Abstract-Obesity is a continuing challenge for any town, city or country faced with this problem. Being obese increases your risk of physical disorders such as high blood pressure (BP), high blood cholesterol, diabetes, coronary heart disease, stroke, cancer and poor reproductive health. Higher obesity rates also leads to increased economic burden on society. In order to better understand and control obesity rates the in uence of various factors on its prevalence should be investigated. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships using a combination of socio-economic and physical factor for counties in Pennsylvania (PA), USA for 2010. Our ndings suggest that the rate of obesity is impacted by local spatial variation and its prevalence positively correlated with diabetes, physical inactivity and the distance that a person must travel to get to a healthy food store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was found to signi cantly improve model tting over OLS (AICc = 299.87; r-squared = 0.34). These results indicate that additional factors, including social, cultural and behavioral, are needed to better explain the distribution of obesity rates across PA.
Assessment and Treatment of Childhood ObesityHEALTHCoalition
Presentation by JoAnn Clark, RN, Magnolia Regional Medical Center, at the Regional Summit on Healthy, Active Living in South Arkansas, November 2, 2010.
JAMA Network: Pregnant women may not be getting recommended nutrientsΔρ. Γιώργος K. Κασάπης
Nutrition during pregnancy is a critical dimension not only for women’s health but
also for the lifelong health of the offspring. Very limited national data exist on the usual dietary
intakes of pregnant women.
Some 3% of women were taking more than the tolerable amount of iron and folic acid. Almost all Pregnant women may not be getting all the vitamins and nutrients that are usually recommended to them during pregnancy, a new survey finds. Researchers asked more than 1,000 pregnant women whether they took dietary supplements, and even though most of the women took supplements, many were not meeting the recommended intake: More than 10% did not were taking less than the recommended amount of magnesium, iron,of the women included in the study were also consuming high amounts of sodium.
The study authors call for better dietary supplement recommendations to better help pregnant women get the nutrients they need.
Which US States Present the Greatest Military Injury RisksJA Larson
The purpose of this study was to investigate state-level distributions of cardiorespiratory fitness, body mass index, and injuries among U.S. Army recruits in order to determine whether or not certain states may also pose disproportionate threats to military readiness and national security.
Assessment and Treatment of Childhood ObesityHEALTHCoalition
Presentation by JoAnn Clark, RN, Magnolia Regional Medical Center, at the Regional Summit on Healthy, Active Living in South Arkansas, November 2, 2010.
JAMA Network: Pregnant women may not be getting recommended nutrientsΔρ. Γιώργος K. Κασάπης
Nutrition during pregnancy is a critical dimension not only for women’s health but
also for the lifelong health of the offspring. Very limited national data exist on the usual dietary
intakes of pregnant women.
Some 3% of women were taking more than the tolerable amount of iron and folic acid. Almost all Pregnant women may not be getting all the vitamins and nutrients that are usually recommended to them during pregnancy, a new survey finds. Researchers asked more than 1,000 pregnant women whether they took dietary supplements, and even though most of the women took supplements, many were not meeting the recommended intake: More than 10% did not were taking less than the recommended amount of magnesium, iron,of the women included in the study were also consuming high amounts of sodium.
The study authors call for better dietary supplement recommendations to better help pregnant women get the nutrients they need.
Which US States Present the Greatest Military Injury RisksJA Larson
The purpose of this study was to investigate state-level distributions of cardiorespiratory fitness, body mass index, and injuries among U.S. Army recruits in order to determine whether or not certain states may also pose disproportionate threats to military readiness and national security.
A presentation of a journal article on the comparison of MUAC and WHZ as diagnostic tools for acute malnutrition
*for classroom presentation purpose only*
*no copyright infringement intended*
Association between variations in the fat mass and obesity-associated gene an...Enrique Moreno Gonzalez
It is clear that genetic variations in the fat mass and obesity-associated (FTO) gene affect body mass index and the risk of obesity. Given the mounting evidence showing a positive association between obesity and pancreatic cancer, this study aimed to investigate the relation between variants in the FTO gene, obesity and pancreatic cancer risk.
Dietary Lifestyle, Way of Life Practices and Corpulence: Towards Present Day Science by Alok Raghav, Aditi, Sneha Gupta, Pratibha Singh, Aman Nikhil, Saba Noor and Jamal Ahmad in Examines in Physical Medicine & Rehabilitation
Global Medical Cures™ | New York State- Diabetes Management & Care Among AdultsGlobal Medical Cures™
Global Medical Cures™ | New York State- Diabetes Management & Care Among Adults
DISCLAIMER-
Global Medical Cures™ does not offer any medical advice, diagnosis, treatment or recommendations. Only your healthcare provider/physician can offer you information and recommendations for you to decide about your healthcare choices.
Taking account of research around the relationship between genetics and our new ‘food environment’, Dr Robyn Toomath (endocrinologist and Clinical Director Wellington Hospital) argues that we are in the middle of an obesity epidemic which impacts widely on public health. She advocates for new approaches to obesity based not on blame or impossible personal goals, but on outcomes. She argues it is the responsibility of all to become informed and active (personally and politically), in working for change to present health policies and gives examples of what can be done.
http://dosomething.org.nz
This assignment is an in-depth, literature-grounded analysis of a .docxchristalgrieg
This assignment is an in-depth, literature-grounded analysis of a significant U.S.health policy issue. The final paper is to be approximately 8–10 ten pages in length (excluding the list of references cited at the end); apply and cite at least ten high-quality references, and address the following ten elements:
1) Overview and Significance of the Health Policy Issue
2) History of the Health Policy Issue (Including Legislative Processes and
Partisan Politics)
3) Current Challenges Associated with the Health Policy Issue
4) Stakeholder Analysis
5) Policy Options and Analysis of Trade-Offs
6) Policy Recommendations
7) Recommended Roles for Federal Government, State Government, and Markets
8) Implications of the Policy Recommendations
a) Analysis of Population Health Implications
b) Analysis of Economic Implications
c) Analysis of Political Implications
d) Analysis of Implications for Health Care Organizations
e) Application of Two Saint Leo University Core Values
9) Conclusion
10) References Cited
The Final Term Paper must also follow APA format including:
· Double-spaced
· 1-inch margins left, right, top, and bottom
· 12-point font
Example U.S. health policy issue topics
Care fraud and abuse Anti-kickback Prohibitions
HIPPA False Claim ACT
Antitrust Compliance Programs Tobacco free policies
Disability legislation Right to die
Right to refuse life treatment Child abuse and neglect
Global pricing on drugs Abortions
Child abuse and neglect Global pricing on drugs
Abortions
Running head: FOOD ACCESS AND HEALTH OUTCOMES IN AMERICAN 1
FOOD ACCESS AND HEALTH OUTCOMES IN AMERICAN 4
Food Access and Health Outcomes in American
Huang
School of Public Health
LM Ho
June 31, 2016
Abstract
In the U.S., food access and food security is a challenge. The lack of convenient access to affordable and healthy food is a considered a national challenge. Socio-economic status of the country’s population affects the consumption and access of health food. Low-income areas usually lack access to adequate food and high-income areas have a challenge of access to health food. Therefore, for the two areas with different socio-economic population statuses, they all have challenges to food access. Lack of healthy foods often lead to poor diet and higher levels of risk to obesity. Due to the persistent food access and food insecurity challenges, the aim of this study is to discuss the link between food access and food consumption among the American population. The paper will also focus on the exploring the variation between food access and food consumption among the American population. A two-stage sampling cross-sectional survey will be used to sample participants from 48 states of the U.S. A self-administered questionnaire will be used as quantitative data collection instrument. The target population will be sampled adult U.S. citizens who have families to feed. Grown-ups with families are likely to demonstrate their understanding of ...
