Open Access

Expenditure and financial burden for the diagnosis and treatment of colorectal cancer in China: a hospital-based, multicenter, cross-sectional survey

  • Hui-Yao Huang1,
  • Ju-Fang Shi1Email author,
  • Lan-Wei Guo1, 2,
  • Ya-Na Bai3,
  • Xian-Zhen Liao4,
  • Guo-Xiang Liu5,
  • A-Yan Mao6,
  • Jian-Song Ren1,
  • Xiao-Jie Sun7,
  • Xin-Yu Zhu1, 3,
  • Le Wang1,
  • Bing-Bing Song8,
  • Ling-Bin Du9,
  • Lin Zhu10,
  • Ji-Yong Gong11,
  • Qi Zhou12,
  • Yu-Qin Liu13,
  • Rong Cao14,
  • Ling Mai15,
  • Li Lan16,
  • Xiao-Hua Sun17,
  • Ying Ren18,
  • Jin-Yi Zhou19,
  • Yuan-Zheng Wang20,
  • Xiao Qi21,
  • Pei-An Lou22,
  • Dian Shi1, 3,
  • Ni Li1,
  • Kai Zhang1,
  • Jie He1 and
  • Min Dai1Email author
Chinese Journal of Cancer201736:41

DOI: 10.1186/s40880-017-0209-4

Received: 6 July 2016

Accepted: 8 December 2016

Published: 28 April 2017

Abstract

Background

The increasing prevalence of colorectal cancer (CRC) in China and the paucity of information about relevant expenditure highlight the necessity of better understanding the financial burden and effect of CRC diagnosis and treatment. We performed a survey to quantify the direct medical and non-medical expenditure as well as the resulting financial burden of CRC patients in China.

Methods

We conducted a multicenter, cross-sectional survey in 37 tertiary hospitals in 13 provinces across China between 2012 and 2014. Each enrolled patient was interviewed using a structured questionnaire. All expenditure data were inflated to the 2014 Chinese Yuan (CNY; 1 CNY = 0.163 USD). We quantified the overall expenditure and financial burden and by subgroup (hospital type, age at diagnosis, sex, education, occupation, insurance type, household income, clinical stage, pathologic type, and therapeutic regimen). We then performed generalized linear modeling to determine the factors associated with overall expenditure.

Results

A total of 2356 patients with a mean age of 57.4 years were included, 57.1% of whom were men; 13.9% of patients had stage I cancer; and the average previous-year household income was 54,525 CNY. The overall average direct expenditure per patient was estimated to be 67,408 CNY, and the expenditures for stage I, II, III, and IV disease were 56,099 CNY, 59,952 CNY, 67,292 CNY, and 82,729 CNY, respectively. Non-medical expenditure accounted for 8.3% of the overall expenditure. The 1-year out-of-pocket expenditure of a newly diagnosed patient was 32,649 CNY, which accounted for 59.9% of their previous-year household income and caused 75.0% of families to suffer an unmanageable financial burden. Univariate analysis showed that financial burden and overall expenditure differed in almost all subgroups (P < 0.05), except for sex. Multivariate analysis showed that patients who were treated in specialized hospitals and those who were diagnosed with adenocarcinoma or diagnosed at a later stage were likely to spend more, whereas those with a lower household income and those who underwent surgery spent less (all P < 0.05).

Conclusions

For patients in China, direct expenditure for the diagnosis and treatment of CRC seemed catastrophic, and non-medical expenditure was non-ignorable. The financial burden varied among subgroups, especially among patients with different clinical stages of disease, which suggests that, in China, CRC screening might be cost-effective.

Keywords

Colorectal neoplasms Direct expenditure Financial burden China

Background

Worldwide, colorectal cancer (CRC) is the third most commonly diagnosed cancer in men and the fourth most common in women [1]. It was estimated that, in 2012, 159,100 new male cases and 142,200 new female cases occurred in China [2]. Significant advances have been made worldwide in improving CRC patient survival, which are bound to increase the financial burden at the aggregate level, especially in the context of high prevalence and rapid population growth [1, 3]. It has been shown that patients and their families suffer both financial burden and emotional hardship [46]. Considerable researches have been conducted on the financial burden of cancer in the United States and other countries [7, 8]. Studies in China have been scarce, and most such studies have focused merely on the medical expenditure, as documented from hospital information systems [9].

Discerning the true financial burden helps explain the general status of a population’s health under current healthcare system, thus enabling the development of optimal policies. Furthermore, a sound understanding of the financial burden is crucial for conducting cost-effective analyses; also, it helps assess the potential expenditures and benefits of related intervention programs [10]—for example, whether the screening strategies involved in the ongoing Cancer Screening Program in Urban China (CanSPUC) are cost-effective at the current scale or an expanded scale in the future [11]. This work is of great importance in the context of limited evidence on the economic evaluation of CRC screening in China [12].

Conducted as part of the health economic evaluation research of the CanSPUC, this study aimed to estimate both medical and non-medical expenditures of overall and subgroups of CRC patients, as well as to discern the subsequent financial burdens imposed on patient families.

Methods

Study design and study sites

This multicenter, hospital-based, cross-sectional study was conducted between September 2012 and December 2014 in 13 study sites (Shandong, Beijing, Jiangsu, Guangdong, Zhejiang, Hebei, Liaoning, Hunan, Heilongjiang, Henan, Xinjiang, Gansu, and Chongqing). The 13 sites joined in the first 2 years after the CanSPUC startup. Thirty-seven tertiary hospitals (23 general hospitals and 14 specialized hospitals) were involved. Table 1 shows further information about the involved cities and hospitals, including population size, gross domestic product (GDP) per capita, and numbers of cities and hospitals for each site [13]. The survey was approved by the Institutional Review Board of the Cancer Hospital of the Chinese Academy of Medical Sciences. All patients provided written informed consent.
Table 1

Summary information and overall expenditure for diagnosis and treatment of patients with colorectal cancer in 13 study sites in China

Province

General information

Specific information on cites and hospitals involved

Overall expenditure (CNY)

Population size in 2014a (×10,000)

GDP per capita in 2014a (CNY)

Number of cities

Total number of hospitals

Number of general hospitals

Number of specialized hospitals

Shandong

9789

60,879

1 (Jinan)

1

0

1

111,813

Beijing

2152

99,995

1 (Beijing)

3

1

2

94,502

Xinjiang

2298

40,648

1 (Urumchi)

2

0

2

88,887

Hunan

6737

40,271

1 (Changsha)

6

5

1

70,168

Guangdong

10,724

58,540

5 (Five citiesb)

2

1

1

69,238

Zhejiang

5508

73,002

2 (Hangzhou, Ningbo)

3

3

0

65,952

Heilongjiang

3833

39,226

2 (Harbin, Daqing)

1

1

0

60,245

Gansu

2591

26,433

2 (Lanzhou, Jinchang)

1

0

1

56,126

Henan

9436

37,072

1 (Zhengzhou)

6

5

1

55,829

Hebei

7384

39,984

1 (Tangshan)

1

0

1

49,332

Jiangsu

7960

81,874

2 (Nantong, Xuzhou)

1

0

1

46,181

Liaoning

4391

65,201

1 (Tieling)

9

7

2

37,103

Chongqing

2991

47,850

1 (Chongqing)

