Small Area Level Research Articles

Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice

BackgroundResponse time to cardiovascular emergency medical requests is an important indicator in reducing cardiovascular disease (CVD) -related mortality. This study aimed to visualize the spatial-time distribution of response time, scene time, and call-to-hospital time of these emergency requests. We also identified patterns of clusters of CVD-related calls.MethodsThis cross-sectional study was conducted in Mashhad, north-eastern Iran, between August 2017 and December 2019. The response time to every CVD-related emergency medical request call was computed using spatial and classical statistical analyses. The Anselin Local Moran’s I was performed to identify potential clusters in the patterns of CVD-related calls, response time, call-to-hospital arrival time, and scene-to-hospital arrival time at small area level (neighborhood level) in Mashhad, Iran.ResultsThere were 84,239 CVD-related emergency request calls, 61.64% of which resulted in the transport of patients to clinical centers by EMS, while 2.62% of callers (a total of 2218 persons) died before EMS arrival. The number of CVD-related emergency calls increased by almost 7% between 2017 and 2018, and by 19% between 2017 and 2019. The peak time for calls was between 9 p.m. and 1 a.m., and the lowest number of calls were recorded between 3 a.m. and 9 a.m. Saturday was the busiest day of the week in terms of call volume. There were statistically significant clusters in the pattern of CVD-related calls in the south-eastern region of Mashhad. Further, we found a large spatial variation in scene-to-hospital arrival time and call-to-hospital arrival time in the area under study.ConclusionThe use of geographical information systems and spatial analyses in modelling and quantifying EMS response time provides a new vein of knowledge for decision makers in emergency services management. Spatial as well as temporal clustering of EMS calls were present in the study area. The reasons for clustering of unfavorable time indices for EMS response requires further exploration. This approach enables policymakers to design tailored interventions to improve response time and reduce CVD-related mortality.

Child Protection Inequalities in New Zealand: Exploring Relationships with Social Gradient Using National Linked Administrative Datasets

IntroductionContact with child protection systems are a key site of the expression of social inequalities. ‘Child welfare inequalities’ by deprivation have been documented in a number of countries including the United Kingdom. The size and nature of this relationship is sparse in the New Zealand system context. Objectives and ApproachThe integration of data routinely collected by NZ’s Ministry for Children into Statistics NZ’s Integrated Data Infrastructure (IDI) provided the opportunity to examine how child protection data relates to deprivation and location. Anonymised person-level data in the IDI was used to obtain a population-based retrospective sample of children estimated to be resident in NZ in 2013/2014 and less than 17 years of age. Using pre-linked data in the IDI, all children with at least one of three child protection outcomes in 2013/2014 were ascertained. Deprivation was assessed using a small-area level national index derived from census data. The most recent residential address recorded in the IDI was used to assign deprivation deciles to each child based on the small area in which they lived. ResultsOf 1,016,928 children, 13,851 had had at least one substantiation of interest, the least serious of the outcomes considered. Compared to children living in the least deprived quintile of small areas, children in the most deprived quintile had 13.1 times (95%CI for Incidence Rate Ratio 12.0, 14.4) the rate of substantiation, 18.0 times (95%CI 15.4, 20.8) the rate of a family group conference, and 6.5 times (95%CI 5.6, 7.6) their rate of placement in foster care. Conclusion / ImplicationsA marked relationship was observed between small-area deprivation and child protection contact. Action is needed to address the causes of deprivation, provide services that respond to families living in poverty, and examine carefully the interactions between demand and supply of services across deprivation levels.

A multilevel mixed effects analysis of informal carers health in Australia: the role of community participation, social support and trust at small area level

