The Normative Underpinnings of Population-Level Alcohol Use: An Individual-Level Simulation Model

How can agent-based modelling provide new insights into the impact of minimum unit pricing in Scotland?

In recent years we have gained insight into the impact of minimum unit pricing (MUP)-a legal floor price below which a given volume of alcohol cannot be sold-on population-level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual-level purchasing changes impact the local economy (e.g., balance between on-licence and off-licence outlets), lead to long-term population-level trends (e.g., youth drinking) and social harms (e.g., violence). Agent-based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent-based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods.

Campus climate impacts on sexual violence: a Bayesian comparison of undergraduate and community colleges

OBJECTIVE

Sexual violence is endemic on college campuses. Four-year campuses present high-risk environments for sexual violence and heavy episodic drinking is a robust risk factor for victimization. However, limited literature exists on sexual violence at two-year institutions, with most research focused on four-year campuses. We examined whether campus climates affect sexual violence prevalence rates.

PARTICIPANTS

Sexual misconduct campus climate data from two-year and four-year campus students.

METHODS

We used Bayesian logistic regressions to compare sexual victimization odds between two- and four-year campuses.

RESULTS

Four-year students were twice as likely to have experienced sexual victimization and 2.5 times more likely to engage in heavy episodic drinking compared to two-year students. The risk of sexual victimization associated with heavy episodic drinking was reliably similar across campus types.

CONCLUSIONS

Campus climates reliably impact student's risk of sexual victimization. Based on these findings, two- and four-year campuses may need to implement distinct prevention services.

On the stability of norms and norm-following propensity: a cross-cultural panel study with adolescents

Norm-based accounts of social behavior in economics typically reflect tradeoffs between maximization of own consumption utility and conformity to social norms. Theories of norm-following tend to assume that there exists a single, stable, commonly known injunctive social norm for a given choice setting and that each person has a stable propensity to follow social norms. We collect panel data on 1468 participants aged 11-15 years in Belfast, Northern Ireland and Bogotá, Colombia in which we measure norms for the dictator game and norm-following propensity twice at 10 weeks apart. We test these basic assumptions and find that norm-following propensity is stable, on average, but reported norms show evidence of change. We find that individual-level variation in reported norms between people and within people across time has interpretable structure using a series of latent transition analyses (LTA) which extend latent class models to a panel setting. The best fitting model includes five latent classes corresponding to five sets of normative beliefs that can be interpreted in terms of what respondents view as "appropriate" (e.g. equality vs. generosity) and how they view deviations (e.g. deontological vs. consequentialist). We also show that a major predictor of changing latent classes over time comes from dissimilarity to others in one's network. Our application of LTA demonstrates how researchers can engage with heterogeneity in normative perceptions by identifying latent classes of beliefs and deepening understanding of the extent to which norms are shared, stable, and can be predicted to change. Finally, we contribute to the nascent experimental literature on the economic behavior of children and adolescents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10683-024-09821-5.

The long-term effectiveness and cost-effectiveness of public health interventions; how can we model behavior? A review

The effectiveness and cost of a public health intervention is dependent on complex human behaviors, yet health economic models typically make simplified assumptions about behavior, based on little theory or evidence. This paper reviews existing methods across disciplines for incorporating behavior within simulation models, to explore what methods could be used within health economic models and to highlight areas for further research. This may lead to better-informed model predictions. The most promising methods identified which could be used to improve modeling of the causal pathways of behavior-change interventions include econometric analyses, structural equation models, data mining and agent-based modeling; the latter of which has the advantage of being able to incorporate the non-linear, dynamic influences on behavior, including social and spatial networks. Twenty-two studies were identified which quantify behavioral theories within simulation models. These studies highlight the importance of combining individual decision making and interactions with the environment and demonstrate the importance of social norms in determining behavior. However, there are many theoretical and practical limitations of quantifying behavioral theory. Further research is needed about the use of agent-based models for health economic modeling, and the potential use of behavior maintenance theories and data mining.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Predicting Alcohol Consumption Patterns for Individuals with a User-Friendly Parsimonious Statistical Model

Many studies on the relationship between alcohol and health outcome focus primarily on average consumption over time and do not consider how heavy per-occasion drinking may influence apparent relationships. Improved methods concerning the most recent drinking occasion are essential to inform the extent of alcohol-related health problems. We aimed to develop a user-friendly and readily replicable computational model that predicts: (i) an individual's probability of consuming alcohol ≥2, 3, 4… drinks; and (ii) the total number of days during which consumption is ≥2, 3, 4… drinks over a specified period. Data from the 2010 and 2011 National Survey on Drug Use and Health (NSDUH) were used to develop and validate the model. Predictors used in model development were age, gender, usual number of drinks consumed per day, and number of drinking days in the past 30 days. Main outcomes were number of drinks consumed on the last drinking occasion in the past 30 days, and number of days of risky levels of consumption. The area under ROC curves ranged between 0.86 and 0.91 when predicting the number of drinks consumed. Coefficients were very close to 1 for all outcomes, indicating closeness between the predicted and observed values. This straightforward modelling approach can be easily adopted by public health behavioral studies.

Improved estimates for individual and population-level alcohol use in the United States, 1984-2020

Aims

While nationally representative alcohol surveys are a mainstay of public health monitoring, they underestimate consumption at the population level. This paper demonstrates how to adjust individual-level survey data using aggregated alcohol per capita (APC) data for improved individual- and population-level consumption estimates.

