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

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 ◽  
Machine Learning Approach ◽  
Lifestyle Risk

AbstractDespite 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. Herein, I developed a machine-learning tool that could be applied to assess the risk in communities based on social, medical, and lifestyle risk factors. As a “proof of concept,” I modeled COVID risk in the Massachusetts communities using 29 risk factors, including the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition. 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 finds the 9 highest risk communities in the state of Massachusetts: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. 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 Physical health disparities and mental illness: the scandal of premature mortality

2011 ◽  
Vol 199 (6) ◽  
pp. 441-442 ◽  
Author(s):  
Graham Thornicroft
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Health Disparities ◽  
Premature Mortality ◽  
Right To Health ◽  
Lifestyle Risk Factors ◽  
People With Mental Illness ◽  
Lifestyle Risk ◽  
The Right ◽  
International Conventions

SummaryA 20-year mortality gap for men, and 15 years for women, is still experienced by people with mental illness in high-income countries. The combination of lifestyle risk factors, higher rates of unnatural deaths and poorer physical healthcare contribute to this scandal of premature mortality that contravenes international conventions for the ‘right to health.’

PARTICIPATING IN HEALTH INSURANCE AND FACTORS AFFECTING AMONG PERSONS WHOSE ENROLMENT IS VOLUNTARY IN THUA THIEN HUE PROVINCE

2015 ◽  
pp. 89-95
Author(s):  
Thi Hoai Thuong Nguyen ◽  
Hoang Lan Nguyen ◽  
Mau Duyen Nguyen
Keyword(s):  
Health Insurance ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
Health Insurance Scheme ◽  
Insurance Scheme ◽  
Cross Sectional ◽  
Factors Affecting ◽  
The People ◽  
Universal Health Insurance Coverage ◽  
The Social

Background:To provide information helps building policy that meets the practical situation and needs of the people with the aim at achieving the goal of universal health insurance coverage, we conducted this study with two objectives (1) To determine the rate of participating health insurance among persons whose enrolment is voluntary in some districts of ThuaThien Hue province; (2) To investigate factor affecting their participation in health insurance. Materials and Methodology:A cross-sectional descriptive study was conducted in three districts / towns / city of ThuaThien Hue in 2014. 480 subjects in the voluntary participation group who were randomly selected from the study settings were directly interviewed to collect information on the social, economic, health insurance participation and knowledge of health insurance. Test χ2 was used to identify factors related to the participation in health insurance of the study subjects. Results:42.5% of respondents were covered by health insurance scheme. Factors related to their participation were the resident location (p = 0.042); gender (p = 0.004), age (p <0.001), chronic disease (p <0.001), economic conditions (p<0.001) and knowledge about health insurance (p <0.001). Conclusion: The rate of participating health insurance among study subjects was low at 42,5%. There was "adverse selection" in health insurance scheme among voluntary participating persons. Providing knowledge about health insurance should be one of solutions to improve effectively these problems. Key words: Health insurance, voluntary, Thua Thien Hue

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 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.

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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