Area-based Socioeconomic Position Research Articles

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study.

Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. Socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women. We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities. Five SEP sub-groups were identified and labelled 'Highest SEP' (48% WE and 52% SA), 'High-Medium SEP' (77% and 23%), 'Medium SEP' (56% and 44%) 'Low-Medium SEP' (21% and 79%), and 'Lowest SEP' (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for 'Medium SEP' and 0.25SD (0.18 to 0.32) for 'Lowest SEP'. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for 'Medium SEP', and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for 'Lowest SEP'. We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.

Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985\u20132015

BackgroundThe aetiology of most childhood cancers is largely unknown. Spatially varying environmental factors such as traffic-related air pollution, background radiation and agricultural pesticides might contribute to the development of childhood cancer. This study is the first investigation of the spatial disease mapping of childhood cancers using exact geocodes of place of residence.MethodsWe included 5947 children diagnosed with cancer in Switzerland during 1985–2015 at 0–15 years of age from the Swiss Childhood Cancer Registry. We modelled cancer risk using log-Gaussian Cox processes and indirect standardisation to adjust for age and year of diagnosis. We examined whether the spatial variation of risk can be explained by modelled ambient air concentration of NO2, modelled exposure to background ionising radiation, area-based socio-economic position (SEP), linguistic region, duration in years of general cancer registration in the canton or degree of urbanisation.ResultsFor all childhood cancers combined, the posterior median relative risk (RR), compared to the national level, varied by location from 0.83 to 1.13 (min to max). Corresponding ranges were 0.96 to 1.09 for leukaemia, 0.90 to 1.13 for lymphoma, and 0.82 to 1.23 for central nervous system (CNS) tumours. The covariates considered explained 72% of the observed spatial variation for all cancers, 81% for leukaemia, 82% for lymphoma and 64% for CNS tumours. There was weak evidence of an association of CNS tumour incidence with modelled exposure to background ionising radiation (RR per SD difference 1.17; 0.98–1.40) and with SEP (1.6; 1.00–1.13).ConclusionOf the investigated diagnostic groups, childhood CNS tumours showed the largest spatial variation. The selected covariates only partially explained the observed variation of CNS tumours suggesting that other environmental factors also play a role.

Spatial Association of Food Sales in Supermarkets with the Mean BMI of Young Men: An Ecological Study.

Supermarket food sales data might serve as a simple indicator of population-level dietary habits that influence the prevalence of excess weight in local environments. To test this possibility, we investigated how variation in store-level food sales composition across Switzerland is associated with the mean Body Mass Index (BMI) of young men (Swiss Army conscripts) living near the stores. We obtained data on annual food sales (2011) for 553 stores from the largest supermarket chain in Switzerland, identified foods commonly regarded as “healthy” or “unhealthy” based on nutrient content, and determined their contribution to each store’s total sales (Swiss francs). We found that the sales percentages of both “healthy” and “unhealthy” food types varied by 2- to 3-fold among stores. Their balance ranged from −15.3% to 18.0% of total sales; it was positively associated with area-based socioeconomic position (r = 0.63) and negatively associated with the mean BMI of young men in the area (r = −0.42). Thus, even though we compared supermarkets from a single chain, different shopping behaviors of customers caused stores in privileged areas to sell relatively more healthy food. Knowledge about such patterns could help in designing in-store interventions for healthier nutrition and monitoring their effects over time.

Long-term exposure to air pollutants from multiple sources and mortality in an industrial area: a cohort study

Background and aimsResidents near industrial areas are exposed to several toxins from various sources and the assessment of the health effects is difficult. The area of Civitavecchia (Italy) has several...

