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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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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:  
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Related Experiment Video

Updated: Jun 12, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using latent variable modelling to identify etiological heterogeneity in preterm delivery.

Kim Steven Betts1, Rosa Alati1, Peter Baker2

  • 1School of Population Health, Curtin University, Perth, Western Australia, Australia.

Journal of Paediatrics and Child Health
|September 21, 2024
PubMed
Summary
This summary is machine-generated.

A small group of mothers with high morbidity consistently experienced preterm births across three consecutive deliveries. Identifying this high-risk subgroup can improve understanding and outcomes for prematurity.

Keywords:
administrative data linkagelatent class analysisneonatal complicationstransition models

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Area of Science:

  • Reproductive Health
  • Perinatal Epidemiology
  • Maternal-Fetal Medicine

Background:

  • Preterm birth remains a leading cause of neonatal morbidity and mortality.
  • Identifying mothers at high risk for recurrent preterm delivery is crucial for targeted interventions.
  • Understanding the patterns of multimorbidity and recurrence across consecutive births is essential for risk stratification.

Purpose of the Study:

  • To identify a specific subgroup of mothers at high risk for preterm delivery.
  • To define this subgroup based on empirical classes of multimorbidity and recurrence across three consecutive births.

Main Methods:

  • Latent Class Analysis (LCA) was used to identify distinct maternal health trajectories.
  • Data from 7714 primiparous mothers with three consecutive singleton births (2009-2015) in Queensland, Australia, were analyzed.
  • Maternal and pregnancy-related factors were assessed for their association with identified classes.

Main Results:

  • A four-class solution best described the data: 'normative' (healthy), preterm/high morbidity, delivery morbidity, and preterm/low morbidity.
  • A small but highly morbid class (<2% of the sample) consistently experienced preterm births.
  • The high morbidity and preterm, low morbidity classes showed strong continuity across consecutive births, independent of other factors.

Conclusions:

  • A distinct, highly morbid class of mothers with recurrent preterm births was identified.
  • This subgroup exhibits strong continuity across consecutive births, suggesting inherent risk factors.
  • Further investigation into this high-risk group may yield insights into the etiology of prematurity and improve birth outcomes.