Census Enumeration Districts Research Articles

Using internet-assisted geocoding of 1940 census addresses to reconstruct enumeration districts for use with redlining and longitudinal health datasets.

Many historical administrative documents, such as the 1940 census, have been digitized and thus could be merged with geographic data. Merged data could reveal social determinants of health, health and social policy milieu, life course events, and selection effects otherwise masked in longitudinal datasets. However, most exact boundaries of 1940 census enumeration districts have not yet been georeferenced. These exact boundaries could aid in analysis of redlining and other geographic and social contextual factors important for health outcomes today. Our objective is to locate and map a large set of 1940 enumeration districts. We use online resources and algorithmic solutions to locate and georeference unknown 1940 enumeration districts. We geocode addresses using the OpenCage API and construct "virtual" enumeration districts by using a convex hull algorithm on those geocoded addresses. We also merge in Home Owners' Loan Corporation (HOLC) redlining maps from the 1930s to demonstrate how 1940 enumeration districts could be used in future work to examine the association between historic redlining and current health. We geocode 7,228,656 1940 census addresses from the largest 191 US cities in 1940 that contained 84% of the 1940 US urban population from the Geographic Reference File and construct 34,472 virtual enumeration districts in areas that had HOLC redlining maps. 18,340 virtual enumeration districts were previously unmapped, covering cities containing an additional 40% of the 1940 US urban population. Where virtual enumeration districts match with previously mapped districts, 96.8% of paired districts share HOLC redlining categorization. Researchers can use algorithmic methods to quickly process, geocode, merge, and analyze large scale repositories of historical documents that provide important data on social determinants of health. These 1940 enumeration district maps could be used with studies such as the Health and Retirement Study, Panel Study for Income Dynamics, and Wisconsin Longitudinal Study.

Individual-Level Exposure to Residential Redlining in 1940 and Mortality Risk

Historic redlining, the practice by the Home Owners' Loan Corporation (HOLC) of systematically denying credit to borrowers in neighborhoods that were inhabited by primarily African American individuals, has been associated with poor community outcomes, but the association with individual risk of death is not clear. To examine if exposure to residential redlining practices by HOLC in 1940 is associated with increased risk of death later in life. The study linked individuals who resided within HOLC-graded neighborhoods (defined as Census Enumeration Districts) in 1940 with administrative death records data. The study estimated hazard ratios as well as age-specific life expectancy gaps (at age 55, 65, and 75 years) for HOLC grading exposure. This was done using methods that adapted standard parametric survival analysis to data with limited mortality coverage windows and incomplete observations of survivors. The analysis sample consisted of 961 719 individual-level observations across 13 912 enumeration districts within 30 of the largest US cities (based on 1940 population counts) across 23 states. Data were analyzed between December 1, 2023, and September 4, 2024. The exposure was HOLC grade based on historic HOLC maps, with A representing "best" or creditworthy areas; B, "still desirable"; C, "definitely declining"; and D, "hazardous" areas not worthy of credit (ie, redlined), and the main outcome was age at death from the Social Security Numident file. The 961 719-person individual sample had a mean (SD) age of 19.26 (9.26) years in 1940 and a mean (SD) age at death of 76.83 (9.22) years. In a model adjusted for sex (52.48% female; 47.52% male), race and ethnicity (7.36% African American; 92.64% White), and latent place effects, a 1-unit lower HOLC grade was associated with an 8% (hazard ratio, 1.08 [95% CI, 1.07-1.09]) increased risk of death. At age 65 years, these hazard differentials translated into an estimated life expectancy gap of -0.49 (95% CI, -0.56 to -0.43) years for each 1-unit decrease of the HOLC grade. This study found that individuals who resided within redlined neighborhoods in 1940 had lower life expectancy later in life than individuals who resided within other HOLC-graded areas.

Racial residential segregation and child mortality in the southern United States at the turn of the 20th century.

