GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

Geospatial disparities in survival of patients with breast cancer in sub-Saharan Africa from the African Breast Cancer-Disparities in Outcomes cohort (ABC-DO): a prospective cohort study

BACKGROUND

There is an urgent need to improve breast cancer survival in sub-Saharan Africa. Geospatial barriers delay diagnosis and treatment, but their effect on survival in these settings is not well understood. We examined geospatial disparities in 4-year survival in the African Breast Cancer-Disparities in Outcomes cohort.

METHODS

In this prospective cohort study, women (aged ≥18 years) newly diagnosed with breast cancer were recruited from eight hospitals in Namibia, Nigeria, South Africa, Uganda, and Zambia. They reported sociodemographic information in interviewer-administered questionnaires, and their clinical and treatment data were collected from medical records. Vital status was ascertained by contacting participants or their next of kin every 3 months. The primary outcome was all-cause mortality in relation to rural versus urban residence, straight-line distance, and modelled travel time to hospital, analysed using restricted mean survival time, Cox proportional hazards, and flexible parametric survival models.

FINDINGS

2228 women with breast cancer were recruited between Sept 8, 2014, and Dec 31, 2017. 127 were excluded from analysis (58 had potentially recurrent cancer, had previously received treatment, or had no follow-up; 14 from minority ethnic groups with small sample sizes; and 55 with missing geocoded home addresses). Among the 2101 women included in analysis, 928 (44%) lived in a rural area. 1042 patients had died within 4 years of diagnosis; 4-year survival was 39% (95% CI 36-42) in women in rural areas versus 49% (46-52) in urban areas (unadjusted hazard ratio [HR] 1·24 [95% CI 1·09-1·40]). Among the 734 women living more than 1 h from the hospital, the crude 4-year survival was 37% (95% CI 32-42) in women in rural areas versus 54% (46-62) in women in urban areas (HR 1·35 [95% CI 1·07-1·71] after adjustment for age, stage, and treatment status). Among women in rural areas, mortality rates increased with distance (adjusted HR per 50 km 1·04, 1·01-1·07) and travel time (adjusted HR per h 1·06, 1·02-1·10). Among women with early-stage breast cancer receiving treatment, women in rural areas had a strong survival disadvantage (overall HR 1·54, 1·14-2·07 adjusted for age and stage; >1 h distance adjusted HR 2·14, 1·21-3·78).

INTERPRETATION

Geospatial barriers reduce survival of patients with breast cancer in sub-Saharan Africa. Specific attention is needed to support patients with early-stage breast cancer living in rural areas far from cancer treatment facilities.

FUNDING

US National Institutes of Health (National Cancer Institute), Susan G Komen for the Cure, and the International Agency for Research on Cancer.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

A gender-specific geodatabase of five cancer types with the highest frequency of occurrence in Iran

OBJECTIVES

Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management. This study aims to create a database containing individual and spatially integrated data on commonly diagnosed cancers in Iran from 2014 to 2017, serving as a valuable resource for spatial-epidemiological approaches.

DATA DESCRIPTION

This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules to identify cancers with the highest incidence. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files.

Melanoma Registry of the Spanish Academy of Dermatology and Venereology (REGESMEL): Description and Data in its First Year of Operation

INTRODUCTION

The incidence of melanoma is rising in Spain. The prognostic stages of patients with melanoma are determined by various biological factors, such as tumor thickness, ulceration, or the presence of regional or distant metastases. The Spanish Academy of Dermatology and Venereology (AEDV) has encouraged the creation of a Spanish Melanoma Registry (REGESMEL) to evaluate other individual and health system-related factors that may impact the prognosis of patients with melanoma. The aim of this article is to introduce REGESMEL and provide basic descriptive data for its first year of operation.

METHODS

REGESMEL is a prospective, multicentre cohort of consecutive patients with invasive cutaneous melanoma that collects demographic and staging data as well as individual and healthcare-related baseline data. It also records the medical and surgical treatment received by patients.

RESULTS

A total of 450 cases of invasive cutaneous melanoma from 19 participant centres were included, with a predominance of thin melanomas≤1mm thick (54.7%), mainly located on the posterior trunk (35.2%). Selective sentinel lymph node biopsy was performed in 40.7% of cases. Most cases of melanoma were suspected by the patient (30.4%), or his/her dermatologist (29.6%). Patients received care mainly in public health centers (85.2%), with tele-dermatology resources being used in 21.6% of the cases.

CONCLUSIONS

The distribution of the pathological and demographic variables of melanoma cases is consistent with data from former studies. REGESMEL has already recruited patients from 15 Spanish provinces and given its potential representativeness, it renders the Registry as an important tool to address a wide range of research questions.

