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

Fournel, Isabelle; Bourredjem, Abderrahmane; Sauleau, Erik-André; Cottet, Vanessa; Dejardin, Olivier; Bouvier, Anne-Marie; Launoy, Guy; Bonithon‐Kopp, Claire

doi:10.1097/cej.0000000000000175

Cited by 4 publications

(1 citation statement)

References 30 publications

(33 reference statements)

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“…In accordance with our findings, previous studies have reported that rural residents enter the healthcare system later and with later stages of disease than urban residents [80][81][82][83][84]. Previous research has also found that individuals living in rural areas have limited access to healthcare services [85][86][87][88][89], fewer physician visits per year, and they wait longer for CRC surgery than individuals living in urban areas [57,[90][91][92]. Furthermore, a previous systematic review found that rural residence was associated with patient delay, while practitioners in rural areas were less likely to refer due to the distance from specialist services [93].…”

Section: Discussionsupporting

confidence: 92%

Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus

Giannakou

Lamnisos

2022

IJERPH

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Colorectal cancer (CRC) is one of the leading causes of death and morbidity worldwide. To date, the relationship between regional deprivation and CRC incidence or mortality has not been studied in the population of Cyprus. The objective of this study was to analyse the geographical variation of CRC incidence and mortality and its possible association with socioeconomic inequalities in Cyprus for the time period of 2000–2015. This is a small-area ecological study in Cyprus, with census tracts as units of spatial analysis. The incidence date, sex, age, postcode, primary site, death date in case of death, or last contact date of all alive CRC cases from 2000–2015 were obtained from the Cyprus Ministry of Health’s Health Monitoring Unit. Indirect standardisation was used to calculate the sex and age Standardise Incidence Ratios (SIRs) and Standardised Mortality Ratios (SMRs) of CRC while the smoothed values of SIRs, SMRs, and Mortality to Incidence ratio (M/I ratio) were estimated using the univariate Bayesian Poisson log-linear spatial model. To evaluate the association of CRC incidence and mortality rate with socioeconomic deprivation, we included the national socioeconomic deprivation index as a covariate variable entering in the model either as a continuous variable or as a categorical variable representing quartiles of areas with increasing levels of socioeconomic deprivation. The results showed that there are geographical areas having 15% higher SIR and SMR, with most of those areas located on the east coast of the island. We found higher M/I ratio values in the rural, remote, and less dense areas of the island, while lower rates were observed in the metropolitan areas. We also discovered an inverted U-shape pattern in CRC incidence and mortality with higher rates in the areas classified in the second quartile (Q2-areas) of the socioeconomic deprivation index and lower rates in rural, remote, and less dense areas (Q4-areas). These findings provide useful information at local and national levels and inform decisions about resource allocation to geographically targeted prevention and control plans to increase CRC screening and management.

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Section: Discussionsupporting

confidence: 92%

Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus

Giannakou

Lamnisos

2022

IJERPH

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A systematic review of methods to estimate colorectal cancer incidence using population-based cancer registries

Alsadhan

Almaiman

Pujades-Rodriguez

et al. 2022

BMC Med Res Methodol

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Background Epidemiological studies of incidence play an essential role in quantifying disease burden, resource planning, and informing public health policies. A variety of measures for estimating cancer incidence have been used. Appropriate reporting of incidence calculations is essential to enable clear interpretation. This review uses colorectal cancer (CRC) as an exemplar to summarize and describe variation in commonly employed incidence measures and evaluate the quality of reporting incidence methods. Methods We searched four databases for CRC incidence studies published between January 2010 and May 2020. Two independent reviewers screened all titles and abstracts. Eligible studies were population-based cancer registry studies evaluating CRC incidence. We extracted data on study characteristics and author-defined criteria for assessing the quality of reporting incidence. We used descriptive statistics to summarize the information. Results This review retrieved 165 relevant articles. The age-standardized incidence rate (ASR) (80%) was the most commonly reported incidence measure, and the 2000 U.S. standard population the most commonly used reference population (39%). Slightly more than half (54%) of the studies reported CRC incidence stratified by anatomical site. The quality of reporting incidence methods was suboptimal. Of all included studies: 45 (27%) failed to report the classification system used to define CRC; 63 (38%) did not report CRC codes; and only 20 (12%) documented excluding certain CRC cases from the numerator. Concerning the denominator estimation: 61% of studies failed to state the source of population data; 24 (15%) indicated census years; 10 (6%) reported the method used to estimate yearly population counts; and only 5 (3%) explicitly explained the population size estimation procedure to calculate the overall average incidence rate. Thirty-three (20%) studies reported the confidence interval for incidence, and only 7 (4%) documented methods for dealing with missing data. Conclusion This review identified variations in incidence calculation and inadequate reporting of methods. We outlined recommendations to optimize incidence estimation and reporting practices. There is a need to establish clear guidelines for incidence reporting to facilitate assessment of the validity and interpretation of reported incidence.

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Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer

Tian

Zhou

et al. 2018

BMC Cancer

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BackgroundAn increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.MethodsPatients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.ResultsBased on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).ConclusionsBased on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4985-2) contains supplementary material, which is available to authorized users.

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

Cited by 4 publications

References 30 publications

Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus

Small-Area Geographic and Socioeconomic Inequalities in Colorectal Cancer in Cyprus

A systematic review of methods to estimate colorectal cancer incidence using population-based cancer registries

Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer

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