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Spatial health inequalities have often been analyzed in terms of socioeconomic and environmental factors. The present study aimed to evaluate spatial relationships between spatial data collected at different spatial scales. The approach was illustrated using health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. Geographically weighted regression (GWR) was used to quantify spatial relationships. The strongest associations were found when low deprivation was associated with lower lip, oral cavity and pharynx cancer mortality and when low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas or to other causes of death or with other indicators requires continuous exploratory analysis to assess the role of the modifiable areal unit problem (MAUP) and downscaling the health data on the study of the relationship, which will allow decision-makers to develop interventions where they are most needed.
Analyzing the relationship between the environment and health has become a major issue for public health in France as forecasted by the national plans for health and environment (NPHE). Two priority areas were selected during the first NPHE: (1) preventing health risks related to the quality of resources and to chemicals and (2) developing environmental health through research, expertise, training and information. In 2009, the second NPHE was prepared from the perspective of the upcoming conference on health and the environment organized by the World Health Organization. Two main axes were prioritized: (1) identifying and managing geographic areas where hotspot exposures to substances present in air, soil, water, and foods resulting from anthropic activities suspected of generating potentially increasing risks to human health and (2) reducing environmental health inequalities. Thus, environmental health inequality has become a substantial topic that guides policy developments in France. To address this aim, there is an urgent need for tools that can quantify the spatial relationships between the environment, socioeconomics and health and that can highlight areas with strong inequalities.
Health inequalities are a quite recent study topic. Previous studies were essentially based, at an individual level, on specific surveys [
Regarding health data, there are strict privacy rules for individual-level health data that prohibit their public release. Aggregated data are only available at the geographic level, from which disclosure and reconstruction of patient identity are impossible. In France these census units could be regions or counties. This aggregation unfortunately results in incidence or mortality rates that can be unreliable over small and/or sparsely populated areas. This effect, known as the “small number problem” [
Several authors have already addressed the spatial relationships between health data and environmental data. One of the issues faced by spatial epidemiologists and for exposure assessment is the combination of data measured for very different spatial scales and with different levels of reliability. In reality, the analysis of cancer mortality maps is often hindered by the presence of noise caused by unreliable extreme rates computed from sparsely populated geographic units. A number of approaches have been developed to improve the reliability of risk estimates [
Poisson kriging, in this context, presents a spatial methodology that allows for filtering the noise caused by the small number problem and enables the estimation of mortality risk and the associated uncertainty at different spatial scales. This approach has been implemented to modeling cancer risk by a number of authors: Oliver
Selection of scale is perhaps the most important factor in creating and analyzing a relationship between environmental exposure and health outcomes [
The present study aims to evaluate spatial relationships at three levels of aggregation: the IRIS level, an intermediate scale (the grid level), and the county level between health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. The approach is illustrated using age-adjusted lip, oral cavity and pharynx, and pleural cancer mortality rates over the period 2000–2009 for the Picardy region. The deprivation index and trace metal exposure indicators are used as putative risk factors.
The region of Picardy covers an area of roughly 19,500 km^{2} and is located between the North Artois, the Ile-de-France in the south, the Bay of the Somme to the west and the East Champagne. It covers the departments of Somme, Oise and Aisne. The urbanization rate in this region is far below the national average (60.4%
The environmental indicators (inhalation and ingestion) used were those described in Caudeville
Map of the study area.
