Marco Boeri

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

This paper introduces the discrete choice model-paradigm of Random Regret Minimisation (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximisation (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimise their regret-regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.

Food fraud and consumers’ choices in the wake of the horsemeat scandal

Purpose – In 2012, the European food industry was hit by a food fraud: horsemeat was found in pre-prepared foods, without any declaration on the package. This is commonly referred to as the “horsemeat scandal”. The purpose of this paper is to investigate consumers’ preferences across Europe for a selected ready meal, ready to heat (RTH) fresh lasagne, to consider whether the effects of potential food frauds on consumers’ choices can be mitigated by introducing enhanced standards of RTH products. Design/methodology/approach – An online survey was administered to 4,598 consumers of RTH lasagne in six European countries (Republic of Ireland, France, Italy, Spain, Germany and Norway), applying discrete choice experiments to estimate consumers’ willingness to pay for enhanced food safety standards and highlight differences between countries. Findings – Many similarities across countries emerged, as well as some differences. Consumers in Europe are highly concerned with the authenticity of the meat in ready mea...

Addressing inequalities in physical activity participation: Implications for public health policy and practice

OBJECTIVE To investigate the characteristics of those doing no moderate-vigorous physical activity (MVPA) (0 days/week), some MVPA (1-4 days/week) and sufficient MVPA (≥ 5 days/week) to meet the guidelines in order to effectively develop and target PA interventions to address inequalities in participation. METHOD A population survey (2010/2011) of 4653 UK adults provided data on PA and socio-demographic characteristics. An ordered logit model investigated the covariates of 1) participating in no PA, 2) participating in some PA, and 3) meeting the PA guidelines. Model predictions were derived for stereotypical subgroups to highlight important policy and practice implications. RESULTS Mean age of participants was 45 years old (95% CI 44.51, 45.58) and 42% were male. Probability forecasting showed that males older than 55 years of age (probability=0.20; 95% CI 0.11, 0.28), and both males (probability=0.31; 95% CI 0.17, 0.45) and females (probability=0.38; 95% CI 0.27, 0.50) who report poor health are significantly more likely to do no PA. CONCLUSIONS Understanding the characteristics of those doing no MVPA and some MVPA could help develop population-level interventions targeting those most in need. Findings suggest that interventions are needed to target older adults, particularly males, and those who report poor health.

Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

Background. In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR. Methods. Psychiatrists were given information about a group of patients’ responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable. Results. Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms – measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists. Limitations. We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients. Conclusions. This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

How do psychiatrists apply the minimum clinically important difference to assess patient responses to treatment

Symptom report scales are used in clinical practice to monitor patient outcomes. Using them permits the definition of a minimum clinically important difference (MCID) beyond which a patient may be judged as having responded to treatment. Despite recommendations that clinicians routinely use MCIDs in clinical practice, statisticians disagree about how MCIDs should be used to evaluate individual patient outcomes and responses to treatment. To address this issue, we asked how clinicians actually use MCIDs to evaluate patient outcomes in response to treatment. Sixty-eight psychiatrists made judgments about whether hypothetical patients had responded to treatment based on their pre- and posttreatment change scores on the widely used Positive and Negative Syndrome Scale. Psychiatrists were provided with the scale’s MCID on which to base their judgments. Our secondary objective was to assess whether knowledge of the patient’s genotype influenced psychiatrists’ responder judgments. Thus, psychiatrists were also informed of whether patients possessed a genotype indicating hyperresponsiveness to treatment. While many psychiatrists appropriately used the MCID, others accepted a far lower posttreatment change as indicative of a response to treatment. When psychiatrists accepted a lower posttreatment change than the MCID, they were less confident in such judgments compared to when a patient’s posttreatment change exceeded the scale’s MCID. Psychiatrists were also less likely to identify patients as responders to treatment if they possessed a hyperresponsiveness genotype. Clinicians should recognize that when judging patient responses to treatment, they often tolerate lower response thresholds than warranted. At least some conflate their judgments with information, such as the patient’s genotype, that is irrelevant to a post hoc response-to-treatment assessment. Consequently, clinicians may be at risk of persisting with treatments that have failed to demonstrate patient benefits.