bs_bs_banner
International Journal of Mental Health Nursing (2014) 23, 479–489
doi: 10.1111/inm.12085
Feature Article
Factors influencing cardiometabolic monitoring practices in an adult community mental health service Freyja Millar,1 Natisha Sands2 and Stephen Elsom3 1
Eastern Health, 3Centre for Psychiatric Nursing, The University of Melbourne, Melbourne, and 2School of Nursing and Midwifery, Deakin University, Geelong, Victoria, Australia
ABSTRACT: People with serious mental illness are reported to live up to 25 years less than the general population. Cardiovascular disease and diabetes risk factors, as well as mental health, treatment, lifestyle, service provision, and socioeconomic factors, all contribute to this health inequity. Cardiometabolic monitoring (CMM) is one strategy used to attend to some cardiometabolic risk factors. The present study aimed to explore factors that influence decisions to undertake CMM in an Australian adult community mental health service. A CMM audit tool was designed to capture demographic, clinical, and care-provision factors. A 6-month retrospective file audit from the total population of consumers of an adult community mental health service was undertaken, where no existing CMM guidelines or practices were in place. The study findings confirmed a higher prevalence of cardiometabolic disorders in the study population compared to the general population. Complete CMM occurred in 24% of the study population (n = 94). No consumer demographic, socioeconomic, or clinical characteristics, or care-provision factors, were found to be predictors of complete CMM. The random manner in which CMM was observed to occur in the study highlights the need for standardized CMM guidelines and capacity-building strategies to improve current CMM practices. KEY WORDS: cardiometabolic, community, mental health service, monitoring, serious mental illness.
INTRODUCTION Research evidence spanning the past decade has reported consistently that people with serious mental illness (SMI) experience poor physical health outcomes, such as increased prevalence of cardiovascular, respiratory, and metabolic disorders; poor treatment outcomes; and a reduced lifespan compared to the general population (Coghlan et al. 2001; Hoang et al. 2011; Lawrence et al.
Correspondence: Freyja Millar, Eastern Health Adult Mental Health Service, Level 1, 43 Carrington Road, Box Hill, Vic. 3128, Australia. Email:
[email protected] Freyja Millar, RN, MHN, MNursP, DE. Natisha Sands, RPN, BN (Hons), PhD. Stephen Elsom, RN, BA, MNurs, PhD. Accepted May 2014.
© 2014 Australian College of Mental Health Nurses Inc.
2013; Saha et al. 2007). In the present study, people are identified as having an SMI if they are diagnosed with a major mental illness, if they have received treatment from a specialist mental health service for a period of greater than 2 years, and as a result, experience significant disability with regards to life activities (Estrine 2011; Ruggeri et al. 2000). The prevalence of metabolic syndrome in people with SMI is reported to be 37–61.6% globally (Brunero et al. 2009; John et al. 2009; McEnvoy et al. 2005; van Winkel et al. 2008), and rates of cardiovascular disease and diabetes are more than double that of the general population (Daumit et al. 2008; De Hert et al. 2009; Goff et al. 2005; Morgan et al. 2011). The literature reviewed for the present study (Barnett et al. 2007; Edward et al. 2010; Lambert 2009; 2011; Lambert & Chapman 2004; Rege
480
2008; Usher et al. 2006) strongly endorses the implementation of a targeted cardiometabolic monitoring (CMM) system, based on metabolic syndrome criteria, as the first step towards addressing the poor cardiometabolic health outcomes for consumers of mental health services. To date, the Victorian Department of Health has not published or authorized a CMM policy for public mental health-care providers, and therefore, CMM is not mandated in Victorian mental health services leaving some Victorian Area Mental Health Services without cardiometabolic guidelines in place. In the present study, we sought to examine the current rate of CMM and the factors influencing current practices in one mental health service in the context of CMM practice that is not influenced by an existing guideline or protocol.
Aim The primary aims of this research were to investigate the rate and type of CMM undertaken within a community mental health team in Melbourne, Australia, and to explore the factors that influence CMM practice. The specific study objectives were: 1 To determine the rate of CMM in the total population of mental health consumers receiving case management from a community mental health team in Melbourne, Australia. 2 To identify the demographic, socioeconomic, illness-, and treatment-related factors most prevalent in the consumer population receiving CMM. 3 To examine any relationships between consumer demographic, socioeconomic, illness-, and treatmentrelated factors and rates of CMM.
METHOD Setting The study setting was a community mental health team in metropolitan Melbourne, Australia. Twelve multidisciplinary mental health professionals, including psychiatrists, mental health nurses, medical officers, social workers, and a psychologist, staffed the clinic. Clinic-based and outreach mental health services are provided through a casemanagement model.
Design The study employed an exploratory design involving a retrospective documentation audit of 6 months of consecutive data on the rate and type of CMM undertaken on the total population of consumers of a community mental health team in Melbourne, Australia.
