Patients With Genital Warts Have a Decreased Quality of Life
Bibliographic record
Abstract
Human papilloma virus (hpv) is one of the most common sexually transmitted infections. It is estimated that 75% of sexually active individuals will have HPV-related infection in their lifetime.1 Of the numerous HPV subtypes, 15 are considered high-risk and lead to the development of cervical cancer.2 The subtypes 6 and 11 are considered low-risk and do not cause cervical cancer but do cause condyloma acuminatum or anogenital warts (AGWs).3 The 2004 National Health and Nutrition Examination Survey of US women between the ages of 14 and 59 years of age showed an overall HPV prevalence of 26.8% (95% CI, 23.3%–30.9%). HPV vaccine type 6 was detected in 1.3% (95% CI, 0.8%–2.3%), HPV-11 in 0.1% (95% CI, 0.03%–0.3%), HPV-16 in 1.5% (95% CI, 0.9%–2.6%), and HPV-18 in 0.8% (95% CI, 0.4%–1.5%) of female participants.3 AGWs appear in the genital area within weeks or months after infection.4 They are normally flesh-coloured and appear in clusters, resembling a cauliflower and are either raised or flat.4 These are present in women on the vulva, cervix, vagina, and anus whereas in men, they appear on the penis, scrotum, or anus.4 In the majority of cases, these lesions resolve untreated. However, in some cases, AGWs can cause both physical discomfort and psychological distress resulting in the need for physician consultation and treatment. Clinic visits and treatment often occur repeatedly and as such, affects the patient's quality of life. Although surveys5–7 have been conducted to evaluate the impact of AGWs on patients, standardized questionnaires and the impact of this disease on health-related quality of life (HRQoL) has not been well characterized in the literature. Therefore, we conducted a study to measure HRQoL decrements associated with genital warts using the Short-Form 6D (SF-6D) and the EuroQol-5D (EQ-5D). Subjects with a history of AGWs were recruited using advertisements in newspapers in urban Vancouver, BC, Canada. Participants were asked questions regarding their sociodemographic and health status and were asked to fill in the EQ-5D and SF-6D HRQoL questionnaires considering the health state they were in while having an episode of AGWs. Descriptions of the Questionnaires The EQ-5D The EQ-5D was designed as a cardinal index of health for describing and valuing HRQL.8–10 The instrument consists of a descriptive health state classification system and a visual analog scale “health thermometer” [the visual analogue score (VAS) component]. The descriptive health state classification system consists of 5 domains (Mobility, Self-Care, Usual Activities, Anxiety/Depression, and Pain/Discomfort), each with 3 response levels (no problems, some problems, extreme problems). The health “thermometer” represents a subjective, global evaluation of the respondent's health status on a scale between 0 and 100, where 0 represents worst imaginable health state and 100 represents best imaginable health. Three types of data are produced for each patient: a health state vector or profile describing the extent of problems on each of the 5 domains, a population-weighted health-index based on the health state vector (the EQ-5D index or utility score), and the health thermometer.8,9 The scoring algorithm typically applied to the descriptive health classification system is the UK-based York scoring system.10 Scores on the EQ-5D range from −0.56 to 1.00 where negative values represent health states worse than death. The SF-6D The SF-6D was created to derive a scoring algorithm to derive preference-based scores from the SF-36.11,12 The SF-6D revised the SF-36 into a 6-dimensional health state classifications system assessing physical functioning, role limitations, social functioning, pain, mental health, and vitality. The SF-6D health classification system defines health states by a respondent selecting 1 level from each of the 6 dimensions. Each dimension has 4 to 6 levels and thus, 18,000 possible health states are defined in this manner.12 The boundaries of the SF-6D utility scores are from 0.30 to 1.00 with a score of 1.00 being indicative of “full health.” Comparisons were made to assess differences in HRQoL scores between males and females and between the scores we obtained and those published in the literature as population norms. These differences were assessed to see if they exceeded the Minimal Important Difference (MID). The MID can be defined as “The smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management.”13 All analyses were conducted using SAS statistical software, version 9.1 (SAS Institute, Cary NC). Seventy-five participants completed the questionnaires. Fifty-two percent of the respondents were female (52%) with a mean age of 40 years (SD 11.4). The majority of participants were white (60%), had some postsecondary education (66%), had no children (63%), and had engaged in unprotected sex within 6 months of the AGW episode (59%). At least 87% received treatment for their AGW, with liquid nitrogen (72%) being the most common modality. Most participants took longer than 2 weeks to clear the lesions (54%) and 61% had at least 1 recurrence. The mean utility score for EQ-5D was 0.76 (95% CI: 0.72–0.80) and the mean EQ-5D VAS score was 65.1 (95% CI: 60.0–70.0), whilst the mean utility score for the SF-6D was 0.74 (95% CI: 0.71–0.77), indicating some level of impairment with quality of life. By examining the frequencies of the SF-6D attributes, most of the decrement was due to mental health and social functioning, with 67% of respondents believing that their health state was causing limitations “more than a little” in social activities and 89% feeling “tense or downhearted and low more than a little of the time.” There were no significant differences in HRQoL scores between males and females (P >0.05). However, when comparing the mean EQ-5D utility scores to age-matched, population norms (with a mean score of 0.82, SD 0.12, n = 330 for 35–45 year olds14), the respondents of our questionnaire had much lower EQ-5D scores on average (P = 0.0005). Similarly, when comparing the SF-6D scores from our respondents to those obtained from the general population (with a mean score of 0.83, SD 0.13, n = 110), there was a similar, significant decrement (P = 0.0001).15 This is one of the first reports to use 2 standardized measures to document a lower HRQoL associated with genital warts. The decrement we observed associated with genital warts was similar in magnitude to that seen with genital herpes.16 Our study showed scores from both the EQ-5D and the SF-6D, whereas the EQ-5D VAS reflected lower HRQoL. Compared to published EQ-5D and SF-6D scores from the general population, there were similar large decrements in HRQoL using both the EQ-5D and the SF-6D in our respondents. There were no differences observed between genders. Recently, Woodhall et al.17 published a report of 81 cases with genital warts who completed the EQ-5D and the VAS. The mean age was 26 years and 53% of their cases were males. The majority of their cases had a mild (43%) to moderate (31%) severity of genital warts (only 12% had a severe case). Their unadjusted EQ-5D score was 0.90 amongst all cases (compared to 0.91 for controls) and 72 on the EQ VAS (compared to 86 for controls). Like our study, these authors found a decrement in HRQoL, which was mostly due to deficits in the EQ-5D's anxiety/depression dimension with 4% of cases suggesting that they were extremely anxious or depressed. However, these authors utilized only 1 standardized questionnaire, whereas we utilized both the SF-6D and the EQ-5D. Although we found similar results with both questionnaires, the domains primarily affected in the SF-6D were social functioning and mental health. Because the EQ-5D does not measure social functioning, this is important information to consider in determining HRQoL. The lower HRQoL associated with genital warts is substantial and similar in magnitude to well-delineated chronic diseases. Compared to age-matched EQ-5D scores from the general population and SF-6D scores from the general population, our participants report a HRQoL decrement that far exceeded the reported MID of each (the MID from the EQ-5D is 0.074 and the MID from the SF-6D is 0.041).18 Given these indicators of a meaningful reduction in HRQol associated with genital warts, the value of HPV prophylactic strategies seems important to affected people's well-being. Specifically, HPV Types 6 and 11, which are associated with 70% of AGWs, may be prevented by a FDA approved HPV vaccine.TABLE 1: Patient Characteristics and Quality of Life Scores
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".