BackgroundInformal carers suffer from worse health outcomes than non-carers due to their caregiving role. Yet, in a society carers health is as important as that of their care recipients. This study investigated the self-assessed mental and general health outcomes of informal carers in Australia. It evaluated the influence of carers’ personal social capital- a logically linked sequence of their social behaviour such as community participation, social support and trust in others- on their health outcomes. The study estimated the magnitude of small area level variation at Statistical Area Level 1 (SA1) along with individual level variation in carers’ health outcomes.MethodsThe study used a multilevel mixed effects cross-sectional design using data from the Household Income and Labour Dynamics of Australia survey, wave 14. It included Australians aged 15 years and older that were surveyed in the year 2014. The sample consisted of 12,767 individuals and 5004 SA1s. The outcome measures included- mental health, general health and physical functioning, domains of the Short Form 36 Questionnaire, a widely used multi-dimensional measure of health-related quality of life.ResultsInformal carers suffered from poor mental (Beta = − 0.587, p = 0.003) and general health (Beta = − 0.670, p = 0.001) outcomes compared to non-carers in Australia. These health outcomes exhibited significant variation acrossSA1s in Australia, with 12–13% variation in general and mental health. However, within small local areas, differences at the individual level, accounted for most of the variation in outcomes. Moreover, levels of community participation, personal social connection and trust, as perceived by individuals in the communities, had a positive influence on both mental and general health of carers and non-carers, and were more beneficial for carers compared to non-carers.ConclusionIt seems that the positive influence of social capital for carers helps them in coping with the negative impact of their caregiving duty on health outcomes. Findings suggested that some targeted community support programs for carers to build on their personal social cohesion and trust in their community could help in improving their poor health profiles. Moreover, improved informal carers’ health may help the health system in better managing their resources.

Estimating small-area demand of urban tourist for groceries: The case of Greater London

Tourist retail demand within urban areas brings both opportunities and challenges to the local economy. Taking Greater London as the study area, this paper integrates conventional statistics and survey datasets with novel crowdsourcing big data sources to identify and estimate four types of tourist grocery demand at the small-area scale: travellers staying with Airbnb, tourists using traditional commercial accommodation, guests staying with relatives or friends and day trip visitors. Based on this combined tourist retail demand layer we show the spatial variations at the small-area level and as an illustration of the demand uplift, we estimate additional grocery expenditure that is associated with this tourist demand. Thus, the paper indicates the neighbourhoods with significant grocery demand uplift from tourist stays. We argue that the new retail demand layer has tremendous potential to be used as an additional input to retail location modelling tools to support new store revenue estimation and store performance evaluation within the grocery retail sector.

Prioritizing the provision of urban ecosystem services in deprived areas, a question of environmental justice.

The distribution of urban ecosystem services (UES) is often uneven across socioeconomic groups, leading to environmental justice issues. Understanding the distribution of UES across a landscape can help managers ensure an equitable distribution of services. While many past studies have focused on the distribution of green spaces in relation to socioeconomic variables, this research analyzes the distribution of UES provided by these green spaces. This research quantified air pollution removal, atmospheric carbon reduction, and surface runoff mitigation provided by urban trees in Strasbourg city (France). The provision of these three UES was studied at the census block scale by creating an index of UES delivery, which was contrasted with a constructed social deprivation index. Our results show that there is no significant association between the delivery of UES and social deprivation. Some deprived populations benefit from high UES delivery. Results also suggest that mapping associations between UES delivery and social deprivation should be integrated with future development plans to enhance the equitable distribution of UES. This study provides insights into the French context where studies about the distribution of UES at a small-area level remain lacking.

Small-area level socio-economic deprivation and tuberculosis rates in England: An ecological analysis of tuberculosis notifications between 2008 and 2012.

BackgroundTuberculosis (TB) rates in England are among the highest in high-income countries. Poverty and historic and current immigration from high TB incidence parts of the world are two major drivers of tuberculosis in England. However, little has been done in recent years to examine socio-economic trends in TB rates in England, and to disentangle the role of deprivation from that of place of birth in the current TB epidemiology.ObjectivesTo assess the association between England’s 2008–2012 TB notification rates and small area-level deprivation, together and separately in the UK-born and foreign-born populations.MethodsEcological analysis of the association between quintiles of England’s 2010 Index of Multiple Deprivation (IMD) and TB rates at the Lower-layer Super Output Area (LSOA; average population ~1500) level, using negative binomial and zero-inflated negative binomial regression models, adjusting for age, sex, urban/rural area classification, and area-level percentage of non-White residents.ResultsThere was a log-linear gradient between area-deprivation levels and TB rates, with overall TB rates in the most deprived quintile areas three times higher than the least deprived quintile after adjustment for age and sex (IRR = 3.35; 95%CI: 3.16 to 3.55). The association and gradient were stronger in the UK-born than the foreign-born population, with UK-born TB rates in the most deprived quintiles about two-and-a-half times higher than the least deprived quintile (IRR = 2.39; 95%CI: 2.19 to 2.61) after controlling for age, sex, urban/rural classification and percentage of non-White residents; whereas the comparable figure for foreign-born persons was 80% higher (IRR = 1.78; 95%CI: 1.66 to 1.91).ConclusionsSocio-economic deprivation continues to play a substantial role in sustaining the TB epidemic in England, especially in the UK-born population. This supports the case for further investigations of the underlying social- determinants of TB.

