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Relevant bibliographies by topics / Population Attributable Fraction

Academic literature on the topic 'Population Attributable Fraction'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Population Attributable Fraction"

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Khosravi, Ahmad, Maryam Nazemipour, Tomohiro Shinozaki, and Mohammad Ali Mansournia. "Population attributable fraction in textbooks: Time to revise." Global Epidemiology 3 (November 2021): 100062. http://dx.doi.org/10.1016/j.gloepi.2021.100062.

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Brooks-Pollock, Ellen, and Leon Danon. "Defining the population attributable fraction for infectious diseases." International Journal of Epidemiology 46, no. 3 (May 4, 2017): 976–82. http://dx.doi.org/10.1093/ije/dyx055.

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Williamson, D. F. "The population attributable fraction and confounding: buyer beware." International Journal of Clinical Practice 64, no. 8 (June 16, 2010): 1019–23. http://dx.doi.org/10.1111/j.1742-1241.2010.02443.x.

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Mishra, Sharmistha, and Stefan D. Baral. "Rethinking the population attributable fraction for infectious diseases." Lancet Infectious Diseases 20, no. 2 (February 2020): 155–57. http://dx.doi.org/10.1016/s1473-3099(19)30618-8.

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Davis, Caroline H., David P. MacKinnon, Amy Schultz, and Irwin Sandler. "Cumulative Risk and Population Attributable Fraction in Prevention." Journal of Clinical Child & Adolescent Psychology 32, no. 2 (May 2003): 228–35. http://dx.doi.org/10.1207/s15374424jccp3202_7.

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Di Maso, Matteo, Francesca Bravi, Jerry Polesel, Eva Negri, Adriano Decarli, Diego Serraino, Carlo La Vecchia, and Monica Ferraroni. "Attributable fraction for multiple risk factors: Methods, interpretations, and examples." Statistical Methods in Medical Research 29, no. 3 (May 10, 2019): 854–65. http://dx.doi.org/10.1177/0962280219848471.

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The attributable fraction is the candidate tool to quantify individual shares of each risk factor on the disease burden in a population, expressing the proportion of cases ascribable to the risk factors. The original formula ignored the presence of other factors (i.e. multiple risk factors and/or confounders), and several adjusting methods for potential confounders have been proposed. However, crude and adjusted attributable fractions do not sum up to their joint attributable fraction (i.e. the number of cases attributable to all risk factors together) and their sum may exceed one. A different approach consists of partitioning the joint attributable fraction into exposure-specific shares leading to sequential and average attributable fractions. We provide an example using Italian case–control data on oral cavity cancer comparing crude, adjusted, sequential, and average attributable fractions for smoking and alcohol and provide an overview of the available software routines for their estimation. For each method, we give interpretation and discuss shortcomings. Crude and adjusted attributable fractions added up over than one, whereas sequential and average methods added up to the joint attributable fraction = 0.8112 (average attributable fractions for smoking and alcohol were 0.4894 and 0.3218, respectively). The attributable fraction is a well-known epidemiological measure that translates risk factors prevalence and disease occurrence in useful figures for a public health perspective. This work endorses their proper use and interpretation.

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Shin, A., S. Park, H. R. Shin, E. H. Park, S. K. Park, J. K. Oh, M. K. Lim, B. Y. Choi, M. Boniol, and P. Boffetta. "Population attributable fraction of infection-related cancers in Korea." Annals of Oncology 22, no. 6 (June 2011): 1435–42. http://dx.doi.org/10.1093/annonc/mdq592.

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Simons, Elinor, Teresa To, and Sharon Dell. "The Population Attributable Fraction of Asthma Among Canadian Children." Canadian Journal of Public Health 102, no. 1 (January 2011): 35–41. http://dx.doi.org/10.1007/bf03404874.

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Taguri, Masataka, and Aya Kuchiba. "Decomposition of the population attributable fraction for two exposures." Annals of Epidemiology 28, no. 5 (May 2018): 331–34. http://dx.doi.org/10.1016/j.annepidem.2018.02.012.

