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Body Mass Index


Theoretical problems with BMI
Practical problems with BMI
Summary - the use of BMI

The body mass index (BMI) is calculated from weight and height measurements using the formula BMI = weight (in kg) divided by height (in m-2). The BMI was first introduced by Quetlet in order to eliminate the confounding effects of height on weight. In normal adults, the ratio of the weight to the square of height is roughly constant, and a person with a low BMI is underweight for their height 16. BMI reflects protein and fat reserves, which in turn reflect functional reserves including the ability to survive nutritional deficit and some diseases.

BMI may be appropriate for population-level assessments of chronic undernutrition. In 1988, researchers proposed the use of BMI to define and diagnose chronic undernutrition14. This classification provides a useful framework for the analysis of height and weight data from chronically undernourished adult populations.

Table 1 The classification of categories of chronic undernutrition

Classification of underweight categories

BMI(kg m-2)

Normal

>=18.5

Grade I

17.0-18.4

Grade II

16.0-16.9

Grade III

<=15.9

(adapted from Ferro-Luzzi et al, 1992 17)
There is an increasing body of evidence that low BMI is related to both increases in morbidity and mortality 18, 19 and, in fertile-age women, to the chances of having low birth weight babies 20. In addition, BMI is known to be highly correlated with both fat and fat-free mass, although these associations may vary with age, sex and ethnicity21.

However, there are several difficulties associated with the use of BMI as an anthropometric index. These difficulties can broadly be separated into theoretical and practical problems.

Theoretical problems with BMI

Body shape - Many factors other than nutritional status determine BMI. Most important of these is body shape, in particular the ratio of leg-length to trunk-length, sometimes called the sitting-height to standing height ratio (SH/S) or Cormic index. This index varies both between populations and within populations. These differences result in world-wide (i.e. between populations) variation in the SH/S ratio from 0.48 in Australian aborigines 22, 23 up to 0.55 in the Japanese 22. This has a considerable influence on BMI, equivalent at the extremes of the range of SH/S ratio, to a variation of over 6 kg m-2

Figure 1 The effect of varying sitting height: stature ratio (SH/S) on BMI for a 70 kg, initially 1.75 metre male (from Norgan 1994 24)

There is also considerable within-population variation in the Cormic Index. In one aboriginal population, the within-population range for the Cormic Index was 0.41 - 0.54 23. This is greater than the world-wide variation in Cormic Index 22 and equivalent to more than 10 kg m-2 variation in BMI, dependant upon shape alone.

Sitting height can be measured by sitting the person on a straight-backed chair with a height board strapped to the back. This measurement is then used to correct BMI by applying a correction factor based on a linear regression model (see box 1) 23.

Comparisons of BMI between different populations can be made using a correction factor based upon the mean Cormic Index for each population. Such corrections should always be made when BMI is used to compare the nutritional status of different populations.

Follow-up surveys for the comparison of within-population data (for example, before and after an intervention, surveillance by repeated surveys etc) do not require the Cormic Index correction.

If BMI is being used to assess an individual for undernutrition the estimation of the individual's Cormic Index should be used as a correction factor. Without this correction the sensitivity and specificity of BMI as a screening indicator may be low 22, 24. During emergencies, especially at the height of a famine relief program, when there are large numbers of people competing for relatively scarce resources, there is almost never sufficient time or staff to perform this standardisation. We therefore feel that BMI is inappropriate for this role.

Age - Adult body size, shape and composition vary with age.25, 26, 27, 28, 29, 30, 31, 32, 33, 34. Adults tend to loose fat free mass (FFM) and increase fat mass (FM) with age 35. These changes may alter the functional significance of BMI at different ages. Some NGOs use different cut-off points for older adults when admitting individuals to a feeding programme. For example, for adults aged 50y+ Action Contre La Faim (ACF) admits adults to therapeutic feeding centres and supplementary feeding centres using the cut-offs of 15kg m-2 and 16 kgm-2 respectively, but admits those aged less than 50 at 16kg m-2 and 17kg m-2.

BOX 1

THE CORRECTION OF BMI USING THE CORMIC INDEX (SH/S)

In order to standardise BMI to take into account changes in SH/S ratio we recommending using the equations below to calculate BMI standardised to the actual SH/S ratio for the population under study.

