http://informahealthcare.com/jmf ISSN: 1476-7058 (print), 1476-4954 (electronic) J Matern Fetal Neonatal Med, 2015; 28(4): 460–463 ! 2014 Informa UK Ltd. DOI: 10.3109/14767058.2014.921670
ORIGINAL ARTICLE
Maternal anthropometric characteristics as determinants of birth weight in north-west Nigeria: prospective study Emmanuel Ajuluchukwu Ugwa Obstetrics and Gynaecology Department, Federal Medical Centre, Birnin Kudu, Jigawa State, Nigeria
Abstract
Keywords
Objective: This study was undertaken to determine the sensitivity and specificity of anthropometric measurements in predicting birth weight. Methods: This was a prospective study. Interviewer-administered questionnaires were used. Two hundred were used. The weight, height and BMI of the women were measured. Unclothed newborns were weighed immediately after delivery. The data obtained were analyzed using SPSS version 16.0 statistical software. The accuracy of maternal weight, height and body mass index in predicting birth weight was compared using chi-squared test and p50.05 was considered statistically significant. Result: The mean maternal age was 28.2 ± 5.7 years. The mean parity was 3 ± 2. The mean gestational age at delivery was 38.5 ± 2 years. The mean actual birth weight was 3.27 ± 0.53 kg. The mean maternal weight was 72.03 ± 11 kg. Maternal weight showed a strong positive correlation with birth weight (r ¼ 0.48) and this was statistically significant (p50.001). The mean maternal height was 1.64 ± 0.55 m. The mean maternal BMI was 27.9 ± 4.33. Maternal weight, height and BMI had sensitivities of 50%, 40% and 50% and specificities of 48%, 57.9% and 67.3%, respectively. Conclusion: Maternal weight, height and BMI are not good predictors of birth weight and cannot be recommended for use as screening test in poor resource setting where ultrasound available.
Anthropometric characteristics, birth weight, determinants, maternal, prospective study
Introduction Maternal exposure to nutrition and other environmental factors during the period from conception to birth may have an impact on fetal growth as well as the child’s health [1–3]. Infant size, such as birth weight and length, was reported to affect not only infant mortality, but also childhood morbidity [4,5]. Although severe undernutrition, which could lead to permanent changes in structure and metabolism in the fetus is uncommon in developed countries, this is not the case in developing countries where the imbalance or relative deficiency of nutrients could affect fetal growth [6,7]. Both fetal macrosomia and intrauterine growth restriction increase the risk of perinatal morbidity and mortality. Therefore, when these conditions are diagnosed in utero appropriate route of delivery is undertaken to forestall these complications [8]. Fetal weight cannot be measured directly in utero, but it can be estimated or predicted from fetal and maternal anatomical characteristics. Maternal anthropometric
Address for correspondence: Dr. Emmanuel Ajuluchukwu Ugwa, Obstetrics and Gynaecology Department, Federal Medical Centre, Birnin Kudu, Jigawa State, Nigeria. Tel: +2348035851872. E-mail: [email protected]
History Received 21 December 2013 Accepted 02 May 2014 Published online 29 May 2014
measurements afford a simple, cheap and available means of predicting birth weight with a variable degree of reliability. These have been widely studied in other countries [1–7], but studies are rather still scanty in developing world where maternal undernutrition is a predisposing factor to poor obstetrics outcome and perinatal morbidity/mortality. Factors that determine birth weight are maternal height, malnutrition, maternal obesity, maternal pregnancy weight gain, parity, fetal sex, ambient attitude, maternal hemoglobin concentration, paternal height, cigarette smoking and glucose intolerance [8]. In third world countries, where poverty among reproductive aged women is prevalent, malnutrition is a common factor that can substantially affect the size of neonates at all gestational ages [8]. Other factors include maternal factors such as race, stature and genetics [9–11], paternal factors such as paternal height [12,13], environmental factors such as attitude, nutrition and physical activities [14], physiological factors such as altered glucose metabolism, hemoglobin concentration and microvascular integrity [15], pathologic factors such as hypertension and uterine malformations, and complications of pregnancy such as gestational diabetes mellitus and pre-eclampsia [16]. According to World Health Organization (WHO), maternal anthropometric aspects and intake of adequate nutrients as
DOI: 10.3109/14767058.2014.921670