A presentation of a journal article on the comparison of MUAC and WHZ as diagnostic tools for acute malnutrition
*for classroom presentation purpose only*
*no copyright infringement intended*
Association between variations in the fat mass and obesity-associated gene an...Enrique Moreno Gonzalez
It is clear that genetic variations in the fat mass and obesity-associated (FTO) gene affect body mass index and the risk of obesity. Given the mounting evidence showing a positive association between obesity and pancreatic cancer, this study aimed to investigate the relation between variants in the FTO gene, obesity and pancreatic cancer risk.
Dietary Lifestyle, Way of Life Practices and Corpulence: Towards Present Day Science by Alok Raghav, Aditi, Sneha Gupta, Pratibha Singh, Aman Nikhil, Saba Noor and Jamal Ahmad in Examines in Physical Medicine & Rehabilitation
Global Medical Cures™ | New York State- Diabetes Management & Care Among AdultsGlobal Medical Cures™
Global Medical Cures™ | New York State- Diabetes Management & Care Among Adults
DISCLAIMER-
Global Medical Cures™ does not offer any medical advice, diagnosis, treatment or recommendations. Only your healthcare provider/physician can offer you information and recommendations for you to decide about your healthcare choices.
Taking account of research around the relationship between genetics and our new ‘food environment’, Dr Robyn Toomath (endocrinologist and Clinical Director Wellington Hospital) argues that we are in the middle of an obesity epidemic which impacts widely on public health. She advocates for new approaches to obesity based not on blame or impossible personal goals, but on outcomes. She argues it is the responsibility of all to become informed and active (personally and politically), in working for change to present health policies and gives examples of what can be done.
http://dosomething.org.nz
This assignment is an in-depth, literature-grounded analysis of a .docxchristalgrieg
This assignment is an in-depth, literature-grounded analysis of a significant U.S.health policy issue. The final paper is to be approximately 8–10 ten pages in length (excluding the list of references cited at the end); apply and cite at least ten high-quality references, and address the following ten elements:
1) Overview and Significance of the Health Policy Issue
2) History of the Health Policy Issue (Including Legislative Processes and
Partisan Politics)
3) Current Challenges Associated with the Health Policy Issue
4) Stakeholder Analysis
5) Policy Options and Analysis of Trade-Offs
6) Policy Recommendations
7) Recommended Roles for Federal Government, State Government, and Markets
8) Implications of the Policy Recommendations
a) Analysis of Population Health Implications
b) Analysis of Economic Implications
c) Analysis of Political Implications
d) Analysis of Implications for Health Care Organizations
e) Application of Two Saint Leo University Core Values
9) Conclusion
10) References Cited
The Final Term Paper must also follow APA format including:
· Double-spaced
· 1-inch margins left, right, top, and bottom
· 12-point font
Example U.S. health policy issue topics
Care fraud and abuse Anti-kickback Prohibitions
HIPPA False Claim ACT
Antitrust Compliance Programs Tobacco free policies
Disability legislation Right to die
Right to refuse life treatment Child abuse and neglect
Global pricing on drugs Abortions
Child abuse and neglect Global pricing on drugs
Abortions
Running head: FOOD ACCESS AND HEALTH OUTCOMES IN AMERICAN 1
FOOD ACCESS AND HEALTH OUTCOMES IN AMERICAN 4
Food Access and Health Outcomes in American
Huang
School of Public Health
LM Ho
June 31, 2016
Abstract
In the U.S., food access and food security is a challenge. The lack of convenient access to affordable and healthy food is a considered a national challenge. Socio-economic status of the country’s population affects the consumption and access of health food. Low-income areas usually lack access to adequate food and high-income areas have a challenge of access to health food. Therefore, for the two areas with different socio-economic population statuses, they all have challenges to food access. Lack of healthy foods often lead to poor diet and higher levels of risk to obesity. Due to the persistent food access and food insecurity challenges, the aim of this study is to discuss the link between food access and food consumption among the American population. The paper will also focus on the exploring the variation between food access and food consumption among the American population. A two-stage sampling cross-sectional survey will be used to sample participants from 48 states of the U.S. A self-administered questionnaire will be used as quantitative data collection instrument. The target population will be sampled adult U.S. citizens who have families to feed. Grown-ups with families are likely to demonstrate their understanding of ...
Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.
Trends in Body Mass Index and Prevalence of Extreme HighObes.docxwillcoxjanay
Trends in Body Mass Index and Prevalence of Extreme High
Obesity Among Pennsylvania Children and Adolescents,
2007–2011: Promising but Cautionary
David Lohrmann, PhD, Ahmed YoussefAgha, PhD, and Wasantha Jayawardene, MD
The economic consequences of obesity in the
United States were estimated at $147 billion
annually in 2008.1 To better understand these
costs, obesity trends to the year 2030 were
predicted.2 Obesity prevalence could reach
51% by 2030, but is more likely to stay at more
than 40% because of recently emerging posi-
tive developments. A subcategory, severe obe-
sity, that is, body mass index (BMI; defined as
weight in kilograms divided by the square of
height in meters) of 40 or greater for adults, has
increased faster than overall obesity and is
projected to grow from 5% of adults in 2010 to
11% of adults by 2030.2 This growth, with its
attendant increased risks of disease, will esca-
late costs even if overall obesity prevalence
stabilizes.2
Because obesity rates vary across states, the
financial burden is not uniform.3 State-specific
differences, such as lower cost of less healthy
foods, can affect obesity and severe obesity
prevalence together with current and projected
health care costs.2 Because of the state-specific
nature of Medicaid and Medicare expenditures,
much of the high cost of obesity-related disease
is borne by public sector health plans.
Today’s children and adolescents will be the
youngest adults in 2030; therefore, obesity
prevention for the future requires monitoring
of obesity prevalence rates among this popu-
lation over time. Prevalence and trends in
obesity among US children from 1999 to 2010
were determined based on National Health and
Nutrition Examination Survey data.4 Preva-
lence of high BMI in US children and adoles-
cents has also been studied.5 By 2010, fewer
than 12% of those aged 2 to 19 years nation-
wide were at or above the 97th percentile
(extreme high obese [ExHi obese]); 17% were
above the 95th percentile (obese), and 32%
were above the 85th percentile (overweight).
A statistically significant increase among 6- to
19-year-old males with a BMI at or above
the 97th percentile was found between 1999
and 2008.4
To inform prevention efforts, state govern-
ments have a vested interest in monitoring
obesity prevalence among all age groups, and
especially among children and adolescents.
Pennsylvania, for example, mandates annual
height and weight screening with BMI calcula-
tion for all public school students statewide.6
One recent study assessed child and adolescent
BMI trends in Pennsylvania, excluding Phila-
delphia and surrounding counties, for 2005 to
20097 and found combined overweight and
obese rates decreased from 28.5% to 23.1% at
the middle school level and from 24.6% to
20.9% at high school levels, but increased from
10.9% to 20% at the elementary level. The
largest shift in BMI over the subset of years
from 2007 to 2009 was among overweight
elementary students; 58 ...
1
Running head: OBESITY
3
Running head: OBESITY
Obesity
Lauren Urquiza
Chamberlain University
NR503 Population Health, Epidemiology, & Statistical Principles
January 2018
Obesity
Obesity is a chronic medical condition and a significant health concern in the United States that is increasing worldwide. More than one third of the adults in the U.S. are obese. It is a leading cause of preventable illness and death (Centers for Disease Control and Prevention [CDC], 2016). This global epidemic is a leading concern for adults and for children who are predisposed to becoming obese as adults. This paper will discuss the significance of obesity in Florida, provide a background of the disease, review current surveillance and reporting methods, conduct a descriptive epidemiological analysis, discuss diagnosis and screening for prevention tools, develop an evidence based plan along with measureable outcomes to address obesity as an advanced practice nurse, and conclude with an overview of the main points presented.
