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Psychosomatic Medicine 68:684-691 (2006)
© 2006 American Psychosomatic Society

ORIGINAL ARTICLES

Medical Comorbidity in Women and Men With Bipolar Disorders: A Population-Based Controlled Study

Caroline P. Carney, MD, MSc and Laura E. Jones, MSc

From the Departments of Psychiatry and Internal Medicine, Indiana University School of Medicine, Indianapolis, Indiana (C.P.C.); Regenstrief Institute, Indianapolis, Indiana (C.P.C.); Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, Iowa (L.E.J.).

Address correspondence and reprint requests to Caroline P. Carney, MD, MSc, Department of Internal Medicine, 449 RT, Indiana University School of Medicine, Indianapolis, IN 46250. E-mail: ccarneyd{at}iupui.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Objective: Rarely has validated information on chronic medical comorbidity been presented for persons with bipolar disorder. To deliver appropriate health services, it is important to understand the prevalence of chronic medical conditions in this population. This study examines chronic medical comorbidity using validated methodology in persons with bipolar disorder.

Methods: This is a retrospective study of a 100% sample of administrative claims (1996–2001) from Wellmark Blue Cross Blue Shield. Three thousand five hundred fifty-seven subjects had bipolar I disorder and did not have claims for schizophrenia or schizoaffective disorder. Controls had no documented claims for psychiatric conditions. Using validated methodology, inpatient and outpatient claims were used to determine prevalence of 44 chronic medical conditions. Odds ratios (ORs) were adjusted for age, gender, residence, and nonmental healthcare utilization.

Results: Persons with bipolar disorder were young (mean age, 38.8 years) and significantly more likely to have medical comorbidity, including three or more chronic conditions (41% versus 12%, p < .001) compared with controls. Elevated ORs were found for conditions spanning all organ systems. Hyperlipidemia, lymphoma, and metastatic cancer were the only conditions less likely to occur in persons with bipolar disorder.

Conclusion: Bipolar disorders are associated with substantial chronic medical burden. Familiarity with conditions affecting this population may assist in programs aimed at providing medical care for the chronically mentally ill.

Key Words: bipolar disorder • medical comorbidity • chronic disease • administrative data

Abbreviations: AIDS = acquired immunodeficiency syndrome; HIV = human immunodeficiency virus; ICD-9 = International Classification of Diseases, Ninth Revision; AOR = adjusted odds ratio; OR = odds ratio; CI = confidence interval; BC/BS = BlueCross BlueShield.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
The co-occurrence of medical illness among persons with severe mental illness such as schizophrenia has been addressed in multiple studies, including mortality studies (1–37). Rarely, though, has much attention been directed at medical comorbidity occurring in persons with other forms of severe mental illness, such as bipolar disorder. Bipolar disorder and related conditions, including bipolar II and cyclothymia, have received little attention in this literature but are more prevalent (totaling nearly 4%) in the United States population than is schizophrenia (0.5% to 1.5%) and are costly (38,39). Two recent studies described the substantial economic burden of this disorder (40,41). In the first study, 1-year healthcare costs in 1997 for persons with bipolar averaged $7,663, nearly four times higher than healthcare costs incurred by age- and gender-matched nonbipolar patients (41) In the other study, 6-month costs in 1995 to 1996 were $3,416, significantly higher than for persons with unipolar depression or diabetes mellitus. Although inpatient hospitalization was the largest driver of cost, both studies reported that a significant proportion of expenditures was related to other healthcare costs, such as laboratory and radiologic diagnostic services and nonmental health care. Further, total annual prevalence-based cost of illness was estimated at $45.2 billion in 1991 values (42). It is significant that indirect costs due to morbidity and premature mortality compose a large portion of the cost of illness (42). Thus, it is important to understand the chronic medical comorbidity experienced by persons with bipolar disorders so that healthcare expenditures can be reduced as a result of providing optimal preventive services.

