AMERICAN JOURNAL OF HUMAN BIOLOGY 7:303-311 (1995)

Prognostic Performance of Several Anthropometric Indicators for Predicting Low and Insufficient Birth Weight

RAFAEL J I a N E Z AND JORGE BACALLAO Departamento de Perrnatologia Basica, Znstituto Supenor de Crencaas Medicas de La Habana, La Habana, Ciudad Habana 11600, Cuba

ABSTRACT Weight gain and several anthropometric indicators were studied in a sample of 181 pregnant women. The period considered ranged from the 12th to the 34th weeks of gestation. Changes in all of the attributes were calculated and related to a birth weight below 3,000 g. Optimal cut-off points for each indicator were defined as those which minimize a loss function which depends on the rates of false positives and false negatives. The independent predictive capacity of each attribute was assessed by means of sensitivity, specificity, positive and negative predictive values, relative risk, and two “measures of detectability” associated to the ROC (relative operating characteristic) curves of the attribute. A logistic regression model was fitted by searching for the best combination of indicators. The individual predictive capacities of the attributes were considerably improved when they were combined into a logistic equation, o 1995 Wiley-Liss, Inc

Maternal nutrition is a critical determi- nant of pregnancy outcome in industrialized and in developing countries. When studying the association of maternal nutrition with pregnancy outcome, birth weight is the most frequently examined variable, given its as- sociation with infant mortality and morbid- ity (Saugstad, 1981). Birth weights below 2,500 g are said to be low, while those be- tween 2,501 and 3,000 g are said to be insuf- ficient (Krasovec, 1991).

Monitoring weight gain has been the most frequent way of assessing the nutritional condition of women during gestation. The association between weight gain and intrau- terine growth is well documented (Kramer, 1987). “he impact of low weight gain on in- trauterine growth is greater for undernour- ished women or for women undergoing acute nutritional stress (Naeye, 1979). Prepreg- nancy weight appears to act as an “effect modifier” in the relationship between weight gain and birth weight (Abrams and Laros, 1986). Further, maternal nutrition accounts for 50% of the difference between rates of intrauterine growth retardation (IUGR) in industrialized vs. developing countries (Kramer, 1987).

Anthropometry is a major tool in the iden- tification of pregnant women at nutritional risk. Many recent studies have devoted spe-

cial attention to the use of several anthropo- metric indicators for nutritional monitoring during gestation (Forsum et al., 1989; Vie- gas e t al., 1987; Maso et al., 1988; Lechtig, 1988; Trowbridge and Staehling, 1980). Shah (1982) recommends arm circumfer- ence (AC) for monitoring purposes. Lechtig (1988) found that AC together with stature and head circumference were at least equiv- alent to weight gain in predicting birth weight of infants.

With the purpose of studying the associa- tion between maternal subcutaneous fat and pregnancy outcomes, considerable at- tention has been devoted to skinfolds. Some authors have used them separately (Frisan- cho et al., 1977; Maso et al., 1988), while others have used their sum as an indicator of body fat (Lawrence et al., 1984; Prentice et al., 1981).

Considerable effort has been directed to- ward identifying “cut-off points for each in- dicator separately, but not much has been done to combine them into a single score with enhanced predictive capacity. The

Received August 4,1993; accepted June 7,1994. Address reprint requests to Dr. Jorge Bacallao, Instituto Supe-

rior de Ciencias Medicas de La Habana, Calle 146 No. 3102, Playa La Habana 11600, Cuba.

0 1995 Wiley-Liss, Inc.

304 R. JIMENEZ AND J. BACALLAO

TABLE 1 . Characterization of the sample of mothers according to age, stature, length of gestation, parity, and smoking habits. and of infants according to birth weight'

Variables Mean (SDI Class intervals or groups n %

Age (years) 25.21 (5.52) < 20

Length of gestation (weeks) 39.46 (1.47) < 37

2 20 and < 35 2 35

3 37 and c 42

38 21.2 133 73.3 10 5.5 4 2.2

175 96.7 > 42 2 1.1

Stature (cm) 158.97 (5.92) Parity Primiparae 113 62.4

With one child 53 29.3 With two or more children 15 8.3

Smoking habit Non-smokers Light smokers Heavy smokers

149 82.3 16 8.8 16 8.8

Birth weight (g) 3,111.4 (478.6) < 2,500 g 25 13.8 2,5OC-3,000 g 41 22.7 > 3,000 g 115 63.5

