Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper)

Rates of anxiety and depression are increasing due to financial stress caused by energy pricing with over half of UK homes unable to afford comfortable heating. UK Government policies to address this energy crisis have been implemented with limited evidence and substantial criticism. This paper applies the dynamic microsimulation MINOS, which utilises longitudinal Understanding Society data, to evidence change in mental well-being under the Energy Price Cap Guarantee and Energy Bill Support Scheme Policies. Results demonstrate an overall improvement in Short Form 12 Mental Component Score (SF12-MCS) both on aggregate and over data zone spatial areas for the Glasgow City region compared with a baseline of no policy intervention. This is work in progress and discussion highlights potential future work in other energy policy areas, such as Net Zero

SIPHER Inclusive Economy Indicator Set: Technical paper

The Systems Science in Public Health and Health Economics Research - SIPHER Consortium is a major investment by the UK Prevention Research Partnership (UKPRP). It is a partnership between scientists across seven universities, four government partners at local, regional and national level, and multiple practice partner organisations. SIPHER seeks to support a shift from ‘health policy’ to ‘healthy public policy’, by understanding how public policies in spheres such as the economy, welfare, housing, education and employment impact on health and health inequalities. Drawing on participatory systems mapping and evidence synthesis, SIPHER is developing system models and decision support tools for use in public policy settings. A key topic of interest for SIPHER is the relationship between inclusive economy policies and wider health outcomes and inequalities. To address this topic, the consortium has developed a set of inclusive economy indicators for use in SIPHER’s modelling work. While the SIPHER Inclusive Economy (IE) Indicators have been selected for specific purposes (discussed below) it is hoped that they will be useful to others concerned with understanding, promoting, and monitoring the development of more inclusive economies. This paper therefore aims to support their wider use by describing the indicator set, data sources and limitations, and explaining the rationale and process for indicator selection

SIPHER Inclusive Economy Indicator Set: Summary

This briefing provides a short summary overview of the SIPHER Inclusive Economy Indicator Set. A key topic of interest for SIPHER is how policies aimed at promoting more inclusive economies can impact on health outcomes and inequalities. To help us understand and model this we need to know what we mean by an inclusive economy, and we need to be able to measure it. To this end, we developed a set of inclusive economy indicators and produced a dataset at Local Authority Level. This also includes summary measures of life expectancy and life span variation. We then use this alongside the SIPHER Synthetic Population Dataset at individual level to show how aggregate place-level characteristics (and changes) are linked to individual and small area characteristics (and changes). Here we introduce the indicators and how they were developed. The SIPHER Inclusive Economy Indicator Set: Technical Paper gives fuller detail on this process, and the derivation of the indicators - available at www.SIPHER.ac.uk

Variations in migration motives over distance

Background: It is often assumed that long-distance migration is dominated by employment or educationally led motives and that local-scale mobility is linked to family and housing adjustments. Unfortunately, few empirical studies examining the relationship between motives and distance exist. Objective: Recognising that the relationships between migration motives and distances are likely to be context-specific, we explore and compare the relationship in three advanced economies: the United Kingdom, Australia, and Sweden. Methods: We use three sources of nationally representative microdata: the United Kingdom Household Longitudinal Study (UKHLS) (2009–2018); the Australian Household, Income and Labour Dynamics (HILDA) survey (2001–2016); and a Swedish survey of motives undertaken in spring 2007. LOESS smooth curves are presented for each of six distance–motive trends (Area, Education, Employment, Family, Housing, and Other) in the three countries. Results: The patterns offer some support to the common assumptions. In all three countries, housing is the most commonly cited motive to move locally. Employment is an important motive for longer-distance migration. Yet, interestingly, and consistent across the three national contexts, family-related considerations are shown to be key in motivating both shorter- and longer-distance moves. Contribution: Our analysis demonstrates how people move for different reasons, across different distances, in different national contexts. While typically associated with local-scale relocations, family-related motives are rarely mentioned in literature focused on longer-distance migration. The role of family in long-distance migration would thus appear to warrant far more attention than it currently receives

A dynamic microsimulation model for epidemics.

