Mapping the prevalence of cancer risk factors at the small area level in Australia.

BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Disease mapping: Geographic differences in population rates of interventional treatment for prostate cancer in Australia.

BACKGROUND: Treatment decisions for men diagnosed with prostate cancer depend on a range of clinical and patient characteristics such as disease stage, age, general health, risk of side effects and access. Associations between treatment patterns and area-level factors such as remoteness and socioeconomic disadvantage have been observed in many countries. OBJECTIVE: To model spatial differences in interventional treatment rates for prostate cancer at high spatial resolution to inform policy and decision-making. METHODS: Hospital separations data for interventional treatments for prostate cancer (radical prostatectomy, low dose rate and high dose rate brachytherapy) for men aged 40 years and over were modelled using spatial models, generalised linear mixed models, maximised excess events tests and k-means statistical clustering. RESULTS: Geographic differences in population rates of interventional treatments were found (p<0.001). Separation rates for radical prostatectomy were lower in remote areas (12.2 per 10 000 person-years compared with 15.0-15.9 in regional and major city areas). Rates for all treatments decreased with increasing socioeconomic disadvantage (radical prostatectomy 19.1 /10 000 person-years in the most advantaged areas compared with 12.9 in the most disadvantaged areas). Three groups of similar areas were identified: those with higher rates of radical prostatectomy, those with higher rates of low dose brachytherapy, and those with low interventional treatment rates but higher rates of excess deaths. The most disadvantaged areas and remote areas tended to be in the latter group. CONCLUSIONS: The geographic differences in treatment rates may partly reflect differences in patients' physical and financial access to treatments. Treatment rates also depend on diagnosis rates and thus reflect variation in investigation rates for prostate cancer and presentation of disease. Spatial variation in interventional treatments may aid identification of areas of under-treatment or over-treatment.

Geographical and spatial variations in bowel cancer screening participation, Australia, 2015-2020.

BACKGROUND: Participation in bowel cancer screening programs remains poor in many countries. Knowledge of geographical variation in participation rates may help design targeted interventions to improve uptake. This study describes small-area and broad geographical patterns in bowel screening participation in Australia between 2015-2020. METHODS: Publicly available population-level participation data for Australia's National Bowel Cancer Screening Program (NBCSP) were modelled using generalized linear models to quantify screening patterns by remoteness and area-level disadvantage. Bayesian spatial models were used to obtain smoothed estimates of participation across 2,247 small areas during 2019-2020 compared to the national average, and during 2015-2016 and 2017-2018 for comparison. Spatial heterogeneity was assessed using the maximized excess events test. RESULTS: Overall, screening participation rates was around 44% over the three time-periods. Participation was consistently lower in remote or disadvantaged areas, although heterogeneity was evident within these broad categories. There was strong evidence of spatial differences in participation over all three periods, with little change in patterns between time periods. If the spatial variation was reduced (so low participation areas were increased to the 80th centile), an extra 250,000 screens (4% of total) would have been conducted during 2019-2020. CONCLUSIONS: Despite having a well-structured evidence-based government funded national bowel cancer screening program, the substantial spatial variation in participation rates highlights the importance of accounting for the unique characteristics of specific geographical regions and their inhabitants. Identifying the reasons for geographical disparities could inform interventions to achieve more equitable access and a higher overall bowel screening uptake.

Geographical and spatial disparities in the incidence and survival of rare cancers in Australia.

Rare cancers collectively account for around a quarter of cancer diagnoses and deaths. However, epidemiological studies are sparse. We describe spatial and geographical patterns in incidence and survival of rare cancers across Australia using a population-based cancer registry cohort of rare cancer cases diagnosed among Australians aged at least 15 years, 2007 to 2016. Rare cancers were defined using site- and histology-based categories from the European RARECARE study, as individual cancer types having crude annual incidence rates of less than 6/100 000. Incidence and survival patterns were modelled with generalised linear and Bayesian spatial Leroux models. Spatial heterogeneity was tested using the maximised excess events test. Rare cancers (n = 268 070) collectively comprised 22% of all invasive cancer diagnoses and accounted for 27% of all cancer-related deaths in Australia, 2007 to 2016 with an overall 5-year relative survival of around 53%. Males and those living in more remote or more disadvantaged areas had higher incidence but lower survival. There was substantial evidence for spatial variation in both incidence and survival for rare cancers between small geographical areas across Australia, with similar patterns so that those areas with higher incidence tended to have lower survival. Rare cancers are a substantial health burden in Australia. Our study has highlighted the need to better understand the higher burden of these cancers in rural and disadvantaged regions where the logistical challenges in their diagnosis, treatment and support are magnified.