Trends in Socioeconomic Inequalities in Ischemic Heart Disease, 2000-2012

IntroductionLow socioeconomic position (SEP) is an important risk factor for ischemic heart disease (IHD). Current surveillance methods use area-based SEP measures to monitor trends in socioeconomic inequalities in IHD. The extent to which these methods underestimate individual-level socioeconomic inequalities in IHD is unclear.
 Objectives and ApproachThe study objective was to estimate socioeconomic trends in IHD by household income and material deprivation in Ontario from 2000 to 2012. A pooled cross-sectional study was conducted using data from 6 Canadian Community Health Survey (CCHS) cycles (2000-2012) linked to the Discharge Abstract Database (n= 119,529 over 35 years of age, 55% female). Relative-weighted Poisson regression models were used to estimate IHD prevalence rates (adjusted for age, sex, ethnicity and immigration) across quintiles of equivalized household income and area-level material deprivation. Socioeconomic inequalities were estimated using the slope index of inequality (SII) and relative index of inequality (RII).
 ResultsSocioeconomic inequalities in IHD were observed across income and material deprivation quintiles. Measured using the SII, adjusted IHD rates were 345 per 10,000 (95%CI: 207,483) higher at the bottom of the income distribution than the top in 2000, decreasing to 167 per 10,000 (95%CI: 40,293) by 2012. These differences represented 2.52 (95%CI: 1.58,3.46) times higher IHD rates in 2000, an increased risk that remained in 2012 (RII: 1.80, 95%CI: 0.97,2.63). A similar pattern was observed across material deprivation quintiles, however with smaller absolute and relative inequalities observed in 2000 (SII:195 per 10,000, 95%CI:79,312; RII:1.64, 95%CI:1.16,2.11) and 2012 (SII:142 per 10,000, 95%CI:16,268; RII:1.54, 95%CI:0.94,2.14).
 Conclusion/ImplicationsConsistent socioeconomic inequalities in IHD were observed in Ontario, with an absolute reduction between 2000 and 2012. Area-level material deprivation underestimated individual-level socioeconomic inequalities in IHD.

Self-selection in a population-based cohort study: impact on health service use and survival for bowel and lung cancer assessed using data linkage

BackgroundIn contrast to aetiological associations, there is little empirical evidence for generalising health service use associations from cohort studies. We compared the health service use of cohort study participants diagnosed with bowel or lung cancer to the source population of people diagnosed with these cancers in New South Wales (NSW), Australia to assess the representativeness of health service use of the cohort study participants.MethodsPopulation-based cancer registry data for NSW residents aged ≥45 years at diagnosis of bowel or lung cancer were linked to the 45 and Up Study, a NSW population-based cohort study (N~ 267,000). We measured hospitalisation, emergency department (ED) attendance and all-cause survival, and risk factor associations with these outcomes using administrative data for cohort study participants and the source population. We assessed bias in prevalence and risk factor associations using ratios of relative frequency (RRF) and relative odds ratios (ROR), respectively.ResultsPeople from major cities, non-English speaking countries and with comorbidites were under-represented among cohort study participants diagnosed with bowel (n = 1837) or lung (n = 969) cancer by 20–50%. Cohort study participants had similar hospitalisation and ED attendance compared with the source population. One-year survival after major surgical resection was similar, but cohort study participants had up to 25% higher post-diagnosis survival (lung cancer 3-year survival: RRF = 1.24, 95% confidence interval 1.12,1.37). Except for area-based socioeconomic position, risk factors associations with health service use measures and survival appeared relatively unbiased.ConclusionsAbsolute measures of health service use and risk factor associations in a non-representative sample showed little evidence of bias. Non-comparability of risk factor measures of cohort study participants and non-participants, such as area-based socioeconomic position, may bias estimates of risk factor associations. Primary and outpatient care outcomes may be more vulnerable to bias.

Ability of ecological deprivation indices to measure social inequalities in a French cohort