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Raut, Janhavi R., Regina M. Simeone, Sarah C. Tinker, Mark A. Canfield, R. Sue Day, and A. J. Agopian. "Proportion of Orofacial Clefts Attributable to Recognized Risk Factors." Cleft Palate-Craniofacial Journal 56, no. 2 (May 4, 2018): 151–58. http://dx.doi.org/10.1177/1055665618774019.

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Objective: Estimate the population attributable fraction (PAF) for a set of recognized risk factors for orofacial clefts. Design: We used data from the National Birth Defects Prevention Study. For recognized risk factors for which data were available, we estimated crude population attributable fractions (cPAFs) to account for potential confounding, average-adjusted population attributable fractions (aaPAFs). We assessed 11 modifiable and 3 nonmodifiable parental/maternal risk factors. The aaPAF for individual risk factors and the total aaPAF for the set of risk factors were calculated using a method described by Eide and Geffler. Setting: Population-based case–control study in 10 US states. Participants: Two thousand seven hundred seventy-nine cases with isolated cleft lip with or without cleft palate (CL±P), 1310 cases with isolated cleft palate (CP), and 11 692 controls with estimated dates of delivery between October 1, 1997, and December 31, 2011. Main Outcome Measures: Crude population attributable fraction and aaPAF. Results: The proportion of CL±P and CP cases attributable to the full set of examined risk factors was 50% and 43%, respectively. The modifiable factor with the largest aaPAF was smoking during the month before pregnancy or the first month of pregnancy (4.0% for CL±P and 3.4% for CP). Among nonmodifiable factors, the factor with the largest aaPAF for CL±P was male sex (27%) and for CP it was female sex (16%). Conclusions: Our results may inform research and prevention efforts. A large proportion of orofacial cleft risk is attributable to nonmodifiable factors; it is important to better understand the mechanisms involved for these factors.

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Dissertations / Theses on the topic "Population Attributable Fraction"

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Cube, Maja von [Verfasser], and Martin [Akademischer Betreuer] Schumacher. "The population-attributable fraction for time-dependent exposures and competing risks." Freiburg : Universität, 2019. http://d-nb.info/119056095X/34.

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Naudin, Sabine. "Le rôle de la consommation d’alcool et du mode de vie sur le risque de cancer du pancréas dans l’étude EPIC." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1133/document.

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Souvent diagnostiqué à un stade avancé, le cancer du pancréas (CP) est un cancer particulièrement létal pour lequel il n’y a, à ce jour, que très peu de possibilités de traitement et de diagnostic anticipé. L’identification de facteurs de risque modifiables pourrait fournir des données épidémiologiques nécessaires au soutien de la mise en place de mesures préventives. Cette thèse a pour but d’étudier l’étiologie du CP en évaluant le rôle de la consommation d’alcool et du mode de vie dans l’étude prospective européenne sur le cancer et la nutrition (EPIC), cohorte multicentrique de plus de 500,000 sujets provenant de 10 pays européens. La consommation d’alcool a d’abord été évaluée en regard du risque de CP. Les rôles des différents types d’alcools ainsi que du tabagisme dans la relation entre l’alcool et le CP ont été examinés. Cette évaluation a montré qu’une consommation d’alcool élevée était associée à une augmentation du risque de CP. Ensuite, la relation entre le Healthy Lifestyle Index, un indicateur combinant le passé tabagique, la consommation d’alcool, l’alimentation, l’anthropométrie et l’activité physique, et le risque de CP a été étudiée. Pour quantifier l’impact de l’amélioration de ces facteurs, des fractions de CP attribuables ont été estimées en considérant des scénarios hypothétiques où les participants adopteraient des modes de vie plus sains. L’adhérence à des habitudes saines était fortement et inversement associée au risque de CP. Ces travaux ont apporté des connaissances informatives sur l’étiologie du CP, et soutiennent le développement de mesures de santé publique promouvant la prévention du CP par l’adoption de modes de vie sains
Commonly diagnosed at late stage, pancreatic cancer (PC) is a highly fatal cancer with limited opportunities for early detection and effective treatment. The identification of modifiable risk factors may offer relevant scientific evidence for PC prevention. This doctoral research program investigated PC etiology through a comprehensive examination of the role of alcohol consumption and other lifestyle determinants in the occurrence of PC within the European Prospective Investigation into Cancer and nutrition (EPIC) study, a multi-center cohort involving more than 500,000 participants from 10 European countries. Alcohol intake was evaluated with respect to the risk of PC. The role of different alcoholic beverages and potential effect modification by smoking habits on PC risk were also examined. Findings from this evaluation provided epidemiological evidence that large intakes of alcohol were associated with an increased risk of PC.The association between the healthy lifestyle index, a score combining information on smoking history, alcohol intake, diet, obesity, and physical activity and the risk of PC was examined. To quantify the impact of modifying several lifestyle factors, population attributable fractions were estimated assuming counterfactual scenarios whereby study participants hypothetically moved towards healthier behaviors. Adherence to healthy lifestyle habits was strongly inversely related to PC. This comprehensive evaluation provides informative insights on the etiology of PC and supports the development and implementation of public health guidelines to promote individuals’ adoption of healthy lifestyle habits for PC prevention

