Background
Literature review
Methods
Data source
Main dependent variables
Main independent variable
Covariates
Statistical models
Multidimensional poverty identification model for health care
Dimensions | Indicators | Threshold of deprivation | Weight |
---|---|---|---|
Enabling | health insurance | Access to the health insurance(Yes = 0, No = 1) | 1/6 |
Health need | Chronic illness | Whether or not chronic illness has been in the last six months(Yes = 1, No = 0) | 1/6 |
Bronchitis illness | Whether or not bronchitis illness has been in the last six months(Yes = 1, No = 0) | 1/6 | |
Asthma illness | Whether or not asthma illness has been in the last six months(Yes = 1, No = 0) | 1/6 | |
Hospitalization | Whether or not entering the hospital in the past 12 months (Yes = 1, No = 0) | 1/6 | |
Health status | The health status by self-reported (range from 0 ~ 7score, below 4 as poor and over 4 as good health level) | 1/6 |
Health care vulnerability as expected poverty model (VEP)
The multivariate logistic regression model
Results
The measurement analysis of the vulnerability of health care poverty
Main statistics | The Year 2014 | The Year of 2016 | The Year of 2018 | |||
---|---|---|---|---|---|---|
Multidimensional poverty threshold (k%) | k = 30% | k = 40% | k = 30% | k = 40% | k = 30% | k = 40% |
Incidence of multidimensional poverty in health care (Rc) | 38.62% | 9.85% | 40.32% | 11.66% | 44.27% | 17.60% |
Health care experts poverty vulnerability (Poverty line = $1.9,
𝑘=30%) | 93.08% | 92.09% | 50.78% | |||
Health care experts poverty vulnerability (Poverty line = $3.2,
𝑘=30%) | 96.28% | 96.02% | 57.13% |
Class | Range of vulnerability (Average Value) | Provinces or Cites of VEP (Poverty line = $1.9) | Provinces or Cites of VEP (Poverty line = $3.2) |
---|---|---|---|
High vulnerability | 60% ~ 70% | Beijing, Shanghai, Zhejiang, Jiangsu, Hainan, Shangxi | Beijing, Shanghai, Zhejiang, Guangdong, Jiangsu, Hainan, Heilongjiang, Chongqing, Hunan, Yunnan, Shandong, Hebei, Shangxi |
Moderate vulnerability | 50% ~ 60% | Guangdong, Heilongjiang, Chongqing, Hunan, Yunnan, Fujian, Shandong, Hebei, Anhui, Shanxi, Liaoning | Fujian, Henan, Hubei, Jiangxi, Anhui, Guizhou, Shanxi, Sichuan, Gansu, Liaoning |
Low vulnerability | Below 50% | Henan, Tianjin, Hubei, Jiangxi, Guangxi, Guizhou, Sichuan, Xinjiang, Tibet, Gansu, Jilin | Tianjin, Guangxi, Xinjiang, Tibet, Jilin |
Multivariate logistic regression
Variables | Year | VEP_1 (Poverty line = $1.9) | VEP_2 (poverty line = $3.2) | (GTP) (Received = 1, Non-received = 0) | Age | Age2 | Gender (Male = 1, Female = 0) | Educational Year (Y_E) | Family Size (F_ S) |
---|---|---|---|---|---|---|---|---|---|
Statistics | |||||||||
Minimum | 2014 | 0 | 0 | 0 | 17 | 2.89 | 0 | 0 | 1 |
2016 | 0 | 0 | 0 | 17 | 2.89 | 0 | 0 | 1 | |
2018 | 0 | 0 | 0 | 17 | 2.89 | 0 | 0 | 1 | |
Maximum | 2014 | 1 | 1 | 1 | 92 | 85 | 1 | 19 | 21 |
2016 | 1 | 1 | 1 | 92 | 85 | 1 | 19 | 21 | |
2018 | 1 | 1 | 1 | 92 | 84.64 | 1 | 19 | 21 | |
Mean | 2014 | 0.93 | 0.96 | 0.72 | 49.92 | 26.77 | 0.76 | 5.79 | 3.92 |
2016 | 0.92 | 0.96 | 0.59 | 49.92 | 26.77 | 0.76 | 5.79 | 3.92 | |
2018 | 0.51 | 0.57 | 0.6 | 49.92 | 26.77 | 0.76 | 5.79 | 3.92 | |
Std. Deviation | 2014 | 0.25 | 0.18 | 0.45 | 13.58 | 13.6 | 0.42 | 4.83 | 2.01 |
2016 | 0.27 | 0.19 | 0.49 | 13.58 | 13.66 | 0.42 | 4.83 | 2.01 | |
2018 | 0.5 | 0.49 | 0.48 | 13.58 | 13.66 | 0.42 | 4.83 | 2.01 | |
Number of Sampling | 2014 | 5754 | |||||||
2016 | |||||||||
2018 |
Series one model:multi-variables logistic model effect analysis
Variables | VEP_1 (Poverty line = $1.9) | VEP_2 (poverty line = $3.2) | ||||
---|---|---|---|---|---|---|
2014 | 2016 | 2018 | 2014 | 2016 | 2018 | |
Statisitics | Coefficient | Coefficient | Coefficient | Coefficient | Coefficient | Coefficient |
