Elsevier

General Hospital Psychiatry

Volume 37, Issue 3, May–June 2015, Pages 199-222
General Hospital Psychiatry

Psychiatric-Medical Comorbidity2
Review of the evidence: prevalence of medical conditions in the United States population with serious mental illness

https://doi.org/10.1016/j.genhosppsych.2015.03.004Get rights and content

Abstract

Objective

Persons with serious mental illness (SMI) have high rates of premature mortality from preventable medical conditions, but this group is underrepresented in epidemiologic surveys and we lack national estimates of the prevalence of conditions such as obesity and diabetes in this group. We performed a comprehensive review to synthesize estimates of the prevalence of 15 medical conditions among the population with SMI.

Method

We reviewed studies published in the peer-reviewed literature from January 2000 to August 2012. Studies were included if they assessed prevalence in a sample of 100 or more United States (US) adults with schizophrenia or bipolar disorder.

Results

A total of 57 studies were included in the review. For most medical conditions, the prevalence estimates varied considerably. For example, estimates of obesity prevalence ranged from 26% to 55%. This variation appeared to be due to differences in measurement (e.g., self-report versus clinical measures) and underlying differences in study populations. Few studies assessed prevalence in representative, community samples of persons with SMI.

Conclusions

In many studies, the prevalence of medical conditions among the population with SMI was higher than among the overall US population. Screening for and monitoring of these conditions should be common practice in clinical settings serving persons with SMI.

Introduction

Persons with serious mental illness (SMI) die prematurely of the same disorders that are common causes of death in the general population, such as cardiovascular disease and cancer [1]. Those with SMI experience high burden of conditions that heighten risk for cardiovascular disease, including obesity, hyperlipidemia, hypertension, and diabetes mellitus [2]. High rates of cardiovascular risk factors among this group appear to be driven by lack of physical activity [3], poor diet [4], high rates of smoking [5], and side effects of commonly prescribed antipsychotic medications that include weight gain and altered glucose metabolism [6]. Importantly, some of these risk behaviors — such as poor diet and smoking — also place persons with SMI at heightened risk of cancer, which prior studies suggest is more likely to occur among persons with SMI [7]. Persons with SMI appear to be at heightened risk of experiencing other medical conditions as well. Prevalence of kidney disease [8], hepatitis [9], and human immunodeficiency virus (HIV) [9] are heightened among persons with SMI. High prevalence of hepatitis and HIV may be driven by high rates of high-risk sexual behaviors and intravenous drug use among this vulnerable population [10].

Elevated rates of comorbid medical conditions in the population with SMI are likely caused by multiple factors. In addition to the health behaviors and metabolic side effects of antipsychotic medications described above, persons with SMI are more likely than those without SMI to experience social risk factors, such as poverty [11], unemployment [12], homelessness [13], and disability [14], [15]. A large body of research in the general United States (US) population has demonstrated that these social risk factors are associated with significantly increased risk of poor health outcomes [16], [17], [18]. These social factors are also associated with the unhealthy behaviors such as smoking and risky sexual activity that are overrepresented in the population with SMI [16]. There are multiple pathways through which social factors influence health behavior and health outcomes: for example, living in a socioeconomically disadvantaged neighborhood can lead to inadequate access to affordable healthy food and safe places to exercise [18], [19]. Research suggests that, overall, persons with SMI are less likely to receive preventive health services, such as screening for cardiovascular risk factors, and high-quality medical care than persons without SMI, although quality of care varies considerably by specific study population [20]. In addition, some research suggests that persons with SMI have difficulty effectively managing their chronic conditions [21], although others show that this group is as or more skilled at some aspects of self-management than persons without mental illness [22].

In several European countries, population registries allow researchers and policy makers to track the prevalence and incidence of medical conditions among persons with SMI over time. Similar to US studies, these national registry-based studies show significantly increased risk for and prevalence of comorbid medical conditions among the population with SMI [23], [24], [25], [26]. Unlike in nations with population registries, to date, no national studies in the US measure both SMI and major medical conditions in the overall US population, making it impossible to generate nationally representative estimates of the prevalence of medical comorbidities in the population with SMI. While the National Comorbidity Survey (NCS) assessed the prevalence of mental health and substance use disorders, including SMI, in a nationally representative sample of participants in 1990–1992 and again in 2001–2002 (NCS Replication), these studies did not measure medical comorbidities. Multiple national studies measure the prevalence of major medical conditions among the overall US population but do not measure SMI diagnoses. Surveys such as the National Health Interview Survey (NHIS) [27], the Behavioral Risk Factor Surveillance System (BRFSS) [28], and the National Health and Nutrition Examination Survey (NHANES) [29] track prevalence of medical conditions over time in nationally representative samples of Americans. The NHANES and BRFSS measure depression using the Patient Health Questionnaire 9 [28], [29], and the NHIS measures psychological distress using the Kessler-6 Psychological Distress Scale [27].

