A comparative analysis of monthly out-of-pocket costs for patients with breast cancer as compared with other common cancers in Ontario, Canada

A comparative analysis of monthly out-of-pocket costs for patients with breast cancer as compared with other common cancers in Ontario, Canada


C.J. Longo , PhD *, B.G. Bereza , MSc
* DeGroote School of Business and Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, and Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON.

Background

Monthly out-of-pocket costs ( oopc ) for Ontario patients with cancer have previously been reported, but little detail has been provided on differences based on tumour type.

Methods

A questionnaire administered in cancer clinics in the province of Ontario, with a mix of urban and rural patients, was analyzed using descriptive statistics and a regression analysis of cross-sectional data. The dependent variable was oopc (Canadian dollars), analyzed separately for total oopc (excluding imputed travel costs), and for each of the individual cost categories.

Results

Compared with colorectal, lung, and prostate cancer patients combined, breast cancer patients had statistically significantly higher total oopc ($393 vs. $149, p = 0.02), device costs ($142 vs. $12, p = 0.018), and family care costs ($38 vs. $3, p = 0.01). By contrast, they trended toward lower costs for travel ($225 vs. $426, p = 0.055) and had lower costs for parking ($32 vs. $53, p = 0.0198). Compared with non-breast cancer patients, patients with breast cancer reported a greater perceived financial burden (31% vs. 17% p = 0.0133).

Interpretation

These findings highlight that financial burden for cancer patients can vary by tumour type, and that patients with breast cancer may require a different mix of supportive services than do patients with other common tumour types. Supportive care programs related to financial burden should consider the likelihood and nature of financial burden when counselling breast cancer patients.

KEYWORDS: Breast cancer , out-of-pocket costs , self-administered questionnaire , health care funding

1. INTRODUCTION

In the Canadian health care setting, cancer patients do not have access to comprehensive cost coverage once care moves outside of the hospital setting. Hence, patients may be required to pay for such direct medical costs as prescription drugs, complementary and alternative medicine ( cam ), and home nursing once they are discharged from the hospital. Similarly, direct nonmedical costs for home or personal care of cancer patients have not traditionally been funded by the public health care system. In Ontario, among approximately 12 million residents, an estimated 65,100 new cases of cancer were diagnosed in 2009 1. Cancers of the prostate, breast, colon and rectum, and lung and bronchus led the way in numbers of new cases by cancer type in Ontario at 11,200, 8700, 8100, and 7800 respectively 1.

The burden of out-of-pocket costs ( oopc ) in cancer patients has been reported in several studies across a variety of Canadian settings, including Ontario, Quebec, and Newfoundland 25. Although there appears to be no standardized way to report oopc , estimated costs were fairly consistent between provinces studied. In Ontario, the mean monthly oopc for a combined patient population (that is, breast, colon, lung, prostate) was $213 [standard deviation (SD): $490.73], with additional imputed travel costs of $372 (SD: $694) 3.

To compensate, patients may adopt strategies that reduce their burden of oopc such as substituting or rationing medication, extending inpatient care, choosing radical treatment (mastectomy vs. lumpectomy and radiotherapy), extending the time between follow-up care appointments, or reducing absenteeism to limit loss of income 2. The need for such strategies implies that these oopc from cancer care represent a substantial burden for some patients 2. In fact, results show that patient-reported financial burden was “problematic” for 20% of the sample population and that loss of time from work for caregivers appeared to substantially influence that burden 3. Based on self-reported financial burden, costs were classified as either “significant” or “unmanageable.” This is not to say that others are not burdened, because another 52% reported that they have “slight” or “somewhat” of a burden (data not shown).

Furthermore, at least one third of the respondents needed caregiver assistance that required time away from paid work for the caregiver 3. On average, caregivers provided assistance for a mean of 7 working days of the preceding 30 calendar days 3. We observed that cancer patients who needed caregivers to take time from work were more likely to report a “significant” or “unmanageable” burden than those who did not (34% vs. 13%).

Categories of direct medical costs such as drugs, devices, and home care accounted for 44% of the mean total monthly oopc . Non-medical costs such as homemaking, cam , vitamins and supplements, family care, accommodation, and meals accounted for the remaining 56% 4. Our survey captured accommodation and meals not under travel costs, but rather as separate entries.

