- Introduction - Anthropometry Nutritional assessment (2024)

The term “nutritional anthropometry” firstappeared in “Body Measure­ments and Human Nutrition”(Brožek, 1956)and was later defined by Jelliffe(1966)as:

“mea­sure­ments of the variationsof the physical dimensions and the gross composition of the human bodyat different age levels and degrees of nutrition”

Subsequently, a number of publications made recom­mendations on specific bodymea­sure­ments for characterizing nutri­tional status, standardizedmea­sure­ment techniques, and suitable reference data(Jelliffe, 1966;WHO, 1968;Weiner and Lourie, 1969).Today, anthro­pometric mea­sure­mentsare widely used for the assessment of nutri­tional status and health, at both theindividual and population levels. One of their main advantages is thatanthro­pometric mea­sure­ments may be related to past exposures, to presentprocesses, or to future events(WHO, 1995).

For individuals in low-income countries,anthro­pometry is partic­ularly useful when there isa chronic imbalance between intakes of energy,protein, and certain micronutrients.Such disturbances modify the patterns of physical growth and the relativeproportions of body tissues such as fat, muscle, and total body water.For individuals in clinicalsettings, anthro­pometry can be used to diagnose failureto thrive in infants and young children,and monitor over­weight and obesity in children and adults.

At the population level, anthro­pometry has an important role in targetinginter­ventions through screening, in assessing the response tointer­ventions, in identi­fying the deter­minants and consequences ofmal­nu­trition, and in conducting nutri­tional surveillance.Increasingly, anthro­pometry is also being used to characterizeand compare the health and nutri­tionalstatus of populations across countries(WHO/UNICEF, 2019).

9.1 Mea­sure­ments, indices, and indicators

Anthro­pometric mea­sure­ments areof two types. One group of mea­sure­mentsassesses body size, the other group appraises bodycomposition. The most widely used mea­sure­ments of body sizeare stature (length or height), weight, and headcircum­ference; see Chapter10 for more details. The anthro­pometricmea­sure­ments of body composition are based onthe classical “two component model” in whichthe body is divided into two major compartments,fat mass and the fat free mass.Skinfold thickness mea­sure­ments are used toestimate of the size of the sub­cu­taneous fat depot, which,in turn provides an estimate of total body fat: overone third of total body fat is estimated to besub­cu­taneous fat. The distri­bution of body fat isalso important, with the mea­sure­ment of waistcircum­ference used increasingly as aproxy for the amount of intra-abdominal visceral fat. Waist circum­ferenceis recom­mended for use in population studies(WHO, 2011),as well as in clinical practice for the evaluation andmanagement of patients with over­weight or obesity(Ross etal., 2020).

The fat-free mass consists of the skeletal muscle,non-skeletal muscle, soft lean tissue, and the skeleton.A major component of the fat-free mass is body muscle.As this is composed of protein, assessment of muscle masscan provide an indirect assessment of the protein reservesof the body. Measure­ments of thigh circum­ference andmid-upper-arm circum­ference (MUAC) can be used toassess skeletal muscle mass(Müller etal., 2016).Measure­ment of MUAC is especially useful for youngchildren <5y in emergency settings such as faminesand refugee crises. In such settings, children often have a smallamount of sub­cutan­eous fat, so changes in MUACtend to parallel changes in muscle mass; see Chapter11 for more details.

Anthro­pometric indices are usually calculated from two ormore raw mea­sure­ments, and are essential forthe inter­pretation and grouping of mea­sure­mentscollected in nutri­tional assessment. For example,the mea­sure­ment of a child's body weight ismeaningless unless it is related to the age orheight of a child. In young children the threemost commonly used growth indices are weight-for-age,height-for-age, and weight-for-height. The first twoindices reflect body weight or heightrelative to chronological age, whereas weight-for-heightassesses body weight relative to height.

Body mass index (BMI) is also widely used in children and adultsto assess under­weight, over­weight, and obesity, and is calculated as(weight kg) / (height m)². When height cannot be measured,as may occur in bed-bound or frail individuals,published equations based on a range of body mea­sure­mentssuch as knee height, lower leg length,arm span, and ulna length can be used to providean approximate estimate of height. Examples of equationsfor estimating height from these body mea­sure­ments in adultsare given in Madden etal.(2016).However, their usefulnessfor hospitalised patients may be questionable, if the equationshave been derived from young and healthy populations(Reidlinger etal., 2014).

Examples of body composition indices include acombination of triceps skinfold and mid-upper-armcircum­ference, which together can be used to estimatemid-upper-arm fat area and mid-upper-arm musclecircum­ference or area, surrogates for total body fatcontent, and muscle mass, respectively.Other mea­sure­ment combinations include the waist-hip ratio(i.e., the waist circum­ference divided by the hip circum­ference),an additional index of the distri­bution of body fat whichcan be measured more precisely than skinfolds.Moreover, mea­sure­mentsof waist-hip ratio as a surrogate for abdominal obesity,appear to be a stronger inde­pen­dent risk factor forrisk of myocardial infarction, stroke and prematuredeath than BMI, especially among men(Larsson etal., 1984; Lapidus etal., 1984).

In an effort to obtain more reliable estimates of per­cent­agebody fat and fat-fat-free mass based on anthro­pometricmea­sure­ments in healthy adults,the sum of skinfold thickness mea­sure­mentsfrom multiple anatomical sites is also used inconjunction with population-specific or generalizedregression equations to predict body density, andin turn, the per­cent­age of body fat using one ofthree empirical equations. Once the per­cent­age of bodyfat is calculated, total body fat content and thefat-free mass can be derived (see Chapter11for more details). Again, many of theprediction equations were developed on young, healthy, lean Caucasianpopulation groups and, hence, are less appro­priate for malnourished,obese, or elderly subjects or for other racial groups.

Anthropometric indices are often evaluated bycomparison with the distri­bution of appro­priate anthropometricreference data using standarddeviation scores (Z‑scores) or per­cen­tiles.(seeSection9.4.3).From this, the number andproportion of individuals (as%) with anthropometricindices below or above a predeterminedreference limit or cutoff are often calculated.A commonly used reference limit for the three main growthindices is a Z‑score of −2 (i.e., below the whor*ference median) (Section9.4.2).When used in this way, the index and its associatedreference limit or cutoff become an “indicator”; theseare discussed below.

