Introduction
This paper is an epidemiological reportage on Obesity. The paper explores various aspects of health conditions. Aspects like morbidity and mortality are explored. Body Mass Index (BMI) is also defined and illustrated as the standard measure of obesity. The paper also entails Meta-analysis in form of the Correlation coefficient as well as confidence interval models analysis of data of different obesity-related researches which serve to minimize the reduction of statistical power of researches conducted on obesity. The paper further touches on the critical extension of child obesity.
This paper presents an epidemiological report on obesity. In the manner of conventional epidemiological, the report will include a multi-dimensional exploration of various qualitative and quantitative aspects of the report focus, obesity. Obesity is defined in mainstream medical realms as a condition that results from an excessive intake of body fat into the human body. The obesity condition obtains when the proportions of the consumed body fat have been such that the human body health system has been negatively affected by the unhealthy levels of body fat.
Scientific medical definitions of obesity entail the Body Mass Index MBI concept in which BMI is equalled to weight divided by height squared of 30kg/m2 or higher. World Health Organization (2000) further outlines that this formula distinguished obesity from mere overweight “As defined by a BMI of between 25-29.kg/m2”
The ills of unhealthy body weight are related to a plethora of diseases. Diseases often related to disproportionate body weight include cardiovascular diseases, obstructive sleep apnea, cancer, osteoarthritis. Diabetes mellitus type 2 has also been associated with excessive body weight. Haslam et al (2005) add, “As a result, obesity has been found to reduce life expectancy. The primary treatment for obesity is dieting and physical exercise. If this fails anti-obesity drugs and (in severe cases) bariatric surgery can be tried.”
Excessive energy levels triggered by disproportionate body fat result in the obesity condition as the energy levels surpass the human body basal metabolic rate. This is often aided or exacerbated by lack of, or significantly low levels of physical exercise. The National Institute for Health and Clinical Excellence (2006) outlines, “Excessive caloric intake and a lack of physical activity in genetically susceptible individuals is thought to explain most cases of obesity, with purely genetic, medical, or psychiatric illness contributing to only a limited number of cases. With rates of adult and childhood obesity increasing, authorities view it as a serious public health problem.”
Global Overview
This chart compares figures in the population of OECD countries. The chart shows the percentage of the total population (aged 15 and above) with a body mass index greater than 30 for the 1996 to 2003 period.
Social Stigma of obesity
In many societies, obesity is fraught with disdain as an anomaly. This view on obesity is nonetheless not fully and accurately representative of views of obesity for all groups of people and global societies. There is an interesting area of dissonance in the views of obesity. Studies have shown that obesity is largely stigmatised in contemporary Western societies whilst on the contrary obesity has been viewed as mark affluence and fertility at various aeons of world societies.
Medical Classification
In mainstream medical jargon and definition obesity is presented as an increase of body adipose tissue mass. Adipose tissues are fat tissue in simpler terms. The feasible method of establishing the condition defined and described as obesity is the Body Mass Index in the way of its distribution through the waist circumference. Obesity as a medical anomaly can not be appropriately explored in isolation of other factors as medical conditions exist in close interlinks and cross–relational effects. Obesity thus has to be examined in a context that will bring other risk factors that can potentially up the risk of complication into perspective.
The BMI Formulae
The Body mass index or (BMI) is the conventional and widely used model of approximating obesity by way of estimating body fat mass. The BMI model was designed by Belgian statistician and anthropometrist Adolphe Quetelet in the 19th century. According to Haslam et al (2005), “BMI is an accurate reflection of body fat percentage in the majority of the adult population, but is less accurate in situations that affect body composition such as in bodybuilders and pregnancy.”
According to WHO (World Health Organisation: 2000), BMI is calculated by dividing the person’s weight value by the square of the person’s height. This is typically represented in metric or US “Customary” units:
Metric: BMI = kg / m2
Kg is the person’s weight in kilograms and m is the person’s height in meters.
In US/Customary and imperial: BMI = lb * 703 / in2
lb is the person’s weight in pounds and it is the person’s height in inches.
The table below presents the common definition of Obesity by WHO based on particular BMI values.
The National Institute for Health and Clinical Excellence (19 July 2002 ), as well as the USC Center for Colorectal and Pelvic Floor Disorders(2006), have presented some extensions and modifications to the BMI delineations of obesity based on BMI values. The NICE (Opcit) has offered that a BMI of 35.0 or higher in the presence of at least one other significant comorbidity is also classified by some bodies as class III obesity. On the other end the USC presents that, “For Asians, overweight is a BMI between 23 and 29.9 kg/m2 and obesity a BMI >30 kg/m2”
By further extension, the surgical literature breaks down “class III” obesity into further categories.
- Any BMI > 40 is severe obesity
- A BMI of 40.0–49.9 is morbid obesity
- A BMI of >50 is super obese
The graph Below Illustrates the BMI breakdowns according to the surgical literature
The graph illustrates that according to surgical literature BMI values that fall over 40 accounts for severe obesity. BMI values that will fall into the 40.0-49.9 account for morbid obesity whilst BMI values that fall over 50 amount to super obesity according to surgical literature.
