Predicting small area health-related behaviour: a comparison of multilevel synthetic estimation and local survey data

Reweighting national survey data for small area behaviour estimates: modelling alcohol consumption in Local Authorities in England

BACKGROUND

There are likely to be differences in alcohol consumption levels and patterns across local areas within a country, yet survey data is often collected at the national or sub-national/regional level and is not representative for small geographic areas.

METHODS

This paper presents a method for reweighting national survey data-the Health Survey for England-by combining survey and routine data to produce simulated locally representative survey data and provide statistics of alcohol consumption for each Local Authority in England.

RESULTS

We find a 2-fold difference in estimated mean alcohol consumption between the lightest and heaviest drinking Local Authorities, a 4.5-fold difference in abstention rates, and a 3.5-fold difference in harmful drinking. The method compares well to direct estimates from the data at regional level.

CONCLUSIONS

The results have important policy implications in itself, but the reweighted data can also be used to model local policy effects. This method can also be used for other public health small area estimation where locally representative data are not available.

Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems

Background: Low health literacy is associated with poorer health. Research has shown that predictive models of health literacy can be developed; however, key variables may be missing from systems where predictive models might be applied, such as health service data. This paper describes an approach to developing predictive health literacy models using variables common to both "source" health literacy data and "target" systems such as health services. Methods: A multilevel synthetic estimation was undertaken on a national (England) dataset containing health literacy, socio-demographic data and geographical (Lower Super Output Area: LSOA) indicators. Predictive models, using variables commonly present in health service data, were produced. An algorithm was written to pilot the calculations in a Family Physician Clinical System in one inner-city area. The minimum data required were age, sex and ethnicity; other missing data were imputed using model values. Results: There are 32,845 LSOAs in England, with a population aged 16 to 65 years of 34,329,091. The mean proportion of the national population below the health literacy threshold in LSOAs was 61.87% (SD 12.26). The algorithm was run on the 275,706 adult working-age people in Lambeth, South London. The algorithm could be calculated for 228,610 people (82.92%). When compared with people for whom there were sufficient data to calculate the risk score, people with insufficient data were more likely to be older, male, and living in a deprived area, although the strength of these associations was weak. Conclusions: Logistic regression using key socio-demographic data and area of residence can produce predictive models to calculate individual- and area-level risk of low health literacy, but requires high levels of ethnicity recording. While the models produced will be specific to the settings in which they are developed, it is likely that the method can be applied wherever relevant health literacy data are available. Further work is required to assess the feasibility, accuracy and acceptability of the method. If feasible, accurate and acceptable, this method could identify people requiring additional resources and support in areas such as medical practice.

The utility of geodemographic indicators in small area estimates of limiting long-term illness

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.

Inequity in cardiovascular care in the English National Health Service (NHS): a scoping review of the literature

There is a general understanding that socioeconomically disadvantaged people are also disadvantaged with respect to their access to NHS care. Insofar as considerable NHS funding has been targeted at deprived areas, it is important to better understand whether and why socioeconomic variations in access and utilisation exist. Exploring this question with reference to cardiovascular care, our aims were to synthesise and evaluate evidence relating to access to and/or use of English NHS services around (i) different points on the care pathway (i.e. presentation, primary management and specialist management) and (ii) different dimensions of inequality (socioeconomic, age- and gender-related, ethnic or geographical). Restricting our search period from 2004 to 2016, we were concerned to examine whether, compared to earlier research, there has been a change in the focus of research examining inequalities in cardiac care and whether the pro-rich bias reported in the late 1990s and early 2000s still applies today. We conducted a scoping study drawing on Arksey & O'Malley's framework. A total of 174 studies were included in the review and appraised for methodological quality. Although, in the past decade, there has been a shift in research focus away from gender and age inequalities in access/use and towards socioeconomic status and ethnicity, evidence that deprived people are less likely to access and use cardiovascular care is very contradictory. Patterns of use appear to vary by ethnicity; South Asian populations enjoying higher access, black populations lower. By contrast, female gender and older age are consistently associated with inequity in cardiovascular care. The degree of geographical variation in access/use is also striking. Finally, evidence of inequality increases with stage on the care pathway, which may indicate that barriers to access arise from the way in which health professionals are adjudicating health needs rather than a failure to seek help in the first place.