Predictive value of maternal anthropometric measurements in birth weight
determinants of fetal growth demonstrate a close association with these parameters and weight and gestational age at birth [17,18]. Anthropometric measurements, among the most frequently applied methods for assessing nutritional status in pregnant women, have undergone remarkable improvement and are recognized as effective tools in the prevention of perinatal morbidity and mortality [19,20]. Excessive pregnancy weight gain is one of the conditions most strongly associated with postpartum weight retention and, consequently, postpartum obesity [21,22]. Evaluation of maternal nutritional status relies on measures such as pre-pregnancy weight, height, body mass index (BMI), weight gain at different trimesters, weight gain during pregnancy and skinfold thickness. Numerous research projects have studied maternal anthropometric characteristics as predictors of birth weight [23–28]. Although there is considerable work done on this topic in other countries, the present work was done among women presenting in a Teaching Hospital in the Sub-Saharan African. The aim of this study was to examine the relationship between the birth weights of babies delivered between 1 September and 31 December 2011 in AKTH at various gestational ages with certain maternal anthropometric measurements (weight, height and BMI) and to determine the sensitivity and specificity of these measurements in predicting birth weight. This can be recommended for use among peripheral health workers for detection of mothers at risk of delivering big or low birth weight babies and need for in utero transfer to centers where caesarean section and neonatal care can be offered.
Methods This research was a prospective study. The study subjects were consecutive pregnant women with singleton pregnancy admitted for either vaginal or planned abdominal delivery between 1 September and 31 December 2011. Ethical approval from Aminu Kano Teaching Hospital Ethics Committee and informed consent from subjects were obtained. Two hundred consecutive pregnant women who fulfilled the inclusion criteria were counseled and, after consenting, were included in the study. Inclusion criteria include; singleton pregnancy above the age of viability (28 weeks and above in Nigeria). Exclusion criteria include; patients’ refusal, and patients with polyhydramnios, ruptured membranes, multiple pregnancies, oligohydramnios, intrauterine fetal death and pregnancy with uterine or adnexal pathology. Sample size was determined using malnutrition prevalence among women of 10–40%. n ¼ z2 p q=d 2 n ¼ sample size, z ¼ standard normal deviation ¼ 1.96 at 95% confidence limit, p ¼ prevalence rate ¼ 15%, q ¼ 1 p ¼ 1– 15% ¼ 0.85, d ¼ error margin ¼ 5%. n¼
1:962 0:15 0:85 ¼ 196 0:052
Interviewer administered questionnaires were used to obtain sociodemographic and clinical information. The weight and height of all the recruited women were measured during the
461
admission for delivery. The study subjects were weighed using spring balance (adult) with minimum clothing after correcting zero error. The weight was recorded to the nearest 50 g. The height was measured keeping the women standing on level ground, without footwear, against a wall, by using measuring tape to the nearest 0.5 cm. The maternal weight and height obtained was used to calculate maternal BMI (kg/m2). Similarly, unclothed newborns were weighed immediately after delivery using a standard analogue Salters (England) scale corrected for zero error. The interval between Table 1. Distribution of sociodemographic characteristics of the study group. Parameter
Frequency
Age (years) 15–24 25–39 40–49 Parity 1–2 3–4 5 Gestational age (weeks) 538 38–40 440 Employment status Unemployed Employed Educational status None Qua’aric Primary Secondary Tertiary
Percentage
Mean ± SD
100 96 4
50 48 2
28.2 ± 5.7
76 100 24
38 50 12
3±2
18 164 18
9 82 9
38.5 ± 2
145 55
72.5 27.5
2 4 9 84 101
1 2 4.5 42 50.5
Table 2. Distribution of actual birth weight of the babies in the study group. Actual birth weight (kg) 52.5 2.5–3.99 4 Total
Frequency
Percentage
Mean ± SD
10 176 14 200
5 88 7 100
3.27 ± 0.53
Table 3. Distribution of anthropometric characteristics and their correlation with birth weight. Parameter Weight (kg) 51–60 61–70 71–80 81–90 Height (m) 1.5–1.59 1.6–1.69 1.7–1.79 BMI (kg/m2) 15–24.99 25–34.99 35–44.99
Frequency
Percentage
35 65 55 45
17.5 32.5 27.5 22.5
80 94 26
40 47 13
65 125 10
32.5 62.5 5
Mean ± SD
r
p value 50.001
72.03 ± 11 0.48
50.001
1.64 ± 0.55 0.25 27.9 ± 4.33
0.28 50.001
BMI, body mass index; SD, standard deviation at 95% confidence interval; r, coefficient of correlation.
