Open Access

Non-medical financial burden in tuberculosis care: a cross-sectional survey in rural China

  • Qiang Li1Email author,
  • Weixi Jiang2,
  • Quanli Wang1,
  • Yuan Shen1,
  • Jingyuan Gao1,
  • Kaori D. Sato3,
  • Qian Long2, 3 and
  • Henry Lucas4
Infectious Diseases of Poverty20165:5

https://doi.org/10.1186/s40249-016-0101-5

Received: 24 August 2015

Accepted: 26 December 2015

Published: 26 January 2016

Abstract

Background

Treatment of tuberculosis (TB) in China is partially covered by national programs and health insurance schemes, though TB patients often face considerable medical expenditures. For some, especially those from poorer households, non-medical costs, such as transport, accommodation, and nutritional supplementation may be a substantial additional burden. In this article we aim to evaluate these non-medical costs induced by seeking TB care using data from a large scale cross-sectional survey.

Methods

A total of 797 TB cases from three cities were randomly selected using a stratified cluster sampling design. Inpatient medical costs, outpatient medical costs, and direct non-medical costs related to TB treatment were collected using in-person interviews by trained interviewers. Mean and median non-medical costs for different sub-groups were calculated and compared using Kruskal-Wallis and Mann–Whitney U tests. Regression analysis was conducted to assess the influence of different patient characteristics on total non-medical cost.

Results

The median non-medical cost was RMB 1429, with interquartile range RMB 424–2793. The median non-medical costs relating to inpatient treatment, outpatient treatment, and additional nutrition supplementation were RMB 540, 91, and 900, respectively. Of the 797 cases, 20 % reported catastrophic expenditure on non-medical costs. Statistically significant differences were detected between different cities, age groups, geographical locations, inpatient/outpatient care, education levels and family income groups.

Conclusions

Non-medical costs relating to TB treatment are a serious financial burden for many TB patients. Financial assistance that can limit this burden is urgently needed during the treatment period, especially for the poor.

Keywords

Non-medical cost Financial burden Tuberculosis China

Multilingual abstracts

Please see Additional file 1 for translations of the abstract into the six official working languages of the United Nations.

Background

China has the second largest burden of tuberculosis (TB) in the world, accounting for 12 % of all cases [1]. Although China more than halved its TB prevalence rate from 1990 to 2010 [2], the rate remained high at the end of this period, at 459 per 100,000 for a population over 15 years old, implying significant social and economic burdens [3]. Prevalence rates are higher in poor, rural areas [4] and the poor have less access to TB care and are less likely to be cured [5]. Substandard living conditions, underlying health problems, malnourishment, a lack of money to pay for health care and inadequate access to health services all play major roles in impeding the successful treatment of TB [6].

The cost of inpatient treatment of TB in China is partly funded by health insurance schemes for those enrolled, and outpatient treatment is funded by China’s national TB control program [7]. Although out of pocket (OOP) payments may also be partially reimbursed by local programs in some areas, patients must meet the largest share of outpatient care expenditures [8]. A number of studies have found that the financial burden relating to treatment was the most cited reason for default [9], and that non-medical costs constituted a substantial portion of this burden [10]. Treatment typically lasts six months and patients make six trips to their outpatient clinics, potentially incurring travel and accommodation costs during treatment. While this is the standard number of visits, a full course of treatment may be delivered with a minimum of four visits where patients live a considerable distance from the facility. These non-medical costs typically include payments for transport, accommodation, and the cost of nutritional supplementation during the treatment period.

Previous international studies have examined the financial burden of non-medical costs and their impact on adherence to treatment. One systematic review of overall costs for TB patients has shown that non-medical cost accounted on average for 20 % of total expenditure [11]. Other studies have found that some TB patients may be discouraged from seeking care or adhering to treatment plans by non-medical costs [12, 13]. Transport and accommodation costs are most often considered, but one study of hospitalization for TB in Ghana, Vietnam, and the Dominican Republic indicates the substantial burden of additional food costs during treatment [14]. In China, while numerous studies have investigated the financial burden on TB patients [1518], some others have focused on non-medical costs or the factors that influence them [16, 19], such as residence location, gender, age, inpatient versus outpatient care, health insurance status, education level, family income, and patient category.

