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Growth-faltering rates in California, Guatemala, and Tamil Nadu: Implications for growth-monitoring programmes

Reynaldo Martorell and Meera Shekar



Criteria used in growth-monitoring programmes in developing countries were applied to three-month weight-gain data for children 12-24 months old in three populations: in Berkeley, California; Guatemala; and Tamil Nadu. A significant proportion of the Berkeley children showed growth faltering: 18% had at least one period in which they failed to gain any weight, and 37% had at least one in which they gained less than 300 g in three months. The frequency of faltering, however, was appreciably greater in Guatemala (45% and 82% respectively) and Tamil Nadu (42% and 74% respectively). These data raise concerns that growth-monitoring criteria, as used in most settings, identify too many children for special attention, perhaps more than can be handled by most programmes. Adjusting the criteria to select fewer children necessarily means departure from the simplest guidelines about focusing on the growth trajectory (i.e., up, flat, or down) to those that specify rates of weight gain by age. This may make growth monitoring impractical in many settings.


Considerable debate continues to take place about the merits of growth monitoring as a component of programmes aimed at improving child health and nutrition in developing countries [1-3]. Some see growth monitoring as the cornerstone to which education, nutrition, and health interventions can be anchored, whereas others see it as a time-consuming, ineffectual ritual.

An often-cited advantage of growth monitoring is the simplicity of measuring weight. A scale plus pencil and paper are all the equipment required. However, analysing this information is not simple, and therefore great effort has been directed at developing easy-to-follow methods for the appropriate plotting of the serial data and interpretation of the results [4, 5]. A much greater problem is achieving adequate coverage rates at timely intervals among children who are likely to grow poorly. How best to use this information to initiate remedial and preventive actions to improve child health is yet another important area of focus and concern [2, 6].

Qualitative as well as quantitative criteria for judging growth have been proposed. The simplest method for interpreting the adequacy of weight gain stresses the direction of the growth trajectory [3]. If it is going up, it is good. A flat line or, even more so, a downward trend suggests concern. Quantitative criteria for identifying children who falter in growth have been proposed and used as well. These generally specify a certain amount of gain over a given period of time; criteria are usually specified by age (e.g., infants as opposed to older children). Some feel that these quantitative criteria are impractical for most programmes [7].

Although much concern has been expressed about the large number of children identified by growth-monitoring programmes [6, 7], studies on the percentages of children in developing countries who are found to be growing poorly according to commonly used criteria are lacking. Also, there is no information on the proportion of healthy children from developed societies who would be identified as faltering in growth by these same criteria. Knowledge about rates of faltering in the populations of interest, as well as in healthy populations, may be useful, together with other information (e.g., about resources available for focusing actions on the at-risk group identified), in defining the criteria to use. A further issue is whether the criteria, once defined, can be applied effectively and at a tolerable cost.


The objective of this study was to compare rates of growth faltering in children from developed and developing countries. Criteria for the selection of samples included adequate sample sizes, the availability of individual-level data, and serial weight data at three-month intervals from 12 to 24 months of age.

The age range 12-24 months was selected for study because growth rates over this time are more or less constant, permitting one to apply uniform criteria for growth faltering. Furthermore, the second year of life coincides with weaning and is associated with significant health and nutrition problems in developing countries. Also, together with infants, children 12-24 months old are often the priority target group of growth-monitoring programmes.

Many definitions of growth faltering have been proposed and used for children in the second year of life. For this investigation, we used two definitions of growth faltering over a three-month period: failure to gain weight or actual loss of weight, and weight gain less than 300 g. These criteria are identical to those used in the Tamil Nadu Integrated Nutrition Project (TINP) to select children for supplementary feeding.



Three populations were selected for study: in Berkeley, California, USA; Guatemala; and Tamil Nadu state, India.