A growing body of research considers racial residential segregation to be a form of systemic racism and a fundamental cause of persistent racial disparities in health and mortality. Historical research examining the impact of segregation on health and mortality, however, is limited to a few studies with poor data and inconsistent results. In this study, we examine the association between racial residential segregation and child mortality in the South at the turn of the 20th century. We rely on the new IPUMS 1900 and 1910 complete-count databases to estimate child mortality in the 5 years before each census and construct segregation measures at the census enumeration district (ED), the lowest level of geography consistently available in the census. We calculate the proportion of households headed by Black individuals in each ED, and the Sequence Index of Segregation (SIS), which is based on the racial sequencing of household heads within each district. We construct models of child mortality for rural and urban areas, controlling for a wide variety of demographic and socioeconomic variables. The results indicate that proportion Black and SIS were strongly and positively associated with the mortality of Black children in most models and in both rural and urban areas. Proportion Black was also positively but more moderately correlated with the mortality of White children, while SIS was not correlated or negatively correlated. These results suggest that racial segregation was a long-standing fundamental cause of race disparities in health and mortality in the United States.

Mixed Land Use as an Intrinsic Feature of Sprawl: A Short-Term Analysis of Settlement Growth and Population Distribution Using European Urban Atlas

This study investigates the land-use/population mix over time as the base to derive an indicator of urban sprawl. Land-use individual patches (provided by Urban Atlas, hereafter UA, with a detailed spatial geometry at 1:10,000 scale) were associated with the total (resident) population based on official statistics (census enumeration districts and other public data sources), providing a comprehensive mapping of the spatial distribution of population density by land-use class in a representative case study for the Mediterranean region (metropolitan Athens, Greece). Data analysis adopted a mix of statistical techniques, such as descriptive statistics, non-parametric curve interpolation (smoothing splines), and exploratory multivariate statistics, namely hierarchical clustering, non-metric multi-dimensional scaling and confirmative factor analysis. The results of this study indicate a non-linear gradient of density decline from downtown (dominated by compact settlements) to peripheral locations (dominated by natural land). Population density in agricultural land was locally high and increasing over time; this result suggests how mixed land use may be the base of intense sprawl in large metropolitan regions. The methodology implemented in this study can be generalized over the whole sample of European cities included in Urban Atlas, providing a semi-automatic assessment of exurban development and population re-distribution over larger metropolitan regions.

Central Locations across Spatial Scales: A Quantitative Evaluation for Italy Using Census Enumeration District Indicators

‘Marginal’ urban settlements can be assumed as specific locations within a metropolitan area that are unable to attract (incoming) commuter flows. The official statistical system of Italy (headed by the National Statistical Institute, Istat) introduced a summary index of ‘urban marginality’ following the original definition proposed by a national, ad hoc Parliamentary Committee and assessing together social vulnerability and material deprivation at a sufficiently detailed spatial scale. More specifically, the index—intended as a composite indicator of territorial marginality with a normative meaning—was calculated as a specific elaboration of the commuting matrix derived from decadal population censuses considering a municipal-level resolution. In this perspective, the ability of a given municipality to attract bigger (or smaller) inflows than outflows, indicates a specific demand for services allowing the identification of (respectively) central places and peripheral locations. Starting from the index described above, our study generalizes this approach to a wider background context, investigating the roles of spatial scale and geographical coverage. By providing a novel (functional) approach to centrality and periphery, we analyzed commuting patterns at a submunicipal level, indirectly focusing on patterns and processes of local development. A spatial clustering of a standardized polarization index quantifying home-to-work daily travels delineated submunicipal (homogeneous) areas taken as sinks (centers) or sources (peripheries) of commuter flows. The empirical results also demonstrate that spatial neighborhoods (i.e., contiguity order) did not affect the functional classification of a given territory as derived from spatial clustering. Our approach provides a dynamic and innovative interpretation of metropolitan hierarchy using simplified data derived from population censuses.

