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Revista de Saúde Pública

On-line version ISSN 1518-8787

Rev. Saúde Pública vol.51  São Paulo  2017  Epub Dec 04, 2017

http://dx.doi.org/10.11606/s1518-8787.2017051006775 

Original Articles

Validity of self-reported weight, height, and BMI in mothers of the research Birth in Brazil

Roberta Gabriela Pimenta da Silva AraújoI  II  IV 

Silvana Granado Nogueira da GamaI 

Denise Cavalcante de BarrosII 

Cláudia SaundersIII 

Inês Echenique MattosI 

IFundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Departamento de Epidemiologia e Métodos Quantitativos em Saúde. Rio de Janeiro, RJ, Brasil

IIFundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Centro de Saúde Escola Germano Sinval Faria. Rio de Janeiro, RJ, Brasil

IIIUniversidade Federal do Rio de Janeiro. Instituto de Nutrição Josué de Castro. Rio de Janeiro, RJ, Brasil

IVFundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Programa de Pós-Graduação em Epidemiologia em Saúde Pública. Rio de Janeiro, RJ, Brasil

ABSTRACT

OBJECTIVE

To evaluate the accuracy of information on pre-gestational weight, height, pre-gestational body mass index, and weight at the last prenatal appointment, according to maternal characteristics and sociodemographic and prenatal variables.

METHODS

The study was developed using data from the face-to-face questionnaire and prenatal card (gold standard) of the study “Birth in Brazil, 2011–2012”. To evaluate the differences between the measured and self-reported anthropometric variables, we used the the Kruskal-Wallis test for the variables divided into quartiles. For the continuous variables, we used the Wilcoxon test, Bland-Altman plot, and average difference between the information measured and reported by the women. We estimated sensitivity and the intraclass correlation coefficient.

RESULTS

In the study, 17,093 women had the prenatal card. There was an underestimation of pre-gestational weight of 1.51 kg (SD = 3.44) and body mass index of 0.79 kg/m2 (SD = 1.72) and overestimation of height of 0.75 cm (SD = 3.03) and weight at the last appointment of 0.22 kg (SD = 2.09). The intraclass correlation coefficients (ICC) obtained for the anthropometric variables were: height (ICC = 0.89), pre-gestational weight (ICC = 0.96), pre-gestational body mass index (ICC = 0.92), and weight at the last appointment (ICC = 0.98).

CONCLUSIONS

The results suggest that the mentioned anthropometric variables were valid for the study population, and they may be used in studies of populations with similar characteristics.

Key words: Pregnant Women; Body Weight; Body Height; Body Mass Index; Self-Assessment; Reproducibility of Results; Validation Studies

INTRODUCTION

The evaluation of the anthropometric nutritional status is part of the clinical practice and is frequently used in health research. Weight and height are important instruments for the anthropometric evaluation of the population, since they are good predictors of the functional conditions of the organism, morbidity, and mortality25. During gestation, these measures are useful anthropometric indicators for the prevention of unfavorable maternal outcomes, such as inadequate weight gain, gestational diabetes, and hypertension, as well as problems with the child, such as macrosomia and perinatal death8,11,25.

Pre-gestational body mass index (BMI) is one of the most relevant indicator to monitor the nutritional status of women during pregnancy. The Institute of Medicine (IOM) and the Ministry of Health of Brazil recommend the classification of BMI to estimate the appropriate total gestational weight gain for each woman, which may reduce the number of complications for the mother-child binomial6,11.

In addition, the IOM recommends that validation studies should be developed for weight, height, and BMI at different stages of the gestational period to support guidelines proposed for weight gain during pregnancy11.

These measurements are obtained using easy to medium complexity techniques that are non-invasive, and the direct measurement is the preferred way to obtain these data. However, because of problems such as lack of equipment and high cost of research, population-based epidemiological studies have used reported measures of weight and height as a valid alternative to those acquired directly, since they produce proxy results of real values9,11,18.

Given the importance of these measures, by verifying the validity of this information we can help in the correct classification of the nutritional status of women, allowing the use of reported data for a population sample with the same characteristics.

This study aimed to evaluate the accuracy of pre-gestational weight, height, pre-gestational BMI, and weight at the last prenatal appointment reported by women, according to maternal characteristics and sociodemographic and prenatal variables.

