| Literature DB >> 21668946 |
Ju-Fang Shi1, Karen Canfell, Jie-Bin Lew, Fang-Hui Zhao, Rosa Legood, Yan Ning, Leonardo Simonella, Li Ma, Yoon-Jung Kang, Yong-Zhen Zhang, Megan A Smith, Jun-Feng Chen, Xiang-Xian Feng, You-Lin Qiao.
Abstract
BACKGROUND: A new lower-cost rapid-throughput human papillomavirus (HPV) test (careHPV, Qiagen, Gaithersburg, USA) has been shown to have high sensitivity for the detection of high grade cervical intraepithelial neoplasia.Entities:
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Year: 2011 PMID: 21668946 PMCID: PMC3141766 DOI: 10.1186/1471-2407-11-239
Source DB: PubMed Journal: BMC Cancer ISSN: 1471-2407 Impact factor: 4.430
Model parameters for screening, diagnosis, treatment and utilities; and ranges for sensitivity analysis
| Screening participation rate for once-lifetime screening (%)† | 71 | 40 - 100 |
| Proportion of women never screened in lifetime in program-based screening (%)†† | 15 | |
| Total participation rate over one screening round for program-based screening (age-standardised to local population for 30-59 years) (%)† | 62 | 35 - 100 |
| Rate of technically inadequate tests for | 0 | 0 - 10 |
| Rate of loss to follow-up after screening, diagnosis or treatment (if follow-up not performed the same day) (%)†† | 10 | 5 - 20 |
| Age-specific rate of unsatisfactory visual tests if performed as first visual test in management process (VIA, VIA/VILI or colposcopy) (%)‡ | ||
| 20-34 years | 5 | 0 - 5 |
| 35-39 years | 6 | 0 - 6 |
| 40-44 years | 7 | 0 - 7 |
| 45-49 years | 16 | 0 - 16 |
| 50-54 years | 35 | 0 - 35 |
| 55+ years | 59 | 0 - 59 |
| Conditional probability of an unsatisfactory colposcopy, given a prior unsatisfactory visual test (%)‡ | 92 | 80 - 100 |
| LEEP treatment success rate (%) [ | 93.6 | 90 - 100 |
| Age-standardised annual progression rate from CIN3 to cancer (%) [ | 1.4 | 0.7 - 2.8 |
| Proportion of CIN3 treated by hysterectomy in rural Chinese settings (%) | 21.1 | 5 - 21.1 |
| 5-year survival by FIGO stage (%) [ | ||
| FIGO I | 88.0 | 79.1 - 96.7 |
| FIGO II | 68.0 | 61.2 - 74.8 |
| FIGO III | 41.3 | 37.2 - 45.5 |
| FIGO IV | 15.5 | 13.9 - 17.0 |
| Per-partnership HPV transmission probability [ | 0.6 | - |
| Age group and sexual behaviour group mixing probability†† | ||
| Same five-year age group mixing | 0.7 | - |
| Random age group mixing | 0.3 | - |
| Same sexual activity group mixing (four activity groups) | 0.7 | - |
| Random sexual activity group mixing | 0.3 | - |
| Average age-specific new annual partnerships (across sexual activity groups) in females/ males‡‡ | ||
| 10-14 years | 0.000/ 0.000 | - |
| 15-19 years | 0.097/ 0.094 | - |
| 20-24 years | 0.316/ 0.263 | - |
| 25-29 years | 0.029/ 0.029 | - |
| 30-34 years | 0.008/ 0.008 | - |
| 35-39 years | 0.045/ 0.053 | - |
| 40-44 years | 0.039/ 0.047 | - |
| 45-50 years | 0.075/ 0.075 | - |
| 50-54 years | 0.021/ 0.021 | - |
| 55+ years | 0.011/ 0.011 | - |
| VIA ($) - once or twice-lifetime (mobile) screening at district hospital | 3.55 | 2.84 - 4.26 |
| VIA ($) - program-based screening at county hospital | 4.30 | 3.44 - 5.16 |
| VILI ($) - district or county hospital | 0.40 | 0.32 - 0.48 |
| 9.20 | 7.20 - 14.20 | |
| 10.34 | 8.34 - 15.34 | |
| LEEP ($) | 55.95 | 44.76 - 67.13 |
| Cancer treatment cost ($): | ||
| FIGO I | 627.64 | 502.11 - 753.16 |
| FIGO II | 1953.20 | 1562.56 - 2343.83 |
| FIGO III | 1810.17 | 1448.13 - 2172.20 |
| FIGO IV | 662.61 | 530.09 - 795.1 |
