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

Modelling multiple hospital outcomes: the impact of small area and primary care practice variation

BACKGROUND: Appropriate management of care – for example, avoiding unnecessary attendances at, or admissions to, hospital emergency units when they could be handled in primary care – is an important part of health strategy. However, some variations in these outcomes could be due to genuine variations in health need. This paper proposes a new method of explaining variations in hospital utilisation across small areas and the general practices (GPs) responsible for patient primary care. By controlling for the influence of true need on such variations, one may identify remaining sources of excess emergency attendances and admissions, both at area and practice level, that may be related to the quality, resourcing or organisation of care. The present paper accordingly develops a methodology that recognises the interplay between population mix factors (health need) and primary care factors (e.g. referral thresholds), that allows for unobserved influences on hospitalisation usage, and that also reflects interdependence between hospital outcomes. A case study considers relativities in attendance and admission rates at a North London hospital involving 149 small areas and 53 GP practices. RESULTS: A fixed effects model shows variations in attendances and admissions are significantly related (positively) to area and practice need, and nursing home patients, and related (negatively) to primary care access and distance of patient homes from the hospital. Modelling the impact of known factors alone is not sufficient to produce a satisfactory fit to the observations, and random effects at area and practice level are needed to improve fit and account for overdispersion. CONCLUSION: The case study finds variation in attendance and admission rates across areas and practices after controlling for need, and remaining differences between practices may be attributable to referral behaviour unrelated to need, or to staffing, resourcing, and access issues. In managerial terms, the analysis points to the utility of formal statistical analysis of hospitalisation rates as a prelude to non-statistical investigation of primary care resourcing and organisation. For example, there may be implications for the location of staff involved in community management of chronic conditions; health managers may also investigate whether some practices have unusual populations (homeless, asylum seekers, students) that explain different hospital use patterns

A model for spatial variations in life expectancy; mortality in Chinese regions in 2000

<p>Abstract</p> <p>Background</p> <p>Life expectancy in China has been improving markedly but health gains have been uneven and there is inequality in survival chances between regions and in rural as against urban areas. This paper applies a statistical modelling approach to mortality data collected in conjunction with the 2000 Census to formally assess spatial mortality contrasts in China. The modelling approach provides interpretable summary parameters (e.g. the relative mortality risk in rural as against urban areas) and is more parsimonious in terms of parameters than the conventional life table model.</p> <p>Results</p> <p>Predictive fit is assessed both globally and at the level of individual five year age groups. A proportional model (age and area effects independent) has a worse fit than one allowing age-area interactions following a bilinear form. The best fit is obtained by allowing for child and oldest age mortality rates to vary spatially.</p> <p>Conclusion</p> <p>There is evidence that age (21 age groups) and area (31 Chinese administrative divisions) are not proportional (i.e. independent) mortality risk factors. In fact, spatial contrasts are greatest at young ages. There is a pronounced rural survival disadvantage, and large differences in life expectancy between provinces.</p

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates

<p>Abstract</p> <p>Background</p> <p>Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease.</p> <p>Methods</p> <p>A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates.</p> <p>Results</p> <p>To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit.</p> <p>Conclusion</p> <p>Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables.</p

A Model for Spatially Varying Crime Rates in English Districts: The Effects of Social Capital, Fragmentation, Deprivation and Urbanicity

Abstract: Geographic variations in crime are often linked to aspects of urban social structure that are latent constructs, not directly observed but instead proxied by a range of observed indicators. Examples are area deprivation and urbanicity, both established risk factors for crime. Little UK based evidence exists for impacts on crime of other potentially relevant influences such as social capital and social fragmentation, which are also latent constructs. Other cited influences on area crime differences include income inequality, but there may be further unobserved factors, which tend to be spatially correlated. The present paper seeks to establish, using appropriate multivariate and spatial regression techniques, the relative importance of social capital, fragmentation, deprivation, urbanicity and income inequality in an analysis of recent crime variations between 324 English Local Authority Districts. Variations in total, violent and property crime are considered

Assessing Persistence in Spatial Clustering of Disease, with an Application to Drug Related Deaths in Scottish Neighbourhoods

