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[[Image:Body mass index chart.svg|right|395px|thumb|A graph of body mass index as a function of body mass and body height is shown above. The dashed lines represent subdivisions within a major class. For instance the "Underweight" classification is further divided into "severe", "moderate", and "mild" subclasses.<ref name="BMI Classification">{{cite web |url=http://www.who.int/bmi/index.jsp?introPage=intro_3.html |title=BMI Classification |work=Global Database on Body Mass Index |publisher=World Health Organization |year=2006 |accessdate=July 27, 2012}}</ref>]]
It is very common to have a dental emergency -- a fractured tooth, an abscess, or severe pain when chewing. Over-the-counter pain medication is just masking the problem. Seeing an emergency dentist is critical to getting the source of the problem diagnosed and corrected as soon as possible.<br><br>Here are some common dental emergencies:<br>Toothache: The most common dental emergency. This generally means a badly decayed tooth. As the pain affects the tooth's nerve, treatment involves gently removing any debris lodged in the cavity being careful not to poke deep as this will cause severe pain if the nerve is touched. Next rinse vigorously with warm water. Then soak a small piece of cotton in oil of cloves and insert it in the cavity. This will give temporary relief until a dentist can be reached.<br><br>At times the pain may have a more obscure location such as decay under an old filling. As this can be only corrected by a dentist there are two things you can do to help the pain. Administer a pain pill (aspirin or some other analgesic) internally or dissolve a tablet in a half glass (4 oz) of warm water holding it in the mouth for several minutes before spitting it out. DO NOT PLACE A WHOLE TABLET OR ANY PART OF IT IN THE TOOTH OR AGAINST THE SOFT GUM TISSUE AS IT WILL RESULT IN A NASTY BURN.<br><br>Swollen Jaw: This may be caused by several conditions the most probable being an abscessed tooth. In any case the treatment should be to reduce pain and swelling. An ice pack held on the outside of the jaw, (ten minutes on and ten minutes off) will take care of both. If this does not control the pain, an analgesic tablet can be given every four hours.<br><br>Other Oral Injuries: Broken teeth, cut lips, bitten tongue or lips if severe means a trip to a dentist as soon as possible. In the mean time rinse the mouth with warm water and place cold compression the face opposite the injury. If there is a lot of bleeding, apply direct pressure to the bleeding area. If bleeding does not stop get patient to the emergency room of a hospital as stitches may be necessary.<br><br>Prolonged Bleeding Following Extraction: Place a gauze pad or better still a moistened tea bag over the socket and have the patient bite down gently on it for 30 to 45 minutes. The tannic acid in the tea seeps into the tissues and often helps stop the bleeding. If bleeding continues after two hours, call the dentist or take patient to the emergency room of the nearest hospital.<br><br>Broken Jaw: If you suspect the patient's jaw is broken, bring the upper and lower teeth together. Put a necktie, handkerchief or towel under the chin, tying it over the head to immobilize the jaw until you can get the patient to a dentist or the emergency room of a hospital.<br><br>Painful Erupting Tooth: In young children teething pain can come from a loose baby tooth or from an erupting permanent tooth. Some relief can be given by crushing a little ice and wrapping it in gauze or a clean piece of cloth and putting it directly on the tooth or gum tissue where it hurts. The numbing effect of the cold, along with an appropriate dose of aspirin, usually provides temporary relief.<br><br>In young adults, an erupting 3rd molar (Wisdom tooth), especially if it is impacted, can cause the jaw to swell and be quite painful. Often the gum around the tooth will show signs of infection. Temporary relief can be had by giving aspirin or some other painkiller and by dissolving an aspirin in half a glass of warm water and holding this solution in the mouth over the sore gum. AGAIN DO NOT PLACE A TABLET DIRECTLY OVER THE GUM OR CHEEK OR USE THE ASPIRIN SOLUTION ANY STRONGER THAN RECOMMENDED TO PREVENT BURNING THE TISSUE. The swelling of the jaw can be reduced by using an ice pack on the outside of the face at intervals of ten minutes on and ten minutes off.<br><br>For those who have just about any concerns concerning where by along with the way to make use of [http://www.youtube.com/watch?v=90z1mmiwNS8 Dentists in DC], you possibly can email us with our own web site.
 
The '''body mass index''' ('''BMI'''), or '''Quetelet index''', is a measure for human body shape based on an individual's mass and height.
 
Devised between 1830 and 1850 by the [[Belgium|Belgian]] [[polymath]] [[Adolphe Quetelet]] during the course of developing "social physics",<ref>{{cite journal |doi=10.1093/ndt/gfm517 |title=Adolphe Quetelet (1796–1874)—the average man and indices of obesity |year=2007 |last1=Eknoyan |first1=Garabed |journal=Nephrology Dialysis Transplantation |volume=23 |issue=1 |pmid=17890752 |pages=47–51}}</ref> it is defined as the individual's body mass divided by the square of their height – with the value universally being given in [[Units of measurement|units]] of kg/m<sup>2</sup>.
 
{|
|-
! <math>\mathrm{BMI}</math>&nbsp;
| <math>= \frac{\text{mass}(\text{kg})}{\left(\text{height}(\text{m})\right)^2}</math>
|-
| &nbsp;
|-
| ||<math>= \frac{\text{mass}(\text{lb})}{\left(\text{height}(\text{in})\right)^2}\times 703</math>&nbsp;<sup>†</sup>||
|}
 
<small><sup>†</sup> The factor for UK/US units is more precisely 703.06957964, but that level of precision is [[Significant figures|not meaningful]] for this calculation.</small>
 
BMI can also be determined using a table<ref group=note>e.g., the [http://www.nhlbi.nih.gov/guidelines/obesity/bmi_tbl.htm Body Mass Index Table] from the [[National Institutes of Health]]'s [[National Heart, Lung, and Blood Institute|NHLBI]].</ref> or from a chart which displays BMI as a function of mass and height using contour lines, or colors for different BMI categories.  Such charts can easily allow two different sets of units of measurement to be used, which is often useful.<ref group=note>For example, in the UK, where people often know their weight in [[Stone (unit)|stone]] and height in feet and inches – see [http://newsimg.bbc.co.uk/media/images/42028000/gif/_42028890_bmi_gra_416x314.gif]</ref>
 
The BMI is used in a wide variety of contexts as a simple method to assess how much an individual's body weight departs from what is normal or desirable for a person of his or her height. There is however often vigorous debate, particularly regarding at which value of the BMI scale the threshold for ''[[overweight]]'' and ''[[obesity|obese]]'' should be set, but also about a range of perceived limitations and problems with the BMI.
 
Despite a wide range of other, differently calculated, ratios having been proposed,<ref group=note>Such as the [[Ponderal index]] and others given in the ''"See also"'' section</ref> none have yet been as widely adopted.
 
==Usage==
While the formula previously called the Quetelet Index for BMI dates to the 19th century, the new term "body mass index" for the ratio and its popularity date to a paper published in the July edition of 1972 in the ''Journal of Chronic Diseases'' by [[Ancel Keys]], which found the BMI to be the best proxy for body fat percentage among ratios of weight and height;<ref>{{cite web|url=http://www.slate.com/id/2223095/ |title=Beyond BMI: Why doctors won't stop using an outdated measure for obesity |author=Jeremy Singer-Vine |publisher=[[Slate (magazine)|Slate.com]] |date=July 20, 2009 |accessdate=15 December 2013}}</ref><ref>{{cite journal |doi=10.1016/0021-9681(72)90027-6 |title=Indices of relative weight and obesity |year=1972 |last1=Keys |first1=Ancel |last2=Fidanza |first2=Flaminio |last3=Karvonen |first3=Martti J. |last4=Kimura |first4=Noboru |last5=Taylor |first5=Henry L. |journal=Journal of Chronic Diseases |volume=25 |issue=6–7 |pmid=4650929 |pages=329–43}}</ref> the interest in measuring body fat being due to obesity becoming a discernible issue in prosperous Western societies. BMI was explicitly cited by Keys as being appropriate for ''population'' studies, and inappropriate for individual diagnosis. Nevertheless, due to its simplicity, it came to be widely used for individual diagnosis.
 
'BMI' provides a simple numeric measure of a person's ''thickness'' or ''thinness'', allowing health professionals to discuss overweight and underweight problems more objectively with their patients. However, BMI has become controversial because many people, including physicians, have come to rely on its apparent numerical authority for medical diagnosis, but that was never the BMI's purpose; it is meant to be used as a simple means of classifying sedentary (physically inactive) individuals, or rather, populations, with an average body composition.<ref>{{cite journal|journal = WHO Technical Report Series| volume = 854 | title = Physical Status: The Use and Interpretation of Anthropometry |page = 9 |url = http://whqlibdoc.who.int/trs/WHO_TRS_854.pdf |location=Geneva, Switzerland |year=1995 |publisher=World Health Organization |pmid=8594834}}</ref> For these individuals, the current value settings are as follows: a BMI of 18.5 to 25 may indicate optimal weight, a BMI lower than 18.5 suggests the person is [[underweight]], a number above 25 may indicate the person is [[overweight]], a number above 30 suggests the person is [[obesity|obese]].
 