Male subjects - BMI = 0.78(SH/S)-18.43

Female subjects - BMI = 1.19(SH/S)-40.34

Note: SH/S ratios should be expressed as a percentage

The observed BMIs can then be standardised to a SH/S ratio of 0.52 by adding the differences between the observed BMI and BMI standardised for the population SH/S ratio to a BMI standardised to 0.52 using the equation below:

BMISstd =BMI0.52 + (BMIob-BMIes),

Where

BMIstd= standardised BMI,
BMI0.52 = estimated BMI at SH/S of 0.52
BMIob = actual BMI
BMIes = estimated BMI at actual SH/S
Examples
1. A Male population "A" has a mean BMI of 18.5 kg m-2 and a mean SH/S ratio of 50%. The BMI0.52= 0.78*52-18.43 = 22.13. The BMIes = 0.78*50-18.43 = 20.57. Therefore the BMIstd = 22.13 + (18.5 - 20.57) = 20.06kg m-2

2. A Female population "A" has a mean BMI of 17.0 kg m-2 and a mean SH/S ratio of 54%. The BMI0.52= 1.19 *52-40.34 = 23.92. The BMIes = 1.19*54-40.34 = 21.54. Therefore the BMIstd = 21.54 + (17.0 - 23.92) = 14.62 kg m-2


These are ad-hoc modifications to the standard cut-off points for the use of BMI to assess undernutrition. As yet there are no published data that support the use of distinct cut-off points for different age groups. Many adults in the developing world do not know their exact age and it may, therefore, be difficult to differentiate the diagnosis of nutritional status according to age in an emergency situation. It may be useful, however, to separate age groups when presenting the results of an adult nutritional survey using BMI in a non-emergency setting, where age can be ascertained using instruments such as local event diaries which would seldom be available in an emergency situation.

The increasing prevalence of kyphosis and scoliosis with age further necessitates the use of proxies for height when assessing the nutritional status of older adults2.

Chronic and acute undernutrition - A great deal of research has focused on use of BMI for the assessment of chronic undernutrition in stable populations. This role is primarily that of prevalence estimation, providing information useful in planning at a population level. This is a different role to that of screening individuals who may be suffering from acute undernutrition in order to regulate admissions to feeding centres. The common assumption in contemporary NGO field manuals and recent academic articles 36, 37, 38 that BMI is also an appropriate indicator for screening during famine, has not been tested.

BMI cut-off points for screening adult admissions to feeding centres, extrapolated directly from CED, may be inappropriate. The cut-off point of 16 kg m2, that indicates severe chronic undernutrition does not necessarily reflect the degree of acute undernutrition that requires specialised treatment 39, 40. During a famine, there is intense competition for entry into feeding centres and it is important that screening indicators are specific, only selecting those who would die if not given specialised treatment.

As adults are usually the primary caregivers and income earners in a household, it is also important not to admit those who do not need therapeutic treatment into a centre as this may have a negative affect on the rest of the household.

In 1996, Ferro-Luzzi and James 36 adjusted their theoretical estimation of the lowest BMI compatible with life down from 12 kg m-2 in order to account for the extremely low BMIs being observed in Somalia during the famine there in 1992. They created two new BMI cut-offs of <13 kg m-2 and <10 kg m-2, denoting severe wasting and extreme wasting respectively. These values did not take into account the Somali long-legged phenotype 41, an important factor explaining the very low BMIs observed 39. Thus the cut-off values they propose are probably too low. In our experience a BMI of 10 kg m-2 after standardisation to a SH/S ratio of 0.52 is probably not compatible with life. One of 13 kg m-2, probably represents a degree of emaciation where peripheral stores have already been exhausted with a corresponding increase in central catabolism. This level is therefore probably inappropriately low to be used as a cut-off for admission into an adult therapeutic centre.

Practical problems with BMI

Difficulties in obtaining the component measures of BMI during famine - The height and weight measurements required to assess BMI are often difficult to obtain during famine. Chair or bed-scales are usually unavailable and thus patients must be able to stand in order to be weighed. Usually, many of the most severely undernourished adults requiring admission to therapeutic feeding centres cannot stand at all and BMI cannot be estimated. In addition, many studies have reported that gross weakness 42, 43, flexor contractions 43, or scoliosis 44 are common. These prevent many patients standing straight enough for accurate height estimation. As height is a squared term, these errors are magnified in BMI calculation.

