• Total number of chronic disease-related hospitalizations attributable to alcohol consumption - the number of hospitalizations for selected chronic disease attributable to alcohol consumption during a given year (fiscal or calendar).
• Total number of injury-related hospitalizations attributable to alcohol consumption - the number of hospitalizations for selected causes of injury attributable to alcohol consumption during a given year (fiscal or calendar).
• Chronic disease-related hospitalization rate attributable to alcohol consumption - the number of hospitalizations for selected chronic disease attributable to alcohol consumption during a given year (fiscal or calendar) per 100,000 population.
• Injury-related hospitalization rate attributable to alcohol consumption - the number of hospitalizations for selected causes of injury attributable to alcohol consumption during a given year (fiscal or calendar) per 100,000 population.

• Total number of chronic disease-related hospitalizations attributable to alcohol consumption
• Total number of injury-related hospitalizations attributable to alcohol consumption
• Alcohol-attributable hospitalization rate for chronic disease
• Alcohol-attributable hospitalization rate for injury

The Ontario Public Health Standards (OPHS) establish requirements for the fundamental public health programs and services carried out by boards of health, which include assessment and surveillance, health promotion and policy development, disease and injury prevention, and health protection. The OPHS consist of one Foundational Standard and 13 Program Standards that articulate broad societal goals that result from the activities undertaken by boards of health and many others, including community partners, non-governmental organizations, and governmental bodies. These results have been expressed in terms of two levels of outcomes: societal outcomes and board of health outcomes. Societal outcomes entail changes in health status, organizations, systems, norms, policies, environments, and practices and result from the work of many sectors of society, including boards of health, for the improvement of the overall health of the population. Board of health outcomes are the results of endeavours by boards of health and often focus on changes in awareness, knowledge, attitudes, skills, practices, environments, and policies. Boards of health are accountable for these outcomes. The standards also outline the requirements that boards of health must implement to achieve the stated results.

http://www.ontario.ca/publichealthstandards

• Board of Health Outcome (Prevention of Injury and Substance Misuse): The board of health is aware of and uses epidemiology to influence the development of healthy public policy and its programs and services for the prevention of injury and substance misuse.
• Board of Health Outcome (Foundational Standard): The public, community partners and health care providers are aware of relevant and current population health information.

• The board of health shall conduct epidemiological analysis of surveillance data in the areas of injury and substance misuse outcomes.

• None

• None

Numerator (chronic diseases and neuro-psychiatric conditions): Hospitalization
Original source: Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD)
Distributed by: Ontario Ministry of Health and Long-Term Care: IntelliHEALTH ONTARIO
Suggested citation (see Data Citation Notes): Inpatient Discharges [years], Ontario Ministry of Health and Long-Term Care, IntelliHEALTH ONTARIO,  Date Extracted: [date].

Numerator (neuro-psychiatric conditions): Hospitalization
Original source: Ontario Mental Health Reporting System (OMHRS), Canadian Institute for Health Information (CIHI)
Distributed by: Ontario Ministry of Health and Long-Term Care: IntelliHEALTH ONTARIO
Suggested citation (see Data Citation Notes): Ontario Mental Health Reporting System [years], Ontario Ministry of Health and Long-Term Care, IntelliHEALTH ONTARIO, Date Extracted: [date].

Numerator: National Ambulatory Care Reporting System
Original source: National Ambulatory Care Reporting System (NACRS), Canadian Institute of Health Information (CIHI)
Distributed by: Ontario Ministry of Health and Long-Term Care (MOHLTC): IntelliHEALTH ONTARIO (IntelliHEALTH)
Suggested citation (see Data Citation Notes): Ambulatory Emergency External Cause [years], Ontario Ministry of Health and Long-Term Care, IntelliHEALTH ONTARIO, Date Extracted: [date].

