Who global database on child growth and malnutrition

11.7 Nepal 1975, 1995, 1996 69.1, 48.7, 46.9 Falling -1.06 Nicaragua 1980-82, 1993 10.0, 11.9 Static +0.17 Niger 1985, 1992 49.4, 42.6 Falling -0.97 Nigeria 1990, 1993 35.3, 39.1 Rising +1.27 Oman 1991, 1994-95 24.3, 14.1 Falling -2.55 Pakistan 1977, 1985-86, 1990- 52.8, 48.8, 40.2, Falling -0.81 91, 1995 38.2 Panama 1980, 1992 15.8, 6.1 Falling -0.81 Peru 1975, 1984, 1991-92, 16.1, 13.4, 10.7, Static, -0.30 1996 7.8 falling -0.47 Philippines 1971-75, 1982, 1987, 49.9, 33.2, 32.9, Falling -1.13 1989-90, 1992, 1993 33.5, 33.4, 29.6 Rwanda 1976, 1992 27.9, 29.4 Static +0.09 42 WHO/NUT/97.4 Country Year of survey % <-2 SD Trend Rate weight-for-age (pp/yr) Russian Federation 1993, 1995 4.2, 3.0 Falling -0.60 Senegal 1986, 1991-92, 1992-93 22.0, 21.6, 22.2 Static +0.03 Sierra Leone 1974-75, 1977-78, 28.2, 23.2, 26.8, Falling, -1.67 1989, 1990 28.7 rising +0.46 Solomon Islands 1970, 1989 21.1, 21.3 Static +0.01 Sri Lanka 1977-78, 1987, 1993 54.3, 37.3, 37.7 Falling, -1.89 static +0.07 Togo 1976-77, 1988 26.1, 24.6 Static -0.14 Trinidad and Tobago 1976, 1987 16.3, 6.7 Falling -0.87 Tunisia 1973-75, 1988, 1994-95 20.2, 10.3, 9.0 Falling -0.56 Turkey 1993, 1995 10.4, 10.3 Static -0.05 Uganda 1988-89, 1995 23.0, 25.5 Rising +0.42 United Republic of 1991-92, 1996 28.9, 30.6 Rising +0.43 Tanzania Uruguay 1987, 1992-93 7.4, 4.4 Falling -0.50 Venezuela 1981-82, 1987, 1990, 10.2, 4.5, 7.7, 6.2, Falling, -1.14 1991, 1992, 1993, 5.1, 4.6, 4.5 rising, +1.07 1994 falling -0.80 Viet Nam 1983-84, 1987-89, 1994 51.5, 45.0, 44.9 Falling, -1.30 static -0.02 Zambia 1992, 1996-97 25.2, 23.5 Falling -0.43 Zimbabwe 1988, 1994 11.5, 15.5 Rising +0.67 Table 10 National country survey data on trends of stunting (<-2 SD height-for- age) in children under five years of age Country Year of survey % <-2 SD Trend Rate height-for-age (pp/yr) Algeria 1987, 1992, 1995 12.4, 18.1, 18.3 Rising, +1.14 static +0.07 Bangladesh 1982-83, 1985-86, 67.7, 67.5, Falling, -0.44 1989-90, 1992, 1996-97 64.6, 64.2, 54.6 falling -1.43 Bolivia 1981, 1989, 1993-94 42.7, 37.7, 26.8 Falling, -1.60 falling -2.18 Brazil 1975, 1989, 1996 32.0, 15.4, 10.5 Falling, -1.19 falling -0.70 Cameroon 1977-78, 1991 35.6, 26.0 Falling -0.74 Cape Verde 1983, 1985 15.3, 25.8 Rising +5.25 Central African 1994-95, 1995 33.6, 28.4 Falling -5.20 Republic 43 WHO Global Database on Child Growth and Malnutrition Country Year of survey % <-2 SD Trend Rate height-for-age (pp/yr) Chile 1984, 1985, 1986, 1993, 9.9, 9.5, 9.6, 6.6, Falling, -0.73 1994, 1995 2.6, 2.4 static -0.20 Colombia 1965-66, 1977-80, 31.9, 22.4, Falling, -0.79 1986, 1989, 1995 25.3, 16.6, 15.0 rising, +0.32 falling, -2.90 static -0.26 Comoros 1991-92, 1995 33.0, 33.8 Static +0.27 Costa Rica (1st grade 1979, 1981, 1983, 20.4, 15.4, 12.7, Falling -1.12 schoolchildren) 1985, 1989 11.3, 9.2 Cụte d’Ivoire 1986, 1994 17.2, 24.4 Rising +0.90 Dominican Republic 1986, 1991 20.6, 16.5 Falling -0.82 Egypt 1978, 1988, 1990, 37.7, 30.9, 30.0, Falling, -0.95 1992-93, 1994-95, 26.0, 21.6, 29.8 rising +8.20 1995-96 El Salvador 1988, 1993 29.9, 23.1 Falling -1.36 Ethiopia (rural) 1983, 1992 59.8, 64.2 Rising +0.49 Ghana 1987-88, 1988, 1993-94 30.5, 29.4, 25.9 Falling -0.77 Guatemala 1987, 1995 57.7, 49.7 Falling -1.00 Guyana 1971, 1981 23.7, 20.7 Static -0.30 Haiti 1978, 1990, 1994-95 39.6, 33.9, 31.9 Falling -0.45 Honduras 1987, 1991-92, 1993-94 37.2, 36.3, 39.6 Static, -0.18 rising +1.65 India (rural) 1974-79, 1988-90, 72.3, 62.1, 61.2 Falling -0.85 1991-92 Jamaica 1978, 1989, 1991, 12.1, 8.7, 6.2, Falling, -0.45 1992, 1993 10.6, 9.6 rising, +4.40 falling -1.00 Kenya 1978-79, 1993, 1994 35.4, 33.3, 33.6 Static -0.12 Lao People’s 1993, 1994 48.0, 47.3 Falling -0.70 Democratic Republic Lesotho 1976, 1992, 1994 41.4, 33.0, 32.9 Falling, -0.53 static -0.05 Madagascar 1983-84, 1992, 33.8, 54.1, Rising, +2.54 1993-94, 1995 48.6, 49.8 falling, -2.75 rising +0.60 Malawi 1981, 1992, 1995 56.4, 49.2, 48.3 Falling, -0.65 static -0.30 Mali 1987, 1995-96 23.8, 30.1 Rising +0.70 Mauritania 1988, 1990-91, 1995-96 34.0, 56.9, 44.0 Rising, +7.63 falling -2.58 44 WHO/NUT/97.4 Country Year of survey % <-2 SD Trend Rate height-for-age (pp/yr) Mauritius 1985, 1995 21.5, 9.7 Falling -1.18 Mexico (rural) 1974, 1979, 1988, 42.6, 26.7, 36.4, Falling, -3.18 1989 35.1 rising, +1.08 falling -1.30 Morocco 1987, 1992 24.9, 24.2 Static -0.14 Myanmar 1980-81, 1983-85, 48.0, 49.7, 40.0, Rising, +0.43 1991, 1994 44.6 falling, -1.62 rising +1.53 Nepal 1975, 1995, 1996 69.4, 63.5, 48.8 Falling -0.98 Nicaragua 1980-82, 1993 21.7, 23.7 Static +0.18 Niger 1985, 1992 37.7, 39.5 Static +0.26 Nigeria 1990, 1993 42.7, 39.0 Falling -1.23 Oman 1991, 1994-95 20.7, 15.7 Falling -1.25 Pakistan 1977, 1985-87, 1990-91, 67.0, 57.9, 49.6 Falling -1.24 Panama 1980, 1992 22.0, 9.9 Falling -1.01 Peru 1975, 1984, 1991-92, 39.7, 37.8, 31.8, Falling -0.66 1996 25.8 Philippines 1971-75, 1982, 1987, 55.3, 42.8, 38.6, Falling -1.26 1989-90, 1992, 1993 37.2, 34.7, 32.7 Rwanda 1976, 1992 36.6, 48.7 Rising +0.76 Russian Federation 1993, 1995 17.0, 12.7 Falling -2.15 Senegal 1986, 1991-92, 1992-93 23.0, 29.1, 24.7 Rising, +1.02 falling -4.40 Sierra Leone 1974-75, 1977-78, 34.1, 42.8, Rising, +2.90 1989, 1990 35.2, 34.7 falling -0.69 Solomon Islands 1970, 1989 25.7, 27.3 Static +0.08 Sri Lanka 1975-76, 1977-78, 49.9, 44.6, Falling -1.54 1980-82, 1987, 1993 36.2, 27.2, 23.8 Togo 1976-77, 1988 33.7, 33.6 Static -0.01 Trinidad and Tobago 1976, 1987 12.4, 4.8 Falling -0.69 Tunisia 1973-75, 1988, 1994-95 39.5, 17.9, 22.5 Falling, -1.66 rising +0.66 Uganda 1988-89, 1995 44.4, 38.3 Falling -1.02 United Republic of 1991-92, 1996 43.2, 43.4 Static +0.20 Tanzania Uruguay 1987, 1992-93 15.9, 9.5 Falling -1.07 Venezuela 1981-82, 1987, 1990, 6.4, 4.6, 13.8, Falling, -0.36 1991, 1992, 1993, 1994 13.5, 13.6, 12.8, rising, +3.07 13.2 static -0.15 45 WHO Global Database on Child Growth and Malnutrition 5 Methods and standardized data presentation The information included in the WHO Global Database on Child Growth and Malnutrition complies with the following standardized format: n systematic use of the NCHS/WHO international reference population (25); n display of growth retardation prevalence for under-5-year-olds, as measured by the proportion of weight-for-age, height-for-age and weight-for-height below -2 and -3 standard deviations (SDs) (Z- scores); n display of the prevalence of overweight, as measured by the proportion of children with weight-for-height above +2 Z-scores; n display of Z-score means and SDs for the three indices; and n stratification of the results according to age, sex, region, and rural/ urban strata. The required criteria for entering surveys in the database are: n A clearly defined population-based sampling frame, permitting inferences to be drawn about an entire population; n A probabilistic sampling procedure involving at least 400 children (allowing for an estimation of prevalence with a random error of •5% at a confidence level of 95%); n Use of appropriate equipment and standard measurement techniques (25); n Presentation of results as Z-scores in relation to the NCHS/WHO reference population. For those surveys where results are presented using a different classification system, reference population, or prevalence cut-offs, the principal investigators are contacted and encouraged to re-analyze their data sets following WHO standardized presentation or, otherwise, to provide the raw data to the WHO Programme of Nutrition for re-analysis. Survey results are systematically checked for inconsistencies and these are brought to the attention of the investigators, with a request for clarification. A hard copy of the survey documentation, together with any corrigendum or additional item of information received from the Country Year of survey % <-2 SD Trend Rate height-for-age (pp/yr) Viet Nam 1983-84, 1987-89, 1994 59.7, 56.5, 46.9 Falling -1.28 Zambia 1992, 1996-97 39.8, 42.4 Rising +0.65 Zimbabwe 1988, 1994 29.0, 21.4 Falling -1.27 46 WHO/NUT/97.4 authors is filed under the survey reference number. The aim is to keep the database as fully documented and comprehensive as possible, so that queries concerning compiled data can be answered quickly. 5.1 Child growth indicators and their interpretation In children the three most commonly used anthropometric indices to assess their growth status are weight-for-height, height-for-age and weight-for-age. These anthropometric indices can be interpreted as follows: Low weight-for-height: Wasting or thinness indicates in most cases a recent and severe process of weight loss, which is often associated with acute starvation and/or severe disease. However, wasting may also be the result of a chronic unfavourable condition. Provided there is no severe food shortage, the prevalence of wasting is usually below 5%, even in poor countries. The Indian subcontinent, where higher prevalences are found, is an important exception. A prevalence exceeding 5% is alarming given a parallel increase in mortality that soon becomes apparent (7). On the severity index, prevalences between 10-14% are regarded as serious, and above or equal 15% as critical. Typically, the prevalence of low weight-for-height shows a peak in the second year of life. Lack of evidence of wasting in a population does not imply the absence of current nutritional problems: stunting and other deficits may be present (26). High weight-for-height: ôOverweightằ is a preferred term for describing high weight-for-height. Even though there is a strong correlation between high weight-for-height and obesity as measured by adiposity, greater lean body mass can also contribute to high weight-for-height. On an individual basis, therefore, ôfatnessằ or ôobesityằ should not be used to describe high weight-for-height. However, on a population- wide basis, high weight-for-height can be considered as an adequate indicator of obesity, because the majority of individuals with high weight- for-height are obese. Strictly speaking, the term obesity should be used only in the context of adiposity measurements, for example skinfold thickness. Low height-for-age: Stunted growth reflects a process of failure to reach linear growth potential as a result of suboptimal health and/or nutritional conditions. On a population basis, high levels of stunting are associated with poor socioeconomic conditions and increased risk of frequent and early exposure to adverse conditions such as illness and/or inappropriate feeding practices. Similarly, a decrease in the national stunting rate is usually indicative of improvements in overall socioeconomic conditions of a country. The worldwide variation of the prevalence of low height- for-age is considerable, ranging from 5% to 65% among the less developed countries (24). In many such settings, prevalence starts to rise at the age of about three months; the process of stunting slows down at around three years of age, after which mean heights run parallel to the reference. Therefore, the