Monitoring the decreasing trend of testicular cancer mortality in Spain during 2005–2019 through a Bayesian approach

Clèries, Ramón; Martı́nez, José Miguel; Escribà, Josep M.; Esteban, Laura; Pareja, Laura; Borràs, Josep M.; Ribes, Josepa

doi:10.1016/j.canep.2010.03.003

Cited by 10 publications

(10 citation statements)

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“…Testicular cancer is one of the most frequently occurring cancers among young men that are aged from 15-44 years old. [1][2][3][4][5][6][7][8][9] Recent trends are suggested that there is an increase in testicular cancers as a diagnosis throughout the world for about 20% and 14% mortality rate are also occurring at this age, for this reason testicular cancer is considered one of the most important health threats in a young men's life. [10,11] According to the American Cancer Society's 2017, it is estimated that about 8,850 men in the United States were diagnosed with testicular cancer.…”

Section: Introductionmentioning

confidence: 99%

Effect of educational guidelines based on health belief model regarding testicular cancer knowledge, practice and beliefs among male nursing students

Mezayen

El-Hay

2019

CNS

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Background: Testicular cancer is considered one of the most important health threats in a young men’s life. However, it is easily detected at an early stage by periodic testicular self-examination (TSE). Awareness of testicular cancer and TSE among university students is very important to aid in early detection and improve health belief. Aim: This study was conducted to evaluate the effect of educational guidelines based on health belief model regarding testicular cancer knowledge, practice and beliefs among male nursing students.Methods: Design: A quasi experimental study design was used and data were collected from Faculty of Nursing, Tanta University. Sample: Proportion stratified random sample of 160 from 500 undergraduate male nursing students were selected as following: about one third of male students from total number of them in different years in the faculty (first, second, third and fourth year). Tools: Three tools were used for data collection. Tool (I): Structure questionnaire sheet to assess students knowledge regarding testicular cancer; Tool (II): Self-reported practices regarding TSE; and Tool (III): Beliefs of students regarding testicular cancer and TSE by Using Health Belief Model (HBM).Results: The results of this study showed that there were significant improvements among students ’knowledge and selfreported practice about testicular cancer & TSE post 3-month and 6-month from educational guidelines, Furthermore, there was improvement in the positive health beliefs about behavioral changes regarding prevention of testicular cancer was observed post 6-month from implementing educational guidelines.Conclusion and Recommendation: These results indicated that educational guidelines about testicular cancer and TSE training are effective in improving knowledge, practice and health belief of young men, but TSE training should be repeated periodically for better efficacy. University faculties should create knowledge and organize workshops about TSE for students and encourage them to perform it regularly.

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Section: Introductionmentioning

confidence: 99%

Effect of educational guidelines based on health belief model regarding testicular cancer knowledge, practice and beliefs among male nursing students

Mezayen

El-Hay

2019

CNS

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“…45 in Bayesian versions of the aPc models, period and cohort effects are smoothed and extrapolated by means of smoothing priors, 16 which can be learned from the data. 13 in situations where rates are low, the Bayesian approach to aPc models can achieve sensible predictions 47 where other methods may fail.…”

Section: Discussionmentioning

confidence: 98%

“…although aPc models have been used increasingly for predicting cancer incidence and mortality, 4,[11][12][13][14]18,20,[45][46][47] projections based on the extrapolation of age, period, and cohort effects require parametric assumptions in non-Bayesian versions of these models. 45 Some models have assumed constant age effects and projected period and cohort effects using a linear regression applied to a fixed number of most recent period and cohort effects.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Change in Breast Cancer Deaths in Spain by 2019

et al. 2013

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“…While age period cohort (APC) models [8,9] have been used increasingly for predicting cancer incidence and mortality [5,[10][11][12][13][14][15][16][17][18], projections based on extrapolating age, period and cohort effects require parametric assumptions in non-Bayesian versions of these models. Osmond [10], for example, assumed constant age-effects and projected period and cohort effects using a linear regression applied to a fixed number of most recent period and cohort effects.…”

Section: Introductionmentioning

confidence: 99%

“…In Bayesian versions of the APC models, period and cohort effects are smoothed and extrapolated by means of autoregressive priors , where an appropriate degree of smoothing can be learned from the data . Moreover, in situations where rates are low and unstable, Bayesian APC models can achieve sensible predictions where other methods may fail . However, APC models require a long period of observed data as a basis for prediction and may present interpretation difficulties in practice with wider credible or prediction intervals than those based on simple linear or log‐linear models .…”

Section: Introductionmentioning

confidence: 99%

Bayesian approach to predicting cancer incidence for an area without cancer registration by using cancer incidence data from nearby areas

Clèries

Ribes

Buxó

et al. 2012

Statistics in Medicine

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This paper compares three different methods for performing cancer incidence prediction in an area without a cancer registry under a Bayesian framework, using linear and log-linear age-period models with either age-specific slopes or a common slope across age groups. The three methods assume that a nearby area with a cancer registration has similar incidence and mortality patterns as the area of interest without a cancer registry where the cancer incidence prediction is carried out. The three methods differ in modeling strategies: (i) modeling the incidence rate directly; (ii) modeling the ratio of the number of incident cases to that of mortality cases; and (iii) modeling the difference between the incidence rate and the mortality rate. Strategy (iii) is a new approach in this type of projection. Empirical assessment is made using real data from the cancer registry of Tarragona, Spain, to predict cancer incidence in Girona, Spain, and vice versa. Predictions of short-term (3-4 years) incidence were made for 2001 in Tarragona using observed cancer incidence and mortality data for 1994-1998 from Girona. Short-term predictions were made for 2002 in Girona using Tarragona's 1994-1998 data. Additionally, long-term (10 years) incidence rate predictions were made for 2002 in Girona using data from Tarragona for the period 1985-1992. Our results suggest that extrapolating time-trends of incidence rates minus mortality rates may have the best predictive performance overall. These methods of population-level disease-incidence prediction are highly relevant to health care planning and policy decisions.

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Monitoring the decreasing trend of testicular cancer mortality in Spain during 2005–2019 through a Bayesian approach

Cited by 10 publications

References 35 publications

Effect of educational guidelines based on health belief model regarding testicular cancer knowledge, practice and beliefs among male nursing students

Effect of educational guidelines based on health belief model regarding testicular cancer knowledge, practice and beliefs among male nursing students

Predicting the Change in Breast Cancer Deaths in Spain by 2019

Bayesian approach to predicting cancer incidence for an area without cancer registration by using cancer incidence data from nearby areas

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