A model for geographical variation in health and total life expectancy

This paper develops a joint approach to life and health expectancy based on 2001 UK Census data for limiting long term illness and general health status, and on registered death occurrences in 2001. The model takes account of the interdependence of different outcomes (e.g. ill health and mortality) as well as spatial correlation in their patterns. A particular focus is on the proportionality assumption or ‘multiplicative model’ whereby separate age and area effects multiply to produce age-area mortality rates. Alternative non-proportional models are developed and shown to be more parsimonious as well as more appropriate to actual area-age interdependence. The application involves mortality and health status in the 33 London Boroughs.disease burden, healthy life expectancy, life tables, proportionality assumption, spatial effects

Space time geography of Malaria and the environmental risks to households, Lagos State, Nigeria

Phd ThesisThe research employs the theoretical lens of human ecology of disease to examine the ecology of malaria in Lagos state, Nigeria. As a first step I examine the spatial and temporal trends in clinical malaria infection using a density-based algorithm to identify two locations (Ikeja and Kosofe LGAs) with one of the highest malaria infection rates and ecologically diverse terrain. They form the focus of this research. I gather data and derive measures on 26 theoretically relevant environment and socio-cultural risk variables in a cross-section of 208 households using mixed methods that comprise semi-structured interviews, a questionnaire, environmental observations, GIS and remote sensing data and GPS mapping. Through these efforts, I build a household spatial database. I assess the contributory influences of the risk variables through the development and assessment of ten ecologically relevant candidate models of urban malaria using statistical and GIS analysis. I also engage with the everyday lives of the households and qualify the quantitative relationships. Findings reveal that the most parsimonious candidate model is grounded on the human ecology of disease principle. While many of the variables are not statistically significant, some, such as travel history, animal presence and household size, are of public health importance. One important finding emerges. The risk variable “working at night without mosquito protection”, though it does not appear in this model, seems to be important across other models. I examine it further and note that its risk within households is higher than those associated with residential locations. In fact, households inhabit low-risk locations and have low vulnerability risk rates. This suggests that in urban areas, infection likely occurs outside homes and mostly from places of work or social gathering, and coincides with older household members rather than vulnerable children. This research suggests further insights for urban-like occupations and behaviours.Dorothy Hodgkin Postgraduate Award (NERC & Shell BP), Newcastle University and the Nigerian Petroleum Technology Development Fund

Spatial and spatio-temporal variability in social, emotional and behavioural development of children

