ArticleCurrent medical science2019
Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme.
Article in Current medical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Long-Term Impact of COVID-19 on Hospital Visits of Rural Residents in Guangdong, China: A Controlled Interrupted Time Series Study.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Throughout the duration of the New Cooperative Medical Scheme (NCMS), it was found that an increasing number of rural patients were seeking out-of-county medical treatment, which posed a great burden on the NCMS fund. Our study was conducted to examine the prevalence of out-of-county hospitalizations and its related factors, and to provide a scientific basis for follow-up health insurance policies. A total of 215 counties in central and western China from 2008 to 2016 were selected. The total out-of-county hospitalization rate in nine years was 16.95%, which increased from 12.37% in 2008 to 19.21% in 2016 with an average annual growth rate of 5.66%. Its related expenses and compensations were shown to increase each year, with those in the central region being higher than those in the western region. Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region (X1), rural population (X2), per capita per year net income (X3), per capita gross domestic product (GDP) (X4), per capita funding amount of NCMS (X5), compensation ratio of out-of-county hospitalization cost (X6), per time average in-county (X7) and out-of-county hospitalization cost (X8). According to Bayesian network (BN), the marginal probability of high out-of-county hospitalization rate was as high as 81.7%. Out-of-county hospitalizations were directly related to X8, X3, X4 and X6. The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors, economy factors, regional characteristics and NCMS policy factors was 95.7%, 91.1%, 93.0% and 88.8%, respectively. And how these factors affect out-of-county hospitalization and their interrelationships were found out. Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations, and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.
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