0202 Aubenton | 6002 Auneuil | 8002 Abbeville Sud |
0203 Bohain-en-Vermandois | 6004 Beauvais Sud-Ouest | 8003 Acheux-en-Amiénois |
0204 Braine | 6005 Betz | 8004 Ailly-le-Haut-Clocher |
0205 La Capelle | 6006 Breteuil | 8005 Ailly-ser-Noye |
0206 Le Catelet | 6007 Chaumont-en-Vexin | 8006 Albert |
0207 Charly | 6008 Clermont | 8007 Amiens Ouest |
0208 Château Thierry | 6009 Compiègne Nord | 8008 Amiens Nord-Ouest |
0209 Chauny | 6010 Le Coudray-Saint-Germer | 8009 Amiens-Nord-Est |
0210 Condé-en-Brie | 6011 Creil-Nogent-sur-Oise | 8010 Amiens-Est |
0211Coucy-le-Château Auffrique | 6012 Crépy-en-Valois | 8011 Ault |
0212 Craonne | 6013 Crèvecoeur-le-Grand | 8012 Bernaville |
0213 Crécy-sr-Serre | 6014 Estrées-Saint-Denis | 8013 Boves |
0214 La Fère | 6015 Formerie | 8014 Bray-sur-Somme |
0215 Fère-en-Tardenois | 6016 Froissy | 8015 Chaulnes |
0216 Guise | 6017 Grandvilliers | 8016 Combles |
0217 Hirson | 6018 Guiscard | 8017 Conty |
0218 Laon Nord | 6019 Lassigny | 8018 Corbie |
0219 Marle | 6020 Liancourt | 8019 Crécy-en-Ponthieu |
0220 Moy-de-l’Aisne | 6021 Maignelay-Montigny | 8020 Domart-en-Ponthieu |
0221 Neufchatel-sur-Aisne | 6022 Marseille-en-Beauvaisis | 8021 Doullens |
0222 Neuilly-Saint-Front | 6023 Méru | 8022 Gamaches |
0223 Le Nouvion-en-Thiérache | 6024 Mouy | 8023 Hallencourt |
0224 Oulchy-le-Château | 6025 Nanteuil-le-Haudoin | 8024 Ham |
0225 Ribemont | 2026 Neuilly-en-Thelle | 8025 Homoy-le-Bourg |
0226 Rozoy-sur-Serre | 2027 Nivillers | 8026 Molliens-Dreuil |
0227 Sains-Richaumont | 6028 Noailles | 8027 Montdidier |
0229 Saint-Simon | 6029 Noyon | 8028 Moreuil |
0230 Sissonne | 6030 Pont-Sainte-Maxence | 8029 Moyenneville |
0231 Soissons-Nord | 6031 Ressons-sur-Matz | 8030 Nesle |
0232 Vailly-sur-Aisne | 6032 Ribécourt-Dreslincourt | 8031 Nouvion |
0233 Vermand | 6033 Saint-Just-en-Chaussée | 8032 Oisemont |
0234 Vervins | 6034 Senlis | 8033 Péronne |
0235 Vic-sur-Aisne | 6035 Songeons | 8034 Picquigny |
0236 Villers-Cotterets | 6036 Chantilly | 8035 Poix-de-Picardie |
0237 Wassigny | 6037 Compiègne-Sud-Est | 8036 Roisel |
0238 Laon-Sud | 6039 Montataire | 8037 Rosières-en-Santerre |
0239 Saint-Quentin-Nord | 6040 Beauvais-Nord-Ouest | 8038 Roye |
0240 Saint-Quentin-Sud | 6041 Compiègne Sud-Oest | 8039 Rue |
0241 Soissons-Sud | 6097 Compiègne | 8040 Saint-Valery-sur-Somme |
0242 Tergnier | 6098 Creil | 8041 Villers-Bocage |
0297 Laon | 6099 Beauvais | 8042 Amiens Sud-Est |
0298 Saint-Quentin | 8043 Amiens Sud-Ouest | |
0299 Soisson | 8044 Amiens Nord | |
8046 Friville-Escarbotin | ||
8098 Abbeville | ||
8099 Amiens | ||
Laon(ville et cantons) comprend les cantons 0218,0238 et 0297 | Compiègne(ville et cantons) comprend les cantons 6009, 6037, 6041 et 6097 | Amiens(ville et cantons) comprend les cantons 8007,8008, 8009, 8010 8042,8044,8045 et 8099 |
Saint-Quentin(ville et cantons) comprend les cantons 0239,0240 et 0298 | Creil-Nogent-sur-Oise comprend les cantons 6011 et 6098 | Abbeville(ville et cantons) comprend les cantons 8001,8002, et 8098 |
Trace elements (nickel-Ni, cadmium-Cd, and lead-Pb) were modeled within the Picardy region [
The deprivation index used was developed by Rey [
The health data came from the Regional Health Observatory of Picardy [
Cumulative, maximum and minimum number of mortality and age-adjusted rates per 100,000 person-years by county, 2000–2009.
