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Global burden of injuries attributable to alcohol consumption in 2004: a novel way of calculating the burden of injuries attributable to alcohol consumption

Population Health Metrics201210:9

https://doi.org/10.1186/1478-7954-10-9

Received: 23 June 2011

Accepted: 3 May 2012

Published: 18 May 2012

Abstract

Background

Alcohol consumption is a major risk factor for injuries; however, international data on this burden are limited. This article presents new methods to quantify the burden of injuries attributable to alcohol consumption and quantifies the number of deaths, potential years of life lost (PYLL), and disability-adjusted life years (DALYs) lost from injuries attributable to alcohol consumption for 2004.

Methods

Data on drinking indicators were obtained from the Comparative Risk Assessment study. Data on mortality, PYLL, and DALYs for injuries were obtained from the World Health Organization. Alcohol-attributable fractions were calculated based on a new risk modeling methodology, which accounts for average and heavy drinking occasions. 95% confidence intervals (CIs) were calculated using a Monte Carlo simulation method.

Results

In 2004, 851,900 (95% CI: 419,400 to 1,282,500) deaths, 19,051,000 (95% CI: 9,767,000 to 28,243,000) PYLL, and 21,688,000 (95% CI: 11,097,000 to 32,385,000) DALYs for people 15 years and older were due to injuries attributable to alcohol consumption. With respect to the total number of deaths, harms to others were responsible for 15.1% of alcohol-attributable injury deaths, 14.5% of alcohol-attributable injury PYLL, and 11.35% of alcohol-attributable injury DALYs. The overall burden of injuries attributable to alcohol consumption corresponds to 17.3% of all injury deaths, 16.7% of all PYLL, and 13.6% of all DALYs caused by injuries, or 1.4% of all deaths, 2.0% of all PYLL, and 1.4% of all DALYs in 2004.

Conclusions

The novel methodology described in this article to calculate the burden of injuries attributable to alcohol consumption improves on previous methodology by more accurately calculating the burden of injuries attributable to one’s own drinking, and for the first time, calculates the burden of injuries attributable to the alcohol consumption of others. The burden of injuries attributable to alcohol consumption is large and is entirely avoidable, and policies and strategies to reduce it are recommended.

Keywords

Alcohol Injury Attributable fraction Burden of disease Mortality Years of potential life lost

Introduction

Alcohol consumption is the sixth leading cause of death and the third leading cause of disability-adjusted life years (DALYs) lost globally, with injuries accounting for a large part of the alcohol-attributable burden of disease [1, 2]. Previous research, with varying study designs such as cross-sectional studies [3], case-crossover studies [4], case–control studies [5], systematic reviews, and meta-analyses [6, 7] has shown a strong association between alcohol consumption and many types of intentional and unintentional injuries. Moreover, it has been shown that alcohol consumption fulfills the standard epidemiological criteria of causality for many injury outcomes [8]. However, reporting of the burden of injuries has not kept pace with new methodology to calculate alcohol-attributable risk. Previous global estimates of the alcohol-attributable burden of injuries were calculated using simplistic methods based on one country and then scaling these estimates according to average volume of consumption and patterns of drinking [9]. The most recently proposed method of calculating the alcohol-attributable burden of disease did not account for injuries caused by other people’s drinking (the burden of which has been estimated to be substantial [10, 11]) or account for the overlap in binge consumption and average consumption, which leads to an overestimation of the alcohol-attributable injuries caused to the drinker [12].

In this article we present new methodology to calculate the alcohol-attributable burden of disease for injuries using formulas that take into consideration the main drivers of alcohol-attributable risk – namely average daily consumption and binge drinking. This calculation of the global burden of injuries attributable to alcohol is possibly due, in part, to the persistent relationship between alcohol consumption and injury risk which has remained strikingly similar throughout the last 50 years of observational research [13, 14] and across cultural and geographical boundaries [1518]. This means that, although drinking patterns may vary across countries, cultures, and age and sex groups, the same risk function can be used for all countries to determine injury burden estimates based on country-specific drinking behavior. This information can be aggregated to regional or global levels to gain a more accurate comparative picture.

It is the aim of this article to utilize the new methodology outlined herein to estimate the burden of alcohol-attributable injuries caused to the drinker and to others, and to estimate for 2004 the global burden of injuries attributable to alcohol consumption in terms of mortality, potential years of life lost (PYLL), and DALYs for each Global Burden of Disease (GBD) region.

Methods

Our methodology has two steps: [1] calculation of the injury-, sex-, age-, consumption-, and region-specific alcohol-attributable fractions (AAFs) and [2] application of these AAFs to mortality, PYLL, and DALY data.

Definition of regions and population data

The GBD regions (2005) are based on geography, child and adult mortality, and major causes of death [19]. Population estimates by country in 2004 were based on data obtained from the 2008 revisions of the United Nations Population Division [20].

Step 1: Calculation of the AAFs by sex and alcohol consumption

Alcohol consumption measures

Two dimensions of alcohol consumption play a role in affecting the probability of injury: binge drinking and average daily alcohol consumption.