Measuring Geographic "Hot Spots" of Racial/Ethnic Disparities: An Application to Mental Health Care

This article identifies geographic "hot spots" of racial/ethnic disparities in mental health care access. Using data from the 2001-2003 Collaborative Psychiatric Epidemiology Surveys(CPES), we identified metropolitan statistical areas(MSAs) with the largest mental health care access disparities ("hot spots") as well as areas without disparities ("cold spots"). Racial/ethnic disparities were identified after adjustment for clinical need. Richmond, Virginia and Columbus, Georgia were found to be hot spots for Black-White disparities, regardless of method used. Fresno, California and Dallas, Texas were ranked as having the highest Latino-White disparities and Riverside, California and Houston, Texas consistently ranked high in Asian-White mental health care disparities across different methods. We recommend that institutions and government agencies in these "hot spot" areas work together to address key mechanisms underlying these disparities. We discuss the potential and limitations of these methods as tools for understanding health care disparities in other contexts.

Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013

Introduction

Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract–level prevalence estimates of 27 measures for the 500 largest US cities.

Methods

To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code–level estimates for the city of Boston, Massachusetts.

Results

By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators.

Conclusion

Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

Integrating national surveys to estimate small area variations in poor health and limiting long-term illness in Great Britain

OBJECTIVES

This study aims to address, for the first time, the challenges of constructing small area estimates of health status using linked national surveys. The study also seeks to assess the concordance of these small area estimates with data from national censuses.

SETTING

Population level health status in England, Scotland and Wales.

PARTICIPANTS

A linked integrated dataset of 23 374 survey respondents (16+ years) from the 2011 waves of the Health Survey for England (n=8603), the Scottish Health Survey (n=7537) and the Welsh Health Survey (n=7234).

PRIMARY AND SECONDARY OUTCOME MEASURES

Population prevalence of poorer self-rated health and limiting long-term illness. A multilevel small area estimation modelling approach was used to estimate prevalence of these outcomes for middle super output areas in England and Wales and intermediate zones in Scotland. The estimates were then compared with matched measures from the contemporaneous 2011 UK Census.

RESULTS

There was a strong positive association between the small area estimates and matched census measures for all three countries for both poorer self-rated health (r=0.828, 95% CI 0.821 to 0.834) and limiting long-term illness (r=0.831, 95% CI 0.824 to 0.837), although systematic differences were evident, and small area estimation tended to indicate higher prevalences than census data.

CONCLUSIONS

Despite strong concordance, variations in the small area prevalences of poorer self-rated health and limiting long-term illness evident in census data cannot be replicated perfectly using small area estimation with linked national surveys. This reflects a lack of harmonisation between surveys over question wording and design. The nature of small area estimates as 'expected values' also needs to be better understood.

Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

'In general, how do you feel today?'--self-rated health in the context of aging in India

This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.

The construction of a decision tool to analyse local demand and local supply for GP care using a synthetic estimation model

BACKGROUND

This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match.

METHODS

National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands.

RESULTS

Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P <0.001). The statistical model could estimate GP consultation time for every postcode area with >1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions.

CONCLUSIONS

The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it.

Small area estimates reveal high cigarette smoking prevalence in low-income cities of Los Angeles county

Los Angeles County has among the lowest smoking rates of large urban counties in the USA. Nevertheless, concerning disparities persist as high smoking prevalence is found among certain subgroups. We calculated adult smoking prevalence in the incorporated cities of Los Angeles County in order to identify cities with high smoking prevalence. The prevalence was estimated by a model-based small area estimation method with utilization of three data sources, including the 2007 Los Angeles County Health Survey, the 2000 Census, and the 2007 Los Angeles County Population Estimates and Projection System. Smoking prevalence varied considerably across cities, with a more than fourfold difference between the lowest (5.3%) and the highest prevalence (21.7%). Higher smoking prevalence was generally found in socioeconomically disadvantaged cities. The disparities identified here add another layer of data to our knowledge of the health inequities experienced by low-income urban communities and provide much sought data for local tobacco control. Our study also demonstrates the feasibility of providing credible local estimates of smoking prevalence using the model-based small area estimation method.

