ABSTRACT
AIM: We examined how, where an overall population is covered by universal health insurance, characteristics of disadvantaged populations interact to influence inequality in primary and secondary medical care utilization. SUBJECTS & METHODS: Disadvantaged populations, the focus of the study, were defined as populations who have lower socio-economic status (SES), who are elderly and/or reside in a peripheral area. Data from the 2009 Israeli National Health Survey were analysed using log-linear models to estimate utilization of medical care. RESULTS: The main findings were: a) pro-poor utilization of primary medical care among elderly populations, with higher odds ratios for low SES populations in the periphery; (b) lack of interaction between SES and primary medical care utilization among younger populations, between SES and secondary medical care utilization among the elderly and pro-rich utilization of secondary medical care among younger populations who did not regularly visit general practitioners (GP); (c) the odds ratios of secondary medical care utilization increased as SES decreased for both elderly and younger populations who also regularly visited a GP. CONCLUSION: Potential policy implications for disadvantaged populations, regarding possible inequality in primary and secondary medical care utilization, can be drawn using log-linear model analysis of interactions among characteristics (SES, age, location) of disadvantaged populations.
Subject(s)
Primary Health Care/statistics & numerical data , Secondary Care/statistics & numerical data , Universal Health Insurance , Vulnerable Populations/statistics & numerical data , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Child , Child, Preschool , Female , Health Surveys , Healthcare Disparities/statistics & numerical data , Humans , Infant , Infant, Newborn , Israel , Linear Models , Male , Middle Aged , Socioeconomic Factors , Young AdultABSTRACT
The aims of this study were to identify the predictors of people's willingness to be vaccinated against influenza and to determine how to improve the inoculation rate. The study was based upon the results of our original large-scale survey conducted in the USA in 2005. A model of bounded rationality can explain vaccination behavior fairly well: (a) people evaluate the costs and benefits of vaccination by applying risk aversion and time preference; (b) the 'status quo bias' of those who were vaccinated in the past affects their decision to be vaccinated in the future; and (c) overconfidence indirectly affects the decision through the moderation of perceived variables. Policy implications include: (a) dissemination of information about the vaccine is especially important among people who are inexperienced with the vaccine since they undervalue the effectiveness of vaccination; (b) lowering the total cost of vaccination, including time costs (for example, by offering the vaccine at workplaces) may raise the inoculation rate, especially among those inexperienced with the vaccine, since those who have experience with the vaccine tend to take it on a regular basis.
