Hospital cost analysis of Chinese patients with diabetes, 2015-2019

doi:10.21203/rs.3.rs-2254213/v1

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Hospital cost analysis of Chinese patients with diabetes, 2015-2019

https://doi.org/10.21203/rs.3.rs-2254213/v1

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Background

To examine the length of stay, hospitalization cost, and cost influencing factors among the Chinese inpatient with diabetes mellitus (DM).

Methods

This is a retrospective study that collected data from the Hospital Information System of a tertiary A general hospital in China, we analyzed 9186 cases of diabetes (type 1 diabetes and type 2 diabetes) during 2015–2019.

Results

The number of inpatients had increased but the per capita hospitalization costs had decreased in the study period. The mean (SD) length of hospital stay among the participants was (15.13 ± 5.66) days. The average per capita hospital cost of 9186 hospitalized patients with diabetes was ¥8689.98. The factors affecting the hospitalization costs of diabetic patients according to the degree of influence from large to small were length of stay, number of complications, age, severe illness, type of diabetes, surgical treatment, consistent admission and discharge, department, route of admission, and payment method.

Conclusions

The length of hospital stay was the main influencing factor of hospitalization costs of diabetic patients. The medical security system and other measures should be further improved to minimize the hospitalization costs of diabetic patients.

hospitalization cost

diabetes mellitus

influencing factors

decision tree model

health economics

Diabetes mellitus (DM) is a metabolic disease with clinical symptoms and higher than normal blood glucose levels, which is a common clinical endocrine disease^[1]. Diabetes mellitus has become a global epidemic, with the number of diabetes mellitus patients increasing annually. According to the International Diabetes Federation (IDF), the number of adults with diagnosed DM in the world was 463 million in 2019, the number that is expected to grow to 700 million by 2045^[2]. In 2019, about three-quarters of all individuals with diabetes lived in low-and-middle-income countries^[2]. China witnessed a marked increase in the prevalence rate of diabetes in recent decades, from 2.5% in 1994^[3] to 9.7% in 2010^[4] and further to 12.8% in 2020^[5].

Diabetes imposes a substantial burden on higher medical costs, lost productivity, and premature mortality^[6]. Wild SH reported that annual health care costs of a patient with type 2 diabetes are approximately two to three times higher than the costs of a patient of similar age and sex but without diabetes^[7]. According to the American Diabetes Association (ADA), the national cost of all diagnosed DM cases in the United States was approximately $245 billion in 2012, while the average medical cost for DM patients was $12,700 annually^[8]. The increase in diabetic patients has also contributed to the growth of health expenditure in China. The Chinese Diabetes Society (CDS) and the International Diabetes Federation (IDF) jointly released a study on the socio-economic impact of diabetes in China, showing that the annual cost of diabetes treatment in China is as high as 173.4 billion RMB (approximately 25 billion USD)^[9,10]. The medical expenditure caused by diabetes has accounted for 13% of the total expenditure in China^[9,10].

Cost-of-illness studies typically include direct and indirect costs of diabetes. Direct costs include all expenditures for the treatment of diabetes, including medication, hospital stays, and treatment of complications^[11]. Indirect costs refer to the productivity or production losses associated with morbidity and premature mortality^[12]. Previous studies have explored the economic burden of diabetes at the national level. The research that the direct and indirect cost of diabetes in Italy, published in 2016, developing a probabilistic prevalence cost of illness model^[13]. The authors concluded that the total economic burden of diabetic patients in Italy amounted to €20.3 billion/year, 54% of which are associated with indirect costs and 46% with direct costs only^[13]. In China, previous studies have found that the main factors influencing hospitalization costs for patients with type 2 diabetes include diabetic complications, duration of hospitalization, and patient age^[14]. Some of the studies used single-factor analysis, so their research outcomes could be influenced by some confounding factors^[15,16]. What is more, the time period of many studies was one year or relatively short, so they could not research the trend in the number of diabetic patients and their hospitalization costs^[17].

