Analysis of influence of physical health factors on subjective wellbeing of middle-aged and elderly women in China

Table 2 Panel ordered Logit regression analysis of subjective well-being

variable	Coef	Std. Err	P value	[95% Conf. Interval]
Age
45–59	0.748	0.130	< 0.001	0.493 ~ 1.003
60–74	0.212	0.053	< 0.001	0.108 ~ 0.317
≥ 75
Registered residence
urban	0.171	0.054	0.001	0.065 ~ 0.276
rural
Education status
college or university degree and above	-0.063	0.090	0.483	-0.240 ~ 0.113
Senior high school	-0.144	0.087	0.100	-0.317 ~ 0.027
junior high school	-0.017	0.065	0.783	-0.145 ~ 0.109
primary school and below
Marital status
Married	0.521	0.076	< 0.001	0.371 ~ 0.670
unmarried
Working situation
Yes	-0.314	0.054	< 0.001	-0.421 ~ -0.208
No
SRH
very bad	-1.155	0.098	< 0.001	-1.347 ~ -0.962
bad	-1.032	0.096	< 0.001	-1.221 ~ -0.843
acceptable	-0.835	0.088	< 0.001	-1.007 ~ -0.663
good	-0.391	0.100	< 0.001	-0.588 ~ -0.195
very good
chronic disease
Yes	-0.001	0.053	0.991	-0.104 ~ 0.103
No
Hospitalization
Yes	0.046	0.062	0.454	-0.075 ~ 0.169
No
two-week morbidity
Yes	-0.158	0.049	0.001	-0.255 ~ -0.062
No
Physical exercise
Yes	0.229	0.046	< 0.001	0.135 ~ 0.317
No
Drinking status
Yes	0.003	0.123	0.978	-0.238 ~ 0.245
No
Smoking status
Yes	0.224	0.119	0.060	-0.009 ~ 0.458
No
BMI
obesity	0.437	0.118	< 0.001	0.205 ~ 0.670
overweight	0.357	0.098	< 0.001	0.164 ~ 0.549
normal weight	0.256	0.092	0.006	0.074 ~ 0.438
underweight
Income status	0.123	0.022	< 0.001	0.079 ~ 0.167
Social status	0.327	0.023	< 0.001	0.281 ~ 0.374
Cut1	-5.249	0.236		-5.711 ~ -4.785
Cut2	-4.216	0.196		-4.600 ~ -3.831
Cut3	-3.305	0.180		-3.658 ~ -2.951
Cut4	-2.506	0.173		-2.845 ~ -2.165
Cut5	-2.091	0.171		-2.426 ~ -1.756
Cut6	-0.252	0.167		-0.579 ~ 0.075
Cut7	0.254	0.167		-0.072 ~ 0.581
Cut8	0.735	0.167		0.407 ~ 1.062
Cut9	2.005	0.169		1.674 ~ 2.334
Cut10	2.506	0.169		2.174 ~ 2.838

Model Wald chi2 = 846.74; Log likelihood = -17,990.021; The number of ‘cut’ in the regression is related to the number of dependent variable classifications. As an auxiliary parameter, the parameter value of ‘cut’ could be interpreted as which value is needed to enter the corresponding dependent variable category. The SWB of MAEW was measured from 0 to 10 as an ordinal categorical variable. Therefore, this study had 10 auxiliary parameters (cut1–10)

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