A binge drinker was defined as a person who consumed at least five drinks (for men) or four drinks (for women) of alcohol on at least one occasion in the past month, assuming that the average drink size is 12 g of pure ethanol. Estimates for the prevalence of binge drinkers, current drinkers, and past year abstainers were obtained from the 2005 Comparative Risk Assessment (CRA) study [19].

Average daily alcohol consumption was calculated based on 80% of per capita consumption of alcohol (to account for alcohol not consumed) and the prevalence of current drinkers. Total adult (age 15 years and over) per capita alcohol consumption for 2004 for each region was calculated by adding the estimated recorded and unrecorded adult per capita consumption and then subtracting tourist (the amount of alcohol consumed by citizens of other countries) adult per capita consumption [21].

Estimates of recorded adult per capita alcohol consumption were obtained from the Global Information System on Alcohol and Health database [22]. These estimates were based on government records (taxation), industry publications for the production and sales of alcohol, and data from the Food and Agriculture Organization [22]. Unrecorded and tourist adult per capita consumption estimates were taken from the ongoing CRA study [22]. The main sources for unrecorded consumption were home production, alcohol intended for industrial, technical, and medical uses, and illegal production or importation of alcohol [22]. As no variance estimates for unrecorded and tourist alcohol consumption existed, we estimated the variance to be five times that of the variance of recorded alcohol consumption proportionate to the mean [23].

For this study we used two different types of drinking days: binge drinking days and average drinking days. Prevalence of binge drinkers and the frequency of binge drinking days and alcohol consumption on binge drinking days (for binge drinkers) were obtained from the 2005 CRA study. Average alcohol consumption on nonbinge drinking days (defined as a drinking day that was not a binge drinking day) was assumed to be the same for binge drinkers and nonbinge drinkers. Average alcohol consumption on nonbinge drinking days was calculated such that every day that a person was not binge drinking was considered to be an average drinking day. The volume of alcohol consumed on an average drinking day was then calculated using per capita consumption and binge drinking consumption, such that the amount of alcohol consumed on the nonbinge drinking days plus the amount of alcohol consumed on binge drinking days was equal to 80% of per capita alcohol consumption.

Risk relations

Sources for relative risk (RR) functions by GBD code are outlined in Table 1. Alcohol-attributable harms were calculated if a meta-analysis existed. The RR functions for injuries, expressed as a function of alcohol consumption in grams per occasion (x), are as follows [6]: R R MVA : 1 n ( R R MVA ) = 3.292589 * x + 0.004 100 2 R R N o n M V A : 1 n ( R R N o n M V A ) = 2.189702 * x + 0.004 100 0.5 where RRMVA represents the RR for motor vehicle accidents, and RRNon-MVA represents the RR for nonmotor vehicle accidents. These RR functions are based on epidemiological studies that measure postinjury blood alcohol content and, thus, can be used to calculate a person’s risk on an average drinking day and on a binge drinking day.
Table 1

Injury categories and the source of the relative risk relationships with alcohol consumption

Condition

GBD code

ICD-10 codes

Relative risk source

Unintentional injuries

III A

  

Motor vehicle accidents

III A 1

§

Taylor et al., 2010 [6] for relative risk

Poisonings

III A 2

X40-X49

Taylor et al., 2010 [6] for relative risk

Falls

III A 3

W00-W19

Taylor et al., 2010 [6] for relative risk

Fires

III A 4

X00-X09

Taylor et al., 2010 [6] for relative risk

Drowning

III A 5

W65-W74

Taylor et al., 2010 [6] for relative risk

Other Unintentional injuries

III A 6

†Rest of V-series and W20-W64, W 75-W99, X10-X39, X50-X59, Y40-Y86, Y88, and Y89

Taylor et al., 2010 [6] for relative risk

Intentional injuries

III B

  

Self-inflicted injuries

III B 1

X60-X84 and Y87.0

Taylor et al., 2010 [6] for relative risk

Violence

III B 2

X85-Y09, Y87.1

Taylor et al., 2010 [6] for relative risk

Other intentional injuries

III B 4

Taylor et al., 2010 [6] for relative risk

§ V021–V029, V031–V039, V041–V049, V092, V093, V123–V129, V133–V139, V143–V149, V194–V196, V203–V209, V213–V219, V223–V229, V233–V239, V243–V249,V253–V259, V263–V269, V273– V279, V283–V289, V294–V299, V304–V309, V314–V319, V324–V329, V334–V339, V344–V349, V354–V359, V364–V369, V374–V379, V384–V389, V394–V399, V404–V409, V414–V419, V424–V429, V434–V439, V444–V449, V454–V459, V464– V469, V474–V479, V484–V489, V494–V499, V504–V509, V514–V519, V524–V529, V534–V539, V544–V549, V554–V559, V564–V569, V574–V579, V584–V589, V594–V599, V604–V609, V614–V619, V624–V629, V634–V639, V644–V649, V654– V659, V664–V669, V674–V679, V684–V689, V694–V699, V704–V709, V714–V719, V724–V729, V734–V739, V744–V749, V754–V759, V764–V769, V774–V779, V784–V789, V794–V799, V803–V805, V811, V821, V830–V833, V840–V843, V850– V853, V860–V863, V870–V878, V892. †Rest of V = V-series MINUS §.

