scholarly journals Polygenic adaptation is not a major driver of disparities in disease mortality across global populations

2021 ◽  
Author(s):  
Ujani Hazra ◽  
Joseph Lachance
Keyword(s):  
Health Disparities ◽  
Human Development Index ◽  
Causes Of Death ◽  
Disease Risk ◽  
Mortality Rates ◽  
Risk Scores ◽  
Common Diseases ◽  
Common Causes ◽  
Polygenic Adaptation ◽  
Disease Risks

Background and objectives: Health disparities are due to a range of socioeconomic and biological causes, and many common diseases have a genetic basis. Divergent evolutionary histories cause allele frequencies at disease-associated loci to differ across global populations. To what extent are differences in disease risks due to natural selection? Methodology: Examining a panel of nine global populations, we identified which of the 20 most common causes of death have the largest health disparities. Polygenic risk scores were computed and compared for 11 common diseases for the same nine populations. We then used PolyGraph to test whether differences in disease risk can be attributed to polygenic adaptation. Finally, we compared human development index statistics and polygenic risk scores to mortality rates for each population. Results: Among common causes of death, HIV/AIDS and tuberculosis exhibited the greatest disparities in mortality rates. Focusing on common polygenic diseases, we found that genetic predictions of disease risk varied across global populations (including elevated risks of lung cancer in Europeans). However, polygenic adaptation tests largely yielded negative results when applied to common diseases. Our analyses revealed that natural selection was not a major cause of differences in disease risks across global populations. We also found that correlations between mortality rates and human development index statistics were stronger than correlations between mortality rates and polygenic predictions of disease risks. Conclusions and implications: Although evolutionary history contributes to differences in disease risks, health disparities are largely due to socioeconomic and other environmental factors.

scholarly journals Persistent and extreme outliers in causes of death by state, 1999-2013

2015 ◽  
Author(s):  
Francis P Boscoe
Keyword(s):  
United States ◽  
Heart Disease ◽  
Causes Of Death ◽  
The United States ◽  
Mortality Rates ◽  
National Rate ◽  
Common Causes ◽  
Specific Mortality

In the United States, state-specific mortality rates that are high relative to national rates can result from legitimate reasons or from variability in coding practices. This paper identifies instances of state-specific mortality rates that were at least twice the national rate in each of three consecutive five-year periods (termed persistent outliers), along with rates that were at least five times the national rate in at least one five-year period (termed extreme outliers). The resulting set of 71 outliers, 12 of which appeared on both lists, illuminates mortality variations within the country, including some that are amenable to improvement either because they represent preventable causes of death or highlight weaknesses in coding techniques. Because the approach used here is based on relative rather than absolute mortality, it is not dominated by the most common causes of death such as heart disease and cancer.

scholarly journals Mortality Characteristics in Sumadija District From 2010 - 2017

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Natasa Mihailovic ◽  
Nebojsa Zdravkovic ◽  
Biljana Milicic ◽  
Sanja Kocic ◽  
Vesna Milicic ◽  
...  
Keyword(s):  
Causes Of Death ◽  
Linear Trend ◽  
Mortality Rates ◽  
Male Population ◽  
Time Period ◽  
Specific Analysis ◽  
Common Causes ◽  
Icd 10 ◽  
Old Men ◽  
The Given

Abstract Mortality rate represents a number of deaths on a particular territory per time unit. There are general and specific mortalities. Th e aims at analysing the characteristics of mortality in Sumadija District for the time period ranging from 2010 - 2017. The study includes all death cases in the District of Sumadija in period 2010-2017, 34681 cases. The data are described and analysed with χ2 test and linear trend. The trend analysis does not indicate any significant variations during the given time span. The gender analysis reveals that there is a significantly higher number of deceased persons among male population (52.2%, 47.8%, р<0.05). The average age of the deceased females (76.2±13.4) was higher than the average age of males (73.8±14.1) (р<0.05). The cause-specific analysis shows that natural causes of death dominate absolutely (96.5%) over violent deaths (2.7%) and undetermined causes (0.8%). The distribution of death causes according to ICD 10 shows that the most frequent causes of death are heart and blood vessel diseases, respiratory and neoplasm diseases. The life expectancy of the inhabitants of Sumadija District is increasing over time. There was a slight decrease in the mortality rates during the observed time period. The highest number of the deceased people is 65 or more years old. Men have higher mortality rates throughout their lives. Natural death and non-communicable diseases are dominant. The most common causes of death are heart and blood vessels diseases, in women, and respiratory and neoplasm diseases, in men.