Anthro­pometric indicators are constructed fromanthro­pometric indices, with the term “indicator” relatingto their use in nutri­tional assessment, often for public health,or socio-medical decision-making at the population level. Indicators are alsoused in clinical settings to identify individuals at risk of malnutrition.To be valid, a substantial proportion of the variabilityof an anthro­pometric indicator should be associatedwith differences in nutri­tional status. WHO(1995)provide a detailed classification of recom­mendedanthro­pometric indicators based on their uses forboth targeting and assessing response to inter­ventions,identi­fying deter­minants of mal­nu­trition,or predicting mal­nu­tritionin populations of infants and children.

Anthro­pometric indicators should be chosen carefully in relation toboth their proposed use and their attributes.Indicators vary in their validity, sensitivity, specificity, and predictivevalue; these characteristics are discussed briefly inSection9.4.3.For example, although the indicatorweight-for-age <−2Z‑score isstill widely used in health centers in many low-income countriesfor screening young children at risk of malnutrition, it is inappro­priate.Children who are stunted but of normal weight,or alternatively, tall and thin may beincorrectly diagnosed as “healthy”.Instead, in these countries, the indicatorlength/height-for-age <−2Z‑scoreshould be used(Ruel etal., 1995).

Further, several factors will affect the magnitude of theexpected response of an anthropometric indicator. These may includethe degree of defi­ciency, age, sex, and physiologicalstate of the target group. Someexamples of frequently used anthro­pometric indicatorsand their corresponding application are shown inTable9.1.

9.2 Advantages and limitations of anthro­pometry

Anthro­pometric mea­sure­ments are of increasing importance in nutri­tional assessment asthey have many advantages (Box9.1). However, anthro­pometric measuresare relatively insensitive and cannot detect disturbances in nutri­tionalstatus over short periods of time. Further­more, nutri­tionalanthro­pometry cannot identify any specific nutrient defi­ciency and,therefore, is unable to distinguish disturbances in growth and bodycomposition induced by nutrient deficiencies (e.g., zinc) from thosecaused by imbalances in protein and energy intake.

Certain non-nutri­tional factors (such as disease, genetic influences,diurnal variation, and reduced energy expenditure) can lower thespecificity and sensitivity of anthro­pometric mea­sure­ments(Section1.4),although such effects generally can be excluded or taken intoaccount by appro­priate sampling and experi­mental design.

Nevertheless, nutri­tional anthro­pometry can be used to monitor changes in both growthand body compo­sition in individuals (e.g., hospital patients) and inpopulation groups, provided sources of mea­sure­ment error and the effectsof confounding factors are minimized(Ulijaszek & Kerr, 1999).

9.3 Errors in anthro­pometry

Errors can occur in nutri­tional anthro­pometry which mayaffect the precision, accuracy, and validity of the mea­sure­ments, andthus indices and indicators. Three major sources of error are significant: mea­sure­menterrors, alterations in the compo­sition and physical properties ofcertain tissues, and the use of invalid assumptions in the derivation ofbody compo­sition from anthro­pometric mea­sure­ments(Heymsfield and Casper, 1987).

Measure­ment errors arise from examiner error resulting frominadequate training, instru­ment error, and difficulties in making themea­sure­ment (e.g., skinfold thicknesses). The major sources ofmea­sure­ment error in anthro­pometry are shown in Boxes 9.2 and 9.3.Both random and sys­tem­atic mea­sure­ment errors may occurwhich reduce the validity of the index and any indicatorconstructed from the index; they have been extensively reviewed byUlijaszek and Kerr(1999).

9.3.1 Random mea­sure­ment errors and precision

Random mea­sure­ment errors limit precision or the extentto which repeated mea­sure­ments of the same variable give the same value.Random mea­sure­ment errors can be minimized by trainingpersonnel to use standardized techniques and precise,correctly calibrated instru­ments(Lohman etal., 1988).Further­more, the precision (and accuracy)of each mea­sure­ment techniqueshould be firmly established prior to use. To improve precision,two or three mea­sure­ments on each individual should be conducted.

A description of the mea­sure­ment techniques used in theWHO Multi­center Growth Reference Study (MGRS)are available in deOnis etal.(2004),as well as in an anthro­pometric training video from WHO. In the WHO MGRSthe equipment was calibrated regularly, using standard weightsover the full weight range for the portable electronicweighing scales, metal rods of known length for both theinfantometer and stadiometer, and calibration blocks ofvarying widths for the skinfold calipers.

Poor precision often reflects within-examiner error, but between-examinererror may also be significant in surveys with multiple examiners. Theprecision of a mea­sure­ment technique can be assessed by calculating:

• Technical error of the mea­sure­ment (TEM)
• per­cent­age technical error (%TEM)
• Coefficient of reliability (R)

These parameters can becalculated for each anthro­pometric mea­sure­ment technique from repeatedmea­sure­ments on each subject made within a few minutes to avoidphysiological fluctuations. A minimum of 10 subjects is recom­mended.

The technical error of the mea­sure­ment (TEM) is the square root of the mea­sure­menterror variance. TEM is expressed in the same units as that of the anthro­pometricmea­sure­ment under study and is often agedependent. The value is also related to the anthro­pometriccharacteristics of the study group. The calculation varies according tothe number of replicate mea­sure­ments made. For one examiner making twomea­sure­ments,TEM =where D = the difference between two mea­sure­ments and N =number of subjects. For more than two mea­sure­ments, the equation ismore complex, and TEM =where N = number ofsubjects, K is the number of deter­minations of the variable taken oneach subject, and M_n is the n^th replicate of the mea­sure­ment, where n varies from 1 to K.

Table9.2:

shows the calculation of TEM frommea­sure­ments of stature performed four times on 10 subjects by a singleanthro­pometrist.

Note that the size of the mea­sure­ment also influencesthe size of the associated TEM, so that comparisonsof precision of different anthro­pometric mea­sure­mentsusing TEM cannot be made easily. This is high­lighted inTable9.3in which the TEM for five anthro­pometricmea­sure­ments taken during theinitial standardization session conducted at the Braziliansite of the WHO Multi­centre Growth Reference Study (MGRS)are presented(deOnis etal., 2004).Table9.3also depicts the maximum allowable differences betweenthe mea­sure­ments of two observers that were used inthe WHO MGRS, and set based on TEMs achievedduring the standardization session.

Per­cent­age TEM has been recom­mended to overcome the difficulty of the TEM being dependent onthe size of the original mea­sure­ment(Norton and Olds, 1996).The per­cent­age technical error ofthe mea­sure­ment is analogous to the coefficient of variation and is calculated as:\[\small\mbox{%TEM = (TEM/mean) × 100% } \]Note that %TEM has no units and can be used to makedirect comparisons of all types of anthro­pometric mea­sure­ments. Itcannot be used, however, when more than one examiner is involved, asthen both within- and between-examiner errors are involved. Ulijaszekand Kerr(1999)describe ways to deal with this more complex case.

The coefficient of reliability (R)is an alternative approach that is widely usedfor comparing mea­sure­ment errorsamong anthro­pometric mea­sure­ments.It ranges from 0to1 and can becalculated using the following equation:\[\small\mbox{ R = 1 −((TEM)}^{2}/ \mbox{s} ^{2}) \]where s² is the between-subject variance. The coefficientindicates the proportion of between-subject variance in a measuredpopulation which is free frommea­sure­ment error. Hence, a mea­sure­mentwith R&equals;0.95 indicates that 95% of the variance is due to factors otherthan mea­sure­ment error.

Whenever possible, a coefficient of reliability>0.95 should be sought. Coefficients of reliability can be used tocompare the relative reliability of different anthro­pometricmea­sure­ments, and the samemea­sure­ments in different age groups, as wellas for calculating sample sizes in anthro­pometric surveys.

More details of standardization procedures and calculation of precision using TEM,per­cent­age TEM, and coefficient of reliability are given in Lohman etal.(1988).In general, the precision of weight and heightmea­sure­ments are high. However, for waist and hip circum­ferences,between-examiner error tends to be large and it is preferable for onlyone examiner to take these mea­sure­ments. Because skinfolds arenotoriously imprecise, both within- and between-examiner errors can belarge. Therefore, rigorous training using standardized techniques andcalibrated equipment are critical when skinfold mea­sure­ments are taken.

9.3.2. Sys­tem­atic mea­sure­ment errors and accuracy

Sys­tem­atic mea­sure­menterrors affect the accuracy of anthro­pometric mea­sure­ments or how closethe mea­sure­ments are to the true value. The most common form ofsys­tem­atic error in anthro­pometry results from equipment bias. Forexample, apparent discrepancies in skinfold mea­sure­ments performed onthe same person but with different calipers may be due to compressiondifferences arising from variations in spring pressure and surface areaof the calipers(Schmidt & Carter, 1990); Harpenden and Holtainskinfold calipers consistently yield smaller values than Lange calipers(Gruber etal., 1990).Errors arising from bias reduce the accuracy of the mea­sure­mentby altering the mean or median value, but have no effect on the variance. Hence, sucherrors do not alter the precision of the mea­sure­ment.

The timing of some anthro­pometric mea­sure­mentsof body size and compo­sition is also known to be critical, partic­ularlyfor short-term growth studies: progressive decreases in the height ofan individual during the day as a consequence of compressionof the spinal column, for example, may seriously compromise the accuracy ofheight velocity mea­sure­ments.

The deter­mination of accuracy inanthro­pometry is difficult because the correct value of anyanthro­pometric mea­sure­ment is never known with absolute certainty. Inthe absence of absolute reference standards, the accuracy ofanthro­pometric mea­sure­ments is estimated by comparing them with thosemade by a criterion anthro­pometrist(Ulijaszek & Kerr, 1999), a personwho has been highly trained in the standardized mea­sure­ment techniquesand whose mea­sure­ments compare well to those from another criterionanthro­pometrist.

In preparation for the compilation of the new WHO Child Growth Standard,four anthro­pometrists were trained and standardized against acriterion anthro­pometrist, designated as the “lead”anthro­pometrist; see deOnis etal.(2004)for more details. All anthro­pometric mea­sure­ments were taken andrecorded inde­pen­dently by two designated anthro­pometrists, andtheir mea­sure­ment values compared for maximum allowabledifferences(Table9.3).Targets for sports anthro­pometrists are also available(Gore etal., 1996).

Attempts should always be made tominimize mea­sure­ment errors. In longitudinal studies involvingsequential anthro­pometric mea­sure­ments on the same group of individuals(e.g., surveillance), it is preferable, whenever possible, to have oneperson carrying out the same mea­sure­ments throughout the study toeliminate between-examiner errors. This is partic­ularly critical whenincrements in growth and body compo­sition are calculated; suchincrements are generally small and are associated with two error terms,one on each mea­sure­ment occasion. Recom­mendations for the minimumintervals necessary to provide reliable data on growth increments duringinfancy and early childhood(Guo etal., 1991)and adolescence(WHO, 1995)are available.

In large regional surveillance studies, it isoften necessary to use several well-trained anthro­pometrists. In suchcirc*mstances, the between-examiner differences among anthro­pometristsmust be monitored throughout the study to maintain the quality of themea­sure­ments and thereby toidentify and correct sys­tem­atic errors inthe mea­sure­ments. This practice was followed during theWHOMGRS(deOnis etal., 2004).

In studies involving two longitudinal mea­sure­ments,the TEM can be calculated to estimate the proportion of the differencethat can be attributed to mea­sure­ment error. For example, with a TEM of0.3 for a given anthro­pometric mea­sure­ment,the TEM for the differencebetween two mea­sure­ments is:because both TEM values contribute to thevariance in the difference. Only if the differenceexceeds 2 × 0.42 &equals; 0.84is there a 95% probability that the difference exceeds themea­sure­ment error alone.

Once assured that such differences are not afunction of mea­sure­ment error, then any changes in growth and bodycompo­sition can be correlated with factors such as age, the onset ofdisease, response to nutri­tion inter­vention therapy, and so on.