In this study, we analyzed the costs of hospitalized patients with diabetes in Heping Hospital, a tertiary A hospital in Changzhi, Shanxi Province from 2015 to 2019. To understand the cost composition, total cost trends and cost influencing factors, as well as to provide a reference for the government to allocate health resources reasonably, improve the medical security system and health economic evaluation.

2.1 Study design and data sources

We studied all hospitalized patients with type 1 or type 2 diabetes in a tertiary A general hospital in Shanxi province from January 1, 2015 to December 31, 2019. All patients with a diagnosis of type 1 or type 2 diabetes [International Classification of Diseases 10th Revision (ICD-10) codes: E10.0- E11.9] were selected^[18], and cases did not meet the requirements were excluded (including cases with unclear clinical diagnosis, no hospitalization, hospital stay of 1 day, and insufficient basic data information). The work has been approved by the Changzhi Medical College’s ethical committees in which it was performed.

2.2 Study variables

A retrospective survey was conducted to collect the medical records of hospitalized patients with type 1 or type 2 diabetes from 2015 to 2019 in this hospital. Patient data collected included demographics (gender, age, occupation, marital status), medical insurance, type of diabetes, complications, length of hospital stay, the total hospitalization costs, and so on. The total hospitalization costs were subdivided into clinical examination cost, drug cost, nursing cost, lab diagnosis cost, imaging diagnosis cost, treatment cost, operation cost, disposable medical materials cost, and other cost.

2.3 Statistical analysis

We used the SPSS (version 22.0) and SPSS Modeler (version 18.0) for statistical analysis and the significance level was set at α = 0.05. Firstly, descriptive statistical analysis was conducted to illustrate the demographic information and hospitalization information of the respondents. Then, the composition of hospitalization costs for patients with diabetes from 2015 to 2019 was statistically described to determine the trend of costs. Finally, the Mann-Whitney test (for 2 groups) and the Kruskal–Wallis test (for more than 2 groups) were used to account for non-normally distributed data. The factors affecting the hospitalization costs of diabetic patients were screened by univariate analysis, and the significant influencing factors were included in the multiple linear stepwise regression model. The Chi-squared Automatic Interaction Detector (CHAID) decision tree model was used to classify hospitalized patients with diabetes and predict the difference in hospitalization costs between different categories of patients.

3.1 General demographic characteristics

9186 patients included in this study, of which 5150 (56.06%) were male and 4036 (43.94%) were female. The age range of diabetic patients was 3ཞ94 years, the mean(SD) age was(54.86 ± 14.40) years, the median age was 56 years. The age groups 45–59 years and 60–74 years accounted for 39.67% and 33.37%, respectively. According to different occupations, there were 3803 farmers, accounting for 41.40% of all participants, and 2025 workers, accounting for 22.04%. There were 8489 married patients (92.41%), 8538 patients (92.95%) had type 2 diabetes, 5621 patients (61.19%) with diabetes had medical insurance. About 93.68% of patients had complications, and 88.82% of patients’ route of admission was outpatient.

3.2 Average hospital stays and hospitalization costs

The number of hospital days for diabetic patients ranged from 2 to 73 days, the mean (SD) length of hospital stay among the participants was (15.13 ± 5.66) days, the median average length of hospital stay was 15 days. As summarized in Table 1, it showed that median average day hospitalization cost was ¥609.14. To remove the effect of inflation (deflation), the per capita hospital cost was adjusted by the national Consumer Price Index of China, based on 2019. The results showed that before adjustment, the average per capita hospital cost of 9186 hospitalized patients with diabetes during the 5 years was ¥8689.98. The overall trend is decreasing, with the largest decrease of 4.53% in 2018. After adjustment, the average per capita hospital cost of 9186 patients with diabetes during the 5 years was ¥9438.66, which was higher than the per capita hospitalization cost before adjustment. Figure 1 shows the changed trend of adjusted hospital costs per capita of patients with diabetes from 2015 to 2019.