Generating small-area prevalence of psychological distress and alcohol consumption: validation of a spatial microsimulation method

PURPOSE

Public mental health surveillance data are rarely available at a fine geographic scale. This study applies a spatial microsimulation procedure to generate small-area (lower super outputs areas [LSOA]) estimates of psychological distress and alcohol consumption. The validity of LSOA estimates and their associations with proximal and broader socioeconomic conditions are examined.

METHODS

A deterministic reweighting methodology assigns prevalence estimates for psychological distress and heavy alcohol consumption through a process of matching individuals from a large, population-representative dataset (Health Survey for England) to known LSOA populations (from the 2001 population Census). 'goodness-of-fit' of LSOA estimates is assessed by their comparison to observed prevalence of these health indicators at higher levels of aggregation (local authority districts [LAD]). Population prevalence estimates are correlated to the mental health needs index (MINI) and other health indicators; ordered logistic regression is applied to investigate their associations with proximal and broader socioeconomic conditions.

RESULTS

Performance of microsimulation models is high with no more than 10% errors in at least 90% of LAD for psychological distress and moderate and heavy alcohol consumption. The MINI is strongly correlated with psychological distress (r = 0.910; p value < 0.001) and moderately with heavy drinking (r = 0.389; p value < 0.001). Psychological distress and heavy alcohol consumption are differently associated with socioeconomic and rurality indicators at the LSOA level. Associations further vary at the LAD level and regional variations are apparent.

CONCLUSION

Spatial microsimulation may be an appropriate methodological approach for replicating social and demographic health patterns at the local level.

Planning the Location of Stop Smoking Services at the Local Level: A Geographic Analysis

Smoking is one of the major causes of premature death and its negative effects on a person's health are a global issue. Therefore, the United Kingdom has introduced new policies aimed at reducing the proportion of smokers from 26% in 2005 down to 21% by 2010. One mechanism to meet this policy target is the provision of stop smoking services. This article aims to estimate the Leeds smoking population at the small area level and especially to highlight the distribution of hard-to-reach groups such as heavy smokers (> 20 cigarettes/day) and pregnant women who smoke. Then optimal location strategies are discussed in relation to stop smoking services. The findings show the importance of adding a spatial component to find out where the smoking population or specific subgroups of smokers are to support policymakers or healthcare planners who are responsible for the planning process of the services.

Small area estimates of smoking prevalence in London. Testing the effect of input data

Small area estimates (SAEs) can provide information about health behaviour at small area levels that is otherwise not available. Because of its increasing use by policy makers, more attention needs to be paid to the reliability of these estimates. This paper reports on smoking prevalence data generated for London at the neighbourhood level using spatial microsimulation modelling. We test the reliability of smoking prevalence estimates at the neighbourhood level using different input datasets. The paper further underlines the importance of estimating health behaviours at the small area level, particularly in diverse cities such as London, where estimation at the city level can mask significant spatial differences.

Counting hard-to-count populations: the network scale-up method for public health

Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation.

An index of unhealthy lifestyle is associated with coronary heart disease mortality rates for small areas in England after adjustment for deprivation

Indices of socio-economic deprivation are often used as a proxy for differences in the health behaviours of populations within small areas, but these indices are a measure of the economic environment rather than the health environment. Sets of synthetic estimates of the ward-level prevalence of low fruit and vegetable consumption, obesity, raised blood pressure, raised cholesterol and smoking were combined to develop an index of unhealthy lifestyle. Multi-level regression models showed that this index described about 50% of the large-scale geographic variation in CHD mortality rates in England, and substantially adds to the ability of an index of deprivation to explain geographic variations in CHD mortality rates.