The collection of longitudinal anthro­pometric data is more time consuming,expensive, and laborious than from cross-sectional surveys, and, as aresult, the sample size is generally smaller. Hence, the probability ofsys­tem­atic sampling bias(Section1.4.2)is generally greater than in more extensive cross-sectional surveys.

For cross-sectional studies,the examiners should be rotated among the subjects to reduce the effectof mea­sure­ment bias of the individual examiners. Statistical methodsexist for removing anthro­pometric mea­sure­ment error from cross-sectionalanthro­pometric data; details are given in Ulijaszek and Lourie(1994).

Cross-sectional surveys are useful for comparing population groups,provided that probability sampling techniques have been used to ensurethat the samples are represen­tative of the populations from which theyare drawn (Chapter1). Recently WHO has provided countries with toolsto develop or strengthen their surveillance systems so theyhave the capacity to monitor changes in the Global Nutri­tionTargets for 2030. They include the followinganthro­pometric indicators: stunting,wasting, low birthweight, and childhood over­weight.For more details see:WHO Nutrition Tracking Tool.

9.3.3 Errors from changes in tissue compo­sition and properties

Variation in the compo­sition and physicalproperties of certain tissues may occur in both healthy and diseasedsubjects, resulting in inaccuracies in certain anthro­pometricmea­sure­ments. Even among healthy individuals, body weight may beaffected by variations in tissue hydration with the menstrual cycle(Heymsfield and Casper, 1987;Madden and Smith, 2016).

Skinfold thickness mea­sure­ments may beinfluenced by variations in compressibility and skin thickness with age,gender, and the level of tissue hydration(Martin etal., 1992;Ward and Anderson, 1993).For example, repeated mea­sure­ments of skinfolds, overa short period (i.e., 5min), may actually decrease accuracy ofskinfolds because later mea­sure­ments are more compressed due to theexpulsion of water from the adipose tissue at the site of the earliermea­sure­ment(Ulijaszek and Kerr, 1999).

The accuracy of waist circum­ference is affected by both the phaseof respiration at the point of mea­sure­ment and by the tensionof the abdominal wall. The phase of respiration is importantbecause it deter­mines the extent of fullness of the lungs andthe position of the diaphragm at the time of the mea­sure­ment.Increasing the tension of the abdominal wall (by sucking in)is frequently an unconscious reaction which is also importantbecause it reduces the waist mea­sure­ment. To minimize theseerrors, WHO(2011)recom­mends advising the subject torelax and take a few deep, natural breaths before the actualmea­sure­ment and at the end of normal expiration.

In addition, during aging, demineral­ization of thebone and changes in body water may result in a decrease inthe density of the fat-free mass(Visser etal., 1994;JafariNasabian etal., 2017),which are not always takeninto account when calculating total body fat and hencefat-free mass from skinfolds via bodydensity (see Chapter11 for more details).

9.3.4 Invalid models and errors in body compo­sition

Invalid assumptions may lead to erroneous estimates of body compo­sitionwhen these are derived from anthro­pometricmea­sure­ments, especially inobese or elderly patients and those with protein-energy mal­nu­trition orcertain disease states. For instance, use of skinfold thicknessmea­sure­ments to estimate total body fat assumes that (a) the thicknessof the sub­cu­taneous adipose tissue reflects a constant proportion of thetotal body fat and (b) the sites selected represent the averagethickness of the sub­cu­taneous adipose tissue. In fact, the relationshipbetween sub­cu­taneous and internal fat is nonlinear and varies with bodyweight, age, and disease state. Very lean subjects have a smallerproportion of body fat deposited sub­cu­taneously than do obese subjects,and in malnourished persons there is probably a shift of fat storagefrom sub­cu­taneous to deep visceral sites. Variations in thedistri­bution of sub­cu­taneous fat also occurs with age, sex, and ethnicityor race(Wagner and Heyward, 2000;He etal., 2002).

Estimates of mid-upper-arm muscle area areused as an index of total body muscle andthe fat-free mass (Chapter11), regardless of age and health statusof the subjects. Such estimates are made, despite the known changes inthe relationship between arm muscle and fat-free mass with age andcertain disease states(Heymsfield and McManus, 1985),and the questionable accuracy of the algorithms used(Martine etal., 1997).Moreover, even the corrected algorithms developed for adultsoverestimate arm muscle area in obese persons when compared with thedeter­mination by computerized tomography(Forbes etal., 1998).

Increasingly, body compo­sition is assessed by laboratory methods; theseare described in Chapter14. Even laboratory methods are based oncertain assumptions that have been challenged in recent years. Forexample, until recently, densitometry, frequently using underwaterweighing, has been the gold standard reference method for thedeter­mination of the per­cent­age of body fat. The assumptions used indensitometry are that the densities of the fat mass and fat-free massare constant at 0.90 and 1.10kg/L, respectively (Chapter11).Several researchers have questioned the validity of using a constantdensity of the fat-free mass for groups who vary in age, gender, levelsof body fatness, and race or ethnicity(Visser etal., 1997).During aging, the density of the fat-free mass may decrease due todemineral­ization of the bone and changes in bodywater, as noted above(Visser etal., 1994;JafariNasabian etal., 2017),which are not always taken into when calculating total body fatfrom skinfolds via body density, leading to a1%–2%overestimate of the body fat content in such subjects(Deurenberg etal., 1989);see Chapter11 for more details.

In contrast, persons of African descent have a larger fat-free massbecause they have a greater bone mineral density and body proteincontent compared to Caucasians(Wagner and Heyward, 2000).Such differences lead to an underestimate of body fat,when generalized equations developed for Caucasians are used.

Per­cent­age of body fat can also be deter­minedusing an isotope dilution technique and dual‑energy X‑rayabsorptiometry (DXA) (Chapter14). Both of these methods assume aconstant hydration of the fat-free mass (i.e., 73.2% watercontent), despite knowledge that itvaries with age(Wang etal., 1999),obesity, and pregnancy(Hopkinson etal., 1997),and throughout the course of a clinical condition (e.g., inflammation)(Müller etal., 2016).When the actual hydration offat-free mass is higher than the assumed value, then the per­cent­age ofbody fat is underestimated by isotope dilution techniques (Chapter14)(Deurenberg-Yap etal., 2001).For example, even whenpregnancy-specific values for hydration have been appliedto account for the increased accretion of water that occursduring pregnancy, individual estimates of fat mass usingisotope dilution differed by >3kg from valuesbased on the four-compartment model(Hopkinson etal., 1997).In contrast, hydration effects on estimates of fat byDXA are not significant(Pietrobelli etal., 1998).

