Prediction of Birth Weight by Using Neonatal Anthropometric Parameters at Birth in Finote Selam Hospital, Ethiopia

Introduction

Birth weight is an indicator of a newborn’s chances for survival and growth. However, developing countries lack enough weighing scales to identify low birth weight babies. Therefore, finding an alternative to weighing scales is vital.

Objective

To predict birth weight from neonatal anthropometric parameters at birth in Finote Selam Hospital, Ethiopia.

Methods

A hospital-based cross-sectional study was carried out from July 13 to October 27, 2020. A total of 424 live-delivered neonates were enrolled. Based on eligibility, birth weight and neonatal anthropometric parameters like crown–heel length, foot length, hand length, mid-upper arm circumference, umbilical–nipple distance, intermammary distance and head circumference were measured within 24 hours of birth. The association between birth weight and neonatal anthropometric parameters was evaluated using correlation analysis. Birth weight predictive regression models were formulated by using simple and multiple linear regression analysis.

Results

All neonatal anthropometric parameters had positive significant correlation with birth weight at p<0.05. Amongst the neonatal anthropometric parameters, the highest significant correlation with birth weight was observed on mid-upper arm circumference (MUAC) followed by foot length (FL), each being r=0.474 and r=0.461, respectively. The best predictive regression models were formulated as birth weight (kg)=0.117+[0.284×MUAC (cm)] and birth weight (kg)=1.137+[0.254×FL (cm)]. As compared to individual neonatal anthropometric parameters, a combination of MUAC, hand length (HL), FL and crown–heel length (CHL) had the highest significant correlation (r=0.661), and a multiple regression equation used to estimate birth weight was formulated as birth weight (kg)=−2.489+[0.192×MUAC(cm)]+[0.078×HL(cm)]+[0.11×FL (cm)]+[0.036×CHL(cm)].

Conclusion

Using a combination of MUAC, HL, FL and CHL followed by individual MUAC neonatal anthropometric parameters has high significance to identify low birth weight. Prediction of neonatal birth weight from neonatal anthropometric parameters is crucial to minimize the death of neonates due to low birth weight.

Chest circumference in full-term newborns: how can it be predicted?

Background

Although over the years a number of studies have used chest circumference (CC) as a sensitive tool to identify the health status of infants, a particularly important aspect for this population is the lack of data on normal values and prediction equations. In order to facilitate and validate the interpretation of CC data in newborn (NB), the aim was to study the relation between CC and other anthropometric variables and develop a predictive equation for CC in a population of full-term newborns.

Methods

Cross-sectional study, carried out with full-term infants. The anthropometric (CC, head circumference - HC, length, age and weight) and hemodynamic variables were evaluated during the first 24 h of life. Bivariate analysis was performed between CC and HC, weight, length and type of delivery, followed by multiple linear regression analysis, including variables that were significant in the bivariate analysis. For data analysis, we used the SPSS program, considering p < 0.05 and 95% CI.

Results

The birth weight of the 120 NB varied between 2580 and 4225 g (mean 3360 g) and the gestational age between 37 and 42 weeks (mean 39 weeks). Approximately 61% of the sample were delivered vaginally and 67 (56%) were boys. The variables that remained statistically associated with CC after multivariate analysis were weight (β 0.003, CI: 0.002: 0.003, p = 0.001) and HC (β 0.287, CI: 0.156: 0.417, p = 0.001). For the linear regression model, the predictive equation of CC was 14.87+ (0.003 x weight) + (0.287 x HC), with a prediction of 76%.

Conclusion

The results show a positive correlation between CC and weight, length and HC, and based on the linear regression model, the predictive equation for CC is based only on weight and HC.

Foot length, chest and head circumference measurements in detection of Low birth weight neonates in Mekelle, Ethiopia: a hospital based cross sectional study

Background

Low birth weight (Birth weight < 2500 g) is a leading cause of prenatal and neonatal deaths. The early identification of Low birth weight (LBW) neonates is essential for any comprehensive initiative to improve their chance of survival. However, a large proportion of births in developing countries take place at home and birth weight statistics are not available. Therefore, there is a need to develop simple, inexpensive and practical methods to identify low birth weight (LBW) neonates soon after birth.

Methods

This is a hospital based cross sectional study. Four hundred twenty two (422) live born neonates were included and anthropometric measurements were carried out within 24 h of birth by three trained nurses. Birth weight was measured by digital scale. Head and chest circumference were measured by using non extendable measuring tape and foot length with hard transparent plastic ruler. Data was entered into SPSS version 20 for analysis. Characteristics of study participants were analyzed using descriptive statistics such as frequency and percentage for categorical data and mean and standard deviation for continuous data. Correlation with birth weight using Pearson’s correlation coefficient and linear regression were used to identify the association between dependent and independent variables. Receiver operating characteristic (ROC) curve was used to evaluate accuracy of the anthropometric measurements to predict LBW.

Results

The prevalence of low birth weight was found to be 27%. All anthropometric measurements had a positive correlation with birth weight, chest circumference attaining the highest correlation with birth weight (r = 0.85) and foot length had the weakest correlation (r = 0.74). Head circumference had the highest predictive value for birth weight (AUC = 0.93) followed by Chest circumference (AUC = 0.91). A cut off point of chest circumference 30.15 cm had 84.2% sensitivity, 85.4% specificity and diagnostic accuracy (P < 0.001). A cut off point of head circumference 33.25 had the highest positive predictive value (77%).

Conclusion

Chest circumference and head circumference were found to be better surrogate measurements to identify low birth weight neonates.