For a given height, BMI is proportional to mass. However, for a given mass, BMI is inversely proportional to the square of the height. So, if all body dimensions double, and mass scales naturally with the cube of the height, then BMI doubles instead of remaining the same. This results in taller people having a reported BMI that is uncharacteristically high compared to their actual body fat levels. In comparison, the [[Ponderal index]] is based on this natural scaling of mass with the third power of the height. However, many taller people are not just "scaled up" short people, but tend to have narrower frames in proportion to their height. Nick Korevaar (a mathematics lecturer from the University of Utah) suggests that instead of squaring the body height (an exponent of 2, as the BMI does) or cubing the body height (an exponent of 3, as the [[Ponderal index]] does), it would be more appropriate to use an exponent of between 2.3 and 2.7<ref name="Nick K.">{{cite web |first=Nick |last=Korevaar |date=July 2003 |url=http://www.math.utah.edu/~korevaar/ACCESS2003/bmi.pdf |title=Notes on Body Mass Index and actual national data}}{{self-published inline|date=July 2012}}{{MEDRS|date=July 2012}}</ref> (as originally noted by Quetelet).  (For a theoretical basis for such values see MacKay.<ref>{{cite journal |arxiv=0910.5834 |doi=10.1016/j.jbiomech.2009.10.038 |title=Scaling of human body mass with height: The body mass index revisited |year=2010 |last1=MacKay |first1=N.J. |journal=Journal of Biomechanics |volume=43 |issue=4 |pages=764–6 |pmid=19909957 |bibcode=2009arXiv0910.5834M}}</ref>)
 
==BMI Prime==
'''BMI Prime''', a simple modification of the BMI system, is the ratio of actual BMI to upper limit BMI (currently defined at BMI 25). As defined, BMI Prime is also the ratio of body weight to upper body weight limit, calculated at BMI 25. Since it is the ratio of two separate BMI values, BMI Prime is a [[dimensionless number]], without associated units. Individuals with BMI Prime less than 0.74 are underweight; those between 0.74 and 1.00 have optimal weight; and those at 1.00 or greater are overweight. BMI Prime is useful clinically because individuals can tell, at a glance, by what percentage they deviate from their upper weight limits. For instance, a person with BMI 34 has a BMI Prime of 34/25 = 1.36, and is 36% over his or her upper mass limit. In South East Asian and South Chinese populations (see international variation section below) BMI Prime should be calculated using an upper limit BMI of 23 in the denominator instead of 25. Nonetheless, BMI Prime allows easy comparison between populations whose upper limit BMI values differ.<ref>{{cite journal |pmid=16768059 |year=2006 |last1=Gadzik |first1=James |title='How much should I weigh?' Quetelet's equation, upper weight limits, and BMI prime |volume=70 |issue=2 |pages=81–8 |journal=Connecticut Medicine}}</ref>
 
==Categories==
 
A frequent use of the BMI is to assess how much an individual's body weight departs from what is normal or desirable for a person of his or her height. The weight excess or deficiency may, in part, be accounted for by body fat ([[adipose tissue]]) although other factors such as muscularity also affect BMI significantly (see discussion below and [[overweight]]). The [[World Health Organization|WHO]] regards a BMI of less than 18.5 as underweight and may indicate [[malnutrition]], an [[eating disorder]], or other health problems, while a BMI greater than 25 is considered overweight and above 30 is considered [[obesity|obese]].<ref name="BMI Classification" /> These ranges of BMI values are valid only as statistical categories
 
{| class="wikitable"
|-
! Category
! BMI range – kg/m<sup>2</sup>
! BMI Prime
|-
| Very severely underweight
| less than 15
 
| less than 0.60
|-
| Severely underweight
| from 15.0 to 16.0
| from 0.60 to 0.64
|-
| Underweight
| from 16.0 to 18.5
| from 0.64 to 0.74
|-
| Normal (healthy weight)
| from 18.5 to 25
| from 0.74 to 1.0
|-
| Overweight
| from 25 to 30
| from 1.0 to 1.2
|-
| Obese Class I (Moderately obese)
| from 30 to 35
| from 1.2 to 1.4
|-
| Obese Class II (Severely obese)
| from 35 to 40
| from 1.4 to 1.6
|-
| Obese Class III (Very severely obese)
| over 40
| over 1.6
<!--
| Very Severely Obese
| from 40 to 45
| from 1.6 to 1.8
|-
| Morbidly Obese
| from 45 to 50
| from 1.8 to 2.0
|-
| Super Obese
| from 50 to 60
| from 2.0 to 2.4
|-
| Hyper Obese
| from 60
| from 2.4-->
|}
 
===BMI-for-age===
{| border="0" style="background:none; float:right;"
|-
|[[Image:BMIBoys 1.svg|thumb|BMI for age percentiles for boys 2 to 20 years of age.]]
|}
BMI is used differently for [[child]]ren. It is calculated the same way as for adults, but then compared to typical values for other children of the same age. Instead of set thresholds for underweight and overweight, then, the BMI [[percentile]] allows comparison with children of the same sex and age.<ref>{{cite web|url = http://www.cdc.gov/nccdphp/dnpa/healthyweight/assessing/bmi/childrens_BMI/about_childrens_BMI.htm|title = Body Mass Index: BMI for Children and Teens|publisher = Center for Disease Control|accessdate = 2013-12-16}}</ref> A BMI that is less than the 5th percentile is considered underweight and above the 95th percentile is considered obese for people 20 and under. People under 20 with a BMI between the 85th and 95th percentile are considered to be overweight.
 
Recent studies in Britain have indicated that females between the ages 12 and 16 have a higher BMI than males of the same age by 1.0&nbsp;kg/m<sup>2</sup> on average.<ref>{{cite web|url = http://www.archive2.official-documents.co.uk/document/deps/doh/survey02/summ03.htm|title = Health Survey for England: The Health of Children and Young People|website=Archive2.official-documents.co.uk|accessdate=16 December 2013}}</ref>
 
===International variations===
These recommended distinctions along the linear scale may vary from time to time and country to country, making global, longitudinal surveys problematic.
 
====Hong Kong====
The [[Hospital Authority]] of [[Hong Kong]] recommends BMI as following:<ref name="ha">{{cite web |url=http://www3.ha.org.hk/bmi/b5_standard.aspx |title={{as written|BMI正常指標}} |trans_title=Normal BMI Index |work=Ideal BMI Disease Prevention Project<!--translated from 理想BMI防病工程--> |publisher=Hospital Authority Health InfoWorld<!--translated from 醫院管理局‧健康資訊天地--> |accessdate=2013-11-12 |language=Chinese}}</ref>
 
{| class="wikitable"
|-
! Category
! BMI range – kg/m<sup>2</sup>
|-
| Underweight
| < 18.5
|-
| Normal Range
| 18.5 - 22.9
|-
| Overweight - At Risk
| 23.0 - 24.9
|-
| Overweight - Moderately Obese
| 25.0 - 29.9
|-
| Overweight - Severely Obese
| ≥ 30.0
|}
 
====Japan====
 
Japan Society for the Study of Obesity (2000)<ref name="himan-mhlw">{{cite web |url=http://www.mhlw.go.jp/topics/bukyoku/kenkou/seikatu/himan/about.html |title={{as written|肥満って、 どんな状態？}} |trans_title=What is obesity, what kind of state?|work=Obesity Homepage<!--肥満ホームページ--> |publisher=Ministry of Health, Labor and Welfare<!--厚生労働省--> |accessdate=2013-05-25 |language=Japanese}}</ref>
 
{| class="wikitable"
|-
! Category
! BMI range – kg/m<sup>2</sup>
|-
| Low
| 18.5 and below
|-
| Normal
| from 18.5 to 25.0 (Standard weight is 22)
|-
|Obese (Level 1)
| from 25.0 to 30.0
|-
|Obese (Level 2)
| from 30.0 to 35.0
|-
|Obese (Level 3)
| from 35.0 to 40.0
|-
|Obese (Level 4)
| 40.0 and above
|}
<ref>{{cite journal |doi=10.1038/sj.ijo.0802486 |title=Overweight Japanese with body mass indexes of 23.0–24.9 have higher risks for obesity-associated disorders: A comparison of Japanese and Mongolians |year=2003 |last1=Shiwaku |first1=K |last2=Anuurad |first2=E |last3=Enkhmaa |first3=B |last4=Nogi |first4=A |last5=Kitajima |first5=K |last6=Shimono |first6=K |last7=Yamane |first7=Y |last8=Oyunsuren |first8=T |displayauthors=6 |journal=International Journal of Obesity |volume=28 |pages=152–8 |pmid=14557832 |issue=1}}</ref>{{clarify|date=March 2013}}
 
====Singapore====
In Singapore, the BMI cut-off figures were revised in 2005, motivated by studies showing that many Asian populations, including Singaporeans, have higher proportion of body fat and increased risk for cardiovascular diseases and [[diabetes mellitus]], compared with Caucasians at the same BMI. The BMI cut-offs are presented with an emphasis on health risk rather than weight.<ref>{{cite web|url = http://www.hpb.gov.sg/hpb/default.asp?TEMPORARY_DOCUMENT=1769&TEMPORARY_TEMPLATE=2|title = Revision of Body Mass Index (BMI) Cut-Offs in Singapore}} {{dead link|date=December 2013}}</ref>
 
{| class="wikitable"
|-
! BMI range – kg/m<sup>2</sup>
! Health Risk
|-
| 27.5 and above
| High risk of developing heart disease, high blood pressure, stroke, diabetes
|-
| 23.0 to 27.4
| Moderate risk of developing heart disease, high blood pressure, stroke, diabetes
|-
| 18.5 to 22.9
| Low Risk (healthy range)
|-
| 18.4 and below
| Risk of developing problems such as nutritional deficiency and osteoporosis
|}
 
====United States====
In 1998, the U.S. [[National Institutes of Health]] and the [[Centers for Disease Control and Prevention]] brought U.S. definitions into line with [[World Health Organization]] guidelines, lowering the normal/overweight cut-off from BMI 27.8 to BMI 25. This had the effect of redefining approximately 29 million Americans, previously ''healthy'' to ''overweight''.<ref>{{cite news|url = http://www.cnn.com/HEALTH/9806/17/weight.guidelines/|title = Who's fat? New definition adopted | publisher=CNN | date=June 17, 1998 |accessdate=2010-04-26}}</ref> It also recommends lowering the normal/overweight threshold for South East Asian body types to around BMI 23, and expects further revisions to emerge from clinical studies of different body types.
 
The U.S. National Health and Nutrition Examination Survey of 1994 indicated that 59% of American men and 49% of women had BMIs over 25. Morbid obesity—a BMI of 40 or more—was found in 2% of the men and 4% of the women. The newest survey in 2007 indicates a continuation of the increase in BMI: 63% of Americans are overweight or obese, with 26% now in the obese category (a BMI of 30 or more). There are differing opinions on the threshold for being underweight in females; doctors quote anything from 18.5 to 20 as being the lowest index, the most frequently stated being 19. A BMI nearing 15 is usually used as an indicator for starvation and the health risks involved, with a BMI less than 17.5 being an informal criterion for the diagnosis of [[anorexia nervosa]].
 