Fortunately, the advent of multicomponentmodels (i.e., the 4‑compartment-model) with minimal assumptionsfor assessing body compo­sition circumvent the use of older methods, which useassumptions that are not always valid for certain ethnic groups or the elderly(Müller etal., 2016).Nevertheless, the use of multicomponent models is expensive,requiring more time and facilities.

9.4 Inter­pretation andevaluation of anthro­pometric data

Anthro­pometric indices are derived from two or moreraw mea­sure­ments, as noted earlier. Normally, it is these indices thatare interpreted and evaluated — not the raw mea­sure­ments.Anthro­pometric indices can be used atboth the individual and the population levels to assessnutri­tional status and to screen and assess a response duringinter­ventions. In addition, in populations, anthro­pometry can be usedto identify the deter­minants andconsequences of mal­nu­trition and fornutri­tional surveillance. To achieve these objectives, knowledge offactors that may modify or “condition”the inter­pretation of abnormalanthro­pometric indices is generally required. These conditioningfactors are briefly discussed below,together with anthropometric reference data andmethods to evaluate anthropometricindices, including classification systems that identifyindividuals and populations as “at risk” for mal­nu­trition.

9.4.1 Conditioning factors

A variety of factors are known to modify or condition the inter­pretationof anthro­pometric data and must be taken into account. Some importantexamples include age, birthweight, birth length, gesta­tional age, sex,parental stature, and feeding mode during infancy. Maturation duringadolescence, prepregnancy weight, maternalheight, parity, smoking, pregnancy, and ethnicityare major conditioning factors for adults(WHO, 1995).

Information on some of these conditioning factors can be obtained by physicalexaminations, questionnaires, or self-reports. An accurate assessmentof age is especially critical for the derivation of many anthro­pometricindices used to identify abnormal anthro­pometry, notably height-for-ageand weight-for-age. See:WHO Child Growth Standards.

Age is also important for categorizing the data intothe age groups recom­mended by WHO for analysis andinter­pretation(WHO/UNICEF, 2019);see Chapter13 for details.In more affluent countries, the assessment of age using birth certificatesis generally easy, but in some low-income countries, local calendars ofspecial events are often constructed to assist in identi­fying the birthdate of a child.

Alternatively, for young children, age is sometimesassessed by counting deciduous teeth. This method is most appro­priateat the population level because of the wide variation among individualsin the timing of deciduous eruption(Delgado etal., 1975).For individuals, bone age can be estimated from the left-hand-wristradiograph using the Tanner WhitehouseII method(Tanner etal., 1983).Gorstein(1989)has high­lighted the marked discrepancies that may occurin the prevalence estimates of undernutri­tion during infancy whendifferent methods are used to deter­mine age.

With infants, an accurateassessment of birth weight, and, if possible, birth length andgesta­tional age, is also important(Hediger etal., 1999). Assessmentof gesta­tional age is especially critical for the inter­pretation ofboth size-for-age mea­sure­ments during infancy and the neuro­develop­mentalprogress of preterm infants. It is also essential for the management ofpregnancy and the treatment of new-born infants.

Several strategies areavailable for estimating gesta­tional age. Prenatal measures ofgesta­tional age include calculating the number of completed weeks sincethe beginning of the last menstrual period, prenatal ultra­sonography,and clinical methods; they are all described inChapter10. In public health settings,the definition of gesta­tional age on the basis of the lastmenstrual period is most frequently used, although it isassociated with several problems: errors may occurbecause of irregular menses, bleeding early in pregnancy,and incorrect recall by mothers. Prenatal ultra­sonographyduring the first or second trimester, although consideredthe gold standard method for assessment of gesta­tionalage, is not universally available, especially in low-income countries.Further­more, the quality of both the equipment used and the technical training varies.

For studies of the adolescent age group, defined by WHO(1995)as 10–19y,information on maturation should also be collected in view of the markedvariation in the timing of the maturational changes during adolescence.The best measure of maturity is bone age — often termed skeletalmaturation — because it can be obtained for both sexes over a wide agerange. However, special equipment and expertise are required for theassessment of bone age. Hence, instead, surrogate measures of somaticmaturation are generally used in nutri­tion surveys. WHO(1995)recom­mends the use of two maturational events for each sex to assist ininterpreting anthro­pometric data during adolescence: one markersignaling the beginning of the adolescent growth spurt in each sex, andone indicating that the peak velocity for height and associated changeshave passed. In girls, the indicator that can be used to signal that theadolescent growth spurt has begun is the start of breast development,which precedes peak height velocity by about 1y.The marker indicating that most of the adolescent growthspurt has been completed is the attainment of menarche,which begins a little more than 1y after peak heightvelocity (Figure9.1). In boys, the corresponding indicatorssignaling the beginning and completion of the adolescent growthspurt are adolescent changes in the penis,characterizing G3, followed by the attainment of adult voice, respectively(Figure9.1).

When an assessment of somatic maturationcannot be obtained by physical examination and questioning, then a self-administeredquestionnaire containing drawings illustrating Tanner's stages ofdevelopment of breasts and pubic hair for females, or pubic hair andmale genitalia, may be used. Adolescents are requested to select thedrawing closest to their stage of development, as described in Morrisand Udry(1980).

9.4.2 Appro­priate anthropometric reference data

In public health settings, appro­priate anthro­pometric reference datafacilitate international comparisons of anthro­pometricindices across populations and enable the proportionof individuals with abnormal indices to be deter­minedrelative to the reference population. Such comparisonsenable the extent and severity of mal­nu­trition in the studygroup to be estimated. In surveillance studies, referencedata allow the evaluation of trends over time, as well asthe effectiveness of inter­vention programs to be assessed.Reference data can also be used in clinical settingsto monitor growth of individuals, detect abnormalchanges in growth, and assess response to treatment(WHO, 1995).

