Developing an openly accessible multi-dimensional small area index of 'Access to Healthy Assets and Hazards' for Great Britain, 2016

Can we empirically derive a geographic definition of 'coastal' for use in cancer data reporting? An ecological modelling study using England's national cancer registry

Background

Reducing avoidable systematic differences in population health requires first understanding which populations are currently disadvantaged. Although the health of coastal communities in England has been of concern for some years, an operationalised definition of 'coastal' is lacking. This study aims to use national cancer statistics to define and validate a small area-level definition of 'coastal' that could be used to better report cancer-related health inequalities in England.

Methods

Information on the geography and demography of English populations at the Lower Super Output Area (LSOA) level were used to define a suite of candidate coastal variables that considered foreshore proximity, resident population location, rurality and deprivation. Adjusted linear models of LSOA-level statistics of cancer incidence, prevalence and mortality in England (2016 to 2020) were used to identify candidate coastal variable(s) that explained the greatest proportion of variation in cancer outcomes after adjustment.

Results

The candidate 'G_25_5' (LSOA's designated as 'coastal' if 25% or more of postcodes were within 5 km of the coastline) was selected as the candidate that explained the most residual variation in cancer incidence and prevalence after adjustment. This variable would assign 7377 2011 LSOAs as coastal, whose populations summed to 12.3 million people (22% of England's population, in 2016). This candidate variable was not significantly associated with cancer mortality.

Conclusions

The coastal variable that we identify can explain some of the 'coastal excess' in poor cancer outcomes. We propose that this variable is now embedded into health inequalities reporting and adopted as the working definition of 'coastal' implicated in NHS England's 'Core20PLUS5' approach for use in cancer data reporting.

Analysis of the Sustainable Development Goal 3 index for Italian municipalities

OBJECTIVES

Improving health at global and local scales is one of the 17 Sustainable Development Goals (SDGs) set by the United Nations (UN) for the period 2015-2030, specifically defined by SDG3, which includes 13 targets described by 28 indicators. In this context, the aim of the current study was to propose a protocol to infer SDG3 values at municipality level with the current openly available data.

STUDY DESIGN

The study incorporated a quantitative research.

METHODS

To calculate the SDG3 index, defined as the average of all 13 target scores, official Italian data at five geographical granularities covering the period 2018-2022 were used, and a spatial downscaling strategy was implemented. The quality of matching between original and inferred indicators was assessed applying a specific standard (International Organisation for Standardisation [ISO]/TS 21564) that matches quality between terminology resources with regards to health care. The significance of regional/provincial differences was assessed by the Kruskal-Wallis test with Bonferroni correction, and the Moran's index with queen contiguity method was applied to evaluate clustering tendency.

RESULTS

The geographical distribution of scores varied considerably (and with statistical significance) across the targets, with municipalities in the central part of the country achieving relatively good overall performance. Matching quality also varied consistently across targets. Clustering tendency was observed and was likely due to regional differences in data collection protocols.

CONCLUSIONS

The SDG3 index, as an internationally standardised measure of health, can be used to validate urban health indices; however, considerable improvement by official data providers in Italy is required to guarantee access to data at the municipal level.

Socioenvironmental sugar promotion and geographical inequalities in dental health of 5-year-old children in England

OBJECTIVES

To investigate the relationship between socioenvironmental sugar promotion and geographical inequalities in the prevalence of dental caries amongst 5-year-olds living across small areas within England.

METHODS

Ecological data from the National Dental Epidemiology Programme (NDEP) 2018-2019, comprising information on the percentage of 5-year-olds with tooth decay (≥1 teeth that are decayed into dentine, missing due to decay, or filled), and untreated tooth decay (≥1 decayed but untreated teeth), in lower-tier local authorities (LAs) of England. These were analysed for association with a newly developed Index of Sugar-Promoting Environments Affecting Child Dental Health (ISPE-ACDH). The index quantifies sugar-promoting determinants within a child's environment and provides standardized scores for the index, and its component domains that is, neighbourhood-, school- and family-environment, with the highest scores representing the highest levels of sugar promotion in lower-tier LAs (N = 317) of England. Linear regressions, including unadjusted models separately using index and each domain, and models adjusted for domains were built for each dental outcome.

