Smoking Cessation and Quality of Life: Insights From Analysis of Longitudinal Australian Data,an Application for Economic Evaluations

ObjectivesA number of studies have shown an association between smoking habit and quality of life, but these have mainly involved cross-sectional data. This study takes advantage of longitudinal panel data to estimate the effect of the transition from “smoker” to “ex-smoker” status (smoking cessation) on health-related quality of life (HRQoL), measured by SF-36, in an Australian general population sample.MethodsPanel data from 13 waves (2001-2013) of a nationally representative longitudinal survey of Household Income and Labour Dynamics of Australia (HILDA) were used; 1858 respondents (5% of total HILDA sample) who experienced only 1 cessation event in their HILDA life were selected. HRQoL trajectories elicited by SF-36 (0-100 scale, worst to best health) were modeled before and after cessation events using a piecewise (segmented) 2-way fixed-effect linear regression, adopted to capture within-person differences. This enabled measurement of changes of regression slopes and intercept while controlling time-invariant characteristics (eg, country of birth, gender) and time-varying changes in health status.ResultsAnnual pre-post intervention improvements were estimated for the following dimensions: role physical 0.65 (95% CI 0.62-1.24), bodily pain 0.48 (95% CI 0.10-0.86), general health 0.55 (95% CI 0.2-0.9), and the physical component summary score 0.22 (95% CI 0.01-0.04). Immediate effects (discontinuity at the time of cessation) of smoking cessation existed for bodily pain –1.5 (95% CI –2.52 to –0.40) and general health 1.82 (95% CI 1.01-2.62). The effects for mental health domains were not significant.ConclusionsAdjusting for all unmeasured time-invariant confounders and controlling the effect of time, this study revealed the varied effects of smoking cessation on HRQoL; it has positive effect on physical and general health but nonsignificant effect on mental aspects. Preference-based utility measures based on SF-6D capture changes that can be measured in several of the domains of the SF-36.     

Predicting healthcare expenditure by multimorbidity groups

Objectives

This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets.

The Early Effects of Medicare's Mandatory Hospital Pay‐for‐Performance Program

Objective

To evaluate the impact of hospital value-based purchasing (HVBP) on clinical quality and patient experience during its initial implementation period (July 2011–March 2012).

Data Sources

Hospital-level clinical quality and patient experience data from Hospital Compare from up to 5 years before and three quarters after HVBP was initiated.

Study Design

Acute care hospitals were exposed to HVBP by mandate while critical access hospitals and hospitals located in Maryland were not exposed. We performed a difference-in-differences analysis, comparing performance on 12 incentivized clinical process and 8 incentivized patient experience measures between hospitals exposed to the program and a matched comparison group of nonexposed hospitals. We also evaluated whether hospitals that were ultimately exposed to HVBP may have anticipated the program by improving quality in advance of its introduction.

Principal Findings

Difference-in-differences estimates indicated that hospitals that were exposed to HVBP did not show greater improvement for either the clinical process or patient experience measures during the program''s first implementation period. Estimates from our preferred specification showed that HVBP was associated with a 0.51 percentage point reduction in composite quality for the clinical process measures (p > .10, 95 percent CI: −1.37, 0.34) and a 0.30 percentage point reduction in composite quality for the patient experience measures (p > .10, 95 percent CI: −0.79, 0.19). We found some evidence that hospitals improved performance on clinical process measures prior to the start of HVBP, but no evidence of this phenomenon for the patient experience measures.

Conclusions

The timing of the financial incentives in HVBP was not associated with improved quality of care. It is unclear whether improvement for the clinical process measures prior to the start of HVBP was driven by the expectation of the program or was the result of other factors.     