METHODS

This is a descriptive study of the validity of the anthropometric information of the research “Birth in Brazil, 2011–2012”, a research of national scope and hospital basis carried out with mothers and their babies, between February 2011 and October 2012, in Brazil. The sample was selected in three stages. The first one consists of hospitals with 500 or more births a year, stratified by the five macro-regions of the country, location (capital or non-capital), and type of hospital (private, public, and mixed). The second stage consists of the number of days in each hospital (minimum of seven days), and the third one consists of the mothers. In each of the 266 hospitals sampled, 90 mothers were interviewed, amounting to 23,894 individuals. More information about the sample design is detailed in Vasconcellos et al.24

Face-to-face interviews were carried out with the mothers during hospitalization, data were extracted from the woman’s and the newborn’s medical records, and pregnancy prenatal care cards were photographed7,15.

In order to meet the objective of this validation study, we considered as eligible women who had the prenatal card, from which we obtained the reference values (gold standard) for the variables: pre-gestational weight in kilograms (kg), height in centimeters (cm), weight at the last appointment (kg), and pre-gestational BMI obtained using the formula [pre-gestational weight (kg) / height2 (m2)].

Another inclusion criterion was the mother who answered at least one of the questions in the face-to-face questionnaire on biometric information, corresponding to the reported measures “What was your weight before pregnancy?”, “What was your weight at the last prenatal appointment?”, “What is your height?”.

The variables of interest for the validation study were: pre-gestational weight, height, weight at the last prenatal appointment, pre-gestational BMI, hospital macro-region, type of service in which the prenatal appointments were performed (public, private, or both), age group (< 20, 20–34, ≥ 35 years), self-reported race (black, brown, white, yellow, and indigenous), marital status (living with or without partner), education level (incomplete basic education, complete basic education, complete high school, complete higher education), economic classification (class A or B, class C, class D or E)1, number of prenatal appointments (1–3, 4–5, 6 or more), and number of previous pregnancies (none, one, two, three or more).

Figure 1 represents the flowchart with the inclusion criteria used to obtain the final sample. To exclude the outliers from the measured and reported anthropometric variables, we chose to use the parameters proposed by Larsen et al.13, and we included the classifications in the interval established by ± 3 z-score of the difference between the measured and reported variables in the analysis and results presented. Therefore, the influence of these points on the agreement of the information was evaluated using the values presented by weighted kappa, using the quadratic weight, which is close to the intraclass correlation coefficient (ICC)12.

PN: prenatal; BMI: body mass index

Figure 1 Flowchart to obtain the sample. 

We assessed the validity of the analysis and the potential for selection bias given 25% that not be include, because they don’t have prenatal card. We compared the characteristics of the sample of cases not include with the ones included. For this step of the analyzes, we considered the complex sampling project, applying sample weights and corrections to give consistency between population estimates24.

In the research Birth in Brazil, the probability of selecting women was different; therefore, we needed to create sample weights so that prevalence results could be representative. For the concordance analyses, we used the original sample data, as the purpose of this study was to validate the answers and not to evaluate some type of prevalence. Therefore, we did not weight them in this step. The chi-square test was used for the concordance analyses.

We calculated the average differences of the anthropometric variables by subtracting the values of the reported variables from the values of the measured variables. Therefore, a negative value indicates overestimation of the reported variable in relation to the measured one and a positive value indicates underestimation9,21.

The values of the anthropometric variables were tested using the Kolmogorov-Smirnov test to verify normality. We used the Kruskal-Wallis test to evaluate the average difference of the reported variables (pre-gestational weight, height, weight at the last appointment, and BMI) in relation to the measured variables (reference), divided into quartiles. Wilcoxon signed-rank test was used to identify the differences between the averages of the direct and reported information of the analyzed variables, in their continuous distribution. We chose to use non-parametric tests, since the variables of interest did not have a normal distribution.

For the validation of the measurements, we estimated the sensitivity of the anthropometric variables in relation to pre-gestational weight, weight at the last appointment, height, and pre-gestational BMI, divided into quartiles (P25 – 1st quartile, P25-50 – 2nd quartile, P50-75 – 3rd quartile, and P75 – 4th quartile), and the variations in sensitivity were evaluated according to the maternal, prenatal, socioeconomic, and demographic variables. Measured and reported pre-gestational BMI were categorized according to what is proposed by the World Health Organization25: low-weight < 18.5 kg/m2, normal range 18.5–24.9 kg/m2, overweight 25.0–29.9 kg/m2, and obesity > 30 kg/m2.