"on-smoker: never smokes; light smoker: fewer than 20 cigarettes per day; heavy smoker: 20 or more cigarettes per day.

main objectives of the present study are threefold: (1) to compare some predictors of "birth weights below 3,000 g" (BW < 3,000 g); (2) to combine several indicators in a sin- gle predictive model in order to increase the individual prognostic capacity of each in re- lation with BW < 3,000 g; and (3) to propose a simple tool for measuring the risk of deliv- ering an infant with BW < 3,000 gas a basis for intervention.

SUBJECTS AND METHODS The sample consisted of all pregnant

women who attended the consultation of- fered as part of the program for *Nutritional Follow-up and Surveillance of Pregnant Women" by the Polyclinic "Carlos J. Finlay" within the first 6 months of 1991. This poly- clinic is located at the municipality of Mari- anao, in the western portion of Havana. The population that it served by the end of 1990 consisted of 45,598 persons of which 2,997 were women in reproductive ages (14 to 47 years of age). During 1991,420 women ben- efited from the program. They were all iden- tified and appointed to the polyclinic by fam- ily doctors, who constitute the primary level of attention of the Health Care System in Cuba. Family doctors are in charge of about 120 families, on average. One of their main tasks is to screen and regularly report to the polyclinic all pregnant women in the popula- tion that they serve. Family doctors involved in the present program were directed during

1991, and particularly throughout the first semester by one of the authors.

Among all pregnant women who benefited from the program during 1991, 86 (20.4%) were below the tenth percentile of weight for stature according to Cuban reference data (Berdasco and Romero, 1985) and were thus at nutritional risk, 76 (18%) were adoles- cents (S18 years), and 168 (40%) lived under poor dwelling conditions. These figures are among the worst compared to the remainder of the populations served by other polyclin- ics in the municipality.

A pregnant woman was admitted to the study and included in the subsequent analy- sis (1) if she was a resident of the area served by the polyclinic, and (2) if she had undergone a complete gynecologic and nu- tritional assessment at the first consulta- tion between the 12th and the 13th weeks of gestation and the ultrasonographic study in the 20th week. The weeks of pregnancy were assessed by asking for the date of the last menstrual period, confirmed by gynecologic examination and reconfirmed with the ul- trasonographic study in the 20th week.

The initial sample consisted of 216 preg- nant women who were enrolled in the pro- gram during the first semester of 1991. Thirty-five did not meet the second inclu- sion criterion and were thus excluded, after which the final sample was reduced to 181 women. The relevant characteristics of the sample are summarized in Table 1.

PREDICTING LOW AND INSUFFICIENT BIRTH WEIGHT 305

For all women in the sample the initial measurement was taken at the 12th or 13th weeks of pregnancy and the final measure- ment between the 33rd and 35th weeks. An- other measurement was taken around the 20th week (*2 weeks). The following mea- surements were taken on the three occa- sions: weight (W), mid-arm circumference (MAC), maximal leg circumference (MLC), the triceps skinfold (TS), and the subscapu- lar skinfold (SS). Stature (ST) was mea- sured at the beginning of the study. All mea- surements were done according to the procedures described in Lohman et al. (1988).

Weight was measured in kilograms on a Seca scale with a capacity of 150 kg and a precision of 0.2 kg. Stature was measured in cm with a fixed Holtain stadiometer. MAC and MLC were measured in cm with a flexi- ble Metrix anthropometric band covered with fiberglass. TS and SS were measured with a Holtain caliper, which has a constant pressure of 10 g/mmz. Both skinfolds were measured to 0.2 mm. All measurements were performed before 10 a.m.