==Health consequences of overweight and obesity in adults==
The BMI ranges are based on the relationship between body weight and disease and death.<ref>{{cite journal|url=<!-- derived from:http://www.who.int/childgrowth/publications/physical_status/en/-->http://whqlibdoc.who.int/trs/WHO_TRS_854.pdf |journal=WHO Technical Report Series |publisher=World Health Organization |author=<!--Staff writer(s); no by-line.--> |title=Physical status: The use and interpretation of anthropometry |location=Geneva, Switzerland |year=1995 |issue=854 |pmid=8594834}}</ref> Overweight and obese individuals are at increased risk for many diseases and health conditions, including the following:<ref>{{cite book |chapter=Executive Summary |chapterurl=http://www.ncbi.nlm.nih.gov/books/NBK2008/ |pages=xi–xxx |nopp=y |date=September 1998 |title=Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report |url=http://www.nhlbi.nih.gov/guidelines/obesity/ob_gdlns.htm |publisher=[[National Heart, Lung, and Blood Institute]]}}</ref>
 
* [[Hypertension]]
* [[Dyslipidemia]] (for example, high LDL cholesterol, low HDL cholesterol, or high levels of triglycerides)
* [[Diabetes mellitus type 2|Type 2 diabetes]]
* [[Coronary disease|Coronary heart disease]]
* [[Stroke]]
* [[Gallbladder disease]]
* [[Osteoarthritis]]
* [[Sleep apnea]] and respiratory problems
* Some cancers ([[Endometrial cancer|endometrial]],<ref name="Oldenburg">{{cite journal|author=Oldenburg C.S. et al. |title=The relationship of body mass index with quality of life among endometrial cancer survivors: a study from the population-based PROFILES registry |journal=Gynecologic Oncology |volume=129 |issue=1 |date=April 2013 |pages=216–21 |url=http://www.ncbi.nlm.nih.gov.ezproxy.ub.unimaas.nl/pubmed/23296262 |pmid=23296262}} {{registration required}}</ref> [[Breast cancer|breast]], and [[Colon cancer|colon]]).
 
==Applications==
 
===Statistical device===
The BMI is generally used as a means of correlation between groups related by general mass and can serve as a vague means of estimating [[adipose tissue|adiposity]]. The duality of the BMI is that, whilst easy-to-use as a general calculation, it is limited in how accurate and pertinent the data obtained from it can be. Generally, the index is suitable for recognizing trends within sedentary or overweight individuals because there is a smaller margin for errors.<ref name="jxbvhf">{{cite book |last1=Jeukendrup |first1=A. |authorlink1=Asker Jeukendrup |last2=Gleeson |first2=M. |year=2005 |title=Sports Nutrition |publisher=Human Kinetics: An Introduction to Energy Production and Performance |isbn=978-0-7360-3404-3}}{{page needed|date=April 2012}}</ref>
 
This general correlation is particularly useful for consensus data regarding obesity or various other conditions because it can be used to build a semi-accurate representation from which a solution can be stipulated, or the [[Recommended Dietary Allowance|RDA]] for a group can be calculated. Similarly, this is becoming more and more pertinent to the growth of children, due to the majority of their exercise habits.<ref>{{cite book |last=Barasi |first=M. E. |year=2004 |title=Human Nutrition – a health perspective |isbn=0-340-81025-4}}{{page needed|date=April 2012}}</ref>
 
The growth of children is usually documented against a BMI-measured growth chart. Obesity trends can be calculated from the difference between the child's BMI and the BMI on the chart.{{Citation needed|date=April 2011}}
 
===Clinical practice===
BMI has been used by the [[World Health Organization|WHO]] as the standard for recording obesity statistics since the early 1980s. In the United States, BMI is also used as a measure of underweight, owing to advocacy on behalf of those suffering with eating disorders, such as [[anorexia nervosa]] and [[bulimia nervosa]].{{Citation needed|date=April 2007}}
 
BMI can be calculated quickly and without expensive equipment. However, BMI categories do not take into account many factors such as [[anthropometry|frame size]] and muscularity.<ref name="jxbvhf"/> The categories also fail to account for varying proportions of fat, bone, cartilage, water weight, and more.{{Citation needed|date=May 2013}}
 
Despite this, BMI categories are regularly regarded as a satisfactory tool for measuring whether sedentary individuals are ''underweight'', ''overweight'' or ''obese'' with various exemptions, such as: athletes, children, the elderly, and the infirm.{{Citation needed|date=May 2013}}
 
One basic problem, especially in athletes, is that muscle weight contributes to BMI. Some professional athletes would be ''overweight'' or ''obese'' according to their BMI, despite carrying little fat, unless the number at which they are considered ''overweight'' or ''obese'' is adjusted upward in some modified version of the calculation.{{Citation needed|date=April 2012}} In children and the elderly, differences in bone density and, thus, in the proportion of bone to total weight can mean the number at which these people are considered ''under''weight should be adjusted downward.{{Citation needed|date=April 2012}}
 
===Medical underwriting===
In the United States, where [[medical underwriting]] of private health insurance plans is widespread, most private health insurance providers will use a particular high BMI as a cut-off point in order to raise insurance rates for or deny insurance to higher-risk patients, thereby reducing the cost of insurance coverage to all other subscribers in a lower BMI range. The cutoff point is determined differently for every health insurance provider and different providers will have vastly different ranges of acceptability. Many will implement phased surcharges, in which the subscriber will pay an additional penalty, usually as a percentage of the monthly premium, based on membership in an actuarially determined risk tier corresponding to a given range of BMI points above a certain acceptable limit, up to a maximum BMI past which the individual will simply be denied admissibility regardless of price. This can be contrasted with [[group insurance]] policies which do not require medical underwriting and where insurance admissibility is guaranteed by virtue of being a member of the insured group, regardless of BMI or other risk factors that would likely render the individual inadmissible to an individual health plan.{{Citation needed|date=April 2008}}
 
==Limitations and shortcomings==
[[Image:Correlation between BMI and Percent Body Fat for Men in NCHS' NHANES 1994 Data.PNG|right|395px|thumb|This graph shows the correlation between body mass index (BMI) and percent body fat (%BF) for 8550 men in [[National Center for Health Statistics|NCHS]]' [[National Health and Nutrition Examination Survey|NHANES]] 1994 data. Data in the upper left and lower right quadrants show some limitations of BMI.<ref name="RomeroCorral2008"/>]]
The medical establishment has acknowledged major shortcomings of BMI.<ref>{{cite web |url=http://www.nhlbi.nih.gov/health/public/heart/obesity/lose_wt/risk.htm#limitations |title=Aim for a Healthy Weight: Assess your Risk |publisher=National Institutes of Health |date=July 8, 2007 |accessdate=15 December 2013}}</ref> Because the BMI depends upon weight and the square of height, it ignores basic scaling laws whereby mass increases to the 3rd power of linear dimensions. Hence, larger individuals, even if they had exactly the same body shape and relative composition, always have a larger BMI.<ref>{{cite journal|author=Taylor, R. S. |year=2010 |title=Use of Body Mass Index For Monitoring Growth and Obesity |journal=Paediatrics & Child Health |volume=15 |issue=5 |page=258 |pmid=21532785 |pmc=2912631}}</ref> Also, its assumptions about the distribution between lean mass and adipose tissue are inexact. BMI generally overestimates [[adipose tissue|adiposity]] on those with more lean body mass (e.g., athletes) and underestimates excess adiposity on those with less lean body mass. A study in June 2008 by Romero-Corral et al. examined 13,601 subjects from the United States' third [[National Health and Nutrition Examination Survey]] (NHANES III) and found that BMI-defined obesity (BMI > 30) was present in 21% of men and 31% of women. Using [[body fat percentage]]s (BF%), however, BF%-defined obesity was found in 50% of men and 62% of women. While BMI-defined obesity showed high [[Sensitivity and specificity|specificity]] (95% for men and 99% for women), BMI showed poor [[Sensitivity and specificity|sensitivity]] (36% for men and 49% for women). Despite this undercounting of obesity by BMI, BMI values in the intermediate BMI range of 20–30 were found to be associated with a wide range of body fat percentages. For men with a BMI of 25, about 20% have a body fat percentage below 20% and about 10% have body fat percentage above 30%.<ref name="RomeroCorral2008"/><!-- Request: A [[WP:RS]] is needed for this [[WP:OR]]
 
It also does not differentiate between short-limbed long-torsoed individuals who generally have more lean weight than average and short-torsoed, long-limbed people from hotter climates who generally have less lean weight than average. BMI was only meant to be used on groups of people and is off as a measure of fitness in at least 8% of individuals. Response: Does the Romero-Corral citation provide a WP:RS answer? -->
 
Mathematician [[Keith Devlin]] and the restaurant industry association [[Center for Consumer Freedom]] argue that the error in the BMI is significant and so pervasive that it is not generally useful in evaluation of health.<ref>{{cite web |url=http://www.maa.org/devlin/devlin_05_09.html |archiveurl=https://web.archive.org/web/20090505180701/http://www.maa.org/devlin/devlin_05_09.html |archivedate=2009-05-05 |title=Do You Believe in Fairies, Unicorns, or the BMI? |publisher=Mathematical Association of America |date=May 1, 2009}}</ref><ref>{{cite news |url=http://www.rockymounttelegram.com/featr/content/shared/health/stories/BMI_INDEX_0830_COX.html |title=Is obesity such a big, fat threat? |publisher=Cox News Service |date=August 30, 2004 |accessdate=2007-07-08 |archiveurl=https://web.archive.org/web/20070804113535/http://www.rockymounttelegram.com/featr/content/shared/health/stories/BMI_INDEX_0830_COX.html<!-- Bot retrieved archive --> |archivedate = 2007-08-04}}</ref> [[University of Chicago]] political science professor Eric Oliver says BMI is a convenient but inaccurate measure of weight, forced onto the populace, and should be revised.<ref>{{cite news |url=http://thedartmouth.com/2005/04/26/news/oliver/ |archiveurl=https://web.archive.org/web/20090804105417/http://thedartmouth.com/2005/04/26/news/oliver/ |archivedate=2009-08-04 |title=Oliver blames 'obesity mafia' for American weight scare |first=Linzi |last=Sheldon |date=April 26, 2005 |newspaper=The Dartmouth}}</ref>
 
