Age and Prostate-Specific Antigen Level Prior to Diagnosis Predict Risk of Death from Prostate Cancer.

A single early prostate-specific antigen (PSA) level has been correlated with a higher likelihood of prostate cancer diagnosis and death in younger men. PSA testing in older men has been considered of limited utility. We evaluated prostate cancer death in relation to age and PSA level immediately prior to prostate cancer diagnosis. Using the Veterans Affairs database, we identified 230,081 men aged 50-89 years diagnosed with prostate cancer and at least one prior PSA test between 1999 and 2009. Prostate cancer-specific death over time was calculated for patients stratified by age group (e.g., 50-59 years, through 80-89 years) and PSA range at diagnosis (10 ranges) using Kaplan-Meier methods. Risk of 10-year prostate cancer mortality across age and PSA was compared using log-rank tests with a Bonferroni adjustment for multiple testing. 10.5% of men diagnosed with prostate cancer died of cancer during the 10-year study period (mean follow-up = 3.7 years). Higher PSA values prior to diagnosis predict a higher risk of death in all age groups (p < 0.0001). Within the same PSA range, older age groups are at increased risk for death from prostate cancer (p < 0.0001). For PSA of 7-10 ng/mL, cancer-specific death, 10 years after diagnosis, increased from 7% for age 50-59 years to 51% for age 80-89 years. Men older than 70 years are more likely to die of prostate cancer at any PSA level than younger men, suggesting prostate cancer remains a significant problem among older men (even those aged 80+) and deserves additional study

Simplicity within complexity: Seasonality and predictability of hospital admissions in the province of Ontario 1988–2001, a population-based analysis

BACKGROUND: Seasonality is a common feature of communicable diseases. Less well understood is whether seasonal patterns occur for non-communicable diseases. The overall effect of seasonal fluctuations on hospital admissions has not been systematically evaluated. METHODS: This study employed time series methods on a population based retrospective cohort of for the fifty two most common causes of hospital admissions in the province of Ontario from 1988–2001. Seasonal patterns were assessed by spectral analysis and autoregressive methods. Predictive models were fit with regression techniques. RESULTS: The results show that 33 of the 52 most common admission diagnoses are moderately or strongly seasonal in occurrence; 96.5% of the predicted values were within the 95% confidence interval, with 37 series having all values within the 95% confidence interval. CONCLUSION: The study shows that hospital admissions have systematic patterns that can be understood and predicted with reasonable accuracy. These findings have implications for understanding disease etiology and health care policy and planning

2016 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1003/thumbnail.jp

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Prostate-Specific Antigen Trends Predict the Probability of Prostate Cancer in a Very Large U.S. Veterans Affairs Cohort

If prostate-specific antigen (PSA) trends help identify elevated prostate cancer (PCa) risk, they might provide early warning of progressing cancer for further evaluation and justify annual testing. Our objective was to determine whether PSA trends predict PCa likelihood. A biopsy cohort of 361,657 men was obtained from a Veterans Affairs database (1999–2012). PSA trends were estimated for the 310,458 men with at least 2 PSA tests prior to biopsy. Cancer tumors may grow exponentially with cells doubling periodically. We hypothesized that PSA from prostate cancer grows exponentially above a no cancer baseline. We estimated PSA trends on that basis along with five descriptive variables: last PSA before biopsy, growth rate in PSA from cancer above a baseline, PSA variability around the trend, number of PSA tests, and time span of tests. PSA variability is a new variable that measures percentage deviations of PSA tests from estimated trends with 0% variability for a smoothly increasing trend. Logistic regression models were used to estimate relationships between the probability of PCa at biopsy and the trend variables and age. All five PSA trend variables and age were significant predictors of prostate cancer at biopsy (p < 0.0001). An overall logistic regression model achieved an AUC of 0.67 for men with at least 4 tests over at least 3 years, which was a substantial improvement over a single PSA (AUC 0.58). High probability of PCa was associated with low PSA variability (smooth trends), high PSA, high growth rate, many tests over a long time-span and older age. For example, at 4.0 PSA the probability of cancer is 32% for 1 PSA test and increases to 68% for 8 tests over 7 years with smooth, fast growth (0% variability and 50% exponential growth). Our results show that smooth, fast exponential growth in PSA above a baseline predicts an increased probability of PCa. The probability increases as smooth (low variability) trends are observed for more tests over a longer time span, which makes annual testing worth considering. Worrisome PSA trends might be used to trigger further evaluation and continued monitoring of the trends—even at low PSA levels

Age and Prostate-Specific Antigen Level Prior to Diagnosis Predict Risk of Death from Prostate Cancer.

A single early prostate-specific antigen (PSA) level has been correlated with a higher likelihood of prostate cancer diagnosis and death in younger men. PSA testing in older men has been considered of limited utility. We evaluated prostate cancer death in relation to age and PSA level immediately prior to prostate cancer diagnosis. Using the Veterans Affairs database, we identified 230,081 men aged 50-89 years diagnosed with prostate cancer and at least one prior PSA test between 1999 and 2009. Prostate cancer-specific death over time was calculated for patients stratified by age group (e.g., 50-59 years, through 80-89 years) and PSA range at diagnosis (10 ranges) using Kaplan-Meier methods. Risk of 10-year prostate cancer mortality across age and PSA was compared using log-rank tests with a Bonferroni adjustment for multiple testing. 10.5% of men diagnosed with prostate cancer died of cancer during the 10-year study period (mean follow-up = 3.7 years). Higher PSA values prior to diagnosis predict a higher risk of death in all age groups (p < 0.0001). Within the same PSA range, older age groups are at increased risk for death from prostate cancer (p < 0.0001). For PSA of 7-10 ng/mL, cancer-specific death, 10 years after diagnosis, increased from 7% for age 50-59 years to 51% for age 80-89 years. Men older than 70 years are more likely to die of prostate cancer at any PSA level than younger men, suggesting prostate cancer remains a significant problem among older men (even those aged 80+) and deserves additional study