Geomapping of chlamydia and gonorrhoea in Birmingham

Abstract

Objective: To investigate if the core population hypothesis is applicable to patients with genital chlamydia infections. Design: Retrospective cross sectional study. Setting: Two genitourinary medicine (GUM) clinics in the city of Birmingham and eight adjacent clinics. Subjects: All patients with chlamydia (n = 665) or gonorrhoea (n = 584) attending between 1 October 1995 and 30 September 1996 with a postcode within the Birmingham health district. Controls were 727 patients seen in the same period with no infection. Methods: Postcodes were used to calculate population prevalence rates per 100 000 aged 15-65 in the 39 wards of the city and to estimate the socioeconomic status using the Super Profile (SP). Ethnic specific rates were also calculated. Data were obtained on gonorrhoea and chlamydia isolation from all the major laboratories of the city over the same time period. Results: GUM clinic attenders accounted for 67.6% and 82.5% of all chlamydia and gonorrhoea isolates reported by the laboratories and that were available for our epidemiological analysis. Both infections were more common in men and in black ethnic groups. However, patients with gonorrhoea only infection were more likely to be of black ethnicity than those with chlamydia only infection (p = 0.0001) and to have diVerent SP distribution (p = 0.0001). On logistic regression age <20 years, male sex, black ethnicity, and living in neighbourhoods with SP J ("have nots") were predictive of both infections compared with controls. Overall chlamydia and gonorrhoea prevalence rates were 129 and 98.4 per 10 5 respectively. Corresponding rates for whites was 64.7 and 37.2 and for black ethnic groups 1105 and 1183 per 10 5 of each ethnic group. Eight adjacent wards accounted for 41% of the chlamydia and 66.5% of the gonorrhoea. Conclusion: In a large urban setting patients attending GUM clinics with chlamydia belong to core population groups with similar, but not identical, sociodemographic characteristics to patients with gonorrhoea infection. (Sex Transm Inf 2000;76:268-272)

Sexual health knowledge, attitudes and behaviours: variations among a religiously diverse sample of young people in London, UK

“…In addition, and of relevance to this paper, certain Black and Minority Ethnic (BME) groups have been identified as bearing a disproportionate burden of sexual illhealth (SEU 1999;Berthoud 2001; Centers for Disease Control and Prevention 2005;Fenton & Hughes 2003;Fenton et al 2005;Sinha et al 2005;Tripp & Viner 2005). National surveillance data (Dougan et al 2004;HPA 2004), and localised surveys within service settings over the last decade (Hughes et al 2001;Low et al 2001;Shahmanesh et al 2000), demonstrate higher numbers of new cases of STIs among Black groups, and lower rates in Asian groups, compared to White groups.…”

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

“…For instance, in the UK, incidence rates of gonorrhoea are much higher in young people and some ethnic minorities (Lacey et al 1997;Low et al 1997;Hickman et al 1999), and the disease is characterized by intense spatial clustering (Shahmanesh et al 2000;Monteiro et al 2005;Risley et al 2007), suggesting that some demographically and geographically distinct subpopulations are at much greater risk of infection. Similar observations have been made in the USA, where gonorrhoea and other sexually transmitted infections (STIs) have been shown in multiple studies to be concentrated in certain geographical areas (Rothenberg 1983;Potterat et al 1985;Bernstein et al 2004;Kerani et al 2005).…”

“…Instead of defining a core group based on individual risk, the population is divided into a number of subpopulations of finite size representing the possible sexual networks that may exist within a given community. The sections that follow will -describe the conceptual and mathematical framework behind the MP model, -use the example of gonorrhoea, modelled as an susceptible-infectious-susceptible (SIS)-type infection to illustrate the model's key features, -explore the influence of key parameters for the MP model, -compare the MP model to the AC model of STI transmission, and -illustrate how the model can explain epidemiological characteristics of gonorrhoea as well as be generalized to other STIs such as chlamydia, which, as gonorrhoea, is an SIS type bacterial STI, but which is more ubiquitous in its distribution (Shahmanesh et al 2000;Monteiro et al 2005). …”

Incidence of sexually transmitted diseases in Hokkaido, Japan, 1998 to 2001

“…In Birmingham, UK, the overall prevalence of chlamydia was reported to be 129 per 100 000. 10 Northern Australia had a reported incidence of female chlamydial infection of 250 per 100 000. 11 The rate for women was approximately six times higher than that for men.…”