Here, a large-scale cross-sectional survey in three Chinese cities was used to assess the non-medical financial burden on TB patients relating to expenditures on transportation, accommodation and supplementary nutrition. We also analyzed the factors influencing these expenditures.

Methods

We designed and conducted a cross-sectional survey in TB patients. In China, the administrative demarcations move downward from country to provinces to prefectures/cities to districts/counties and to towns. The study was undertaken in Zhenjiang City, Jiangsu Province in eastern China; Yichang City, Hubei Province in central China; and Hanzhong City, Shaanxi Province in western China. Sample size calculations indicated that a minimum of 792 TB cases (264 in each city) were necessary as the assumed sample proportion of catastrophic expenditure on non-medical costs were 20 %, with 5 % as half the width of the confidence interval, and α = 0.05. In each city one county/district was randomly selected by a random number from each category of those with high, middle and low GDP per capita. Three townships/streets were then selected at random in each selected county/district and thirty TB cases were randomly selected from each township/street using a list of registered cases. Patients who completed normal treatment or stopped treatment during 2012 were included in the study. We excluded patients with communication barriers, such as those with hearing impairments. We also excluded patients with serious diseases and migrant workers who did not join the survey within the study period. Patients with mental illnesses were excluded as well. In total, 797 TB patients were recruited and informed consent of each participant was obtained.

Interviews were conducted by trained enumerators using a structured questionnaire to collect the medical and non-medical costs (transportation, accommodation, and nutritional supplementation) of TB treatment. Information regarding personal demographic and socio-economic status (age, sex, education, family income/expenditure, etc.; Table 1), reimbursements from health insurance, and financial assistance from government agencies was also collected. The field survey was conducted between April 2013 and May 2013.
Table 1

Basic characteristics of TB patients according to study site a

Characteristic

Hanzhong

Yichang

Zhenjiang

Total

Age in year, (Mean ± SDb, years)

55.8 ± 14.4

53.6 ± 15.0

59.3 ±14.7

56.2 ±14.9

Sex

Male

211 (78.1)

191 (72.3)

192 (73.0)

594 (74.5)

 

Female

59 (21.9)

73 (27.7)

71 (27.0)

203 (25.5)

Family size, (Mean ± SD, people)

3.1 ± 1.4

3.0 ± 1.4

3.4 ±1.7

3.2 ±1.5

Area

Rural

252 (93.3)

249 (94.3)

235 (89.4)

736 (92.3)

 

Urban

18 (6.7)

15 (5.7)

28 (10.6)

61 (7.7)

Patient category

New patient

218 (80.7)

227 (86.0)

193 (73.4)

638 (80.1)

 

Relapse patient

43 (15.9)

37 (14.0)

68 (25.9)

148 (18.6)

 

Failure of previously untreated patient

5 (1.9)

0 (0.0)

2 (0.8)

7 (0.9)

 

Failure of re-treated patient /chronic

4 (1.5)

0 (0.0)

0 (0.0)

4 (0.5)

Education

Never attended school

75 (27.8)

30 (11.4)

58 (22.1)

163 (20.5)

 

Primary school

85 (31.5)

91 (34.5)

86 (32.7)

262 (32.9)

 

Junior high school or at the same level

89 (33.0)

96 (36.4)

84 (31.9)

269 (33.8)

 

High school or at the same level

21 (7.8)

47 (17.8)

35 (13.3)

103 (12.9)

Health insurance

With health insurance

267 (98.9)

260 (98.5)

257 (97.7)

784 (98.4)

 

Without health insurance

3 (1.1)

4 (1.5)

6 (2.3)

13 (1.6)

Family income, (Median (P25–P75b), RMB 1000c)

15.0 (5.0–30.0)

20.0 (9.2–32.6)

36.0 (13.0–63.6)

