8.4: Inter­pretation and evaluation of anthro­pometric data

Anthro­pometric indices are derived from two or more raw mea­sure­ments, as noted earlier. Normally, it is these indices that are interpreted and evaluated — not the raw mea­sure­ments. Anthro­pometric indices can be used at both the individual and the population levels to assess nutri­tional status and to screen and assess a response during inter­ventions. In addition, in populations, anthro­pometry can be used to identify the deter­minants and consequences of mal­nu­trition and for nutri­tional surveillance. To achieve these objectives, knowledge of factors that may modify or “condition” the inter­pretation of abnormal anthro­pometric indices is generally required. These conditioning factors are briefly discussed below, together with anthropometric reference data and methods to evaluate anthropometric indices, including classification systems that identify individuals and populations as “at risk” for mal­nu­trition.

9.4.1 Conditioning factors

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

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

Age is also important for categorizing the data into the age groups recom­mended by WHO for analysis and inter­pretation (WHO/UNICEF, 2019); see Chapter 13 for details. In more affluent countries, the assessment of age using birth certificates is generally easy, but in some low-income countries, local calendars of special events are often constructed to assist in identi­fying the birth date of a child.

Alternatively, for young children, age is sometimes assessed by counting deciduous teeth. This method is most appro­priate at the population level because of the wide variation among individuals in the timing of deciduous eruption (Delgado et al., 1975). For individuals, bone age can be estimated from the left-hand-wrist radiograph using the Tanner Whitehouse II method (Tanner et al., 1983). Gorstein (1989) has high­lighted the marked discrepancies that may occur in the prevalence estimates of undernutri­tion during infancy when different methods are used to deter­mine age.

With infants, an accurate assessment of birth weight, and, if possible, birth length and gesta­tional age, is also important (Hediger et al., 1999). Assessment of gesta­tional age is especially critical for the inter­pretation of both size-for-age mea­sure­ments during infancy and the neuro­develop­mental progress of preterm infants. It is also essential for the management of pregnancy and the treatment of new-born infants.

Several strategies are available for estimating gesta­tional age. Prenatal measures of gesta­tional age include calculating the number of completed weeks since the beginning of the last menstrual period, prenatal ultra­sonography, and clinical methods; they are all described in Chapter 10. In public health settings, the definition of gesta­tional age on the basis of the last menstrual period is most frequently used, although it is associated with several problems: errors may occur because of irregular menses, bleeding early in pregnancy, and incorrect recall by mothers. Prenatal ultra­sonography during the first or second trimester, although considered the gold standard method for assessment of gesta­tional age, 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 marked variation in the timing of the maturational changes during adolescence. The best measure of maturity is bone age — often termed skeletal maturation — because it can be obtained for both sexes over a wide age range. However, special equipment and expertise are required for the assessment of bone age. Hence, instead, surrogate measures of somatic maturation are generally used in nutri­tion surveys. WHO (1995) recom­mends the use of two maturational events for each sex to assist in interpreting anthro­pometric data during adolescence: one marker signaling the beginning of the adolescent growth spurt in each sex, and one indicating that the peak velocity for height and associated changes have passed. In girls, the indicator that can be used to signal that the adolescent 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 growth spurt has been completed is the attainment of menarche, which begins a little more than 1y after peak height velocity (Figure 9.1). In boys, the corresponding indicators signaling the beginning and completion of the adolescent growth spurt are adolescent changes in the penis, characterizing G3, followed by the attainment of adult voice, respectively (Figure 9.1).

Figure 9.1. Approximate timing of recom­mended maturational events relative to peak height velocity (PHV) in boys and girls. B2 = Start of breast development. Identified by examination. Precedes PHV by about 1y. Indicates adolescent spurt has begun. Menarche. Determined by questioning. Menstruation usually begins a little more than 1y after PHV. Indicates that most of the adolescent spurt has been completed. G3 = Adolescent changes in the penis, characterizing G3. Identified by examination. Precedes PHV by about 1y. Indicates adolescent spurt has begun. AV= Attainment of adult voice. Determined by questioning. Usually attained about 1y after PHV. Indicates that most of the adolescent spurt has been completed. Redrawn from: WHO (1995).

When an assessment of somatic maturation cannot be obtained by physical examination and questioning, then a self-administered questionnaire containing drawings illustrating Tanner's stages of development of breasts and pubic hair for females, or pubic hair and male genitalia, may be used. Adolescents are requested to select the drawing closest to their stage of development, as described in Morris and Udry (1980).

9.4.2 Appro­priate anthropometric reference data

In public health settings, appro­priate anthro­pometric reference data facilitate international comparisons of anthro­pometric indices across populations and enable the proportion of individuals with abnormal indices to be deter­mined relative to the reference population. Such comparisons enable the extent and severity of mal­nu­trition in the study group to be estimated. In surveillance studies, reference data allow the evaluation of trends over time, as well as the effectiveness of inter­vention programs to be assessed. Reference data can also be used in clinical settings to monitor growth of individuals, detect abnormal changes in growth, and assess response to treatment (WHO, 1995).

The WHO recom­mends the use of the WHO Child Growth Standards for young children from birth to 5y for international use (WHO, 2006) in view of the small effect of ethnic and genetic differences on the growth of infants and young children compared with the environmental, nutri­tional, and socio-economic effects, some of which may persist across generations. The WHO Child Growth Standard was developed as a result of the technical and biological limitations identified with the earlier NCHS/WHO growth reference (Garza and de Onis, 1999). A prescriptive approach depicting physiological human growth under optimal conditions was used for the new Child Growth Standards so they represent how young children should grow, rather than as a “reference” describing how children do grow. To achieve this goal, a set of individual eligibility criteria were developed: term singleton infants with non-smoking mothers, a health status that did not constrain growth, and mothers who were willing to follow current WHO feeding recom­mendations. The design combined a longitudinal study from birth to 24mos with a cross-sectional study of children aged18–71mos based on pooled data from 6 participating countries (Brazil, Ghana, India, Norway, Oman, and the United States) (de Onis et al., 2004). WHO has developed a tool for the application of the WHO Child Growth Standards which includes instructions on how to take the mea­sure­ments, interpret growth indicators, investigate causes 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-age children and adolescents 5–19y should be used (de Onis et al., 2007). This is a reconstruction of the original 1977 National Centre for Health Statistics (NCHS) data set supplemented with data from the WHO Child Growth Standard. The statistical methodology used to construct this reference was the same as that used for the WHO Child Growth Standard.

A series of prescriptive standards for monitoring fetal, newborn growth, and gesta­tional weight gain have also been developed for international use by the INTERGROWTH‑21^st project. This project adhered to the WHO recom­mendations for assessing human size and growth, and followed healthy pregnant women longitudinally from 9wks of fetal life to 2y (Papageorghiou et al., 2018; Ismail et al., 2016). Populations from urban areas of 8 countries in which maternal 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 growth standards were generated that represent how fetuses should grow. Postnatal growth standards for preterm infants were also developed by this group (Villar et al., 2015).

Updated childhood growth charts have been prepared by the Center for Disease Control (CDC) for U.S children for two age groups: 0–36mos and2–20y. These CDC 2000 growth charts are based primarily on physical measure­ments taken during five 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 participated in the NHANES III survey. The latter data were excluded from both the revised weight and BMI growth charts because their inclusion shifted the upper per­cen­tile curves. Hence, the exclusion of these selected data resulted in a modified growth reference that is not a purely descriptive growth reference because it does not contain represen­tative national data for all variables (Kuczmarski et al., 2000). A comparison of these CDC 2000 Growth Charts with the WHO Child Growth Standards is available in de Onis et al. (2007).