Fragmented dry grasslands preserve unique components of plant species and phylogenetic diversity in agricultural landscapes

In intensively used landscapes biodiversity is often restricted to fragmented habitats. Exploring the biodiversity potential of habitat fragments is essential in order to reveal their complementary role in maintaining landscape-scale biodiversity. We investigated the conservation potential of dry grassland fragments in the Great Hungarian Plain, i.e. patch-like habitats on ancient burial mounds and linear-shaped habitats in verges, and compared them to continuous grasslands. We focused on plant taxonomic diversity, species richness of specialists, generalists and weeds, and the phylogenetic diversity conserved in the habitats. Verges meshing the landscape are characterised by a small core area and high level of disturbance. Their species pool was more similar to grasslands than mounds due to the lack of dispersal limitations. They held high species richness of weeds and generalists and only few specialists. Verges preserved only a small proportion of the evolutionary history of specialists, which were evenly distributed between the clades. Isolated mounds are characterised by a small area, a high level of environmental heterogeneity, and a low level of disturbance. Steep slopes of species accumulation curves suggest that high environmental heterogeneity likely contributes to the high species richness of specialists on mounds. Mounds preserved the same amount of phylogenetic diversity represented by the branch-lengths as grasslands. Abundance-weighted evolutionary distinctiveness of specialists was more clustered in these habitats due to the special habitat conditions. For the protection of specialists in transformed landscapes it is essential to focus efforts on preserving both patch-like and linear grassland fragments containing additional components of biodiversity.

The role of socio-demographic determinants in the geo-spatial distribution of newly diagnosed HIV infections in small areas of Catalonia (Spain)

BackgroundSpatial visualization of HIV surveillance data could improve the planning of programs to address the HIV epidemic. The objectives of the study were to describe the characteristics and the spatial distribution of newly diagnosed HIV infection in Catalonia and to identify factors associated with HIV infection rates.MethodsSurveillance data from the national registry were presented in the form of descriptive and ring maps and used to study the spatial distribution of new HIV diagnoses in Catalonia (2012–2016) and associated risk factors at the small area level (ABS, acronym for “basic health area” in Catalan). Incident cases were modeled using the following as predictors: type of municipality, prevalence of young men and migrant groups, GBMSM activity indicators, and other variables at the aggregated level.ResultsNew HIV diagnoses are heterogeneously distributed across Catalonia. The predictors that proved to be significantly associated with a higher rate of new HIV diagnoses were ABS located in the city of Barcelona (IRR, 2.520; P < 0.001), a higher proportion of men aged 15–44 years (IRR, 1.193; P = 0.003), a higher proportion of GBMSM (IRR, 1.230; P = 0.030), a higher proportion of men from Western Europe (IRR, 1.281; P = 0.003), a higher proportion of men from Latin America (IRR, 1.260; P = 0.003), and a higher number of gay locations (IRR, 2.665; P < 0.001). No association was observed between the HIV diagnosis rate and economic deprivation.ConclusionsRing maps revealed substantial spatial associations for the rate of new HIV diagnoses. New HIV diagnoses are concentrated in ABS located in urban areas. Our results show that, in the case of HIV infection, the socioeconomic deprivation index on which the Catalan government bases its budget allocation policies among the ABS should not be the only criterion used.