scholarly journals The Enduring Association between Education and Mortality

2011 ◽  
Vol 76 (6) ◽  
pp. 913-934 ◽  
Author(s):  
Richard Miech ◽  
Fred Pampel ◽  
Jinyoung Kim ◽  
Richard G. Rogers
Keyword(s):  
United States ◽  
Health Disparities ◽  
Causes Of Death ◽  
The United States ◽  
Mortality Rates ◽  
Social Forces ◽  
Educational Disparities ◽  
Fundamental Cause ◽  
Almost All ◽  
Over Time

This article examines how educational disparities in mortality emerge, grow, decline, and disappear across causes of death in the United States, and how these changes contribute to the enduring association between education and mortality over time. Focusing on adults age 40 to 64 years, we first examine the extent to which educational disparities in mortality persisted from 1989 to 2007. We then test the fundamental cause prediction that educational disparities in mortality persist, in part, by shifting to new health outcomes over time. We focus on the period from 1999 to 2007, when all causes of death were coded to the same classification system. Results indicate (1) substantial widening and narrowing of educational disparities in mortality across causes of death, (2) almost all causes of death with increasing mortality rates also had widening educational disparities, and (3) the total educational disparity in mortality would be about 25 percent smaller today if not for newly emergent and growing educational disparities since 1999. These results point to the theoretical and policy importance of identifying social forces that cause health disparities to widen over time.

scholarly journals Persistent and extreme outliers in causes of death by state, 1999-2013

2015 ◽  
Author(s):  
Francis P Boscoe
Keyword(s):  
United States ◽  
Heart Disease ◽  
Causes Of Death ◽  
The United States ◽  
Mortality Rates ◽  
National Rate ◽  
Common Causes ◽  
Specific Mortality

In the United States, state-specific mortality rates that are high relative to national rates can result from legitimate reasons or from variability in coding practices. This paper identifies instances of state-specific mortality rates that were at least twice the national rate in each of three consecutive five-year periods (termed persistent outliers), along with rates that were at least five times the national rate in at least one five-year period (termed extreme outliers). The resulting set of 71 outliers, 12 of which appeared on both lists, illuminates mortality variations within the country, including some that are amenable to improvement either because they represent preventable causes of death or highlight weaknesses in coding techniques. Because the approach used here is based on relative rather than absolute mortality, it is not dominated by the most common causes of death such as heart disease and cancer.

scholarly journals Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App (Preprint)

2020 ◽  
Author(s):  
Bum Chul Kwon ◽  
Courtland VanDam ◽  
Stephanie E Chiuve ◽  
Hyung Wook Choi ◽  
Paul Entler ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Disease ◽  
Blood Sugar ◽  
Diet Quality ◽  
Disease Risk ◽  
Mobile App ◽  
Risk Scores ◽  
Disease Risks ◽  
Heart Disease Risk ◽  
Mhealth Apps