The WHO recom­mends the use of the WHO Child Growth Standards foryoung children from birth to 5y for international use(WHO, 2006)in view of the small effect of ethnic and genetic differences on the growth ofinfants and young children compared with theenvironmental, nutri­tional, and socio-economic effects,some of which may persist across generations.The WHO Child Growth Standard was developedas a result of the technical and biological limitationsidentified with the earlier NCHS/WHO growth reference(Garza and deOnis, 1999).A prescriptive approachdepicting physiological human growth under optimal conditionswas used for the new Child Growth Standards so they representhow young children should grow, rather than asa “reference” describing how children do grow.To achieve this goal, a set of individual eligibility criteriawere developed: term singleton infants with non-smoking mothers,a health status that did not constrain growth, and mothers whowere willing to follow current WHO feeding recom­mendations.The design combined a longitudinal study from birth to 24moswith a cross-sectional study of children aged18–71mosbased on pooled data from 6 participating countries(Brazil, Ghana, India, Norway, Oman, and the UnitedStates)(deOnis etal., 2004).WHO has developed a tool forthe application of the WHO Child Growth Standardswhich includes instructions on how to take themea­sure­ments, interpret growth indicators, investigatecauses of growth problems, and how to counsel caregivers.An anthro­pometry training video is also available. For more details, see:WHO Child Growth Training Module.

For older children, the WHO growth reference data for school-agechildren and adolescents 5–19y should be used(deOnis etal., 2007).This is a reconstruction of the original 1977 National Centre forHealth Statistics (NCHS) data set supplemented withdata from the WHO Child Growth Standard. The statisticalmethodology used to construct this reference was thesame as that used for the WHO Child Growth Standard.

A series of prescriptive standards for monitoring fetal,newborn growth, and gesta­tional weight gain have alsobeen developed for international use by theINTERGROWTH‑21^st project.This project adhered to the WHO recom­mendations forassessing human size and growth, and followed healthypregnant women longitudinally from 9wks of fetal lifeto 2y(Papageorghiou etal., 2018; Ismail etal., 2016).Populations from urban areas of 8 countries in whichmaternal health care and nutri­tional needs were met(Brazil, China, India, Italy, Kenya, Oman, the UK and the USA)were involved to ensure universal multi-ethnic growthstandards were generated that represent how fetusesshould grow. Postnatal growth standards for preterminfants were also developed by this group(Villar etal., 2015).

Updated childhood growth charts have been preparedby the Center for Disease Control (CDC) for U.S children for two age groups:0–36mos and2–20y.These CDC2000 growth charts are based primarily onphysical measure­ments taken duringfive nationally represen­tative surveys conducted between 1963 and 1994,although some supplemental data were also used.When creating these revised growth charts, two data sets were excluded:growth data for very low birthweight infants (<1500g)whose growth differs from that of normal birth-weight infants,and weight data for children >6y who participatedin the NHANESIII survey. The latter data wereexcluded from both the revised weight and BMI growthcharts because their inclusion shifted the upper per­cen­tile curves.Hence, the exclusion of these selected data resultedin a modified growth reference that is not a purelydescriptive growth reference because it does notcontain represen­tative national data for all variables(Kuczmarski etal., 2000).A comparison of theseCDC 2000 Growth Charts with the WHO Child GrowthStandards is available in deOnis etal.(2007).

For body compo­sition indices, use of localreference data are preferred becauseracial differences exist in both body proportions, and theamount and distri­bution of sub­cu­taneous andintra-abdominal fat(Wagner and Heyward, 2000;He etal., 2002;Lim etal., 2019).In practice, however, only a few countrieshave local body compo­sition referencedata. In the absence of such local data,WHO recom­mend the use of the reference datafor mid-upper-arm circum­ference (MUAC),triceps and subscapular skinfolds based on thedata collected during the WHO MGRS on children age0–5y.Electronic copies of the WHO tables and charts ofper­cen­tiles and Z‑scores for MUAC‑for-age,triceps-for-age, and subscapular-for age by sexare available for children from age 3mos to 71mosand are included inWHO Child Growth Standards.

In clinical settings, abnormal changes in the rate ofgrowth of a child can be detected much earlier whengrowth velocity charts, rather than distance growth charts,are used; see Chapter13 for more details.Growth velocity charts are based on longitudinalstudies during which the same child is measured serially,and the growth rate calculated for each interval.WHO has developed a set of growth velocity charts basedon the WHO MGRS described earlier forinternational use; see deOnis etal.(2011)for more details.

Several different distance growth standards have beencompiled, depending on the specific deter­minants ofgrowth. For example, the new international postnatalgrowth standards should be used for preterminfants(Villar etal., 2015)in view of the differencein weight, length, and head circum­ference between pretermand full-term infants. More­over, the time period over which thesedifferences extend varies with the growth mea­sure­ment.Differences are significant until 18 mos for head circum­ference,until 24mos for weight-for-age, and up to 3.5y for length/height-for-age.

Alternatively, tempo-conditional growth charts can be used for monitoring thegrowth of individual children during adolescence(Figure9.2).

These growth charts are based on mixed cross-sectionaland longitudinal data, and take into account differencesin the timing of the adolescent growth spurt — termedthe “phase difference effect”(Tanner & Buckler, 1997).

Parent-allowed-for growth reference data are available forchildren from2–9y when nonfamilial short stature isof concern. Cole(2000)developed a novel parent-allowed-for-heightchart that adjusts for mid-parent, single parent, or siblingheight based on the UK90-height reference. Specialgrowth charts have also been compiled for childrenwith certain genetic disorders such as Down's syndromeor other developmental disorders in which growth patternsdiffer from the reference growth curves.

