Food safety vulnerability: Neighbourhood determinants of non-compliant establishments in England and Wales

Inequalities in local government expenditure on environmental and regulatory services in England from 2009 to 2020: a longitudinal ecological study

Background

Gastrointestinal (GI) infections affect one in five people in the UK and local authorities play a crucial role in controlling these infections. However, there have been substantial reductions in funding for environmental and regulatory (ER) services that enable GI infectious disease prevention and control via food safety and infection control (FSIC) services. This study investigates how local funding cuts to these services have varied across England to understand the potential consequences of inequalities in GI infections.

Methods

We carried out a longitudinal observational ecological study, using a panel of annual data between 2009/2010 and 2020/2021. Analysis of ER service expenditure and FSIC service expenditure included 312 and 303 local authorities respectively. Generalised estimating equation models were used to estimate the annual per cent change of ER service expenditure between 2009/2010 and 2020/2021 in addition to FSIC expenditure change overall, and as a share of total ER expenditure. Models analysed trends by local authority structure, population density and deprivation level.

Results

ER services saw the largest cuts in unitary authorities, declining by 1.9%. London boroughs had the greatest reductions in FSIC expenditure, decreasing by 9.9%. Both ER and FSIC expenditure decreased with increasing population density. Areas of higher deprivation had the largest reduction in expenditure, with ER and FSIC cuts of 2.4% and 22.8%, respectively, compared with a 1.2% and 7.5% reduction in the least deprived areas. The share of ER expenditure spent on FSIC decreased by 13.4% in the most deprived authorities compared with 6.3% in the least deprived areas.

Conclusion

The unequal distribution of cuts shows the need for increased and equitable investment into these services to enable resilience to emerging infectious disease threats and to prevent the widening of health inequalities.

Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach

Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance.