scholarly journals Multilevel mediation analysis on time-to-event outcomes: Exploring racial/ethnic disparities in breast cancer survival in California

2021 ◽  
pp. 263208432110612
Author(s):  
Qingzhao Yu ◽  
Mandi Yu ◽  
Joe Zou ◽  
Xiaocheng Wu ◽  
Scarlett L Gomez ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Census Tract ◽  
Ethnic Disparities ◽  
R Package ◽  
Individual Risk ◽  
Multilevel Mediation ◽  
Census Tract Level ◽  
The Individual ◽  
Individual Risk Factors ◽  
Racial Ethnic

Background Third-variable effect refers to the effect from a third-variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is a causal relationship from the exposure to the third variable, the third-variable is called a mediator or a confounder. The multilevel mediation analysis is used to differentiate third-variable effects from data of hierarchical structures. Data Collection and Analysis We developed a multilevel mediation analysis method to deal with time-to-event outcomes and implemented the method in the mlma R package. With the method, third-variable effects from different levels of data can be estimated. The method uses multilevel additive models that allow for transformations of variables to take into account potential nonlinear relationships among variables in the mediation analysis. We apply the proposed method to explore the racial/ethnic disparities in survival among patients diagnosed with breast cancer in California between 2006 and 2017, using both individual risk factors and census tract level environmental factors. The individual risk factors are collected by cancer registries and the census tract level factors are collected by the Public Health Alliance of Southern California in partnership with the Virginia Commonwealth University's Center on Society and Health. The National Cancer Institute work group linked variables at the census tract level with each patient and performed the analysis for this study. Results We found that the racial disparity in survival were mostly explained at the census tract level and partially explained at the individual level. The associations among variables were depicted. Conclusion: The multilevel mediation analysis method can be used to differentiate mediation/confounding effects for factors originated from different levels. The method is implemented in the R package mlma.

scholarly journals CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Gerard A Bouland ◽  
Joline W J Beulens ◽  
Joey Nap ◽  
Arno R van der Slik ◽  
Arnaud Zaldumbide ◽  
...  
Keyword(s):  
Risk Factors ◽  
Association Studies ◽  
R Package ◽  
Integrative Approach ◽  
Individual Risk ◽  
Genome Wide Association Studies ◽  
Genome Database ◽  
Intergenic Regions ◽  
Functional Consequences ◽  
Individual Risk Factors

Abstract Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.

Comparative assessment of the level of physical activity and physical fitness IIn patients treated for sciatica versus healthy individuals

2020 ◽  
Vol 1 (3) ◽  
pp. 27-38
Author(s):  
Grzegorz MAŃKO ◽  
Anna STROKOWSKA ◽  
Magda PIENIĄŻEK ◽  
Marcin ZIELIŃSKI ◽  
Robert DZIURA ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Fitness ◽  
Healthy People ◽  
Individual Risk ◽  
Control Group ◽  
Degree Of Disability ◽  
Disability Questionnaire ◽  
The Individual ◽  
Individual Risk Factors ◽  
Age Range

Introduction: Sciatica (Ischias) is a set of symptoms associated with the compression of L4, L5 and S1 spinal nerves, forming the sciatic nerve, the largest peripheral human nerve. Pain can radiate to the buttock, lateral surface of the hip, calf and foot. The individual risk factors for sciatica include height, body mass, muscle strength, and physical condition. I n d i v i d u a l f a c t o r s a l s o i n c l u d e pregnancies and births. The aim of this study was to assess and compare the level of physical activity and physical fitness of people treated for sciatica and compare the results with the results of tests carried out among healthy people, not treated for sciatica or other diseases of the lower spine. Material and methods: The study was conducted among 60 people, both sexes, aged 50-69. The I n t e r n a t i o n a l P h y s i c a l A c t i v i t y Questionnaire IPAQ and the Oswestry Disability Questionnaire were used for the assessment. In the research group there were 30 people diagnosed with sciatica, treated for this disease or other diseases of the lumbar spine. The control group consisted of 30 healthy people in the same age range. Results and conclusion: On the basis of the conducted research, there were significant differences between the degree of disability of healthy people and patients, which confirmed that sciatica is a disease significantly affecting physical fitness during everyday activities.

