Does affordability matter? Examining the trends and patterns in health care expenditure in India

2020 ◽  
Vol 33 (4) ◽  
pp. 207-218 ◽  
Author(s):  
Rinshu Dwivedi ◽  
Jalandhar Pradhan

Background Absence of better financing mechanism results in higher out of pocket expenditure and catastrophe, which leads to impoverishment and poverty especially among low- and middle-income countries like India. This paper examines the major characteristics associated with the higher out of pocket expenditure and provides an insight from Andersen’s behavioural model that how predisposing, enabling and need factors influence the level and pattern of out of pocket expenditure in India. Methods Data has been extracted from three rounds of nationally representative consumer expenditure surveys, i.e. 1993–1994, 2004–2005 and 2011–2012 conducted by the Government of India. States were categorized based on regional classification, and adult equivalent scale was used to adjust the household size. Multiple Generalized-Linear-Regression-Model was employed to explore the relative effect of various socio-economic covariates on the level of out of pocket expenditure. Results The gap has widened between advantaged and disadvantaged segment of the population along with noticeable regional disparities among Indian states. Generalized-Linear-Regression-Model indicates that the most influential predisposing and enabling factor determining the level of out of pocket expenditure were age composition, religion, social-group, household type, residence, economic status, sources of cooking and lighting arrangements among the households. Conclusions Present study suggests the need for strengthening the affordability mechanism of the households to cope with the excessive burden of health care payments. Furthermore, special consideration is required to accommodate the needs of the elderly, rural, backward states and impoverishment segment of population to reduce the unjust burden of out of pocket expenditure in India.

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Nicolas Pröllochs ◽  
Dominik Bär ◽  
Stefan Feuerriegel

AbstractEmotions are regarded as a dominant driver of human behavior, and yet their role in online rumor diffusion is largely unexplored. In this study, we empirically study the extent to which emotions explain the diffusion of online rumors. We analyze a large-scale sample of 107,014 online rumors from Twitter, as well as their cascades. For each rumor, the embedded emotions were measured based on eight so-called basic emotions from Plutchik’s wheel of emotions (i.e., anticipation–surprise, anger–fear, trust–disgust, joy–sadness). We then estimated using a generalized linear regression model how emotions are associated with the spread of online rumors in terms of (1) cascade size, (2) cascade lifetime, and (3) structural virality. Our results suggest that rumors conveying anticipation, anger, and trust generate more reshares, spread over longer time horizons, and become more viral. In contrast, a smaller size, lifetime, and virality is found for surprise, fear, and disgust. We further study how the presence of 24 dyadic emotional interactions (i.e., feelings composed of two emotions) is associated with diffusion dynamics. Here, we find that rumors cascades with high degrees of aggressiveness are larger in size, longer-lived, and more viral. Altogether, emotions embedded in online rumors are important determinants of the spreading dynamics.


Author(s):  
Alemayehu Siffir Argawu ◽  
Gizachew Gobebo ◽  
Ketema Bedane ◽  
Temesgen Senbeto ◽  
Reta Lemessa ◽  
...  

The aims of this study was to predict COVID-19 new cases using multiple linear regression model based on May to June 2020 data in Ethiopia. The COVID-19 cases data was collected from the Ethiopia Ministry of Health Organization Facebook page. Pearson’s correlation analysis and linear regression model were used in the study. And, the COVID-19 new cases was positively correlated with the number of days, daily laboratory tests, new cases of males, new cases of females, new cases from Addis Ababa city, and new cases from foreign natives. In the multiple linear regression model, COVID-19 new cases was significantly predicted by the number of days at 5%, the number of daily laboratory tests at 10%, and the number of new cases from Addis Ababa city at 1% levels of significance. Then, the researchers recommended that Ethiopian Government, Ministry of Health, and Addis Ababa city administrative should give more awareness and protections for societies, and they should open again more COVID-19 laboratory testing centers. And, this study will help the government and doctors in preparing their plans for the next times.


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