scholarly journals 59 A FORMULA OF POPULATION ATTRIBUTABLE RISK FOR A DISEASE WITH MULTIPLE RISK FACTORS

Epidemiology ◽  
1995 ◽  
Vol 6 (2) ◽  
pp. S17
Author(s):  
C. QLYi
Keyword(s):  
Risk Factors ◽  
Population Attributable Risk ◽  
Attributable Risk ◽  
Multiple Risk Factors ◽  
Multiple Risk

ESTIMATING THE POPULATION ATTRIBUTABLE RISK FOR MULTIPLE RISK FACTORS USING CASE-CONTROL DATA

1985 ◽  
Vol 122 (5) ◽  
pp. 904-914 ◽  
Author(s):  
PAOLO BRUZZI ◽  
SYLVAN B. GREEN ◽  
DAVID P. BYAR ◽  
LOUISE A. BRINTON ◽  
CATHERINE SCHAIRER
Keyword(s):  
Risk Factors ◽  
Population Attributable Risk ◽  
Case Control ◽  
Attributable Risk ◽  
Control Data ◽  
Multiple Risk Factors ◽  
Multiple Risk

A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Yibing Ruan ◽  
Stephen D. Walter ◽  
Christine M. Friedenreich ◽  
Darren R. Brenner ◽  
_ _
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Population Attributable Risk ◽  
Individual Level ◽  
Joint Distributions ◽  
Multiple Risk Factors ◽  
Relative Risks ◽  
Multiple Risk ◽  
The Individual ◽  
Population Attributable Risks

AbstractObjectivesThe methods to estimate the population attributable risk (PAR) of a single risk factor or the combined PAR of multiple risk factors have been extensively studied and well developed. Ideally, the estimation of combined PAR of multiple risk factors should be based on large cohort studies, which account for both the joint distributions of risk exposures and for their interactions. However, because such individual-level data are often lacking, many studies estimate the combined PAR using a comparative risk assessment framework. It involves estimating PAR of each risk factor based on its prevalence and relative risk, and then combining the individual PARs using an approach that relies on two key assumptions: that the distributions of exposures to the risk factors are independent and that the relative risks are multiplicative. While such assumptions rarely hold true in practice, no studies have investigated the magnitude of bias incurred if the assumptions are violated.MethodsUsing simulation-based models, we compared the combined PARs obtained with this approach to the more accurate estimates of PARs that are available when the joint distributions of exposures and risks can be established.ResultsWe show that the assumptions of exposure independence and risk multiplicativity are sufficient but not necessary for the combined PAR to be unbiased. In the simplest situation of two risk factors, the bias of this approach is a function of the strength of association and the magnitude of risk interaction, for any values of exposure prevalence and their associated risks. In some cases, the combined PAR can be strongly under- or over-estimated, even if the two assumptions are only slightly violated.ConclusionsWe encourage researchers to quantify likely biases in their use of the M–S method, and here, we provided level plots and R code to assist.

The relative and interactive impact of multiple risk factors in schizophrenia spectrum disorders: a combined register-based and clinical twin study

2021 ◽  
pp. 1-11
Author(s):  
C. Lemvigh ◽  
R. Brouwer ◽  
R. Hilker ◽  
S. Anhøj ◽  
L. Baandrup ◽  
...  
Keyword(s):  
Risk Factors ◽  
Environmental Factors ◽  
Childhood Trauma ◽  
Cannabis Use ◽  
Paternal Age ◽  
Interactive Effects ◽  
Polygenic Risk Score ◽  
Multiple Risk Factors ◽  
Schizophrenia Spectrum ◽  
Multiple Risk

