Détail du document
Identifiant
oai:pubmedcentral.nih.gov:9909...
Sujet
ResearchAuteur
Banerjee, Joyita Petrosyan, Sarah Rao, Abhijith R. Jacob, Steffi Khobragade, Pranali Yogiraj Weerman, Bas Chien, Sandy Angrisani, Marco Agarwal, Arunika Madan, Nirupam Sethi, Tanya Dey, Sharmistha Schaner, Simone Bloom, David E. Lee, Jinkook Dey, A. B.Langue
enEditeur
BioMed Central
Catégorie
BMC Public Health
Année
2023
Date de référencement
12/12/2023
Mots clés
changes survey phone compared diagnosis prevalence economic india reported respondents lasi-dad education covid-19 households studyRésumé
BACKGROUND: The coronavirus disease (COVID) pandemic caused disruption globally and was particularly distressing in low- and middle-income countries such as India.
This study aimed to provide population representative estimates of COVID-related outcomes in India over time and characterize how COVID-related changes and impacts differ by key socioeconomic groups across the life course.
METHODS: The sample was leveraged from an existing nationally representative study on cognition and dementia in India: Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD).
The wave-1 of LASI-DAD enrolled 4096 older adults aged 60 years and older in 3316 households from 18 states and union territories of India.
Out of the 3316 LASI-DAD households, 2704 with valid phone numbers were contacted and invited to participate in the Real-Time Insights COVID-19 in India (RTI COVID-India) study.
RTI COVID-India was a bi-monthly phone survey that provided insight into the individual’s knowledge, attitudes, and behaviour towards COVID-19 and changes in the household’s economic and health conditions throughout the pandemic.
The survey was started in May 2020 and 9 rounds of data have been collected.
FINDINGS TILL DATE: Out of the 2704 LASI-DAD households with valid phone numbers, 1766 households participated in the RTI COVID-India survey at least once.
Participants were in the age range of 18–102 years, 49% were female, 66% resided in rural area.
Across all rounds, there was a higher report of infection among respondents aged 60–69 years.
There was a greater prevalence of COVID-19 diagnosis reported in urban (23.0%) compared to rural areas (9.8%).
Respondents with higher education had a greater prevalence of COVID-19 diagnosis compared to those with lower or no formal education.
Highest prevalence of COVID-19 diagnosis was reported from high economic status compared to middle and low economic status households.
Comparing education gradients in experiencing COVID-19 symptoms and being diagnosed, we observe an opposite pattern: respondents with no formal schooling reported the highest level of experiencing COVID-19 symptoms, whereas the greatest proportion of the respondents with secondary school or higher education reported being diagnosed with COVID-19.
FUTURE PLANS: The study group will analyse the data collected showing the real-time changes throughout the pandemic and will make the data widely available for researchers to conduct further studies.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-15084-1.
Banerjee, Joyita,Petrosyan, Sarah,Rao, Abhijith R.,Jacob, Steffi,Khobragade, Pranali Yogiraj,Weerman, Bas,Chien, Sandy,Angrisani, Marco,Agarwal, Arunika,Madan, Nirupam,Sethi, Tanya,Dey, Sharmistha,Schaner, Simone,Bloom, David E.,Lee, Jinkook,Dey, A. B., 2023, Cohort Profile: Real-Time Insights of COVID-19 in India (RTI COVID-India), BioMed Central
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
methods salience efficiency quantization language slim-llm llms
Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques
using data ml prediction 0 suicide adolescents
