| Literature DB >> 26266416 |
Fei Chen1,2, Xiang-Long Xu3, Zhan Yang4, Hua-Wei Tan5, Liang Zhang6.
Abstract
BACKGROUND: In 2012, a pilot health policy of contractual service relations between general practitioners and patients was implemented in China. Due to the decline in body and cognitive function, as well as the lack of family care and narrow social support networks, the demand of health services among the elderly is much higher than that among the general population. This study aims to probe into the empty nesters' willingness-to-pay for general practitioners using a contractual service policy, investigating empty nesters' payment levels for the service, and analyze the main factors affecting the willingness of empty-nesters' general practitioners using contractual service supply cost.Entities:
Keywords: Contingent Valuation Method; Cox’s proportional hazards regression model; contractual service; empty nesters; general practitioners; willingness-to-pay
Mesh:
Year: 2015 PMID: 26266416 PMCID: PMC4555283 DOI: 10.3390/ijerph120809330
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
The interval distribution of general practitioners by contractual service.
| Payment Level | Method 1 2 | Method 2 3 | ||
|---|---|---|---|---|
| Number/Frequency | Cumulative Frequency/Frequency | Number/Frequency | Cumulative Frequency/Frequency | |
| (0,10) 1 | 232/26.8 | 865/100 | 30/4.5 | 663/100 |
| (10,20) | 90/10.4 | 633/73.2 | 90/13.6 | 633/95.5 |
| (20,30) | 177/20.5 | 543/62.8 | 177/26.7 | 543/81.9 |
| (30,40) | 122/14.1 | 366/42.3 | 122/18.4 | 366/55.2 |
| (40,50) | 98/11.4 | 244/28.2 | 98/14.8 | 244/36.8 |
| (50,60) | 73/8.4 | 146/16.8 | 73/11.0 | 146/22.0 |
| (60,+∞ ) | 73/8.4 | 73/8.4 | 73/11.0 | 73/11.0 |
| Total | 865/100 | − | 663/100 | − |
Notes: If empty nesters are willing to pay but do not accept any given price level, we set that willingness-to-pay as 0; If empty nesters are not willing to pay, we set that willingness-to-pay as 0; Method 1 (0, 10) range includes being willing to pay but at any given price level not being willing to accept the service and the total number of the elderly were not willing to pay; Method 2 (0,10) range contains the number of elderly only being willing to pay but at any given price level not being willing to accept.
The probability of payment interval unit analysis.
| Parameter | Method 1 | Method 2 | ||||||
|---|---|---|---|---|---|---|---|---|
| Estimate | SE | z | 95% CI | Estimate | SE | z | 95% CI | |
| B | −1.077 * | 0.033 | −32.735 | (−1.142, −1.013) | −2.065 * | 0.064 | −32.441 | (−2.190, −1.940) |
| A | 3.332 * | 0.112 | 29.754 | (3.220,3.444) | 7.289 * | 0.230 | 31.697 | (7.059,7.519) |
Note: * p < 0.01.
Payment level measurement among empty nesters.
| Cumulative Probability | Method 1 | Method 2 | ||
|---|---|---|---|---|
| Estimated Value | 95% CI | Estimated Value | 95% CI | |
| 0.30 | 35.882 | (28.392,49.523) | 43.963 | (41.886,46.389) |
| 0.35 | 31.535 | (24.778,41.687) | 41.100 | (39.239,43.197) |
| 0.40 | 27.899 | (21.492,35.869) | 38.556 | (36.843,40.416) |
| 0.45 | 24.780 | (18.491,31.412) | 36.244 | (34.624,37.939) |
| 0.50 | 22.052 | (15.770,27.874) | 34.104 | (32.534,35.690) |
| 0.55 | 19.623 | (13.330,24.958) | 32.091 | (30.536,33.611) |
| 0.60 | 17.430 | (11.160,22.460) | 30.167 | (28.604,31.654) |
| 0.65 | 15.420 | (9.239,20.248) | 28.300 | (26.711,29.778) |
| 0.70 | 13.552 | (7.540,18.226) | 26.457 | (24.831,27.944) |
Variable definition and statistical description.
