s first facility type, age, residential status, education level, first symptom, and stage.

Factors System delay Crude OR (95% CI) Adjusted ORa (95% CI) Age (years)   <50 Referent Referent   50+ 0.6 (0.3, 1.2) 0.6 (0.3, 1.4) Residential status   Rural Referent Referent   Urban 0.8 (0.4, 1.5) 1.1 (0.5, 2.3) Education level   <Bachelor Referent Referent   Bachelor+ 1.2 (0.7, 2.3) 1.3 (0.5, 2.9) First symptom   Other Referent Referent   Breast mass 0.8 (0.4, 1.8) 1.3 (0.6, 3.1) Stage   Early (stage1 or 2) Referent Referent   Late (stage 3 or 4) 1.4 (0.7, 2.7) 1.6 (0.8, 3.5) First facility type   TCCb Referent Referent   General surgeon 5.5 (1.7, 18.0) 7.6 (2.1, 27.6)   Primary care 11.0 (2.9, 41.7) 12.2 (2.9, 51.0)   Surgical oncologist 3.0 (0.7, 13.4) 3.4 (0.7, 16.0)   Medical oncologist 5.6 (1.0, 30.9) 8.3 (1.3, 55.0)   Gynecologist 9.0 (1.6, 52.3) 8.6 (1.4, 53.4)   Other 12.0 (2.2, 66.5) 11.0 (1.9, 63.3) Table 7.

Relationship between dystem delay and patient’s navigation pathway, age, residential status, education level, first symptom, and stage.

Factors System delay Crude OR (95% CI) Adjusted ORa (95% CI) Age (years)   <50 Referent Referent   50+ 0.6 (0.3, 1.2) 0.8 (0.3, 1.9) Residential status   Rural Referent Referent   Urban 0.8 (0.4, 1.5) 1.2 (0.5, 2.8) Education level   <Bachelor Referent Referent   Bachelor+ 1.2 (0.7, 2.3) 1.3 (0.5, 3.5) First symptom   Other Referent Referent   Breast mass 0.8 (0.4, 1.8) 1.3 (0.5, 3.3) Stage   Early (stage 1 or 2) Referent Referent   Late (stage 3 or 4) 1.4 (0.7, 2.7) 1.8 (0.8, 4.1) Navigation pathwayb   TCC Referent Referent   General surgeon → TCC 2.1 (0.6, 8.0) 2.6 (0.6, 11.0)   General surgeon → surgical oncologist → TCC 29.3 (4.6, 184.4) 35.4 (5.3, 237.5)   Surgical oncologist → TCC 2.3 (0.5, 11.3) 2.4 (0.4, 12.9)   Primary care → TCC 2.3 (0.4, 13.1) 2.6 (0.4, 16.6)   Medical oncologist → TCC 3.4 (0.5, 21.4) 5.4 (0.7, 46.1)   General surgeon → medical oncologist → TCC 6.0 (0.9, 38.1) 8.1 (1.0, 62.2)   Primary care → others → TCCc 19.5 (3.7, 102.4) 23.2 (4.0, 134.5)   Otherd 16.9 (4.4, 64.1) 17.8 (4.2, 74.1) Discussion

Understanding how breast cancer patients navigate the health care system is critical to providing timely diagnosis and treatment, particularly in developing countries like Egypt. In previous studies, we found that patient-mediated factors such as lack of knowledge and time constraints were associated with late-stage presentation.7 This study is the first to investigate the role of physician- and system-mediated factors in seeking care in the Nile Delta region of Egypt. Patients navigation is oftentimes complex and can result in unnecessary delay due to the health care system. In this study we observed the following interesting results. First, over 50% of patients’ initial contact with the health care system was through a general surgeon or primary care provider. Second, patients whose initial contact was with a general surgeon, primary care provider, or gynecologist were significantly more likely to experience a delay in reaching the TCC due to the system. Finally, patients who initially visited a general surgeon followed by a visit to a surgical oncologist were more likely face a delay in reaching the TCC as a result of the Egyptian health care system. Thus, the type of physician a patient comes in contact with initially as well as the patient’s navigation pathway play an important role in obtaining timely treatment,10,21 indicating that barriers to breast cancer diagnosis and treatment exist within the Egyptian health care system.