Few studies exist regarding the chronic medical comorbidity experienced by persons with bipolar disorders. Studies of persons with medical conditions such as hepatitis C, acquired immunodeficiency syndrome (AIDS), and diabetes have described increased associations with bipolar disorder compared with persons in the general population (43–45). For instance, Medicaid recipients with major affective disorders (unipolar and bipolar depression) were 3.8 times as likely to have a diagnosis of human immunodeficiency virus (HIV) infection (35). All natural causes of deaths for Swedes with bipolar disorder were increased, except cancer and diseases of the nervous system for males (46). Standardized mortality ratios were 2.5 in males and 2.7 in females (46). Work by Dixon’s group (37) showed that a clinic-based sample of persons with either major depression or bipolar disorders had greater odds of respiratory diseases, diabetes, stroke, and rheumatoid arthritis. Notably, these subjects reported receiving higher or the same levels of medical care as controls (36). Finally, a recent study of persons (90% men) seeking health care through the Veterans Affairs Healthcare System reported that hepatitis C, diabetes, low back pain, and pulmonary conditions were more common among subjects with bipolar disorder (47). A recently published review discussed the importance of learning whether medical comorbidity is truly comorbid, a consequence of treatment, or a combination of both (48).

We thought that the current literature could be enhanced by the addition of a broad-based study of women and men seeking care in a variety of medical settings, spanning multiple years and using criteria validated for the detection of chronic medical conditions. We hypothesized that men and women with bipolar disorders have a greater overall burden of chronic medical comorbidity and specific medical conditions compared with persons without claims for mental illness.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Data Source
The data source was a 100% sample of Wellmark Blue Cross/Blue Shield (BC/BS) of Iowa administrative claims data from January 1, 1996, to December 31, 2001. The data include all inpatient and outpatient claims submitted by healthcare providers of all specialties in the BC/BS network, including primary care, specialty medicine, and mental health. The data include International Classification of Diseases, Ninth Revision (ICD-9) diagnostic codes for inpatient and outpatient data.

Population
The eligible study population included all subjects aged 18 to 64 years who filed at least one claim for medical service during the study period, 1996 to 2001. All subjects were residents of Iowa and were classified as residing in a rural or urban Iowa county, based on the metropolitan statistical area definition (49). The basic medical insurance coverage was similar among subjects, with only a small proportion (<10%) enrolled in a managed care plan.

Mental Disorders
The case population included subjects who were diagnosed with bipolar I disorder at any time during 1996 to 2001. Patients were considered to have bipolar I disorder if they had an ICD-9 code for bipolar I disorder (296.0–296.1, 296.4–296.8) recorded in any inpatient encounter by a psychiatrist or in two or more outpatient encounters by nonpsychiatrists (50,51). Two separate outpatient visits were required to help increase the diagnostic specificity. Prescription data were not available, which may further help to corroborate the diagnosis. However, a prior study indicated that the false-positive rate of bipolar disorder in administrative claims is less than 10%, which helps to validate our case-finding methodology (52).

We realize that this case-finding methodology may also identify patients with other comorbid psychiatric disorders that are common in the bipolar population and may be recorded in claims data or may be coded before provider recognition of bipolar disorder (47). Therefore, the most clinically predominant disorder was used to identify patients according to the following decision hierarchy: schizophrenia, schizoaffective disorder, and then bipolar disorder. Patients who were identified as having claims for both bipolar disorder and schizophrenic disorders were not included in this analysis and are included in a companion manuscript describing medical comorbidity associated with schizophrenia. Only subjects identified with bipolar disorder with or without other nonschizophrenic disorders are included in this analysis. This taxonomy was used because schizophrenia is the most severe disorder.

Controls
The control population consisted of subjects who did not have claims for any DSM-IV disorder, with the exception of substance abuse, at any time during 1996 to 2001.

Comorbidity
Medical comorbidities and substance abuse disorders assessed for these analyses include conditions in the Elixhauser Comorbidity Index, prevalence of conditions common in the general population, descriptions in prior research of medical comorbidity in bipolar disorder, and the likelihood of prevention and primary care treatment for chronic conditions (53). The Elixhauser Index was first used to describe chronic medical conditions most commonly occurring in hospitalized persons. As modified and validated by Klabunde et al. (54), comorbid outpatient conditions were also counted if the condition occurred either in the inpatient setting or in two or more outpatient claims coded in a period of no fewer than 30 days during 1996 to 2001. This period was used to ensure that acute or miscoded outpatient comorbidities were not included in the total comorbidity count. Twenty-six comorbidities were selected from the Elixhauser Comorbidity Index. Using the same methodology, we also examined an additional nine chronic medical comorbidities prevalent in the adult population (stroke, ischemic heart disease, hyperlipidemia, pancreatitis, backache, arthritis, asthma, accidents/injuries, and headache). Five specific comorbidities unique to women’s healthcare (cystitis, mammary dysplasia, endometriosis, inflammatory disease of ovary, and disorders of menstruation) and one comorbidity unique to men’s healthcare (benign prostatic hyperplasia) are also included. Finally, we have included nicotine and polysubstance and alcohol abuse/dependence conditions as comorbidities, given the likelihood of these conditions to complicate the course of underlying medical illness. Comorbidities were further classified into nine groups based on the major organ systems.