For body compo­sition indices, use of local reference data are preferred because racial differences exist in both body proportions, and the amount and distri­bution of sub­cu­taneous and intra-abdominal fat (Wagner and Heyward, 2000; He et al., 2002; Lim et al., 2019). In practice, however, only a few countries have local body compo­sition reference data. In the absence of such local data, WHO recom­mend the use of the reference data for mid-upper-arm circum­ference (MUAC), triceps and subscapular skinfolds based on the data collected during the WHO MGRS on children age 0–5y.Electronic copies of the WHO tables and charts of per­cen­tiles and Z‑scores for MUAC‑for-age, triceps-for-age, and subscapular-for age by sex are available for children from age 3mos to 71mos and are included in WHO Child Growth Standards.

In clinical settings, abnormal changes in the rate of growth of a child can be detected much earlier when growth velocity charts, rather than distance growth charts, are used; see Chapter 13 for more details. Growth velocity charts are based on longitudinal studies 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 based on the WHO MGRS described earlier for international use; see de Onis et al. (2011) for more details.

Several different distance growth standards have been compiled, depending on the specific deter­minants of growth. For example, the new international postnatal growth standards should be used for preterm infants (Villar et al., 2015) in view of the difference in weight, length, and head circum­ference between preterm and full-term infants. More­over, the time period over which these differences 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 the growth of individual children during adolescence (Figure 9.2).

Figure 9.2. Diagrammatic height per­cen­tile chart for early-, median-, and late-maturing girls. Adapted from Tanner and Buckler (1997).

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

Parent-allowed-for growth reference data are available for children from 2–9y when nonfamilial short stature is of concern. Cole (2000) developed a novel parent-allowed-for-height chart that adjusts for mid-parent, single parent, or sibling height based on the UK90-height reference. Special growth charts have also been compiled for children with certain genetic disorders such as Down's syndrome or other developmental disorders in which growth patterns differ from the reference growth curves.

9.4.3 Methods of evaluating anthropometric indices

For studies of both individuals and populations, the anthro­pometric indices can be compared to the reference population using per­cen­tiles or Z‑scores derived from the distri­bution of the anthropometric reference data. A per­cen­tile refers to the position of the mea­sure­ment value in relation to all the mea­sure­ments for the reference population, ranked in order of magni­tude. A Z‑score (or standard deviation score) measures the deviation of the value for an individual from the median value of the reference population, divided by the standard deviation of the reference, as shown below:

\[\text { Z-score or SD score }=\frac{(\text { observed value })-(\text { median reference value })}{(\text { standard deviation of reference population })}\nonumber\]

In most industrialized countries, per­cen­tiles are used because no errors are introduced if the data have a skewed distri­bution. Weight-for-age, weight-for-height, and many circumferential and skinfold indices have skewed distri­butions.

Per­cen­tiles are not appro­priate for use in low- and middle-income countries (LMICs) where many children may fall below the lowest per­cen­tile. In these settings Z‑scores should be used because they can be calculated accurately beyond the limits of the original reference data. Hence, individuals with indices below the extreme per­cen­tiles of the reference data can then be classified accurately. For more discussion of per­cen­tiles and Z‑scores, see Chapter 13. Both per­cen­tiles and Z‑scores based on the WHO Child Growth Standards (0-5y) and the WHO Growth Reference for school-aged children and adolescents (5-19y) can be readily calculated in population studies using the WHO software program: WHO AnthroPlus (2009). Alternatively, for individuals in clinical settings, the per­cen­tile or Z‑score range within which the mea­sure­ment of an individual falls can be read from sex-specific charts or tables of the appro­priate reference data, as shown in Figure 9.3.

Figure 9.3. WHO Growth chart for girls aged 2&ndash5y for height-for-age. From: Training Course on Child Growth Assessment (WHO).

Three methods have been recom­mended by WHO to evaluate cross-sectional anthro­pometric data for use in public health; these are summarized in Box 9.4.

Box 9.4. Methods recommended by WHO to assess anthropometry

• Comparison of the frequency distri­bution of anthro­pometric indices with appro­priate WHO growth reference data using Z‑scores
• Summary statistics of the Z‑scores: mean; median; SD; standard error (SE) with the 95% confidence interval (CI) for each growth indicator
• Calculation of number and proportion of individuals (as %)with anthropometric indices below or above a designated cutoff (95% CIs) for each age and sex group.

