Covid-19 and sex disaggregated data -Voice from India

Globally a daily surge is observed in cases of COVID-19.   There are varied reports on spread of infection. The information of hotspot areas of infection spread is shared through media. This might affect the perspective towards SARS-COV-2 infection. As we need to study the epidemiology of disease closely and results should be published to the community. This will pave path to design cost effective strategies and spread awareness of more specific preventive measures. These could be more specific and acceptable. Ultimately, having impact on the controlling the infection.

The media flashing news updates of trends in the form of numbers and graph. However, there were few mentions of more men being positive with SARS-COV-2 than women. Obviously, it’s due to the gender norms of men stepping out for utilities and finance related works. The genetics might play role in affecting the males because X linked chromosome has lower risk to get the infection (Devlin 2020). However, clear evidence of genetic predominance for men is yet to be proven. Very few countries (excluding India) are showcasing the data related descriptive statistics to community. This information will strengthen the understanding of epidemiological investigations. And this will be valuable for designing interventional strategies and prime for the discovery of vaccine. Thus, it is need for global health development to display sex disaggregated data analysis.

Considering the fatality rate in China was reported in March 2020 was 4.7 % in male and 2.8 % female respectively. It was reported that the fatality rate is low among women. Similarly, In Denmark death occurred in twice more in males than females, out of the total number confirmed cases. However, the results are based on assumption and need to be studied further. On the contrary, majority of the health workers who contracted the infection were women (90%). Thus, there is need to study specifically in the Indian population. Because of the high infectivity in no time the infection will spread family members. In Iran there was minor difference between male and female mortality (5.4 vs 5.9). Notably, the infection rate is higher among females as compared to males(Sandoui 2020).

We cannot forget that India is called “diabetes capital of world”, so we have to put extra efforts while being cautious. Women have longer life expectancy and in the given situation it will increase the vulnerability. Furthermore, the social determinants play major role in a disease causation.  Therefore, just on the basis of mortality trends concluding the higher propensity of COVID 19 is dubious at this stage. The stereotypical roles as caregiver and family expectations based on gender influence the risk of Pandemic for women. So, in the gendered society it will be very much important to know the reasons for gender gap in the COVID-19 infection, as the decision making is rarely given to woman, for instance to access health care, to excise reproductive rights or even use of contraceptive. In the previous epidemics it was observed that the maternal health services, violence against women are given secondary importance. It happens as a result of strained health institutions and inpatient emergencies.  Hence, increasing the vulnerability of women. Furthermore, there is lack of evidence showing the variation of infection rates and certain reason behind mortality rates in India. So, the breakdown of data for death and cases should be available in open domain. In view of the socio- cultural influences in India, it’s pivotal to analyze the sex disaggregated data using gender lens.

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