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.

Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare.

OBJECTIVE: Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls. MATERIALS AND METHODS: Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance. RESULTS: The proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration. DISCUSSION: Our results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research. CONCLUSIONS: This study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

Changes in prostate specific antigen (PSA) "screening" patterns by geographic region and socio-economic status in Australia: Analysis of medicare data in 50-69 year old men.

BACKGROUND: While it is known that national PSA testing rates have decreased in Australia since 2007, it is not known whether these trends are consistent by broad geographical areas, nor whether previously reported area-specific differences have remained in more recent time periods. METHODS: Population-based cohort study of Australian men (n = 2793,882) aged 50-69 who received at least one PSA test (Medicare Benefit Schedule item number 66655) during 2002-2018. Outcome measures included age-standardised participation rate, annual percentage change using JoinPoint regression and indirectly standardised participation rate ratio using multivariable Poisson regression. RESULTS: During 2005-09, two thirds (68%) of Australian men aged 50-69 had at least one PSA test, reducing to about half (48%) during 2014-18. In both periods, testing rates were highest among men living in major cities, men aged 50-59 years, and among men living in the most advantaged areas. Nationally, the Australian PSA testing rate increased by 9.2% per year between 2002 and 2007, but then decreased by 5.0% per year to 2018. This pattern was generally consistent across States and Territories, and socio-economic areas, however the magnitude of the trends was less pronounced in remote and very remote areas. CONCLUSIONS: The decreasing trends are consistent with a greater awareness of the current guidelines for clinical practice in Australia, which recommend a PSA test be done only with the informed consent of individual men who understand the potential benefits and risks. However, given there remain substantial geographical disparities in prostate cancer incidence and survival in Australia, along with the equivocal evidence for any benefit from PSA screening, there remains a need for more effective diagnostic strategies for prostate cancer to be implemented consistently regardless of where men live.

Global trends in the incidence of hospital admissions for diabetes-related foot disease and amputations: a review of national rates in the 21st century.

AIMS/HYPOTHESIS: Diabetic foot disease (DFD) is a leading cause of hospital admissions and amputations. Global trends in diabetes-related amputations have been previously reviewed, but trends in hospital admissions for multiple other DFD conditions have not. This review analysed the published incidence of hospital admissions for DFD conditions (ulceration, infection, peripheral artery disease [PAD], neuropathy) and diabetes-related amputations (minor and major) in nationally representative populations. METHODS: PubMed and Embase were searched for peer-reviewed publications between 1 January 2001 and 5 May 2022 using the terms 'diabetes', 'DFD', 'amputation', 'incidence' and 'nation'. Search results were screened and publications reporting the incidence of hospital admissions for a DFD condition or a diabetes-related amputation among a population representative of a country were included. Key data were extracted from included publications and initial rates, end rates and relative trends over time summarised using medians (ranges). RESULTS: Of 2527 publications identified, 71 met the eligibility criteria, reporting admission rates for 27 countries (93% high-income countries). Of the included publications, 14 reported on DFD and 66 reported on amputation (nine reported both). The median (range) incidence of admissions per 1000 person-years with diabetes was 16.3 (8.4-36.6) for DFD conditions (5.1 [1.3-7.6] for ulceration; 5.6 [3.8-9.0] for infection; 2.5 [0.9-3.1] for PAD) and 3.1 (1.4-10.3) for amputations (1.2 [0.2-4.2] for major; 1.6 [0.3-4.3] for minor). The proportions of the reported populations with decreasing, stable and increasing admission trends were 80%, 20% and 0% for DFD conditions (50%, 0% and 50% for ulceration; 50%, 17% and 33% for infection; 67%, 0% and 33% for PAD) and 80%, 7% and 13% for amputations (80%, 17% and 3% for major; 52%, 15% and 33% for minor), respectively. CONCLUSIONS/INTERPRETATION: These findings suggest that hospital admission rates for all DFD conditions are considerably higher than those for amputations alone and, thus, the more common practice of reporting admission rates only for amputations may substantially underestimate the burden of DFD. While major amputation rates appear to be largely decreasing, this is not the case for hospital admissions for DFD conditions or minor amputation in many populations. However, true global conclusions are limited because of a lack of consistent definitions used to identify admission rates for DFD conditions and amputations, alongside a lack of data from low- and middle-income countries. We recommend that these areas are addressed in future studies. REGISTRATION: This review was registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/4TZFJ ).