Neighbourhood differences in early development can be explored by incorporating spatial and spatio-temporal information with population data. Spatial refers to the relationship between neighbouring areas, while spatio-temporal refers to the relationship between neighbouring areas over time. At the time of writing, most population studies have focused on spatial variation in early development over a single year or short time period. This project identifies spatial and spatio-temporal referenced data that can be linked with population data on child social, emotional and behavioural difficulties in Glasgow, United Kingdom (UK). The Child Mental Health in Education (ChiME) study is a unique resource that can be used to model long term trends in a preschool population. In the ChiME study, Strengths and Difficulties Questionnaire (SDQ) forms were analysed for 35,171 children aged 4–5 years old across 180 preschools in Glasgow, UK, between 2010 and 2017 as part of routine monitoring. Using ChiME data, this work examined how early development varies over space and time, how the neighbourhood is defined, how important the neighbourhood is and what neighbourhood characteristics are related to early development. A literature review of 71 studies (from 2012-2022) in Chapter 2 discusses the neighbourhood constructs that are associated with variation in early development for children in Scotland. These constructs included the physical environment (e.g. greenspace) and social environment (e.g. social networks). The availability of data and the strength of evidence to support each construct varied. For many constructs, there was limited understanding of their relevance to younger children as opposed to adolescent or adult populations. There are gaps in the literature in the extent that neighbourhood constructs relate to developmental outcomes at an individual level or how this may change over time. To address these gaps, much more multilevel research, using population data is required. Chapter 3 provides a methodological review of the multilevel spatio-temporal approaches used to date. There is limited methodological guidance on how to model spatio-temporal variation for multilevel data. There is a risk of over complicating the model when attempting to account for spatial, temporal and/or spatio-temporal effects. Choosing the appropriate spatio-temporal multilevel model depends on the structure of the data, the degree of correlation, the goal of the analysis and overall model fit. Using a Bayesian workflow, each component of the model is reviewed in an iterative process to provide the best model for the data in Chapter 4. This includes evaluation of the outcome (total difficulties scores vs high scores) and comparing discrete distributions (Poisson, Negative Binomial and Zero-Inflated Negative Binomial models). Workflow analysis supported the use of Zero Inflated Negative Binomial distribution for total difficulties scores and the use of approximation methods for estimation. The total difficulties score for an individual child nested in their preschool, electoral ward and ward:year was modelled using a multilevel model with the components selected in Chapter 4. In Chapter 5, models were built incrementally, considering the value of each context. Boys, those of increasing deprivation and children outside the average age, had more difficulties on average. Preschool and ward variation, although minimal, highlight potential priority areas for local service provision. After consideration of demographics (sex, age, and deprivation), the overall spatial effect found the electoral wards of Anderston, Craigton, North East and Pollokshields were worse than expected (Relative Risk > 1) from 2010 to 2017. There were 72 preschools that were worse than expected based on their demographics. Approximately half of the children who lived in a ward that was worse than expected also attended a preschool that was worse than expected. There were independent spatio-temporal patterns in total difficulties, that exist in addition to the overall spatial effect. Spatial effects were not solely due to consistently poor performing areas. Instead, there is evidence of yearly variations in performance. Spatial analysis using only a single or few years may lead to misleading conclusions about area level variability. For example, once considering the spatio-temporal effect, Pollokshields was no longer considered worse than expected. There were differences in spatial and spatio-temporal variation depending on the neighbourhood definition (electoral ward, locality, Intermediate Zone (2001 and 2011) and Consistent Areas Through Time (CATTs)) found in Chapter 6. Looking at the different spatial scales together, can help support diffuse or more concentrated intervention delivery. Localities and 2011 Intermediate Zones had a similar spatial distribution to the ward. The relative importance of the neighbourhood compared to other contexts can be quantified through the Variance Partition Coefficient (VPC). Estimated VPC of the neighbourhood on early development was expected to be between 0 and 9% according to recent literature. Though the typical VPC equation does not apply to discrete distributions, recent approximations have been developed. Using these approximations, it was found that proportionally, the neighbourhood context alone does not make a considerable contribution to variation in difficulties scores. VPC values ranged from 0.39-1.1% depending on the neighbourhood definition. From the perspective of decision-making, the partitioned variance suggests that considering the neighbourhood along with other contexts would be more meaningful than the neighbourhood alone. Preschool and neighbourhood characteristics are thought to provide a more feasible target for intervention compared to individual level characteristics. Cross-level effects (which describe the association between a higher level covariate and a lower level outcome) are investigated in Chapter 7. Preschool and neighbourhood indicators were derived from openly available administrative data. The quality of these indicators and their relevance to this project varied. Preschools were classified as small/medium/large local authority, private business or voluntary. Most children were in local authority preschools. Total difficulties scores were lower in private business compared to small local authority preschools. Spatial variation was in part explained by a child’s prosocial behaviour and its interaction with their preschool provider. The mechanisms underlying these differences are at present unknown. There were ecological correlations between total difficulties and the neighbourhood indicators (participation, child poverty, domestic abuse, free time places, vandalism and proximity to greenspace (at 400 m and 800m)). These correlations did not translate to a cross-level association with individual level total difficulties. In conclusion, there are multiple contexts that account for variation in total difficulties. The preschool and spatio-temporal context and their composition could provide additional information about how the neighbourhood relates to early development. There is a need for more spatio-temporal data, that can be linked to population data, to understand how the neighbourhood is associated with development at an individual level, beyond deprivation. Multi-level spatio-temporal models can be used to understand early development and support the selection of delivery areas for place-based interventions.Neighbourhood