1

0

1

36,292

National total

136,782

46,652

21

37

23

14

67,408c

CNY Chinese Yuan, GDP gross domestic product

aBased on China Statistical Yearbook 2015. http://www.stats.gov.cn/tjsj/ndsj/2015/indexch.htm [13]

bIncluding Guangzhou, Shenzhen, Zhongshan, Dongguan, and Foshan

cThe average overall expenditure for colorectal cancer diagnosis and treatment based on data from the 13 study sites in China

Patient selection

Considering the budget from the government and previous experience, a total of 3120 CRC patients were expected for the 13 study sites. For each site, in accordance with a uniform design scheme, a stratified convenience sampling approach was used for selecting 240 clinically confirmed, primary prevalent CRC patients who were undergoing treatment in hospitals (including both newly diagnosed and existing cancer patients). To reach a sufficient power for subgroup analyses, sample sizes were balanced among cancer stages (20%–30% for each stage, from stage I to stage IV) and sex (maximum 60% for either sex). All respondents were interviewed face-to-face using a structured questionnaire at the time of discharge when most treatment expenses were incurred. Prior to the survey, we registered participation of all invited patients; also, we recorded basic information to facilitate exclusion, including age, sex, and cancer stage. For patients who were in very poor condition, family member(s) or other caregivers helped with the interview; all other interviewees were the patients themselves.

Questionnaire contents

The questionnaire included the following five parts: (A) demographic and societal information (e.g., hospital ID, name, sex, age, education, occupation, previous-year household income, and healthcare insurance type); (B) clinical information (e.g., clinical stage, pathologic diagnosis, confirmed date, and therapeutic regimen); (C) expenditure information of the to-date whole course of illness until the survey date by clinical visit—both outpatient and inpatient, occurring both within and outside the surveyed hospitals—i.e., the start date of treatment, hospitalization duration, overall medical expenditure, overall and detailed non-medical expenditure (including additional meals, additional nutrition, transportation, accommodation, hired informal nursing, and other expenditures), predicted reimbursement ratio, and self-reported financial pressure; (D) time loss of the to-date whole course to clinical visits (both outpatient and inpatient, occurring both within and outside the surveyed hospitals)—patient working days lost and accompanying person-days of informal caregivers (relatives and friends); and (E) quality control items (e.g., investigator-evaluated reliability (excellent, good, general, or poor) of the above four parts, and signature of investigator and auditor). If the former four parts were evaluated as excellent or good reliability, the record was deemed as high quality; otherwise, it was considered as low quality.

Estimation of expenditure and financial burden

We estimated the overall expenditure per patient for the whole course of illness, including both medical and non-medical expenditures. Medical expenditures were paid partly by the insurers; non-medical expenditures were paid entirely by the patients. We defined a newly diagnosed course as 2 months before diagnosis and 10 months after diagnosis, which is not exactly the same as the commonly used definition (1 year after diagnosis) because, in China, a large amount of money is usually spent for diagnosis before pathologic confirmation. We defined all patient-paid medical expenditure items and non-medical expenditure of a newly diagnosed course as out-of-pocket expenditure. Expenditure data presented estimates for the whole course of the illness if they were unspecified. Except when calculating the proportional breakdown of non-medical expenditures, all expenditure data were converted to the 2014 Chinese Yuan (CNY; 1 CNY = 0.163 USD) by the year-specific healthcare consumer price index of China [13].

To qualify financial pressure, we asked, “Which of the following accurately describes your family’s financial pressure from your disease?” and offered four response options: “not at all,” “somewhat but manageable,” “heavy,” and “overwhelmed.” We classified “not at all” and “somewhat but manageable” as manageable burdens; we classified the other two responses as unmanageable burdens. In addition, to objectively reflect the financial burden, we adapted the indicator of the expense-income ratio, which equals to the average out-of-pocket expense of a newly diagnosed course divided by the average previous-year household income. We used the threshold proposed by Xu et al. [14] that financial catastrophe occurs with the expense-income ratio at or exceeding 40%.

Statistical analysis

For quality control purposes, all investigators were trained and required to check each questionnaire before ending the survey; a second research staff member would then double-check each questionnaire within 2 days of completion. All data were double-entered into EpiData 3.1 software (EpiData Association, Odense, Denmark). In addition, extensive data checking was performed using SAS 9.2 statistical software (SAS Institute, Cary/NC, USA).

SAS 9.2 statistical software was also used for data analysis. For descriptive analysis, we used percentages for qualitative variables; due to the skewed nature of quantitative variables (such as expenditure estimates), several descriptive statistics was derived as needed, including means, standard deviations, medians, and ranges. We conducted a subgroup analysis of the overall expenditure, expense-income ratio, financial pressure, and time loss by using the following variables: hospital type, age at diagnosis, sex, education, occupation, healthcare insurance type, previous-year household income, clinical stage, pathologic type, and therapeutic regimen. For the overall expenditure after logarithm transition, expense-income ratio, and time loss, a two-sample Student’s t test was used for a two-group comparative analysis; the analysis of variance test was used for more than two groups; and the SNK-q test was used for multiple comparisons. The overall expenditure of each study site was also calculated, and its spearman correlation with site-specific GDP per capita in 2014 was explored. To determine financial pressure, the Chi square test was used. To determine the influencing factors of overall expenditure, we also performed generalized linear modeling with a gamma distribution. P values less than 0.05 were considered statistically significant.

Results

Descriptive characteristics

We invited 2710 CRC patients; however, 354 (15.5%) did not participate in the survey. The main reasons for non-participation were strong refusal by patients (74.0%), followed by communication difficulties (13.0%), and strong refusal by relatives (3.4%). A total of 2356 CRC patients were finally included, with a mean age at diagnosis of 57.4 years. Of these patients, 1660 (70.5%) came from specialized hospitals; 1345 (57.1%) were men; 253 (10.7%) had college education or above; and 845 (35.9%) were farmers (Table 2). The two principal insurance types were the urban employee basic medical insurance (916 of 2356, 38.9%) and the new rural cooperative medical scheme (897 of 2356, 38.1%). The mean previous-year household income was 54,525 CNY.
Table 2

Characteristics of 2356 patients with colorectal cancer

Characteristic

No. of patients (%)

Hospital type

 General

696 (29.5)

 Specialized

1660 (70.5)

Age at diagnosis (years)

 Mean ± SDa

57.4 ± 12.1

  <45

361 (15.3)

  45–54

542 (23.0)

  55–64

787 (33.4)

  ≥65

666 (28.3)

Sex

 Men

1345 (57.1)

 Women

1011 (42.9)

Education

 Primary school or below

727 (30.9)

 Junior high school

784 (33.3)

 Senior high school

592 (25.1)

 Undergraduate or over

253 (10.7)

Occupation

 Farmer

845 (35.9)

 Enterprise or company employee/worker

570 (24.2)

 Self-employee or unemployee

354 (15.0)

 Retiree

274 (11.6)

 Public sector employee

259 (11.0)

 Others

54 (2.3)

Previous-year household incomea,b (CNY)

 Mean ± SD

54,525 ± 45,822

 Median (P25–P75)

40,000 (20,000–70,000)

 <20,000

339 (14.9)

 20,000–39,999

632 (27.7)

 40,000–69,999

669 (29.4)

 ≥70,000

639 (28.0)

Income per patient in last 5 yearsa,c (CNY)

 Mean ± SD

30,355 ± 29,841

Number of family membersa,d [median (P25–P75)]

4 (2–5)

Healthcare insurance typee

 Urban employee basic medical insurance

916 (38.9)

 Urban resident basic medical insurance

446 (18.9)

