Dataset Information

The pattern of health insurance economic resilience in the Covid 19 pandemic shock.

ABSTRACT:

Objective

Health insurance is based on people's significant risks in receiving health services that they cannot afford alone. Since the outbreak of the corona epidemic, the health insurance system has suffered many economic problems. Designing a model of a health insurance system based on the requirements of a resilient economy can improve the functions of this system in the corona crisis.

Results

In this research 12, structural components were obtained in the form of 4 conceptual components. The 4 main conceptual components are Knowledge-based economy, Economic stability, Economic resilience, and justice. The knowledge-based economy is the basis for the formation of economic resilience in the health insurance systems. Health insurance systems will achieve two crucial intermediate results, namely economic resilience, and economic stability, by building the basic infrastructure of a knowledge-based economy. In the long run, maintaining such intermediate results is the foundation of justice in the health insurance system.

SUBMITTER: Kharazmi E

PROVIDER: S-EPMC8460182 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Previous pandemics have rarely affected everyone equally and, so far, the COVID-19 pandemic is no exception. Emerging evidence has shown that incidence rate, hospitalisation rate, and mortality due to COVID-19 are higher among people in lower socio-economic position (SEP). In addition, first investigations indicate that not everyone is equally affected by this pandemic's collateral public health damage. Using a stratified random sample of 1,004 participants living in Vienna, a Central European city with approximately 1.9 million inhabitants, this study analysed the distribution of 10 adverse health-related and socio-economic outcomes attributable to the COVID-19 pandemic across socio-economic strata. To this end, we estimated differences in the incidence rate of these outcomes by SEP and each of its indicators using zero-inflated Poisson and logistic regression models, adjusted for age and gender. Data were collected during first lockdown measures between 27 April and 17 May 2020. Differences in the incidence rate between the two lowest and two highest SEP groups were clearly visible. Participants in the lowest SEP category had a 32.96% higher incidence rate (IRR = 1.333 [95% CI: 1.079-1.639]), and participants in the second lowest SEP category had a 44.69% higher incidence rate (IRR = 1.447 [95% CI: 1.190-1.760]) compared with participants in the highest SEP category. In sum, 6 out of 10 adverse COVID-19-related outcomes were, to a greater or lesser extent, disproportionately experienced by Viennese residents in lower SEP. Inequalities were most visible between income groups and for the outcomes job loss, worsening of the financial situation, and worse mental health. These results strengthen and extend the current evidence on the unequally distributed burden of the COVID-19 pandemic. In light of effect heterogeneity across SEP indicators, we encourage future investigators to pay increased attention to their operationalisation of SEP. Such awareness will help to correctly identify those in most urgent need of supportive polices.

Dataset Information

The pattern of health insurance economic resilience in the Covid 19 pandemic shock.

Objective

Results

Similar Datasets

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