Estimating Chlamydia Re-infection Rates
Monitoring chlamydia re-infection rates is a routine part of disease surveillance. Re-infection rates are used to inform chlamydia control programmes, including developing re-screening guidelines. The calculation of a re-infection rate is mathematically simple: the number of re-infections divided by the population at-risk for re-infection in a specified time period. However, there are a number of challenges in estimating re-infection rates, such as misclassification of re-infections as initial infections and variation in case identification over time, including switching to more sensitive diagnostic tests or increasing screening coverage. The choice of denominator (ie, what population best describes the 'at-risk' population for being re-infected) poses an additional challenge. Often, denominator choice is limited by available data. For example, surveillance datasets may not contain negative test results to identify who was rescreened, so re-infection rates are calculated among all women with a previous infection. Other researchers have calculated re-infection rates using population or census data as a denominator. Using data from a Pacific Northwest health plan, we investigated the impact of denominator selection when estimating chlamydia re-infection rates.
Background
Monitoring chlamydia re-infection rates is a routine part of disease surveillance. Re-infection rates are used to inform chlamydia control programmes, including developing re-screening guidelines. The calculation of a re-infection rate is mathematically simple: the number of re-infections divided by the population at-risk for re-infection in a specified time period. However, there are a number of challenges in estimating re-infection rates, such as misclassification of re-infections as initial infections and variation in case identification over time, including switching to more sensitive diagnostic tests or increasing screening coverage. The choice of denominator (ie, what population best describes the 'at-risk' population for being re-infected) poses an additional challenge. Often, denominator choice is limited by available data. For example, surveillance datasets may not contain negative test results to identify who was rescreened, so re-infection rates are calculated among all women with a previous infection. Other researchers have calculated re-infection rates using population or census data as a denominator. Using data from a Pacific Northwest health plan, we investigated the impact of denominator selection when estimating chlamydia re-infection rates.
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