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China’s 1-3-7 surveillance and response strategy for malaria elimination: Is case reporting, investigation and foci response happening according to plan?

Overview of attention for article published in Infectious Diseases of Poverty, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 416)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
59 Mendeley
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Title
China’s 1-3-7 surveillance and response strategy for malaria elimination: Is case reporting, investigation and foci response happening according to plan?
Published in
Infectious Diseases of Poverty, December 2015
DOI 10.1186/s40249-015-0089-2
Pubmed ID
Authors

Shui-Sen Zhou, Shao-Sen Zhang, Li Zhang, Aafje E. C. Rietveld, Andrew R. Ramsay, Rony Zachariah, Karen Bissell, Rafael Van den Bergh, Zhi-Gui Xia, Xiao-Nong Zhou, Richard E. Cibulskis

Abstract

The China's 1-3-7 strategy was initiated and extensively adopted in different types of counties (geographic regions) for reporting of malaria cases within 1 day, their confirmation and investigation within 3 days, and the appropriate public health response to prevent further transmission within 7 days. Assessing the level of compliance to the 1-3-7 strategy at the county level is a first step towards determining whether the surveillance and response strategy is happening according to plan. This study assessed if the time-bound targets of the 1-3-7 strategy were being sustained over time. Such information would be useful to improve implementation of the 1-3-7 strategy in China. This cross-sectional study involved country-wide programmatic data for the period January 1st 2013 to June 30th 2014. Data variables were extracted from the national malaria information system and included socio-demographic information, type of county, date of diagnosis, date of reporting, date of case investigation, case classification (indigenous, or imported, or unknown), focus investigation, date of reactive case detection (RACD), and date of indoor residual spraying (IRS). Summary statistics and proportions were used and comparisons between groups were assessed using the chi-square test. Level of significance was set at a P-value ≤ 0.05. Of a total of 5,688 malaria cases from 731 counties, there were 55 (1 %) indigenous cases (only in Type 1 and Type 2 counties) and 5,633 (99 %) imported cases from all types of counties. There was no delay in reporting malaria cases by type of county. In terms of case investigation, 97.5 % cases were investigated within 3 days with the proportion of delays (1.5 %) in type 2 counties, being significantly lower than type 1 counties (4.1 %). Regarding active foci, 96.4 % were treated by RACD and/or IRS. The performance of 1-3-7 strategy was encouraging but identified some challenges that if addressed can further improve implementation.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Thailand 1 2%
Belgium 1 2%
Unknown 57 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Master 14 24%
Student > Bachelor 5 8%
Student > Ph. D. Student 5 8%
Other 3 5%
Other 7 12%
Unknown 10 17%
Readers by discipline Count As %
Medicine and Dentistry 16 27%
Social Sciences 6 10%
Nursing and Health Professions 5 8%
Environmental Science 3 5%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 10 17%
Unknown 17 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 25 October 2017.
All research outputs
#1,287,013
of 12,050,803 outputs
Outputs from Infectious Diseases of Poverty
#44
of 416 outputs
Outputs of similar age
#44,680
of 322,264 outputs
Outputs of similar age from Infectious Diseases of Poverty
#2
of 13 outputs
Altmetric has tracked 12,050,803 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 416 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 88% of its peers.
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 322,264 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.