21.4 (7.6–40.0)

Family expenditure, (Median (P25–P75b), RMB 1000c)

15.0 (7.5–23.5)

20.0 (9.0–30.0)

20.0 (10.0–35.0)

20.0 (9.8–30.0)

aData are n(%) unless otherwise indicated. TB: tuberculosis

bSD: standard deviation. P25: 25th percentile. P75: 75th percentile

cRMB 1000: a currency exchange rate of Chinese RMB 628 Yuan to US$100 Yuan at the end of 2012,RMB1000 = US$159

Only patients with a ‘confirmed’ TB diagnoses were included. Most had at least one sputum smear test and one chest x-ray. Indirect expenditures on transport and accommodation incurred by patients, their families and others related to seeking and accessing TB treatment during pre-diagnostic, diagnostic and post-diagnostic periods, as well as during hospitalization where applicable, were recalled by patients and their caregivers,. The cost for nutritional supplementation during TB treatment was estimated by extracting the cost of extra food expenditure (such as meat, milk, vitamins, etc.). We attempted to minimize the recall bias via in-depth interviews with the patient.

Ethical approval was sought and granted for this research by the Ethical Committee of China CDC. It was recognized that the right and the welfare of the subject were adequately protected; the potential risks were outweighed by the potential benefits. The ethical approval number was 201307.

Statistical analysis

We quantified non-medical costs by aggregating the transport, accommodation, and nutritional supplementation expenditures related to TB health care. Overall, 752 patients reported complete non-medical costs for transport, accommodation, and nutritional supplementation, while others missed some portion of the above. Cases with missing data were deleted when analyzing the corresponding costs. Mean and median non-medical expenditures were calculated and compared across subgroups using Mann–Whitney U and Kruskal-Wallis tests and a 5 % significance level. Linear regression was then used to model the relationships between non-medical costs and the explanatory variables available from the survey data. We also separated the transport plus accommodation costs and the additional nutrition cost for the multi-variate analysis. All the statistical analysis was done using the SAS version 9.3 statistical software package (SAS Institute Inc., Cary, North Carolina).

We considered the following patient variables to be potentially correlated with non-medical costs as they were major risk factors for the non-medical cost: residence location (the three study cities), gender, age (<65 years or > =65 years), residence type (urban or rural), inpatient care (with or without), health insurance (covered or uncovered), education level (never attended school, primary school, junior high school, high school or higher), family income (as a proportion of the median in each city), and patient category (new or relapse patient).

Results

Table 1 shows the basic demographic characteristics of the participants. In total, 797 TB patients were included in the study, with mean ages ranging from 53.6 to 59.3 across the three study sites. Some 74.5 % were male, and 80.1 % were new patients. Most came from rural areas, and their degree of education was limited. Overall one-fifth (almost 28 % in Hanzhong) had no formal education. Only 12.9 % of the participants had received a high school education or similar. The average family income was RMB 21,400 (equaling to US$3408, a currency exchange rate of Chinese RMB 628 Yuan to US$100 at the end of 2012), ranging from RMB 15,000 (US$2389) in Hanzhong to RMB 36,000 (US$5732) in Zhenjiang. It was notable that the average family expenditure (RMB 20,000) was almost equal to the average income, indicating that many families would have very limited ability to save. As expected, given the government promotion of the urban and rural health insurance schemes, almost all patients were covered by one of these schemes. The largest percentage (41.9 %) of participants made six trips to their outpatient clinic during treatment, potentially incurring travel and accommodation costs.

