Hugo Grisales

Age-specific seroprevalence of human papillomavirus 16, 18, 31, and 58 in women of a rural town of Colombia.

Objective The study’s objective was to estimate human papillomavirus (HPV) genotype–specific seroprevalence to determine population HPV exposure and inform vaccine policy. Methods This study is a cross-sectional prevalence survey of 878 women of Pueblorrico, a rural town of Colombia. A standardized questionnaire was used to obtain information on demographic characteristics, sexual and reproductive history, and smoking habits. Seropositivity to HPV-16, -18, -31, and -58 was determined by virus-like particles in an enzyme-linked immunosorbent assay. Results Overall seropositivity to any HPV genotype was 27.9%. The combined seroprevalence of women 15 to 19 and 20 to 24 years old was 35.4% (95% confidence interval [CI], 25.9–46.2) and 36.0% (95% CI, 27.7–45.3), respectively. Seroprevalence for HPV-16 was 17% (95% CI, 14.6–19.6); for HPV-18, 9.8% (95% CI, 8.0–11.9); for HPV-31, 11.4% (95% CI, 9.5–13.7); and for HPV 58, 12.5% (95% CI, 10.5–14.9). Higher HPV seropositivity was associated with the lifetime number of occasional sexual partners (odds ratio, 3.05; 95% CI, 1.26–7.37) and having more than 2 regular sexual partners (odds ratio, 3.00; 95% CI, 1.21–7.45) in women younger than 44 and older than 45 years old, respectively. Use of oral contraceptives and tobacco/cigarettes was significantly associated with reduced HPV seropositivity in women older than 45 but not in women younger than 44 years old. Conclusions Human papillomavirus seropositivity is associated with measures of sexual behavior, particularly a greater lifetime number of sexual partners. Hormonal and tobacco/cigarette use may be factors influencing the HPV seropositivity in women older than 45 years old.

The effect of misclassification error on risk estimation in case-control studies

INTRODUCTION In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. OBJECTIVE To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to describe the trends in DME and NDME. METHODS Different sensitivity levels, specificity levels, prevalence rates and odds ratios were simulated. Interaction graphics were constructed to study bias in the different settings, and the effect of the different factors on bias was described using linear models. RESULTS One hundred per cent of the biases caused by NDME were negative. DME biased the association positively more often than it did negatively (70 versus 30%), increasing or decreasing the OR estimate towards the null hypothesis. CONCLUSIONS The effect of the sensitivity and specificity in classifying exposure, the prevalence of exposure in controls and true OR differed between positive and negative biases. The use of valid exposure classification instruments with high sensitivity and high specificity is recommended to mitigate this type of bias.