BackgroundDespite the increasing interest in place effect to explain health inequalities, there is currently no consensus on which kind of area-based socioeconomic measures researchers should use to assess neighborhood socioeconomic position (SEP). The study aimed to evaluate the reliability of different area-based deprivation indices (DIs) in capturing socioeconomic residential conditions of French elderly women cohort.MethodsWe assessed area-based SEP using 3 DIs: Townsend Index, French European Deprivation Index (FEDI) and French Deprivation index (FDep), among women from E3N (Etude épidémiologique auprès des femmes de la Mutuelle Générale de l’Education Nationale). DIs were derived from the 2009 French census at IRIS level (smallest geographical units in France). Educational level was used to evaluate individual-SEP. To evaluate external validity of the 3 DIs, associations between two well-established socially patterned outcomes among French elderly women (smoking and overweight) and SEP, were compared. Odd ratios were computed with generalized estimating equations to control for clustering effects from participants within the same IRIS.ResultsThe analysis was performed among 63,888 women (aged 64, 47% ever smokers and 30% overweight). Substantial agreement was observed between the two French DIs (Kappa coefficient = 0.61) and between Townsend and FEDI (0.74) and fair agreement between Townsend and FDep (0.21). As expected among French elderly women, those with lower educational level were significantly less prone to be ever smoker (Low vs. High; OR [95% CI] = 0.43 [0.40–0.46]) and more prone to being overweight (1.89 [1.77–2.01]) than women higher educated. FDep showed expected associations at area-level for both smoking (most deprived vs. least deprived quintile; 0.77 [0.73–0.81]) and overweight (1.52 [1.44–1.62]). For FEDI opposite associations with smoking (1.13 [1.07–1.19]) and expected association with overweight (1.20 [1.13–1.28]) were observed. Townsend showed opposite associations to those expected for both smoking and overweight (1.51 [1.43–1.59]; 0.93 [0.88–0.99], respectively).ConclusionFDep seemed reliable to capture socioeconomic residential conditions of the E3N women, more educated in average than general French population. Results varied strongly according to the DI with unexpected results for some of them, which suggested the importance to test external validity before studying social disparities in health in specific populations.

A tall order: Small area mapping and modelling of adult height among Swiss male conscripts

Adult height reflects an individual’s socio-economic background and offers insights into the well-being of populations. Height is linked to various health outcomes such as morbidity and mortality and has consequences on the societal level. The aim of this study was to describe small-area variation of height and associated factors among young men in Switzerland.Data from 175,916 conscripts (aged between 18.50 and 20.50 years) was collected between 2005 and 2011, which represented approximately 90% of the corresponding birth cohorts. These were analysed using Gaussian hierarchical models in a Bayesian framework to investigate the spatial pattern of mean height across postcodes. The models varied both in random effects and degree of adjustment (professional status, area-based socioeconomic position, and language region).We found a strong spatial structure for mean height across postcodes. The range of height differences between mean postcode level estimates was 3.40cm according to the best fitting model, with the shorter conscripts coming from the Italian and French speaking parts of Switzerland. There were positive socioeconomic gradients in height at both individual and area-based levels. Spatial patterns for height persisted after adjustment for individual factors, but not when language region was included. Socio-economic position and cultural/natural boundaries such as language borders and mountain passes are shaping patterns of height for Swiss conscripts. Small area mapping of height contributes to the understanding of its cofactors.

Long-term Exposure to Air Pollution from Power Plants and Mortality in a Cohort of People Living in an Industrial Area of Southern Italy

Introduction The area around Brindisi (Apulia region) is characterized by three thermoelectric power plants established in different time periods (1961, 1969, and 1991), two of which remain active. These plants vary in terms of combustible material (oil and coal) and pollution control technology. The study aimed to evaluate the effects of emissions from the power plants on mortality in a cohort of people living in the area, taking into account the effect of socioeconomic position (SEP) and other contextual variables. Methods A cohort of residents from 2000 to 2013 were enrolled from Municipal Registers and followed until 2013. Individual data were linked with the regional mortality register. Census data from 2001 at census block level were used to assess an area-based SEP index. Exposure to power plants emissions in 1998 (PM10 as tracer) at each residential address was estimated using Lagrangian dispersion models. Cox mortality Hazard Ratios were calculated including a linear term for industrial PM10 exposure (95th-5th percentiles difference), age, gender, calendar period, and SEP (HRs, 95%CI). Results A total of 223,934 people were enrolled (48% male, 40% low SEP). The annual average population exposure levels [mean (SD, 50th percentile, 95th-5th percentile)] of industrial PM10 were 0.30 µg/m3 (0.25, 0.33, 0.62). For increases of PM10 equal to the difference between 95th-5th percentile, we found increasing risks for cancer mortality (HR 1.08, 95%CI 1.04-1.13), pancreatic (HR 1.11, 95%CI 1.01-1.23) and bladder cancers (HR 1.16, 95%CI 1.04-1.30), respiratory disease (HR 1.12, 95%CI 1.03-1.22), and acute coronary events (HR 1.11, 95%CI 1.00-1.23). Conclusions Industrial pollution from power plants was associated with cohort mortality of the people resident in the area. The effect estimates are large although there are uncertainties in the retrospective estimation of the absolute concentrations of industrial pollutants.

