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

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.

Spatial patterns of prostate-specific antigen testing in asymptomatic men across Australia: a population-based cohort study, 2017-2018

OBJECTIVES

In Australia, while prostate-specific antigen (PSA) testing rates vary by broad area-based categories of remoteness and socio-economic status, little is known about the extent of variation within them. This study aims to describe the small-area variation in PSA testing across Australia.

STUDY DESIGN

This was a retrospective population-based cohort study.

METHODS

We received data for PSA testing from the Australian Medicare Benefits Schedule. The cohort included men (n = 925,079) aged 50-79 years who had at least one PSA test during 2017-2018. A probability-based concordance was applied across multiple iterations (n = 50) to map each postcode to small areas (Statistical Areas 2; n = 2,129). For each iteration, a Bayesian spatial Leroux model was used to generate smoothed indirectly standardized incidence ratios across each small area, with estimates combined using model averaging.

RESULTS

About a quarter (26%) of the male population aged 50-79 years had a PSA test during 2017-2018. Testing rates among small areas varied 20-fold. Rates were higher (exceedance probability>0.8) compared with the Australian average in the majority of small areas in southern Victoria and South Australia, south-west Queensland, and some coastal regions of Western Australia but lower (exceedance probability<0.2) in Tasmania and Northern Territory.

CONCLUSIONS

The substantial geographical variation in PSA testing rates across small areas of Australia may be influenced by differences in access to and guidance provided by clinicians and attitudes and preferences of men. Greater understanding of PSA testing patterns by subregions and how these patterns relate to health outcomes could inform evidence-based approaches to identifying and managing prostate cancer risk.

The associations of air pollution and socioeconomic factors with esophageal cancer in China based on a spatiotemporal analysis

Rapid urbanization and industrialization in China have incurred serious air pollution and consequent health concerns. In this study, we examined the modifying effects of urbanization and socioeconomic factors on the association between PM_2.5 and incidence of esophageal cancer (EC) in 2000-2015 using spatiotemporal techniques and a quasi-Poisson generalized linear model. The results showed a downward trend of EC and high-risk areas aggregated in North China and Huai River Basin. In addition, a stronger association between PM_2.5 and incidence was observed in low urbanization group, and the association was stronger for females than males. When exposure time-windows were adjusted as 0, 5, 10, 15 years, the incidence risk increased by 2.48% (95% CI: 2.23%, 2.73%), 2.20% (95% CI: 1.91%, 2.49%), 2.18% (95% CI%: 1.92%, 2.43%), 1.87% (95% CI%:1.64, 2.10%) for males, respectively and 4.03% (95% CI: 3.63%, 4.43%), 2.20% (95% CI: 1.91%, 2.49%), 3.97% (95% CI: 3.54%, 4.41%), 3.06% (95% CI: 2.71%, 3.41%) for females, respectively. The findings indicated people in low urbanization group faced with a stronger EC risk caused by PM_2.5, which contributes to a more comprehensive understanding of combating EC challenges related to PM_2.5 pollution.

Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia

OBJECTIVE

To understand the spatial pattern of dengue fever (DF) patients' survival and investigated factors influencing DF patients' survival.

METHOD

A Bayesian spatial survival method via a conditional autoregressive approach was used to analyze the factors that influence DF patients' survival in 14 sub-districts from January 2015 to May 2017 in Makassar city, Indonesia. Bayesian spatial and a non-spatial model were compared by using deviance information criterion.

RESULTS

The spatial model was more suitable than a non-spatial model. Under the Bayesian spatial model, there was a substantive relationship between age, grade and DF patients' survival time.

CONCLUSIONS

The relative risk map and related factors of DF patients' survival can indicate the health policy makers to give special attention to the high risk areas in order to faster and more targeted treatment.