As shown in Table 2, non-medical financial costs related to TB treatment varied considerably across study sites. The overall mean and median expenditure was considerably lower in Yichang than in Hanzhong and Zhenjiang. However, this was a simply reflection of the much more limited expenditure on supplementary nutrition – around half that in the other cities. Both travel and accommodation costs were substantially higher in Yichang, which was probably a reflection of the increased distance that patients had to travel to a designated TB facility.
Table 2

Non-medical financial cost related to TB treatment by study site (RMBa)

 

n

Hanzhong

Yichang

Zhenjiang

Total

Mean

Median

P25-P75b

Mean

Median

P25-P75

Mean

Median

P25-P75

Mean

Median

P25-P75

Total

752c

2308

1785

853-3185

1453

968

318-1880

2061

1800

381-3000

1943

1429

424-2793

Inpatient

371

            

 Travel fee

 

130

30

10-100

257

50

20-270

124

15

0-100

158

30

6-120

 Accommodation fee

 

822

530

150-1060

1018

760

300-1350

547

315

108-630

766

500

150-1000

 Subtotal

 

958

600

210-1240

1285

815

350-1710

673

400

200-823

928

540

208-1200

Outpatient

753

            

 Travel fee

 

98

60

24-120

150

120

40-204

77

28

0-96

109

60

12-140

 Accommodation fee

 

124

30

0-100

56

20

0-75

20

0

0-0

68

0

0-60

 Subtotal

 

223

91

30-240

206

144

60-270

97

36

0-120

176

91

20-210

Additional nutrition fee

770

1586

1200

300-2400

828

435

0-1200

1596

1200

0-2400

1337

900

0-1800

aRMB 1000: a currency exchange rate of Chinese RMB 628 Yuan to US$100 Yuan at the end of 2012, RMB1000 = US$159

bP25: 25th percentile. P75: 75th percentile

cThe total non-medical cost is the sum of the inpatient cost, outpatient cost and the additional nutrition fee. The total number of patients is less than that in the subgroup because of missing data

Table 3 reveals the non-medical economic burden of TB care, as measured by the numbers/proportions of patients with catastrophic health care expenditure on care. Overall, some 20 % of all respondents reported that their non-medical costs exceeded 40 % of their non-food expenditure, while 37 % reported that these costs exceeded 10 % of their annual household income. The non-medical burden was highest in Hanzhong using both measures.
Table 3

Description of the non-medical burden of TB health care, n (%)

Non-medical cost in TB health care

Hanzhong

Yichang

Zhenjiang

Total

P_value a

≥ 40 % of the annual capacity to pay

65 (26.9)

29 (11.9)

51 (21.2)

145 (20.0)

<0.011

≥ 10 % of the annual household income

132 (52.4)

81 (32.7)

62 (25.3)

275 (36.9)

<0.011

Groups of non-medical cost (RMB)

     

  ≤ 200

35 (13.8)

45 (18.1)

53 (21.3)

133 (17.7)

<0.011

 201 ~ 1000

45 (17.7)

82 (32.9)

31 (12.4)

158 (21.0)

 

 1001 ~ 2000

64 (25.2)

64 (25.7)

60 (24.1)

188 (25.0)

 

 2001 ~ 4000

66 (26.0)

38 (15.3)

70 (28.1)

174 (23.1)

 

 4001 ~ 6000

29 (11.4)

14 (5.6)

26 (10.4)

69 (9.2)

 

  ≥ 6001

15 (5.9)

6 (2.4)

9 (3.6)

30 (4.0)

 

a Chi-square test was used to compare the difference of percentage between study sites

Table 4 shows the influence of different patient characteristics on total non-medical costs. In addition to the variation between the study cities, the relationships between non-medical costs and age group, residence type (urban/rural), receiving inpatient care, education and family income were statistically significant.
Table 4

Comparison of non-medical cost of TB care between different characteristics, RMB

Characteristic

Mean

Median

P_value a

City

  

<0.001

 Hanzhong

2308

1785

 

 Yichang

1453

968

 

 Zhenjiang

2060

1800

 

Sex

  

0.445

 Men

1940

1410

 

 Women

1950

1470

 

Age

  

<0.001

  < 65 years

2163

1605

 

  > =65 years

1490

1050

 

Residence type

  

<0.001

 Urban

3128

2600

 

 Rural

1842

1320

 

Inpatient care

  

<0.001

 With

2567

2115

 

 Without

1370

900

 

Health insurance

  

0.889

 Covered

1794

1495

 