 New rural cooperative medical scheme

897 (38.1)

 Commercial insurance

22 (0.9)

 Self-paid

49 (2.1)

 Others

24 (1.0)

Clinical stage

 I

328 (13.9)

 II

630 (26.7)

 III

815 (34.6)

 IV

559 (23.7)

 Not reported

24 (1.0)

Pathologic type

 Adenocarcinoma

2081 (88.3)

 Others

176 (7.5)

 Not reported

99 (4.2)

Therapeutic regimenf

 Surgery

886 (37.8)

 Chemotherapy

784 (33.4)

 Surgery and postoperative chemotherapy

333 (14.2)

 Symptomatic treatment

222 (9.5)

 Concurrent chemoradiotherapy

62 (2.6)

 Radiotherapy

42 (1.8)

 Neoadjuvant chemotherapy and surgery

16 (0.7)

Number of clinical visitsa [median (P5–P95)]

2 (1–5)

Number of admissionsa [median (P5–P95)]

1 (1–5)

Hospital staya,g (days)

 Mean ± SD

37 ± 38

 Median (P25–P75)

25 (17–42)

Quality of the questionnaire

 High quality

2230 (94.7)

 Low quality

126 (5.3)

SD standard deviation, CNY Chinese Yuan, P 25 –P 75 percentile 25 to percentile 75, P 5 –P 95 percentile 5 to percentile 95

aExcept for these values, other values are presented as number of patients followed by percentage in parentheses

bThe data of 77 patients were missing

cThe data of 44 patients were missing

dThe data of 24 patients were missing

eThe data of 2 patients were missing

fThe data of 11 patients were missing

gThe data of 4 patients were missing

The proportions of stage I, II, III, and IV disease were 13.9, 26.7, 34.6, and 23.7%, respectively. In terms of pathologic type, most was adenocarcinoma (88.3%). Approximately one-third (37.8%) of the patients underwent surgery alone; another third (33.4%) received chemotherapy alone. The median numbers of clinical visits and admissions were 2 (percentile 5 to percentile 95 [P5–P95]: 1–5) and 1 (P5–P95: 1–5), respectively. The median hospital duration was 25 days (percentile 25 to percentile 75 [P25–P75]: 17–42 days), and the median course was 36 days (P25–P75: 12–124 days). Of the whole patient cohort, 2230 (94.7%) provided responses that were considered high quality. More information about the questionnaires is shown in Table 2.

Overall expenditure

Overall mean expenditure per CRC patient was estimated to be 67,408 CNY, with 91.7% (61,829 CNY) used for medical expenditure. Overall expenditure showed a notable increase with the progression of disease (P < 0.001): for stages I, II, III, and IV disease, the expenditures were 56,099 CNY (95% confidence interval [CI] 51,918–60,281 CNY), 59,952 CNY (95% CI 56,971–62,932 CNY), 67,292 CNY (95% CI 63,673–70,910 CNY), and 82,729 CNY (95% CI 77,231–88,228 CNY), respectively. Multiple comparisons of overall expenditure showed that no significant difference was found between stage I and II, I and III, or II and III diseases (all P > 0.05); overall expenditure for stage IV disease was significantly higher than that for stages I–III diseases (P < 0.001). Multiple comparison of medical expenditure showed similar results; medical expenditure ranged from 51,366 CNY (95% CI 47,644–55,087 CNY) for stage I disease to 75,673 CNY (95% CI 70,551–80,794 CNY) for stage IV disease, with a 47.5% growth rate. Detailed information about medical expenditure for CRC diagnosis and treatment is shown in Fig. 1.
Fig. 1

Medical and non-medical expenditures for diagnosis and treatment of patients with stage I–IV colorectal cancer. CNY Chinese Yuan, CI confidence interval. Of the 2356 patients included, 24 have no information of clinical stage

We found that the overall expenditure varied among different study sites (range 36,292–111,813 CNY), with Chongqing the lowest and Shandong the highest (Table 1). Spearman correlation analysis showed that the expenditure was not associated with the local economy (r = 0.143, P = 0.626). Thus, the GDP per capita was not considered in the later univariate and multivariate analyses.

Univariate analysis showed significant differences in all subgroup comparisons, except for sex (P = 0.181). Patients in specialized hospitals (P < 0.001), those who were diagnosed with adenocarcinoma (P < 0.001) or were diagnosed at an earlier age (P = 0.034), or those who were well-educated (undergraduate or higher) (P < 0.001) were likely to spend more compared with their control groups, whereas self-employed or unemployed patients (P < 0.001), underinsured patients (self-paid) (P = 0.007), those with a lower household income (P < 0.001), or who were treated with surgery (P < 0.001) spent less. Multivariate analysis confirmed that patients in specialized hospitals (P < 0.001), patients who were diagnosed with adenocarcinoma (P < 0.001), or patients who were diagnosed at stage IV (P < 0.001) were likely to spend more, whereas patients with lower household income (P = 0.006) or who received surgery (P < 0.001) spent less compared with their control groups. Detailed expenditure comparison results are shown in Tables 3 and 4.
Table 3

Univariate analysis of overall expenditure for diagnosis and treatment of 2356 patients with colorectal cancer

Variable

Expenditure (CNY)

Statisticsa

P a

Medical

Non-medical

Overall

Total

61,829

5579

67,408

Hospital type

 General

52,392

4893

57,285

−4.46

<0.001

 Specialized

65,786

5866

71,652

  

Age at diagnosis (years)

 <45

65,887

6122

72,009

2.90

0.034

 45–54

66,303

6049

72,352

  

 55–64

59,416

5328

64,744

  

 ≥65

58,840

5198

64,038

  

Sex

 Men

62,526

5751

68,277

1.34

0.181

 Women

60,903

5350

66,253

  

Education

 Primary school or below

57,354

4883

62,237

8.36

<0.001

 Junior high school

58,898

5191

64,089

  

 Senior high school

65,095

5805

70,900

  

 Undergraduate or higher

76,132

8250

84,382

  

Occupation

 Farmer

57,796

4973

62,769

8.19

<0.001

 Enterprise or company employee/worker

63,563

5859

69,422

  

 Self-employee or unemployee

54,449

4640

59,089

  

 Retiree

70,629

7835

78,464

  

 Public sector employee

72,707

5628

78,335

  

 Others

58,197

6579

64,776

  

Healthcare insurance type

 Urban employee basic medical insurance

66,458

6289

72,747

3.17

0.007

 Urban resident basic medical insurance

60,118

4861

64,979

  

 New rural cooperative medical scheme

58,046

5062

63,108

  

 Commercial insurance

61,233

2958

64,191

  

 Self-paid

53,914

5855

59,769

  

 Others

78,293

13,023

91,316

  

Previous-year household income (CNY)

 <20,000

53,581

4570

58,151

5.75

<0.001

 20,000–39,999

61,461

5193

66,654

  

 40,000–69,999

62,972

5586

68,558

  

 ≥70,000

65,371

6188

71,559

  

Pathologic type

 Adenocarcinoma

62,759

5636

68,395

3.35

<0.001

 Others

53,279

4501

57,780

  

Therapeutic regimen

 Surgery

51,759

3812

55,571

14.04

<0.001

 Chemotherapy

68,749

6818

75,567

  

 Surgery and postoperative chemotherapy

63,068

5702

68,770

  

 Symptomatic treatment

55,970

5237

61,207

  

 Concurrent chemoradiotherapy

114,491

12,944

127,435

  