| Discount rate (%) [ | 3.6 | 0 - 5 |
| Having a screening test | 0.999945 | 0.999616 - 0.999956 |
| Screening test positive with no treatment on the same day | 0.999918 | 0.998849 - 0.999934 |
| Colposcopy negative | 0.999877 | 0.998274 - 0.999901 |
| Colposcopy positive and biopsy negative | 0.9965 | 0.992603 - 0.997238 |
| Colposcopy positive then biopsy confirmed CIN1 with no treatment | 0.9965 | 0.992603 - 0.997238 |
| Colposcopy positive then biopsy confirmed CIN1 with LEEP treatment | 0.984 | 0.935178 - 0.987178 |
| Colposcopy positive then biopsy confirmed CIN2-3 with LEEP treatment | 0.984 | 0.935178 - 0.987178 |
| Colposcopy positive then biopsy confirmed CIN3 with hysterectomy treatment | 0.85 | 0.82 - 0.88 |
| Cancer - FIGO I | 0.76 | 0.65 - 0.76 |
| Cancer - FIGO II | 0.67 | 0.56 - 0.67 |
| Cancer - FIGO III | 0.67 | 0.56 - 0.67 |
| Cancer - FIGO IV | 0.67 | 0.48 - 0.67 |
† Data source: Professor You-Lin Qiao, personal communication (based on a demonstration screening project in Xiangyuan County, Shanxi Province 2006);
†† Assumption (no data available);
‡ Re-analysis of data on colposcopy unsatisfactory rates from Dai et al [5];
‡‡ Female data were based on re-analysis of the IARC/CICAMS study [5], and male sexual contact data were assumed based on female data;
* Micro-costing study results as described in text. Range for sensitivity analysis +/-20% of baseline values.
Figure 1Screening and management pathways. pos = positive result; neg = negative result; sat = original cervical squamocolumnar junction fully visible. † A proportion (10%) of women with unsatisfactory and negative VIA or VILI test results were assumed to undergo endocervical curettage (ECC) on the same day. †† If colposcopy performed on the same day as HPV sampling and laboratory testing, assumed no loss to follow-up. ‡ Model assumes stage-specific cancer survival as described in text. ‡‡ A proportion (21%) of women with CIN3 were assumed to receive hysterectomy treatment, based on data from the micro-costing study.* CIN1 is assumed to be treated in once or twice-lifetime strategies only. ** Go to a sub-model of post-treatment natural history and recurrence [12].
Summary of test characteristics for detection of CIN2+†
| Sensitivity | 41% | 58% | 84% (provider) | 90% (provider) | 81% |
| Specificity | 95% | 82% | 88% (provider) | 84% (provider) | 77% |
† Note that this table summarises the finding for sensitivity and specificity in the original study populations, taking into account the relative proportions of CIN1, 2 and 3 in the population. However, the model implementation used these data to derive a test probability matrix separately describing the relationship between each health state (Normal; PCR HPV positive; CIN1, CIN2, CIN3+, etc.) and each test, rather than utilising the study sensitivity and specificity estimates directly, as described in the text [3,5,6].
Figure 2Predicted and observed HPV prevalence and cervical cancer incidence. Data on HPV prevalence in cytologically / histologically normal women (Figure 2A) obtained from analysis of Hybrid Capture II testing performed as part of the IARC/CICAMS study to obtain information on HPV positivity rates by 5-year age group [5]. The average incidence observed in unscreened populations of 24 less developed countries (Figure 2B) was calculated using data from IARC's Cancer Incidence in Five Continents Vol. VIII, which uses the Segi world standard population [14,15].
