Heterogeneity of risk within racial groups, a challenge for public health programs

Valles, Sean A.

doi:10.1016/j.ypmed.2012.08.022

Cited by 18 publications

(13 citation statements)

References 15 publications

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“…However, a considerable part of the population still represents genetically typical Finns and even continues living in the regional isolates. So the health care has to flexibly respect and implement the experience of the Finnish genetic research but be prepared to also take into account the different needs of the genetically distinct parts of the population (Valle 2012 ). For genetic discoveries for which performing research in our isolated population would be beneficial, the time to proceed is optimal just now: the genetic knowledge and the technologies are ready for this research and the main part of the population still represents the original genetic isolate.…”

Section: Is the Finnish Disease Heritage Disappearing In The Future?mentioning

confidence: 99%

Genetics in an isolated population like Finland: a different basis for genomic medicine?

et al. 2017

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A unique genetic background in an isolated population like that of Finland offers special opportunities for genetic research as well as for applying the genetic developments to the health care. On the other hand, the different genetic background may require local attempts to develop diagnostics and treatment as the selection of diseases and mutations differs from that in the other populations. In this review, we describe the experiences of research and health care in this genetic isolate starting from the identification of specific monogenic diseases enriched in the Finnish population all the way to implementing the knowledge of the unique genetic background to genomic medicine at population level.

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Section: Is the Finnish Disease Heritage Disappearing In The Future?mentioning

confidence: 99%

Genetics in an isolated population like Finland: a different basis for genomic medicine?

et al. 2017

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“…In Europe, ethnic differences in CV events persist after adjusting for income differences, suggesting that other variables might play a role [ 11 ]. Detection and control of risk factors such as hypertension and diabetes, identification of potential drivers predisposing ethnic minority groups to CV disease, and recognizing at risk sub-populations may be crucial to build effective prevention strategies [ 12 , 13 ]. Currently only four European Union member states (Netherlands, France, Portugal and Spain) cover the costs of long-term medical care for chronic disease in undocumented migrants [ 1 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis

et al. 2016

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BackgroundPeople of Sub Saharan Africa (SSA) and South Asians(SA) ethnic minorities living in Europe have higher risk of stroke than native Europeans(EU). Study objective is to provide an assessment of gender specific absolute differences in office systolic(SBP) and diastolic(DBP) blood pressure(BP) levels between SSA, SA, and EU.Methods and FindingsWe performed a systematic review and meta-analysis of observational studies conducted in Europe that examined BP in non-selected adult SSA, SA and EU subjects. Medline, PubMed, Embase, Web of Science, and Scopus were searched from their inception through January 31st 2015, for relevant articles. Outcome measures were mean SBP and DBP differences between minorities and EU, using a random effects model and tested for heterogeneity. Twenty-one studies involving 9,070 SSA, 18,421 SA, and 130,380 EU were included. Compared with EU, SSA had higher values of both SBP (3.38 mmHg, 95% CI 1.28 to 5.48 mmHg; and 6.00 mmHg, 95% CI 2.22 to 9.78 in men and women respectively) and DBP (3.29 mmHg, 95% CI 1.80 to 4.78; 5.35 mmHg, 95% CI 3.04 to 7.66). SA had lower SBP than EU(-4.57 mmHg, 95% CI -6.20 to -2.93; -2.97 mmHg, 95% CI -5.45 to -0.49) but similar DBP values. Meta-analysis by subgroup showed that SA originating from countries where Islam is the main religion had lower SBP and DBP values than EU. In multivariate meta-regression analyses, SBP difference between minorities and EU populations, was influenced by panethnicity and diabetes prevalence.Conclusions1) The higher BP in SSA is maintained over decades, suggesting limited efficacy of prevention strategies in such group in Europe;2) The lower BP in Muslim populations suggests that yet untapped lifestyle and behavioral habits may reveal advantages towards the development of hypertension;3) The additive effect of diabetes, emphasizes the need of new strategies for the control of hypertension in groups at high prevalence of diabetes.

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“…It was first necessary to determine whether the ML models were adequately calibrated in 'test' cohorts different from the ML modeldiscovery 'training' cohorts. Controlling for age distribution, geographic differences, gender, common contraindications for the treatment-of-interest (discharge beta-blocker post-CAB), and other factors [34][35][36][37][38][39][40][41][42] is important, to insure adequate statistical power for these assessments and to mitigate confounding [27,43]. Establishing that the ML model was adequately well-calibrated for each racial group prior to performing procedures to evaluate the presence of discrimination or disparities was performed using the Hosmer-Lemeshow test by model score deciles.…”

Section: Evaluation Approachmentioning

confidence: 99%

Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models

McNair¹

2018

New Insights Into Bayesian Inference

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Machine-learning (ML) methods are finding increasing application to guide human decision-making in many fields. Such guidance can have important consequences, including treatments and outcomes in health care. Recently, growing attention has focused on the potential that machine-learning might automatically learn unjust or discriminatory, but unrecognized or undisclosed, patterns that are manifested in available observational data and the human processes that gave rise to them, and thereby inadvertently perpetuating and propagating injustices that are embodied in the historical data. We applied two frequentist methods that have long been utilized in the courts and elsewhere for the purpose of ascertaining fairness (Cochran-Mantel-Haenszel test and beta regression) and one Bayesian method (Bayesian Model Averaging). These methods revealed that our ML model for guiding physicians' prescribing discharge beta-blocker medication for post-coronary artery bypass patients do not manifest significant untoward race-associated disparity. The methods also showed that our ML model for directing repeat performance of MRI imaging in children with medulloblastoma did manifest racial disparities that are likely associated with ethnic differences in informed consent and desire for information in the context of serious malignancies. The relevance of these methods to ascertaining and assuring fairness in other ML-based decision-support model-development and-curation contexts is discussed.

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Heterogeneity of risk within racial groups, a challenge for public health programs

Cited by 18 publications

References 15 publications

Genetics in an isolated population like Finland: a different basis for genomic medicine?

Genetics in an isolated population like Finland: a different basis for genomic medicine?

Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis

Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models

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