Abstract A11: Neighborhood clustering of bladder cancer incidence in Utah: Analyzing census data linked to cancer records

Abstract Evaluating risk factors across the lifespan (using the life course perspective) is important when considering the role of environment on BCa risk. Spatial cluster detection methods offer unique opportunities to identify shared exposures of individuals with BCa on the local level and formulate hypotheses about the potential role of environmental exposures. This study tests for evidence of spatial clustering of BCa incidence based on neighborhoods defined as 1940 US Census enumeration districts (ED). Data were extracted from the Utah Population Database (UPDB). UPDB holds birth, death, historic US Census, and driver license records that allow tracking of an individual’s location across the lifespan. UPDB is linked to the Utah Cancer Registry, an original member of the Surveillance, Epidemiology, and End Results program. Incident BCa cases from 1966 to 2017 were identified in the UCR or by death records and linked to UPDB. We geocoded all 1940 US Census ED centroids in Utah using US Census maps and ArcGIS software. The cohort comprises all individuals age 0–40 years at the time of the 1940 Census (Ncohort=343,521). We excluded individuals with an absence of follow-up information (Nexcluded=82,726). Our final cohort size was 260,795 individuals who contributed 13,816,670 person-years. SatScan software was used to identify spatial clustering of EDs with high rates of BCa. BCa was diagnosed in 2,654 individuals during follow-up (rate=19.2 cases per 100,000 person-years). Approximately 78.4% of cases were male. Average follow-up time was 53 years (range 1-78 years). We identified a circular cluster of 1.54 kilometers in diameter containing 23 ED centroids in an urban area, with a rate of 32.7 cases per 100,000 person-years (relative risk = 1.73, p=0.0023). We found evidence of spatial clustering of BCa incidence in an urban area of Salt Lake City, Utah for individuals in early life (0-40 years) at the time of the 1940 US Census. Individuals residing in this shared space were significantly more likely to develop BCa later in life, with a 73% increase in the incidence rate. Shared space in this context is presumed to be a proxy for environmental exposures. Environmental exposures including arsenic in drinking water and exposure to carcinogenic chemicals are associated with bladder cancer incidence. While this study was hypothesis generating and did not test for contaminants directly, future studies would benefit from the inclusion of contamination data. Additionally, we were limited by the use of residential history at a single time point and lack of data on personal behaviors (e.g., smoking). Strengths of this study include large cohort size, long follow-up time, and ability to expand to an early census. A life course perspective on cancer incidence provides for innovative methods to explore the environmental contexts of disease. Citation Format: Claire L. Leiser, Marissa Taddie, Rebecca Richards-Steed, James A. VanDerslice, Brock O’Neil, Heidi A. Hanson. Neighborhood clustering of bladder cancer incidence in Utah: Analyzing census data linked to cancer records [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr A11.

Spatial clustersof cancer incidence: analyzing 1940 census data linked to 1966-2017 cancer records.

A life course perspective to cancer incidence is important for understanding effects of the environment during early life on later cancer risk. We assessed spatial clusters of cancer incidence based on early life location defined as 1940 US Census Enumeration District (ED). A cohort of 260,585 individuals aged 0-40years in 1940 was selected. Individuals were followed from 1940 to cancer diagnosis, death, or last residence in Utah. We geocoded ED centroids in Utah for the 1940 Census. Spatial scan statistics with purely spatial elliptic scanning window were used to identify spatial clustersof EDs with excess cancer rates across26 cancer types, assuming a discrete Poisson model. Cancer was diagnosed in 66,904 (25.67%) individuals during follow-up across 892 EDs. Average follow-up was 50.9years. We detected 15 clusters of excess risk for bladder, breast, cervix, colon, lung, melanoma, oral, ovary, prostate, and soft tissue cancers. An urban area had dense overlap of multiple cancer types, including two EDs at increased risk for five cancer types each. Early environments may contribute to cancer risk later in life. Life course perspectives applied to the study of cancer incidence can provide insights for increasing understanding of cancer etiology.