Figure 9.4 is an example of the first method summarized in Box 9.4. Here the frequency distri­bution of the Z‑scores for height-for-age children from the Indian National Family Health Survey (2005-2006) are compared with the corresponding reference distri­bution of height-for-age Z‑scores for the WHO Child Growth Standards. The figure high­lights that nearly all the children surveyed were affected by some degree of linear growth retardation and would benefit from an inter­vention; this approach is termed a “population approach to targeting”.

Figure 9.4 distri­bution of length/height-for-age Z‑scores of children from the Indian National Family Health Survey 2005–2006. Modified from de Onis and Branca (2016)

Summary statistics can also be used when anthro­pometric indices are expressed as Z‑scores in population studies. In the example given in Figure 9.4, 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 indicates that the entire distri­bution has shifted downward, suggesting that most, if not all of the individuals, are affected by linear growth retardation, as was clear from the frequency distri­bution of the height-for-age Z‑scores compared with the corresponding reference distri­bution depicted, in Figure 9.4.

Note, summary statistics cannot be calculated in this way for data from a population expressed in terms of per­cen­tiles which are often not normally distributed.

Figure 9.5 Two populations with the same mean Z‑score, but different standard deviations.

Alternatively, calculation of the mean Z‑score and SD can be used to compare directly different populations or the status of the same population at different times (Goldstein and Tanner, (1980). However, even if the populations have the same mean Z‑score, their SDs may differ, with the population with the larger SD having a greater proportion below the reference limit or cutoff point, as shown in (Figure 9.5). Here, the growth reference is not being used for comparative purposes as shown in Figure 9.4.

The third method itemized in Box 9.4 involves calculating the per­cent­age of individuals with anthro­pometric indices below or above predeter­mined reference limits or cutoff points. When used in this way, the anthro­pometric index and its associated reference limit or cutoff point are termed an “indicator” as described earlier. This approach is used to classify individuals as “at risk” to mal­nu­trition and is used by governments and International Agencies (e.g., WHO and UNICEF) to generate prevalence estimates of malnutrition for information and comparisons across countries, as well as for advocacy. The approach is described in more detail below.

9.4.4 Classification systems

Classification systems are used in both clinical settings and in public health. All use at least one anthro­pometric mea­sure­ment and one or more reference limit derived from appro­priate reference data (i.e., indicator) to classify at risk individuals. Alternatively, cut-off points are used. In practice, classification schemes are not perfect, some misclassification will always occur so that some individuals identified as “at risk” to mal­nu­trition will not be truly malnourished (false positives), and others classified as “not at risk” to mal­nu­trition will in fact be malnourished (false negatives). Misclassification arises because there is always biological variation among individuals (and hence in the normal levels defined by the mea­sure­ment) (Fraser, 2004); see Chapter 13 for more details.

Reference limits for anthro­pometric indices are derived from a reference distri­bution and can be expressed in terms of Z‑scores or per­cen­tiles. In low income countries reference limits defined by Z‑scores are frequently applied, with scores below −2 or above +2 Z‑scores of the WHO Child Growth Standard or WHO Growth Reference used to designate individuals with either unusually low or unusually high anthro­pometric indices (WHO, 1995). This approach is used because statistically 95% of the international reference population fall within the central range assumed to be “healthy”. Therore, theoretically, the proportion of children with a Z‑score less than −2 or greater than +2 in a study population should be ≈ 2.3%. Clearly, if the proportion in the study population with such low or high Z‑scores is significantly greater than this, then the study population is seriously affected. The WHO uses the below −2 Z‑scores of the WHO reference 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 designating individuals as “at risk” to mal­nu­trition are either below the 3^rd or 5^th and above the 97^th or 95^th per­cen­tiles. The limits chosen depend on the reference data used: see Chapter 13 for more details.