We used the intraclass correlation coefficient (ICC), which takes into account systematic bias, two-way mixed, with absolute concordance for the continuous anthropometric variables. We evaluated the existence of interobserver reproducibility, that is, if the tests obtained the same result with different methods, for comparability purposes. To evaluate the observed values, we used the criteria of Landis and Koch12, in which ICC < 0 is poor; from 0 to 0.20, weak; from 0.21 to 0.40, probable; from 0.41 to 0.60, moderate; from 0.61 to 0.80, substantial; and from 0.81 to 1.00, almost perfect. We also used the Bland-Altman plot2 to evaluate the possible systematic patterns and errors of the average differences between the measured and reported variables (ordinate axis), in relation to their average (abscissa axis).

We used Pearson’s chi-square test to analyze the distribution between those with or without accurate pre-gestational weight, weight at the last appointment, height, and pre-gestational BMI. Accuracy was considered acceptable when the average difference between the measured and reported values for weight if within ± 2 kg, for height if within ± 2 cmand between ± 1.4 kg/m2 for BMI5. The statistical level of significance adopted was 5%.

Statistical analysis was performed using the software IBM SPSS for Windows 8, version 20, and winpepi, version 11.43.

The study has been approved by the Ethics Committee of the Escola Nacional de Saúde Pública (92/10) under CAE 0096.0.031.000-10.

RESULTS

A total of 17,093 (71.5%) women had the prenatal card, approximately 23% had pre-gestational weight measured in the first trimester, measured height was present in 43% of the cards, allowing the calculation of the measured pre-gestational BMI in 19.1% of them, and 41.1% of the cards had weight at the last prenatal appointment (Figure 1). For the validation study, of the percentages presented previously, we considered the anthropometric variables within the range of ± 3 z-score, from which we found the record of 50% for measured pre-gestational weight, 79.8%, for height, 44% for pre-gestational BMI, and 93% for weight at the last appointment.

Of those who went to prenatal appointments, 77% in the SUS and 69.5% in the private sector had the prenatal card at the time of the interview. Women of the South and Southeast regions, adolescents, those from class C+D or E, brown, and primigravida were more likely to have the card (data not shown).

The women who reported their height tended to overestimate it by an average of 0.75 cm when compared to the measurements. We verified that the weight reported at the last appointment is close to the one measured, with a difference of 0.2 kg (Table 1).

Table 1 Average values of height, pre-gestational weight, weight at the last appointment, and pre-gestational BMI, according to SD and quartiles. Brazil, 2011–2012. 

Variable n SD Quartile Total

1st 2nd 3rd 4th
Height Average measured 5,882 6.774 149.8 155.6 160.4 167.7 158.57
Average reported 5,882 6.993 150.0 156.5 162.5 169.9 159.32
Average height differencec 5,882 3.033 -4.7a -1.0a 0 2.9a -0.750b
Pre-gestational weight Average measured 3,714 12.822 48.0 56.0 63.9 80.1 61.98
Average reported 3,714 12.654 46.6 54.3 61.6 77.9 60.47
Average pre-gestational weight differencec 3,714 3.436 -3.0a 0 1.8a 6.6a 1.510b
Weight at the last appointment Average measured 6,546 13.396 57.2 65.9 74.6 91.4 72.436
Average reported 6,546 13.282 57.5 66.6 75.2 91.1 72.654
Average weight difference at the last appointmentc 6,546 2.092 -2.6a -0.6a 0 2.4a -0.218b
Pre-gestational BMI Average measured 1,446 4.835 19.4 22.1 25.3 31.2 24.479
Average reported 1,446 4.717 18.7 21.6 24.2 30.2 23.69
Average pre-gestational BMI differencec 1,446 1.723 -1.3a 0 0.8a 3.1a 0.790b

n: total of mothers per variable; BMI: body mass index; SD: standard deviation

a Significant differences, according to Kruskal-Wallis test with p < 0.05.

b Significant differences, according to Wilcoxon test, for continuous variables, with p < 0.05.

c Average difference: difference between the measured and reported variables, calculated for each woman within the quartiles, reported as average values in the table. Therefore, there was underestimation if the value is positive and overestimation if the value is negative.

