CanJPsychiatry 2013;58(9):529–537

Original Research

The Burden of Mental Illness and Addiction in Ontario Sujitha Ratnasingham, MSc1; John Cairney, PhD2; Heather Manson, MD3; Jürgen Rehm, PhD4; Elizabeth Lin, PhD5; Paul Kurdyak, MD, PhD6 1

Epidemiologist, Institute for Clinical Evaluative Sciences, Toronto, Ontario.

2

Associate Professor, Departments of Family Psychiatry and Behavioural Neurosciences and Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario.

3

Chief, Health Promotion, Chronic Disease and Injury Prevention, Public Health Ontario, Toronto, Ontario.

4

Director, Social and Epidemiological Research Department, Centre for Addiction and Mental Health, Toronto, Ontario.

5

Scientist, Provincial System Support Program, Centre for Addiction and Mental Health, Toronto, Ontario.

6

Chief, General and Health System Psychiatry, Centre for Addiction and Mental Health, Toronto, Ontario; Adjunct Scientist, Institute for Clinical Evaluative Sciences, Toronto, Ontario. Correspondence: Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8; [email protected].

Key Words: burden, mental illness, addictions, healthadjusted life years, population health, major depressive disorder, bipolar disorder, substance use disorder Received October 2012, revised, and accepted March 2013.

Objective: Public Health Ontario and the Institute for Clinical Evaluative Sciences have collaborated to estimate the burden of illness attributable to mental disorder and addictions in Ontario. Methods: Health-adjusted life years were used to estimate burden. It is conceptually similar to disability-adjusted life years that were used in the global burden of disease studies. Data sources for the mental illnesses and addictions used in our study included health administrative data for the province of Ontario, survey data from Statistics Canada and the Centre for Addiction and Mental Health, vital statistics data from the Ontario Office of the Registrar General, and US epidemiologic survey data. Results: The 5 conditions with the highest burden are: major depression, bipolar affective disorder, alcohol use disorders (AUDs), social phobia, and schizophrenia. The burden of depression is double the next highest mental health condition (that is, bipolar affective disorder) and is more than the combined burden of the 4 most common cancers in Ontario. AUDs were the only disease group that had a substantial proportion of burden attributable to early death. The burden estimates for the other conditions were primarily due to disability. Conclusions: The burden of these conditions in Ontario is as large or larger than other conditions, such as cancer and infectious diseases, owing in large part to the high prevalence, chronicity, and age of onset for most mental disorders and addiction problems. The findings serve as an important baseline for future evaluation of interventions intended to address the burden of mental health and addictions. WWW

Le fardeau de la maladie mentale et de la toxicomanie en Ontario Objectif : Le service de Santé publique de l’Ontario et l’Institut pour les sciences évaluatives cliniques ont collaboré pour estimer le fardeau de la maladie attribuable aux trouble mentaux et aux toxicomanies en Ontario. Méthodes : Les années de vie ajustées en fonction de la santé ont servi à estimer le fardeau. Le concept est semblable aux années de vie ajustées en fonction de l’incapacité qui ont servi aux études sur le fardeau mondial de la maladie. Les sources de données sur les maladies mentales et les toxicomanies utilisées étaient notamment des données administratives sur la santé de la province de l’Ontario, des données d’enquête de Statistique Canada et du Centre de toxicomanie et de santé mentale, des données de l’état civil du Bureau du registraire général de l’Ontario, et des données de l’enquête épidémiologique américaine. Résultats : Les 5 affections dont le fardeau est le plus élevé sont : la dépression majeure, le trouble bipolaire affectif, les troubles liés à l’utilisation d’alcool (TUA), la phobie sociale, et la schizophrénie. Le fardeau de la dépression est le double de celui de l’affection mentale la plus proche (c’est-à-dire, le trouble bipolaire affectif) et est plus lourd que le fardeau combiné des 4 cancers les plus répandus en Ontario. Les TUA étaient le seul groupe de maladies dont une proportion substantielle du fardeau était attribuable au décès précoce. www.TheCJP.ca