Table 1

Average hospital stays and hospitalization costs of patients with diabetes from 2015 to 2019
Year	Number	Average length of stay Median (days)	Hospital cost per day Median (¥)	Hospital cost per capita Median (¥)	Adjusted hospital cost per capita * Median (¥)	Annual growth rate (%)
2015	1799	15	615.78	9016.50	10595.60
2016	1552	16	593.81	8984.48	10171.45	-0.36
2017	1774	15	592.56	8663.47	9252.86	-3.57
2018	2048	14	589.53	8270.73	8469.23	-4.53
2019	2013	14	646.70	8704.16		5.24
Mean value	1837	15	609.14	8689.98	9438.66
Note: * Based on the national Consumer Price Index, based on 2019

3.3 Composition of hospitalization expenses

The composition of hospitalization expenses was shown in Table 2. From 2015 to 2019, the cost of hospitalization for patients with diabetes was mainly composed of three major parts: drug cost (24.21%), lab diagnosis cost (29.78%) and imaging diagnosis cost (14.55%), accounting for 68.54% of the total cost. Among them, laboratory fees account for the largest proportion of expenses, and the overall trend is increasing. The proportion of drug cost generally showed a downward trend, decreasing to the lowest in 2019, at 19.69%. The proportion of Imaging diagnosis cost (14.55%) increased year by year, with the largest increase in 2018, an increase of 4.36%, and it was relatively stable in other years. The proportion of other costs showed an overall downward trend, with the largest decline in 2017, a decrease of 7.51%. The proportion of disposable medical materials cost (8.63%), clinical examination cost (5.78%), nursing cost (1.34%) and operation cost (0.76%) was small, and the degree of change was not significant during the past 5 years. (Fig. 2)

Table 2

Composition of hospitalization costs of patients with diabetes in 2015–2019 (%)
Year	Clinical examination cost	Drug cost	Nursing cost	Lab diagnosis cost	Imaging diagnosis cost	Treatment cost	Operation cost	Disposable medical materials cost	Other cost
2015	4.68	24.02	0.99	25.62	9.40	16.23	0.51	6.83	11.72
2016	4.77	31.40	1.08	24.60	11.32	9.50	0.57	8.39	8.37
2017	6.59	26.16	1.47	31.86	14.33	8.12	0.85	9.76	0.86
2018	6.59	19.80	1.58	34.93	18.69	7.19	0.90	8.75	1.57
2019	6.22	19.69	1.52	31.88	19.00	9.72	0.99	9.42	1.56
Mean value	5.77	24.21	1.33	29.78	14.55	10.15	0.76	8.63	4.82

3.4 Factors associated with hospitalization costs

3.4.1 Univariate analysis

As shown in Table 3, after non-parametric test, except for gender and occupation (P > 0.05), age, marital status, payment method, type of diabetes, whether had complications, the number of complications, whether severe illness, length of hospital stays, whether surgery, whether consistent admission and discharge department and route of admission were the influencing factors of hospitalization costs in patients with diabetes (all P < 0.001).