The mothers in 1.51 kg and 0.80 kg/m2, respectively, underestimate pre-gestational weight and pre-gestational BMI. From the second quartile, we can notice a difference in the average of the reported values.

The differences between the measured and reported variables are greater in the extremes, the first and fourth quartiles (Q). We highlight the accuracy of the anthropometric variables; the highest, 76%, was found for weight at the last prenatal appointment, and the lowest, 50%, for pre-gestational weight (Table 2).

Table 2 Distribution of mothers by variables selected for accuracy according to the variables of pre-gestational weight, height, weight at the last appointment, and pre-gestational BMI. Brazil, 2011–2012. 

Variable Height p Pre-gestational weight p Weight at the last appointment p BMI p




Accuracy* Total Accuracy* Total Accuracy* Total Accuracy* Total




N % per category n N % per category N N % per category N N % per category N
Place of the PN < 0.05 < 0.05 0.158 0.091
Public service 3,388 63.9 5,303 1,305 47.1 2,772 3,928 75.9 5,176 794 62.1 1,279
Private service 223 70.8 315 467 59.3 788 894 78.6 1,138 66 69.5 95
Both 158 64.0 247 75 50.7 148 172 76.4 225 52 72.2 72
Total 3,769 64.3 5,865 1,847 49.8 3,708 4,994 76.4 6,539 909 62.8 1,446
Geographic region < 0.05 < 0.05 < 0.05 0.129
North 505 63.8 792 113 42.5 266 359 67.6 531 114 68.7 166
Northeast 1,209 63.5 1,903 456 44.7 1,019 1,531 76.6 2,000 279 59.4 470
Southeast 1,250 62.4 2,002 878 54.4 1,615 2,067 77.8 2,658 298 63.5 469
South 568 70.0 811 336 49.6 677 861 77.8 1,107 164 62.8 261
Midwest 248 66.0 376 65 47.8 136 182 72.5 251 56 70.9 79
Total 3,780 64.2 5,884 1,848 49.8 3,713 4,987 76.4 6,547 908 63.0 1,445
Age group (years) 0.283 0.272 < 0.05 0.347
12–19 865 66.0 1,310 281 46.8 600 1,039 72.5 1,434 172 62.1 277
20–34 2,638 63.8 4,132 1,383 50.3 2,751 3,496 77.4 4,515 657 62.6 1,049
> 34 273 62.8 435 185 51.1 362 459 77.5 592 83 69.2 120
Total 3,776 64.3 5,877 1,849 49.8 3,713 4,994 76.3 6,541 912 63.1 1,446
Race < 0.05 < 0.05 0.276 < 0.05
White 1,131 66.8 1,692 746 54.1 1,380 1,721 77.9 2,208 331 70.9 467
Black 301 61.2 492 110 42.5 259 424 76.4 555 46 44.2 104
Brown 2,277 63.2 3,602 975 48.0 2,032 2,789 75.5 3,693 526 61.2 859
Yellow 53 76.8 69 16 48.5 33 47 72.3 65 8 57.1 14
Indigenous 17 65.4 26 2 20.0 10 17 77.3 22 1 33.3 3
Total 3,779 64.3 5,881 1,849 49.8 3,714 4,998 76.4 6,543 912 63.0 1,447
Marital status of the mother 0.946 0.056 0.067 0.255
Without partner 724 64.4 1,125 292 53.6 545 914 74.4 1,229 121 66.9 181
With partner 3,055 64.2 4,755 1,557 49.1 3,168 4,083 76.8 5,314 791 62.5 1,266
Total 3,779 64.3 5,880 1,849 49.8 3,713 4,997 76.4 6,543 912 63.0 1,447
Education of the mother < 0.05 < 0.05 < 0.05 < 0.05
Incomplete BE 1,008 62.8 1,604 319 40.1 795 1,390 74.3 1,872 183 52.6 348
Complete BE 1,175 63.2 1,858 440 46.2 953 1,368 75.3 1,817 278 63.9 435
Complete HS 1,421 65.2 2,178 891 54.0 1,649 1,893 77.9 2,431 394 66.7 591
Complete HE and more 164 74.2 221 191 63.7 300 324 81.8 396 54 81.8 68
Total 3,768 64.3 5,861 1,841 49.8 3,697 4,975 76.4 6,516 909 63.0 1,442
Economic class < 0.05 < 0.05 0.189 0.073
Classes A+B 581 69.2 839 507 55.8 909 984 78.3 1,257 162 69.5 233
Class C 2,106 63.4 3,321 968 48.4 1,999 2,683 75.9 3,534 534 62.0 861
Classes D+E 1,069 63.9 1,673 366 46.6 786 1,291 75.7 1,705 209 60.9 343
Total 3,756 64.4 5,833 1,841 49.8 3,694 4,958 76.3 6,496 0.178 905 63.0 1,437
Number of prenatal appointments 0.629 < 0.05 < 0.05 0.348
1–3 320 63.0 508 67 47.9 140 347 60.9 570 23 57.5 40
4–5 668 63.4 1,054 136 40.6 335 964 70.5 1,367 81 58.3 139
6 or more 2,790 64.6 4,319 1,647 50.8 3,240 3,688 80.0 4,608 807 63.7 1,267
Total 3,778 64.2 5,881 1,850 49.8 3,715 4,999 76.4 6,546 908 63.0 1,446
Number of previous pregnancies < 0.05 < 0.05 < 0.05 < 0.05
Zero 1,632 65.4 2,495 939 55.2 1,701 2,130 77.0 2,765 458 67.9 675
1 1,059 64.6 1,638 525 50.0 1,049 1,376 77.9 1,767 237 60.8 390
2 553 63.6 869 244 44.8 545 817 77.4 1,056 126 57.3 220
3 or more 536 61.0 878 142 33.8 419 676 70.6 957 91 55.8 163
Total 3,780 64.3 5,880 1,850 49.8 3,714 4,999 76.4 6,545 912 63.0 1,448

