Cancer Screening Among U.S. Medicaid Enrollees with Chronic Comorbidities or Residing in Long-Term Care Facilities

Random survival forests using linked data to measure illness burden among individuals before or after a cancer diagnosis: Development and internal validation of the SEER-CAHPS illness burden index

PURPOSE

To develop and internally validate an illness burden index among Medicare beneficiaries before or after a cancer diagnosis.

METHODS

Data source: SEER-CAHPS, linking Surveillance, Epidemiology, and End Results (SEER) cancer registry, Medicare enrollment and claims, and Medicare Consumer Assessment of Healthcare Providers and Systems (Medicare CAHPS) survey data providing self-reported sociodemographic, health, and functional status information. To generate a score for everyone in the dataset, we tabulated 4 groups within each annual subsample (2007-2013): 1) Medicare Advantage (MA) beneficiaries or 2) Medicare fee-for-service (FFS) beneficiaries, surveyed before cancer diagnosis; 3) MA beneficiaries or 4) Medicare FFS beneficiaries surveyed after diagnosis. Random survival forests (RSFs) predicted 12-month all-cause mortality and drew predictor variables (mean per subsample = 44) from 8 domains: sociodemographic, cancer-specific, health status, chronic conditions, healthcare utilization, activity limitations, proxy, and location-based factors. Roughly two-thirds of the sample was held out for algorithm training. Error rates based on the validation ("out-of-bag," OOB) samples reflected the correctly classified percentage. Illness burden scores represented predicted cumulative mortality hazard.

RESULTS

The sample included 116,735 Medicare beneficiaries with cancer, of whom 73 % were surveyed after their cancer diagnosis; overall mean mortality rate in the 12 months after survey response was 6%. SEER-CAHPS Illness Burden Index (SCIBI) scores were positively skewed (median range: 0.29 [MA, pre-diagnosis] to 2.85 [FFS, post-diagnosis]; mean range: 2.08 [MA, pre-diagnosis] to 4.88 [MA, post-diagnosis]). The highest decile of the distribution had a 51 % mortality rate (range: 29-71 %); the bottom decile had a 1% mortality rate (range: 0-2 %). The error rate was 20 % overall (range: 9% [among FFS enrollees surveyed after diagnosis] to 36 % [MA enrollees surveyed before diagnosis]).

CONCLUSIONS

This new morbidity measure for Medicare beneficiaries with cancer may be useful to future SEER-CAHPS users who wish to adjust for comorbidity.

Impact of state-specific Medicaid reimbursement and eligibility policies on receipt of cancer screening

BACKGROUND

Although state Medicaid programs cover cancer screening, Medicaid beneficiaries are less likely to be screened for cancer and are more likely to present with tumors of an advanced stage than are those with other insurance. The current study was performed to determine whether state Medicaid eligibility and reimbursement policies affect the receipt of breast, cervical, and colon cancer screening among Medicaid beneficiaries.

METHODS

Cross-sectional regression analyses of 2007 Medicaid data from 46 states and the District of Columbia were performed to examine associations between state-specific Medicaid reimbursement/eligibility policies and receipt of cancer screening. The study sample included individuals aged 21 years to 64 years who were enrolled in fee-for-service Medicaid for at least 4 months. Subsamples eligible for each screening test were: Papanicolaou test among 2,136,511 patients, mammography among 792,470 patients, colonoscopy among 769,729 patients, and fecal occult blood test among 753,868 patients. State-specific Medicaid variables included median screening test reimbursement, income/financial asset eligibility requirements, physician copayments, and frequency of eligibility renewal.

RESULTS

Increases in screening test reimbursement demonstrated mixed associations (positive and negative) with the likelihood of receiving screening tests among Medicaid beneficiaries. In contrast, increased reimbursements for office visits were found to be positively associated with the odds of receiving all screening tests examined, including colonoscopy (odds ratio [OR], 1.07; 95% confidence interval [95% CI], 1.06-1.08), fecal occult blood test (OR, 1.09; 95% CI, 1.08-1.10), Papanicolaou test (OR, 1.02; 95% CI, 1.02-1.03), and mammography (OR, 1.02; 95% CI, 1.02-1.03). Effects of other state-specific Medicaid policies varied across the screening tests examined.

CONCLUSIONS

Increased reimbursement for office visits was consistently associated with an increased likelihood of being screened for cancer, and may be an important policy tool for increasing screening among this vulnerable population.