Estimating the AAFs for harms caused to oneself

The AAFs for injuries were modeled according to methodology that takes into account two dimensions of alcohol consumption:
  1. 1

    binge drinking (both the number of occasions and the amount consumed per occasion)

     
  2. 2

    average daily alcohol consumption (on nonbinge drinking days)

     

When calculating the AAFs, we also included alcohol metabolism rates for men and women to calculate a person’s time at risk of an injury outcome, according to methods outlined by Taylor and colleagues [12].

The AAFs for intentional and unintentional injuries attributable to alcohol consumption were calculated as follows: A A F = P abs + P c u r r e n t ( n o n b i n g e ) R R c u r r e n t ( n o n b i n g e ) + P c u r r e n t ( b i n g e ) R R c u r r e n t ( b i n g e ) 1 P abs + P c u r r e n t ( n o n b i n g e ) R R c u r r e n t ( n o n b i n g e ) + P c u r r e n t ( b i n g e ) R R c u r r e n t ( b i n g e ) where Pabs represents the prevalence of current abstainers, and Pcurrent(binge) and Pcurrent(non-binge) are the prevalence of current drinkers who engage in binge drinking and the prevalence of current drinkers who do not engage in binge drinking, respectively. The RRs were calculated separately for current drinkers who engage in binge drinking and current drinkers who do not engage in binge drinking. RRcurrent(non-binge) was calculated as follows: R R c u r r e n t ( n o n b i n g e ) = ( R R a v e r a g e 1 ) * P nonbingedays + 1 and RRcurrent(binge) was calculated as follows: R R c u r r e n t ( b i n g e ) = ( R R average 1 ) * P nonbingedays + ( R R binge 1 ) * P bingedays + 1 where risk on average drinking days (RRaverage) was calculated as follows: R R average = P dayatrisk ( x ) * ( R R injury ( x ) 1 ) + 1 and where risk on binge drinking days (RRbinge) was calculated as follows: R R binge = P dayatrisk ( x ) * ( R R injury ( x ) 1 ) + 1 where Pdayatrisk represents the proportion of a day at risk given an alcohol consumption on that day (x), and RRinjury is the relative risk for injury given an amount of alcohol consumed (x), where x is the amount of alcohol consumed on binge drinking days for RRbinge and the amount of alcohol consumed on nonbinge drinking days for RRaverage*Pdayatrisk is calculated based on the average rate at which alcohol is metabolized.

Since these AAFs were calculated based on samples of emergency room patients, we estimated the AAF for mortality from motor vehicle accidents by multiplying the AAF for morbidity for motor vehicle accidents by 3/2. Similarly, to estimate the AAF for mortality due to nonmotor vehicle accidents, we multiplied the AAF for morbidity for nonmotor vehicle accidents by 9/4. These methods were based on two studies that compared blood alcohol levels of emergency room patients with blood alcohol levels obtained from coroners’ reports of patients who died from an injury [24, 25].

For women, the AAF for motor vehicle accidents was calculated by multiplying the AAF for motor vehicle accidents for men by the product of the per capita consumption of alcohol for women divided by the per capita consumption of alcohol for men.

Estimating the AAFs for harms caused to others

The AAFs for deaths and morbidity caused by drinkers to others due to motor vehicle accidents were calculated based on recent data reported by Laslett et al., 2011 [11]. The AAFs for the alcohol-attributable injuries to others were calculated as follows: A A F Otherage = ( 1 A A F selfagecountryi ) * 1 exp 1 n 1 A A F otherageAustralia * A A F selfcountryi A A F sefAustralia where AAFOtherage represents the AAF for motor vehicle accident injuries caused by others, AAFselfcountryi represents the AAF for motor vehicle accident injuries caused to oneself for an entire country i, and AAFselfagecountryi represents the AAF for motor vehicle accident injuries caused to oneself for each specific age group. AAFselfAustralia represents the AAF for motor vehicle accident injuries caused to oneself in Australia, and AAFotherageAustralia represents the AAF for motor vehicle accident injuries caused by others for each specific age group in Australia.

The AAFs for deaths and injuries caused by an assault by someone who had been drinking were calculated based on recent data reported by Laslett et al., 2011. These AAFs were calculated as follows: A A F A s s a u l t a g e \_ c o u n t r y i = A A F A s s a u l t a g e \_ A u s t r a l i a * ( A A F A s s a u l t \_ c o u n t r y i / A A F A s s a u l t \_ A u s t r a l i a ) where AAFAssaultage_countryi represents the age-specific AAF for deaths or injuries caused by assault, AAFAssault_countryi represents the AAF for assaults for an entire country, AAFAssault_Australia represents the AAF for deaths or injuries caused by assaults for Australia and AAFAssaultage_Australiai represents the AAF for deaths or injuries caused by assaults for each specific age group in Australia.

