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Describing a complex and changing landscape: metrics for understanding and planning chlamydia control
  1. Sarah C Woodhall,
  2. John Saunders
  1. HIV and STI Department, Public Health England, London, UK
  1. Correspondence to Dr Sarah C Woodhall, HIV and STI Department, Public Health England, 61 Colindale Avenue, London NW9 5EQ, UK; sarah.woodhall{at}phe.gov.uk

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In this edition, den Heijer et al1 describe the complex landscape of chlamydia testing practices among people aged 16–29 years in the South Limburg region of the Netherlands, between 2006 and 2010.1 The study combined data from sexually transmitted infection (STI) clinics and laboratories with that from the Chlamydia Screening Implementation project, which used a population registry to invite residents to order a chlamydia test via the internet.2 The authors report on a number of key metrics: the proportion of tests that resulted in a positive diagnosis (‘positivity’), the percentage of all tests contributed by different testing service providers and the percentage of all diagnoses contributed by service type and patient demographic characteristics. They show that general practice (GP) and internet screening each accounted for just less than one-third of all tests, whereas STI clinics and gynaecology each accounted for around one-fifth of all tests. The majority of diagnoses were made in GP and STI clinics. They also highlight differences in positivity between settings, with >10% of urogenital tests in GP and STI clinics resulting in a positive result compared with <5% of those carried out in gynaecology settings.

Data such as these can inform service planning and delivery by providing an indication of testing activity as well as prevalence of infection in different populations. This can help to highlight gaps in service provision, to identify opportunities for service improvement or to identify key professionals for clinical and quality assurance guidelines. However, in interpreting these types of data there are some things that readers should keep in mind.

First, as chlamydia is largely asymptomatic, positivity will depend on who is being tested, individual and clinician motivations for testing and the venue in which they are tested.3 Variations in positivity may therefore reflect differences in the population accessing testing in different settings. This is seen quite clearly in South Limburg where those tested in STI and GP clinics had a much higher positivity (12.3% and 11.8%, respectively) than those accessing testing via gynaecology (4.3%) or the internet-based chlamydia screening programme (4.4%).

However, it would be a mistake to focus only on positivity when considering the relative contribution of each service type to overall chlamydia control activities. While positivity is one metric of the efficiency of testing, this must be understood within the context of the age-specific coverage of testing (ie, the proportion of the population tested) as well as the populations being served. In South Limburg, tests performed in gynaecology may have been performed as part of the clinical workup for the investigation of infertility, or prior to the fitting an intrauterine device. Thus, it is likely that testing in this setting would still be important for the care of the individual, even if the overall contribution to infections detected is relatively small. Chlamydia is much more widely spread through the population than other STI such as gonorrhoea.4 Therefore, chlamydia testing in specialist services alone is unlikely to reach a majority of those who have chlamydia and are at risk of complications from untreated infection or transmitting to sexual partners. Thus, although positivity was <5% among those tested via the internet-based chlamydia screening programme, chlamydia screening may have been reaching a group at risk of chlamydia infection that would not otherwise have accessed testing.

Finally, while den Heijer provides a comprehensive overview of testing patterns, there are some important components missing from the picture. Control of infection is much more than testing and identifying infection and as such there are other metrics that would be helpful in understanding the implications of these findings. Even before a test is performed, there will be many missed opportunities to test. Is everyone who ‘should’ be tested being offered a test? To understand this, we need a denominator. This will vary by country depending on clinical recommendations, but may include the sexually active population of a given age, those who attend a specific service or those who report specific risk factors.5–9 Such denominators would allow consideration of whether or not health systems and services are operating at maximum capacity, could contribute more to the testing landscape or if resources should be reallocated within the system. Following a diagnosis, effective treatment and partner notification are essential. Therefore, key metrics around case management are invaluable. These include time to results, time to treatment, proportion of index cases receiving treatment and measures of partner notification. As reinfection with chlamydia is common, several countries recommend retesting after a positive chlamydia test.7 10 Measures of retesting rates therefore offer another metric of service and programme quality.

In summary, den Heijer's findings are one part of a complex picture of regional chlamydia control activities. Understanding the relative contributions of tests from different test settings relies on a deeper understanding of the populations served and reasons for testing. A more comprehensive overview of the impact of testing requires the collection of other key variables that can be challenging to measure in national, regional or local public health monitoring systems. However, without considering the bigger picture and the interactions between key components of comprehensive case management, it is extremely difficult to design, deliver and evaluate programmes to control the spread and consequences of chlamydia.

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