Cancer Mortality | Numbers of Cases | Age-adjusted Rates Per 100,000 Person-years |
---|---|---|
Cumulative | 1,327 | 16.26 |
Minimum | 1 | 2.81 |
Maximum | 128 | 37.4 |
Cumulative | 263 | 3.78 |
Minimum | 0 | 0 |
Maximum | 18 | 11.94 |
(
Spatially resolved data types and approaches used to homogenize spatial coverage.
Indicator | Variables | Sources | Spatial Scale or Resolution | Spatial Operation |
---|---|---|---|---|
Socioeconomic | SE: Deprivation index | French census Rey |
Vector data from the IRIS. | Spatial population-weighted aggregation |
Exposure | F1: Exposure inhalation indicator |
Caudeville |
Raster data of 1 km^{2} grid | Spatial aggregation |
Health | Lip, oral cavity and pharynx cancer mortality | Regional Health Observatory of Picardy [ |
Vector data from the county database | Poisson kriging |
Pleural cancer mortality |
To correct for the instability attributable to the small number problem, a number of algorithms have been developed that aim at estimating risk. The geostatistical approach, in this context, presents an interesting alternative; it conducts the noise filtering and allows for risk estimation along with the associated uncertainty at different scales. This section provides a brief overview of the geostatistical methodology for estimating risk values. See Goovaerts [
The cancer mortality count
The mortality risk and the associated kriging variance for an area
Kriging variance is computed as follows:
Global Moran’s I was calculated for all of the explanatory variables as well as for the dependent variables within three spatial structures to determine the role of spatial representation using global spatial autocorrelation. The Global Moran’s I spatial autocorrelation statistic measures the self-similarity of a spatial variable’s value as a function of adjacency [
Analyses of correlations between health data and putative factors are traditionally performed using a global or “aspatial” regression model, under the implicit assumption that the impact of variables is constant over the entire study area. This assumption is likely unrealistic for large areas, which can display large geographic variations. Fotheringham and colleagues developed Geographically Weighted Regression (GWR) to explore spatial non-stationarity and map statistics to visualize the spatial patterns of the relationships between dependent and independent variables [
The explanatory power of SE and exposure indicators was first investigated using the following multiple linear regression model:
In geographically weighted regression, the regression is conducted within local windows centered around each observation. The regression coefficients are thus location-dependent:
Within each window, observations are weighted according to their proximities to the center of the window. A variety of distance decay functions are available. In this paper, we used the XX function, which is characterized by a bandwidth that corresponds to the distance beyond which the weight rapidly approaches zero.
The bandwidth is estimated by minimizing the AICc value:
where
The maps of the kriging variance indicate the higher reliability of risks estimated in densely populated areas such as Amiens, Beauvais, Saint Quentin, and Abbeville. The variance of the risk estimates decreased as the geographic unit area increased: from the IRIS level to the grid level and then to the county level (
The risk estimates are characterized by positive spatial autocorrelation within the three spatial scales (
Maps of the lip, oral cavity and pharynx cancer mortality risk estimates and the corresponding prediction variance computed by Poisson kriging at three spatial scales: (
Maps of the pleural cancer mortality risk estimates and the corresponding prediction variances computed by Poisson kriging at three spatial scales: (
Summary statistics for health indicators after applying Poisson kriging.
Lip. Oral Cavity and Pharynx Cancer Mortality | ||||
---|---|---|---|---|
Estimation Type | Mean | Min | Max | Morans’I |
15.59 | 8.88 | 25.14 | 0.65 (0.001) | |
8.36 | 1.87 | 13.42 | ||
15.32 | 8.31 | 25.92 | 0.78 (0.001) | |
16.06 | 2.81 | 30.09 | ||
15.35 | 7.38 | 26.56 | 0.96 (0.001) | |
Kriging variance | 22.52 | 4.1 | 33.24 | |
3.16 | 1 | 8.72 | 0.52 (0.001) | |
1.92 | 0.43 | 3.07 | ||
2.99 | 0.87 | 8.48 | 0.62 (0.001) | |
3.65 | 0.63 | 6.67 | ||
3.21 | 0.87 | 9.04 | 0.93 (0.001) | |
Kriging variance | 4.99 | 0.89 | 7.32 |
The mean values of the explanatory variables under each of the three spatial structures are similar (
All of the variables were characterized by positive spatial autocorrelation within the three spatial scales at levels of
Summary statistics for the explanatory variables.