Statistical Analyses
Demographic and clinical characteristics were analyzed using chi-square tests for categorical variables and t tests for continuous variables. The demographic variables analyzed in this study include gender, age, urban or rural residence, number of months eligible for medical care as calculated from the first medical claim date to the last medical claim date (membership files with actual dates of enrollment were unavailable), and nonmental healthcare utilization (calculated as the sum of the days hospitalized overnight for medical and/or psychiatric conditions and the number of visits to primary care providers) during the study period. Differences in duration of eligibility for care may influence the degree to which comorbidity was ascertained and recorded. Comorbidity was further categorized as the presence of 0, 1, 2, and ≥3 total conditions.

Logistic regression was used to calculate odds ratios (OR) and confidence intervals (CI) for each of the 44 comorbidities examined. ORs are adjusted for gender (except gender-specific comorbidities), age, residence (rural versus urban), and nonmental healthcare utilization. We adjusted for utilization by controlling for the number of known nonmental health visits in order to take into account potential differences in diagnostic patterns attributed by contact with a provider during the time of observation from first to last known claim. Increased utilization may be associated with greater opportunity to diagnose conditions (i.e., Berkson’s bias) or conversely, presence of medical symptoms may also drive increased utilization. Increased utilization may artificially diminish the reported associations because it may indicate that persons were seeking care/treatment for diagnosed comorbidity.

Only adjusted ORs are reported. Unadjusted findings are available on request from the authors. {alpha} was set at 0.05 (two-sided). p values were not adjusted for multiple comparisons but are reported in the tables to allow the reader to make his or her own assessment of significance. The reader is also referred to Rothman’s (55) 1990 article for further discussion regarding the lack of need to adjust for multiple comparisons. All analyses were performed with SAS (Cary, NC), version 9.1 (56).

The institutional review boards at Indiana University and the University of Iowa approved this study.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 NOTES
 REFERENCES
 
Overall, 2,162 (61%) women and 1,395 (39%) men were identified with bipolar disorder. The control population consisted of 726,262 subjects; 53% (n = 381,116) were female. Subjects with bipolar disorder were approximately 1.6 years older than subjects without mental health comorbidity (39.3 versus 37.7 years, p < .0001). They were also more likely to reside in an urban setting (50% versus 45%, p < .0001) (Table 1). Subjects with bipolar disorder had approximately one additional year of eligibility for medical care, based on length of time from first healthcare visit to last healthcare visit, than controls (38.8 versus 26.8 months, p < .0001). The percentage of subjects with bipolar disorder with three or more chronic medical comorbidities was more than three-fold higher than among controls (40.5% versus 12.1%, p < .0001).


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  		TABLE 1. Demographic Characteristics of Subjects With Bipolar I Disorder Compared to Subjects Without Mental Health Conditions

 

In the adjusted analyses, subjects with bipolar disorder had increased odds for conditions spanning nearly every organ system and markedly higher ORs for substance abuse and dependence (Table 2). Our findings show that conditions occurring at increased odds among those with bipolar disorder likely result as comorbid conditions; conditions related to treatment effects, such as weight gain or lithium use; and conditions from behaviors possibly related to the disorder itself, such as increased nicotine consumption or sexual behaviors.