Abstract Background Research has yielded evidence for genetic and environmental factors influencing the risk of schizophrenia. Numerous environmental factors have been identified; however, the individual effects are small. The additive and interactive effects of multiple risk factors are not well elucidated. Twin pairs discordant for schizophrenia offer a unique opportunity to identify factors that differ between patients and unaffected co-twins, who are perfectly matched for age, sex and genetic background. Methods Register data were combined with clinical data for 216 twins including monozygotic (MZ) and dizygotic (DZ) proband pairs (one or both twins having a schizophrenia spectrum diagnosis) and MZ/DZ healthy control (HC) pairs. Logistic regression models were applied to predict (1) illness vulnerability (being a proband v. HC pair) and (2) illness status (being the patient v. unaffected co-twin). Risk factors included: A polygenic risk score (PRS) for schizophrenia, birth complications, birth weight, Apgar scores, paternal age, maternal smoking, season of birth, parental socioeconomic status, urbanicity, childhood trauma, estimated premorbid intelligence and cannabis. Results The PRS [odds ratio (OR) 1.6 (1.1–2.3)], childhood trauma [OR 4.5 (2.3–8.8)], and regular cannabis use [OR 8.3 (2.1–32.7)] independently predicted illness vulnerability as did an interaction between childhood trauma and cannabis use [OR 0.17 (0.03–0.9)]. Only regular cannabis use predicted having a schizophrenia spectrum diagnosis between patients and unaffected co-twins [OR 3.3 (1.1–10.4)]. Conclusion The findings suggest that several risk factors contribute to increasing schizophrenia spectrum vulnerability. Moreover, cannabis, a potentially completely avoidable environmental risk factor, seems to play a substantial role in schizophrenia pathology.

Multiple Risk Factors Before Pediatric Cardiac Transplantation Are Associated With Increased Graft Loss

2011 ◽  
Vol 33 (1) ◽  
pp. 49-54 ◽  
Author(s):  
Scott R. Auerbach ◽  
Marc E. Richmond ◽  
Jonathan M. Chen ◽  
Ralph S. Mosca ◽  
Jan M. Quaegebeur ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiac Transplantation ◽  
Graft Loss ◽  
Multiple Risk Factors ◽  
Multiple Risk

Abstract P672: Black White Disparity in Multiple Risk Factors is Increased in Older Young Adults

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Prachi Mehndiratta ◽  
Kathleen Ryan ◽  
Adeolu Morawo ◽  
Seemant Chaturvedi ◽  
Carolyn A Cronin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Young Adults ◽  
Stroke Risk ◽  
Reference Group ◽  
Population Based ◽  
Multiple Risk Factors ◽  
Stroke In Young Adults ◽  
Multiple Risk ◽  
The Relationship ◽  
Control Study

Background: Stroke in young adults constitutes 15-18% of all ischemic strokes. Vascular risk factors contribute to stroke risk in young adults particularly older young adults. Few studies have addressed Black White differences in risk, stratified by age. We evaluated the prevalence of risk factors in the younger young (less than 40 years) vs. the older young adults (40 and above). Methods: A population based case control study with 1034 cases and 1091 controls, ages 15-49 was used to investigate the relationship between risk factors (DM, HTN, Smoking and Obesity) and stroke. Groups were defined by the number of risk factors (RF) among cases and controls : no risk factors (ref group), one RF, two RF, three RF and four RF. Prevalence of risk factors was determined in the entire population and stratified by age, sex and race. Logistic regression was used to determine odds of stroke based on the number of risk factors compared to the reference group. Results: The percent of cases with three or more risk factors was compared in different subgroups: ages 15-39 vs. 40-49 was 8.4 vs. 21.6, women vs. men was 15.6 vs. 18.6 and White vs. Black was 12.3 vs. 22.7. Among cases 40 years and older, Blacks were 3 times more likely than Whites (5.9 vs. 2) to have four or more risk factors.Across all age, race and sex subgroups, the odds of having a stroke increased exponentially with an increase in the number of risk factors. Conclusion: Blacks are more likely to have multiple risk factors than Whites. This difference is accentuated in those 40 years and older. Targeting young adults with multiple risk factors for preventive interventions would address a root case of excess stroke risk especially among Blacks.

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