Mortality
Mokdad AH et al (2004) state, “Obesity is one of the leading preventable causes of death”. Mortality threat is understood in accordance with the manner in which it varies with BMI. Mainstream BMI based obesity mortality delineations present that the lowest risk is pegged at a BMI of 22-24kg/m2 “And increases with changes with either direction.” (Opcit) As BMI values escalate to values over 32 the figures account for a doubled risk of death. In the United States obesity is approximated to be the basis of a surfeit of 111,909 to 365,000 deaths in a single year according to Allison DB (1999). The researcher also indicates, “Obesity on average reduces one life expectancy by 6–7 years. For subjects with severe obesity (BMIs >40) life expectancy is reduced by 20 years in men and 5 years in women”.
Morbidity
There are growing numbers of physical and mental health conditions that have been identified as related to the condition of obesity. The effects of obesity on the human body can be presented in a manner of categorisation according to the results of inflated fat mass. This category will include consequences such as obstructive sleep apnea and osteoarthritis. The other category will entail the classification basing on the multiplied number of fat cells. This category will constitute consequences like cancer, cardiovascular disease, non-alcoholic fatty liver disease and diabetes. Notable also is the fact that excessive levels of body fat temper albeit negatively with the human body’s response to insulin a scenario that culminates in insulin resistance and the creation of a pro-inflammatory state. This heightens the threat of thrombosis.
From another dimension, the dangers of exposure to metabolic syndrome result from what is termed central obesity which is characterised by the high waist-hip ratio. Grundy SM (2004) explains, “Metabolic syndrome is a combination of medical disorders which often includes diabetes mellitus type 2, high blood pressure, high blood cholesterol, and triglyceride levels. Obesity is related to a variety of other complications.”
Grundy outlines that most of these health conditions are triggered by obesity and that others are obliquely related through means such as sharing a familiar cause “ie. Poor diet or a sedentary lifestyle.” (Optic) Peter G. Kopelman et al (2005) adds, “The strength of the link between obesity and specific conditions is variable. One of the strongest is the link with type 2 diabetes. 64% of diabetes in men and 77% of diabetes in women can be attributed to excess weight”
Research methodology
The research methodology is taking the thrust of secondary research wherein the quantitative analyses are done of data obtained from secondary sources. The research exercise will make use of data obtained from various research models to gather the data germane to arriving at meaningful positions on the research matter. Surveys are important forms of getting data from research. Surveys are useful for getting a great deal of specific information.
The survey entails both open-ended questions e.g., “Which canned food brand do you prefer? Or closed-ended where the respondent is asked to select answers from a brief list e.g. “- Male – Female.” Open-ended questions have been advantageous in that the respondent is not limited to the options listed, and that the respondent is not being influenced by a presented list of responses. Nonetheless, the survey falls short where open-ended questions are skipped by respondents. On the other dimension coding survey, data can be quite a challenge.
Confidence interval and interpretation: Prevalence of obesity and hypertension and associated behavioural risk factors
Invalidating the statistical components of this epidemiological research this section of the report will slot in confidence interval and interpretation of findings obtained on obesity. The merits of Meta-analysis come through the combination of the outcomes of various studies conducted on a set of closely related subject focuses. Meta-analysis models such as confidence interval strive to surmount the setback of diminished statistical power in typical epidemiological studies. The Meta-analysis section of this study will make use of data of outcomes of obesity research conducted to examine the prevalence of obesity and hypertension and associated behavioural risk factors in adult men and women In Uzbekistan.
The confidence interval analysis will make use of data derived from the 2002 Uzbekistan Health Examination Survey (European Journal of Clinical Nutrition (2006). The survey entailed a nationally representative sample of 2333 men aged 15–59 years and 5463 women aged 15–49 years. The survey obtained values on height, weight and blood pressure. From a qualitative tending thrust the research included questions on physical activity, dietary habits, tobacco smoking, and alcohol use among other features.
The results of the confidence interval in the researches of the outlined elements of obesity show that males and females suffering from obesity were around three times as prone to experience hypertension as those with a normal BMI. The odds ratio of the study is, (OR)=3.01; 95% and confidence interval (CI): 1.67–5.44; P<0.001 for men and OR=2.82; 95% CI: 2.05–3.86; P<0.001 for women). These were established as independent of physical activity level, dietary habits, tobacco smoking and other factors according to the research. The results come in the background of the long-held notion that
The consumption of animal-source protein and tobacco smoking were highly linked to conditions of obesity. The dispelled loophole in this epidemiological research is that there have been no consistent links with certain dietary indicators, physical activity proportions as well alcohol intake.
According to the European Journal of Clinical Nutrition (2006), “For men, the risk of hypertension was strongly positively associated with BMI only at BMI levels above 25 kg/m2, but for women, a positive relationship was observed at all BMI levels.” The researchers came to the derived conclusion that “There a positive association between obesity and hypertension in adult men and women in Uzbekistan. The shape of the relationship between BMI and hypertension is different for women than for men, requiring further research to explore this relationship”.
