Who is at the highest risk from COVID-19 in India? Analysis of health, healthcare access, and socioeconomic indicators at the district level
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Abstract
Introduction
Despite measures such as travel restrictions and lockdowns, the novel coronavirus (SARS-COV-2) is projected to spread across India. Considering that a vaccine for COVID-19 is will not be available soon, it is important to identify populations with high risk from COVID-19 and take measures to prevent outbreaks and build healthcare infrastructure at the local level.
Methods
We used data from two large nationally representative household surveys, administrative sources, and published studies to estimate the risk of COVID-19 at the district level in India. We employed principal component analysis to create an index of the health risk of COVID-19 from demographic and comorbidity indicators such as the proportions of elderly population and rates of diabetes, hypertension, and respiratory illnesses. Another principal component index examined the socioeconomic and healthcare access risk from COVID-19, based on the standard of living, proportion of caste groups, and per capita access to public healthcare in each district.
Results
Districts in northern, southern and western Indian states such as Punjab, Tamil Nadu, Kerala, and Maharashtra were at the highest health risk from COVID-19. Many of these districts have been designated as COVID-19 hotspots by the Indian government because of emergent outbreaks. Districts in eastern and central states such as Uttar Pradesh, Bihar, and Madhya Pradesh have higher socioeconomic and healthcare access risk as compared with other areas.
Conclusion
Districts at high risk of COVID-19 should prioritize policy measures for preventing outbreaks, and improving critical care infrastructure and socioeconomic safety nets.
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SciScore for 10.1101/2020.04.25.20079749: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2020.04.25.20079749: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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