Utilizing residential histories to assess environmental exposure and socioeconomic status over the life course among mesothelioma patients

Background

Exposure misclassification based solely on the address at cancer diagnosis has been widely recognized though not commonly assessed.

Methods

We linked 1,015 mesothelioma cases diagnosed during 2011-2015 from the New York State Cancer Registry to inpatient claims and LexisNexis administrative data and constructed residential histories. Percentile ranking of exposure to ambient air toxics and socioeconomic status (SES) were based on the National Air Toxic Assessment and United States Census data, respectively. To facilitate comparisons over time, relative exposures (REs) were calculated by dividing the percentile ranking at individual census tract by the state-level average and subtracting one. We used generalized linear regression models to compare the RE in the past with that at cancer diagnosis, adjusting for patient-level characteristics.

Results

Approximately 43.7% of patients had residential information available for up to 30 years, and 96.0% up to 5 years. The median number of unique places lived was 4 [interquartile range (IQR), 2-6]. The time-weighted-average RE from all addresses available had a median of -0.11 (IQR, -0.50 to 0.30) for air toxics and -0.28 (IQR, -0.65 to 0.25) for SES. RE associated with air toxics (but not SES) was significantly higher for earlier addresses than addresses at cancer diagnosis for the 5-year [annual increase =1.24%; 95% confidence interval (CI): 0.71-1.77%; n=974] and 30-year (annual increase =0.36%; 95% CI: 0.25-0.48%; n=444) look-back windows, respectively.

Conclusions

Environmental exposure to non-asbestos air toxics among mesothelioma patients may be underestimated if based solely on the address at diagnosis. With geospatial data becoming more readily available, incorporating cancer patients' residential history would lead to reduced exposure misclassification and accurate health risk estimates.

The Spatial-Temporal Dimension of Oncological Prevalence and Mortality in Romania

The objective of this study was to identify spatial disparities in the distribution of cancer hotspots within Romania. Additionally, the research aimed to track prevailing trends in cancer prevalence and mortality according to a cancer type. The study covered the timeframe between 2008 and 2017, examining all 3,181 territorial administrative units. The analysis of spatial distribution relied on two key parameters. The first parameter, persistence, measured the duration for which cancer prevalence exceeded the 75th percentile threshold. Cancer prevalence refers to the total number of individuals in a population who have been diagnosed with cancer at a specific time point, including both newly diagnosed cases (occurrence) and existing cases. The second parameter, the time continuity of persistence, calculated the consecutive months during which cancer prevalence consistently surpassed the 75th percentile threshold. Notably, persistence of elevated values was also evident in lowland regions, devoid of any discernible direct connection to environmental conditions. In conclusion, this work bears substantial relevance to regional health policies, by aiding in the formulation of prevention strategies, while also fostering a deeper comprehension of the socioeconomic and environmental factors contributing to cancer.

Self-reported critical gaps in the essential knowledge and capacity of spatial epidemiology between the current university education and competency-oriented professional demands in preparing for a future pandemic among public health postgraduates in China: a nationwide cross-sectional survey

BACKGROUND

Spatial epidemiology plays an important role in public health. Yet, it is unclear whether the current university education in spatial epidemiology in China could meet the competency-oriented professional demands. This study aimed to understand the current situation of education and training, practical application, and potential demands in spatial epidemiology among public health postgraduates in China, and to assess the critical gaps in a future emerging infectious diseases (EID) pandemic preparedness and response.

METHODS

This study was divided into three parts. The first part was a comparative study on spatial epidemiology education in international public health postgraduate training. The second part was a cross-sectional survey conducted among public health professionals. The third part was a nationwide cross-sectional survey conducted among public health postgraduates at Chinese universities from October 2020 to February 2021. Data was collected by the WeChat-based questionnaire star survey system and analyzed using the SPSS software.

RESULTS

International education institutions had required public health postgraduates to master the essential knowledge and capacity of spatial epidemiology. A total of 198 public health professionals were surveyed, and they had a median of 4.00 (IQR 3.13-4.53) in demand degree of spatial epidemiology. A total of 1354 public health postgraduates were surveyed from 51 universities. Only 29.41% (15/51) of universities offered spatial epidemiology course. Around 8.05% (109/1354) of postgraduates had learned spatial epidemiology, and had a median of 1.05 (IQR 1.00-1.29) in learning degree and a median of 1.91 (IQR 1.05-2.78) in practical application degree of spatial epidemiology. To enhance professional capacity, 65.95% (893/1354) of postgraduates hoped that universities would deliver a credit-course of spatial epidemiology.

CONCLUSIONS

A huge unmet education and training demand in spatial epidemiology existed in the current education system of public health postgraduates in China. To enhance the competency-oriented professional capacity in preparedness and response to a future pandemic, it is urgent to incorporate the teaching and training of spatial epidemiology into the compulsory curriculum system of public health postgraduates in China.

