Evidence map›Paper›PMID 36962751›Full record

ArticlePLOS global public health2022

All-cause excess mortality across 90 municipalities in Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021).

Rolando J Acosta, Biraj Patnaik, Caroline Buckee, Mathew V Kiang, Rafael A Irizarry, Satchit Balsari, Ayesha Mahmud

Open access · goldFull text read
In one paragraph

Article in PLOS global public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 28% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. 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

7 authors at 6 institutions in 2 countries.

Rolando J AcostaDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0002-7690-9590
Biraj PatnaikNational Foundation for India, New Delhi, India.
Caroline BuckeeCenter for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Mathew V KiangDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Palo Alto, California, United States of America.
Rafael A IrizarryDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Satchit BalsariDepartment of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0002-5492-1900
Ayesha MahmudDepartment of Demography, University of California, Berkeley, California, United States of America.ORCID https://orcid.org/0000-0002-2282-8613
Harvard University · USBeth Israel Deaconess Medical Center · USCenter for Disease Dynamics, Economics & Policy · USNational Foundation for India · INStanford University · USUniversity of California, Berkeley · US

Funding

GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9M
Data Analysis Tools for Emerging High-Throughput TechnologiesR35GM131802 · NIGMS · DANA-FARBER CANCER INST · PI Rafael Angel Irizarry · 2019 to 2026
$4.1M
NIEHS NIH HHS T32 ES007142NIGMS NIH HHS R35 GM131802
6 · The paper itself

Abstract

Official COVID-19 mortality statistics are strongly influenced by local diagnostic capacity, strength of the healthcare and vital registration systems, and death certification criteria and capacity, often resulting in significant undercounting of COVID-19 attributable deaths. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock-both direct and indirect-of the COVID-19 pandemic. Here, we use data from civil death registers from a convenience sample of 90 (of 162) municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to weekly data from January 2019 to February 2020, we estimated excess mortality over the course of the pandemic from March 2020 to April 2021. During this period, the official government data reported 10,098 deaths attributable to COVID-19 for the entire state of Gujarat. We estimated 21,300 [95% CI: 20, 700, 22, 000] excess deaths across these 90 municipalities in this period, representing a 44% [95% CI: 43%, 45%] increase over the expected baseline. The sharpest increase in deaths in our sample was observed in late April 2021, with an estimated 678% [95% CI: 649%, 707%] increase in mortality from expected counts. The 40 to 65 age group experienced the highest increase in mortality relative to the other age groups. We found substantial increases in mortality for males and females. Our excess mortality estimate for these 90 municipalities, representing approximately at least 8% of the population, based on the 2011 census, exceeds the official COVID-19 death count for the entire state of Gujarat, even before the delta wave of the pandemic in India peaked in May 2021. Prior studies have concluded that true pandemic-related mortality in India greatly exceeds official counts. This study, using data directly from the first point of official death registration data recording, provides incontrovertible evidence of the high excess mortality in Gujarat from March 2020 to April 2021.

Identifiers

PMID36962751
PMCPMC10021770
OpenAlexW4291993681

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read117
identifiers read3
Read underepoch 390

Registered trials

None linked

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.