How to interpret and choose a Bayesian spatial model and a Poisson regression model in the context of describing small area cancer risks variations

How do territorial characteristics affect spatial inequalities in the risk of coronary heart disease?

BACKGROUND

Cardiovascular diseases remain the leading cause of death and disabilities worldwide, with coronary heart diseases being the most frequently diagnosed. Their multifactorial etiology involves individual, behavioral and territorial determinants, and thus requires the implementation of multidimensional approaches to assess links between territorial characteristics and the incidence of coronary heart diseases.

CONTEXT AND OBJECTIVES

This study was carried out in a densely populated area located in the north of France with multiple sources of pollutants. The aim of this research was therefore to establish complex territorial profiles that have been characterized by the standardized incidence, thereby identifying the influences of determinants that can be related to a beneficial or a deleterious effect on cardiovascular health.

METHODS

Forty-four variables related to economic, social, health, environment and services dimensions with an established or suspected impact on cardiovascular health were used to describe the multidimensional characteristics involved in cardiovascular health.

RESULTS

Three complex territorial profiles have been highlighted and characterized by the standardized incidence rate (SIR) of coronary heart diseases after adjustment for age and gender. Profile 1 was characterized by an SIR of 0.895 (sd: 0.143) and a higher number of determinants that revealed favorable territorial conditions. Profiles 2 and 3 were characterized by SIRs of respectively 1.225 (sd: 0.242) and 1.119 (sd: 0.273). Territorial characteristics among these profiles of over-incidence were nevertheless dissimilar. Profile 2 revealed higher deprivation, lower vegetation and lower atmospheric pollution, while profile 3 displayed a rather privileged population with contrasted territorial conditions.

CONCLUSION

This methodology permitted the characterization of the multidimensional determinants involved in cardiovascular health, whether they have a negative or a positive impact, and could provide stakeholders with a diagnostic tool to implement contextualized public health policies to prevent coronary heart diseases.

Fine-scale geographic variations of rates of renal replacement therapy in northeastern France: Association with the socioeconomic context and accessibility to care

Background

The strong geographic variations in the incidence rates of renal replacement therapy (RRT) for end-stage renal disease are not solely related to variations in the population's needs, such as the prevalence of diabetes or the deprivation level. Inequitable geographic access to health services has been involved in different countries but never in France, a country with a generous supply of health services and where the effect of the variability of medical practices was highlighted in an analysis conducted at the geographic scale of districts. Our ecological study, performed at the finer scale of townships in a French area of 8,370,616 inhabitants, investigated the association between RRT incidence rates, socioeconomic environment and geographic accessibility to healthcare while adjusting for morbidity level and medical practice patterns.

Methods

Using data from the Renal Epidemiology and Information Network registry, we estimated age-adjusted RRT incidence rates during 2010–2014 for the 282 townships of the area. A hierarchical Bayesian Poisson model was used to examine the association between incidence rates and 18 contextual variables describing population health status, socioeconomic level and health services characteristics. Relative risks (RRs) and 95% credible intervals (95% CrIs) for each variable were estimated for a 1-SD increase in incidence rate.

Results

During 2010–2014, 6,835 new patients ≥18 years old (4231 men, 2604 women) living in the study area started RRT; the RRT incidence rates by townships ranged from 21 to 499 per million inhabitants. In multivariate analysis, rates were related to the prevalence of diabetes [RR (95% CrI): 1.05 (1.04–1.11)], the median estimated glomerular filtration rate at dialysis initiation [1.14 (1.08–1.20)], and the proportion of incident patients ≥ 85 years old [1.08 (1.03–1.14)]. After adjusting for these factors, rates in townships increased with increasing French deprivation index [1.05 (1.01–1.08)] and decreased with increasing mean travel time to reach the closest nephrologist [0.92 (0.89–0.95]).

Conclusion

These data confirm the influence of deprivation level, the prevalence of diabetes and medical practices on RRT incidence rates across a large French area. For the first time, an association was found with the distance to nephrology services. These data suggest possible inequitable geographic access to RRT within the French health system.

A Comparative Study of Spatial Distribution of Gastrointestinal Cancers in Poverty and Affluent Strata (Kermanshah Metropolis, Iran)

INTRODUCTION

The trend of cancers has witnessed a twofold rise in the last three decades, which is expected to be fivefold by 2030. On the other hand, gastrointestinal cancers have turned into one of the health issues in many societies. Given the presence of gastrointestinal cancer hot spots and evidence of health inequalities across Kermanshah Metropolis and the results of studies signaling the association between gastrointestinal cancers and socioeconomic status of individuals as well as evidence of unequal socioeconomic opportunities in this metropolis, the present study aimed to investigate the spatial distribution of gastrointestinal cancers in the poverty and affluent strata of Kermanshah Metropolis, Iran.

MATERIALS AND METHODS

In this descriptive-analytical study, the recorded data of patients, suffering from gastrointestinal cancers, in Kermanshah-based Pathology Centers and Vice Chancellery of Kermanshah University of Medical Sciences (2007-2012) were used. Moreover, to examine the status of gastrointestinal cancers in socioeconomic classes based on the census data collected during 2007-2012, 33 social, cultural, and structural indexes were extracted from the statistical blocks. Additionally, for data analysis and factor analysis, Kruskal-Wallis Test in the environment of SPSS and kernel density estimation (KDE) and Moran's I tests in the GIS environment were employed.