Population-Level Access to Breast Cancer Early Detection and Diagnosis in Nigeria

PURPOSE

Mammography, breast ultrasound (US), and US-guided breast biopsy are essential services for breast cancer early detection and diagnosis. This study undertook a comprehensive evaluation to determine population-level access to these services for breast cancer early detection and diagnosis in Nigeria using a previously validated geographic information system (GIS) model.

METHODS

A comprehensive list of public and private facilities offering mammography, breast US, and US-guided breast biopsy was compiled using publicly available facility data and a survey administered nationally to Nigerian radiologists. All facilities were geolocated. A cost-distance model using open-source population density (GeoData Institute) and road network data (OpenStreetMap) was used to estimate population-level travel time to the nearest facility for mammography, breast US, and US-guided biopsy using GIS software (ArcMAP).

RESULTS

In total, 1,336 facilities in Nigeria provide breast US, of which 47.8% (639 of 1,336) are public facilities, and 218 provide mammography, of which 45.4% (99 of 218) are public facilities. Of the facilities that provide breast US, only 2.5% (33 of 1,336) also provide US-guided breast biopsy. At the national level, 83.1% have access to either US or mammography and 61.7% have access to US-guided breast biopsy within 120 minutes of a continuous one-way travel. There are differences in access to mammography (64.8% v 80.6% with access at 120 minutes) and US-guided breast biopsy (49.0% v 77.1% with access at 120 minutes) between the northern and southern Nigeria and between geopolitical zones.

CONCLUSION

To our knowledge, this is the first comprehensive evaluation of breast cancer detection and diagnostic services in Nigeria, which demonstrates geospatial inequalities in access to mammography and US-guided biopsy. Targeted investment is needed to improve access to these essential cancer care services in the northern region and the North East geopolitical zone.

Association of multi-criteria derived air toxics hazard score with lung cancer incidence in a major metropolitan area

Background

Lung cancer remains a major health problem world-wide. Environmental exposure to lung cancer carcinogens can affect lung cancer incidence. We investigated the association between lung cancer incidence and an air toxics hazard score of environmental carcinogen exposures derived previously under the exposome concept.

Methods

Lung cancer cases diagnosed in Philadelphia and the surrounding counties between 2008 and 2017 were identified from the Pennsylvania Cancer Registry. Age-adjusted incidence rates at the ZIP code level were calculated based on the residential address at diagnosis. The air toxics hazard score, an aggregate measure for lung cancer carcinogen exposures, was derived using the criteria of toxicity, persistence, and occurrence. Areas with high incidence or hazard score were identified. Spatial autoregressive models were fitted to evaluate the association, with and without adjusting for confounders. Stratified analysis by smoking prevalence was performed to examine potential interactions.

Results

We observed significantly higher age-adjusted incidence rates in ZIP codes that had higher air toxics hazard score values after controlling for demographic variables, smoking prevalence, and proximity to major highways. Analyzes stratified by smoking prevalence suggested that exposure to environmental lung carcinogens had a larger effect on cancer incidence in locations with higher smoking prevalence.

Conclusion

The positive association between the multi-criteria derived air toxics hazard score and lung cancer incidence provides the initial evidence to validate the hazard score as an aggregate measure of carcinogenic exposures in the environment. The hazard score can be used to supplement the existing risk factors in identifying high risk individuals. Communities with higher incidence/hazard score may benefit from greater awareness of lung cancer risk factors and targeted screening programs.

Scientometric Analysis of Articles on Spatial Epidemiology of Cancer in Iran: A Systematic Review

Background

Geographic information system (GIS) plays an important role in identifying areas with a high incidence of cancer. In the present study, based on a systematic review of studies by Iranian researchers, we performed a scientometric analysis of the published articles on the spatial epidemiology of cancer. In addition, the geographical distribution of certain types of cancer in Iran is presented.

Methods

A literature search was conducted using electronic databases such as PubMed and NLM Gateway, Institute for Scientific Information, Scopus, Google Scholar, and Cochrane Library for relevant articles published from 2000 to 2021. The search was performed using a combination of medical subject heading terms and keywords. A narrative synthesis was performed, and descriptive data were expressed as frequency and percentage.

Results

Of the 200 identified articles, 31 studies published in 15 different journals were included in this systematic review. Results showed a wide variation in high-risk breast cancer clusters. However, a similar incidence of gastrointestinal cancers has been reported, and high-risk clusters were identified in the north and the northwest of Iran. Skin cancer and acute lymphoblastic leukemia were more prevalent in the central provinces.