A study published by [[JAMA (journal)|JAMA]] in 2005 showed that ''overweight'' people had a similar relative risk of mortality to ''normal'' weight people as defined by BMI, while ''underweight'' and ''obese'' people had a higher death rate.<ref>{{cite journal |doi=10.1001/jama.293.15.1861 |title=Excess Deaths Associated with Underweight, Overweight, and Obesity |year=2005 |last1=Flegal |first1=K. M. |journal=[[Journal of the American Medical Association|JAMA]] |volume=293 |issue=15 |pmid=15840860 |pages=1861–7 |last2=Graubard |first2=BI |last3=Williamson |first3=DF |last4=Gail |first4=MH}}</ref> High BMI is associated with [[diabetes mellitus type 2|type&nbsp;2 diabetes]] only in persons with high serum [[gamma-glutamyl transpeptidase]].<ref name="pmid21976023">{{cite journal | author=Lim JS, Lee DH, Park JY, Jin SH, Jacobs DR Jr | title=A strong interaction between serum gamma-glutamyltransferase and obesity on the risk of prevalent type 2 diabetes: results from the Third National Health and Nutrition Examination Survey | journal=Clinical Chemistry  | volume=53 | issue=6 | year=2007 | pages=1092–1098  |url=http://www.clinchem.org/content/53/6/1092.long | pmid=17478563 | doi=10.1016/j.jacl.2011.05.004}}</ref>
 
In an analysis of 40 studies involving 250,000 people, patients with coronary artery disease with ''normal'' BMIs were at higher risk of death from cardiovascular disease than people whose BMIs put them in the ''overweight'' range (BMI 25–29.9).<ref>{{cite journal |doi=10.1016/S0140-6736(06)69251-9 |title=Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: A systematic review of cohort studies |year=2006 |last1=Romero-Corral |first1=Abel |last2=Montori |first2=Victor M |last3=Somers |first3=Virend K |last4=Korinek |first4=Josef |last5=Thomas |first5=Randal J |last6=Allison |first6=Thomas G |last7=Mookadam |first7=Farouk |last8=Lopez-Jimenez |first8=Francisco |displayauthors=6 |journal=The Lancet |volume=368 |issue=9536 |pmid=16920472 |pages=666–78}}</ref>
In the ''overweight'', or intermediate, range of BMI (25–29.9), the study found that BMI failed to discriminate between bodyfat percentage and lean mass. The study concluded that "the accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. These results may help to explain the unexpected better survival in overweight/mild obese patients."<ref name="RomeroCorral2008">{{cite journal |doi=10.1038/ijo.2008.11 |title=Accuracy of body mass index in diagnosing obesity in the adult general population |year=2008 |last1=Romero-Corral |first1=A |last2=Somers |first2=V K |last3=Sierra-Johnson |first3=J |last4=Thomas |first4=R J |last5=Collazo-Clavell |first5=M L |last6=Korinek |first6=J |last7=Allison |first7=T G |last8=Batsis |first8=J A |last9=Sert-Kuniyoshi |first9=F H |last10=Lopez-Jimenez |first10=F |displayauthors=6 |journal=International Journal of Obesity |volume=32 |issue=6 |pages=959–66 |pmid=18283284 |pmc=2877506}}</ref>
 
A 2010 study that followed 11,000 subjects for up to eight years concluded that BMI is not a good measure for the risk of heart attack, stroke or death. A better measure was found to be the [[waist-to-height ratio]].<ref>{{cite journal |doi=10.1210/jc.2009-1584 |title=The Predictive Value of Different Measures of Obesity for Incident Cardiovascular Events and Mortality |year=2010 |last1=Schneider |first1=H. J. |last2=Friedrich |first2=N. |last3=Klotsche |first3=J. |last4=Pieper |first4=L. |last5=Nauck |first5=M. |last6=John |first6=U. |last7=Dorr |first7=M. |last8=Felix |first8=S. |last9=Lehnert |first9=H. |last10=Pittrow |first10=D. |last11=Silber |first11=S. |last12=Volzke |first12=H. |last13=Stalla |first13=G. K. |last14=Wallaschofski |first14=H. |last15=Wittchen |first15=H. U. |displayauthors=6 |journal=Journal of Clinical Endocrinology & Metabolism |volume=95 |issue=4 |pmid=20130075 |pages=1777–85}}</ref> However, a 2011 study that followed 60,000 participants for up to 13 years found that [[waist–hip ratio]] was a better predictor of ischaemic heart disease mortality.<ref name="MørkedalRomundstad2011">{{cite journal|last1=Mørkedal|first1=Bjørn|last2=Romundstad|first2=Pål R|last3=Vatten|first3=Lars J.|title=Informativeness of indices of blood pressure, obesity and serum lipids in relation to ischaemic heart disease mortality: the HUNT-II study|journal=European Journal of Epidemiology|volume=26|issue=6|year=2011|pages=457–461|issn=0393-2990|doi=10.1007/s10654-011-9572-7|pmid=21461943|pmc=3115050}}</ref>
 
BMI is particularly inaccurate for people who are fit or athletic, as the higher muscle mass tends to put them in the ''overweight'' category by BMI, even though their body fat percentages frequently fall in the 10–15% category, which is below that of a more sedentary person of average build who has a ''normal'' BMI number. Body composition for athletes is often better calculated using measures of [[Body fat percentage|body fat]], as determined by such techniques as skinfold measurements or underwater weighing and the limitations of manual measurement have also led to new, alternative methods to measure obesity, such as the [[body volume index]]. However, recent studies of [[American football]] [[lineman (American football)|linemen]] who undergo intensive weight training to increase their muscle mass show that they frequently suffer many of the same problems as people ordinarily considered obese, notably [[sleep apnea]].<ref>{{cite news |last=Brown |first=David |title=Linemen More Likely To Have Sleep Condition |publisher=The Washington Post |date=January 23, 2003 |url=http://www.highbeam.com/doc/1P2-234641.html}}</ref><ref>{{cite news |url=http://www.washingtonpost.com/wp-dyn/content/article/2007/01/29/AR2007012900606.html |title=Ex-NFL Linemen prone to Heart Disease |publisher=The Washington Post |date=January 29, 2007 |first=Steven |last=Reinberg |accessdate=2010-04-26}}</ref>
 
BMI also does not account for body frame size; a person may have a small frame and be carrying more fat than optimal, but their BMI reflects that they are ''normal''. Conversely, a large framed individual may be quite healthy with a fairly low body fat percentage, but be classified as ''overweight'' by BMI. Accurate frame size calculators use several measurements (wrist circumference, elbow width, neck circumference and others) to determine what category an individual falls into for a given height. The standard is to use frame size in conjunction with ideal height/weight charts and add roughly 10% for a large frame or subtract roughly 10% for a smaller frame.{{Citation needed|date=November 2012}}
 
For example, a chart may say the ideal weight for a man {{convert|5|ft|10|in|cm|abbr=on}} is {{convert|165|lb|kg}}. But if that man has a slender build (small frame), he may be overweight at {{convert|165|lb|kg}} and should reduce by 10%, to roughly {{convert|150|lb|kg}}. In the reverse, the man with a larger frame and more solid build can be quite healthy at {{convert|180|lb|kg}}. If one teeters on the edge of small/medium or medium/large, a dose of common sense should be used in calculating their ideal weight. However, falling into your ideal weight range for height and build is still not as accurate in determining health risk factors as waist/height ratio and actual body fat percentage.
 
A further limitation of BMI relates to loss of height through aging. In this situation, BMI will increase without any corresponding increase in weight.
 
The exponent of 2 in the denominator of the formula for BMI is arbitrary. It is meant to reduce variability in the BMI associated only with a difference in size, rather than with differences in weight relative to one's ideal weight. If taller people were simply scaled-up versions of shorter people, the appropriate exponent would be 3, as weight would increase with the cube of height. However, on average, taller people have a slimmer build relative to their height than do shorter people, and the exponent which matches the variation best is less than 3. An analysis based on data gathered in the U.S. suggested an exponent of 2.6 would yield the best fit for children aged 2 to 19 years old.<ref name="Nick K." /> For U.S. adults, exponent estimates range from 1.92 to 1.96 for males and from 1.45 to 1.95 for females.<ref>{{cite journal|title=Weight-height relationships and body mass index: some observations from the Diverse Populations Collaboration|journal=American Journal of Physical Anthropology|year=2005|pmid=15761809|doi=10.1002/ajpa.20107|volume=128|issue=1|pages=220–9|author1=Diverse Populations Collaborative Group}}</ref><ref>{{cite journal|title=Physiological models of body composition and human obesity|author=David G Levitt et al.|journal=Nutrition & Metabolism (London)|year=2007|  pmid=17883858|pmc=2082278|first2=SB|first3=RN|first4=SA|first5=JG|volume=4|page=19|doi=10.1186/1743-7075-4-19}}</ref> The exponent 2 is used by convention and for simplicity.
 
As a possible alternative to BMI, the concepts [[fat-free mass index]] (FFMI) and [[fat mass index]] (FMI) were introduced in the early 1990s,<ref>{{cite journal |pmid=2239792 |year=1990 |last1=Vanitallie |first1=TB |last2=Yang |first2=MU |last3=Heymsfield |first3=SB |last4=Funk |first4=RC |last5=Boileau |first5=RA |title=Height-normalized indices of the body's fat-free mass and fat mass: Potentially useful indicators of nutritional status |volume=52 |issue=6 |pages=953–9 |journal=The American Journal of Clinical Nutrition}}</ref> and [[Body Shape Index]] in 2012.
 
===Varying standards===
 
It is not clear where on the BMI scale the threshold for ''[[overweight]]'' and ''[[obesity|obese]]'' should be set. Because of this the standards have varied over the past few decades. Between 1980 and 2000 the U.S. Dietary Guidelines have defined overweight at a variety of levels ranging from a BMI of 24.9 to 27.1. In 1985 the [[National Institutes of Health]] (NIH) consensus conference recommended that overweight BMI be set at a BMI of 27.8 for men and 27.3 for women. In 1988 a NIH report concluded that a BMI over 25 is overweight and a BMI over 30 is obese. In the 1990s the [[World Health Organization]] (WHO) decided that a BMI of 25 to 30 should be considered overweight and a BMI over 30 is obese, the standards the NIH set. This became the definitive guide for determining if someone is overweight.
 
