Homeopathy
DOI: 10.1055/s-0041-1731314
Original Research Article

Impact of Bias in Data Collection of COVID-19 Cases

1  Directorate of AYUSH, Health and Family Welfare Department, Government of NCT of Delhi, New Delhi, India
,
Anjali Miglani
2  Homeopathic Unit, Delhi Government Health Centre, Dwarka Sector 12 Government of NCT of Delhi, New Delhi, India
,
Moumita Chakraborty
3  Homeopathic Unit, GTB Hospital, Government of NCT of Delhi, New Delhi, India
,
Baljeet Singh Meena
4  Homeopathic Unit, SRHC Hospital, Government of NCT of Delhi, New Delhi, India
,
Kavita Sharma
2  Homeopathic Unit, Delhi Government Health Centre, Dwarka Sector 12 Government of NCT of Delhi, New Delhi, India
,
Meeta Gupta
5  Directorate of AYUSH, Government of NCT of Delhi, New Delhi, India
,
Ashok Sharma
6  Homeopathic Unit, Delhi Secretariat, Government of NCT of Delhi, New Delhi, India
,
Vishal Chadha
7  Homeopathic Unit, DHAS Hospital, Government of NCT of Delhi, New Delhi, India
,
Purnima Rani
3  Homeopathic Unit, GTB Hospital, Government of NCT of Delhi, New Delhi, India
,
Rahul Kumar Singh
4  Homeopathic Unit, SRHC Hospital, Government of NCT of Delhi, New Delhi, India
,
Lex Rutten
8  Independent Researcher, Breda, The Netherlands
› Author Affiliations
Funding Support The infrastructure of the Government of NCT of Delhi, India, was used in the conduct of this study. No special funding was provided.

Abstract

Background/Objective Prognostic factor research (PFR), prevalence of symptoms and likelihood ratio (LR) play an important role in identifying prescribing indications of useful homeopathic remedies. It involves meticulous unbiased collection and analysis of data collected during clinical practice. This paper is an attempt to identify causes of bias and suggests ways to mitigate them for improving the accuracy in prescribing for better clinical outcomes and execution of randomized controlled studies.

Methods A prospective, open label, observational study was performed from April 2020 to December 2020 at two COVID Health Centers. A custom-made Excel spreadsheet containing 71 fields covering a spectrum of COVID-19 symptoms was shared with doctors for regular reporting. Cases suitable for PFR were selected. LR was calculated for commonly occurring symptoms. Outlier values with LR ≥5 were identified and variance of LRs was calculated.

Results Out of 1,889 treated cases of confirmed COVID-19, 1,445 cases were selected for pre-specified reasons. Nine medicines, Arsenicum album, Bryonia alba, Gelsemium sempervirens, Pulsatilla nigricans, Hepar sulphuricus, Magnesia muriaticum, Phosphorus, Nux vomica and Belladonna, were most frequently prescribed. Outlier values and large variance for Hepar sulphuricus and Magnesia muriaticum were noticed as indication of bias. Confirmation bias leading to lowering of symptom threshold, keynote prescribing, and deficiency in checking of all symptoms in each case were identified as the most important sources of bias.

Conclusion Careful identification of biases and remedial steps such as training of doctors, regular monitoring of data, checking of all pre-defined symptoms, and multicenter data collection are important steps to mitigate biases.



Publication History

Received: 23 February 2021

Accepted: 15 April 2021

Publication Date:
09 September 2021 (online)

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