Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia.
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ABSTRACT:
SUBMITTER: Wah W
PROVIDER: S-EPMC8151486 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCancer 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.MethodsUsing 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.ResultsWe 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.ConclusionsOur 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.
| S-EPMC10729400 | biostudies-literature
Project description:ObjectiveHospitalisation rates for many chronic conditions are higher in socioeconomically disadvantaged and less accessible areas. We aimed to map diabetes hospitalisation rates by local government area (LGA) across Western Victoria, Australia, and investigate their association with socioeconomic status (SES) and accessibility/remoteness.DesignCross-sectional study METHODS: Data were acquired from the Victorian Admitted Episodes Dataset for all hospitalisations (public and private) with a diagnosis of type 1 or type 2 diabetes mellitus during 2011-2014. Crude and age-standardised hospitalisation rates (per 1000 population per year) were calculated by LGA for men, women and combined data. Associations between accessibility (Accessibility/Remoteness Index of Australia, ARIA), SES (Index of Relative Socioeconomic Advantage and Disadvantage, IRSAD) and diabetes hospitalisation were investigated using Poisson regression analyses.ResultsHigher LGA-level accessibility and SES were associated with higher rates of type 1 and type 2 diabetes hospitalisation, overall and for each sex. For type 1 diabetes, higher accessibility (ARIA category) was associated with higher hospitalisation rates (men incidence rate ratio [IRR]=2.14, 95% CI 1.64 to 2.80; women IRR=2.45, 95% CI 1.87 to 3.19; combined IRR=2.30, 95% CI 1.69 to 3.13; all p<0.05). Higher socioeconomic advantage (IRSAD decile) was also associated with higher hospitalisation rates (men IRR=1.25, 95% CI 1.09 to 1.43; women IRR=1.32, 95% CI 1.16 to 1.51; combined IRR=1.23, 95% CI 1.07 to 1.42; all p<0.05). Similarly, for type 2 diabetes, higher accessibility (ARIA category) was associated with higher hospitalisation rates (men IRR=2.49, 95% CI 1.81 to 3.43; women IRR=2.34, 95% CI 1.69 to 3.25; combined IRR=2.32, 95% CI 1.66 to 3.25; all p<0.05) and higher socioeconomic advantage (IRSAD decile) was also associated with higher hospitalisation rates (men IRR=1.15, 95% CI 1.02 to 1.30; women IRR=1.14, 95% CI 1.01 to 1.28; combined IRR=1.13, 95% CI 1.00 to 1.27; all p<0.05).ConclusionOur observations could indicate self-motivated treatment seeking, and better specialist and hospital services availability in the advantaged and accessible areas in the study region. The determinants for such variations in hospitalisation rates, however, are multifaceted and warrant further research.
| S-EPMC6537986 | biostudies-literature
Project description:BackgroundDengue, a vector-borne infectious disease caused by the dengue virus, has spread through tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in the equatorial city state of Singapore, and frequent localised outbreaks occur, sometimes leading to national epidemics. Vector control remains the primary and most effective measure for dengue control and prevention. The objective of this study is to develop a novel framework for producing a spatio-temporal dengue forecast at a neighbourhood level spatial resolution that can be routinely used by Singapore's government agencies for planning of vector control for best efficiency.MethodsThe forecasting algorithm uses a mixture of purely spatial, purely temporal and spatio-temporal data to derive dynamic risk maps for dengue transmission. LASSO-based regression was used for the prediction models and separate sub-models were constructed for each forecast window. Data were divided into training and testing sets for out-of-sample validation. Neighbourhoods were categorised as high or low risk based on the forecast number of cases within the cell. The predictive accuracy of the categorisation was measured.ResultsClose concordance between the projections and the eventual incidence of dengue were observed. The average Matthew's correlation coefficient for a classification of the upper risk decile (operational capacity) is similar to the predictive performance at the optimal 30% cut-off. The quality of the spatial predictive algorithm as a classifier shows areas under the curve at all forecast windows being above 0.75 and above 0.80 within the next month.ConclusionsSpatially resolved forecasts of geographically structured diseases like dengue can be obtained at a neighbourhood level in highly urban environments at a precision that is suitable for guiding control efforts. The same method can be adapted to other urban and even rural areas, with appropriate adjustment to the grid size and shape.
