[Is cancer incidence different between type 2 diabetes patients compared to non-diabetics in hemodialysis? A study from the REIN registry]. / Patients en hémodialyse : l'incidence de cancer est-elle différente entre les diabétiques de type 2 comparés aux non-diabétiques ? Une étude du registre REIN.

BACKGROUND AND OBJECTIVES: In France, diabetes mellitus is now the second cause of end stage renal disease. In a large previous French national study, we observed that dialyzed diabetics have a significant lower risk of death by cancer. This first study was focused on cancer death but did not investigate cancer incidence. In this context, the aim of this second study was to compare the incidence of cancer in diabetic dialyzed patients compared to non-diabetic dialyzed patients in a French region. METHODS: This epidemiologic multicentric study included 588 diabetic and non-diabetic patients starting hemodialysis between 2002 and 2007 in Bretagne. Data were issued from REIN registry and cancer incidence were individually collected from medical records. Diabetics and non-diabetics were matched one by one on age, sex and year of dialysis initiation. RESULTS: During the follow-up, we observed 28 cancers (9.4%) in diabetic patients and 26 cancers (8.9%) in non-diabetics patients. The cumulative incidence to develop a cancer 2 years after the dialysis start was approximately 6% in both diabetics and non-diabetics patients. In univariate Fine and Gray analysis, BMI, hemoglobin, statin use had P-value<0.2. However, in the adjusted model, these variables were not significantly associated with cancer incidence. CONCLUSION: This study lead on a little number of dialyzed patients did not show any significant difference on cancer incidence between diabetic and non-diabetic patients after hemodialysis start.

Does the edge effect impact on the measure of spatial accessibility to healthcare providers?

BACKGROUND: Spatial accessibility indices are increasingly applied when investigating inequalities in health. Although most studies are making mentions of potential errors caused by the edge effect, many acknowledge having neglected to consider this concern by establishing spatial analyses within a finite region, settling for hypothesizing that accessibility to facilities will be under-reported. Our study seeks to assess the effect of edge on the accuracy of defining healthcare provider access by comparing healthcare provider accessibility accounting or not for the edge effect, in a real-world application. METHODS: This study was carried out in the department of Nord, France. The statistical unit we use is the French census block known as 'IRIS' (Ilot Regroupé pour l'Information Statistique), defined by the National Institute of Statistics and Economic Studies. The geographical accessibility indicator used is the "Index of Spatial Accessibility" (ISA), based on the E2SFCA algorithm. We calculated ISA for the pregnant women population by selecting three types of healthcare providers: general practitioners, gynecologists and midwives. We compared ISA variation when accounting or not edge effect in urban and rural zones. The GIS method was then employed to determine global and local autocorrelation. Lastly, we compared the relationship between socioeconomic distress index and ISA, when accounting or not for the edge effect, to fully evaluate its impact. RESULTS: The results revealed that on average ISA when offer and demand beyond the boundary were included is slightly below ISA when not accounting for the edge effect, and we found that the IRIS value was more likely to deteriorate than improve. Moreover, edge effect impact can vary widely by health provider type. There is greater variability within the rural IRIS group than within the urban IRIS group. We found a positive correlation between socioeconomic distress variables and composite ISA. Spatial analysis results (such as Moran's spatial autocorrelation index and local indicators of spatial autocorrelation) are not really impacted. CONCLUSION: Our research has revealed minor accessibility variation when edge effect has been considered in a French context. No general statement can be set up because intensity of impact varies according to healthcare provider type, territorial organization and methodology used to measure the accessibility to healthcare. Additional researches are required in order to distinguish what findings are specific to a territory and others common to different countries. It constitute a promising direction to determine more precisely healthcare shortage areas and then to fight against social health inequalities.

The need for, and value of, a spatial scan statistical tool for tackling social health inequalities.

Today, one important challenge in developed countries is health inequalities. Research conducted in public health policy issues supply little evidence for effective interventions aiming to improve population health and to reduce health inequalities. There is a need for a powerful tool to support priority setting and guide policy makers in their choice of health interventions, and that maximizes social welfare. This paper proposes to divert a spatial tool based on Kulldorff's scan method to investigate social inequalities in health. This commentary argues that this spatial approach can be a useful tool to tackle social inequalities in health by guiding policy makers at three levels: (i) supporting priority setting and planning a targeted intervention; (ii) choosing actions or interventions which will be performed for the whole population, but with a scale and intensity proportionate to need; and (iii) assessing health equity of public interventions.

Neighborhood Deprivation and Risk of Congenital Heart Defects, Neural Tube Defects and Orofacial Clefts: A Systematic Review and Meta-Analysis.