Disparities in Geographical Access to Hospitals in Portugal

Geographical accessibility to health care services is widely accepted as relevant to improve population health. However, measuring it is very complex, mainly when applied at administrative levels that go beyond the small-area level. This is the case in Portugal, where the municipality is the administrative level that is most appropriate for implementing policies to improve the access to those services. The aim of this paper is to assess whether inequalities in terms of access to a hospital in Portugal have improved over the last 20 years. A population-weighted driving time was applied using the census tract population, the roads network, the reference hospitals’ catchment area and the municipality boundaries. The results show that municipalities are 25 min away from the hospital—3 min less than in 1991—and that there is an association with premature mortality, elderly population and population density. However, disparities between municipalities are still huge. Municipalities with higher rates of older populations, isolated communities or those located closer to the border with Spain face harder challenges and require greater attention from local administration. Since municipalities now have responsibilities for health, it is important they implement interventions at the local level to tackle disparities impacting access to healthcare.

Evaluating the impact of a small number of areas on spatial estimation.

BackgroundThere is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study.MethodsBoth the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered.ResultsThe simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised G = 2 models performed similarly and better than the independent and localised G = 3 models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice.ConclusionDetecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.

Effects of long-term exposure to air pollutants on the spatial spread of COVID-19 in Catalonia, Spain

BackgroundThe risk of infection and death by COVID-19 could be associated with a heterogeneous distribution at a small area level of environmental, socioeconomic and demographic factors. Our objective was to investigate, at a small area level, whether long-term exposure to air pollutants increased the risk of COVID-19 incidence and death in Catalonia, Spain, controlling for socioeconomic and demographic factors. MethodsWe used a mixed longitudinal ecological design with the study population consisting of small areas in Catalonia for the period February 25 to May 16, 2020. We estimated Generalized Linear Mixed models in which we controlled for a wide range of observed and unobserved confounders as well as spatial and temporal dependence. ResultsWe have found that long-term exposure to nitrogen dioxide (NO2) and, to a lesser extent, to coarse particles (PM10) have been independent predictors of the spatial spread of COVID-19. For every 1 μm/m3 above the mean the risk of a positive test case increased by 2.7% (95% credibility interval, ICr: 0.8%, 4.7%) for NO2 and 3.0% (95% ICr: -1.4%,7.44%) for PM10. Regions with levels of NO2 exposure in the third and fourth quartile had 28.8% and 35.7% greater risk of a death, respectively, than regions located in the first two quartiles. ConclusionAlthough it is possible that there are biological mechanisms that explain, at least partially, the association between long-term exposure to air pollutants and COVID-19, we hypothesize that the spatial spread of COVID-19 in Catalonia is attributed to the different ease with which some people, the hosts of the virus, have infected others. That facility depends on the heterogeneous distribution at a small area level of variables such as population density, poor housing and the mobility of its residents, for which exposure to pollutants has been a surrogate.

Impact of a national primary care pay-for-performance scheme on ambulatory care sensitive hospital admissions: a small-area analysis in England.

ObjectiveWe aimed to spatially describe hospital admissions for ambulatory care sensitive conditions (ACSC) in England at small-area geographical level and assess whether recorded practice performance under one of the world’s largest primary care pay-for-performance schemes led to reductions in these potentially avoidable hospitalisations for chronic conditions incentivised in the scheme.SettingWe obtained numbers of ACSC hospital admissions from the Hospital Episode Statistics database and information on recorded practice performance from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/2016. We fitted three sets of negative binomial models to examine ecological associations between incentivised ACSC admissions, general practice performance, deprivation, urbanity and other sociodemographic characteristics.ResultsHospital admissions for QOF incentivised ACSCs varied within and between regions, with clusters of high numbers of hospital admissions for incentivised ACSCs identified across England. Our models indicated a very small effect of the QOF on reducing admissions for incentivised ACSCs (0.993, 95% CI 0.990 to 0.995), however, other factors, such as deprivation (1.021, 95% CI 1.020 to 1.021) and urbanicity (0.875, 95% CI 0.862 to 0.887), were far more important in explaining variations in admissions for ACSCs. People in deprived areas had a higher risk of being admitted in hospital for an incentivised ACSC condition.ConclusionSpatial analysis based on routinely collected data can be used to identify areas with high rates of potentially avoidable hospital admissions, providing valuable information for targeting resources and evaluating public health interventions. Our findings suggest that the QOF had a very small effect on reducing avoidable hospitalisation for incentivised conditions. Material deprivation and urbanicity were the strongest predictors of the variation in ACSC rates for all QOF incentivised conditions across England.