The research indicates that obesity dynamics for men and women differ quite significantly and yet hence researches on the subject must be customised to suffice for the disparities of each research group to come up with feasible recommendations and valuable insights.
Correlation co-efficient and interpretation: Obesity and Functional Disability
This component of the report presents nuances and insights derived from the correlation co-efficient model. The model is based on the outcomes of the research conducted to investigate whether indicators of obesity are associated with functional disabilities among elderly US women and men.
Population Characteristics According to Quartiles of Body Mass Index and Waist Circumference, The National Health and Nutrition Examination Survey, 1999–2004
“*Q stands for quartile: Q1 is the lowest quartile and Q4 is the highest quartile. Cutoffs for BMI are Q1<24.3, Q2 24.3–27.5, Q3 27.6–31.3, Q4≥ 31.4 for women and Q1<24.7, Q2 24.7–27.1, Q3 27.2–30.0, and Q4 ≥ 30.1 for men; cutoffs for waist circumference are Q1 < 87.0, Q2 87.0–96.1, Q3 96.2–104.1, and Q4 ≥104.2 for women and Q1<94.7, Q2 94.7–101.6, Q3 101.7–109.6, and Q4 ≥109.7 for men.
†: means and standard deviations are provided for continuous variables.”
The table presents the aspects of study participants according to quartiles of BMI and waist circumference distributions. In line with the research hypothesis, the two obesity measures are strongly connected with a Pearson correlation coefficient of 0.84 (p < 0.001). For Both the males and females, “higher BMI or larger waist circumference was associated with younger age, current nonsmoker, low physical activity, and the presence of some chronic diseases.” (Optic)
For women, body mass index (BMI) and waist circumference were each related to a higher prevalence of all measures of disabilities. The comparison with the lowest quartile of waist circumference, according to the research findings “the multivariate odds ratios (OR) and 95% confidence intervals of the highest quartile for having difficulties in functional domains were 2.4 (1.6, 3.6) for the activity of daily living, 2.3 (1.6, 3.3) for the instrumental activity of daily living, 2.6 (1.6, 4.1) for leisure and social activities, 4.8 (3.4, 6.9) for lower extremity mobility and 2.9 (2.1, 4.0) for general physical activity. In men, these associations were moderate: the corresponding ORs were 1.2 (0.8, 2.0), 1.3 (0.9, 2.1), 2.1 (1.2, 3.7), 1.8 (1.2, 2.7), and 2.1 (1.5, 2.8) respectively.”
According to the cited research journal, “Similar results were obtained for BMI. These associations could not be explained by the presence of major chronic conditions. When adjusted simultaneously, waist circumference appeared to be a better predictor than BMI of disabilities in women.” By and large, the research outcomes and the quantitative analyses thereof imply that the indicators of obesity are related to functional disabilities among elderly Americans.
Child Obesity
Child obesity is one of the grim realities that face societies in contemporary societies, especially in well to do economies Like the UK and US. The proliferation of fast food dependence and advertising has been blamed largely on this epidemic in various areas.
Ogden CL et al, (2008) asserts, “Childhood obesity is a condition where excess body fat negatively affects a child’s health or wellbeing. As methods to determine body fat directly are difficult, the diagnosis of obesity is often based on BMI. Due to the rising prevalence of obesity in children and its many adverse health effects, it is being recognized as a serious public health concern”. Child obesity is a growing concern from various societies across the world and various health concern groups are lobbying governments to ban fast food advertising especially the marketing strategies aimed at children.
References
- Allison DB, Fontaine KR, Manson JE, Stevens J, VanItallie TB, “Annual deaths attributable to obesity in the United States”. JAMA , 1994.
- European Journal of Clinical Nutrition (2006) 60, 1355–1366. Web.
- Grundy SM, “Obesity, metabolic syndrome, and cardiovascular disease”,J. Clin. Endocrinol. Metab, 2004.
- Haslam DW, James WP, “Obesity”. Lancet 366 (9492), 2005.
- Ogden CL, Carroll MD, Flegal KM,”High body mass index for age among US children and adolescents, 2008.
- Peter G. Kopelman, Ian D. Caterson, Michael J. Stock, William H. Dietz , Clinical obesity in adults and children: In Adults and Children. Blackwell Publishing, 2005.
- Mokdad AH, Marks JS, Stroup DF, Gerberding JL, “Actual causes of death in the United States, 2000”. JAMA , 2004.
- NICE issues guidance on surgery for morbid obesity”. National Institute for Health and Clinical Excellence, 2002.
- “Bariatrice Surgery”. USC Center for Colorectal and Pelvic Floor Disorders. University of Southern California, 2006.
- World Health Organization, Technical report series 894: “Obesity: preventing and managing the global epidemic.” Geneva: World Health Organization, 2000.