F. MILLAR ET AL.
Data collection The Cardio Metabolic Monitoring Audit Tool collects cardiometabolic risk data, as well as socioeconomic elements, such as living situation and source of income. Socioeconomic factors have not been investigated by previous studies that examine CMM of people with a SMI, even though social disadvantage can have a negative impact on their physical health (Barnes et al. 2008; Morrato et al. 2008; Organ et al. 2010; Shi et al. 2009; Wildgust & Beary 2010). Waist circumference was not included in this audit tool, as the service had not been introduced to or educated about this type of health assessment. Other items incorporated into the audit tool were service-provision factors, including case manager gender, case manager discipline, and the number of case managers the participant had during the audit period. The Cardio Metabolic Monitoring Audit Tool was tested and validated on the first five consumer files prior to undertaking further auditing for this study (Fig. 1). The 6-month audit timeframe for this study was based on a review of CMM standards, guidelines, and consensus statements for people with an SMI (ADA et al. 2004; Cohn & Sernyak 2006; De Hert et al. 2009; Lambert & Chapman 2004; NSW Department of Health 2009a; Waterreus & Laugharne 2009). Data were collected from all relevant documentation held in the clinical file from 1 October 2010 to 31 March 2011, inclusive. Data were collected from consumer registration forms, progress notes, medical reviews, Mental Health Act legal documents (Department of Health 2008), medication charts, diagnostic reports, treatment plans, clinical review, riskassessment documents, and correspondence with other health professionals.
Data analysis All quantitative data collected in this study were categorical and analysed using the Statistical Package for Social Science version 19 data analysis package (SPSS, Chicago, IL, USA). Descriptive statistics were calculated for the following independent variables: consumer age, sex, living situation, income, mental health legal status, psychiatric disability rehabilitation support services (PDRSS), and general practitioner (GP) involvement, as well as mental health diagnosis and pre-existing diagnoses of diabetes, hypertension, or dyslipidaemia. Descriptive statistics were also used to determine the frequencies and percentages of tests for blood pressure, weight, hyperglycaemia, and dyslipidaemia, as well as the level of CMM. Cross-tabulation and χ2-test for independence were conducted using the Pearson χ2-test and Yates’s correction for continuity, which compensated for overestimating © 2014 Australian College of Mental Health Nurses Inc.
CARDIOMETABOLIC MONITORING PRACTICES
481
Participant age
60
Psychotic disorder
Mood disorder
< 5years
other
6– 10 years
>11 years
yes
No
Yes
No
yes
no
1
2
3
4
1
2
3
4
Cardio Metabolic Screening Documented in six month period
FIG. 1: Cardio Metabolic Monitoring Audit Tool.
Blood pressure
Yes
No
Consumer declined
Weight
Yes
No
Consumer declined
Hyperglycaemia
Yes
No
Consumer declined
Dyslipidaemia
Yes
No
Consumer declined
Cardio metabolic monitoring
0-1
2-3
4
the χ2-test value for a 2 × 2 table (Pallant 2010; Peat 2001). All data collected using the Cardio Metabolic Monitoring Audit Tool were categorical, and independent variables (age, sex, living situation, income, community treatment order, PDRSS and GP involvement, mental health diagnosis, duration of illness, CMM test variables, and pre-existing diagnoses of diabetes, hypertension, and dyslipidaemia) underwent cross-tabulation with the dependent variable (CMM). A χ2-test analysis was then performed to determine independent variables that met the assumptions for a binomial logistic regression analysis. No independent variables reached asymptotic significance (P < 0.05); therefore, bino© 2014 Australian College of Mental Health Nurses Inc.
mial logistic regression analysis was not conducted (Pallant 2010; Tabachnick & Fidell 2007). Due to the large number of prescribed medications, psychiatric medications were recoded to create new independent variables, the first labelled ‘pharmacotherapy’, which comprised of two categories: mono or poly pharmacy. The second variable was labelled ‘type of antipsychotic medication’, also with two categories: first-generation antipsychotic and second-generation antipsychotic.
Ethics The research was conducted according to the guidelines for ethical research published in the National Statement
482
F. MILLAR ET AL.
The majority of the population (72.3%) was living in privately-owned or rented accommodation, with the remaining cases (20.2%) living in Department of Human Services-managed housing or reported as being itinerant or homeless (7.4%). Almost 80% of the population received an income from a government pension, with 20.2% receiving an income from other sources. The majority of cases had a primary mental health diagnosis of a psychotic disorder, with considerably less cases reported as having a diagnosis of mood disorder or other mental illnesses. The mean number of years of illness of the total population (n = 94) was calculated to be 9.00 (SD = 6.84). Nine cases had a recorded duration of illness less than 2 years, and 10 cases were new to the CCT with no available record of duration of illness. Females were shown to have a greater duration of illness than males; however, the distribution of illness duration was consistent. Half of the study population was treated involuntarily under the Victorian Mental Health Act (Department of Health 2008) during the audit period. The number of cases treated with more than one psychotropic medication was nearly twice that of cases treated with a single medication. Thirty different psychotropic medications were prescribed to the total population within the audit period, with Quetiapine, a second-generation antipsychotic, the most frequently-prescribed drug. Of the cases where an antipsychotic medication was prescribed (n = 90), second-generation antipsychotic medications were more frequently prescribed than first-generation antipsychotic medications. Four of 94 cases were not prescribed an antipsychotic medication, and all cases were prescribed at least one psychotropic medication. Table 1 reports on the diagnostic and treatment-related factors of the study population by sex. Table 2 reports on the
(National Health and Medical Research Council et al. 2007). A low-risk and negligible-risk research ethics application was approved by the Area Mental Health Service and the university Research and Ethics Committee, and in accordance with the National Statement on Ethical Conduct in Human Research. The research proposal met the National Statement on Ethical Conduct in Human Research, Waiver of Consent criteria, and participant consent was not sought for this project (National Health and Medical Research Council et al. 2007, p. 23).
