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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Socioeconomic inequalities of outpatient and inpatient service utilization in China: personal and regional perspectives
|
|---|---|
| Published in |
International Journal for Equity in Health, December 2017
|
| DOI | 10.1186/s12939-017-0706-8 |
| Pubmed ID | |
| Authors |
Dawei Zhu, Na Guo, Jian Wang, Stephen Nicholas, Li Chen |
| Abstract |
China's health system has shown remarkable progress in health provision and health outcomes in recent decades, however inequality in health care utilization persists and poses a serious social problem. While government pro-poor health policies addressed affordability as the major obstacle to equality in health care access, this policy direction deserves further examination. Our study examines the issue of health care inequalities in China, analyzing both regional and individual socioeconomic factors associated with the inequality, and provides evidence to improve governmental health policies. The China Health and Nutrition Survey (CHNS) 1991-2011 data were used to analyze the inequality of health care utilization. The random effects logistic regression technique was used to model health care utilization as the dependent variable, and income and regional location as the independent variables, controlling for individuals' age, gender, marital status, education, health insurance, body mass index (BMI), and period variations. The dynamic trend of 1991-2011 regional disparities was estimated using an interaction term between the regional group dummy and the wave dummy. The probability of using outpatient service and inpatient services during the previous 4 weeks was 8.6 and 1.1% respectively. Compared to urban residents, suburban (OR: 0.802, 95% CI: 0.720-0.893), town (OR: 0.722, 95% CI: 0.648-0.804), rich (OR: 0.728, 95% CI: 0.656-0.807) and poor village (OR: 0.778, 95% CI: 0.698-0.868) residents were less likely to use outpatient service; and rich (OR: 0.609, 95% CI: 0.472-0.785) and poor village (OR: 0.752, 95% CI: 0. 576-0.983) residents were less likely to use inpatient health care. But the differences between income groups were not significant, except the differences between top and bottom income group in outpatient service use. Regional location was a more important factor than individual characteristics in determining access to health care. Besides demand-side subsidies, Chinese policy makers should pay enhanced attention to health care resource allocation to address inequity in health care access. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 87 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 15% |
| Student > Ph. D. Student | 7 | 8% |
| Student > Bachelor | 7 | 8% |
| Researcher | 6 | 7% |
| Student > Doctoral Student | 4 | 5% |
| Other | 10 | 11% |
| Unknown | 40 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 10 | 11% |
| Economics, Econometrics and Finance | 9 | 10% |
| Medicine and Dentistry | 8 | 9% |
| Social Sciences | 8 | 9% |
| Psychology | 4 | 5% |
| Other | 9 | 10% |
| Unknown | 39 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 December 2017.
All research outputs
#14,960,072
of 23,009,818 outputs
Outputs from International Journal for Equity in Health
#1,501
of 1,924 outputs
Outputs of similar age
#252,410
of 439,388 outputs
Outputs of similar age from International Journal for Equity in Health
#31
of 42 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,924 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 439,388 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.