462
E. A. Ugwa
J Matern Fetal Neonatal Med, 2015; 28(4): 460–463
Table 4. Distribution of the power of maternal anthropometric parameters in predicting birth weight. Maternal anthropometric parameters Weight Height BMI
True positive
False positive
False negative
True negative
Sensitivity (%)
Specificity (%)
Positive predictive value (%)
Negative predictive value (%)
5 4 5
95 76 62
5 10 5
95 110 128
50 40 50
48 57.9 67.3
5 5 7.46
95 91.7 96.2
anthropometric measurements and delivery of the babies was within 24 h. The data obtained were analyzed using SPSS version 16.0 statistical software (SPSS Inc., Chicago, IL). Absolute numbers and simple percentages were used to describe categorical variables. Similarly, quantitative variables were described using measures of central tendency (mean, median) and measures of dispersion (range, SD) as appropriate. The accuracy of maternal weight, height and body mass index in predicting birth weight was compared using chi-squared test and p50.05 was considered statistically significant.
Results As shown in Table 1, the mean maternal age was 28.2 ± 5.7 years, mean parity was 3 ± 2 and average gestational age at delivery was 38.5 ± 2 years. Table 2 shows a mean actual birth weight of 3.27 ± 0.53 kg. Table 3 shows a mean weight 72.03 ± 11 kg and maternal weighed showed a strong positive correlation with birthweight (r ¼ 0.48). As shown in Table 4, maternal weight and BMI had sensitivities of 50% each and specificities of 48 and 67.3% respectively.
Discussion Studies in other countries [25,26] have shown that mothers who are malnourished are most likely going to give birth to low birth weight babies and vice visa. Furthermore, studies in Nigeria by Kemiki and Akindele [27], and also by Fakeye and Adetoro [28] have shown that birth weight correlated well with maternal weight. The mean birth weight from this study was 3.27 ± 0.53 kg. This was similar to studies in Ile-Ife, Nigeria, where Shittu et al. [8] reported an actual average birth weight of 3.255 ± 0.625 kg and Ayoola et al. [29] who reported mean birth weight of 3.238 ± 0.452 kg [29]. These reports from developing countries were comparable with those of studies from USA, Great Britain and Singapore that showed that the mean birth weight at 38–42 completed weeks’ gestation was 3.060–3.520 g (range, 0.460 g) [11], 3.201–3.753 g (range, 0.551 g) [30] and 2.880–3.290 g (range, 0.410 g) [31], respectively. The mean birth weight is however 42.746 ± 0.40 kg reported in India where 17.30% of the newborns had low birth weight compared to 5% in the present study [32]. The present study showed significant positive correlations among maternal weight and birth weight (r ¼ 0.48), maternal height and birth weight (r ¼ 0.25), maternal body mass index and birth weight (r ¼ 0.28). This was comparable to significant positive correlations observed among maternal weight and birth weight (r ¼ 0.38), maternal height and birth weight (r ¼ 0.25), and maternal body mass index (BMI) and birth weight (r ¼ 0.30) reported by Mohanty et al. [33]. From the
above report, maternal weight was the strongest determinant of birth weight. This was also confirmed in regression analyses by Karim and Mascie-Taylor [34] who reported correlation coefficient between maternal weight and birth weight of 0.49. This study has also shown that the correlation between maternal weight, height and BMI and birth weight was statistically significant but Jananthan et al. [35] did not report any significant influence of height on birth weight. This study has shown that maternal weight and BMI had sensitivities of 50% each and specificities of 48% and 67.3%, respectively. Therefore, both maternal weight and BMI are equally good predictors of low birth weight but BMI is a better predictor of normal birth weight babies than maternal weight. The predictive value of maternal height for low birth weight is poor (40%), however its value is in prediction of normal birth weight (57.9% specificity). This finding is comparable to those of Jananthan et al. [35] and other workers [23–28,34]. This is, however, in contrast to study by Elshibly and Schmalisch [36] who reported that maternal height was the second most important parameter which influences the risk for LBW in Sudanese mothers. They also reported that the lower predictive value of maternal weight measured at delivery in their study could be due to the high individual differences in the changes that occur in the body weight during pregnancy. In conclusion, maternal weight, height and BMI are not good predictors of birth weight and cannot be recommended for use as screening test in poor resource setting where ultrasound available. The limitation of this study is that anthropometric measurements such as BMI are more reliable before pregnancy. However, in our environment, preconceptional care is an evolving field and women commonly present to health facilities only when they are advanced in pregnancy and there may be no record of their pre-pregnancy weight. A community-based study is anticipated during which anthropometric parameters like BMI can be taken before pregnancy and followed up throughout pregnancy.
