Evidence map›Paper›PMID 31601887›Full record

ArticleScientific reports2019

Epidemiological and time series analysis of haemorrhagic fever with renal syndrome from 2004 to 2017 in Shandong Province, China.

Chao Zhang, Xiao Fu, Yuanying Zhang, Cuifang Nie, Liu Li, Haijun Cao, Junmei Wang, Baojia Wang, Shuying Yi, Zhen Ye

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

10 authors.

Chao ZhangCollege of Basic Medicine, Shandong First Medical University & Shandong academy of medical sciences, Tai'an, 271016, China.
Xiao FuClinical Skills Training Center, Shandong First Medical University & Shandong academy of medical sciences, Tai'an, 271016, China.
Yuanying ZhangCollege of Basic Medicine, Shandong First Medical University & Shandong academy of medical sciences, Tai'an, 271016, China.
Cuifang NieDepartment of Infectious Disease, Tai'an Central Hospital, Tai'an, 271000, China.
Liu LiDepartment of Infectious Disease, Tai'an Central Hospital, Tai'an, 271000, China.
Haijun CaoDepartment of Infectious Disease, Tai'an Central Hospital, Tai'an, 271000, China.
Junmei WangCollege of Information Science and Engineering, College of Information Science and Engineering Shandong Agricultural University, Tai'an, 271000, China.
Baojia WangCollege of Information Science and Engineering, College of Information Science and Engineering Shandong Agricultural University, Tai'an, 271000, China.
Shuying YiCollege of Basic Medicine, Shandong First Medical University & Shandong academy of medical sciences, Tai'an, 271016, China.
Zhen YeCollege of Basic Medicine, Shandong First Medical University & Shandong academy of medical sciences, Tai'an, 271016, China. yezichess@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Shandong Province is an area of China with a high incidence of haemorrhagic fever with renal syndrome (HFRS); however, the general epidemic trend of HFRS in Shandong remains unclear. Therefore, we established a mathematical model to predict the incidence trend of HFRS and used Joinpoint regression analysis, a generalised additive model (GAM), and other methods to evaluate the data. Incidence data from the first half of 2018 were included in a range predicted by a modified sum autoregressive integrated moving average-support vector machine (ARIMA-SVM) combination model. The highest incidence of HFRS occurred in October and November, and the annual mortality rate decreased by 7.3% (p < 0.05) from 2004 to 2017. In cold months, the incidence of HFRS increased by 4%, -1%, and 0.8% for every unit increase in temperature, relative humidity, and rainfall, respectively; in warm months, this incidence changed by 2%, -3%, and 0% respectively. Overall, HFRS incidence and mortality in Shandong showed a downward trend over the past 10 years. In both cold and warm months, the effects of temperature, relative humidity, and rainfall on HFRS incidence varied. A modified ARIMA-SVM combination model could effectively predict the occurrence of HFRS.

Indexed as

Models, StatisticalAdultChinaEpidemicsEpidemiologic StudiesFarmersFeasibility StudiesFemaleForecastingHemorrhagic Fever with Renal SyndromeHumansIncidenceMaleMiddle AgedMortalitySeasons

Identifiers

PMID31601887
PMCPMC6787217

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.