 Radiotherapy

88,857

7989

96,846

  

 Neoadjuvant chemotherapy and surgery

71,557

9682

81,239

  

CNY Chinese Yuan

aTwo-sample Student’s t test after logarithm transition was used for binary classification variables, including hospital type, sex, and pathologic type; analysis of variance test after logarithm transition was used for other multiple categorical variables, including age at diagnosis, education, occupation, insurance type, household income, and therapeutic regimen

Table 4

Multivariate analysis of overall expenditure for diagnosis and treatment of 2356 patients with colorectal cancer

Characteristic

Estimate (95% CI)

P

Intercept

10.4 (10.2, 10.5)

<0.001

Hospital type (Ref = general)

 Specialized

0.2 (0.1, 0.2)

<0.001

Age at diagnosis (years) (Ref = ≥65)

 <45

0.0 (−0.1, 0.1)

0.419

 45–54

0.1 (0.0, 0.2)

0.131

 55–64

0.0 (−0.1, 0.1)

0.522

Sex (Ref = women)

 Men

0.0 (−0.1, 0.1)

0.969

Education (Ref = primary school or below)

 Junior high school

0.0 (−0.1, 0.1)

0.815

 Senior high school

0.0 (0.0, 0.1)

0.310

 Undergraduate or higher

0.1 (0, 0.2)

0.134

Occupation (Ref = self-employee or unemployee)

 Farmer

0.0 (−0.1, 0.2)

0.493

 Enterprise or company employee/worker

−0.1 (−0.2, 0.0)

0.057

 Retiree

0.1 (−0.1, 0.2)

0.377

 Public sector employee

0.2 (0.1, 0.3)

0.004

 Other

0.0 (−0.3, 0.2)

0.778

Healthcare insurance type (Ref = new rural cooperative medical scheme)

  

 Urban employee basic medical insurance

0.0 (−0.1, 0.1)

0.641

 Urban resident basic medical insurance

0.0 (−0.1, 0.1)

0.707

 Commercial insurance

0.0 (−0.3, 0.4)

0.792

 Self-paid

0.0 (−0.2, 0.2)

0.905

 Other

0.4 (0.1, 0.7)

0.020

Previous-year household income (CNY) (Ref = ≤20,000)

 20,000–39,999

0.1 (0.0, 0.2)

0.006

 40,000–69,999

0.1 (0.0, 0.2)

0.046

 ≥70,000

0.1 (0.0, 0.2)

0.006

Clinical stage (Ref = I)

 II

0.0 (−0.1, 0.1)

0.822

 III

0.1 (0.0, 0.2)

0.099

 IV

0.3 (0.1, 0.4)

<0.001

Pathologic type (Ref = others)

 Adenocarcinoma

0.2 (0.1, 0.3)

<0.001

Therapeutic regimen (Ref = surgery)

 Chemotherapy

0.2 (0.1, 0.3)

<0.001

 Surgery and postoperative chemotherapy

0.2 (0.1, 0.3)

<0.001

 Symptomatic treatment

0.1 (0.0, 0.2)

0.170

 Concurrent chemoradiotherapy

0.8 (0.6, 1.0)

<0.001

 Radiotherapy

0.5 (0.3, 0.7)

<0.001

 Neoadjuvant chemotherapy and surgery

0.2 (−0.2, 0.6)

0.251

CNY Chinese Yuan, CI confidence interval

Non-medical expenditure

Non-medical expenditure accounted for 8.3% of the overall expenditure (5588 CNY per CRC patient). Additional meal contributed the largest proportion (1566 CNY, 28.0%), followed by transportation (1089 CNY, 19.5%) and additional nutrition (1075 CNY, 19.2%). Multiple comparisons of non-medical expenditure between CRC patients stage I-IV disease showed differences between all the two subgroups except stages II and III. Figures 1 and 2 show more detailed information about non-medical expenditure.
Fig. 2

Proportional breakdown of non-medical expenditures for diagnosis and treatment of colorectal cancer

Financial burden

As a whole, the overall expenditure of a newly diagnosed illness course was 58,778 CNY, accounting for 87.2% of that of the to-date whole course of illness. With the predicted reimbursement ratio equaling 46.5%, out-of-pocket expenditure amounted to 32,649 CNY, accounting for 59.9% of the previous-year household income. That made 75.0% of the families perceive an unmanageable burden (47.4% heavy, 27.6% overwhelmed); only 18.3% perceived a somewhat but manageable burden, and 6.7% perceived no burden at all. The influencing factors associated with the expense-income ratio and the proportion of families with an unmanageable burden coincide with each other quite well; moreover, these factors were also allied with the overall expenditure of CRC patients in China.

Compared with patients from general hospitals, patients from specialized hospitals tended to have a higher expense-income ratio (0.684 vs. 0.432, P < 0.001) and more families with unmanageable burden (76.4% vs. 71.4%, P < 0.001). Similarly, patients who were diagnosed at an earlier age (younger than 45 years) were likely to expend more than those diagnosed at older age (P = 0.019); among these patients, 79.6% experienced an unmanageable burden. The expense-income ratio for patients with poor education (i.e., primary school or less) was 0.769, making 84.0% of them experience an unmanageable burden, which was much higher than that of well-educated patients (P < 0.001). The gap was even apparent in terms of the household income: those with lower household income (i.e., less than 20,000 CNY) spent a larger share of the household income for CRC diagnosis and treatment, and more families felt stressed (P < 0.001). Those with income lower than 20,000 CNY spent more than three times their household income for CRC diagnosis and treatment, making 92.3% of these families unable to afford treatment. In terms of therapeutic regimen, the expense-income ratios of patients who received symptomatic treatment and those who received neoadjuvant chemotherapy and surgery were the lowest and highest (0.489 vs. 1.192, P < 0.001); the proportion of families who perceived an unmanageable burden was lowest for patients who received symptomatic treatment and highest for those who received radiotherapy (69.0% vs. 85.7%, P < 0.001). Although the expense-income ratio was similar among patients with CRC of various stages (P = 0.054), we still found that families of stage IV CRC patients suffered the highest pressure (27.8% heavy, 50.9% overwhelmed; P < 0.001). As expected, the expense-income ratio of farmers was the highest (0.977, P < 0.001): 90.1% of farmer families found treatment expenses unmanageable. However, patients who had new rural cooperative medical scheme insurance faced a similar dilemma and spent 85.8% of their household income, resulting in 88.6% of these families experiencing unmanageable financial burden. Neither the expense-income ratio nor financial pressure was statistically sensitive to sex (P = 0.053) or pathologic type (P = 0.083). More information about the financial burden of overall expenditure is shown in Table 5.
Table 5

Financial effect of overall expenditure on colorectal cancer patient’s family

Characteristic

Expenditure of a newly diagnosed disease coursea (CNY)

Self-reported predicted reimbursement ratiob (%)

Out-of-pocket expenditureb,c (CNY) (A)

Previous-year household income (CNY) (B)

Expense-income ratio (A/B)

Self-reported financial pressuree [n (%)]

Value

P d

Not at all

Somewhat but manageable

Heavy

Overwhelmed

P f

Total

58,778

46.5

32,649

54,525

0.599

158 (6.7)

429 (18.3)

647 (27.6)

1111 (47.4)

Hospital type

 General

48,258

50.2

26,742

61,866

0.432

<0.001

70 (10.1)

128 (18.5)

200 (28.9)

294 (42.5)