9.4.3 Methods of evaluating anthropometric indices

For studies of both individuals and populations, the anthro­pometricindices can be compared to the reference population usingper­cen­tiles or Z‑scores derived from the distri­butionof the anthropometric reference data. A per­cen­tile refers to the positionof the mea­sure­ment value in relation to all the mea­sure­mentsfor the reference population, ranked in order of magni­tude.A Z‑score (or standard deviation score) measuresthe deviation of the value for an individual from the medianvalue of the reference population, divided by the standarddeviation of the reference, as shown below:\[\small \mbox{Z‑score or SD score = } \frac {\mbox {(observed value) − (median reference value)}}{\mbox {(standard deviation of reference population)}}\]In most industrialized countries, per­cen­tiles are used becauseno errors are introduced if the data have a skewed distri­bution.Weight-for-age, weight-for-height, and many circumferentialand skinfold indices have skewed distri­butions.

per­cen­tiles are not appro­priate for use inlow- and middle-income countries (LMICs) wheremany children may fall below the lowest per­cen­tile.

In these settings Z‑scores should be usedbecause they can be calculated accurately beyond thelimits of the original reference data. Hence, individualswith indices below the extreme per­cen­tiles of thereference data can then be classified accurately.For more discussion of per­cen­tiles and Z‑scores, see Chapter13.Both per­cen­tiles and Z‑scores based on the WHOChild Growth Standards (0-5y) and the WHOGrowth Reference for school-aged children andadolescents (5-19y) can be readily calculatedin population studies using the WHO softwareprogram: WHO AnthroPlus (2009). Alternatively, forindividuals in clinical settings, the per­cen­tile orZ‑score range within which the mea­sure­ment of anindividual falls can be read from sex-specificcharts or tables of the appro­priate reference data, as shown inFigure9.3.

Three methods have been recom­mended by WHO to evaluatecross-sectional anthro­pometric data for use inpublic health; these are summarized in Box9.4.

Figure9.4

is an example of the first method summarizedin Box9.4. Here the frequency distri­bution of theZ‑scores for height-for-age children from the IndianNational Family Health Survey (2005-2006) are comparedwith the corresponding reference distri­bution of height-for-ageZ‑scores for the WHO Child Growth Standards.The figure high­lights that nearly all the children surveyedwere affected by some degree of linear growth retardationand would benefit from an inter­vention; this approach is termed a “population approach to targeting”.

Summary statistics can also be used whenanthro­pometric indices are expressed as Z‑scoresin population studies. In the example given in Figure9.4,the calculated mean height-for-age Z‑score (-1.83)for the children was markedly lower than zero —the expected value for the reference distri­bution.This statistic alone indicatesthat the entire distri­bution has shifted downward,suggesting that most, if not all of the individuals,are affected by linear growth retardation, as was clearfrom the frequency distri­bution of the height-for-ageZ‑scores comparedwith the corresponding reference distri­bution depicted, in Figure9.4.

Note, summary statisticscannot be calculated in this way for data from a populationexpressed in terms of per­cen­tiles which are often not normally distributed.

The third method itemized in Box9.4 involvescalculating the per­cent­age of individuals withanthro­pometric indices below or abovepredeter­mined reference limits or cutoff points.When used in this way, the anthro­pometric indexand its associated reference limit or cutoff pointare termed an “indicator” as described earlier.This approach is used to classify individuals as “at risk”to mal­nu­trition and isused by governments and International Agencies (e.g., WHO and UNICEF)to generate prevalence estimates of malnutritionfor information and comparisons across countries,as well as for advocacy. The approach is describedin more detail below.

9.4.4 Classification systems

Classification systems are used in both clinicalsettings and in public health. All use at least oneanthro­pometric mea­sure­ment and one or morereference limit derived from appro­priate referencedata (i.e., indicator) to classify at risk individuals.Alternatively, cut-off points are used. In practice,classification schemes are not perfect, somemisclassification will always occur so that someindividuals identified as “at risk” tomal­nu­trition will not be truly malnourished (false positives),and others classified as “not at risk” tomal­nu­trition will in fact be malnourished (false negatives).Misclassification arises because there is alwaysbiological variation among individuals (and hence inthe normal levels defined by themea­sure­ment)(Fraser, 2004); see Chapter13 for more details.

Reference limits for anthro­pometric indices arederived from a reference distri­bution and canbe expressed in terms of Z‑scores or per­cen­tiles.In low income countries reference limits defined byZ‑scores are frequently applied, with scoresbelow −2or above &plus;2Z‑scoresof the WHO Child Growth Standard or WHO Growth Referenceused to designate individuals with eitherunusually low or unusually high anthro­pometricindices(WHO, 1995).This approach is usedbecause statistically 95% of the international referencepopulation fall within the central range assumed tobe “healthy”.Therore, theoretically,the proportion of children with a Z‑score lessthan −2or greater than&plus;2 in astudy population should be ≈2.3%.Clearly, if the proportion in the study populationwith such low or high Z‑scores is significantlygreater than this, then the study population isseriously affected. The WHO uses thebelow −2Z‑scores of the whor*ference median for weight-for-age, length/height-for-age,or weight-for-height to classify children as under­weight,stunted, or wasted, respectively.

In industrialized countries,the per­cen­tiles commonly used for designatingindividuals as “at risk” to mal­nu­tritionare either below the 3^rd or 5^th andabove the 97^th or 95^th per­cen­tiles.The limits chosen depend on the reference data used: see Chapter13 for more details.

There is nothing immutable about a referencelimit at −2Z‑score, despite theiruse by countries and agencies to generate prevalenceestimates, most notably for stunting and wasting.As a consequence, attempts have been made toestablish “cutoffs” for someanthropometric indicators to improve the abilityto discriminate between children who aremalnourished and those who are “healthy”.These cutoffs have beenestablished by a review of the anthro­pometriccharacteristics of individuals with either clinicallymoderate or severe mal­nu­trition or who subsequently die.However, many other characteristics of individuals suchas age, sex, life-stage, race/ethnicity, genetics, andmorbidity or nutri­tional status may affect the relationship under study(Hondru etal., 2019; Yaghootkar etal., 2020; Wright etal., 2021).Hence, in practice, defining cutoffs is difficult becausethe relationship between the indices and the biologicalfactors cannot be generalized from one region to another(WHO Expert Consultation, 2004)..Consequently, in some studies universal cutoff points are used,whereas in others the methods used to identifythe cutoff points applied are not always well documented.

