Ideological notions in public health care rationing : Case studies from Oregon, New Zealand and Finland

The purpose of this dissertation is to analyze and explicate the ideological content, which is often implicit, in the health care rationing discussion. The phrase "ideological content" refers to viewpoints and assumptions expressed in the rationing discussion that may be widespread and accepted, but without clear evidential support. The study method is philosophical text analysis. The study begins by exploring the literature from the 1970s that affects the present-day rationing discussion. Since ideological contents may have different emphases in realm of health care, three representative cases were studied. The first was a case study of the first and best-known rationing experiment in the American state of Oregon, namely, an experimental rationing plan within the public health program Medicaid, which is designed to provide care for the poor and underprivileged. The second was a study of the only national-level public priority setting that has been conducted in New Zealand. The third examined the Finnish Care Guarantee plan introduced in March 2005. The findings show that several problematic and scientifically mostly unproven concepts have remained largely uncontested in the debate about public health care rationing. Some of these notions already originated decades ago in studies that relied on outdated data or research paradigms. The problematic ideological contents have also been taken up from one publication into another, thereby affecting the rationing debate. The study suggests that before any new public health care rationing experiments are undertaken, these ideological factors should be properly examined, especially in order to avoid repetitious research and perhaps erroneous rationing decisions.

Paradigms in Statistical Inference for Finite Populations; Up to the 1950s

Modern sample surveys started to spread after statistician at the U.S. Bureau of the Census in the 1940s had developed a sampling design for the Current Population Survey (CPS). A significant factor was also that digital computers became available for statisticians. In the beginning of 1950s, the theory was documented in textbooks on survey sampling. This thesis is about the development of the statistical inference for sample surveys. For the first time the idea of statistical inference was enunciated by a French scientist, P. S. Laplace. In 1781, he published a plan for a partial investigation in which he determined the sample size needed to reach the desired accuracy in estimation. The plan was based on Laplace s Principle of Inverse Probability and on his derivation of the Central Limit Theorem. They were published in a memoir in 1774 which is one of the origins of statistical inference. Laplace s inference model was based on Bernoulli trials and binominal probabilities. He assumed that populations were changing constantly. It was depicted by assuming a priori distributions for parameters. Laplace s inference model dominated statistical thinking for a century. Sample selection in Laplace s investigations was purposive. In 1894 in the International Statistical Institute meeting, Norwegian Anders Kiaer presented the idea of the Representative Method to draw samples. Its idea was that the sample would be a miniature of the population. It is still prevailing. The virtues of random sampling were known but practical problems of sample selection and data collection hindered its use. Arhtur Bowley realized the potentials of Kiaer s method and in the beginning of the 20th century carried out several surveys in the UK. He also developed the theory of statistical inference for finite populations. It was based on Laplace s inference model. R. A. Fisher contributions in the 1920 s constitute a watershed in the statistical science He revolutionized the theory of statistics. In addition, he introduced a new statistical inference model which is still the prevailing paradigm. The essential idea is to draw repeatedly samples from the same population and the assumption that population parameters are constants. Fisher s theory did not include a priori probabilities. Jerzy Neyman adopted Fisher s inference model and applied it to finite populations with the difference that Neyman s inference model does not include any assumptions of the distributions of the study variables. Applying Fisher s fiducial argument he developed the theory for confidence intervals. Neyman s last contribution to survey sampling presented a theory for double sampling. This gave the central idea for statisticians at the U.S. Census Bureau to develop the complex survey design for the CPS. Important criterion was to have a method in which the costs of data collection were acceptable, and which provided approximately equal interviewer workloads, besides sufficient accuracy in estimation.