Finding big shots: small-area mapping and spatial modelling of obesity among Swiss male conscripts.

BackgroundIn Switzerland, as in other developed countries, the prevalence of overweight and obesity has increased substantially since the early 1990s. Most of the analyses so far have been based on sporadic surveys or self-reported data and did not offer potential for small-area analyses. The goal of this study was to investigate spatial variation and determinants of obesity among young Swiss men using recent conscription data.MethodsA complete, anonymized dataset of conscription records for the 2010–2012 period were provided by Swiss Armed Forces. We used a series of Bayesian hierarchical logistic regression models to investigate the spatial pattern of obesity across 3,187 postcodes, varying them by type of random effects (spatially unstructured and structured), level of adjustment by individual (age and professional status) and area-based [urbanicity and index of socio-economic position (SEP)] characteristics.ResultsThe analysed dataset consisted of 100,919 conscripts, out of which 5,892 (5.8 %) were obese. Crude obesity prevalence increased with age among conscripts of lower individual and area-based SEP and varied greatly over postcodes. Best model’s estimates of adjusted odds ratios of obesity on postcode level ranged from 0.61 to 1.93 and showed a strong spatial pattern of obesity risk across the country. Odds ratios above 1 concentrated in central and north Switzerland. Smaller pockets of elevated obesity risk also emerged around cities of Geneva, Fribourg and Lausanne. Lower estimates were observed in North-East and East as well as south of the Alps. Importantly, small regional outliers were observed and patterning did not follow administrative boundaries. Similarly as with crude obesity prevalence, the best fitting model confirmed increasing risk of obesity with age and among conscripts of lower professional status. The risk decreased with higher area-based SEP and, to a lesser degree – in rural areas.ConclusionIn Switzerland, there is a substantial spatial variation in obesity risk among young Swiss men. Small-area estimates of obesity risk derived from conscripts records contribute to its understanding and could be used to design further studies and interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s40608-016-0092-6) contains supplementary material, which is available to authorized users.

Socioeconomic Position and Premature Mortality in the AusDiab Cohort of Australian Adults.

To determine the association of socioeconomic position indicators with mortality, without and with adjustment for modifiable risk factors. We examined the relationships of 2 area-based indices and educational level with mortality among 9338 people (including 8094 younger than 70 years at baseline) of the Australian Diabetes Obesity and Lifestyle (AusDiab) from 1999-2000 until November 30, 2012. Age- and gender-adjusted premature mortality (death before age 70 years) was more likely among those living in the most disadvantaged areas versus least disadvantaged (hazard ratio [HR] = 1.48; 95% confidence interval [CI] = 1.08, 2.01), living in inner regional versus major urban areas (HR = 1.36; 95% CI = 1.07, 1.73), or having the lowest educational level versus the highest (HR = 1.64; 95% CI = 1.17, 2.30). The contribution of modifiable risk factors (smoking status, diet quality, physical activity, stress, cardiovascular risk factors) in the relationship between 1 area-based index or educational level and mortality was more apparent as age of death decreased. The relation of area-based socioeconomic position to premature mortality is partly mediated by behavioral and cardiovascular risk factors. Such results could influence public health policies.