Conclusion

The current volume of studies on the spatial epidemiology of cancer in Iran, with a CiteScore quartile of Q1, is inadequate to guide health policymakers. The geographical distribution of many prevalent types of cancer has not been assessed by Iranian researchers. Furthermore, the classification of high- and low-risk geographical clusters of cancers was not completely homogeneous.

Investigation of Geographical Disparities: The Use of An Interpolation Method For Cancer Registry Data

The American Cancer Society estimated 1.9 million diagnosed cancer cases and 608,570 cancer deaths in 2021 in the US; for Oklahoma, they estimated 22,820 cases and 8,610 deaths. This project aimed to demonstrate a method to systematically describe cancer in an accurate and visually attractive, yet simple to make, interpolated map using ZIP Code level registry data, as it is the smallest area unit with high accuracy using inverse distance weighting. We describe a process of creating smoothed maps with an appropriate, well-described, simple, replicable method. These smoothed maps display low (cold) or high (hot) areas of incidence rates of: (a) all cancer combined, (b) colorectal cancer and lung cancer rates by gender, (c) female breast cancer, and (d) prostate cancer, by ZIP Codes for Oklahoma from 2013-2017. The methods we present in this paper provide an effective visualization to pinpoint low (cold) or high (hot) areas of cancer incidence.

Disparities in Cutaneous T-Cell Lymphoma Incidence by Race/Ethnicity and Area-Based Socioeconomic Status

Cutaneous T-cell lymphoma (CTCL) is a rare type of extranodal non-Hodgkin lymphoma (NHL). This study uses population-based data from the New Jersey (NJ) State Cancer Registry to examine geographic variation in CTCL incidence and evaluates whether CTCL risk varies by race/ethnicity and census tract socioeconomic status (SES). The study included 1163 cases diagnosed in NJ between 2006 and 2014. Geographic variation and possible clustering of high CTCL rates were assessed using Bayesian geo-additive models. The associations between CTCL risk and race/ethnicity and census tract SES, measured as median household income, were examined using Poisson regression. CTCL incidence varied across NJ, but there were no statistically significant geographic clusters. After adjustment for age, sex, and race/ethnicity, the relative risk (RR) of CTCL was significantly higher (RR = 1.47, 95% confidence interval: 1.22-1.78) in the highest income quartile than in the lowest. The interactions between race/ethnicity and SES indicated that the income gradients by RR were evident in all groups. Compared to non-Hispanic White individuals in low-income tracts, CTCL risk was higher among non-Hispanic White individuals in high-income tracts and among non-Hispanic Black individuals in tracts of all income levels. Our findings suggest racial disparities and a strong socioeconomic gradient with higher CTCL risk among cases living in census tracts with higher income compared to those living in lower-income tracts.

Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection

Background

Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants.

Objectives

(1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line.

Methods

Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing.

Results

Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86–2.96) during birth and childhood in the 1950’s–1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state.

Conclusion

This study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person’s geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00313-4.

What Geohistory Can Teach Us About Fundamental Causes of Health Inequities

The causes of cancer health inequities are complex, multilevel, and intersectional. The typical disciplines and data used to address these inequities focus on public health, health services, clinical, and fundamental science. Fundamental causes such as systemic racism are a source of much health inequity, but a broader scope of fundamental causes may be considered. Geohistorical events may intersect with other fundamental causes of health inequities. In this study, an example of relationships between ancient geological events, slavery, and subsequent effects of systematic racism are identified. These relationships support the hypothesis that health inequities have deep and complex origins. Geohistorical factors precede social, economic, and political influences on health inequities, and suggest that a full understanding of cancer health inequities and their elimination may be informed by geohistorical events. Thus, addressing inequities may involve disciplines not typically involved in health equity collaborations, including geography, history, economics, political science, and others.

Application of Bayesian Regression for the Identification of a Catchment Area for Cancer Cases in Dogs and Cats

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

The Geographic Context of Racial Disparities in Aggressive Endometrial Cancer Subtypes: Integrating Social and Environmental Aspects to Discern Biological Outcomes

The number of Endometrial Carcinoma (EC) diagnoses is projected to increase substantially in coming decades. Although most ECs have a favorable prognosis, the aggressive, non-endometrioid subtypes are disproportionately concentrated in Black women and spread rapidly, making treatment difficult and resulting in poor outcomes. Therefore, this study offers an exploratory spatial epidemiological investigation of EC patients within a U.S.-based health system's institutional cancer registry (n = 1748) to search for and study geographic patterns. Clinical, demographic, and geographic characteristics were compared by histotype using chi-square tests for categorical and t-tests for continuous variables. Multivariable logistic regression evaluated the impact of risks on these histotypes. Cox proportional hazard models measured risks in overall and cancer-specific death. Cluster detection indicated that patients with the EC non-endometrioid histotypes exhibit geographic clustering in their home address, such that congregate buildings can be identified for targeted outreach. Furthermore, living in a high social vulnerability area was independently associated with non-endometrioid histotypes, as continuous and categorical variables. This study provides a methodological framework for early, geographically targeted intervention; social vulnerability associations require further investigation. We have begun to fill the knowledge gap of geography in gynecologic cancers, and geographic clustering of aggressive tumors may enable targeted intervention to improve prognoses.

