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Med Care. 2014 Mar;52 Suppl 3:S52-9. doi: 10.1097/MLR.0000000000000033.

Assessment of morbidity over time in predicting health outcomes.

Author information

1
*Institute for Health Research, Kaiser Permanente, Denver †Department of Family Medicine, University of Colorado Denver, Aurora, CO.

Abstract

BACKGROUND:

Administratively derived morbidity measures are often used in observational studies as predictors of outcomes. These typically reflect a limited time period before an index event; some outcomes may be affected by rate of morbidity change over longer preindex periods.

OBJECTIVES:

The aim of the study was to develop statistical models representing the trajectory of individual morbidity over time and to evaluate the performance of trajectory versus other summary morbidity measures in predicting a range of health outcomes.

METHODS:

From a retrospective cohort study of integrated health system members aged 65 years or older with 3 or more common chronic medical conditions, we used available diagnoses for up to 10 years to examine associations between variations of the Charlson Comorbidity Index (CCI, Quan adaptation) and health outcomes. A linear mixed effects model was used to estimate the trajectory of individual CCI over time; estimated parameters describing individual trajectories were used as predictors for health outcomes. Other variations of CCI were: a "snapshot" measure, a cumulative measure, and actual baseline and rate of change. Models were developed in an initial cohort for whom we had survey data, and verified in a larger cohort.

RESULTS:

Among 961 surveyed members and 13,163 members of a secondary cohort, cumulative and snapshot measures provided best fit and predictive ability for utilization outcomes. Incorporating trajectory resulted in a slightly better model for self-reported health status.

CONCLUSIONS:

Modeling longitudinal morbidity trajectories did not add substantially to the association between morbidity and utilization or mortality. Standard snapshot morbidity measures likely sufficiently capture multimorbidity in assessing these outcomes.

PMID:
24561759
DOI:
10.1097/MLR.0000000000000033
[Indexed for MEDLINE]

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