RESULTS All consumer files registered at the community mental health team at the time of audit were screened for inclusion in the study. The consumer files that were registered with the community mental health team for the entire file audit period were included in this study. If a consumer was not registered for the 6-month audit period, they were excluded from this study. The total consumer population of the community mental health team was 136, of which 30.8% were not registered with the community mental health team for the entire audit period, leaving a study population of 94. Basic demographic data were collected on the total population (n = 94) that met the study inclusion criteria. The sex split of cases was 50% male and 50% female. The mean age of the total population was 41.89 years (standard deviation (SD) = 11.16). An independent-samples t-test was conducted to compare the mean age in years for males (mean (M) = 41.62, SD = 11.57) and females (M = 42.16, SD=10.86; t (94) = −0.23, P = 0.82), with no significant difference found.
TABLE 1: Diagnostic and treatment factors of total population by sex Consumer demographic Mental health diagnosis
Duration of illness (years)
CTO Pharmacotherapy Type of antipsychotic
Psychotic disorder Mood disorder Other 11 Yes No Mono pharmacy Poly pharmacy First-generation antipsychotic Second-generation antipsychotic
Female (n = 47)
Male (n = 47)
Female (%)
Male (%)
Overall (%)
Total (n = 94)
41 4 2 13 14 20 16 31 18 29 7 38
39 6 2 14 19 14 23 24 16 31 7 38
87 9 4 28 30 43 34 66 38 62 15 81
83 13 4 30 40 29 49 51 34 66 15 81
85 11 4 29 35 36 41 59 36 64 15 81
80 10 4 27 33 34 39 55 34 60 14 76
CTO, community treatment order.
© 2014 Australian College of Mental Health Nurses Inc.
CARDIOMETABOLIC MONITORING PRACTICES
483
TABLE 2: Care-provision and physical health factors of total population by sex Female (n = 47)
Male (n = 47)
Female (%)
Male (%)
Overall (%)
Total (n = 94)
Mental health nurse Social worker Psychologist Yes No Yes No Yes No
21 19 7 35 12 36 11 16 31
32 13 2 30 17 26 21 19 28
45 40 15 74 26 77 23 34 66
69 28 4 64 36 55 45 40 60
56 34 10 69 31 66 34 37 63
53 32 9 65 29 62 32 35 59
Yes No Yes No Yes No
11 36 4 43 9 38
4 43 4 43 11 36
23 77 9 91 19 81
9 2 9 91 23 77
16 84 9 91 21 79
15 79 8 86 20 74
Variable Care-provision factors Case manager discipline
GP name documented Communication with GP documented PDRSS involvement Physical health factors Pre-existing diagnosis of diabetes Pre-existing diagnosis of hypertension Pre-existing diagnosis of dyslipidaemia
GP, general practitioner; PDRSS, psychiatric disability rehabilitation support services.
TABLE 3:
Rate of testing for cardiometabolic risk factors by sex
Cardiometabolic risk factor
Female (n = 47)
Male (n = 47)
Female (%)
Male (%)
Overall (%)
Total (n = 94)
Weight Blood pressure Lipids Glucose
35 19 25 28
22 17 21 27
74 40 53 60
47 36 45 57
61 38 49 59
57 36 46 55
care-provision and physical health factors of total population by sex. The range of case manager disciplinary background over the 6-month audit period included mental health nurses (55%), social workers (31%), and psychologists (9%); 5% of consumers had more than one case manager discipline during the audit period. Sixty-nine percent of consumers had a GP name recorded in their clinical files, with 66% of consumers having had a record of communication between a GP and the community mental health team. Communication between the community mental health team and GP was more likely to occur if the consumer was female (77%) than male (55%). One-third of consumers (37%) received a service from a PDRSS. Twenty-one percent of the population had a recorded diagnosis of dyslipidaemia, with no notable difference in prevalence between males (23%) and females (19%). The diagnosis of hypertension was also spread evenly between males and females, and occurred in 8% of the population. Sixteen percent of consumers had a recorded diagnosis of diabetes; this cohort was comprised of four males and 11 © 2014 Australian College of Mental Health Nurses Inc.
females. Complete CMM, defined as receiving a test for weight, blood pressure, hyperglycaemia, and dyslipidaemia, occurred in 24% of the study population (n = 94). The rate of tests for cardiometabolic risk factors by sex is shown in Table 3. The most commonly assessed cardiometabolic risk factors within the 6-month audit period were weight (61%) and blood glucose levels (59%), followed by blood lipids levels (49%); the least-assessed cardiometabolic risk factor was blood pressure (38%). Each study participant (n = 94) received an average of 2.06 (SD = 1.45) cardiometabolic risk assessments over the 6-month time period. A cardiometabolic risk assessment for weight predominately occurred in female (n = 35) consumers than males (n = 22). An independent-samples t-test, which was conducted to compare complete cardiometabolic riskfactor assessments (maximum of 4) for males and females, found no significant difference in mean CMM risk-factor assessments for males (M = 1.85, SD = 1.56) and females (M = 2.28, SD = 1.31; t (94) = −1.43, P = 0.16, two tailed).