Numerator and Denominator (alcohol consumption prevalence): Canadian Community Health Survey (CCHS)
Original Source: Statistics Canada
Distributed by:
1. Ontario Ministry of Health and Long-Term Care (MOHLTC)
2. Statistics Canada
Suggested citation (see Data Citation Notes):
1. Canadian Community Health Survey [year], Statistics Canada, Share File, Ontario MOHLTC
2. Canadian Community Health Survey [year], Statistics Canada, Public Use Microdata File, Statistics Canada

• The IntelliHEALTH licensing agreement does not require suppression of small cells, but caution should be used when reporting at a level that could identify individuals, (e.g. reporting at the postal code level by age and sex). Please note that privacy policies may vary by organization. Prior to releasing data, ensure adherence to the privacy policy of your organization.
• NOTE**IntelliHEALTH has made a change and will now expose both historical data and current fiscal year data in the NACRS maps - folder 04. Note that when creating reports using maps in folder 04,the most recent year may not contain a complete year of data.

Chronic Diseases and Neuro-psychiatric conditions- DAD - Inpatient Discharges

• For in-patient discharges in IntelliHEALTH:
• Use Inpatient Discharge Main Table data source from the ‘05 Inpatient Discharges' folder. Do not use the ‘Inpatient Mental Health' table available in folder 12, as this may not contain all psychiatric hospitalizations due to alcohol consumption.
• Use # Dschg as measure.
• Filter for Hospital Type = AT (Acute Treatment) or AP (Acute Psychiatric) - to include only acute care hospitals.
• Filter for ICD-10 Codes.  Use ‘ICD10-CA Code ONLY MRDx' for 4 digit ICD-10 codes and ‘ICD10-CA Code ONLY MRDx' for 3 digit ICD-10 codes. (Please note: ICD-10 codes must be entered into IntelliHEALTH without decimal points)
• Filter for years of interest (Fyear or CYear)
• Use the pre-defined filter to select the appropriate geography (e.g. public health unit, LHIN) before running the report. Hospital information (hospital name, PHU or LHIN) can also be selected in the report.

Neuro-psychiatric conditions - OMHRS

• Note: Hospitals started reporting data to OMHRS in April 1, 2006
• Note: OMHRS database uses the Diagnostic and Statistical Manual of Mental Disorders, version 4 (DSM-IV), and not ICD-10 for diagnosis codes.
• For in-patient mental health admissions in IntelliHEALTH:
• o Use ‘IP Adult MH Diagnosis, Treatment & Assessment' data source from the '12 Inpatient Mental Health' folder
• o Use # Admits (D) as measure.
• o Select for specific DSM-IV codes (listed in Tables 1 and 2 above) located in "MH Assessment", then "Section Q - Psychiatric Diagnostic Information" , then "Psychiatric Diagnosis", then "DSM-IV Axis I Primary Dx". (Please note: DSM-IV codes must be entered into IntelliHEALTH without decimal points)
• o Filter for years of interest (Fyear or CYear)

• Use the pre-defined filter to select the appropriate geography (e.g. public health unit, LHIN) before running the report. Hospital information (hospital name, PHU or LHIN) can also be selected in the report.

For more information on DAD and OMHRS, please see the following presentation:

http://www.apheo.ca/resources/events/2011/conf11/Mental%20Disorders%20-JHeale%20-%20APHEO%202011-May-16.pdf

