ArticleClinical epidemiology and global health
Comparing COVID-19 mortality across selected states in India: The role of age structure.
Article in Clinical epidemiology and global health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Factors associated with unexplained sudden deaths among adults aged 18-45 years in India - A multicentric matched case-control study.The Indian journal of medical research · 2023Article
- Gender and age group-wise inequality in health burden and value of premature death from COVID-19 in India.Aging and health research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMortality rates provide an opportunity to identify and act on the health system intervention for preventing deaths. Hence, it is essential to appreciate the influence of age structure while reporting mortality for a better summary of the magnitude of the epidemic.
objectivesWe described and compared the pattern of COVID-19 mortality standardized by age between selected states and India from January to November 2020.
methodsWe initially estimated the Indian population for 2020 using the decadal growth rate from the previous census (2011). This was followed by estimations of crude and age-adjusted mortality rate per million for India and the selected states. We used this information to perform indirect-standardization and derive the age-standardized mortality rates for the states for comparison. In addition, we derived a ratio for age-standardized mortality to compare across age groups within the state. We extracted information regarding COVID-19 deaths from the Integrated Disease Surveillance Programme special surveillance portal up to November 16, 2020.
resultsThe crude mortality rate of India stands at 88.9 per million population (118,883/1,337,328,910). Age-adjusted mortality rate (per-million) was highest for Delhi (300.5) and lowest for Kerala (35.9). The age-standardized mortality rate (per million) for India is (<15 years = 1.6, 15-29 years = 6.3, 30-44 years = 35.9, 45-59 years = 198.8, 60-74 years = 571.2, ≥75 years = 931.6). The ratios for age-standardized mortality increase proportionately from 45 to 59 years age group across all the states.
conclusionThere is high COVID-19 mortality not only among the elderly ages, but we also identified heavy impact of COVID-19 on the working population. Therefore, we recommend further evaluation of age-adjusted mortality for all States and inclusion of variables like gender, socio-economic status for standardization while identifying at-risk populations and implementing priority public health actions.
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.