Enabling a just transition: A composite indicator for assessing home-heating energy-poverty risk and the impact of environmental policy measures

Home-heating energy-poverty risk presents both challenge and opportunity for policymakers, businesses and communities. Effective measurement and management of this risk requires an evidence base that accounts for characteristics of the householder, building, and heating system. A composite index utilising 10 indicators refined to Small Area level is created to deliver spatially refined analysis of home-heating energy-poverty risk. The index is used to assess home-heating energy-poverty risk across 18,641 Small Area clusters in Ireland. This risk index is a scalable and internationally transferrable methodology that can be extended to cover other energy uses. Importantly the index is also dynamic and offers the capacity to analyse changes in energy-poverty risk associated with specific policy intervention proposals, including major contemporary environmental policy transitions such as residential fabric retrofit, residential heating system changes, energy price changes and carbon taxation. The application of the index to the Irish case affords refined insight into the impact and incidence of various market, technology and policy driven interventions such as fuel price changes, retrofit strategies and carbon tax increases. Risk and impacts vary geographically, and this index is designed to inform targeted policy interventions to mitigate home heating energy-poverty risk and thereby support ambitions for a ‘just transition’.

Augmenting disease maps: a Bayesian meta-analysis approach.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England.

Few studies have investigated associations between metal components of particulate matter on mortality due to well-known issues of multicollinearity. Here, we analyze these exposures jointly to evaluate their associations with mortality on small area data. We fit a Bayesian profile regression (BPR) to account for the multicollinearity in the elemental components (iron, copper, and zinc) of PM10 and PM2.5. The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008-2011 at a small area level, for a population of 13.6 million in the London-Oxford area of England. From the BPR, we identified higher risks in the PM10 fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality relative risk (RR) 1.07 (95% credible interval [CI] 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared with the study mean. For PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the "highways" cluster. We did not find relevant associations for lung cancer incidence. Our analysis showed small but not fully consistent adverse associations between health outcomes and particulate metal exposures. The BPR approach identified subpopulations with unique exposure profiles and provided information about the geographical location of these to help interpret findings.

SMALL AREA ESTIMATION OF NUTRITIONAL STATUS OF UNDER-FIVE CHILDREN IN SYLHET DIVISION: AN M-QUANTILE APPROACH:

Under nutrition is one of the severe problems around the globe and finds its place in the global agenda. Sustainable Development Goals (SDGs) highlight the need for special attention to eradicate malnutrition. Bangladesh having high prevalence of malnutrition is committed to fulfill the targets of SDGs. Though Bangladesh achieved remarkable success in improving nutritional status of under-five children at national level, there have been regional variations. Government is planning to target need based resource allocation to small administrative levels. To do that real time, small area level estimates of nutrition will be required. Sylhet division was severely suffering from one or all form of malnutrition (BBS, 2014). This research tried to address these issues for which a primary sample of size 300 was collected from Dharampasha Upazila of Sunamgonj district of Sylhet division for in-depth analysis. M-Quantile estimation method was used to identify small area estimates at Upazila level of Sylhet division. The Upazilas exhibiting poorest nutritional status was identified in maps for comparison. Special care should be given to help these Upazilas to come out of the cycle of malnutrition in addition to the common national programmes. The results are efficient and may be adopted in the future, especially where we have doubted in the distributional assumption of the data.

Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

BackgroundThis study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.MethodsWe utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.ResultsWe found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas.ConclusionsIn a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small area.

A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile

BackgroundThere is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile.MethodsTo deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socio-economic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data.ResultsWe found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other.ConclusionsIn this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and socioeconomic data. The method explored and uncovered valuable spatial patterns for health decision-making. In turn, spatial microsimulation.

Industrial pollution and mortality from digestive cancers at the small area level in a Spanish industrialized province.