Table 3

Univariate analysis of hospitalization costs in diabetic patients with different characteristics
Factors	Number	Hospital cost (¥) Median (IQR)	Statistics	P
Gender			-3.417	0.126
Male	5150(56.06)	8766.34(6783.28 ~ 11370.06)
Female	4036(43.94)	8555.33(6581.16 ~ 10981.37)
Age(years)			316.617	< 0.001
༜45	1852(20.16)	7677.87(5880.86 ~ 9804.94)
45–59	3644(39.67)	8574.57(6707.70 ~ 11091.20)
60–74	3065(33.37)	9231.07(7225.74 ~ 11879.19)
≥ 75	625(6.80)	9735.85(7364.93 ~ 13093.44)
Occupation			249.099	0.535
Administrative staff	1114(12.13)	8609.74(6728.78 ~ 11322.73)
Worker	2025(22.04)	8891.70(7001.55 ~ 11374.64)
Farmer	3803(41.40)	8413.10(6519.65 ~ 10806.73)
Student	209(2.28)	6220.97(3867.01 ~ 7542.13)
Teacher	38(0.41)	9001.88(7587.67 ~ 11275.93)
Other	1997(21.74)	9197.14(7114.03 ~ 11899.26)
Marital status			171.962	< 0.001
Married	8489(92.41)	8772.04(6791.38 ~ 11268.98)
Unmarried	425(4.63)	6798.60(4727.07 ~ 8995.98)
Widowed	204(2.22)	9531.43(6650.45 ~ 13460.28)
Divorce	68(0.74)	8936.74(6157.94 ~ 11886.69)
Payment method			124.866	< 0.001
Self-pay Medical Care	2848(31.00)	8291.20(6315.71 ~ 10754.55)
Free medical care	1(0.01)	12190.64(12190.64 ~ 12190.64)
Social Basic Medical Insurance	4071(44.32)	9010.12(7068.23 ~ 11666.06)
New rural cooperative medical system	361(3.93)	8036.83(6076.42 ~ 10288.50)
Commercial Medical insurance	1188(12.93)	8683.21(6768.23 ~ 11003.92)
Other	20(0.22)	9335.94(7414.05 ~ 14817.31)
Type of diabetes			-9.073	< 0.001
T1DM	648(7.05)	7476.78(5140.16 ~ 10365.59)
T2DM	8538(92.95)	8769.12(6788.31 ~ 11267.87)
Complications			-15.786	< 0.001
Yes	8605(93.68)	8822.63(6848.75 ~ 11311.91)
No	581(6.32)	6477.62(4598.76 ~ 8726.87)
Number of complications			537.884	< 0.001
0	581(6.32)	6477.62(4598.76 ~ 8726.87)
1	2624(28.57)	7740.59(5961.05 ~ 10090.39)
2	3558(38.73)	8818.65(7013.36 ~ 11131.62)
≥ 3	2423(26.38)	9979.20(7937.54 ~ 12749.46)
Severe illness			-8.047	< 0.001
Yes	1117(12.16)	9730.83(6923.68 ~ 13446.78)
No	8069(87.84)	8574.18(6669.87 ~ 10961.30)
Length of stay(days)			3434.237	< 0.001
༜15	4472(48.68)	6961.23(5556.76 ~ 8471.90)
15–29	4555(49.59)	10480.82(8733.61 ~ 12880.40)
≥ 30	159(1.73)	22261.03(17448.48 ~ 30514.72)
Surgical treatment			-5.198	< 0.001
Yes	422(4.59)	9550.53(6567.73 ~ 14939.83)
No	8764(95.41)	8670.35(6701.86 ~ 11120.08)
Consistent admission and discharge department			-15.904	< 0.001
Yes	8946(97.39)	8584.24(6648.46 ~ 11064.96)
No	240(2.61)	14063.79(10151.97 ~ 19696.30)
Route of admission			82.458	< 0.001
Emergency	885(9.63)	10042.55(7197.29 ~ 14058.86)
Outpatient	8159(88.82)	8568.68(6669.86 ~ 11006.28)
Other	142(1.55)	8895.42(6481.13 ~ 11265.54)

3.4.2 Stepwise multiple linear regression analysis

Because hospitalization costs were not normally distributed, they were used as the dependent variable after logarithmic transformation with a base of 10. The factors selected by univariate analysis to affect the hospitalization costs of diabetic patients were used as independent variables (age, marital status, payment method, type of diabetes, whether had complications, the number of complications, whether severe illness, length of hospital stays, whether surgery, whether consistent admission and discharge department, and route of admission). Among them, binary variables were assigned with 0 and 1; ordinal variables were assigned according to the order of grades; disordered variables were assigned in the form of dumb variables and entered into the stepwise multiple linear regression analysis. The results showed that F = 527.365, P < 0.001, R² = 0.446, and the regression equation was acceptable. The factors affecting the hospitalization costs of diabetic patients according to the degree of influence from large to small are length of stay, number of complications, age, severe illness, type of diabetes, surgical treatment, consistent admission and discharge, department, route of admission, and payment method (all P < 0.001) (Table 4).