Variables | Mean | Min | Max | Variance | Moran's I | |
---|---|---|---|---|---|---|
0.61 | −4.5 | 3.48 | 2.84 | 0.63(0.001) | ||
F1: Exposure inhalation indicator | 0.08 | 0.06 | 0.13 | 0.0002 | 0.81(0.001) | |
F2: Exposure ingestion indicator | 0.27 | 0.27 | 0.39 | 0.002 | 0.61(0.001) | |
0.58 | −5.1 | 4.1 | 3.02 | 0.70(0.001) | ||
F1: Exposure inhalation indicator | 0.08 | 0.06 | 0.13 | 0.0002 | 0.88(0.001) | |
F2: Exposure ingestion indicator | 0.27 | 0.28 | 0.48 | 0.002 | 0.61(0.001) | |
0.48 | −7.3 | −8 | 4.62 | 0.55(0.001) | ||
F1: Exposure inhalation indicator | 0.08 | 0.06 | 0.15 | 2,00E−04 | 0.91(0.001) | |
F2: Exposure ingestion indicator | 0.26 | 0.31 | 0.68 | 0.003 | 0.65(0.001) |
Maps of the deprivation index computed at three spatial scales: (
Maps of the
Application of the linear model for lip, oral cavity and pharynx cancer mortality data explained a moderate proportion of the total variance (adjusted R^{2} = 0.22 and 0.19) at the county level and grid level, respectively. This proportion was lower when the analysis was conducted at the IRIS level (adjusted R^{2} = 0.11). It is noteworthy that the correlation coefficients for the SE factor were always significant for the different aggregation levels and were higher at the county level than at the IRIS level, which was the expected result because aggregation is known to increase the strength of correlation [
Results of the correlation analysis.
Lip. Oral Cavity and Pharynx Cancer Mortality | ||||
---|---|---|---|---|
SE | F1 | F2 | Adjusted R^{2} | |
0.32 * | −0.11 | 0.04 | 0.11 | |
Kriging risk | 0.53 * | −0.27 | 0.06 | 0.26 |
Kriging risk (weighted) | 0.49 * | −0.26 | 0.03 | 0.22 |
0.49 * | −0.28 | 0.03 | 0.24 | |
Kriging risk (weighted) | 0.43 * | −0.26 | 0.01 | 0.19 |
0.37 * | −0.21 | 0.01 | 0.15 | |
Kriging risk (weighted) | 0.32 * | −0.13 * | −0.03 | 0.11 |
SE | F1 | F2 | Adjusted R^{2} | |
Age-adjusted rate | −0.13 | 0.35 * | 0.03 | 0.11 |
Kriging risk | −0.18 | 0.51 * | 0.02 | 0.25 |
Kriging risk (weighted) | −0.16 | 0.52 * | −0.01 | 0.28 |
−0.18 | 0.47 * | 0.04 | 0.20 | |
Kriging risk (weighted) | −0.17 | 0.49 * | 0.03 | 0.24 |
−0.01 | 0.46 * | 0.06 | 0.22 | |
Kriging risk (weighted) | 0.04 | 0.50 * | 0.05 | 0.28 |
Notes:
The model explains a moderate proportion of the total variance when the dependent variable
In the aspatial analysis, we implicitly assumed that the impact of covariates was constant across the study area. This assumption is likely unrealistic for large areas, which can display substantial geographic variation in socioeconomic and environmental conditions. Therefore, the global statistics reported by this traditional regression model could potentially hide a number of interesting local relationships. The question is then to examine whether there are any meaningful spatial variations in these relationships.
County-level data were used to optimize the bandwidth of the GWR distance decay function.
