Финансовая токсичность для взрослых, переживших рак в Сингапуре: существует ли она?

INTRODUCTION

Financial impact of cancer diagnosis can be significant. Compared to noncancer control groups, cancer patients are more likely to experience financial toxicity (FT). FT in oncology practice can be defined as the ‘detrimental effects of the excess financial strain caused by the diagnosis of cancer on the well-being of patients, their families and society’.[ 1 ] Based on studies carried out in Western countries, the prevalence of FT among cancer patients is reported to range between 30% and 50%.[ 2 ]

The extent of FT depends on many factors, including costly treatment-related expenditures, loss of productivity or employment and, consequently, loss of access to insurance schemes provided by the employer. The impact of FT has also been studied and is increasingly recognised to adversely affect quality of life (QoL).[ 3 ]

It is, however, important to note that the payment schemes utilised in Western countries and Singapore differ tremendously. Most patients from Western countries rely on social or private insurance for direct medical cost payments. Contrastingly, Singapore has a unique healthcare model, with emphasis on co-payment and greater self-responsibility. In addition, we have government-instituted mechanisms in place to assist the underprivileged to access necessary medical treatments. For the above reasons, it is important to study the differing prevalence and magnitude of FT in Singapore.

In Singapore, citizens or permanent residents rely on their personal savings and typically have three additional sources of funding to pay for their medical bills. These three sources are known as MediSave, MediShield and MediFund.[ 4 ] MediSave is a national compulsory medical savings policy in which employed Singapore residents contribute part of their income to an account for their future use in approved medical expenses.[ 5 ] MediShield Life is a government-administered universal basic health insurance plan for Singapore residents that helps to pay for large hospital bills and selected costly outpatient treatments such as chemotherapy and radiotherapy.[ 6 ] This insurance plan permits patients to make less out-of-pocket (OOP) payments using MediSave and/or cash. Individuals have an option to purchase additional private insurance plans to further reduce their OOP payment. Lastly, MediFund is an endowment fund provided by the government to citizens who face financial difficulties with their remaining bills after drawing on other means of payment, including MediShield Life, MediSave and cash.[ 7 ] The amount of MediFund subsidy is determined by means-testing. Besides these three sources, elderly Singapore citizens (born before 1960) and selected government employees are eligible for generous government subsidies, which further defray OOP expenses, with Pioneer Generation (PG)[ 8 ] Package and Civil Service Card (CSC), respectively.

With these unique medical payment schemes in place, findings in Western countries may not be applicable to our setting. We have thus designed this pilot study to provide preliminary data on the prevalence and magnitude of FT, as well as the correlation of FT with QoL in our local patients.

METHODS

This pilot study was conducted at the National University Hospital and was approved by the institution ethics review board. Our eligibility criteria included patients with nonmetastatic breast cancer or nasopharyngeal cancer (NPC) who had completed curative intent radiotherapy, with age ranging from 21 to 85 years, and who were disease free during the 9–24 months postdiagnosis. We have chosen these two cancer types as they usually affect middle-aged adults, who are likely to be employed and have other financial commitments such as housing loan and young family to support. They are also more likely to be susceptible to the impacts of FT. We used validated COmprehensive Score for financial Toxicity (COST)[ 9 ] and Functional Assessment of Cancer Therapy-General (FACT-G)[ 10 ] questionnaires, and permission from the Functional Assessment of Chronic Illness Therapy (FACIT) was obtained to modify the recall period of both questionnaires from 7 days to 9–24 months. The lower time point of this period was chosen to avoid acute treatment-related adverse effects from influencing QoL and the upper time point was chosen to reduce recall bias.

Eligible patients were first sent postal correspondence and contacted via telephone a week later. Verbal consent was obtained for participation in the study, and study participants were given the option to complete the enclosed questionnaires and return them via mail or have the survey administered over telephone. Other details of this process are described in Supplemental Digital Appendix 1.

We hypothesised that higher OOP expenditure correlate positively with FT, and higher FT correlates with a worse QoL. Description of how OOP expenditure is obtained is also given in Supplemental Digital Appendix 1.

Statistical and regression analysis

Statistical analysis was performed using Stata version 16 (StataCorp LP, College Station, TX, USA). Differences in means between groups were compared using an independent-samples t -test. Categorical or ordinal variables were analysed using the chi-square test. Statistical significance was defined by a P value of <0.05. Selected variables were cross tabulated to explore the relationship between them.