Table 4

Stepwise multiple linear regression analysis of the influencing factors of hospitalization costs in diabetic patients
Independent variable	β	SE	β'	t	P	VIF
Constant	3.662	0.028		131.334	< 0.001
Length of stay	0.193	0.003	0.529	65.552	< 0.001	1.077
Number of complications	0.042	0.002	0.189	19.221	< 0.001	1.602
Age	0.025	0.002	0.110	13.109	< 0.001	1.171
Severe illness	0.055	0.005	0.092	10.292	< 0.001	1.308
Type of diabetes	0.069	0.007	0.090	10.288	< 0.001	1.279
Surgical treatment	0.083	0.007	0.089	11.126	< 0.001	1.063
Consistent admission and discharge department	0.104	0.010	0.085	10.547	< 0.001	1.073
Route of admission	0.041	0.005	0.068	7.746	< 0.001	1.283
Payment method	0.006	0.001	0.041	5.217	< 0.001	1.046
Hospital cost analysis of Chinese patients with diabetes, 2015–2019

3.4.3 Decision Tree Model for Factors Influencing Hospitalization Costs

Establishment of decision tree model by pre-pruning with CHAID. The decision tree model analysis produced a total of three layers of nodal variables. Six variables were included: length of hospital stay, number of complications, whether surgery was performed, type of diabetes, age, and whether the condition was critical (Fig. 4). The longer the length of hospital stay and the higher the number of complications, the higher the hospitalization costs. The highest cost of hospitalization was for patients with ≥ 30 days of hospitalization. Hospitalization days were < 15 days, and patients with uncomplicated and type 1 diabetes had the least cost of hospitalization (all P < 0.001). Hospitalization costs have relatively small variation within groups, which can justify the relatively reasonable grouping of this model. The predicted risk score of the decision tree was 0.063.

According to the bar chart of the importance of predictor variables (Fig. 3), the length of hospital stay had the greatest effect on the hospitalization costs of diabetic patients, with the predictive importance as high as 67.4%. The order of importance of other predictive factors were surgical treatment (11.6%), the number of complications (10.2%), age of patients (5.1%), whether the patients were critical (3.2%), and the types of diabetes (2.7%).

The huge economic burden of diabetes makes the disease an important public health problem^[19,20]. The economic burden of diabetes mellitus has been studied in many countries^[21–23]. At present, the unreasonable growth of medical costs has become one of the focuses of considerable attention to health services in China. Our main purpose in this study was to analyze the composition and influencing factors of hospitalization costs for diabetic patients in a tertiary A general hospital in Shanxi Province from 2015 to 2019.

The results showed that in terms of average length of hospital stay and per capita

hospitalization costs, the median length of hospital stay revealed a slight decrease from 16 days in 2016 to 14 days in 2019, and per capita costs showed a trend of decrease between 2015 and 2019 (decrease from ¥9016.50 to ¥8704.16). It shows that the state and society have strengthened the management of public health expenditure, and the structure of medical expenditure has changed. The decrease in hospitalization costs from 2015 to 2019 may be related to the increased control of medical insurance expenses after the state initiated the "New health care reform"^[24]. As well as may be related to the improvement of national health awareness in China, because of the "Healthy China 2030" policy, more diabetic patients control blood glucose through a healthy lifestyle and delay disease progression in order to reduce the dependence on hospitalization and reduce hospitalization costs. Among them, the cost of drugs decreased significantly in consecutive years after 2016, suggesting that a series of measures taken by "New health care reform" to control drug costs and standardize the diagnosis and treatment behaviors of public hospitals have played an important role. After 2018, the hospitalization expenses have regained the momentum of growth, but generally still showed a downward trend. Laboratory costs and imaging examination costs have increased rapidly in various expenses, suggesting that there are still many difficulties in controlling the excessive growth of medical expenses. The reason for the excessively rapid increase in costs may be the acceleration of the upgrading of medical technology and the more examinations and laboratory tests performed by the hospital during the diagnosis and treatment process^[25].

