Simulation of alcohol control policies for health equity (SIMAH) project: study design and first results

Since about 2010, life expectancy at birth in the United States has stagnated and begun to decline, with concurrent increases in the socioeconomic divide in life expectancy. The Simulation of Alcohol Control Policies for Health Equity (SIMAH) Project uses a novel microsimulation approach to investigate the extent to which alcohol use, socioeconomic status (SES), and race/ethnicity contribute to unequal developments in US life expectancy and how alcohol control interventions could reduce such inequalities. Representative, secondary data from several sources will be integrated into one coherent, dynamic microsimulation to model life-course changes in SES and alcohol use and cause-specific mortality attributable to alcohol use by SES, race/ethnicity, age, and sex. Markov models will be used to inform transition intensities between levels of SES and drinking patterns. The model will be used to compare a baseline scenario with multiple counterfactual intervention scenarios. The preliminary results indicate that the crucial microsimulation component provides a good fit to observed demographic changes in the population, providing a robust baseline model for further simulation work. By demonstrating the feasibility of this novel approach, the SIMAH Project promises to offer superior integration of relevant empirical evidence to inform public health policy for a more equitable future

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

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

Healthcare seeking behaviour as a link between tuberculosis and socioeconomic factors

Socioeconomic barriers to tuberculosis care-seeking and costs due to care-seeking lead to unfavourable treatment, epidemiological and economic outcomes. Especially in the post-2015 era, socioeconomic interventions for tuberculosis control are receiving increasing attention. In Taiwan, the National Health Insurance programme minimises out-of-pocket expenses for patients, but important delays to tuberculosis treatment still exist. Based on the population and tuberculosis epidemiology in Taiwan, I develop an analysis for profiling the efficacy of tuberculosis care provision and patients' care-seeking pathways. The results highlight that the interrupted tuberculosis evaluation processes and low diagnostic capacity in small local hospitals stands as key causes of extended delays to treatment, unfavourable outcomes, and costs. I analyse socioeconomic status (SES) of employment, vulnerability, and residential contexts, to identify risk factors for different aspects of care-seeking. To link the care-seeking pathways to the nationwide tuberculosis epidemiology, I develop a data-driven hybrid simulation model. The model integrates the advantages of agent-based approaches in representing detail, and equation-based approaches in simplicity and low computational cost. This approach makes feasible Monte-Carlo experiments for robust inferences without over-simplifying the care-seeking details of interest. By comparing the hybrid model simulations with a corresponding equation-based comparator, I confirm its validity. I considered interventions to improve universal health coverage by decentralising tuberculosis diagnostic capacity. I modelled specific interventions increasing the coverage of tuberculosis diagnostic capacity using various SES-targeted scale-up strategies. These show potential benefits in terms of reducing dropouts and reducing the tuberculosis burden, without significant increases in the inequality of care-seeking costs. I suggest considering additional SES variables such as education, health illiteracy, and social segregation to find other care-seeking barriers. Further investigations of SES-related interventions against tuberculosis, including formal impact and health economic evaluation, should be pursued in collaboration with policymakers able to advise on feasibility and patients able to advise on acceptability

Can social norms explain long-term trends in alcohol use? Insights from inverse generative social science

Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact

Markovian-based clustering of internet addiction trajectories

A hidden Markov clustering procedure is applied to a sample of n=185 longitudinal Internet Addiction Test trajectories collected in Switzerland. The best solution has 4 groups. This solution is related to the level of emotional wellbeing of the subjects, but no relation is observed with age, gender and BMI

Using dynamic microsimulation to understand professional trajectories of the active Swiss population

Within the social and economic sciences and of particular interest to demographers are life course events. Looking at life sequences we can better understand which states, or life events, precede or are precursors to vulnerability. A tool that has been used for policy evaluation and recently has been gaining ground in life course sequence simulation is dynamic microsimulation. Within this context dynamic microsimulation consists in generating entire life courses from the observation of portions of the trajectories of individuals of different ages. In this work, we aim to use dynamic microsimulation in order to analyse individual professional trajectories with a focus on vulnerability. The primary goal of this analysis is to deepen upon current literature by providing insight from a longitudinal perspective on the signs of work instability and the process of precarity. The secondary goal of this work which is to show how, by using microsimulation, data collected for one purpose can be analysed under a different scope and used in a meaningful way. The data to be used in this analysis are longitudinal and were collected by NCCR-LIVES IP207 under the supervision of Prof. Christian Maggiori and Dr. Gregoire Bollmann. Individuals aged 25 to 55 residing in the German-speaking and French-speaking regions of Switzerland were followed annually for four years. These individuals were questioned regarding, amongst their personal, professional and overall situations and well-being. At the end of the fourth wave, there were 1131 individuals who had participated in all waves. The sample remained representative of the Swiss population with women and the unemployed slightly over represented. Using the information collected from these surveys, we use simulation to construct various longitudinal data modules where each data module represents a specific life domain. We postulate the relationship between these modules and layout a framework of estimation. Within certain data modules a set of equations are created to model the process therein. For every dynamic (time-variant) data module, such as the labour-market module, the transition probabilities between states (ex. labour market status) are estimated using a Markov model and then the possible outcomes are simulated. The benefit of using dynamic microsimulation is that longitudinal sample observations instead of stylised profiles are used to model population dynamics. This is one of the main reasons large-scale dynamic microsimulation models are employed by many developed nations. There has been limited use, however, of such approaches with Swiss data. This work contributes to the analysis of professional trajectories of the active Swiss population by utilising dynamic microsimulation methods