General surgeons and primary care providers in Egypt are generally not equipped to diagnose and treat breast cancer patients, especially considering the limited continuing medical education available for these physicians.8,22 Previous studies have also reported an overall sense of mistrust of local community primary care providers. In our study, however, we found that many breast cancer patients seeking treatment at the TCC had their initial contact with the health care system through general surgeons or primary care providers. Furthermore, the percentage of patients referred to the TCC after initial contact with general surgeons or primary care providers was lower than the percentage of patients referred to the TCC after initial contact with surgical and medical oncologists. Thus, while general surgeons and primary care physicians may not have complete, accurate, and up-to-date knowledge on appropriate breast cancer diagnostic methods and treatments, patients continue to visit them. Primary care facilities in Egypt are widely accessible, even in rural regions.6 This availability of primary care providers as well as the cultural taboos associated with cancer centers may explain the continued reliance on primary care providers as initial nodes of care. The differences in referral patterns between different providers indicate the need for educational programs targeted toward general surgeons and primary care providers that will encompass proper breast cancer diagnostic methods and referral techniques.13,22,23

Unlike a recent study in Ethiopia examining breast cancer patient navigation through the health care system which found that patients typically visited three health facilities before being referred to a specialized cancer center,21 most patients in our study had arrived at the TCC by the third node of care. However, on average, patients in our study experienced longer system delays than patients in studies conducted on other international populations in both developed and developing countries.10,2426 This suggests that problems may exist with the Egyptian health care system’s infrastructure.13 As is the case in other developing countries, Egypt’s health care system is fragmented. Health care in Egypt is financed by three main groups: the government, professional societies, and out-of-pocket payments, which account for over half of the financing.27 Unlike other developing countries, however, lack of access to care and the unavailability of proper technology and equipment are not issues in Egypt.6,27,28 Given the availability of medical personnel in Egypt, the main concerns with breast cancer care and with the Egyptian health system in general are related to cost of management, training and education of medical personnel, and guidelines for early detection. Egypt’s relatively low breast cancer incidence rate indicates that population-wide screening programs would be cost-ineffective.29 Instead, focus should be placed on early detection, particularly among women who are already experiencing breast cancer symptoms.29 In our study we found that women who experience a breast mass may be more likely to delay seeking treatment than women who did not. Furthermore, patients are frequently unable to effectively navigate the health care system, which can result in an advanced stage of breast cancer. These deficiencies in the current Egyptian health care system call for the reallocation of resources by the Egyptian government toward decreasing barriers within the system.13,29

As data were collected retrospectively, there is the possibility of recall bias. However, this was limited by validating patient-reported information using medical records. Furthermore, our study recruited approximately 50% of the new patient population at the TCC. However, we found no significant difference in age, residential status distribution, and breast cancer tumor stage distribution between our study sample and the TCC breast cancer patient population. Furthermore, given that the TCC is the only major cancer center in the Gharbiah region and the surrounding Nile Delta area, we are confident that our results are representative of the breast cancer patient population in this region.

Multiple studies on other communities have analyzed the effectiveness of patient navigators in assisting patients with the breast cancer care navigation process and found these navigators to be useful in negotiating cultural and systemic barriers.21,3039 The first breast cancer patient navigation program, created in Harlem, New York, was found to increase the percentage of early stage diagnosis from 6% among low-income women who did not use patient navigators to 41% among low-income women who used patient navigators.40,41 Patient navigators may be especially useful in Egypt as feasible solutions to the inefficient Egyptian health care system. In conclusion, this study highlighted gaps in the navigation of breast cancer patients through the health care system in Egypt. Future studies should examine the potential implementation of patient navigators and assess their effectiveness in downstaging and early detection of breast cancer. This study may have implications for other middle-income developing countries with available human resources but limited capacity for health management and policies for medical education and early detection. We believe that both patient- and system-mediated factors should be investigated to target tailored interventions simultaneously in Egypt and other developing countries. Because biological differences exist between breast cancer tumors within women in the same country and population,6,4245 understanding tumor biology and rapid referral of aggressive breast cancers should be a prime factor in patient navigation and system-based referrals. In addition, public, patient, and professional education may have major emphasis on early patient referrals in developing countries7,8 where technological and financial resources for early detection are not available or impractical on population-based levels.29

Acknowledgements

We wish to acknowledge the staff of the Gharbiah Cancer Society and the Tanta Cancer Center for their dedication and willingness to aid this research. Shimaa Mousa was supported by the Cancer Epidemiology Education in Special Populations (CEESP) Program of the University of Michigan (R25 CA112383).

Footnotes

Conflict of interest

All authors have no conflicts of interest.

References

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