N: total of mothers per category for accuracy (between 2 kg/2 cm); n: total of mothers per variable; BMI: body mass index; BE: basic education; HS: high school; HE: higher education; PN: prenatal

* Defined as reported information between ± 2 units (kg or cm) for weight and height, between ± 1.4 units (kg/m2) for BMI, of the measured variable.

Regarding height, Table 3 shows greater accuracy among women with prenatal (PN) in the private sector, from the South region, white, and belonging to the A+B class.

Table 3 Distribution of mothers by variables selected in quartiles for sensitivity, according to height, pre-gestational weight, weight at the last appointment, and pre-gestational BMI. Brazil, 2011–2012. 

Variable Height Pre-gestational weight Weight at the last appointment Pre-gestational BMI




N Sensitivity (%) N Sensitivity (%) n Sensitivity (%) n Sensitivity (%)




1st Q 2nd Q 3rd Q 4th Q 1st Q 2nd Q 3rd Q 4th Q 1st Q 2nd Q 3rd Q 4th Q Low weight Adequate Overweight Obesity
Place of the PN 5,863 3,710 6,538 1,447
Public service 5,302 85.9 56.6 61.6 82.0 2,774 85.5 67.7 66.5 87.1 5,175 93.8 76.2 94.2 92.9 1,280 44.8 81.1 74.1 90.6
Private service 314 90.2 58.1 71.2 94.2 787 86.1 76.8 83.4 87.2 1,138 91.4 73.1 94.2 97.2 95 50.0 79.0 75.0 53.3
Both 247 80.0 47.8 59.3 88.1 149 92.3 65.5 75.7 87.7 225 94.6 76.0 97.4 91.7 72 33.3 86.0 64.3 58.3
Geographic region 5,883 3,715 6,545 1,450
North 793 83.6 73.7 62.2 48.2 266 91.4 67.9 69.3 83.3 529 91.7 89.4 70.2 97.0 167 72.7 94.4 46.8 68.4
Northeast 1,901 82.7 68.7 63.6 52.3 1,019 84.4 66.3 67.6 88.6 2,000 93.3 86.5 84.5 95.3 470 63.2 86.4 60.2 58.0
Southeast 2,003 75.6 68.3 69.5 63.9 1,616 80.0 57.0 79.7 91.1 2,658 91.6 92.6 80.5 97.3 470 92.0 86.9 55.1 84.2
South 811 86.9 77.4 70.2 66.8 677 86.6 71.4 79.0 93.3 1,107 88.4 89.4 83.2 97.9 263 87.5 88.4 55.3 68.8
Midwest 375 77.9 66.2 68.9 50.5 137 80.0 69.7 77.8 82.1 251 78.7 90.7 85.1 96.8 80 80.0 84.8 64.7 28.6
Age group (years) 5,877 3,711 6,540 1,447
12–19 1,310 85.1 68.0 68.4 57.2 599 86.5 67.5 78.5 82.7 1,434 90.5 86.6 77.8 95.9 278 84.6 78.3 39.3 42.9
20–34 4,133 80.2 72.1 66.2 59.1 2,750 82.7 62.0 75.3 91.1 4,514 91.8 90.2 82.5 96.9 1,050 75.0 90.5 57.0 70.8
> 34 434 81.8 63.1 69.0 59.3 362 78.3 66.7 75.9 90.3 592 93.1 95.1 81.0 98.5 119 100 90.0 73.9 86.4
Race 5,887 3,790 6,489 1,448
White 1,693 82.4 72.6 67.6 63.6 1,377 87.1 61.3 79.7 90.7 2,207 89.7 90.3 82.2 97.9 466 94.1 92.2 69.4 75.0