Geographic distribution of malignant mesothelioma incidence and survival in Australia.

OBJECTIVES: To understand the geographic distribution of and area-level factors associated with malignant mesothelioma incidence and survival in Australia. MATERIALS AND METHODS: Generalised linear models and Bayesian spatial models were fitted using population registry data. Area-level covariates were socioeconomic quintile, remoteness category and state or territory. The maximised excess events test was used to test for spatial heterogeneity. RESULTS: There was strong evidence of spatial differences in standardised incidence rates for malignant mesothelioma but survival was uniformly poor. Incidence rates varied by state or territory and were lower in remote areas. Patterns in the geographic distribution of modelled incidence counts for malignant mesothelioma differed substantially from patterns of standardised incidence rates. CONCLUSIONS: Geographic variation in the modelled incidence counts of malignant mesothelioma demonstrates varying demand for diagnostic and management services. The long latency period for this cancer coupled with migration complicates any associations with patterns of exposure, however some of the geographic distribution of diagnoses can be explained by the location of historical mines and asbestos-related industries.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics.

BACKGROUND: Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancer types over 2148 small areas across Australia. METHODS: The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standardised incidence ratios for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. The correlation between the pairs of cancers included in each multivariate model for a group was examined by computing the posterior correlation matrix for each cancer group in each region. The posterior correlation matrices in different remoteness regions were compared using Jennrich's test of equality of correlation matrices (Jennrich in J Am Stat Assoc. 1970;65(330):904-12. https://doi.org/10.1080/01621459.1970.10481133 ). RESULTS: Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. For example, there has been significant negative correlation between prostate and lung cancer in major cities, but zero correlation found in regional and remote areas for the same pair of cancer types. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model. CONCLUSIONS: Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

Augmenting disease maps: a Bayesian meta-analysis approach.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

County-level variation in the long-term association between PM2.5 and lung cancer mortality in China.

INTRODUCTION: The relative risk (RR) of long-term exposure to PM2.5 in lung cancer mortality (LCM) may vary spatially in China. However, previous studies applying global regression have been unable to capture such variation. We aimed to employ a geographically weighted Poisson regression (GWPR) to estimate the RRs of LCM among the elderly (≥65 years) related to long-term exposure to PM2.5 and the LCM attributable to PM2.5 at the county level in China. METHODS: We obtained annual LCM in the elderly between 2013 and 2015 from the National Death Surveillance. We linked annual mean concentrations of PM2.5 between 2000 and 2004 with LCM using GWPR model at 148 counties across mainland China, adjusting for smoking and socioeconomic covariates. We used county-specific GWPR models to estimate annual average LCM in the elderly between 2013 and 2015 attributable to PM2.5 exposure between 2000 and 2004. RESULTS: The magnitude of the association between long-term exposure to PM2.5 and LCM varied with county. The median of county-specific RRs of LCM among elderly men and women was 1.52 (range: 0.90, 2.40) and 1.49 (range: 0.88, 2.56) for each 10 µg/m3 increment in PM2.5, respectively. The RRs were positively significant (P < 0.05) at 95% (140/148) of counties among both elderly men and women. Higher RRs of PM2.5 among elderly men were located at Southwest and South China, and higher RRs among elderly women were located at Northwest, Southwest, and South China. There were 99,967 and 54,457 lung cancer deaths among elderly men and women that could be attributed to PM2.5, with the attributable fractions of 31.4% and 33.8%, respectively. CONCLUSIONS: The relative importance of long-term exposure to PM2.5 in LCM differed by county. The results could help the government design tailored and efficient interventions. More stringent PM2.5 control is urgently needed to reduce LCM in China.