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.

Inpatient Fall Prediction Models: A Scoping Review.

INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction. METHODS: This scoping review used methods recommended by the Arksey and O'Malley framework and its recent advances. PubMed, CINAHL, IEEE Xplore, and EMBASE databases were systematically searched. Studies reporting the development of inpatient fall risk prediction approaches were included. There was no restriction on language or recency. Reference lists and manual searches were also completed. Reporting quality was assessed using adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement (TRIPOD), where appropriate. RESULTS: Database searches identified 1,396 studies, 63 were included for scoping assessment and 45 for reporting quality assessment. There was considerable overlap in data sources and methods used for model development. Fall prediction models typically relied on features from patient assessments, including indicators of physical function or impairment, or cognitive function or impairment. All but two studies used patient information at or soon after admission and predicted fall risk over the entire admission, without consideration of post-admission interventions, acuity changes or length of stay. Overall, reporting quality was poor, but improved in the past decade. CONCLUSION: There was substantial homogeneity in data sources and prediction model development methods. Use of artificial intelligence, including machine learning with high-dimensional data, remains underexplored in the context of hospital falls. Future research should consider approaches with the potential to utilize high-dimensional data from digital hospital systems, which may contribute to greater performance and clinical usefulness.

Diabetes and physical activity: A prospective cohort study.

Diabetes is on the rise as the worldwide population ages. While physical activity can help protect against diabetes, ageing is commonly associated with reduced physical activity. This study aimed to examine if physical activity differs by diabetes status in mid-aged adults, how this association changes over time, and whether physical activity-related sociodemographic factors and health indicators differ in those with and without diabetes. Data came from four waves of the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT), a longitudinal study of mid-age adults living in Brisbane, Australia. Random effects/Expectation-maximisation (RE-EM) regression trees were used to identify factors affecting physical activity among those with and without diabetes, both separately and combined. At study entry, those with diabetes had a higher median age of 58 years (95% CI: 57-60) and a lower median physical activity of 699 MET.min/week (95% CI: 599-799) than people without diabetes (53 years (95% CI: 53-53) and 849 MET.min/week (95% CI: 799-899)). However, the strongest factors influencing physical activity were BMI and gender, not diabetes status. It is vital to promote physical activity among adults, in particular among those with high BMI and women, as well as those with and at high risk of diseases like diabetes.

Clinical prediction models for hospital falls: a scoping review protocol.

INTRODUCTION: Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients' fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment. METHODS AND ANALYSIS: This scoping review will follow the Arksey and O'Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis. ETHICS AND DISSEMINATION: Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.

Correction to 'Augmenting disease maps: a Bayesian meta-analysis approach'.

[This corrects the article DOI: 10.1098/rsos.192151.][This corrects the article DOI: 10.1098/rsos.192151.].

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.

Quantifying the Number of Cancer Deaths Avoided Due to Improvements in Cancer Survival since the 1980s in the Australian Population, 1985-2014.

BACKGROUND: This study quantifies the number of potentially "avoided"cancer deaths due to differences in 10-year relative survival between three time periods, reflecting temporal improvements in cancer diagnostic and/or treatment practices in Australia. METHODS: National population-based cohort of 2,307,565 Australians ages 15 to 89 years, diagnosed with a primary invasive cancer from 1985 to 2014 with mortality follow-up to December 31, 2015. Excess mortality rates and crude probabilities of cancer deaths were estimated using flexible parametric relative survival models. Crude probabilities were then used to calculate "avoided cancer deaths" (reduced number of cancer deaths within 10 years of diagnosis due to survival changes since 1985-1994) for all cancers and 13 leading cancer types. RESULTS: For each cancer type, excess mortality (in the cancer cohort vs. the expected population mortality) was significantly lower for more recently diagnosed persons. For all cancers combined, the number of "avoided cancer deaths" (vs. 1985-1994) was 4,877 (1995-2004) and 11,385 (2005-2014) among males. Prostate (1995-2004: 2,144; 2005-2014: 5,099) and female breast cancer (1,127 and 2,048) had the highest number of such deaths, whereas <400 were avoided for pancreatic or lung cancers across each period. CONCLUSIONS: Screening and early detection likely contributed to the high number of "avoided cancer deaths" for prostate and female breast cancer, whereas early detection remains difficult for lung and pancreatic cancers, highlighting the need for improved preventive and screening measures. IMPACT: Absolute measures such as "avoided cancer deaths" can provide a more tangible estimate of the improvements in cancer survival than standard net survival measures.