Black 494 62.9 67.9 72.4 58.9 260 79.1 67.6 69.6 94.0 555 91.7 89.6 81.9 98.2 105 100 87.5 31.1 87.5
Brown 3,601 82.7 68.9 65.5 56.8 2,030 81.8 64.3 74.0 90.0 3,693 92.0 89.2 81.2 96.1 861 72.0 85.1 55.1 67.6
Yellow 70 100 85.0 72.7 60.0 32 87.5 80.0 75.0 85.7 11 94.7 94.1 77.8 90.9 14 100 100 25.0 50.0
Indigenous 29 80.0 100 57.1 50.0 10 100 0 100 100 23 100 100 100 100 2 0 0 0 0
Marital status of the woman 5,882 3,715 2,519 1,447
Without partner 1,126 82.6 62.3 65.8 57.7 546 73.7 47.0 65.9 94.7 1,228 92.0 91.7 80.4 98.7 181 81.8 88.9 53.7 85.7
With partner 4,756 81.0 72.2 67.2 59.7 3,169 85.4 67.0 77.1 90.1 1,291 91.2 89.1 81.9 96.7 1,266 78.7 87.6 56.7 69.8
Education level 5,860 3,697 5,061 1,444
Incomplete BE 1,604 79.3 70.2 66.0 56.8 794 84.3 63.5 67.5 82.3 1,872 92.6 85.4 80.5 94.8 349 63.2 84.1 47.6 69.0
Complete BE 1,858 83.7 66.4 67.0 53.4 954 83.8 64.6 76.9 88.8 359 91.3 88.5 80.8 98.6 436 88.9 84.7 52.7 64.6
Complete HS 2,178 81.0 72.9 66.6 63.9 1,650 81.7 60.3 77.8 94.2 2,432 90.2 93.8 82.2 97.4 591 80.0 91.3 60.8 79.2
Complete HE or more 220 93.3 79.4 71.9 65.7 299 90.9 75.0 80.0 92.0 398 92.5 91.7 87.2 96.4 68 100 97.2 72.7 55.6
Economic class 5,830 3,692 6,497 1,438
Classes A+B 837 80.5 70.3 69.3 66.3 910 86.2 73.8 82.3 93.7 1,260 89.9 90.5 82.4 98.0 234 90.9 86.7 65.0 64.3
Class C 3,322 80.9 70.6 67.5 60.1 1,997 82.5 62.0 73.9 90.8 3,532 90.0 91.0 81.8 96.5 862 82.9 88.6 56.2 73.9
Classes D+E 1,671 81.9 69.9 63.6 50.9 785 83.5 57.6 67.8 84.1 1,705 93.3 86.9 80.7 96.7 342 69.2 85.9 50.6 67.6
Number of PN appointments 5,879 3,714 6,545 1,445
1–3 508 79.5 71.7 59.5 55.7 140 82.1 19.7 73.3 90.5 569 91.3 85.8 64.8 91.2 41 100 88.9 26.7 71.4
4–5 1,053 84.1 71.8 67.1 50.9 334 84.0 67.5 70.3 68.3 1,368 88.2 85.5 82.2 94.9 137 50.0 86.1 47.6 50.0
6 or more 4,318 80.9 69.8 67.7 61.5 3,240 83.5 66.3 76.4 92.2 4,608 92.9 91.6 82.9 97.8 1,267 83.6 87.9 58.8 72.6
Number of previous pregnancies 5,877 3,713 6,544 1,446
Zero 2,495 78.8 70.4 59.1 61.4 1,702 81.7 63.5 79.8 94.2 2,765 91.6 89.6 81.1 97.2 673 88.6 89.8 47.1 67.9
1 1,638 81.2 70.5 66.8 60.8 1,049 87.4 63.1 76.2 89.1 1,766 89.9 91.2 83.0 97.5 390 57.9 83.6 64.4 71.1
2 868 84.0 73.2 59.4 50.0 544 77.6 67.5 70.7 89.9 1,057 92.8 92.6 82.9 95.5 218 71.4 90.3 63.2 80.0
3 or more 876 85.0 67.2 69.7 58.8 418 88.1 58.8 65.5 85.8 956 92.0 83.9 79.0 97.7 165 100 85.3 59.0 65.7
Total 5,884 81.4 70.3 66.9 59.5 3,715 83.5 63.4 75.7 90.5 6,543 91.4 89.6 81.7 97.1 1,448 79.2 87.7 56.2 71.6

