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Methods for inference on transmission in seroprevalence data for multiple infections
A A Evans1, M Lefkopoulou, N E Mueller
1Department of Epidemiology, Harvard School of Public Health, Boston, MA.
Abstract:
When risk factors for an infectious disease are unknown, a method commonly employed is to investigate parallels with known infections (covariate infections). Data sets of value here are those for specified populations in which the seroprevalence of antibodies for multiple infections has been ascertained. The use of markers of covariate infections in multivariable analyses is problematic when the covariate infection is not itself an independent risk factor for the outcome of interest. In the performance of these analyses, the authors recommend the following strategy: 1) For estimates of the effects of measured risk factors on the outcome, adjustment for the covariate infection should not be done; this will avoid problems of overadjustment. 2) After control for the measured risk factors, an estimate of the "effect" of the covariate infection may be used as an indicator of the presence of unmeasured shared risk factors. 3) When shared, measured risk factors exist, the authors propose the use of methods developed for analysis of repeated measures of categorical variables to assist in inference about shared mechanisms of action of these risk factors. This analytic strategy takes advantage of the method of analogy for building understanding of transmission of new agents through their parallels with better known ones and is useful in the development of hypotheses.
Insights
Investigating known infections can reveal unknown infectious disease risk factors. This strategy helps identify shared risk factors and understand disease transmission through analogy.
Area of Science:
- Epidemiology
- Infectious Disease Research
- Biostatistics
Background:
- Identifying risk factors for novel infectious diseases is challenging.
- Utilizing data on seroprevalence of multiple infections in populations is valuable.
- Using covariate infections in analyses can be problematic if not independent risk factors.
Purpose of the Study:
- To propose a strategy for analyzing infectious disease risk factors when unknowns exist.
- To leverage parallels with known infections (covariate infections) to understand new agents.
- To develop hypotheses for disease transmission using the method of analogy.
Main Methods:
- Avoid adjusting for covariate infections when estimating effects of measured risk factors to prevent overadjustment.
- Use the estimated effect of covariate infections after controlling for measured factors as an indicator of unmeasured shared risk factors.
- Employ methods for repeated measures of categorical variables to infer shared mechanisms when shared measured risk factors are present.
Main Results:
- The proposed analytic strategy helps identify the presence of unmeasured shared risk factors.
- It allows for inference about shared mechanisms of action when shared measured risk factors exist.
- This approach facilitates hypothesis development for understanding transmission of new infectious agents.
Conclusions:
- The method of analogy, by studying parallels with known infections, aids in understanding novel infectious diseases.
- The recommended analytic strategy provides a robust framework for investigating infectious disease risk factors.
- This approach is particularly useful for building understanding of disease transmission and generating research hypotheses.