age of the child modifies the interpretation of the findings: for children in the age group below 2-3 years, low height-for-age probably 47 WHO Global Database on Child Growth and Malnutrition reflects a continuing process of “failing to grow” or “stunting”; for older children, it reflects a state of “having failed to grow” or “being stunted”. It is important to distinguish between the two related terms, length and stature: length refers to the measurement in recumbent position, the recommended way to measure children below 2 years of age or less than 85 cm; whereas stature refers to standing height measurement. For simplification, the term height is used all throughout the database to cover both measurements. Low weight-for-age: Weight-for-age reflects body mass relative to chronological age. It is influenced by both the height of the child (height-for-age) and his or her weight (weight-for-height), and its composite nature makes interpretation complex. For example, weight- for-age fails to distinguish between short children of adequate body weight and tall, thin children. However, in the absence of significant wasting in a community, similar information is provided by weight-for- age and height-for-age, in that both reflect the long-term health and nutritional experience of the individual or population. Short-term change, especially reduction in weight-for-age, reveals change in weight- for-height. In general terms, the worldwide variation of low weight-for- age and its age distribution are similar to those of low height-for-age. 5.2 The international reference population The designation of a child as having impaired growth implies some means of comparison with a ôreferenceằ child of the same age and sex. Thus, in practical terms, anthropometric values need to be compared across individuals or populations in relation to an acceptable set of reference values. This need has made the choice of a growth reference population an important issue that has received considerable attention in the last decades (1). The database uses as a basis for comparison across countries the National Center for Health Statistics (NCHS) growth reference, the so-called NCHS/WHO international reference population. The international reference growth curves were formulated in the 1970s by combining growth data from two distinct data sets, which were originally planned to serve as a reference for the USA. The reference for ages 0 to 23 months is based on a group of children in the Ohio Fels Research Institute Longitudinal Study which was conducted from 1929 to 1975. The height curves for this part of the reference are based on recumbent length measurements. The reference from 2 to 18 years of age is based on data of three cross-sectional USA representative surveys conducted between 1960 and 1975. The height curves for this part of the reference are based on standing height measurements. All samples consisted of healthy well-nourished US children. A detailed account of the historical background of the NCHS/WHO growth charts can be found elsewhere (1, 27). The World Health Organization adopted the reference curves of the NCHS for international use in the late 1970s (28) based on the then 48 WHO/NUT/97.4 growing evidence that the growth patterns of well-fed, healthy preschool children from diverse ethnic backgrounds are very similar (29). Differences of genetic origin are evident for some comparisons; however, these variations are relatively minor compared with the large worldwide variation in growth related to health and nutrition (30). The adoption by WHO of the NCHS-based growth curves resulted in their wide international dissemination. Throughout the 1980s, several microcomputer-based software versions of the NCHS/WHO international growth reference were developed and supported by CDC and WHO (27). These software-based references have contributed to the wide acceptance of the concept of the international growth reference because they simplified the handling of anthropometric data from surveys, surveillance, and clinical studies. Although the NCHS/WHO international growth curves have served many useful purposes throughout these years, because of a number of serious drawbacks, the suitability of these curves for international purposes has recently been challenged (1,31). Work supported by WHO has demonstrated that the current international reference is sufficiently flawed as to interfere with the sound health and nutritional management of infants and young children. These flaws arise from both technical and biological considerations. In particular, the current reference may lead to the early introduction of complementary foods in exclusively breast-fed infants, which often has adverse consequences for the health and nutritional well-being of infants (32,33). As a result, an international effort is currently underway to develop a new international growth reference (34). Until the new reference is developed, the NCHS/ WHO growth reference curves will remain the reference values recommended for international use. General issues that need to be considered when using international reference values are discussed elsewhere (31). One essential consideration is the appropriate use of the reference data. The way in which a reference is interpreted and the clinical and public health decisions that will be based upon it are often more important than the choice of reference. The reference should be used as a general guide for screening and monitoring and not as