BMI: body mass index; Q: Quartile; n: total of mothers per variable; n: total of mothers per category; BE: basic education; HS: high school; HE: higher education; PN: prenatal

For the pre-gestational weight of those who had prenatal care in the private sector, accuracy was higher in the group of Southeast residents, white, with higher education, and ≥ 6 prenatal appointments. For the weight at the last prenatal appointment, better results were found for women from the South or Southeast regions, with higher education, ≥ 6 prenatal appointments, adult, and up to two pregnancies. Regarding pre-gestational BMI, the mothers who were white, had higher education, and primigravida presented statistically significant differences in accuracy.

In Figure 2, the Bland-Altman plot was used to show the difference between the measured and reported pre-gestational weight. The average difference of pre-gestational weight and the highest concentration of points are above the zero point, which shows an underestimation of the reported pre-gestational weight values, that is, women tend to report a lower pre-gestational weight. The same pattern can be observed for pre-gestational BMI. The Wilcoxon test was used to compare these measures, confirming the underestimation of the information.

Figure 2 Differences between measured and reported anthropometric variables (pre-gestational weight, pre-gestational BMI, weight at the last visit, and height), according to the averages of the anthropometric variables in mothers. Brazil, 2011–2012. 

Conversely, weight at the last appointment and height, according to the chart, show that the reported measures are overestimated by the mothers, that is, women tended to report greater weight at the last appointment and greater height than the measure of reference, which was present on the prenatal card. However, the Wilcoxon test showed that the differences between the measured and reported values were significant, even though most of the women reported the same value as the measured variable, both for weight at the last appointment and height.

The ICC showed high agreement between the measured and reported information for height (ICC = 0.898, 95%CI 0.880–0.912), pre-gestational weight (ICC = 0.957, 95%CI 0.930–0.971), weight at the last appointment (ICC = 0.988, 95%CI 0.987–0.988), and pre-gestational BMI (ICC = 0.922, 95%CI 0.871–0.948) (data not shown in tables).

Table 3 compares the reported and measured variables, divided into quartiles, for sensitivity analysis. For height, sensitivity was high in the first quartile. As the quartiles increased, the validity of information decreased, reaching 59.5% in the fourth quartile. For pre-gestational weight, we found the highest sensitivity in the fourth quartile.

Sensitivity for height indicated that the lowest percentages were among women who had prenatal appointments in both public and private services, in the North region, adolescents, brown, with complete basic education, and in class D+E. For pre-gestational weight, sensitivity was higher among women from private establishments, in the South region, and aged between 20 and 34 years.

For weight at the last prenatal appointment, sensitivity was generally high. In the first quartile, we found 91.5%, reaching 97.1% in the fourth quartile. When we evaluated the sensitivity of the strata, the lowest values were found among women from the North and adolescents.

Reported BMI showed a sensitivity of 88% for women with adequate classification and the lowest percentage was for overweight women; that is, the validity of the information was lower among overweight women, and, when we evaluated the sensitivity of the information using the variables selected by strata, we observed the lowest percentages of reported BMI.

DISCUSSION

This study showed that most mothers accurately report their anthropometric data. The tendency to underestimate pre-gestational weight, as well as BMI, corroborates with the results of the literature14,18,19,22.