The hospitalization cost of diabetic patients was affected by many factors. The length of hospital stay is the most important factor affecting the hospitalization costs of diabetes. Many previous studies have agreed that reducing the number of hospital days can control the direct medical economic burden on patients^[26,27]. The expenditure of hospitalization costs varies according to the payment method. Patients participating in health insurance had higher hospitalization costs than self-paid patients. This is in line with what was found in earlier studies^[14]. This may be related to the fact that after people participate in health insurance, some medical costs were paid by third parties, and there was an increase in the hospitalization costs. The hospitalization costs of patients who underwent surgical treatment in this hospital were much higher than those of patients without surgical treatment, which was consistent with the study by Bommer C, et al.^[28]. It may be because patients who need surgery have a relatively long hospital stay, which leads to additional expenses such as accompanying fees and bed fees. Patients with comorbidities have higher hospitalization costs than those without comorbidities, which may be because those with comorbidities had more severe conditions, more diseases, and require longer hospitalization for observation, which resulted in higher total costs.

At present, it is necessary to strengthen the publicity and education of diabetes risk factors and disease-related knowledge, and take effective measures to carry out population screening and regular follow-up in order to achieve early detection, early diagnosis, and early treatment. Comprehensive measures should be taken for hospitalized patients with diabetes. By improving the quality of medical services and the standardized management level, scientifically and rationally use drugs and shorten the length of hospital stay. Further, improve the medical security system and other measures to minimize the hospitalization costs of diabetic patients.

There are some limitations in our study: Firstly, we did not research the data related to glucose, blood pressure, blood lipids, and so on, but these factors could have a significant influence on diabetic complications and hospitalization costs. Then, the data were obtained from a tertiary A hospital in one region, and may not be representative of DM patients from other socioeconomic different areas. Finally, the association with diabetes hospital costs and complications needs more in-depth research: for example, the kinds of complications and the relationships between different kinds of complications and costs. These problems should be solved in future research.

In the study, the hospitalization costs of inpatients with diabetes in China generally showed a decreasing trend during 2015–2019. The proportion of each hospitalization cost has changed. The proportion of drug costs decreased significantly before comparison, and the laboratory diagnosis costs and imaging diagnosis costs increased rapidly among various costs. The influencing factors of hospitalization costs of patients with diabetes included: length of stay, number of complications, age, consistent admission and discharge, severe illness, surgical treatment, type of diabetes, route of admission, and payment method. Although controlling the increase in medical expenses has had some effects, there are still many difficulties.

Ethics approval and consent to participate

This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Human Investigation Committee (IRB) of Changzhi Medical College approved this study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding statement

The authors disclosed the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Project of Philosophy and Social Science Planning of Shanxi Province (Grant numbers No. 2020YJ128) and the Innovation Project of Graduate Education of Shanxi Province (Grant numbers No. 2020SY446). However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ Contributions

Wenjun Cao and Jianzhong Zheng conceived and designed the research. Ziwei Fang drafted the manuscript. Ruimin Chang and Lijun Zhang revised the manuscript. Shaoxia Li, Yujiang Wang and Ziwei Fang curated and analyzed the data. Yuyu Zheng took part in the discussion. All authors read and approved the final manuscript.

Acknowledgements

The authors sincerely thanks for the Medical Record Information Department, Changzhi Medical College Affiliated Heping Hospital for supporting this study in the work of data collection, and also gratefully thank the all co-authors for their help.