484
Factors influencing CMM The majority of consumers who received complete CMM were aged above 40 years (60.9%), lived in private accommodation (56.5%), and received a pension (73.9%). Complete CMM was found to occur predominately in consumers diagnosed with a psychotic disorder (87%), were prescribed second-generation antipsychotic medication (95.7%), or were treated with more than one psychiatric medication (65.2%). Over half of the consumers who received complete CMM had an illness duration greater than 11 years. Two-thirds of consumers who had a mental health nurse case manager, and over 80% of consumers who had evidence of communication with a GP in the file, received complete CMM. Of the consumers attending a PDRSS, one-third received complete CMM. One-third of consumers who had a pre-existing cardiometabolic disorder received complete CMM.
DISCUSSION Rates of CMM A key aim of this study was to determine the rate and type of CMM provided for case-managed consumers by a community mental health team over a 6-month period. The present study found that a relatively small proportion (24%) of case-managed consumers with SMI received full CMM during the audit period, despite a high proportion of the study population prescribed second-generation antipsychotics. The CMM rates for weight (61%), blood pressure (38%), hyperglycaemia (59%), and dyslipidaemia (49%) are in keeping with findings from previous research, although most previous studies have used a 12-month audit period (Barnes et al. 2008; Gonzalez et al. 2010; Organ et al. 2010). The present study identified that blood pressure was the least-recorded cardiometabolic risk test undertaken, despite the availability of sphygmomanometers and the non-invasive nature of the test. This outcome is consistent with Organ et al. (2010), who found blood pressure testing to be less frequently performed than other tests. In the absence of an appropriate evidence-based guideline or CMM protocol to inform clinicians, such as nurses, psychiatrists, registrars, and GPs, with regards to the expected type and frequency of cardiometabolic risk testing, a blood pressure assessment might be considered a ‘good idea’ compared to an evidenced-based clinical risk activity. This rationale is supported when placed in contrast to the Morgan et al. (2011) study, which reported that blood pressure was the most frequent cardiometabolic assessment undertaken on people with a psychotic illness. The study by Morgan et al. (2011) included assessments by non-government services
F. MILLAR ET AL.
and GPs, which might have influenced the documentation of blood pressure assessments, thereby supporting the call (Happell et al. 2013a; Scott & Happell 2012) for a more collaborative approach to physical health care for people with mental illness. The present study showed that community mental health team clinicians have started to incorporate CMM into their practice, as evidenced by the 24% of the sample who received complete CMM. This is the baseline rate, given that the service did not have a procedure or policy to guide CMM. In the absence of an Australian CMM frequency benchmark to compare this rate to, it is problematic to categorize this rate of CMM. However, given the unacceptable level of cardiometabolic health of people with SMI, describing 24% as inadequate is more than appropriate. Previous research has identified that mental health services lack guidance in identifying appropriate rates of cardiometabolic screening and monitoring processes to address the poor cardiometabolic health outcomes of people with a SMI treated with secondgeneration antipsychotic medication (De Hert et al. 2006a; Hyland et al. 2003). However, CMM guidelines and consensus statements in mental health settings have resulted in mixed outcomes in terms of rates of CMM (Citrome & Yeomans 2005; Mitchell et al. 2012). A study of CMM practices in an Australian early psychosis service with a CMM policy in place found that baseline CMM occurred in up to 60% of cases, but that adherence reduced significantly over the course of treatment (Hetrick et al. 2010).
Factors influencing CMM practice The CMM practices undertaken by staff at the study site during the audit period were not significantly associated with any consumer demographic or clinical variables, indicating no current pattern of CMM based on clinical indicators, such as diagnosis or prescribed medication. There are limited published studies that describe CMM practices of clinicians in community mental health services, or which factors influence decisions to perform CMM, but studies suggest that CMM is poorly done in mental health settings (Fagiolini 2008; Gill et al. 2009; Lambert & Newcomer 2009). A pre-existing diagnosis of cardiometabolic disorders, such as diabetes, hypertension, and dyslipidaemia, is consistently reported as a predictor of complete CMM in the literature (Barnes et al. 2008; Morrato et al. 2008; Shi et al. 2009). The present study did not confirm diabetes, hypertension, and dyslipidaemia to be statisticallysignificant predictors of complete CMM. Consumers with a pre-existing diagnosis of a cardiometabolic disorder © 2014 Australian College of Mental Health Nurses Inc.