<0.001

 Specialized

63,189

44.9

35,154

51,403

0.684

 

88 (5.3)

301 (18.2)

447 (27.0)

4817 (9.4)

 

Age at diagnosis (years)

 <45

59,930

41.3

36,670

56,888

0.645

0.019

12 (3.4)

61 (17.0)

75 (20.9)

210 (58.7)

<0.001

 45–54

62,643

46.8

34,621

57,088

0.606

 

34 (6.3)

86 (16.0)

142 (26.3)

277 (51.4)

 

 55–64

58,340

45.8

32,173

51,441

0.625

 

55 (7.0)

133 (16.9)

228 (29.0)

369 (47.0)

 

 ≥65

55,527

49.8

29,425

54,792

0.537

 

57 (8.6)

149 (22.5)

202 (30.5)

255 (38.5)

 

Sex

 Men

59,496

47.8

32,180

55,654

0.578

0.293

85 (6.4)

256 (19.2)

390 (29.2)

605 (45.3)

0.053

 Women

57,823

44.7

33,273

52,999

0.628

 

73 (7.2)

173 (17.1)

257 (25.5)

506 (50.1)

 

Education

 Primary school or below

56,196

40.8

33,563

43,647

0.769

<0.001

28 (3.9)

88 (12.1)

206 (28.4)

404 (55.6)

<0.001

 Junior high school

55,499

44.7

33,151

51,301

0.646

 

52 (6.7)

130 (16.7)

207 (26.6)

389 (50.0)

 

 Senior high school

62,019

50.1

31,598

62,863

0.503

 

44 (7.5)

136 (23.1)

172 (29.3)

236 (40.1)

 

 Undergraduate or higher

68,776

59.8

30,943

75,844

0.408

 

34 (13.4)

75 (29.6)

62 (24.5)

82 (32.4)

 

Occupation

 Farmer

56,812

37.0

36,961

37,845

0.977

<0.001

11 (1.3)

72 (8.6)

214 (25.4)

544 (64.7)

<0.001

 Enterprise or company employee/worker

59,787

56.7

28,593

60,396

0.473

 

53 (9.3)

123 (21.7)

166 (29.3)

225 (39.7)

 

 Self-employed or unemployee

51,594

35.1

33,266

57,242

0.581

 

23 (6.6)

48 (13.7)

93 (26.5)

187 (53.3)

 

 Retiree

64,059

60.3

28,342

69,155

0.410

 

27 (9.9)

89 (32.5)

82 (29.9)

76 (27.7)

 

 Public sector employee

66,992

58.8

29,102

72,026

0.404

 

34 (13.2)

84 (32.6)

82 (31.8)

58 (22.5)

 

 Other

59,806

31.1

42,908

74,245

0.578

 

10 (18.5)

13 (24.1)

10 (18.5)

21 (38.9)

 

Healthcare insurance type

 Urban employee basic medical insurance

62,021

58.8

28,021

65,258

0.429

<0.001

108 (11.8)

222 (24.3)

275 (30.1)

308 (33.7)

<0.001

 Urban resident basic medical insurance

56,065

49.1

29,482

50,466

0.584

 

18 (4.0)

110 (24.7)

116 (26.1)

201 (45.2)

 

 New rural cooperative medical scheme

56,880

35.1

37,684

43,898

0.858

 

20 (2.2)

81 (9.1)

235 (26.3)

556 (62.3)

 

 Commercial insurance

59,424

25.2

42,769

60,273

0.710

 

4 (18.2)

4 (4.5)

1 (22.7)

5 (54.5)

 

 Self-paid

47,771

0.0

47,771

79,956

0.597

 

5 (10.2)

5 (18.4)

9 (18.4)

9 (53.1)

 

 Other

80,807

64.9

39,869

57,855

0.689

 

3 (12.5)

6 (25.0)

7 (29.2)

8 (33.3)

 

Previous-year household income (CNY)

 <20,000

51,323

37.6

33,207

10,258

3.237

<0.001

6 (1.8)

20 (6.0)

61 (18.2)

249 (74.1)

<0.001

 20,000–39,999

58,978

44.8

34,618

26,072

1.328

 

34 (5.4)

66 (10.5)

160 (25.4)

371 (58.8)

 

 40,000–69,999

58,885

49.4

30,979

49,932

0.620

 

41 (6.2)

155 (23.3)

198 (29.8)

271 (40.8)

 

 ≥70,000

62,086

50.0

31,769

110,959

0.286

 

68 (10.7)

179 (28.1)

207 (32.5)

182 (28.6)

 

Clinical stage

 I

50,425

50.0

26,950

48,454

0.556

0.054

49 (15.0)

64 (19.6)

79 (24.2)

135 (41.3)

<0.001

 II

56,097

45.9

31,305

53,801

0.582

 

38 (6.1)

133 (21.2)

176 (28.1)

280 (44.7)

 

 III

59,782

45.0

32,969

53,401

0.617

 

43 (5.3)

139 (17.1)

233 (28.7)

398 (49.0)

 

 IV

65,209

47.7

36,610

61,333

0.597

 

27 (4.9)

91 (16.4)

154 (27.8)

282 (50.9)

 

Pathologic type

 Adenocarcinoma

59,653

46.9

32,756

55,065

0.595

0.139

142 (6.9)

373 (18.0)

565 (27.3)

990 (47.8)

0.083

 Other

52,955

44.8

31,272

51,593

0.606

 

12 (6.8)

38 (21.6)

59 (33.5)

67 (38.1)

 

Type of therapy

 Surgery

53,083

45.2

29,521

48,047

0.614

<0.001

82 (9.3)

172 (19.5)

262 (29.7)

367 (41.6)

<0.001

 Chemotherapy

62,476

47.9

34,846

61,024

0.571

 

43 (5.5)

123 (15.8)

205 (26.3)

409 (52.4)

 

 Surgery and postoperative chemotherapy

62,217

48.9

33,251

54,218

0.613

 

8 (2.4)

63 (18.9)

95 (28.5)

167 (50.2)

 

 Symptomatic treatment

47,408

41.3

29,608

60,538

0.489

 

22 (10.0)

46 (21.0)

51 (23.3)

100 (45.7)

 

 Concurrent chemoradiotherapy

97,958

52.8

46,001

46,232

0.995

 

2 (3.2)

13 (21.0)

17 (27.4)

30 (48.4)

 

 Radiotherapy

79,945

42.0

46,824

56,359

0.831

 

0 (0.0)

6 (14.3)

10 (23.8)

26 (61.9)

 

 Neoadjuvant chemotherapy and surgery

77,269

48.1

43,154

36,200

1.192

 

0 (0.0)

5 (31.3)

4 (25.0)

7 (43.8)

 

CNY Chinese Yuan

aTwo months before and ten months after diagnosis

bThe data of 12 patients were missing

cOut-of-pocket expenditure = [∑(1 − self-reported predicted reimbursement ratio) × medical expenditure of a newly diagnosed course + non-medical expenditure of a newly diagnosed course]/n; n refers to the sample size

dTwo-sample Student’s t test after logarithm transition was used for binary classification variables, including hospital type, sex, and pathologic type; analysis of variance test after logarithm transition was used for other multiple categorical variables, including age at diagnosis, education, occupation, insurance type, household income, and therapeutic regimen

eThe data of 11 patients were missing for hospital type, age at diagnosis, sex, education, occupation and insurance type; the data of 21, 35, 88 and 110 patients were missing for type of therapy, clinical stage, household income and pathologic type, respectively

fWe classified “not at all” and “somewhat but manageable” as manageable burdens and the other two responses as unmanageable burdens. The Chi square test was used for subgroup comparisons