Social inequalities in alcohol consumption in the Czech Republic: a multilevel analysis

Czech Republic traditionally ranks among the countries with the highest alcohol, consumption. This paper examines both risk and protective factors for frequent of alcohol, consumption in the Czech population using multilevel analysis. Risk factors were measured at the, individual level and at the area level. The individual-level data were obtained from a survey for a, sample of 3526 respondents aged 18-64 years. The area-level data were obtained from the Czech, Statistical Office. The group most inclinable to risk alcohol consumption and binge drinking are mainly, men, who live as single, with low education and also unemployed. Only the variable for divorce rate, showed statistical significance at both levels, thus the individual and the aggregated one. No cross-level interactions were found to be statistically significant.

Small-area estimation and prioritizing communities for tobacco control efforts in Massachusetts

OBJECTIVES

We developed a method to evaluate geographic and temporal variations in community-level risk factors and prevalence estimates, and used that method to identify communities in Massachusetts that should be considered high priority communities for smoking interventions.

METHODS

We integrated individual-level data from the Behavioral Risk Factor Surveillance System from 1999 to 2005 with community-level data in Massachusetts. We used small-area estimation models to assess the associations of adults' smoking status with both individual- and community-level characteristics and to estimate community-specific smoking prevalence in 398 communities. We classified communities into 8 groups according to their prevalence estimates, the precision of the estimates, and temporal trends.

RESULTS

Community-level prevalence of current cigarette smoking among adults ranged from 5% to 36% in 2005 and declined in all but 16 (4%) communities between 1999 and 2005. However, less than 15% of the communities met the national prevalence goal of 12% or less. High smoking prevalence remained in communities with lower income, higher percentage of blue-collar workers, and higher density of tobacco outlets.

CONCLUSIONS

Prioritizing communities for intervention can be accomplished through the use of small-area estimation models. In Massachusetts, socioeconomically disadvantaged communities have high smoking prevalence rates and should be of high priority to those working to control tobacco use.

Disease prevalence in the English population: a comparison of primary care registers and prevalence models

The Quality and Outcomes Framework (QOF) is a UK system for monitoring general practitioner (GP) activity and performance, introduced in 2004. The objective of this paper is to explore the potential of QOF datasets as a basis for better understanding geographical variations in disease prevalence in England. In an ecological study, prevalence estimates for four common disease domains (coronary heart disease (CHD), asthma, hypertension and diabetes) were derived from the 2004-2005 QOF primary care disease registers for 354 English Local Authority Districts (LADs). These were compared with synthetic estimates from four prevalence models and with self-reported measures of general health from the 2001 census. Prevalence models were recalculated for LADs using demographic and deprivation data from the census. Results were mapped spatially and cross-tabulated against a national classification of local authorities. The four disease domains display different spatial distributions and different spatial relationships with the corresponding prevalence model. For example, the prevalence model for CHD under-estimated QOF cases in northern England, but this north-south pattern was not evident for the other disease domains. The census-derived health measures were strongly correlated with CHD, but not with the other disease domains. The relationship between modelled prevalence and QOF disease registers differs by disease domain, implying that there is no simple cross-domain effect of the QOF process on prevalence figures. Given reliable synthetic estimates of small area prevalence for the QOF disease domains, one potential application of the QOF dataset may be in assessing the geographical extent of under-diagnosis for each domain.

Validation of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for wards in England

Several sets of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for small areas in England have been developed. These have been used in policy documents to indicate which areas are in need of intervention. In general, these models have not been subjected to validity assessment. This paper describes a validity assessment of 16 sets of synthetic estimates, by comparison of the models with national, regional and local survey-based estimates, and local mortality rate estimates. Model-based estimates of the prevalence of smoking, low fruit and vegetable consumption, obesity, hypertension and raised cholesterol are found to be valid.

Flaccid theory and the geographies of sexual health in the age of Viagra™

The discipline of geography is largely absent in discussions and debates about drug use practices and their relationships to sexual health. Given the important relationships among the use of drugs, performances of sexualized identities, and the practices of sex, it behooves medical and health geographers particularly, and social and cultural geographers more generally, to engage in the wider interdisciplinary debates about these relationships. Through a discussion of one drug, Viagra, this brief intervention offers an agenda for studying the geographies of sex, sexuality, and drug use. It is argued that drug use is an inherently geographic practice that reshapes how places are resituated in relation to the fluid and dynamic meanings of sex, sexuality, and sexual health, areas of research and practice that medical and health geographers ought to consider more seriously.

