Geographical variations in the epidemiology of bacterial sexually transmitted infections in Manitoba, Canada

Elliott, Lawrence; Blanchard, James; Beaudoin, Carole; Green, C G; Nowicki, Deborah L.; Matusko, P; Moses, Stephen

doi:10.1136/sti.78.suppl_1.i139

Cited by 66 publications

(70 citation statements)

References 6 publications

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“…In addition, studies in different settings have estimated different values for the Gini coefficient. One Canadian study calculated a value of 0.66 for Winnipeg, Manitoba (Elliott et al 2002), and one USAbased study calculated a value of 0.57 for King County, Washington (Kerani et al 2005). The different estimates obtained suggest that estimates of the Gini coefficient for an STI could be dependent on the methodology used, the context of the data, or both factors, and questions could therefore be raised about the accuracy and the validity of calibrating our model outputs against the Gini coefficient published by Monteiro and colleagues, which was based on data from Leeds from 1994 to 1995 (Monteiro et al 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Lorenz curves have been used to visualize and compare the distribution of STIs across geographically defined subpopulations (Elliott et al 2002;Kerani et al 2005;Monteiro et al 2005). In such studies, the Lorenz curves plot the cumulative proportion of incident infections (y-axis) against that of the population (x -axis), after ranking population subunits (e.g.…”

Section: Generating a Distribution Of High-activity Individuals Acrosmentioning

confidence: 99%

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A metapopulation modelling framework for gonorrhoea and other sexually transmitted infections in heterosexual populations

Chen

Ghani

Edmunds

2008

J. R. Soc. Interface.

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Gonorrhoea continues to be a public health problem in the UK, and is the second most common bacterial sexually transmitted infection (STI ) after chlamydia. In the UK, gonorrhoea is disproportionately concentrated in epidemiologically distinct subpopulations, with much higher incidence rates in young people, some ethnic minorities and inner city subpopulations. The original model of STI transmission proposed by Hethcote and Yorke explained some of these features through the concept of the 'core group'. Since then, several authors have modified the original model approach to include multiple sexual activity classes, but found this modelling approach to be inadequate when applied to low-prevalence settings such as the UK. We present a metapopulation framework for modelling gonorrhoea and other STIs. The model proposes that the epidemiology of gonorrhoea is largely driven by subpopulations with higher than average concentrations of individuals with high sexual risk activity. We show how this conceptualization of gonococcal epidemiology overcomes key limitations associated with some of the prior efforts to model gonorrhoea. We also use the model to explain several epidemiological features of gonorrhoea, such as its asymmetric distribution across subpopulations, and the contextual risk experienced by members of at-risk subpopulations. Finally, we extend the model to explain the distribution of other STIs, using chlamydia as an example of a more ubiquitous bacterial STI.

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Section: Discussionmentioning

confidence: 99%

Section: Generating a Distribution Of High-activity Individuals Acrosmentioning

confidence: 99%

A metapopulation modelling framework for gonorrhoea and other sexually transmitted infections in heterosexual populations

Chen

Ghani

Edmunds

2008

J. R. Soc. Interface.

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“…The regional variation in these rates for each racial group is different than that noted for the combined rates of the two racial groups (Farley, 2006). From a structural perspective, higher STD rates are affected by the social, medical, and economic environment (Adimora et al, 2001;Aral & Holmes, 1999;Bunnell et al, 1999;Cohen et al, 2000;Elliott et al, 2002;Gunn, Fitzgerald, & Aral, 2000;St.Louis, Farley, & Aral, 1996). Given these three perspectives, it seems important when analyzing the association between social capital and STD rates at the state level to control for compositional, clustering, and structural determinants that affect exposure to and infection with STDs and that affect state variation in STD rates.…”