RESULTS

Participants lived across 272 of 317 lower-tier LAs measured within the index. The average percentage of children with tooth decay and untreated tooth decay was 22.5 (SD: 8.5) and 19.6 (SD: 8.3), respectively. The mean index score was (0.1 [SD: 1.01]). Mean domain scores were: neighbourhood (0.02 [SD: 1.03]), school (0.1 [SD: 1.0]), and family (0.1 [SD: 0.9]). Unadjusted linear regressions indicated that the LA-level percentage of children with tooth decay increased by 5.04, 3.71, 4.78 and 5.24 with increased scores of the index, and neighbourhood, school and family domains, respectively. An additional model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.37, and by family domain it increased to 6.33. Furthermore, unadjusted models indicated that the LA-level percentage of children with untreated tooth decay increased by 4.72, 3.42, 4.45 and 4.97 with increased scores of the index, and neighbourhood, school, and family domains, respectively. The model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.24 and by family domain rose to 6.47. School-domain was not significantly associated with either outcome in adjusted models.

CONCLUSIONS

This study reveals that socioenvironmental sugar promotion, particularly within neighbourhood- and family-environments, may contribute to geographical inequalities in dental caries in children. Further research involving data on individual-level dental outcomes and confounders is required.

Psychosis prevalence in London neighbourhoods; A case study in spatial confounding

Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.

Ageing in Place Classification: Creating a geodemographic classification for the ageing population in England

Population ageing is one of the most significant demographic changes underway in many countries. Far from being a homogenous group, older people and their experiences of ageing are diverse. A better understanding of the characteristics and geography of the older population, including the older workforce, is important. It allows policymakers and stakeholders to better adapt to the opportunities and challenges that the ageing population brings. This paper describes the implementation of the Ageing in Place Classification (AiPC) in England. AiPC is a multidimensional geodemographic classification, and it employs a wide range of spatially representative attributes of older people's sociodemographic characteristics and their living environment at the small area level. The openly available product provides valuable insights that can be implemented in both local and national contexts, in particular to improve service delivery and inform targeted policy interventions. AiPC is readily updateable with the arrival of new Census data; the concept and framework are also transferable to other countries.

Household food insecurity risk indices for English neighbourhoods: Measures to support local policy decisions

BACKGROUND

In England, the responsibility to address food insecurity lies with local government, yet the prevalence of this social inequality is unknown in small subnational areas. In 2018 an index of small-area household food insecurity risk was developed and utilised by public and third sector organisations to target interventions; this measure needed updating to better support decisions in different settings, such as urban and rural areas where pressures on food security differ.

METHODS

We held interviews with stakeholders (n = 14) and completed a scoping review to identify appropriate variables to create an updated risk measure. We then sourced a range of open access secondary data to develop an indices of food insecurity risk in English neighbourhoods. Following a process of data transformation and normalisation, we tested combinations of variables and identified the most appropriate data to reflect household food insecurity risk in urban and rural areas.

RESULTS

Eight variables, reflecting both household circumstances and local service availability, were separated into two domains with equal weighting for a new index, the Complex Index, and a subset of these to make up the Simple Index. Within the Complex Index, the Compositional Domain includes population characteristics while the Structural Domain reflects small area access to resources such as grocery stores. The Compositional Domain correlated well with free school meal eligibility (rs = 0.705) and prevalence of childhood obesity (rs = 0.641). This domain was the preferred measure for use in most areas when shared with stakeholders, and when assessed alongside other configurations of the variables. Areas of highest risk were most often located in the North of England.

CONCLUSION

We recommend the use of the Compositional Domain for all areas, with inclusion of the Structural Domain in rural areas where locational disadvantage makes it more difficult to access resources. These measures can aid local policy makers and planners when allocating resources and interventions to support households who may experience food insecurity.

