ArticleAging and health research2023
Gender and age group-wise inequality in health burden and value of premature death from COVID-19 in India.
Article in Aging and health research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Productivity Losses due to Health Problems Arising from COVID-19 Pandemic: A Systematic Review of Population-Level Studies Worldwide.Applied health economics and health policy · 2025Pooled it
- The burden of COVID-19 based on disability-adjusted life years: a systematic review of available evidence.Frontiers in public health · 2025Pooled it
- India's death toll in 2021 during the COVID-19 pandemic: insights from delayed official civil registration data.Journal of global health · 2025Article
- Serum proteomics for the identification of biomarkers to flag predilection of COVID19 patients to various organ morbidities.Clinical proteomics · 2024Article
- Health Hazard Among Shrimp Cultivators in India: A Quantitative Burden of Disease Study.International journal of general medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Earlier studies have focused on the age-group-wise health burden of COVID-19 while few studies have focused on the gender-wise analysis of the burden of COVID-19. The present study estimated the health burden and value of premature mortality from COVID-19 based on gender and age. Methods: This study was based on secondary data collected from several sources of the government of India. To quantify the health burden, the disability-adjusted life years (DALY) method was used. An abridged life table was used to estimate the fall in life expectancy due to COVID-19. The value of premature mortality was estimated by using the human capital approach. Results: Among COVID-19 cases, 65.08% were males and 34.92% were females. The overall health burden caused by COVID-19 was 19,24,107 DALY in 2020, 43,40,526 DALY in 2021, and 8,08,124 DALY in 2022. The health burden per 1000 males was more than double that per 1000 females. This was due to higher rates of infection and case fatality rate among males compared to females. The age group 60-64 years experienced the highest loss of healthy life years per 1000 people, while the age group 55-59 years had the highest overall loss. Due to additional deaths from COVID-19, life expectancy decreased by 0.24 years in 2020, 0.47 years in 2021, and 0.07 years in 2022. The total value of premature death in the first three years of the COVID-19 pandemic amounted to 15,849.99 crores INR. Conclusion: In India, males and the older population were more susceptible to the impact of COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.