a fixed standard that can be applied in a rigid fashion to individuals from different ethnic, socioeconomic, and nutritional and health backgrounds. For clinical or individual-based application, reference values should be used as a screening tool to detect individuals at greater risk of health or nutritional disorders; and they should not be viewed as a self-sufficient diagnostic tool. For population- based application, the reference values should be used for comparison and monitoring purposes. In a given population, a high prevalence of anthropometric deficit will be indicative of significant health and nutritional problems, however, it is not only those individuals below the cut-off point who are at risk; the entire population is at risk, and the cut-off point should be used only to facilitate the application of the indicator. 49 WHO Global Database on Child Growth and Malnutrition 5.3 The Z-score or standard deviation classification system There are three different systems by which a child or a group of children can be compared to the reference population: Z-scores (standard deviation scores), percentiles, and percent of median. For population- based assessment—including surveys and nutritional surveillance—the Z-score is widely recognized as the best system for analysis and presentation of anthropometric data because of its advantages compared to the other methods (1). At the individual level, however, although there is substantial recognition that Z-score is the most appropriate descriptor of malnutrition, health and nutrition centers (e.g. supplementary feeding programmes in refugee camps) have been in practice reluctant to adopt its use for individual assessment. A detailed description of the three systems, including a discussion of their strengths and weaknesses, can be found elsewhere (1,35). In this database, weight-for-height, height-for-age and weight-for-age are interpreted by using the Z-score classification system. The Z-score system expresses the anthropometric value as a number of standard deviations or Z-scores below or above the reference mean or median value. A fixed Z-score interval implies a fixed height or weight difference for children of a given age. For population-based uses, a major advantage is that a group of Z-scores can be subjected to summary statistics such as the mean and standard deviation (see section 5.4). The formula for calculating the Z-score is (1): Z-score (or SD-score) = observed value - median value of the reference population standard deviation value of reference population Interpreting the results in terms of Z-scores has several advantages: (1) The Z-score scale is linear and therefore a fixed interval of Z-scores has a fixed height difference in cm, or weight difference in kg, for all children of the same age. For example, on the height-for-age distribution for a 36-month-old boy, the distance from a Z-score of -2 to a Z-score of -1 is 3.8 cm. The same difference is found between a Z-score of 0 and a Z-score of +1 on the same distribution. In other words, Z-scores have the same statistical relation to the distribution of the reference around the mean at all ages, which makes results comparable across ages groups and indicators. (2) Z-scores are also sex-independent, thus permitting the evaluation of children’s growth status by combining sex and age groups. (3) These characteristics of Z-scores allow further computation of summary statistics such as means, standard deviations, and standard error to classify a population’s growth status. 50 WHO/NUT/97.4 5.4 Cut-off points and summary statistics For population-based assessment, there are two ways of expressing child growth survey results using Z-scores. One is the commonly used cut- off-based prevalence; the other includes the summary statistics of the Z-scores: mean, standard deviation, standard error, and frequency distribution. Prevalence-based reporting: For consistency with clinical screening, prevalence-based data are commonly reported using a cut-off value, often +2 Z-scores. The rationale for this is the statistical definition of the central 95% of a distribution as the “normal” range, which is not necessarily based on the optimal point for predicting functional outcomes. The WHO Global Database on Child Growth and Malnutrition uses a Z-score cut-off point of <-2 SD to classify low weight-for-age, low height- for-age and low weight-for-height as moderate and severe undernutrition, and +2 SD classifies high weight-for-height as overweight in children. The use of -2 Z-scores as a cut-off implies that 2.3% of the reference population will be classified as malnourished even if they are truly “healthy” individuals with no growth impairment. Hence, 2.3% can be regarded as the baseline or expected prevalence. To be precise the reported values in the surveys would need to subtract this baseline value in order to calculate the prevalence above normal. It is important to note, however, that the 2.3% figure is customarily not subtracted from the observed value. In reporting underweight and stunting rates this is not a serious problem because prevalences in deprived populations are usually much higher than 2.3%. However, for wasting, with much lower prevalence levels, not subtracting this baseline level undoubtedly affects the interpretation of findings. Summary statistics of the Z-scores: A major advantage of the Z-score system is that a group of Z-scores can be subjected to summary statistics such as the mean and standard deviation. The