Key variable combinations for identifying non-participants in the Japan National Health and Nutrition Survey through record linkage with the Comprehensive Survey of Living Conditions

Objectives The identification of non-participants in the Japan National Health and Nutrition Survey (NHNS) requires record linkage with its master sample from the Comprehensive Survey of Living Conditions (CSLC). In principle, we can merge individual records between the two surveys by using key identifiers including household ID, but false matches and nonmatches can occur. We examined combinations of key variables for improving record linkage to identify nonparticipants in the NHNS.Methods We used individual-level data from the NHNS and the CSLC from 1988 to 2015 (except 2012). We extracted from CSLC data individuals in participating unit blocks in the NHNS to merge records between the two surveys. We used four combinations of key variables: prefecture ID, census enumeration district ID, unit block ID, household ID, and household member ID (A); household member ID in A was replaced with sex and birth year and month or age (B); sex and birth year and month or age were added to A (C); two-stage linkage of B and C (D). We classified a sample of individuals into matched participants, unmatched NHNS participants, and unmatched CSLC participants (a proxy for nonparticipants). We compared the percentages of matched NHNS participants and unmatched CSLC participants across the four combinations of key variables.Results We obtained a sample of 455,854 participants from the CSLC and 335,010 from the NHNS. The percentage of matched NHNS participants was highest in A (the upper 90%), followed by D (the lower 90%), B (the lower 90%), and C (the 80%). Compared to C, the percentage of matched NHNS participants was higher by 8-14 percentage points in A and 5-10 percentage points in B. Compared to B, it was higher by 0.1-0.4 percentage points in D. The percentage of unmatched CSLC participants was highest in C, followed by B, D, and A. The percentage of unmatched CSLC participants increased in D from the 20% level in the late 1980s to around 30% in the 1990s and stayed between the 30% level and the lower 40% level in the 2000s.Conclusion The highest percentage of accurate matches of NHNS participants was obtained by considering changes in household member ID and incorrect entries on sex and birth year/month and age, and same-sex multiple births. However, there are limitations in handling unmatched participants due to changes in household ID or other reasons. It is therefore necessary to consider the possibility of false nonmatches included in unmatched CSLC participants in regarding them as non-participants in the NHNS.

Effects of different building blocks designs on the statistical characteristics of Automated Zone-design Tool output areas

Prior to any census, the country usually gets demarcated into small geographic units called census enumeration areas, districts or blocks. In most countries, these small geographic units are also used for census dissemination. In cases where they are not used for census release, they are normally used as building blocks for developing output areas or they are aggregated to higher spatial levels in an effort to preserve privacy or confidentiality. Buildings blocks are therefore, of significant importance towards results that could be drawn from either aggregated higher level or from output areas developed using these small geographic areas. This paper aimed at evaluating the effects of different building blocks on the statistical characteristics of output areas generated using the Automated Zone-design Tool (AZTool) computer program. Different spatial layers (such as Enumeration Areas (EAs), Small Area Layers (SALs) and SubPlaces) from the 2001 census data were used as building blocks for the generation of census output areas with AZTool program in both rural and urban areas of South Africa. One way-Analysis of Variance (ANOVA) was also performed to determine statistical significance of the AZTool results. Results showed that the AZTool output areas generated from smaller areas (EAs and SALs) tend to be more homogeneous than the ones generated from larger areas (SubPlaces) when using dwelling type and geotype as homogeneity variables. The output areas from smaller areas also had narrower population distribution and more compact shapes than their counter-parts. In addition, the AZTool optimised output areas from the smaller areas allowed a clear distinction of the scale effects than output areas from larger areas. It was concluded that indeed different building blocks did have an impact on the statistical qualities of the AZTool optimised output areas in both rural and urban settings in South Africa.