The data from Berkeley are from a guidance study and refer to a core sample of subjects born in 1928 and 1929 for whom complete longitudinal data were kept from birth to adolescence [8]. The 66 boys and 70 girls in the sample were white and apparently from middle- and upper-class backgrounds. At 18 years of age, the average height was 179.0 cm for the boys and 166.6 cm for the girls. National data from NHANES I and 11 indicate that the average stature of United States white adults 18.0-24.9 years old was 176.9 cm for men and 163.3 cm for women [9], values lower than those of the Berkeley sample. The Berkeley sample is among the tallest in the world, taller than the English and as tall as present-day Dutch and Scandinavians [10].

All of the anthropometric data for the core sample are available in the public domain [8]. From these records, length and weight data were abstracted for subjects at ages 12, 15, 18, 21, and 24 months. Length was measured to the nearest millimetre and weight to the nearest tenth of a kilogram.


The Guatemalan data are from the INCAP longitudinal study [11]. The children came from four Ladino (i.e., Spanish-speaking, of mixed Spanish-Indian ancestry) villages from eastern Guatemala. Growth retardation was marked, particularly in the first three years of life [12]. The causes of these effects were infections, particularly diarrhoeal diseases, and dietary factors [13].

Examinations took place within 15 days of the target date. Length was measured to the nearest millimetre and weight to the nearest hundredth of a kilogram. All available data from this study were used. A total of 1,021 individuals contributed data for one or more child-periods. For some analyses, only the 637 subjects with complete serial data were selected.

Tamil Nadu

The Tamil Nadu data were generated by the TINP through its activity of monthly weighing of children under 36 months of age [14]. The data were from the subset abstracted by Shekar [15] from 42 villages in the Kottampatti Block of Madurai district [15]. The information was extracted by trained personnel from village-based records for the 12 months from April 1986 to March 1987. Weight data at ages 12, 15, 18, 21, and 24 months were selected for analyses. Weight was measured to the nearest 50 g. Length was not measured. A total of 1,348 individuals contributed data for one or more child-periods.

As noted below, some analyses focus on the subset of children with complete data for all four periods. Because the information abstracted from the Tamil Nadu records was only for 12 months [15], the maximum number of child-periods for which an individual may have information is three; for this research, the ages 12-15, 1518, and 18-21 months were selected for analysis. A total of 209 children had data for all these intervals. To obtain data that would provide four child-periods per individual, one would have to extend data collection to 13 months.



The WHO/NCHS reference curves [16] were used to contrast growth patterns in the three populations. The data for children under two years of age come from the Fels Research Institute and were collected from 1928 to 1978 [17]. There were no secular trends in increments over this period [18].

The distribution of weight increments in Berkeley, Guatemala, and Tamil Nadu were compared after classifying the values into any of four categories: negative or 0, 1-299, 300-499, and 500 g and above (the exit criterion from supplementation in the TINP is weight gain greater than 500 g over a three-month period).

Some analyses used all the available period data, and some used only those for children with data for all possible periods. The latter approach was followed to estimate the number of episodes of growth faltering, defined alternatively as a weight gain either of 0 g or less or of less than 300 g in a three-month period per child. For Berkeley and Guatemala, the possible number of episodes of faltering ranged from zero to four, but for Tamil Nadu, for the reasons already given, the range was zero to three. The raw data in Tamil Nadu were adjusted to make the results comparable to those of Berkeley and Guatemala. The probability of faltering in n periods (Fn) can be expressed as

Fn = 1 - pn,

where p is the probability of not faltering in any one period. The TINP data available for analyses provided values for F3 (i.e., for three periods), and this allows one to solve for p. The probability of faltering in four periods (F4) was then estimated as

Fn = 1 - p4

The above method assumes that the probability of faltering in different periods is independent of other periods and that p is constant across periods. Although neither of these assumptions may be true, the degree to which they are violated is minor (e.g., successive increments are only weakly correlated after adjustment for common error terms) and should not appreciably affect the estimates provided.

Also, estimates of the probability of faltering in four periods were obtained through a different procedure. The average probability of not faltering (p) was obtained using pooled data for all child-periods, and F4 was estimated as above. These values were only slightly higher than those obtained from longitudinal data.

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