Two anthropometrists with experience in Cuban National Child Growth Studies (Jor- dan, 1979) and well informed about the pur- poses of the study made all of the measure- ments. The measurements were taken in replicate and averaged. In each measuring session one of the authors verified the ap- propriate conditions of the site, and ran- domly selected 5 women and remeasured them for purposes of quality control. This included the calculation of the technical er- ror of measurement (TEM) and reliability (R). (Habicht, 1973; Marks et al., 1989). The TEM for MAC, TS, and SS were 0.21 cm, 1.03 mm, and 1.02 111111, and the reliabilities were 0.94,0.83, and 0.86, respectively.

Statistical analysis The subsequent analysis relates “BW <

3,000 g” with changes between the first and last measurements for each indicator. This cut-off defines the two groups: (1) below 3,000 g, and (2) 3,000 g or more (normal birth weight). Descriptive statistics were calculated for all maternal variables (WG, MAC, TS, SS, and MLC) and for their abso- lute changes between the first and last mea- surements. All women whose gestational age differed by more than 1 week from 12 weeks at the time of the initial measure- ment or by more than 1 week from 34 weeks

at the time of the final measurement were not considered for the analysis. The absolute changes were then corrected for differences in the time interval between the first and last measurements, and adjusted to a total elapsed time of 22 weeks. This has no practi- cal consequences since variations in both measurements of gestational age are not greater than one weeks, and since most of the variables change in linear fashion dur- ing the period of pregnancy considered (Fescina, 1983).

Optimal cut-off points were determined for each predictor by minimizing a loss func- tion (Bacallao, 1986) defined as follows:

where a1 and az are penalties assigned to a false

positive and a false negative case, respec- tively (in the present study we set a1 = 1 anda, = 2). ~

FP(t): number of false positives for cut-off point t

FN(t): number of false negatives for cut- off point t

t E 1,2,. . . , k: designates an ordinal value representing a rank of the cut-off point for a set of plausible values of the indicator.

Minimizing this loss function amounts to optimizing the overall performance of a pre- diction rule by properly weighing a false pos- itive and a false negative.

Sensitivity, specificity, relative risk, and predictive values (both positive and nega- tive) were calculated for each predictor in relation to their sample-based optimal cut- off point. Two additional measures of perfor- mance were estimated for each predictor. Both measures are “indicators of detectabil- ity” corresponding to a relative operating characteristic (ROC) curve over a range of different selected cut-off points including the optimal one, in the sense defined by the loss function (Erdreich and Lee, 1981). The ROC curve is a plot of the rate of false posi- tives, on the x-axis, vs. the rate of true posi- tives, on the y-axis, for different cut-offs,

These indicators are defined as:

where

306 R. JIMENEZ AN1

XNBW and XLIBW are the means of the indi- cator in normal and low or insdicient birth weight infants;

szmw and szLIBw are the standard devia- tions of the indicator for normal and low or insufficient birth weight infants, respec- tively, and 9-l (x) is the density function of the standard normal distribution (Brownie et al., 1986; Goddard and Hinberg, 1990).

Two predictors can be compared with re- spect to the areas under their ROC curves. A larger area corresponds to a better predic- tive capacity. Z, is a measure of the degree of separability between two populations (normal and insufficient or low birth weight, in this instance) yielded by the predictor, and Az can be shown to be the-area under its ROC curve. (Note that if XNBw = XI,IBw, then Z, = 0; Az = 0.5, and the prediction would perform as a random one with equal relative frequencies of false and true posi- tives).

The original values of the absolute changes of each predictor were converted into categorical dichotomous variables (above or below cut-off point) and all possi- ble logistic regression models consisting of two and three variables were fitted. The best models chosen were those with the highest asymptotic chi-square goodness of fit value. As will be seen later, they were those which combined sensitive and specific predictors.

A cut-off point for the estimated individ- ual risks of BW < 3,000 g was determined in a similar manner and the same measures of performance were computed for the probabi- listic scores yielded by the logistic models as estimates of risk.

RESULTS Table 1 contains relevant background

data of the sample. Included are the relative frequencies of low and insufficient birth weight, and descriptive statistics on birth weight for low, insufficient, and normal weight infants. The percentage of infants with low birth weight is high compared to the reported national figure, -7.6%, in 1990 (MINSAP, 1992).