Do Computers Follow Rules Once Followed by Workers?

Do Computers Follow Rules Once Followed by Workers? Bjorn Westergard (bio) In his 2014 paper “Polanyi’s Paradox and the Shape of Employment Growth,” economist David Autor puts forward a very general historical thesis (emphasis added): When a computer processes a company’s payroll, alphabetizes a list of names, or tabulates the age distribution of residents in each U.S. Census enumeration district, it is “simulating” a work process that would, in a previous era, have been done by humans using nearly identical procedures. The principle of computer simulation of workplace tasks has not fundamentally changed since the dawn of the computer era. But its cost has. … This remarkable cost decline creates strong economic incentives for firms to substitute ever-cheaper computing power for relatively expensive human labor, with attendant effects on employers’ demand for employees. How could a historian of computing adjudicate this claim? How can we determine whether the procedures used by humans and computers are similar, let alone “nearly identical”? Part and parcel with this framing of the issue is Autor’s assertion that the inability of workers to articulate the rules they follow when carrying out a task constitutes an impediment to writing software to automate it and his suggestion that this impediment might be overcome with machine learning techniques, which putatively infer these “tacit rules” from a wealth of examples. Underwriting this view is a theory—henceforth, “the ALM theory”—first laid out by Autor, Levy, and Murnane in The Skill Content Content of Recent Technological Change (2003) and The New Division of Labor (2004), which builds upon Michael Polanyi’s epistemology and attendant conceptions of rule following. The ALM theory was developed in response to an economic literature that argued that adoption of computer technology—at the level of the industry, firm, or worksite—increases demand for the labor of those with a postsecondary education at the expense of those without. It was thought that in the race between education (supplying computer-complementary skills) and technology (creating demand for them), technology had and would prevail, driving up the wage premia of more educated workers.1 This “canonical model” of “skills-biased technical change” employed a binary classification scheme of “more-and less-skilled workers, often operationalized as college-and non-college-educated workers.” As the 1990s wore on economists found slowing growth in the college wage premium and nonmonotonic inequality growth difficult to account for in this framework. Subtler distinctions needed to be drawn.2 For these, economists pursuing the “task approach” looked to databases of job descriptions, such as the Department of Labor’s Dictionary of Occupational Titles and its successor O*NET, to “[measure] the tasks performed in jobs rather than the educational credentials of workers performing those jobs.”3 They would conclude, contrary to the existing skill-biased technical change literature, that beginning in the late 1970s, computerization had issued in “job polarization” or “the simultaneous growth of high-education, high-wage and low-education, low-wages jobs.”4 The task approach drops the assumption that educational attainment determines work activity in favor of two production functions: one characterizing how labor and computer capital inputs combine to perform tasks, another characterizing how task performances combine to produce outputs (i.e., goods, services). The firm is taken to be a locus of task assignment and execution in which managers play a key role in “organizing tasks into jobs.”5 The heart of the ALM theory, which is meant to provide an interpretation of the data collected using the “task approach,” is the “ALM hypothesis”:6 (1) that computer capital substitutes for workers in carrying out a limited and well-defined set of cognitive and manual activities, those that can be accomplished by following explicit rules (what we term “routine tasks”); and (2) that computer capital complements workers in carrying out problem-solving and complex communication activities (“nonroutine tasks”). [End Page 5] In addition to being “routine” or “nonroutine,” tasks are also either “manual” or “cognitive.” Example classifications include record keeping, calculation, repetitive customer service (routine cognitive), medical diagnosis, legal writing, managing others (nonroutine cognitive), picking/sorting, repetitive assembly (routine manual), janitorial work, truck driving, and removing paper clips from documents7 (nonroutine...

Attitudes towards smoking restrictions and tobacco advertisement bans in Georgia

ObjectivesThis study aims to provide data on a public level of support for restricting smoking in public places and banning tobacco advertisements.DesignA nationally representative multistage sampling design, with sampling strata...