Global, regional, and national burden of lung cancer and its attributable risk factors, 1990 to 2017.

BACKGROUND: China's lung cancer (LC) burden plays a pivotal role in the global cancer epidemic. Comparing LC burden and population attributable fractions (PAFs) of risk factors between China and other countries/regions is essential to inform effective intervention. The Global Burden of Disease (GBD) study provides a unique opportunity for such comparisons. METHODS: We extracted the number of LC deaths, age-standardized death rates (ASDRs), age-standardized disability-adjusted life-year (DALY) rates, and PAFs of risk factors for LC deaths between 1990 and 2017 from GBD 2017. The annual percentage change (APC) was used to quantify the trends of LC ASDRs and age-standardized DALY rates. The relationship between the APC of LC ASDR and Socio-demographic Index was assessed among China and other countries. RESULTS: Globally, the ASDR for LC decreased in men (APC, -0.66% [95% CI, -0.69 to -0.62]) but increased in women (APC, 0.31% [95% CI, 0.26 to 0.36]) from 1990 to 2017. The ASDRs in China increased both for men (APC, 1.12% [95% CI, 1.03 to 1.20]) and women (APC, 0.80% [95% CI, 0.70 to 0.89]). The increased LC death numbers among men (312,798) and women (139,115) in China accounted for 59.39% and 43.01% of global increases. LC years of life lost accounted for the majority of LC DALYs globally and in China. The risk factors with the highest PAFs of LC death in China were smoking and ambient particulate matter. The ASDRs for LC associated with ambient particulate matter in China ranked second globally. CONCLUSIONS: The trends of LC ASDRs and age-standardized DALY rates and the PAFs of risk factors vary markedly by region, indicating a need for tailored measures to reduce LC burden and improve health equality. China's LC ASDRs are among the highest in the world, and the primary intervention priorities in China should be control of ambient particulate matter and tobacco usage.

Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing.

BACKGROUND: Many methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or global mean. Smoothing is important for several reasons, not least of all because it increases predictive robustness and reduces uncertainty of the estimates. Despite the benefits of smoothing, this attribute is all but ignored when it comes to model selection. Traditional goodness-of-fit measures focus on model fit and model parsimony, but neglect "goodness-of-smoothing", and are therefore not necessarily good indicators of model performance. Comparing spatial models while taking into account the degree of spatial smoothing is not straightforward because smoothing and model fit can be viewed as opposing goals. Over- and under-smoothing of spatial data are genuine concerns, but have received very little attention in the literature. METHODS: This paper demonstrates the problem with spatial model selection based solely on goodness-of-fit by proposing several methods for quantifying the degree of smoothing. Several commonly used spatial models are fit to real data, and subsequently compared using the goodness-of-fit and goodness-of-smoothing statistics. RESULTS: The proposed goodness-of-smoothing statistics show substantial agreement in the task of model selection, and tend to avoid models that over- or under-smooth. Conversely, the traditional goodness-of-fit criteria often don't agree, and can lead to poor model choice. In particular, the well-known deviance information criterion tended to select under-smoothed models. CONCLUSIONS: Some of the goodness-of-smoothing methods may be improved with modifications and better guidelines for their interpretation. However, these proposed goodness-of-smoothing methods offer researchers a solution to spatial model selection which is easy to implement. Moreover, they highlight the danger in relying on goodness-of-fit measures when comparing spatial models.

Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.