mean Z-score, though less commonly used, has the advantage of describing the nutritional status of the entire population directly without resorting to a subset of individuals below a set cut-off. A mean Z-score significantly lower than zero—the expected value for the reference distribution—usually means that the entire distribution has shifted downward, suggesting that most, if not all, individuals have been affected. Using the mean Z-score as an index of severity for health and nutrition problems results in increased awareness that, if a condition is severe, an intervention is required for the entire community, not just those who are classified as “malnourished” by the cut-off criteria (36). The observed SD value of the Z-score distribution is very useful for assessing data quality. With accurate age assessment and anthropometric 51 WHO Global Database on Child Growth and Malnutrition measurements, the SD of the observed height-for-age, weight-for-age, and weight-for-height Z-score distributions should be relatively constant and close to the expected value of 1.0 for the reference distribution. An SD that is significantly lower than 0.9 describes a distribution that is more homogenous, or one that has a narrower spread, compared to the distribution of the reference population. If the surveyed standard deviation of the Z-score ranges between 1.1 and 1.2, the distribution of the sample has a wider spread than the reference. Any standard deviation of the Z-scores above 1.3 suggests inaccurate data due to measurement error or incorrect age reporting. The expected ranges of standard deviations of the Z-score distributions for the three anthropometric indicators are as follows (1): n height-for-age Z-score: 1.10 to 1.30 n weight-for-age Z-score: 1.00 to 1.20 n weight-for-height Z-score: 0.85 to 1.10 Available means and SDs of Z-scores of survey data are included in the Global Database. However, as this has been possible only for a number of surveys, these summary statistics do not appear in the printouts that follow in section 9. Given the importance of the mean and SD of Z- scores, it is hoped that an increasing number of survey reports will include them in the future. ‘Trigger-levels’ as a basis of public health decisions Experience with surveillance has contributed to emphasizing the usefulness of identifying prevalence ranges to assess the severity of a situation as the basis for making public health decisions. For example, when 10% of a population is below the -2SD cut-off for weight-for- height, is that too much, too little, or average? The intention of the so- called ‘trigger-levels’ is to assist in answering this question by giving some kind of guideline for the purpose of establishing levels of public health importance of a situation. Such classifications are very helpful for summarizing prevalence data and can be used for targeting purposes when establishing intervention priorities. The prevalence ranges shown in Table 11 are those currently used by WHO to classify levels of stunting, underweight, and wasting. It should be borne in mind, however, that this classification is largely arbitrary and simply reflects a convenient statistical grouping of prevalence levels worldwide. Moreover, the designations of a prevalence as “low” or “medium” should be interpreted cautiously and not be taken as grounds for complacency. Since only 2.3% of the children in a well-nourished population would be expected to fall below the cut-off, the “low” weight- for-age group, for example, includes communities with up to four times that expected prevalence, and the “medium” group communities with up to an eightfold excess. 52 WHO/NUT/97.4 Table 11 Classification for assessing severity of malnutrition by prevalence ranges among children under 5 years of age Indicator Severity of malnutrition by prevalence ranges (%) Low Medium High Very high Stunting <20 20-29 30-39 ž40 Underweight <10 10-19 20-29 ž30 Wasting < 5 5-9 10-14 ž15 From reference (1) 6 How to read the database printouts The country printouts, i.e. data and references, of the WHO Global Database on Child Growth and Malnutrition are found on pages 67 to 710. 6.1 Data The data printouts are structured in tabular form (see Figure 11) with a top row bearing the name of the country and 11 columns containing the following information: Column 1 Administrative level. Country data are specified as national, regional, province, district or local, depending on where the survey took place and for which administrative level it is representative: National: A nationally representative sample. Regional: The survey covers several sub- national levels. Province: The survey was performed in a province or state (name presented in the Notes column). District: The survey is representative of a district (name presented in the Notes column). Local: A community, a village or a town was surveyed (name presented in the Notes column). Column 2 Dates of survey. This column gives the month and year during which the survey took place. Column 3 Area. URBAN and RURAL specify the setting. If no specification is given, data refer to both urban and rural areas. 