differences in early development can be explored by incorporating spatial and spatio-temporal information with population data. Spatial refers to the relationship between neighbouring areas, while spatio-temporal refers to the relationship between neighbouring areas over time. At the time of writing, most population studies have focused on spatial variation in early development over a single year or short time period. This project identifies spatial and spatio-temporal referenced data that can be linked with population data on child social, emotional and behavioural difficulties in Glasgow, United Kingdom (UK). The Child Mental Health in Education (ChiME) study is a unique resource that can be used to model long term trends in a preschool population. In the ChiME study, Strengths and Difficulties Questionnaire (SDQ) forms were analysed for 35,171 children aged 4–5 years old across 180 preschools in Glasgow, UK, between 2010 and 2017 as part of routine monitoring. Using ChiME data, this work examined how early development varies over space and time, how the neighbourhood is defined, how important the neighbourhood is and what neighbourhood characteristics are related to early development. A literature review of 71 studies (from 2012-2022) in Chapter 2 discusses the neighbourhood constructs that are associated with variation in early development for children in Scotland. These constructs included the physical environment (e.g. greenspace) and social environment (e.g. social networks). The availability of data and the strength of evidence to support each construct varied. For many constructs, there was limited understanding of their relevance to younger children as opposed to adolescent or adult populations. There are gaps in the literature in the extent that neighbourhood constructs relate to developmental outcomes at an individual level or how this may change over time. To address these gaps, much more multilevel research, using population data is required. Chapter 3 provides a methodological review of the multilevel spatio-temporal approaches used to date. There is limited methodological guidance on how to model spatio-temporal variation for multilevel data. There is a risk of over complicating the model when attempting to account for spatial, temporal and/or spatio-temporal effects. Choosing the appropriate spatio-temporal multilevel model depends on the structure of the data, the degree of correlation, the goal of the analysis and overall model fit. Using a Bayesian workflow, each component of the model is reviewed in an iterative process to provide the best model for the data in Chapter 4. This includes evaluation of the outcome (total difficulties scores vs high scores) and comparing discrete distributions (Poisson, Negative Binomial and Zero-Inflated Negative Binomial models). Workflow analysis supported the use of Zero Inflated Negative Binomial distribution for total difficulties scores and the use of approximation methods for estimation. The total difficulties score for an individual child nested in their preschool, electoral ward and ward:year was modelled using a multilevel model with the components selected in Chapter 4. In Chapter 5, models were built incrementally, considering the value of each context. Boys, those of increasing deprivation and children outside the average age, had more difficulties on average. Preschool and ward variation, although minimal, highlight potential priority areas for local service provision. After consideration of demographics (sex, age, and deprivation), the overall spatial effect found the electoral wards of Anderston, Craigton, North East and Pollokshields were worse than expected (Relative Risk > 1) from 2010 to 2017. There were 72 preschools that were worse than expected based on their demographics. Approximately half of the children who lived in a ward that was worse than expected also attended a preschool that was worse than expected. There were independent spatio-temporal patterns in total difficulties, that exist in addition to the overall spatial effect. Spatial effects were not solely due to consistently poor performing areas. Instead, there is evidence of yearly variations in performance. Spatial analysis using only a single or few years may lead to misleading conclusions about area level variability. For example, once considering the spatio-temporal effect, Pollokshields was no longer considered worse than expected. There were differences in spatial and spatio-temporal variation depending on the neighbourhood definition (electoral ward, locality, Intermediate Zone (2001 and 2011) and Consistent Areas Through Time (CATTs)) found in Chapter 6. Looking at the different spatial scales together, can help support diffuse or more concentrated intervention delivery. Localities and 2011 Intermediate Zones had a similar spatial distribution to the ward. The relative importance of the neighbourhood compared to other contexts can be quantified through the Variance Partition Coefficient (VPC). Estimated VPC of the neighbourhood on early development was expected to be between 0 and 9% according to recent literature. Though the typical VPC equation does not apply to discrete distributions, recent approximations have been developed. Using these approximations, it was found that proportionally, the neighbourhood context alone does not make a considerable contribution to variation in difficulties scores. VPC values ranged from 0.39-1.1% depending on the neighbourhood definition. From the perspective of decision-making, the partitioned variance suggests that considering the neighbourhood along with other contexts would be more meaningful than the neighbourhood alone. Preschool and neighbourhood characteristics are thought to provide a more feasible target for intervention compared to individual level characteristics. Cross-level effects (which describe the association between a higher level covariate and a lower level outcome) are investigated in Chapter 7. Preschool and neighbourhood indicators were derived from openly available administrative data. The quality of these indicators and their relevance to this project varied. Preschools were classified as small/medium/large local authority, private business or voluntary. Most children were in local authority preschools. Total difficulties scores were lower in private business compared to small local authority preschools. Spatial variation was in part explained by a child’s prosocial behaviour and its interaction with their preschool provider. The mechanisms underlying these differences are at present unknown. There were ecological correlations between total difficulties and the neighbourhood indicators (participation, child poverty, domestic abuse, free time places, vandalism and proximity to greenspace (at 400 m and 800m)). These correlations did not translate to a cross-level association with individual level total difficulties. In conclusion, there are multiple contexts that account for variation in total difficulties. The preschool and spatio-temporal context and their composition could provide additional information about how the neighbourhood relates to early development. There is a need for more spatio-temporal data, that can be linked to population data, to understand how the neighbourhood is associated with development at an individual level, beyond deprivation. Multi-level spatio-temporal models can be used to understand early development and support the selection of delivery areas for place-based interventions