CARDIOMETABOLIC MONITORING PRACTICES
received nearly 50% less complete CMM than consumers who did not have a pre-existing cardiometabolic disorder. Of the 16 cases (n = 94) who had a documented preexisting diagnosis of diabetes, seven had a nominated GP listed in the file, five were registered with a PDRSS, and 13 had evidence of communication between the community mental health team and primary health service. It is possible that collaborative care partnerships between the consumer, community mental health team, and GP are a disincentive for community mental health team clinicians to perform CMM; case managers might assume that the CMM is being managed by the GP or that other primary health-care arrangements are in place. Education and collaborative capacity-building strategies might improve knowledge about primary health-care activities and care of consumers with a pre-existing cardiometabolic disorder (Happell et al. 2013b; Millar & Majoor 2013; Osborn et al. 2010). The prescription of second-generation antipsychotic medication was shown to be a predictor of complete CMM in previous research (Barnes et al. 2008; Morrato et al. 2008; Shi et al. 2009). In the present study, 95.7% of consumers who received complete CMM were prescribed a second-generation antipsychotic. This finding is not surprising, given the volume of research published about the cardiometabolic side-effects of secondgeneration antipsychotics (ADA et al. 2004; Chaudhry et al. 2010; Daumit et al. 2008; Lambert 2009; 2011Montejo 2010; Steylen et al. 2013). Other psychotropic medications, such as antidepressants, are also reported to have cardiometabolic side-effects. In order to provide the best possible mental health care, community mental health service clinicians need to acknowledge that all consumers with SMI are at high risk of cardiovascular disease and diabetes, and therefore require CMM, but also that consumers are interested in and are seeking support to attend to their physical health issues, such as weight gain (Lawrence et al. 2013; Tweedle et al. 2004). Shi et al. (2009) reported that advancing age increased the likelihood of complete CMM by 10%. Barnes et al. (2008) also determined that age was a predictor of full CMM. Age was not found to be a statistically-significant predictor of full CMM in the present study. Twenty-seven percent of community mental health team consumers over the age of 40 years received complete CMM compared to 21% of those under 40 years, indicating that clinicians might not consider increased age as a cardiometabolic risk factor. No previous studies investigating predictors of CMM in mental health settings have reported on the influence © 2014 Australian College of Mental Health Nurses Inc.
485
of social factors, such as housing and financial status, on CMM rates. Overall, consumers who had limited financial means were more likely to receive complete CMM in the present study; however, this was not the case for consumers living in unstable accommodation. Homeless consumers and those in unstable accommodation received the least amount of complete CMM (8.7%), despite research confirming that homeless or itinerant people with a SMI are a highly-vulnerable group that require additional support to manage physical health care (Kim et al. 2007). The majority of consumers in the present study received a government pension (79.8%), a finding similar to Morgan et al. (2011), where 85% of the sample received a pension. The present study found that 74% of consumers who received complete CMM were on a pension, compared to 26% who had an alternative income source. Limited financial means is reported to be a barrier to accessing physical health care (De Hert & Cohen 2010; Kim et al. 2007), and case managers have an important role in supporting consumers achieve physical health-care goals through the provision of targeted health promotion, cardiometabolic health screening, referral to appropriate services, and supporting the consumer to attend medical appointments (Happell et al. 2011; 2013b; Ministerial Advisory Committee on Mental Health 2010; Usher et al. 2006). The present study found that there was no statisticallysignificant difference between mental health diagnosis and complete CMM activity, and that more consumers diagnosed with a psychotic disorder (n = 20) received complete CMM compared to those diagnosed with a mood disorder (n = 1) or other mental health diagnosis (n = 2). Barnes et al. (2008) also reported that psychiatric diagnosis did not predict complete CMM; however, the study by Morrato et al. (2008) found that individuals with a diagnosis of schizophrenia were 46% more likely to receive complete CMM than people with non-psychotic disorders. Much of the published research focuses on the cardiometabolic risk of people with schizophrenia (Barnett et al. 2007; Beebe & Harris 2013; Brunero & Lamont 2010; Chaudhry et al. 2010; Daumit et al. 2008; De Hert et al. 2006b; Galletly et al. 2012; Heald et al. 2010; Holt et al. 2004; Lambert & Chapman 2004; McEnvoy et al. 2005; Morgan et al. 2011; Rege 2008; Usher et al. 2006). Research investigating the cardiometabolic risk of people with mood disorders indicates that people with bipolar disorder are 19% more likely to have diabetes and 44% more likely to have coronary artery disease than people with a psychotic disorder (John et al. 2009; Kilbourne et al. 2007; van Winkel
486
et al. 2008). The premise that all people with an SMI, regardless of psychiatric diagnosis, have an increased risk of metabolic syndrome is beginning to be accepted by health policy makers. The inclusion of all case-managed consumers in Australian state physical health-care guidelines is an example of the growing acknowledgement of the cardiometabolic health risks of all people with an SMI (NSW Department of Health 2009a,b; Stanley & Laugharne 2011). A longer duration of mental illness was reported by Shi et al. (2009) to increase the likelihood of complete CMM by 10%; however, this finding is not supported by other studies that investigated predictors of complete CMM (Barnes et al. 2008; Lambert & Newcomer 2009; Morrato et al. 2008). From a consumer point of view, the present study did not investigate strategies that might increase their likelihood of engaging in regular CMM. Australian studies have suggested that the use of a hand-held record card and targeted consumer health education might improve consumer self-care and health management (Brunero et al. 2008; Coghlan et al. 2001). From a clinician perspective, ‘who’ should be conducting the CMM is a question that has been taken up by Australian mental health nursing experts, who assert that mental health nurses are ideally placed to manage and or conduct CMM of people with SMI (Brunero & Lamont 2009; Brunero et al. 2009; Edward et al. 2010; Happell et al. 2011; 2013b; Millar 2012; Osborn et al. 2010; Usher et al. 2006). The outcomes of this study show that more consumers who had a mental health nurse case manager (n = 16) received complete CMM than those with an allied health case manager (n = 5). There are limited studies that investigate the influence of case manager discipline on CMM practices within a community mental health setting (Hyland et al. 2003); however, the introduction of a cardiometabolic health nurse as a means to improve cardiometabolic risk management in mental health services is a notion that has received increased interest in recent years (Brunero & Lamont 2009; Happell et al. 2013b).
