Big Data-Enabled Analysis of Factors Affecting Medical Expenditure in the Cerebral Infarction of a Developing City in Western China

Figures & data

Table 1 Variable and Assignment Description

Variable Types	Variables	Assignment and Code of Influencing Factors
Dependent variable	Expense	Log of the original value
Independent variable	Gender	1 = male 2 = female
	Insurance Type	1 = basic medical insurance of urban employees 2 = basic medical insurance of urban resident
	Hospital Level	1 = community hospital 2 = secondary hospital 3 = tertiary hospital
	Charge Level	1 = Grade A tertiary hospital 2 = Grade B tertiary hospital 3 = Grade A secondary hospital 4 = Grade B secondary hospital 5 = Grade B secondary hospital up 10% 6 = Grade B secondary hospital down 10%
	Age	1 = Age ≤45 2 =Age between 45 and 65 3 = Age between 65 and 75 4 = Age between 75 and 85 5 =Age >85
	LOS	1 = ≤9d 2 = 9–12d 3 = ≥12d
	CCs	1 = severe complications and comorbidities, 2 = general complications and comorbidities, 3 = mild or no complications and comorbidities

Table 2 Basic Characteristics and Results of Single Factor Analysis of the Factors Influencing Inpatient Medical Expenditure (n = 109,314)

Variable		Frequency	Percentage	Expenditure (Mean)	Expenditure (M±IQR)	SD	Statistical Magnitude (Kruskal-Chi-P Squared)	p
Gender	1	54,115	49.50%	10,945.99	7988.51±4640	11,626.87	117.51	≤0.000
	2	55,199	50.50%	10,201.17	7551.12±4564.29	10,509.95
Insurance Type	1	68,498	62.66%	12,007.94	8824.28±5660.55	11,918.44	7110.3	≤0.000
	2	40,816	37.34%	8156.524	5947.93±3157.35	9016.71
Hospital Level	1	21,463	19.63%	3591.99	2944.66±1767.68	2790.01	43,360	≤0.000
	2	35,771	32.72%	8096.49	6978.04±4881.02	5967.52
	3	52,080	47.64%	15,144.43	11,453.43±7598.25	13,600.34
Charge Level	1	29,946	27.39%	18,372.53	14,028.89±972.61	15,473.92	50,296	≤0.000
	2	22,130	20.24%	10,077.52	8579.095±5958.610	8837.721
	3	28,595	26.16%	8584.808	7388.99±5288.19	6220.438
	4	5462	5.00%	5838.394	4780.75±3240.46	4510.595
	5	3111	2.85%	6038.798	5938.310±3886.160	3259.391
	6	20,070	18.36%	3517.074	2867.245±1721.82	2799.864
Age	1	2080	1.90%	12,390.72	8912.130±4899.665	13,420.83	222.56	≤0.000
	2	27,673	25.32%	10,194.13	7591.86±4391.21	10,593.93
	3	35,512	32.49%	10,208.15	7613.205±4768.56	10,208.15
	4	34,175	31.26%	10,928.76	7872.40±4768.56	10,928.76
	5	9874	9.03%	11,298.32	8151.585±4968.080	11,298.32
LOS	1	41,510	37.97%	5885.77	4913.15±2845.85	5674.106	33,536	≤0.000
	2	22,920	20.97%	8722.17	7601.795±5102.670	6457.386
	3	44,884	41.06%	15,845.42	12,011.18±7997.98	14,079.58
CCs	1	18,469	16.90%	15,737.03	10,655.10±6563.06	16,566.24	6334.3	≤0.000
	2	75,662	69.22%	9886.77	7629.54±4681.52	9224.57
	3	15,183	13.89%	7688.62	5372.33±2718.00	9133.86

Figure 1 The effect of gender on hospitalization expenditure.

Figure 2 The effect of insurance type on hospitalization expenditure.

Figure 3 The effect of hospital level on hospitalization expenditure.

Figure 4 The effect of charge level on hospitalization expenditure.

Figure 5 The effect of age on hospitalization expenditure.

Figure 6 The effect of LOS on hospitalization expenditure.

Figure 7 The effect of CCs on hospitalization expenditure.

Table 3 Multiple Linear Stepwise Regression Results of Factors Influencing Hospitalization Expenditure in CI Disease Patients

Variable		k	SE	z	p	Exp(k)	Confidence Interval
Coefficient (reference No.)		9.5112	0.2401	39.6161	<0.0000	13,509.7566	[8438.9410, 21,627.5389]
Gender (2)	1	−0.0306	0.0029	−10.5122	<0.0000	0.9699	[0.9643, 0.9754]
Insurance Type (2)	1	−0.1494	0.0031	−48.057	<0.0000	0.8612	[0.8560, 0.8665]
Hospital Level (2)	1	−0.0177	0.0168	−1.0529	0.2924	0.9825	[0.9507, 1.0153]
	3	−0.6075	0.2399	−2.532	0.0113	0.5447	[0.3404, 0.8718]
Charge Level (2)	1	−0.3804	0.0043	−88.3598	<0.000	0.6836	[0.6778, 0.6894]
	3	−1.173	0.2393	−4.9012	<0.000	0.3095	[0.1936, 0.4947]
	4	−1.4892	0.2394	−6.22	<0.000	0.2256	[0.1411, 0.3606]
	5	−1.7958	0.2396	−7.494	<0.000	0.166	[0.1038, 0.2655]
	6	−2.0422	0.2399	−8.5192	<0.000	0.1295	[0.0809, 0.2072]
Age	Age values	0.0012	0.0001	9.0059	<0.000	1.0012	[1.0009, 1.0014]
LOS	LOS values	0.0434	0.0002	42.0821	<0.000	1.0443	[1.0440, 1.0447]
CCs (2)	1	0.1685	0.004	42.0821	<0.000	1.1835	[1.1743, 1.1928]
	3	−0.1666	0.0043	−38.4811	<0.000	0.8465	[0.8394, 0.8537]

Note: All the categorical variables in the model take the second category as the reference level during operation.