We specified two regression models corresponding to the two primary variables of interest. The first model examines the association of covariates presented in Table 1 with the COST score, which measures FT as follows:

COST score = α 0 + α 1 Cancer Site + α 2 Treatment + α 3 Sociodemographics + α 4 Direct Medical Costs + α 5 Direct Nonmedical Costs + α 6 Indirect Costs + α 7 Household Finances + α 8 Living Arrangement + α 9 Medical Bill + u 1 (1)

The second model examines the association of FT and other covariates with the FACT-G score, which measures QoL:

FACT-G score = β 0 + δ COST score + β 1 Cancer Site + β 2 Treatment + β 3 Sociodemographics + β 4 Direct Medical Costs + β 5 Direct Nonmedical Costs + β 6 Indirect Costs + β 7 Household Finances + β8 Living Arrangement + β 9 Medical Bill + u 2 (2)

The intercept terms are denoted by α 0 and β 0 and represent the average COST and FACT-G scores, respectively, holding other factors constant. The coefficients for the vector of covariates are denoted by α k and β k , and the stochastic error term is u k . The coefficient δ in Model 2, which measures the association between the COST and FACT-G scores, is also a coefficient of interest, as it demonstrates the correlation between FT and QoL. Regression analysis of the medical bill variables is applied only to the subset of respondents for which data are available.

RESULTS

Descriptive statistics

Of the 483 eligible patients, 389 (81%) and 94 (19%) patients had breast cancer and NPC, respectively. Detailed sample characteristics and their relevant statistical tests are presented in Table 1 . Statistical tests indicated significant differences between survey participants ( n = 76) and nonparticipants ( n = 407, either uncontactable or declined to participate in the survey) for cancer site, gender and race ( P <0.001). These differences are expected and directly attributed to our intention to sample similar proportions of breast cancer and NPC patients. Patients who had been involved in a clinical trial were more likely to participate in our survey ( P < 0.01). We also compared survey participants whose medical bill data were extracted for further analysis ( n = 41) to those who did not provide consent for us to do so ( n = 35, either refused or did not respond). There were no statistically significant differences between these two groups, except that there were more patients from the Malay ethnicity in the former group ( P < 0.01, results not shown).

Characteristics of our 76 survey participants are presented in Table 2 . Most respondents were married (76%), had an educational level of secondary and below (63%) and lived in a public Housing Development Board (HDB) flat (90%). For medical expenses, around 10% of the respondents qualified for MediFund, which provides further financial assistance based on means-testing. Another 10% received PG and CSC subsidies. About one-fifth of the respondents reported using their family’s MediSave to pay for treatment.

Direct nonmedical expenses included transport cost, use of complementary and alternative medicine (CAM) and hire of a formal caregiver. Most respondents took public transport (66%), followed by taxi/private hire (25%) and private car (25%); some respondents selected more than one mode of transport in the questionnaire. The use of CAM was not prevalent. Of the 31 (41%) respondents who used CAM, 11 (31%) respondents stopped due to cancer treatment. Other factors included CAM being unsuitable or ineffective, financial and other reasons. Only six (8%) respondents hired a formal caregiver after their cancer diagnosis.

Respondents who adjusted their living arrangement because of their cancer diagnosis formed a minority ( n = 18, 24%). These adjustments were made to improve access to care or for financial reasons. Some respondents had rented out rooms or moved to smaller flats to raise funds, and some had moved in with family or friends to receive care. Seven (9%) respondents indicated that they or their household member(s) took loans, including borrowing money from relatives and friends, to finance the cost of cancer treatment. The loan amounts were <$10,000 ( n =4), $20,000–$30,000 ( n =1) and ≥$50,000 ( n =1).

The mean COST and FACT-G scores among the survey participants were 18.0 (out of 44) and 68.3 (out of 108), respectively. Higher scores indicate better feelings of financial well-being and better QoL, respectively. The mean scores, as well as distribution of COST scores, were not significantly different between the breast cancer and NPC patients [ Table 3 ]. In our sample, the two scores were positively and moderately correlated ( r = 0.45).

Cross tabulations

To gain a deeper insight into employment status and income for both patients and their households, we cross tabulated the survey results for these two variables. Indirect costs of cancer included lost economic productivity for the patients and their household members. Only a quarter of the respondents who earned less than $1,000 per month before cancer diagnosis reported being affected by changes in employment status or income, compared to respondents with higher income [ Table 4 ]. One reason is that these respondents were typically homemakers or retirees or had been out of the workforce for a significant period. In contrast, about 70% of the respondents who had higher earnings experienced an adverse impact on employment and income.

