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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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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:
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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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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
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Gestational age data completeness, quality and validity in population-based surveys: EN-INDEPTH study.

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New survey questions for estimating gestational age (GA) show potential for global child mortality research. While accurate, GA reporting in surveys needs refinement to improve sensitivity for preterm birth detection.

Keywords:
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Area of Science:

  • Public Health
  • Demography
  • Reproductive Health

Background:

  • Preterm birth is a leading cause of child mortality globally.
  • Gestational age (GA) assessment is crucial but rarely included in population-based surveys, especially in low/middle-income countries.
  • Accurate GA data is vital for understanding preterm birth rates and informing interventions.

Purpose of the Study:

  • To evaluate new survey questions for measuring gestational age (GA).
  • To assess the accuracy and completeness of GA data collected through household surveys.
  • To compare survey-based GA estimates with ultrasound dating to determine preterm birth rates.

Main Methods:

  • A large-scale population-based survey (EN-INDEPTH) involving 69,176 women across five countries.
  • Inclusion of questions on GA in months (GAm) and weeks (GAw), and 'born before expected' status.
  • Validation against linked surveillance data and early pregnancy ultrasound GA data in specific sites.

Main Results:

  • GAm questions were well-answered but showed heaping, underestimating preterm birth. GAw reporting exhibited heaping at even numbers, overestimating preterm birth.
  • Ultrasound validation in one site showed 60% sensitivity and 93% specificity for survey-GAw in detecting preterm birth.
  • Focus groups revealed women perceive GA as important, often counting in months; antenatal care, education, and health cards may improve reporting.

Conclusions:

  • This study is the first to compare survey-based GA reporting with ultrasound standards.
  • Survey reporting of GAw is feasible with high completeness but accuracy is impacted by heaping, requiring improved sensitivity.
  • Revised survey questions and interviewer training are recommended for better GA data collection in population surveys.