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Monte Carlo simulation-based estimation for the minimum mortality temperature in temperature-mortality association study

Overview of attention for article published in BMC Medical Research Methodology, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

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2 tweeters

Citations

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12 Dimensions

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10 Mendeley
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Title
Monte Carlo simulation-based estimation for the minimum mortality temperature in temperature-mortality association study
Published in
BMC Medical Research Methodology, September 2017
DOI 10.1186/s12874-017-0412-7
Pubmed ID
Authors

Whanhee Lee, Ho Kim, Sunghee Hwang, Antonella Zanobetti, Joel D. Schwartz, Yeonseung Chung

Abstract

Rich literature has reported that there exists a nonlinear association between temperature and mortality. One important feature in the temperature-mortality association is the minimum mortality temperature (MMT). The commonly used approach for estimating the MMT is to determine the MMT as the temperature at which mortality is minimized in the estimated temperature-mortality association curve. Also, an approximate bootstrap approach was proposed to calculate the standard errors and the confidence interval for the MMT. However, the statistical properties of these methods were not fully studied. Our research assessed the statistical properties of the previously proposed methods in various types of the temperature-mortality association. We also suggested an alternative approach to provide a point and an interval estimates for the MMT, which improve upon the previous approach if some prior knowledge is available on the MMT. We compare the previous and alternative methods through a simulation study and an application. In addition, as the MMT is often used as a reference temperature to calculate the cold- and heat-related relative risk (RR), we examined how the uncertainty in the MMT affects the estimation of the RRs. The previously proposed method of estimating the MMT as a point (indicated as Argmin2) may increase bias or mean squared error in some types of temperature-mortality association. The approximate bootstrap method to calculate the confidence interval (indicated as Empirical1) performs properly achieving near 95% coverage but the length can be unnecessarily extremely large in some types of the association. We showed that an alternative approach (indicated as Empirical2), which can be applied if some prior knowledge is available on the MMT, works better reducing the bias and the mean squared error in point estimation and achieving near 95% coverage while shortening the length of the interval estimates. The Monte Carlo simulation-based approach to estimate the MMT either as a point or as an interval was shown to perform well particularly when some prior knowledge is incorporated to reduce the uncertainty. The MMT uncertainty also can affect the estimation for the MMT-referenced RR and ignoring the MMT uncertainty in the RR estimation may lead to invalid results with respect to the bias in point estimation and the coverage in interval estimation.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 10%
Student > Doctoral Student 1 10%
Student > Bachelor 1 10%
Professor 1 10%
Student > Ph. D. Student 1 10%
Other 2 20%
Unknown 3 30%
Readers by discipline Count As %
Nursing and Health Professions 2 20%
Environmental Science 2 20%
Mathematics 1 10%
Unspecified 1 10%
Unknown 4 40%

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 26 November 2019.
All research outputs
#1,780,389
of 15,099,074 outputs
Outputs from BMC Medical Research Methodology
#309
of 1,407 outputs
Outputs of similar age
#47,981
of 272,658 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 15,099,074 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,407 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 77% 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 272,658 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 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them