Inequity in healthcare needs, health service use and financial burden of medical expenditures in China: results from a consecutive household monitoring study in Jiangsu Province

Table 5 regression analysis of factors associated out-of-pocket health expenditure and CHE

	OOP health expenditure				Catastrophic health expenditure
	part1-logit		part2-GLM		logit
	OR	95% CI	Coef.	95% CI	OR.	95% CI
Age
< 30	ref.				ref.
30–59	1.46	0.73, 2.92	57.7	− 1810.2, 1925.7	1.01	0.40, 2.59
> =60	1.07	0.51, 2.21	1898.1	− 722.9, 4519.0	1.13	0.44, 2.94
Male	0.8	0.58, 1.11	− 2207.4	− 4337.5, −77.3	0.58	0.38, 0.89
Rural residence	6.6	3.95, 11.03	1094.2	− 802.5, 2991.0	2.92	1.61, 5.30
Married	1.16	0.72, 1.86	595	− 620.0, 1810.0	0.94	0.53, 1.67
Education Level
no education	ref.				ref.
primary and junior high	0.68	0.42, 1.11	1788.4	− 634.9, 4211.7	0.97	0.57, 1.64
senior high school and above	0.62	0.32, 1.18	2199.7	− 761.7, 5161.1	0.73	0.34, 1.54
Employed	0.9	0.58, 1.39	712.9	− 1453.9, 2879.8	0.61	0.37, 1.02
Insurance
UEBMI	ref.				ref.
NRCMS/URBMI	1.33	0.8, 2.19	238.5	− 640.6, 1117.7	2.02	1.10, 3.73
Income level
poorest 33.3%	ref.				ref.
middle 33.3%	1.06	0.69, 1.61	− 237.4	− 1661.7, 1186.8	0.72	0.43, 1.20
richest 33.3%	1.09	0.69, 1.74	877.2	− 2167.2, 3921.6	0.57	0.31, 1.05
With NCD	1.99	1.42, 2.78	212.8	− 1245.0, 1670.6	2.97	1.93, 4.58

(Part 1 of the two-part model used logit regression to estimate the likelihood of incurring OOP health expenditure, and part 2 used GLM to model the amount of OOP health expenditure if occurred. All estimates were adjusted.)

ISSN: 1472-6963