key: cord-0696437-4t8tgrbh
authors: Kumar, Ashish; Arora, Anil; Sharma, Praveen
title: Letter to the editor regarding article: Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis (Ghoshal et al.)
date: 2020-08-31
journal: Diabetes & Metabolic Syndrome: Clinical Research & Reviews
DOI: 10.1016/j.dsx.2020.04.027
sha: 8137142d74e18405353d93707aee8b6cf9bf5731
doc_id: 696437
cord_uid: 4t8tgrbh

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Letter to the editor regarding article: Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetesrelated complications: A simulation model using multivariate regression analysis (Ghoshal et al.) To The Editor,

We read with interest the article by Ghoshal et al. [1] , in which a simulation model is made to estimate the effect of nationwide lockdown on the glycosylated hemoglobin (HbA1c) of diabetes patients. Based on their regression equation [Post-disaster A1c ¼ 0.95þ (0.09* number of days of lock-down) þ (1.04*Baseline A1c)], the authors calculated that from a baseline HbA1c value of 8.9, by the end of 30-day lockdown the HbA1c will rise to 11.16, and similarly, by the end of 90-day lockdown it will rise to an alarming level of 16.84! While the attempt by the investigators to study the effect of current lockdown on glycemic control of diabetes patients is commendable; however, their results are faulty and unreliable. The main problem with the model is the wrong assumption that the present lockdown is similar to the previous calamities such as hurricane Katrina (USA, 2005) , Kobe earthquake (Japan, 1995), Hanshin-Awashi earthquake (Japan, 1995), Gulf war (Iraq 1991), Murmura earthquake (Turkey, 1999) , and the Great Eastern Japan Earthquake plus tsunami (Japan, 2011). We wish to point out that use of data from these calamities for generation of this model is wrong. Except for the Gulf war, the other disasters were natural calamities (mainly earthquakes and hurricanes), where almost every family of the population was directly or indirectly affected by the disaster due to sudden destruction of houses; disruption of electric and water supply; disruption of sewer lines; and trauma or death occurring in a large number of families. So, during post-disaster periods, most diabetic patients would have had sudden disruption of their anti-diabetic medications. Similarly, their diabetes restricted diets would also have been replaced by the only available carbohydrate rich diet supplied by the relief agencies. In addition, with most houses being damaged, or family members being injured, the focus of the families would have shifted to restoration and rehabilitation of houses and care of injured family members, rather than ensuring diabetes control of their diabetic family members.

The present lockdown is no way like those post-earthquake or post-hurricane situations. During the present lockdown, most families are staying at home and are safe. Most diabetic patients have access to their chronic anti-diabetic medications. All pharmacy shops are open, and availability and delivery of medications are been ensured. Most physicians are available on phone, and if called, are guiding their diabetic patients telephonically. The dietary habits of most middle-class families are also not expected to have changed much and it is unlikely that diabetic patients would have switched their diets to high glycemic index foods.

The only exception are daily-wage workers whose livelihoods are disrupted due to the present lockdown: however, this population is not expected to constitute much to the diabetic population of India.

Hence, we do not expect any alarming rise of HbA1c in diabetic patients of India during the current lockdown, as predicted by the authors [1] .

Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: a simulation model using multivariate regression analysis

Praveen Sharma Sir Ganga Ram Hospital