Correlation between anthropometric measures and birthweight of infants: value in measuring actual birthweight

Eltahir M Elshibly1, Gerd Schmalisch

  • 1Departments of Paediatrics and Child Health, University of Khartoum, Sudan.

Birthweight (BW) is an important predictor of newborn morbidity and mortality. In Africa, infant mortality is very high mainly due to low birthweight (LBW). Most deliveries occur at home where scales are not always available. The aim of this study was to find a simple formula to predict birthweight using anthropometric measurements. In 1000 singleton Sudanese newborns, anthropometric measurements were taken within 24 hours of birth. Multiple regression analysis with backward selection was used to analyze data. The mean (standard deviation) of BW was 3131.7 (538.9) g and that of gestational age was 39.1 (1.8) weeks. All anthropometric parameters were strongly correlated with BW ( P < 0.001). The highest correlations were obtained with chest (CC), midthigh (MT), and head circumferences (HC). Using these three parameters, a simple formula was obtained to predict BW as follows: BW(g) = 97*CC + 74*MT + 85*HC - 4000 with a standard error of 285 g. For birthweights < 2000 g, specificity is near 100% and the sensitivity is > 80%. Applying a cutoff point of 2500 g, all infants (100%) with a birthweight < 2000 g are correctly identified. Our model by allowing for actual measurement of BW will enable the health worker in developing countries to select appropriate LBW infants for referral to an equipped health facility.

Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...