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  		TABLE 2. Adjusted Odds Ratios for Chronic Medical Conditions Among Subjects With Bipolar I Disorder as Compared to Subjects Without Mental Health Conditions

 

First, subjects with bipolar disorder were more likely to have claims for substance disorders. Nicotine abuse/dependence was 192% more likely (adjusted odds ratio [AOR] = 2.92; 95% CI, 2.59–3.29) among subjects with bipolar compared with subjects without mental health comorbidity. For example, chronic obstructive pulmonary disease (AOR = 2.32; 95% CI, 2.08–2.59) and asthma (AOR = 2.67; 95% CI, 2.32–3.06) were more common in the bipolar population and are associated with nicotine abuse. The ORs for alcohol abuse/dependence and polysubstance disorders were also markedly higher (alcohol: AOR = 19.63; 95% CI, 17.59–21.90; polysubstance: AOR = 42.91; 95% CI, 37.83–48.66). Conditions related to alcohol use, such as peptic ulcer disease (AOR = 2.6), liver disease (AOR = 4.0), and pancreatitis (AOR = 2.5) were more common among those with bipolar disorder. It is also possible that liver disease and pancreatitis resulted from anticonvulsant use in this population.

Increased odds for cardiovascular conditions could be the expected result of weight gain, unhealthy diet, and increased nicotine use among subjects with bipolar disorder. With few exceptions, cardiovascular conditions were more common among those with bipolar disorder, ranging from a 23% increase in hypertension (AOR = 1.23; 95% CI, 1.11–1.37) to a 185% increase in claims for stroke (AOR = 2.85; 95% CI, 2.18–3.72).

Next, some endocrine, renal, gynecological, and neurological conditions may be the result of treatment for bipolar disorder. For example, results show that both obesity (AOR = 2.63; 95% CI, 2.21–3.12) and weight loss (AOR = 3.31; 95% CI, 1.71–6.39) were more common among those with bipolar disorder, although this may partially be explained by small sample size, as a result of unhealthy lifestyle common in the bipolar population or as a result of treatment differences within the population (e.g., lithium is associated with weight gain, and topiramate [anticonvulsant] is associated with weight loss). Likewise, hypothyroidism, a condition related to both bipolar disorder itself and to lithium treatment, was expectedly more common (AOR = 2.57; 95% CI, 2.27–2.91). Renal failure was two-fold more common (AOR = 2.31; 95% CI, 1.56–3.40) in the bipolar population, and might also be a result of lithium treatment, hypertension, or diabetes. Notably, diabetes with complications occurred at increased odds (AOR = 1.54; 95% CI, 1.16–2.03) among subjects with bipolar disorder. Furthermore, increased ORs for gynecological conditions may be the result of drug treatment itself, or the weight gain associated with treatment. For instance, valproic acid is associated with polycystic ovarian syndrome. Likewise, some neurological disorders, such as headache (AOR = 2.47; 95% CI, 2.25–2.71) may be associated with side effects of treatment or may be a more benign association recorded as a function of increased medical contact.

It is possible that some medical conditions were more common among those with bipolar disorder as a result of behavioral characteristics. For example, increased odds for HIV/AIDS (AOR = 9.53; 95% CI, 3.84–23.64) may be associated with substance abuse or unsafe sexual behaviors, although the analysis was limited by small sample size. Likewise, accidents and injuries were 74% more likely (AOR = 1.74; 95% CI, 1.61–1.87) among those with bipolar disorder and may be associated with drug overdose or suicidal ideation or attempt, among poor behavioral choices associated with mania.

Furthermore, conditions unexpected to occur at significantly higher odds in persons with bipolar were also noted. This included benign prostatic hypertrophy (AOR = 1.78; 95% CI, 1.33–2.37) and coagulopathy (AOR = 1.92; 95% CI, 1.34–2.74) among others.

Finally, few conditions were significantly less common among subjects with bipolar disorder. Only metastatic cancer (AOR = 0.42; 95% CI, 0.25–0.70), lymphoma (AOR = 0.39; 95% CI, 0.17–0.88), and hyperlipidemia (AOR = 0.87; 95% CI, 0.78–0.99) were significantly less common in this population.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
NOTES
 REFERENCES
 
This study supports prior conclusions in other populations that persons with bipolar disorder have significant medical comorbidity. Compared with control subjects, and controlling for important factors such as age, gender, rurality, and healthcare utilization, subjects with bipolar were more likely to have a greater total number of conditions and to have multiple conditions occurring at elevated odds that spanned nearly every organ system. The average age of the bipolar subjects was only 38.8 years, indicating that these chronic conditions are prevalent at a very young age. This research contributes to the growing literature on medical and psychiatric comorbidity by describing a commercially insured population of men and women with bipolar disorder compared with contemporaries without administrative claims for mental illness. It further supports that the wide variety of conditions present is likely the result of preventable or modifiable behaviors (e.g., tobacco use) and treatment of bipolar disorder (e.g., lithium use). Evidence such as this is necessary to ensure that appropriate medical services are delivered to those with bipolar disorder.

It is appropriate to compare these findings to other recent publications reporting medical comorbidity in different populations of persons with bipolar disorder. Similar to the Kilbourne et al. (47) work in the VA population, in our adjusted analyses, we also found a higher risk for low back pain and chronic obstructive pulmonary disease. And, like the sample studied by Sokal et al. (37), we also confirm increased risk for stroke, diabetes, and respiratory conditions. However, our findings extend beyond these other reports and reveal that the risk for other chronic and costly conditions (e.g., renal disease, liver disease) is also markedly increased.

The conditions that had increased odds in persons with bipolar disorder can be grouped into cardiovascular, metabolic, pulmonary, hematological, neurological, infectious, and endocrine. Increased rates of smoking and obesity among persons with bipolar disorder likely contribute to the elevated odds for cardiac conditions, stroke, pulmonary conditions, and diabetes mellitus with complications in these subjects. The finding that subjects with bipolar disorder were not significantly more likely to have uncomplicated diabetes was unexpected, given the association between diabetes and behavioral characteristics and antipsychotic treatment. Regardless, prevalence of diabetes, with or without complications, was higher among subjects with bipolar disorder. Hypothyroidism is certainly not surprising in this population of persons with the likelihood of prior lithium treatment. That lithium may cause hypothyroidism and that hypothyroidism is present in persons with bipolar disorder have been well documented (47,57–62). However, the increased association found in our study has not been shown in other recent articles describing medical comorbidity and bipolar disorder. It is also possible that the increased association between anemia and bipolar disorder can be explained by pharmacological treatments. However, medications such as carbamazepine and valproic acid typically cause alterations in white blood cell counts and platelet counts (10,63,64). Elevated risks for infectious diseases, such as HIV, have been previously described in this population (65–67). Importantly, though, prevalence of infectious disease in this study was low and may result in unreliability of the estimated OR and wide CIs. The finding that subjects with bipolar disorder were less likely to have been diagnosed with hyperlipidemia was surprising, particularly given the association with obesity and that metabolic dysregulation has been associated with second-generation antipsychotic use. It is possible that this association was found as a result of chance and should be confirmed in future research.

The impact of medical comorbidity associated with bipolar disorder is significant for reasons including quality of life, delivery of psychiatric and medical services, and mortality. Persons with bipolar disorder have higher rates of mortality from all natural causes except cancer, according to a study of Swedish patients (68). Our data support that these patients suffer from a wide variety of medical conditions, many of which could contribute to early mortality, such as cardiovascular conditions, stroke, and AIDS. Mortality is related to factors including other medical comorbidity, delivery of services, and adherence to services. These subjects were significantly more likely to have more comorbid medical conditions than the controls. They also had access to primary medical care, but we were unable to assess the quality of that care.

A recent study by Dickerson et al. (36) found that persons with affective disorders who were enrolled in psychiatric care were more likely to have visited a general medical doctor in the last year (OR, 2.37) and to have had a complete physical examination (OR, 1.74) than population controls. However, those patients were more likely to report substantial barriers to care, including lack of transportation (OR, 8.43), inability to afford prescription medications (OR, 5.57), and delays in seeking care (OR, 6.23). It may be reasonable to assume that the same barriers apply to persons in this population.

Whether treatment is received by persons with bipolar disorder may be dependent on the type of medical conditions present, physician factors, and patient factors. The burden of risk factors, including smoking, obesity, and sedentary lifestyle, for medical disease among persons with serious mental illness has been described (69,70). Despite these known risk factors, clinical and systems intervention strategies to modify risks are needed (18,20,22,71,72). For instance, receipt of three or more ambulatory care visits among homeless veterans was negatively associated with a diagnosis of schizophrenia. This suggests that persons with severe mental illness may be less likely to regularly utilize healthcare services, despite the high prevalence of medical comorbidity (73). Even among insured persons with mental disorders, risks for delaying care or not receiving needed care are substantial (74). Reasons include failure of psychiatric providers to ask about medical issues and patient inability to identify primary care providers by name (75).