Tables 2 and 3 contain descriptive statis- tics for all variables of interest in the study. Means and standard deviations of maternal variables: W, MAC, TS, SS, and MLC for different gestational ages are given in Table 2, which means and standard devations of the adjusted absolute changes for the same variables as shown in Table 3 for two

D J. BACALLAO

TABLE 2. Means and (standard deviations) for maternal anthropometric variables at three different moments

during gestation'

First Second Third measurement measurement measurement

Predictor 112 ? 1 week) (20 ? 2 weeks) 134 ? 1 week)

Weight (kg) 57.7 (10.9) 61.6 (11.1) 65.2 (11.4) MAC (em) 25.8 (3.2) 26.0 (3.2) 26.5 (3.2) TS (mm) 16.3 (5.9) 17.2 (6.0) 18.1 (6.0) ss (mm) 18.4 (7.1) 19.4 (7.2) 20.6 (7.3) MLC (cm) 34.1 (3.4) 34.6 (3.5) 35.4 (3.5)

'MAC mid-arm cimunforence; Ts: triceps skinfold SS. subscapular skinfold; ME: maximal leg cimnferenee.

TABLE 3. Means and (standard deviations) of adjusted increments for the maternal predictors of birth

weight < 3,000 g in infants with birth weight < 3,000 g and with birth weight a 3.000 d

Birth Birth weight weight Both

Predictors < 3,000 g 2 3,OW g WOUPS

WG (kg) 5.18 13.04) 9.22 (2.42) 7.66 (3.22) MAC (cm) -0.58 (0.87) 1.10 (1.00) 0.65 (1.10) TS (mm) 0.75 (1.22) 2.40 (1.54) 1.76 (1.63) SS (mm) 0.76 (1.31) 3.01 (1.80) 2.14 (1.96) MLC (em) 0.58 (0.86) 1.71 (1.21) 1.27 (1.22)

IWG. weight gain: MAC: mid-arm cireomference; TS: triceps skmfold; SS. subscap ular skinfold; MLC maximal leg circumference.

TABLE 4. Optimal sample-based cut-off points, values of the loss function and rates of false positives and true

positives for each predictor'

False True Cut-off Loss positives positives

Predictor point function ('$6) (%I WG 7kg 49 16.7 77.6 MAC 3 mm 58 7.1 62.7 TS 2.4mm 64 49.1 94.0 ss 2.4 mm 54 35.4 89.6 MLC 13mm 61 34.5 83.3

'WG: weight gain; MAC: mid-arm circumference; TS: triceps skinfold; S S subscapular skinfold MLC: maximal leg circumference.

groups: (1) infants with birth weight below 3,000 g, and (2) infants with birth weight 2 3,000 g. It is worth noting that the MAC of gravidae who deliver infants with BW < 3,000 g decreases whereas TS in- creases slightly.

Table 4 shows the optimal sample-based cut-off points for each predictor, the value of the loss function, and the rates of false and true positives corresponding to the optimal cut-off point for each predictor, while Table 5 contains the different analytic measures of performance for all of the predictors. WG and SS are the best predictors in the sense defined by the loss function, which assigns a penalty two times larger to a false negative

PREDICTING LOW AND INSUFFICIENT BIRTH WEIGHT 307

TABLE 5. Measures of performance for all of the predictors'

Sensitivity Specificity PPV NPV Predictor (a) (a) (%) (%) RR 2.4 Az

WG 77.6 83.3 73.2 86.4 5.4 1.04 .85 MAC 62.7 92.9 84.0 80.6 4.3 0.87 .81 TS 94.0 50.9 52.9 93.6 8.2 0.84 .so ss 89.6 64.6 60.0 91.3 6.9 1.01 .84 MLC 83.3 65.5 58.5 87.1 4.6 0.77 .78

'WG weight gain; MAC: mid-arm circumference; TS: triceps skinfold; SS: subscapular skinfold; MLC: maximal leg circumference; PPV positive predictive value; NW: negative predictive value; RFC relative r isk; Z,: separability between groups; Az: area under the operating characteristic (ROC) curve.