Estimating the 95% confidence intervals for the AAFs

The 95% confidence intervals (CIs) for the AAFs were calculated using a Monte Carlo approach, with 40,000 simulations that estimated the lowest-level parameters used in the AAF formulas [23]. For the AAFs of binge and average consumption for each simulation, we generated estimates for the prevalence of past year abstainers from which a prevalence of current drinkers was estimated. Additionally, we generated estimates of the RR function betas and the formula used to calculate the average rate at which alcohol is metabolized, which, in turn, was used to calculate Pdayatrisk. For the calculation of the 95% CIs, we also generated estimates for the prevalence of binge drinkers among current drinkers and the average number of drinks consumed on binge drinking occasions and on nonbinge drinking occasions. The resulting 40,000 AAF estimates for binge and average consumption were used to calculate the variance of the AAFs and the 95% CIs for each disease category.

Step 2: Application of the AAFs to region-specific mortality, PYLL, and DALY data

This step required multiplying the sex-, age-, consumption- ,and injury-specific AAFs by mortality, PYLL, and DALY data, respectively.

Estimates of mortality and morbidity

To quantify the burden of injuries attributable to alcohol consumption we used an event-based measure (mortality) and time-based measures (PYLL and DALYs). DALYs combine years of life lost due to premature mortality and years lived with disabilities. Comprehensive revision estimates for 2004 of mortality, PYLL, and DALYs for the 160 GBD disease and injury categories were provided by the World Health Organization (WHO)[26]. Methods to estimate the mortality, PYLL, and DALYs in the GBD project are described elsewhere [27, 28]. Estimates of mortality, PYLL, and DALYs were available for each country, which were used to calculate regional estimates. This meant that for each region, sex-, age-, injury-, and consumption-specific AAFs were applied to sex-, age-, and injury-specific outcome data.

All statistics and analyses were performed using R version 2.11.1.

Results

Table 2 outlines the prevalence of current drinkers, people who engage in binge drinking, average number of binge drinking days in a year, the average number of drinks consumed during a binge drinking occasion, and the per capita consumption by sex for each CRA region. Men exhibited a higher per capita consumption and a higher prevalence of current drinkers and of binge drinkers than did women in every region. The prevalence of current drinkers and per capita consumption varied greatly, with Western Europe having the highest prevalence of current drinkers and North Africa/Middle East having the lowest prevalence of current drinkers. Eastern Europe had the highest per capita consumption for men and women, while Southern Asia had the lowest per capita consumption for women and North Africa/Middle East had the lowest for men.
Table 2

Drinking indicators by Global Burden of Disease region for 2005

 

Men

Women

GBD Region

Current drinkers

Prevalence of binge drinkers [among current drinkers]

Binge drinking occasions per year

Drinks consumed during a binge drinking occasion

Per capita consumption (l/year per person)

Current drinkers

Prevalence of binge drinkers [among current drinkers]

Binge drinking occasions per year

Drinks consumed during a binge drinking occasion

Per capita consumption (l/year per person)

Asia, Pacific [High Income]

87.43%

17.17%

26

7

15.23

75.62%

3.93%

26

6

4.63

Asia, Central

63.87%

52.76%

52

7

10.62

46.83%

8.78%

52

6

3.23

Asia, East

71.71%

13.48%

26

7

9.88

37.50%

0.54%

26

6

1.95

Asia, South

16.68%

45.09%

52

7

3.80

2.64%

9.22%

52

6

0.24

Asia, Southeast

27.21%

10.58%

52

7

5.21

5.63%

2.89%

52

6

0.47

Australasia

87.08%

10.00%

26

7

14.29

80.02%

2.84%

26

6

5.78

Caribbean

65.17%

20.17%

26

7

9.36

34.23%

5.11%

26

6

2.74

Europe, Central

77.41%

25.21%

52

7

21.81

59.05%

3.26%

52

6

6.70

Europe, Eastern

71.74%

59.39%

78

9

25.19

50.77%

13.25%

78

7

8.07

Europe, Western

87.80%

12.04%

26

7

17.64

77.56%

1.80%

26

6

7.06

Latin America, Andean

67.92%

18.47%

52

7

11.35

47.14%

3.84%

52

6

3.43

Latin America, Central

57.83%

22.54%

78

8

11.73

34.88%

1.56%

78

6

3.23

Latin America, Southern

86.48%

16.80%

26

7

13.91

66.75%

0.17%

26

6

5.28

Latin America, Tropical

58.67%

21.15%

52

7

14.11

41.48%

5.36%

52

6

4.39

Northern Africa / Middle East

8.90%

7.21%

26

7

2.04

2.40%

4.10%

26

6

0.26

North America [High Income]