The current WHO and NIH ranges of ''normal'' weights are proved to be associated with decreased risks of some diseases such as diabetes type II; however using the same range of BMI for men and women is considered arbitrary, and makes the definition of underweight quite unsuitable for men.<ref>{{cite web |url=http://www.halls.md/ideal-weight/medical.htm |title=About the 'Medical Recommendation' of Ideal Weight |author=Steven B. Halls |date=November 10, 2003 |accessdate=2010-12-18}}</ref>
 
==Global statistics==
Researchers at the [[London School of Hygiene & Tropical Medicine]] calculated the average BMI for 177 countries using UN data on population, WHO estimates of global weight, and mean height from national health examination surveys.<ref>{{cite news |url=http://www.bbc.co.uk/news/health-18770328#G1A24H1.58W42C167 |publisher=BBC |title=Where are you on the global fat scale?<!--Data extracted from [http://www.bbc.co.uk/news/health-18770328#G1A24H1.58W42C167 BBC News - Where are you on the global fat scale?]--> |date=July 12, 2012 |accessdate=2013-12-16}}</ref>
{| class="wikitable sortable collapsible" style="text-align:center;"
|-
! Country || Average BMI<ref group=note>Assuming equal male and female population (generally correct within 5%)</ref>|| Relative size of average BMI || Male BMI || Relative size of male BMI || Female BMI || Relative size of female BMI || Ratio of male to female BMI || Relative size of ratio
|-
| style="text-align:left;"      |Afghanistan                  || {{bartable|21.01||4}} || {{bartable|21.36||4}} || {{bartable|20.65||4}} || {{bartable|1.034||100}}
|-
| style="text-align:left;"      |Albania                      || {{bartable|24.53||4}} || {{bartable|27.60||4}} || {{bartable|21.45||4}} || {{bartable|1.287||100}}
|-
| style="text-align:left;"      |Algeria                      || {{bartable|23.87||4}} || {{bartable|24.38||4}} || {{bartable|23.36||4}} || {{bartable|1.044||100}}
|-
| style="text-align:left;"      |Angola                        || {{bartable|22.73||4}} || {{bartable|23.24||4}} || {{bartable|22.22||4}} || {{bartable|1.046||100}}
|-
| style="text-align:left;"      |Argentina                    || {{bartable|26.44||4}} || {{bartable|27.76||4}} || {{bartable|25.11||4}} || {{bartable|1.106||100}}
|-
| style="text-align:left;"      |Armenia                      || {{bartable|24.26||4}} || {{bartable|25.72||4}} || {{bartable|22.80||4}} || {{bartable|1.128||100}}
|-
| style="text-align:left;"      |Australia                    || {{bartable|26.10||4}} || {{bartable|27.24||4}} || {{bartable|24.95||4}} || {{bartable|1.092||100}}
|-
| style="text-align:left;"      |Austria                      || {{bartable|25.00||4}} || {{bartable|26.97||4}} || {{bartable|23.03||4}} || {{bartable|1.171||100}}
|-
| style="text-align:left;"      |Azerbaijan                    || {{bartable|24.65||4}} || {{bartable|26.21||4}} || {{bartable|23.08||4}} || {{bartable|1.136||100}}
|-
| style="text-align:left;"      |Bahamas                      || {{bartable|27.09||4}} || {{bartable|27.60||4}} || {{bartable|26.57||4}} || {{bartable|1.039||100}}
|-
| style="text-align:left;"      |Bahrain                      || {{bartable|26.33||4}} || {{bartable|27.97||4}} || {{bartable|24.69||4}} || {{bartable|1.133||100}}
|-
| style="text-align:left;"      |Bangladesh                    || {{bartable|20.32||4}} || {{bartable|21.00||4}} || {{bartable|19.63||4}} || {{bartable|1.070||100}}
|-
| style="text-align:left;"      |Barbados                      || {{bartable|27.70||4}} || {{bartable|26.84||4}} || {{bartable|28.55||4}} || {{bartable|0.940||100}}
|-
| style="text-align:left;"      |Belarus                      || {{bartable|26.72||4}} || {{bartable|26.32||4}} || {{bartable|27.11||4}} || {{bartable|0.971||100}}
|-
| style="text-align:left;"      |Belgium                      || {{bartable|24.15||4}} || {{bartable|25.93||4}} || {{bartable|22.36||4}} || {{bartable|1.160||100}}
|-
| style="text-align:left;"      |Belize                        || {{bartable|26.09||4}} || {{bartable|26.60||4}} || {{bartable|25.58||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Benin                        || {{bartable|22.48||4}} || {{bartable|22.52||4}} || {{bartable|22.43||4}} || {{bartable|1.004||100}}
|-
| style="text-align:left;"      |Bhutan                        || {{bartable|20.37||4}} || {{bartable|20.88||4}} || {{bartable|19.85||4}} || {{bartable|1.052||100}}
|-
| style="text-align:left;"      |Bolivia                      || {{bartable|25.86||4}} || {{bartable|26.07||4}} || {{bartable|25.65||4}} || {{bartable|1.016||100}}
|-
| style="text-align:left;"      |Bosnia and Herzegovina        || {{bartable|23.94||4}} || {{bartable|26.18||4}} || {{bartable|21.69||4}} || {{bartable|1.207||100}}
|-
| style="text-align:left;"      |Botswana                      || {{bartable|24.45||4}} || {{bartable|24.96||4}} || {{bartable|23.94||4}} || {{bartable|1.043||100}}
|-
| style="text-align:left;"      |Brazil                        || {{bartable|24.79||4}} || {{bartable|25.85||4}} || {{bartable|23.72||4}} || {{bartable|1.090||100}}
|-
| style="text-align:left;"      |Brunei                        || {{bartable|22.67||4}} || {{bartable|23.18||4}} || {{bartable|22.16||4}} || {{bartable|1.046||100}}
|-
| style="text-align:left;"      |Bulgaria                      || {{bartable|23.77||4}} || {{bartable|26.53||4}} || {{bartable|21.01||4}} || {{bartable|1.263||100}}
|-
| style="text-align:left;"      |Burkina Faso                  || {{bartable|21.25||4}} || {{bartable|21.86||4}} || {{bartable|20.64||4}} || {{bartable|1.059||100}}
|-
| style="text-align:left;"      |Burundi                      || {{bartable|20.40||4}} || {{bartable|20.91||4}} || {{bartable|19.89||4}} || {{bartable|1.051||100}}
|-
| style="text-align:left;"      |Cambodia                      || {{bartable|21.51||4}} || {{bartable|22.30||4}} || {{bartable|20.72||4}} || {{bartable|1.076||100}}
|-
| style="text-align:left;"      |Cameroon                      || {{bartable|24.70||4}} || {{bartable|26.65||4}} || {{bartable|22.75||4}} || {{bartable|1.171||100}}
|-
| style="text-align:left;"      |Canada                        || {{bartable|25.70||4}} || {{bartable|27.04||4}} || {{bartable|24.36||4}} || {{bartable|1.110||100}}
|-
| style="text-align:left;"      |Cape Verde                    || {{bartable|23.44||4}} || {{bartable|23.95||4}} || {{bartable|22.93||4}} || {{bartable|1.044||100}}
|-
| style="text-align:left;"      |Central African Republic      || {{bartable|20.99||4}} || {{bartable|20.97||4}} || {{bartable|21.01||4}} || {{bartable|0.998||100}}
|-
| style="text-align:left;"      |Chad                          || {{bartable|21.42||4}} || {{bartable|22.04||4}} || {{bartable|20.80||4}} || {{bartable|1.060||100}}
|-
| style="text-align:left;"      |Chile                        || {{bartable|26.05||4}} || {{bartable|25.94||4}} || {{bartable|26.15||4}} || {{bartable|0.992||100}}
|-
| style="text-align:left;"      |China                        || {{bartable|22.86||4}} || {{bartable|23.78||4}} || {{bartable|21.93||4}} || {{bartable|1.084||100}}
|-
| style="text-align:left;"      |Colombia                      || {{bartable|24.94||4}} || {{bartable|26.30||4}} || {{bartable|23.58||4}} || {{bartable|1.115||100}}
|-
| style="text-align:left;"      |Comoros                      || {{bartable|22.99||4}} || {{bartable|23.39||4}} || {{bartable|22.59||4}} || {{bartable|1.035||100}}
|-
| style="text-align:left;"      |Congo                        || {{bartable|21.91||4}} || {{bartable|22.30||4}} || {{bartable|21.52||4}} || {{bartable|1.036||100}}
|-
| style="text-align:left;"      |Costa Rica                    || {{bartable|24.87||4}} || {{bartable|26.06||4}} || {{bartable|23.68||4}} || {{bartable|1.101||100}}
|-
| style="text-align:left;"      |Côte d'Ivoire                || {{bartable|22.03||4}} || {{bartable|21.64||4}} || {{bartable|22.42||4}} || {{bartable|0.965||100}}
|-
| style="text-align:left;"      |Croatia                      || {{bartable|26.61||4}} || {{bartable|30.21||4}} || {{bartable|23.00||4}} || {{bartable|1.313||100}}
|-
| style="text-align:left;"      |Cuba                          || {{bartable|25.64||4}} || {{bartable|26.78||4}} || {{bartable|24.49||4}} || {{bartable|1.094||100}}
|-
| style="text-align:left;"      |Cyprus                        || {{bartable|26.70||4}} || {{bartable|27.21||4}} || {{bartable|26.18||4}} || {{bartable|1.039||100}}
|-
| style="text-align:left;"      |Czech Republic                || {{bartable|23.78||4}} || {{bartable|26.50||4}} || {{bartable|21.06||4}} || {{bartable|1.258||100}}
|-
| style="text-align:left;"      |Denmark                      || {{bartable|24.24||4}} || {{bartable|25.75||4}} || {{bartable|22.73||4}} || {{bartable|1.133||100}}
|-
| style="text-align:left;"      |Djibouti                      || {{bartable|22.96||4}} || {{bartable|23.47||4}} || {{bartable|22.44||4}} || {{bartable|1.046||100}}
|-
| style="text-align:left;"      |Dominican Republic            || {{bartable|25.45||4}} || {{bartable|25.55||4}} || {{bartable|25.34||4}} || {{bartable|1.008||100}}
|-
| style="text-align:left;"      |DR Congo                      || {{bartable|20.25||4}} || {{bartable|20.76||4}} || {{bartable|19.74||4}} || {{bartable|1.052||100}}
|-
| style="text-align:left;"      |East Timor                    || {{bartable|20.72||4}} || {{bartable|21.23||4}} || {{bartable|20.20||4}} || {{bartable|1.051||100}}
|-
| style="text-align:left;"      |Ecuador                      || {{bartable|25.58||4}} || {{bartable|26.09||4}} || {{bartable|25.06||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Egypt                        || {{bartable|26.70||4}} || {{bartable|27.14||4}} || {{bartable|26.25||4}} || {{bartable|1.034||100}}
|-
| style="text-align:left;"      |El Salvador                  || {{bartable|25.80||4}} || {{bartable|26.31||4}} || {{bartable|25.28||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Equatorial Guinea            || {{bartable|24.75||4}} || {{bartable|25.26||4}} || {{bartable|24.24||4}} || {{bartable|1.042||100}}
|-
| style="text-align:left;"      |Eritrea                      || {{bartable|19.85||4}} || {{bartable|20.27||4}} || {{bartable|19.43||4}} || {{bartable|1.043||100}}
|-