Funder: Aerospace Technology InstituteFunder: UK Research and InnovationFunder: The Alan Turing InstituteA large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations

Estimating Population Attribute Values in a Table: “Get Me Started in” Iterative Proportional Fitting

Iterative proportional fitting (IPF) is a technique that can be used to adjust a distribution reported in one data set by totals reported in others. IPF is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample. Although widely applied, the IPF methodology is rarely presented in a way that is accessible to nonexpert users. This article fills that gap through discussion of how to operationalize the method and argues that IPF is an accessible and transparent tool that can be applied to a range of data situations in population geography and demography. It offers three case study examples where IPF has been applied to geographical data problems; the data and algorithms are made available to users as supplementary material

Infrastructure and cities ontologies

The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use of cases that rely on real-world integration of disparate systems, the need for semantic congruence across boundaries and the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems, but they are particularly apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated, undermining the potential for integration to solve societal challenges such as net zero, resilience to climate change, equity and affordability. This paper reports on findings of a literature review on infrastructure and city ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to the literature and provide avenues for further research on (a) belief systems that underpin non-top-level ontologies and the potential for interference from them, (b) the need for a small number of top-level ontologies and translation mechanisms between them and (c) clarity on the role of standards and information systems in the adaptability and quality of data sets using ontologies. A gap is also identified in the extent that ontologies can support more complex automated coupling and data transformation when dealing with different scales

Evaluating the influence of taxation and social security policies on psychological distress: a microsimulation study of the UK during the COVID-19 economic crisis

Economic determinants are important for population health, but actionable evidence of how policies can utilise these pathways remains scarce. This study employs a microsimulation framework to evaluate the effects of taxation and social security policies on population mental health. The UK economic crisis caused by the COVID-19 pandemic provides an informative context involving an economic shock accompanied by one of the strongest discretionary fiscal responses amongst OECD countries. The analytical setup involves a dynamic, stochastic, discrete-time microsimulation model (SimPaths) projecting changes in psychological distress given predicted economic outcomes from a static tax-benefit microsimulation model (UKMOD) based on different policy scenarios. We contrast projections of psychological distress for the working-age population from 2017 to 2025 given the observed policy environment against a counterfactual scenario where pre-crisis policies remained in place. Levels of psychological distress and potential cases of common mental disorders (CMDs) were assessed with the 12-item General Health Questionnaire (GHQ-12). The UK policy response to the economic crisis is estimated to have prevented a substantial fall (over 12 percentage points, %pt) in the employment rate in 2020 and 2021. In 2020, projected psychological distress increased substantially (CMD prevalence increase >10%pt) under both the observed and the counterfactual policy scenarios. Through economic pathways, the policy response is estimated to have prevented a further 3.4%pt [95%UI 2.8%pt, 4.0%pt] increase in the prevalence of CMDs, approximately 1.2 million cases. Beyond 2021, as employment levels rapidly recovered, psychological distress returned to the pre-pandemic trend. Sustained preventative effects on poverty are estimated, with projected levels 2.1%pt [95%UI 1.8%pt, 2.5%pt] lower in 2025 than in the absence of the observed policy response. The study shows that policies protecting employment during an economic crisis are effective in preventing short-term mental health losses and have lasting effects on poverty levels. This preventative effect has substantial public health benefits

Is adolescent body mass index and waist circumference associated with the food environments surrounding schools and homes? A longitudinal analysis

Background: There has been considerable interest in the role of access to unhealthy food options as a determinant of weight status. There is conflict across the literature as to the existence of such an association, partly due to the dominance of cross-sectional study designs and inconsistent definitions of the food environment. The aim of our study is to use longitudinal data to examine if features of the food environment are associated to measures of adolescent weight status. Methods: Data were collected from secondary schools in Leeds (UK) and included measurements at school years 7 (ages 11/12), 9 (13/14), and 11 (15/16). Outcome variables, for weight status, were standardised body mass index and standardised waist circumference. Explanatory variables included the number of fast food outlets, supermarkets and ‘other retail outlets’ located within a 1 km radius of an individual’s home or school, and estimated travel route between these locations (with a 500 m buffer). Multi-level models were fit to analyse the association (adjusted for confounders) between the explanatory and outcome variables. We also examined changes in our outcome variables between each time period. Results: We found few associations between the food environment and measures of adolescent weight status. Where significant associations were detected, they mainly demonstrated a positive association between the number of amenities and weight status (although effect sizes were small). Examining changes in weight status between time periods produced mainly non-significant or inconsistent associations. Conclusions: Our study found little consistent evidence of an association between features of the food environment and adolescent weight status. It suggests that policy efforts focusing on the food environment may have a limited effect at tackling the high prevalence of obesity if not supported by additional strategies