Variables such breast cancer, hospitalization, age, and number of clinic trips influenced the extent of oopc incurred by cancer patients 4. It is interesting to note that these costs represent only a portion of the financial burden that cancer patients may face. We observed only 30 days of oopc , but patients in our study were on active treatment for more than 1 year on average, and for some patients, treatment can last several years. Will et al. 6 noted that lifetime costs for breast cancer exceed US$36,000 (1995 dollars) for stage iv disease and that those costs extend over several years.

It is also well documented that costs vary considerably by tumour type, with data from the United States showing variation in mean monthly costs from a low of US$2187 for prostate cancer to a high of US$7616 for pancreatic cancer (2000 dollars) 7. Hence, one might expect that the related oopc to patients could vary significantly by tumour type.

The present article describes secondary outcomes from a survey previously reported by Longo and colleagues 3,4. Secondary outcomes include disaggregating oopc incurred by breast cancer patients as compared with non-breast-cancer patients combined, disaggregating oopc by age category, and by payer (public, private). The intent of the analysis was to identify whether oopc differs by cancer population. The findings may prove helpful in identifying tumour-specific gaps in existing programs and in informing health care providers of the variable financial needs of cancer patients in the Ontario setting.

2. METHODS

2.1 Study Design

Our study used a cross-sectional design to elicit data from patients with breast, colorectal, lung, or prostate cancer. The study parameters and the design of the questionnaire were described at length in earlier publications 3,4. Briefly, the four cancer types were selected as being the most common (representing 56% of all new cases in Ontario in 2009) 1. A pilot study helped to refine the survey and data-gathering procedures, and to test content and face validity (October–December 2001). No significant changes were made after the pilot, and the full survey was initiated after ethics review.

2.2 Ethics Approval

Ethics approval for the survey was obtained from the University of Toronto. Additionally, site approvals were obtained from each of the 5 cancer clinics involved in the study.

2.3 Data Sources

The questionnaire was administered to a mix of urban and rural patients in 5 of the 8 cancer clinics in the province of Ontario. Although all 8 sites were approached, 3 sites did not participate primarily because of human resource constraints or lack of financial support. The participating sites were an urban teaching centre [Toronto Sunnybrook Regional Cancer Centre ( tsrcc )], 3 intermediate centers associated with teaching hospitals having a mix of urban and rural patients [the London Regional Cancer Program ( lrcp ), the Juravinski Cancer Centre in Hamilton ( jcc ), and the Ottawa Hospital Regional Cancer Centre ( ohrcc )], and a smaller centre serving northern Ontario and having predominantly rural patients [Northwestern Ontario Regional Cancer Centre in Thunder Bay ( nworcc )]. The 3 non-participating sites included centres in Kingston and Sudbury, and the Princess Margaret Hospital in Toronto. Their absence did not contribute to sample bias.

2.4 Sample Size

The sample size calculation was based on a differential in costs across age category. From the results of the pilot study, we determined that, to detect a difference of less than $30, we would require sample sizes beyond the capacity of the participating sites, given the time allotted for the study. The $30 differential was based on a daily difference of $1, which was felt to be meaningful to patients. The sample size inputs included an alpha of 0.05 and a power of 80%. Clinics were instructed to accrue equal numbers of patients with each tumour type, with a quota of 12 patients per tumour type, yielding a target sample of 240 patients.

2.5 Subjects

All patients recruited at the outpatient cancer clinic were to be 18 years of age or older and receiving treatment for breast, colorectal, lung, or prostate cancer. Eligible patients had to have been on active treatment for their cancer for a minimum of 30 days (those on follow-up only were excluded) and had to be able to read and write English or French. We note that most patients remained in an ambulatory setting during the period of study. Only 5.3% of the sample required overnight hospitalization during the study period. We also captured information on other procedures and treatments (chemotherapy, radiation, surgery) to determine the role that they played in determining patient oopc .

2.6 Questionnaire Design and Application

The survey was a self-administered questionnaire. Questions captured information on patient demographics, general health, duration of the current cancer treatment, current treatments being provided (chemotherapy, radiation, surgery, doctor visits, emergency room visits, hospitalizations, in-home nursing services, physiotherapy services), level of insurance coverage, and employment details. Given that the questionnaire was self-administered, no information on disease stage was captured, as the potential responses were felt likely to be unreliable.