| style="text-align:left;"      |Estonia                      || {{bartable|23.06||4}} || {{bartable|25.21||4}} || {{bartable|20.90||4}} || {{bartable|1.206||100}}
|-
| style="text-align:left;"      |Ethiopia                      || {{bartable|20.46||4}} || {{bartable|20.97||4}} || {{bartable|19.94||4}} || {{bartable|1.052||100}}
|-
| style="text-align:left;"      |Fiji                          || {{bartable|24.99||4}} || {{bartable|25.25||4}} || {{bartable|24.72||4}} || {{bartable|1.021||100}}
|-
| style="text-align:left;"      |Finland                      || {{bartable|25.06||4}} || {{bartable|26.76||4}} || {{bartable|23.36||4}} || {{bartable|1.146||100}}
|-
| style="text-align:left;"      |France                        || {{bartable|23.56||4}} || {{bartable|24.90||4}} || {{bartable|22.22||4}} || {{bartable|1.121||100}}
|-
| style="text-align:left;"      |Gabon                        || {{bartable|23.40||4}} || {{bartable|23.75||4}} || {{bartable|23.05||4}} || {{bartable|1.030||100}}
|-
| style="text-align:left;"      |Gambia                        || {{bartable|21.73||4}} || {{bartable|21.94||4}} || {{bartable|21.52||4}} || {{bartable|1.020||100}}
|-
| style="text-align:left;"      |Georgia                      || {{bartable|25.27||4}} || {{bartable|25.78||4}} || {{bartable|24.75||4}} || {{bartable|1.042||100}}
|-
| style="text-align:left;"      |Germany                      || {{bartable|25.32||4}} || {{bartable|27.17||4}} || {{bartable|23.46||4}} || {{bartable|1.158||100}}
|-
| style="text-align:left;"      |Ghana                        || {{bartable|23.15||4}} || {{bartable|24.64||4}} || {{bartable|21.65||4}} || {{bartable|1.138||100}}
|-
| style="text-align:left;"      |Greece                        || {{bartable|26.13||4}} || {{bartable|27.68||4}} || {{bartable|24.57||4}} || {{bartable|1.127||100}}
|-
| style="text-align:left;"      |Grenada                      || {{bartable|26.43||4}} || {{bartable|26.94||4}} || {{bartable|25.91||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Guatemala                    || {{bartable|25.88||4}} || {{bartable|26.42||4}} || {{bartable|25.34||4}} || {{bartable|1.043||100}}
|-
| style="text-align:left;"      |Guinea                        || {{bartable|22.06||4}} || {{bartable|22.41||4}} || {{bartable|21.71||4}} || {{bartable|1.032||100}}
|-
| style="text-align:left;"      |Guinea-Bissau                || {{bartable|21.04||4}} || {{bartable|21.55||4}} || {{bartable|20.53||4}} || {{bartable|1.050||100}}
|-
| style="text-align:left;"      |Guyana                        || {{bartable|25.10||4}} || {{bartable|25.61||4}} || {{bartable|24.59||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Haiti                        || {{bartable|23.12||4}} || {{bartable|22.21||4}} || {{bartable|24.03||4}} || {{bartable|0.924||100}}
|-
| style="text-align:left;"      |Honduras                      || {{bartable|25.12||4}} || {{bartable|25.63||4}} || {{bartable|24.61||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Hungary                      || {{bartable|24.45||4}} || {{bartable|26.50||4}} || {{bartable|22.39||4}} || {{bartable|1.184||100}}
|-
| style="text-align:left;"      |Iceland                      || {{bartable|25.93||4}} || {{bartable|26.80||4}} || {{bartable|25.06||4}} || {{bartable|1.069||100}}
|-
| style="text-align:left;"      |India                        || {{bartable|21.05||4}} || {{bartable|22.50||4}} || {{bartable|19.60||4}} || {{bartable|1.148||100}}
|-
| style="text-align:left;"      |Indonesia                    || {{bartable|21.59||4}} || {{bartable|21.91||4}} || {{bartable|21.26||4}} || {{bartable|1.031||100}}
|-
| style="text-align:left;"      |Iran                          || {{bartable|24.28||4}} || {{bartable|25.21||4}} || {{bartable|23.35||4}} || {{bartable|1.080||100}}
|-
| style="text-align:left;"      |Iraq                          || {{bartable|24.53||4}} || {{bartable|25.04||4}} || {{bartable|24.01||4}} || {{bartable|1.043||100}}
|-
| style="text-align:left;"      |Ireland                      || {{bartable|24.40||4}} || {{bartable|26.14||4}} || {{bartable|22.65||4}} || {{bartable|1.154||100}}
|-
| style="text-align:left;"      |Israel                        || {{bartable|25.05||4}} || {{bartable|26.72||4}} || {{bartable|23.37||4}} || {{bartable|1.143||100}}
|-
| style="text-align:left;"      |Italy                        || {{bartable|23.49||4}} || {{bartable|25.78||4}} || {{bartable|21.19||4}} || {{bartable|1.217||100}}
|-
| style="text-align:left;"      |Jamaica                      || {{bartable|26.21||4}} || {{bartable|24.82||4}} || {{bartable|27.60||4}} || {{bartable|0.899||100}}
|-
| style="text-align:left;"      |Japan                        || {{bartable|21.93||4}} || {{bartable|23.52||4}} || {{bartable|20.34||4}} || {{bartable|1.156||100}}
|-
| style="text-align:left;"      |Jordan                        || {{bartable|25.09||4}} || {{bartable|26.65||4}} || {{bartable|23.52||4}} || {{bartable|1.133||100}}
|-
| style="text-align:left;"      |Kazakhstan                    || {{bartable|22.99||4}} || {{bartable|25.02||4}} || {{bartable|20.96||4}} || {{bartable|1.194||100}}
|-
| style="text-align:left;"      |Kenya                        || {{bartable|21.41||4}} || {{bartable|21.59||4}} || {{bartable|21.23||4}} || {{bartable|1.017||100}}
|-
| style="text-align:left;"      |Kuwait                        || {{bartable|27.92||4}} || {{bartable|28.77||4}} || {{bartable|27.07||4}} || {{bartable|1.063||100}}
|-
| style="text-align:left;"      |Kyrgyzstan                    || {{bartable|22.90||4}} || {{bartable|23.99||4}} || {{bartable|21.80||4}} || {{bartable|1.100||100}}
|-
| style="text-align:left;"      |Laos                          || {{bartable|21.99||4}} || {{bartable|22.50||4}} || {{bartable|21.48||4}} || {{bartable|1.047||100}}
|-
| style="text-align:left;"      |Latvia                        || {{bartable|23.73||4}} || {{bartable|25.38||4}} || {{bartable|22.07||4}} || {{bartable|1.150||100}}
|-
| style="text-align:left;"      |Lebanon                      || {{bartable|24.57||4}} || {{bartable|26.60||4}} || {{bartable|22.54||4}} || {{bartable|1.180||100}}
|-
| style="text-align:left;"      |Lesotho                      || {{bartable|24.56||4}} || {{bartable|22.96||4}} || {{bartable|26.16||4}} || {{bartable|0.878||100}}
|-
| style="text-align:left;"      |Liberia                      || {{bartable|21.00||4}} || {{bartable|21.51||4}} || {{bartable|20.49||4}} || {{bartable|1.050||100}}
|-
| style="text-align:left;"      |Libya                        || {{bartable|26.06||4}} || {{bartable|26.57||4}} || {{bartable|25.55||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Lithuania                    || {{bartable|24.29||4}} || {{bartable|26.44||4}} || {{bartable|22.14||4}} || {{bartable|1.194||100}}
|-
| style="text-align:left;"      |Luxembourg                    || {{bartable|25.06||4}} || {{bartable|25.60||4}} || {{bartable|24.51||4}} || {{bartable|1.044||100}}
|-
| style="text-align:left;"      |Macedonia                    || {{bartable|23.81||4}} || {{bartable|24.25||4}} || {{bartable|23.36||4}} || {{bartable|1.038||100}}
|-
| style="text-align:left;"      |Madagascar                    || {{bartable|21.60||4}} || {{bartable|22.31||4}} || {{bartable|20.89||4}} || {{bartable|1.068||100}}
|-
| style="text-align:left;"      |Malawi                        || {{bartable|21.96||4}} || {{bartable|22.02||4}} || {{bartable|21.90||4}} || {{bartable|1.005||100}}
|-
| style="text-align:left;"      |Malaysia                      || {{bartable|22.58||4}} || {{bartable|23.06||4}} || {{bartable|22.09||4}} || {{bartable|1.044||100}}
|-
| style="text-align:left;"      |Maldives                      || {{bartable|22.21||4}} || {{bartable|23.54||4}} || {{bartable|20.88||4}} || {{bartable|1.127||100}}
|-
| style="text-align:left;"      |Mali                          || {{bartable|22.18||4}} || {{bartable|22.11||4}} || {{bartable|22.24||4}} || {{bartable|0.994||100}}
|-
| style="text-align:left;"      |Malta                        || {{bartable|26.04||4}} || {{bartable|27.91||4}} || {{bartable|24.17||4}} || {{bartable|1.155||100}}
|-
| style="text-align:left;"      |Mauritania                    || {{bartable|23.74||4}} || {{bartable|24.17||4}} || {{bartable|23.30||4}} || {{bartable|1.037||100}}
|-
| style="text-align:left;"      |Mauritius                    || {{bartable|24.46||4}} || {{bartable|25.05||4}} || {{bartable|23.87||4}} || {{bartable|1.049||100}}
|-
| style="text-align:left;"      |Mexico                        || {{bartable|26.54||4}} || {{bartable|27.70||4}} || {{bartable|25.37||4}} || {{bartable|1.092||100}}
|-
| style="text-align:left;"      |Micronesia                    || {{bartable|32.82||4}} || {{bartable|32.80||4}} || {{bartable|32.84||4}} || {{bartable|0.999||100}}
|-
| style="text-align:left;"      |Moldova                      || {{bartable|25.24||4}} || {{bartable|25.75||4}} || {{bartable|24.73||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Mongolia                      || {{bartable|25.94||4}} || {{bartable|24.78||4}} || {{bartable|27.10||4}} || {{bartable|0.914||100}}
|-
| style="text-align:left;"      |Morocco                      || {{bartable|23.76||4}} || {{bartable|23.71||4}} || {{bartable|23.80||4}} || {{bartable|0.996||100}}
|-
| style="text-align:left;"      |Mozambique                    || {{bartable|21.27||4}} || {{bartable|21.27||4}} || {{bartable|21.27||4}} || {{bartable|1.000||100}}
|-
| style="text-align:left;"      |Myanmar                      || {{bartable|22.40||4}} || {{bartable|22.91||4}} || {{bartable|21.89||4}} || {{bartable|1.047||100}}
|-
| style="text-align:left;"      |Namibia                      || {{bartable|22.00||4}} || {{bartable|22.01||4}} || {{bartable|21.99||4}} || {{bartable|1.001||100}}
|-
| style="text-align:left;"      |Nepal                        || {{bartable|20.55||4}} || {{bartable|20.82||4}} || {{bartable|20.27||4}} || {{bartable|1.027||100}}
|-
| style="text-align:left;"      |Netherlands                  || {{bartable|24.14||4}} || {{bartable|25.72||4}} || {{bartable|22.56||4}} || {{bartable|1.140||100}}
|-
| style="text-align:left;"      |New Zealand                  || {{bartable|26.61||4}} || {{bartable|27.55||4}} || {{bartable|25.67||4}} || {{bartable|1.073||100}}
|-