Background: The upward trend in drug related deaths in some countries is a major public health concern. Regarding geographic location within countries, many studies report spatial clustering in drug related deaths. We consider drug related deaths in Scottish small areas, and investigate probabilities that clusters of adjacent neighbourhoods have elevated risk. We focus especially on assessing persistence in spatial clustering, relevant to prioritising area based interventions. We assess impacts of area risk factors on drug deaths, finding a strong link to poverty, and a clear overlap between drug death clustering and spatial poverty clustering.&nbsp; Methods: We analyse drug related deaths in 1279 Scotland neighbourhoods over two periods, 2009-13 and 2014- 18, during which drug related mortality in Scotland has more than doubled. A fully Bayesian approach is used to identify zones with high mortality risk in both a neighbourhood and its spatial lag (“high-high” clusters), and extended to identify recurring high risk clustering over more than one period. Estimation of mortality risks, and of cluster probabilities through periods, is developed in conjunction with a regression model including area risk factors such as deprivation.&nbsp; Results: Persistent clustering is concentrated in major urban centres, for example, Glasgow and Dundee. Deprivation is the paramount observed risk factor underlying elevated mortality risk, and persistent clustering in drug related mortality shows strong overlaps with poverty clustering. Social fragmentation modifies the paramount influence of poverty on drug mortality risk.&nbsp; Conclusion: Cluster persistence is a central feature in small area variability in drug related death risk in Scotland intermediate zones, especially in some urban areas.&nbsp

Comparing two models for disease mapping data not varying systematically in space

Baptista, H., Mendes, J., & Congdon, P. (2018). Comparing two models for disease mapping data not varying systematically in space. In METMA 9: Book of Extended Abstracts (pp. 68-71). [METMA IX, 9th Workshop on spatio-temporal modeling, 13-15 june, 2018, Montepellier, France].Conditionally specified Gaussian Markov random field (GMRF) models with adjacencybased neighborhood weight matrix, commonly known as eighborhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. However, there are cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Two models have been proposed for those cases, a conditionally specified Gaussian random field (GRF) model using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping, and a spatially adaptive conditional autoregressive prior model. The former model, named similarity-based GRF, is motivated for modeling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not varying systematically in space, and the similarity is defined by similarity with respect to the associated disease determinant factors. In the presence of disease data with no evidence of positive spatial correlation, a simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. The latter model considers a spatially adaptive extension of Leroux et al. [9] prior to reflect the fact that the appropriate mix between local and global smoothing may not be constant across the region being studied. Local smoothing will not be indicated when an area is disparate from its neighbours (e.g. in terms of social or environmental risk factors for the health outcome being considered). The prior for varying spatial correlation parameters may be based on a regression structure which includes possible observed sources of disparity between neighbours. We will compare the results of the two models.publishersversionpublishe

Thermoresponsive, well-defined, poly(vinyl alcohol) co-polymers

Thermoresponsive polymers have attracted huge interest as adaptable biomaterials based on their reversible solubility behaviour which can be exploited for controlled drug delivery or cellular uptake. The most famous and successful of these is poly(ethylene glycol) (PEG), but the thermal transition temperatures that are practically accessible are not physiologically useful. There are some notable examples of synthetic, responsive, polymers that are highly tunable over a physiologically relevant range, but there is still a need for these to be clinically validated in terms of toxicology and immunogenity for in vivo usage, in addition to their widely used in vitro applications. Poly(vinyl alcohol), PVA, is an appealing biocompatible polymer which is already used for a huge range of biomedical applications. Here, PVA is shown to be a highly tunable, thermoresponsive polymer scaffold. RAFT/MADIX polymerization is used to obtain a library of well-defined polymers between 8 and 50 kDa. Selective alkanoylation of the obtained PVA enabled the effect of side-chains, end-groups and molecular weight on the observable transition temperatures to be studied by turbidimetry. It was found that increasingly hydrophobic side chains (acetyl, propanoyl, butanoyl), or increasing their density led to corresponding decreases in cloud point. PVA with just 10 mol% butanoylation was shown to have a thermal transition temperature close to physiological temperatures (37 °C), compared to 70 mol% for acetylation, with temperatures in between accessible by controlling both the relative degree of functionalization, or by altering the chain length. Finally, a secondary response to esterase enzymes was demonstrated as a route to ‘turn off’ the responsive behaviour on demand. This study suggests that PVA-derived polymers may be a useful platform for responsive biomaterials

Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology

Emerging infectious diseases, and the resurgence of previously controlled infectious disease (e.g., malaria, tuberculosis), are a major focus for public health concern, as well as providing challenges for establishing aetiology and transmission. [...