A discussion on hidden Markov models for life course data

This is an introduction on discrete-time Hidden Markov models (HMM) for longitudinal data analysis in population and life course studies. In the Markovian perspective, life trajectories are considered as the result of a stochastic process in which the probability of occurrence of a particular state or event depends on the sequence of states observed so far. Markovian models are used to analyze the transition process between successive states. Starting from the traditional formulation of a first-order discrete-time Markov chain where each state is liked to the next one, we present the hidden Markov models where the current response is driven by a latent variable that follows a Markov process. The paper presents also a simple way of handling categorical covariates to capture the effect of external factors on the transition probabilities and existing software are briefly overviewed. Empirical illustrations using data on self reported health demonstrate the relevance of the different extensions for life course analysis

Evaluating local-level interventions to address alcohol-related harms in England: the development and application of a complex systems perspective to process evaluations

Background: Alcohol misuse is a wicked problem that may be best addressed by applying a complex systems perspective to the development and evaluation of alcohol interventions. Public health researchers have increasingly advocated this perspective, but the methods for complex systems process evaluations are under-developed. This thesis aims to develop and apply a framework for the application of a complex systems perspective to process evaluations of interventions to reduce alcohol-associated harms. Methods: The research involved 4 elements: i) a qualitative study involving interviews (n=30) and a focus group to evaluate the mechanisms by which the intervention ‘Reducing the Strength’ (RtS) may generate multi-level changes; ii) a scoping review of 87 primary studies and 3 systematic reviews to describe the scope of complex systems alcohol research; iii) a systematic review of 21 complex systems process evaluations and the development of a framework for qualitative process evaluation from a complex systems perspective; and iv) the application of this framework to evaluate the ‘Late Night Levy’ (LNL) using documentary analysis, interviews (n=21) and observations (35.5 hours). Findings: Alcohol interventions may generate multiple changes within and beyond the systems into which they are implemented. Alcohol research taking a complex systems perspective focuses on individual and local systems, with far less analysis of regional, national and international systems. Process evaluations from a complex systems perspective describe systems at a single timepoint, but utilise few complexity concepts to analyse system change. A two-phase process evaluation framework illustrates how to assess mechanisms of system change following intervention implementation. Applying the framework to evaluate the LNL demonstrated how the levy generated system changes which were both anticipated and unanticipated by system actors. Conclusion: The process evaluation framework can produce holistic appraisals of how interventions generate system changes across system levels; evaluators should further apply and refine the framework

Populační perspektivy Kazachstánu do roku 2030

Population prospects of Kazakhstan till 2030 Abstract Population change affects national income, national expenditure, and the demand for services such as education, health and transport. Therefore, information about future population size and structure obtained with the help of population forecasts, which can be used for a wide range of decision-making purposes, is of paramount importance. The primary aim of this dissertation is to produce three different types of population forecasts for Kazakhstan till 2030 and by comparing and analysing the differences to find out the most important factors determining the population development process in the country. Kazakhstan is a country with significant size and regional diversity which makes it relevant to consider those dimensions in population forecasting. Most southern oblasts of the country have a young population structure meaning that much of future population growth, particularly of working age, will come from these regions. Also, native population tends to concentrate in rural areas, while industrialized cities are mostly populated by non-natives with considerably different nuptiality and fertility behaviour. Despite such regional and residential demographic differences, presently the country is experiencing an overall increase in birth rates. Many claims...Population prospects of Kazakhstan till 2030 Abstract Population change affects national income, national expenditure, and the demand for services such as education, health and transport. Therefore, information about future population size and structure obtained with the help of population forecasts, which can be used for a wide range of decision-making purposes, is of paramount importance. The primary aim of this dissertation is to produce three different types of population forecasts for Kazakhstan till 2030 and by comparing and analysing the differences to find out the most important factors determining the population development process in the country. Kazakhstan is a country with significant size and regional diversity which makes it relevant to consider those dimensions in population forecasting. Most southern oblasts of the country have a young population structure meaning that much of future population growth, particularly of working age, will come from these regions. Also, native population tends to concentrate in rural areas, while industrialized cities are mostly populated by non-natives with considerably different nuptiality and fertility behaviour. Despite such regional and residential demographic differences, presently the country is experiencing an overall increase in birth rates. Many claims...Department of Demography and GeodemographyKatedra demografie a geodemografieFaculty of SciencePřírodovědecká fakult