Time loss

Mean overall time loss amounted to 95.9 person-days—54.0 person-days (56.3%) for patients and 41.9 person-days (43.7%) for caregivers. If crudely converted by the 2014 minimum monthly wage of 1560 CNY in Beijing [15], mean wage loss amounted to 6652 CNY. Patients from specialized hospitals (P < 0.001) or those who were diagnosed with adenocarcinoma (P = 0.026) suffered relatively more time loss than patients from general hospitals or those who were diagnosed with other pathologic types; conversely, patients who were self-employed or unemployed (P < 0.001), covered by the Urban Resident Basic Medical Insurance or underinsured (P = 0.003), diagnosed with stage I–II disease (P < 0.001), or underwent surgery (P < 0.001) suffered less than their corresponding control groups. Nevertheless, when stratified by age at diagnosis (P = 0.516), sex (P = 0.191), education (P = 0.138), or household income (P = 0.219), no difference was observed. Detailed findings for time loss are shown in Table 6.
Table 6

Time loss due to colorectal cancer diagnosis and treatment

Characteristic

Time loss (person-days)a

Statisticsb

P b

Overall

Patients

Caregivers

Total

95.9

54.0

41.9

Hospital type

 General

40.0

38.7

78.6

−4.10

<0.001

 Specialized

59.9

43.3

103.2

  

Age at diagnosis (years)

 <45

103.5

61.7

41.7

0.76

0.516

 45–54

98.3

55.6

42.7

  

 55–64

95.4

53.6

41.8

  

 ≥65

90.7

49.0

41.6

  

Sex

 Men

98.8

55.7

43.1

1.31

0.191

 Women

92.2

51.8

40.3

  

Education

 Primary school or below

95.4

53.7

41.7

1.84

0.138

 Junior high school

94.9

54.3

40.6

  

 Senior high school

95.2

52.0

43.2

  

 Undergraduate or higher

102.6

58.7

43.9

  

Occupation

 Farmer

101.1

58.1

43.0

4.80

<0.001

 Enterprise or company employee/worker

98.8

53.7

45.1

  

 Self-employee or unemployee ununemployee

78.8

45.8

33.0

  

 Retiree

93.5

52.7

40.8

  

 Public sector employee

98.6

53.8

44.8

  

 Others

96.5

52.9

43.6

  

Healthcare insurance type

 Urban employee basic medical insurance

99.7

54.6

45.0

3.64

0.003

 Urban resident basic medical insurance

82.2

43.3

38.9

  

 New rural cooperative medical scheme

99.3

58.8

40.4

  

 Commercial insurance

87.0

60.4

26.5

  

 Self-paid

85.0

46.2

38.8

  

 Other

127.0

65.0

62.0

  

Previous-year household income (CNY)

 <20,000

98.1

56.1

42.0

1.48

0.219

 20,000–39,999

99.3

57.3

42.0

  

 40,000–69,999

96.7

54.5

42.2

  

 ≥70,000

92.1

50.3

41.8

  

Clinical stage

 I

76.1

40.1

36.0

16.05

<0.001

 II

77.4

42.8

34.6

  

 III

98.0

55.6

42.4

  

 IV

126.8

73.7

53.1

  

Pathologic type

 Adenocarcinoma

97.0

55.0

42.0

2.25

0.026

 Others

82.4

46.6

35.8

  

Therapeutic regimen

 Surgery

70.6

39.9

30.7

18.28

<0.001

 Chemotherapy

115.7

66.1

49.6

  

 Surgery and postoperative chemotherapy

98.2

55.8

42.5

  

 Symptomatic treatment

93.3

49.2

44.1

  

 Concurrent chemoradiotherapy

196.2

103.0

93.2

  

 Radiotherapy

188.0

105.1

82.9

  

 Neoadjuvant chemotherapy and surgery

91.8

53.7

38.1

  

CNY Chinese Yuan

aThe data of 89 patients were missing

bTwo-sample Student’s t test after logarithm transition was used for binary classification variables, including hospital type, sex, and pathologic type; analysis of variance test after logarithm transition was used for other multiple categorical variables, including age at diagnosis, education, occupation, insurance type, household income, and therapeutic regimen

Discussion

Our study provided much-needed data on direct medical and non-medical expenditures associated with prevalent CRC and the resulting financial burden. We found that direct expenditure was catastrophic and burdensome and varied greatly among different subgroups.

In our study, we found that the mean direct expenditure per CRC patient was 67,408 CNY. According to a recent review of the financial burden of CRC in China, only one study included both medical and non-medical expenditures per patient; others focused only on medical expenditure [9]. The earliest study was reported in 1999, and the most recent was in 2014 [9]. Except for one study (~50,000 CNY), all others showed expenses amounting to less than half of that in our study (61,829 CNY), mainly because of their relatively short course and our uncovering of expenses outside the surveyed hospitals [9]. According to the latest data published in Lancet, CRC was the most costly cancer among the six most common cancers in urban China [16]. Compared internationally, the absolute quantity of overall expenditure in China was much less than that in the United States and Canada [17, 18]. The ratio of the expenditure in annual GDP per capita was 1.4 and 0.6 in our survey, whereas that for both the United States and Canada was lower than 1.0 [19].

We found that several variables were significant for overall expenditure, including hospital type, occupation, household income, clinical stage, pathologic type, and therapeutic regimen. Notably, in line with a recent research [20] and review [21], we found that expenditure was higher for patients with late stage (stage III and IV) CRC than for those with early stage (stage I and II) CRC, which may be linked to longer hospitalization stays and more expensive treatments, such as targeted biological therapies. In contrast, patients with stage I disease mostly underwent surgery and spent much less. These findings suggest the potential cost-effectiveness of early detection and treatment. Although we attempted to balance stage-specific cases, only 13.9% of all cases were stage I, which reflects the lack of early diagnosis and treatment. However, under the resource and financial constraints in China, policymakers require more evidence of cost-effectiveness before expanding the scope of CRC screening.

Additionally, we found that non-medical expenditure for the diagnosis and treatment of CRC was a significant component, reaching 8.3% of the overall expenditure. It was higher than that found in the only previous relevant study in China (5.4%) [22]. Of the 55 overseas studies on financial burden, only nine investigated non-medical expenditure. The detailed non-medical expenditure were mainly comprising the wage losses of both caregivers and patients [23], which was different from those reported by Drummond et al. [24] and Cheng et al. [25], as well as those reported in our study.

Regarding time loss, CRC diagnosis and treatment caused an average wage loss of 1560 CNY, accounting for 3.3% of GDP per capita of China in 2014, which was substantially underestimated. The ratio of wage loss in annual GDP per capita for the only previous study in China was 12.2% [22], whereas those found in studies in the United States and in Canada were 25.6% [26] and 289.5% [27], respectively. This substantial gap could be partly explained by the low labor cost and special healthcare delivery model in China and potentially by methodological differences. Numeric differences exist among these studies, but it is more important to observe the consistent conclusions and directions. The time loss of both patients and caregivers was a considerable component of the financial burden, and more attention was recently paid to time cost and productivity cost [2830]. However, in China, these components have been rarely calculated previously [9].