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.This work has been supported by Projects MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033). Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá

[EN] Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people's health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogota is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study's approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.Many thanks to the members of the Intelligence and Territorial Analysis Group of the Universidad Santo Tomás for their collaboration in conducting the fieldwork.Molina-Gomez, NI.; Calderón-Rivera, DS.; Sierra-Parada, R.; Díaz Arévalo, JL.; López Jiménez, PA. (2021). 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Dispersal in microbes: fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances.

The indoor microbiome is a complex system that is thought to depend on dispersal from the outdoor biome and the occupants' microbiome combined with selective pressures imposed by the occupants' behaviors and the building itself. We set out to determine the pattern of fungal diversity and composition in indoor air on a local scale and to identify processes behind that pattern. We surveyed airborne fungal assemblages within 1-month time periods at two seasons, with high replication, indoors and outdoors, within and across standardized residences at a university housing facility. Fungal assemblages indoors were diverse and strongly determined by dispersal from outdoors, and no fungal taxa were found as indicators of indoor air. There was a seasonal effect on the fungi found in both indoor and outdoor air, and quantitatively more fungal biomass was detected outdoors than indoors. A strong signal of isolation by distance existed in both outdoor and indoor airborne fungal assemblages, despite the small geographic scale in which this study was undertaken (<500 m). Moreover, room and occupant behavior had no detectable effect on the fungi found in indoor air. These results show that at the local level, outdoor air fungi dominate the patterning of indoor air. More broadly, they provide additional support for the growing evidence that dispersal limitation, even on small geographic scales, is a key process in structuring the often-observed distance-decay biogeographic pattern in microbial communities

Towards efficient large scale epidemiological simulations in EpiGraph

The work we present in this paper focuses on understanding the propagation of flu-like infectious outbreaks between geographically distant regions due to the movement of people outside their base location. Our approach incorporates geographic location and a transportation model into our existing region-based, closed-world EpiGraph simulator to model a more realistic movement of the virus between different geographic areas. This paper describes the MPI-based implementation of this simulator, including several optimization techniques such as a novel approach for mapping processes onto available processing elements based on the temporal distribution of process loads. We present an extensive evaluation of EpiGraph in terms of its ability to simulate large-scale scenarios, as well as from a performance perspective.We would like to acknowledge the assistance provided by David del Río Astorga and Alberto Martín Cajal. This work has been partially supported by the Spanish Ministry of Science TIN2010-16497, 2010.Peer ReviewedPostprint (author's final draft