| style="text-align:left;"      |Nicaragua                    || {{bartable|25.61||4}} || {{bartable|25.83||4}} || {{bartable|25.38||4}} || {{bartable|1.018||100}}
|-
| style="text-align:left;"      |Niger                        || {{bartable|21.49||4}} || {{bartable|22.27||4}} || {{bartable|20.71||4}} || {{bartable|1.075||100}}
|-
| style="text-align:left;"      |Nigeria                      || {{bartable|22.88||4}} || {{bartable|23.98||4}} || {{bartable|21.77||4}} || {{bartable|1.102||100}}
|-
| style="text-align:left;"      |North Korea                  || {{bartable|20.78||4}} || {{bartable|21.29||4}} || {{bartable|20.27||4}} || {{bartable|1.050||100}}
|-
| style="text-align:left;"      |Norway                        || {{bartable|24.69||4}} || {{bartable|26.28||4}} || {{bartable|23.10||4}} || {{bartable|1.138||100}}
|-
| style="text-align:left;"      |Oman                          || {{bartable|24.15||4}} || {{bartable|25.41||4}} || {{bartable|22.89||4}} || {{bartable|1.110||100}}
|-
| style="text-align:left;"      |Pakistan                      || {{bartable|21.53||4}} || {{bartable|21.92||4}} || {{bartable|21.14||4}} || {{bartable|1.037||100}}
|-
| style="text-align:left;"      |Panama                        || {{bartable|26.16||4}} || {{bartable|26.67||4}} || {{bartable|25.65||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Papua New Guinea              || {{bartable|23.79||4}} || {{bartable|23.16||4}} || {{bartable|24.41||4}} || {{bartable|0.949||100}}
|-
| style="text-align:left;"      |Paraguay                      || {{bartable|25.32||4}} || {{bartable|25.83||4}} || {{bartable|24.81||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Peru                          || {{bartable|25.23||4}} || {{bartable|25.87||4}} || {{bartable|24.59||4}} || {{bartable|1.052||100}}
|-
| style="text-align:left;"      |Philippines                  || {{bartable|22.35||4}} || {{bartable|22.73||4}} || {{bartable|21.96||4}} || {{bartable|1.035||100}}
|-
| style="text-align:left;"      |Poland                        || {{bartable|23.21||4}} || {{bartable|25.88||4}} || {{bartable|20.54||4}} || {{bartable|1.260||100}}
|-
| style="text-align:left;"      |Portugal                      || {{bartable|24.59||4}} || {{bartable|26.49||4}} || {{bartable|22.69||4}} || {{bartable|1.167||100}}
|-
| style="text-align:left;"      |Qatar                        || {{bartable|27.47||4}} || {{bartable|27.98||4}} || {{bartable|26.96||4}} || {{bartable|1.038||100}}
|-
| style="text-align:left;"      |Romania                      || {{bartable|22.98||4}} || {{bartable|24.62||4}} || {{bartable|21.33||4}} || {{bartable|1.154||100}}
|-
| style="text-align:left;"      |Russian Federation            || {{bartable|23.25||4}} || {{bartable|24.80||4}} || {{bartable|21.69||4}} || {{bartable|1.143||100}}
|-
| style="text-align:left;"      |Rwanda                        || {{bartable|21.67||4}} || {{bartable|21.15||4}} || {{bartable|22.19||4}} || {{bartable|0.953||100}}
|-
| style="text-align:left;"      |Saint Lucia                  || {{bartable|25.23||4}} || {{bartable|24.59||4}} || {{bartable|25.86||4}} || {{bartable|0.951||100}}
|-
| style="text-align:left;"      |Samoa                        || {{bartable|28.34||4}} || {{bartable|28.79||4}} || {{bartable|27.88||4}} || {{bartable|1.033||100}}
|-
| style="text-align:left;"      |São Tomé and Príncipe        || {{bartable|21.75||4}} || {{bartable|22.26||4}} || {{bartable|21.24||4}} || {{bartable|1.048||100}}
|-
| style="text-align:left;"      |Saudi Arabia                  || {{bartable|26.11||4}} || {{bartable|27.88||4}} || {{bartable|24.33||4}} || {{bartable|1.146||100}}
|-
| style="text-align:left;"      |Senegal                      || {{bartable|22.68||4}} || {{bartable|23.73||4}} || {{bartable|21.62||4}} || {{bartable|1.098||100}}
|-
| style="text-align:left;"      |Sierra Leone                  || {{bartable|23.45||4}} || {{bartable|23.87||4}} || {{bartable|23.03||4}} || {{bartable|1.036||100}}
|-
| style="text-align:left;"      |Singapore                    || {{bartable|22.19||4}} || {{bartable|22.80||4}} || {{bartable|21.58||4}} || {{bartable|1.057||100}}
|-
| style="text-align:left;"      |Slovakia                      || {{bartable|25.34||4}} || {{bartable|25.85||4}} || {{bartable|24.83||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Slovenia                      || {{bartable|25.38||4}} || {{bartable|25.89||4}} || {{bartable|24.87||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Solomon Islands              || {{bartable|27.34||4}} || {{bartable|27.85||4}} || {{bartable|26.83||4}} || {{bartable|1.038||100}}
|-
| style="text-align:left;"      |Somalia                      || {{bartable|20.48||4}} || {{bartable|20.99||4}} || {{bartable|19.97||4}} || {{bartable|1.051||100}}
|-
| style="text-align:left;"      |South Africa                  || {{bartable|24.96||4}} || {{bartable|24.95||4}} || {{bartable|24.97||4}} || {{bartable|0.999||100}}
|-
| style="text-align:left;"      |South Korea                  || {{bartable|24.06||4}} || {{bartable|25.34||4}} || {{bartable|22.78||4}} || {{bartable|1.112||100}}
|-
| style="text-align:left;"      |Spain                        || {{bartable|24.52||4}} || {{bartable|26.47||4}} || {{bartable|22.57||4}} || {{bartable|1.173||100}}
|-
| style="text-align:left;"      |Sri Lanka                    || {{bartable|20.51||4}} || {{bartable|21.44||4}} || {{bartable|19.57||4}} || {{bartable|1.096||100}}
|-
|  style="text-align:left; white-space:nowrap;"|St Vincent and the Grenadines || {{bartable|26.04||4}} || {{bartable|26.55||4}} || {{bartable|25.53||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Sudan                        || {{bartable|21.97||4}} || {{bartable|22.48||4}} || {{bartable|21.46||4}} || {{bartable|1.048||100}}
|-
| style="text-align:left;"      |Suriname                      || {{bartable|25.71||4}} || {{bartable|26.22||4}} || {{bartable|25.20||4}} || {{bartable|1.040||100}}
|-
| style="text-align:left;"      |Swaziland                    || {{bartable|23.39||4}} || {{bartable|23.90||4}} || {{bartable|22.88||4}} || {{bartable|1.045||100}}
|-
| style="text-align:left;"      |Sweden                        || {{bartable|24.54||4}} || {{bartable|26.11||4}} || {{bartable|22.97||4}} || {{bartable|1.137||100}}
|-
| style="text-align:left;"      |Switzerland                  || {{bartable|24.94||4}} || {{bartable|25.47||4}} || {{bartable|24.40||4}} || {{bartable|1.044||100}}
|-
| style="text-align:left;"      |Syria                        || {{bartable|25.00||4}} || {{bartable|25.51||4}} || {{bartable|24.49||4}} || {{bartable|1.042||100}}
|-
| style="text-align:left;"      |Tajikistan                    || {{bartable|25.21||4}} || {{bartable|25.72||4}} || {{bartable|24.70||4}} || {{bartable|1.041||100}}
|-
| style="text-align:left;"      |Tanzania                      || {{bartable|21.83||4}} || {{bartable|21.87||4}} || {{bartable|21.78||4}} || {{bartable|1.004||100}}
|-
| style="text-align:left;"      |Thailand                      || {{bartable|22.34||4}} || {{bartable|23.36||4}} || {{bartable|21.32||4}} || {{bartable|1.096||100}}
|-
| style="text-align:left;"      |Togo                          || {{bartable|22.22||4}} || {{bartable|22.72||4}} || {{bartable|21.72||4}} || {{bartable|1.046||100}}
|-
| style="text-align:left;"      |Tonga                        || {{bartable|32.90||4}} || {{bartable|32.03||4}} || {{bartable|33.77||4}} || {{bartable|0.948||100}}
|-
| style="text-align:left;"      |Trinidad and Tobago          || {{bartable|26.90||4}} || {{bartable|26.46||4}} || {{bartable|27.33||4}} || {{bartable|0.968||100}}
|-
| style="text-align:left;"      |Tunisia                      || {{bartable|23.86||4}} || {{bartable|24.63||4}} || {{bartable|23.08||4}} || {{bartable|1.067||100}}
|-
| style="text-align:left;"      |Turkey                        || {{bartable|24.92||4}} || {{bartable|25.33||4}} || {{bartable|24.50||4}} || {{bartable|1.034||100}}
|-
| style="text-align:left;"      |Turkmenistan                  || {{bartable|23.55||4}} || {{bartable|25.13||4}} || {{bartable|21.96||4}} || {{bartable|1.144||100}}
|-
| style="text-align:left;"      |Uganda                        || {{bartable|21.53||4}} || {{bartable|21.03||4}} || {{bartable|22.02||4}} || {{bartable|0.955||100}}
|-
| style="text-align:left;"      |Ukraine                      || {{bartable|23.34||4}} || {{bartable|24.84||4}} || {{bartable|21.84||4}} || {{bartable|1.137||100}}
|-
| style="text-align:left;"      |United Arab Emirates          || {{bartable|26.66||4}} || {{bartable|27.60||4}} || {{bartable|25.71||4}} || {{bartable|1.074||100}}
|-
| style="text-align:left;"      |United Kingdom                || {{bartable|26.19||4}} || {{bartable|27.62||4}} || {{bartable|24.76||4}} || {{bartable|1.116||100}}
|-
| style="text-align:left;"      |United States                || {{bartable|27.82||4}} || {{bartable|28.64||4}} || {{bartable|27.00||4}} || {{bartable|1.061||100}}
|-
| style="text-align:left;"      |Uruguay                      || {{bartable|25.06||4}} || {{bartable|26.88||4}} || {{bartable|23.24||4}} || {{bartable|1.157||100}}
|-
| style="text-align:left;"      |Uzbekistan                    || {{bartable|23.80||4}} || {{bartable|24.99||4}} || {{bartable|22.60||4}} || {{bartable|1.106||100}}
|-
| style="text-align:left;"      |Vanuatu                      || {{bartable|25.53||4}} || {{bartable|26.46||4}} || {{bartable|24.60||4}} || {{bartable|1.076||100}}
|-
| style="text-align:left;"      |Venezuela                    || {{bartable|26.19||4}} || {{bartable|27.52||4}} || {{bartable|24.86||4}} || {{bartable|1.107||100}}
|-
| style="text-align:left;"      |Vietnam                      || {{bartable|19.96||4}} || {{bartable|21.18||4}} || {{bartable|18.73||4}} || {{bartable|1.131||100}}
|-
| style="text-align:left;"      |Yemen                        || {{bartable|22.07||4}} || {{bartable|22.91||4}} || {{bartable|21.22||4}} || {{bartable|1.080||100}}
|-
| style="text-align:left;"      |Zambia                        || {{bartable|21.02||4}} || {{bartable|21.02||4}} || {{bartable|21.01||4}} || {{bartable|1.000||100}}
|-
| style="text-align:left;"      |Zimbabwe                      || {{bartable|22.38||4}} || {{bartable|21.70||4}} || {{bartable|23.06||4}} || {{bartable|0.941||100}}
|}
 