Spatial-temporal disease mapping of illicit drug abuse or dependence in the presence of misaligned ZIP codes

Geo-referenced data often are collected in small, administrative units such as census enumeration districts or postal code areas. Such areas vary in geographic area and population size and may change over time. In research into drug-related health issues within the United States, U.S. Postal Service ZIP codes represent a commonly used unit for data collection, storage, and spatial analysis because of their widespread availability in health databases through patient contact and billing information. However, the ZIP code was developed for the specific purpose of delivering mail and may be changed at any time, and its design and development does not take into consideration problems that may arise in data collection, analysis, and presentation in health studies. In this paper, we propose a spatial hierarchical modeling approach to quantify trends within ZIP-code based counts when some fraction of ZIP codes change over the study period, that is, when the data are spatially misaligned across time. We propose a data vector approach and adjust the spatial auto-correlation structure within our Bayesian hierarchical model to provide inference for our misaligned data. We motivate and illustrate our approach to explore spatio-temporal patterns of amphetamine abuse and/or dependence in Tracy, California over the years 1995-2005. Uncertainty associated with misaligned data is modeled, quantified, and visualized. The approach offers a framework for further investigation into other risk factors in order to more fully understand the dynamics of illicit drug abuse or dependence across time and space in imperfectly measured data.

A graph-theory method for pattern identification in geographical epidemiology – a preliminary application to deprivation and mortality

BackgroundGraph theoretical methods are extensively used in the field of computational chemistry to search datasets of compounds to see if they contain particular molecular sub-structures or patterns. We describe a preliminary application of a graph theoretical method, developed in computational chemistry, to geographical epidemiology in relation to testing a prior hypothesis. We tested the methodology on the hypothesis that if a socioeconomically deprived neighbourhood is situated in a wider deprived area, then that neighbourhood would experience greater adverse effects on mortality compared with a similarly deprived neighbourhood which is situated in a wider area with generally less deprivation.MethodsWe used the Trent Region Health Authority area for this study, which contained 10,665 census enumeration districts (CED). Graphs are mathematical representations of objects and their relationships and within the context of this study, nodes represented CEDs and edges were determined by whether or not CEDs were neighbours (shared a common boundary). The overall area in this study was represented by one large graph comprising all CEDs in the region, along with their adjacency information. We used mortality data from 1988–1998, CED level population estimates and the Townsend Material Deprivation Index as an indicator of neighbourhood level deprivation. We defined deprived CEDs as those in the top 20% most deprived in the Region. We then set out to classify these deprived CEDs into seven groups defined by increasing deprivation levels in the neighbouring CEDs. 506 (24.2%) of the deprived CEDs had five adjacent CEDs and we limited pattern development and searching to these CEDs. We developed seven query patterns and used the RASCAL (Rapid Similarity Calculator) program to carry out the search for each of the query patterns. This program used a maximum common subgraph isomorphism method which was modified to handle geographical data.ResultsOf the 506 deprived CEDs, 10 were not identified as belonging to any of the seven groups because they were adjacent to a CED with a missing deprivation category quintile, and none fell within query Group 1 (a deprived CED for which all five adjacent CEDs were affluent). Only four CEDs fell within Group 2, which was defined as having four affluent adjacent CEDs and one non-affluent adjacent CED. The numbers of CEDs in Groups 3–7 were 17, 214, 95, 81 and 85 respectively. Age and sex adjusted mortality rate ratios showed a non-significant trend towards increasing mortality risk across Groups (Chi-square = 3.26, df = 1, p = 0.07).ConclusionGraph theoretical methods developed in computational chemistry may be a useful addition to the current GIS based methods available for geographical epidemiology but further developmental work is required. An important requirement will be the development of methods for specifying multiple complex search patterns. Further work is also required to examine the utility of using distance, as opposed to adjacency, to describe edges in graphs, and to examine methods for pattern specification when the nodes have multiple attributes attached to them.

