| Literature DB >> 30939970 |
Mi-Ryeong Gil1, Cheon Geun Choi2.
Abstract
The present study analyzed factors underlying outpatient service users' choice of national and public (rather than private) hospitals. Based on Andersen's Behavioral Model, we developed a framework that covered needs, enabling, and personal factors. Data of outpatient service usage were obtained from the Korean medical panel survey during 2008 to 2013. Logistic regression analyses were conducted, and results revealed that the rate of national and public hospital use was very low (5.57%), and our model adequately explained variance in service use. Specifically, several demographic factors-older age, low income, national merit and medical care, being chronically ill, and having a disability-were predictive of whether an individual is likely to choose national and public hospitals. We discuss the need to strengthen national and public hospitals' medical services in order to better manage care for low-income vulnerable groups.Entities:
Keywords: Andersen’s Behavioral Model; national and public hospitals; outpatient medical services
Mesh:
Year: 2019 PMID: 30939970 PMCID: PMC6448112 DOI: 10.1177/0046958019833256
Source DB: PubMed Journal: Inquiry ISSN: 0046-9580 Impact factor: 1.730
Figure 1.Research model.
Use of National and Public Hospitals by Outpatients (Unit: Case, %).
| Year | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | Sum |
|---|---|---|---|---|---|---|---|
| Frequency (%) | Frequency (%) | Frequency (%) | Frequency (%) | Frequency (%) | Frequency (%) | Frequency (%) | |
| Sex | |||||||
| Female | 115 087 | 118 503 | 124 145 | 130 985 | 132 040 | 131 398 | 752 158 |
| Male | 66 285 | 68 685 | 72 997 | 77 641 | 80 860 | 79 473 | 445 941 |
| Publicity | |||||||
| Private hospitals | 167 510 | 175 384 | 186 346 | 198 273 | 202 923 | 200 873 | 1 131 309 |
| National and public hospitals | 13 862 | 11 804 | 10 796 | 10 353 | 9977 | 9998 | 66 790 |
Chi-Square Analysis of Factors Determining Selection of National and Public Hospitals (Unit: Case, %).
| Category | Selection of hospitals | χ2 | ||||
|---|---|---|---|---|---|---|
| Private hospitals | National and public hospitals | |||||
| Frequency | (%) | Frequency | (%) | |||
| Chronic diseases | ||||||
| No | 150 758 | (97.25) | 4258 | (2.75) | 2700 | .000 |
| Yes | 980 551 | (94.01) | 62 532 | (5.99) | ||
| Disability | ||||||
| No | 982 888 | (94.58) | 56 290 | (5.42) | 370.99 | .000 |
| Yes | 148 421 | (93.39) | 10 500 | (6.61) | ||
| Household income | ||||||
| First quintile | 285 508 | (91.91) | 25 146 | (8.09) | 8600 | .000 |
| Second quintile | 253 995 | (93.45) | 17 811 | (6.55) | ||
| Third quintile | 215 208 | (95.01) | 11 306 | (4.99) | ||
| Fourth quintile | 190 274 | (96.41) | 7079 | (3.59) | ||
| Fifth quintile | 182 111 | (97.18) | 5288 | (2.82) | ||
| Medical assistance | ||||||
| Health insurance | 1 011 886 | (94.53) | 58 513 | (5.47) | 1500 | .000 |
| Medical benefits | 104 672 | (94.41) | 6200 | (5.59) | ||
| National meritorious | 14 751 | (87.66) | 2077 | (12.34) | ||
| Private insurance | ||||||
| Purchase | 614 370 | (93.12) | 45 381 | (6.88) | 49.06 | .000 |
| No-purchase | 516 939 | (96.02) | 21 409 | (3.98) | ||
| Sex | ||||||
| Female | 713 736 | (94.89) | 38 422 | (5.11) | 835.19 | .000 |
| Male | 417 573 | (93.64) | 28 368 | (6.36) | ||
| Age | ||||||
| 18~29 years | 46 607 | (98.17) | 871 | (1.83) | 13 000 | .000 |
| 30s | 91 724 | (98.12) | 1758 | (1.88) | ||
| 40s | 146 725 | (97.36) | 3984 | (2.64) | ||
| 50s | 207 374 | (96.15) | 8311 | (3.85) | ||
| 60s | 286 007 | (93.34) | 20 418 | (6.66) | ||
| 70s or older | 352 872 | (91.82) | 31 448 | (8.18) | ||
| Spouse | ||||||
| No | 305 835 | (94.32) | 18 431 | (5.68) | 10.08 | .001 |
| Yes | 825 474 | (94.47) | 48 359 | (5.53) | ||
| Education | ||||||
| Middle school or less | 636 621 | (92.80) | 49 388 | (7.20) | 1500 | .000 |
| High school graduate | 291 792 | (96.11) | 11 805 | (3.89) | ||
| University graduate or more | 202 796 | (97.31) | 5 597 | (2.34) | ||
Logistic Regression Analysis for Predicting Selection of National and Public Hospitals Among Outpatient Service Users.