==See also==
* [[Allometric law]]
* [[Body water]]
Other measures of obesity:
* [[Body adiposity index]]
* [[Body fat percentage]]
* [[Body volume index]]
* [[Ponderal index]]
* [[Rohrer's index]]
* [[Sagittal Abdominal Diameter|Sagittal Abdominal Diameter (SAD)]]
* [[Waist-hip ratio]]
*[[Waist-to-height ratio]]
 
==Notes==
{{reflist|group=note}}
 
==References==
{{reflist|2}}
 
==Further reading==
*{{cite book |editor1-first=Linda A. |editor1-last=Ferrera |year=2006 |title=Focus on Body Mass Index And Health Research |publisher=Nova Science |location=New York |isbn=978-1-59454-963-2}}
*{{cite book |editor1-first=Thomas T. |editor1-last=Samaras |year=2007 |title=Human Body Size and the Laws of Scaling: Physiological, Performance, Growth, Longevity and Ecological Ramifications |publisher=Nova Science |location=New York |isbn=978-1-60021-408-0}}
* {{cite book |editor1-first=Melinda S. |editor1-last=Sothern |editor2-first=Stewart T. |editor2-last=Gordon |editor3-first=T. Kristian |editor3-last=von Almen |year=2006 |title=Handbook of Pediatric Obesity: Clinical Management |publisher=CRC Press |edition=illustrated |isbn=978-1-4200-1911-7}}
 
==External links==
{{wiktionary|body mass index}}
<!--Please do not add more links to more calculators. If you feel a particular online calculator has specific merits, please propose the link on the talk page.-->
* U.S. National Center for Health Statistics:
**[http://www.cdc.gov/growthcharts/ BMI Growth Charts for children and young adults]
**[http://apps.nccd.cdc.gov/dnpabmi/Calculator.aspx BMI calculator ages 2–19]
**[http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/bmi_calculator.html BMI calculator ages 20 and older]
 
{{DEFAULTSORT:Body Mass Index}}
[[Category:Body shape]]
[[Category:Human weight]]
[[Category:Human height]]
[[Category:Medical signs]]
[[Category:Ratios]]
[[Category:Belgian inventions]]

Revision as of 05:24, 3 March 2014

It is very common to have a dental emergency -- a fractured tooth, an abscess, or severe pain when chewing. Over-the-counter pain medication is just masking the problem. Seeing an emergency dentist is critical to getting the source of the problem diagnosed and corrected as soon as possible.

Here are some common dental emergencies:
Toothache: The most common dental emergency. This generally means a badly decayed tooth. As the pain affects the tooth's nerve, treatment involves gently removing any debris lodged in the cavity being careful not to poke deep as this will cause severe pain if the nerve is touched. Next rinse vigorously with warm water. Then soak a small piece of cotton in oil of cloves and insert it in the cavity. This will give temporary relief until a dentist can be reached.

At times the pain may have a more obscure location such as decay under an old filling. As this can be only corrected by a dentist there are two things you can do to help the pain. Administer a pain pill (aspirin or some other analgesic) internally or dissolve a tablet in a half glass (4 oz) of warm water holding it in the mouth for several minutes before spitting it out. DO NOT PLACE A WHOLE TABLET OR ANY PART OF IT IN THE TOOTH OR AGAINST THE SOFT GUM TISSUE AS IT WILL RESULT IN A NASTY BURN.

Swollen Jaw: This may be caused by several conditions the most probable being an abscessed tooth. In any case the treatment should be to reduce pain and swelling. An ice pack held on the outside of the jaw, (ten minutes on and ten minutes off) will take care of both. If this does not control the pain, an analgesic tablet can be given every four hours.

Other Oral Injuries: Broken teeth, cut lips, bitten tongue or lips if severe means a trip to a dentist as soon as possible. In the mean time rinse the mouth with warm water and place cold compression the face opposite the injury. If there is a lot of bleeding, apply direct pressure to the bleeding area. If bleeding does not stop get patient to the emergency room of a hospital as stitches may be necessary.

Prolonged Bleeding Following Extraction: Place a gauze pad or better still a moistened tea bag over the socket and have the patient bite down gently on it for 30 to 45 minutes. The tannic acid in the tea seeps into the tissues and often helps stop the bleeding. If bleeding continues after two hours, call the dentist or take patient to the emergency room of the nearest hospital.

Broken Jaw: If you suspect the patient's jaw is broken, bring the upper and lower teeth together. Put a necktie, handkerchief or towel under the chin, tying it over the head to immobilize the jaw until you can get the patient to a dentist or the emergency room of a hospital.

Painful Erupting Tooth: In young children teething pain can come from a loose baby tooth or from an erupting permanent tooth. Some relief can be given by crushing a little ice and wrapping it in gauze or a clean piece of cloth and putting it directly on the tooth or gum tissue where it hurts. The numbing effect of the cold, along with an appropriate dose of aspirin, usually provides temporary relief.

In young adults, an erupting 3rd molar (Wisdom tooth), especially if it is impacted, can cause the jaw to swell and be quite painful. Often the gum around the tooth will show signs of infection. Temporary relief can be had by giving aspirin or some other painkiller and by dissolving an aspirin in half a glass of warm water and holding this solution in the mouth over the sore gum. AGAIN DO NOT PLACE A TABLET DIRECTLY OVER THE GUM OR CHEEK OR USE THE ASPIRIN SOLUTION ANY STRONGER THAN RECOMMENDED TO PREVENT BURNING THE TISSUE. The swelling of the jaw can be reduced by using an ice pack on the outside of the face at intervals of ten minutes on and ten minutes off.

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