The Canadian Journal of Psychiatry, Vol 58, No 9, September 2013 W 529

Original Research

Les estimations du fardeau pour les autres affections étaient principalement attribuables à l’incapacité. Conclusions : Le fardeau de ces affections en Ontario est aussi plus lourd que celui d’autres affections, comme le cancer et les maladies infectieuses, ce qui s’explique en grande partie par la prévalence élevée, la chronicité, et l’âge de début de la plupart des troubles mentaux et des problèmes de toxicomanie. Les résultats servent de base importante à l’évaluation future des interventions destinées à aborder le fardeau de la maladie mentale et des toxicomanies.

I

n 1994, the first GBD study was published, highlighting the previously underrecognized illness burden associated with neuropsychiatric disorders in general and, more specifically, the significant burden of illness associated with major depression.1,2 Since that time, there have been other studies documenting the large burden of mental health and addiction disorders.3,4 In Canada, the burden of mental illness and addictions has not been well studied. Without good evidence on disease burden, health care priorities may focus on more easily measured outcomes, such as mortality. The burden of mental illness and addictions is less likely to be associated with early mortality than other conditions because, unlike chronic medical conditions, such as cardiovascular diseases and diabetes, most people suffering from mental illness and addictions have illness onset in adolescence and early adulthood and can struggle with their mental illness and addictions over prolonged periods of time with striking impact on quality of life. The advantages of burden of disease methods are 2-fold: they account for both early mortality and reductions in quality of life; and they enable comparisons between conditions. These 2 aspects of the methods employed in our study allow for a more accurate estimate of the burden of mental illness and addictions, and therefore, a more accurate and complete source of evidence with which to examine health care priorities.

Abbreviations AUD

alcohol use disorder

BAD

bipolar affective disorder

CAMH

Centre for Addiction and Mental Health

CCHS 1.2 Canadian Community Health Survey: Mental Health and Well-Being CLAMES Classification and Measurement System of Functional Health DALY

disability-adjusted life year

GBD

global burden of disease

HALY

health-adjusted life year

ICES

Institute for Clinical Evaluative Sciences

NESARC National Epidemiologic Survey of Alcohol and Related Conditions NSDUH

National Survey on Drug Use and Health

PHO

Public Health Ontario

YERF

year-equivalents of reduced functioning

YLL

years of life lost due to premature mortality

530 W La Revue canadienne de psychiatrie, vol 58, no 9, septembre 2013

Clinical Implications •

The burden of mental illness and addictions is larger than the burden of illness for other medical conditions, such as cancer and infectious diseases.

•

The burden estimates for mental illness and addictions are largely explained by reduced functioning and disability, not by premature mortality.

•

The findings from our study provide a baseline for the evaluation of future interventions aimed at improving the burden of mental illness and addictions.

Limitations •

Mortality, due to mental illness and addictions, was not fully incorporated and attributed in the analysis

•

Burden estimates were only calculated for individual disorders; we were not able to estimate burden related to comorbid conditions.

•

Only conditions with available data were included in our study.

ICES, in collaboration with PHO, launched a project to measure the burden of mental illness and addictions in Ontario. The objective was to use existing data and current burden of disease methods to quantify the burden of mental illness and addictions among Ontarians. The methods used allow a comparison of the burden of mental illness and addictions with other conditions examined in previous burden of disease studies in a standardized manner.5,6 A systematic measurement of mental illness and addictions burden also provides us with a metric to measure the impact of future interventions. The specific objectives of our study were to determine the burden of disease-related mental illness and addictions in Ontario; and to establish a baseline for future evaluation of interventions that impact on the burden of mental illness and addictions in Ontario.