Injuries

• The Report Inventory & Webinar Materials tab in IntelliHEALTH contains information on pre-defined reports, and webinar materials, including information on external causes of injury from a webinar on the pre-defined report titled 'Hospitalizations - External Causes of Injury - PHU'. The webinar material provides guidance on how to extract external cause hospitalization and mortality data.
• It is important to note that an individual can have more than one external cause diagnosis for each hospitalization. Unlike with other ICD-10 diagnostic codes, no ‘most responsible diagnosis' exists for external cause diagnosis.
• For in-patient hospitalization in IntelliHEALTH: Use Ambulatory Emergency External Cause (Chapter 20) source from the '04 Ambulatory visits' folder. Please note that this source differs from the Ambulatory All Visits Main Table in that it only includes unscheduled ED visits. If using the Ambulatory All Visits Main Table, the filter "AM Case Type = EMG" must be used to extract only unscheduled ED visits and disposition status = 6, 7 or 8.
• Unintentional Injuries:
• For all unintentional injury hospitalizations, filter for ‘ICD10 Block All Dx' and select blocks V01 through X59, and Y85-Y89.
• Add in a filter on ‘ICD10-CA Problem (3 char) All Dx' to filter not equal to for 'Y87', 'Y88' and 'Y89', as these codes are not part of unintentional injuries.
• The ‘ICD10 Block All Dx' and ‘ICD10-CA Problem (3 char) All Dx' MUST be hidden under the Assign Data option, in order to avoid double counting. For example, a person who has both a V03 code and W10, will only be counted once.
• To select clients admitted as inpatients, use the 'Disposition Status' variables = '6' - (i.e.'Client admitted as inpatient to critical care unit/operating room in reporting facility difect from amb care visit functional center') OR = '7' ('Client admitted as inpatient to other units in reporting facility direct from amb. care visit functional') OR = '8' (Transferred to another acute care facility directly from an ambulatory care visit functional centre).
• Select # Visits (D) measure.
• Filter for years of interest (Fyear or CYear)
• Use the pre-defined filter to select the appropriate geography (e.g. public health unit, LHIN) before running the report. Hospital information (hospital name, PHU or LHIN) can also be selected in the report.
• For intentional injuries:
• For all intentional hospitalizations, filter for ‘ICD10 Block All Dx' and select blocks X60-X84 and X85-Y09 (Filter A).
• Add in a filter on ‘ICD10-CA Problem (4 char) All Dx' to filter equal to for 'Y870', and 'Y871' (Filter B). (Please note: decimal points normally found in these ICD10-CA codes (e.g. ‘Y87.1') have been removed as four character ICD10-CA codes must be entered into IntelliHEALTH without decimal points)
• In the ‘Combine filter', cases must consist of Filter A OR Filter B. If using other custom filters (eg. Filter C), brackets must be used around this request. Eg. (Filter A or Filter B) AND Filter C.
• The ‘ICD10 Block All Dx' and ‘ICD10-CA Problem (4 char) All Dx' MUST be hidden under the Assign Data option, in order to avoid double counting.
• To select clients admitted as inpatients, use the 'Disposition Status' variables = '6' - (i.e.'Client admitted as inpatient to critical care unit/operating room in reporting facility direct from amb care visit functional center') OR = '7' ('Client admitted as inpatient to other units in reporting facility direct from amb. care visit functional') OR = '8' (Transferred to another acute care facility directly from an ambulatory care visit functional centre).
• Select # Visits (D) measure.
• Filter for years of interest (Fyear or CYear)
• Use the pre-defined filter to select the appropriate geography (e.g. public health unit, LHIN) before running the report. Hospital information (hospital name, PHU or LHIN) can also be selected in the report.
• For all unintentional injury hospitalizations by ICD10-CA block, include the ‘ICD10 Block All Dx‘ variable (not hidden) in the report. For example, a person who has both a V03 code and W10, will be counted twice, once in the V01-V09 block and once in the W00-W19 block.
• Distinct counts - a patient can have more than one diagnosis code for one visit (eg. cardiovascular disease). In such cases, one code is deemed ‘the most responsible diagnosis code' or main problem Dx (or MRDx is some data sources). Diagnoses that are identified as due to external causes (eg. fractured elbow) also have a companion "external cause" ICD-10-CA code. A person can have more than one external cause diagnosis for one visit. However, unlike the diagnoses codes mentioned above, external cause diagnoses do not have a main problem diagnosis in emergency (or other hospital) data. Thus, the external cause diagnoses are only included in the multi-record per visit data sources such as the Ambulatory Emergency External Cause (Chapter 20) source. Because the source has multiple records per visit, only distinct counts (# Visits (D)) can be used in order tally number of visits. IntelliHEALTH can now create crosstab tables and sum across distinct counts for external causes (Note: because the column total is a distinct count, it may be smaller than the sum of the cells within the column). For more information, please see the Report Inventory and Webinar Materials and Training sections within IntelliHEALTH.
• Note that ambulatory care data (and in-patient data) are reported by fiscal year (April 1 - March 31). Any changes in the source data occur on a fiscal year basis (e.g., ICD10 reporting began on April 1, 2002) and will affect reporting by calendar year.