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative

Natural environments, such as parks, woodlands and lakes, have positive impacts on health and wellbeing. Urban Green and Blue Spaces (UGBS), and the activities that take place in them, can significantly influence the health outcomes of all communities, and reduce health inequalities. Improving access and quality of UGBS needs understanding of the range of systems (e.g. planning, transport, environment, community) in which UGBS are located. UGBS offers an ideal exemplar for testing systems innovations as it reflects place-based and whole society processes , with potential to reduce non-communicable disease (NCD) risk and associated social inequalities in health. UGBS can impact multiple behavioural and environmental aetiological pathways. However, the systems which desire, design, develop, and deliver UGBS are fragmented and siloed, with ineffective mechanisms for data generation, knowledge exchange and mobilisation. Further, UGBS need to be co-designed with and by those whose health could benefit most from them, so they are appropriate, accessible, valued and used well. This paper describes a major new prevention research programme and partnership, GroundsWell, which aims to transform UGBS-related systems by improving how we plan, design, evaluate and manage UGBS so that it benefits all communities, especially those who are in poorest health. We use a broad definition of health to include physical, mental, social wellbeing and quality of life. Our objectives are to transform systems so that UGBS are planned, developed, implemented, maintained and evaluated with our communities and data systems to enhance health and reduce inequalities. GroundsWell will use interdisciplinary, problem-solving approaches to accelerate and optimise community collaborations among citizens, users, implementers, policymakers and researchers to impact research, policy, practice and active citizenship. GroundsWell will be shaped and developed in three pioneer cities (Belfast, Edinburgh, Liverpool) and their regional contexts, with embedded translational mechanisms to ensure that outputs and impact have UK-wide and international application.

Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England

While social and spatial determinants of biomarkers have been reported, no previous study has examined both together within an intersectional perspective. We present a novel extension of quantitative intersectional analyses using cross-classified multilevel models to explore how intersectional positions and neighbourhood deprivation are associated with biomarkers, using baseline UK Biobank data (collected from 2006 to 2010). Our results suggest intersectional inequalities in biomarkers of healthy ageing are mostly established by age 40-49, but different intersections show different relationships with deprivation. Our study suggests that certain biosocial pathways are more strongly implicated in how neighbourhoods and intersectional positions affect healthy ageing than others.

Measuring Obesogenicity and Assessing Its Impact on Child Obesity: A Cross-Sectional Ecological Study for England Neighbourhoods

Both major influences on changing obesity levels (diet and physical activity) may be mediated by the environment, with environments that promote higher weight being denoted obesogenic. However, while many conceptual descriptions and definitions of obesogenic environments are available, relatively few attempts have been made to quantify obesogenic environments (obesogenicity). The current study is an ecological study (using area units as observations) which has as its main objective to propose a methodology for obtaining a numeric index of obesogenic neighbourhoods, and assess this methodology in an application to a major national dataset. One challenge in such a task is that obesogenicity is a latent aspect, proxied by observed environment features, such as poor access to healthy food and recreation, as well as socio-demographic neighbourhood characteristics. Another is that obesogenicity is potentially spatially clustered, and this feature should be included in the methodology. Two alternative forms of measurement model (i.e., models representing a latent quantity using observed indicators) are considered in developing the obesogenic environment index, and under both approaches we find that both food and activity indicators are pertinent to measuring obesogenic environments (though with varying relevance), and that obesogenic environments are spatially clustered. We then consider the role of the obesogenic environment index in explaining obesity and overweight rates for children at ages 10-11 in English neighbourhoods, along with area deprivation, population ethnicity, crime levels, and a measure of urban-rural status. We find the index of obesogenic environments to have a significant effect in elevating rates of child obesity and overweight. As a major conclusion, we establish that obesogenic environments can be measured using appropriate methods, and that they play a part in explaining variations in child weight indicators; in short, area context is relevant.

The environment an adult resides within is associated with their health behaviours, and their mental and physical health outcomes: a nationwide geospatial study

BACKGROUND

The determinants of health behaviours and health outcomes are multifaceted and the surrounding environment is increasingly considered as an important influence. This pre-registered study investigated the associations between the geospatial environment people live within and their health behaviours as well as their mental and physical health outcomes.

METHOD

We used the newly developed Healthy Location Index (HLI) to identify health-promoting and health-constraining environmental features where people live. We then used Time 10 (2018) data from the New Zealand Attitudes and Values Survey (NZAVS; N = 47,951), a national probability sample of New Zealand adults, to gauge mental health outcomes including depression, anxiety and psychological distress, physical health outcomes including BMI and type II diabetes, and health behaviours such as tobacco smoking and vaping. Linear and logistic multilevel mixed effect regression models with random intercepts of individuals nested within geographical areas (meshblocks) were employed.