53 WHO Global Database on Child Growth and Malnutrition Column 4 Sex. Male is signified as M and female as F. If no specification is given, data refer to both sexes. Column 5 Age group (years). Every data entry refers to a specific age group, specified in years using the decimal system, i.e. the month ranges have been transformed into the decimal system and are presented in years with two decimals. The following age groups appear in the printouts: 0. -4.99 years meaning 0-59 completed months; 0.00-0.49 years meaning 0-5 completed months; 0.50-0.99 years meaning 6-11 completed months; 0.00-0.99 years meaning 0-11 completed months; 1 year meaning 12-23 completed months; 2 years meaning 24-35 completed months; 3 years meaning 36-47 completed months; 4 years meaning 48-59 completed months; 5 years meaning 60-71 completed months. Several survey reports contain also smaller age group breakdowns, e.g.: 0.00-0.24 years meaning 0-2 completed months; 0.25-0.49 years meaning 3-5 completed months. Column 6 Sample size. This column contains the sample sizes for all disaggregations of the data presented. Column 7 Percentage below/above the median: WEIGHT/ HEIGHT. Prevalence for the cut-offs below -3 SD, below -2 SD (including the % below -3 SD) and above +2 SD are presented. Prevalence data for the three nutritional indicators are presented as percentages with one decimal. Column 8 Percentage below the median: HEIGHT/AGE. Prevalence for the cut-offs below -3 SD and below -2 SD (including the % below -3 SD) are presented. Column 9 Percentage below the median: WEIGHT/AGE. Prevalence for the cut-offs below -3 SD and below -2 SD (including the % below -3 SD) are presented. Column 10 Notes. Any additional information or clarification of the data are presented. Column 11 Ref.No. Which stands for reference number, is used to identify data sources (see section 6.2), which are listed in the reference sections following each country’s data printout. 54 WHO/NUT/97.4 Figure 11 Tabular form of data printouts 55 WHO Global Database on Child Growth and Malnutrition 6.2 References Data are derived from a variety of sources, e.g. published articles in the scientific press, government health statistics, and survey reports from international and nongovernmental organizations. To complement the scarce nutritional information from some countries data from national surveillance systems, e.g. Chile, Uruguay and Zimbabwe, are also included. For a few countries, e.g. Ecuador and South Africa, height censuses of school children have been included because little other data are available to describe the child growth status. When the data source is either a surveillance system or a height census, this is stated in the NOTES column of the data printout. Data references follow immediately after the data printout, providing the user with the necessary information to easily trace data sources. Within each country’s reference printout there are two separate sections: 1) ôdata referencesằ, i.e. those related to the data included in the data printout, and 2) ôadditional referencesằ, i.e. those that provide supplementary information about status of child growth in the country. These ôadditional referencesằ contain nutritional data that do not fulfil the database entry criteria but which might, nevertheless, provide relevant information for interested researchers. Finally, whenever survey data have been reanalysed, either by responsible national authorities or by WHO, this is clearly indicated in the reference section by the words ôand additional analysisằ. 56 WHO/NUT/97.4 7 Bibliography (1) Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. WHO Technical Report Series No. 854. Geneva: World Health Organization, 1995. (2) Tomkins A and Watson F. Malnutrition and infection: a review. ACC/SCN State-of-the-Art Series, Nutrition Policy Discussion Paper No. 5. Geneva: Administrative Committee on Coordination/Subcommittee on Nutrition, 1989. (3) Lutter CK, Mora JO, Habicht JP et al. Nutrition supplementation: effects on child stunting because of diarrhea. American Journal of Clinical Nutrition 1989;50:1-8. (4) Victora CG, Barros FC, Kirkwood BR, Vaughan JP. Pneumonia, diarrhea and growth in the first four years of life. A longitudinal study of 5,914 Brazilian infants. American Journal of Clinical Nutrition 1990;52:391-396. (5) Briend A. Is diarrhoea a major cause of malnutrition among the under-fives in developing countries? A review of available evidence. European Journal of Clinical Nutrition 1990;44:611-628. (6) Victora CG, Fuchs SC, Flores A, Fonseca W, Kirkwood BR. Risk factors for pneumonia in a Brazilian metropolitan area. Pediatrics 1994;93(6 Pt 1);977-985. (7) Toole MJ, Malkki RM. Famine-affected refugee and displaced populations: Recommendations for public health issues. Morbidity and Mortality Weekly Report 1992;41:1-25. (8) Pelletier D. Relationships between child anthropometry and mortality in developing countries. Monograph 12, Cornell Food and Nutrition Policy Program. Ithaca: Cornell University Press, 1991. (9) Pelletier D, Frongillo JrA, Habicht JP. Epidemiologic evidence for a potentiating effect of malnutrition on child mortality. American Journal of Public Health 1993;83:1130-1133. (10) Bailey K, de Onis M, Blửssner M. Protein-energy malnutrition. In: Murray CJL, Lopez AD, eds. Malnutrition and the Burden of Disease: the global epidemiology of protein-energy malnutrition, anemias and vitamin deficiencies. Vol 8. The Global Burden of Disease and Injury Series. Cambridge, MA: Harvard University Press, 1998 (in press). (11) Pollitt E, Gorman KS, Engle PL, Martorell R, Rivera J. 