| S-EPMC6091171 | biostudies-literature
Project description:BackgroundOpioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation.ObjectiveThe overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts.MethodsWe obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas.ResultsBayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019.ConclusionsOur Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
| S-EPMC9960038 | biostudies-literature
Project description:BackgroundIn the Nordic countries, universal healthcare access has been effective in reducing socioeconomic disparities in non-small-cell lung cancer (NSCLC) management. However, other factors, such as proximity to healthcare facilities, may still affect access to care. This study aimed at investigating the influence of residential area on NSCLC survival.MethodsThis population-based study utilized hospital records to identify NSCLC patients who underwent their initial treatment at Vaasa Central Hospital between January 1, 2016, and December 31, 2020. Patients were categorized based on their postal codes into urban areas (≤50 km from the hospital) and rural areas (>50 km from the hospital). Survival rates between these two groups were compared using Cox regression analysis.ResultsA total of 321 patients were included in the study. Patients residing in rural areas (n = 104) exhibited poorer 12-month survival rates compared to their urban counterparts (n = 217) (unadjusted Hazard Ratio [HR]: 1.38; 95% Confidence Interval [CI]: 1.01-1.89; p = 0.042). After adjusting for factors such as performance status, frailty, and stage at diagnosis in a multivariate Cox regression model, the adjusted HR increased to 1.47 (95% CI: 1.07-2.01; p = 0.017) for patients living in rural areas compared to those in urban areas.InterpretationThe study findings indicate that the distance to the hospital is associated with increased lung cancer mortality. This suggests that geographical proximity may play a crucial role in the disparities observed in NSCLC survival rates. Addressing these disparities should involve strategies aimed at improving healthcare accessibility, particularly for patients residing in rural areas, to enhance NSCLC outcomes and reduce mortality.
| S-EPMC11332544 | biostudies-literature
Project description:ImportanceCOVID-19 mRNA vaccine-associated myocarditis has previously been described; however specific features in the adolescent population are currently not well understood.ObjectiveTo describe myocarditis adverse events following immunisation reported following any COVID-19 mRNA vaccines in the adolescent population in Victoria, Australia.DesignStatewide, population-based study.SettingSurveillance of Adverse Events Following Vaccination in the Community (SAEFVIC) is the vaccine-safety service for Victoria, Australia.ParticipantsAll SAEFVIC reports of myocarditis and myopericarditis in 12-17-year-old COVID-19 mRNA vaccinees submitted between 22 February 2021 and 22 February 2022, as well as accompanying diagnostic investigation results where available, were assessed using Brighton Collaboration criteria for diagnostic certainty.ExposuresAny mRNA COVID-19 vaccine.Main outcomes/mmeasureConfirmed myocarditis as per Brighton Collaboration criteria (levels 1-3).ResultsClinical review demonstrated definitive (Brighton level 1) or probable (level 2) diagnoses in 75 cases. Confirmed myocarditis reporting rates were 8.3 per 100 000 doses in this age group. Cases were predominantly male (n=62, 82.7%) and post dose 2 (n=61, 81.3%). Rates peaked in the 16-17-year-old age group and were higher in males than females (17.7 vs 3.9 per 100 000, p=<0.001).The most common presenting symptoms were chest pain, dyspnoea and palpitations. A large majority of cases who had a cardiac MRI had abnormalities (n=33, 91.7%). Females were more likely to have ongoing clinical symptoms at 1-month follow-up (p=0.02).ConclusionAccurate evaluation and confirmation of episodes of COVID-19 mRNA vaccine-associated myocarditis enabled understanding of clinical phenotypes in the adolescent age group. Any potential vaccination and safety surveillance policies needs to consider age and gender differences.
| S-EPMC9240449 | biostudies-literature
Project description:BackgroundDelays to breast cancer treatment can lead to more aggressive and extensive treatments, increased expenses, increased psychological distress, and poorer survival. We explored the individual and area level factors associated with the interval between diagnosis and first treatment in a population-based cohort in Queensland, Australia.MethodsData from 3216 Queensland women aged 20 to 79, diagnosed with invasive breast cancer (ICD-O-3 C50) between March 2010 and June 2013 were analysed. Diagnostic dates were sourced from the Queensland Cancer Registry and treatment dates were collected via self-report. Diagnostics-treatment intervals were modelled using flexible parametric survival methods.ResultsThe median interval between breast cancer diagnosis and first treatment was 15 days, with an interquartile range of 9-26 days. Longer diagnostic-treatment intervals were associated with a lack of private health coverage, lower pre-diagnostic income, first treatments other than breast conserving surgery, and residence outside a major city. The model explained a modest 13.7% of the variance in the diagnostic-treatment interval [Formula: see text]. Sauerbrei's D was 0.82, demonstrating low to moderate discrimination performance.ConclusionWhilst this study identified several individual- and area-level factors associated with the time between breast cancer diagnosis and first treatment, much of the variation remained unexplained. Increased socioeconomic disadvantage appears to predict longer diagnostic-treatment intervals. Though some of the differences are small, many of the same factors have also been linked to screening and diagnostic delay. Given the potential for accumulation of delay at multiple stages along the diagnostic and treatment pathway, identifying and applying effective strategies address barriers to timely health care faced by socioeconomically disadvantaged women remains a priority.