Long-Term Air Pollution Exposure and Heart Failure in the Rome Longitudinal Study

Background Some studies have shown an association between acute air pollution exposure and aggravation of heart failure, but the role of long-term exposure has been less investigated. The objective of this study was to analyze the association between long- term exposure to fine particles (PM2.5) and nitrogen dioxide (NO2) with first hospitalization for heart failure in Rome, Italy. Methods The Rome Longitudinal Study includes 1,265,058 adults enrolled in 2001 and followed for 9 years. Average residential exposure to NO2 (by land use regression model) and PM2.5 (by Chemical Transport dispersion model) for the 5 years before the inclusion were available. We used Cox regression to evaluate the association between pollutants and first hospitalization for heart failure or fatal heart failure (deaths within 28 days from the hospital admission), adjusting for several factors. We used age as time scale, stratified the models by sex, and adjusted for level of education, marital status, place of birth, occupation, area-based socioeconomic position, comorbidity, including previous acute coronary events. Results During the follow-up, among the 1,252,880 subjects never admitted to hospital for heart failure at baseline, 37,200 had a hospital admission for the condition under study (and 17.8% died within 28 days). There was a 2% increased risk in being hospitalized for heart failure each 10ug/m3 increase in PM2.5 exposure during 1996-2000 (HR=1.02, 95%CI: 1.00-1.05), while the NO2 effect was lower (HR=1.01, 95%CI: 0.99-1.02 for 10ug/m3 increase in NO2). There was a strong association between long-term exposure to both pollutants and fatal events: HR=1.05 (95%CI: 1.02-1.09) for 10ug/m3 increase in NO2, and HR=1.16 (95%CI: 1.09-1.23) for 10ug/m3 increase in PM2.5 average exposure in the 5 years before the inclusion. Conclusions We found evidence of a link between long-term exposure to air pollution and first hospital admission for heart failure, in particular with fatal events.

Socioeconomic, Temporal and Regional Variation in Body Mass Index among 188,537 Swiss Male Conscripts Born between 1986 and 1992

BackgroundRising levels of overweight and obesity are important public-health concerns worldwide. The purpose of this study is to elucidate their prevalence and trends in Switzerland by analyzing variations in Body Mass Index (BMI) of Swiss conscripts.MethodsThe conscription records were provided by the Swiss Army. This study focussed on conscripts 18.5–20.5 years of age from the seven one-year birth cohorts spanning the period 1986–1992. BMI across professional status, area-based socioeconomic position (abSEP), urbanicity and regions was analyzed. Two piecewise quantile regression models with linear splines for three birth-cohort groups were used to examine the association of median BMI with explanatory variables and to determine the extent to which BMI has varied over time.ResultsThe study population consisted of 188,537 individuals. Median BMI was 22.51 kg/m2 (22.45–22.57 95% confidence interval (CI)). BMI was lower among conscripts of high professional status (−0.46 kg/m2; 95% CI: −0.50, −0.42, compared with low), living in areas of high abSEP (−0.11 kg/m2; 95% CI: −0.16, −0.07 compared to medium) and from urban communities (−0.07 kg/m2; 95% CI: −0.11, −0.03, compared with peri-urban). Comparing with Midland, median BMI was highest in the North-West (0.25 kg/m2; 95% CI: 0.19–0.30) and Central regions (0.11 kg/m2; 95% CI: 0.05–0.16) and lowest in the East (−0.19 kg/m2; 95% CI: −0.24, −0.14) and Lake Geneva regions (−0.15 kg/m2; 95% CI: −0.20, −0.09). Trajectories of regional BMI growth varied across birth cohorts, with median BMI remaining high in the Central and North-West regions, whereas stabilization and in some cases a decline were observed elsewhere.ConclusionsBMI of Swiss conscripts is associated with individual and abSEP and urbanicity. Results show regional variation in the levels and temporal trajectories of BMI growth and signal their possible slowdown among recent birth cohorts.

