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myEpi. Epidemiology of One.

Bobashev G - Front Public Health (2014)

Bottom Line: Traditional epidemiology requires that results be generalizable to a predefined population.The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population.These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use).

View Article: PubMed Central - PubMed

Affiliation: RTI International , Durham, NC , USA.

ABSTRACT
A new concept of within-individual epidemiology termed "myEpi" is introduced. It is argued that traditional epidemiological methods, which are usually applied to populations of humans, can be applicable to a single individual and thus used for self-monitoring and forecasting of "epidemic" outbreaks within an individual. Traditional epidemiology requires that results be generalizable to a predefined population. The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population. Applications of myEpi are aimed for, but not limited to, the analysis of data collected by individuals with the help of wearable sensors and digital diaries. These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use). Although many examples of within-individual epidemiology exist, there is a pressing need for systematic guidance to the analysis and interpretation of intensive individual-level data. myEpi serves this need by adapting statistical methods (e.g., regressions, hierarchical models, survival analysis, agent-based models) to individual-level data.

No MeSH data available.


Related in: MedlinePlus

A “moving windows” method to identify patterns of alcohol use trajectories. Distributional properties of sliding windows Wt and Wt+s−1 are compared to each other. The point when the distributions become significantly different signifies the change in patterns. We illustrate the point at which the pattern switched from type 5 to 7 as the number of drinks increases. The figure is reproduced with permission from Ref. (8).
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Figure 2: A “moving windows” method to identify patterns of alcohol use trajectories. Distributional properties of sliding windows Wt and Wt+s−1 are compared to each other. The point when the distributions become significantly different signifies the change in patterns. We illustrate the point at which the pattern switched from type 5 to 7 as the number of drinks increases. The figure is reproduced with permission from Ref. (8).

Mentions: Infectious diseases such as measles and influenza have been extensively studied using epidemic surveillance and mathematical models (11). These models are based on daily or regular incidence reports and if model parameters are well calibrated the model can predict the course of an epidemic (11–13) and evaluate strategies to contain it (12, 13). The same methods, such as distinguishing an emerging epidemic from occasional outbreaks, can be used when considering individual data. The application again requires a change of mindset. Similar to considering regular (e.g., weekly or monthly) disease incidence reports one can consider regular (e.g., daily or weekly) reports of specific events. These events could be categorical (e.g., exercised or not), count (e.g., drank five beers), or continuous (e.g., consumed 3680 calories). In substance use studies, a number of tools are available to track alcohol and tobacco consumption; drawing an analogy with an infectious epidemic, the tools can detect the start of increased use (8) (Figure 2), predict most likely moment for relapse (14), predict future use (15), and identify strategies to influence the recovery process (16, 17).


myEpi. Epidemiology of One.

Bobashev G - Front Public Health (2014)

A “moving windows” method to identify patterns of alcohol use trajectories. Distributional properties of sliding windows Wt and Wt+s−1 are compared to each other. The point when the distributions become significantly different signifies the change in patterns. We illustrate the point at which the pattern switched from type 5 to 7 as the number of drinks increases. The figure is reproduced with permission from Ref. (8).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129497&req=5

Figure 2: A “moving windows” method to identify patterns of alcohol use trajectories. Distributional properties of sliding windows Wt and Wt+s−1 are compared to each other. The point when the distributions become significantly different signifies the change in patterns. We illustrate the point at which the pattern switched from type 5 to 7 as the number of drinks increases. The figure is reproduced with permission from Ref. (8).
Mentions: Infectious diseases such as measles and influenza have been extensively studied using epidemic surveillance and mathematical models (11). These models are based on daily or regular incidence reports and if model parameters are well calibrated the model can predict the course of an epidemic (11–13) and evaluate strategies to contain it (12, 13). The same methods, such as distinguishing an emerging epidemic from occasional outbreaks, can be used when considering individual data. The application again requires a change of mindset. Similar to considering regular (e.g., weekly or monthly) disease incidence reports one can consider regular (e.g., daily or weekly) reports of specific events. These events could be categorical (e.g., exercised or not), count (e.g., drank five beers), or continuous (e.g., consumed 3680 calories). In substance use studies, a number of tools are available to track alcohol and tobacco consumption; drawing an analogy with an infectious epidemic, the tools can detect the start of increased use (8) (Figure 2), predict most likely moment for relapse (14), predict future use (15), and identify strategies to influence the recovery process (16, 17).

Bottom Line: Traditional epidemiology requires that results be generalizable to a predefined population.The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population.These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use).

View Article: PubMed Central - PubMed

Affiliation: RTI International , Durham, NC , USA.

ABSTRACT
A new concept of within-individual epidemiology termed "myEpi" is introduced. It is argued that traditional epidemiological methods, which are usually applied to populations of humans, can be applicable to a single individual and thus used for self-monitoring and forecasting of "epidemic" outbreaks within an individual. Traditional epidemiology requires that results be generalizable to a predefined population. The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population. Applications of myEpi are aimed for, but not limited to, the analysis of data collected by individuals with the help of wearable sensors and digital diaries. These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use). Although many examples of within-individual epidemiology exist, there is a pressing need for systematic guidance to the analysis and interpretation of intensive individual-level data. myEpi serves this need by adapting statistical methods (e.g., regressions, hierarchical models, survival analysis, agent-based models) to individual-level data.

No MeSH data available.


Related in: MedlinePlus