Acknowledgements The author acknowledges the useful contribution of Dr. Abiodun Omole Ohonsi of Obstetrics and Gynaecology Department and Professor Zubairu Ilyasu of Community Medicine Department both of Bayero University, Kano, Nigeria in preparation of this article.
Declaration of interest The author received no funding for this study. There were no conflicts of interest. The author contributed solely in literature review, study design, statistical analysis and preparation of the article.
DOI: 10.3109/14767058.2014.921670
Predictive value of maternal anthropometric measurements in birth weight
References 1. Strauss RS, Dietz WH. Growth and development of term children born with low birth weight: effects of genetic and environmental factors. J Pediatr 1998;133:67–72. 2. Godfrey KM, Barker DJ. Fetal nutrition and adult disease. Am J Clin Nutr 2000;71:S1344–52. 3. Kramer MS. Why are babies getting bigger? Temporal trends in fetal growth and its determinants. J Pediatr 2002;141:538–42. 4. Mutch L, Ashurst H, Macfarlane A. Birth weight and hospital admission before the age of 2 years. Arch Dis Child 1992;67:900–4. 5. Hack M, Fanaroff AA. Outcomes of children of extremely low birthweight and gestational age in the 1990s. Early Hum Dev 1999; 53:193–218. 6. Mathews F, Yudkin P, Neil A. Influence of maternal nutrition on outcome of pregnancy: prospective cohort study. Br Med J 1999; 319:339–43. 7. Fall CH, Yajnik CS, Rao S, et al. Micronutrients and fetal growth. J Nutr 2003;133:S1747–56. 8. Shittu AS, Oluwafemi K, Orji EO, et al. Clinical versus sonographic estimation of fetal weight in Southwest Nigeria. J Health Popul Nutr 2007;25:14–23. 9. Dombrowski MP, Wolfe HM, Brans YW, et al. Neonatal morphometry. Relation to obstetric, pediatric, and menstrual estimates of gestational age. Am J Dis Child 1992;146:852–6. 10. Brenner WE, Edelman DA, Hendricks CH. A standard of fetal growth for the United States of America. Am J Obstet Gynecol 1976;126:555–64. 11. Amini SB, Catalano PM, Hirsch V, Mann LI. An analysis of birth weight by gestational age using a computerized perinatal data base, 1975–1992. Obstet Gynecol 1994;83:342–52. 12. Morrison J, Williams GM, Najman JM, Anderson MJ. The influence of paternal height and weight on birth-weight. Aust N Z J Obstet Gynaecol 1991;31:114–16. 13. Wilcox MA, Newton CS, Johnson IR. Paternal influences on birthweight. Acta Obstet Gynecol Scand 1995;74:15–18. 14. Lam ETC, Black JM, Little KD, et al. The effects of exercise on birth weight: a meta-analysis. Am J Health Stud 2002;18: 38–45. 15. Lindsay MK, Graves W, Klein L. The relationship of one abnormal glucose tolerance test value and pregnancy complications. Obstet Gynecol 1989;73:103–6. 16. Velentgas P, Benga-De E, Williams MA. Chronic hypertension, pregnancy-induced hypertension, and low birthweight. Epidemiology 1994;5:345–8. 17. World Health Organization. Report of the advisory group meeting on maternal nutrition and low birth weight. Geneva: WHO; 2002. 18. World Health Organization. Promoting optimal fetal development: report of a technical consultation. Geneva: WHO; 2006. 19. Oliveira AF, Gadelha AM, Leal MC, Szwarcwald CL. Estudo da validac¸a˜o das informac¸o˜es de peso e estatura em gestantes atendidas em maternidades municipais no Rio de Janeiro, Brasil. Cad Saude Publica 2004;20:S92–100.