Around 60% of the respondents reported no change in the employment status of their household members [ Table 5 ]. Accordingly, most of these respondents did not experience a fall in household income. Among the affected households, most had employed household members taking on extra work or unemployed members seeking employment to supplement the income, while a minority had employed household members seeking alternative work arrangements (such as taking unpaid leave from work, working fewer hours, resigning from job and/or early retirement) to devote more time towards caregiving. The reduction in household income ranged from $250 to $4,000 per month.

Medical bill

All 41 patients whose medical bill data were retrieved incurred OOP expenses. Table 6 reports the mean and median OOP expenses for inpatient and outpatient settings, as well as both combined. There was no significant difference in the total mean OOP expense between breast cancer and NPC patients ($26,818 vs. $24,206, P = 0.79). The outpatient component ranged between 26% and 34% of the total costs. However, the mean total OOP expense was skewed by five breast cancer patients with extremely high OOP [ Figure S1 , Supplemental Digital Appendix 2]. On comparing the median total OOP costs, a significant difference was found between breast cancer and NPC patients ($15,910 vs. $21,593, P = 0.008).

Financial toxicity

For the first set of regression analysis, COST score was the dependent variable and the sample consisted of 76 survey participants without medical bill data. The results are reported in Table S2 [see Supplemental Digital Appendix 2]. Specification 1 contains all the variables listed in Equation 1, except that due to the low utilisation of CAM and the possibility of recall bias for the transport mode used for hospital visit, these two variables were not included. Based on the adjusted R 2 , its explanatory power is relatively lower compared to Specification 2, which was obtained after systematically dropping the variables with coefficients that were small in magnitude or highly insignificant.

According to the parsimonious specification, patients who lived in HDB flats were still found to have significantly lower scores (−8.3, P = 0.02), with education level being marginally significant (−4.4, P = 0.06). Patients who hired formal a caregiver due to the cancer diagnosis, as well as those whose household member(s) needed to earn extra income to finance medical expenses were found to be associated with significantly lower scores (−7 and − 6, respectively, P = 0.05). On the contrary, patients requiring inpatient admission had significantly higher scores (4, P = 0.05), symbolising increased feelings of financial well-being. In the second set of regression analysis, COST score was the dependent variable and total OOP expenses was an additional variable, for which data were limited to a subset of 36 survey participants excluding five outliers [ Table S3 , Supplemental Digital Appendix]. We did not find any meaningful correlation between the OOP expenses and FT for both the ordinary least squares and two-stage least squares specifications.

Quality of life

Results of the preplanned regression analysis with FACT-G score as the dependent variable are reported in Table S4 [see Supplemental Digital Appendix 2]. We found that the feeling of financial well-being was positively and significantly correlated with a patient’s QoL, controlling for other factors. Particularly, a one-point increase in COST score was associated with a 0.7-point increase in FACT-G score ( P = 0.002). A loss of QoL was found to be significantly associated with certain adverse changes that household member(s) experienced, such as when the household member(s) had to work more to supplement the household income (−15.2, P = 0.02) or when a household member’s MediSave account was being used to pay for medical expenses (−0.3, P = 0.04). Patients of Malay and Indian ethnicities were more likely to report higher QoL.

DISCUSSION

Financial toxicity is often an overlooked subject in cancer care and survivorship. The prevalence and impact of FT may vary among healthcare systems, depending on the model of care. Singapore is unique as it emphasises shared responsibilities with a co-payment model and means-tests healthcare subsidises. The prevalence of FT in Singapore is unclear and is possibly higher than those of countries with universal healthcare. Knowledge about FT (and the predictors associated with FT) will provide valuable information to administrators and healthcare providers to improve healthcare policies and redirect resources.

In the Singapore context, Chan et al. have qualitatively reported the affordability of cancer treatment among patients above 50 years of age. They found that patients undergoing targeted therapy were 2.5 times more likely to have difficulty paying for treatment. However, about 70% of the cohort felt that the existing financial schemes were helpful to reduce OOP costs.[ 12 ] To the best of our knowledge, we are the first to systematically assess and report on the prevalence of FT among Singaporean cancer survivors using the widely utilised COST tool.