Background and Significance
According to the CDC (2016), obesity is defined as “weight that is higher than what is considered as a healthy weight for a given height.” It involves excessive weight gain and accumulation of fat. In order to determine obesity, Body Mass Index or BMI is used to indirectly calculate a person’s body fat and health risk based on weight in relation to height. A BMI of 25.0 or above is considered overweight and 30.0 or greater is considered obese. Athletes with a greater amount of muscle mass may have a higher BMI even though they do not have excess body fat. Waist circumference is also used as a tool to diagnose obesity.
There are many causes that contribute to obesity, including behavioral, genetic, hormonal, environmental, and social factors. Increase in caloric intake, unhealthy eating habits, decrease in physical activity, certain medications, age, lack of sleep, quitting smoking, pregnancy, and certain medical disorders can contribute to weight gain (Mayo Clinic, 2018). Driving cars has replaced walking and riding bikes, technology has replaced engaging in physical activity, and easy access to cheaper foods has replaced nutritional importance. Most people are aware when weight is gained. Obvious signs and symptoms are tighter clothes, excess fat, and increased weight on a scale. Being overweight or obese increases the risk for many health diseases. Obesity may cause low endurance, breathing issues, excessive sweating, and joint discomfort. It can also lead to diabetes, gastroesophageal reflux disease, coronary heart disease, hypertension, high cholesterol, stroke, depression, and even certain types of cancer such as bowel, breast, and prostate cancer (Mayo Clinic, 2018).
Below is a map that highlights the obesity prevalence across the U.S. in 2016 according to the CDC. There is no significant difference in overall prevalence between men and women. The prevalence of women with a BMI > 35 ...
1Running head OBESITY 4Running head OBESITY.docxvickeryr87
1
Running head: OBESITY
4
Running head: OBESITY
Obesity
NR503 Population Health, Epidemiology, & Statistical Principles
January 2018
Obesity
Obesity is a chronic medical condition and a significant health concern in the United States that is increasing worldwide. More than one third of the adults in the U.S. are obese. It is a leading cause of preventable illness and death (Centers for Disease Control and Prevention [CDC], 2016). This global epidemic is a leading concern for adults and for children who are predisposed to becoming obese as adults. This paper will discuss the significance of obesity in Florida, provide a background of the disease, review current surveillance and reporting methods, conduct a descriptive epidemiological analysis, discuss diagnosis and screening for prevention tools, develop an evidence based plan along with measureable outcomes to address obesity as an advanced practice nurse, and conclude with an overview of the main points presented.
Background and Significance
According to the CDC (2016), obesity is defined as “weight that is higher than what is considered as a healthy weight for a given height.” It involves excessive weight gain and accumulation of fat. In order to determine obesity, Body Mass Index or BMI is used to indirectly calculate a person’s body fat and health risk based on weight in relation to height. A BMI of 25.0 or above is considered overweight and 30.0 or greater is considered obese. Athletes with a greater amount of muscle mass may have a higher BMI even though they do not have excess body fat. Waist circumference is also used as a tool to diagnose obesity.
There are many causes that contribute to obesity, including behavioral, genetic, hormonal, environmental, and social factors. Increase in caloric intake, unhealthy eating habits, decrease in physical activity, certain medications, age, lack of sleep, quitting smoking, pregnancy, and certain medical disorders can contribute to weight gain (Mayo Clinic, 2018). Driving cars has replaced walking and riding bikes, technology has replaced engaging in physical activity, and easy access to cheaper foods has replaced nutritional importance. Most people are aware when weight is gained. Obvious signs and symptoms are tighter clothes, excess fat, and increased weight on a scale. Being overweight or obese increases the risk for many health diseases. Obesity may cause low endurance, breathing issues, excessive sweating, and joint discomfort. It can also lead to diabetes, gastroesophageal reflux disease, coronary heart disease, hypertension, high cholesterol, stroke, depression, and even certain types of cancer such as bowel, breast, and prostate cancer (Mayo Clinic, 2018).
Below is a map that highlights the obesity prevalence across the U.S. in 2016 according to the CDC. There is no significant difference in overall prevalence between men and women. The prevalence of women with a BMI > 35 is 18.3% compared to 12.5% of men. The.
Management of Excess Weight and Obesity: A Global PerspectiveCrimsonPublishersIOD
Non-communicable diseases (NCDs), especially, hypertension, excess weight, obesity, metabolic syndrome, type-2 diabetes, and vascular diseases,
have increased rapidly in the last two decades and have reached an epidemic status worldwide. Some experts have compared this increase in the
incidence of these diseases as “tsunamis”. Tsunamis’ are seasonal and unpredictable whereas, these diseases are predictable and not seasonal. So, what
are we going to do about this situation? Are we going to sit and wait for some miracle to happen? What are the member nations of the United Nations,
World Health Organization, NCD Task Force going to do about this, besides writing and publishing scary reports of future economic and healthcare
disasters? In this overview, we would like to discuss briefly the salient findings on this topic, initiate a healthy dialogue, request suggestions, positive
comments, and offer few suggestions.
The Effect of a Pilot Nutrition Education Intervention on Perc.docxmehek4
The Effect of a Pilot Nutrition Education Intervention on Perceived Cancer Risk
in a Rural Texas Community
Liliana Correa, MS', Debra B. Reed, PhD, RDN, LD:, Barent N. McCool, PhD3, Mary Murimi, PhD, RDN, LD2, Conrad
Lyford, PhD4
'Former M.S. Nutritional Sciences Graduate Student, Texas Tech University, Lubbock, TX
departm ent of Nutritional Sciences, Texas Tech University, Lubbock, TX
departm ent of Hospitality and Retail Management, Texas Tech University, Lubbock, TX
departm ent of Agricultural & Applied Economics, Texas Tech University, Lubbock, TX
Correspondence to:
Debra B. Reed, PhD, RDN, LD
[email protected]
ABSTRACT
Background: A high consumption o f fruits, vegetables, and whole
grain foods and adequate levels o f physical activity are associated
with a lower risk o f obesity and lower risk o f lifestyle cancers. Re
search suggests that rural communities have a high risk o f unhealthy
behaviors that may contribute to excessive weight gain and risk o f
lifestyle related cancers. The purpose o f this pilot study was to deter
mine the effect o f an educational intervention in a rural Texas com
munity on the intermediate outcomes o f eating behavior (increasing
the intake o f fruits, vegetables, and whole grain foods) and physical
activity behavior, and the distal outcome o f body mass index (BM1).
Methods: The intervention, guided by the Social Cognitive Theory,
was implemented over a 10-month period and included a variety o f
community-based education activities related to nutrition, physical
activity, and cancer in a variety o f settings. The effect o f the inter
vention was assessed by analyzing pre- and post-data (N=67) using
independent and paired samples t-tests and bivariate correlations.
Results: Participants were mainly Hispanic (53.7%) and White
(44.8%). At pre-intervention, 6% o f participants reported consuming
>5 servings o f fruits and vegetables daily, 19.4% consumed >3 serv
ings o f whole grain foods daily, and 85.1% were either overweight
or obese. Only 31% o f participants were aware that cancer risk was
related to overweight at pre-intervention. At post-intervention, His-
panics showed a significant increase in the consumption o f fruits and
vegetables (p<0.05). Participation in sports or physical activity pro
grams showed a significant increase (p<0.05). However, no signifi
cant decrease in BM1 was shown.
