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An econometric method for estimating population parameters from non-random samples: An application to clinical case finding

Overview of attention for article published in Health economics (Online), August 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 1,994)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
22 news outlets
twitter
1 tweeter
facebook
1 Facebook page
reddit
1 Redditor

Readers on

mendeley
42 Mendeley
Title
An econometric method for estimating population parameters from non-random samples: An application to clinical case finding
Published in
Health economics (Online), August 2017
DOI 10.1002/hec.3547
Pubmed ID
Authors

Rulof P. Burger, Zoë M. McLaren

Abstract

The problem of sample selection complicates the process of drawing inference about populations. Selective sampling arises in many real world situations when agents such as doctors and customs officials search for targets with high values of a characteristic. We propose a new method for estimating population characteristics from these types of selected samples. We develop a model that captures key features of the agent's sampling decision. We use a generalized method of moments with instrumental variables and maximum likelihood to estimate the population prevalence of the characteristic of interest and the agents' accuracy in identifying targets. We apply this method to tuberculosis (TB), which is the leading infectious disease cause of death worldwide. We use a national database of TB test data from South Africa to examine testing for multidrug resistant TB (MDR-TB). Approximately one quarter of MDR-TB cases was undiagnosed between 2004 and 2010. The official estimate of 2.5% is therefore too low, and MDR-TB prevalence is as high as 3.5%. Signal-to-noise ratios are estimated to be between 0.5 and 1. Our approach is widely applicable because of the availability of routinely collected data and abundance of potential instruments. Using routinely collected data to monitor population prevalence can guide evidence-based policy making.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 19%
Student > Ph. D. Student 7 17%
Student > Postgraduate 5 12%
Researcher 4 10%
Student > Doctoral Student 4 10%
Other 6 14%
Unknown 8 19%
Readers by discipline Count As %
Medicine and Dentistry 10 24%
Social Sciences 4 10%
Economics, Econometrics and Finance 4 10%
Computer Science 3 7%
Nursing and Health Professions 2 5%
Other 7 17%
Unknown 12 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 180. 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 23 November 2020.
All research outputs
#110,176
of 16,304,436 outputs
Outputs from Health economics (Online)
#4
of 1,994 outputs
Outputs of similar age
#3,870
of 274,519 outputs
Outputs of similar age from Health economics (Online)
#1
of 42 outputs
Altmetric has tracked 16,304,436 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,994 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has done particularly well, scoring higher than 99% 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 274,519 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.