Stable Chlamydia Prevalence Does Not Exclude Increasing Burden of Disease
Bibliographic record
Abstract
To the Editor: In the November 2011 issue of Sexually Transmitted Diseases, Satterwhite et al. report the results of over 5 million chlamydia screening tests in 15- to 24-year-old females attending Infertility Prevention Project (IPP) family planning clinics in the United States (US) from 2004 through 2008.1 The results of their analysis suggest that chlamydia test positivity in this population did not change significantly from 2004 to 2008 after controlling for age, race, test usage, and geography. Based on their results, they propose that the increase in chlamydia case rates of more than 20% over this same period2 is not evidence of an increasing burden of disease. Rather, the authors contend that increasing cases are the result of increasing usage of more sensitive nucleic acid amplification testing combined with increasing coverage of chlamydia screening. They conclude that the chlamydia burden of disease in the US is not increasing despite increasing case reports. There is an alternate explanation for increasing chlamydia case reports nationally coincident with a stable positivity rate of chlamydia screening tests at IPP clinics. The burden of disease in a population includes both incident (new) cases and prevalent (existing) cases. IPP patients are screened annually regardless of symptoms, as per Centers for Disease Control and Prevention (CDC) guidelines,3 and thus reflect chlamydia prevalence at a specific point in time. However, national chlamydia case report data are accumulated over an entire year and include both prevalent and incident cases. According to CDC guidelines, sexually active 15- to 24-year-old females should be screened annually regardless of symptoms and when they have symptoms, a high-risk sexual exposure or become pregnant.3 In fact, between scheduled clinic visits, IPP patients themselves may acquire chlamydia infection, test positive and be treated and cured. In this scenario, they would be included in new case reports but not in IPP prevalence data. Chlamydia test positivity in the IPP population would be representative of chlamydia burden of disease in 15- to 24-year-old sexually active females in the general population only if IPP patients received all of their health care (and chlamydia testing) at IPP clinics so that any positive test over a specific 12-month period could be included in the numerator of the test positivity calculation. The increase in new chlamydia case reports in the US and elsewhere undoubtedly reflects better case finding, as the authors suggest, but increased incidence may also be an important contributing factor. Increased incidence may be attributable to more new infections from an increase in high-risk sexual behavior or more reinfections from arrested immunity, i.e., early, expanded control programs interrupting the acquisition of natural immunity to chlamydia resulting in an increased risk of reinfection.5 In order to interpret the determinants behind the stable prevalence rates, it would be helpful for the authors to report time trends in reinfection rates and changes in the estimated duration of infection between exposure and test positivity. The arrested immunity hypothesis predicts that incidence rates rise while prevalence rates fall as a result of shortening the average duration of infection. Michael L. Rekart, MD Robert C. Brunham, MD British Columbia Centre for Disease Control (BCCDC) Vancouver, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".