Method of Calculation - alcohol attributable fractions (AAFs)

Please use this Excel SpreadSheet to help keep track of data and calculate numbers of hospitalizations attributed to alcohol consonsumption.

Please use this Stata Syntax file to help create disease categories from downloaded  IntelliHEALTH data.

Please use this Stata Syntax file to help create the four alcohol consumption categories used in this indicator and Excel spreadsheet.

This appendix describes the method used to calculate or estimate the number of hospitalizations that are alcohol-related.  Three pieces of information are needed to calculate the numbers of hospitalizations caused or prevented by alcohol:  an estimate of exposure to alcohol (prevalence of alcohol consumption), the sex-specific relative risks of certain diseases and the total number of disease-specific hospitalizations. 

1. The prevalence of alcohol consumption is determined using the Canadian Community Health Survey. The various alcohol consumption categories used are shown in Table 5. The categories were used based on average volume of alcohol consumed per day, with 10g as the amount of alcohol in a standard drink.¹ Most meta-analyses provide relative risks for disease and injuries based on these four drinking categories.²

Table 5.                     

2. Next, the relative risks were taken from Rehm et al.² The relative risk is the ratio of the probability of developing a disease among those exposed to alcohol compared with the probability of those not exposed, or abstainers.

3. Lastly, numbers of chronic diseases, psychiatric conditions, and injuries were obtained from IntelliHEALTH.

These three pieces of information can be used to calculate the AAF. For chronic conditions, AAFs are calculating by combining alcohol consumption and relative risk estimates from meta-analyses Knowing the  AAF (i.e., the percentage of deaths for a certain disease (i.e., the AAF) caused (or prevented) by alcohol ), it is multiplied by the number of disease-specific hospitalizations to obtain the estimated number of hospitalizations caused by alcohol. 

The basic calculation³ for AAF is:

(Prevalence)(Relative Risk - 1)
1+(Prevalence)(Relative Risk - 1)

Where prevalence is the percentage of the population consuming alcohol at a specified level of average daily consumption within a given timeframe, and relative risk is the likelihood of death from a particular cause at a specified level of average daily alcohol consumption.⁴

The detailed formula for an AAF calculation² is:

AAF = [∑^k_i=1  P_i(RR - 1)] / ∑ ^K_i= 0 P_i (RR_i - 1) +1]

Where is the category with usage (i=1) or no alcohol (i=0), RR(i) is the relative risk at exposure level i compared with no alcohol consumption.  P(i) is the prevalence of the i^th category of alcohol consumption and k is the highest drinking category (that is, category III). 

Sample AFF Calculation:

AFF for liver cancer in males, ages 15-69, (i.e., the number of liver disease deaths that can be attributed to alcohol consumption), for the Kingston, Frontenac, Lennox & Addington (KFL&A) Public Health:  for males 15-69).    For simplicity, the calculations for the numerator and denominator are shown separately. The four drinking categories used, as well as the symbol definitions for use in the AAF calculation, are outlined in Table 6.

Table 6.

*The superscripts on ‘RR' and ‘P' represent the four drinking categories, starting with subscript '0' for the reference category or abstainers.

Calculation part 1. Numerator from AAF formula shown above.