If BMI is to be used during an emergency there is a need to obtain robust, reliable and precise scales that can withstand repeated measurements under dry, dusty and hot conditions. These may be expensive.

Difficulties in the calculation of BMI - Even in non-famine situations the calculation of BMI and Cormic Index may be unfamiliar to field workers and therefore difficult. ACF have developed tables of weight-for-height that show BMI ranges (like those used for children) that may reduce this difficulty.

Difficulties in obtaining the component measures of BMI in elderly and handicapped people - As adults become older, spinal disease (predominantly osteoarthritis and osteoporosis) affects an increasing proportion of people. These conditions affect the ability to stand straight and make the accurate measurement of height impossible. BMI, based on height cannot therefore be used in older adults 45. Recognition of this problem has prompted research into the use of proxy measures of height. Researchers have shown a good relationship between arm-span, demi-span, femur length, knee height and height 46, 47, 48, 49, 50. These proxies are converted to estimates of height using correction factors derived from regression equations. As the relationship between proxies and height has been shown to vary between ethnic groups and by age, different correction factors should be applied to different populations 47, 48, 50. Suitable population-specific correction factors to apply to proxy measures of height are usually unavailable in emergencies.

In elderly individuals, there are no viable alternatives to estimating height from arm span or demi-span. It should be recognised, however, that at the individual level, there is significant error involved in the estimation of height using correction factors based on population means. For example, the standard error of the estimate of height from arm span is reported to be between 2.5 and 3.8cm46. The squaring of the height element in calculating BMI magnifies these differences.

Famine oedema - Adult nutritional oedema is common during famine and its presence increases weight, producing an upward bias in BMI. In adults the frequent co-existence of pitting oedema and ascites means that oedema fluid can often account for over 10% of body weight. Famine oedema is also associated with poor prognosis (see Figure 2). Consequently, patients with severe famine oedema often have a poorer prognosis the higher their admission BMI, the opposite of the situation in marasmic patients (see Figure 3). BMI is therefore, not an appropriate indicator for people suffering from famine oedema. This may be corrected by using a modified screening criteria (i.e. BMI below a cut-off point OR the presence of oedema). However, as the presence of oedema, particularly in older adults, may not always be indicative of undernutrition, it will be necessary to train field workers to differentiate between the causes of oedema in adults. Alternatively, adults presenting with oedema will have to be referred to a clinician who is able to differentiate between the types of oedema.

As the prevalence of famine oedema is frequently high during emergencies (see Table 2), the inability of BMI to assess oedematous adults limits the usefulness of BMI as a screening tool to assess acute adult undernutrition.

Table 2 Incidence of famine oedema

Study

Location

Year

Prevalence of famine oedema

Collins

Somalia

1992

24%

Collins

Angola

1993

90%

Collins

Sudan

1993

0%

Zimmer et al.

France

1942

50%

Mollinson

Germany

1945

typical finding

McCance et al.

Germany

1946

common

Debray

France

1945

100%

Keys et al.*

U.S.A.

1945

87%

* In a selected population of previously well-nourished American volunteers starved under experimental conditions
Figure 2 Pitting oedema and the odds of mortality, based upon the grading system described in Table 4 (adapted from Collins 1995) 39

Figure 3 The odds of mortality below different thresholds of admission BMI for oedematous (N=75) and marasmic (N=218) patients admitted to a therapeutic centre in Baidoa, Somalia during 1992/3 (adapted from Collins 1995)39

Summary - the use of BMI

In our opinion, the many problems with the use of BMI for screening acutely undernourished adults admissions to feeding programmes during famine relief programmes make the indicator inappropriate for this role.

BMI combined with an assessment of the prevalence of famine oedema is an appropriate indicator for population-level assessment of chronic undernutrition. These data can be categorised according to the classification given in Table 1 above. Such surveys should also assess MUAC.

If BMI survey data are used to compare BMIs between populations, estimates should be corrected by standardisation to a SH/S ratio of 0.52 using the mean SH/S ratio for the specific populations being studied.

Inside feeding centres it is useful to assess a standardised BMI on each patient admitted (standardised using individualised SH/S ratios).


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