Risk Factor Analysis for Injuries in Football Players

2000 ◽  
Vol 28 (5_suppl) ◽  
pp. 69-74 ◽  
Author(s):  
Jiri Dvorak ◽  
Astrid Junge ◽  
Jiri Chomiak ◽  
Toni Graf-Baumann ◽  
Lars Peterson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Wide Spectrum ◽  
Practical Experience ◽  
Individual Risk ◽  
Football Players ◽  
Psychosocial Characteristics ◽  
The Individual ◽  
Individual Risk Factors ◽  
Physical Findings ◽  
Baseline Examination

Review of the literature shows that information concerning risk factors for football injuries is incomplete and partly contradictory. The aim of this study was to analyze the influence of medical history, physical findings, football skills, and football performance, as well as psychosocial characteristics on the occurrence and severity of football injuries. The prospective outline of the study was as follows: after a baseline examination was performed to ascertain possible predictors of injury, all players were followed up weekly for 1 year to register subsequent injuries and complaints. Two hundred sixty-four of 398 players (67%) had complete weekly follow-ups over 1 year. A majority of the players ( N = 216; 82%) were injured during the observation period. In comparing injured and uninjured players, several differences were observed. To create a multidimensional predictor score for football injuries, 17 risk factors were selected. These risk factors covered a wide spectrum, such as previous injuries, acute complaints, inadequate rehabilitation, poor health awareness, high life-event stress, playing characteristics, poor reaction time, poor endurance, and insufficient preparation for games. By summing up the individual risk factors, a predictive sum was calculated for each player. The more risk factors present at the baseline examination, the higher the probability of that player incurring an injury in the ensuing year. Using two risk factors as the cut-off score, more than 80% of the players were correctly classified as to whether they went on to incur an injury. Based on these findings, knowledge from the literature, and practical experience, possibilities for a prevention program are suggested.

Abstract P346: Modeling Novel Biomarkers with Individual Risk Factors and an “Improved” Bayes Approach are Preferred Strategies for Assessing Predictive Utility of Biomarkers

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Vanessa Xanthakis ◽  
Michael J Pencina ◽  
Lisa M Sullivan
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Absolute Risk ◽  
Individual Risk ◽  
Composite Risk ◽  
The Individual ◽  
Individual Risk Factors ◽  
Standard Risk ◽  
Prior Risk ◽  
Bayes Approach

Given the rapid growth of new prognostic biomarkers, it is critical to assess their incremental utility for risk prediction while considering standard risk factors. This assessment may be influenced by the approach used to model new biomarkers. We hypothesized that the performance of a putative biomarker is best assessed by adding it to a model that includes standard risk factors as individual variables, as compared to adding it to a composite risk score (based on standard risk factors) estimated from the current study or to a composite risk score from a published study. We also compared 3 approaches of adjusting the prior absolute risk of an event using the information from a new biomarker, when data regarding prior risk are limited, hypothesizing that conditioning the biomarker residuals on prior risk (Improved Bayes approach) or adjusting the intercept of a model that includes the prior risk estimate are superior to the Naïve Bayes approach. Incremental performance was evaluated by comparing measures of improvement in discrimination. Using 1000 simulated datasets, similar incremental performance was observed when a putative biomarker was added to a model with the individual risk factors as compared to adding it to a model with a risk score estimated from the current study. Including a biomarker in a model with a published risk score resulted in an overestimation of its contribution ( Table ).These findings were supported by Framingham Heart Study data predicting incident atrial fibrillation using CRP and BNP.The Improved Bayes approach was a better strategy for updating the prior risk estimate as compared to the Naïve Bayes approach, using information from a new biomarker (Table). Our theoretical and empirical results identified that adding a new biomarker into a multivariable prediction model that includes the individual risk factors is the preferred strategy for assessing the incremental yield of a novel biomarker, and using the Naive Bayes approach (when information on the prior absolute risk of an event is scarce) is suboptimal.

scholarly journals Association of antecedent cardiovascular risk factor levels and trajectories with cardiovascular magnetic resonance-derived cardiac function and structure

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Roberto Lorbeer ◽  
Susanne Rospleszcz ◽  
Christopher L. Schlett ◽  
Sophia D. Rado ◽  
Barbara Thorand ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Cardiac Function ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Individual Risk ◽  
The Individual ◽  
And Function ◽  
Individual Risk Factors