We found a mean COST score of 18.0 (out of 44) in our surveyed patients. This is 3–4 points lower than that of cohorts from the USA (indicating more FT). For example, de Souza et al . validated the COST tool in 233 North American patients with Stage IV solid tumour who had been receiving palliative chemotherapy for at least 2 months. They reported a mean COST score of 22.2 ± 11.9.[ 9 ] Huntington et al . reported on FT in 100 patients with multiple myeloma receiving systemic therapy in a tertiary academic centre in the USA. The mean COST score was 23 ± 11.1, with one-third of patients applying for financial assistance.[ 13 ] Within the Asian context, Honda et al . reported FT in a cohort of 156 Japanese patients receiving chemotherapy for solid tumours. They reported a median COST score of 21.[ 14 ] The differences in COST scores seen in our cohort are unclear. One possible explanation is the higher proportion of co-payments required within our health system as compared to the USA and Japan.

Factors associated with a lower socioeconomic status, such as residence in subsided housing and lower educational levels, were understandably associated with a higher COST score. This suggests that vulnerable patients still experience FT despite available subsidies. In our study, one-third of the household members of cancer patients had to take on additional employment to supplement their household income, and this may directly affect the amount of care and help received by the patient from these working family members.

One surprising finding from our regression analysis is that patients who spent part of their treatment requiring hospitalised care had lower COST scores (signifying increased financial well-being). This is contrary to the findings by de Souza et al . who reported that more than three hospital admissions were associated with a worse COST score.[ 9 ] We postulate that this may be due to patients’ access to specific subsidies and insurance coverage that were made available during and after hospitalisation. Certain private medical insurance policies extend the coverage of outpatient treatment costs for up to 3 months after hospitalisation. This benefit alleviates costs that would have otherwise been borne by the patient.

With regard to QoL, similar to other studies,[ 15 16 ] we found that a higher COST score (reduced FT) is positively correlated with a better QoL (improved FACT-G score) ( P = 0.002). Another interesting finding based on our regression model is that QoL suffered when the patient’s household members had to engage in additional employment to sustain their finances or if the household members’ MediSave had to be utilised for the patient’s medical expenses. The decline in QoL may arise from the negative emotions surrounding the excess financial strain on the family.

The financial impact of a cancer diagnosis is significant. The average OOP expenses in our cohort was about $25,000, which is more than five times the national median gross monthly income of $4,534 in 2020.[ 17 ] We found that about half of our patients reported a change in their employment and income status after their diagnosis. About one-third of the household members had to take on extra employment to supplement income. Despite this, half of them still reported a decrease in household income. We could not find an association between OOP expenses and FT in our cohort. While this could be partially attributable to our small number of patients with available billing data, another plausible explanation is the effectiveness of the means-tested subsidies. Effective means-testing gives the most vulnerable patients access to much needed financial assistance.

The main strength of our study is the successful application of the COST tool in our population to assess FT. In the process, we also translated the COST tool to Chinese and Malay languages via a stipulated procedure defined by FACIT (the copyright holder of the COST questionnaire). Furthermore, this is the first study to report on FT in adult cancer survivors with breast cancer and NPC who had undergone curative treatment within the Singapore healthcare system. Although patients may feel uneasy talking about their financial situation, we managed to obtain data on OOP expenses from approximately 50% of the eligible patients.

Regarding study limitations, we acknowledge the potential of recall bias, as patients were made to recollect their treatment expenses and QoL 1–2 years after completion of treatment. Secondly, there is a possibility of selection bias — patients in an extreme financial situation may have declined treatment entirely and were therefore not included in our study. Thirdly, the treatments for both breast cancer and NPC are typically multimodal. Treatment of breast cancer involves surgery followed by a combination of chemotherapy, radiotherapy, targeted therapy and endocrine therapy, while treatment of NPC typically involves radiotherapy with or without concurrent chemotherapy. It remains unclear which aspect of the treatment contributes more to FT. Certainly, some treatments like targeted therapy (with monoclonal antibodies) can be costly and contribute more to FT. However, we must acknowledge that the patient’s treatment should be considered as an entirety.

In conclusion, some degree of FT exists within our population, with lower socioeconomic patients being at higher risk. Financial toxicity should not be considered in isolation, as it correlates with poorer QoL. Out-of-pocket expenses was surprisingly not directed related to FT, likely due to the presence of effective means-tested subsidies. Finally, cancer diagnosis and treatment have an impact on patients’ household members, and additional resources should be devoted to reduce this burden for patients at risk. Looking ahead, our methodology can be scaled up to study FT in other cancer primaries. We would suggest collecting information (such as COST, QoL data) prospectively to eliminate the risk of recall bias.

Conflicts of interest

There are no conflicts of interest.