Patients were instructed to recall the previous 30 days and to list the costs related to their cancer treatment. The oopc were based on responses under the heading “paid by yourself.” Those costs were classified by “type of expense” into these categories:

  • Travel

  • Prescription drugs

  • In-home health care

  • Homemaking services

  • cam

  • Vitamins and supplements

  • Family care

  • Accommodations and meals

  • Devices and equipment

  • Other costs

Travel costs associated with transportation by vehicle were imputed first by determining the distance traveled to the clinic, and then by applying the Canadian Revenue Agency’s 2003 reimbursement rate of $0.43/km 8. The structure of the questionnaire was built to a significant degree on previous work undertaken with cancer patients in the United States 9,10. Patients were also asked if these expenditures were “less than other months,” “typical,” or “more than other months.” Questions that captured data related to perception by the patients of their financial burden included “How much of a financial burden are these out-of-pocket expenses?” Available categorical responses were “not a burden at all,” “only a slight burden,” “somewhat of a burden,” “significant burden, but manageable,” or “unmanageable burden.”

2.7 Data Collection and Extraction Methods (Analyses)

Patient demographics, treatment patterns, and categorical costs are captured and presented as means, standard deviations, and ranges. Where required, average family income for each participant was taken as the midpoint of the family income category chosen in the questionnaire. The value for families earning more than $80,000 annually was entered as $90,000.

The dependent variable was oopc , analyzed separately for total oopc (excluding imputed travel costs) and for each of the individual cost categories. Travel costs and total costs were kept separate because travel costs were largely imputed and did not represent actual patient expenditures. We felt that it was not appropriate to combine costs not captured in the same manner. All other cost categories represent actual oopc .

We compared total costs for each tumour type with costs for all other tumour types, but only the comparison of breast cancer costs with costs for all other types showed a statistically significant difference; hence, no analysis of differences for the other primary tumour types was undertaken.

All analyses were performed using the statistical software Stata (version 7.0: Stata Corporation, College Station, TX, U.S.A.). This software has the ability to handle weighting of cases and clustering when running standard analyses, including linear or logistic regressions. Stata also has the ability to detect collinearity in multiple regression analysis and omits the offending variables from the final result. Our analysis generated means, standard deviations, confidence intervals, and p values from the survey data.

2.8 Outcomes

The oopc for breast cancer and for other cancer types (colorectal, lung, and prostate combined) were the primary outcome in this study. Differences by cost categories are also presented. Secondary outcomes include oopc by age category (older or younger than 65) and patient perception of financial burden, both segregated by the breast and non-breast cancer populations. The independent variables tested in the initial analyses included age, sex, education, income, marital status, tumour type, treatment type, treatment duration, clinic site, and insurance coverage. In the multiple regression, we used backward stepwise regression and iteratively dropped the variables with the highest p values (and those above 0.2) from the model. Other outcomes (demographic and treatment variables) were reported in earlier publications 3,4.

3. RESULTS

3.1 Sample Population

The study sample has been extensively described elsewhere; Table I presents a concise version 4. Briefly, data on total oopc was retrieved from surveys of 74 patients with breast cancer and 208 patient with colorectal, lung, or prostate cancer. Approximately 28% of breast cancer patients reported a mean family income of $80,000 or more, which compares with 10%, 17%, and 16% in the lung, colorectal, and prostate cancer groups respectively. Compared with breast cancer patients at 27%, a higher percentage of the lung, prostate, and colorectal cancer groups reported incomes of less than $40,000 (43%, 40%, and 34% respectively). A mean income figure for each population group was not possible because the survey presented the question in a categorical manner.

TABLE I .  Study population: demographic characteristics by tumour type

 

Treatment duration varied between cancer types. The mean treatment duration of 424 days was highest for the breast cancer group (lung cancer, 219 days; colorectal cancer, 328 days; prostate cancer, 339 days) 4. Among patients meeting the entry criteria, the response rate was high, with 83% agreeing to participate. We therefore expect that selection bias is minimal. Also, no statistically significant difference was observed for oopc between clinic sites. However, the sites are relatively homogeneous, given that they all represent cancer centres with a full suite of cancer care services. We do not suggest that other centres operating within community hospitals would have similar expenditures, because those sites were not sampled.

Of the breast cancer respondents, 50% had been on treatment for more than 6 months; that percentage was 28% in the non-breast-cancer patients. Although this difference may bias the sample, previous research suggests that costs are highest in the first 6 months of treatment, which would suggest the breast cancer oopc estimates are conservative. The mean age of the breast, lung, colorectal, and prostate respondents was 52, 64, 63, and 68 years respectively.