There is significant variation in measurement techniques across these national studies, which have important implications for prevalence estimates. Self-report of medical conditions through national surveys has been shown to lead to underestimation of true prevalence, as has use of administrative claims data to identify medical conditions [30]. Supplementing self-reported medical history with physical examination and laboratory data, such as available in the NHANES, provides more valid measurements for most conditions [30]. Critically, none of these national studies measure diagnoses of psychotic disorders, such as schizophrenia or bipolar disorder. Researchers using the NCS data have defined SMI as presence of any mental disorder associated with substantial interference in one or more major life activities [31], and the Kessler-6 scale included in the NHIS is a symptomatology-based measure shown to have poor specificity [32]. These broad definitions of SMI include individuals with a variety of diagnoses and are more inclusive than diagnosis-based definitions of SMI that include people with schizophrenia or bipolar disorder. Schizophrenia and bipolar disorder, the two diagnoses most commonly used to define SMI in prior research [33], [34], [35], are rare conditions: an estimated 1.1% [36] of Americans have schizophrenia and 2.6% [37] have bipolar disorder. As a result, oversampling persons with SMI in national epidemiologic studies — a strategy commonly used to create representative samples of other subpopulations of interest [38] — would be very costly: if 1% of the population has schizophrenia, 100,000 individuals would need to be screened in order to identify 1000 potential survey participants. In addition, prior research has shown that SMI diagnoses are underreported, potentially due to stigma. As a result, the gold standard for identifying SMI is use of a structured diagnostic interview, which takes significant time and is administered in-person by a trained interviewer [39]. Including such an interview in national surveys is likely infeasible due to cost and interviewer and respondent burden.

Persons with SMI comprise a vulnerable population with high rates of premature mortality from preventable medical conditions. In the absence of national data, it is important to assess the variation in and quality of existing prevalence estimates. For researchers and practitioners working to ameliorate the burden of medical conditions among the population with SMI, a synthesis of existing prevalence estimates could inform the design and implementation of interventions targeting this vulnerable group. To our knowledge, to date, no such synthesis exists. To fill this gap, we reviewed studies published between January 2000 and August 2012 to summarize the prevalence of fifteen medical comorbidities in study populations with SMI. The goal of this review is to summarize prevalence estimates of comorbid conditions among study populations published in the recent peer-reviewed literature. We envision this review as a first step in synthesizing the existing research on this topic. While studies of the incidence and etiology of comorbid medical conditions among persons with SMI are critically important, they are outside the scope of this review. Examination of the state of the science on these topics warrants consideration in future reviews. Results of our review should inform data collection efforts, service delivery, and research on the burden of prevalent medical comorbid conditions in this high risk population. As different nations have very different data collection and service delivery systems, as well as different social and cultural risk factors for medical comorbidities in SMI (e.g., socioeconomic status and dietary habits), the National Institute of Mental Health (NIMH), which funded the review, asked us to limit our review to US study populations.

Section snippets

PICOT Framework

Development of our review followed a modified PICOT (population, intervention, comparison, outcome, and time) framework, the accepted standard for designing literature reviews [40]. The goal of our review was to summarize prevalence estimates of medical conditions in the population with SMI, and as a result, the intervention and comparison categories of the PICOT framework did not apply to our study. We used the remaining elements of the framework to inform our design. The population of

Results

A total of 57 studies were included in our review (see Fig. 1). Key results for each condition from all studies are summarized in Table 2. The same results presented in Table 2 are displayed visually in Figs. 1–30 in Appendix C. For most medical conditions, the range in prevalence estimates among study populations with SMI was large. For example, estimates of obesity prevalence among persons with SMI ranged from 26.0% to 55.0% and estimates of hypertension prevalence ranged from 10.0% to 68.0%.

Discussion

In many of the studies we reviewed, the prevalence of major medical conditions among the population with SMI was higher than among the overall US population. For many conditions, such as hypertension, hyperlipidemia, and overall cardiovascular disease, prevalence estimates across studies varied considerably, likely due to differences in age, gender, race, socioeconomic status, disability, antipsychotic medication use, and other factors — as well as measurement error — across specific study

Conclusions

Our findings suggest a need for nationally representative estimates of medical conditions among persons with SMI using research-quality clinical measurements. The consistently high prevalence of medical conditions among persons with SMI in the studies we reviewed suggests that screening for and monitoring of these conditions should be common practice in clinical settings serving persons with SMI. Future research should examine methods to ensure consistent screening, monitoring, and linkage to

Competing Interests

The authors declare that they have no competing interests.

Authors’ Contributions

All authors (EJ, EM, SA, DJB, and GD) (1) have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; (2) have been involved in drafting the manuscript or revising it critically for important intellectual content; and (3) have given final approval of the version to be published.

Acknowledgements

This study was commissioned and supported by the NIMH.

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