72.70%

13.74%

26

7

14.38

60.98%

3.39%

26

6

5.05

Oceania

79.59%

25.20%

52

7

5.55

47.59%

10.74%

52

6

0.94

Sub-Saharan Africa, Central

49.95%

32.01%

52

7

5.83

29.88%

16.58%

52

6

2.18

Sub-Saharan Africa, East

29.83%

20.98%

52

7

7.37

19.34%

1.00%

52

6

2.19

Sub-Saharan Africa, Southern

37.53%

39.00%

78

8

14.28

13.60%

21.29%

78

6

3.07

Sub-Saharan Africa, Western

41.39%

32.40%

52

7

11.69

24.76%

20.88%

52

6

3.94

World

50.22%

24.24%

-

-

9.74

30.82%

5.48%

-

-

2.6

Table 3 outlines the deaths attributable to alcohol consumption by region and sex. 851,900 (95% CI: 419,400 to 1,282,500) deaths were due to alcohol-attributable injuries, of which 221,100 (95% CI: 140,000 to 312,000) deaths were caused by alcohol-attributable harms to others (alcohol-attributable injuries caused by others are outlined in Additional file 1). Figures 1 and 2 outline the burden of injuries in deaths per 100,000 people for men and women, respectively. Alcohol-attributable injuries account for 1.43% (95% CI: 0.70% to 2.15%) of all deaths and 15.10% (95% CI: 7.43% to 22.73%) of all deaths from injuries. Of this total, 761,300 (95% CI: 390,800 to 1,088,900) deaths were among men, representing 2.42% (95% CI: 1.24% to 3.46%) of all deaths and 20.26% (95% CI: 10.40% to 28.98%) of all deaths from injuries for men. 90,600 (95% CI: 28,500 to 193,600) deaths were among women, representing 0.32% (95% CI: 0.10% to 0.69%) of all deaths and 4.81% (95% CI: 1.51% to 10.27%) of all deaths from injuries for women. Adjusting the figures in Table 3 for the population of each region, we observed that Eastern Europe had the largest population-standardized mortality rate, with 135.4 (95% CI: 82.0 to 161.4) deaths per 100,000 people caused by injuries attributable to alcohol consumption, and North Africa/Middle East had the lowest death rate, with 2.0 (95% CI: 0.5 to 5.5) deaths per 100,000 people caused by injuries attributable to alcohol consumption. All AAFs for injuries as well as alcohol-attributable injury deaths by age, sex, region, and cause are provided in Additional file 2, Additional file 3, Additional file 4 and Additional file 5.
Table 3

Alcohol-attributable deaths caused by injuries by Global Burden of Disease region for 2004

 

Men

Women

Total

 

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Asia, Pacific [High Income]

17,030

8,340

25,730

1,530

620

2,440

18,560

8,960

28,170

Asia, Central

8,640

4,940

12,340

1,010

410

1,620

9,650

5,340

13,960

Asia, East

80,300

37,750

122,860

10,310

1,760

19,810

90,620

39,510

142,670

Asia, South

77,810

21,330

144,490

7,070

290

49,630

84,890

21,620

194,120

Asia, Southeast

37,260

13,000

62,920

3,280

320

8,990

40,540

13,320

71,910

Australasia

1,210

630

1,800

160

90

230

1,380

720

2,030

Caribbean

2,410

1,350

3,460

300

120

490

2,710

1,470

3,950

Europe, Central

27,480

13,820

39,030

2,120

480

3,760

29,600

14,290

42,790

Europe, Eastern

258,530

161,260

298,650

29,310

12,980

44,570

287,840

174,240

343,220

Europe, Western

30,270

14,990

45,550

4,490

1,360

7,620

34,760

16,340

53,170

Latin America, Andean

3,790

1,660

5,930

370

100

690

4,160

1,760

6,620

Latin America, Central

38,050

23,870

52,240

4,010

1,950

6,130

42,060

25,820

58,370

Latin America, Southern

3,880

1,920

5,840

390

110

670

4,270

2,040

6,510

Latin America, Tropical

33,810

18,020

49,600

3,060

1,070

5,110

36,870

19,090

54,710

Northern Africa / Middle East

7,530

1,710

19,660

670

0

2,840

8,200

1,710

22,490

North America [High Income]

25,840

13,290

38,400

3,850

1,450

6,250

29,690

14,740

44,650

Oceania

430

270

580

70

30

120

500

300

700

Sub-Saharan Africa, Central

9,500

6,180

12,810

1,410

600

2,220

10,910

6,780

15,030

Sub-Saharan Africa, East

31,430

12,860

50,150

5,080

850

10,000

36,510

13,710

60,150

Sub-Saharan Africa, Southern

32,110

17,060

45,430

5,570

2,290

9,030

37,680

19,350

54,460

Sub-Saharan Africa, Western

33,990

16,560

51,420

6,510

1,680

11,380

40,500

18,240

62,800

World

761,300

390,800

1,088,900

90,600

28,500

193,600

851,900

419,400

1,282,500

Figure 1

Population-standardized alcohol-attributable deaths per 100,000 people by GBD region for men.

Figure 2

Population-standardized alcohol-attributable deaths per 100,000 people by GBD region for women.