LIMITATIONS The small sample size, the retrospective design, and the fact that it was a single site study limit the generalizability of these study findings. The 6 months audit time-frame used for this study was determined as an appropriate timeframe; however, it limited the capacity to compare the findings of this study with similar studies that used a longer audit timeframe. The strengths of the study
F. MILLAR ET AL.
include the comprehensive Cardio Metabolic Monitoring Audit Tool developed for data collection, which could be used in future research into CMM practice in mental health settings.
CONCLUSIONS AND RECOMMENDATIONS This study confirmed previous research findings identifying the high prevalence of metabolic syndrome in people with SMI and inadequate rates of CMM in mental health settings. The arbitrary manner in which CMM occurred in the study population indicates that further work is needed to inform CMM practice in community settings. A key recommendation arising from the study is that evidence-based CMM clinical guidelines should be mandated in Victorian mental health services, and be supported by targeted CMM education and capacity-building strategies. CMM guidelines and protocols need to be endorsed by government health department policy to improve the consistency and accountability of CMM practice.
REFERENCES ADA, APA, AACE & NAASO (2004). Consensus development conference on antipsychotic drugs and obesity and diabetes. Diabetes Care, 27, 596–601. Barnes, T., Paton, C., Hancock, E., Cavanagh, M., Taylor, D. & Lelliott, P. (2008). Screening for the metabolic syndrome in community psychiatric patients prescribed antipsychotics: A quality improvement programme. Acta Psychiatrica Scandinavica, 118, 26–33. Barnett, A., Mackin, P., Chaudhry, I. et al. (2007). Minimising metabolic and cardiovascular risk in schizophrenia: Diabetes, obesity and dyslipidaemia. Journal of Psychopharmacology, 21, 357–373. Beebe, L. & Harris, R. (2013). Description of physical activity in outpatients with schizophrenia spectrum disorders. International Journal of Mental Health Nursing, 22, 430– 436. Brunero, S. & Lamont, S. (2009). Systematic screening for metabolic syndrome in consumers with severe mental illness. International Journal of Mental Health Nursing, 18, 144– 150. Brunero, S. & Lamont, S. (2010). Health behaviour beliefs and physical health risk factors for cardiovascular disease in an outpatient sample of consumers with a sever mental illness: A cross-sectional survey. International Journal of Nursing Studies, 47, 753–760. Brunero, S., Lamont, S., Myrtle, L. & Fairbrother, G. (2008). The Blue Card: A hand-held health record card for mental health consumers with comorbid physical health risk. Australas Psychiatry, 16, 238–243. © 2014 Australian College of Mental Health Nurses Inc.
CARDIOMETABOLIC MONITORING PRACTICES Brunero, S., Lamont, S. & Fairbrother, G. (2009). Prevalence and predictors of metabolic syndrome among patients attending an outpatient clozapine clinic in Australia. Archives of Psychiatric Nursing, 23, 261–268. Chaudhry, I., Jordan, J., Cousin, F., Cavallaro, R. & Mostaza, J. (2010). Management of physical health in patients with schizophrenia: International insights. European Psychiatry, 25 (Suppl. 2), S37–S40. Citrome, L. & Yeomans, D. (2005). Do guidelines for severe mental illness promote physical health and well-being? Journal of Psychopharmacology, 19, 102–109. Coghlan, R., Lawrence, D., Holman, C. & Jablensky, A. (2001). Duty to Care: Physical Illness in People with Mental Illness. Perth: The University of Western Australia. Cohn, T. & Sernyak, M. (2006). Metabolic monitoring for patients treated with antipsychotic medications. Canadian Journal of Psychiatry, 51, 492–501. Daumit, G., Goff, D., Meyer, J. et al. (2008). Antipsychotic effects on estimated 10-year coronary heart disease risk in the CATIE schizophrenia study. Schizophrenia Research, 105, 175–187. De Hert, M. & Cohen, D. (2010). Barriers to physical health care in people with mental illness. European Psychiatry, 25, 1627–1627. De Hert, M., Van Eyck, D. & De Nayer, A. (2006a). Metabolic abnormalities associated with second generation antipsychotics: Fact or fiction? Development of guidelines for screening and monitoring. International Clinical Psychopharmacology, 21, S11–S15. De Hert, M., Van Eyck, D., Hanssens, L. et al. (2006b). Oral glucose tolerance tests in treated patients with schizophrenia.: Data to support an adaptation of the proposed guidelines for monitoring of patients on second generation antipsychotics? European Psychiatry, 21, 224–226. De Hert, M., Dekker, J., Wood, D., Kahl, K., Holt, R. & Möller, H. (2009). Cardiovascular disease and diabetes in people with severe mental illness position statement from the European Psychiatric Association (EPA), supported by the European Association for the Study of Diabetes (EASD) and the European Society of Cardiology (ESC). European Psychiatry, 24, 412–424. Department of Health (2008). The Mental Health Act 1986. Melbourne. Edward, K., Rasmussen, B. & Munro, I. (2010). Nursing care of clients treated with atypical antipsychotics who have a risk of developing metabolic instability and/or type 2 diabetes. Archives of Psychiatric Nursing, 24, 46–53. Estrine, S. (2011). Service Delivery for Vulnerable Populations: New Directions in Behavioral Health. New York: Springer Pub. Fagiolini, A. (2008). Overcoming hurdles to achieving good physical health in patients treated with atypical antipsychotics. European Neuropsychopharmacology, 18, S101–S107. Galletly, C. A., Foley, D. L., Waterreus, A. et al. (2012). Cardiometabolic risk factors in people with psychotic disor© 2014 Australian College of Mental Health Nurses Inc.