BACKGROUND: We conducted this systematic review and meta-analysis to address the open question of a possible association between the socioeconomic level of the neighborhoods in which pregnant women live and the risk of Congenital Heart Defects (CHDs), Neural Tube Defects (NTDs) and OroFacial Clefts (OFCs). METHODS: We searched MEDLINE from its inception to December 20th, 2015 for case-control, cohort and ecological studies assessing the association between neighborhood socioeconomic level and the risk of CHDs, NTDs and the specific phenotypes Cleft Lip with or without Cleft Palate (CLP) and Cleft Palate (CP). Study-specific risk estimates were pooled according to random-effect and fixed-effect models. RESULTS: Out of 245 references, a total of seven case-control studies, two cohort studies and two ecological studies were assessed in the systematic review; all studies were enrolled in the meta-analysis with the exception of the two cohort studies. No significant association has been revealed between CHDs or NTDs and neighborhood deprivation index. For CLP phenotype subgroups, we found a significantly higher rate in deprived neighborhoods (Odds Ratios (OR) = 1.22, 95% CI: 1.10, 1.36) whereas this was not significant for CP phenotype subgroups (OR = 1.20, 95%CI: 0.89, 1.61). CONCLUSION: In spite of the small number of epidemiological studies included in the present literature review, our findings suggest that neighborhood socioeconomic level where mothers live is associated only with an increased risk of CLP phenotype subgroups. This finding has methodological limitations that impede the formulation of firm conclusions, and further investigations should confirm this association.

Assessment of the spatial accessibility to health professionals at French census block level.

BACKGROUND: The evaluation of geographical healthcare accessibility in residential areas provides crucial information to public policy. Traditional methods - such as Physician Population Ratios (PPR) or shortest travel time - offer only a one-dimensional view of accessibility. This paper developed an improved indicator: the Index of Spatial Accessibility (ISA) to measure geographical healthcare accessibility at the smallest available infra-urban level, that is, the Îlot Regroupé pour des Indicateurs Statistiques. METHODS: This study was carried out in the department of Nord, France. Healthcare professionals are geolocalized using postal addresses available on the French state health insurance website. ISA is derived from an Enhanced Two-Step Floating Catchment Area (E2FCA). We have constructed a catchment for each healthcare provider, by taking into account residential building centroids, car travel time as calculated by Google Maps and the edge effect. Principal Component Analyses (PCA) were used to build a composite ISA to describe the global accessibility of different kinds of health professionals. RESULTS: We applied our method to studying geographical healthcare accessibility for pregnant women, by selecting three types of healthcare provider: general practitioners, gynecologists and midwives. A total of 3587 healthcare providers are potentially able to provide care for inhabitants of the department of Nord. On average there are 92 general practitioners, 22 midwives and 21 gynecologists per 100,000 residents. The composite ISA for the three types of healthcare provider is 39 per 100,000 residents. A comparative analysis between ISA and physician-population ratios indicates that ISA represents a more even distribution whereas the physician-population ratios show an 'all-or-nothing' approach. CONCLUSION: ISA is a multidimensional and improved measure, which combines the volume of services relative to population size with the proximity of services relative to the population's location, available at the smallest feasible geographical scale. It could guide policy makers towards highlighting critical areas in need of more healthcare providers, and these areas should be earmarked for further knowledge-based policy making.

Neighbourhood Characteristics and Long-Term Air Pollution Levels Modify the Association between the Short-Term Nitrogen Dioxide Concentrations and All-Cause Mortality in Paris.

BACKGROUND: While a great number of papers have been published on the short-term effects of air pollution on mortality, few have tried to assess whether this association varies according to the neighbourhood socioeconomic level and long-term ambient air concentrations measured at the place of residence. We explored the effect modification of 1) socioeconomic status, 2) long-term NO2 ambient air concentrations, and 3) both combined, on the association between short-term exposure to NO2 and all-cause mortality in Paris (France). METHODS: A time-stratified case-crossover analysis was performed to evaluate the effect of short-term NO2 variations on mortality, based on 79,107 deaths having occurred among subjects aged over 35 years, from 2004 to 2009, in the city of Paris. Simple and double interactions were statistically tested in order to analyse effect modification by neighbourhood characteristics on the association between mortality and short-term NO2 exposure. The data was estimated at the census block scale (n=866). RESULTS: The mean of the NO2 concentrations during the five days prior to deaths were associated with an increased risk of all-cause mortality: overall Excess Risk (ER) was 0.94% (95%CI=[0.08;1.80]. A higher risk was revealed for subjects living in the most deprived census blocks in comparison with higher socioeconomic level areas (ER=3.14% (95%CI=[1.41-4.90], p<0.001). Among these deprived census blocks, excess risk was even higher where long-term average NO2 concentrations were above 55.8 µg/m3 (the top tercile of distribution): ER=4.84% (95%CI=[1.56;8.24], p for interaction=0.02). CONCLUSION: Our results show that people living in census blocks characterized by low socioeconomic status are more vulnerable to air pollution episodes. There is also an indication that people living in these disadvantaged census blocks might experience even higher risk following short-term air pollution episodes, when they are also chronically exposed to higher NO2 levels.