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20. Coelho KS, Souza AI, Batista Filho M. Avaliac¸a˜o antropome´trica do estado nutricional da gestante: visa˜o retrospective e prospectiva. Rev Bras Saude Matern Infant 2002;2:57–61. 21. Lacerda EMA, Leal MC. Fatores associados com a retenc¸a˜o e o ganho de peso po´s-parto: uma revisa˜o sistema´tica. Rev Bras Epidemiol 2004;7:187–200. 22. Ivyna Spinola CJ, Kac G. Determinantes da retenc¸a˜o de peso po´s-parto Segundo a cor da pele em mulheres do Rio de Janeiro, Brasil. Rev Panam Salud Publ 2005;18:403–12. 23. Nahar S, Mascie-Taylor CGN, Housne AB. Maternal anthropometry as a predictor of birth weight. Publ Health Nutr 2007;10: 965–70. 24. Loaiza E. Maternal nutritional status 1997. DHS Comparative Studies No. 24. Calverton (MD): Macro International Inc. 25. Woldemariam G, Timotiows G. Determinants of nutritional status of women and children in Ethiopia. Report of Ethiopian Health and Nutrition Research Institute, 2002. Available from: http://www. measuredhs.com/pubs/pdf/FA39/02-nutrition.pdf [last accessed 01 Oct 2013]. 26. Kemiki AO, Akindele JA. Influence of maternal biosocial factors on birthweight. Nig J Paediatr 1993;20:6–12. 27. Fakeye OO, Adetoro OO. The effects of sociobiochemical factors on birthweights of nigeria full-size, live-born infants. Nig J Paediatr 1989;16:7–13. 28. Nahum GG, Stanislaw H, Huffaker BJ. Accurate prediction of term birth weight from prospectively measurable maternal characteristics. J Reprod Med 199;44:705–12. 29. Ayoola OO, Orji EO, Adetiloye VA, Nzeh DA. Accuracy of various ultrasonographic formula in predicting fetal weight in a Nigerian population. J Chin Clin Med 2008;1:1. 30. Wilcox M, Gardosi J, Mongelli M, et al. Birth weight from pregnancies dated by ultrasonography in a multicultural British population. Br Med J 1993;307:588–91. 31. Cheng MC, Chew PC, Ratnam SS. Birth weight distribution of Singapore Chinese, Malay and Indian infants from 34 weeks to 42 weeks gestation. J Obstet Gynaecol Br Commonw 1972;79: 149–53. 32. Jaydip S, Arpita R, Nitish M. Composition and socio-economic variables with low birth weight in India. J Trop Pediatr 2010;56: 254–9. 33. Mohanty C, Rajniti P, Reddy SA, et al. Maternal anthropometry as predictors of low birth weight. J Trop Pediatr 2006;52:24–9. 34. Karim E, Mascie-Taylor CG. The association between birthweight, sociodemographic variables and maternal anthropometry in an urban sample from Dhaka, Bangladesh. Ann Hum Biol 1997;24: 387–401. 35. Jananthan R, Wijesinghe DG, Sivananthawerl T. Maternal anthropometry as a predictor of birth weight. Trop Agric Res 2009;21: 89–98. 36. Elshibly EM, Schmalisch G. The effect of maternal anthropometric characteristics and social factors on gestational age and birth weight in Sudanese newborn infants. BMC Publ Health 2008;8:244.
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ORIGINAL ARTICLE
Maternal anthropometric characteristics as determinants of birth weight in north-west Nigeria: prospective study Emmanuel Ajuluchukwu Ugwa Obstetrics and Gynaecology Department, Federal Medical Centre, Birnin Kudu, Jigawa State, Nigeria
Abstract
Keywords
Objective: This study was undertaken to determine the sensitivity and specificity of anthropometric measurements in predicting birth weight. Methods: This was a prospective study. Interviewer-administered questionnaires were used. Two hundred were used. The weight, height and BMI of the women were measured. Unclothed newborns were weighed immediately after delivery. The data obtained were analyzed using SPSS version 16.0 statistical software. The accuracy of maternal weight, height and body mass index in predicting birth weight was compared using chi-squared test and p50.05 was considered statistically significant. Result: The mean maternal age was 28.2 ± 5.7 years. The mean parity was 3 ± 2. The mean gestational age at delivery was 38.5 ± 2 years. The mean actual birth weight was 3.27 ± 0.53 kg. The mean maternal weight was 72.03 ± 11 kg. Maternal weight showed a strong positive correlation with birth weight (r ¼ 0.48) and this was statistically significant (p50.001). The mean maternal height was 1.64 ± 0.55 m. The mean maternal BMI was 27.9 ± 4.33. Maternal weight, height and BMI had sensitivities of 50%, 40% and 50% and specificities of 48%, 57.9% and 67.3%, respectively. Conclusion: Maternal weight, height and BMI are not good predictors of birth weight and cannot be recommended for use as screening test in poor resource setting where ultrasound available.