RESULTS

The presence of health-constraining environmental features were adversely associated with self-reported mental health outcomes of depression, anxiety and psychological distress, physical health outcomes of BMI and type II diabetes, and negative health behaviours of tobacco smoking and vaping. By contrast, health-promoting environmental features were uniquely associated only with physical health outcomes of BMI and type II diabetes.

CONCLUSION

The current study advances research on environmental determinants of health behaviours by demonstrating that close proximity to health-constraining environmental features is related to a number of self-reported physical and mental health outcomes or behaviours. We provide some evidence to support the notion that preventive population-health interventions should be sought.

Assessing Response Readiness to Health Emergencies: A Spatial Evaluation of Health and Socio-Economic Justice in Pakistan

COVID19 pandemic has put the global health emergency response to the test. Providing health and socio-economic justice across communities/regions helps in resilient response. In this study, a Geographic Information Systems-based framework is proposed and demonstrated in the context of public health-related hazards and pandemic response, such as in the face of COVID19. Indicators relevant to health system (HS) and socio-economic conditions (SC) are utilized to compute a response readiness index (RRI). The frequency histograms and the Analysis of Variance approaches are applied to analyze the distribution of response readiness. We further integrate spatial distributional models to explore the geographically-varying patterns of response readiness pinpointing the priority intervention areas in the context of cross-regional health and socio-economic justice. The framework's application is demonstrated using Pakistan's most developed and populous province, namely Punjab (districts scale, n = 36), as a case study. The results show that ~ 45% indicators achieve below-average scores (value < 0.61) including four from HS and five from SC. The findings ascertain maximum districts lack health facilities, hospital beds, and health insurance from HS and more than 50% lack communication means and literacy-rates, which are essential in times of emergencies. Our cross-regional assessment shows a north-south spatial heterogeneity with southern Punjab being the most vulnerable to COVID-like situations. Dera Ghazi Khan and Muzaffargarh are identified as the statistically significant hotspots of response incompetency (95% confidence), which is critical. This study has policy implications in the context of decision-making, resource allocation, and strategy formulation on health emergency response (i.e., COVID19) to improve community health resilience.

Assessing physical access to healthy food across United Kingdom: A systematic review of measures and findings

Background

Existing research suggests that physical access to food can affect diet quality and thus obesity rates. When defining retail food environment (RFE) quantitatively, there is a little agreement on how to measure "lack of healthy food" and what parameters to use, resulting in a heterogeneity of study designs and outcome measures. In turn, this leads to a conflicting evidence base being one of the many barriers to using evidence in policy-making.

Aims

This systematic review aimed to identify and describe methods used to assess food accessibility in the United Kingdom (UK) to overcome heterogeneity by providing a classification of measures.

Materials & Methods

The literature search included electronic and manual searches of peer-reviewed literature and was restricted to studies published in English between January 2010 and March 2021. A total of 9365 articles were assessed for eligibility, of which 44 articles were included in the review. All included studies were analysed with regards to their main characteristics (e.g., associations between variables of interest, setting, sample, design, etc.) and definition of RFE and its metrics. When defining these metrics, the present review distinguishes between a point of origin (centroid, address) from which distance was calculated, summary statistic of accessibility (proximity, buffer, Kernel), and definition of distance (Euclidean, network distance). Trends, gaps and limitations are identified and recommendations made for food accessibility research in UK.

Results

Multiple theoretical and methodological constructs are currently used, mostly quantifying distance by means of Euclidean and ring-buffer distance, using both proximity- and density-based approaches, and ranging from absolute to relative measures. The association between RFE and diet and health in rural areas, as well as a spatiotemporal domain of food access, remains largely unaccounted.

Discussion

Evidence suggests that the duration of exposure may bear a greater importance than the level of exposure and that density-based measures may better capture RFE when compared with proximity-based measures, however, using more complex measures not necessarily produce better results. To move the field forward, studies have called for a greater focus on causality, individual access and the use of various measures, neighbourhood definitions and potential confounders to capture different aspects and dimensions of the RFE, which requires using univariate measures of accessibility and considering the overall context in terms of varying types of neighbourhoods.

Conclusion

In order to render ongoing heterogeneity in measuring RFE, researchers should prioritise measures that may provide a more accurate and realistic account of people's lives and follow an intuitive approach based on convergence of results until consensus could be reached on using some useful standards.