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Geneva: Administrative Committee on Coordination/ Subcommittee on Nutrition, 1992. n ACC/SCN. How Nutrition Improves. ACC/SCN State-of-the-Art Series, Nutrition Policy Discussion Paper No. 15. Geneva: Administrative Committee on Coordination/ Subcommittee on Nutrition, 1996. n de Onis M, Yip R, Mei Z. The development of MUAC-for-age reference data recommended by a WHO Expert Committee. Bulletin of the World Health Organization 1997;75:11-18. n Dibley MJ, Goldsby JB, Staehling NW, Trowbridge FL. Development of normalized curves for international growth reference historical and technical considerations. American Journal of Clinical Nutrition 1987;46:736-748. n Dibley MJ, Staehling N, Nieburg P, Trowbridge FL. Interpretation of Z-score anthropometric indicators derived from the international growth reference. American Journal of Clinical Nutrition 1987;46:749- 762. n Eveleth BP and Tanner JM. Worldwide variation in human growth. Cambridge: Cambridge University Press, 1990. n FAO/WHO. World Declaration and Plan of Action for Nutrition. International Conference on Nutrition, Rome, December, 1992. n FAO. The Sixth World Food Survey. Rome: Food and Agriculture Organization, 1996. n Gorstein J. Assessment of nutritional status: effects of different methods to determine age on the classification of undernutrition. Bulletin of the World Health Organization 1989;67:143-150. n Mei Z, Grummer-Strawn LM, de Onis M, Yip R. The development of a MUAC-for-height reference, inlcuding a comparison to other nutritional status screening indicators. Bulletin of the World Health Organization 1997;75:333-342. n Pinstrup-Andersen P, Pelletier D and Alderman H, eds. Child Growth and Nutrition in Developing Countries. -Priorities for Action. Ithaca: Cornell University Press, 1995. 60 WHO/NUT/97.4 n UNDP. Human Development Report 1996. New York: Oxford University Press, 1996. n Van den Broeck J, Meulemanns W, Eeckels R. Nutritional assessment: the problem of clinical-anthropometrical mismatch. European Journal of Clinical Nutrition 1994;48:60-65. n Waterlow JC. Protein Energy Malnutrition. London: Edward Arnold, 1992. n The Growth Chart.- A tool for use in infant and child health care. Geneva: World Health Organization, 1986. n WHO Working Group. Use and interpretation of anthropometric indicators of nutritional status. Bulletin of the World Health Organization 1986;64:929-941. n WHO. Management of severe malnutrition. A manual for physicians and other senior health workers. Geneva: World Health Organization, 1998. n WHO. Integrated management of childhood illness. Document WHO/ CHD/95.14.A-K. Geneva: World Health Organization, 1995. n Yip R. Expanded usage of anthropometry Z-scores for assessing population nutritional status and data quality. Abstract of the 15th International Congress of Nutrition; 1993 Sep 26-Oct 1; Adelaide: Smith Gordon, 1993;297. 61 WHO Global Database on Child Growth and Malnutrition 8 List of countries 8.1 UN regions and subregions Africa Eastern Africa Middle Africa Northern Africa Southern Africa Western Africa Burundi Angola Algeria Botswana Benin Comoros Cameroon Egypt Lesotho Burkina Faso Djibouti Central African Libyan Arab Namibia Cape Verde Eritrea Republic Jamahiriya South Africa Cụte d’Ivoire Ethiopia Chad Morocco Swaziland Gambia Kenya Congo Sudan Ghana Madagascar Democratic Tunisia Guinea Malawi Republic of Western Sahara ‡ Guinea-Bissau Mauritius the Congo Liberia Mozambique Equatorial Guinea Mali Rộunion (F) Gabon Mauritania Rwanda Sao Tome and Niger Seychelles Principe Nigeria Somalia St Helena (UK) Uganda Senegal United Republic Sierra Leone of Tanzania Togo Zambia Zimbabwe Asia Eastern Asia South-central South-eastern Western Asia Asia Asia China Afghanistan Brunei Armenia Syrian Arab Democratic Bangladesh Darussalam Azerbaijan Republic People’s Bhutan Cambodia Bahrain Turkey Republic India East Timor Cyprus United Arab of Korea Iran (Islamic Indonesia Georgia Emirates Japan Republic of) Lao People’s Iraq Yemen Macau† Kazakstan Democratic Israel Mongolia Kyrgyzstan Republic Jordan Republic of Maldives Malaysia Kuwait Korea Nepal Myanmar Lebanon Pakistan Philippines Oman Sri Lanka Singapore Palestinian self- Tajikistan Thailand rule areas Turkmenistan Viet Nam Qatar Uzbekistan Saudi Arabia Europe Eastern Europe Northern Europe Southern Europe Western Europe Belarus Channel Albania Austria Bulgaria Islands (UK) Andorra Belgium Czech Republic Denmark Bosnia and France Hungary Estonia Herzegovina Germany Poland Faeroe Croatia Liechtenstein Republic of Islands (DK) Gibraltar (UK) Luxembourg Moldova Finland Greece Monaco Romania Iceland Holy See Netherlands Russian Ireland Italy Switzerland Federation Isle of Man (UK) Malta Slovakia Latvia Portugal Ukraine Lithuania San Marino Norway Slovenia Sweden Spain 62 WHO/NUT/97.4 Europe (continued) Northern Europe Southern Europe United Kingdom The former of Great Britain Yugoslav and Northern Republic of Ireland Macedonia Yugoslavia Latin America and the Caribbean Caribbean Central America South America Anguilla (UK) Jamaica Belize Argentina Antigua and Martinique (F) Costa Rica Bolivia Barbuda Montserrat (UK) El Salvador Brazil Aruba (NE) Netherlands Guatemala Chile Bahamas Antilles (NE) Honduras Colombia Barbados Puerto Rico (US) Mexico Ecuador British Virgin St Kitts and Nevis Nicaragua Falkland Islands Island (UK) St Lucia Panama (Malvinas) (UK) Cayman St Vincent and the French Guiana (F) Islands (UK) Grenadines Guyana Cuba Trinidad and Paraguay Dominica Tobago Peru Dominican Turks and Caicos Suriname Republic Islands (UK) Uruguay Grenada US Virgin Venezuela Guadeloupe (F) Islands (US) Haiti Northern America Bermuda (UK) Canada Greenland (DK) St Pierre and