 Uncovered

1945

1429

 

Education

  

<0.001

 Never attended school

1389

1040

 

 Primary school

1864

1268

 

 Junior high school

2146

1658

 

 High school or higher

2479

1837

 

Family income

  

<0.001

 Lower half

1560

1066

 

 Higher half

2298

1700

 

Category

  

0.540

 New patient

1884

1406

 

 Relapse patient

2182

1600

 

a P_value was calculated by univariate analysis

The results of the regression analysis are reported in Table 5. In this analysis we combined transportation and accommodation costs as both are related to geographic factors such as distance from home to facility and availability of transportation options. The table indicates that after controlling for other variables, living in Yichang was still associated with higher transportation and accommodation costs and lower additional nutrition costs. For transportation and accommodation costs specifically, only care type and age were likely to have a significant influence in addition to city of residence. However, additional nutrition costs were also positively correlated with higher education level, family income and urban residence.
Table 5

Regression analysis of total non-medical cost and patient characteristics a

Variable

 

Total non-medical cost

Transportation plus accommodation cost

Additional nutrition cost

 

Parameter

SE

P value

Parameter

SE

P value

Parameter

SE

P value

Location

          

 Hanzhong

 

8.00

1.86

<.0001

−1.98

1.19

0.0970

13.31

2.02

<.0001

 Yichang

Ref

         

 Zhenjiang

 

4.45

1.86

0.0167

−6.42

1.19

<.0001

10.87

2.02

<.0001

Sex

Ref: male

2.66

1.73

0.1241

0.40

1.11

0.7203

2.31

1.89

0.2209

age

Ref: aged <65

−6.12

1.70

0.0003

−2.38

1.08

0.0285

−5.24

1.84

0.0046

Residence type

Ref: rural

10.18

2.82

0.0003

−0.62

1.83

0.7356

11.35

3.05

0.0002

Inpatient care

Ref: without

14.86

1.50

<.0001

18.53

0.96

<.0001

4.00

1.62

0.0140

Health insurance

Ref: covered

7.12

5.91

0.2290

6.88

3.84

0.0737

1.86

6.22

0.7657

Education

          

 Never attended school

Ref

         

 Primary school

 

6.22

2.15

0.0040

0.31

1.37

0.8197

6.47

2.33

0.0057

 Junior high school

 

7.32

2.24

0.0011

1.36

1.43

0.3432

6.49

2.44

0.0079

 High school or higher

 

9.19

2.90

0.0016

0.27

1.84

0.8815

9.25

3.15

0.0034

Family income

Ref: lower half

4.12

1.57

0.0090

0.45

1.00

0.6517

6.16

1.70

0.0003

Category

Ref: new patients

1.61

1.87

0.3891

−1.54

1.19

0.1983

2.69

2.02

0.1838

a Following variables were enrolled in the regression medel: residence location (the three study cities), gender, age (<65 years or > =65 years), residence type (urban or rural), inpatient care (with or without), health insurance (covered or uncovered), education level (never attended school, primary school, Junior high school, high school or higher), family income (as a proportion of the median in each city), and patient category (new or relapse patient)

Discussion

China’s national TB control program requires patients to visit the TB outpatient clinic every month for six months or at least four times during the first, second, fifth and last month of the treatment regimen [20]. A majority of participants in this study adhered to this requirement, although many of them suffered from heavy financial burden caused by TB treatment. In many cases, especially where patients lived in more remote areas – which were typically associated with lower household incomes – this entailed considerable expenditure on travel and accommodation, leading to non-medical expenditures that may be comparable to out-of-pocket payments for hospitalization.

Non-medical costs of TB treatment placed a considerable financial burden on patients. Some 25 % of participants spent RMB 1001 ~ 2000 on non-medical costs while over 23 % spent RMB 2001 ~ 4000. These costs were considerable when compared to household expenditures, which ranged from RMB 15,000 to 20,000 across the three cities, and to incomes, which ranged from RMB 15,000 to 36,000.