Methods Disorders and Inclusion Criteria

Mental illness and addictions with high prevalence were considered for our study. The main criterion for study inclusion was availability of data to calculate HALYs; only conditions that could be readily identified using surveys (that is, the CCHS 1.2 or the CAMH Monitor7) or health care use data were included (Table 1). Therefore, some high prevalence conditions (for example, generalized www.LaRCP.ca

The Burden of Mental Illness and Addiction in Ontario

anxiety disorder) were not included in our study. As only 9 mental illnesses and addictions were included in our study, our results are not a comprehensive measure of the total burden of mental illness and addictions in Ontario. In addition, the inability to include the impact of comorbidity using these methods likely contributed to a conservative estimate of the total burden of mental illness and addictions in Ontario. As the primary prevalence data source, the CCHS 1.2, only included people aged 15 years and older, and the sample size of the survey data in people aged 15 to 18 years was quite small, we excluded people aged younger than 18 years in our study. In addition, incident cases in people aged 65 years or older were excluded because most of the mental illnesses and addictions examined in our study develop in young adulthood and rarely manifest in the elderly.9,10 The following age groups were used for the YLL, YERF, and HALY calculations: 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 years or older. Although attempts were made to obtain data stratified by these age groups, this was not always possible owing to limitations in data availability. In such cases, age groups were aggregated. 8

Outcome Measures

The HALY is the unit of measure used in our study. HALYs are a measure of future YLL and YERF owing to incident cases of disease in an average year; it is thus an incidencerather than a prevalence-based measure. It is a composite health gap measure that allows for the simultaneous description of mortality and morbidity by incorporating deaths occurring before a pre-specified life expectancy (YLL) and time spent in a suboptimal state of health associated with conditions or diseases (YERF). HALYs are conceptually similar to DALYs used in the GBD studies, but have some distinct characteristics. The GBD studies used a standard life expectancy (based on the highest life expectancy attainable globally), nonuniform age weighting (that is, giving higher weights to time lost in young adulthood), discounted for future years, and expert medical opinion when determining disability weights. In our study, we used Ontario life expectancies,11 did not age weight or employ discounting, and used the CLAMES to calculate utility weights (which were then converted to severity weights).12 The CLAMES used a combination of medical and lay panels in Canada when scoring and used the standard gamble approach to ascertain a scoring function; thus making the scores representative of the preferences of Canadians. This tool also has a focus on functional limitations and includes attributes that are more relevant to mental illness and addictions (for example, social relationships, emotional state, memory, and thinking) that were not found in the tools used in the GBD study.1,2 The methods used in our study were also used in the Ontario Burden of Infectious Diseases Study5 and the Population Health Impact of Disease Study,6 thus allowing a direct comparison of our results. www.TheCJP.ca

Table 1 Mental illnesses and addictions and associated health states included in this study Mental illness or addiction

Health state

Agoraphobia

Mild, moderate, severe

BAD

Mild, moderate, severe

Major depression

Mild, moderate, severe

Panic disorder

Overall

Schizophrenia

Overall

Social phobia

Mild, moderate, severe

AUDs

Overall

Cocaine use disorder

Overall

Prescription opioid misuse

Overall

Years of Life Lost Due to Premature Mortality

YLL is the measure of years of life lost due to premature mortality YLLc,a,s = Nc,a,s × La,s It is calculated for each condition by age group and sex. To calculate the YLL for each condition (c), age group (a), and sex (s), the number of deaths (N) in an age group from a particular cause is multiplied by the standard loss function, L, the life expectancy at which the death occurs. The YLL for each age group and sex is added to yield the total YLL for each condition. For all conditions, YLL mortality data was obtained from Ontario Vital Statistics data, which are generated when the Ontario Office of the Registrar General collects mortality data from death certificates completed by physicians.

Year-Equivalents of Reduced Functioning

YERF is the measure of equivalent YLL owing to reduced functioning as a result of the condition YERFc,h,a,s = Ic,h,a,s × Dc,h,a,s × SWc,h To calculate YERFs, each condition was broken down into health states, where possible. The YERF for each health state (h) and condition (c) was calculated by multiplying the incidence (I) by duration (D) and severity weight (SW) for each age group (a) and sex(s). The total YERF for each condition (c) was determined by adding the YERFs for its respective health states (h).