| S-EPMC10805972 | biostudies-literature
Project description:Buruli ulcer (BU) is a destructive soft-tissue infection caused by the environmental pathogen Mycobacterium ulcerans. In response to rising BU notifications in the state of Victoria, Australia, we reviewed all cases that occurred during 2011-2016 to precisely map the time and likely place of M. ulcerans acquisition. We found that 600 cases of BU had been notified; just over half were in residents and the remainder in visitors to defined BU-endemic areas. During the study period, notifications increased almost 3-fold, from 66 in 2013 to 182 in 2016. We identified 4 BU-endemic areas: Bellarine Peninsula, Mornington Peninsula, Frankston region, and the southeastern Bayside suburbs of Melbourne. We observed a decline in cases on the Bellarine Peninsula but a progressive increase elsewhere. Acquisitions peaked in late summer. The appearance of new BU-endemic areas and the decline in established areas probably correlate with changes in the level of local environmental contamination with M. ulcerans.
| S-EPMC6199991 | biostudies-other
Project description:BackgroundExtreme heat is a leading weather-related cause of illness and death in many locations across the globe, including subtropical Australia. The possibility of increasingly frequent and severe heat waves warrants continued efforts to reduce this health burden, which could be accomplished by targeting intervention measures toward the most vulnerable communities.ObjectivesWe sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlight regions of the city with the greatest risk. We also aimed to find area-level social and environmental determinants of high risk within Brisbane.MethodsWe used a series of hierarchical Bayesian models to examine city-wide and intracity associations between temperature and morbidity using a 2007-2011 time series of geographically referenced hospital admissions data. The models accounted for long-term time trends, seasonality, and day of week and holiday effects.ResultsOn average, a 10°C increase in daily maximum temperature during the summer was associated with a 7.2% increase in hospital admissions (95% CI: 4.7, 9.8%) on the following day. Positive statistically significant relationships between admissions and temperature were found for 16 of the city's 158 areas; negative relationships were found for 5 areas. High-risk areas were associated with a lack of high income earners and higher population density.ConclusionsGeographically targeted public health strategies for extreme heat may be effective in Brisbane, because morbidity risk was found to be spatially variable. Emergency responders, health officials, and city planners could focus on short- and long-term intervention measures that reach communities in the city with lower incomes and higher population densities, including reduction of urban heat island effects.
| S-EPMC4123028 | biostudies-literature
Project description:BackgroundSpatiotemporal modelling techniques allow one to predict injury across time and space. However, such methods have been underutilised in injury studies. This study demonstrates the use of statistical spatiotemporal modelling in identifying areas of significantly high injury risk, and areas witnessing significantly increasing risk over time.MethodsWe performed a retrospective review of hospitalised major trauma patients from the Victorian State Trauma Registry, Australia, between 2007 and 2019. Geographical locations of injury events were mapped to the 79 local government areas (LGAs) in the state. We employed Bayesian spatiotemporal models to quantify spatial and temporal patterns, and analysed the results across a range of geographical remoteness and socioeconomic levels.ResultsThere were 31,317 major trauma patients included. For major trauma overall, we observed substantial spatial variation in injury incidence and a significant 2.1% increase in injury incidence per year. Area-specific risk of injury by motor vehicle collision was higher in regional areas relative to metropolitan areas, while risk of injury by low fall was higher in metropolitan areas. Significant temporal increases were observed in injury by low fall, and the greatest increases were observed in the most disadvantaged LGAs.ConclusionsThese findings can be used to inform injury prevention initiatives, which could be designed to target areas with relatively high injury risk and with significantly increasing injury risk over time. Our finding that the greatest year-on-year increases in injury incidence were observed in the most disadvantaged areas highlights the need for a greater emphasis on reducing inequities in injury.
| S-EPMC9258853 | biostudies-literature