To overcome the absence of socio-economic information in administrative databases and to monitor social inequalities in health, a material and social deprivation index was developed for Québec and Canada.
The index is based on the smallest area unit used in Canadian censuses, with 400 to 700 persons on average. It includes six socio-economic indicators grouped along two dimensions - material and social - produced from principal component analyses. The index exists for 1991, 1996, 2001 and 2006 and in different versions, from local areas to the whole of Canada. Numerous products related to the index are available online free of charge.
The index has been used extensively in the field of health and social services, mainly in the province of Québec but also elsewhere in Canada. It has had four main uses, all related to public health: describing geographic variations of deprivation, illustrating inequalities in population health status and in service use according to deprivation, supporting the development of health reports and policies, and guiding regional resource allocation. These applications are facilitated by a close partnership between the producers and users of the index.
The deprivation index is a marker of social inequalities in health. It allows for monitoring of inequalities over time and space, and constitutes a useful tool for public health planning, intervention and service delivery.
Variations in the rate of use of common medical procedures/therapies are widely documented. Previous studies tend to focus on variations between either hospitals or geographic areas. Few studies examine within centre practice variations.
To examine if variation in treatment recommendations exist among highly trained interventional cardiologists (n=9) working in a single, highly collaborative tertiary care centre.
Data was collected from a local registry. A logistic regression model was used to estimate each physician's odds of recommending revascularization therapy over medical therapy for patients with significant CAD. The analysis was repeated to estimate each physician's odds of recommending percutaneous coronary intervention (PCI) over coronary artery bypass graft surgery (CABG) when the physician indicated the need for revascularization. Each physician's odds were compared to those for a reference physician to yield odds ratios. The odds ratios were adjusted for multiple patient characteristics.
The adjusted odds ratios of four physicians differed significantly from the reference physician (range: 0.8-2.9). Variation was also seen among physicians in the decision to recommend CABG rather than PCI once revascularization therapy was selected. The odds ratios ranged from 1.5 to 4.2.
Practice variations were seen despite case mix adjustment, similar resource and environmental constraints. The existence of within centre variations may have implications on service delivery and planning. Research is needed to both identify the existence, and explain the determinants of "an even smaller area variation".
INTRODUCTION: Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California's Cancer Detection Programs: Every Woman Counts. METHODS: We used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model. RESULTS: Our method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races. CONCLUSION: The small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.
Hospital records lack information about country of birth. This study describes a method for calculating hospitalization rates by the percentage of foreign-born in Census Dissemination Areas (DAs).
Data from the 2006 Census were used to classify DAs by the percentage of the foreign-born population who lived in them. Quintile and tercile thresholds were created to classify DAs as having low to high percentages of foreign-born residents. This information was appended to the 2005/2006 Hospital Morbidity Database via postal codes. Age-sex standardized hospitalization rates were calculated for low to high foreign-born concentration DAs, nationally and subnationally.
Nationally, quintile thresholds had better discriminatory power to detect variations in hospitalization rates by foreign-born concentration, but tercile thresholds produced reliable results at subnational levels. All-cause hospitalization rates were lowest among residents of the high foreign-born concentration terciles. Similar gradients emerged in hospitalization rates for heart disease, diseases of the circulatory system, and mental health conditions. The pattern varied more at the subnational level.
With this approach, administrative data can be used to calculate hospitalization rates by foreign-born concentration.
Many studies of health inequalities use household income as an indicator of socioeconomic status. Because household income is usually subject to high item non-response rates area or census-based measures have been suggested as an alternative. A number of studies have shown that these are as good as or better than conventional measures of socioeconomic status at identifying variations in health status and use of health services. This paper examines the association of the median household income of the enumeration area in which a subject lives, with a variety of oral and general health behaviours. After the confounding effects of age and sex were controlled for, this area-based indicator was significantly associated with six of ten health behaviours for which data were collected. Four of the associations remained significant after the effects of house hold income were controlled for. These results suggest that area-based measures of socioeconomic status may have a useful role in understanding the influence of social contexts on health behaviours.
To present a geographic information systems (GIS) method for exploring the spatial pattern of injuries and to demonstrate the utility of using this method in conjunction with classic ecological models of injury patterns.
Profiles of patients' socioeconomic status (SES) were constructed by linking their postal code of residence to the census dissemination area that encompassed its location. Data were then integrated into a GIS, enabling the analysis of neighborhood contiguity and SES on incidence of injury.
Data for this analysis (2001-2006) were obtained from the British Columbia Trauma Registry. Neighborhood SES was calculated using the Vancouver Area Neighborhood Deprivation Index. Spatial analysis was conducted using a join-count spatial autocorrelation algorithm.
