By G. Kamak. Prescott College.

In addition to Poland order trazodone 100 mg on-line, France (29%) order trazodone 100 mg without prescription, Slovenia (27%) buy 100 mg trazodone mastercard, Spain (24%), Portugal (23%) and Switzerland (19%) belong to the countries where the perceived likelihood of being checked for alcohol is above the European average (18%). Not only in Denmark, but also in Finland (4%), in Germany (8%), in the United Kingdom (9%), in Ireland (9%) and in the Netherlands (10%), the car drivers have a particularly low expectation of being checked for alcohol. In most countries where the expectation to be checked for alcohol is high, the anticipation of possible drugs controls is also rather high. In Poland, the gap between the perceived likelihood of being checked for alcohol (44%) and for drugs (16%) is quite big, but the anticipation of possible drug controls (16%) is still above the European average (11%). In the countries with low expectations of alcohol controls, the expectations for drug controls are even lower. There is also an association between the perceived likelihood of being checked for impaired driving and the level of enforcement in the different countries. This link is presented and discussed in the thematic report Enforcement and support for road safety policy measures. There is almost no difference between men and women concerning the perceived likelihood of being controlled for alcohol or drugs (Figure 19). The percentage of car drivers estimating that the chance of being checked is big or very big is slightly higher among women than men, but it is statistically not significant (p>. Female On a typical journey, how likely is it that you 12% Male (as a driver) will be checked by the police for the use of illegal drugs? The perceived likelihood of being controlled for alcohol or drugs clearly depends on the age groups (Figure 20). The younger the respondents, the more likely they are to expect a control for alcohol or for drugs. The differences between the age groups are more pronounced in the case of expected alcohol controls than of expected drugs controls. On a typical journey, how likely is it that you (as a 15% driver) will be checked by the police for alcohol, in 17% other words, being subjected to a Breathalyser test? Notes: (1) % of (very) big chance: scores 4 and 5 on a 5-point scale from 1 ‘very small chance’ to 5 ‘very big chance’. Further analysis What are the factors affecting driving under the influence of an impairing substance? In order to investigate the association of self-declared impaired driving with the various predictors, we developed four logistic regression models. The outcome variable in these models is the dichotomized variable indicating the absence (never) or presence (at least once) of self-declared impaired driving. The following explanatory variables were considered: socio-demographic variables (gender, age group and level of education), driving frequency, acceptability of impaired driving, attitudes towards impaired driving, support for road safety measures, risk perception, reported police checks and perceived likelihood of being checked for impaired driving. In models 2 and 4 the variable ‘countries’ has also been taken into consideration. Factors affecting drink-driving Possible factors affecting (self-declared) drink-driving are presented in this section, in the first logistic regression model without the variable ‘countries’ (Table 3), and in the second logistic regression model with the variable ‘countries’ (Table 4). For the logistic regression models on drink-driving, we chose the less restrictive question, because of the similarity of the formulation of the question on drug-driving. Other advantages of the question on drink-driving we selected for the logistic regression is that it does not take into account the differences in national alcohol limits and encompasses a longer time period. In the analysis, we selected only car drivers who answered that they drive at least a few days a year and for whom the level of education was known. It is the only statistically significant result concerning the level of education. According to this model, the likelihood of (self- declared) drink-driving is lower among drivers with a lower level of education. In comparison with the persons who drive only a few days per year, the odds ratios are respectively 1. There is also a significant association between self-declared drink-driving and perception of risk. Drivers who think that an important part of the road traffic accidents are caused by alcohol are less likely to report that they drink-drive than drivers who think that this percentage is below 6%. Table 3: Logistic regression model for drink-driving in the past 12 months (Model 1). Compared to Austria (reference category in the logistic regression model of Table 4), the odds of self- declared drink-driving increase significantly by 77% in Belgium, 38% in Denmark and 29% in Portugal. Factors affecting drug-driving In this section, the first logistic regression model presents possible factors affecting (self-declared) drug-driving without the variable ‘countries’ (Table 5) and the second logistic regression model includes these same factors as well as the variable ‘countries’ (Table 6). No association can be pointed out either between level of education and drug-driving or between driving frequency and drug-driving. This means that drivers fully in agreement with these statements report less often that they drug-drive.

