The value of improving failures within a cervical cancer screening program: an example from Norway

Abstract

Failures in cervical cancer (CC) screening include nonparticipation, underscreening and loss to follow-up of abnormal results. We estimated the long-term health benefits from and maximum investments in interventions targeted to improving compliance to guidelines while remaining cost-effective. We used a mathematical model empirically calibrated to simulate the natural history of CC in Norway. A baseline scenario reflecting current practice using cytology-based screening was compared to scenarios that target different sources of noncompliance: (i) failure to follow-up women with abnormal results, (ii) screening less frequently than recommended (i.e., underscreening) and (iii) absence of screening. A secondary analysis included human papillomavirus (HPV)-based screening as the primary test. Model outcomes included reductions in lifetime cancer risk and incremental net monetary benefit (INMB) resulting from improvements with compliance. Compared to the status quo, improving all sources of noncompliance leads to important health gains and produced positive INMBs across a range of developed-country willingness-to-pay (WTP) thresholds. For example, a 2% increase in compliance could reduce lifetime cancer risk by 1-3%, depending on the targeted source of noncompliance and primary screening method. Assuming a WTP threshold of $83,000 per year of life saved and cytology-based screening, interventions that increase follow-up of abnormal results yielded the highest INMB per 2% increase in coverage [$19 ($10-21)]. With HPV-based screening, recruiting nonscreeners resulted in the largest INMB [$23 ($18-32)]. Considerable funds could be allocated toward policies that improve compliance with screening under the current cytology-based program or toward adoption of primary HPV-based screening while remaining cost-effective.

Keywords: Pap smear; cervical cancer; compliance; cost-effectiveness; human papillomavirus; mass screening.

Figure 1

Additional cancer risk reduction and net monetary benefit (INMB) associated with targeted interventions to improve compliance to screening guidelines. The height of each bar corresponds to the y-axis (additional cancer risk reduction compared to status quo screening using cytology) for increasing compliance to100% within each scenario or for an incremental (2%) increase in screening adherence, designated by white lines. The INMBs associated with human papillomavirus (HPV)-based scenario are inclusive of the benefit (designated by the darkest solid box) and costs associated with switching current screeners to primary HPV testing for women age 30 years or older. For the primary HPV testing strategy, we assumed that women with a positive HPV test underwent additional cytology testing. Those who were both HPV and cytology-positive (i.e., atypical cells or worse) were referred directly to colposcopy; for women HPV-positive but cytology-negative, two additional persistent HPV-positive, cytology-negative results were required (each 12 months apart) prior to prompting referral to colposcopy.

Figure 2

Incremental net monetary benefit (INMB) of A) targeting follow-up, B) targeting under-screeners, C) targeting non-screeners and D) perfect compliance. Point estimate (solid line) and credible bounds (dotted lines) for the INMB assuming perfect compliance compared to status quo participation using cytology and are shown as a function of the willingness to pay (WTP) threshold (λ) per year of life saved (YLS). Credible bounds reflect uncertainty in input values across the good-fitting parameter sets.

Figure 3

Tornado plot of influential parameters on the incremental net monetary benefit (INMB) of increasing screening among different subgroups of noncompliant women for primary cytology (black bars) and primary human papillomavirus (HPV) testing for women > age 30 (grey bars). A) targeting follow-up, B) targeting under-screeners, C) targeting non-screeners and D) perfect compliance. *HPV sensitivity defined as the probability of high-risk HPV DNA positivity given high-risk HPV.