Data analysis techniques: a tool for cumulative exposure assessment.

Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise, etc.or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare. We propose to use statistical factor and cluster analyses to create a composite exposure index with a data-driven approach, in view to assess the environmental burden experienced by populations. We illustrate this approach in a large French metropolitan area. The study was carried out in the Great Lyon area (France, 1.2 M inhabitants) at the census Block Group (BG) scale. We used as environmental indicators ambient air NO2 annual concentrations, noise levels and proximity to green spaces, to industrial plants, to polluted sites and to road traffic. They were synthesized using Multiple Factor Analysis (MFA), a data-driven technique without a priori modeling, followed by a Hierarchical Clustering to create BG classes. The first components of the MFA explained, respectively, 30, 14, 11 and 9% of the total variance. Clustering in five classes group: (1) a particular type of large BGs without population; (2) BGs of green residential areas, with less negative exposures than average; (3) BGs of residential areas near midtown; (4) BGs close to industries; and (5) midtown urban BGs, with higher negative exposures than average and less green spaces. Other numbers of classes were tested in order to assess a variety of clustering. We present an approach using statistical factor and cluster analyses techniques, which seem overlooked to assess cumulative exposure in complex environmental settings. Although it cannot be applied directly for risk or health effect assessment, the resulting index can help to identify hot spots of cumulative exposure, to prioritize urban policies or to compare the environmental burden across study areas in an epidemiological framework.

Traffic-related air pollution and the onset of myocardial infarction: disclosing benzene as a trigger? A small-area case-crossover study.

BACKGROUND AND OBJECTIVES: Exposure to traffic is an established risk factor for the triggering of myocardial infarction (MI). Particulate matter, mainly emitted by diesel vehicles, appears to be the most important stressor. However, the possible influence of benzene from gasoline-fueled cars has not been explored so far. METHODS AND RESULTS: We conducted a case-crossover study from 2,134 MI cases recorded by the local Coronary Heart Disease Registry (2000-2007) in the Strasbourg Metropolitan Area (France). Available individual data were age, gender, previous history of ischemic heart disease and address of residence at the time of the event. Nitrogen dioxide, particles of median aerodynamic diameter <10 µm (PM10), ozone, carbon monoxide and benzene air concentrations were modeled on an hourly basis at the census block level over the study period using the deterministic ADMS-Urban air dispersion model. Model input data were emissions inventories, background pollution measurements, and meteorological data. We have found a positive, statistically significant association between concentrations of benzene and the onset of MI: per cent increase in risk for a 1 µg/m3 increase in benzene concentration in the previous 0, 0-1 and 1 day was 10.4 (95% confidence interval 3-18.2), 10.7 (2.7-19.2) and 7.2 (0.3-14.5), respectively. The associations between the other pollutants and outcome were much lower and in accordance with the literature. CONCLUSION: We have observed that benzene in ambient air is strongly associated with the triggering of MI. This novel finding needs confirmation. If so, this would mean that not only diesel vehicles, the main particulate matter emitters, but also gasoline-fueled cars--main benzene emitters-, should be taken into account for public health action.

Gender differences in the association between socioeconomic status and subclinical atherosclerosis.

OBJECTIVES: This study explored the pattern of associations between socioeconomic status (SES) and atherosclerosis progression (as indicated by carotid intima media thickness, CIMT) across gender. DESIGN: Cross-sectional analysis of a sample of 5474 older persons (mean age 73 years) recruited between 1999 and 2001 in the 3C study (France). We fitted linear regression models including neighborhood SES, individual SES and cardiovascular risk factors. RESULTS: CIMT was on average 24 µm higher in men (95% CI: 17 to 31). Neighborhood SES was inversely associated with CIMT in women only (highest versus lowest tertiles: -12.2 µm, 95%CI -22 to -2.4). This association persisted when individual SES and risk factors were accounted for. High individual education was associated with lower CIMT in men (-21.4 µm 95%CI -37.5 to -5.3) whereas high professional status was linked to lower CIMT among women (-15.7 µm 95%CI: -29.2 to -2.2). Adjustment for cardiovascular risk factors resulted in a slightly more pronounced reduction of the individual SES-CIMT association observed in men than in women. CONCLUSION: In this sample, neighborhood and individual SES displayed different patterns of associations with subclinical atherosclerosis across gender. This suggests that the causal pathways leading to SES variations in atherosclerosis may differ among men and women.

A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis.

INTRODUCTION: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. METHODS: The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. RESULTS: Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. CONCLUSIONS: The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.