Spatiotemporal Analysis of Gastrointestinal Tumor (GI) with Kernel Density Estimation (KDE) Based on Heterogeneous Background

The purpose of this study is to explore hotspots or clusters of gastrointestinal tumors (GI) and their spatiotemporal distribution characteristics and the changes over time in 293 villages and communities in Jianze County, central China, through the kernel density estimation (KDE) method based on the rarely considered heterogeneous background. The main findings were: (1) Heterogeneous background impact: there were substantial differences in the GI case rate among people of different ages and genders in Jianze County. Specifically, the GI case rate was significantly higher in the elderly population over 65 than in the population under 65, and higher in men than in women. (2) GI in Jianze County exhibited spatial specific and aggregated hotspots. The high-value spatial clusters were mainly located in Hujindian Town in the northern county, Wupu Town and Geputan Town in the middle, and Xiaxindian Town in the south. Some villages had persistent hot spots for multiple years. (3) Most GI hotspots in Jianze County were concentrated in areas with both high density of local chemical plants and with water systems in the neighbourhood. We expect that this study provides a scientific basis for exploring unknown risk factors of tumor occurrence from a spatial perspective in the future.

Identification of spatio-temporal clusters of lung cancer cases in Pennsylvania, USA: 2010-2017

Background

It is known that geographic location plays a role in developing lung cancer. The objectives of this study were to examine spatio-temporal patterns of lung cancer incidence in Pennsylvania, to identify geographic clusters of high incidence, and to compare demographic characteristics and general physical and mental health characteristics in those areas.

Method

We geocoded the residential addresses at the time of diagnosis for lung cancer cases in the Pennsylvania Cancer Registry diagnosed between 2010 and 2017. Relative risks over the expected case counts at the census tract level were estimated using a log-linear Poisson model that allowed for spatial and temporal effects. Spatio-temporal clusters with high incidence were identified using scan statistics. Demographics obtained from the 2011–2015 American Community Survey and health variables obtained from 2020 CDC PLACES database were compared between census tracts that were part of clusters versus those that were not.

Results

Overall, the age-adjusted incidence rates and the relative risk of lung cancer decreased from 2010 to 2017 with no statistically significant space and time interaction. The analyses detected 5 statistically significant clusters over the 8-year study period. Cluster 1, the most likely cluster, was in southeastern PA including Delaware, Montgomery, and Philadelphia Counties from 2010 to 2013 (log likelihood ratio = 136.6); Cluster 2, the cluster with the largest area was in southwestern PA in the same period including Allegheny, Fayette, Greene, Washington, and Westmoreland Counties (log likelihood ratio = 78.6). Cluster 3 was in Mifflin County from 2014 to 2016 (log likelihood ratio = 25.3), Cluster 4 was in Luzerne County from 2013 to 2016 (log likelihood ratio = 18.1), and Cluster 5 was in Dauphin, Cumberland, and York Counties limited to 2010 to 2012 (log likelihood ratio = 17.9). Census tracts that were part of the high incidence clusters tended to be densely populated, had higher percentages of African American and residents that live below poverty line, and had poorer mental health and physical health when compared to the non-clusters (all p < 0.001).

Conclusions

These high incidence areas for lung cancer warrant further monitoring for other individual and environmental risk factors and screening efforts so lung cancer cases can be identified early and more efficiently.

Investigation of Relationships Between the Geospatial Distribution of Cancer Incidence and Estimated Pesticide Use in the U.S. West

The objective of the study was to evaluate the potential geospatial relationship between agricultural pesticide use and two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western United States at state and county resolution. The pesticide usage data were collected from the U.S. Geological Survey Pesticide National Synthesis Project database, while cancer data for each state were compiled from the National Cancer Institute State Cancer Profiles. At the state spatial scale, this study identified a significant positive association between the total mass of fumigants and pediatric cancer incidence, and also between the mass of one fumigant in particular, metam, and total cancer incidence (P-value < 0.05). At the county scale, the relationship of all cancer incidence to pesticide usage was evaluated using a multilevel model including pesticide mass and pesticide mass tertiles. Low pediatric cancer rates in many counties precluded this type of evaluation in association with pesticide usage. At the county scale, the multilevel model using fumigant mass, fumigant mass tertiles, county, and state predicted the total cancer incidence (R-squared = 0.95, NSE = 0.91, and Sum of square of residuals [SSR] = 8.22). Moreover, this study identified significant associations between total fumigant mass, high and medium tertiles of fumigant mass, total pesticide mass, and high tertiles of pesticide mass relative to total cancer incidence across counties. Fumigant application rate was shown to be important relative to the incidence of total cancer and pediatric cancer, at both state and county scales.

