Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk

Background

Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases.

Methods

Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases.

Results

The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city.

Conclusions

This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.

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.

Sequential tests for monitoring methods to detect elevated incidence - a simulation study

Background

Common cancer monitoring practice is seldom prospective and rather driven by public requests. This study aims to assess the performance of a recently developed prospective cancer monitoring method and the statistical tools used, in particular the sequential probability ratio test in regard to specificity, sensitivity, observation time and heterogeneity of size of the geographical unit.

Methods

A simulation study based on a predefined selection of cancer types, geographical unit and time period was set up. Based on the population structure of Lower Saxony the mean number of cases of three diagnoses were randomly assigned to the geographical units during 2008–2012. A two-stage monitoring procedure was then executed considering the standardized incidence ratio and sequential probability ratio test. Scenarios were constructed differing by the simulation of clusters, significance level and test parameter indicating a risk to be elevated.

Results

Performance strongly depended on the choice of the test parameter. If the expected numbers of cases were low, the significance level was not fully exhausted. Hence, the number of false positives was lower than the chosen significance level suggested, leading to a high specificity. Sensitivity increased with the expected number of cases and the amount of risk and decreased with the size of the geographical unit.

Conclusions

The procedure showed some desirable properties and is ready to use for a few settings but demands adjustments for others. Future work might consider refinements of the geographical structure.

Inhomogeneous unit size could be addressed by a flexible choice of the test parameter related to the observation time.

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.

Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology

Background

Monitoring spatial disease risk (e.g. identifying risk areas) is of great relevance in public health research, especially in cancer epidemiology. A common strategy uses case-control studies and estimates a spatial relative risk function (sRRF) via kernel density estimation (KDE). This study was set up to evaluate the sRRF estimation methods, comparing fixed with adaptive bandwidth-based KDE, and how they were able to detect ‘risk areas’ with case data from a population-based cancer registry.

Methods

The sRRF were estimated within a defined area, using locational information on incident cancer cases and on a spatial sample of controls, drawn from a high-resolution population grid recognized as underestimating the resident population in urban centers. The spatial extensions of these areas with underestimated resident population were quantified with population reference data and used in this study as ‘true risk areas’. Sensitivity and specificity analyses were conducted by spatial overlay of the ‘true risk areas’ and the significant (α=.05) p-contour lines obtained from the sRRF.

Results

We observed that the fixed bandwidth-based sRRF was distinguished by a conservative behavior in identifying these urban ‘risk areas’, that is, a reduced sensitivity but increased specificity due to oversmoothing as compared to the adaptive risk estimator. In contrast, the latter appeared more competitive through variance stabilization, resulting in a higher sensitivity, while the specificity was equal as compared to the fixed risk estimator. Halving the originally determined bandwidths led to a simultaneous improvement of sensitivity and specificity of the adaptive sRRF, while the specificity was reduced for the fixed estimator.

Conclusion

The fixed risk estimator contrasts with an oversmoothing tendency in urban areas, while overestimating the risk in rural areas. The use of an adaptive bandwidth regime attenuated this pattern, but led in general to a higher false positive rate, because, in our study design, the majority of true risk areas were located in urban areas. However, there is a strong need for further optimizing the bandwidth selection methods, especially for the adaptive sRRF.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-015-0005-9) contains supplementary material, which is available to authorized users.

Spatial epidemiology of dry eye disease: findings from South Korea

BACKGROUND

DED rate maps from diverse regions may allow us to understand world-wide spreading pattern of the disease. Only few studies compared the prevalence of DED between geographical regions in non-spatial context. Therefore, we examined the spatial epidemiological pattern of DED prevalence in South Korea using a nationally representative sample.

METHODS

We analyzed 16,431 Korean adults aged 30 years or older of the 5th Korea National Health and Nutrition Examination Survey. DED was defined as previously diagnosed by an ophthalmologist as well as symptoms experienced. Multiple logistic regression analysis was used to assess the spatial pattern in the prevalence of DED, and effects of environmental factors.

RESULTS

Among seven metropolitan cities and nine provinces, three metropolitan cities located in the southeast of Korea revealed the highest prevalence of DED. After adjusting for sex, age and survey year, people living in urban areas had higher risk of having DED. Adjusted odds ratio for having previously diagnosed DED was 1.677 (95% CI 1.299-2.166) for metropolitan cities and 1.580 (95% CI 1.215-2.055) for other cities compared to rural areas. Corresponding odds ratio for presenting DED symptoms was 1.388 (95% CI 1.090-1.766) for metropolitan cities and 1.271 (95% CI 0.999-1.617) for other cities. Lower humidity and longer sunshine duration were significantly associated with DED. Among air pollutants, SO2 was associated with DED, while NO2, O3, CO, and PM10 were not.

CONCLUSION

Our findings suggest that prevalence of DED can be affected by the degree of urbanization and environmental factors such as humidity and sunshine duration.