Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

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Constructing socio-economic status indices: how to use principal components analysis.

Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns. Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001–2008, we report whether earlier trends continued during 2009–2012. This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms. A total of 179 health-related DCEs for 2009–2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project. The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.

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Discrete Choice Experiments in Health Economics: A Review of the Literature

Understanding the preferences of patients and health professionals is useful for health policy and planning. Discrete choice experiments (DCEs) are a quantitative technique for eliciting preferences that can be used in the absence of revealed preference data. The method involves asking individuals to state their preference over hypothetical alternative scenarios, goods or services. Each alternative is described by several attributes and the responses are used to determine whether preferences are significantly influenced by the attributes and also their relative importance. DCEs are widely used in high-income contexts and are increasingly being applied in low- and middle-income countries to consider a range of policy concerns. This paper aims to provide an introduction to DCEs for policy-makers and researchers with little knowledge of the technique. We outline the stages involved in undertaking a DCE, with an emphasis on the design considerations applicable in a low-income setting.

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How to do (or not to do) ... Designing a discrete choice experiment for application in a low-income country.

Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004–5. Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.

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Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries

Many low- and middle-income countries continue to search for better ways of financing their health systems. Common to many of these systems are problems of inadequate resource mobilisation, as well as inefficient and inequitable use of existing resources. The poor and other vulnerable groups who need healthcare the most are also the most affected by these shortcomings. In particular, these groups have a high reliance on user fees and other out-of-pocket expenditures on health which are both impoverishing and provide a financial barrier to care. It is within this context, and in light of recent policy initiatives on user fee removal, that a debate on the role of user fees in health financing systems has recently returned. This paper provides some reflections on the recent user fees debate, drawing from the evidence presented and subsequent discussions at a recent UNICEF consultation on user fees in the health sector, and relates the debate to the wider issue of access to adequate healthcare. It is argued that, from the wealth of evidence on user fees and other health system reforms, a broad consensus is emerging. First, user fees are an important barrier to accessing health services, especially for poor people. They also negatively impact on adherence to long-term expensive treatments. However, this is offset to some extent by potentially positive impacts on quality. Secondly, user fees are not the only barrier that the poor face. As well as other cost barriers, a number of quality, information and cultural barriers must also be overcome before the poor can access adequate health services. Thirdly, initial evidence on fee abolition in Uganda suggests that this policy has improved access to outpatient services for the poor. For this to be sustainable and effective in reaching the poor, fee removal needs to be part of a broader package of reforms that includes increased budgets to offset lost fee revenue (as was the case in Uganda). Fourthly, implementation matters: if fees are to be abolished, this needs clear communication with a broad stakeholder buy-in, careful monitoring to ensure that official fees are not replaced by informal fees, and appropriate management of the alternative financing mechanisms that are replacing user fees. Fifthly, context is crucial. For instance, immediate fee removal in Cambodia would be inappropriate, given that fees replaced irregular and often high informal fees. In this context, equity funds and eventual expansion of health insurance are perhaps more viable policy options. Conversely, in countries where user fees have had significant adverse effects on access and generated only limited benefits, fee abolition is probably a more attractive policy option. Removing user fees has the potential to improve access to health services, especially for the poor, but it is not appropriate in all contexts. Analysis should move on from broad evaluations of user fees towards exploring how best to dismantle the multiple barriers to access in specific contexts.

To retain or remove user fees?: reflections on the current debate in low- and middle-income countries.

This study reports on the results of a discrete choice experiment undertaken in Zambia to assess the factors influencing the demand for hospital care in Zambia, in particular the role of (perceived) quality and trade-offs between price and quality. Valuations of quality were evaluated for the treatment of two acute medical conditions, cerebral malaria in adults and acute pneumonia in children. Marginal utilities and willingness-to-pay for attributes of quality of hospital care were estimated, together with the influence of socioeconomic characteristics on these valuations and the extent of non-linearities in valuations of time and money. We find the technical quality of care, as represented by the thoroughness of examination, to be the most important quality attribute, followed by staff attitudes and drug availability. Valuations of examination thoroughness increase with increasing socioeconomic status. The disutility of cost was found to decrease with higher socioeconomic status, as was the value of drug availability. The implications of the findings for Zambian hospital sector reforms are discussed.

Preferences for hospital quality in Zambia: results from a discrete choice experiment.

Hospitals have been relatively neglected in the debate over
health system development in low-income countries, perhaps
because of the recognition that those services most
effective in improving population health in developing
countries are better delivered in a different setting. However, the efficiency of its hospital sector is vital to the overall effciency of any health system, for several reasons: the hospital sector's high resource consumption implies high gains from increased efficiency; functional problems at the hospital level can create problems lower down the system; the functioning of hospitals has become symbolic of the functioning of the overall system: hospital efficiency is important for public confidence.

Luke Archard

Papers

Preferences for hospital quality in Zambia: results from a discrete choice experiment.

Creating markets in hospital care: the adoption of developed country health sector reforms by developing countries. Is it appropriate?