Spatial epidemiology of skin cancer in Iran: separating sun-exposed and non-sun-exposed parts of the body

Background

Skin cancer is among the most common cancer types with an increasing global trend of incidence rate. This study explores the spatial distribution of skin cancer, considering body sites exposed and not exposed to sunshine separately.

Methods

We used 4302 skin cancer cases recorded by Fars Cancer Registry in south-western Iran for over 6 years (2011–2017). The variables included in the study were patients’ residence address, gender, age, report date, and final topographical code. The patients’ addresses were geocoded to the counties of the study area. Skin cancer sites were categorized based on sun exposure in male and female cases. We used the empirical Bayesian smoothing approach to smooth the skin cancer incidence rate at the county level to remove any potential population size bias. Finally, Anselin’s Local Moran’s Index and Getis Ord G* were used to identify the clustered and high-risk skin cancer geographical areas.

Results

The incidence rates had an increasing trend from 14.28 per 100,000 people in 2011 to 17.87 per 100,000 people in 2016, however, it was decreased to 13.05 per 100,000 people in 2017. Out of 4302 patients with skin cancer, 2602 cases (60%) were male. The cancer cumulative incidence rate in males and females who were not exposed to sunshine was 7.80 and 14.18 per 100,000, respectively. The rates increased to 86.22 and 48.20 in males and females who were exposed to the sun. There were some high-risk spatial clusters of skin cancer in the study area. Further investigations are required to identify the underlying cause of the formation of these clusters.

Conclusions

Patients exposed to sunshine, especially among the male group, experienced much higher rates of cancer occurrence as compared to unexposed individuals. With a heterogeneous spatial pattern, hotspots were identified in non-sun-exposed and sun-exposed categories in the study area. Researchers and policymakers can significantly benefit from the spatial analyses of skin cancer incidence. These analyses can provide useful and timely prevention policies as well as tailored monitoring techniques in high-risk regions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00798-2.

Assessing the effect of environmental and socio-economic factors on skin melanoma incidence: an island-wide spatial study in Gran Canaria (Spain), 2007-2018

INTRODUCTION

Skin melanoma incidence has risen in the last decades becoming a major public health problem in many regions of the world. Geographic variation of rates is not well understood.

PURPOSE

To assess the spatial distribution of skin melanoma in Gran Canaria Island (Canary Islands, Spain) and to evaluate the role of environmental, socio-economic, and demographic factors in this distribution.

METHODS

We performed a small-area study with disease mapping at the census-tract level (CT) in Gran Canaria between 2007 and 2018. After testing for spatial autocorrelation, we integrated individual-level health data with census-based demographic and socio-economic indicators, and satellite-based environmental data. Finally, we assessed the role of demographic, socio-economic and environmental factors on skin melanoma incidence using a Bayesian analytical framework, with options for non-spatial and spatial random effects.

RESULTS

1058 patients were diagnosed with invasive skin melanoma in the study period and geolocated to a CT (number of CT in Gran Canaria = 565). We found evidence of global spatial autocorrelation in skin melanoma incidence (Moran's I = 0.09, pseudo p-value = 0.001). A few hotspots were detected, fundamentally in urban northern tracts. A radial pattern of high values was also observed in selected ravines with historical isolation. Multivariable conditional autoregressive models identified urbanicity, percent of females, and a high socio-economic status as risk factors for disease. Solar radiation did not show a significant role.

CONCLUSION

Urbanicity and a high socio-economic status were identified as the main risk factors for skin melanoma. These associations might reflect differential melanoma susceptibilities or be explained by health inequalities in detection. This study also uncovered high-risk areas in particular ravines. Future targeted research in these regions might help better understand the role of genetic and toxic factors in melanoma pathogenesis.

Area-Based Geocoding: An Approach to Exposure Assessment Incorporating Positional Uncertainty

While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case-control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point-based method along with a novel area-based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low-certainty locations. In contrast, area-based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point-based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes.

Eudaimonia: An Aristotelian approach to transplantation

Despite extraordinary achievements in over the past 20 years, the field of transplantation remains hindered by relatively narrow metrics for success. Eudaimonia is an Aristotelian concept that refers to flourishing, or achieving the best conditions possible, in every sense. The vast amounts of patient data that are collected throughout the transplant care continuum, ranging from social determinants of health to genomic profiles and patient-reported outcomes, afford us unprecedented opportunity to enhance our definition of success for our transplant patients. We must engage the technologies available for data integration and analysis and apply them in an insightful way, such that our clinical practice evolves beyond patient and graft survival and toward a more comprehensive state of wellness.

