Internet-Based Birth-Cohort Studies: Is This the Future for Epidemiology?
Ridvan Firestone1, Soo Cheng, Neil Pearce
1Centre for Public Health Research, Massey University, Wellington, New Zealand. r.t.firestone@massey.ac.nz.
JMIR Research Protocols
|June 14, 2015
Summary
Internet-based mother-child cohorts like NINFEA and ELF are feasible for studying early life exposures and non-communicable diseases. However, these online recruitment methods result in self-selected participants, requiring careful bias management for long-term epidemiology research.
Area of Science:
- Epidemiology
- Public Health
- Reproductive Health
Background:
- NINFEA and ELF are international mother-child cohorts utilizing internet-based recruitment and follow-up.
- These studies investigate associations between early life exposures and non-communicable diseases.
Purpose of the Study:
- To report the research methodology of internet-based cohort studies.
- To highlight the advantages and limitations of an internet-based design in epidemiological research.
- To present findings from studies conducted in Turin, Italy, and Wellington, New Zealand.
Main Methods:
- Employed online and offline strategies for participant recruitment.
- Utilized online questionnaires for baseline data collection from pregnant women.
- Implemented support processes to minimize participant attrition.
Main Results:
- Recruited 7003 participants in the NINFEA study and 2197 in the ELF study.
- Online participants were generally older, highly educated, and in their final pregnancy trimester.
- Demonstrated the feasibility of nationwide cohort recruitment via the internet.
Conclusions:
- Internet-based cohort studies are a feasible and potentially cost-effective methodology for long-term epidemiology.
- Participants in internet-based cohorts are often self-selected, similar to traditional birth cohorts.
- Further research is needed to address biases and limitations inherent in internet-based epidemiological studies.
Related Concept Videos
Introduction to Epidemiology
2.5K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
2.5K
Longitudinal Research
13.7K
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...
13.7K
Study Designs in Epidemiology
1.6K
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.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
1.6K
Statistical Methods for Analyzing Epidemiological Data
1.2K
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:
1.2K
Cross-Sectional Research
12.9K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
12.9K
Bias in Epidemiological Studies
1.6K
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:
1.6K