Anthropometric characteristics, birth weight, determinants, maternal, prospective study
Introduction Maternal exposure to nutrition and other environmental factors during the period from conception to birth may have an impact on fetal growth as well as the child’s health [1–3]. Infant size, such as birth weight and length, was reported to affect not only infant mortality, but also childhood morbidity [4,5]. Although severe undernutrition, which could lead to permanent changes in structure and metabolism in the fetus is uncommon in developed countries, this is not the case in developing countries where the imbalance or relative deficiency of nutrients could affect fetal growth [6,7]. Both fetal macrosomia and intrauterine growth restriction increase the risk of perinatal morbidity and mortality. Therefore, when these conditions are diagnosed in utero appropriate route of delivery is undertaken to forestall these complications [8]. Fetal weight cannot be measured directly in utero, but it can be estimated or predicted from fetal and maternal anatomical characteristics. Maternal anthropometric
Address for correspondence: Dr. Emmanuel Ajuluchukwu Ugwa, Obstetrics and Gynaecology Department, Federal Medical Centre, Birnin Kudu, Jigawa State, Nigeria. Tel: +2348035851872. E-mail: [email protected]
History Received 21 December 2013 Accepted 02 May 2014 Published online 29 May 2014
measurements afford a simple, cheap and available means of predicting birth weight with a variable degree of reliability. These have been widely studied in other countries [1–7], but studies are rather still scanty in developing world where maternal undernutrition is a predisposing factor to poor obstetrics outcome and perinatal morbidity/mortality. Factors that determine birth weight are maternal height, malnutrition, maternal obesity, maternal pregnancy weight gain, parity, fetal sex, ambient attitude, maternal hemoglobin concentration, paternal height, cigarette smoking and glucose intolerance [8]. In third world countries, where poverty among reproductive aged women is prevalent, malnutrition is a common factor that can substantially affect the size of neonates at all gestational ages [8]. Other factors include maternal factors such as race, stature and genetics [9–11], paternal factors such as paternal height [12,13], environmental factors such as attitude, nutrition and physical activities [14], physiological factors such as altered glucose metabolism, hemoglobin concentration and microvascular integrity [15], pathologic factors such as hypertension and uterine malformations, and complications of pregnancy such as gestational diabetes mellitus and pre-eclampsia [16]. According to World Health Organization (WHO), maternal anthropometric aspects and intake of adequate nutrients as
DOI: 10.3109/14767058.2014.921670
Predictive value of maternal anthropometric measurements in birth weight
determinants of fetal growth demonstrate a close association with these parameters and weight and gestational age at birth [17,18]. Anthropometric measurements, among the most frequently applied methods for assessing nutritional status in pregnant women, have undergone remarkable improvement and are recognized as effective tools in the prevention of perinatal morbidity and mortality [19,20]. Excessive pregnancy weight gain is one of the conditions most strongly associated with postpartum weight retention and, consequently, postpartum obesity [21,22]. Evaluation of maternal nutritional status relies on measures such as pre-pregnancy weight, height, body mass index (BMI), weight gain at different trimesters, weight gain during pregnancy and skinfold thickness. Numerous research projects have studied maternal anthropometric characteristics as predictors of birth weight [23–28]. Although there is considerable work done on this topic in other countries, the present work was done among women presenting in a Teaching Hospital in the Sub-Saharan African. The aim of this study was to examine the relationship between the birth weights of babies delivered between 1 September and 31 December 2011 in AKTH at various gestational ages with certain maternal anthropometric measurements (weight, height and BMI) and to determine the sensitivity and specificity of these measurements in predicting birth weight. This can be recommended for use among peripheral health workers for detection of mothers at risk of delivering big or low birth weight babies and need for in utero transfer to centers where caesarean section and neonatal care can be offered.