Conclusion: This intervention had a limited effect in increasing tar
geted behaviors and no effect on reducing BMI. More assessment is
needed in this rural community to identify barriers to healthy behav
iors and to improve interventions to increase consumption o f fruits,
vegetables, and whole grain foods, levels o f physical activity, and
awareness o f the cancer and obesity relationship.
INTRODUCTION
During the last 20 years, there has been an increase in the rates o f
excessive weight in the U.S. population with more than 69% o f the
adult population classified as overwei ...
Running head PICOT STATEMENT 1PICOT STATEMENT 5.docxtoltonkendal
Running head: PICOT STATEMENT 1
PICOT STATEMENT 5
PICOT Statement: Childhood Obesity
P-I-C-O-T Statement
P- Patients who suffer from obesity (BMI of more than 30)
I- Undertaking nutritional education, diet, and exercise
C- Comparison to nutritional education, endoscopic bariatric surgical intervention
O- Improved health outcomes in terms of overall weight
T - A year’s time limit
PICOT Statement: Childhood Obesity
Introduction
Childhood obesity poses serious health problems in the US as the number of overweight and obese population increases at a rapid pace every year. The effects of this problem have arrested the attention of policymakers, societal members, and government agencies. This has resulted in ranking childhood obesity as a national health concern. The adverse impacts of this disease go beyond the health realms to include economic burden on both personal and national budgets. While there are numerous risk factors and various evidence-based interventions to address this challenge, no single approach is consistently efficacious in curbing the disease. Consequently, it is imperative that efficacious initiatives and policies be developed to address the never-ending problem of childhood obesity. Multidisciplinary approaches are often broad and cut across all dimensions of personal health problems. Instead of placing emphasis solely on biomedical models, health care professionals should also seek to promote behavior change among obesity patients and their family members. A PICOT statement can be utilized as an effective tool to seek interventions of addressing childhood obesity.
PICOT Statement
Population
In the US, obesity prevalence is highest among children aged from 6 to 11 years (Cheung et al. 2016). The disease has tripled among this age group from 4.2 percent to 15.3 percent from 1963 to 2012. In the last three decades, increased cases of obesity prevalence have been noted among children of all ages, although the differences in obesity prevalence have been recorded in terms of age, race, ethnicity, and gender (Cheung et al. 2016). In this respect, children from socioeconomically disadvantaged families and some racial and ethnic minorities experience the higher median score on obesity than the dominant white population. Higher obesity rates are often recorded among blacks and Hispanics compared to whites. For instance, a survey on girls in the Southwest revealed that the yearly cases of obesity stood at 4.5 percent among Blacks, 2 percent among Hispanics, and 0.7 percent among white girls aged from 13 to 17 years (Cheung et al. 2016). For low-income earners, American Indians rank highest at 6.3 percent, followed closely by Hispanics at 5.5 percent.
Intervention
Evidence-based interventions that seek to reduce childhood obesity incidences in the country should target two major areas: prevention and treatment. High-quality RCT has been proven as one of the most effective preventative ...
Disparities in Overweight and ObesityAmong US College Studen.docxelinoraudley582231
Disparities in Overweight and Obesity
Among US College Students
Toben F. Nelson, ScD; Steven L. Gortmaker, PhD; S.V. Subramanian, PhD
Lilian Cheung, ScD; Henry Wechsler, PhD
Objectives: To examine social dis-
parities and behavioral correlates
of overweight and obesity over time
among college students. Methods:
Multilevel analyses of BMI, physi-
cal activity, and television viewing
from 2 representative surveys of
US college students (n=24,613).
Results: Overweight and obesity
increased over time and were higher
among males, African Americans,
and students of lower socioeco-
nomic position and lower among
Asians. Television viewing and in
activity were associated with obe-
sity, and disparities in these behav-
iors partially accounted for excess
weight among African Americans.
Conclusions: Social disparities in
overweight and obesity exist among
college students. Promoting physi-
cal activity and reducing televi-
sion viewing may counteract in-
creasing trends.
Key words: obesity, college stu-
dents, physical activity, televi-
sion viewing, social disparities
Am J Health Behav. 2007;31(4):363-373
Overweight and obesity have in-creased dramatically over the past30 years among both adults and
children in the United States.''^ The in-
crease in overweight and obesity h a s
been observed in all age, gender, and
racial/ethnic groups^'^ and is rising more
rapidly among women, young adults, His-
panics and non-Hispanic blacks, and
people with some college education,^'^
Higher rates are observed among minor-
Toben F. Nelson, Research Associate, Depart-
ment of Society, Human Development and Health;
Steven L. Gortmaker, Professor, Department of
Society, Human Development and Health; S.V.
Subramanian, Assistant Professor, Department
of Society, Human Development and Health; Lilian
Cheung, Lecturer, Department of Nutrition; Henry
Wechsler, Lecturer on Society, Human Develop-
ment and Health, all from the Harvard School of
Public Health, Boston, MA.
Address correspondence to Dr Nelson, Harvard
School of Public Health, Department of Society,
Human Development and Health, 677 Hunting-
ton Avenue, Boston, MA 02115. E-mail:
[email protected] harvard, edu
ity racial/ethnic groups, most notably
African Americans and Hispanics.*"® Per-
sons of lower socioeconomic position gen-
erally also have higher rates of obesity,^'
Healthy People 2010 goals for the nation's
health include a reduction in the preva-
lence of obesity and the elimination of
disparities in health across different seg-
ments of the population.'"
Obesity is a s s o c i a t e d with major
chronic diseases, such as cardiovascu-
lar disease, some cancers, type 2 diabe-
t e s , ' ' ' ^ and creates a major burden for
health care systems.'^'''^ Although the full
population health consequences of this
epidemic have not yet been realized, the
potential impact for future decreased life
expectancy and poor health due to obesity
is considerable.'* The poor health out-
comes of obesity usually mani.
Peer-Reviewed Highlights From Obesity Among Children and.docxdanhaley45372
Peer-Reviewed
Highlights From
Obesity Among Children and
Adolescents in America
Written by Phil Vinall
According to a report from the Centers for Disease Control and Prevention (CDC), overweight
or obese preschoolers are five times as likely to become overweight or obese adults as their
normal weight peers [Centers for Disease Control and Prevention. Morb Mortal Wkly Rep 2013].
Additionally, high cholesterol, high blood sugar, asthma, and mental health problems are linked
to obesity in older children and adolescents.
Although small decreases in the prevalence of obesity were observed among low-income
preschool children in certain parts of the United States and its territories between 2008 and 2011,
the rate of obesity remains high with ~1 of 8 children aged 2 to 5 years having an age- and sex-
specific body mass index (BMI) ≥95th percentile, according to the 2000 CDC growth charts (Figure
1) [Centers for Disease Control and Prevention. Morb Mortal Wkly Rep 2013]. The study included
data for ~11.6 million low-income children aged 2 to 4 years who were participants in federally
funded child health and nutrition programs.
Figure 1. Changes in Obesity Prevalence: 2008 to 2011
DC=Washington, DC; PR=Puerto Rico; V I=Virgin Islands.
Source: Morbidit y and Mortalit y Week ly Report 2013.
Using a subset of the CDC data, Pan and colleagues [Pediatrics 2013] reported an overall
incidence of childhood obesity of 11.0% but with several important differences among population
subgroups. Obesity was more common among boys versus girls and among children aged 0 to 11
months in 2008 versus older children. The risk of obesity was 35% higher among Hispanics and 49%
Figure 1. Changes in Obesity Prevalence: 2008 to 2011
Increase
No change
Decrease
Not included
DC
PR
VI
S E L E C T E D U P D A T E S O N O B E S I T Y
21Official Peer-Reviewed Highlights From the American Society for Nutrition 2013
ASN2013.indd 21 10/20/2014 3:00:55 PM
higher among American Indians/Alaska Natives compared
with non-Hispanic whites, but among non-Hispanic
African Americans, it was 8% lower. Of the children who
were obese at baseline, 36.5% remained obese at follow-
up while 63.5% were nonobese. Obesity remission was
proportionally significantly lower among Hispanics and
American Indians/Alaska Natives compared with other
racial/ethnic groups.
