计量经济分析 = Econometric analysis

The detection of psychiatric illness by questionnaire

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.

/pdf/negative-binomial-regression-29q4k5nu4u.pdf

Negative Binomial Regression

This is the latest updated volume in a well-established series bringing together information from a variety of sources to estimate national unit costs for a wide range of health and social care services. This report consists of tables for more than 130 types of service which, as well as providing the most detailed and comprehensive information possible, also quotes sources and assumptions so users can adapt the information for their own purposes. This year we have included a guest editorial on ‘Big Data’: increasing productivity while reducing costs in health and social care and three short articles on The cost of integrated care; Shared lives – improving understanding of the costs of family-based support and the costs of providing a group intervention to people with dementia and their family carers:SHIELD: RYCT & CSP intervention costs (Remembering Yesterday Caring Today and Carer Support Programme). There are also several new schema providing the costs of: advocacy for people with learning disabilities; a hospice rapid response service; the early years classroom management programme; residential parenting assessments; independent review officers; time banks; dentists and care home costs for people with dementia. The 2014 edition is also available in full, free of charge, at the PSSRU website www.pssru.ac.uk/unit-costs/2014/ as an Acrobat file.

Unit Costs of Health and Social Care 2014

BACKGROUND: Cost-effectiveness analysis involves the comparison of the incremental cost-effectiveness ratio of a new technology, which is more costly than existing alternatives, with the cost-effectiveness threshold. This indicates whether or not the health expected to be gained from its use exceeds the health expected to be lost elsewhere as other health-care activities are displaced. The threshold therefore represents the additional cost that has to be imposed on the system to forgo 1 quality-adjusted life-year (QALY) of health through displacement. There are no empirical estimates of the cost-effectiveness threshold used by the National Institute for Health and Care Excellence. OBJECTIVES: (1) To provide a conceptual framework to define the cost-effectiveness threshold and to provide the basis for its empirical estimation. (2) Using programme budgeting data for the English NHS, to estimate the relationship between changes in overall NHS expenditure and changes in mortality. (3) To extend this mortality measure of the health effects of a change in expenditure to life-years and to QALYs by estimating the quality-of-life (QoL) associated with effects on years of life and the additional direct impact on QoL itself. (4) To present the best estimate of the cost-effectiveness threshold for policy purposes. METHODS: Earlier econometric analysis estimated the relationship between differences in primary care trust (PCT) spending, across programme budget categories (PBCs), and associated disease-specific mortality. This research is extended in several ways including estimating the impact of marginal increases or decreases in overall NHS expenditure on spending in each of the 23 PBCs. Further stages of work link the econometrics to broader health effects in terms of QALYs. RESULTS: The most relevant 'central' threshold is estimated to be £12,936 per QALY (2008 expenditure, 2008-10 mortality). Uncertainty analysis indicates that the probability that the threshold is < £20,000 per QALY is 0.89 and the probability that it is < £30,000 per QALY is 0.97. Additional 'structural' uncertainty suggests, on balance, that the central or best estimate is, if anything, likely to be an overestimate. The health effects of changes in expenditure are greater when PCTs are under more financial pressure and are more likely to be disinvesting than investing. This indicates that the central estimate of the threshold is likely to be an overestimate for all technologies which impose net costs on the NHS and the appropriate threshold to apply should be lower for technologies which have a greater impact on NHS costs. LIMITATIONS: The central estimate is based on identifying a preferred analysis at each stage based on the analysis that made the best use of available information, whether or not the assumptions required appeared more reasonable than the other alternatives available, and which provided a more complete picture of the likely health effects of a change in expenditure. However, the limitation of currently available data means that there is substantial uncertainty associated with the estimate of the overall threshold. CONCLUSIONS: The methods go some way to providing an empirical estimate of the scale of opportunity costs the NHS faces when considering whether or not the health benefits associated with new technologies are greater than the health that is likely to be lost elsewhere in the NHS. Priorities for future research include estimating the threshold for subsequent waves of expenditure and outcome data, for example by utilising expenditure and outcomes available at the level of Clinical Commissioning Groups as well as additional data collected on QoL and updated estimates of incidence (by age and gender) and duration of disease. Nonetheless, the study also starts to make the other NHS patients, who ultimately bear the opportunity costs of such decisions, less abstract and more 'known' in social decisions. FUNDING: The National Institute for Health Research-Medical Research Council Methodology Research Programme.

