Publications

BACKGROUND

Individuals with severe mental illness (e.g., schizophrenia, bipolar disorder) die 10-25 years earlier than the general population, primarily from premature cardiovascular disease (CVD). Contributing factors are complex, but include systemic-related factors of poorly integrated primary care and mental health services. Although evidence-based models exist for integrating mental health care into primary care settings, the evidence base for integrating medical care into specialty mental health settings is limited. Such models are referred to as "reverse" integration. In this paper, we describe the application of an implementation science framework in designing a model to improve CVD outcomes for individuals with severe mental illness (SMI) who receive services in a community mental health setting.

METHODS

Using principles from the theory of planned behavior, focus groups were conducted to understand stakeholder perspectives of barriers to CVD risk factor screening and treatment identify potential target behaviors. We then applied results to the overarching Behavior Change Wheel framework, a systematic and theory-driven approach that incorporates the COM-B model (capability, opportunity, motivation, and behavior), to build an intervention to improve CVD risk factor screening and treatment for people with SMI.

RESULTS

Following a stepped approach from the Behavior Change Wheel framework, a model to deliver primary preventive care for people that use community mental health settings as their de facto health home was developed. The CRANIUM (cardiometabolic risk assessment and treatment through a novel integration model for underserved populations with mental illness) model focuses on engaging community psychiatrists to expand their scope of practice to become responsible for CVD risk, with significant clinical decision support.

CONCLUSION

The CRANIUM model was designed by integrating behavioral change theory and implementation theory. CRANIUM is feasible to implement, is highly acceptable to, and targets provider behavior change, and is replicable and efficient for helping to integrate primary preventive care services in community mental health settings. CRANIUM can be scaled up to increase CVD preventive care delivery and ultimately improve health outcomes among people with SMI served within a public mental health care system.

BACKGROUND

Prebeta-1 high-density lipoprotein (HDL) is a small subspecies of HDL that functions as the HDL quantum particle and is the principal acceptor of cholesterol effluxed from macrophages through the ATP-binding cassette transporter, ABCA1. High levels of prebeta-1 HDL are associated with increased risk of structural coronary artery disease and myocardial infarction.

OBJECTIVE

We aimed to compare prebeta-1 HDL levels in normal subjects and in 3 phenotypes of dyslipidemia.

METHODS

We studied 2435 individuals (1388 women; 1047 men). Of these, 2018 were not taking lipid-lowering medication when enrolled: 392 were normolipidemic controls; 713 had elevated levels of low-density lipoprotein cholesterol; 623 had combined hyperlipidemia; and 290 had hypertriglyceridemia.

RESULTS

Relative to controls, prebeta-1 HDL levels were increased in all 3 dyslipidemic phenotypes, particularly the combined and hypertriglyceridemia groups. This increase possibly reflects increased acceptor capacity of apolipoprotein B-100 containing lipoproteins for entropically driven transfer of cholesteryl esters from HDL via cholesteryl ester transfer protein. Multiple regression analysis revealed that the main predictor variables significantly associated with prebeta-1 HDL levels were apolipoprotein A-I (apoA-1) (β = 0.500), triglyceride (β = 0.285), HDL-C (β = -0.237), and age (β = -0.169). There was an interaction between apoA-1 and sex (female vs male; β = -0.110). Among postmenopausal women, estrogenized subjects had a similar level of prebeta-1 HDL compared to those not receiving estrogens.

CONCLUSIONS

Prebeta-1 HDL levels are elevated in the 3 most common types of hyperlipidemia and are most strongly influenced by the levels of apoA-1, triglyceride, and HDL-C.