Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Saver BG

PROVIDER: S-EPMC3901312 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Saver Barry G BG Wang Ching-Yun CY Dobie Sharon A SA Green Pamela K PK Baldwin Laura-Mae LM

European journal of public health 20130328 1

<h4>Background</h4>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.<h4>Methods</h4>Using 1998-99 US Medicare claims for 1 06 930 SEER-M ...[more]

PMID: 23543676

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Project description:ImportanceThe association of the COVID-19 pandemic with the quality of ambulatory care is unknown. Hospitalizations for ambulatory care-sensitive conditions (ACSCs) are a well-studied measure of the quality of ambulatory care; however, they may also be associated with other patient-level and system-level factors.ObjectiveTo describe trends in hospital admissions for ACSCs in the prepandemic period (March 2019 to February 2020) compared with the pandemic period (March 2020 to February 2021).Design, setting, and participantsThis cross-sectional study of adults enrolled in a commercial health maintenance organization in Michigan included 1 240 409 unique adults (13 011 176 person-months) in the prepandemic period and 1 206 361 unique adults (12 759 675 person-months) in the pandemic period.ExposureCOVID-19 pandemic (March 2020 to February 2021).Main outcomes and measuresAdjusted relative risk (aRR) of ACSC hospitalizations and intensive care unit stays for ACSC hospitalizations and adjusted incidence rate ratio of the length of stay of ACSC hospitalizations in the prepandemic (March 2019 to February 2020) vs pandemic (March 2020 to February 2021) periods, adjusted for patient age, sex, calendar month of admission, and county of residence.ResultsThe study population included 1 240 409 unique adults (13 011 176 person-months) in the prepandemic period and 1 206 361 unique adults (12 759 675 person-months) in the pandemic period, in which 51.3% of person-months (n = 6 547 231) were for female patients, with a relatively even age distribution between the ages of 24 and 64 years. The relative risk of having any ACSC hospitalization in the pandemic period compared with the prepandemic period was 0.72 (95% CI, 0.69-0.76; P < .001). This decrease in risk was slightly larger in magnitude than the overall reduction in non-ACSC, non-COVID-19 hospitalization rates (aRR, 0.82; 95% CI, 0.81-0.83; P < .001). Large reductions were found in the relative risk of respiratory-related ACSC hospitalizations (aRR, 0.54; 95% CI, 0.50-0.58; P < .001), with non-statistically significant reductions in diabetes-related ACSCs (aRR, 0.91; 95% CI, 0.83-1.00; P = .05) and a statistically significant reduction in all other ACSC hospitalizations (aRR, 0.79; 95% CI, 0.74-0.85; P < .001). Among ACSC hospitalizations, no change was found in the percentage that included an intensive care unit stay (aRR, 0.99; 95% CI, 0.94-1.04; P = .64), and no change was found in the length of stay (adjusted incidence rate ratio, 1.02; 95% CI, 0.98-1.06; P = .33).Conclusions and relevanceIn this cross-sectional study of adults enrolled in a large commercial health maintenance organization plan, the COVID-19 pandemic was associated with reductions in both non-ACSC and ACSC hospitalizations, with particularly large reductions seen in respiratory-related ACSCs. These reductions were likely due to many patient-level and health system-level factors associated with hospitalization rates. Further research into the causes and long-term outcomes associated with these reductions in ACSC admissions is needed to understand how the pandemic has affected the delivery of ambulatory and hospital care in the US.

Project description:BackgroundHospitalizations for ambulatory care-sensitive conditions (ACSCs), conditions that should not require inpatient treatment if timely and appropriate ambulatory care is provided, may be an important contributor to rising healthcare costs and public health burden.ObjectiveTo examine if probable major depression is independently associated with hospitalization for an ACSC in patients with diabetes.DesignSecondary analysis of data from a prospective cohort study.ParticipantsPopulation-based cohort of 4,128 patients with diabetes ≥ 18 years old seen in primary care, who were enrolled between 2000 and 2002 and followed for 5 years (through 2007).Main measuresDepressive symptoms were assessed with the Patient Health Questionnaire-9. Outcomes of interest included time to initial hospitalization for an ACSC and total number of ACSC-related hospitalizations. We used Cox proportional hazards regression models to ascertain an association between probable major depression and time to ACSC-related hospitalization, as well as Poisson regression for models examining probable major depression and number of ACSC-related hospitalizations.Key resultsPatients' mean age at study enrollment was 63.4 years (Standard Deviation: 13.4 years). Over the 5-year follow-up period, 981 patients in the study were hospitalized a total of 1,721 times for an ACSC, comprising 45.1 % of all hospitalizations. After adjusting for baseline demographic, clinical and health-risk behavioral factors, probable major depression was associated with initial ACSC-related hospitalization (Hazard Ratio: 1.41, 95 % Confidence Interval [95 % CI]: 1.15, 1.72) and number of ACSC-related hospitalizations (Relative Risk: 1.37, 95 % CI: 1.12, 1.68).ConclusionsProbable major depression in patients with diabetes is independently associated with hospitalization for an ACSC. Additional research is warranted to ascertain if effective interventions for depression in patients with diabetes could reduce the risk of hospitalizations for ACSCs and their associated adverse outcomes.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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