The central role of comorbidity in predicting ambulatory care sensitive hospitalizations.

BACKGROUND: Ambulatory care sensitive hospitalizations (ACSHs) are commonly used as measures of access to and quality of care. They are defined as hospitalizations for certain acute and chronic conditions; yet, they are most commonly used in analyses comparing different groups without adjustment for individual-level comorbidity. We present an exploration of their roles in predicting ACSHs for acute and chronic conditions. METHODS: Using 1998-99 US Medicare claims for 1 06 930 SEER-Medicare control subjects and 1999 Area Resource File data, we modelled occurrence of acute and chronic ACSHs with logistic regression, examining effects of different predictors on model discriminatory power. RESULTS: Flags for the presence of a few comorbid conditions-congestive heart failure, chronic obstructive pulmonary disease, diabetes, hypertension and, for acute ACSHs, dementia-contributed virtually all of the discriminative ability for predicting ACSHs. C-statistics were up to 0.96 for models predicting chronic ACSHs and up to 0.87 for predicting acute ACSHs. C-statistics for models lacking comorbidity flags were lower, at best 0.73, for both acute and chronic ACSHs. CONCLUSION: Comorbidity is far more important in predicting ACSH risk than any other factor, both for acute and chronic ACSHs. Imputations about quality and access should not be made from analyses that do not control for presence of important comorbid conditions. Acute and chronic ACSHs differ enough that they should be modelled separately. Unaggregated models restricted to persons with the relevant diagnoses are most appropriate for chronic ACSHs.

Do primary care physicians lose contact with their colorectal cancer patients?

BACKGROUND: Primary care physicians and patients perceive that they lose contact with each other after a cancer diagnosis. The objective of this study was to determine whether colorectal cancer (CRC) patients are less likely to see their primary care physicians after cancer diagnosis. METHODS: This was a longitudinal cohort study using 1993 to 2001 Surveillance Epidemiology and End Results (SEER)-Medicare claims data. Eligible patients were those with stage 0 to 1 and 2 to 3 CRC aged 67 to 89 years at diagnosis. Main measures included the proportion of individuals with a face-to-face primary care visit and mean annual primary care visits per patient at baseline and during 5 years after treatment. RESULTS: Fewer than half of the cancer patients visited with a primary care physician at baseline. In the first year after treatment, patients with stage 0 to 1 CRC (48.9% vs 53.3%; P ≤ .001) and stage 2 to 3 CRC (43.6% vs 53.4%; P ≤ .001) significantly increased their likelihood of visiting a primary care physician from baseline. The proportion of patients with stage 0 to 1 CRC with a primary care visit remained relatively stable, and the proportion of patients with stage 2 to 3 CRC decreased somewhat between the first and fifth year after treatment. The findings for mean annual primary care visits per patient roughly paralleled those for the proportion of individuals with a primary care visit. CONCLUSIONS: Elderly patients with CRC, especially stage 2 to 3 CRC, increase rather than decrease contact with primary care providers after diagnosis. More work is needed to understand the care that different physician specialties provide cancer patients and to support their collaboration.

No pain, but no gain? The disappearance of angina hospitalizations, 1992-1999.

BACKGROUND: Hospitalization for angina is commonly considered an ambulatory care sensitive hospitalization and used as a measure of access to primary care. OBJECTIVE: To analyze time trends in angina-related hospitalizations and seek possible explanations for an observed, marked decline during 1992 to 1999. RESEARCH DESIGN: We analyzed Medicare claims of SEER-Medicare control subjects for occurrence of angina hospital discharges, using the Agency for Healthcare Research and Quality Prevention Quality Indicator (PQI) definition, along with occurrence of related events including angina admissions with revascularization, angina admissions discharged as coronary artery disease (CAD) or myocardial infarction, and overall ischemic heart disease discharges. SUBJECTS: Approximately 124,000 cancer-free Medicare beneficiary/ies, with subjects contributing data for 1 to 8 years. RESULTS: Angina PQI hospital discharges declined 75% between 1992 and 1999. CAD hospital discharges rose in a reciprocal pattern, while angina discharges with revascularization declined and discharges for myocardial infarction and ischemic heart disease were relatively constant during this time period. CONCLUSIONS: The marked decline in angina PQI hospital discharges during 1992-1999 does not appear to represent improvements in access to care or prevention of heart disease, but rather increased coding of more specific discharge diagnoses for CAD. Our findings suggest that angina hospitalization is not a valid measure for monitoring access to care and, more generally, demonstrate the need for careful, periodic re-evaluation of quality measures.

The contribution of longitudinal comorbidity measurements to survival analysis.

BACKGROUND: Many clinical and health services research studies are longitudinal, raising questions about how best to use an individual's comorbidity measurements over time to predict survival. OBJECTIVES: To evaluate the performance of different approaches to longitudinal comorbidity measurement in predicting survival, and to examine strategies for addressing the inevitable issue of missing data. RESEARCH DESIGN: Retrospective cohort study using Cox regression analysis to examine the association between various Romano-Charlson comorbidity measures and survival. SUBJECTS: Fifty thousand cancer-free individuals aged 66 or older enrolled in Medicare between 1991 and 1999 for at least 1 year. RESULTS: The best fitting model combined both time independent baseline comorbidity and the time dependent prior year comorbidity measure. The worst fitting model included baseline comorbidity only. Overall, the models fit best when using the "rolling" comorbidity measures that assumed chronic conditions persisted rather than measures using only prior year's recorded diagnoses. CONCLUSIONS: Longitudinal comorbidity is an important predictor of survival, and investigators should make use of individuals' longitudinal comorbidity data in their regression modeling.