487 ders: The second Australian national survey of psychosis. Australian & New Zealand Journal of Psychiatry, 46, 753– 761. Gill, K., Murphy, A., Zechner, M., Swarbrick, M. & Spagnolo, A. (2009). Co-morbid psychiatric and medical disorders: Challenges and strategies. Journal of Rehabilitation, 75, 32–40. Goff, D., Sullivan, L., McEvoy, J. et al. (2005). A comparison of ten-year cardiac risk estimates in schizophrenia patients from the CATIE study and matched controls. Schizophrenia Research, 80, 45–53. Gonzalez, C., Ahammed, N. & Fisher, R. (2010). Improving physical health monitoring for out-patients on antipsychotic medication. The Psychiatrist, 34, 91–94. Happell, B., Platania-Phung, C. & Scott, D. (2011). Placing physical activity in mental health care: A leadership role for mental health nurses. International Journal of Mental Health Nursing, 20, 310–318. Happell, B., Platania-Phung, C. & Scott, D. (2013a). Proposed nurse-led initiatives in improving physical health of people with serious mental illness: A survey of nurses in mental health. Journal of Clinical Nursing, 23, 1018–1029. Happell, B., Scott, D. & Platania-Phung, C. (2013b). Nurse views on the cardiometabolic health nurse as an approach to improving the physical health of people with serious mental illness in Australia. International Journal of Mental Health Nursing, 22, 418–429. Heald, A., Montejo, A., Millar, H., De Hert, M., McCrae, J. & Correll, C. (2010). Management of physical health in patients with schizophrenia: Practical recommendations. European Psychiatry, 25 (Suppl. 2), S41–S45. Hetrick, S., Alvarez-Jimenez, M., Parker, A. et al. (2010). Promoting physical health in youth mental health services: Ensuring routine monitoring of weight and metabolic indices in a first episode psychosis clinic. Australasian Psychiatry, 18, 451–455. Hoang, U., Stewart, R. & Goldacre, M. (2011). Mortality after hospital discharge for people with schizophrenia or bipolar disorder: Retrospective study of linked English hospital episode statistics, 1999–2006. British Medical Journal, 343, d5422. Holt, R., Pevelert, R. & Byrne, C. (2004). Schizophrenia, the metabolic syndrome and diabetes. Diabetic Medicine, 21, 515–523. Hyland, B., Judd, F., Davidson, S., Jolley, D. & Hocking, B. (2003). Case managers’ attitudes to the physical health of their patients. The Australian And New Zealand Journal Of Psychiatry, 37, 710–714. John, A., Koloth, R., Dragovic, M. & Lim, S. (2009). Prevalence of metabolic syndrome among Australians with severe mental illness. The Medical Journal of Australia, 190, 176– 179. Kilbourne, A., Brar, J., Drayer, R., Xu, X. & Post, E. (2007). Cardiovascular disease and metabolic risk factors in male patients with schizophrenia, schizoaffective disorder, and bipolar disorder. Psychosomatics, 48, 412–417.
488
F. MILLAR ET AL.
Kim, M., Swanson, J., Swartz, M., Bradford, D., Mustillo, S. & Elbogen, E. (2007). Healthcare barriers among severely mentally ill homeless adults: Evidence from the five-site health and risk study. Administration and Policy in Mental Health, 34, 363–375.
National Health and Medical Research Council, Australian Research Council & Australian Vice-Chancellors’ Committee (2007). National Statement on Ethical Conduct in Human Research. Canberra: National Health and Medical Research Council.
Lambert, T. (2009). The medical care of people with psychosis. The Medical Journal of Australia, 190, 171–172.
NSW Department of Health (2009a). Physical Health Care of Mental Health Consumers – Guidelines. Sydney: NSW Department of Health.