An exploration of factors characterising unusual spatial clusters of COVID-19 cases in the East Midlands region, UK: A geospatial analysis of ambulance 999 data

Complex interactions between physical landscapes and social factors increase vulnerability to emerging infections and their sequelae. Relative vulnerability to severe illness and/or death (VSID) depends on risk and extent of exposure to a virus and underlying health susceptibility. Identifying vulnerable communities and the regions they inhabit in real time is essential for effective rapid response to a new pandemic, such as COVID-19. In the period between first confirmed cases and the introduction of widespread community testing, ambulance records of suspected severe illness from COVID-19 could be used to identify vulnerable communities and regions and rapidly appraise factors that may explain VSID. We analyse the spatial distribution of more than 10,000 suspected severe COVID-19 cases using records of provisional diagnoses made by trained paramedics attending medical emergencies. We identify 13 clusters of severe illness likely related to COVID-19 occurring in the East Midlands of the UK and present an in-depth analysis of those clusters, including urban and rural dynamics, the physical characteristics of landscapes, and socio-economic conditions. Our findings suggest that the dynamics of VSID vary depending on wider geographic location. Vulnerable communities and regions occur in more deprived urban centres as well as more affluent peri-urban and rural areas. This methodology could contribute to the development of a rapid national response to support vulnerable communities during emerging pandemics in real time to save lives.

Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Identifying the effect of retail brands on private residential rental prices in Great Britain

This study extends our understanding of the influence of proximity to retail grocery provision on housing rental prices. To achieve this, extensive data on the size and location of retail outlets are combined with neighbourhood rental values for small areas across a two year period, together with varied contextual data for each area. In order to control the influence of many confounding variables in the determination of housing rentals, the technique of propensity score matching is applied. This provides a sophisticated means for the comparison between areas where there is substantial natural variation, rather than manageable controls. For a variety of types of retail brands, only a significant relationship is found between the proximity of a Premium retail outlet and the housing rental value. The findings of this research allow local planning officers to further understand the impact of planning applications on the potential for gentrification and the affordability of neighbouring housing.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10901-021-09904-2.

COVID-19 Mortality in English Neighborhoods: The Relative Role of Socioeconomic and Environmental Factors

Factors underlying neighborhood variation in COVID-19 mortality are important to assess in order to prioritize resourcing and policy intervention. As well as characteristics of area populations, such as health status and ethnic mix, it is important to assess the role of more specifically environmental variables (e.g., air quality, green space access). The analysis of this study focuses on neighborhood mortality variations during the first wave of the COVID-19 epidemic in England against a range of postulated area risk factors, both socio-demographic and environmental. We assess mortality gradients across levels of each risk factor and use regression methods to control for multicollinearity and spatially correlated unobserved risks. An analysis of spatial clustering is based on relative mortality risks estimated from the regression. We find mortality gradients in most risk factors showing appreciable differences in COVID mortality risk between English neighborhoods. A regression analysis shows that after allowing for health deprivation, ethnic mix, and ethnic segregation, environment (especially air quality) is an important influence on COVID mortality. Hence, environmental influences on COVID mortality risk in the UK first wave are substantial, after allowing for socio-demographic factors. Spatial clustering of high mortality shows a pronounced metropolitan-rural contrast, reflecting especially ethnic composition and air quality.

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand's Ministry of Health (MoH) and the University of Canterbury's GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.

The good, the bad, and the environment: developing an area-based measure of access to health-promoting and health-constraining environments in New Zealand

Background

Accounting for the co-occurrence of multiple environmental influences is a more accurate reflection of population exposure than considering isolated influences, aiding in understanding the complex interactions between environments, behaviour and health. This study examines how environmental ‘goods’ such as green spaces and environmental ‘bads’ such as alcohol outlets co-occur to develop a nationwide area-level healthy location index (HLI) for New Zealand.

Methods

Nationwide data were collected, processed, and geocoded on a comprehensive range of environmental exposures. Health-constraining ‘bads’ were represented by: (i) fast-food outlets, (ii) takeaway outlets, (iii) dairy outlets and convenience stores, (iv) alcohol outlets, (v) and gaming venues. Health-promoting ‘goods’ were represented by: (i) green spaces, (ii) blue spaces, (iii) physical activity facilities, (iv) fruit and vegetable outlets, and (v) supermarkets. The HLI was developed based on ranked access to environmental domains. The HLI was then used to investigate socio-spatial patterning by area-level deprivation and rural/urban classification.