TABLE 6. Goodness of fit of two-uariahle and three-uarrable predictive models hased

on logistic regression'

Asymptotic Predictors in the model chi-square

Two-variable models WG + MAC WG + TS WG + SS WG + MLC MAC + TS MAC + ss MAC + MLC TS + SS TS + MLC SS + MLC

Three-variable models WG + MAC + TS WG + MAC + SS WG + MAC t MLC WG + TS + SS WG + TS + MLC WG T SS + MLC MAC + TS + ss MAC i TS + MLC &AC + SS + M E TS + SS + MLC

91.35 88.05 91.902 85.99 82.37 88.54 75.54 59.62 61.84 71.77

105.36 107.943

99.07 95.03 89.76 93.82 92.83 87.24 93.94 74.68

'WG weight gain; MAC: mid-arm circumference; TS: triceps skinfold; SS: subscapular skinfold; MLC: maximal leg circumference. 'Best two-variable model. 'Best three-variable model.

than to a false positive. WG and MAC are highly specific (specificity = 1 - rate of false positives), while TS, SS, and MLC are highly sensitive (sensitivity = rate of true positives). Consequently, WG and MAC are the best positive, and TS, SS, and MLC the best negative, predictors. These tables show that WG and SS perform better than the other predictors not only with respect to the sample-based optimal cut-offs, but also along the series of all possible cut-off points (the loss is lower and A, is higher for WG and SS).

Table 6 shows the asymptotic chi-square used as a measure of goodness of fit for all the regressions consisting of two or three predictors. The best model with just two pre-

dictors includes WG and SS; the best with three predictors includes WG, MAC, and SS.

Performance measures are provided in Table 7 for the estimated risk of BW < 3,000 g corresponding to the best two-variable and three-variable models. A clear improvement in predictive capacity is observed with re- spect to individual predictors. The losses de- crease and the overall performance (A,) in- creases. Not much predictive power would be gained by adding MAC to a model which already includes WG and SS, although the sample performance of the three-variable model about the optimal cut-off points is considerably better, as given by the losses.

Finally, Table 8 shows a matrix of corre- spondence between values of the categorized predictors and the conditional estimated risks for the two- and three-variable predic- tive models. A pregnant woman has an esti- mated risk of 0.04 of delivering an infant with BW < 3,000 g if her increments for WG, MAC, and SS are above the cut-off points, and of 0.93 if all of the increments are below the same cut-offs.

DISCUSSION The percentage of low birth weight shown

in Table 1 is substantially higher than the reported national percentage. Although no equivalent figures for the rate of insufficient birth weight are available, the 22.7% of the present sample is also quite high. This is probably due to the composition of the sam- ple which contained high proportions of un- derweight and adolescent women. This is a characteristic of the community in which the study took place and was the reason for having a program of nutritional surveil- lance.

There is a striking difference for the in- crements of all of the indicators between mothers of normal body weight infants and mothers of infants with BW < 3,000 g. Par- ticularly interesting is the fact that the MAC of the latter decreases while both TS and SS

308 R. JIMENEZ AND J. BACALLAO

TABLE 7. Measures of performance for the two- and three-uariable logistic predictive models'

Model Sensitivity (5%) Specificity (%) PPV (%I NPV (%I RR ZA AZ

WG + SS 79.1 83.3 73.6 87.2 5.8 1.32 .90 W G + M A C +

91.0 79.8 93.8 72.6 11.7 1.36 .91 + ss 'WG: weight gain; MAC: mid-arm circumference; SS: subscapular skinfold; PPV positive predictive value; NW: negative predictive value; K R relative risk ZA: separability between groups; Az: area under relative operating characteristic (ROC) curve.