Male and female patients over the age of 18 and hospitalized from severe injury (Injury Severity Score >12) resulting from an assault or intentional self-harm and included in the British Columbia Trauma Registry were analyzed.
Male patients injured by assault and who resided in adjoining census areas were observed 1.3 to 5 times more often than would be expected under a random spatial pattern. Adjoining neighborhood clustering was less visible for residential patterns of patients hospitalized with injuries sustained from self-harm. A social gradient in assault injury rates existed separately for men and neighborhood SES, but less than would be expected when stratified by age, gender, and neighborhood. No social gradient between intentional injury from self-harm and neighborhood SES was observed.
This study demonstrates the added utility of integrating GIS technology into injury prevention research. Crucial information on the associated social and environmental influences of intentional injury patterns may be under-recognized if a spatial analysis is not also conducted. The join-count spatial autocorrelation is an ideal approach for investigating the interconnectedness of injury patterns that are rare and occur in only a small percentage of the population.
To assess the ability of an Adjusted Clinical Group (ACG)-based morbidity measure to assess the overall health service needs of populations. Data Sources/Study Setting. Three population-based secondary data sources: registration and health service utilization data from fiscal year 1995-1996; mortality data from vital statistics reports from 1996-1999; and Canadian census data. The study included all continuously enrolled residents in the universal health care plan in Manitoba.
Using 60 small geographic areas as the units of analysis, we compared a population-based "ACG morbidity index," derived from individual ACG assignments in fiscal year 1995-1996, with the standardized mortality ratio (ages
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In this article, recently developed Bayesian spatial and ecological regression models are applied to analyse small-area variation in accident and injury. This study serves to demonstrate how Bayesian modelling techniques can be implemented to assess potential risk factors measured at group (e.g. area) level. Presented here is a unified modelling framework that enables thorough investigations into associations between injury rates and regional characteristics, residual variation and spatial autocorrelation. Using hospital separation data for 83 local health areas in British Columbia (BC), Canada, in 1990-1999, we explore and examine ecological/contextual determinants of motor vehicle accident injury (MVAI) among male children and youth aged 0-24 and for those of six age groups (
To examine falls in older people in the Wellington-Dufferin-Guelph (WDG) health region of Ontario, Canada, and to identify areas with excess RR and associated risk factors, particularly those related to private dwellings.
Cases of hospitalisation following falls among older people in the WDG health region between 2002 and 2006 were geocoded to the dissemination area level and used in the spatial analysis. The falls data and covariates from the 2006 Canadian census were analysed using Poisson log-linear models with (spatial and non-spatial) random effects at the dissemination area level. A Bayesian approach with Markov chain Monte Carlo simulation allowed the spatial random effects models to be fitted. Map decomposition was used to visualise the results.
The percentage of occupied private dwellings requiring repairs and median income were significantly associated with falls in older people in the WDG health region. Twenty-six dissemination areas with high RR of falls in older people in the WDG health region were identified. Map decomposition revealed that RR were also driven by unknown factors that have spatial patterns.
This research identified an association between falls in older people and housing conditions; the higher the percentage of dwellings requiring repairs in an area, the higher its risk of falls in older people. Bayesian spatial modelling accounts for measurement errors and unobserved or unknown risk factors that have spatial patterns. The findings have the potential to contribute to future research in reducing falls in older people and generate more interest in using Bayesian spatial modelling approaches in injury and public health research.
BACKGROUND: Analysing the geographical variation of cancer incidence is an important issue in epidemiological research. It might suggest new aetiologic hypotheses, provide guidelines for the design of new surveys and give ideas for preventive campaigns. METHODS: Four different methods for estimating the variation of cancer risks between small areas and three homogeneity tests were evaluated by simulation. In three of the methods the systematic variation of the relative risks (RR) was estimated by subtracting the expected Poisson variation from the total variation. The last method assumes that RR are gamma distributed and the maximum likelihood estimate (MLH) of the systematic variation parameter is calculated. A likelihood ratio test (LRT) of heterogeneity of RR based on this method is also evaluated, and compared with an ordinary chi2 test and the Potthoff and Whittinghill test (P&W). RESULTS: For most of the simulated data-sets, the estimates obtained by MLH are most precise, even if the assumption of gamma distribution of RR is violated. The LRT and P&W tests of homogeneity are also shown to perform better than the chi2 test. Most of the real cancer data-sets exhibited at least some geographical variation. Cancer of the lung, melanoma and other skin cancers, and cancers of the urinary bladder, pancreas and stomach, have the highest systematic variation. DISCUSSION: The study suggests that likelihood-based approaches are suitable, both for estimating the variation between small areas and for testing the null hypothesis of equal RR. Such geographical analyses might suggest new aetiological hypothesis.