Results

Results showed environmental ‘goods’ and ‘bads’ co-occurred together and were patterned by area-level deprivation. The novel HLI shows that the most deprived areas of New Zealand often have the most environmental ‘bads’ and less access to environmental ‘goods’.

Conclusions

The index, that is now publicly available, is able to capture both inter-regional and local variations in accessibility to health-promoting and health-constraining environments and their combination. Results in this study further reinforce the need to embrace the multidimensional nature of neighbourhood and place not only when designing health-promoting places, but also when studying the effect of existing built environments on population health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-021-00269-x.

Unhealthy environments are associated with adverse mental health and psychological distress: Cross-sectional evidence from nationally representative data in New Zealand

This study combines data on the location of health-constraining 'bads' (i: fast-food outlets, ii: takeaway outlets, iii: dairy outlets and convenience stores, iv: alcohol outlets, and v: gaming venues) and health-promoting 'goods' (i: green spaces, ii: blue spaces, iii: physical activity facilities, and iv: fruit and vegetable outlets) into a nationwide Healthy Living Index. This was applied to pooled (2015/16-2017/18) nationally representative New Zealand Health Survey data, with mental health conditions (depression, bipolar, and anxiety) and psychological distress as population-level outcomes. Mental health was associated with proximity to environmental 'goods' and 'bads'. Compared to those individuals who reside within the unhealthiest environments, there was a steady reduction in the odds of adverse mental health outcomes and psychological distress as the environment became more health-promoting.

Open data products-A framework for creating valuable analysis ready data

This paper develops the notion of "open data product". We define an open data product as the open result of the processes through which a variety of data (open and not) are turned into accessible information through a service, infrastructure, analytics or a combination of all of them, where each step of development is designed to promote open principles. Open data products are born out of a (data) need and add value beyond simply publishing existing datasets. We argue that the process of adding value should adhere to the principles of open (geographic) data science, ensuring openness, transparency and reproducibility. We also contend that outreach, in the form of active communication and dissemination through dashboards, software and publication are key to engage end-users and ensure societal impact. Open data products have major benefits. First, they enable insights from highly sensitive, controlled and/or secure data which may not be accessible otherwise. Second, they can expand the use of commercial and administrative data for the public good leveraging on their high temporal frequency and geographic granularity. We also contend that there is a compelling need for open data products as we experience the current data revolution. New, emerging data sources are unprecedented in temporal frequency and geographical resolution, but they are large, unstructured, fragmented and often hard to access due to privacy and confidentiality concerns. By transforming raw (open or "closed") data into ready to use open data products, new dimensions of human geographical processes can be captured and analysed, as we illustrate with existing examples. We conclude by arguing that several parallels exist between the role that open source software played in enabling research on spatial analysis in the 90 s and early 2000s, and the opportunities that open data products offer to unlock the potential of new forms of (geo-)data.

Beyond the map: evidencing the spatial dimension of health inequalities

Background

Spatial inequalities in health result from different exposures to health risk factors according to the features of geographical contexts, in terms of physical environment, social deprivation, and health care accessibility. Using a common geographical referential, which combines indices measuring these contextual features, could improve the comparability of studies and the understanding of the spatial dimension of health inequalities.

Methods

We developed the Geographical Classification for Health studies (GeoClasH) to distinguish French municipalities according to their ability to influence health outcomes. Ten contextual scores measuring physical and social environment as well as spatial accessibility of health care have been computed and combined to classify French municipalities through a K-means clustering. Age-standardized mortality rates according to the clusters of this classification have been calculated to assess its effectiveness.

Results

Significant lower mortality rates compared to the mainland France population were found in the Wealthy Metropolitan Areas (SMR = 0.868, 95% CI 0.863–0.873) and in the Residential Outskirts (SMR = 0.971, 95% CI 0.964–0.978), while significant excess mortality were found for Precarious Population Districts (SMR = 1.037, 95% CI 1.035–1.039), Agricultural and Industrial Plains (SMR = 1.066, 95% CI 1.063–1.070) and Rural Margins (SMR = 1.042, 95% CI 1.037–1.047).