TABLE 8. Matrix ofcorrespondence between values ofthe predictors and estimated risks of insufftcient birth weight for the two-variable and three-uarinble uredictiue moakls'

Predictorsz

Model WG MAC SES

WG + SS 1 1 1 2 2 1 2 2

W G + M C A + S S 1 1 1 1 1 2 1 2 1 1 2 2 2 1 1 2 1 2 2 2 1 2 2 2

Risk of birth weight

below 3,000 g

.a1

.35

.27

.05

.93

.68

.63

.20

.69

.24

.20

.04

'For all three predictors 1 means below cuhff, and 2 at or above cuboff. W G weight Eain; MAC: mid-arm circumference; SS: subscapular s h - Cold. 'Below or above cueoff point.

increase slightly. Although the controversy around the relative importance of incre- ments in body fat and lean body mass re- mains essentially unanswered, there is some moderate evidence that the distribu- tion of the fat deposits could be related to birth weight. Both the loss function and the separability index Z, (and consequently also A,) perform slightly better for SS than for MAC, TS, or MLC. This result is consistent with Genaro and Bacallao (1991), who found that the relative location of the body fat de- posits, as described by a second component in a principal components analysis, was more strongly related with birth weight than the absolute increment of maternal body fat. The second component was charac- terized by the contrast between central and peripheral fat. Frisancho et al. (1977) also reported a good correlation between mater- nal arm fat and infant fatness, but not be- tween the former and birth weight.

Weight gain is the best predictor of BW < 3,000 g. It shows both the lowest loss and the highest overall measure of perfor- mance A,.

In the study by Fescina (1983), the aver- age growth rate reported between gesta- tional weeks 18 and 25 is 0.4 kgiweek. Lechtig (1988) found a mean monthly weight gain of 1.2 kg/month in Guatemalan women during the last two trimesters of ges- tation. Between weeks 12 and 34, the aver- age increase was 1.49 kglmonth in this study. There is substantial difference, how- ever, in the amount of weight gain between mothers of children whose BW is below 3,000 g and those with normal birth weight. The relative risk associated with the cut-off point is considerably greater than that re- ported by Kramer (1987, 5.4 compared to 2.0). Although the cut-off point is the same in both cases (7 kg), the target events are different (BW < 3,000 g in this study and IUGR in the study by Kramer) and the pe- riod of pregnancy is also different.

The changes in MAC in Cuban women are comparable to those reported in studies of women in industrialized countries, but two clearly distinguishable subpopulations emerge when infants with normal birth weight and those with low or insufficient birth weight are separated (Table 3). Bis- senden (1981) found an average increase of 0.3 cm during the second trimester of preg- nancy, while Metcoff (1986) reported an in- crease of 0.5 cm between 20 and 35 weeks of gestation. This pattern of change is different from those reported in developing countries where consistent losses in MAC during preg- nancy are usually found. For example, Hull (1983) found that mean MAC decreased from about 24.2 cm at 4-12 weeks of gesta- tion to 23.8 at 30-38 weeks, and Krasovec (1989) reported an average decrement of 0.6 cm during an entire pregnancy.

Lechtig (1988) has compared anthropo- metric indicators for predicting risk of low birth weight, along the same lines that have been used in the present study, in terms of sensitivity, specificity, relative risk, and predictive values. However, attention was limited to crude values of the indicators and

PREDICTING LOW AND INSUFFICIENT BIRTH WEIGHT

APPENDlX A. Parameter estimates and standard errors in all two-variable models'

309

Indicators Models WG MAC TS ss MLC WG + MAC 2.33 2.31 - - -

.44 .so WG + TS 2.52 - 2.11 - -

.60 .41 WG + SS 2.41 - - 2.05 -

.42 - - .49 - WG + MLC 2.40 - -

.42 MAC + TS - 2.47 2.29 - -

MAC + ss - 2.43 - 2.28 - - .45 - .48 -

MAC + MLC - 2.17 - - .45 -

TS + SS - - 1.62 1.99 -

- .62 .49 - 1.95 - 1.91 TS + ML€ -

5 8 - .59 ss t MLC - - - 2.13 1.89

- - 48 45

- - -

- - -

- 1.68 - .46 __ -

- - .44 .58 -

- 1.66 - .45

- -

- -

-

'WG weight gain; MAC: mid-arm circumference; TS: triceps skinfnld; SS: subscapular skinfnld; MLC: maximal leg circumference.