Given the extensive medical comorbidity in persons with bipolar disorder, it is integral that further work address the treatment utilization and outcomes of medical care. Given the high mortality rates reported in other studies, access to primary care alone is likely not sufficient.

The recognition of treatment barriers has led to calls for integration of physical and mental health treatment services (30,69,74,76–79). Integrated delivery of medical and psychiatric services has been successfully demonstrated in the inpatient setting, an outpatient clinic, a detoxification unit, and smoking cessation programs (15,79–81). As our study demonstrates, chronic medical comorbidity among persons with bipolar disorder is both common and significant. We cannot measure the potential added burden among those who did not seek care and may not have had claims for medical comorbidity. The development and evaluation of integrated treatment venues is essential.

The strengths of our study should be considered. Unlike studies conducted in a single hospital or clinic setting, our study analyzed a large sample of adults seeking care in a variety of medical settings and who were seen by a diverse group of health care providers. Because these subjects were commercially insured, the findings represent a chronically ill population rarely studied: the commercially insured chronically mentally ill. We examined 6 years of claims data, with a follow-up period of approximately 40 months for subjects with bipolar disorder. The use of rigorous case-finding methodology further ensures specificity of the bipolar diagnosis and generalizability of these finding to other men and women suffering from bipolar disorder who may have a different socioeconomic status. These findings may not be generalizable to the chronically mentally ill with bipolar disorder that do not have access to healthcare because of lack of health insurance. However, there is no reason to suspect that medical comorbidity in the uninsured would be less than that described, given the similar risk factors.

Limitations of this work warrant mention. This study included insured adults from Iowa, a racially homogeneous state. Reliance on administrative data to identify mental health conditions may affect diagnostic accuracy, but we note that many prior analyses have used such data and that a prior report suggests that the false-positive rate is less than 10% for identification of bipolar disorder in administrative data based on medical record review (52). Further, to enhance diagnostic specificity, we used validated criteria for identification but may have inadvertently excluded potential subjects who lacked supporting claims (e.g., those with a single outpatient diagnosis) (82). Conversely, because standardized diagnostic interview data were unavailable and likely not done in physician offices, some subjects with bipolar disorder may have been misclassified. Likewise, it is also possible that some controls were misclassified if mental health conditions were not recorded in the claims data, particularly if mental health was paid out of pocket. "True" rates of comorbidity may be underestimated in these analyses. Subjects who did not visit healthcare providers during the study period or who filed claims with other insurers are not represented in these data. Physician failure to bill for services or failure to code medical diagnoses (e.g., obesity) may have resulted in lowered rates of comorbidity. We have no reason to suspect that either of these differentially affected either the cases or controls. However, physician failure to provide needed medical assessments of persons with bipolar disorder may have resulted in lower rates of claims for specific medical diagnoses. We also noted that disparate length of eligibility for healthcare occurred among subjects with and without bipolar disorder, but we did adjust for healthcare utilization, which may account for duration of eligibility and increased opportunity for diagnosis. It is possible that persons with bipolar disorder had longer follow-up times for fear of losing health benefits (74). We did not adjust for nicotine use, because it is rarely coded in these data and would bias the results due to misclassification. The odds for some conditions may have been elevated due to the lack of control for nicotine use, but we note that logistic models controlling for smoking did not change risk for respiratory conditions in the Sokal et al. (37) study. Finally, some conditions that were more (or less) likely in patients with bipolar disorder may be the result of chance because we did not adjust the p values for multiple comparisons. We have reported p values in the table and invite the reader to make his or her own decision regarding statistical significance. However, it is unlikely that the significance of many of these conditions would change, given that most p values were <.0001.

In summary, the use of rigorous methodology revealed significant chronic medical comorbidity, including conditions previously described (diabetes, cardiovascular disease) and those not (hypothyroidism, anemia) in this population of insured persons with bipolar disorder. These results provide further evidence that the burden of chronic medical conditions in persons with bipolar disorders is substantial. Our findings support the education of primary care providers regarding diagnosis and treatment of persons with bipolar disorders. Based on this and other work, we advocate the development and dissemination of coordinated medical and psychiatric systems of care.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
REFERENCES
 

Received for publication September 9, 2005; revision received April 26, 2006.

DOI:10.1097/01.psy.0000237316.09601.88


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 

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