Globally, in 2004, 19,051,000 (95% CI: 9,767,000 to 28,243,000) PYLL or 320.1 (95% CI: 161.1 to 477.9) PYLL per 100,000 people were caused by injuries attributable to alcohol consumption, of which 5,989,300 (95% CI: 3,788,700 to 8,423,600) were caused by harms to others. Alcohol-attributable injuries accounted for 2.02% (95% CI: 1.03% to 2.99%) PYLL and 14.54% (95% CI: 7.45% to 21.55%) of all PYLL caused by injuries worldwide. Table 4 outlines estimates of the PYLL attributable to alcohol consumption by region and sex for 2004. Figures 3 and 4 outline the burden of injuries in PYLL per 100,000 people for men and women, respectively. Alcohol-attributable injury PYLL among men were far in excess of the estimates calculated for women, with 605.8 (95% CI: 317.9 to 860.0) PYLL per 100,000 men (3.29% [95% CI: 1.76% to 4.65%] of all PYLL and 19.11% [95% CI: 10.22% to 26.95%] of all PYLL caused by injuries for men) compared to 59.9 (95% CI: 19.4 to 126.7) PYLL per 100,000 women (0.48% [95% CI: 0.16% to 1.00%] of all PYLL and 4.92% [95% CI: 1.62% to 10.20%] of all PYLL caused by injuries for women). Eastern Europe had the highest burden in terms of PYLL for men and women, with 6,003.8 (95% CI: 3813.4 to 6785.6) PYLL per 100,000 men and 499.1 (95% CI: 236.1 to738.2) PYLL per 100,000 women. North Africa/Middle East had the lowest burden of PYLL, with 107.3 (95% CI: 22.9 to 282.9) PYLL per 100,000 men and 7.8 (95% CI: 0.0 to 160) PYLL per 100,000 women.
Table 4

Alcohol-attributable PYLL caused by injuries by Global Burden of Disease region for 2004

 

Men

Women

Total

 

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Asia, Pacific [High Income]

274,300

138,300

410,300

24,800

10,100

39,500

299,100

148,400

449,700

Asia, Central

200,500

116,000

284,900

23,900

9,800

37,900

224,300

125,700

322,900

Asia, East

1,511,900

775,000

2,248,800

212,600

42,000

400,600

1,724,500

817,000

2,649,400

Asia, South

1,670,100

520,100

3,086,300

167,100

7,300

1,049,400

1,837,200

527,400

4,135,800

Asia, Southeast

799,800

310,200

1,316,400

78,100

7,900

208,300

878,000

318,100

1,524,600

Australasia

24,400

13,200

35,500

3,100

1,800

4,300

27,400

15,000

39,800

Caribbean

55,100

32,400

77,800

8,100

3,100

13,200

63,300

35,500

91,000

Europe, Central

494,400

258,900

693,600

43,800

11,000

76,600

538,300

269,900

770,200

Europe, Eastern

5,699,200

3,622,900

6,438,200

632,500

296,200

939,000

6,331,700

3,919,100

7,377,200

Europe, Western

520,300

274,000

766,500

64,400

19,400

109,500

584,700

293,400

875,900

Latin America, Andean

90,000

40,600

139,400

9,500

2,600

17,700

99,500

43,200

157,100

Latin America, Central

984,400

624,100

1,344,600

106,500

52,800

162,500

1,090,900

676,900

1,507,100

Latin America, Southern

87,300

45,000

129,700

8,000

2,700

13,500

95,300

47,700

143,200

Latin America, Tropical

926,900

504,500

1,349,400

82,000

30,700

134,600

1,008,900

535,200

1,484,000

Northern Africa / Middle East

219,500

47,000

587,600

18,800

0

76,900

238,300

47,000

664,500

North America [High Income]

610,100

324,400

895,800

89,600

34,500

144,700

699,700

358,900

1,040,500

Oceania

11,400

7,400

15,400

2,000

800

3,200

13,400

8,200

18,600

Sub-Saharan Africa, Central

259,500

171,000

348,100

40,400

17,300

63,600

299,900

188,200

411,600

Sub-Saharan Africa, East

785,900

344,300

1,230,700

138,700

24,800

268,400

924,700

369,100

1,499,100

Sub-Saharan Africa, Southern

882,400

473,800

1,245,100

146,600

61,600

236,100

1,029,100

535,400

1,481,200

Sub-Saharan Africa, Western

865,800

439,200

1,292,300

176,900

48,300

306,800

1,042,700

487,500

1,599,100

World

16,973,000

9,082,000

23,936,000

2,078,000

685,000

4,306,000

19,051,000

9,767,000

28,243,000

Figure 3

Population-standardized alcohol-attributable potential years of life lost per 100,000 people by GBD region for men.

Figure 4

Population-standardized alcohol-attributable potential years of life lost per 100,000 people by GBD region for women.