Design and Methods

For the period 1984-2020, data on self-reported alcohol consumption in the past 30 days were taken from the Behavioral Risk Factor Surveillance System (BRFSS) involving participants (18+ years) in the United States (US). Monthly abstainers were reallocated into lifetime abstainers, former drinkers, and 12-month drinkers using the 2005 National Alcohol Survey data. To correct for under-coverage of alcohol use, we triangulated APC and survey data by upshifting quantity (average grams/day) and frequency (drinking days/week) of alcohol use based on national- and state-level APC data. Results were provided for the US as a whole and for selected states to represent different drinking patterns.

Findings

The corrections described above resulted in improved correspondence between survey and APC data. Following our procedure, national estimates of alcohol quantity increased from 45% to 77% of APC estimates. Both quantity and frequency of alcohol use were upshifted; by upshifting to 90% of APC, we were able to fit trends and distributions in APC patterns for individual states and the US.

Conclusions

An individual-level dataset which more accurately reflects the alcohol use of US citizens was achieved. This dataset will be invaluable as a research tool and for the planning and evaluation of alcohol control policies for the US. The methodology described can also be used to adjust individual-level alcohol survey data in other geographical settings.

An integrated dual process simulation model of alcohol use behaviours in individuals, with application to US population-level consumption, 1984-2012

INTRODUCTION

The Theory of Planned Behaviour (TPB) describes how attitudes, norms and perceived behavioural control guide health behaviour, including alcohol consumption. Dual Process Theories (DPT) suggest that alongside these reasoned pathways, behaviour is influenced by automatic processes that are determined by the frequency of engagement in the health behaviour in the past. We present a computational model integrating TPB and DPT to determine drinking decisions for simulated individuals. We explore whether this model can reproduce historical patterns in US population alcohol use and simulate a hypothetical scenario, "Dry January", to demonstrate the utility of the model for appraising the impact of policy interventions on population alcohol use.

METHOD

Constructs from the TPB pathway were computed using equations from an existing individual-level dynamic simulation model of alcohol use. The DPT pathway was initialised by simulating individuals' past drinking using data from a large US survey. Individuals in the model were from a US population microsimulation that accounts for births, deaths and migration (1984-2015). On each modelled day, for each individual, we calculated standard drinks consumed using the TPB or DPT pathway. In each year we computed total population alcohol use prevalence, frequency and quantity. The model was calibrated to alcohol use data from the Behavioral Risk Factor Surveillance System (1984-2004).

RESULTS

The model was a good fit to prevalence and frequency but a poorer fit to quantity of alcohol consumption, particularly in males. Simulating Dry January in each year led to a small to moderate reduction in annual population drinking.

CONCLUSION

This study provides further evidence, at the whole population level, that a combination of reasoned and implicit processes are important for alcohol use. Alcohol misuse interventions should target both processes. The integrated TPB-DPT simulation model is a useful tool for estimating changes in alcohol consumption following hypothetical population interventions.

Commentary on Robinson et al. (2021): Evaluating theories of change for public health policies using computer model discovery methods

Recent developments in computer modelling—known as model discovery—could help to confirm the mechanisms underpinning Robinson and colleagues’ important early findings for the effectiveness of minimum unit pricing, and to test the complete theory of change underpinning this crucial evaluation.

Intra-group decision-making in agent-based models

Many agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women’s empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.

Using Multi-objective Grammar-based Genetic Programming to Integrate Multiple Social Theories in Agent-based Modeling

Different theoretical mechanisms have been proposed for explaining complex social phenomena. For example, explanations for observed trends in population alcohol use have been postulated based on norm theory, role theory, and others. Many mechanism-based models of phenomena attempt to translate a single theory into a simulation model. However, single theories often only represent a partial explanation for the phenomenon. The potential of integrating theories together, computationally, represents a promising way of improving the explanatory capability of generative social science. This paper presents a framework for such integrative model discovery, based on multi-objective grammar-based genetic programming (MOGGP). The framework is demonstrated using two separate theory-driven models of alcohol use dynamics based on norm theory and role theory. The proposed integration considers how the sequence of decisions to consume the next drink in a drinking occasion may be influenced by factors from the different theories. A new grammar is constructed based on this integration. Results of the MOGGP model discovery process find new hybrid models that outperform the existing single-theory models and the baseline hybrid model. Future work should consider and further refine the role of domain experts in defining the meaningfulness of models identified by MOGGP.

Introducing CASCADEPOP: an open-source sociodemographic simulation platform for us health policy appraisal

Largescale individual-level and agent-based models are gaining importance in health policy appraisal and evaluation. Such models require the accurate depiction of the jurisdiction's population over extended time periods to enable modeling of the development of non-communicable diseases under consideration of historical, sociodemographic developments. We developed CASCADEPOP to provide a readily available sociodemographic micro-synthesis and microsimulation platform for US populations. The micro-synthesis method used iterative proportional fitting to integrate data from the US Census, the American Community Survey, the Panel Study of Income Dynamics, Multiple Cause of Death Files, and several national surveys to produce a synthetic population aged 12 to 80 years on 01/01/1980 for five states (California, Minnesota, New York, Tennessee, and Texas) and the US. Characteristics include individuals' age, sex, race/ethnicity, marital/employment/parental status, education, income and patterns of alcohol use as an exemplar health behavior. The microsimulation simulates individuals' sociodemographic life trajectories over 35 years to 31/12/2015 accounting for population developments including births, deaths, and migration. Results comparing the 1980 micro-synthesis against observed data shows a successful depiction of state and US population characteristics and of drinking. Comparing the microsimulation over 30 years with Census data also showed the successful simulation of sociodemographic developments. The CASCADEPOP platform enables modelling of health behaviors across individuals' life courses and at a population level. As it contains a large number of relevant sociodemographic characteristics it can be further developed by researchers to build US agent-based models and microsimulations to examine health behaviors, interventions, and policies.

A software architecture for mechanism-based social systems modelling in agent-based simulation models

This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.