Abd Ellah NH, Ahmed EA, Abd-Ellatief RB, Ali MF, Zahran AM, Hetta HF. Metoclopramide nanoparticles modulate immune response in a diabetic rat model: association with regulatory T cells and proinflammatory cytokines. Int J Nanomedicine. 2019; 14: 2383–2395. https://doi.org/10.2147/IJN.S196842.
International Diabetes Federation. IDF Diabetes Atlas, 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.
Pan XR, Yang WY, Li GW, Liu J. Prevalence of diabetes and its risk factors in China, 1994. National Diabetes Prevention and Control Cooperative Group. Diabetes Care. 1997; 20(11): 1664–1669. https://doi.org/10.2337/diacare.20.11.1664.
Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. N Engl J Med. 2010; 362(12): 1090–1101. https://doi.org/10.1056/NEJMoa0908292.
Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020; 369: m997. https://doi.org/10.1136/bmj.m997.
American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care. 2018; 41(5): 917–928. https://doi.org/10.2337/dci18-0007.
Wild SH, Hanley J, Lewis SC, McKnight JA, McCloughan LB, Padfield PL, et al. Supported Telemonitoring and Glycemic Control in People with Type 2 Diabetes: The Telescot Diabetes Pragmatic Multicenter Randomized Controlled Trial. PLoS Med. 2016; 13(7): e1002098. https://doi.org/10.1371/journal.pmed.1002098.
American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes care. 2013; 36(4), 1033–1046. https://doi.org/10.2337/dc12-2625.
Xiang P, Cao P. Analysis of Effect of Different Health Insurance Policies on the Economic Burdens of Inpatients with Diabetes. China Health Industry. 2016, 13 (28): 1–2.
Chen D, Liu S, Tan X, Zhao Q. Assessment of hospital length of stay and direct costs of type 2 diabetes in Hubei Province, China. BMC Health Serv Res. 2017; 17(1): 199. https://doi.org/10.1186/s12913-017-2140-4.
Bommer C, Heesemann E, Sagalova V, Manne-Goehler J, Atun R, Bärnighausen T, Vollmer S. The global economic burden of diabetes in adults aged 20–79 years: a cost-of-illness study. Lancet Diabetes Endocrinol. 2017; 5(6): 423–430. https://doi.org/10.1016/S2213-8587(17)30097-9.
Seuring T, Archangelidi O, Suhrcke M. The Economic Costs of Type 2 Diabetes: A Global Systematic Review. PharmacoEconomics. 2015; 33(8): 811–831. https://doi.org/10.1007/s40273-015-0268-9.
Marcellusi A, Viti R, Mecozzi A, Mennini FS. The direct and indirect cost of diabetes in Italy: a prevalence probabilistic approach. Eur J Health Econ. 2016; 17(2): 139–147. https://doi.org/10.1007/s10198-014-0660-y.
Cao P, Wang K, Zhang H, Zhao R, Li C. Factors influencing the hospitalization costs of patients with type 2 diabetes. Asia Pac J Public Health. 2015; 27(2 Suppl): 55S-60S. https://doi.org/10.1177/1010539515573831.
Bahia LR, da Rosa MQM, Araujo DV, Correia MG, Dos Rosa RDS, Duncan BB, Toscano CM. Economic burden of diabetes in Brazil in 2014. Diabetol Metab Syndr. 2019; 11: 54. https://doi.org/10.1186/s13098-019-0448-4.
Chandra P, Gogate B, Gogate P, Thite N, Mutha A, Walimbe A. Economic burden of diabetes in urban indians. Open Ophthalmol J. 2014; 8: 91–94. https://doi.org/10.2174/1874364101408010091.
Wang W, Yu M, Jin H, Liu Y, Fang Y, Huang Q, Wan F. Analysis of the Influencing Factors for the Hospitalization Expenses of 1391 Patients with Diabetes. Chinese Health Economics. 2017; 36(12): 88–90. https://doi.org/10.7664/CHE20171224.
Dugan J, Shubrook J. International Classification of Diseases, 10th Revision, Coding for Diabetes. Clin Diabetes. 2017; 35(4): 232–238. https://doi.org/10.2337/cd16-0052.