American society is characterized by environments that
promote poor eating habits and physical inactivity. William
H. Dietz, MD, PhD, Centers for Disease Control and
Prevention, Atlanta, Georgia, USA, discussed intervention
strategies to address some of these issues.
Beyond the effects of poverty, pregnancy and postnatal
influences have an important influence on childhood
obesity. These include maternal weight prior to pregnancy
and the amount of weight gained during pregnancy,
breastfeeding duration, the child’s overall feeding
experience and sleep patterns, as well as media exposure to
f.
A Critical Review of High and Very High-Resolution Remote Sensing Approaches ...rsmahabir
Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.
Impact of road networks on the distribution of dengue fever cases in Trinidad...rsmahabir
This study examined the impact of road networks on the distribution of dengue fever cases in Trinidad, West Indies. All confirmed cases of dengue hemorrhagic fever (DHF) observed during 1998 were georef- erenced and spatially located on a road map of Trinidad using Geographic Information Systems software. A new digital geographic layer representing these cases was created and the distances from these cases to the nearest classified road category (5 classifications based on a functional utility system) were examined. The distance from each spatially located DHF case to the nearest road in each of the 5 road subsets was determined and then subjected to an ANOVA and t-test to determine levels of association between minor road networks (especially 3rd and 4th class roads) and DHF cases and found DHF cases were located away from forests, especially 5th class roads). The frequency of DHF cases to different road classes was: 0% (1st class roads), 7% (2nd class roads), 32% (3rd class roads), 57% (4th class roads) and 4% (5th class road). The data clearly demonstrated that both class 3 and class 4 roads account for 89% of nearby dengue cases. These results represent the first evidence of dengue cases being found restricted between forested areas and major highways and would be useful when planning and implementing control strategies for dengue and Aedes aegypti mosquitoes.
The Rabies Epidemic in Trinidad of 1923 to 1937: An Evaluation with a Geograp...rsmahabir
Background.—Rabies, although not preeminent among current infectious diseases, continues to afflict humans with as many as 55,000 deaths annually. The case fatality rate remains the highest among infectious diseases, and medical treatments have proven ineffective.
Objective.—This study analyzes the rabies epidemic of 1929 to 1937 in Trinidad from a geograph- ical perspective, using Geographic Information System (GIS) software as an analytical tool.
Setting.—A small island developing country at a time when infectious diseases were rampant.
Methods.—A review of the literature was undertaken, and data were collected on the occurrence of disease in both animal and humans populations and mapped using GIS software. Several factors identified in the literature were further explored such as land use/land cover, rainfall and magnetic declination.
Results.—The bat rabies epidemic of 1923 to 1937 in Trinidad was migratory and seasonal, shifting to new locations along a definite path. The pattern of spread appears to be spatially linked to land use/land cover. The epidemic continues to present many unexplained peculiarities.
Conclusion.—Despite the fact that this epidemic occurred almost 7 decades ago, the application of new tools available for public health use can create new knowledge and understanding of events. We showed that the spatial of distribution of the disease followed a distinct pathway possible due to the use of electromagnetic capabilities of bats.
The Role of Spatial Data Infrastructure in the Management of Land Degradation...rsmahabir
Abstract
Land degradation involves a wide array of natural and human induced factors affecting the productivity of land. These factors can exist in various non unique and complex combinations of different environmental settings, making detection and monitoring of land degradation an often difficult undertaking. As a result, no universal solution exists to eliminate the problem of land degradation altogether. In order to reduce its rate of encroachment, this phenomenon should be assessed and quantified in order to identify the causes, processes and factors leading to land degradation.
In small tropical and Caribbean islands, there exists a severe shortage of good, reliable and up- to-date information bases for the contributing factors of land degradation. In addition to the limited knowledge about what spatial datasets already exist, there is also no agreed minimum level of quality for datasets and metadata documentation standards. As a result, datasets produced to help in understanding and treating land degradation problems may have unknown or unacceptable levels of uncertainty. This may require re-development of already existing datasets, hence consuming further efforts, financial resources, and time. In critical circumstances where land degradation posses severe threat to the environment and therefore indirectly to humans, the incurred price of a slow or ill informed decision may eventually render the state of land unrecoverable.
It is postulated that Spatial Data Infrastructure (SDI) would present the opportunity for much more strategic and cooperative management of land degradation datasets in Small Tropical Caribbean Islands. It is therefore expected to be a vital tool in the treatment of land degradation, and also to assist in creating a network of critical resources to drive further research in the area. This paper reviews the challenges faced by Small Tropical Caribbean Islands when managing land degradation, with special emphasis on Trinidad, and discusses how SDI can be used to better facilitate land degradation management in these areas.
Advancing the Use of Earth Observation Systems for the Assessment of Sustaina...rsmahabir
Abstract: Decisions made on the use of land in Trinidad and Tobago, with little considerations to environmental impact or physical constraints, have resulted in physical, socio-economic, and environmental problems. As a result of the country’s economic progress, urbanisation and development are fragmenting natural areas and reducing the viability of the environment to support the population. Spatial information is a crucial component in the characterisation and examination of the spatio-temporal dynamics and the consequences of the interaction between human and the environment. This information is of critical importance in the development of models to predict future trends in land cover change and therein, best land use practices to be implemented. However, the lack of data at appropriate scales has made it difficult to accurately examine the land use/cover patterns in the country. This paper argues that the gap in data and information can be managed through the adoption of earth observation technology. Moreover, it reports on the developed methodology, and highlights key results of examining the use of geo-spatial images in addressing sustainability issues associated with development. The developed methodology involves several critical steps in using multi-spectral imagery including cloud and cloud shadow removal, image classification and image fusion. Additionally, a method for improving classification performance using high resolution imagery is discussed. The results demonstrated the accuracy, flexibility and cost-effectiveness of these technologies for mapping the land cover and producing other environmental measures and indicators. Further, these results confirmed the effectiveness of this technology in establishing the necessary baseline and support information for sustainable development in the Caribbean region.
Dengue Fever Epidemiology and Control in the Caribbean: A Status Report (2012)rsmahabir
The epidemiology of Dengue fever in the English speaking Caribbean over the last two decades is reviewed. Dengue cases reported to the World Health Organization, Pan American Health Organization, Caribbean Epidemiology Centre and in recent published papers were collated and analysed to determine the incidence and geographical distribution among the various countries. Dengue fever was observed among most Caribbean countries with various intensities of transmission. During 2010 all four dengue serotypes were found co-circulating within the Caribbean islands with crude fatality rates of 6 in Barbados, 4 in Jamaica, 3 in the Bahamas and 2 in Dominica. Similar numbers of males and females from the 20-39 age group were found with DHF but the 10-19 age group shows a slight increase in disease levels. Overall more males were reported with DF/DHF than females. The results show significant (P<0.002) increases in the number of DF/DHF cases and in Ae. aegypti indices during the rainy season compared to the dry season. Little data is available on the density of the Aedes aegypti population in the Caribbean region, and most information comes from Jamaica and Trinidad and Tobago.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Exploratory space-time analysis of dengue incidence in trinidad: a retrospect...rsmahabir
The increased geographic spread and intensity of dengue is due to numerous factors including, increased urbanization, human migrations and air travel, flooding and global warming. In the Caribbean, outbreaks continue to occur with hyperendemic occurrence of the disease. This is mainly due to the use of reactive programs and limited resources available to control the disease. Using the island of Trinidad as a case study, we show that higher rates of infection occur in areas with a history of dengue incidence. Also, a general pattern in the movement of dengue cases is found leading up to and transitioning away from an epidemic occurrence, and associated with the locations of transportation hubs. These findings can be used to contain the disease in a more efficient and effective manner. Also, few studies have examined the space and time relationship of dengue incidents at local scales in the Caribbean islands. Other islands can adopt the approach used to better allocate resources and understand the disease. This information can then be used to gain regional perspective and understanding about the spatio-temporal persistence of dengue in the Caribbean.