Lambert, T. (2011). Managing the metabolic adverse effects of antipsychotic drugs in patients with psychosis. Australian Prescriber, 34, 97–99. Lambert, T. & Chapman, L. (2004). Diabetes, psychotic disorders and antipsychotic therapy: A consensus statement. The Medical Journal of Australia, 181, 544–548. Lambert, T. & Newcomer, J. (2009). Are the cardiometabolic complications of schizophrenia still neglected? Barriers to care. MJA, 190, S39–S42. Lawrence, D., Hancock, K. & Kisely, S. (2013). The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: Retrospective analysis of population based registers. BMJ, 346, f2539. McEnvoy, J., Meyer, J., Goff, D. et al. (2005). Prevalence of the metabolic syndrome in patients with schizophrenia: Baseline results from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III. Schizophrenia Research, 80, 19–32. Millar, F. (2012). Better monitoring for better health. Australian Nursing Journal, 20, 35–35. Millar, F. & Majoor, J. (2013). Changing the cardiometabolic monitoring practices in a Victorian mental health service: Lessons learnt. In: Australian College of Mental Health Nursing (Ed.). Australian College of Mental Health Nursing 39th International Mental Health Nursing Conference – Collaboration and Partnership in Mental Health Nursing. (p. 23); 22–24 October 2013, Perth. Ministerial Advisory Committee on Mental Health (2010). Improving the physical health of people with severe mental illness.. Melbourne: Mental Health and Drugs Division. Mitchell, A., Delaffon, V., Vancampfort, D., Correll, C. & De Hert, M. (2012). Guideline concordant monitoring of metabolic risk in people treated with antipsychotic medication: Systematic review and meta-analysis of screening practices. Psychological Medicine, 42, 125–147. Montejo, A. (2010). The need for routine physical health care in schizophrenia. European Psychiatry, 25 (Suppl. 2), S3–S5.
NSW Department of Health (2009b). The Physical Health Mental Health Handbook. Sydney: NSW Department of Health. Organ, B., Nicholson, E. & Castle, D. (2010). Implementing a physical health strategy in a mental health service. Australasian Psychiatry, 18, 456–459. Osborn, D., Nazareth, I., Wright, C. & King, M. (2010). Impact of a nurse-led intervention to improve screening for cardiovascular risk factors in people with severe mental illnesses. Phase-two cluster randomised feasibility trial of community mental health teams. BMC Health Services Research, 10, 61–73. Pallant, J. (2010). SPSS Survival Manual. Berkshire: McGrawHill. Peat, J. (2001). Health Science Research: A Handbook of Quantitative Methods. Sydney: Allen and Unwin. Rege, S. (2008). Antipsychotic induced weight gain in schizophrenia:Mechanisms and management. Australian & New Zealand Journal of Psychiatry, 42, 369–381. Ruggeri, M., Leese, M., Thornicroft, G., BIisoffi, G. & Tansella, M. (2000). Definition and prevalence of severe and persistent mental illness. The British Journal of Psychiatry, 177, 149–155. Saha, S., Chant, D. & McGrath, J. (2007). A systematic review of mortality in schizophrenia: Is the differential mortality gap worsening over time? Archives of General Psychiatry, 64, 1123–1131. Scott, D. & Happell, B. (2012). Utilization and perceptions of primary health care services in australian adults with mental illness. Population Health Management, 16, 208– 213. Shi, L., Ascher-Svanum, H., Chiang, Y.-J., Zhao, Y., Fonseca, V. & Winstead, D. (2009). Predictors of metabolic monitoring among schizophrenia patients with a new episode of secondgeneration antipsychotic use in the Veterans Health Administration. BMC Psychiatry, 9, 80.
Morgan, V., Waterreus, A., Jablensky, A. et al. (2011). People living with psychotic illness 2010. National survey of people with psychotic illness. Canberra: Commonwealth Department of Health and Ageing.
Stanley, S. & Laugharne, J. (2011). Clinical guidelines for the physical care of mental health consumers: A comprehensive assessment and monitoring package for mental health and primary care clinicians. Australian and New Zealand Journal of Psychiatry, 45, 824–829.
Morrato, E., Newcomer, J., Allen, R. & Valuck, R. (2008). Prevalence of baseline serum glucose and lipid testing in users of second-generation antipsychotic drugs: A retrospective, population-based study of Medicaid claims data. Journal of Clinical Psychiatry, 69, 316–322.
Steylen, P., van der Heijden, F., Kok, H., Sijben, N. & Verhoeven, W. (2013). Cardiometabolic comorbidity in antipsychotic treated patients: Need for systematic evaluation and treatment. International Journal of Psychiatry in Clinical Practice, 17, 125–130. © 2014 Australian College of Mental Health Nurses Inc.
CARDIOMETABOLIC MONITORING PRACTICES Tabachnick, B. & Fidell, L. (2007). Using Multivariate Statistics. Boston: Allyn and Bacon. Tweedle, D., Sutter, R. & Doran, K. (2004). Managing neuroleptic weight gain: Consumers’ perspectives. International Journal of Psychosocial Rehabilitation, 9, 37–40. Usher, K., Foster, K. & Park, T. (2006). The metabolic syndrome and schizophrenia: The latest evidence and nursing guidelines for management. Journal of Psychiatric & Mental Health Nursing, 13, 730–734. Waterreus, A. & Laugharne, J. (2009). Screening for the metabolic syndrome in patients receiving antipsychotic treatment:
© 2014 Australian College of Mental Health Nurses Inc.
489 A proposed algorithm. The Medical Journal of Australia, 190, 185–189. Wildgust, H. & Beary, M. (2010). Review: Are there modifiable risk factors which will reduce the excess mortality in schizophrenia? Journal of Psychopharmacology, 24, 37–50. van Winkel, R., De Hert, M., Van Eyck, D. et al. (2008). Prevalence of diabetes and the metabolic syndrome in a sample of patients with bipolar disorder. Bipolar Disorders, 10, 342– 348.
