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The American Journal of Managed Care February 2020
Care Coordination for Veterans With COPD: A Positive Deviance Study
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Co-payment Policies and Breast and Cervical Cancer Screening in Medicaid
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Co-payment Policies and Breast and Cervical Cancer Screening in Medicaid

Lindsay M. Sabik, PhD; Anushree M. Vichare, PhD; Bassam Dahman, PhD; and Cathy J. Bradley, PhD
Co-payments for preventive services can discourage breast and cervical cancer screening among Medicaid enrollees, particularly breast cancer screening, which is more costly and time-consuming.
The study sample consists of nonelderly, nondisabled, nonpregnant women in the recommended age range for each screening service (50-64 years for mammograms and 21-64 years for Pap tests) who were enrolled in Medicaid. We excluded men; women younger than 21 or older than 64 years; women who had claims for pregnancy or labor and delivery within the calendar year; and individuals with dual eligibility or who were enrolled under the disabled eligibility category, had missing enrollment length, or appeared in the MAX data in more than 1 state within a calendar year. We restricted the sample to enrollees in FFS Medicaid for 2 reasons: First, managed care data may not be reliable for all states and years,29,30 and second, cost-sharing requirements may differ for managed care plans compared with FFS plans. We excluded Maine and Kansas because MAX data for those states were unavailable for the study years. Given that MAX data are based on standardization of state reports that vary in completeness, particularly for states with many enrollees in managed care who may, for example, be in FFS Medicaid only upon initial enrollment in the program, we focused on states with robust FFS claims during our study period. To gauge data completeness by state, we examined the count of FFS claims available in the MAX data per state and per year. We excluded Hawaii, Maryland, Arizona, New Mexico, and Tennessee because the FFS claim count for each study year (ie, 2003, 2008, and 2010) was 1.5 SD below the average number of FFS claims that year for each of these states. We chose the 1.5 SD threshold to exclude the extreme outliers based on examination of the distribution of claims by state. We also excluded Rhode Island because the number of eligible women was less than 100 in each year for both samples. Thus, the final sample included data from 42 states and the District of Columbia (DC).

Study Measures

Claims were used to measure the primary outcomes: receipt of mammography or Pap test within the given calendar year, based on procedure and diagnosis codes (eAppendix Table 1 [eAppendix available at]). Explanatory variables of interest indicate whether the state required co-payments for different types of outpatient visits. Specifically, states fall into 1 of 3 categories in any year: those requiring co-payments for all visits, including preventive services; those requiring co-payments for outpatient visits, but with co-payments waived for preventive services; or those without co-payments, regardless of visit type. We constructed co-payment policy variables that compare states with co-payments for preventive services with states without co-payments for preventive services and then compared enrollees’ utilization of breast and cervical cancer screening in each of the 3 co-payment policy groups.

We controlled for potential confounders that may differ across states or years and may be associated with receipt of screening, including individual and area-level variables identified in models of healthcare access and shown in previous literature to be associated with preventive services and healthcare access.18,31-34 Individual-level controls include age, race/ethnicity, basis of Medicaid eligibility (ie, 1115 waiver adult, medically needy, parent, poverty, or other eligibility pathway), number of months in Medicaid that year, and whether the woman was enrolled in a primary care case management (PCCM) program, because those in PCCM may experience more coordinated primary care, which could increase use of preventive services.35 County-level sociodemographic variables include percentage of population 25 years or older with less than a high school diploma, percentage of population that is white non-Hispanic, percentage of population living in an urban area, percentage of population that is unemployed, and median household income. We also controlled for the availability of healthcare providers in the county of residence, measured per 1000 population in the county: primary care physicians (including obstetricians/gynecologists [OB/GYNs]), specialists, hospital beds, federally qualified health centers (FQHCs), and rural health centers (RHCs).


We estimated 2 sets of multivariable logistic regression models adjusting for all covariates, as well as state fixed effects, to control for time-invariant state characteristics, and year fixed effects, to control for trends in screening over time. In the first model, we compared enrollees for whom co-payments apply to preventive services with those without co-payments for preventive services (regardless of co-payments for other types of visits). In the second model, we compared enrollees across the 3 co-payment groups described previously. We estimated all models for a sample of women enrolled in Medicaid for the entire calendar year and for the overall sample with at least 1 month of Medicaid enrollment. Using the estimated coefficients, we derived predicted probabilities of receiving screening. Because most states increased enrollment in managed care over time, we also estimated models including an indicator for the percentage of enrollees in managed care. Analyses were conducted using SAS software version 9.4 (SAS Institute; Cary, North Carolina).

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