Obviously, much work remains to determine the comprehensive expenditure, especially including direct non-medical expenditure, indirect expenditure, and intangible expenditure. Moreover, in China, no attention has been given to lifetime expenditure or specifics such as treatment phase (initial, continuing, or terminal). In studies of financial burden, opportunities and challenges coexist.

Concerning financial burden, patients spent 59.9% of their household income for one year of CRC diagnosis and treatment, and 75.0% of the families perceived an unmanageable financial burden. In contrast, in the United States, 25.0% of insured patients spent approximately one-third of their annual income on healthcare, and 39.9% spent approximately one-fifth of their annual income [30]. In Canada, the proportion of patients who perceived an unmanageable financial burden was only 3.9%; even including those who perceived a significant but manageable burden, it came to only 20.4% [31], which was much lower than that in our study. Thus, we can surmise that the plight of CRC patients and their families in China is worse than that of CRC patients in the United States and Canada. Heterogeneity in terms of data source or methodology occasionally makes it difficult to compare across studies; nevertheless, such a large gap does deserve the government’s attention. Developing corresponding policies to control out-of-pocket expenses can help patients and their families in China better cope with serious diseases such as cancer.

Several key influencing factors for the expense-income ratio and the proportion of families perceiving an unmanageable financial burden were found to be similar to those for overall expenditure, which perhaps was not coincidental when we combined evidence reported elsewhere [3234]. Notably, patients in the lowest household income group were in the worst financial situation, although the absolute quantity of expenditure was the least. Not surprisingly, the inability to pay prevents them from receiving sufficient healthcare [35]. Well-educated patients generally had higher incomes and spent more but were less stressed compared with their control groups. Because farmers generally had new rural cooperative medical scheme insurance and lower income, it seems self-evident that their direct expenditure was the lowest, and vice versa for public sector employees or retirees. These differences suggest that, in keeping with principles of justice and fairness, health service delivery reforms, such as to the medical insurance system, should consider providing more assistance to vulnerable populations.

This survey had several limitations. First, recall bias may have resulted from the retrospective nature of the questionnaire survey. Second, selection bias may have resulted from high-level hospitals and non-random sampling, although in China CRC patients are almost exclusively treated in tertiary hospitals. Third, the expenditure may have been underestimated because it covered only the to-date whole course. Another issue is risk factors for high expenditure; further multiple factor analysis is needed. Finally, although we could determine the potential effect of wage loss on perceived financial pressure, we considered only medical and non-medical expenditures.

Conclusions

Research on calculating the financial burden of CRC in China is still in its initial phase, and more is needed. We found that, for patients in China, expenditure for diagnosis and treatment of CRC seemed catastrophic, and non-medical expenditure cannot be ignored. Expenditure and financial burden varied among subgroups, especially for patients with disease of different clinical stages, suggesting that, in China, CRC screening might be cost-effective. Our findings also support the policy of medical insurance and payment system reform for affordable and equitable access to quality healthcare, which should be considered before further research on comprehensive expenditure is done.

Abbreviations

CRC: 

colorectal cancer

CanSPUC: 

Cancer Screening Program in Urban China

CNY: 

Chinese Yuan

Declarations

Authors’ contributions

HYH contributed to framework planning and draft writing, as well as data quality control, analysis, and interpretation. JFS led the health economic study design, overall analysis framework planning, and data interpretation. LWG, XYZ, LW, and DS supported data quality control, analysis, and interpretation. YNB, XZL, GXL, AYM, JSR, and XJS were the expert panel of the Health Economic Evaluation Working Group of the CanSPUC program, contributing to field data collection and participating in data analysis planning. BBS, LBD, LZ, JYG, QZ, YQL, RC, LM, LL, XHS, YR, JYZ, YZW, XQ, and PAL contributed to field data collection. MD, KZ, and JH contributed the overall design and were the co-managers of the Cancer Screening Program in Urban China. All authors read and approved the final manuscript.

Acknowledgements

We thank all the participants, without whom this survey would not have been possible. We also thank all the staff involved in the CanSPUC for their hard work and dedication, including experts from the National Cancer Center of China, field personnel in 13 provinces, and the external expert panel.

Competing interests

The authors declare that they have no competing interests.

Grant support

This study was supported by the grants from the Beijing Hope Run Special Fund (# LC2012YF44), National Natural Science Foundation of China (No. 81402740), Specialized Research Fund for the Doctoral Program of Higher Education (No. 20131106120014), and The National Health and Family Planning Committee of P. R. China.

Open AccessThis 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.

Authors’ Affiliations

(1)
Program Office for Cancer Screening in Urban China, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College
(2)
Department of Cancer Epidemiology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University
(3)
Institute of Epidemiology and Health Statistics, Lanzhou University
(4)
Hunan Office for Cancer Control and Research, Hunan Provincial Cancer Hospital
(5)
Department of Health Economics, School of Health Management, Harbin Medical University
(6)
Public Health Information Research Office, Institute of Medical Information, Chinese Academy of Medical Sciences
(7)
Center for Health Management and Policy, Key Lab of Health Economics and Policy, Shandong University
(8)
Heilongjiang Office for Cancer Control and Research, Affiliated Cancer Hospital of Harbin Medical University
(9)
Zhejiang Office for Cancer Control and Research, Zhejiang Cancer Hospital
(10)
Teaching and Research Department, Affiliated Cancer Hospital of Xinjiang Medical University
(11)
Science and Education Department of Public Health Division, Shandong Tumor Hospital
(12)
Chongqing Office for Cancer Control and Research, Chongqing Cancer Hospital
(13)
Cancer Epidemiology Research Center, Gansu Provincial Cancer Hospital
(14)
Department of Health Policy and Economic Research, Guangdong Provincial Institute of Public Health
(15)
Department of Institute of Tumor Research, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University
(16)
Institute of Chronic Disease Prevention and Control, Harbin Center for Disease Control and Prevention
(17)
Ningbo Clinical Cancer Prevention Guidance Center, Ningbo No. 2 Hospital
(18)
Urban Office of Cancer Early Detection and Treatment, Tieling Central Hospital
(19)
Institute of Chronic Non-communicable Diseases Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention
(20)
Department of Economic Operation, Kailuan General Hospital
(21)
Department of Occupational Medicine, Tangshan People’s Hospital
(22)
Department of Control and Prevention of Chronic Non-communicable Diseases, Xuzhou Center for Disease Control and Prevention