3.2 Out-of-Pocket Costs

Breast cancer patients incurred significantly higher mean oopc over a 30-day period ($393 vs. $149 for all other tumour types combined, p = 0.0006). Furthermore, the range in reported oopc was wider for breast cancer patients ($0–$5230 vs. $0–$2300). Breast cancer patients incurred an additional $225 for imputed travel; non-breast-cancer patients incurred an addition $426 for imputed travel.

For each category, the mean oopc were higher in the breast cancer patient group, with the exception of the “accommodation/meals” and “other” categories. The most notable and significant difference between the breast and non-breast populations with regard to categorical costs was that for “devices”: $141.83 and $11.74 respectively ( p = 0.0001). Costs incurred for “family care” were also significantly different between populations: $38.38 for breast cancer as compared with $3.22 for non-breast-cancer patients ( p = 0.0093). Other categorical costs between the populations were also substantially, but not significantly, different. For example, breast cancer patients incurred higher mean monthly oopc for cam products ($80.35 vs. $11.28, p = 0.0996) and for drugs ($57.31 vs. $40.99, p = 0.3984) than did non-breast-cancer patients. However, breast cancer patients incurred lower imputed costs for travel ($225 vs. $426, p = 0.055) and for parking ($32 vs. $53, p = 0.0198). Tables II and III present 30-day total and categorical costs for the breast cancer and non-breast-cancer populations respectively.

TABLE II .  Total 30-day out-of-pocket costs for breast cancer patients, by category

 

TABLE III .  Total 30-day out-of-pocket costs for patients with other cancer types (combined), by category

 

Tables IV and V stratify oopc costs by age category and by cancer type. Compared with non-breast-cancer patients in the same age group, breast cancer patients younger than 65 incurred significantly higher oopc ($408.40 vs. $201.84, p = 0.0499). For patients 65 years of age and older, we observed no statistical difference in oopc ; however, there were only 6 respondents with breast cancer in that age group.

TABLE IV .  Out-of-pocket cost comparison for patients under 65 years of age with breast cancer or with other cancer types (combined)

 

TABLE V .  Out-of-pocket cost comparison for patients 65 years of age or older with breast cancer or with other cancer types (combined)

 

3.3 Regressions

In a limited number of regression analyses, we found that the differences between tumour types persisted even after controlling for education, income, and age, which are the factors typically cited in the literature as affecting expenditures. Although evidence of collinearity might be expected in our model, it appears that the relationship between income and oopc is quite strong, but that the relationship between education and oopc is much weaker—as evidenced in our primary backward stepwise regression analysis, which initially tested tumour type, treatment type, treatment duration, marital status, age category, education, income, sex, and insurance coverage. The final model showed that, in addition to tumour type, only income and insurance coverage were statistically significant predictors of expenditure, although the model accounted for only 10.5% of the variance ( R 2 = 0.1051, Table VI)

TABLE VI .  Final (backward stepwise) regression of total out-of-pocket costs, excluding travel, with final independent variables (tumour type, income, insurance coverage)

 

3.4 Likelihood of Burden

Compared with non-breast-cancer patients, patients with breast cancer tended to perceive a greater financial burden. A significant or unmanageable burden was reported by 31% of breast cancer patients, but by only 17% of non-breast-cancer patients ( p = 0.0133)

4. DISCUSSION

Results show that mean total oopc —and categorical costs “devices” and “family care”—are greater for breast cancer patients than for patients with other common cancers combined. The significance difference in mean total oopc remained true even after controlling for age, education, and income, although the only statistically significant predictors of oopc were tumour type, income, and insurance coverage. Our initial regression analysis considered a variety of variables including marital status, treatment duration, treatment type, and clinic site, but those were dropped during the backward stepwise regression, suggesting that they were not good predictors of oopc . Furthermore, oopc are significantly higher for breast cancer patients younger than 65 than for patients in the same age group with other types of cancer. Our survey also shows that the likelihood of personal perception of significant or unmanageable financial burden is greater in the breast cancer cohort than in the comparator group. Furthermore, recent changes in coverage for physiotherapy in Ontario (2005) and the opening of private infusion clinics (2004) are just two examples of how costs borne by patients have expanded since our research was undertaken. Hence, we can expect that an even higher percentage of the population will experience a significant or unmanageable financial burden.

Previous work in this area by CJL 3,4 has shown that patients with a significant or unmanageable burden are more likely to be uninsured and to be under the age of 65. The higher oopc experienced by breast cancer patients has not been investigated in as great detail as it has been here. Hence, this work adds additional insights to this body of literature.

