scholarly journals The effect of risk factor misclassification on the partial population attributable risk

2018 ◽  
Vol 37 (8) ◽  
pp. 1259-1275 ◽  
Author(s):  
Benedict H.W. Wong ◽  
Sarah B. Peskoe ◽  
Donna Spiegelman
Keyword(s):  
Risk Factor ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Partial Population

Examination of the population attributable risk of different risk factor domains for suicidal thoughts and behaviors

2015 ◽  
Vol 187 ◽  
pp. 66-72 ◽  
Author(s):  
Ronny Bruffaerts ◽  
Ronald C Kessler ◽  
Koen Demyttenaere ◽  
Anke Bonnewyn ◽  
Matthew K Nock
Keyword(s):  
Risk Factor ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Suicidal Thoughts ◽  
And Behaviors ◽  
Suicidal Thoughts And Behaviors

A Catch in `a Catch in the Reye'

PEDIATRICS ◽  
1988 ◽  
Vol 82 (4) ◽  
pp. 676-677
Author(s):  
PATRICK L. REMINGTON ◽  
KEVIN SULLIVAN ◽  
JAMES S. MARKS
Keyword(s):  
Risk Factor ◽  
Relative Risk ◽  
Small Proportion ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Reye Syndrome ◽  
History Of ◽  
The Relationship

To the Editor.— The "Catch in the Reye"1 is not in the data but rather in the authors' interpretation. They incorrectly surmise that finding that only one in 20 patients with Reye syndrome had taken aspirin makes it unlikely that aspirin is a risk factor for Reye syndrome. This conclusion ignores the relationship between the prevalence of a risk factor (such as aspirin), the relative risk (RR), and the population attributable risk (PAR). In a population in which aspirin use is uncommon, such as Australia, only a small proportion of all patients with Reye syndrome will have a history of aspirin use.

scholarly journals Estimating the current and future cancer burden in Canada: methodological framework of the Canadian population attributable risk of cancer (ComPARe) study

BMJ Open ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. e022378 ◽  
Author(s):  
Darren R Brenner ◽  
Abbey E Poirier ◽  
Stephen D Walter ◽  
Will D King ◽  
Eduardo L Franco ◽  
...  
Keyword(s):  
Risk Factor ◽  
Cancer Risk ◽  
Cancer Incidence ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Population Exposure ◽  
Cancer Society ◽  
National Network ◽  
Canadian Population ◽  
Risk Of Cancer

IntroductionThe Canadian Population Attributable Risk of Cancer project aims to quantify the number and proportion of cancer cases incident in Canada, now and projected to 2042, that could be prevented through changes in the prevalence of modifiable exposures associated with cancer. The broad risk factor categories of interest include tobacco, diet, energy imbalance, infectious diseases, hormonal therapies and environmental factors such as air pollution and residential radon.Methods and analysisUsing a national network, we will use population-attributable risks (PAR) and potential impact fractions (PIF) to model both attributable (current) and avoidable (future) cancers. The latency periods and the temporal relationships between exposures and cancer diagnoses will be accounted for in the analyses. For PAR estimates, historical exposure prevalence data and the most recent provincial and national cancer incidence data will be used. For PIF estimates, we will model alternative or ‘counterfactual’ distributions of cancer risk factor exposures to assess how cancer incidence could be reduced under different scenarios of population exposure, projecting incidence to 2042.DisseminationThe framework provided can be readily extended and applied to other populations or jurisdictions outside of Canada. An embedded knowledge translation and exchange component of this study with our Canadian Cancer Society partners will ensure that these findings are translated to cancer programmes and policies aimed at population-based cancer risk reduction strategies.

Estimation of Population Attributable Risk from Longitudinal Data: A Quasi­Likelihood Approach

1996 ◽  
Vol 46 (3-4) ◽  
pp. 253-262
Author(s):  
Srabashi Basu ◽  
Ayanendranath Basu
Keyword(s):  
Risk Factor ◽  
Longitudinal Data ◽  
Logit Model ◽  
Population Attributable Risk ◽  
Real Life ◽  
Attributable Risk ◽  
Model Parameters ◽  
Data Set ◽  
Life Data ◽  
Real Life Data

This note focuses on the following scenario common in real life : Data are collected on K subjects for a length of time on exposure to a binary risk factor and a binary disease outcome. The problem considered is the estimation of population attributable risk independent of time, which quantifies the marginal impact of the risk factor on the disease. At each of T time points, the population attributable risk is formulated as a function of risk prevalence rate and the logit model parameters relating the risk and the disease. Quasilikelihood methods for longitudinal data are applied to estimate these model parameters. Finally, applying the quasi­likelihood for a second time, T estimates of the population attributable risk are combined. The methodology is illustrated with a real life data set.

