Assessing the potential of upper respiratory tract point-of-care testing: a systematic review of the prognostic significance of upper respiratory tract microbes.

OBJECTIVES: Microbial point-of-care testing (POCT) has potential to revolutionize clinical care. Understanding the prognostic value of microbes identified from the upper respiratory tract (a convenient sampling site) is a necessary first step to understand potential for upper respiratory tract POCTs in assisting antimicrobial treatment decisions for respiratory infections (RTIs). The aim was to investigate the relationship between upper respiratory tract microbial detection and disease prognosis, including effects of antimicrobial use. METHODS: Data sources were the MEDLINE and Embase databases. Study eligibility criteria consisted of quantitative studies reporting microbiological and prognostic data from patients of all age groups presenting with RTI. Patients presenting to healthcare or research settings with RTI participated. Interventions included upper respiratory tract swab. The methods used were systematic review and meta-analysis. RESULTS: Searches identified 5156 articles, of which 754 were duplicates and 4258 excluded on title or abstract. A total of 144 full texts were screened; 21 articles were retained. Studies reported data for 15 microbes and 26 prognostic measures (390 potential associations). One hundred and seven (27%) associations were investigated statistically, of which 38 (36%) were significant. Most studies reported only prognostic value of test positive results. Meta-analyses suggested hospitalization duration was longer for patients with respiratory syncytial virus than adenovirus and influenza, but significant heterogeneity was observed between studies. CONCLUSIONS: A quarter of potential prognostic associations have been investigated. Of these, a third were significant, suggesting considerable potential for POCT. Future research should investigate prognostic value of positive and negative tests, and interactions between test results, use of antimicrobials and microbial resistance.

Predicting the population impact of chlamydia screening programmes: comparative mathematical modelling study.

BACKGROUND: Published individual-based, dynamic sexual network modelling studies reach different conclusions about the population impact of screening for Chlamydia trachomatis. The objective of this study was to conduct a direct comparison of the effect of organised chlamydia screening in different models. METHODS: Three models simulating population-level sexual behaviour, chlamydia transmission, screening and partner notification were used. Parameters describing a hypothetical annual opportunistic screening program in 16-24 year olds were standardised, whereas other parameters from the three original studies were retained. Model predictions of the change in chlamydia prevalence were compared under a range of scenarios. RESULTS: Initial overall chlamydia prevalence rates were similar in women but not men and there were age and sex-specific differences between models. The number of screening tests carried out was comparable in all models but there were large differences in the predicted impact of screening. After 10 years of screening, the predicted reduction in chlamydia prevalence in women aged 16-44 years ranged from 4% to 85%. Screening men and women had a greater impact than screening women alone in all models. There were marked differences between models in assumptions about treatment seeking and sexual behaviour before the start of the screening intervention. CONCLUSIONS: Future models of chlamydia transmission should be fitted to both incidence and prevalence data. This meta-modelling study provides essential information for explaining differences between published studies and increasing the utility of individual-based chlamydia transmission models for policy making.

Modelling the effectiveness of chlamydia screening in England.

BACKGROUND: Several developed countries have initiated chlamydia screening programmes. Screening for a sexually transmitted infection has both direct individual and indirect population-wide effects. Mathematical models can incorporate these non-linear effects and estimate the likely impact of different screening programmes and identify areas where more data are needed. METHODS: A stochastic, individual based dynamic network model, parameterised from UK screening studies and data on sexual behaviour and chlamydia epidemiology, was used to investigate the likely impact of opportunistic screening on chlamydia prevalence. Three main strategies were considered for <25 year olds: (1) annual offer to women; (2) annual offer to women or if changed partner within last 6 months; (3) annual offer to men and women. Sensitivity analyses were performed for key screening parameters including uptake rate, targeted age range, percentage of partners notified, and screening interval. RESULTS: Under strategy 1, continuous opportunistic screening of women <25 years of age is expected to reduce the population prevalence by over 50% after 5 years. Prevalence is also expected to decrease in unscreened older women and in men. For all three strategies screening those aged over 25 results in small additional reductions in prevalence. Including men led to a faster and greater reduction in overall prevalence, but involved approximately twice as many tests as strategy 1 and 10% more than strategy 2. The frequency of attendance at healthcare sites limits the number of opportunities to screen and the effect of changing the screening interval. CONCLUSIONS: The model suggests that continuous opportunistic screening at high uptake rates could significantly reduced chlamydia prevalence within a few years. Opportunistic programmes depend on regular attendance at healthcare providers, but there is a lack of high quality data on patterns of attendance. Inequalities in coverage may result in a less efficient and less equitable outcome.

Investigating ethnic inequalities in the incidence of sexually transmitted infections: mathematical modelling study.

OBJECTIVES: To investigate ethnic differences in rates of gonorrhoea using empirical sexual behaviour data in a simple mathematical model. To explore the impact of different intervention strategies in this simulated population. METHODS: The findings from cross sectional studies of gonorrhoea rates and sexual behaviour in three ethnic groups in south east London were used to determine the parameters for a deterministic, mathematical model of gonorrhoea transmission dynamics, in a population stratified by sex, sexual activity (rate of partner change), and ethnic group (white, black African, and black Caribbean). We compared predicted and observed rates of infection and simulated the effects of targeted and population-wide intervention strategies. RESULTS: In model simulations the reported sexual behaviours and mixing patterns generated major differences in the rates of gonorrhoea experienced by each subpopulation. The fit of the model to observed data was sensitive to assumptions about the degree of mixing by level of sexual activity, the numbers of sexual partnerships reported by men and women, and the degree to which observed data underestimate female infection rates. Interventions to reduce duration of infection were most effective when targeted at black Caribbeans. CONCLUSIONS: Average measures of sexual behaviour in large populations are inadequate descriptors for the epidemiology of gonorrhoea. The consistency between the model results and empirical data shows that profound differences in gonorrhoea rates between ethnic groups can be explained by modest differences in a limited number of sexual behaviours and mixing patterns. Targeting effective services to particular ethnic groups can have a disproportionate influence on disease reduction in the whole community.

The impact of the phase of an epidemic of sexually transmitted infection on the evolution of the organism.

The evolution of any sexually transmitted organism will be influenced by prevailing epidemiological interactions. The optimum strategy for an organism to overcome treatment, either through drug resistance or cryptic infections, depends upon whether the method for identifying patients is passive (treating symptoms alone favours asymptomatic organisms) or active (screening favours resistant organisms). The use of mathematical models of competing strains of infection allows theoretical predictions for the outcome of evolution under a range of assumptions about potential phenotypes. The course of pathogen evolution has implications for the success of interventions, but the predictions presented need to be tested at the level of the community in carefully monitored interventions.