Methods This research was a prospective study. The study subjects were consecutive pregnant women with singleton pregnancy admitted for either vaginal or planned abdominal delivery between 1 September and 31 December 2011. Ethical approval from Aminu Kano Teaching Hospital Ethics Committee and informed consent from subjects were obtained. Two hundred consecutive pregnant women who fulfilled the inclusion criteria were counseled and, after consenting, were included in the study. Inclusion criteria include; singleton pregnancy above the age of viability (28 weeks and above in Nigeria). Exclusion criteria include; patients’ refusal, and patients with polyhydramnios, ruptured membranes, multiple pregnancies, oligohydramnios, intrauterine fetal death and pregnancy with uterine or adnexal pathology. Sample size was determined using malnutrition prevalence among women of 10–40%. n ¼ z2 p q=d 2 n ¼ sample size, z ¼ standard normal deviation ¼ 1.96 at 95% confidence limit, p ¼ prevalence rate ¼ 15%, q ¼ 1 p ¼ 1– 15% ¼ 0.85, d ¼ error margin ¼ 5%. n¼
1:962 0:15 0:85 ¼ 196 0:052
Interviewer administered questionnaires were used to obtain sociodemographic and clinical information. The weight and height of all the recruited women were measured during the
461
admission for delivery. The study subjects were weighed using spring balance (adult) with minimum clothing after correcting zero error. The weight was recorded to the nearest 50 g. The height was measured keeping the women standing on level ground, without footwear, against a wall, by using measuring tape to the nearest 0.5 cm. The maternal weight and height obtained was used to calculate maternal BMI (kg/m2). Similarly, unclothed newborns were weighed immediately after delivery using a standard analogue Salters (England) scale corrected for zero error. The interval between Table 1. Distribution of sociodemographic characteristics of the study group. Parameter
Frequency
Age (years) 15–24 25–39 40–49 Parity 1–2 3–4 5 Gestational age (weeks) 538 38–40 440 Employment status Unemployed Employed Educational status None Qua’aric Primary Secondary Tertiary
Percentage
Mean ± SD
100 96 4
50 48 2
28.2 ± 5.7
76 100 24
38 50 12
3±2
18 164 18
9 82 9
38.5 ± 2
145 55
72.5 27.5
2 4 9 84 101
1 2 4.5 42 50.5
Table 2. Distribution of actual birth weight of the babies in the study group. Actual birth weight (kg) 52.5 2.5–3.99 4 Total
Frequency
Percentage
Mean ± SD
10 176 14 200
5 88 7 100
3.27 ± 0.53
Table 3. Distribution of anthropometric characteristics and their correlation with birth weight. Parameter Weight (kg) 51–60 61–70 71–80 81–90 Height (m) 1.5–1.59 1.6–1.69 1.7–1.79 BMI (kg/m2) 15–24.99 25–34.99 35–44.99
Frequency
Percentage
35 65 55 45
17.5 32.5 27.5 22.5
80 94 26
40 47 13
65 125 10
32.5 62.5 5
Mean ± SD
r
p value 50.001
72.03 ± 11 0.48
50.001
1.64 ± 0.55 0.25 27.9 ± 4.33
0.28 50.001
BMI, body mass index; SD, standard deviation at 95% confidence interval; r, coefficient of correlation.
462
E. A. Ugwa
J Matern Fetal Neonatal Med, 2015; 28(4): 460–463
Table 4. Distribution of the power of maternal anthropometric parameters in predicting birth weight. Maternal anthropometric parameters Weight Height BMI
True positive
False positive
False negative
True negative
Sensitivity (%)
Specificity (%)
Positive predictive value (%)
Negative predictive value (%)
5 4 5
95 76 62
5 10 5
95 110 128
50 40 50
48 57.9 67.3
5 5 7.46
95 91.7 96.2
anthropometric measurements and delivery of the babies was within 24 h. The data obtained were analyzed using SPSS version 16.0 statistical software (SPSS Inc., Chicago, IL). Absolute numbers and simple percentages were used to describe categorical variables. Similarly, quantitative variables were described using measures of central tendency (mean, median) and measures of dispersion (range, SD) as appropriate. The accuracy of maternal weight, height and body mass index in predicting birth weight was compared using chi-squared test and p50.05 was considered statistically significant.
