Related Experiment Video

Updated: Nov 4, 2025

Author Spotlight: Collecting the Brain and Serum from the Same Mice Fetus to Study Brain Tumor Development
05:44

Author Spotlight: Collecting the Brain and Serum from the Same Mice Fetus to Study Brain Tumor Development

Published on: May 17, 2024

818

Retrospective analysis to identify the association of various determinants on birth weight

Suprava Patel1, Neha R Verma2, Phalguni Padhi3

  • 1Associate Professor, Department of Biochemistry, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India.

Journal of Family Medicine and Primary Care
|May 21, 2021
PubMed

Insights

Low birth weight (LBW) affects neonatal health, with maternal anemia, inadequate antenatal care, and premature delivery being key risk factors. Addressing maternal health is crucial to reduce LBW prevalence and improve infant outcomes.

Area of Science:

  • Obstetrics and Gynecology
  • Neonatal Health
  • Public Health

Background:

  • Low birth weight (LBW) is a significant determinant of neonatal morbidity and mortality, with global prevalence at nearly 15%.
  • India faces a substantial LBW burden, ranging from 16-30%, influenced by maternal factors.
  • Understanding these influencing parameters is critical for targeted interventions.

Purpose of the Study:

  • To determine the key influencing parameters for the occurrence of low birth weight (LBW) in a tertiary care setting.
  • To identify independent risk factors associated with LBW.
  • To explore the association between LBW and neonatal complications.

Main Methods:

  • A retrospective observational study was conducted over 24 months.
  • Data were collected from 1216 live newborns delivered at a tertiary care center.
  • Information was recorded using a predesigned questionnaire from institutional medical records.

Main Results:

  • The prevalence of LBW was 27.55% (335/1216), with a 1:3 ratio of LBW to normal birth weight (NBW) babies.
  • Significant risk factors for LBW included maternal anemia (59.39%), young maternal age (30.39%), parity ≥ 3 (35.71%), fewer than 3 antenatal care (ANC) visits (56.88%), and premature delivery (71.57%).
  • Maternal anemia (OR 4.7), inadequate ANC (OR 2.2), and prematurity (OR 7.6) were independent risk factors. LBW was associated with neonatal complications (OR 1.6).

Conclusions:

  • Inadequate antenatal care, maternal anemia, and illnesses leading to premature delivery are critical determinants of LBW.
  • These factors contribute significantly to neonatal mortality and morbidity.
  • Improving maternal health status is essential for reducing the burden of LBW.
Abstract

Keywords:
Antenatal carelow birth weightmaternal anemiamaternal determinantsneonatal complicationsprematuritytertiary care hospital

More Related Videos

Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography03:35

Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography

Published on: December 1, 2023

428
Assessment of Child Anthropometry in a Large Epidemiologic Study09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.4K

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...
6.6K
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.4K
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...
3.4K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
715
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
663
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...
15.7K