As an example in a study of low BMI and morbidityin Pakistan, a reported a cutoff of <18.5 wasassociated with higher morbidity, whereas in Calcuttait was <16.0(Campbell and Ulijaszek, 1994;Kennedy and Garcia, 1994).

Cut-offs for BMI, waistcircum­ference, and waist-hip ratio associatedwith risk of cardiovascular disease and type2 diabeteshave also been extensively investigated among differentethnic groups(Ding etal., 2020; WHO Expert Consultation, 2004; Lear etal., 2010).Some expert groups have defined lower waist circumference cutoffs for adults ofAsian descent compared to Europeans(IDF, 2006).

Receiver operating characteristic (ROC) curves are oftenused to deter­mine cutoff points. This is a graphical methodof comparing indices and portraying the trade-offs thatoccur in the sensitivity and specificity of a mea­sure­mentor index when the cutoffs are altered.To use this approach, a spectrum of cutoffs over theobserved range of the indicator results is used, and thesensitivity and specificity for each cutoff calculated.Next, the sensitivity (or true-positive) rate is plotted on thevertical axis against the true negative rate (1−specificity)on the horizontal axis for each cutoff point, as shown in(Figure9.6).

The closer the curve follows the left-hand of the ROC space,the more accurate is the cutoff under study indistinguishing the health or nutri­tional status conditionunder investigation from optimal status. The optimalROC curve is the line connecting the points highest and farthestto the left of the upper corner. The closer the curve comesto the 45° diagonal of the ROC space,the less accurate the indicator cutoffs(Søreide, 2009).Most statistical programs (e.g., SPSS) provide ROC curve analysis.Details of alternative statistical methods for selecting thebest cutoff point are given in Brownie etal.(1986).

The choice of the cutoff may vary depending on the circ*mstances.When resources are scarce, a low cutoff point may be selected.As a consequence, the sensitivity decreases, which mean thatmore truly malnourished children are missed. However, at thesame time, the specificity increases, which means thatfewer well-nourished children are misdiagnosed as malnourished.Conversely, when resources are generous, the cutoffcan be high, because it does not matter if some children receivetreatment when they do not need it. The inverse relationshipbetween sensitivity and specificity and relative risk of mortalityassociated with various values for MUAC in children6–36mosin rural Bangladesh is shown inTable9.4.

More details of the inter-relationships between these variables is given in Chapter1.

Unfortunately, because sensitivity and specificity datafor the anthro­pometric indices selected are usuallynot known for the population under study, the datarequired to plot ROC curves areoften obtained elsewhere, even though the valuesmay not be appro­priate for the population understudy because of the many factors known toinfluence cutoff values, as noted earlier; see Chapter13for more details. Ultimately the choice of anindex and an associated cutoff point (i.e., an indicator)depends on the resources available and the purpose for which it is being used.The latter can range from screening for disease or monitoringto detect changes in prevalence of mal­nu­trition etc.For more discussion on the selection of anthro­pometric indicators, see Brownie etal.(1986)and WHO(1995).

Screening tools to identify those who are malnourishedin clinical or public health settings can be basedon single or multiple mea­sure­ments and associatedreference limits or cutoff points.An example of a screening tool used in clinical settings is theMal­nu­trition Universal Screening Tool (MUST),widely used to identify adults who are at riskof undernutri­tion or obesity (See Chapter27).MUST is based on height and weight mea­sure­mentsfrom which both a BMI score (from 3 cutoffs)assessed via a chart and weight loss in previous3–6mos,can be derived, followed by establishing acute diseaseeffect and score. From the sum of the three scores,the overall risk of mal­nu­trition is calculated,with management guidelines provided according to the levelof risk (low; medium; high). More details are available:Mal­nu­trition Universal Screening Tool .

In public health emergencies, a screening toolbased on a single mea­sure­ment (i.e., MUAC)and associated cutoff (i.e., <115mm)is often used to identify severe acute mal­nu­trition (SAM)in children 6–60mos. This MUAC cutoff was chosenbecause children with a MUAC <115mm wereobserved to have a highly elevated risk of death compared to those with aMUAC >115mm(Myatt etal., 2006).

For defining over­weightand obesity in children and adolescents,WHO recom­mends the use of BMI-for-ageZ‑scores and reference limits based on Z‑scores.For children (0–5y), a Z‑score for BMI-for-ageabove&plus;1 is described as being “at riskof over­weight”, above&plus;2 as “over­weight”,and above&plus;3 as “obese”based on the WHO Child Growth Standard. Forchildren 5–19y,BMI-for-age Z‑scores above&plus;1and above&plus;2 based on the WHO 2007growth reference data are recom­mended(deOnis & Lobstein, 2010).To classify over­weight and obesity in adults,however, WHO recom­mends a graded classification scheme, as shown in(Table9.5).

Increasingly, it is recognized that in low-income countries,multiple anthropometric deficits may occursimultaneously in children and amplify theirrisk of morbidity and mortality. Consequently, a composite index of anthropometricfailure (CIAF) has been developed, andis described in Chapter13.

In public health, screening tools are also used to mapcountries according to levels of severity ofmal­nu­trition(UNICEF/WHO/World Bank, 2021)in orderto identify priority countries.Five prevalence thresholds (as %) basedwasting (i.e., WHZ <−2),over­weight (BMIZ >&plus;2), andstunting (i.e., HAZ <−2) have beendeveloped by WHO and UNICEF; these aredepicted in(Table9.6).The fifth thresholdlabelled “very low” and of no publichealth concern was included across all threeindicators to reflect the expected prevalenceof 2.3% (rounded to 2.5%) below/above 2SDsfrom the median of the WHO Child Growth Standard.

The number of countries in different thresholdcategories for wasting, over­weight, and stunting,also shown in(Table9.6),is based on data from 134countries.Comparison of the prevalence estimatesfor each anthro­pometric indicator can triggercountries to identify the most appro­priateinter­vention program toachieve “low” or “very low” prevalence thresholds.

Details of the techniques used to measure body size andbody compo­sition, together with the indices derived from thesemea­sure­ments, are discussed in Chapters10 and11,whereas Chapter13 discussesmethods used for evaluation of anthro­pometric indicesand their application at the individual and population level.