PURPOSE: Limiting the dose to the rectum can be one of the most challenging aspects of creating a dosimetric external beam radiation therapy (EBRT) plan for prostate cancer treatment. Rectal sparing devices such as hydrogel spacers offer the prospect of increased space between the prostate and rectum, causing reduced rectal dose and potentially reduced injury. This study sought to help identify patients at higher risk of developing rectal injury based on estimated rectal dosimetry compliance prior to the EBRT simulation and planning procedure. Three statistical machine learning methods were compared for their ability to predict rectal dose outcomes with varied classification thresholds applied. METHODS: Prostate cancer patients treated with conventionally fractionated EBRT to a reference dose of 74-78 Gy were invited to participate in the study. The dose volume histogram data from each dosimetric plan was used to quantify planned rectal volume receiving 50%, 83% 96%, and 102% of the reference dose. Patients were classified into two groups for each of these dose levels: either meeting tolerance by having a rectal volume less than a clinically acceptable threshold for the dose level (Y) or violating the tolerance by having a rectal volume greater than the threshold for the dose level (N). Logistic regression, classification and regression tree, and random forest models were compared for their ability to discriminate between class outcomes. Performance metrics included area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. Finally, three classification threshold levels were evaluated for their impact on model performance. RESULTS: A total of 176 eligible participants were recruited. Variable importance differed between model methods. Area under the receiver operator characteristic curve performance varied greatly across the different rectal dose levels and between models. Logistic regression performed best at the 83% reference dose level with an AUC value of 0.844, while random forest demonstrated best discrimination at the 96% reference dose level with an AUC value of 0.733. In addition to the standard classification probability threshold of 50%, the clinically representative threshold of 10%, and the best threshold from each AUC plot was applied to compare metrics. This showed that using a 50% threshold and the best threshold from the AUC plots yields similar results. Conversely, applying the more conservative clinical threshold of 10% maximized the sensitivity at V83_RD and V96_RD for all model types. Based on the combination of the metrics, logistic regression would be the recommendation for rectal protocol compliance prediction at the 83% reference dose level, and random forest for the 96% reference dose level, particularly when using the clinical probability threshold of 10%. CONCLUSIONS: This study demonstrated the efficacy of statistical machine learning models on rectal protocol compliance prediction for prostate cancer EBRT dosimetric planning. Both logistic regression and random forest modeling approaches demonstrated good discriminative ability for predicting class outcomes in the upper dose levels. Application of a conservative clinical classification threshold maximized sensitivity and further confirmed the value of logistic regression and random forest models over classification and regression tree.

Application of ensemble methods to analyse the decline of organochlorine pesticides in relation to the interactions between age, gender and time.

Organochlorine pesticides (OCPs) are toxic chemicals that persist in human tissue. Short and long term exposure to OCPs have been shown to have adverse effects on human health. This motivates studies into the concentrations of pesticides in humans. However these studies typically emphasise the analysis of the main effects of age group, gender and time of sample collection. The interactions between main effects can distinguish variation in OCP concentration such as the difference in concentrations between genders of the same age group as well as age groups over time. These are less studied but may be equally or more important in understanding effects of OCPs in a population. The aim of this study was to identify interactions relevant to understanding OCP concentrations and utilise them appropriately in models. We propose a two stage analysis comprising of boosted regression trees (BRTs) and hierarchical modelling to study OCP concentrations. BRTs are used to discover influential interactions between age group, gender and time of sampling. Hierarchical models are then employed to test and infer the effect of the interactions on OCP concentrations. Results of our analysis show that the best fitting model of an interaction effect varied between OCPs. The interaction between age group and gender was most influential for hexachlorobenzene (HCB) concentrations. There was strong evidence of an interaction effect between age group and time for ß-hexachlorocyclohexane (ß-HCH) concentrations in >60 year olds as well as an interaction effect between age group and gender for HCB concentrations for adults aged >45 years. This study highlights the need to consider appropriate interaction effects in the analysis of OCP concentrations and provides further insight into the interplay of main effects on OCP concentration trends.

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates.

BACKGROUND: It is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia. METHODS: The Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually. RESULTS: The results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas ( https://atlas.cancer.org.au ) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making. CONCLUSIONS: The success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

Short-term association between ambient air pollution and lung cancer mortality.