A Review of Models Used for Investigating Barriers to Healthcare Access in Australia

Understanding barriers to healthcare access is a multifaceted challenge, which is often highly diverse depending on location and the prevalent surroundings. The barriers can range from transport accessibility to socio-economic conditions, ethnicity and various patient characteristics. Australia has one of the best healthcare systems in the world; however, there are several concerns surrounding its accessibility, primarily due to the vast geographical area it encompasses. This review study is an attempt to understand the various modeling approaches used by researchers to analyze diverse barriers related to specific disease types and the various areal distributions in the country. In terms of barriers, the most affected people are those living in rural and remote parts, and the situation is even worse for indigenous people. These models have mostly focused on the use of statistical models and spatial modeling. The review reveals that most of the focus has been on cancer-related studies and understanding accessibility among the rural and urban population. Future work should focus on further categorizing the population based on indigeneity, migration status and the use of advanced computational models. This article should not be considered an exhaustive review of every aspect as each section deserves a separate review of its own. However, it highlights all the key points, covered under several facets which can be used by researchers and policymakers to understand the current limitations and the steps that need to be taken to improve health accessibility.

A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality

OBJECTIVES

This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality.

METHODS

This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018.

RESULTS

A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization.

CONCLUSIONS

Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.

Bayesian spatial analysis of cholangiocarcinoma in Northeast Thailand

Cholangiocarcinoma (CCA) is a malignant neoplasm of the biliary tract. Thailand reports the highest incidence of CCA in the world. The aim of this study was to map the distribution of CCA and identify spatial disease clusters in Northeast Thailand. Individual-level data of patients with histopathologically confirmed CCA, aggregated at the sub-district level, were obtained from the Cholangiocarcinoma Screening and Care Program (CASCAP) between February 2013 and December 2017. For analysis a multivariate Zero-inflated, Poisson (ZIP) regression model was developed. This model incorporated a conditional autoregressive (CAR) prior structure, with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. Covariates included in the models were age, sex, normalized vegetation index (NDVI), and distance to water body. There was a total of 1,299 cases out of 358,981 participants. CCA incidence increased 2.94 fold (95% credible interval [CrI] 2.62–3.31) in patients >60 years as compared to ≤60 years. Males were 2.53 fold (95% CrI: 2.24–2.85) more likely to have CCA when compared to females. CCA decreased with a 1 unit increase of NDVI (Relative Risk =0.06; 95% CrI: 0.01–0.63). When posterior means were mapped spatial clustering was evident after accounting for the model covariates. Age, sex and environmental variables were associated with an increase in the incidence of CCA. When these covariates were included in models the maps of the posterior means of the spatially structured random effects demonstrated evidence of spatial clustering.

Variations in outcomes by residential location for women with breast cancer: a systematic review

OBJECTIVES

To systematically assess the evidence for variations in outcomes at each step along the breast cancer continuum of care for Australian women by residential location.

DESIGN

Systematic review.

METHODS

Systematic searches of peer-reviewed articles in English published from 1 January 1990 to 24 November 2017 using PubMed, EMBASE, CINAHL and Informit databases. Inclusion criteria were: population was adult female patients with breast cancer; Australian setting; outcome measure was survival, patient or tumour characteristics, screening rates or frequencies, clinical management, patterns of initial care or post-treatment follow-up with analysis by residential location or studies involving non-metropolitan women only. Included studies were critically appraised using a modified Newcastle-Ottawa Scale.

RESULTS

Seventy-four quantitative studies met the inclusion criteria. Around 59% were considered high quality, 34% moderate and 7% low. No eligible studies examining treatment choices or post-treatment follow-up were identified. Non-metropolitan women consistently had poorer survival, with most of this differential being attributed to more advanced disease at diagnosis, treatment-related factors and socioeconomic disadvantage. Compared with metropolitan women, non-metropolitan women were more likely to live in disadvantaged areas and had differing clinical management and patterns of care. However, findings regarding geographical variations in tumour characteristics or diagnostic outcomes were inconsistent.

CONCLUSIONS

A general pattern of poorer survival and variations in clinical management for Australian female patients with breast cancer from non-metropolitan areas was evident. However, the wide variability in data sources, measures, study quality, time periods and geographical classification made direct comparisons across studies challenging. The review highlighted the need to promote standardisation of geographical classifications and increased comparability of data systems. It also identified key gaps in the existing literature including a lack of studies on advanced breast cancer, geographical variations in treatment choices from the perspective of patients and post-treatment follow-up.

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.