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Risk pathways for gonorrhea acquisition in sex workers: can we distinguish confounding from an exposure effect using a priori hypotheses?

Gomez GB, Ward H, Garnett GP - J. Infect. Dis. (2014)

Bottom Line: Theoretical frameworks describing the interrelationships among risk determinants are useful in directing both the design and analysis of research studies and interventions.We also propose an analysis strategy to interpret the associations found to be significant in uniform analyses of observational data.The framework and the hierarchical analysis strategy are of particular relevance in the understanding of risk formation and might prove useful in identifying determinants that are part of the causal pathway and therefore amenable to prevention strategies across populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Health and Amsterdam Institute for Global Health and Development, AMC, University of Amsterdam, The Netherlands.

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Proposed framework of gonorrhea risk from studies of female sex worker populations. R0, the reproductive number, is at the center of the figure. At the first level, directly influencing R0, are its 3 components: probability of exposure to infected, transmission probability, and the duration of infectiousness. At a second level, we positioned all the proximate determinants that influence each of the components of R0. At a third level, there are the distal/underlying determinants. When different types of measures or proxies of the same determinant were found in the literature, we indicated the determinant and proceeded to list the different measures found (eg, characteristics of sex work: age at first sex work, duration of sex work, registration, place of sex work, place of client recruitment, price per intercourse). Abbreviations: CT, Chlamydia trachomatis; HIV, human immunodeficiency virus; NG, Neisseria gonorrhoeae; No, number; R0, reproductive number; STI, sexually transmitted infection; TV, Trichomonas vaginalis.
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JIU484F1: Proposed framework of gonorrhea risk from studies of female sex worker populations. R0, the reproductive number, is at the center of the figure. At the first level, directly influencing R0, are its 3 components: probability of exposure to infected, transmission probability, and the duration of infectiousness. At a second level, we positioned all the proximate determinants that influence each of the components of R0. At a third level, there are the distal/underlying determinants. When different types of measures or proxies of the same determinant were found in the literature, we indicated the determinant and proceeded to list the different measures found (eg, characteristics of sex work: age at first sex work, duration of sex work, registration, place of sex work, place of client recruitment, price per intercourse). Abbreviations: CT, Chlamydia trachomatis; HIV, human immunodeficiency virus; NG, Neisseria gonorrhoeae; No, number; R0, reproductive number; STI, sexually transmitted infection; TV, Trichomonas vaginalis.

Mentions: The findings of our review are summarized in Figure 1. As mentioned above, at a population level, the spread of an infection is defined by its R0, representing the average number of new cases generated by 1 primary case [5]. It is implied that the higher the R0, the greater the potential for the infection to spread (Figure 1). However, it is worth noting that whereas R0 is theoretically related to the potential for spread through the population, the reviewed studies relate to the risk of an infection occurring in the individual. Theoretically, this will be determined by the force of infection that combines the transmission probability, number of contacts, and prevalence of infection in these contacts [], along with the duration of infection and the associated chance of an infection remaining at the period of sampling (access to treatment and healthcare). Thus, the importance of the 3 components of R0 differs from the reason they are central in our framework for risk, but the correspondence is useful in translating findings from the individual to the population. Subsequently, risk factors found in the literature were organized in 2 levels (underlying and proximate) influencing the 3 components of R0: probability of transmission, probability of exposure to an infected person, and average duration of infectiousness [5, 9, 10].Figure 1.


Risk pathways for gonorrhea acquisition in sex workers: can we distinguish confounding from an exposure effect using a priori hypotheses?

Gomez GB, Ward H, Garnett GP - J. Infect. Dis. (2014)

Proposed framework of gonorrhea risk from studies of female sex worker populations. R0, the reproductive number, is at the center of the figure. At the first level, directly influencing R0, are its 3 components: probability of exposure to infected, transmission probability, and the duration of infectiousness. At a second level, we positioned all the proximate determinants that influence each of the components of R0. At a third level, there are the distal/underlying determinants. When different types of measures or proxies of the same determinant were found in the literature, we indicated the determinant and proceeded to list the different measures found (eg, characteristics of sex work: age at first sex work, duration of sex work, registration, place of sex work, place of client recruitment, price per intercourse). Abbreviations: CT, Chlamydia trachomatis; HIV, human immunodeficiency virus; NG, Neisseria gonorrhoeae; No, number; R0, reproductive number; STI, sexually transmitted infection; TV, Trichomonas vaginalis.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231644&req=5

JIU484F1: Proposed framework of gonorrhea risk from studies of female sex worker populations. R0, the reproductive number, is at the center of the figure. At the first level, directly influencing R0, are its 3 components: probability of exposure to infected, transmission probability, and the duration of infectiousness. At a second level, we positioned all the proximate determinants that influence each of the components of R0. At a third level, there are the distal/underlying determinants. When different types of measures or proxies of the same determinant were found in the literature, we indicated the determinant and proceeded to list the different measures found (eg, characteristics of sex work: age at first sex work, duration of sex work, registration, place of sex work, place of client recruitment, price per intercourse). Abbreviations: CT, Chlamydia trachomatis; HIV, human immunodeficiency virus; NG, Neisseria gonorrhoeae; No, number; R0, reproductive number; STI, sexually transmitted infection; TV, Trichomonas vaginalis.
Mentions: The findings of our review are summarized in Figure 1. As mentioned above, at a population level, the spread of an infection is defined by its R0, representing the average number of new cases generated by 1 primary case [5]. It is implied that the higher the R0, the greater the potential for the infection to spread (Figure 1). However, it is worth noting that whereas R0 is theoretically related to the potential for spread through the population, the reviewed studies relate to the risk of an infection occurring in the individual. Theoretically, this will be determined by the force of infection that combines the transmission probability, number of contacts, and prevalence of infection in these contacts [], along with the duration of infection and the associated chance of an infection remaining at the period of sampling (access to treatment and healthcare). Thus, the importance of the 3 components of R0 differs from the reason they are central in our framework for risk, but the correspondence is useful in translating findings from the individual to the population. Subsequently, risk factors found in the literature were organized in 2 levels (underlying and proximate) influencing the 3 components of R0: probability of transmission, probability of exposure to an infected person, and average duration of infectiousness [5, 9, 10].Figure 1.

Bottom Line: Theoretical frameworks describing the interrelationships among risk determinants are useful in directing both the design and analysis of research studies and interventions.We also propose an analysis strategy to interpret the associations found to be significant in uniform analyses of observational data.The framework and the hierarchical analysis strategy are of particular relevance in the understanding of risk formation and might prove useful in identifying determinants that are part of the causal pathway and therefore amenable to prevention strategies across populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Global Health and Amsterdam Institute for Global Health and Development, AMC, University of Amsterdam, The Netherlands.

Show MeSH
Related in: MedlinePlus