Weight at the last prenatal appointment was overestimated, but with a lower variation than that found for pre-gestational weight, which differs from the results found by Oliveira et al.18, in which pregnant women tended to underestimate the information. The lower variation found for weight at the last prenatal appointment may be related to memory, because of the lower interval between the last appointment (when weight was measured) and the information collected in the research. Considering that the interval between prenatal appointments decreases in the months before birth, women have greater access to prenatal care and information, which can improve their report16.

Women with lower weight and height tended to overestimate information, while those with greater weight and height tended to underestimate. The patterns established in search of a social ideal, generating a distortion of the self-image, can lead to errors when the information is reported, be it for weight or height4,5,7,21.

The overestimation of height, found in this study, has also been identified by other authors4,8,10,18. The shortest and highest women presented greater variation of information, differing from the results of Fonseca et al.9, who have found that the higher the individual, the smaller the difference found for this measure.

The accuracy of the information on reported height may change because of the presence of age-related bias. Younger women are measured only once in the gestation period by health professionals, who do not mind the fact that they are growing. Older women refer to a stature that they had in the past. Socioeconomic status and race may also contribute with the decrease in both the accuracy for height and weight, as they are associated with access to care and information about the health status. Therefore, non-white persons in less favorable conditions are those who have less accurate information3,8,17,20.

In the graphical analysis for pre-gestational weight and pre-gestational BMI, we observed a spacing between the points for women weighting approximately 70 kg and in the overweight range, respectively, in addition to a tendency for the underestimation of both measures, also observed in other studies18,23.

The ICC, which take systematic errors into account, were high for all anthropometric variables, showing almost perfect agreement and agreeing with other studies9,14.

Sensitivity values were high. Sensitivity showed a greater concordance of information for pre-gestational weight and weight at the last appointment, in the first and fourth quartiles, and for women who were classified as low weight and obese according to pre-gestational BMI, in agreement with the results of other studies7,18,22. This could be because women with inadequate weight (low or higher than expected) or inadequate pre-gestational BMI are diagnosed as with nutritional risk and are better monitored in the prenatal care; therefore, they present greater access to information and better perception regarding their anthropometric data.

In relation to height, the shortest women had better sensitivity and the tallest ones (fourth quartile) had a lower percentage of sensitivity, differing from the study of Boström and Diderichsen4, in which the lowest value was in the second quartile.

In this study, women who had prenatal care in the private service, more years of education, white or brown, from the South or Southeast regions, better economic classification, six or more appointments, and less parity presented the best results for validation of the anthropometric variables, reinforcing the strong relation between socioeconomic conditions and the quality of information4,6.

This validation study did not intend to be representative of the Brazilian population. However, the sample size allowed us to evaluate the validity of the information and possible differences between measured and reported measures18.

We highlight that, although the gold standard method used was the prenatal card, the differences between the information resembled those found in national and international studies that have obtained the measures directly, showing that the card is a relevant instrument for the anthropometric evaluation of pregnant women.

The lack of records of the anthropometric variables on the card limited the inclusion of more women who could represent the Brazilian population. However, as the anthropometric data presented high agreement for the self-reported measures, they could be used to outline the nutritional profile of women in the gestational period, as well as their weight gain, allowing their use in population-based studies when no resources for measurement are present.

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Funding: National Council for Scientific and Technological Development (CNPq); Department of Science and Technology, Secretariat of Science, Technology and Strategic Inputs, Ministry of Health; Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz (Project INOVA/ENSP – MCT/CNPq/CT-Saúde/MS/SCTID/DECIT # 057/2009); and, Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ – Process E-26/103.083/2011).

Received: October 19, 2015; Accepted: October 18, 2016

Correspondence: Silvana Granado Nogueira da Gama. Departamento de Epidemiologia e Métodos Quantitativos em Saúde – ENSP/FIOCRUZ Rua Leopoldo Bulhões, 1480. Sala 808 Manguinhos. 21041-210 Rio de Janeiro, RJ, Brasil. E-mail: roberta.araujo.nut@gmail.com

Authors’ Contribution: Design and planning of the study: RGPSA, SGNG, DCB. Collection, analysis, and interpretation of the data: RGPSA, SGNG, DCB. Preparation of the study: RGPSA, SGNG, DCB. Critical review of the study: SGNG, DCB, CS, IEM. Approval of the final version: RGPSA, SGNG, DCB, CS, IEM. Public responsibility for the content of the article: RGPSA, SGNG, DCB, CS, IEM.

Conflict of Interest: The authors declare no conflict of interest.

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