Data Sources

The calculation of HALYs required data on cause-specific mortality, disease incidence, health state distribution, and health state duration all disaggregated by age group and sex. In addition, the analysis required severity weights associated with each health state. These estimates were collected from the data sources outlined in Table 2.

Health Care Use Data

The prevalence of BAD was measured using health care use data. Ontarians were considered to have BAD if they had a single health care interaction in the hospitalization The Canadian Journal of Psychiatry, Vol 58, No 9, September 2013 W 531

Original Research

Table 2 Data sources used to calculate HALYs YLL

YERF

Mortality

Incidence

Health state distribution

Duration

Severity weights

Agoraphobia

Vital statistics

CCHS, NESARC using DisModII

Not broken down into health states

NESARC

CLAMES

BAD

Vital statistics

Health care use data, NESARC using DisModII

Scientific literature

NESARC

CLAMES

Major depression

Vital statistics

CCHS, NESARC using DisModII

Scientific literature

NESARC

CLAMES

Panic disorder

Vital statistics

CCHS, NESARC using DisModII

Not broken down into health states

NESARC

CLAMES

Schizophrenia

Vital statistics

Health care use data

Not broken down into health states

Lifelong, with a 20% reduced life expectancy12

CLAMES

Social phobia

Vital statistics

CCHS, NESARC using DisMod II

Not broken down into health states

NESARC

CLAMES

AUDs

Vital statistics

CCHS, NESARC using DisModII

Scientific literature

NESARC

CLAMES

Cocaine use disorders

Vital statistics

CCHS, NSDUH using DisModII

Not broken down into health states

NSDUH

CLAMES

Prescription opioid misuse

Vital statistics

CAMH Monitor, NSDUH using DisModII

Not broken down into health states

NSDUH

CLAMES

Mental illness or addiction

data (Canadian Institute for Health Information Discharge Abstract Data and Ontario Mental Health Reporting System), or ambulatory visit data (National Ambulatory Care Reporting System), or at least 2 physician billings in the Ontario Health Insurance Plan within 2 years. Prevalence estimates were converted to incidence estimates using DisMod II13 and survey data. A similar algorithm was used to identify prevalent cases of schizophrenia. Cases were identified to be incident based on their first appearance of the case in the prevalent cohort using a washout period of 2 years in each data source. This method helps ensure the included cases are incident cases and not just prevalent cases. Canadian Community Health Survey: Mental Health and Well-Being The CCHS 1.2 is a cross-sectional survey that collects information related to health status, health care use, and health determinants for the Canadian population.8 The content for CCHS 1.2 is partly based on a selection of mental disorders from the World Mental Health—Composite International Diagnostic Interview Instrument.14 We used the CCHS 1.2 to measure the prevalence of agoraphobia, major depression, panic disorder, AUDs, and cocaine use disorders. Prevalence estimates were converted to incidence estimates using DisMod II and survey data. CAMH Monitor The CAMH Monitor7 is the longest ongoing representative survey of adult substance use in Canada. The 2009 cycle of 532 W La Revue canadienne de psychiatrie, vol 58, no 9, septembre 2013

the CAMH Monitor was based on telephone interviews with Ontario adults aged 18 and older. In our study, the 2009 CAMH Monitor was used to estimate the prevalence of prescription opioid misuse. Prevalence estimates were converted to incidence estimates using DisMod II13 and survey data. National Epidemiologic Survey of Alcohol and Related Conditions NESARC is a longitudinal survey, with its first wave of interviews fielded in 2001–2002, followed by a second wave in 2004–2005.15 NESARC is a representative sample of the noninstitutionalized US population aged 18 years and older. For our study, NESARC was used to obtain estimates of remission rates and case fatality rates for the following disorders: agoraphobia, BAD, major depression, panic disorder, social phobia, and AUDs. National Survey on Drug Use and Health The NSDUH provides national and state-level data on mental illnesses and the use of tobacco, alcohol, illicit drugs (including nonmedical use of prescription drugs) in the United States.16 NSDUH is an annual, nationwide survey involving interviews. We used the NSDUH to obtain data on cocaine use disorder and prescription opioid misuse. Evidence From Epidemiologic Studies To calculate HALYs, estimates of the incidence of each health state are needed. The list of health states can be found in Table 1. The distribution of cases by health states www.LaRCP.ca

