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This thesis is concerned with the estimation of costs in economic evaluation. The thesis reviews the theoretical and applied literature on costing and highlights that studies generally ignore cost variation across health care settings. The thesis aims to assess why costs vary across health care settings, and the implications for economic evaluations. The study uses microeconomic theory to pose hypotheses for cost variation across health care settings and uses a consistent methodology to collect costs across a range of health care settings. The analysis uses multilevel models (MLMs) to test hypotheses concerning cost variation. Statistical theory suggests that MLMs accommodate the hierarchical structure of the data and may therefore be more appropriate than ordinary least squares (OLS) models for identifying reasons for cost variation across settings. The use of MLMs and OLS models for analysing reasons for cost variation are compared. The OLS models find that both patient and higher-level covariates are associated with length of hospital stay (LOS) and total cost, but these models overestimate the precision of the higher-level variables. By contrast, the MLMs show that none of the higher-level variables are associated with LOS, and the national level of spending on health care is the only higher-level variable associated with total cost. The empirical investigation also illustrates that using OLS regression analysis to report cost-effectiveness can lead to inaccurate estimates. By contrast, the MLMs recognise the structure of the data and accurately quantify mean incremental cost- effectiveness and the associated levels of uncertainty. The thesis concludes that ignoring cost variation across health care settings can lead to inaccurate estimates of cost and cost-effectiveness. Basing decision-making on inaccurate information can move the allocation of health care resources away from the target of allocative efficiency. This thesis presents a methodology for improving the conduct of cost analyses that future economic evaluations can adopt.

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The aim of this thesis is to broaden work in the area of discrete choice experiments (DCEs) in health economics by focusing on the development of some key areas which have to date received relatively little attention.  By firstly outlining the background, theory and context of DCEs in health economics, areas are identified that deserve further exploration due to their particular relevance to health economics.  Specific contributions of the thesis are in three main areas:  modelling the participation decision in DCEs in health care; the use of strength of preference choice modelling approaches; and the applicability of a new approach, best attribute scaling (BSc), to health economics.  The contributions of the thesis are to the design, analysis and interpretation of DCE studies. The seven key recommendations for the conduct of DCE surveys arising from this thesis have been made on the basis of the best available knowledge gleaned from empirical analysis carried out.  They are as follows:  (1) Researchers conducting DCE studies in health care should carefully consider the participation decision when designing and analysing a DCE and where appropriate pay particular attention to describing the opt-out or status quo option as realistically as possible (2) When incorporating status quo or opt-out scenarios within a DCE design researchers should check that the statistical properties of the resulting design are still valid (3) Strength of preference models appear to improve the statistical efficiency of models and produce more accurate estimates of welfare however this may come at the expense of the predictive ability of the model and consistency rates (4) Where possible DCE surveys should provide respondents with the most realistic range of preference ‘capture’ mechanisms as possible, this includes providing opting out, status quo and indifference options where appropriate.  (5) As shown in the environmental economics literature strength of preference models facilitates the elicitation of preferences in health economics (6)  BSc methods can be used to predict choices in health care preference surveys (7) BSc has the ability to separate scale and weight in choice experiments and hence place all attribute levels on a common scale, unlike traditional choice experiments however future work with larger samples should clarify this result.

Results Based Financing (RBF) models are results oriented, linking performance indicators to incentives to motivate health workers to deliver quality care in anticipation of rewards attached to service delivery. The study sought to explore nurses’ views and experiences regarding the implementation of RBF in Zimbabwe with the aim of recommending measures to strengthen the programme. The researcher used a qualitative, exploratory and descriptive design in this study. The population of this study comprised 21 nurses. Non-probability purposive sampling was used to select professional nurses involved in implementing RBF in Mrewa District, Mashonaland East Province, Zimbabwe. Data were collected through focus group discussions using an interview guide. Three (3) focus group discussions were conducted during this study, following a pilot study consisting of six (6) conveniently sampled nurses in Mashonaland East Province. Interviews were tape recorded and transcribed verbatim. Permission to proceed with this study was granted by the Ministry of Health and Child Care and the University of South Africa. Measures to ensure credibility, dependability, conformability and transferability were followed. Data were analysed using Creswell’s data analysis steps. Data were transcribed and thematically analysed, and emerging patterns were noted. The researcher examined these categories closely and compared them for similarities and differences, identifying the most frequent or significant codes in order to develop the main categories. These were summarised in narrative form. Four themes emerged from data: interpretation of RBF; role of nurses in the implementation of RBF; evaluation of RBF; and strengthening implementation of RBF. The study revealed various interpretations of RBF that converged to definitions of RBF in literature. Nurses viewed themselves as key and important players in the successful implementation of RBF. The successes and challenges of RBF were presented. Several measures that could strengthen the implementation of donor funds were highlighted, including subsidisation of low catchment health facilities, inclusion of district hospitals on the RBF programme, increasing financial autonomy of health facilities and the review of procurement guidelines. The study assumed that these measures will enhance nurses’ work experience in donor funded health care delivery, and improve health outcomes. / Health Studies / M.P.H.