Li X, Xu Z, Ji L, Gou L, Liu J, Feng K, et al. Direct medical costs for patients with type 2 diabetes in 16 tertiary hospitals in urban China: A multicenter prospective cohort study. J Diabetes Investig. 2019; 10(2): 539–551. https://doi.org/10.1111/jdi.12905.
Gregg EW, Cheng YJ, Saydah S, Cowie C, Garfield S, Geiss L, Barker L. Trends in death rates among U.S. adults with and without diabetes between 1997 and 2006: findings from the National Health Interview Survey. Diabetes Care. 2012; 35(6): 1252–1257. https://doi.org/10.2337/dc11-1162.
Emery C, Torreton E, Dejager S, Levy-Bachelot L, Bineau S, Detournay B. Cost of Managing Type 2 Diabetes Before and After Initiating Dipeptidyl Peptidase 4 Inhibitor Treatment: A Longitudinal Study Using a French Public Health Insurance Database. Diabetes Ther. 2020; 11(2): 535–548. https://doi.org/10.1007/s13300-020-00760-x.
Afroz A, Alam K, Ali L, Karim A, Alramadan MJ, Habib SH, Magliano DJ, Billah B. Type 2 diabetes mellitus in Bangladesh: a prevalence based cost-of-illness study. BMC Health Serv Res. 2019; 19(1): 601. https://doi.org/10.1186/s12913-019-4440-3.
Deerochanawong C, Ferrario A. Diabetes management in Thailand: a literature review of the burden, costs, and outcomes. Global Health. 2013; 9: 11. https://doi.org/10.1186/1744-8603-9-11.
Sruamsiri R, Ferchichi S, Jamotte A, Toumi M, Kubo H, Mahlich J. Impact of patient characteristics and treatment procedures on hospitalization cost and length of stay in Japanese patients with influenza: A structural equation modelling approach. Influenza Other Respir Viruses. 2017; 11(6): 543–555. https://doi.org/10.1111/irv.12505.
Songer TJ, Haymond MW, Glazner JE, Klingensmith GJ, Laffel LM, Zhang P, Hirst K, TODAY Study Group. Healthcare and associated costs related to type 2 diabetes in youth and adolescence: the TODAY clinical trial experience. Pediatr Diabetes. 2019; 20(6): 702–711. https://doi.org/10.1111/pedi.12869.
Naser AY, Alwafi H, Alsairafi Z. Cost of hospitalisation and length of stay due to hypoglycaemia in patients with diabetes mellitus: a cross-sectional study. Pharm Pract (Granada). 2020; 18(2): 1847. https://doi.org/10.18549/PharmPract.2020.2.1847.
Chen J, Xu S, Gao J. The Mixed Effect of China's New Health Care Reform on Health Insurance Coverage and the Efficiency of Health Service Utilisation: A Longitudinal Approach. Int J Environ Res Public Health. 2020; 17(5): 1782. https://doi.org/10.3390/ijerph17051782.
Bommer C, Sagalova V, Heesemann E, Manne-Goehler J, Atun R, Bärnighausen T, et al. Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030. Diabetes Care. 2018; 41(5): 963–970. https://doi.org/10.2337/dc17-1962.

No competing interests reported.

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Hospital cost analysis of Chinese patients with diabetes, 2015-2019

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Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Methods

2.1 Study design and data sources

2.2 Study variables

2.3 Statistical analysis

3. Results

3.1 General demographic characteristics

3.2 Average hospital stays and hospitalization costs

3.3 Composition of hospitalization expenses

3.4 Factors associated with hospitalization costs

3.4.1 Univariate analysis

3.4.2 Stepwise multiple linear regression analysis

3.4.3 Decision Tree Model for Factors Influencing Hospitalization Costs

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

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