There is nothing immutable about a reference limit at −2 Z‑score, despite their use by countries and agencies to generate prevalence estimates, most notably for stunting and wasting. As a consequence, attempts have been made to establish “cutoffs” for some anthropometric indicators to improve the ability to discriminate between children who are malnourished and those who are “healthy”. These cutoffs have been established by a review of the anthro­pometric characteristics of individuals with either clinically moderate or severe mal­nu­trition or who subsequently die. However, many other characteristics of individuals such as age, sex, life-stage, race/ethnicity, genetics, and morbidity or nutri­tional status may affect the relationship under study (Hondru et al., 2019; Yaghootkar et al., 2020; Wright et al., 2021). Hence, in practice, defining cutoffs is difficult because the relationship between the indices and the biological factors 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 identify the cutoff points applied are not always well documented.

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

Cut-offs for BMI, waist circum­ference, and waist-hip ratio associated with risk of cardiovascular disease and type 2 diabetes have also been extensively investigated among different ethnic groups (Ding et al., 2020; WHO Expert Consultation, 2004; Lear et al., 2010). Some expert groups have defined lower waist circumference cutoffs for adults of Asian descent compared to Europeans (IDF, 2006).

Receiver operating characteristic (ROC) curves are often used to deter­mine cutoff points. This is a graphical method of comparing indices and portraying the trade-offs that occur in the sensitivity and specificity of a mea­sure­ment or index when the cutoffs are altered. To use this approach, a spectrum of cutoffs over the observed range of the indicator results is used, and the sensitivity and specificity for each cutoff calculated. Next, the sensitivity (or true-positive) rate is plotted on the vertical axis against the true negative rate (1−specificity) on the horizontal axis for each cutoff point, as shown in (Figure 9.6).

Figure 9.6. Receiver-operating characteristic curves. Three plots and their respective areas under the curve (AUC) are given. The diagnostic accuracy of marker C (white area) is better than that of B and A, as the AUC of C > B > A. X = point of best cut-off for the bio­marker. From: Søreide, 2009, with permission of the BMJ Publishing Group Ltd.

The closer the curve follows the left-hand of the ROC space, the more accurate is the cutoff under study in distinguishing the health or nutri­tional status condition under investigation from optimal status. The optimal ROC curve is the line connecting the points highest and farthest to the left of the upper corner. The closer the curve comes to 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 the best cutoff point are given in Brownie et al. (1986).

The choice of the cutoff may vary depending on the circumstances. When resources are scarce, a low cutoff point may be selected. As a consequence, the sensitivity decreases, which mean that more truly malnourished children are missed. However, at the same time, the specificity increases, which means that fewer well-nourished children are misdiagnosed as malnourished. Conversely, when resources are generous, the cutoff can be high, because it does not matter if some children receive treatment when they do not need it. The inverse relationship between sensitivity and specificity and relative risk of mortality associated with various values for MUAC in children6–36mos in rural Bangladesh is shown in Table 9.4. More details of the inter-relationships between these variables is given in Chapter 1.

Table 9.4. Sensitivity, specificity, and relative risk of death associated with various values for mid-upper-arm circumference in children 6–36mos in rural Bangladesh. Data from Briend et al.(1987).

Arm circum- ference (mm)	Sensitivity (%)	Specificity (%)	Relative Risk of death
≤ 100	42	99	48
100–110	56	94	20
110–120	77	77	11
120–130	90	40	6

Unfortunately, because sensitivity and specificity data for the anthro­pometric indices selected are usually not known for the population under study, the data required to plot ROC curves are often obtained elsewhere, even though the values may not be appro­priate for the population under study because of the many factors known to influence cutoff values, as noted earlier; see Chapter 13 for more details. Ultimately the choice of an index 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 monitoring to detect changes in prevalence of mal­nu­trition etc. For more discussion on the selection of anthro­pometric indicators, see Brownie et al. (1986) and WHO (1995).