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Amere, Genet A. MD. "Population Attributable Fraction of Smoking for Tuberculosis (TB) Disease Incidence and TB Mortality in High-Burden TB Countries." 2017. http://scholarworks.gsu.edu/iph_theses/506.

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Background: Globally, there are 10 million new cases of tuberculosis (TB) disease annually and 95% of cases occur in low- and middle-income countries (LMIC). More than 1 billion people use tobacco, and 80% of tobacco users reside in LMIC. Smoking approximately doubles the risk of TB disease and is associated with excess mortality during TB treatment. We aimed to estimate the proportion of annual incident TB cases and TB mortality attributable to tobacco smoking in high burden TB countries. Methods: To estimate population attributable fractions (PAF), we obtained country specific estimates of TB incidence and TB mortality rates from the WHO 2015 Global TB Report. Country specific smoking prevalence was estimated from WHO 2015 tobacco surveillance reports and the Tobacco Atlas. Risk ratios for the effect of smoking on TB incidence and TB mortality were obtained from previously published meta-analyses. Country specific PAF of smoking for TB disease were age and sex adjusted. Results: In high burden countries during 2014, an estimated 4.5 million adults developed TB disease and 163,000 people died from TB. An estimated 740 million adult smokers lived in those high burden countries in 2014. We estimated that tobacco smoking was attributable for 17.7% (95% confidence interval [CI] 8.6-21.9%) of TB cases and 15.0% (95% CI 1.9-31.6%) of TB mortality. Of the high burden countries, Russia had the highest proportion of smoking attributable TB disease (31.8%, 95% CI 16.0-37.8%) and death (28.1%, 95% CI 3.8-51.3%). India had the greatest absolute number of TB cases (233,000) and TB deaths (7,400) attributable to smoking. Men (30.5%, 95% CI 14.9%-36.9%) had a greater proportion of TB cases attributable to smoking than women (4.7%, 95% CI 1.9%-6.2%). Conclusion: In high-burden TB countries, nearly one-sixth of all TB cases and TB deaths were attributable to smoking. Our findings highlight the need for tobacco control in high TB burden regions and specifically among patients with TB. Reaching key populations and integrating smoking cessation efforts into TB programs will be essential to achieve global TB control goals.

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Book chapters on the topic "Population Attributable Fraction"

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Torén, Kjell, and Paul D. Blanc. "Population-attributable fraction for occupation and asthma." In Occupational Asthma, 57–70. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-7643-8556-9_4.

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"Population Attributable Fraction." In Handbook of Disease Burdens and Quality of Life Measures, 4289. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-78665-0_6371.

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"PAF (population attributable fraction)." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics, 1432. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_12179.

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Wolfson, Michael C. "Computational Simulation Modeling." In Complex Systems and Population Health, 241–52. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190880743.003.0017.