Disparities in accessibility to evidence-based breast cancer care facilities by rural and urban areas in Bavaria, Germany

BACKGROUND

Breast cancer (BC), which is most common in elderly women, requires a multidisciplinary and continuous approach to care. With demographic changes, the number of patients with chronic diseases such as BC will increase. This trend will especially hit rural areas, where the majority of the elderly live, in terms of comprehensive health care.

METHODS

Accessibility to several cancer facilities in Bavaria, Germany, was analyzed with a geographic information system. Facilities were identified from the national BC guideline and from 31 participants in a proof-of-concept study from the Breast Cancer Care for Patients With Metastatic Disease registry. The timeframe for accessibility was defined as 30 or 60 minutes for all population points. The collection of address information was performed with different sources (eg, a physician registry). Routine data from the German Census 2011 and the population-based Cancer Registry of Bavaria were linked at the district level.

RESULTS

Females from urban areas (n = 2,938,991 [ie, total of females living in urban areas]) had a higher chance for predefined accessibility to the majority of analyzed facilities in comparison with females from rural areas (n = 3,385,813 [ie, total number of females living in rural areas]) with an odds ratio (OR) of 9.0 for cancer information counselling, an OR of 17.2 for a university hospital, and an OR of 7.2 for a psycho-oncologist. For (inpatient) rehabilitation centers (OR, 0.2) and genetic counselling (OR, 0.3), women from urban areas had lower odds of accessibility within 30 or 60 minutes.

CONCLUSIONS

Disparities in accessibility between rural and urban areas exist in Bavaria. The identification of underserved areas can help to inform policymakers about disparities in comprehensive health care. Future strategies are needed to deliver high-quality health care to all inhabitants, regardless of residence.

Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017

Background

The most common gender-specific malignancies are cancers of the breast and the prostate. In developing countries, cancer screening of all at risk is impractical because of healthcare resource limitations. Thus, determining high-risk areas might be an important first screening step. This study explores incidence patterns of potential high-risk clusters of breast and prostate cancers in southern Iran.

Methods

This cross-sectional study was conducted in the province of Kerman, South Iran. Patient data were aggregated at the county and district levels calculating the incidence rate per 100,000 people both for cancers of the breast and the prostate. We used the natural-break classification with five classes to produce descriptive maps. A spatial clustering analysis (Anselin Local Moran’s I) was used to identify potential clusters and outliers in the pattern of these cancers from 2014 to 2017.

Results

There were 1350 breast cancer patients (including, 42 male cases) and 478 prostate cancer patients in the province of Kerman, Iran during the study period. After 45 years of age, the number of men with diagnosed prostate cancer increased similarly to that of breast cancer for women after 25 years of age. The age-standardised incidence rate of breast cancer for women showed an increase from 29.93 to 32.27 cases per 100,000 people and that of prostate cancer from 13.93 to 15.47 cases per 100,000 during 2014–2017. Cluster analysis at the county level identified high-high clusters of breast cancer in the north-western part of the province for all years studied, but the analysis at the district level showed high-high clusters for only two of the years. With regard to prostate cancer, cluster analysis at the county and district levels identified high-high clusters in this area of the province for two of the study years.

Conclusions

North-western Kerman had a significantly higher incidence rate of both breast and prostate cancer than the average, which should help in designing tailored screening and surveillance systems. Furthermore, this study generates new hypotheses regarding the potential relationship between increased incidence of cancers in certain geographical areas and environmental risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07674-8.

A spatial database of colorectal cancer patients and potential nutritional risk factors in an urban area in the Middle East

OBJECTIVES

Colorectal cancer (CRC) is the third most common cancer across the world that multiple risk factors together contribute to CRC development. There is a limited research report on impact of nutritional risk factors and spatial variation of CRC risk. Geographical information system (GIS) can help researchers and policy makers to link the CRC incidence data with environmental risk factor and further spatial analysis generates new knowledge on spatial variation of CRC risk and explore the potential clusters in the pattern of incidence. This spatial analysis enables policymakers to develop tailored interventions. This study aims to release the datasets, which we have used to conduct a spatial analysis of CRC patients in the city of Mashhad, Iran between 2016 and 2017.

DATA DESCRIPTION

These data include five data files. The file CRCcases_Mashhad contains the geographical locations of 695 CRC cancer patients diagnosed between March 2016 and March 2017 in the city of Mashhad. The Mashhad_Neighborhoods file is the digital map of neighborhoods division of the city and their population by age groups. Furthermore, these files include contributor risk factors including average of daily red meat consumption, average of daily fiber intake, and average of body mass index for every of 142 neighborhoods of the city.

Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention

Objectives

Liver cancer (LC) continues to rise, partially due to limited resources for prevention. To test the precision public health (PPH) hypothesis that fewer areas in need of LC prevention could be identified by combining existing surveillance data, we compared the sensitivity/specificity of standard recommendations to target geographic areas using U.S. Census demographic data only (percent (%) Hispanic, Black, and those born 1950–1959) to an alternative approach that couples additional geospatial data, including neighborhood socioeconomic status (nSES), with LC disease statistics.

Methods

Pennsylvania Cancer Registry data from 2007-2014 were linked to 2010 U.S. Census data at the Census tract (CT) level. CTs in the top 80th percentile for 3 standard demographic variables, %Hispanic, %Black, %born 1950–1959, were identified. Spatial scan statistics (SatScan) identified CTs with significantly elevated incident LC rates (p-value<0.05), adjusting for age, gender, diagnosis year. Sensitivity, specificity, and positive predictive value (PPV) of a CT being located in an elevated risk cluster and/or testing positive/negative for at least one standard variable were calculated. nSES variables (deprivation, stability, segregation) significantly associated with LC in regression models (p < 0.05) were systematically evaluated for improvements in sensitivity/specificity.

Results

9,460 LC cases were diagnosed across 3,217 CTs. 1,596 CTs were positive for at least one of 3 standard variables. 5 significant elevated risk clusters (CTs = 402) were identified. 324 CTs were positive for a high risk cluster AND standard variable (sensitivity = 92%; specificity = 37%; PPV = 17.4%). Incorporation of 3 new nSES variables with one standard variable (%Black) further improved sensitivity (93%), specificity (62.9%), and PPV (26.3%).

Conclusions

We introduce a quantitative assessment of PPH by applying established sensitivity/specificity assessments to geospatial data. Coupling existing disease cluster and nSES data can more precisely identify intervention targets with a liver cancer burden than standard demographic variables. Thus, this approach may inform prioritization of limited resources for liver cancer prevention.

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Precision Public Health calls for linking surveillance data to identify fewer neighborhoods for intervention.

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Sensitivity/specificity methods can measure the utility of Precision Public Health by identifying optimal data combinations.

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Select combinations of linked Census and liver cancer registry data reduced neighborhood targets more than Census data alone.

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Precision Public Health improves the prioritization of liver cancer prevention efforts.

Using GIS to Explore the Potential of Business Rating Data to Analyse Stock and Value Change for Land Administration: A Case Study of York

This study explores the potential of GIS to map and analyse the distribution, stock and value of commercial and industrial property using rating data compiled for the purposes of charging business rates taxation on all non-residential property in the UK. Rating data from 2010, 2017 and 2019, comprising over 6000 property units in the City of York, were filtered and classified by retail, office and industrial use, before geocoding by post code. Nominal rateable values and floor areas for all premises were aggregated in 100 m diameter hexagonal grid and average rateable value calculated to reveal changes in the distribution and value of all employment floorspace in the City over the last decade. Temporospatial analysis revealed polarisation of York’s retail property market between the historic city centre and out-of-town locations. Segmenting traditional retail from food and drink premises revealed growth in the latter has mitigated the hollowing out of the city core. This study is significant in developing a replicable and efficient method of using GIS, using a nationally available rating dataset, to represent changes in the quantum, spatial distribution and relative value of employment floorspace over time to inform local and national land administration, spatial planning and economic development policy making.

A Bivariate Mapping Tutorial for Cancer Control Resource Allocation Decisions and Interventions

Bivariate choropleth mapping is a straightforward but underused method for displaying geographic health information to use in public health decision making. Previous studies have recommended this approach for state comprehensive cancer control planning and similar efforts. In this method, 2 area-level variables of interest are mapped simultaneously, often as overlapping quantiles or by using other classification methods. Variables to be mapped may include area-level (eg, county level) measures of disease burden, health care use, access to health care services, and sociodemographic characteristics. We demonstrate how geographic information systems software, specifically ArcGIS, can be used to develop bivariate choropleth maps to inform resource allocation and public health interventions. We used 2 types of county-level public health data: South Carolina’s Behavioral Risk Factor Surveillance System estimates of ever having received cervical cancer screening, and a measure of availability of cervical cancer screening providers that are part of South Carolina’s Breast and Cervical Cancer Early Detection Program. Identification of counties with low screening rates and low access to care may help inform where additional resources should be allocated to improve access and subsequently improve screening rates. Similarly, identifying counties with low screening rates and high access to care may help inform where educational and behavioral interventions should be targeted to improve screening in areas of high access.