Influence of spatial heterogeneity in socioeconomic deprivation on birth outcomes and air pollution epidemiology in New York City, USA

Background: Numerous studies have linked area-level socioeconomic deprivation with adverse birth outcomes. Deprivation is often operationalized as a composite index, which efficiently combines diverse components of socioeconomic position (SEP), but also may obscure spatial heterogeneity in variable distributions, potentially biasing epidemiological models. Aims: We aimed to build (and test) a deprivation index reflecting spatial heterogeneity in New York City (NYC), to include with fine-scale air pollution in birth outcomes models. Methods: We explored intra-urban spatial variability across contextual SEP factors previously associated with birth outcomes (n=20). We applied spatially-stratified Principle Components Analysis (borough-level) to reflect spatially-varying prevalence and combinations of SEP factors in a composite deprivation score. For comparison, we calculated a crude citywide index following the same data reduction process. Results: The spatial deprivation index retained seven area-based SEP variables (college degree, unemployment, management/professional occupation, residential crowding, poverty, households receiving public assistance, non-white racial composition), explaining 55% of overall variance in SEP factors. The crude index included fewer and somewhat different SEP variables, explaining 41% of overall variance, and correlated with the spatial index at r=0.70. Residential outdoor PM2.5 exposure was differentially correlated with the spatial (r=-0.11) and crude (r=0.02) indices. After adjusting for individual covariates and exposure to PM2.5, spatial and crude deprivation indices were associated with 5.32g and 6.56g decrements in birth weight per IQR change in SEP index – small changes, yet within the range of concern for air pollution effects on birth outcomes. Conclusions: Spatially-informed data reduction techniques can improve estimation of area-level deprivation, and elucidate potential confounding and bias in epidemiological analyses.

Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data.

BackgroundGlioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveillance Epidemiology and End Results (SEER) data.MethodsCases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence.ResultsThose residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit.ConclusionDemographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship.

Using a Socioeconomic Position Index to Assess Disparities in Cancer Incidence and Mortality, Puerto Rico, 1995-2004

Evaluation of the extent of socioeconomic inequalities in cancer incidence and mortality is essential to generate hypotheses in population health research and provides evidence for population-based strategies for comprehensive cancer control. The objective of this study was to create an area-based socioeconomic position (SEP) index to assess possible socioeconomic disparities in incidence and mortality of selected cancers in Puerto Rico. Data for cancer incidence and mortality from 1995 to 2004 were obtained from the Puerto Rico Central Cancer Registry and the Puerto Rico Department of Health, and Puerto Rico socioeconomic data were obtained from the US Census 2000. We used principal component and factor analysis methods to construct the SEP index at the municipality level. We calculated age-adjusted incidence and mortality for each SEP area and used rate ratios to evaluate the differences by SEP. Incidence and mortality of cancer in Puerto Rico varied by SEP area. In general, the incidence and mortality for cancers of the esophagus and stomach were higher for municipalities with the lowest SEP; in contrast, rates for breast, colorectal, kidney, pancreas, prostate, and thyroid were higher for areas with the highest SEP. These results highlight cancer disparities in Puerto Rico by SEP level that warrant further research.

LONG-TERM EXPOSURE TO AIR POLLUTION (PM 2.5 AND NO 2 ) AND MORTALITY: RESULTS OF THE ROME LONGITUDINAL STUDY (ROLS)

Background and aims: There are few longitudinal studies evaluating the role of chronic exposure to air pollutants within the same urban setting. We analyzed the association of long-term exposure to PM2.5 and NO2 with mortality in a large cohort. Methods: The Rome Longitudinal Study is a fixed cohort enrolled at the 2001 census. We selected subjects aged 45-80 years at the baseline who had not changed their address in the previous five years (n=684,204). Exposure at residence to PM2.5, was predicted by means of a chemical transport model (FARM) (1 km2 grid) using 2005 emissions data. Spatial levels of NO2 were derived from a specific land use regression model (R2=0.66). We had information on age, gender, education, occupation, place of birth, and area-based socioeconomic position at baseline. All subjects were followed till December 31, 2006. Cox regression models were used to study the association between the exposure and cause-specific mortality, taking several confounders into account. Results: During the study period, 45,006 natural deaths occurred (33% cardiovascular, 5% respiratory disorders, 3.3% diabetes, and 12% lung cancer). The average exposure was 23 (sd 4) μg/m3 for PM2.5 and 44 (8) μg/m3 for NO2. The correlation of the two exposure indicators was 0.75. With increased exposure of PM2.5 and NO2 there was an increased risk of dying for natural causes: 7% (95%CI:4%-9%) for 10μg/m3 increase of PM2.5, and 9% (95%CI:6%-13%) for 10μg/m3 increase of NO2. The relationships were linear with no evidence of a threshold. The strongest associations were found for cardiovascular diseases and diabetes. Conclusions: This is one of the largest European urban cohort study on PM2.5. The results indicate that long-term exposures to PM2.5, and NO2 at residence are associated with mortality in Rome and the effect estimates are very similar to what has been suggested in studies from the USA.