=  P₁(RR₁ - 1) + P₂(RR₂ - 1) + P₃(RR₃ - 1)

                                                     = 0.34(1.8-1) + 0.08(2.38-1) + 0.02(4.36-1)

                                                     = 0.272 + 0.1104 + 0.0672

                                                    = 0.4496

Calculation part 2. Denominator from AAF formula shown above.

             =  [P₀(RR₀ - 1) + P₁(RR₁ - 1) + P₂(RR₂ - 1) + P₃(RR₃ - 1)]  +1

                                                     = [0(1-1) + 0.34(1.8-1) + 0.08(2.38-1) + 0.02(4.36-1)] +1

                                                     = [0 + 0.272 + 0.1104 + 0.0672] + 1

                                                    = 1.4496

 AAF= numerator/denominator

= 0.4496/1.4496 = 0.310 or 31%

This means that 31% of liver cancer hospitalizations in males ages 15-69 in the KFL&A area can be attributable to alcohol.  Or, in other words, if males 15-69 did not consume alcohol, there would be 31% fewer liver cancer hospitalizations.  If there are 200 hundred hospitalizations due to alcohol every year, multiply 200 by the AAF of 31%:  62 of the 200 hospitalizations would be attributed to alcohol consumption.

Method of Calculation for Specific Indicator Rates

Alcohol and Chronic Diseases

• The relative risks from chronic disease were taken from pooled risk estimates obtained from meta-analyses.²
• Chronic disease conditions, either wholly or partially attributable to alcohol consumption, were identified through literature review.²  These disease must have: 1) an established biological pathway 2) a temporal order (cause before effect), 3) consistent effects and 4) a dose-response relationships.  The nature of the effect can be detrimental or beneficial.
• Although most meta-analyses² use the four drinking categories developed by English et al., 1995¹, note that these categories were based on a standard drink containing 10 grams of alcohol.  In Canada, a standard drink contains 17.05 mL or 13.45g of alcohol.^5,6  There are no published Canadian relative risks for selected chronic disease and injuries based on a standard drink of 13.45g.

Health Benefits from Alcohol?

• When consumed in moderation, alcohol has also been shown to be protective against a number of conditions including ischaemic heart disease (IHD), diabetes and cholethiasis.^7,8 Alcohol consumption and risk of mortality (from certain diseases) follows a J- or U-shaped pattern. Those who drink lightly to moderately experience a protective effect, with lower mortality risks than abstainers. Heavy alcohol consumption is associated with an increased risk of mortality, higher than both abstainers and light to moderate drinkers.^7,9 Any health benefits can be achieved at one drink per day, or less⁸, and only applies only to adults aged 45 or older.¹⁰
• However, the strength of the evidence on the health benefits of alcohol has been questioned. Newer studies which better distinguish lifetime abstainers from those who used to drink are now suggesting that the protective effect from alcohol has likely been overstated.¹¹ Stockwell suggests that the apparent health benefit of low to moderate alcohol use could in part be due to biases (including many types of selection bias)^12,13 and competing risks in large observational studies.¹⁴ There is also the ‘sick quitter' hypothesis, where former drinkers have quit drinking due to their health and have a higher risk for health problems than abstainers. This likely leads to inflated estimates of health benefits among current lower level consumers.^8,10,13

Alcohol and Perinatal Outcomes

• Adverse birth outcomes due to maternal alcohol consumption during pregnancy such as low birth weight, preterm birth, spontaneous abortion, intra-uterine growth retardation and fetal alcohol syndrome spectrum¹⁴ are not covered in this indicator.  Until recently, there has been no consistent source of data on maternal alcohol consumption during pregnancy. Better Outcomes and Registry Network (BORN) now collects this data. Information about the completeness and quality of the BORN data will become available after a suitable period of data collection has occurred.  .