The Burden of Mental Illness and Addiction in Ontario

Table 3a Number of deaths, YLL, YERF, and HALYs lost for selected mental illnesses and addictions among Ontario men Number of deaths

YLL

Incident cases

YERF

HALYs

Major depression

15

174

19 316

72 377

72 551

AUDs

573

13 263

138 368

53 854

67 117

0

0

9055

32 968

32 968

Mental illness or addiction

Social phobia BAD

1

19

16 834

49 484

49 503

Schizophrenia

17

400

3264

31 213

31 613

Panic disorder

0

0

21 276

10 443

10 443

Cocaine use disorder

6

244

10 071

7275

7519

Prescription opioid misuse

1

50

13 555

5953

6003

Agoraphobia

0

0

1465

5316

5316

613

14 150

233 204

268 883

283 033

Total

Table 3b Number of deaths, YLL, YERF, and HALYs lost for selected mental illnesses and addictions among Ontario women Number of deaths

YLL

Incident cases

YERF

HALYs

Major depression

31

319

36 271

131 593

131 912

BAD

5

88

25 397

67 330

67 417

Social phobia

0

0

11 036

42 400

42 400

Mental illness or addiction

Schizophrenia

25

387

2471

23 196

23 583

AUDs

190

5202

30 466

11 880

17 082

Panic disorder

0

0

41 521

14 908

14 908

Agoraphobia

0

0

3230

13 919

13 919

Cocaine use disorder

2

110

7211

4648

4759

Prescription opioid misuse Total

1

27

10 753

4731

4759

254

6133

165 126

314 605

320 739

was not available directly from the data, thus epidemiologic studies were used to derive this information. The disease modelling software, DisMod II, was developed by the World Health Organization for the consistent estimation of key population health-relevant epidemiologic parameters (that is, prevalence, incidence, remission rates, and duration) within GBD.13 Disease incidence, prevalence, remission, case fatality, and mortality are not independent variables; as a result, these interrelations can be used to estimate parameters that are missing, if the other parameters are known.17 In our study, we used DisMod II and prevalence, remission, and mortality parameters to generate incidence estimates for the following conditions without existing incidence estimates: agoraphobia, major depression, panic disorder, social phobia, AUDs, cocaine use disorder, BAD, and prescription opioid misuse. In addition to the prevalence data stemming from the health care use data or from CCHS 1.2, remission rates or average duration were obtained www.TheCJP.ca

from either NESARC or NSDUH, the mortality risks from Harris and Barraclough18 (Table 2).

Comparison With Other Conditions

One of the key advantages of using standardized burden of disease methods is the capacity to compare the burden of disease across multiple conditions. In our study, we used Ontario disease burden estimates for other types of conditions (that is, cancers and infectious diseases) that were generated from previous Ontario and Canadian burden of disease reports.5,6

Age-Standardized Mortality

Because the use of vital statistics and our inability to account for comorbidity and likely underestimates YLL for people with mental illness and addictions, we estimated age-standardized mortality rates for the 2 conditions, BAD and schizophrenia, for which we had comprehensive health administrative data. The Canadian Journal of Psychiatry, Vol 58, No 9, September 2013 W 533

Original Research

Figure 1 HALYs lost for selected mental illnesses and addictions in Ontario, by age group 200 000 Schizophrenia