| Variable Categories | Code | Variable Name | The Definition of the Variables and Measurements | Mean Value | Standard Deviation |
|---|---|---|---|---|---|
| Willingness-to-pay | y | Willing to pay | The general practitioners type of pension insurance’ willing to pay | – | – |
| Demographic characteristics | X1 | Gender | Male = 1, Female = 0 | 0.429 | 0.495 |
| X2 | Age | Actual age | 69.558 | 6.976 | |
| X3 | Education level | Primary schooling and below = 1, junior middle schooling = 2, High schooling and above = 3 | 1.501 | 0.751 | |
| X4 | Occupation before retire | Public institution personnel = 0, enterprise staff = 1, individual businessmen = 2, famer = 3 | 1.983 | 0.836 | |
| X5 | Marital status | Married=1, Other = 0 | 0.845 | 0.362 | |
| Physical condition | X6 | The number of chronic diseases | Actual kind of chronic diseases | 1.025 | 0.940 |
| X7 | Health status by self-reported | Poor = 1, General = 2, Well = 3 | 2.186 | 0.781 | |
| Socioeconomic characteristics | X8 | Family disease financial burden by self-reported | Cannot stand=1,Can stand in general = 2, Can stand = 3 | 2.123 | 0.769 |
| X9 | Social support | one type support = 1, two types of supports = 2, three types of supports = 3 | 1.871 | 0.364 | |
| X10 | Family per capita income | Under 1000 yuan = 1, 1000–2000 yuan = 2, Above 2000 yuan = 3 | 1.810 | 0.621 | |
| Community doctor service | X11 | Awareness of general practitioners by contractual service | Know = 1, Do not know = 0 | 0.813 | 0.389 |
| X12 | The choice of general practitioners by contractual service | Do not choose = 1, Not sure = 2, Choose = 3 | 2.221 | 0.831 | |
| X13 | Community health service satisfaction | Not satisfied = 1, Indifferent = 2, Satisfied = 3 | 2.552 | 0.614 | |
| Pension mode | X14 | The choice of old-age care | Traditional family caring =1 | 0.787 | 0.410 |
Notes: . This research adopts the contingent valuation method (CVM) the one-way decreasing inquiry mode for general practitioners service data of willingness-to-pay. If consumers’ willingness-to-pay are located in (+ up, 60), the willingness-to-pay = 7; if consumers’ willingness-to-pay are located in (0,10), the willingness to pay = 1, and so on; . Other includes four cases: single, divorced, separated, and widowed. . Social supports include social support, offspring support and other support, the actual kind being counted. . Home endowment refers to endowment place in the family pension, including the traditional family endowment and community endowment.
COX proportional hazards model estimation.
| Influence Factors | B | SE | Wald | Exp (B) | 95% CI (Exp (B)) | Significance Level (Sig.) |
|---|---|---|---|---|---|---|
| X1 | 0.106 | 0.04 | 7.152 | 1.112 | (1.029,1.193) | 0.025 |
| X2 | −0.004 | 0.005 | 0.620 | 0.996 | (0.986,1.006) | 0.246 |
| X3 | −0.162 | 0.056 | 8.429 | 1.176 | (1.054,1.312) | 0.014 |
| X4 | −0.126 | 0.072 | 3.066 | 0.881 | (0.765,1.015) | 0.262 |
| X5 | 0.077 | 0.046 | 2.8 | 1.037 | (0.960,1.121) | 0.066 |
| X6 | 0.037 | 0.040 | 0.864 | 0.926 | (0.846,1.013) | 0.329 |
| X7 | −0.206 | 0.098 | 4.431 | 1.228 | (1.014,1.487) | 0.003 |
| X8 | −0.003 | 0.051 | 0.003 | 0.997 | (0.902,1.102) | 0.441 |
| X9 | −0.159 | 0.095 | 2.789 | 0.853 | (0.708,1.028) | 0.072 |
| X10 | −0.265 | 0.061 | 18.806 | 0.768 | (0.681,0.865) | 0.000 |
| X11 | −0.140 | 0.089 | 3.508 | 0.869 | (0.730,1.034) | 0.068 |
| X12 | 0.008 | 0.042 | 0.034 | 1.008 | (0.928,1.094) | 0.331 |
| X13 | −0.199 | 0.054 | 13.858 | 0.819 | (0.738,0.930) | 0.000 |
| X14 | 0.078 | 0.088 | 0.788 | 1.082 | (0.910,1.286) | 0.153 |
| Goodness of fit test | Chi-square = 39.084 | −2LogLikelihood = 5243.276 | ||||