Alcohol and Injury          

• Injury codes were restricted to what is considered standard unintentional and intentional injuries, as defined by other APHEO injury Core Indicators and the Recommended ICD10-CA codes for Injury Core Indicators Core Indicator resource, as well as any additional injuries 100% attributable to alcohol (eg. Y15) and Y35 - legal intervention. The grouping of ‘other' unintentional injury codes excludes codes for "misadventures to patients during surgical and medical care" (Y40-Y84) as well as Y88 and Y99, which is included in some AAF calculations for ‘other' unintentional injuries.¹⁵
• This indicator does not account for injuries caused by other people's drinking.

General Indicator Comments

• The AAF calculation is restricted to ages 15 to 69 as death certificates for the elderly tend to be less valid than for those who are young due to the multiple cause of death that are often involved.²
• If the coefficient of variation for prevalence of alcohol consumption calculated from CCHS is >33.3, several years of CCHS data can be combined to produce a more stable estimate, or the estimate for Ontario can be used.
• A person may be hospitalized for more than one occurrence of the same injury classification or discharged from more than one hospital for the same injury event in a given time period. Therefore, hospitalization data cannot be used to measure the incidence of a specific chronic disease or injury.
• ICD-10-CA has a greater level of specificity and different code titles than ICD-9. CIHI does not endorse forward conversions because of differences in the classification systems. Refer to Core Indicator resource: ICD-10-CA for more information.
• Hospitalization data will not capture those treated and released from emergency departments, those treated in doctors' offices or clinics, or those who did not seek treatment in hospital for a alcohol-attributable injury and therefore will underestimate burden of alcohol-attributable  injury.
• Since many age-specific rates are cumbersome to present, age standardized rates have the advantage of providing a single summary number that allows different populations to be compared; however, they present an "artificial" picture of the death /disease pattern in a community. For more information about standardization, refer to the Resources section: Standardization of Rates.

• Alcohol-attributable Fraction (AAF): the proportion of a disease or outcome that is due to alcohol consumption.¹⁵ Said another way, alcohol-attributable fractions are usually defined as the proportion of a disease in a population that will disappear if alcohol is removed.²
• Canadian Standard Drink:  Currently, the standard drink in Canada contains 17.05 mL or 13.45 grams of alcohol.^5,6

Cross References to Other Sections

• Drinking in Excess of the Low-Risk Alcohol Drinking Guidelines (Section 5B. Alcohol)
• Injury-related hospitalizations  (Section 4C. Injury Prevention and Substance Abuse Prevention)    
• Chronic Disease hospitalizations (Section 4A. Chronic Diseases)

