ABSTRACT
This study aims to examine socio-economicfactors, the perceptionofpersonal health insurance products and individual’s personality traits to unbundle the paradox that prevents people from subscribing to health insurance schemes. In spite of its significance, a subscription paralysis has been observed in India for health insurance product. People who can afford Medicare are also found to be either unaware or aversive to it. This survey was conducted in the region of Punjab, India. For a primary data an offline/online questionnaire was sent and collected from the sample respondents from Amritsar, Jalandhar, Mohali, Patiala and Ludhiana. Response evinced by 502 respondents was formed as a part of study for the further data analysis. For assessing the relationships between variables Correlation and Anova were applied as a part of quantitative measuring tool. Finally, regression technique was used to estimate the factors that influence respondents’ decision to purchase health insurance. Age, income of family members, marital status and level of education product perception were found to be significantly associated with health insurance subscription in the region. COVID-19, Marketing forces, Hospital Cost & Tax Benefit showed a positive relationship with respondent’s insurance status whereas financial Literacy and wealth maximization has no significant effect on health insurance. No such research study has been conducted so far on impact of COVID-19 on health insurance sector in this region. This paper reveals the importance of health insurance during the pandemic and the awareness level of people increased. The socio-economic factors, individuals’ product knowledge, awareness perception of cost induce health insurance demand and its subscription in the region. © 2022 Transnational Press London
ABSTRACT
Objectives: This study aims to assess the magnitude of anxiety, stress, and depression among private practitioners during the COVID pandemic 2020. Materials and Methods: A descriptive, observational and cross-sectional study was done using a semi-structured questionnaire through electronic media in the form of Google Forms. Patient health questionnaire 4 (PHQ-4) and Perceived Stress Scale-10 were incorporated into the questionnaire to assess the psychological status in November 2020. Results: A total of 157 private practitioners participated in the study. Males constituted 60%. Participants have mean age (SD) of 44.2 (6.9) years with work experience and a mean (SD) of 16.3 (6.78) years. The anxiety and depression subscales of the PHQ-4 scale showed that 54.7% of the private practitioners have anxiety subscale ≥3 and 28% have depression subscale >3 needing further psychiatric evaluation. Significant predictors of anxiety and depression were female gender, younger and less experienced, and those providing inpatient services in COVID care isolation facilities. Conclusion: There is a high prevalence of anxiety, stress, and depression among private practitioners comparable with other countries. ©2022 Published by Scientific Scholar on behalf of Indian Journal of Physiology and Pharmacology.
ABSTRACT
Worldwide increasing cases of COVID-19 are putting high pressure on healthcare services. The coronavirus epidemic caused announcing emergency cases in India. The virus started with one infected case by 30th January, 2020, in Kerala, where the first death due to corona in Karnataka and 73 announced cases were reported by 12th March, 2020. 1024 announced cases were reported by 29th March, 2020.This indicates that the number of confirmed cases is increasing rapidly, causing national crises for India. This study aims is to fill a gap between previous studies and the current development of COVID-19 spreading, by extracting a relationship between corona positives as independent and corresponding deaths as a dependent variable. This research statistically analyses the mortality in 10 days of every month. A mathematical model to predict the new deceased cases corresponding infected cases in a practical scenario is proposed. An approximate prediction of mortality corresponding to new predicted cases can be easily performed using the proposed model. As most of the other countries have reached their peaks, confirmed cases start decreasing. By analyzing these countries’ data considering the lockdown, the peak ratio is identified using all countries’ population and the decreasing rate of confirmed cases after the peak has been achieved. The same calculation has been done for death and recovered cases. This average peak ratio is used to identify India’s COVID patients’ peak value. The decreasing rate is also used to define the rate of confirmed cases and mortality after the peak has occurred. The model has also been verified in different countries to identify the significance of the model. © 2020, Novel Carbon Resource Sciences. All rights reserved.