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This chapter illustrates computer simulation “model thinking,” with brief descriptions of five recent health models along an abstract to applied spectrum. The author starts with a very simple model to assess not only the cross-sectional but also the lifetime redistributive impact of Canada’s publicly funded healthcare. Next is a multilevel interacting agent model seeking to understand why the correlations between city-level income inequality and mortality are so different between Canada and the United States. Following are models that significantly generalize the concept of attributable fraction applied to health-adjusted life expectancy and a genetic missing model to support cost-effectiveness of risk-based breast cancer screening policy options. The fifth model is the most detailed and is being applied to develop projections of long-term care utilization and costs. While this is a diverse set of models, collectively, they illustrate the range of possibilities, and the benefits of “model thinking.”

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Conference papers on the topic "Population Attributable Fraction"

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Riyadina, Woro, Ekowati Rahajeng, Iwan Ariawan, and Pradana Soewondo. "Population Attributable Fraction and Predictors of Type 2 Diabetes Mellitus Incidence in Adult Population in Bogor, West Java." In The 8th International Conference on Public Health 2021. Masters Program in Public Health, Universitas Sebelas Maret, 2021. http://dx.doi.org/10.26911/ab.epidemiology.icph.08.2021.43.

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Hutchings, Sally, Lesley Rushton, Steven Sadhra, and David Fishwick. "P017 An age-time window method to estimate a population attributable fraction for the prevalence of copd in the uk." In Occupational Health: Think Globally, Act Locally, EPICOH 2016, September 4–7, 2016, Barcelona, Spain. BMJ Publishing Group Ltd, 2016. http://dx.doi.org/10.1136/oemed-2016-103951.342.

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Riyadina, Woro, Dewi Kristanti, Julianty Pradono, Ekowati Rahajeng, and Yuda Turana. "A Cohort Study on The Primary Prevention of Stroke Incidence in Adult Population in Bogor, West Java." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.01.25.

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ABSTRACT Background: Incidence of stroke showed an increasing trend in Indonesia, 12 new cases per 1,000 in 2013, and 19 new cases per 1,000 in 2018 despite the stroke control programs from the government. This study aimed to estimate Cumulative Incidence, Incidence Rate, and Population Attributable Fraction (PAF). This study also estimated the effect of blood pressure, physical activity, blood sugar levels, and smoking habits on the risk of stroke in adults. Subjects and Method: This was a cohort study conducted for 6 years in 5 Districts, Bogor City, West Java. An adult population of 5,189 subjects with aged ≥25 years was selected. The dependent variable was stroke. The independent variables included blood pressure, physical activity, blood sugar level, and smoking habits. Data for independent variables (except blood sugar level) were collected by using questioners. Data were analyzed by Cox Proportional Hazard Model. Results: The Cumulative Incidence was 2.09% and Incidence Rate was 480 new stroke cases per 100,000 person-year (CI 95%= 130 to 670) over 6 years. Predictors of stroke incidence were hypertension (HR= 2.50; CI 95%= 1.50 to 4.10; p= 0.001), low physical activity (HR= 2.40; CI 95%= 1.50 to 3.90; p< 0.001), high blood sugar level (HR= 3; CI 95%= 1.70 to 5.50; p= 0.001), and heavy smoking (HR= 3.90; CI 95%= 1.40 to 11.10; p= 0.007). PAF of blood sugar level and physical activity was 67.75%, and able to reduce stroke incidence by 1,990 from 2,937. Conclusion: Hypertension, low physical activity, high blood sugar level, and heavy smoking are predictors of stroke in adult. Primary prevention by increasing physical activity and decreasing blood sugar level can reduce incidence of stroke. Keywords: stroke, Cumulative Incidence, Incidence Rate, PAF, primary prevention Correspondence: Woro Riyadina. National Institute of Health Research and Development, Ministry of Health, Indonesia. Email: w.riyadina02@gmail.com. Mobile: +6281297673074. DOI: https://doi.org/10.26911/the7thicph.01.25

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