Temporal trends in loss of life expectancy after a cancer diagnosis among the Australian population.

BACKGROUND: Loss of life expectancy (LOLE) provides valuable insights into the impact of cancer. We evaluated the temporal trends in LOLE for Australian cancer patients and the gain in life years for recently diagnosed patients due to survival improvements. METHODS: Analysis was conducted using an Australian population-based cohort (n = 1,865,154) aged 50-89 years, who were primarily diagnosed with one of 19 leading cancers between 1982-2015. Flexible parametric survival models were used to estimate LOLE and the proportion of life lost (POLL) by year, age group, sex, and, for New South Wales only, spread of disease. The total years of LOLE and gain in life years due to survival improvements were estimated for those diagnosed in 2014. RESULTS: For 19 cancers combined, LOLE and POLL were significantly lower for more recent diagnoses. Cancer-specific temporal trends were consistent by age, sex, and spread of disease (where relevant) although the magnitude varied. Prostate, kidney, or non-Hodgkin lymphoma experienced the largest decreases in POLL over time. For the 2014 diagnoses, an estimation of 403,094 life years lost will be caused by the 19 cancers. With the increase in cancer survival over time, the 2014 cohort will gain an extra 432,588 life years (52 %) compared to that experienced by the 1982 cohort. CONCLUSION: While reduced impact of a cancer diagnosis on LOLE over time is encouraging, the growing number of cancer survivors in Australia is likely to pose complex challenges for cancer patients, their care givers, and health-care systems.

Temporal Trends in Population-Level Cure of Cancer: The Australian Context.

BACKGROUND: With the improvements in cancer diagnosis and treatment, more patients with cancer are surviving for longer periods than before. This study aims to quantify the proportion cured and median survival time for those who are not cured for major cancers in Australia. METHODS: Australian population-based cohort of 2,164,172 cases, ages 15 to 89 years, whose first cancer diagnosis between 1982 and 2014 was one of 22 leading cancers, were followed up to December 2014. Flexible parametric cure models were used to estimate the proportion cured and median survival time for those uncured by age, sex, and spread of disease, and temporal trends in these measures. RESULTS: Cure estimates could be generated for 19 of the 22 cancer types. The unadjusted proportion cured ranged from 5.0% for pancreatic cancer to 90.0% for melanoma. Median survival time for those uncured ranged from 0.35 years for pancreatic cancer to 6.05 years for prostate cancer. Cancers were divided into four groups according to their proportion cured in the 1980s and the degree of improvement over 28 years. Esophageal, stomach, pancreatic, liver, gallbladder, lung, and brain cancer had lower proportion cured and smaller improvements over time. CONCLUSIONS: For cancers with poor survival in which little has changed over time either in prolonging life or achieving statistical cure, efforts should be focused on reducing the prevalence of known risk factors and earlier detection, thereby enabling more effective treatment. IMPACT: Cure models provide unique insights into whether survival improvements are due to prolonging life or through curing the disease.

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.

Individual- and Area-Level Socioeconomic Inequalities in Esophageal Cancer Survival in Shandong Province, China: A Multilevel Analysis.

BACKGROUND: China contributes to almost half of the esophageal cancer cases diagnosed globally each year. However, the prognosis information of this disease in this large population is scarce. METHODS: Data on a population-based cohort consisting of residents of Shandong Province, China who were diagnosed with esophageal cancer during the period from 2005 to 2014 were analyzed. The cancer-specific survival rates were estimated using Kaplan-Meier analysis. Discrete-time multilevel mixed-effects survival models were used to investigate socioeconomic status (SES) disparities on esophageal cancer survival. RESULTS: The unadjusted 1-, 3-, and 5-year cause-specific survival rates were 59.6% [95% confidence interval (CI), 59.2%-59.9%], 31.9% (95% CI, 31.5%-32.3%), and 23.6% (95% CI, 23.1%-24.0%), respectively. Patients of blue-collar occupations had higher risk of esophageal cancer-related death than those of white-collar occupations in the first 2 years after diagnosis. Rural patients had higher risk of death than urban patients in the first 3 years after diagnosis. The risks of esophageal cancer-related death among patients living in low/middle/high SES index counties were not different in the first 2 years after diagnosis. However, patients living in high SES index counties had better long-term survival (3-5 years postdiagnosis) than those living in middle or low SES index counties. CONCLUSIONS: Socioeconomic inequalities in esophageal cancer survival exist in this Chinese population. Higher individual- or area-level SES is associated with better short-term or long-term cancer survival. IMPACT: Elucidation of the relative roles of the SES factors on survival could guide interventions to reduce disparities in the prognosis of esophageal cancer.