Remote sensing-derived national land cover land use maps: a comparison for Ma...rsmahabir
Reliable land cover land use (LCLU) information, and change over time, is impor- tant for Green House Gas (GHG) reporting for climate change documentation. Four different organizations have independently created LCLU maps from 2010 satellite imagery for Malawi for GHG reporting. This analysis compares the procedures and results for those four activities. Four different classification methods were employed; traditional visual interpretation, segmentation and visual labelling, digital clustering with visual identification and supervised signature extraction with application of a decision rule followed by analyst editing. One effort did not report classification accuracy and the other three had very similar and excellent overall thematic accura- cies ranging from 85 to 89%. However, despite these high thematic accuracies there were very significant differences in results. National percentages for forest ranged from 18.2 to 28.7% and cropland from 40.5 to 53.7%. These significant differences are concerns for both remote-sensing scientists and decision-makers in Malawi.
Comparison and integration of spaceborne optical and radar data for mapping i...rsmahabir
The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using sepa- rate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or inte- grated with optical imagery has great potential in improving the accuracy of land- cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.
Radar and optical remote sensing data evaluation and fusion; a case study for...rsmahabir
The recent increase in the availability of spaceborne radar in different wavelengths with multiple polarisations provides new opportunities for land surface analysis. This research effort explored how different radar data, and derived texture values, indepen- dently and in combination with optical imagery influence land cover/use classification accuracies for a study site in Washington, DC, USA. Two spaceborne radar images, Radarsat-2L-band and Palsar C-band quad-polarised radar, were registered with Aster optical data for this study. Traditional methods of classification were applied to various components and combinations of this data set, and overall and class-specific thematic accuracies obtained for comparison. The results for the two despeckled radar data sets were quite different, with Radarsat-2 obtaining an overall accuracy of 59% and Palsar 77%, while that of the optical Aster was 90%. Combining the original radar and a variance texture measure increased the accuracy of Radarsat-2 to 71% but that of Palsar only to 78%. One of the sensor fusions of optical and radar obtained an accuracy of 93%. For this location, radar by itself does not obtain classification accuracies as high as optical data, but fusion with optical imagery provides better overall thematic accuracy than the optical independently, and results in some useful improvements on a class-by-class basis. For those regions with high cloud cover, quad polarisation radar can independently provide viable results but it may be wavelength-dependent.
VDIS: A System for Morphological Detection and Identification of Vehicles in ...rsmahabir
With the growth of urban centers worldwide, the number of vehicles in and around these areas has also increased. Traffic-related data plays an important role in spatial planning, for example, optimizing road networks and in the estimation or simulation of air and noise pollution. This information is important as it reflects the changes taking place around us. Additionally, data collected can be used for a wide array of applications including law enforcement, fleet management, and supporting other analyses at varying scales. In this paper, we present a method for the detection and identification of vehicles from low altitude, high spatial resolution Red Blue Green (RGB) images, utilizing both object spectra and image morphology. Results show an identification performance upwards of 62% with false positives occurring from the use of images with sun glare and vehicles with similar spectra values.
Climate Change and Forest Management: Adaptation of Geospatial Technologiesrsmahabir
eraction with the environment, has led to increased concerns about the impact of such disruption on major areas of sustainable development. This has resulted in various innovations in technology, policy and forged alliances at regional and international scales in an effort to reduce humans’ impact on climate. Forests provide a suitable option for reducing the net amount of carbon dioxide in the atmosphere by acting as carbon sinks, thereby forming one part of a more complete solution for combating climate change. At the same time, forests are also sensitive to changes in climate, making sustainable forest management a critical component of present and future climate change strategies. This paper examines the contribution of geospatial technologies in supporting sustainable forest management, emphasizing its use in the classification of forests, estimation of their structure, detecting change and modeling of carbon stocks.
Black holes no more the emergence of volunteer geographic informationrsmahabir
More than one billion people currently live in slums, which are growing at unprecedented rates leading to the rise of vulnerable communities. Slums are usually viewed as areas of extreme poverty and neglect and further, their development as an impediment to progress. Although slums exist in all areas of the world, their presence is most noticeable in the less developed countries of the global south. These countries are among the poorest worldwide as suggested by the Human Development Index and the substantial disbursement of and dependence on international aid. With the added burden of having to absorb the majority of projected population growth, further challenges can be expected at these locations if the situation of slum dwellers does not improve.
An evaluation of Radarsat-2 individual and combined image dates for land use/...rsmahabir
Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarized images were obtained for Washington, D.C., USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73% and 82%.
Radar speckle reduction and derived texture measures for land cover/use class...rsmahabir
This study examined the appropriateness of radar speckle reduction for deriving texture measures for land cover/use classifications. Radarsat-2 C-band quad-polarized data were obtained for Washington, D.C., USA. Polarization signatures were extracted for multiple image components, classified with a maximum-likelihood decision rule and thematic accuracies determined. Initial classifications using original and despeckled scenes showed despeckled radar to have better overall thematic accuracies. However, when variance texture measures were extracted for several window sizes from the original and despeckled imagery and classified, the accuracy for the radar data was decreased when despeckled prior to texture extraction. The highest classification accuracy obtained for the extracted variance texture measure from the original radar was 72%, which was reduced to 69% when this measure was extracted from a 5x5 despeckled image. These results suggest that it may be better to use despeckled radar as original data and extract texture measures from the original imagery.
Coral Reefs: Challenges, Opportunities and Evolutionary Strategies for Surviv...rsmahabir
Coral reefs are one of the most diverse marine ecosystems on Earth. They are renowned hotspots of species biodiversity and provide home to a large array of marine plants and animals. Over the past 100 years, many tropical regions’ sea surface temperatures have increased by almost 1 °C and are currently increasing at about 1–2 °C per century. Corals have very specific thermal thresholds beyond which their temperature sensitive symbiont Zooxanthellae becomes affected and causes corals to bleach. Mass bleaching has already caused significant losses to live coral in many parts of the world. In the Caribbean, the problem of coral bleaching has especially been problematic, with as much as 90% bleaching in some parts of the Caribbean due to thermal anomalies in some instances. This paper looks at the key role that temperature plays in the health and spatial distribution of coral in the Caribbean. The relationship between coral and symbiont is examined along with some evolutionary strategies necessary to ensure the future survival of coral with the changing climate.