Section: Introductionmentioning

confidence: 99%

“…States with higher STD rates have a larger number of persons who engage in risky behaviors associated with STD transmission (Bernstein et al, 2004;Jennings, Curriero, Celentano, & Ellen, 2005). From an infectious disease perspective and from a clustering perspective, STD rates in a given state are associated with similar STD rates in neighboring states and hence are not randomly distributed in space (Aral, Fullilove, Coutinho, & Van Den Hoek, 1992;Bernstein et al, 2004;Elliott et al, 2002;Fox et al, 1998;Jennings et al, 2005;Kerani, Handcock, Handsfield, & Holmes, 2005;Koumans et al, 2000;Webster, Rolfs, Nakashima, & Greenspan, 1991;Wylie, Cabral, & Jolly, 2005). Regional variation in rates of gonorrhea and syphilis, for example, continue to exist even when these rates are examined separately for Whites and for African-Americans (Farley, 2006).…”

Section: Introductionmentioning

confidence: 99%

Social capital and rates of gonorrhea and syphilis in the United States: Spatial regression analyses of state-level associations

Semaan

Sternberg

Zaidi

et al. 2007

Social Science & Medicine

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We conducted spatial regression analysis to account for spatial clustering of sexually transmitted diseases (STDs) and to examine the state-level association between social capital (using Putnam's public use data set) and rates of gonorrhea and syphilis. We conducted the analysis for the 48 contiguous states of the United States for 1990, 1995, and 2000 and controlled for the effects of regional variation in STD rates, and for state variation in poverty, income inequality, racial composition, and percentage aged 15-34 years. We compared the results of the spatial regression analysis with those of ordinary least squares (OLS) regression. Controlling for all population-level variables, the percentage of variation explained by the OLS regression and by the spatial regression were similar (mid-90s for gonorrhea and low-70s for syphilis), the standardized parameter estimates were similar, and the spatial lag parameter was not statistically significant. Social capital was not associated with STD rates when state variation in racial composition was included in the regression analysis. In this analysis, states with a higher proportion of residents who were African-American had higher STD rates. When we did not control for racial composition, regression analysis showed that states with higher social capital had lower STD rates. We conjecture that sexual networks and sexual mixing drive the association between social capital and STD rates and highlight important measurement and research questions that need elucidation to understand fully the relationship between social capital and STDs. r

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“…It is most well known as a method for representing inequality in income or wealth [31]. Recently, it has been applied to visualize the geographical distribution of several bacterial STIs at different levels [32][33][34][35] and over time [36]. In a mathematical modelling study, it has also been used as a method to describe the distribution of infections across certain subpopulations [37].…”

Section: Lorenz Curve and Gini Coefficientmentioning

confidence: 99%

Transmission ofChlamydia trachomatisthrough sexual partnerships: a comparison between three individual-based models and empirical data

Althaus

Turner

Schmid

et al. 2011

J. R. Soc. Interface.

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Chlamydia trachomatis is the most common bacterial sexually transmitted infection (STI) in many developed countries. The highest prevalence rates are found among young adults who have frequent partner change rates. Three published individual-based models have incorporated a detailed description of age-specific sexual behaviour in order to quantify the transmission of C. trachomatis in the population and to assess the impact of screening interventions. Owing to varying assumptions about sexual partnership formation and dissolution and the great uncertainty about critical parameters, such models show conflicting results about the impact of preventive interventions. Here, we perform a detailed evaluation of these models by comparing the partnership formation and dissolution dynamics with data from Natsal 2000, a population-based probability sample survey of sexual attitudes and lifestyles in Britain. The data also allow us to describe the dispersion of C. trachomatis infections as a function of sexual behaviour, using the Gini coefficient. We suggest that the Gini coefficient is a useful measure for calibrating infectious disease models that include risk structure and highlight the need to estimate this measure for other STIs.

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Geographical variations in the epidemiology of bacterial sexually transmitted infections in Manitoba, Canada

Cited by 66 publications

References 6 publications

A metapopulation modelling framework for gonorrhoea and other sexually transmitted infections in heterosexual populations

A metapopulation modelling framework for gonorrhoea and other sexually transmitted infections in heterosexual populations

Social capital and rates of gonorrhea and syphilis in the United States: Spatial regression analyses of state-level associations

Transmission ofChlamydia trachomatisthrough sexual partnerships: a comparison between three individual-based models and empirical data

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