Results As shown in Table 1, the mean maternal age was 28.2 ± 5.7 years, mean parity was 3 ± 2 and average gestational age at delivery was 38.5 ± 2 years. Table 2 shows a mean actual birth weight of 3.27 ± 0.53 kg. Table 3 shows a mean weight 72.03 ± 11 kg and maternal weighed showed a strong positive correlation with birthweight (r ¼ 0.48). As shown in Table 4, maternal weight and BMI had sensitivities of 50% each and specificities of 48 and 67.3% respectively.
Discussion Studies in other countries [25,26] have shown that mothers who are malnourished are most likely going to give birth to low birth weight babies and vice visa. Furthermore, studies in Nigeria by Kemiki and Akindele [27], and also by Fakeye and Adetoro [28] have shown that birth weight correlated well with maternal weight. The mean birth weight from this study was 3.27 ± 0.53 kg. This was similar to studies in Ile-Ife, Nigeria, where Shittu et al. [8] reported an actual average birth weight of 3.255 ± 0.625 kg and Ayoola et al. [29] who reported mean birth weight of 3.238 ± 0.452 kg [29]. These reports from developing countries were comparable with those of studies from USA, Great Britain and Singapore that showed that the mean birth weight at 38–42 completed weeks’ gestation was 3.060–3.520 g (range, 0.460 g) [11], 3.201–3.753 g (range, 0.551 g) [30] and 2.880–3.290 g (range, 0.410 g) [31], respectively. The mean birth weight is however 42.746 ± 0.40 kg reported in India where 17.30% of the newborns had low birth weight compared to 5% in the present study [32]. The present study showed significant positive correlations among maternal weight and birth weight (r ¼ 0.48), maternal height and birth weight (r ¼ 0.25), maternal body mass index and birth weight (r ¼ 0.28). This was comparable to significant positive correlations observed among maternal weight and birth weight (r ¼ 0.38), maternal height and birth weight (r ¼ 0.25), and maternal body mass index (BMI) and birth weight (r ¼ 0.30) reported by Mohanty et al. [33]. From the
above report, maternal weight was the strongest determinant of birth weight. This was also confirmed in regression analyses by Karim and Mascie-Taylor [34] who reported correlation coefficient between maternal weight and birth weight of 0.49. This study has also shown that the correlation between maternal weight, height and BMI and birth weight was statistically significant but Jananthan et al. [35] did not report any significant influence of height on birth weight. This study has shown that maternal weight and BMI had sensitivities of 50% each and specificities of 48% and 67.3%, respectively. Therefore, both maternal weight and BMI are equally good predictors of low birth weight but BMI is a better predictor of normal birth weight babies than maternal weight. The predictive value of maternal height for low birth weight is poor (40%), however its value is in prediction of normal birth weight (57.9% specificity). This finding is comparable to those of Jananthan et al. [35] and other workers [23–28,34]. This is, however, in contrast to study by Elshibly and Schmalisch [36] who reported that maternal height was the second most important parameter which influences the risk for LBW in Sudanese mothers. They also reported that the lower predictive value of maternal weight measured at delivery in their study could be due to the high individual differences in the changes that occur in the body weight during pregnancy. In conclusion, maternal weight, height and BMI are not good predictors of birth weight and cannot be recommended for use as screening test in poor resource setting where ultrasound available. The limitation of this study is that anthropometric measurements such as BMI are more reliable before pregnancy. However, in our environment, preconceptional care is an evolving field and women commonly present to health facilities only when they are advanced in pregnancy and there may be no record of their pre-pregnancy weight. A community-based study is anticipated during which anthropometric parameters like BMI can be taken before pregnancy and followed up throughout pregnancy.
Acknowledgements The author acknowledges the useful contribution of Dr. Abiodun Omole Ohonsi of Obstetrics and Gynaecology Department and Professor Zubairu Ilyasu of Community Medicine Department both of Bayero University, Kano, Nigeria in preparation of this article.
Declaration of interest The author received no funding for this study. There were no conflicts of interest. The author contributed solely in literature review, study design, statistical analysis and preparation of the article.
DOI: 10.3109/14767058.2014.921670
Predictive value of maternal anthropometric measurements in birth weight
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