BACKGROUND Diet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet quality—focusing on high-quality dietary patterns rather than quantifying diet into calories—has shown effectiveness in improving heart disease risk. The Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on the consumption of foods from quality-focused food categories, rather than detailed serving sizes. No studies have examined how mobile health (mHealth) apps focusing on diet quality can bring promising results in health outcomes and ease of adoption. OBJECTIVE This study aims to design a mobile app to support the HHS-informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of the HHS. We measured the app’s feasibility and efficacy in improving individuals’ clinical and behavioral factors that affect future heart disease risks and app use. METHODS We recruited 38 participants who were overweight or obese with high heart disease risk and who used the app for 5 weeks and measured weight, blood sugar, blood pressure, HHS, and diet score (DS)—the measurement for diet quality—at baseline and week 5 of the intervention. RESULTS Most participants (30/38, 79%) used the app every week and showed significant improvements in DS (baseline: mean 1.31, SD 1.14; week 5: mean 2.36, SD 2.48; 2-tailed <i>t</i> test <i>t</i><sub>29</sub>=−2.85; <i>P</i>=.008) and HHS (baseline: mean 22.94, SD 18.86; week 4: mean 22.15, SD 18.58; <i>t</i><sub>29</sub>=2.41; <i>P</i>=.02) at week 5, although only 10 participants (10/38, 26%) checked their HHS risk scores more than once. Other outcomes, including weight, blood sugar, and blood pressure, did not show significant changes. CONCLUSIONS Our study showed that our logging tool significantly improved dietary choices. Participants were not interested in seeing the HHS and perceived logging diet categories irrelevant to improving the HHS as important. We discuss the complexities of addressing health risks and quantity- versus quality-based health monitoring and incorporating secondary behavior change goals that matter to users when designing mHealth apps.

scholarly journals Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App

10.2196/21733 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e21733
Author(s):  
Bum Chul Kwon ◽  
Courtland VanDam ◽  
Stephanie E Chiuve ◽  
Hyung Wook Choi ◽  
Paul Entler ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Disease ◽  
Blood Sugar ◽  
Diet Quality ◽  
Disease Risk ◽  
Mobile App ◽  
Risk Scores ◽  
Disease Risks ◽  
Heart Disease Risk ◽  
Mhealth Apps

Background Diet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet quality—focusing on high-quality dietary patterns rather than quantifying diet into calories—has shown effectiveness in improving heart disease risk. The Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on the consumption of foods from quality-focused food categories, rather than detailed serving sizes. No studies have examined how mobile health (mHealth) apps focusing on diet quality can bring promising results in health outcomes and ease of adoption. Objective This study aims to design a mobile app to support the HHS-informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of the HHS. We measured the app’s feasibility and efficacy in improving individuals’ clinical and behavioral factors that affect future heart disease risks and app use. Methods We recruited 38 participants who were overweight or obese with high heart disease risk and who used the app for 5 weeks and measured weight, blood sugar, blood pressure, HHS, and diet score (DS)—the measurement for diet quality—at baseline and week 5 of the intervention. Results Most participants (30/38, 79%) used the app every week and showed significant improvements in DS (baseline: mean 1.31, SD 1.14; week 5: mean 2.36, SD 2.48; 2-tailed t test t29=−2.85; P=.008) and HHS (baseline: mean 22.94, SD 18.86; week 4: mean 22.15, SD 18.58; t29=2.41; P=.02) at week 5, although only 10 participants (10/38, 26%) checked their HHS risk scores more than once. Other outcomes, including weight, blood sugar, and blood pressure, did not show significant changes. Conclusions Our study showed that our logging tool significantly improved dietary choices. Participants were not interested in seeing the HHS and perceived logging diet categories irrelevant to improving the HHS as important. We discuss the complexities of addressing health risks and quantity- versus quality-based health monitoring and incorporating secondary behavior change goals that matter to users when designing mHealth apps.

scholarly journals Towards clinical utility of polygenic risk scores

2019 ◽  
Vol 28 (R2) ◽  
pp. R133-R142 ◽  
Author(s):  
Samuel A Lambert ◽  
Gad Abraham ◽  
Michael Inouye
Keyword(s):  
Risk Factors ◽  
Family History ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Disease Risk ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Common Diseases ◽  
History Of Disease ◽  
History Of

Abstract Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

scholarly journals Improving Heart disease risk through quality-focused diet logging: pre-post study of a diet quality tracking app

2020 ◽  
Author(s):  
Courtland VanDam ◽  
Bum Chul Kwon ◽  
Stephanie Chiuve ◽  
Hyung-Wook Choi ◽  
Paul Entler ◽  
...  
Keyword(s):  
Heart Disease ◽  
Behavior Change ◽  
Mobile Health ◽  
Diet Quality ◽  
Disease Risk ◽  
Mobile App ◽  
Risk Scores ◽  
Cardiovascular Risks ◽  
Disease Risks ◽  
Heart Disease Risk

AbstractDiet-tracking mobile apps have been effective in behavior change. At the same time, quantity-focused diet tracking (e.g., calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet Quality—focusing on high-quality dietary patterns rather than quantifying diet into calories—has shown effectiveness in improving heart disease risk. Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on quality-focused food category consumptions, rather than detailed serving sizes. No studies have examined how mobile health apps focusing on diet quality can bring promising results on health outcomes and ease of adoption. We designed a mobile app to support the HHS informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of HHS. We measured the app’s feasibility and efficacy on improving individuals’ clinical and behavioral factors affecting future heart disease risks and app use. We recruited 38 overweight or obese participants at high heart disease risk, who used the app for 5 weeks and measured weight, blood sugar, and blood pressure, HHS, and Diet Score (DS) measuring diet quality at baseline and the fifth week of the intervention. The majority used the application every week (84%) and significantly improved DS and HHS at the fifth week (p<0.05), although only 10 participants (31%) checked their risk scores more than once. Other outcomes did not show significant changes. Our study showed logging simplified diet quality significantly improved dietary behavior. The participants were not interested in seeing HHS, and the participants perceived logging diet categories irrelevant to improving HHS as important. We discuss the complexities of addressing health risks, quantity vs. quality-based health monitoring, and incorporating secondary behavior change goals that matter to users when designing mobile health.

scholarly journals Persistent and extreme outliers in causes of death by state, 1999-2013

2015 ◽  
Author(s):  
Francis P Boscoe
Keyword(s):  
United States ◽  
Heart Disease ◽  
Causes Of Death ◽  
The United States ◽  
Mortality Rates ◽  
National Rate ◽  
Common Causes ◽  
Specific Mortality

In the United States, state-specific mortality rates that are high relative to national rates can result from legitimate reasons or from variability in coding practices. This paper identifies instances of state-specific mortality rates that were at least twice the national rate in each of three consecutive five-year periods (termed persistent outliers), along with rates that were at least five times the national rate in at least one five-year period (termed extreme outliers). The resulting set of 71 outliers, 12 of which appeared on both lists, illuminates mortality variations within the country, including some that are amenable to improvement either because they represent preventable causes of death or highlight weaknesses in coding techniques. Because the approach used here is based on relative rather than absolute mortality, it is not dominated by the most common causes of death such as heart disease and cancer.

scholarly journals Persistent and extreme outliers in causes of death by state, 1999–2013

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1336 ◽  
Author(s):  
Francis P. Boscoe
Keyword(s):  
United States ◽  
Heart Disease ◽  
Causes Of Death ◽  
The United States ◽  
Mortality Rates ◽  
National Rate ◽  
Common Causes ◽  
Specific Mortality

In the United States, state-specific mortality rates that are high relative to national rates can result from legitimate reasons or from variability in coding practices. This paper identifies instances of state-specific mortality rates that were at least twice the national rate in each of three consecutive five-year periods (termed persistent outliers), along with rates that were at least five times the national rate in at least one five-year period (termed extreme outliers). The resulting set of 71 outliers, 12 of which appear on both lists, illuminates mortality variations within the country, including some that are amenable to improvement either because they represent preventable causes of death or highlight weaknesses in coding techniques. Because the approach used here is based on relative rather than absolute mortality, it is not dominated by the most common causes of death such as heart disease and cancer.

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