Anthropometric measurements: options for identifying low birth weight newborns in Kumasi, Ghana

Background

In Ghana, 32% of deliveries take place outside a health facility, and birth weight is not measured. Low birth weight (LBW) newborns who are at increased risk of death and disability, are not identified; 13%–14% of newborns in Ghana are LBW. We aimed at determining whether alternative anthropometrics could be used to identify LBW newborns when weighing scales are not available to measure birth weight.

Methods

We studied 973 mother and newborn pairs at the Komfo Anokye Teaching and the Suntreso Government hospitals between November 2011 and October 2012. We used standard techniques to record anthropometric measurements of newborns within 24 hours of birth; low birth weight was defined as birth weight <2.5kg. Pearson's correlation coefficient and the area under the curve were used to determine the best predictors of low birth weight. The sensitivity, specificity and predictive values were reported with 95% confidence intervals at generated cut-off values.

Results

One-fifth (21.7%) of newborns weighed less than 2.5 kg. Among LBW newborns, the following measurements had the highest correlations with birth weight: chest circumference (r = 0.69), mid-upper arm circumference (r = 0.68) and calf circumference (r = 0.66); the areas under the curves of these three measurements demonstrated the highest accuracy in determining LBW newborns. Chest, mid-upper arm and calf circumferences at cut-off values of ≤29.8 cm, ≤9.4 cm and ≤9.5 cm respectively, had the best combination of maximum sensitivity, specificity and predictive values for identifying newborns with LBW.

Conclusions

Anthropometric measurements, such as the chest circumference, mid-upper arm circumference and calf circumference, offer an opportunity for the identification of and subsequent support for LBW newborns in settings in Ghana, where birth weights are not measured by standardized weighing scales.

Relationships between neonatal weight, limb lengths, skinfold thicknesses, body breadths and circumferences in an Australian cohort

Background

Low birth weight has been consistently associated with adult chronic disease risk. The thrifty phenotype hypothesis assumes that reduced fetal growth impacts some organs more than others. However, it remains unclear how birth weight relates to different body components, such as circumferences, adiposity, body segment lengths and limb proportions. We hypothesized that these components vary in their relationship to birth weight.

Methods

We analysed the relationship between birth weight and detailed anthropometry in 1270 singleton live-born neonates (668 male) from the Mater-University of Queensland Study of Pregnancy (Brisbane, Australia). We tested adjusted anthropometry for correlations with birth weight. We then performed stepwise multiple regression on birth weight of: body lengths, breadths and circumferences; relative limb to neck-rump proportions; or skinfold thicknesses. All analyses were adjusted for sex and gestational age, and used logged data.

Results

Circumferences, especially chest, were most strongly related to birth weight, while segment lengths (neck-rump, thigh, upper arm, and especially lower arm and lower leg) were relatively weakly related to birth weight, and limb lengths relative to neck-rump length showed no relationship. Skinfolds accounted for 36% of birth weight variance, but adjusting for size (neck-rump, thigh and upper arm lengths, and head circumference), this decreased to 10%. There was no evidence that heavier babies had proportionally thicker skinfolds.

Conclusions

Neonatal body measurements vary in their association with birth weight: head and chest circumferences showed the strongest associations while limb segment lengths did not relate strongly to birth weight. After adjusting for body size, subcutaneous fatness accounted for a smaller proportion of birth weight variance than previously reported. While heavier babies had absolutely thicker skinfolds, this was proportional to their size. Relative limb to trunk length was unrelated to birth weight, suggesting that limb proportions at birth do not index factors relevant to prenatal life.

Determining an anthropometric surrogate measure for identifying low birth weight babies in Uganda: a hospital-based cross sectional study

Background

Achieving Millennium Development Goal 4 is dependent on significantly reducing neonatal mortality. Low birth weight is an underlying factor in most neonatal deaths. In developing countries the missed opportunity for providing life saving care is mainly a result of failure to identify low birth weight newborns. This study aimed at identifying a reliable anthropometric measurement for screening low birth weight and determining an operational cut-off point in the Uganda setting. This simple measurement is required because of lack of weighing scales in the community, and sometimes in the health facilities.

Methods

This was a hospital-based cross-sectional study. Two midwives weighed 706 newborns and measured their foot length, head, chest, thigh and mid-upper arm circumferences within 24 hours after birth.

Data was analysed using STATA version 10.0. Correlation with birth weight using Pearson’s correlation coefficient and Receiver Operating Characteristics curve analysis were done to determine the measure that best predicts birth weight. Sensitivity and specificity were calculated for a range of measures to obtain operational cut-off points; and Likelihood Ratios and Diagnostic Odds Ratio were determined for each cut-off point.

Results

Birth weights ranged from 1370–5350 grams with a mean of 3050 grams (SD 0.53) and 85 (12%) babies weighed less than 2500 grams. All anthropometric measurements had a positive correlation with birth weight, with foot length showing the strongest (r = 0.76) and thigh circumference the weakest (r = 0.62) correlations. Foot length had the highest predictive value for low birth weight (AUC = 0.97) followed by mid-upper arm circumference (AUC = 0.94). Foot length and chest circumference had the highest sensitivity (94%) and specificity (90%) respectively for screening low birth weight babies at the selected cut-off points. Chest circumference had a significantly higher positive likelihood ratio (8.7) than any other measure, and foot length had the lowest negative likelihood ratio. Chest circumference and foot length had diagnostic odds ratios of 97% and 77% respectively. Foot length was easier to measure and it involved minimal exposure of the baby to cold. A cut-off of foot length 7.9 cm had sensitivity of 94% and specificity of 83% for predicting low birth weight.

Conclusions

This study suggests foot length as the most appropriate predictor for low birth weight in comparison to chest, head, mid-upper arm and thigh circumference in the Uganda setting. Use of low cost and easy to use tools to identify low birth weight babies by village health teams could support community efforts to save newborns.