Socioeconomic position and health status of people who live near busy roads: the Rome Longitudinal Study (RoLS)

BackgroundSubjects living close to high traffic roads (HTR) are more likely to suffer from air-pollution related morbidity and mortality. The issue has large public health consequences but few studies have described the main socio-demographic characteristics of people exposed to traffic.ObjectivesTo characterise a large cohort of residents in Rome according to different measures of traffic exposure, socioeconomic position (SEP), and baseline health status.MethodsResidents of Rome in October 2001 were selected. Individual and area-based SEP indices were available. GIS was used to obtain traffic indicators at residential addresses: distance from HTR (> = 10,000 vehicles/day), length of HTR, average daily traffic count, and traffic density within 150 meters of home. Hospitalisations in the 5-year period before enrolment were used to characterise health status. Logistic and linear regression analyses estimated the association between traffic exposure and socio-demographic characteristics.ResultsWe selected 1,898,898 subjects with complete SEP information and GIS traffic indicators. A total of 320,913 individuals (17%) lived within 50 meters of an HTR, and 14% lived between 50 and 100 meters. These proportions were higher among 75+ year-old subjects. Overall, all traffic indicators were directly associated with SEP, with people living in high or medium SEP areas or with a university degree more likely to be exposed to traffic than people living in low SEP areas or with a low level of education. However, an effect modification by area of residence within the city was seen and the association between traffic and SEP was reversed in the city centre.ConclusionsA large section of the population is exposed to traffic in Rome. Elderly people and those living in areas of high and medium SEP tend to be more exposed. These findings are related to the historical stratification of the population within the city according to age and socioeconomic status.

Traffic Exposure and Mortality in Rome: Results of a Large Cohort Study

ISEE-0234 Background and Objective: There are few investigations on the long-term effects of traffic-related pollutants and mortality. We analyzed the association between different proxy measures of exposure to traffic air pollution and mortality in the Rome Longitudinal Study (RoLS). Methods: We selected all 684,204 city residents (October 21, 2001) aged 45-80 years who had not changed their address in the previous five years. Different GIS indicators were available for each subject's residence: distance from high traffic roads (HTR) (>10,000 vehicles per day), traffic density within 150m from home (number of vehicles * meters of street in the buffer of 150m from home/area of the buffer), meters and daily traffic count of HTR within 150m of home. Data on individual education and area-based socioeconomic position (SEP) were also available. We followed the participants until December 31, 2006. Cox regression models were used to study traffic indicators and mortality taking into account gender, age, education, and area-based SEP. Results: During the study period, 45,006 natural deaths occurred (mortality was 13.4% per year, 96% of all deaths). All GIS indicators had a statistically significant association (P for trend <0.03) with mortality. Subjects who lived fewer than 50m from a HTR had a higher risk of dying (HR = 1.05, 95%CI:1.02-1.08) than those more than 250m away. Traffic density showed the strongest association with an 8% (95%CI = 4-11%) higher risk for those in the greatest traffic density quartile than those with zero traffic density. The results were stronger for cardiovascular diseases and diabetes. No effect modification was seen for age, education and area based SEP. Conclusion: An association between traffic variables and mortality was detected, especially for cardiovascular diseases and diabetes.

Census-based and personally reported income measures as long-term risk-adjusted mortality predictors

Census-based and personally reported income measures as long-term risk-adjusted mortality predictors