In 2004, alcohol-attributable injuries accounted for 21,668,000 (95% CI: 11,097,000 to 32,385,000) DALYs, of which 6,917,300 (95% CI: 4,396,000 to 9,731,100) were caused by alcohol-attributable injuries caused by others. Alcohol-attributable injuries in 2004 accounted for 1.40% (95% CI: 0.71% to 2.09%) of all DALYs and 11.35% (95% CI: 5.81% to 16.96%) of DALYs caused by injuries among men and women. Table 5 outlines estimates of the DALYs attributable to alcohol consumption by region and by sex for 2004. Figures 5 and 6 outline the burden of injuries in DALYs per 100,000 people for men and women, respectively. Alcohol accounted for 681.8 (95% CI: 354.8 to977.9) DALYs per 100,000 men and 70.4 (95% CI: 25.3 to 146.4) DALYs per 100,000 women. Eastern Europe had the highest DALYs per 100,000 people, with 6,561.7 (95% CI: 4191.3 to 7523.2) DALYs per 100,000 men and 590.2 (95% CI: 296.3 to 860.0) DALYs per 100,000 women. The North Africa/Middle East region was estimated to have the lowest alcohol-attributable injury DALYs worldwide, with 138.5 (95% CI: 22.2 to 364.1) DALYs per 100,000 men and 9.9 (95% CI: 1.1 to 40.6) DALYs per 100,000 women. An outline of the alcohol-attributable years of life lived with disability is provided in Additional file 6.
Table 5

Alcohol-attributable DALYs caused by injuries by Global Burden of Disease region for 2004

 

Men

Women

Total

 

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Point estimate

Lower 95% confidence interval

Upper 95% confidence interval

Asia, Pacific [High Income]

301,300

152,000

450,600

28,900

12,500

45,300

330,200

164,500

495,900

Asia, Central

230,300

131,200

329,300

28,400

12,600

44,100

258,600

143,900

373,400

Asia, East

1,690,700

858,200

2,523,100

240,200

56,100

443,700

1,930,900

914,300

2,966,800

Asia, South

1,918,200

586,800

3,558,600

198,700

26,800

1,215,700

2,117,000

613,600

4,774,200

Asia, Southeast

902,500

347,700

1,489,100

102,700

23,300

255,600

1,005,200

371,000

1,744,700

Australasia

26,900

14,500

39,200

3,600

2,100

5,100

30,500

16,600

44,300

Caribbean

69,300

40,300

98,300

11,200

4,800

17,600

80,500

45,100

115,900

Europe, Central

578,300

300,000

820,200

53,400

14,900

92,000

631,700

314,900

912,100

Europe, Eastern

6,227,900

3,981,100

7,137,100

745,100

369,700

1,090,500

6,972,900

4,350,800

8,227,600

Europe, Western

587,200

307,300

867,200

77,700

25,000

130,300

664,900

332,300

997,500

Latin America, Andean

109,000

48,300

169,600

12,200

4,000

22,200

121,200

52,400

191,800

Latin America, Central

1,193,900

743,100

1,644,600

133,300

71,500

198,300

1,327,200

814,600

1,842,900

Latin America, Southern

108,000

56,400

159,600

10,900

4,400

17,600

118,800

60,800

177,100

Latin America, Tropical

1,128,000

606,700

1,649,300

103,400

42,700

166,100

1,231,400

649,400

1,815,400

Northern Africa / Middle East

284,800

52,200

758,300

24,400

3,100

94,700

309,200

55,300

853,000

North America [High Income]

678,700

359,500

997,800

104,000

42,900

165,100

782,700

402,500

1,162,900

Oceania

12,500

8,000

17,000

2,500

1,200

3,800

15,000

9,200

20,800

Sub-Saharan Africa, Central

292,700

191,400

394,000

48,400

22,900

73,800

341,100

214,400

467,800

Sub-Saharan Africa, East

895,900

386,700

1,408,500

163,400

37,700

307,800

1,059,300

424,400

1,716,300

Sub-Saharan Africa, Southern

966,400

511,300

1,367,100

165,100

72,300

263,800

1,131,600

583,600

1,630,800

Sub-Saharan Africa, Western

997,300

499,500

1,495,200

210,600

64,500

358,000

1,207,900

563,900

1,853,100

World

19,200,000

10,182,000

27,374,000

2,468,000

915,000

5,011,000

21,668,000

11,097,000

32,385,000

Figure 5

Population-standardized alcohol-attributable disability-adjusted years of life lost per 100,000 people by GBD region for men.

Figure 6

Population-standardized alcohol-attributable disability-adjusted years of life lost per 100,000 people by GBD region for women.

Discussion

Alcohol is a substantial risk factor for the global burden of injuries in terms of death, PYLL, and DALYs. We found that this burden of injuries for 2004 varied by region corresponding with drinking prevalence: those regions exhibiting a low prevalence of current drinkers, such as in North Africa/Middle East and Southern Asia, carried a relatively low burden of injuries attributable to alcohol. This can be contrasted with Eastern Europe, where drinking prevalence, binge drinking, and the burden due to alcohol-attributable injury were all high.

