Related Experiment Video

Updated: Jun 22, 2026

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

Published on: January 29, 2018

Socio-economic determinants of low birth weight in Bangladesh: a multivariate approach

Selina Khatun1, Mahmudur Rahman

  • 1Institute of Epidemiology, Disease Control and Research, Dhaka, Bangladesh. knselina@gmail.com

Bangladesh Medical Research Council Bulletin
|May 30, 2009
PubMed

Insights

Maternal age, education, and income are key factors influencing low birth weight (LBW) in Bangladesh. Improving these socioeconomic determinants can help reduce LBW rates and improve infant health outcomes.

Area of Science:

  • Maternal and Child Health
  • Public Health
  • Epidemiology

Background:

  • Despite overall health improvements in Bangladesh, the incidence of low birth weight (LBW) remains a significant public health concern.
  • Identifying key determinants of LBW is crucial for developing effective interventions.

Purpose of the Study:

  • To ascertain the significant determinants associated with low birth weight (LBW) in the Bangladeshi population.
  • To identify maternal factors that contribute to the incidence of LBW.

Main Methods:

  • A comparative study involving 108 LBW babies and 357 normal birth weight babies.
  • Analysis of 20 potential risk variables using univariate analysis and stepwise logistic regression.

Main Results:

  • Nine out of twenty risk variables were found to be significant determinants of LBW.
  • Maternal age, education, number of antenatal care visits, and yearly income were identified as the strongest predictors of LBW through logistic regression.
  • The final model accurately predicted 86.1% of LBW cases and 94.4% of normal birth weight cases.

Conclusions:

  • Maternal age, educational attainment, and economic status are critical factors influencing the incidence of low birth weight.
  • Antenatal care attendance is also a significant factor associated with LBW.
  • Targeting socioeconomic and demographic factors in maternal health programs can help reduce LBW rates.

Related Concept Videos

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...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
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.