Abstract Background The association of longitudinal trajectories of cardiovascular risk factors with cardiovascular magnetic resonance (CMR)-measures of cardiac structure and function in the community is not well known. Therefore we aimed to relate risk factor levels from different examination cycles to CMR-measures of the left ventricle (LV) and right ventricle in a population-based cohort. Methods We assessed conventional cardiovascular disease risk factors in 349 participants (143 women; aged 25–59 years) at three examination cycles (Exam 1 [baseline], at Exam 2 [7-years follow-up] and at Exam 3 [14-years follow-up]) of the KORA S4 cohort and related single-point measurements of individual risk factors and longitudinal trajectories of these risk factors to various CMR-measures obtained at Exam 3. Results High levels of diastolic blood pressure, waist circumference, and LDL-cholesterol at the individual exams were associated with worse cardiac function and structure. Trajectory clusters representing higher levels of the individual risk factors were associated with worse cardiac function and structure compared to low risk trajectory clusters of individual risk factors. Multivariable (combining different risk factors) trajectory clusters were associated with different cardiac parameters in a graded fashion (e.g. decrease of LV stroke volume for middle risk cluster β = − 4.91 ml/m2, 95% CI − 7.89; − 1.94, p < 0.01 and high risk cluster β = − 7.00 ml/m2, 95% CI − 10.73; − 3.28, p < 0.001 compared to the low risk cluster). The multivariable longitudinal trajectory clusters added significantly to explain variation in CMR traits beyond the multivariable risk profile obtained at Exam 3. Conclusions Cardiovascular disease risk factor levels, measured over a time period of 14 years, were associated with CMR-derived measures of cardiac structure and function. Longitudinal multivariable trajectory clusters explained a greater proportion of the inter-individual variation in cardiac traits than multiple risk factor assessed contemporaneous with the CMR exam.

Beyond Ideology in Drug Policy: The Primary Prevention Model

1992 ◽  
Vol 22 (4) ◽  
pp. 959-976 ◽  
Author(s):  
Derek T. Mason ◽  
Mark W. Lusk ◽  
Michael Gintzler
Keyword(s):  
Primary Prevention ◽  
Drug Policy ◽  
The United States ◽  
Policy Formulation ◽  
Individual Risk ◽  
Common Denominator ◽  
Prevention Model ◽  
Definition Of ◽  
The Individual ◽  
Individual Risk Factors

Drug policy in the United States is heavily influenced by popular and expert ideologies and a social definition of the problem. As a result, public substance abuse policies reflect an incoherent compromise between medical and criminal definitions and approaches to intervention. The effect is that in both conceptions, the problem locus is in the individual user. Thus, contemporary prevention, treatment and rehabilitation strategies fail to account for the myriad socioeconomic correlates of abuse and tend to atomize the problem by reducing it to the lowest common denominator — the drug-abusing person. Primary prevention approaches to drug abuse hold the greatest promise for remediation of this social problem because of the inclusion of macroenvironmental factors in tandem with individual risk factors to form a comprehensive approach to policy formulation.

Abstract 005: Violent Crime, Physical Inactivity, And Obesity: Examining Differences In Spatial Relationships By Racial/ethnic Composition Of Community Residents

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Chelsea R Singleton ◽  
Fikriyah Winata ◽  
Oluwafikayo S Adeyemi ◽  
Kaustubh V Parab ◽  
Susan Aguiñaga
Keyword(s):  
Violent Crime ◽  
Census Tract ◽  
Crime Rate ◽  
Physical Inactivity ◽  
Ethnic Composition ◽  
Census Tracts ◽  
Census Tract Level ◽  
Violent Crime Rate ◽  
Community Residents ◽  
Racial Ethnic