| Outpatient service selection variable | Selection of national and public hospitals | ||||
|---|---|---|---|---|---|
|
|
| Odds ratio |
| ||
| <Need factors> | |||||
| Chronic disease (criterion: no disease) | −0.423 | 0.01 | 0.654 | −21.07 | .000 |
| Disability (criterion: no disability) | −0.049 | 0.012 | 0.951 | −3.66 | .000 |
| <Enabling factors> | |||||
| Household Income (criterion: first quintile low-income earners) | |||||
| Second quintile | −0.128 | 0.011 | 0.879 | −10.07 | .000 |
| Third quintile | −0.287 | 0.011 | 0.750 | −19.41 | .000 |
| Fourth quintile | −0.510 | 0.010 | 0.599 | −29.48 | .000 |
| Fifth quintile | −0.69 | 0.009 | 0.497 | −35.73 | .000 |
| Medical Benefits (criterion: health insurance) | |||||
| Medical benefits | −0.101 | 0.026 | 0.903 | −3.48 | .001 |
| National merits | 0.102 | 0.031 | 1.10 | 3.58 | .000 |
| Private insurance purchase | −0.111 | 0.01 | 0.894 | −9.81 | .000 |
| Payment amount of medical expenses | −9.10 | 2.17 | 0.999 | −42.17 | .000 |
| <Personal factors> | |||||
| Sex (criterion: male) | 0.294 | 0.014 | 1.34 | 27.40 | .000 |
| Age | 0.029 | 0.00 | 1.0 | 53.78 | .000 |
| Spouse | 0.022 | 0.011 | 1.02 | 1.94 | .052[ |
| Education level (criterion: middle school graduation or less) | |||||
| High school graduate | −0.263 | 0.010 | 0.768 | −19.37 | .000 |
| University graduate | −0.41 | 0.012 | 0.663 | −21.43 | .000 |
| <Control variables | |||||
| Economic activity (criterion: no economic activity) | 0.382 | 0.015 | 1.4 | 36.08 | .000 |
| Receiving basic national security | 0.036 | 0.030 | 1.03 | 1.27 | .206 |
| Medical institutions (criterion: tertiary hospital) | |||||
| General hospitals | 0.316 | 0.030 | 1.37 | 14.11 | .000 |
| Hospitals | −1.89 | 0.005 | 0.151 | −54.63 | .000 |
| Clinics | −6.10 | 0.000 | 0.002 | −86.33 | .000 |
| Others | 0.89 | 0.052 | 2.45 | 42.13 | .000 |
| Medical expenses financial compensation | 0.059 | 0.018 | 1.06 | 3.46 | .001 |
| Reason for visit (criterion: accident, poisoning) | |||||
| Disease treatment | 0.910 | 0.094 | 2.48 | 23.87 | .000 |
| Other hospital diagnosis | 2.93 | 0.769 | 18.8 | 71.85 | .000 |
| Traffic time | −0.0 | 0.000 | 0.99 | −29.91 | .000 |
| By region (criterion: Seoul) | |||||
| Busan | −0.468 | 0.016 | 0.625 | −17.62 | .000 |
| Daegu | −0.549 | 0.017 | 0.577 | −18.65 | .000 |
| Incheon | 0.155 | 0.032 | 1.16 | 5.54 | .000 |
| Gwangju | −0.346 | 0.026 | 0.707 | −9.16 | .000 |
| Daejeon | 0.508 | 0.043 | 1.66 | 19.30 | .000 |
| Ulsan | −1.38 | 0.015 | 0.250 | −22.52 | .000 |
| Gyonggi | −0.00 | 0.019 | 0.993 | −0.32 | .750 |
| Gangwon | 0.351 | 0.041 | 1.42 | 12.06 | .000 |
| Chungbuk | 0.886 | 0.068 | 2.42 | 31.41 | .000 |
| Chungnam | 0.931 | 0.061 | 2.53 | 38.25 | .000 |
| Jeonbuk | 0.841 | 0.052 | 2.32 | 37.32 | .000 |
| Jeonnam | 0.662 | 0.041 | 1.9 | 30.81 | .000 |
| Kyungbuk | 0.552 | 0.03 | 1.73 | 24.50 | .000 |
| Gyeongnam | 0.67 | 0.041 | 1.96 | 32.14 | .000 |
| Jeju | 0.379 | 0.046 | 1.4 | 11.94 | .000 |
| Year (criterion: 2008) | |||||
| 2009 | −0.304 | 0.011 | 0.737 | −19.35 | .000 |
| 2010 | −0.631 | 0.008 | 0.531 | −39.32 | .000 |
| 2011 | −0.747 | 0.007 | 0.47 | −46.59 | .000 |
| 2012 | −0.841 | 0.00 | 0.430 | −52.06 | .000 |
| 2013 | −0.958 | 0.006 | 0.383 | −58.67 | .000 |
| Constant | −4.30 | 0.060 | − | −70.92 | .000 |
|
| 1 196 685 | ||||
| Model χ2 | 217 4290.30 | ||||
| LL | –148 290.71 | ||||
| Model hit ratio | 95.11% | ||||
Note. LL = log likelihood.
P < .1. *P < .05. **P < .01. ***P < .001.