The province of Cadiz, Spain, is a highly industrialized area with numerous registered industrial plants, which has led to major concern regarding the possible influence of these facilities on the high rate of cancer-related mortality observed. Our objective was to evaluate the association between digestive cancer mortality and proximity to industrial installations in the province of Cadiz over the period 1992-2014 and to analyse this risk according to different categories of carcinogenic substances. An ecological study at the census tract level was carried out. Mortality due to digestive cancer (involving the oral cavity, pharynx, oesophagus, stomach, liver, pancreas, gallbladder, colon and rectum) was analysed. Using the spatial Besag, York and Mollié (BYM) approach, we assessed the relative risk of dying from these cancers for people living between 500 m and 5 km from industrial installations. The models were adjusted to account for socioeconomic deprivation. We detected a significant, excess risk of dying due to cancer in the following organs (expressed as relative risk with 95% confidence intervals): colon/rectum (1.13; 1.04-1.22 at 4 km), stomach (1.13; 1.00-1.29 at 2 km), liver (1.28; 1.02-1.61 at 1 km), pancreas (1.19; 1.03-1.39 at 2 km), oral and pharyngeal (1.40; 1.08-1.82 at 1 km), oesophagus (2.05; 1.18-3.56 at 500 m) and gallbladder (2.80; 1.14-6.89 at 500 m) for men; and from colorectal (1.21; 1.00-1.46 at 1 km), stomach (1.15; 1.01-1.31 at 4 km) and liver (1.58; 1.20- 2.07 at 1 km) cancers for women. The results support the hypothesis of an association between several digestive cancers and proximity to polluting industrial plants.

O1.3. BACK TO THE FUTURE: PREDICTING POPULATION NEED FOR PSYCHOSIS CARE BASED ON THE EPIDEMIOLOGY OF PSYCHOTIC DISORDERS IN ENGLAND, AN APPLIED BAYESIAN METHODOLOGY

BackgroundProviding timely, adequate and appropriately-resourced care to people experiencing their first episode of psychosis needs to be informed by evidence-based models of future need in the population. We sought to develop a validated prediction model of need for provision of early intervention in psychosis [EIP] services at the small area level in England up to 2025, based on current epidemiological evidence and demographic projections of the at-risk population.MethodsWe developed a Bayesian population-level prediction tool. First, we obtained small area incidence data on first episode psychoses, aged 16–64 years, from three major empirical studies of psychosis risk (ÆSOP, ELFEP and SEPEA). Second, we identified suitable prior information from the published literature on variation in psychosis risk by age, sex, ethnicity, deprivation and cannabis use. Third, we combined this empirical data with prior beliefs in six Bayesian Poisson regression models to obtain a full characterisation of the underlying uncertainty in the form of suitable posterior distributions for the relative risks for different permutations of covariate data. Fourth, model coefficients were applied to population projections for 2017 to predict the expected incidence of psychotic disorders, aggregated to Commissioning Group [CCG] and national levels. Fifth, we compared these predictions to observed national FEP data from the NHS Mental Health Services Data Set in 2017 to establish the most valid model. Sixth, we used the best-fitting model to predict three nested indicators of need for psychosis care: (i) total annual referrals to early intervention in psychosis [EIP] for “suspected” FEP (ii) total annual cases accepted onto EIP service caseloads, and (iii) total annual new cases of probable FEP in England up until 2025, using small area population projections.ResultsA model with an age-sex interaction, ethnicity, small area-level deprivation, social fragmentation and regional cannabis use provided best internal and apparent validity, predicting 8112 (95% Credible Interval 7623 to 8597) individuals with FEP in England in 2017, compared with 8038 observed cases (difference: n=74; 0.94%). Apparent validity was acceptable at CCG level, and by sex and ethnicity, although we observed greater-than-expected need before 35 years old. Predicted new referrals, caseloads and probable incidences of FEP rose over the forecast period by 6.2% to 25,782, 23,187 and 9,541 new cases in 2025, respectively.DiscussionOur translational epidemiological tool provides an accurate, validated method to inform planners, commissioners and providers about future population need for psychosis care at different stages of the referral pathway, based on individual and small area level determinants of need. Such tools can be used to underpin evidence-based decision-making in public mental health and resource allocation in mental health systems.