Conclusions

Our results evidence the comprehensive contribution of the geographical context in the constitution of health inequalities. To our knowledge, GeoClasH is the first nationwide classification that combines social, environmental and health care access scores at the municipality scale. It can therefore be used as a proxy to assess the geographical context of the individuals in public health studies.

Developing an index to estimate the association between the food environment and CVD mortality rates

The food environment has been shown to influence dietary patterns, which ultimately affects nutrition-related diseases such as diabetes, obesity, and cardiovascular disease (CVD). Measures of food accessibility and socioeconomics were combined to develop the Food Environment Index (FEI), characterizing all U.S. counties between 2008 and 2016. Multi-level regression models showed that this index is significantly negatively associated with CVD death rates across the two time periods studied (2008-2010 and 2013-2016). The FEI may be a useful proxy for identifying differences in the food environment to inform future interventions.

Developing composite indices of geographical access and need for nursing home care in Ireland using multiple criteria decision analysis

Background: Spatial accessibility has consistently been shown to influence utilisation of care and health outcomes, compared against local population needs. We sought to identify how appropriately nursing homes (NHs) are distributed in Ireland, as its NH market lacks central planning. Methods: We used multiple criteria decision analysis (MCDA) approaches to develop composite indices of both access (incorporating measures of availability, choice, quality and affordability) and local NH need for over 65s (relating to the proportion living alone, with cognitive disabilities or with low self-rated health, estimated scores for activities of daily living and instrumental activities of daily living, the average number of disabilities per person and the average age of this group). Data for need were derived from census data. Results were mapped to better understand underlying geographical patterns. Results: By comparing local accessibility and need, underserved areas could be identified, which were clustered particularly in the country's northwest. Suburbs, particularly around Dublin, were by this measure relatively overserved. Conclusions: We have developed multi-dimensional indices of both accessibility to, and need for, nursing home care. This was carried out by combining granular, open data sources and elicited expert/stakeholder opinion from practitioners. Mapping these data helped to highlight clear evidence of inequitable variation in nursing home distribution.

Methodology for building a geographical accessibility health index throughout metropolitan France

Spatial accessibility to health services is a key factor in terms of public health. Even though some tools are available, establishing accessibility criteria applicable from one geographic scale to another remains difficult. Therefore, we propose a method for creating a health accessibility index applicable on a large geographic scale, based on a methodology that overcomes the limitations of political-administrative divisions and which allows a multi-scalar approach to be implemented. The index highlights, on a national scale, areas of cumulative health disadvantages. This index of accessibility to health care combines accessibility and availability and can be adapted to many geographical scales. As accessibility can be understood in various dimensions, a score could be calculated for various fields such as education and culture. The index can help policymakers to identify under-endowed areas and find optimal locations. In terms of public health, it may be used to understand the mechanisms underlying geographic health disparities.

Open data on health-related neighbourhood features in Great Britain

Our study details the creation of a series of national open source low-level geographical measures of accessibility to health-related features for Great Britain. We create 14 measures across three domains: retail environment (fast food outlets, gambling outlets, pubs/bars/nightclubs, off-licences, tobacconists), health services (General Practitioners, pharmacies, dentists, hospitals, leisure centres) and the physical environment (green space and air quality). Using the network analysis process of Routino, postcode accessibility (km) to each of these features were calculated for the whole of Great Britain. An average score for each domain was calculated and subsequently combined to form an overall Index highlighting 'Access to Healthy Assets and Hazards'. We find the most accessible healthy areas are concentrated in the periphery of the urban cores, whilst the least accessible healthy areas are located in the urban cores and the rural areas. The open data resource is important for researchers and policy makers alike with an interest in measuring the role of spatial features on health.

Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one ‘self’ diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012–2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: ‘coughs and colds’, ‘Hayfever’, ‘pain relief’ and ‘sun preps’. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

INTRODUCTION

Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market.

CASE DESCRIPTION

This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a 2 year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach.

DISCUSSION AND EVALUATION

The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price.

CONCLUSIONS

From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machine learning algorithms used, the two tree based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, a practitioner based approach is seen to outperform the modelling approaches. A practical finding is that the application of sophisticated machine learning algorithms to big data is still a challenge for modern desktop PCs.