scholarly journals ROLE OF "ATYPICAL" MICROORGANISMS ON THE FORMATION OF BRONCHIAL ASTHMA IN CHILDREN WITH ACUTE AND RECURRENT OBSTRUCTIVE BRONCHITIS

2017 ◽  
Vol 10 (4) ◽  
pp. 239 ◽  
Author(s):  
Olga V Zhukova ◽  
Svetlana V Kononova ◽  
Tatjana M Konyshkina
Keyword(s):  
Risk Factor ◽  
Relative Risk ◽  
Bronchial Asthma ◽  
Absolute Risk ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Risk Population ◽  
Group Frequency ◽  
The Absolute ◽  
Number Needed To Harm

Objective: This study was undertaken to determine the link between the presence of “atypical” infections in patients with acute obstructive andrecurrent obstructive bronchitis (AOB/ROB) and bronchial asthma (BA) development based on the concept of risk.Methods: The materials for the study were the data records of patients hospitalized with AOB or ROB and whose analysis was performed to identifyantibodies to “atypical” microflora (796 patients). The study period was 4 years from 2008 to 2011. In the analyzed period, immunosorbent assay forthe detection of antibodies to “atypical” microflora (Chlamydophila pneumoniae, Mycoplasma pneumoniae, Mycoplasma hominis) was performed. Theconcept of risk identification was based on the determination of the absolute risk, attributable risk (AtR), relative risk, population attributable risk, aswell as on the definition of the standard errors for each type of risk and the confidence interval.Results and Conclusion: Methodical aspects of determining the relationship between the presence of “atypical” infections in patients with AOBor ROB and BA development were based on the concept of risk. The analysis showed a direct link between the increase of cases of BA formationagainst the backdrop of “atypical” infections. Therefore, the performed analysis of atypical pathogens influences on BA occurrence in patients withAOB/ROB which indicates direct dependence increase of BA incidence on atypical infection. In experimental group, Frequency of event is 14.84% inexperimental group. Frequency of event is 1.67% in control group. The risk factor increases probability of event by 13.17%, the risk factor increasesprobability of event by 13.17%. Presence of atypical infection leads to increase of BA incidence by 8.9 times. Number needed to harm (NNH) is 7.59,i.e., in the presence of atypical infection in patients with AOB/ROB, each eighth exposed person develops BA in addition to background level of BAincidence.Keywords: Acute obstructive bronchitis, Recurrent obstructive bronchitis, Bronchial asthma, The concept of risk, Risk factor, The absolute risk,Relative risk, Attributable risk, Population attributable risk, Number needed to harm.

scholarly journals Secular trends of population attributable risk of overweight and obesity for hypertension among Chinese adults from 1991 to 2011

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Gou ◽  
Huiying Wu
Keyword(s):  
Population Attributable Risk ◽  
Attributable Risk ◽  
Secular Trends ◽  
Overweight And Obesity ◽  
Regression Coefficients ◽  
Chinese Adults ◽  
Intervention Effect ◽  
Obesity Epidemic ◽  
The Past ◽  
Increasing Trend

AbstractWe determined if the increasing trend in hypertension can be partly attributed to increasing prevalence of overweight/obesity in China over the past two decades. Data were collected from 1991 to 2011 and the population attributable risk (PAR), which is used to estimate the intervention effect on hypertension if overweight/obese, were eliminated. Linear regression was used to evaluate the secular trends. The age-standardized prevalence of overweight and obesity increased by 26.32% with an overall slope of 1.27% (95% CI: 1.12–1.43%) per year. Hypertension also increased by 12.37% with an overall slope of 0.65% (95% CI: 0.51–0.79%) per year. The adjusted ORs of overweight/obesity for hypertension across the survey years remained unchanged; however, the trend in PAR increased steadily from 27.1 to 44.6% with an overall slope of 0.81% (95% CI: 0.34–1.28%) per year (P = 0.006). There was no significant gender difference in the slopes of increasing PAR, as measured by regression coefficients (β = 0.95% vs. β = 0.63% per year, P = 0.36). Over the past two decades, the increase in the prevalence of hypertension in China was partly attributed to the overweight/obesity epidemic, which highlights the importance of controlling weight and further reducing the burden of hypertension.

scholarly journals Population attributable risk of aflatoxin-related liver cancer: Systematic review and meta-analysis

2012 ◽  
Vol 48 (14) ◽  
pp. 2125-2136 ◽  
Author(s):  
Yan Liu ◽  
Chung-Chou H. Chang ◽  
Gary M. Marsh ◽  
Felicia Wu
Keyword(s):  
Systematic Review ◽  
Liver Cancer ◽  
Meta Analysis ◽  
Population Attributable Risk ◽  
Attributable Risk
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