Miquelon (F) United States of America Oceania Australia- New Melanesia Micronesia Polynesia Zealand Australia Fiji Guam (US) American New Zealand New Caledonia (F) Kiribati Samoa (US) Papua New Marshall Islands Cook Islands Guinea Micronesia French Solomon Islands (Federated Polynesia (F) Vanuatu States of ) Niue Nauru Pitcairn (NZ) Northern Mariana Western Samoa Islands (US) Tokelau (NZ) Palau Tonga Tuvalu (UK) Wallis and Futuna Islands (F) Source: World Populations Prospects 1994. New York: United Nations, 1995. (F) Overseas Departments of France, French territorial collectivity, (UK) UK crown dependent territory, British colony, or British protectorate (US) United States of America † Overseas territory of Portugal ‡ recognized by the Organization of African Unity (DK) Kingdom of Denmark (NE) Netherlands (NZ) Overseas territory of New Zealand 63 WHO Global Database on Child Growth and Malnutrition 8.2 WHO regions Africa Algeria Democratic Republic Malawi South Africa Angola of the Congo Mali Swaziland Benin Equatorial Guinea Mauritania Togo Botswana Eritrea Mauritius Uganda Burkina Faso Ethiopia Mozambique United Republic of Burundi Gabon Namibia Tanzania Cameroon Gambia Niger Zambia Cape Verde Ghana Nigeria Zimbabwe Central African Guinea Rwanda Republic Guinea Bissau Sao Tome and Chad Kenya Principe Comoros Lesotho Senegal Congo Liberia Seychelles Cụte d’Ivoire Madagascar Sierra Leone Americas Antigua and Barbuda Costa Rica Honduras Suriname Argentina Cuba Jamaica Trinidad and Bahamas Dominica Mexico Tobago Barbados Dominican Republic Nicaragua United States of Belize Ecuador Panama America Bolivia El Salvador Paraguay Uruguay Brazil Grenada Peru Venezuela Canada Guatemala St Kitts and Nevis Chile Guyana St Lucia Colombia Haiti St Vincent and the Grenadines South-East Asia Bangladesh Indonesia Sri Lanka Bhutan Maldives Thailand Democratic People’s Myanmar Republic of Korea Nepal India Europe Albania France Malta Switzerland Armenia Georgia Monaco Tajikistan Austria Germany Netherlands The former Azerbaijan Greece Norway Yugoslav Republic Belarus Hungary Poland of Macedonia Belgium Iceland Portugal Turkey Bosnia and Ireland Republic of Moldova Turkmenistan Herzegovina Israel Romania Ukraine Bulgaria Italy Russian Federation United Kingdom of Croatia Kazakstan San Marino Great Britain and Czech Republic Kyrgyzstan Slovakia Northern Ireland Denmark Latvia Slovenia Uzbekistan Estonia Lithuania Spain Yugoslavia Finland Luxemburg Sweden 64 WHO/NUT/97.4 Eastern Mediterranean Afghanistan Iraq Oman Tunisia Bahrain Jordan Pakistan Unite Arab Emirates Cyprus Kuwait Qatar Yemen Djibouti Lebanon Saudi Arabia Egypt Libyan Arab Somalia Iran (Islamic Jamahiriya Sudan Republic of) Morocco Syrian Arab Republic Western Pacific Australia Lao People’s New Zealand Solomon Islands Brunei Darussalam Democratic Republic Niue Tonga Cambodia Malaysia Palau Tuvalu China Marshall Islands Papua New Guinea Vanuatu Cook Islands Micronesia, Federated Philippines Viet Nam Fiji States of Republic of Korea Japan Mongolia Samoa Kiribati Nauru Singapore 8.3 Level of development Developed market economy countries (Level 1) Andorra Greece Norway Australia Iceland Portugal Austria Ireland San Marino Belgium Italy Spain Canada Japan Sweden Denmark Luxembourg Switzerland Finland Monaco United Kingdom of Great France Netherlands Britain and Northern Ireland Germany New Zealand United States of America Developing countries (excluding least developed countries) (Level 2) Algeria Fiji Nigeria Antigua and Barbuda Gabon Niue Argentina Ghana Oman Bahamas Grenada Pakistan Bahrain Guatemala Palau Barbados Guyana Panama Belize Honduras Papua New Guinea Bolivia India Paraguay Bosnia and Herzegovina Indonesia Peru Botswana Iran, Islamic Republic of Philippines Brazil Iraq Qatar Brunei Darussalam Israel Republic of Korea Cụte d’Ivoire Jamaica St Kitts and Nevis Cameroon Jordan St Lucia Chile Kenya St Vincent and the China Kuwait Grenadines Colombia Lebanon Saudi Arabia Congo Libyan Arab Jamahiriya Senegal Cook Islands Malaysia Seychelles Costa Rica Malta Singapore Croatia Marshall Islands Slovenia Cuba Mauritius South Africa Cyprus Mexico Sri Lanka Democratic People’s Micronesia, Federated Suriname Republic of Korea States of Swaziland Dominica Mongolia Syrian Arab Republic Dominican Republic Morocco Thailand 65 WHO Global Database on Child Growth and Malnutrition Developing countries (continued) Ecuador Namibia The former Yugoslav Egypt Nauru Republic of Macedonia El Salvador Nicaragua Tonga Trinidad and Tobago Tunisia Turkey United Arab Emirates Uruguay Venezuela Viet Nam Zimbabwe Least developed countries (Level 3) Afghanistan Haiti Sudan Angola Kiribati Togo Bangladesh Lao People’s Tuvalu Benin Democratic Republic Uganda Bhutan Lesotho United Republic Burkina Faso Liberia of Tanzania Burundi Madagascar Vanuatu Cambodia Malawi Yemen Cape Verde Maldives Zambia Central African Republic Mali Chad Mauritania Comoros Mozambique Democratic Republic of Myanmar the Congo Nepal Djibouti Niger Equatorial Guinea Rwanda Eritrea Samoa Ethiopia Sao Tome and Principe Gambia Sierra Leone Guinea Solomon Islands Guinea-Bissau Somalia Economies in transition (Level 4) Albania Kazakstan Tajikistan Armenia Kyrgyzstan Turkmenistan Azerbaijan Latvia Ukraine Belarus Lithuania Uzbekistan Bulgaria Poland Czech Republic Republic of Moldova Estonia Romania Georgia Russian Federation Hungary Slovakia 66 WHO/NUT/97.4 67 WHO Global Database on Child Growth and Malnutrition 9 Country data and references