Physician responses to insurance benefit restrictions: The case of ophthalmology.

This study examines the impact of social insurance benefit restrictions on physician behaviour, using ophthalmologists as a case study. We examine whether ophthalmologists use their market power to alter their fees and rebates across services to compensate for potential policy-induced income losses. The results show that ophthalmologists substantially reduced their fees and rebates for services directly targeted by the benefit restriction compared to other medical specialists' fees and rebates. There is also some evidence that they increased their fees for services that were not targeted. High-fee charging ophthalmologists exhibited larger fee and rebate responses while the low-fee charging group raise their rebates to match the reference price provided by the policy environment.

Preterm Birth and Total Health Care Use and Costs in the First 5 Years of Life: A Population-based Study.

OBJECTIVES: To investigate the relationship between preterm birth and hospital/out-of-hospital care and costs over the first 5 years of life. STUDY DESIGN: Birth data from a population-based cohort of 631 532 infants born between 2007 and 2013 were linked probabilistically with data on hospitalizations, primary and secondary care, and the use of medications. We analyzed the distribution of health care use and public health care costs for infants who survived at least 5 years, comparing the outcomes of extremely preterm (<28 weeks of gestation), very preterm (28-32 weeks), moderate to late preterm (32-37 weeks), and term infants (at least 37 weeks). A linear regression model was used to investigate the effect of preterm birth on these outcomes, controlling for important confounders including pregnancy and birth complications, neonatal morbidity, survival, and maternal socioeconomic characteristics. RESULTS: Preterm birth has a statistically significant and economically relevant effect on health care use and costs in the first 5 years of life. Compared with a term infant, preterm infants born at 32-36 weeks, 28-32 weeks, and <28 weeks of gestation had, respectively, an average of 7.0 (SE 0.06), 41.6 (0.18), and 68.7 (0.35) more hospital days; 3.1 (0.04), 11.0 (0.13), and 13.2 (0.25) more outpatient specialist physician visits; and 1.2-fold (<0.01), 6.8-fold (0.01), and 10.9-fold (0.02) higher 5-year public health care costs. Preterm infants also had statistically significantly higher levels of general practitioner visits and use of medications. CONCLUSIONS: Higher levels of accessible care are needed for preterm infants across health care settings and over sustained periods. As our understanding of the impact of preterm birth on long-term clinical outcomes continues to improve, clinicians and policymakers should develop an accurate recognition of these needs to enable appropriate resource allocation toward research priorities and early intervention strategies.

Private provider incentives in health care: The case of caesarean births.

Private doctors and hospitals face incentives to intervene in the process of childbirth because they are employed and paid differently from their public counterparts. While private obstetric care has been associated with higher rates of caesarean birth, it is unclear to what extent this is attributable to unobserved selection effects related to clinical need or patient preferences. Using administrative birth data on over 280,000 births in Australia between 2007 and 2012, we implement an instrumental variables framework to account for the endogeneity of choice of care. We also exploit Australia's institutional framework to examine the differences in doctor-level and hospital-level incentives. We find that giving birth in a private hospital leads to a 4 percentage point increase in the probability of having an unplanned caesarean birth. Over our study period, this equates to an additional 3241 caesarean births.

Health care use in response to health shocks: Does socio-economic status matter?

We investigate how utilization of primary care, specialist care, and emergency department (ED) care (and the mix across the three) changes in response to a change in health need. We determine whether any changes in utilization are impacted by socio-economic status. The use of a unique Australian data set that consists of a large survey linked to multiple years of detailed administrative records enables us to better control for individual heterogeneity and allows us to exploit changes in health that are related to the onset of two health shocks: a new diagnosis of diabetes and heart disease. We extend the analysis by also examining changes to patient out-of-pocket costs. We find significant differences in the mix between primary and specialist care use according to income and type of health shock but no evidence of using ED as a substitute for other care. Our results indicate that low- and high-income patients navigate very different pathways for their care following the onset of diabetes and to a lesser extent heart disease. These pathways appear to be chosen on the basis of ability to pay, rather than the most effective or efficient bundle of care delivered through a combination of GP and specialist care.

Birth models of care and intervention rates: The impact of birth centres.

Birth centres offer a midwifery-led model of care which supports a non-medicalised approach to childbirth. They are often reported as having low rates of birth intervention, however the precise impact is obscured because less disadvantaged mothers with less complex pregnancies, and who prefer and often select little intervention, are more likely to choose a birth centre. In this paper, we use a methodology that purges the impact of these selection effects and provides a causal interpretation of the impact of birth centres on intervention outcomes. Using administrative birth data on over 364,000 births in Australia's most populous state between 2001 and 2012, we implement an instrumental variables framework to address confounding factors influencing choice of birth setting. We find that giving birth in a birth centre results in significantly lower probabilities of intervention, and that critically, this impact has been increasing over time. Our estimates are larger than those in existing studies, reflecting our newer data, diverging intervention rates across birth settings, and our accounting for important selection effects. The results emphasise the greater role of birth centres in delivering on policy priorities which include greater maternal autonomy, lower intervention rates, and lower health system costs.

Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated Preferences: A Systematic Review.

BACKGROUND: Scale heterogeneity, or differences in the error variance of choices, may account for a significant amount of the observed variation in the results of discrete choice experiments (DCEs) when comparing preferences between different groups of respondents. OBJECTIVE: The aim of this study was to identify if, and how, scale heterogeneity has been addressed in healthcare DCEs that compare the preferences of different groups. METHODS: A systematic review identified all healthcare DCEs published between 1990 and February 2016. The full-text of each DCE was then screened to identify studies that compared preferences using data generated from multiple groups. Data were extracted and tabulated on year of publication, samples compared, tests for scale heterogeneity, and analytical methods to account for scale heterogeneity. Narrative analysis was used to describe if, and how, scale heterogeneity was accounted for when preferences were compared. RESULTS: A total of 626 healthcare DCEs were identified. Of these 199 (32%) aimed to compare the preferences of different groups specified at the design stage, while 79 (13%) compared the preferences of groups identified at the analysis stage. Of the 278 included papers, 49 (18%) discussed potential scale issues, 18 (7%) used a formal method of analysis to account for scale between groups, and 2 (1%) accounted for scale differences between preference groups at the analysis stage. Scale heterogeneity was present in 65% (n = 13) of studies that tested for it. Analytical methods to test for scale heterogeneity included coefficient plots (n = 5, 2%), heteroscedastic conditional logit models (n = 6, 2%), Swait and Louviere tests (n = 4, 1%), generalised multinomial logit models (n = 5, 2%), and scale-adjusted latent class analysis (n = 2, 1%). CONCLUSIONS: Scale heterogeneity is a prevalent issue in healthcare DCEs. Despite this, few published DCEs have discussed such issues, and fewer still have used formal methods to identify and account for the impact of scale heterogeneity. The use of formal methods to test for scale heterogeneity should be used, otherwise the results of DCEs potentially risk producing biased and potentially misleading conclusions regarding preferences for aspects of healthcare.

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software.

We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a 'way in' for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics.

Consideration Sets and Their Role in Modelling Doctor Recommendations About Contraceptives.

Decisions about prescribed contraception are typically the result of a consultation between a woman and her doctor. In order to better understand contraceptive choice within this environment, stated preference methods are utilized to ask doctors about what contraceptive options they would discuss with different types of women. The role of doctors is to confine their discussion to a subset of products that best match their patient. This subset of options forms the consideration set from which the ultimate recommendation is made. Given the existence of consideration sets we address the issue of how to model appropriately the ultimate recommendations. The estimated models enable us to characterize doctor recommendations and how they vary with patient attributes and to highlight where recommendations are clear and when they are uncertain. The results also indicate systematic variation in recommendations across different types of doctors, and in particular we observe that some doctors are reluctant to embrace new products and instead recommend those that are more familiar. Such effects are one possible explanation for the relatively low uptake of more cost effective longer acting reversible contraceptives and indicate that further education and training of doctors may be warranted. Copyright © 2015 John Wiley & Sons, Ltd.

Big Data: Will It Improve Patient-Centered Care?

Within a generation, empirical researchers have experienced unprecedented increases in the availability of data. 'Big data' has arrived with considerable hype and a sense that these are dramatic shifts in the research environment that have wide-reaching implications across many disciplines. There is no doubt that the analysis of new and varied sources of data currently available to researchers in health have the potential to better measure, monitor and describe health outcomes of patients and to uncover interesting patterns in how patients respond to treatments and interact with the health system. What is less clear is whether answers are readily available to more nuanced and substantive research questions. Here, the data-rich environment needs to be complemented by considerable research effort developing novel research designs and generating new and improved methods of analysis. Importantly, this will require researchers to be able to combine data from multiple sources and to be pro-active in data collection.

Hips and hearts: the variation in incentive effects of insurance across hospital procedures.

The separate identification of effects due to incentives, selection and preference heterogeneity in insurance markets is the topic of much debate. In this paper, we investigate the presence and variation in moral hazard across health care procedures. The key motivating hypothesis is the expectation of larger causal effects in the case of more discretionary procedures. The empirical approach relies on an extremely rich and extensive dataset constructed by linking survey data to administrative data for hospital medical records. Using this approach we are able to provide credible evidence of large moral hazard effects but for elective surgeries only.