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Health-GPS is a modular and flexible microsimulation framework developed in collaboration between the Centre for Health Economics & Policy Innovation (CHEPI), Imperial College London; and INRAE, France; as part of the STOP project. Health-GPS models the impacts of behavioural and metabolic risk factors on chronic diseases, and measure lifelong outcomes to allow researchers to test the effectiveness of a variety of health policies and interventions designed for tackling childhood obesity in European countries.
Health-GPS creates a virtual population representative of a distinct country’s population and simulates close to reality life histories from birth to death of each member of the population including key characteristics such as gender, age, socio-economic status, risk factors, and disease profiles. These characteristics evolve over time and are updated in discrete time annually using statistical and probabilistic models which are calibrated to reproduce key demographic and epidemiological statistics from the specific country.
The model uses proprietary equations to account for a variety of complex interactions such as risk factor-disease interactions and disease-disease interactions. Modellers are then able to evaluate health-related policies by changing some of the parameters and comparing the outputs with a baseline simulation. The model produces detailed quantitative outputs covering demographics, risk factors, diseases, mortality, global health estimates and health care expenditure, which could then be used to complement qualitative policy evaluation tools.
The Health-GPS workflow is summarised below, datasets from many disconnected sources are used to define the various modules and components of the framework. Commonly used datasets are processed, aggregated, indexed by country, and stored in the backend datastore, while research specific datasets are analysed externally to build the risk factors and socio-economic status modules, design and parameterise intervention to be tested.
|Health-GPS General Workflow Diagram|
The simulation creates the virtual population, simulates the synthetic individuals over time, collects population statistics and publish to the outside world at the end of each simulated time step. It is the user’s responsibility to analyse and quantify the model results, which are saved to a chosen output folder in JSON format.
Health-GPS is a flexible and modular framework, written in modern C++, designed using object-oriented principles to provide the building blocks necessary to compose the overall microsimulation, several data sources, modules, and sub-model are required as shown below.
|Health-GPS Concept Diagram|
The framework defines multi-dimensional interactions on demographics, risk factors, diseases and intervention modules as shown below. The model dynamics capture the effects of the various interacting modules overtime to stablish the population baseline projection and quantify the impact of interventions on risk factors, the burden of diseases (BoD) module estimate population outcomes such as risk factors, disease prevalence, and health care expenditure. Finally, the different between the two scenarios is the effect of the intervention.
|Health-GPS Dynamics Diagram|
A brief overview of each module is provided next, the framework has been designed to allow module composition, modellers can experiment with different module implementations at run-time.
The population historical trends and projections are used to define the baseline scenario for the model. The model requires historical and projected populations by year, age, and gender for each country of interest, covering the entire duration of the experiment. All data processing, units’ conversion, gap filling, smoothing, etc, must be carried out outside to produce the complete datasets required. The following demographic measures are required by the model:
Births, deaths, and immigration are the only drivers of changes in demographics in a population. While the births and deaths modelling are data driven, finding accurate data about immigration is more challenging. Net migration, the net flow of migrants between two consecutive years, is estimated as the difference by age and gender between the simulated population and the expected population from the country’s input data.
SES plays an important role in the levels of risk factors observed within the population. The levels of income and education can influence the nature of diet, and physical activity. Health-GPS models SES as a proxy value, combining education, income, and other factors to represent specific groups within the population. Each individual SES value is attributed at birth by sampling from a standard normal distribution and stays constant over time.
The population cultural and social behaviours are represented by risk factors, defined as any attributes that can influence the likelihood of acquiring a disease. Individual choices such as smoking, alcohol consumption, physical activity, and diet, can lead to long-term consequences such as hypertension, obesity, and diabetes. Furthermore, certain diseases can be risk factors for other diseases or certain types of cancers.
The dynamics of risk factors modelling is a major challenge for health policy modellers, there are divergent opinions on the types and directions of causality between risk factors and diseases. Health-GPS defines a dynamic hierarchical risk factor model structure, modellers can configure the hierarchy outside for different problems, fit parameters to data and provide to fully built model as part of the experiment configuration.
