The Oxford University-AstraZeneca’s vector-based vaccine called Covishield (ChAdOx1 nCoV- 19 Vaccine) is being manufactured and distributed by Serum Institute of India (SII). National roll out of this vaccine was in a phased manner starting from 16th January 2021. At present, many states are facing shortage of vaccines. Government of India kept changing its policy on dosing gap of Covishield vaccine based on researches. The latest recommendation citing “real-life evidence” from the UK is to extend the two doses of Covishield to 12-16 weeks. This reasonable approach will not only a breathing space for the government but also aids in free up doses for a larger number of people to get their first dose of the vaccine.
The COVID-19 Pandemic has changed the global picture in the field of public health in the past few years. This critical time has made life very uncertain for everyone. Man has been constantly striving to discover measures to stop the spread of this deadly virus; the COVID-19 vaccine is one such endeavour. The fear and anxiety towards a new vaccine is high among the general public. With every new vaccination, constant monitoring and documentation of the vital happenings is very important. Thus, the reporting of Adverse events following immunization (AEFI) needs to be strengthened and implemented more efficiently to analyse the efficacy of this vaccination drive with respect to protection from the disease without causing any harmful adverse effects.
Background: Early diagnosis is the mainstay for reducing complications associated with diabetes. Though blood sugar testing was made freely available, there is gap in utilization of the same. Hence, the current study was conducted to assess the detection gap in diabetes compared to regional estimates. Also, to assess the extent of screening and factors associated with not getting screened.Methods: A community based cross-sectional analytical study was conducted in the selected rural areas of Puducherry. House to house enumeration survey was conducted in the purposively selected rural areas during September-2016 to February-2017. Information on diabetes status, status of screening in last one year was collected from individuals aged more than eighteen years. Data was entered in Epidata 3.1 and analyzed using Stata 12 software. Percentages with 95% CI were used to estimate the detection gap in diabetes on comparing with regional estimates given by IDF (8.6%). Generalized Linear Models were used to assess the individual level characteristics associated with not screening for diabetes.Results: Of the total 1844 enumerated individuals, 1423 (78.6%) were above 18 years of age. The detection gap for diabetics based on IDF was 39% and based on DLHS-4 was 69.8%. Of the eligible individuals for screening 81.9% (95% CI: 79.7%-83.9%) were not screened for diabetes in last one year.Conclusions: With high detection gap and low utilization of screening, there is need for developing innovative strategies like task shifting, ICT utilization for screening and targeted screening.
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