Spatial variation in cancer incidence and survival over time across Queensland, Australia.

Interpreting changes over time in small-area variation in cancer survival, in light of changes in cancer incidence, aids understanding progress in cancer control, yet few space-time analyses have considered both measures. Bayesian space-time hierarchical models were applied to Queensland Cancer Registry data to examine geographical changes in cancer incidence and relative survival over time for the five most common cancers (colorectal, melanoma, lung, breast, prostate) diagnosed during 1997-2004 and 2005-2012 across 516 Queensland residential small-areas. Large variation in both cancer incidence and survival was observed. Survival improvements were fairly consistent across the state, although small for lung cancer. Incidence changes varied by location and cancer type, ranging from lung and colorectal cancers remaining relatively constant over time, to prostate cancer dramatically increasing across the entire state. Reducing disparities in cancer-related outcomes remains a health priority, and space-time modelling of different measures provides an important mechanism by which to monitor progress.

Spatio-temporal relative survival of breast and colorectal cancer in Queensland, Australia 2001-2011.

Despite improvements in cancer survival across many developed countries, it is unclear how survival is changing over time in small areas. This study investigated changes in breast and colorectal cancer survival across 478 areas over 11 years (2001-2011), and the influence of early diagnosis on changes. Queensland Cancer Registry data were analysed using an introduced Bayesian spatio-temporal flexible parametric relative survival model. All areas showed survival improvements between 2001-2003 and 2008-2011. The median absolute 5-year survival improvement for localised breast cancer was small (1.8%), compared to advanced (4.8%) and unknown (7.9%) breast cancer, as well as localised (2.6%), advanced (5.0%) and unknown (4.8%) colorectal cancers. Improvements in non-diagnostic factors, such as patient treatment and management, appear to be the main influence on recent survival increases for breast and colorectal cancers. Important inequalities in cancer survival between small areas remain.

A flexible parametric approach to examining spatial variation in relative survival.

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

The International Epidemiology of Lung Cancer: Latest Trends, Disparities, and Tumor Characteristics.

INTRODUCTION: Our aim was to update global lung cancer epidemiology and describe changing trends and disparities. METHODS: We presented country-specific incidence and mortality from GLOBOCAN 2012 by region and socioeconomic factors via the Human Development Index (HDI). Between- and within-country incidence by histological type was analyzed by using International Agency for Research on Cancer data on cancer incidence on five continents. Trend analyses including data from the International Agency for Research on Cancer, cancer registries, and the WHO mortality database were conducted using joinpoint regression. Survival was compared between and within countries and by histological type. RESULTS: In 2012, there were 1.82 and 1.59 million new lung cancer cases and deaths worldwide, respectively. Incidence was highest in countries with a very high HDI and lowest in countries with a low HDI (42.2 versus 7.9 in 100,000 for males and 21.8 versus 3.1 in 100,000 for females, respectively). In most countries with a very high HDI, as incidence in males decreased gradually (ranging from -0.3% in Spain to -2.5% in the United States each year), incidence in females continued to increase (with the increase ranging from 1.4% each year in Australia to 6.1% in recent years in Spain). Although histological type varied between countries, adenocarcinoma was more common than squamous cell carcinoma, particularly among females (e.g., in Chinese females, the adenocarcinoma-to-squamous cell carcinoma ratio was 6.6). Five-year relative survival varied from 2% (Libya) to 30% (Japan), with substantial within-country differences. CONCLUSIONS: Lung cancer will continue to be a major health problem well through the first half of this century. Preventive strategies, particularly tobacco control, tailored to populations at highest risk are key to reducing the global burden of lung cancer.

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data.

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.