/pdf/methods-for-the-estimation-of-the-national-institute-for-4gigdoqvdq.pdf

Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold

This paper considers the dynamics of a categorical indicator of self-assessed health using eight waves (1991–1998) of the British Household Panel Survey (BHPS). Our analysis has three focal points: the relative contributions of state dependence and heterogeneity in explaining the dynamics of health, the existence and consequences of health-related sample attrition, and the investigation of the effects of measures of socioeconomic status, with a particular focus on educational attainment and income. To investigate these issues we use dynamic panel ordered probit models. There is clear evidence of health-related attrition in the data but this does not distort the estimates of state dependence and of the socioeconomic gradient in health. The models show strong positive state dependence and heterogeneity accounts for around 30% of the unexplained variation in health. Copyright © 2004 John Wiley & Sons, Ltd.

http://people.stern.nyu.edu/wgreene/Lugano2013/Jones-et-al-HealthSatisfaction.pdf

The dynamics of health in the British Household Panel Survey

Methods for assessing the cost-effectiveness of public health interventions: key challenges and recommendations.

This paper presents an introductory account of multilevel models, highlighting the potential benefits that may be gained by the use of these methods. It draws on recent applications in health services research that have appeared in the literature. Methodological advances in these statistical techniques have taken place in the field of education, where empirical studies have mainly been concerned with comparing pupil achievement across different schools by exploring the relationship between individual and institutional factors. Although recent widespread availability of suitable software packages has enabled other disciplines to adopt these methods, to date they have received little attention in the health services research literature (the investigation of effects of geographical areas on health being a possible exception) despite their obvious application in many areas of current interest. Key areas that could benefit greatly from these techniques include the exploration of variations in clinical practice, comparisons of institutional performance and resource allocation.

Multilevel models: applications to health data.

Objective: To identify the socioeconomic determinants of consultation rates in general practice. Design: Analysis of data from the fourth national morbidity survey of general practices (MSGP4) including sociodemographic details of individual patients and small area statistics from the 1991 census. Multilevel modelling techniques were used to take account of both individual patient data and small area statistics to relate socioeconomic and health status factors directly to a measure of general practitioner workload. Results: Higher rates of consultations were found in patients who were classified as permanently sick, unemployed (especially those who became unemployed during the study year), living in rented accommodation, from the Indian subcontinent, living with a spouse or partner (women only), children living with two parents (girls only), and living in urban areas, especially those living relatively near the practice. When characteristics of individual patients are known and controlled for the role of “indices of deprivation” is considerably reduced. The effect of individual sociodemographic characteristics were shown to vary between different areas. Conclusions: Demographic and socioeconomic factors can act as powerful predictors of consultation patterns. Though it will always be necessary to retain some local planning discretion, the sets of coefficients estimated for individual level factors, area level characteristics, and for practice groupings may be sufficient to provide an indicative level of demand for general medical services. Although the problems in using socioeconomic data from individual patients would be substantial, these results are relevant to the development of a resource allocation formula for general practice. Key messages Characteristics of individual patients are much more powerful predictors of consulting patterns than the characteristics of the areas in which patients live The effects of individual socioeconomic factors themselves vary in different geographical areas Resource allocation methods based on area of residence (for example, Jarman score) will always be inferior to an approach that takes into account the characteristics of individual patients

/pdf/socioeconomic-determinants-of-rates-of-consultation-in-53px1e0tr5.pdf

Nigel Rice

Papers

Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold

The dynamics of health in the British Household Panel Survey

Methods for assessing the cost-effectiveness of public health interventions: key challenges and recommendations.

Multilevel models: applications to health data.

Socioeconomic determinants of rates of consultation in general practice based on fourth national morbidity survey of general practices