These results strongly suggest that the relationships between cancer (lip, oral cavity and pharynx-pleural) mortality and the environmental and deprivation indexes are not stationary but instead vary over the study area. The application of GWR clearly enhances the explanatory power of the covariates for the three spatial levels: the proportion of variance explained (adjusted R^{2}) is almost doubled (
Impact of bandwidth size on the AICc of geographically weighted regression for each cancer.
The three spatial scales share the same southeast-northwest trend in the explanatory power of the local regression models for lip, oral cavity and pharynx cancer mortality: the lower mortality values in the south are better explained by the SE index than is the higher risk recorded in the northwest. As a recall, the largest R^{2} observed in the south (
The lower pleural cancer mortality values are better explained in areas of low F1 variation (
Comparison of local and global regression models at the three spatial scales.
Lip. Oral Cavity and Pharynx Cancer Mortality | |||
---|---|---|---|
Regression model | Bandwidth Size | Adjusted R2 | AICc |
Global model | 47 km | 0.22 | 567.00 |
Local model | 0.52 | 513.47 | |
47 km | 0.19 | 1,530.76 | |
Local model | 0.48 | 1,280.26 | |
47 km | 0.11 | 10,932.00 | |
Local model | 0.21 | 10,112.32 | |
Bandwidth Size | Adjusted R2 | AICc | |
0.28 | 374.35 | ||
Local model | 54 km | 0.48 | 348.09 |
0.24 | 931.65 | ||
Local model | 54 km | 0.49 | 803.08 |
0.28 | 6,219.21 | ||
Local model | 54 km | 0.46 | 5,852.26 |
Results of the geographically weighted regression applied to the lip mortality kriged rates: (
Results of the geographically weighted regression applied to the pleural cancer mortality kriged rates: (
Our results substantiate the work on noise filtering described in the introduction section from Oliver
The other issue was the so-called modifiable areal unit problem (MAUP), for which different geographic scales can lead to inconsistent results for relationships analysis. For example, the mortality rate reported at (1) the county level requires an aggregated deprivation index at the same resolution, and this aggregation obscures the intra-county variation and thus the relationship and (2) the IRIS level, at which the disaggregation leads to a large variance in estimated risk. Exploratory methods, such as the univariate Moran’s can serve as indications of the potential effect of the MAUP in the study how relationships based on the homogeneity and heterogeneity of spatial data are affected by the study level and may affect the ability of the study to detect a relationship [
In this study, very similar results were obtained for the different spatial scales between:
pleural cancer mortality and the exposure indicator F1.
lip, oral cavity, pharynx cancer mortality and the SE index.
Whereas other studies on the relationship between heath and deprivation showed that the use of spatial representations other than the census tract produced different analytical results [
This difference between the significant correlation coefficients is the result of aggregation because the aggregation level is known to increase the strength of correlation [
Based on the results obtained, we can confirm that the presence of this significant statistical association was likely not induced by the use of a particular geography. At the three spatial scales, the strongest correlation coefficients were found where low deprivation was associated with low lip, oral cavity and pharynx cancer mortality and where low environmental pollution was associated with low pleural cancer mortality.
This paper presents an approach for evaluating spatial relationships between health outcomes (mortality attributable to cancer) initially aggregated at the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. The approach was illustrated using age-adjusted lip, oral cavity and pharynx, and pleural cancer mortality rates measured over the period 2000–2009 for the Picardy region. The deprivation index and trace metal exposure indicators were used as putative risk factors. For the different spatial scales, the strongest associations were found where low deprivation was associated with low lip, oral cavity and pharynx cancer mortality and where low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas, for other causes of death, or with other indicators always requires exploratory analysis to assess the role of the MAUP and downscaling health data in the study of the relationships that will allow decision-makers to develop interventions where they are the most needed.
The authors wish to acknowledge the financial support by the French Environment and Energy Management Agency ADEME and the French Picardy Region provided within the framework of the CIRCE project.
Work presented here was conceived of, carried out and analyzed by Mahdi-Salim Saib, Julien Caudeville and Florence Carre. Olivier Ganry, Alain Trugeon and Andre Cicolella gave important suggestions, and supervised the study.All authors read, revised the manuscript and approved the final version.
The authors declare no conflict of interest