APPENDIX B. Parameter estimates and standard errors in all three-variable models1

Indicators Models WG MAC TS ss MLC

W G + M A C + T S

WG + MAC + SS

WG + MAC + MLC

WG + TS + SS

WG + TS + MLC

WG + SS + MLC

MAC + TS + SS

MAC + TS + MLC

MAC t SS + MLC

TS I SS + MLC

2.06 .44

1.96 .45

2.10 .44

2.29 .43

2.19 .43

2.05 .45 - - - - - - - -

1.91 .47

1.90 .49

1.74 .49

- 2.31

.46 2.27

.49 2.24

.52 -

1.94 .64 -

- -

1.16 .68

1.69 .64 - -

1.29 .65

1.98 .64 - -

1.09 .66

- 1.84

5 2 -

1.47 .58

- 1.73 5 2

1.68 .55 - -

2.07 .52

1.75 .52

- - - -

1.11 .50 - -

1.31 .49

1.28 .50 - -

1.16 .49

1.13 .50

1.71 .46

'WG weight gain; MAC: mid-arm circumference; TS: triceps skinfnld; SS: subscapular skinfold; MLC: maximal leg circumference.

not to changes over periods of pregnancy as in the present case.

In developing countries, the use of MAC is recommended for screening in order to iden- tify women at nutritional risk, but not for monitoring purposes on the ground that it changes little during pregnancy and that re- peated measurements increase error (Shah, 1991). However, in the present study, changes in MAC have high predictive per-

formance and very good separability be- tween normal and low or insufficient birth weight infants. This may be due to the low technical error of the measurements. How- ever, if the number of women to be measured in a working session is small, errors can be kept low even by a staffwith limited anthro- pometric experience.

Skinfold thicknesses have not been fre- quently used as predictors of pregnancy out-

R. JIMENEZ AND J. BACALIAO 31 0

come. The main reason lies in the difficulty in consistently achieving reproducible mea- surements, particularly under the condi- tions of pregnancy. Taggart et al. (1967) cau- tion that “. . . skinfold measurements are relatively inaccurate and . . . a high degree of standardization is required to obtain reli- able comparisons.” The present study re- veals, however, that if the TEM can be kept reasonably low, SS (more than TS) is highly sensitive and could be combined with a spe- cific measure such as WG or MAC to predict BW < 3,000 g.

WG alone is a good predictor of low birth weight but its impact depends very strongly upon prepregnancy weight (Garn, 1991). The predictive capacity of WG increases if it is combined into a single model with two (MAC and SS) or at least one (SS) variable. From the statistical point of view, this im- provement is brought about by the addition of an indicator which is made sensitive by the selection of a proper cut-off. From the biological point of view, the improvement is achieved by the addition of one or two indi- cators which account for changes in body fat and lean body mass. This seems to be the reason for which adding SS alone, or both SS and MAC, improves the overall performance of the prediction rule.

An important methodological aspect that needs emphasis is the use of the ROC curves for comparisons among indicators. The ad- vantage of this procedure is that predictors are compared over their whole range of vari- ation and not only with respect to a single, sample-dependent cut-off point that could very likely change from one setting to an- other.

The conclusions derived from the present study concerning the relative merits of the chosen indicators are limited to the period of pregnancy comprised between weeks 12 and 34, but they are reliable within that period. Estimates of effects (as given by the param- eters of the predictive models) and their standard errors are remarkably consistent across the 20 logistic models that have been fitted (see Appendix). This is the most solid criterion (Achen, 1983) of the reliability of any estimates of effect.

The matrix of correspondence shown in Table 8 represents the essential difference between the present approach and previous ones dealing with the same or related prob- lems, because it is not limited to a classify-

ing strategy for identifying women at nutri- tional risk. It also yields estimates of such risk of delivering an infant with birth weight below 3,000 g. As a strategy for screening this seems to be the best alternative, al- though these particular estimates can only be applied in settings with similar condi- tions to those prevailing in the described population. The methodological procedure, however, remains valid.

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Achen C (1983) Interpreting and Using Regression. Bev- erly Hills: Sage Publications.

Bacallao J (1986) Un test estadistico para evaluar la eficiencia de 10s modelos de prediccion. Rev. Ciencias Matemat, 3:49-58.

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APPENDIX Tables A and B show the stability of the

estimates of the parameters of the logistic models and their standard deviations, for models having the same number of predic- tors. This is a very reliable criterion to as- sess the impact of any given regressor in predictive models.