Cited References

1. English DR, Holman CDJ, Milne E, et al. The quantification of drug caused morbidity and mortality in Australia. 1995 edition. Canberra: Commonwealth Department of Human Services and Health.
2. Rehm J, Giesbrecht N, Patra J, Roerecke M. Estimating Chronic Disease Deaths and Hospitalizations Due to Alcohol Use in Canada in 2002: Implications for Policy and Prevention Strategies. Preventing Chronic Disease: Public Health Research, Practice, and Policy. 2006 October; 3(4): 1-19
3. Centers for Disease Control and Prevention.  Alcohol and Public Health: Alcohol-Related Disease Impact (ARDI).  Frequently Asked Questions. 2011 [cited 2014 Aug 26]. Available from: http://apps.nccd.cdc.gov/DACH_ARDI/Info/FAQ.aspx#Q2.1
4. Centers for Disease Control and Prevention.  Alcohol and Public Health: Alcohol-Related Disease Impact (ARDI).  Methods.  2011 [cited 2014 Aug 26]. Available from: http://apps.nccd.cdc.gov/DACH_ARDI/Default/Default.aspx
5. Kerr, WC, Stockwell, T.  Understanding standard drinks and drinking guidelines.  Drug Alcohol Rev. 2012 March; 31 (2) 200-205
6. Butt, P., Beirness, D., Gliksman, L., Paradis, C., & Stockwell, T. Alcohol and health in Canada: A summary of evidence and guidelines for low risk drinking. 2011. Ottawa, ON: Canadian Centre on Substance Abuse.
7. Rehm J, Patr J, Popova S. Alcohol-attributable mortality and potential years of life lost in Canada 2001: implications for prevention and policy. Addiction.  2006; 101:373-384
8. Manafo E, Giesbrecht N.  Alcohol, cancer and other health issues: An action plan for prevention.  A report to the Toronto Cancer Prevention Coalition; 2011.  [cited 2014 Aug 28]. Available from : http://www.toronto.ca/health/resources/tcpc/
9. Goldstein LB. Is There a Causal Relationship Between the Amount of Alcohol Consumption and Stroke Risk? Stroke 2006; 37:1-2 
10. Rehm, J.T., Gutjahr, E., & Gmel, G. Alcohol and all-cause mortality: A pooled analysis. Comtemporary Drug Problems. 2001; 28:337-61.
11. Stockwell, Tim; Chikritzhs, Tanya; Bostrom, Alan; Fillmore, Kaye; Kerr, William; Rehm, Jürgen; Taylor, Ben.  Alcohol-caused mortality in Australia and Canada: Scenario Analyses using different assumptions about cardiac benefit.  Journal of Studies on Alcohol & Drugs. 2007 May; 68(3): 345-352.
12. Fillmore KM, Kerr WC, Stockwell T, Chikritzha T, Bostrom A. Moderate alcohol use and reduced mortality risk: Systematic error in prospective studies.  Addiction research and Theory.  2006; 14 (2): 101-132
13. Stockwell T, Chikritzhs T. Commentary: Another serious challenge to the hypothesis that moderate drinking is good for health? International Journal of Epidemiology. 2013 Dec; 42(6):1792-4
14. Patra J, Bakker R, Irving H, Jaddoe VWV, Shobha M, Rehm J.  Dose-response relationshop between alcohol consumption before and during pregnancy and the risks of low birth weight, preterm birth and small-size-for-gestational age (SGA) - A systemic review and meta-analyses.  BJOG. 2011; 118(2): 1411-1421
15. Taylor BJ, Shield D, Rehm J.  Combining best evidence: A novel method to calculate the alcohol-attributable fraction and its variance for injury mortality.  BMC Public Health. 2011; 11: 265-274

1. Rehm J, Room R, et al. Comparative Quantification of Health Risks Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, Volume 1, Chapter 12: Alcohol Use. World Health Organization; 2004
2. Rehm J; Mathers C; Popova S; Thavorncharoensap M; Teerawattananon Y; Patra J; Global burden on disease and injury and economic cost attributable to alcohol use and alcohol-use disoreders.  Lancet. 2009 June 17; 373 (9682): 2223-33
3. Gutjahr, E. Gmel, G. Rehm, J. Relation between Average Alcohol Consumption and Disease: An Overview. European Addiction Research.  2001; 7:117-127
4. Rehm J, Baliunas D, Brochu S, Fischer B, Gnam W, Patra J et al. The costs of substance abuse in Canada 2002. Ottawa: 2006. Available from:
5. http://www.ccsa.ca/2006%20CCSA%20Documents/ccsa-011332-2006.pdf
6. Rehm J, Gmel G, Sempos CT, Trevisan, M.  Alcohol-related morbidity and mortality. 2002 Alcohol Research and Health. 2003; 27 (1): 39-51
7. Reynolds K, Lewis LB, Nolen JDL, Kinney GL, Sathya B, He J.  Alcohol consumption and risk of stroke.  JAMA 2003 Feb 5; 289(5): 579-588
8. Bergman MM, Rehm J, Klipstein-Grobusch K, Boeing H et al. The association of pattern of lifetime alcohol use and cause of death in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.  International Journal of Epidemiology. 2013; 42: 1772-1790.
9. Babor T, Caetano R, Casswell S, Edwards G, et al.  Alcohol: No ordinary commodity - a summary of the second edition.  Addiction. 2010. 105:769-779