The SEER-CAHPS Illness Burden Index (SCIBI)

Two versions are available to users requesting the SEER-CAHPS data:

• Concurrent Basic (SCIBI-CB): Available for all respondents. Includes self-reported information from survey data that is based on the respondent thinking about the prior 6 months. For FFS enrollees, also includes claims-based predictors that are based on a 24 month period – that is, the 12 months before and the 12 months after the survey. Indicators such as the number of hospitalizations and durable medical equipment (DME) use are measured across the full 24-month period (i.e., the predictors are measured concurrently with the outcome).
• Prospective Basic (SCIBI-PB): Only available for FFS population. These alternate scores include claims-based predictor data from the 12 months before survey response only (which increases the error rate but may be preferred based on the analysis). While claims data are available for FFS enrollees, claims are not available for MA enrollees in the SEER-CAHPS linkage. Thus, these scores are not generated for MA enrollees.

Note that case-mix adjustors are not included as predictor variables in either version of the scores (refer to the website and section 6.3 of the User Guide (PDF, 1.4 MB) for more information on case-mix adjustor variables).

See below for a list of all predictor variables used to generate prediction scores for SCIBI-CB and SCIBI-PB. The variables used for each individual differ depending on whether the beneficiary is enrolled in Medicare FFS or MA and whether the beneficiary was diagnosed with cancer.

The SCIBI scores vary in their distributions depending on

• year,
• whether a person is in MA or FFS, and
• whether they were surveyed before or after their cancer diagnosis

Thus, in addition to the cohort-specific raw out-of-bag scores* (developed within each year-group slice), we provide standardized z-scores centered on the population mean (i.e., people with and without cancer, MA and FFS, all years) for each version of the scores (SCIBI-CB and SCIBI-PB). These z-scores, which have a mean of 0 and an SD of 1, can thus be used to compare illness burden using the same “measuring stick” for every person in the linked data.

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* Out-of-bag (OOB) predictions take advantage of bootstrap aggregation (bagging). OOB predictions are generated by using only the 1/3 of the trees that do not have a particular data point in them to generate the predictions, and this is repeated for all data points. This provides a more unbiased estimate of the prediction error of the random forest compared to standard prediction scores, which are susceptible to overfitting. For this reason, the values in the SCIBI dataset are all based on out-of-bag prediction scores.

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The SCIBI dataset contains 6 variables in total:

• Patient_id: a unique, encrypted identifier, specific to SEER-CAHPS, that can be used to link the scores to the other SEER-CAHPS data files
• Scibi_year: indicates the year the CAHPS survey was taken upon which the SCIBI scores for an individual are based on. Individuals may have taken more than one survey across the period 2007-2017, but the SCIBI scores are unique at the patient_id level and pertain to only a single survey for each patient_id.
• Scibi_cb: the raw prediction scores for the concurrent basic version
• Scibi_cb_z: the standardized prediction scores for the concurrent basic version
• Scibi_pb: the raw prediction scores for the prospective basic version
• Scibi_pb_z: the standardized prediction scores for the prospective basic version

The SCIBI was designed specifically for this data resource to be used as a comorbidity adjustor. It can be used for both MA and FFS enrollees surveyed between 2007 and 2017. SCIBI scores are particularly useful for analyses that encompass both FFS and MA beneficiaries because they use information that is found not just in claims but also in enrollment files, CAHPS surveys, and cancer registries. Given that the SEER-CAHPS data resource does not include claims data for MA beneficiaries, the scores may allow researchers to account for illness burden among MA enrollees as well as FFS enrollees.

SCIBI scores can be used as a substitute for, or adjunct to, the NCI Combined or Charlson comorbidity indices. As an omnibus measure incorporating multiple individual indicators of physical function, frailty, and being at higher risk for mortality, the SCIBI scores allow researchers to substitute a single variable for the dozens of individual variables that exist to measure illness burden. Many of the individual variables have high rates of missing information; in addition, it can be helpful to have an omnibus measure for estimating more parsimonious models.

Researchers may also find it helpful to use SCIBI scores as predictor variables. For example, the SCIBI has been used in an analysis of SEER-CAHPS using data from 2007-2015.² The research team found that, after controlling for other case-mix adjustors, higher SCIBI scores (indicating greater illness burdens) were significantly associated with better ratings of Health Plan and better Getting Care Quickly scores. These results suggest that illness burden may influence how people experience care or report those experiences, independently of standard case-mix adjustors (such as self-reported general and mental health status).

In another study, in a sample of Medicare FFS and MA enrollees with regional, distant, or unstaged cancers, the SCIBI was shown to be significantly associated with forgone care (surgery or radiation contraindicated, not recommended, or refused), independent of other factors.³ The study also reported that care experience measures were not associated with forgone care.

Please contact the SEER-CAHPS team with questions.

¹ Lines LM, Cohen J, Kirschner J, Halpern MT, Kent EE, Mollica M, et al. 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. Int J Medical Informatics. 2020. [View]
² Lines L, Cohen J, Kirschner J, Barch DH, Halpern M, Kent EE, et al. Associations between illness burden and care experiences among Medicare beneficiaries before or after a cancer diagnosis. Cancer Causes & Control. 2022. [View]
³ Lines LM, Danforth K, Zabala D, Halpern MT, Mollica M. Illness Burden is Associated with Forgone Care Among Medicare Beneficiaries with Cancer. American Public Health Association Annual Meeting; Oct. 22, 2021; Denver/Online. Cancer Forum; 2021. [View]