Evidence map›Paper›PMID 35937218›Full record

ArticleFrontiers in public health2022

Ranking Methodology for Evaluating Region-Wise COVID-19 Testing Performance in India.

Jasmine Kaur, Jasleen Kaur, Harpreet Singh

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jasmine KaurDivision of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India.
Jasleen KaurDivision of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India.
Harpreet SinghDivision of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For containment of COVID-19, most countries are following the isolate, test, treat and trace approach. Following the approach, India scaled up COVID-19 testing from about 5,000 tests per day at the end of March 2020 to more than 1 M tests per day in September 2020. Testing scale-up has seen a huge variation across states and union territories (UTs) with respect to growth rates, testing strategies, testing infrastructure, and deployment of various kit types (RT-PCR, Antigen, CBNAAT, etc). To understand the gaps in testing and prioritize appropriate interventions, it is important for national stakeholders to evaluate and rank states/UTs based on their testing performance. Indicators like total samples tested, total samples positive, tests per million, and positives per million are currently being used by epidemiologists and researchers for comparing the performance of various regions. This article proposes a robust ranking methodology to rank the states/UTs in India based on a comprehensive scoring developed by combining multiple variables for evaluating the testing performance of states/UTs. Since RT-PCR testing is considered the gold standard for COVID-19 testing, the composite score for testing performance in this article is defined by the ability of states/UTs to successfully deploy RT-PCR testing and reduce its positivity over time. Evaluating region-wise ranking can enable the identification of areas requiring immediate attention in poorly performing regions, thus channelizing efforts and resources in the right direction.

Indexed as

COVID-19COVID-19 TestingHumansIndiaCOVID-19COVID-19 testing performanceranking methodologyregion-wise COVID-19 testingtesting performance

Identifiers

PMID35937218
PMCPMC9355680

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.