Evidence map›Paper›PMID 36792764›Full record

ArticleScientific reports2023

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model.

Lu Bai, Ke Lu, Yongfei Dong, Xichao Wang, Yaqin Gong, Yunyu Xia, Xiaochun Wang, Lin Chen, Shanjun Yan, Zaixiang Tang and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Lu Bai *Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
Ke Lu *Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, No. 91 West of Qianjin Road, Suzhou, 215300, Jiangsu, China.
Yongfei DongDepartment of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
Xichao WangDepartment of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
Yaqin GongInformation Department, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, 215300, Jiangsu, China.
Yunyu XiaMeteorological Bureau of Kunshan City, Suzhou, 215337, Jiangsu, China.
Xiaochun WangMeteorological Bureau of Kunshan City, Suzhou, 215337, Jiangsu, China.
Lin ChenEcology and Environment Bureau of Kunshan City, Suzhou, 215330, Jiangsu, China.
Shanjun YanEcology and Environment Bureau of Kunshan City, Suzhou, 215330, Jiangsu, China.
Zaixiang TangDepartment of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China. tangzx@suda.edu.cn.
Chong LiDepartment of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, No. 91 West of Qianjin Road, Suzhou, 215300, Jiangsu, China. lichong1705@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

Indexed as

Models, StatisticalOutpatientsBayes TheoremChinaForecastingHospitalsHumansIncidence

Identifiers

PMID36792764
PMCPMC9930044

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.