RATIONALE: Long-term exposure to air pollution has been associated with increased lung cancer incidence and mortality. However, the short-term association between air pollution and lung cancer mortality (LCM) remains largely unknown. METHODS: We collected daily data on particulate matter with diameter <2.5 µm (PM2.5), particulate matter with diameter < 10 µm (PM10), sulfur dioxide (SO2), and ozone (O3), and LCM in three of the biggest cities in China, i.e. Beijing, Chongqing, and Guangzhou, from 2013 to 2015. We first estimated city-specific relationships between air pollutants and LCM using time-series generalized linear models, adjusting for potential confounders. A classification and regression tree (CART) model was used to stratify LCM risk based on combinations of air pollutants and meteorological factors in each city. Then we pooled the city-specific associations using random-effects meta-analysis. Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association. Finally, we stratified the analyses by season, age, and sex. RESULTS: Over the entire period, the current-day concentrations of PM2.5 and PM10 in Chongqing and PM2.5, PM10, and SO2 in Guangzhou were positively associated with LCM (Excess risk ranged from 0.72% (95% CI 0.27%-1.17%) to 6.06% (95% CI 0.76%-11.64%) with each 10 µg/m3 increment in different pollutants), but the association between current-day air pollution and LCM in Beijing was not significant (P > 0.05). When considering the environmental and weather factors simultaneously, current-day PM2.5, relative humidity, and PM10 were the most important factors associated with LCM in Beijing, Chongqing, and Guangzhou, respectively. LCM risk related with daily PM2.5, PM10, and SO2 significantly increased with the increasing annual mean temperature and humidity of the city, while LCM risk related with daily O3 significantly increased with the increases of latitude, annual mean O3 concentration, and socioeconomic level. After stratification, the current-day PM2.5, PM10, and O3 during the warm season in Beijing and PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou were positively associated with LCM (Excess risk ranged from 0.93% (95% CI 0.42%-1.45%) to 7.16% (95% CI 0.64%-14.09%) with each 10 µg/m3 increment in different pollutants). Male and the elderly lung cancer patients were more sensitive to the short-term effect of air pollution. CONCLUSIONS: Lung cancer patients should enhance protection measures against air pollution. More attentions should be paid for the high PM2.5, PM10, and O3 during the warm season in Beijing, and high PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou.

Lung Cancer Mortality in China: Spatial and Temporal Trends Among Subpopulations.

BACKGROUND: This study aimed to identify changing spatial and temporal trends of lung cancer mortality rates (LCMRs) among subpopulations in China (according to region, age, and sex). METHODS: Data on LCMRs from 2006 to 2015 were extracted from the Chinese National Death Surveillance. Joinpoint regression and seasonal decomposition were used to assess the temporal trends. A geographic information system and spatial kriging interpolation were used to examine the spatial trends. RESULTS: LCMRs in men aged 30 to 49 years significantly declined nationally from 2009 to 2015 (annual percentage change, -2.7%; P < .05), but they continued to rise in men aged ≥ 70 years and women aged ≥ 50 years in the east, people aged 50 to 69 years in the south, and most groups in the southwest. Among provincial capital cities, Shenyang, Changsha, and Hohhot had the highest 10-year average LCMR for men aged 30 to 49 years, 50 to 69 years, and ≥ 70 years, respectively; among all ages of women, Harbin had the highest average LCMR. Over the 10 years, the odds of the increases in LCMRs in men and women aged 30 to 69 years decreased by 3% to 7% with the longitudes or latitudes increasing by 1° (ORs ranged from 0.93 [95% CI, 0.90-0.95) to 0.97 [95% CI, 0.95-0.99]). CONCLUSIONS: Disparities in the spatial and temporal trends of LCMRs among subpopulations highlight the need for investigation into potential drivers, especially for the east, south, and southwest of China. These findings may help health authorities target interventions to those most in need to reduce the lung cancer burden in China.

An image-guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool.

