Yuanyuan Gu

Attributes and weights in health care priority setting: A systematic review of what counts and to what extent

In most societies resources are insufficient to provide everyone with all the health care they want. In practice, this means that some people are given priority over others. On what basis should priority be given? In this paper we are interested in the general publics views on this question. We set out to synthesis what the literature has found as a whole regarding which attributes or factors the general public think should count in priority setting and what weight they should receive. A systematic review was undertaken (in August 2014) to address these questions based on empirical studies that elicited stated preferences from the general public. Sixty four studies, applying eight methods, spanning five continents met the inclusion criteria. Discrete Choice Experiment (DCE) and Person Trade-off (PTO) were the most popular standard methods for preference elicitation, but only 34% of all studies calculated distributional weights, mainly using PTO. While there is heterogeneity, results suggest the young are favoured over the old, the more severely ill are favoured over the less severely ill, and people with self-induced illness or high socioeconomic status tend to receive lower priority. In those studies that considered health gain, larger gain is universally preferred, but at a diminishing rate. Evidence from the small number of studies that explored preferences over different components of health gain suggests life extension is favoured over quality of life enhancement; however this may be reversed at the end of life. The majority of studies that investigated end of life care found weak/no support for providing a premium for such care. The review highlights considerable heterogeneity in both methods and results. Further methodological work is needed to achieve the goal of deriving robust distributional weights for use in health care priority setting.

The effect of adverse information and positive promotion on women's preferences for prescribed contraceptive products

Recent rapid growth in the range of contraceptive products has given women more choice, but also adds complexity to the resultant decision of which product to choose. This paper uses a discrete choice experiment (DCE) to investigate the effect of adverse information and positive promotion on womens stated preferences for prescribed contraceptive products. In November 2007, 527 Australian women aged 18-49 years were recruited from an online panel. Each was randomly allocated to one of three information conditions. The control group only received basic information on contraceptive products. One treatment group also received adverse information on the risks of the combined oral pill. The other group received basic information and promotional material on the vaginal ring, newly introduced into Australia and on the transdermal patch, which is unavailable in Australia. Respondents completed 32 choice sets with 3 product options where each option was described by a product label: either combined pill, minipill, injection, implant, hormonal IUD, hormonal vaginal ring, hormonal transdermal patch or copper IUD; and by the attributes: effect on acne, effect on weight, frequency of administration, contraceptive effectiveness, doctors recommendation, effect on periods and cost. Womens choices were analysed using a generalized multinomial logit model (G-MNL) and model estimates were used to predict product shares for each information condition. The predictions indicated that adverse information did not affect womens preferences for products relative to only receiving basic information. The promotional material increased womens preferences for the transdermal patch. Women in all groups had a low preference for the vaginal ring which was not improved by promotion. The findings highlight the need for researchers to pay attention to setting the context when conducting DCEs as this can significantly affect results.

Bayesian Estimation of a Random Effects Heteroscedastic Probit Model

Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison. Copyright

Resource use, costs and quality of end-of-life care: observations in a cohort of elderly Australian cancer decedents

BackgroundThe last year of life is one of the most resource-intensive periods for people with cancer. Very little population-based research has been conducted on end-of-life cancer care in the Australian health care setting. The objective of this program is to undertake a series of observational studies examining resource use, costs and quality of end-of-life care in a cohort of elderly cancer decedents using linked, routinely collected data.Methods/DesignThis study forms part of an ongoing cancer health services research program. The cohorts for the end-of-life research program comprise Australian Government Department of Veterans’ Affairs decedents with full health care entitlements, residing in NSW for the last 18 months of life and dying between 2005 and 2009. We used cancer and death registry data to identify our decedent cohorts and their causes of death. The study population includes 9,862 decedents with a cancer history and 15,483 decedents without a cancer history. The median age at death is 86 and 87 years in the cancer and non-cancer cohorts, respectively. We will examine resource use and associated costs in the last 6 months of life using linked claims data to report on health service use, hospitalizations, emergency department visits and medicines use. We will use best practice methods to examine the nature and extent of resource use, costs and quality of care based on previously published indicators. We will also examine factors associated with these outcomes.DiscussionThis will be the first Australian research program and among the first internationally to combine routinely collected data from primary care and hospital-based care to examine comprehensively end-of-life care in the elderly. The research program has high translational value, as there is limited evidence about the nature and quality of care in the Australian end-of-life setting.

ESTIMATING HEALTH STATE UTILITY VALUES FROM DISCRETE CHOICE EXPERIMENTS—A QALY SPACE MODEL APPROACH

Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based on the mixed logit: one using preference space and the other using quality-adjusted life year (QALY) space, a concept adapted from the willingness-to-pay literature. These methods are applied to a dataset collected using the EQ-5D. The results showcase the advantages of using QALY space and demonstrate that the preferred QALY space model provides lower estimates of the utility values than the conditional logit, with the divergence increasing with worsening health states.

Understanding what matters: An exploratory study to investigate the views of the general public for priority setting criteria in health care

Health care policy makers internationally are increasingly expressing commitment to consultation with, and incorporation of, the views of the general public into the formulation of health policy and the process of setting health care priorities. In practice, however, there are relatively few opportunities for the general public to be involved in health care decision-making. In making resource allocation decisions, funders, tasked with managing scarce health care resources, are often faced with difficult decisions in balancing efficiency with equity considerations. A mixed methods (qualitative and quantitative) approach incorporating focus group discussions and a ranking exercise was utilised to develop a comprehensive list of potential criteria for setting priorities in health care formulated from the perspective of members of the general public in Australia. A strong level of congruence was found in terms of the rankings of the key criteria with the size of the health gain, clinical effectiveness, and the ability to provide quality of life improvements identified consistently as the three most important criteria for prioritising the funding of an intervention. Findings from this study will be incorporated into a novel DCE framework to explore how decision makers and members of the general public prioritize and trade off different types of health gain and to quantify the weights attached to specific efficiency and equity criteria in the priority setting process.