180 000

BAD Agoraphobia

160 000

Panic disorder

HALYs

140 000

Social phobia Major depression

120 000

AUDs Prescription opioid misuse

100 000

Cocaine use disorders

80 000 60 000 40 000 20 000 0

Age groups

Results

The mental illness and addictions selected for this report contributed to more than 600 000 HALYs lost in Ontario. The number of deaths, YLL, incident cases, YERF, and HALYs for each condition by sex are listed in Tables 3a and 3b. The largest contributor to HALYs was major depression (more than 200 000), followed by BAD (more than 100 000) and AUDs (more than 80 000). The smallest contributor, among the conditions examined, was prescription opioid misuse; however, it is important to note that deaths owing to this condition were not well captured by the mortality data source. The vast majority of HALYs were owing to YERF; YLL contributed 20 283 (3%) and YERF contributed 583 489 (97%) to the overall HALYs. The only condition or addiction for which YLL had a significant contribution was AUDs, where it contributed 22% of the HALYs. AUDs contributed to the greatest number of deaths (88% of total deaths) and, in turn, the largest percentage of YLL (91%) of the conditions examined. Overall, YLL and number of deaths are directly correlated; however, there were some exceptions. Major depression had a slightly higher average number of deaths than schizophrenia, but schizophrenia had almost twice as many YLL. Compared with major depression, deaths in people with schizophrenia occurred at younger ages, resulting in a greater number of future YLL. Likewise, 87% of deaths owing to depression were among people aged 70 years and older, compared with 534 W La Revue canadienne de psychiatrie, vol 58, no 9, septembre 2013

62% of deaths due to schizophrenia in that age group. No deaths were attributed to panic disorder, social phobia, or agoraphobia. Only 2 deaths were attributed to prescription opioid misuse, but this outcome is thought to be an underreport of the true mortality burden. There were significant differences in the burden of illness estimates depending on sex. For men, AUDs had a higher incidence, contributed to a significant number of deaths and subsequent YLLs, and had a very large HALY estimate (67 117, second only to major depression) (Table 3a). By comparison, women had fewer incident cases and fewer deaths related to alcohol use and a much lower HALY attributable to AUDs (17 082). The HALYs attributable to the 2 mood disorders, BAD and major depression, were higher in women than in men (Table 3b), mostly owing to their greater incidence among women. The burden associated with mental illness and addictions is also highest in the younger age groups as most cases present early on (Figure 1). To put the burden estimates of mental illness and addictions in Ontario into context, we compared the HALY estimates for mental illness and addictions with nonpsychiatric conditions calculated using the same methods5,6 (Figure 2). The burden associated with mental illness and addictions in our study is nearly double the burden of illness associated with the most prevalent cancers, and is more than 1.5 times the burden of all cancers in Ontario. The burden of mental illness and addictions is also predominantly owing to YERF, www.LaRCP.ca

The Burden of Mental Illness and Addiction in Ontario

Figure 2 HALYs lost for selected mental illnesses, addictions, cancersa, and infectious diseasesa in Ontario, by YLL and YERF All mental illness and addiction examined Major depression BAD AUDs Social phobia Schizophrenia Panic disorder Agoraphobia Cocaine use disorders Prescription opioid misuse All cancers Lung cancer Colorectal cancer Breast cancer Prostate cancer All infectious diseases Hepatitis C S pneumoniae Human papillomavirus E coli Hepatitis B

YLL

100 000

200 000

YERF

300 000

400 000

500 000

600 000

700 000

HALYs

Only the conditions with the highest burden from these categories are displayed, but the All cancers and All infectious diseases categories include all cancers and all infectious diseases, respectively.

a

unlike cancers and infectious diseases, for which the burden is primarily due to YLL (Figure 2). Finally, we estimated relative age-standardized mortality rate ratios for people with BAD (2.00, 95% CI 1.95 to 2.05; and 1.89, 95% CI 1.85 to 1.94 for males and females, respectively) and with schizophrenia (2.51, 95% CI 2.43 to 2.60; and 2.34, 95% CI 2.26 to 2.42 for males and females, respectively), compared with those without each condition.