Boxplot diagram of OOP expenses by cancer site

Acknowledgement

We would like to acknowledge Koh Jun Kai Joel, Chai Jia Hui Chai and Chu Chia Huay for their contributions towards the study.

APPENDIX 1

Detailed description of the process of data collection, participant enrolment and questionnaire administration is as shown.

This supplementary material depicts the specific parameters obtained from the three sources in our study. From the hospital database, we obtained the name, contact details, and basic characteristics of the patients who fit the eligibility criteria. The basic characteristics include demographics, primary cancer, treatment received (including if they were treated on a clinical trial where costs would have been borne by the sponsor), indicators of direct medical expenses (such as whether the patient received further subsidy or assistance from family members Medisave accounts), and hospital admission. Admissions during the neo-adjuvant therapy or adjuvant therapy period(s) were recorded; elective admissions for surgery for breast cancer patients, as well as unrelated admissions relating to neither radiotherapy nor chemotherapy toxicities (e.g. a patient developed right-sided weakness and was diagnosed with a stroke one year after breast cancer treatment), were not included.

To sample an approximate equal number of patients with breast cancer and NPC, we contacted all eligible NPC patients and a subset of breast cancer patients whilst ensuring that patients belonging to the minority races are represented. During May 2019, we sent postal correspondence to 238 patients—145 (61%) and 93 (39%) had breast cancer and NPC, respectively—to invite them to participate in the study. The contents of the mail included an invitation letter, participant information sheet, and three sets of questionnaires consisting COST questionnaire, FACT-G questionnaire, and a specifically-designed questionnaire (see Appendices). All the materials contained Chinese and Malay translations; the COST and FACT-G questionnaires, including the translated versions, were licensed by FACIT. The specifically-designed questionnaire elicited additional information that, subject to statistical analyses, can reveal variables associated with financial toxicity and quality of life. The variables include socio-demographic characteristics, indicators of how direct medical expenses are financed, and indicators of indirect medical expenses. Respondents were asked to specify, for indirect cost of cancer treatment, the changes they and their household members had experienced in employment status and income, as well as living arrangement and household finances.

About a week after the letters were sent out, we called the recipients to seek their verbal consent for participation. We also asked if they preferred to self-complete the questionnaires and mail back to the study team using the return envelope, or to have the survey administered over the telephone. We received a total of 76 responses (32% response rate), of which 48 (63%) were self-completed questionnaires and 28 (37%) were surveys administered over the phone. The average duration of a phone survey was 27 minutes. There were 47 (62%) and 29 (38%) breast cancer and NPC patients respectively.

During the phone call, we also sought consent from patients to access their medical bill data from the hospital system for further analysis. Identification numbers of 41 consenting patients—of which 30 (73%) and 11 (27%) had breast cancer and NPC, respectively—were recorded and forwarded to the NUH Academic Informatics Office for data extraction. The medical bill data for all outpatient visits and inpatient admissions related to breast cancer and NPC diagnosis or treatment were obtained. The data has a breakdown of out-of-pocket expenses, amount from MediSave account, amount paid by MediShield, amount paid by Medifund, amount paid by third party (e.g. voluntary welfare organisation [VWO]), means-tested direct subsidy amount, and PG subsidy amount.

APPENDIX 2

Specification 1 contains all the variables listed in Equation 1, except that due to the low utilisation of CAM and the possibility of recall bias for the transport mode used for hospital visit, these two variables were not included. Based on the adjusted R-squared, its explanatory power is relatively lower compared to Specification 2, which was obtained after systematically dropping the variables with coefficients that were small in magnitude or highly insignificant.

This second set of regression analysis also has COST score as the dependent variable, but includes the total OOP expense as an additional variable, for which data is limited to a subset of 36 survey participants excluding five outliers.

The variable for hiring of formal caregiver was dropped due to insufficient variation among the subset of patients, and other insignificant variables in the previous specification were also excluded to preserve the degrees of freedom given the small sample size. We did not find any meaningful correlation between OOP expense and FT for both the ordinary least squares (OLS) and two-stage least squares (2SLS) specifications; the latter specification uses housing type as an instrument variable for OOP expense to control for the potential confounding effect of socioeconomic status, which influences the amount of subsidy and in turn a patient's OOP expense.

Specification 5 contains all the variables listed in Equation 2, excluding CAM and transport variables for reasons previously stated, whereas Specification 6 is parsimonious. Based on the latter specification, the coefficient (on COST Score) indicates that feeling of financial well-being is positively and significantly correlated with a patient's quality of life, controlling for other factors.

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