References

  1. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11. Lyon, France: International Agency for Research on Cancer, December 2013. http://globocan.iarc.fr. Accessed 2 Oct 2015.
  2. Chen W, Zheng R, Zeng H, Zhang S. The incidence and mortality of major cancers in China, 2012. Chin J Cancer. 2016;35(1):73. doi:10.1186/s40880-016-0137-8.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Sullivan R, Peppercorn J, Sikora K, Zalcberg J, Meropol NJ, Amir E, et al. Delivering affordable cancer care in high-income countries. Lancet Oncol. 2011;12(10):933–80. doi:10.1016/s1470-2045(11)70141-3.View ArticlePubMedGoogle Scholar
  4. Arndt V, Merx H, Stegmaier C, Ziegler H, Brenner H. Restrictions in quality of life in colorectal cancer patients over three years after diagnosis: a population based study. Eur J Cancer. 2006;42(12):1848–57. doi:10.1016/j.ejca.2006.01.059.View ArticlePubMedGoogle Scholar
  5. Cotrim H, Pereira G. Impact of colorectal cancer on patient and family: implications for care. Eur J Oncol Nurs. 2008;12(3):217–26. doi:10.1016/j.ejon.2007.11.005.View ArticlePubMedGoogle Scholar
  6. Maher J, McConnell H. New pathways of care for cancer survivors: adding the numbers. Br J Cancer. 2011;105(Suppl 1):S5–10. doi:10.1038/bjc.2011.417.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Altice CK, Banegas MP, Tucker-Seeley RD, Yabroff KR. Financial hardships experienced by cancer survivors: a systematic review. J Natl Cancer Inst. 2017;109(2):205. doi:10.1093/jnci/djw205.View ArticleGoogle Scholar
  8. Girgis A, Lambert S, Johnson C, Waller A, Currow D. Physical, psychosocial, relationship, and economic burden of caring for people with cancer: a review. J Oncol Pract. 2013;9(4):197–202. doi:10.1200/jop.2012.000690.View ArticlePubMedGoogle Scholar
  9. Shi JF, Shi CL, Yue XP, Huang HY, Wang L, Li J, et al. Economic burden of cancer in China during 1996–2014: a systematic review. Chinese Health Econ. 2016;38(12):929–41 (in Chinese).Google Scholar
  10. Yip W, Hsiao W. Harnessing the privatisation of China’s fragmented health-care delivery. Lancet. 2014;384(9945):805–18. doi:10.1016/s0140-6736(14)61120-x.View ArticlePubMedGoogle Scholar
  11. Dai M, Shi JF, Li N. Cancer Screening Program in Urban China: the program design and the expectancies. Zhonghua Yu Fang Yi Xue Za Zhi. 2013;47(2):179–82 (in Chinese).Google Scholar
  12. Huang HY, Shi JF, Dai M. Reasearch progress in health economic evaluation of colorectal cancer screening in China. Zhonghua Yu Fang Yi Xue Za Zhi. 2015;8:747–51. doi:10.3760/cma.j.issn.0253-9624.2015.08.017 (in Chinese).Google Scholar
  13. National Bureau of Statistics of China. China statistical yearbook, 2015. http://www.stats.gov.cn/tjsj/ndsj/2015/indexch.htm. Accessed 5 Jun 2016 (in Chinese).
  14. Xu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health Aff (Millwood). 2007;26(4):972–83. doi:10.1377/hlthaff.26.4.972.View ArticleGoogle Scholar
  15. Beijing Human Resources and Social Security Bureau. http://www.bjldgovcn/LDJAPP/search/searchdetailjsp?no=28109. Accessed 3 Jun 2015 (in Chinese).
  16. Huang HY, Shi JF, Guo LW, Zhu XY, Wang L, Liao XZ, et al. Expenditure and financial burden for common cancers in China: a hospital-based multicentre cross-sectional study. Lancet. 2016;388:S10.View ArticleGoogle Scholar
  17. Lang K, Lines LM, Lee DW, Korn JR, Earle CC, Menzin J. Lifetime and treatment-phase costs associated with colorectal cancer: evidence from SEER-Medicare data. Clin Gastroenterol Hepatol. 2009;7(2):198–204. doi:10.1016/j.cgh.2008.08.034.View ArticlePubMedGoogle Scholar
  18. Shankaran V, Jolly S, Blough D, Ramsey SD. Risk factors for financial hardship in patients receiving adjuvant chemotherapy for colon cancer: a population-based exploratory analysis. J Clin Oncol. 2012;30(14):1608–14. doi:10.1200/JCO.2011.37.9511.View ArticlePubMedGoogle Scholar
  19. The World Bank. GDP per capita based on purchasing power parity (PPP). http://data.worldbank.org/indicator. Accessed 10 Oct 2015.
  20. Hall PS, Hamilton P, Hulme CT, Meads DM, Jones H, Newsham A, et al. Costs of cancer care for use in economic evaluation: a UK analysis of patient-level routine health system data. Br J Cancer. 2015;112(5):948–56. doi:10.1038/bjc.2014.644.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Kriza C, Emmert M, Wahlster P, Niederlander C, Kolominsky-Rabas P. An international review of the main cost-effectiveness drivers of virtual colonography versus conventional colonoscopy for colorectal cancer screening: is the tide changing due to adherence? Eur J Radiol. 2013;82(11):e629–36. doi:10.1016/j.ejrad.2013.07.019.View ArticlePubMedGoogle Scholar
  22. Xu WY, Shi W, Chen P. Analysis of economic burden of malignant tumor in-patients in Xinjiang. Ji Bing Jian Ce. 2006;21(1):38–40 (in Chinese).Google Scholar
  23. Yabroff KR, Borowski L, Lipscomb J. Economic studies in colorectal cancer: challenges in measuring and comparing costs. J Natl Cancer Inst Monogr. 2013;2013(46):62–78. doi:10.1093/jncimonographs/lgt001.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005.Google Scholar
  25. Cheng XM, Wj Luo, Liu GX, Ma J, Meng QY, Wang XW, et al. Health Economics. Beijing: People’s Medical Publishing House; 2012 (in Chinese).Google Scholar
  26. Van Houtven CH, Ramsey SD, Hornbrook MC, Atienza AA, van Ryn M. Economic burden for informal caregivers of lung and colorectal cancer patients. Oncologist. 2010;15(8):883–93. doi:10.1634/theoncologist.2010-0005.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Hopkins RB, Goeree R, Longo CJ. Estimating the national wage loss from cancer in Canada. Curr Oncol. 2010;17(2):40–9.PubMedPubMed CentralGoogle Scholar
  28. Hanly P, Soerjomataram I, Sharp L. Measuring the societal burden of cancer: the cost of lost productivity due to premature cancer-related mortality in Europe. Int J Cancer. 2015;136(4):E136–45. doi:10.1002/ijc.29105.View ArticlePubMedGoogle Scholar
  29. Hollenbeak CS, Short PF, Moran J. The implications of cancer survivorship for spousal employment. J Cancer Surviv. 2011;5(3):226–34. doi:10.1007/s11764-011-0175-9.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Neuman P, Cubanski J, Desmond KA, Rice TH. How much ‘skin in the game’ do medicare beneficiaries have? The increasing financial burden of health care spending, 1997-2003. Health Aff (Millwood). 2007;26(6):1692–701. doi:10.1377/hlthaff.26.6.1692.View ArticleGoogle Scholar
  31. Longo CJ, Fitch M, Deber RB, Williams AP. Financial and family burden associated with cancer treatment in Ontario, Canada. Support Care Cancer. 2006;14(11):1077–85. doi:10.1007/s00520-006-0088-8.View ArticlePubMedGoogle Scholar
  32. Mathews M, West R, Buehler S. How important are out-of pocket costs to rural patients’ cancer care decisions? Can J Rural Med. 2009;14:54–60.PubMedGoogle Scholar
  33. Weaver KE, Rowland JH, Alfano CM, McNeel TS. Parental cancer and the family: a population-based estimate of the number of US cancer survivors residing with their minor children. Cancer. 2010;116(18):4395–401. doi:10.1002/cncr.25368.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Shi Q, Smith TG, Michonski JD, Stein KD, Kaw C, Cleeland CS. Symptom burden in cancer survivors 1 year after diagnosis: a report from the American Cancer Society’s Studies of Cancer Survivors. Cancer. 2011;117(12):2779–90. doi:10.1002/cncr.26146.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Puts MT, Tu HA, Tourangeau A, Howell D, Fitch M, Springall E, et al. Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Ann Oncol. 2014;25(3):564–77. doi:10.1093/annonc/mdt433.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement