Effects of Health Information in Youth on Adult Biological and Lifestyle Risk Factors for Chronic Diseases

2003 ◽  
pp. 167-182
Author(s):  
H.C.G. Kemper ◽  
L.L.J. Koppes ◽  
W. de Vente ◽  
G.B. Post ◽  
W. van Mechelen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Information ◽  
Chronic Diseases ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

scholarly journals Smoking, drinking, diet and physical activity—modifiable lifestyle risk factors and their associations with age to first chronic disease

2019 ◽  
Vol 49 (1) ◽  
pp. 113-130 ◽  
Author(s):  
Ryan Ng ◽  
Rinku Sutradhar ◽  
Zhan Yao ◽  
Walter P Wodchis ◽  
Laura C Rosella
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Lifestyle Factors ◽  
Competing Risk ◽  
Chronic Obstructive ◽  
Common Disease ◽  
Heavy Smoking ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

AbstractBackgroundThis study examined the incidence of a person’s first diagnosis of a selected chronic disease, and the relationships between modifiable lifestyle risk factors and age to first of six chronic diseases.MethodsOntario respondents from 2001 to 2010 of the Canadian Community Health Survey were followed up with administrative data until 2014 for congestive heart failure, chronic obstructive respiratory disease, diabetes, lung cancer, myocardial infarction and stroke. By sex, the cumulative incidence function of age to first chronic disease was calculated for the six chronic diseases individually and compositely. The associations between modifiable lifestyle risk factors (alcohol, body mass index, smoking, diet, physical inactivity) and age to first chronic disease were estimated using cause-specific Cox proportional hazards models and Fine-Gray competing risk models.ResultsDiabetes was the most common disease. By age 70.5 years (2015 world life expectancy), 50.9% of females and 58.1% of males had at least one disease and few had a death free of the selected diseases (3.4% females; 5.4% males). Of the lifestyle factors, heavy smoking had the strongest association with the risk of experiencing at least one chronic disease (cause-specific hazard ratio = 3.86; 95% confidence interval = 3.46, 4.31). The lifestyle factors were modelled for each disease separately, and the associations varied by chronic disease and sex.ConclusionsWe found that most individuals will have at least one of the six chronic diseases before dying. This study provides a novel approach using competing risk methods to examine the incidence of chronic diseases relative to the life course and how their incidences are associated with lifestyle behaviours.

New Onset of Chronic Diseases and Changes in Lifestyle Risk Factors Among Gulf War Veterans

2016 ◽  
Vol 58 (8) ◽  
pp. 770-777 ◽  
Author(s):  
Stella M. Gwini ◽  
Helen L. Kelsall ◽  
Jil F. Ikin ◽  
Malcolm R. Sim ◽  
Alexander C. McFarlane ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Diseases ◽  
Gulf War ◽  
War Veterans ◽  
Gulf War Veterans ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
New Onset

scholarly journals World War II Effects on Prevalence and Pattern of Multimorbidity in Older Adults: Results From the KORA-Age Study

2021 ◽  
Author(s):  
Ava Arshadipour ◽  
Barbara Thorand ◽  
Birgit Linkohr ◽  
Susanne Rospleszcz ◽  
karl-heinz Ladwig ◽  
...  
Keyword(s):  
Risk Factors ◽  
World War Ii ◽  
Chronic Diseases ◽  
Early Life ◽  
World War ◽  
Disease Clusters ◽  
Logistic Regression Models ◽  
Lifestyle Risk Factors ◽  
Age Related ◽  
Lifestyle Risk

Abstract BackgroundWhile risk factors for age-related diseases may increase multimorbidity (MM), early life deprivation may also accelerate the development of chronic diseases and MM.MethodsThis study explores the prevalence and pattern of MM in 65-71 year-old individuals born before, during, and after World War II in Southern Germany based on two KORA (Cooperative Health Research in the Region of Augsburg) -Age studies. MM was defined as having at least two chronic diseases, and birth periods were classified into five phases: pre-war, early war, late war, famine, and after the famine period. Logistic regression models were used to analyze the effect of the birth phases on MM with adjustment for sociodemographic and lifestyle risk factors. Furthermore, we used agglomerative hierarchical clustering to investigate the co-occurrence of diseases.ResultsParticipants born during the late war phase had the highest prevalence of MM (62.2%) and single chronic diseases compared to participants born during the other phases. Being born in the late war phase was significantly associated with a higher odds of MM (OR = 1.83, 95% CI: 1.15-2.91) after adjustment for sociodemographic and lifestyle factors. In women, the prevalence of joint, gastrointestinal, eye diseases, and anxiety was higher, while heart disease, stroke, and diabetes were more common in men. Moreover, three main chronic disease clusters responsible for the observed associations were identified: joint and psychosomatic, cardiometabolic and, internal organs diseases.ConclusionsOur findings imply that adverse early-life exposure may increase the risk of MM in adults aged 65-71 years. Moreover, identified disease clusters are not coincidental and require more investigation.

scholarly journals The Relationship Between the Healthy Diet Index, Chronic Diseases, Obesity and Lifestyle Risk Factors Among Adults in Kaunas City, Lithuania

2021 ◽  
Vol 8 ◽  
Author(s):  
Audrius Dėdelė ◽  
Žydrūnė Bartkutė ◽  
Yevheniia Chebotarova ◽  
Auksė Miškinytė
Keyword(s):  
Risk Factors ◽  
Chronic Diseases ◽  
Dietary Habits ◽  
Healthy Diet ◽  
World Health ◽  
Food Groups ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

A healthy and balanced diet is an important factor contributing to overall health and wellness. The aim of this study was to develop a Healthy Diet Index (HDI) score and assess its association with various chronic diseases and lifestyle risk factors. A cross-sectional survey included 1,111 adults aged 18 years and older. Information on dietary habits was collected using a questionnaire. Additional demographic, socioeconomic and lifestyle risk factors data were also collected. Sixteen food groups were used to develop the HDI score for the residents of Kaunas city, Lithuania based on the national recommendations, World Health Organization (WHO) and other guidance on a healthy diet. We used logistic regression models to assess the association of the HDI score with chronic diseases, obesity and lifestyle risk factors. We found that both males and females were lacking the optimal consumption of the base components of a healthy diet–fruits and vegetables, starchy carbohydrates, and proteins. We also observed significant associations between the HDI score and several outcomes such as hypertension, arrhythmia, physical activity, and obesity. The suggested HDI score could serve as a valuable tool in assessing and improving dietary habits beneficial for promoting health and preventing many diseases.

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

scholarly journals The Prevalence of Insulin Resistance and the Associated Risk Factors in a Sample of 14–18-Year-Old Slovak Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 909
Author(s):  
Jana Jurkovičová ◽  
Katarína Hirošová ◽  
Diana Vondrová ◽  
Martin Samohýl ◽  
Zuzana Štefániková ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Insulin Resistance ◽  
Venous Blood ◽  
Significant Risk ◽  
Lifestyle Changes ◽  
Skewed Distribution ◽  
Model Assessment ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

The prevalence of cardiometabolic risk factors has increased in Slovakian adolescents as a result of serious lifestyle changes. This cross-sectional study aimed to assess the prevalence of insulin resistance (IR) and the associations with cardiometabolic and selected lifestyle risk factors in a sample of Slovak adolescents. In total, 2629 adolescents (45.8% males) aged between 14 and 18 years were examined in the study. Anthropometric parameters, blood pressure (BP), and resting heart rate were measured; fasting venous blood samples were analyzed; and homeostasis model assessment (HOMA)-insulin resistance (IR) was calculated. For statistical data processing, the methods of descriptive and analytical statistics for normal and skewed distribution of variables were used. The mean HOMA-IR was 2.45 ± 1.91, without a significant sex differences. IR (cut-off point for HOMA-IR = 3.16) was detected in 18.6% of adolescents (19.8% males, 17.6% females). IR was strongly associated with overweight/obesity (especially central) and with almost all monitored cardiometabolic factors, except for total cholesterol (TC) and systolic BP in females. The multivariate model selected variables such as low level of physical fitness, insufficient physical activity, breakfast skipping, a small number of daily meals, frequent consumption of sweetened beverages, and low educational level of fathers as significant risk factors of IR in adolescents. Recognizing the main lifestyle risk factors and early IR identification is important in terms of the performance of preventive strategies. Weight reduction, regular physical activity, and healthy eating habits can improve insulin sensitivity and decrease the incidence of metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD).

scholarly journals Lifestyle Risk Factors and Cardiovascular Disease in Cubans and Cuban Americans

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Melissa S. Burroughs Peña ◽  
Dhaval Patel ◽  
Delfin Rodríguez Leyva ◽  
Bobby V. Khan ◽  
Laurence Sperling
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Type 2 Diabetes Mellitus ◽  
Cardiovascular Prevention ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Cuban Americans

Cardiovascular disease is the leading cause of mortality in Cuba. Lifestyle risk factors for coronary heart disease (CHD) in Cubans have not been compared to risk factors in Cuban Americans. Articles spanning the last 20 years were reviewed. The data on Cuban Americans are largely based on the Hispanic Health and Nutrition Examination Survey (HHANES), 1982–1984, while more recent data on epidemiological trends in Cuba are available. The prevalence of obesity and type 2 diabetes mellitus remains greater in Cuban Americans than in Cubans. However, dietary preferences, low physical activity, and tobacco use are contributing to the rising rates of obesity, type 2 diabetes mellitus, and CHD in Cuba, putting Cubans at increased cardiovascular risk. Comprehensive national strategies for cardiovascular prevention that address these modifiable lifestyle risk factors are necessary to address the increasing threat to public health in Cuba.

scholarly journals Lifestyle risk factors for lower limb venous reflux in the general population: Edinburgh Vein Study

2001 ◽  
Vol 30 (4) ◽  
pp. 846-852 ◽  
Author(s):  
FGR Fowkes ◽  
AJ Lee ◽  
CJ Evans ◽  
PL Allan ◽  
AW Bradbury ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Lower Limb ◽  
Venous Reflux ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

scholarly journals Influence of lifestyle risk factors on work ability and sick leave in a general working population in Norway: a 5-year longitudinal study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e045678
Author(s):  
Marit Müller De Bortoli ◽  
Inger M. Oellingrath ◽  
Anne Kristin Moeller Fell ◽  
Alex Burdorf ◽  
Suzan J. W. Robroek
Keyword(s):  
Risk Factors ◽  
Longitudinal Study ◽  
Sick Leave ◽  
Work Ability ◽  
Working Population ◽  
Short Term ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

ObjectivesThe aim of this study is to assess (1) whether lifestyle risk factors are related to work ability and sick leave in a general working population over time, and (2) these associations within specific disease groups (ie, respiratory diseases, cardiovascular disease and diabetes, and mental illness).SettingTelemark county, in the south-eastern part of Norway.DesignLongitudinal study with 5 years follow-up.ParticipantsThe Telemark study is a longitudinal study of the general working population in Telemark county, Norway, aged 16 to 50 years at baseline in 2013 (n=7952) and after 5-year follow-up.Outcome measureSelf-reported information on work ability (moderate and poor) and sick leave (short-term and long-term) was assessed at baseline, and during a 5-year follow-up.ResultsObesity (OR=1.64, 95% CI: 1.32 to 2.05) and smoking (OR=1.62, 95% CI: 1.35 to 1.96) were associated with long-term sick leave and, less strongly, with short-term sick leave. An unhealthy diet (OR=1.57, 95% CI: 1.01 to 2.43), and smoking (OR=1.67, 95% CI: 1.24 to 2.25) were associated with poor work ability and, to a smaller extent, with moderate work ability. A higher lifestyle risk score was associated with both sick leave and reduced work ability. Only few associations were found between unhealthy lifestyle factors and sick leave or reduced work ability within disease groups.ConclusionLifestyle risk factors were associated with sick leave and reduced work ability. To evaluate these associations further, studies assessing the effect of lifestyle interventions on sick leave and work ability are needed.

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