Authoritative and Volunteered Geographical Information in a Developing Countr...rsmahabir
Abstract: With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan ...rsmahabir
Abstract-The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
Relative value of radar and optical data for land cover/use mapping: Peru exa...rsmahabir
This study determined using divergence measures the best indivi- dual and combinations of various numbers of bands for six land cover/use classes around the city of Arequipa, Peru. A 15 band data stack consisting of PALSAR L-band dual-polarised radar, Landsat optical data, as well as six variance texture measures extracted from the PALSAR images, was used in this study. Spectral signatures were obtained for each class for the diver- gence examination. The band having the highest separability was the Landsat visible red band followed by the two largest window PALSAR texture measures. The best three band combina- tion included three very different data types, Landsat visible red, near infrared and the PALSAR HH variance texture from a 17 × 17 pixel window. There was no need based upon the diver- gence values to use more than five bands for classification.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Healthy Food Accessibility and Obesity: Case Study of Pennsylvania, USA
1. Healthy Food Accessibility and Obesity: Case Study
of Pennsylvania, USA
Ranjay Shrestha
Earth System and Geoinformation Sciences, GMU
Center for Spatial Information Science and Systems
Fairfax, VA
rshresth@masonlive.gmu.edu
Ron Mahabir
Earth System and Geoinformation Sciences, GMU
Fairfax, VA
rmahabir@masonlive.gmu.edu
Abstract-Obesity is a continuing challenge for any town, city
or country faced with this problem. Being obese increases your
risk of physical disorders such as high blood pressure (BP), high
blood cholesterol, diabetes, coronary heart disease, stroke, cancer
and poor reproductive health. Higher obesity rates also leads to
increased economic burden on society. In order to better
understand and control obesity rates the influence of various
factors on its prevalence should be investigated. We used
Ordinary Least Squares (OLS) and Geographically Weighted
Regression (GWR) models to analyze spatial relationships using
a combination of socio-economic and physical factor for counties
in Pennsylvania (PA), USA for 2010. Our findings suggest that
the rate of obesity is impacted by local spatial variation and its
prevalence positively correlated with diabetes, physical inactivity
and the distance that a person must travel to get to a healthy food
store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was
found to significantly improve model fitting over OLS (AICc =
299.87; r-squared = 0.34). These results indicate that additional
factors, including social, cultural and behavioral, are needed to
better explain the distribution of obesity rates across PA.
Keywords-Obesity; Food Accessibility; Ordinal Least Squares;
Geographically Weighted Regression
A. What is obesity?
I. INTRODUCTION
According to the Word Health Organization (WHO) [16],
obesity is defined as abnormal or excessive fat accumulation
that presents a risk to an individuals' health. People are
considered obese when their Body Mass Index (BMI)
surpasses 30 kg/m2[8, 16].
B. Why is it important?
Increasingly, obesity has becomea major health risk
concern in both developed and developing countries [10, 12].
People with excessive weight are more likely to suffer various
physical disorders such as high blood pressure (BP), high
blood cholesterol, diabetes, coronary heart disease, stroke,
cancer andpoor reproductive health. Besides physical health,
obese people are also vulnerable to mental disorders such as
depression and eating irregularity [12]. In the United States
approximately 365,000 deaths per year are related to obesity,
only second to tobacco [1]. These high death rates are just a
LipingDi
Center for Spatial Science and Systems
Fairfax, VA
Idi@gmu.edu
small part of the problem. The major issue with higher obesity
ratesis the economic consequences they have on society. In
2000, the total direct and indirect cost due to people being
overweight and obese was about 117 billion US dollars [13].
As mentioned earlier, obese people are prone to various health
issues and for any town, city, or country an unhealthy
community presentsan economical strain. Majority of the
taxpayers' money will be spent towards healthcare and health
services, which otherwise could have been used for other
purposes to the benefit of the community.
C. Factors Influencing Obesity
Many studies have investigatedthe influence of various
socio-economic and physical factors on obesity. Such
information is important in order to better understand and
control the prevalence of the diseaseat both local and national
level. Grabner [10] in his research found a positive
correlation with education and health. People who are
educated are less likely to be obese compared to people
without education. Similar results were obtained by Kenkelet.
al [5] who found that individuals who completed high school
or GED were less likely to suffer from obesity. This makes
sense since educated individuals are most likely to understand
the role of a balance diet and nutrition values and are likely to
practice a healthier lifestyle.
Another important factor found related to obesity is
accessibility (or lack of it) to food option. Brennan &
Carpenter [4] looking at child obesity examined whether
having easy accessibility to fast food could influence weight
gain. Theresultsof this study showed astrong positive
relationship between fast food access and child obesity.
Furthermore, they (Brennan & Carpenter) suggested that it
would be useful to consider changes in school eating policies
to provide children alternatives to fast food access in schools.
This suggests that having easy accessibility to healthier food
optionsmay help in reducingobesity. Numerous other factors
have been found to influence the prevalence of obesity. These
have been known to vary with location [15] and behavioral
characteristics [6, 11] with research ongoing in these areas.
The main objectives of this research are (1) to determine
the impact of accessibility to healthy foods and other social
2. andeconomic factors on the prevalence of obesity
inPennsylvania, United States, and (2) to further determine the
influence of space in modeling obesity rates. In section II a
brief introduction of the study area, data, and tools utilized is
given. Section III explainsthe methods used to process and
analyze the data while results and analysis are discussed in
section IV. Finally, this paper concludes with final remarks
given in section V.
A. Study Area
II. STUDY AREA/DATA
The study area used in this research is the state of
Pennsylvania (PA), USA. Geographically it is located in the
Northeastern region of the United States. It is considered the
9th most densely populated and 6th most populated states in the
US [14]. The state of PA has total of 67 counties and all are
included within the scope of this research. Fig 1 shows the
geographic location and county divisionsfor statesin PA.
Fig. 1. Study Area- State of PA,USA
B. Data and Tools
Data on obesity rates and various factors which affect its
prevalence based on literature review were collected for PA
for 2010 (Table I). In order to preprocess and analyze these
datasets, the ArcGIS geographic information system software
suite (version 10.1) was utilized.
Obesity rates for counties in PA are shown Fig 2. This
figure shows that counties in the middle of the state are more
heavily impacted by obesity compared to counties in the
eastern and western parts of PA.
0.001 0.002 Kilometers
Legend
Obesity rates
D 21.20·24.24
D 24.24 - 27.28
_ 27.28 - 30.32
_ 30.32 - 33.36
_ 33.36 - 36.40
Fig. 2. Obesity Distribution in the State of PA - County Level
TABLET. DATASETS
Category Factors Data source Geographic
Scale
Health Obesity,Physical American County
Inactivity, FindTheData
Diabetes
Administrative State,County, Tiger Line Varies with
boundaries Census Tract dataset
Socioeconomic Total poverty, American County
Median household FactFinder
income,median
family income,
mean population
age,males,
females,Age 16 to
19 in school,Age
16 to 19not in
school.
Access to Healthy food Reference Point
healthy food stores locationsI USA locations
I HEALTHY FOOD STORES WERE IDENTIFIED AS GROCERY STORES AND FARMER
MARKETS
III. METHODS
A. Data Preprocessing
1) Aggregation to County level.·Health and socioeconomic
factors collected in tabular format were appended as aspatial
information to county polygons.
2) Food Stores Filtering: Only grocery stores which
provide healthier food options were included. Small convinent
and gas station food stores were excluded.
3) Average Nearest Distance: To determine food
accessibility the average nearest health food facility to the
centroid of each census tract in PA was calculated using the
Network Analyst Tool in ArcGIS. This information was then
used to calculate the average nearest distance travelled per
person for each county using Eq 1.
Dist_TraveLPerPopulationcounty
Where,
I(Disttrack * POptra ck)
TotPopcounty
()
Dist_Trave(PerPopulationcounty= Average network distance a
person has to travel to the nearest food store in a particular
county
Disttrack= Network Distance to the nearest food store for that
particular census track
POPtrack= Total population in that particular census track
TotPoPcounty= Total population within that particular county
B. Ordinal Least Squares Regression
The use of OLS was twofold. First it was used to remove
multicollinear relationships between variables. Second, it was
used to build and test the suitability of a non-spatial model
between obesity and explanatory factors. In order to remove
3. multicollinearity, variables were screened using a combination
of Variable Inflation Factor (VIF) and Variable Significance
(VS) values. A value greater than 7.5 for VIF was used to
suggest collinear participation [9]. The variable with the
highest VIF value greater than the given threshold was
removed and the OLS procedure was re-run. If two variables
both failed the VIF test and had similar VIF values these were
further evaluated based on their VS values. Variables with
higher VS values were kept since these reflect overall greater
model participation. Because the removal of one or more
explanatory variable impacts overall model variance, different
combination of variables were tested. This approach was used
to ensure that the best model was selected. Models' suitability
was evaluatedbased on a combination of Akaike's Information
Criterion (AICc) and r-squared values. Lower AICc and
higher r-squared values indicated an overall better model.