Evaluating Direct Medical Expenditures Estimation Methods of Adults Using the Medical Expenditure Panel Survey: An Example Focusing on Head and Neck Cancer

ObjectiveTo inform policymakers of the importance of evaluating various methods for estimating the direct medical expenditures for a low-incidence condition, head and neck cancer (HNC).MethodsFour methods of estimation have been identified: 1) summing all health care expenditures, 2) estimating disease-specific expenditures consistent with an attribution approach, 3) estimating disease-specific expenditures by matching, and 4) estimating disease-specific expenditures by using a regression-based approach. A literature review of studies (2005–2012) that used the Medical Expenditure Panel Survey (MEPS) was undertaken to establish the most popular expenditure estimation methods. These methods were then applied to a sample of 120 respondents with HNC, derived from pooled data (2003-2008).ResultsThe literature review shows that varying expenditure estimation methods have been used with MEPS but no study compared and contrasted all four methods. Our estimates are reflective of the national treated prevalence of HNC. The upper-bound estimate of annual direct medical expenditures of adult respondents with HNC between 2003 and 2008 was $3.18 billion (in 2008 dollars). Comparable estimates arising from methods focusing on disease-specific and incremental expenditures were all lower in magnitude. Attribution yielded annual expenditures of $1.41 billion, matching method of $1.56 billion, and regression method of $1.09 billion.ConclusionsThis research demonstrates that variation exists across and within expenditure estimation methods applied to MEPS data. Despite concerns regarding aspects of reliability and consistency, reporting a combination of the four methods offers a degree of transparency and validity to estimating the likely range of annual direct medical expenditures of a condition.     

The Hausman test in a Cliff and Ord panel model

Summary This paper studies the random effects model and the fixed effects model for spatial panel data. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one‐way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specifications and propose a spatial Hausman test which compares these two models accounting for spatial autocorrelation in the disturbances. We derive the large sample properties of our estimation procedures and show that the test statistic is asymptotically chi‐square distributed. A small Monte Carlo study demonstrates that this test works well even in small panels.     

Sur model with spatial effects: an application to mental health expenditure

This study analyses, through the adoption of a seemingly unrelated regression approach, the temporal evolution of policy interactions among local authorities in England when allocating mental health resources. This new approach in health economics may shed light on the degree of interdependence between adjacent municipalities at a specific point in time (e.g. before, during, or after a change in policy), exploiting the information carried by the panel, rather than that of a single cross-section.     

Health expenditure growth: reassessing the threat of ageing

In this paper we evaluate the respective effects of demographic change, changes in morbidity and changes in practices on growth in health care expenditures. We use microdata, i.e. representative samples of 3441 and 5003 French individuals observed in 1992 and 2000. Our data provide detailed information about morbidity and allow us to observe three components of expenditures: ambulatory care, pharmaceutical and hospital expenditures.We propose an original microsimulation method to identify the components of the drift observed between 1992 and 2000 in the health expenditure age profile. On the one hand, we find empirical evidence of health improvement at a given age: changes in morbidity induce a downward drift of the profile. On the other hand, the drift due to changes in practices is upward and sizeable. Detailed analysis attributes most of this drift to technological innovation.After applying our results at the macroeconomic level, we find that the rise in health care expenditures due to ageing is relatively small. The impact of changes in practices is 3.8 times larger. Furthermore, changes in morbidity induce savings which more than offset the increase in spending due to population ageing.     

Switching processes in financial markets

For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating upward trends and downward trends, on time scales ranging from macroscopic trends persisting for hundreds of days to microscopic trends persisting for a few minutes. The question arises whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the transaction volume after each switching. Our findings can be interpreted as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in time intervals between transactions. We suggest that the well known catastrophic bubbles that occur on large time scales--such as the most recent financial crisis--may not be outliers but single dramatic representatives caused by the formation of increasing and decreasing trends on time scales varying over nine orders of magnitude from very large down to very small.     

Mendelian randomization: using genes as instruments for making causal inferences in epidemiology

Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.     

Maximization by parts in extremum estimation

In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the Newton–Raphson algorithm is difficult, if not impossible. While the Newton–Raphson algorithm makes use of the full Hessian matrix, which may be difficult to evaluate, our algorithms use parts of the Hessian matrix only, the parts that are easier to compute. We establish consistency and asymptotic efficiency of our iterative estimators under regularity and information dominance conditions. We argue that the economic interpretation of a structural econometric model will often allow us to give credibility to a well‐suited information dominance condition. We apply our algorithms to the estimation of the Merton structural credit risk model and to the Heston stochastic volatility option pricing model.