There are associated costs of patients travelling overseas – the necessity to monitor/regulate advertising and provide detailed information and advice to support potential or actual medical tourists carries its own costs trazodone 100mg generic. There is the likelihood that large numbers of medical tourists will impact on the source country‘s own health system trusted trazodone 100mg, perhaps increasing trends that are encouraged by the current domestic private provision generic trazodone 100mg line. Such flows also reduce the pressure for investment in particular facilities and technology. Indeed, there is an argument that some types of outflows of medical tourists for treatments that could be provided locally signal a failure of policy and delivery in the sender country. But it is also within higher income countries where the possibilities of a exacerbating two-tier system can emerge. If, for example, eligibility for services such as fertility or dental work is tightened, then 30 those with private resources may choose to travel overseas to maintain access (thus exercising choice and exit). Patients who are able to circumvent waiting times highlight the familiar issues of access and equity. In those countries where third-party insurers are exploring medical tourism as a provider option, those that are insured under these plans – perhaps unable to get alternative cover – may find themselves disadvantaged. Clearly, however, source-country payers may benefit from outflows of patients – including employers and employees contributing to health plans, and the public insurance system itself. There may be some opportunities for financial benefit if medical tourism is an option. Mattoo and Rathindran (2006), for example, highlight that for the United States 15 treatment that would show savings of $1. Some subsets of the population, such the Indian Diaspora, may prefer to go back ―home‖ for treatment, and may be happy to cross-subsidise some of the costs, or may not need an accompanying adult, further increasing the amount saved. Plausibly, the health systems within source countries could develop relations with off-shore medical tourism facilities to leverage cost savings – providing individuals with a choice of overseas destinations. This could also reduce waiting lists – and reflects a form of outsourcing or more ‗collective‘ medical travel (Smith et al. One of the drivers for medical tourism is price because treatments may often be available locally within the private sector, but at greater cost. There are arguments that some medical systems are inefficient and face restrictive barriers to entry. A development such as medical tourism can potentially exert competitive pressure on systems importing health care and help drive down the costs and prices offered in domestic systems (Herrick, 2007). Medical tourism may encourage economies to maximize their comparative advantage in labour costs, technology and/or capacity. We have seen in Section 4 that source counties – or those importing health services – may benefit from medical tourism through alleviating waiting lists and lowering healthcare costs, but may risk quality of care and legal liability. In this section we turn our attention to destination countries – or those exporting health services. Medical tourism has historically been from lower to higher income countries, with better medical facilities and more highly trained and qualified professionals. However, this trend is now reversing, and most recently ―hubs‖ of medical excellence have developed which attract people regionally (Horowitz et al. The main importing countries (those where the medical tourists come from) are in North America and Western Europe. Although current levels of movement are relatively limited, as outlined in Section Four , the potential, if payment was covered by third-party payers, is significant. The main exporting countries (those who provide the services to medical tourists) are located across all continents, including Latin America, Eastern Europe, Africa and Asia. For instance, Thailand and India specialise in orthopaedic and cardiac surgery, whereas Eastern European countries are hotspots for dental surgery (Smith et al. Nonetheless, many of the issues are quite generic and will affect any destination country, regardless of the level of economic development, just to a greater or lesser extent. It is also worth noting that the magnitude of the possible effects being discussed is largely unknown – in many cases the potential or actual occurrence of these effects has been observed, but the scale of effect, and how this scale may differ between countries is an unknown quantity (Smith et al. Most countries that engage in delivering care to medical tourists do so to increase the level of direct foreign exchange earnings coming into their country; to improve their balance-of-payments position (Timmermans, 2004, Ramírez de Arellano, 2007, Turner, 2007). To some extent this might be income thought of as accruing directly to the health system. For instance, in Singapore the authorities stress that involvement in medical tourism enables them to provide a broader range of clinical services to the indigenous population than would be the case if income was not being generated through medical tourism (India and Malta use such arguments) (Lee, 2010, Lee and Hung, 2010). Similarly, Ramírez de Arellano (2011) suggests that the Cuban experience is to reinvest income from foreign patients into the national system. It is therefore possible that some countries may seek foreign patients in order to develop facilities to better serve local patients (e. However, one must remember that foreign patients are merely an addition to domestic private patients; and this may be a significant or insignificant addition. There may also be different economic implications depending if these patients are simply using spare capacity or competing with domestic patients. For instance, the push by Thailand to be a hub for medical tourists in the 1990‘s was a result of the economic crisis in Asia generating a fall in domestic private patients and hence leading to spare capacity in their private sector. In this case, increasing foreign patients was more or less a net benefit to the private health system with substantial income and little real opportunity cost.

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