C. Spatial Autocorrelation
Moran's I was used to test for spatial autocorrelation using
the standardized residuals from the OLS modeland polygon
contiguity as the spatial relationship between observations.
This was necessary to ensure that there was no systematic
pattern indicating clustering and therefore an unsuitable or
bias model.
D. Geographically Weighted Regression (GWR)
GWR was used to build a model showing the impact of
local spatial variation of explanatory variables on obesity rates.
A fixed distance kernel using AICc to determine its spatial
extent was used to develop the final model.A fixed kernel was
chosen since the centroids of counties were used as
observations which because scales are expected to be relatively
stable across space. As it relates to AICc, this method chooses
the optimum bandwidth for the kernel based on tradeoff
between model bias and variance explained by the model.
IV. RESULTS AND ANALYSIS
Results of the findings and its analysis achieve from this
research are shown in following sections
A. Average distance travelled per person tofood store
The average nearest distance travelled per person to food
store is shown in Figure 3. This figure shows that persons
living in counties in the middle to northern parts of PA have to
travel distance upwards of 8km on average to get to the
nearest healthy food store.
0.001 0.002 Kilometers
I
Fig. 3. Average Nearest Distance to Food Stores
Legend
0<8
08-16
.16-24
.24-32
.>=32
B. Ordinal Least Squares Regression
The results of OLS produced a linear model with 3
explanatory variables accounting for most of the variance
observedfor obesity rates (Table II). As table II shows, all
explanatory variables show a positive relationship with the
rate of obesity with variables in decreasing order of influence
being diabetes, physical inactivity and average distance. The t
statistic and probability value suggests that the coefficient for
physical inactivity is statistically significant at the 95%
level.Whereas, low VIF values for explanatory variables
indicate all multicollinearity has been removed from this
model. Although other explanatory variables (diabetes and
average distance) as Table II shows were not statistically
significant, one of the objectives of this study was to compare
the influence of space on the distribution of obesity rates. It
was therefore necessary to keep all explanatory variables so
that comparison could be made between both spatial and non
spatial model outputs.
Variable
Intercept
Diabetes
Physical
Inactivity
Average
Distance
01
TABLE I!. OLS MoDEL
Coefficient t-Statistic Probability VIF
15.514312 5.674965 0 NA
0.51696 1.869992 0.066135 1.434994
0.325889 3.30219 0.001586 1.367493
0.000037 0.98089 0.330395 1.068323
II
- • �, I
. .
�
Fig. 4. Histogram of standardized residuals of OLS
Values for multiple r-squared and adjusted r-squared
were 0.34 and 0.31 respectively with a resulting AICc value of
299.87. These values suggest poor or less than optimum
model fit. Furthermore, the p-value for Koenker (BP) statistic
was 0.58 suggesting a stationary relationship between obesity
and explanatory factors. Moreover, the OLS model has a Joint
F-Statistic p-value of 0.000009 indicating that the model is
unbiased. This is also shown in Fig 4 which shows that the
standardized residuals of the model follow a normal
distribution (blue line).
4. C. Spatial Autocorrelation
!iillnif;c.nc.L.....!
(.....,;01...)
Fig. 5. Moran's 1
-2.�8- ·1.96
t=J ·1.9"- ·1.�
� Ti::L::-
The output of Moran's I is shown in Fig 5. Given the
z-score of 0,53, the pattern does not appear to be
significantly different than random. Fig 6 shows the
standardized distribution of residuals across PA counties.
This figure does not show any spatial pattern and therefore
agrees with the result of Moran's I. Additionally Fig 6
shows the model performs reasonably well with one
location, Columbia County in dark red, being under
predicted (standard deviation of residual > 2.5).
0.001 0.002 Kilometers
!
Fig. 6. Spatial distribution of OLS standardized residuals
D. Geographically Weighted Regression
The results of GWR for all observations are shown in
Table III, column 2. These results are similar to the output of
OLS (Table II). Fig 7 shows the spatial distribution of
residuals from GWR. This figure shows very similar results to
the residuals of OLS in Fig 6.
0.001 0.002 Kilomelers
!
Fig. 7. Spatial distribution of GWR Standardized Residuals (All
observations)
TABLE III. GWR OUTPUT
GWR GWR GWR Outlier +
All Outlier Clusters
observ removed removed
Parameter ations
Observations 67
66 61
Bandwidth 47.73
2.31 2.95
ResidualSquares 292.44
240.97 208.72
293.25 261.59
AICc 299.87
R2 0.34
0.44 0.45
R2Adjusted 0.31
0.35 0.40
Because of similar results between OLS and GWR cluster
and outlier analysis was performed using Anselin Local
Moran's I to test for local spatial autocorrelation using the
standardized residuals of GWR. Fig 8 shows the results of this
analysis with several areas identified as having local clustering
and outliers. Table III shows the results of re-running GWR
with outliers removed (column 3) followed by the removal of
clustered observations (column 4). This table shows improved
model parameters for AICc and r-squared (including adjusted
r-squared) with subsequent removal of observations identified
as problem areas. Evident from Table III also is that bandwidth
and sum of residuals squares also reduced indicating better
model fitting with increase local variation and moving away
from a model of global to local influence.
0.001 0.002 Kilometers
!
Fig. 8. Culter and Outlier Results
0.001 0.002 Kilometers
Legend
_Not Significant
_High-HghCluster
_ High-low Outlier
D Low-Higtl Outlier
_ Low-low Cluster
Fig. 9. Spatial Distribution of GWR Standardized Residuals (local clusters
and outliers removed)
5. V. CONCLUSION
Obesity continues to be a pressing problem affecting the
health and well being of persons worldwide. Especially
affected is the United States, which ranks 18th in the world [3].
The results of this study showed that obesity rates in PA is
impacted by various factors, in decreasing order of influence,
diabetes, physical inactivity and average distance to the
nearest healthy food store according to the OLS model. The
AICc and r-squared values for this model was 299.87 and 0.34
respectively. These values suggest that only about one third of
the variance in obesity rates can be explained using this
model. Similar results were found using GWR, which
subsequently led to an improved model (AICc =261.59; r
squared = 0.45) with the removal of local spatial clustering of
observations. These results indicate that GWR significantly
improves model fitting over OLS. Furthermore, although both
OLS and GWR accounted for low model variances (OLS =
34%; GWR = 45%), these results indicate that the rate of
obesity is impacted by local spatial variation.
The low variance of these models suggests that additional
factors are needed to better explain the distribution of obesity
rates across PA. This could be due to the limited coverage of
variables used which does not include factors such as social
(e.g. influence of people and surrounding environment),
cultural (e.g. cooking at home) and behavioral (e.g. habit of
eating healthy or fast food). Furthermore, because of the scale
of analysis of this study, county level, results may be
generalized and not reflect possible underlying local
variationoccurring between obesity and explanatory variables.
This issue will be addressed in future work using more
disaggregated datasets. Also, because obesity may be
impacted by changes in season (e.g. people may be more
likely to eat more during winter compared to summer since
being indoors for longer periods) this factor will also be
investigated. Finally, further analysis will incorporate the
distances to fast food outlets to examine their prevalence on
obesity rates since several studies have identified this to be a
contributing factor [2, 7].
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