PURPOSE: To describe a Bayesian network (BN) and complementary visualization tool that aim to support decision-making during online cone-beam computed tomography (CBCT)-based image-guided radiotherapy (IGRT) for prostate cancer patients. METHODS: The BN was created to represent relationships between observed prostate, proximal seminal vesicle (PSV), bladder and rectum volume variations, an image feature alignment score (FASTV_OAR ), delivered dose, and treatment plan compliance (TPC). Variables influencing tumor volume (TV) targeting accuracy such as intrafraction motion, and contouring and couch shift errors were also represented. A score of overall TPC (FASglobal ) and factors such as image quality were used to inform the BN output node providing advice about proceeding with treatment. The BN was quantified using conditional probabilities generated from published studies, FASTV_OAR/global modeling, and a survey of IGRT decision-making practices. A new IGRT visualization tool (IGRTREV ), in the form of Mollweide projection plots, was developed to provide a global summary of residual errors after online CBCT-planning CT registration. Sensitivity and scenario analyses were undertaken to evaluate the performance of the BN and the relative influence of the network variables on TPC and the decision to proceed with treatment. The IGRTREV plots were evaluated in conjunction with the BN scenario testing, using additional test data generated from retrospective CBCT-planning CT soft-tissue registrations for 13/36 patients whose data were used in the FASTV_OAR/global modeling. RESULTS: Modeling of the TV targeting errors resulted in a very low probability of corrected distances between the CBCT and planning CT prostate or PSV volumes being within their thresholds. Strength of influence evaluation with and without the BN TV targeting error nodes indicated that rectum- and bladder-related network variables had the highest relative importance. When the TV targeting error nodes were excluded from the BN, TPC was sensitive to observed PSV and rectum variations while the decision to treat was sensitive to observed prostate and PSV variations. When root nodes were set so the PSV and rectum variations exceeded thresholds, the probability of low TPC increased to 40%. Prostate and PSV variations exceeding thresholds increased the likelihood of repositioning or repeating patient preparation to 43%. Scenario testing using the test data from 13 patients, demonstrated two cases where the BN provided increased high TPC probabilities, despite some of the prostate and PSV volume variation metrics not being within tolerance. The IGRTREV tool was effective in highlighting and quantifying where TV and OAR variations occurred, supporting the BN recommendation to reposition the patient or repeat their bladder and bowel preparation. In another case, the IGRTREV tool was also effective in highlighting where PSV volume variation significantly exceeded tolerance when the BN had indicated to proceed with treatment. CONCLUSIONS: This study has demonstrated that both the BN and IGRTREV plots are effective tools for inclusion in a decision support system for online CBCT-based IGRT for prostate cancer patients. Alternate approaches to modeling TV targeting errors need to be explored as well as extension of the BN to support offline IGRT decisions related to adaptive radiotherapy.

A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer.

PURPOSE: To develop a method for scoring online cone-beam CT (CBCT)-to-planning CT image feature alignment to inform prostate image-guided radiotherapy (IGRT) decision-making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. METHODS: Radiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT-IGRT case examples and completed a paper-based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FASTV_OAR ). Scaled and weighted FASTV_OAR were then used to calculate a score of overall treatment compliance (FASglobal ) for a given CBCT-planning CT registration. The FASTV_OAR were assessed for sensitivity, specificity, and predictive power. FASglobal thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. RESULTS: Thirty-two participants completed the prostate CBCT-IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose ± 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FASTV_OAR >0 is indicative of delivery of planned dose. For calculated FASTV_OAR for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FASglobal of were low <60, medium 60-80, and high >80, with a 27% misclassification rate for the test data. CONCLUSIONS: A FASglobal incorporating nested FASTV_OAR and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy.

Lung cancer and particulate pollution: A critical review of spatial and temporal analysis evidence.

BACKGROUND: Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs. METHODS: We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM10 (inhalable particles with diameters that are 10 µm and smaller) with lung cancer. RESULTS: We identified 17 articles eligible for the review. Of these, 11 focused on PM2.5, five on PM10, and one on both. These studies suggested a significant positive association between PM2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07-1.09) to 1.60 (95%CI: 1.09-2.33) for 10 µg/m3 increase in PM2.5 exposure. The association between PM10 and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively. CONCLUSIONS: Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies.

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

BACKGROUND: When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. METHODS: The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. RESULTS: The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. CONCLUSIONS: The specification of the spatial weights matrix can have a colossal impact on model fit and parameter estimation. The results provide some evidence that a smaller number of neighbours used in defining the spatial weights matrix yields a better model fit, and may provide a more accurate representation of the underlying spatial random field. The commonly used binary, first-order adjacency weights still appear to be a good choice for implementing spatial smoothing.