Neighbourhood effects on health: Does it matter where you draw the boundaries?

Neighbourhood effects on health: Does it matter where you draw the boundaries?

Perceptions of the neighbourhood environment and self rated health: a multilevel analysis of the Caerphilly Health and Social Needs Study

BackgroundIn this study we examined whether (1) the neighbourhood aspects of access to amenities, neighbourhood quality, neighbourhood disorder, and neighbourhood social cohesion are associated with people's self rated health, (2) these health effects reflect differences in socio-demographic composition and/or neighbourhood deprivation, and (3) the associations with the different aspects of the neighbourhood environment vary between men and women.MethodsData from the cross-sectional Caerphilly Health and Social Needs Survey were analysed using multilevel modelling, with individuals nested within enumeration districts. In this study we used the responses of people under 75 years of age (n = 10,892). The response rate of this subgroup was 62.3%. All individual responses were geo-referenced to the 325 census enumeration districts of Caerphilly county borough.ResultsThe neighbourhood attributes of poor access to amenities, poor neighbourhood quality, neighbourhood disorder, lack of social cohesion, and neighbourhood deprivation were associated with the reporting of poor health. These effects were attenuated when controlling for individual and collective socio-economic status. Lack of social cohesion significantly increased the odds of women reporting poor health, but did not increase the odds of men reporting poor health. In contrast, unemployment significantly affected men's health, but not women's health.ConclusionThis study shows that different aspects of the neighbourhood environment are associated with people's self rated health, which may partly reflect the health impacts of neighbourhood socio-economic status. The findings further suggest that the social environment is more important for women's health, but that individual socio-economic status is more important for men's health.

Selection of SES Factors for Construction of Socioeconomic Deprivation Index in the Czech Republic

ISEE-154 Objective: In the Czech Republic any socioeconomic deprivation index (SESDI) has not been constructed for the use in ecologic studies yet. The aim of the study financed by the grant agency of the Czech MoH was to create such SESDI. Material and Methods: The SESDI components were selected from the census data (2001). The selection was done according to domains of material and social deprivation. Material deprivation was represented by ownership of accommodation (including cottage houses), car, phone, density of housing; social deprivation by education, singles, economic activity, and unemployment. The first step of selection was done on the level of census enumeration districts (ED; 5114) in the Moravian region (6 districts) with the total population of 1,253,000 inhabitants. Following factors were chosen for the first analysis: type of ownership of accommodation (weighted distribution for each ED was grouped into 5 categories; the districts were assigned by the average rating), density of housing (average/ED), ownership of summer cottage, car, phone (%), singles (%), education (weighted distribution), economic activity, and unemployment. The results of the analysis classified districts by deprivation level and were visualized by GIS. Then the order of distribution of all available SES factors from census was prepared for 6 pilot districts, verified by the results of the detailed analysis on ED level. The final selection of factors was done by omitting the similar ones with the same distribution and a factor with different distribution was included (incomplete families with children). Finally the same method was applied for all 77 districts in the Czech Republic. Results: Based on detailed analysis, the following factors were selected: material—ownership of accommodation, car, phone, and density of housing; social—proportion of basic education, unemployed, singles, and incomplete families with children. Conclusions: The final set of SESDI components was a basis for construction of the index.