Estimating CHD prevalence by small area: integrating information from health surveys and area mortality

The risk of coronary heart disease (CHD) is strongly linked both to deprivation and ethnicity and so prevalence will vary considerably between areas. Variations in prevalence are important in assessing health care needs and how far CHD service provision and surgical intervention rates match need. This paper uses a regression model of prevalence rates by age, sex, region and ethnicity from the 1999 and 2003 Health Surveys for England to estimate CHD prevalence for 354 English local authority areas. To allow for the impact of social factors on prevalence, survey information on the deprivation quintile in the respondents' micro-area of residence is also used. Allowance is also made for area CHD mortality rates (obtained from aggregated vital statistics data) which are positively correlated with, and hence a proxy for, CHD prevalence rates. An application involves assessment of surgical intervention rates in relation to prevalence at the level of 28 Strategic Health Authorities.

Fat nation: deciphering the distinctive geographies of obesity in England

Much attention is focused on obesity by both the media and by public health. As a health risk, obesity is recognised as a contributing factor to numerous health problems. Recent evidence points to a growth in levels of obesity in many countries and particular attention is usually given to rising levels of obesity among younger people. England is no exception to these generalisations with recent studies revealing a clear geography to what has been termed an 'obesity epidemic.' This paper examines the complexities inherent in the geography of adult obesity in England. Existing knowledge about the sub-national geography of obesity is examined and assessed. Multilevel synthetic estimation is then used to construct an age-sex-ethnicity disaggregated geography of obesity. These differing geographies are compared and contrasted with pre-existing findings and explored at multiple scales. A complex picture of the geography of obesity in England is revealed.

Small-area estimation of health insurance coverage for California legislative districts

OBJECTIVES

To aid state and local policymakers, program planners, and community advocates, we created estimates of the percentage of the population lacking health insurance in small geographic areas of California.

METHODS

Finally, calibration ensured the consistency and stability of the estimates when they were aggregated.

RESULTS

Health insurance coverage among nonelderly persons varied widely across assembly districts, from 10% to 44%. The utility of local-level estimates was most apparent when the variations in subcounty uninsured rates in Los Angeles County (19%-44%) were examined.

CONCLUSIONS

Stable and useful estimates of health insurance rates for small areas such as legislative districts can be created through use of multiple sources of publicly available data.

Estimating population prevalence of psychiatric conditions by small area with applications to analysing outcome and referral variations

This paper considers the development of estimates of mental illness prevalence for small areas and applications in explaining psychiatric outcomes and in assessing service provision. Estimates of prevalence are based on a logistic regression analysis of two national studies that provides model based estimates of relative morbidity risk by demographic, socio-economic and ethnic group for major psychiatric conditions; household/marital and area status also figure in the regression. Relative risk estimates are used, along with suitably disaggregated census populations, to make prevalence estimates for 354 English local authorities (LAs). Two applications are considered: the first involves analysis of variations in schizophrenia referrals and suicide mortality over English LAs that takes account of prevalence differences, and the second involves assessing hospital referral and bed use in relation to prevalence (for ages 16-74) for a case study area, Waltham Forest in NE London.

Social capital, geography and health: a small-area analysis for England

There has recently been much debate about the influence of social capital on health outcomes. In particular it has been suggested that levels of social capital vary from place to place and that such variations may account for previously unexplained between-place variations in health outcomes. As yet few studies exist of the influence of small-area variations in social capital on health outcomes. One reason for this is the difficulty of obtaining indicators for small areas such as electoral wards in England, and we describe a method used to derive what we term 'synthetic estimates' of aspects of social capital by linking coefficients produced from multi-level analyses of national survey datasets to census data. We produce estimates for electoral wards in England and apply these in multi-level models of our response variable, the probability of survival of individuals surveyed in the Health and Lifestyle Survey of England. We report various combinations of models incorporating individual attributes, health-related behaviours, area measures of deprivation, and area measures of social capital. Our overall conclusion is that we find little support, at this spatial scale, for the proposition that area measures of social capital exert a beneficial effect on health outcomes.