Pap-level covariates included timing of Pap test (before or after the 2009 guideline change) as well as family planning services delivered on the same day as the Pap test.
We used patient-level data to calculate descriptive statistics of the cohort and interval-based data to provide more information on time intervals between Pap tests and to define our categorical outcomes. To determine patient demographic and health status factors associated with underscreening or overscreening, we performed multivariable multinomial logistic regression. We used appropriate screening as our base outcome for multinomial models and built models using clinically meaningful covariates. The primary independent variable was timing of a Pap test (performed before or after the 2009 guideline change). Within-patient correlation was accounted for using robust standard error estimation. We then derived average predicted probabilities from multinomial models. As discussed previously, we included a 6-month window period in the guideline intervals. We performed sensitivity analyses using no window period and 3-month window periods to vary definitions for guideline-based intervals. Results from these analyses were similar in magnitude and direction to our primary analysis; we present the 6-month window period as our main results. Statistical significance was determined using a P value of <.05. We performed analyses using Stata 13.
Our sample included 53,160 Pap tests performed in 29,650 women who met our continuous enrollment and inclusion criteria. Table 4 presents clinical characteristics of the cohort stratified by age group. Mean age was 22 years among 17,360 younger than 30 years old and 39 among 12,290 30 years old or older. The 168,909 continuously enrolled women who had zero Pap tests in our study period were more likely to be white (63%), less likely to be black (25%), and were similarly likely to be Hispanic (11%) compared with our cohorts.
Our findings demonstrate a decrease in guideline-based cervical cancer screening after the 2009 guideline change. The interval between Pap tests did not meaningfully change, but the proportion of appropriately screened women decreased sharply. Our results reflect a dichotomy: overscreening increased dramatically, accounting for 60% of screening, whereas underscreening decreased in those younger than 30 years old but increased in those 30 years old or older. Women who are pregnant, black women younger than 30 years, and women younger than 30 years enrolled in managed care were more likely to experience underscreening, whereas white women older than 30 years and those with more office visits and more STI testing were more likely to be overscreened.
Our results reflect lower rates of appropriate screening and higher rates of overscreening than survey-based studies with self-reported measures of screening.10–15 Our population-based study of Medicaid claims, which is better suited than survey data to measure intervals between screening,24 uniquely identifies dramatic overscreening. One laboratory database study reported median years between Pap tests increased from 1.5 in 2008 to 1.87 in 2011, consistent with our results.24 Among our study's participants younger than 30 years old, premature screening substantially contributed to overscreening post-2009, which is consistent with studies showing high rates of screening in younger women.25
Potential reasons for overscreening include limited health care provider knowledge of guidelines, patient expectations, health care provider concerns that lengthening the screening interval causes loss of health system engagement, and continued reimbursement.4,5,26–30 It is possible that increased overscreening after guideline changes was the result of an expected lag in guideline implementation. However, we ensured that all postguideline women had an index Pap test in the first 6 months after the guideline change and defined adherence based on the time to subsequent Pap tests, thus giving health care providers 2–3 years to adapt.
Worsening underscreening among those 30 years old or older was concerning given that low-income women are at fourfold increased risk for developing cervical cancer. We hypothesize that for a segment of our population, health care providers were already following the U.S. Preventive Services Task Force and American Cancer Society 2012 guidelines that recommended screening every 5 years among women with negative cytology and HPV cotesting. Although HPV results were not widely available in 2009–2010 in clinical settings, health care providers with access to cotesting results potentially followed the newest guidelines.
Strengths in our design include large sample size, assessment of multiple guidelines, and interval-based adherence measurement. It is worth noting some limitations to our analysis. First, we limited our analyses to women with Pap tests in “index” periods with one or more outpatient visits. Although this approach may underestimate underscreening because we did not capture women without health system engagement, our interval-based method allows for accurate measurement of underscreening and overscreening in a defined population. It is very unlikely that these inclusion criteria biased our estimates of the effect of the guideline change on adherence because these criteria were applied equally to women enrolled in Medicaid before and after the change. Second, because the newest 2012 guidelines lengthened the screening interval for some to 5 years, not enough time had elapsed to permit assessment of adherence to these guidelines. If anything, rates of appropriate screening may be even lower, and overscreening may be even higher under the newest guidelines. Third, as a result of limitations with claims data, we were unable to assess how health care provider-related factors and HPV vaccination affected adherence outcomes, although it is notable that screening guidelines in our study period were consistent across specialties and did not differ based on vaccination status. Finally, our study design did not allow inference of causal relationships between patient characteristics and cervical cancer screening.
Our findings suggest opportunities to improve adherence in Medicaid through an examination of risk factors for underscreening and overscreening identified in administrative data. For example, pregnancy was associated with underscreening, which is consistent with national quality data suggesting that women access health care less postpartum.31 Interventions to address underscreening can target postpartum mothers who should continue accessing the health care system after delivery. Given that Medicaid programs contract with managed care in most states32 and managed care enrollment was significantly associated with underscreening, states may incentivize plans to increase guideline adherence among their enrollees. Black women (younger than 30 years) were more likely to be underscreened, and white women (30 years or older) were more likely to be overscreened. Importantly, black women are more likely to develop and to die from cervical cancer.17 System-based efforts are therefore essential to eliminating this disparity.
Interventions to reduce overscreening could ensure that women with greater health care utilization and those with frequent STI testing not be overscreened. Specific interventions to address overscreening include electronic medical record alerts that flag overscreening (like existing alerts for underscreening) at a clinical level and disincentivizing overscreening in quality-based reimbursements at a health-system level. A reduction in overscreening could lead to a more than 50% reduction in Pap tests performed annually, which will lead to reductions in expenditures, unnecessary procedures with potential complications, and inconvenience.
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