Introduction: Violent crime (e.g., homicide, aggravated assault) is a major public health issue that disproportionately affects communities of color in large urban centers. Studies have reported that residents in high crime communities are less likely to engage in physical activity. There is limited understanding of how violent crime influences physical inactivity and obesity at the community level. We aimed to address this gap by examining differences in spatial relationships between violent crime rate, physical inactivity, and obesity by racial/ethnic composition of community residents in Chicago, IL. Hypothesis: We assessed the hypothesis that violent crime rate is associated with the prevalence of physical inactivity and obesity at the census tract level in Chicago, IL. Methods: We conducted an ecological assessment of 2018 census tract data obtained from various sources. We used data from the City of Chicago to calculate per capita violent crime rate (number of incidents per 1,000 residents) for all census tracts (N = 801). Data on physical inactivity and obesity prevalence (%) were acquired from the CDC. Socio-demographic data (i.e., % Non-Hispanic (NH) White, % NH Black, % Hispanic, median household income) were obtained from the census bureau. We examined spatial lag and error models to determine if violent crime rate is associated with % physical inactivity and % obesity after controlling for socio-demographic characteristics and amenity availability (i.e., per capita outdoor parks and grocery stores). Stratified models were examined to identify differences in associations among majority NH White, NH Black, and Hispanic census tracts (defined as ≥ 50% representation). Results: NH Black census tracts (n = 278) had significantly higher rates of violent crime, physical inactivity, and obesity than Hispanic (n = 169) and NH White tracts (n = 240). Overall, violent crime rate was positivity associated with % physical inactivity (p<0.001) but not % obesity (p=0.77) in Chicago after controlling for covariates. Stratified models revealed that violent crime rate was positively associated with % physical inactivity (p<0.001) and % obesity (p=0.01) among NH Black tracts. Violent crime rate was not associated with % physical inactivity or % obesity among Hispanic and NH White census tracts. Conclusions: Racial/ethnic composition of residents appears to influence census-tract level associations between violent crime rate, physical inactivity, and obesity. Violent crime appears to be more relevant to physical inactivity and obesity in Chicago’s NH Black communities compared to Hispanic and NH White communities.

scholarly journals The Relative Importance of Worker, Firm, and Market Characteristics for Racial/Ethnic Disparities in Employer-Sponsored Health Insurance

2007 ◽  
Vol 44 (3) ◽  
pp. 280-302 ◽  
Author(s):  
Jennifer Haas ◽  
Katherine Swartz
Keyword(s):  
Race And Ethnicity ◽  
Ethnic Disparities ◽  
Individual Characteristics ◽  
Relative Importance ◽  
Racial And Ethnic Disparities ◽  
Firm Characteristics ◽  
Working People ◽  
The Individual ◽  
Racial Ethnic

The characteristics of an individual, the local labor market, and the firm where an individual is employed each may be associated with racial and ethnic disparities in employer-sponsored insurance (ESI). This study estimates two models to determine the relative effects of each of these three sets of characteristics on the likelihood a worker has a job with ESI. One model has two outcomes: the job comes with ESI or not. The other model has five possible outcomes: the individual is not offered ESI and is uninsured; the individual is not offered ESI and is insured; the individual is offered ESI but turns it down and is uninsured; the individual is offered ESI but turns it down and is insured; and the individual is offered ESI and accepts. Findings indicate that individual characteristics and firm characteristics are more likely to have significant and substantial effects on the probability that a person has ESI, while the effects of market characteristics appear to be conveyed through firm characteristics. Being African American or Hispanic is not significantly associated with having ESI in the two-outcomes model, but in the five-outcomes model each is associated significantly with being uninsured, either because the person has not been offered ESI or has declined offered coverage. Clearly, examining more nuanced outcomes is more informative about the role of race and ethnicity in why working people are uninsured.

scholarly journals DETERMINANTS OF NEET (NOT IN EMPLOYMENT, EDUCATION OR TRAINING) ON THE POLISH LABOUR MARKET

2022 ◽  
Author(s):  
Anna M. Rak
Keyword(s):  
Risk Factors ◽  
Young People ◽  
Labour Market ◽  
Formal Education ◽  
Individual Risk ◽  
Job Seeking ◽  
Employment Education ◽  
The Individual ◽  
Individual Risk Factors ◽  
The Eu

The principal purpose of the study is to identify the individual risk factors of young people becoming the NEET generation on the Polish labour market. The first part of the paper comprises a literature-based overview of definitions of the NEET category based and a presentation of the risk factors of young people becoming NEET. The second part presents the results of empirical analyses conducted employing a questionnaire on a group of 120 individuals, aged 15 through 30, who met all criteria of the NEET definition set forth by the Employment Committee of the EU. The research demonstrates that among the major determinants of young people becoming NEET are financial hardship of their households, low motivation to continue formal education or change professional qualifications, and low level of job-seeking activity.

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