Before we discuss the implications of these findings, the potential weaknesses associated with this analysis should be discussed in detail. First, quantification of the global burden using insurance and police records, which would provide the most accurate data, appears not to be possible as no such reporting system exists [29]. Second, there are limitations regarding the quality of global mortality data (see [27]). For most of the world, there are no vital registries, i.e., there is scarce or no available information on causes of death. In these instances, data on the missing causes of death have to be statistically estimated [28, 30, 31]. Although we do not incorporate the variation of the mortality estimates in these countries into our analysis, estimations by the WHO/GBD of the number of deaths in countries where little or no data are available increase the uncertainty of our estimates of the number of deaths, PYLL, and DALYs attributable to alcohol consumption [27]. Third, there are weaknesses associated with the assumptions made in the calculation of the DALYs (see [32]), particularly the calculation of weights for DALYs, although these assumptions have been shown to exert only a minor effect on the variation of DALY estimates for injuries [27]. Fourth, alcohol consumption variables used in our analysis came from population surveys which have limitations with respect to coverage, and the survey instruments involved commonly have inherent biases due to self-reporting of data, leading to an underestimation of level of drinking and number of binge drinkers [33]. While the distribution of average volume of alcohol consumption can be adjusted for the amount of alcohol actually consumed [34], there is currently no methodology to correct the underestimation of irregular binge drinking [35], thereby causing an underestimation of the global burden of injuries attributable to alcohol consumption.

Our study is further limited by the use of the RR for all nonmotor vehicle accident injuries. We suspect that the risk relationship may change by injury type, but the body of research relating alcohol consumption to injury is relatively sparse (except with respect to motor vehicle accidents), meaning that meta-analytic techniques used to generate stable risk curves are not usable due to a scarcity of data points. This is especially important for alcohol consumption and resulting intentional and unintentional nonmotor vehicle accident injuries, due to alcohol playing a very different role in intentional and unintentional nonmotor vehicle accident injuries [6]. However, the risk estimates for intentional and unintentional injuries were not stable in the meta-analyses performed by Taylor and colleagues and, thus, were combined [6]. The resulting aggregate RR showed little heterogeneity among studies that examined intentional and unintentional nonmotor vehicle accident injuries [6]. Additionally, although not taken into consideration in our analysis, previous research has suggested that the RRs for injury may be dependent upon previous alcohol consumption patterns, with heavy consumers of alcohol at a lower risk than those people who do not frequently consume large amounts of alcohol [3638].

Our study is limited also by the information on harms to others available in the literature; we used data from Australia to model harms to others. Because of limited data, we were unable to determine if a linear relationship exists between the AAFs for motor vehicle accident injuries to passengers, pedestrians, and drivers who did not cause the accident and the AAFs for motor vehicle accident deaths to drivers who caused the accident. We were also unable to quantify the relationship between age-specific AAFs for assaults and population AAFs for assaults. This lack of data leads to two different formulas being used for AAFs for motor vehicle accidents caused to others and for AAFs for assaults; the AAFs for assaults were calculated assuming a linear relationship, and the AAFs for motor vehicle accidents caused to others had to be log transformed. Log transformation of the AAFs for motor vehicle accidents caused to others was required in order to keep the resulting total AAFs within the boundaries of 0 and 1. In the case of the AAFs for assaults, log transformation was not necessary since the age-specific AAFs were very similar.

Regardless of these limitations, the method of calculating the burden of injury attributable to alcohol consumption presented in this article is an improvement on previous methods used to calculate the alcohol-attributable burden. For the first time at a global level the burden of alcohol-attributable injuries has been estimated using consumption data and RRs, and alcohol-related harms to others have been calculated.

Conclusion

Given the severity of the alcohol-attributable burden of injuries and the expectation that it will increase in developing countries [39, 40], it is imperative to accurately characterize this burden and develop strategies aimed at reducing it. This article presents a new method to calculate the burden of disease attributable to alcohol consumption and is an improvement over previous methods. Additionally, given the size of the estimated alcohol-attributable burden of injuries, strategies aimed at reducing this burden should target two key areas of concern: 1) the need to decrease the harmful consumption of alcohol, by methods such as regulating the availability of alcohol [41], and 2) the need to decrease the frequency of drunk driving by, for example, lowering the maximum allowable blood alcohol concentration level for driving, especially in the case of younger drivers [36].

Additional files

Declarations

Acknowledgments

This work was supported by financial support provided to the last author listed above by the National Institute for Alcohol Abuse and Alcoholism (NIAAA) with contract # HHSN267200700041C to conduct the study titled “Alcohol- and Drug-attributable Burden of Disease and Injury in the US.” In addition, the last author received a salary and infrastructure support from the Ontario Ministry of Health and Long-Term Care.

Authors’ Affiliations

(1)
Centre for Addiction and Mental Health (CAMH), Centre for Addiction and Mental Health (CAMH)
(2)
Institute of Medical Science, University of Toronto
(3)
Ecole Polytechnique Fédérale de Lausanne, Ecole Polytechnique Fédérale de Lausanne
(4)
Dalla Lana School of Public Health (DLSPH), University of Toronto
(5)
Institute for Clinical Psychology and Psychotherapy, TU Dresden, Institute for Clinical Psychology and Psychotherapy
(6)
Department of Psychiatry, University of Toronto

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Copyright

© Shield et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.