Inpatient care was positively associated with both types of costs (P < 0.05), which can be explained by the fact that receiving inpatient care increased the accommodation costs for patients and their companions, and the likelihood of purchasing additional food during hospitalization. The negative impacts of higher age on both cost components may be ascribed to older people’s limited ability to travel to health facilities (especially for patients in remote areas) and to their lower willingness and capacity to pay for additional food. This is consistent with results from previous studies which revealed that older people do not want use their children’s money for treatment as they believe the money could be used for more meaningful purposes, for example, the education of their grandchildren [16, 21].

The impact of other patient characteristics on non-medical costs were mixed for the two types of costs. Geographic constraints played a major role in the influence of location on costs, as Yichang and Hanzhong have vast mountainous areas, which increases travel costs and adds to the difficulty of purchasing additional food. Indicators of socio-economic status – residence type, education and family income – were all positively associated with additional nutrition costs but had no significant influence on travel and accommodation costs. This would suggest that patients with higher levels of education, a greater ability to pay and wider availability of nutritional supplements (in urban areas) were more willing and able to consume more nutritious foods to help with recovery.

The results of the regression analyses revealed the different nature of the two types of non-medical costs, and suggested how to reduce patient costs in following the prescribed treatment regimen. Future policies might best serve to focus on reducing travel and accommodation costs, which have the greatest impact on poorer households in remote areas. One study has confirmed the role of transportation subsidies in reducing the financial burden of TB patients but suggests that the amount provided needs to be more substantial [19, 22]. Our study indicates that geographic factors exert such a major influence on non-medical costs that the amount of travel subsidy should be determined by location of residence. Additional subsidies may also be appropriate to encourage adherence to treatment by older patients. For the effect of health insurance, our results were similar to a previous study which indicated higher non-medical costs for those not covered by any health insurance [16].

This study is not without its limitations. Firstly, investigators were trained to extract information as reliably as possible. However, the recall bias of annual income and non-direct medical costs can hardly be avoided due to the long treatment duration. Patients’ estimation of transportation, accommodation and additional food costs may not be accurate. Secondly, some patients received therapy mainly in 2011 and so major non-medical costs were incurred in 2011; however, these patients reported their 2012 income which may not have reflected their economic statuses during treatment when incomes may have been reduced. In addition, only three cities form eastern China, central China and western China were selected by location instead of by TB burden level. Cities with the highest TB burden, such as Tibet, Xinjiang and Guizhou, were not involved. Thus, the present results were limited when considering TB burden levels. Future studies on the economic burden of non-medical costs could gather more accurate information through timely monitoring during treatment in broader areas.

Conclusion

Non-medical costs related to TB treatment imposed a considerable financial burden for TB patients, often accounting for a considerable proportion of their annual income. Geographic factors played such an important role in transportation and accommodation costs that transportation subsidies should be provided based on the patients’ places of residence.

Abbreviations

GDP: 

Gross Domestic Product

OOP: 

Out-of-Pocket payment

TB: 

Tuberculosis

Declarations

Acknowledgments

The study upon which this paper was written is part of the large program entitled “China National Health and Family Planning Commission and the Gates Foundation TB Project” - a collaboration between the Government of China and the Bill and Melinda Gates Foundation (Grant No. 51914), and implemented by the China Center of Disease Control and Prevention (CDC). The Duke Global Health Institute, USA/Duke Kunshan University in China, and Nanjing Medical University, Huazhong University of Science and Technology and Xi’an Jiaotong University in China were contracted by the Foundation and China CDC to undertake the baseline survey, and monitoring and evaluation of innovative financial models of TB/MDRTB control and care in China. The authors are also grateful for the supports and valuable comments provided by Prof. Shenglan Tang, Duke Global Health Institute.

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)
Department of Biostatistics and Epidemiology, School of Public Health, Xi’an Jiaotong University
(2)
Global Health Research Center, Duke Kunshan University
(3)
Duke Global Health Institute, Duke University
(4)
Institute of Development Studies at the University of Sussex

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© Li et al. 2016