Screening tools to identify those who are malnourished in clinical or public health settings can be based on single or multiple mea­sure­ments and associated reference limits or cutoff points. An example of a screening tool used in clinical settings is the Mal­nu­trition Universal Screening Tool (MUST), widely used to identify adults who are at risk of undernutri­tion or obesity (See Chapter 27). MUST is based on height and weight mea­sure­ments from which both a BMI score (from 3 cutoffs) assessed via a chart and weight loss in previous 3–6mos, can be derived, followed by establishing acute disease effect and score. From the sum of the three scores, the overall risk of mal­nu­trition is calculated, with management guidelines provided according to the level of risk (low; medium; high). More details are available: Mal­nu­trition Universal Screening Tool .

In public health emergencies, a screening tool based 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 chosen because children with a MUAC < 115mm were observed to have a highly elevated risk of death compared to those with a MUAC > 115mm (Myatt et al., 2006).

For defining over­weight and obesity in children and adolescents, WHO recom­mends the use of BMI-for-age Z‑scores and reference limits based on Z‑scores. For children (0–5y), a Z‑score for BMI-for-age above +1 is described as being “at risk of over­weight”, above +2 as “over­weight”, and above +3 as “obese” based on the WHO Child Growth Standard. For children 5–19y, BMI-for-age Z‑scores above +1 and above +2 based on the WHO 2007 growth reference data are recom­mended (de Onis & Lobstein, 2010). To classify over­weight and obesity in adults, however, WHO recom­mends a graded classification scheme, as shown in (Table 9.5).

Table 9.5. WHO classification of obesity in adults according to body mass index (BMI). From: WHO (2000).

Classification	BMI (kg/m^2)	Risk of comorbidities
Under­weight	< 18.50	Low (but risk of clinical problems is increased)
Normal range	18.50–24.99	Average
Over­weight	≥ 25.00
Pre-obese	25.00–29.99	Increased
Obese class I	30.00–34.99	Moderate
Obese class II	35.00–39.99	Severe
Obese class III	≥ 40.00	Very severe

Increasingly, it is recognized that in low-income countries, multiple anthropometric deficits may occur simultaneously in children and amplify their risk of morbidity and mortality. Consequently, a composite index of anthropometric failure (CIAF) has been developed, and is described in Chapter 13.

In public health, screening tools are also used to map countries according to levels of severity of mal­nu­trition (UNICEF/WHO/World Bank, 2021) in order to identify priority countries. Five prevalence thresholds (as %) based wasting (i.e., WHZ < −2), over­weight (BMIZ > +2), and stunting (i.e., HAZ < −2) have been developed by WHO and UNICEF; these are depicted in (Table 9.6). The fifth threshold labelled “very low” and of no public health concern was included across all three indicators to reflect the expected prevalence of 2.3% (rounded to 2.5%) below/above 2 SDs from the median of the WHO Child Growth Standard.

Table 9.6. Prevalence thresholds, corresponding labels, and the number of countries (n) in different preva­lence threshold categories for wasting, over­weight and stunting in children under 5y using the “novel approach”. From: de Onis et al. (2018).

Wasting			Over­weight			Stunting
Prevalence thresholds (%)	Labels	(n)	Prevalence thresholds (%)	Labels	(n)	Prevalence thresholds (%)	Labels	(n)
< 2·5	Very low	36	< 2·5	Very low	18	< 2·5	Very low	4
2·5 – < 5	Low	33	2·5 – < 5	Low	33	2·5 – < 10	Low	26
5 – < 10	Medium	39	5 – < 10	Medium	50	10 – < 20	Medium	30
10 – < 15	High	14	10 – < 15	High	18	20 – < 30	High	30
≥ 15	Very high	10	≥ 15	Very high	9	≥ 30	Very high	44

The number of countries in different threshold categories for wasting, over­weight, and stunting, also shown in (Table 9.6), is based on data from 134 countries. Comparison of the prevalence estimates for each anthro­pometric indicator can trigger countries to identify the most appro­priate inter­vention program to achieve “low” or “very low” prevalence thresholds.

Details of the techniques used to measure body size and body compo­sition, together with the indices derived from these mea­sure­ments, are discussed in Chapters 10 and 11, whereas Chapter 13 discusses methods used for evaluation of anthro­pometric indices and their application at the individual and population level.