Evidence map›Paper›PMID 35288381›Full record

ArticleBMJ open2022

Epidemiology of COVID-19 and effect of public health interventions, Chennai, India, March-October 2020: an analysis of COVID-19 surveillance system.

Jagadeesan M, Parasuraman Ganeshkumar, Prabhdeep Kaur, Hemalatha Masanam Sriramulu, Manikandanesan Sakthivel, Polani Rubeshkumar, Mohankumar Raju, Lakshmidevi Murugesan, Raajkumar Ganapathi, Mahalakshmi Srinivasan and 6 more

Abstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Modelling the first wave of COVID-19 in India.PLoS computational biology · 2022
    Article
  5. 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

16 authors.

Jagadeesan MGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Parasuraman GaneshkumarDivision of Epidemiology, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Prabhdeep KaurDivision of Non-communicable Diseases, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India kprabhdeep@gmail.com.ORCID 0000-0002-2605-3739
Hemalatha Masanam SriramuluGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Manikandanesan SakthivelField Epidemiology Training Program, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Polani RubeshkumarField Epidemiology Training Program, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.ORCID 0000-0002-0418-7592
Mohankumar RajuField Epidemiology Training Program, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.ORCID 0000-0001-9316-8977
Lakshmidevi MurugesanGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Raajkumar GanapathiGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Mahalakshmi SrinivasanGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Aswini SukumarDivision of Non-communicable Diseases, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Kumaravel IlangovanField Epidemiology Training Program, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.ORCID 0000-0003-0111-511X
Madhusudhan ReddyGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Divyadharshini ShanmugamGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Prakash GovindasamyGreater Chennai Corporation, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
Manoj MurhekarDivision of Epidemiology, ICMR - National Institute of Epidemiology, Chennai, Tamil Nadu, India.ORCID 0000-0002-1720-7628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo describe the public health strategies and their effect in controlling the COVID-19 pandemic from March to October 2020 in Chennai, India.

settingChennai, a densely populated metropolitan city in Southern India, was one of the five cities which contributed to more than half of the COVID-19 cases in India from March to May 2020. A comprehensive community-centric public health strategy was implemented for controlling COVID-19, including surveillance, testing, contact tracing, isolation and quarantine. In addition, there were different levels of restrictions between March and October 2020.

participantsWe collected the deidentified line list of all the 192 450 COVID-19 cases reported from 17 March to 31 October 2020 in Chennai and their contacts for the analysis. We defined a COVID-19 case based on the real-time reverse transcriptase-PCR (RT-PCR) positive test conducted in one of the government-approved labs. OUTCOME MEASURES: The primary outcomes of interest were incidence of COVID-19 per million population, case fatality ratio (CFR), deaths per million, and the effective reproduction number (R

resultsOf the 192 450 RT-PCR confirmed COVID-19 cases reported in Chennai from 17 March to 31 October 2020, 114 889 (60%) were males. The highest incidence was 41 064 per million population among those 61-80 years. The incidence peaked during June 2020 at 5239 per million and declined to 3627 per million in October 2020. The city reported 3543 deaths, with a case fatality ratio of 1.8%. In March, R

conclusionThe combination of public health strategies might have contributed to controlling the COVID-19 epidemic in a large, densely populated city in India. We recommend continuing the test-trace-isolate strategy and appropriate restrictions to prevent resurgence.

Indexed as

COVID-19HumansIndiaMalePandemicsPublic HealthQuarantineCOVID-19epidemiologypublic health

Identifiers

PMID35288381
PMCPMC8921469

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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.