Predicted effect of screening strategies on cancer rates and cost-effectiveness
| Once-lifetime (35 years) | ||||
| VIA only | 7% | 8% | 8% | 557 |
| VIA/VILI | 8% | 10% | 10% | 629 |
| 10% | 12% | 12% | 959 | |
| 10% | 12% | 13% | 909 | |
| Twice-lifetime (35+45 years) | ||||
| VIA only | 13% | 16% | 17% | 611 |
| VIA/VILI | 16% | 18% | 20% | 689 |
| 19% | 22% | 24% | 1,032 | |
| 21% | 24% | 25% | 985 | |
| 10 yearly (30-59 years) | ||||
| VIA only | 17% | 19% | 20% | 665 |
| VIA/VILI | 19% | 22% | 23% | 744 |
| 24% | 28% | 29% | 1,074 | |
| 24% | 28% | 29% | 1,071 | |
| 5 yearly (30-59 years) | ||||
| VIA only | 27% | 32% | 33% | 796 |
| VIA/VILI | 31% | 36% | 37% | 916 |
| 37% | 43% | 44% | 1,395 | |
| 37% | 43% | 44% | 1,391 | |
| IARC recommended | ||||
| VIA only | 37% | 43% | 41% | 1,213 |
| VIA/VILI | 41% | 47% | 45% | 1,427 |
| 47% | 54% | 52% | 2,269 | |
| 48% | 54% | 52% | 2,263 | |
† Comparison GDP per capita Shanxi Province 2008: US$ 2,975 [25].
Predicted number of procedures per 100,000 women screened at age 35 years
| VIA only | 6,068 | 1,920 | 791 | 7.7 |
| VIA/VILI | 17,881 | 4,640 | 1,036 | 17.3 |
| 8,124 | 3,010 | 1,493 | 5.4 | |
| 8,478 | 3,164 | 1,587 | 5.3 | |
† Biopsy does not include diagnoses from ECC.
Figure 3Analysis of most effective age of once-lifetime screening, showing life years saved (undiscounted) as a function of age at screening.
Figure 4Cost-effectiveness frontier for screening strategies.
Figure 5The proportional breakdown of costs of screening and treatment for selected strategies. A. Once-lifetime screening with VIA/VILI at age 35 years; B. Once-lifetime screening with careHPV@0.5 pg/ml at age 35 years; C. Routine screening with VIA/VILI at IARC-recommended intervals; D. Routine screening with careHPV@0.5 pg/ml at IARC-recommended intervals. The greatest component of the total lifetime cost is the cancer treatment cost for once-lifetime screening strategies. In contrast, for screening according to the IARC-recommended intervals (the most intensive regular screening strategy examined) the screening test costs dominate.
Figure 6Sensitivity analysis for cost per life year saved (LYS). Sensitivity analysis findings are shown for cost-effectiveness ratios relative to no intervention for selected test technologies (VIA/VILI and careHPV@0.5 pg/ml) and screening strategies (once-lifetime and IARC-recommended intervals); similar results were obtained for other screening strategies. In general, the cost-effectiveness findings were most sensitive to assumptions about the progression rate from CIN3 to invasive cervical cancer, the discount rate, mortality to incidence ratio, the cost of careHPV testing and VIA, test accuracy and cancer treatment costs. The baseline values and range for sensitivity analysis are provided in Table 1.
Figure 7Utilities sensitivity analysis for cost per quality-adjusted life years (QALY) saved. Sensitivity analysis findings are shown for cost-effectiveness ratios calculated as cost per QALY saved relative to no intervention for careHPV testing at 0.5 pg/ml (once-lifetime screening and IARC-recommended intervals); similar relative results were obtained for other screening technologies. The baseline values and range for sensitivity analysis are provided in Table 1. Because this is a secondary sensitivity analysis, we examined two selected strategies as examples for QALY sensitivity analysis rather than the full four strategies considered in YLS analysis. Because the cost-effectiveness frontier curves calculated using YLS and QALYs in Figure 4A and 4B were similar, we focused the secondary sensitivity analysis on health state utility parameters.