Gaps remain in the understanding of why breast cancer patients have higher expenditures than are seen with other common tumours. Breast cancer patients are predominately female, and yet, in the regression analysis, sex was not a good predictor of oopc . We do note that insurance coverage is a factor: it remained in the final regression model for this analysis as it did in earlier publications. We note that people under the age of 65 are less likely to have comprehensive coverage because many health programs are not covered through the public purse until a person reaches the age of 65, and this factor likely plays an important role.

Despite the fact that breast cancer patients have a higher average income, they may also be more affected by the loss of income associated with their illness and the resulting impact on their lifestyle. This impact may account for the more frequent reports of significant or unmanageable burden. The observation of higher costs for devices and family care is likely influenced by the fact that women with breast cancer are more likely than are patients with any of the other tumour types to invest in items such as wigs and prostheses. The explanation for lower travel and parking costs may be related to the fact that breast cancer patients travel shorter distances on average (45.7 km vs. 59.0 km) and, over a period of 30 days, make fewer trips to the primary clinic (6.4 trips vs. 10.8 trips). Clearly, additional studies are required to more completely understand the factors that influence oopc across tumour types.

The present research has a number of limitations worth highlighting. The literature suggests that patients tend to underreport their use of health care resources 11,12, and therefore the costs reported here are likely to be a lower bound. The data for all tumour types was highly skewed, because a significant number of patients had no costs in many of the categories. In many cases, a relatively small population with very high costs influenced the mean—a common occurrence in the health care costing data seen in other published literature 13. Although we considered transforming the costing data, we felt that transformation would reduce the transparency of the results. More-sophisticated methods (for example, retransformation or bootstrap methods) could potentially have been used to address the skewness of the data, and we recognize that not undertaking these more sophisticated techniques could be considered a limitation of our analysis.

Collecting data for the most recent 30 days increased reliability by limiting recall bias, but it also meant that the resulting data would underestimate costs that tend to be episodic in nature (devices, for example). Research in the United States has shown that cancer treatment costs are higher in the first 6 months after diagnosis and just before death; costs between those two points in time are lower 14. An estimated 50% of our breast cancer sample and 29% of our comparator sample had been on treatment for more than 6 months, and thus many of these individuals would be in the relatively low-cost period of their treatment. It is important also to note that surgery was likely to have occurred early in the treatment course for the sample population. The mean number of treatment days for breast cancer and non-breast-cancer patients was 424 and 295 respectively, and so patients in our sample would likely have already undergone surgery.

Another limitation is that a follow-up of respondents was not undertaken; therefore no data are available on the number of patients surveyed just before death. It is also important to note that indirect costs (lost wages) are not addressed in the present paper but have been shown to be problematic in many cases 3. Such losses can further exacerbate the financial burden on patients.

5. CONCLUSIONS

Health care providers should be aware of factors that are predictive of a higher likelihood of significant oopc . Because breast cancer patients appear to be more likely to have high expenditures (partly because of their younger age and higher income) it may be appropriate to counsel these patients differently and to offer different supportive services that address their added burden, whether financial or otherwise. Government programs to assist in this regard may also prove useful for those under the age of 65 and those whose private health insurance coverage is limited or insufficient.

6. ACKNOWLEDGMENTS

This research was supported by an opportunities grant through the Medicare to Home and Community program, funded through the Canadian Institutes of Health Research, with Dr. Raisa Deber as lead investigator. Clinic-based staff were also instrumental in the completion of this project, including, but not limited to, Allen Edwardson ( nworcc ), Scott Sellick ( nworcc ), Elenor Dilullo ( lrcp ), Susan Wolnick ( lrcp ), Dr. David D’Souza ( lrcp ), Diane Manii ( ohrcc ), Diane Dilnot ( tsrcc ), Susan Dimitry ( jcc ), Adrieanne Hasler ( jcc ), Mary O’Brien ( jcc ), Dr. Richard Tozer ( jcc ), and Dr. Tim Whelan ( jcc ). Invaluable clinical expertise concerning cancer care was provided by Dr. Neill Iscoe.

7. CONFLICT OF INTEREST DISCLOSURES

The authors declare that no financial conflict of interest exists.

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Correspondence to: Christopher J. Longo, Strategic Market Leadership and Health Services Management, DeGroote School of Business (MGD-210), and Centre for Health Economics and Policy Analysis, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4M4; and Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario L8S 4M4. E-mail: cjlongo@mcmaster.ca

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Current Oncology , VOLUME 18 , NUMBER 1 , 2011








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