The spatial structure of chronic morbidity: evidence from UK census returns

Background: Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity.Methods: 2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power.Results: The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions.Conclusions: Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data

The spatial structure of chronic morbidity: evidence from UK census returns

BACKGROUND: Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity. METHODS: 2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power. RESULTS: The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions. CONCLUSIONS: Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data

Incidence of First Abortions: Integration of Administrative and Survey Data within a Joint Cohort Life Table Model

Introduction Integration of administrative and survey data to address sources of error is a fast-growing area of research. This paper examines the case of abortion, where survey data are susceptible to self-report bias, while administrative data provide crude but comprehensive and relatively unbiased information. Objectives and Approach Although abortion is a common and legal procedure, information is lacking on the proportion of women having one or more abortions during their lifetime. A Bayesian joint cohort life table model estimates age-specific rates of incidence of a first abortion for cohorts of women born between 1936 and 2003 an residing in England and Wales. The model is fitted using (1) waves II and III of the British National Surveys of Sexual Attitudes and Lifestyles (NATSAL) and (2) administrative counts of first ever abortions published by the UK's Office for National Statistics and Department of Health. Results Model parameters controlling for underreporting indicate that survey reports are plausible for abortions occurring before the age of 20 years. Beyond that age, the model shows a fast increasing propensity to underreport abortions depending on the age at which they occurred. Underreporting also appears to be higher in NATSAL III. The study produces corrected estimates of the overall lifetime prevalence of an abortion in England and Wales, which is higher than previously thought. Conclusion/Implications Joint modelling of survey and administrative data can provide robust statistics, while reducing the need for record linkage where it is not feasible or acceptable. This approach is relevant in other contexts to correct the bias of particular population datasets, when audit data exist (e.g. underascertained diagnoses/causes of death)

The effects of oxygen depletion due to upwelling groundwater on the post-hatch fitness of Atlantic salmon (Salmo salar)

The conditions experienced by incubating Atlantic salmon (Salmo salar) eggs are strongly influenced by hyporheic exchange. In some rivers, periods of intense groundwater upwelling can reduce oxygen levels in the incubation zone to 0% saturation. The present study investigated the effect of oxygen sags on the post-hatch fitness of Atlantic salmon. A laboratory experiment allowed fine-scale control of oxygen concentrations to replicate those induced by low oxygen groundwater in rivers. Extreme oxygen sags in the earlier stages of embryo development resulted in a developmental lag with alevin hatching later and at an underdeveloped state. At the latest stages of development, oxygen sags caused premature hatching of severely underdeveloped alevin. These findings combined with a review of the literature suggest post-hatch survival of embryos exposed to groundwater induced hypoxia will be lower due to predation and poor competitiveness

Lifelong exposure to air pollution and cognitive development in young children: the UK Millennium Cohort Study

Abstract Evidence about the impact of air pollution on cognitive development of children has been growing but remains inconclusive. To investigate the association of air pollution exposure and the cognitive development of children in the UK Millennium Cohort Study. Longitudinal study of a nationally representative sample of 13 058–14 614 singleton births, 2000–2002, analysed at age 3, 5 and 7 years for associations between exposure from birth to selected air pollutants and cognitive scores for: School Readiness, Naming Vocabulary (age 3 and 5), Picture Similarity, Pattern Construction (age 5 and 7), Number Skills and Word Reading. Multivariable regression models took account of design stratum, clustering and sampling and attrition weights with adjustment for major risk factors, including age, gender, ethnicity, region, household income, parents’ education, language, siblings and second-hand tobacco smoke. In fully adjusted models, no associations were observed between pollutant exposures and cognitive scores at age 3. At age 5, particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) were associated with lower scores for Naming Vocabulary but no other outcome except for SO2 and Picture Similarity. At age 7, PM2.5, PM10 and NO2 were associated with lower scores for Pattern Construction, SO2 with lower Number Skills and SO2 and ozone with poorer Word Reading scores, but PM2.5, PM10 and NO2 were associated with higher Word Reading scores. Adverse effects of air pollutants represented a deficit of up to around four percentile points in Naming Vocabulary at age 5 for an interquartile range increase in pollutant concentration, which is smaller than the impact of various social determinants of cognitive development. In a study of multiple pollutants and outcomes, we found mixed evidence from this UK-wide cohort study for association between lifetime exposure to air pollutants and cognitive development to age 7 years.</jats:p

Alcohol use and breast cancer risk:A qualitative study of women’s perspectives to inform the development of a preventative intervention in breast clinics

OBJECTIVE: This study aimed to explore women's views about breast cancer risk and alcohol use, to inform the design of a prototype for an intervention in breast clinics about alcohol as a modifiable risk factor for breast cancer. METHODS: Women recruited in NHS breast screening and symptomatic clinics in Southampton, UK, were invited to take part in semi‐structured telephone interviews or a focus group to discuss their perspectives of breast cancer risk, alcohol consumption and their information needs about these topics. Data were analysed thematically. Twenty‐eight women took part in telephone interviews, and 16 attended one of three focus groups. RESULTS: While most women reported a personal responsibility for their health and were interested in advice about modifiable risk factors, few without (or prior to) experience of breast symptoms independently sought information. Many considered alcohol advice irrelevant as the association with breast cancer was largely unknown, and participants did not consider their drinking to be problematic. Women reported trusting information from health organisations like the NHS, but advice needs to be sensitive and non‐blaming. CONCLUSION: NHS breast screening and symptomatic clinics offer a “teachable moment” to engage women with context‐specific advice about alcohol and cancer risk that, if targeted correctly, may assist them in making informed lifestyle choices