Discussion

Our study demonstrates that the population-level burden associated with a wide range of mental health and addiction disorders is very high, especially in comparison with medical conditions when evaluated using standardized measures. The burden of mental illness and addictions differs from that of medical conditions, such as cancer and infectious diseases, in that disability is the primary contributor to burden rather than YLL. The relatively young age of onset and chronicity explains the large contribution of disability by mental illness and addictions. That people rarely die from a mental illness or addiction explains the small contribution of YLL to mental illness and addictions burden. However, our definition of causespecific mortality is highly restrictive and potentially biased. In our analyses of age-standardized mortality rate ratios for people with schizophrenia and BAD indicate that while these people may not necessarily die from their psychiatric illness, they nonetheless have elevated risk of www.TheCJP.ca

mortality, compared with the rest of the population. Our findings are consistent with recent GBD results from North American high-income countries.19 To our knowledge, this is the first Canadian study evaluating the burden of mental illness and addictions and provides evidence that a substantial number of people are significantly burdened by these conditions. The high burden associated with mental illness and addictions is similar to findings from previous burden of illness studies that included mental illness and addictions.2–4 The persistent finding of high burden of illness among people with mental illness and addiction issues is troubling because effective treatments exist; however, only a minority of people get the treatment they need.20,21 Our study sought to highlight the need for an organized system of care that identifies people who would benefit from treatment and ensures that they receive it. Our study also provides condition-specific baseline measures of burden that can be used to assess the impact of any large-scale interventions. It is highly likely that the findings from our study are generalizable to the rest of the country as Ontario contains a large proportion of the Canadian population. Our study was initiated and funded by PHO. Local public health agencies perform various important functions, including population health assessment and surveillance, health protection and promotion, and disease prevention. Historically, public health agencies have contributed to mental health promotion through early years programming, The Canadian Journal of Psychiatry, Vol 58, No 9, September 2013 W 535

Original Research

the integration of mental health promotion into school health, and work with partners on the determinants of health. However, the intention to impact mental health has not always been explicit, and there are opportunities to enlist and engage the public health infrastructure to help decrease the burden of mental illness and addictions. The nature of the interaction between public and mental health systems would require collaborative engagement and discussion. Our study has numerous limitations. As mentioned above, we only had the main underlying cause of death as part of our YLL measurement. Our significantly higher agestandardized mortality rate estimates for people with BAD and schizophrenia suggest that this would result in an underestimate of the burden associated with premature mortality, particularly for more severe mental illnesses and addiction issues. Our diagnoses for BAD and schizophrenia relied on health care use codes that have not been validated. However, they are identical to algorithms for other diagnoses using Ontario health administrative data that have good sensitivities and specificities.22 Further, the schizophrenia algorithm has been used in a previous publication,23 and yields a prevalence of schizophrenia in Ontario that is less than 1%, similar to previous Canadian estimates.24,25 We were also unable to measure the impact of co-occurrence of mental illnesses or comorbid physical health conditions. Our study provides a Canadian benchmark for the burden of illness for numerous mental illness and addictions. The very high burden underscores the early onset and chronicity of these conditions. Importantly, effective treatments and management strategies exist to address this burden of illness, but there is consistent evidence that the current availability and use of services that provide treatment for these conditions are not adequately addressing the burden at a population level. Applying public health strategies to better align resources to address burden may be a way to enhance mental health and addiction system performance.

Acknowledgements

Our study was supported by a grant from PHO and by ICES, a nonprofit research institute sponsored by the Ontario Ministry of Health and Long-Term Care. These data sets were held securely in a linked, de-identified form and analyzed at the Institute for Clinical Evaluative Sciences. A longer version of the data and findings was published by the Institute for Clinical Evaluative Sciences and Public Health Ontario.26 Tables 1, 3a, and 3b, and Figures 1 and 2 were reproduced with permission from Public Health Ontario. The opinions, results, and conclusions reported in our paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. We thank the following people who assisted this study with project coordination, expert review of drafts, analysis, library searches, and other vital activities: Natasha Crowcroft, Vivek Goel, Phat Ha, Saba Khan, Jen Levi, Hong Lu, Chris 536 W La Revue canadienne de psychiatrie, vol 58, no 9, septembre 2013

Mackie, Jennifer Modica, Aline Nizigama, George Pasut, and the PHO Library Services.

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