p -value | p -value | | p -value | p -value | p -value | p -value | |
GTP | −0.0105 (0.139) | −0.0015 (0.831) | −0.0340*** (0.010) | − 0.0031 (0.157) | −0.0048 (0.354) | − 0.0604*** (0.000) |
Age | 0.0014 (0.257) | 0.0019 (0.151) | 0.0044* (0.099) | 0.0002 (0.788) | 0.0006 (0.560) | 0.0002 (0.925) |
Age2 | 0.0016 (0.187) | 0.0024* (0.066) | 0.0016** (0.045) | 0.0003 (0.739) | 0.0002 (0.762) | 0.0044* (0.087) |
Gender | 0.0221*** (0.004) | 0.0232*** (0.005) | −0.1028*** (0.000) | 0.0149*** (0.008) | 0.0178*** (0.002) | 0.0799*** (0.000) |
Y_E | −0.0009 (0.181) | −0.0005 (0.502) | − 0.0113*** (0.000) | −0.00003 (0.949) | − 0.0014** (0.016) | −0.0122*** (0.000) |
F_S | 0.021*** (0.000) | 0.0131*** (0.000) | −0.0152*** (0.000) | 0.0153*** (0.000) | 0.0131 *** (0.000) | −0.0457*** (0.000) |
Log likelihood | − 1352.12 | − 1514.91 | − 3909.70 | − 843.12 | − 891.95 | − 3688.69 |
LR Statistics | 189.85 | 152.19 | 155.91 | 142.54 | 141.43 | 482.09 |
Number of Sampling | 5754 | 5754 | 5754 | 5754 | 5754 | 5754 |
Series two model:multi-variables logistic model effect analysis
Variables | GTP*E_H (VEP_1) | GTP*E_H (VEP_2) |
---|---|---|
Wald(Prob.) | 0.0000*** | 0.0000*** |
Chi2 (1) | 261.61 | 173.17 |
Variables | VEP_1 (Poverty line = $1.9) | VEP_2 (Poverty line = $3.2) | ||||
---|---|---|---|---|---|---|
2014 | 2016 | 2018 | 2014 | 2016 | 2018 | |
E_H | −0.0281*** (0.000) | − 0.0220*** (0.000) | − 0.0791*** (0.000) | −0.0174*** (0.000) | − 0.0512*** (0.000) | −0.0925*** (0.000) |
GTP*E_H | −0.0258*** (0.000) | − 0.5279*** (0.000) | − 0.1981*** (0.000) | −0.0124*** (0.009) | − 0.3205*** (0.000) | −0.2283*** (0.000) |
Age | 0.0006 (0.602) | 0.0009 (0.400) | 0.0042* (0.100) | 0.0002 (0.770) | 0.0007 (0.421) | 0.0004(0.856) |
Age2 | 0.0009 (0.437) | −0.0003 (0.746) | − 0.0014* (0.588) | 0.0001 (0.913) | 0.0009 (0.251) | 0.0052** (0.043) |
Gender | 0.0217*** (0.003) | 0.0075* (0.070) | −0.1026*** (0.000) | 0.0154*** (0.005) | 0.0108** (0.028) | −0.0822*** (0.000) |
Y_E | - 0.0004 (0.511) | −0.0015** (0.026) | −0.0096*** (0.000) | − 0.00036 (0.514) | −0.0005 (0.347) | 0.0105*** (0.000) |
F_S | 0.0229*** (0.000) | 0.0199*** (0.000) | −.0133*** (0.000) | 0.0158*** (0.000) | 0.0136*** (0.000) | −0.0440*** (0.000) |
Loglikelihood | − 1288.93 | − 1022.16 | − 3793.30 | − 802.56 | − 626.31 | − 3524.67 |
LR Statistics | 316.23 | 1137.70 | 388.73 | 223.67 | 672.69 | 810.15 |
Number of Sampling | 5754 | 5754 | 5754 | 5754 | 5754 | 5754 |
Cross-validation regression
Variables | VEP_1 (Poverty line = $1.9) | VEP_2 (poverty line = $3.2) | ||||
---|---|---|---|---|---|---|
2014 | 2016 | 2018 | 2014 | 2016 | 2018 | |
E_H | −0.0302*** (0.000) | − 0.1371*** (0.000) | − 0.1510*** (0.000) | −0.0184*** (0.000) | − 0.0578*** (0.000) | −0.1502*** (0.000) |
GTP*E_H | −0.0356*** (0.000) | −0.5946*** (0.000) | − 0.1991*** (0.000) | −0.0183*** (0.001) | − 0.3210 *** (0.000) | − 0.2261*** (0.000) |
Age | 0.0007 (0.545) | 0.0013 (0.236) | 0.0043* (0.097) | 0.0002 (0.774) | 0.0005 (0.545) | 0.0001 (0.977) |
Age2 | 0.0010 (0.393) | 0.0009 (0.384) | 0.0015* (0.053) | 0.0001 (0.879) | 0.000 (0.380) | 0.0046* (0.068) |
Gender | 0.0221*** (0.003) | 0.0108*** (0.113) | 0.1013*** (0.000) | 0.0153*** (0.005) | 0.0103** (0.032) | 0.0822*** (0.000) |
Y_E | −0.0005 (0.475) | −0.0010 (0.117) | − 0.0097*** (0.000) | − 0.0004 (0.504) | −0.0001 (0.248) | − 0.0105*** (0.000) |
F_S | 0.0209*** (0.000) | 0.0176*** (0.000) | −0.0133*** (0.000) | 0.0151*** (0.000) | 0.0121*** (0.000) | −0.0438*** (0.000) |
Loglikelihood | − 1282.257 | − 1023.15 | −3793.51 | − 796.9304 | − 611.08 | − 3529.13 |
LR Statistics | 329.57 | 1135.72 | 388.30 | 234.92 | 703.17 | 801.22 |
Number of Sampling | 5754 | 5754 | 5754 | 5754 | 5754 | 5754 |