FINDINGS

The results of the present study revealed that the distribution of poverty (Z score = 48.916518, p value = 0.000000) and affluent strata (Z score = 14.345028, p value = 0.000000) followed clustered patterns (p < 0.01). Additionally, the results indicated that the spatial distribution pattern of the upper gastrointestinal cancer was clustered (Z score = 1.896996, p value = 0.007828), whereas the spatial distribution pattern of the lower gastrointestinal cancer was inclined to a randomized clustered pattern (Z score = 1.338121, p value = 0.000857) (p < 0.01). Finally, seven main hot spots were identified from the poverty stratum in Kermanshah, which perfectly overlapped the hot spots of upper gastrointestinal cancer. Similarly, four main hot spots were identified from the affluent stratum in Kermanshah, which overlapped the hot spots of lower gastrointestinal cancer. The results of the Kruskal-Wallis Test demonstrated that the poverty and affluent strata were significantly different from each other in terms of gastrointestinal cancer: upper gastrointestinal cancer (p < 0.05 and X²=10.064) and lower gastrointestinal cancer (p < 0.05 and X²=10.253).

CONCLUSION

The results of the present study showed that the ratio of patients with lower gastrointestinal cancers was higher than the incidence of upper gastrointestinal cancers over the 5-year period under study. Moreover, in Kermanshah Metropolis, there was a significant difference between the upper gastrointestinal cancer in the poverty stratum and the lower gastrointestinal cancer in the affluent stratum. Hence, it is suggested that GIS be applied as a tool for identifying the patterns of effective factors of this type of cancer in each social class, and it is recommended that some effective policies be presented and adopted by health managers according to the role and importance of socioeconomic, environmental, and nutritional factors in the poverty and affluent strata of society, and people at risk be equipped with preventive training programs in this respect.

Small-area geographic and socioeconomic inequalities in colorectal tumour detection in France

The aim of this study was to assess the impact of area deprivation and primary care facilities on colorectal adenoma detection and on colorectal cancer (CRC) incidence in a French well-defined population before mass screening implementation. The study population included all patients aged 20 years or more living in Côte d'Or (France) with either colorectal adenoma or invasive CRC first diagnosed between 1995 and 2002 and who were identified from the Burgundy Digestive Cancer Registry and the Côte d'Or Polyp Registry. Area deprivation was assessed using the European deprivation index on the basis of the smallest French area available (Ilots Regroupés pour l'Information Statistique). Healthcare access was assessed using medical density of general practitioners (GPs) and road distance to the nearest GP and gastroenterologist. Bayesian regression analyses were used to estimate influential covariates on adenoma detection and CRC incidence rates. The results were expressed as relative risks (RRs) with their 95% credibility interval. In total, 5399 patients were diagnosed with at least one colorectal adenoma and 2125 with invasive incident CRC during the study period. Remoteness from GP [RR=0.71 (0.61-0.83)] and area deprivation [RR=0.98 (0.96-1.00)] independently reduced the probability of adenoma detection. CRC incidence was only slightly affected by GP medical density [RR=1.05 (1.01-1.08)] without any area deprivation effect [RR=0.99 (0.96-1.02)]. Distance to gastroenterologist had no impact on the rates of adenoma detection or CRC incidence. This study highlighted the prominent role of access to GPs in the detection of both colorectal adenomas and overall cancers. Deprivation had an impact only on adenoma detection.

Variability of cancer risk within an area: time to complement the incidence rate

The aim of this study was to show that age-adjusted cancer incidence rates for an area may not be representative of the incidence in subareas. We propose a simple measure to show the amount of geographical variability. European age-standardized incidence rates (ASRs) for 'all sites excluding nonmelanoma skin cancer', for men, in 2014, for Nordic countries as a whole, for each country (Denmark, Faroe Islands, Finland, Greenland, Iceland, Sweden and Norway) and for their regions, were retrieved from the Nordcan with corresponding standard errors SEs. We compared the ASR for Nordic countries versus single country and single country versus specific regions. The overlapping of 95% confidence intervals was used for ASRs comparisons. As a measure of variability, we computed the range between the highest and the lowest ASR within an area and the ratio between this range and the ASR of the overall area, r/R=(range/ASR)×100. The 95% confidence interval of the ASR for Nordic countries as a whole did not overlap those of the majority of the single countries; in fact, the r/R - which provides a clue for the amount of underlying geographical variability - was rather large (57.1%). Within countries, the variability was negligible in Iceland (r/R=9.6%), whereas the highest value was found in Sweden (37.1%). The ASR does not provide any information on underlying geographical variability. Therefore, its interpretation could be misleading. When data for subareas are available, the r/R, which is simple to compute and to understand, should be added to the ASR for providing more truthful information.

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping

BACKGROUND

The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence.

METHODS

Moran's I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only.

RESULTS

The study found a spatial heterogeneity (p < 0.01) and an autocorrelation for prostate (EBI = 0.02; p = 0.001), lung (EBI = 0.01; p = 0.019) and bladder (EBI = 0.007; p = 0.05) cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes (p < 0.05): four in the Western and one in the Northern parts of the study area (standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively). In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods (RR >1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6).

CONCLUSIONS

In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.