To represent childhood obesity, national dietary surveys from various European countries are analysed to build the risk factor model. Estimates of yearly changes in physical activity, diet, energy balance, and Body Mass Index (BMI) are created using dietary and anthropometric surveys. These include measures of physical activity expressed in metabolic equivalents (METs) and macronutrients intakes measures including grams of fat, carbohydrates, protein, fibre, salt, and sugar. The general concept for an EBM is shown below (top diagram), and a possible Health-GPS translation is provided for illustration purpose.
|Energy balance model structure example|
The calibration of the equations is carried out outside of the model by gender for children and adults separately to ensure capturing gender- and age-related differences. Emphasis is placed on capturing the rapid growth and changes in children BMIs. International anthropometric references are used to properly classify individuals as normal weight, overweight or obese. This classification is used throughout the simulation to identify children with growth problems and devise appropriate policies and interventions to tackle the health issues.
Individuals may acquire new diseases for many reasons, including genetics, environment, and lifestyles. The associations between risk factors and the incidence of certain types of diseases is a major subject being widely study. Health-GPS accounts for the associations between risk factors and diseases by using equations to translate exposures to risk factors into probabilities that are used to simulate the incidence of diseases in the population.
The association between risk factor and disease stays constant throughout the simulation, however any changes in the distribution of a risk factor, will still translate to more/fewer disease cases through relative risk equations. Any change in the prevalence of a disease is therefore solely caused by changes in risk factors distributions alongside the ageing effect on the simulated population. Health-GPS supports two groups of diseases: general noncommunicable diseases, and types of cancer respectively.
Collects statistical indicators about the simulated population, life expectancy, disease prevalence, risk factors exposure; and standardised metrics such as years of life lost due to premature mortality (YLL), years of healthy life lost due to disability (YLD), disability-adjusted life years (DALY), and healthcare expenditure (HCE) to reflect the impact of the intervention compared to a status-quo simulation.
The overall approach adopted to evaluate the impacts and cost-effectiveness of intervention policies to reduce childhood obesity using the policy simulation tool is based upon “what-if” analyses to quantify the causal relations between variables, scenarios can be classified as:
Baseline scenarios: elaborated to define the trends in observed childhood obesity we measure outcomes against (e.g., population, calories intake, diseases prevalence trends).
Intervention scenarios: policies designed to change the observed trends in childhood obesity during a specific time frame (e.g., food labelling, healthy eating promotion, BMI reduction).
The choice of baseline scenario is critical for analyses as it serves as a reference for comparison and can influence outcomes. Having defined the baseline scenario, the simulation assesses the impacts of different intervention policies by projecting populations, risk factors, diseases, and life trajectories into the future comparing pairs of no-intervention and intervention scenarios.
The first run evaluates the no-intervention, “baseline scenario” where demographics, risk factors, and diseases are projected based solely on estimates from historical data. The second run evaluates the “intervention scenario” where a specific policy is applied to the same population with the aim of modifying the underlying trends and risk factor distribution.
Finally, detailed analysis can be carried out, externally, to compare the two simulated scenarios results in terms of population demographics and burden of diseases to estimate the cost-effectiveness and impacts of the targeted intervention in tackling childhood obesity.
The microsimulation follows a two-step process to capture time-serial and cross-sectional correlations between risk factors, preserve the cross-sectional correlations between factors, and their hierarchical structure to allow changes to be propagated from lower to high level variables. The workflow consists of two main algorithms to initialise and project the virtual population over time respectively. Creating and initialising the virtual population is the first step of a simulation run, while the algorithm projecting the population over time is the core of the microsimulation as shown below.
|Health-GPS Workflow Diagram|
The initialisation sets the simulation world clock, in years, to the user’s defined start time, and requests the simulation modules to initialise the relevant properties of the virtual population individuals. The projection moves the simulation clock, in years, forwards until the user’s defined end time is reached, at which point the algorithm terminates.
Health-GPS makes use of various types of data such as cross-sectional and longitudinal surveys to produce consistent estimates of a particular variable of interest. To reconcile large swathes of datasets describing determinants of health, demographics, socio-economic, behavioural, risk exposure, diseases, healthcare delivery and expenditure from otherwise unconnected sources, the Health-GPS data model adopts the ISO 3166 country code to link all datasets as shown below.
|Health-GPS Data Reconciliation|
The reconcile process can be extremely laborious with each dataset having to be processed individually to for general data cleansing, map country code, use consistent unit of measurement, filling gaps, and smoothing. Health-GPS assumes complete datasets, all data processing must take place out outside of the model.