Does social cohesion modify the association between area income deprivation and mental health? A multilevel analysis

Despite the increasing belief that the places where people live influence their health, there is surprisingly little consistent evidence for their associations with mental health. We investigated the joint effect of community and individual-level socio-economic deprivation and social cohesion on individual mental health status. Multilevel analysis of population survey data on 10,653 adults aged 18-74 years nested within the 325 census enumeration districts in Caerphilly county borough, Wales, UK. The outcome measure was the Mental Health Inventory (MHI-5) subscale of the SF-36 instrument. A social cohesion subscale was derived from a factor analysis of responses to the Neighbourhood Cohesion scale and was modelled at individual and area level. Area income deprivation was measured by the percentage of low income households. Poor mental health was significantly associated with area-level income deprivation and low social cohesion after adjusting for individual risk factors. High social cohesion significantly modified the association between income deprivation and mental health: the difference between the predicted mean area mental health scores at the 10th and 90th centiles of the low income distribution was 3.7 in the low cohesion group and 0.9 in the high cohesion group (difference of the difference in means = 2.8, 95% CI: 0.2, 5.4). Income deprivation and social cohesion measured at community level are potentially important joint determinants of mental health. Further research on the impact of the social environment on mental health should investigate causal pathways in a longitudinal study.

An ecometric analysis of neighbourhood cohesion

BackgroundIt is widely believed that the social environment has an important influence on health, but there is less certainty about how to measure specific factors within the social environment that could link the neighbourhood of residence to a health outcome. The objectives of the study were to examine the underlying constructs captured by an adapted version of Buckner's neighbourhood cohesion scale, and to assess the reliability of the scale at the small-area-level by combining ecometric methodology with ordinal modelling of a five-point scale.MethodsData were analysed from 11,078 participants in the Caerphilly Health and Social Needs Study, who were sampled from within 325 UK census enumeration districts in Caerphilly county borough, Wales, UK. The responses of interest came from 15 question items designed to capture different facets of neighbourhood cohesion. Factor analysis was used to identify constructs underlying the neighbourhood cohesion item responses. Using a multilevel ecometric model, the variability present in these ordinal responses was decomposed into contextual, compositional, item-level and residual components.ResultsTwo constructs labelled neighbourhood belonging and social cohesion were identified, and variability in both constructs was modelled at each level of the multilevel structure. The intra-neighbourhood correlations were 6.4% and 1.0% for the neighbourhood belonging and social cohesion subscales, respectively. Given the large sample size, contextual neighbourhood cohesion scores can be estimated reliably. The wide variation in the observed frequency of occurence of the scale item activities suggests that the two subscales have desirable ecometric properties. Further, the majority of between-neighbourhood variation is not explained by the socio-demographic characteristics of the individual respondents.ConclusionAssessment of the properties of the adapted neighbourhood cohesion scale using factor analysis and ecometric analysis extended to an ordinal scale has shown that the items allow fine discrimination between individuals. However, large sample sizes are needed in order to accurately estimate contextual neighbourhood cohesion. The scale is therefore appropriate for use in the measurement of neighbourhood cohesion at small-area-level in future studies of neighbourhoods and health.

Population access to hospital emergency departments and the impacts of health reform in New Zealand

In the current political climate of evidence-based research, GIS has emerged as a powerful research tool as it allows spatial and social health inequality to be explored efficiently. This article explores the impact health reforms had on geographical accessibility to hospital emergency department (ED) services in New Zealand from 1991 to 2001. Travel time was calculated using least-cost path analysis, which identified the shortest travel time from each census enumeration district through a road network to the nearest ED. This research found that the population further than 60 minutes from an ED has increased with some areas being affected more than others. Some of this increase is attributed to increases in population rather than the closing of hospitals. The findings will be discussed within the context of the health policy reform era and changes to health service provision.

Providing the evidence: Geographic accessibility of maternity units in New Zealand

Abstract: Public health planners should be providing evidence to the public that maternity units are fairly distributed. This research demonstrates how a Geographical Information System (GIS) can be used to provide information on travel time to the closest maternity unit from the 38 000 population census enumeration districts in New Zealand. The distribution of accessibility is mapped and regions and population groups that appear under‐serviced are highlighted. We conclude by stating that GIS accessibility models provide important evidence for health policy and that the information generated from these models should be routinely produced for a wide range of health services and communicated to the public.