BACKGROUND
One in 5 individuals has elevated depressive symptoms following acute coronary syndromes (ACS), increasing the risk of recurrent cardiovascular events and mortality.1 Multiple advisories recommend depression screening and comprehensive treatment in post-ACS patients.1 While depression rates vary by patient characteristics, little is known about differences in depression screen-positive rates at the health system level.
OBJECTIVE
We sought to determine whether post-ACS depression detection rates differ by healthcare system after adjusting for patient characteristics.
METHODS
We used baseline visit data from the CODIACS-QoL, a multi-health system 3-group randomized clinical trial of depression screening with and without enhanced depression care versus no depression screening.2 We recruited post-ACS patients between November 2013 and April 2017 from 4 geographically diverse health systems: HealthPartners (Minneapolis, MN); Duke University Health System (Durham, NC); Kaiser Permanente Northwest (Portland, OR); and New York-Presbyterian Hospital/Columbia (New York, NY). We screened electronic health records for eligible English- or Spanish-speaking individuals ≥21 years old and with documented ACS within 2–12 months of enrollment based on standardized International Classification of Diseases discharge codes for acute myocardial infarction and unstable angina. We excluded individuals with prior or current depression diagnosis or treatment; life expectancy < 1 year; prior or current history of bipolar disorder, suicidal risk, or psychosis; current substance abuse; dementia; current pregnancy; or severe chronic illness requiring frequent hospitalizations.
Eligible individuals completed a baseline assessment by phone, which included age, sex, race, ethnicity, education, partner status, employment status, health insurance coverage, and physical health-related quality of life (Short Form-12 Health Survey, Version 2™ (SF-12).3 We restricted analyses to patients randomized to the two depression screening groups (vs. no screen groups) who completed the 8-item Patient Health Questionnaire (PHQ-8), which was selected because of institutional review board stipulations, limited resources to handle positive suicide screens, and similar validity and reliability as the more commonly used PHQ9.4 A PHQ-8 score ≥ 10 indicated a positive screen. We used logistic regression to assess the association between health system and screen-detected depression adjusting for patient demographics and physical health.
FINDINGS
Among 1001 post-ACS individuals, the mean (SD) age was 66.0 (11.5); 28.0% were women, 16.3% Hispanic, 8.6% black, and 17.8% either black or white. The prevalence of screen-detected depression was 2.5% (5 of 202) at site 1; 6.0% (21 of 349) at site 2; 20.7% (6 of 29) at site 3; and 10.2% (43 of 421) at site 4, the referent site with the largest patient enrollment. The adjusted odds of screening positive for depression was significantly associated with healthcare system (compared to site 4: site 1 AOR=0.09; 95%CI 0.005–0.48, p=0.02; site 2 AOR=0.76, 95%CI 0.36–1.61, p=0.47; site 3 AOR=2.87, 95%CI 0.86–8.81, p=0.07). Age (0.95, 95%CI 0.93–0.98, p<0.001) and physical health (SF-12 physical subscale score 0.95 [0.93–0.97], p<0.001) were also associated with likelihood of screening positive for depression (Table 1).
Table 1.
Association Between Health System and Odds of Screen-Detected Depression in the CODIACS-QoL Trial, With and Without Adjusting for Patient Characteristics
| Model 1, unadjusted OR, 95%CI | p-value | Model 2, adjusted OR, 95%CI | p-value | |
|---|---|---|---|---|
| Health system | ||||
| Site 3 | 2.28, 0.81–5.60 | 0.09 | 2.87, 0.86–8.81 | 0.07 |
| Site 2 | 0.57, 0.32–0.96 | 0.04 | 0.76, 0.36–1.61 | 0.47 |
| Site 1 | 0.04, 0.002–0.20 | 0.002 | 0.09, 0.005–0.48 | 0.02 |
| Site 4 (referent) | 1 (referent) | 1 (referent) | - | |
| Age, per year increase | 0.95, 0.93–0.98 | <0.001 | ||
| Male | 0.64, 0.35–1.17 | 0.14 | ||
| Ethnicity | ||||
| Hispanic | 1.25, 0.48–3.18 | 0.64 | ||
| Declined | 1.31, 0.07–8.20 | 0.81 | ||
| Non-Hispanic | 1 (referent) | - | ||
| Race | ||||
| Black | 1.39, 0.56–3.29 | 0.47 | ||
| Other | 1.76, 0.78–3.93 | 0.17 | ||
| White | 1 (referent) | - | ||
| Born outside the US | 1.30, 0.50–3.06 | 0.57 | ||
| First language English | 2.51, 0.74–8.18 | 0.13 | ||
| Married | 0.97, 0.54–1.75 | 0.91 | ||
| Education | ||||
| College or higher | 0.60, 0.30–1.16 | 0.13 | ||
| Some college | 0.67, 0.32–1.33 | 0.26 | ||
| Less than college | 1 (referent) | - | ||
| Employed | 0.67, 0.34–1.29 | 0.23 | ||
| Problem affording health care | 1.20, 0.46–2.80 | 0.69 | ||
| Physical health, per point increase (SF-12 Physical Composite Scale) | 0.95, 0.93–0.97 | <0.001 | ||
DISCUSSION
There was substantial heterogeneity in the prevalence of screen-detected depression in ACS survivors among health systems not explained by differences in patient characteristics. Depression screening is increasingly common in primary care, potentially decreasing opportunities to identify new positive screens through centralized screening. Low prevalence in certain sites may be due to integrated care with greater trial exclusion documentation (e.g., prior depression), low community prevalence, variability in PHQ9 use for screening and/or treatment monitoring, or higher rates of depression case finding and treatment, such that universal screening resulted in low detection rates of incident depression post-ACS.
Study limitations were low numbers of women ACS patients limiting generalizability, that depression screening occurred in the context of a clinical trial requiring patient consent, lack of consideration of community and healthcare system–level characteristics (e.g., size, case finding/screening/depression treatment rates) as correlates of depression screening, and the influence of site sample sizes on outcomes (i.e., the smallest site3 had the highest prevalence rates). Nevertheless, these findings suggest that health systems should explore how system-level factors may be contributing to lower incident detection rates. Systems should also consider making detection of recurrent or unremitted depression a larger priority.5 Future studies should also seek to understand the reason for health system differences in screen-detected depression, the value of allocating additional resources towards monitoring, and the utility of depression screening implementation, particularly when treatment resources are not available.
Author Contribution
Ian Kronish, MD, MS, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper, and its final contents.
Clarke: Investigation, project administration, resources, supervision, review and editing
Davidson: Conceptualization, funding acquisition, investigation, methodology, resources, supervision, review and editing
Dolor: Investigation, project administration, resources, supervision, review and editing
Kronish: Investigation, methodology, project administration, supervision, original draft, review and editing
Margolis: Investigation, project administration, resources, supervision, review and editing
Moise: Investigation, methodology, project administration, original draft, review and editing
Funding
This work was supported by funds from the National Institutes of Health (R01 HL114924; 3 R01 HL114924-03S1; R01 HL141609).
Declarations
Conflict of Interest
The authors declare that they do not have a conflict of interest.
Disclaimer
The sponsors had no role in the design and conduct of the study, including the collection, management, analysis, interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
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