Analysis of Environmental and Social Significant Factors Affecting the Flow of Maternal Patients in Jilin, China
Bibliographic Data
| ID | 22075255 |
|---|---|
| Authors | Dongmei Mu (0000-0002-4119-3844, Jilin University), Hua Li (0000-0002-1778-8071, Jilin University, corresponding author), Dongxuan Wang (0000-0003-4275-6327, Jilin University), Xinyu Yang (0009-0007-3844-9830, Jilin University), Shutong Wang (0000-0001-6708-8738, Jilin University) |
| Year | 2022 |
| Volume | 10 |
| Pages | 780452-780452 |
| Publication date | 2022-05-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.780452 |
| PMID | 35669749 |
| OpenAlex | W4229368533 |
| Language | EN |
| References cited | 30 |
Background: With the implementation of China's Two-child policy, the number of pregnant women has been increasing year by year in recent years. However, the pregnancy success rate of pregnant women is declining year by year, and it is almost necessary for all the elderly mothers to do pregnancy protection. Objective: The purpose of this study is to analyze the social and environmental factors that affect the patient flow of pregnant women in Jilin area of China, and further utilize the favorable factors to avoid the negative effects of adverse factors, so as to improve the pregnancy success rate and eugenics level. Methods: Monthly patient flow data from 2018 to 2020 were collected in the obstetrics department of the First Hospital of Jilin University. The decompose function in R software was used to decompose the time series data, and the seasonal and trend change rules of the data were obtained; the significant factors influencing patient flow were analyzed by using Poisson regression model, and the prediction model was verified by using assumptions, such as the normal distribution of residuals and the constant difference of residuals. Results: = 0.00299). The patient flow was accompanied by random flow, but also showed trend change and seasonal change. The trend of change has been increasing year by year. The seasonal variation rule is that the flow of patients presents a trough in February every year, and reaches the peak in July. Conclusion: In this article, Poisson regression model is used to obtain the social and environmental significant factors of obstetric patient flow. According to the significant factors, we should give full play to significant factors to further improve the level of eugenics. By using time series decomposition model, we can obtain the rising trend and seasonal trend of patient flow, and then provide the management with decision support, which is conducive to providing pregnant women with higher level of medical services and more comfortable medical experience
Biology · China · Environmental health · Geography · Poisson distribution · Poisson regression · Population · Pregnancy · Statistics · Advanced Technologies in Various Fields · AI and Big Data Applications · Applied Advanced Technologies · Demography · Mathematics · Medicine · Psychology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |