1. 2014年2月xx日 某勉強会
世界における疾病および
死亡リスク要因の定量化
Global Burden of Disease Study 2010 の論文紹介
林岳彦
＠国立環境研究所環境リスク研究センター

2. 本日の段取り
■ 前説：そもそもなぜこの論文なのか
- この論文は何をやっているのか
- 障害調整生命年（DALY）の説明
■ 論文の中身の紹介
- 背景
- 方法
- 結果
- 議論

3. 今回の論文
IF39ですが何か？
Lim et al. (2012) in The Lancet Vol.380 No. 9859
Articles
A comparative risk assessment of burden of disease and
injury attributable to 67 risk factors and risk factor clusters
in 21 regions, 1990–2010: a systematic analysis for the
Global Burden of Disease Study 2010
Stephen S Lim‡, Theo Vos, Abraham D Flaxman, Goodarz Danaei, Kenji Shibuya, Heather Adair-Rohani*, Markus Amann*, H Ross Anderson*,
Kathryn G Andrews*, Martin Aryee*, Charles Atkinson*, Loraine J Bacchus*, Adil N Bahalim*, Kalpana Balakrishnan*, John Balmes*,
Suzanne Barker-Collo*, Amanda Baxter*, Michelle L Bell*, Jed D Blore*, Fiona Blyth*, Carissa Bonner*, Guilherme Borges*, Rupert Bourne*,
Michel Boussinesq*, Michael Brauer*, Peter Brooks*, Nigel G Bruce*, Bert Brunekreef*, Claire Bryan-Hancock*, Chiara Bucello*, Rachelle Buchbinder*,
Fiona Bull*, Richard T Burnett*, Tim E Byers*, Bianca Calabria*, Jonathan Carapetis*, Emily Carnahan*, Zoe Chafe*, Fiona Charlson*, Honglei Chen*,
Jian Shen Chen*, Andrew Tai-Ann Cheng*, Jennifer Christine Child*, Aaron Cohen*, K Ellicott Colson*, Benjamin C Cowie*, Sarah Darby*,
Susan Darling*, Adrian Davis*, Louisa Degenhardt*, Frank Dentener*, Don C Des Jarlais*, Karen Devries*, Mukesh Dherani*, Eric L Ding*,
E Ray Dorsey*, Tim Driscoll*, Karen Edmond*, Suad Eltahir Ali*, Rebecca E Engell*, Patricia J Erwin*, Saman Fahimi*, Gail Falder*, Farshad Farzadfar*,
Alize Ferrari*, Mariel M Finucane*, Seth Flaxman*, Francis Gerry R Fowkes*, Greg Freedman*, Michael K Freeman*, Emmanuela Gakidou*,
Santu Ghosh*, Edward Giovannucci*, Gerhard Gmel*, Kathryn Graham*, Rebecca Grainger*, Bridget Grant*, David Gunnell*, Hialy R Gutierrez*,
Wayne Hall*, Hans W Hoek*, Anthony Hogan*, H Dean Hosgood III*, Damian Hoy*, Howard Hu*, Bryan J Hubbell*, Sally J Hutchings*,
Sydney E Ibeanusi*, Gemma L Jacklyn*, Rashmi Jasrasaria*, Jost B Jonas*, Haidong Kan*, John A Kanis*, Nicholas Kassebaum*, Norito Kawakami*,
Young-Ho Khang*, Shahab Khatibzadeh*, Jon-Paul Khoo*, Cindy Kok*, Francine Laden*, Ratilal Lalloo*, Qing Lan*, Tim Lathlean*, Janet L Leasher*,
James Leigh*, Yang Li*, John Kent Lin*, Steven E Lipshultz*, Stephanie London*, Rafael Lozano*, Yuan Lu*, Joelle Mak*, Reza Malekzadeh*,
Leslie Mallinger*, Wagner Marcenes*, Lyn March*, Robin Marks*, Randall Martin*, Paul McGale*, John McGrath*, Sumi Mehta*, George A Mensah*,
Tony R Merriman*, Renata Micha*, Catherine Michaud*, Vinod Mishra*, Khayriyyah Mohd Hanafiah*, Ali A Mokdad*, Lidia Morawska*,
Dariush Mozaffarian*, Tasha Murphy*, Mohsen Naghavi*, Bruce Neal*, Paul K Nelson*, Joan Miquel Nolla*, Rosana Norman*, Casey Olives*,
Saad B Omer*, Jessica Orchard*, Richard Osborne*, Bart Ostro*, Andrew Page*, Kiran D Pandey*, Charles D H Parry*, Erin Passmore*, Jayadeep Patra*,
Neil Pearce*, Pamela M Pelizzari*, Max Petzold*, Michael R Phillips*, Dan Pope*, C Arden Pope III*, John Powles*, Mayuree Rao*, Homie Razavi*,

4. なぜこの論文を紹介するのか
textbook example
■ リスク評価という考え方 の良い典型例
- リスクを可視化する
- リスクを数量化して比較する
- 公共政策を支援する
- 曝露-影響関係からリスクを見る
- すごくざっくり計算する
■ たまにはグローバルなことも考えよう
手強い実務案件を抱えてるとついつい視点が狭くなりがち

5. この論文は何をやっているのか
disease burden
■ 世界規模での疾病負荷の定量化
@Wikipedia先生

6. この論文は何をやっているのか
disease burden
■ 世界規模での疾病負荷の定量化
DALYって何？
@Wikipedia先生

7. 障害調整生命年
キー用語説明：「DALY」とは？
■ 死亡数 は影響の指標として限界がある
- 死ななければ良い、というものでもない
- 死亡@20歳も死亡＠80歳も同じ？
- 死亡 の生涯リスクは比較不可（常に1だから）
discount
リスク要因による悪影響の 軽視 に繋がる

8. 障害調整生命年
キー用語説明：「DALY」とは？
YLL
YLD
■ DALY =「損失生命年」+「損失健康年」
http://en.wikipedia.org/wiki/File:DALY_disability_affected_life_year_infographic.svg

9. 障害調整生命年
キー用語説明：「DALY」とは？
YLL
YLD
■ DALY =「損失生命年」+「損失健康年」
総人口について死亡が早まることによって失われた年数
Years of Life Lost
YLL
=
N
x
L
死亡数
死亡時の
平均余命

10. 障害調整生命年
キー用語説明：「DALY」とは？
YLL
YLD
■ DALY =「損失生命年」+「損失健康年」
人々の健康状態に生じた事故による障害によって失われた年数
Years of Life due to Disability
YLD
=
I
x
DW
x
L
件数
障害
障害の
ウェイト 持続年

11. 障害ウェイトの値
池田・田端（1998）「わが国における障害調整生存年（DALY）」より引用
＊障害の当事者からみると「障害と共に生きる生命の価値」を
discountされているようにも見えるのに注意

12. 障害調整生命年
キー用語説明：「DALY」とは？
YLL
YLD
■ DALY =「損失生命年」+「損失健康年」
年齢に応じた
社会的重み付け も
考慮されている
http://en.wikipedia.org/wiki/Disability-adjusted_life_year より引用

13. この論文は何をやっているのか
disease burden
■ 世界規模での疾病負荷の定量化
DALYって何？
@Wikipedia先生

14. この論文は何をやっているのか
disease burden
■ 世界規模での疾病負荷の定量化
■ 障害調整生命年（DALY）の算出
- 67のリスク要因
- 21の地域
- 1990年と2010年で解析
■ リスク要因による曝露と影響の解析
では実際に中身を見て行きましょう...

15. 本日の段取り
■ 前説：そもそもなぜこの論文なのか
- この論文は何をやっているのか
- 障害調整生命年（DALY）の説明
■ 論文の中身の紹介
- 背景
- 方法
- 結果
- 議論

16. Global Burden of Disease
背景：GBD studyとは...
■ GBD study 1990
リスク要因とDALYを結びつけた
初めてのグローバルな解析
1996
■ GBD ＠WHO
2002
2009
WHOのGBDのページ

17. Global Burden of Disease
背景：GBD studyとは...
■ GBD study 1990
リスク要因とDALYを結びつけた
初めてのグローバルな解析
1996
■ GBD ＠WHO
■ GBD study 2010 ←イマココ
- 現在のリスクの定量化
- 1990年データの再解析
- 1990年と2010年のGBDの比較
では実際の解析方法を見てみましょう...

18. 本日の段取り
■ 前説：そもそもなぜこの論文なのか
- この論文は何をやっているのか
- 障害調整生命年（DALY）の説明
■ 論文の中身の紹介
- 背景
- 方法
- 結果
- 議論

19. 方法：概要
■ 各リスク要因により起こる死亡や疾病の
割合を計算
- 他の独立な要因の影響は変わらないとする
- 20の齢グループ、男女、187カ国

20. 方法：概要
■ 全部で64のリスク要因を解析した
- 大中小の階層構造（10個の大カテゴリー）
(1) 不十分な水と衛生
(2) 大気汚染 PM、室内汚染、オゾン
(3) 他の環境汚染 ラドン、鉛
(4) 児童および母体の栄養不足 母乳不足、低体重、亜鉛不足etc
(5) タバコ（副流煙含む）
(6) アルコールとドラッグ
(7) 生理的リスク 高血圧、高コレストロール、高BMI etc
(8) 食生活と運動不足 野菜不足、肉不足、塩分過剰、運動不足 etc
(9) 職業的リスク 発がん物質への曝露、負傷、腰痛 etc
(10) 性的虐待、DV

21. 方法：概要
■ 疾病負荷推定の５段階のプロセス
outcome
(1) 因果的関連に基づくリスク-帰結ペアの選択
(2) 集団の各リスク要因への 曝露 量を推定
(3) リスク要因の帰結への単位当り影響量の推定
theoretical-minimum-risk exposure
(4) 仮想的な理論的最小曝露量の推定
(5) 各リスク因子の（DALY等への）負荷量を算出

22. latrines, open defecation or no
occupational groups
facilities, and other unspecified
(professional, technical, and
facilities)
related workers; administrative
and managerial workers; clerical
and related workers; sales
workers; service workers;
Exposure definition
Outcomes
2. Air pollution agriculture, animal husbandry,
and forestry workers, fishermen
Ambient concentration of
2.1. Ambient
Lower respiratory infections;
and hunters; production and
particles with an aerodynamic
particulate
trachea, bronchus, and lung
related workers; and transport
diameter smaller than 2·5 μm,
matter
cancers; IHD; cerebrovascular
equipment operators3 and
(Continued from previous page)
measured
pollution
disease; COPD
labourers) in μg/m
方法(1) リスク-帰結ペアの選択
■ リスク-帰結ペアとは？
8.9. Diet low in Dietary intake of fibre from all
10. Sexual abuse and sources including fruits,
violence
fibre
vegetables, grains, legumes,
10.1. Childhood Proportion of the population
and pulses
sexual abuse*
who have ever experienced
Colon and rectum cancers; IHD
Alcohol use disorders, unipolar
depressive disorders, intentional
self-harm
Su
Ag
childhood sexual abuse,
defined as the experience with
2.2. Household Proportion of households using Lower respiratory infections;
an older person of unwanted
trachea, bronchus, and lung
solid fuels for cooking (coal,
air pollution
non-contact, contact abuse, or
cancers; rectum cancers; prostate
from solid fuels wood, charcoal,of calcium from Colon and IHD; cerebrovascular Ag
8.10. Diet low intercourse, whendung, 15 years
Dietary intake aged and
disease;
agricultural residues) milk,
cancer COPD; cataracts
in calcium
all sources, including
or younger
yogurt, and cheese

23. 方法(1) リスク-帰結ペアの選択
■ 各「リスク-帰結ペア」の採用基準
(1) 既往知見から疾病負荷への影響がありそうである
exposure distribution
(2) 国ごとの曝露量を推定するための十分なデータ
と手法がある
(3) リスク-帰結間に因果的影響があると判断できる
一定の質を満たす疫学的エビデンスがある
(4) 集団への影響の知見が一般化（外挿）可能なも
のである

24. unspecified sources)
tubewell or
borehole, a
protected well or
spring, or rainwater
collection)
Article
方法(2) 各リスク要因の曝露量推定
ntestinal infectious diseases
All
1.2. ages
Unimproved
sanitation
Population of households using Intestinal infectious diseases use New
Spatiotemporal All households
All ages
Proportion surveys and
censuses sanitation
Gaussian
improved sanitation meta-analy
unimproved
process
(public sewers,
(traditional latrines, open
regression19–21
septic systems, flush
latrines without squatting slabs,
or pour-flush
bucket latrines, hanging
facilities, ventilated
latrines, open defecation or no
improved latrines,
facilities, and other unspecified
simple pit latrines
facilities)
with squatting
Source o
TheoreticalSubgroup
Main data sources for
Exposure
slabs, and
relative
minimum-risk
exposure
estimation
composting toilets)
exposure
method
2. Air pollution
distribution
Ambient concentration of
2.1. Ambient
Age <5 ye
Lower
Surface monitor
5·8–8·8 μg/m³
Integrated
Age <5 years
Average ofrespiratory infections;
particles with an aerodynamic satellite and bronchus, and lung
particulate
for lower
trachea,
measurements, aerosol
exposure–
for lower
All ages
Population surveys and μm, chemistry IHD; cerebrovascular use response c
Spatiotemporal All households
New
diameter smaller than
matter
respirator
cancers;
optical depth from 2·5
respiratory
measured
pollution
disease;
Gaussian
meta-an
satellites, in μg/m3
tract infection; censuses and TM5 global
transport COPD an improved water tract infec
process
source (household ≥25 years
atmospheric chemistry
≥25 years for
estimates,
regression19–21
connection, a public all others
transport model22–26
all others
calibrated to
surface
tap or standpipe, a
monitor
tubewell or
measurements
borehole, a
Age <5 ye
Proportion surveys and
2.2. Household Population of households using Lower respiratoryhouseholds or Integrated
protected well
Mixed effect
All infections;
Age <5 years
trachea,
solid fuels
air pollution
spring, or fuels
censuses for cooking (coal, regression bronchus, and lung for exposure–
using clean rainwater for lower
for lower
respirator
cancers; IHD; cerebrovascular
from solid fuels wood, charcoal, dung, and
collection)
response cu
cooking (vented
respiratory
tract infec
disease; COPD; cataracts
agricultural
tract infection; Population residues)and
All ages
surveys
Spatiotemporal gas, electricity) use for lower
New
All households
≥25 years
■ 文献の体系的サーチにより曝露データを収集
■ データは全ての国・集団・年代で得られるわけ
ではなく、モデルにより推定を完成させた
utcomes
Lower respiratory infections;
rachea, bronchus, and lung
testinal IHD; cerebrovascular
cancers; infectious diseases
disease; COPD
Lower respiratory infections;
rachea, bronchus, and lung
cancers; IHD; cerebrovascular
disease; COPD; cataracts
testinal infectious diseases

25. 方法(3) リスク要因の疾病への影響
■ 疫学データのメタ解析から「単位曝露量
当りの影響量」を計算
■ 実際の計算法はリスク要因につれて異なる

26. 方法(4) 理論的最小曝露量の推定
■「現在の曝露量がもたらす疾病負荷量」は
仮想的／理論的な最小曝露による負荷量との
比較として計算される
タバコ → ０本
血圧 → 110-115mm Hg
PM2.5 → 5.8∼8.5 μg/m3
↑最小の設定値、低い！
＊疾病負荷の推定値はここの設定値に大きく
依存することに注意！

27. We calculated the burden attributable to risk factor
方法(5) リスク要因の負荷量の計算
continuous exposure by comparing the present
■ 疾病負荷量は以下の式で計算された
bution of exposure to the theoretical-minimum
exposure distribution for each age group, sex, year
曝露量xの元でのRelative Risk
仮想的最小
and 2010), and cause according to the following for
現実の曝露量xの分布
m
PAF=
x=0
RR x P1 x dx –
m
x=0
m
x=0
RR x P2 x dx
RR x P1 x dx
Population Attributable Fraction
人口寄与割合PAF
Where
曝露量xの分布
is the population attributable fr
集団における疾病（例えば肺がん）の全事例のうち!
(burden attributable to risk factor), RR(x) is the
曝露（例えば喫煙）に起因する事例の割合

28. 本日の段取り
■ 前説：そもそもなぜこの論文なのか
- この論文は何をやっているのか
- 障害調整生命年（DALY）の説明
■ 論文の中身の紹介
- 背景
- 方法
- 結果
- 議論

29. 1 768 073
3 762 356
5 018 051
(79 774–96 333)
(107 244–130 842) (30 42
45 612) (1 588 197–1 935 072)
(3 508 021–4 030 022) (4 680 954–5 321 362)
Alcohol use
77 550
1014 860 168
875
28 4
720 059
3 700 324
No data1indicates that attributable deaths were not quantified.
(70451 511–3 967 436) (4 533 106–5 153 283) 76
706–84 835)
(92 405–111 165)
(25
22 080) (1 541 469–1 886 125) (3
n
Both
Drug use 385 attributable 1068 577sexes and risk factor clusters,49
178 factors
16 248 805
48
157
Table 3: Deaths
to risk
w
(7787–13 073)
679–20 132)
3)
(36 780–64 303)
(50 706–102 395) (12 (124 639–209 873) (3811
0
結果：生理学的リスク因子
2010
Physiological risk factors
1990
··
2010
··
3
10 165
52 169
21 187
High fasting plasma
30 177
49 148 271
26 1
1 607 214 650)
2 104 174 073)
3 356 957)
45 792)
(428–19
(2700–93
(866–40
619–57 197)
(22 24
0 327)glucose367 465–1 839 764) (25797 633–2 401 170)(41 (2 917 520–3 782 483) m
(1Sneha, Chennai, India(1 148–34 980)
the combined 7775
effects of
7
3694
21 172
22 519
23 179 811
17 0
1 057 196
2 018
(ProfHigh total cholesterol Royal1 945 920 491)
L Vijayakumar MBBS);
approximated 572 853–2
the705) (13
joint effe
9 424)
(242–7511)
(1517–40029) 054) (514–15650)479 097) 94
(18625 929–2 318
(17 (1
148–29
8 442)
(793 595–1 350 633)
(1 230–27
Children’s Hospital and Critical
きゅうひゃくさんじゅうきゅうまん
1
7192
14
assuming that 927 factors8
293 185
High 4 645 279
blood pressure Theme, 7 36 050
73 120
99 9 395 860
566 risk
63
Care and Neurosciences
2 889)
(187–14 092 003) (65701 203–7 859 894) (377–29 705) 147 805)90
(1115–66 871)
250 099) (4 198 029–5 099)
(6 538–81 302)
579 630–10
(88(8However, (57
independent.193–110 943) risk f
Murdoch Children’s Research
さんびゃくさんじゅうななまん
··
3
High 1 738 466 ··index VIC,1 963 549 necessarily371 232
body-mass
25 391 ··
48 310 ··
26 1
Institute, Melbourne,
not 345 133) (2 817 774–3 951 127)
independent;
86 482)
(1 454 008–2 036 059) (19590 282–2
(1 752–31 108)
(39 429–57 750)
(20 91
2
29 431 Weintraub); 81 699
76 163
Australia (R
of the burden086–85 171) 13
84mineral density
146
103 270 859)
187attributable
4–40 402) bone 722–33 273)
(25 of Nottingham, (71 012–92
(68 586
Low
1764
3105
University
10)
(57 863–102 441)
(90 672–124
(140 636–219
(1448–2208)230) through high (1102
mediated (2295–3831) 906) blo
2
49 317
110 962
Nottingham, UK 175 909
数値は死亡数／年
(Continues on next page)
5–96 472)
(38 818–60 315)
(141 870–207 095) (86 848–137 813)

30. 1 299 715)
401 478
(325 516–484 452)
2 263 952 627–1 484 105) 117
860 (366 866–561
(997
(1 927 356–2 735 821) (715 742–1 033 573)
(62 505–107 021)
(130 444–197 085)
Iron deficiency
結果：タバコ
39 409
32 287
87 321
168 084 677–47 108) 119 608
No data indicates that attributable deaths were not quantified.(21 925–37 449
(30
6 884)
181 151
63 291
56 472
349 354
Table 3: Deaths attributable toBoth(85 775–341 439) 119 762(32 070–104 03
risk factors and risk factor clusters, w
n
sexes
98 163)
(28 192–91 464)
(170 504–632 149)
(61 723–191 846)
0
3 841)
Vitamin A deficiency
(93 261–139 985)
Zinc deficiency
44 940
2010
(7696–87 711)
275 590143 518
97 330 52 390
1990
2010
(27 451)
(9382–105 728
(51 274–529797–276 850) 575–190 527)
(17
3 Tobacco smoking (including
10 165
52 1693 680 571 ろっぴゃくにじゅうまん 059
21 187 4 507
1 790 228
5 329 808
6 297 287
the(3 combined395 (3 006 of
45144 408) Sneha, Chennai, India(2700–93 073)427–4 229 530)769–7757 779–5m
–2 792)
(1(428–19 650)
278 666–2
(5 effects
second-hand smoke) 094 260) (4 778 526–6 049 296) (866–40 957) 942) 09
213
L Vijayakumar
approximated5 695 349joint effe
the
7(ProfTobacco 443 924MBBS); Royal 421 1723 332 192
3694
7775 4 251 424
1 smoking
576 298
Children’s Hospital and Critical (4 068 753–5 312 438)840 033) 705) 674–4
(4 755 779–6
9781 819) (920 763–1 743 849) (1517–40 491)957–3that risk 503 611) 8
424)
(242–7511)
(514–15 (3 421
(2 871
assuming ろくじゅうまん factors
Care and Neurosciences Theme, 36 050
346 304
753 510348 378
601 938
1
7192
14 927 255 634
Second-hand smoke
independent.(447 705–745 328)risk 5
However,
f
00 100)
(252 702–439 439)
(585 131–912 313)
Murdoch(187–14 099)
Children’s Research(1115–66 871)
(273 555–425 310)
(191
2 889)
(377–29 705) 587–314
1 768 073
5 independent;
051
Institute, Melbourne, VIC, 3 762 356367 579
not2necessarily018 ·· 3 249 978
Alcohol
use
·· 545 612) and drug ·· 935 072) (3 508 021–4 030 022) (4 680 954–5 321 362)
··
1
(1 588 197–1
Australia (R Weintraub); of (2 201 233–2 555 818) (3 004 655–3 4
the burden163
attributable
2
29 431
81 699
76 168
1 720 059
3 700 324
4 860
University of Nottingham,
Alcohol use 469–1 886 125) (71451 511–3 967 436) (68533 106–5140 109blo
2 325 747
–40 402)
(25
1 522 080) (1 541722–33 273)
(3 012–92 859) through 3 171)
mediated (4 086–85 153 283)
high
Nottingham, UK
(2 153 733–2 512 207) 数値は死亡数／年3
(2 902 204–3
48 317
2
49 385
175 68 577
909
110157 805
962

31. (27 797–276 850)
(9382–105
(7696–87 711)
(51 274–529 451)
(17 575–190 527)
Tobacco228
1data indicates that attributable deaths were571 quantified. 4 507 059
790 smoking (including5 329 808 3 680 not 6 297 287
No
08) second-hand smoke)
(1 278 666–2 094 260) (4 778 526–6(3 213 427–4 229 530) 006 942)
049 296) (5 395 769–7 (3 757 779
Tobacco smoking
1 443 Deaths attributable to298 factors and risk factor clusters, w
4 576 risk 3 332 192 5 695 349 4 251 424
Table 3:924
n
Both sexes 957–3 840 033) (3 503 674
)
(920 763–1 743 849)
(4 068 753–5(2 871
312 438) (4 755 779–6 421 611)
0
2010
Second-hand smoke
255 634
346 304 2010
7531990 348 378
510
601 938
310)
(252 702–439 439)
(58552 169 (273 555–42521 187
131–912 313)
(447 705–745(191 587–3
328)
3
10 165
ごひゃくいちまん
Sneha, Chennai, India(2700–93 073) 579 (866–40 957)3 249 978
the combined018 051
Alcohol 073
1
3 762 356 2 367
5 effects of m
45 792)768 and drug use
(428–19 650)
2) (1 588 197–1 935 072) Royal 21021–4(2 201 233–2 555 818)joint 362)
(Prof L Vijayakumar MBBS); (3 508 approximated 680 954–5 (3 004 655
the 321 effe
7
3694
172 030 022) (4 7775
Children’s use
1 720 059
3 700 324 2 325
4 860 168
9 424)Alcohol Hospital and Critical(1517–40 491) 747 that risk factors
(242–7511)
(514–15 705) 3 140 109
assuming (4 533 106–5 153 283)
0)Care and Neurosciences Theme, 36511–3 967 436)
(1 541 469–1 886 125) (3 451 050 (2 153 733–2 512927
1
7192
14 207) (2 902 204
independent. However, risk
Murdoch(187–14 099)
Children’s Research(1115–66 871) 682 (377–29 705) 109 420f
68 577
157 805
Drug 385
46
2 889) 48use
Institute, Melbourne, VIC, 706–102necessarily independent;
(36 780–64 303)
(50 not 395)
(124 ··
873)
(33 063–78 398)639–209(82 297–1
··
··
··
Australia (R Weintraub); of the burden attributable
2 Physiological risk factors
29 431
81 699
76 163
University of Nottingham,
1 607 (25
3 356 271 1 749
–40 402) fasting plasma glucose104 012–92 859)401 (68 086–85 171) blo
High 214 722–33 273) 2 (71 174 1 051 through high 058
mediated
Nottingham, UK 633–2 401 170) (2 917 520–3 782 483)
)
(1 367 465–1 839 764) (1 797
(865 949–1 110 962数値は死亡数／年
250 550)
(1 455 169
2
49 317
175 909
結果：アルコールとドラッグ

32. 2010
1990
1990
2010
2010
結果：大気汚染
171 097
9 Unimproved water and sanitation
166 379
715 244
337 476
No data indicates that attributable365 873 were not quantified.
deaths
638 433)
(6690–326 989)
(36 817–1 279 551)
(13 150–648262)
(18 940–662 220)
(6841–326 205)
0 Table 3: Deaths source
56 663
116 463
Unimproved water attributable to288 007
147 857
risk factors and risk59 126 clusters, w
factor
n 546)
Both sexes
271
(3604–115 704)
(20 641–553 293)
(7518–233 136)
(10 566–282 890)
(3880–120 264)
7
120 851
0 Unimproved sanitation
2010
60 913)
(3104–242 452)
496 986
252 779
1990
(15 380–927822)
(8032–480 845)
244 106
123 255
2010
(6027–478 186)
(2924–242 588)
3 Air pollution 10 165··
52 169 ··
21 187 ··
··
··
··
さんびゃくにじゅうまん
Sneha, Chennai, India (2700–93 combined850540957) of m
the 073)
effects
45 792)
(428–19 650)
(866–40
2 Ambient particulate matter
1 373 113
2 910 161
3 223 428
1 549 448
1
(Prof L Vijayakumar MBBS); Royal (2 approximated 828 854–3 619 148)
92–1 pollution (1 187 639–1 563 793)
(1546 894–1 752 880) (1 7775 joint effe
the
7 559 747)
3694
21345184–3 286 508) (2 614 010–2 082 474)
172
さんびゃくよんじゅうまん
9 Children’s Hospital and Critical (1517–40 491)
1 pollution
4 309 715
3 546 399
Household air645 956 from solid
2 579 166
1 900 443
9 424)
(242–7511)
(514–15 705)factors
51–2 fuels411) (1 265 509–2 089 785) (3 assuming that risk 516 722)
655
(1717 246–2 824 893) (1 378 832–2
Care and Neurosciences Theme, 36720711–5 365 013) (2 679 627–4 518 572)
1
7192
050
14 927
4 Ambient ozone pollutionResearch 143 362
66 100
152 434
independent. However, risk f
77 087
86 335
Murdoch(187–14 099)
Children’s
2 889)
(1115–66 871)
(377–29 705)
116 663)
(21 362–115 225)
(47 539–251 885)
(52 272–267 431)
(25 256–134 021)
(30 551–153 776)
Institute, Melbourne, VIC, not necessarily independent;
··
··
··
··
9 Other environmental risks
346 751
209 923
773 280
109 224
426030
119 745) Australia (R Weintraub); (177805–131 511)
(281 555–413 370)
(640 893–929 465)
of the burden 163 935)
attributable
(91 673–243 565)
(341 744–541
2
29 431
81 699
76
University of Nottingham,
·· Residential radon 978 273)
28
··
··
70 014
4–40 402)
(25 722–33
(71 mediated through high blo
012–92 859)
(6898 992 171)
086–85
(4098–64 387)
(13 133–215 237)
Nottingham, UK
(9140–154 460)
数値は死亡数／年
2
49 317
175 909
110 962

33. 745–57 799)
(13 085–80 413)
(20 479–114 435)
(29 728–171 340)
(10 464–58 211)
(16 106–91 527)
(9745–57
12 551 Diet high in red meat
16 762
38 092
1326 439
888
21 330
12 551
425–22 054)
(4306–29 007)
(7374–45 232)
(10 749–65
(3859–23 763)
(6175–37 340)727)
(3425–22
No data indicates that attributable deaths were not quantified.
Men sexes
Wome
34 476
367 296
731
840 857
Women Diet high in processed meat
Both 675
397 198 sexes
473 562
334 476
n
Both
1 692–584 050)
(83 446–637 120)
(158
(188
(85 536–688 905)423) (103 608–842460 279) (71 692–5
1990 044–1 257
2010 952–1 923)
1990
990Table 3: Deaths attributable to risk factors and2010 factor clusters, w
2010
1990
risk 521
83
138
183 799
299
0 548(Continuedin sugar-sweetened
1990
2010
Diet high 2010480
100 250
161 042
83 548
from previous page)
3 949–117 567)
(127 938–240 028) (111 700–219 563)
(212 310–403 716)
beverages (91 257–203 236)
(69 485–134 139)
(53 949–1
いっせんにひゃくごじゅうまん
Dietary risk factors815 748
and physical
4 8584 144
473 276
6 687 621
4 057 5
057
5
12 742 888
503 187
3 558
52 530 835
169
21 370
250 541 Diet low in 10 165994
300
fibre
333 603
441 895
250 541
inactivity
(4 110065–914 729)
(6
206
31 867–394 088) (428–19 650)261 225) (2700–93 073) 556) (201172379–1 166 933) (111 704 3
704 325–4 431 571) (134 201–470 634)
(5 380 274–6
(7 007–521 852
(260 262–4712)
(334 230–7234) 283) (3 867–
(149907 898–9 150 862) (11 710 741–13 324 770)
062–693 957)
45 792)
(866–40
よんひゃくきゅうじゅうまん
2 4882 305
013 415
24 902 242
837 481
1 653 7
653 787 Diet low in calcium 181
2 064
3 667 202
33 330 Diet low in fruits49 761
125 594
975
76 413
33 330
7008–43 904) 693) 3694
21 870 267–4535)
(1 016–63 592)
(2 324–108 394
(3 818651–3 881
356–5
3 269 335–2 006Sneha, 593 495–2 507 876) (32 570 347–2 435 112) (51 653–103 188) 649) (23 008–4
(34Chennai, India (1 814–66 562) 152) (2 2037775 414 561) (1 269
(57172
(88 323–164 800)
11 389 896 705)
017 500
674 3
966 440 LDiet low in seafoodMBBS); fatty (1517–40 491)
424)
(242–7511)
(514–15
674 309 Diet low in vegetables
779 754
1043 085
454 057
1 797 254
(Prof Vijayakumar omega-3 Royal 576 646
596 246
1779 747
793 650
466 440
(535 472–1 041 627)
441 649–910 150)
(521 285–1 040 304) (418 376–735327517) (574 205 059–2 930)401) (337 205–
(978 665–1 924
(1 010 300–1 394 366) (441 64
37 205–601 988)
(437 287–764 762)
(757 418–1 746)334) (687 787–1 378 721)
(1 241–1 010 781
acids
結果：食事と運動不足
the combined effects of m
approximated the joint effe
1 Children’s Hospital and Critical 36 050
7192
14 927
649 676
963 640
580
580 600 Diet low in polyunsaturated fatty
762 171
1assuming that 603
230 276
1 725 risk factors6
99 388 Diet low in whole grains
227 307
448 065
248 677
306533 812
296
199 388
2 418–305 733)Neurosciences Theme,(1115–66 871) 812) (140342 896–2705) (95 418–3
889) acids
(377–29 067
(503 984–787 057)
(748 116–1 162 854)
447 140–706 303) (187–14 099) 194)
(592 879–919
(958 136–1 787)
489
(1 873–473 149) 224) (447 14
5 Care and
(108 675–350 709)
(213 262–687 396)
(245 096–820 721)
(117 929–381
However, risk 4f
Diet low Children’s
1 389 433
872
872 483 Diet highin nuts222 173 Research 1 041 726
1 082 seeds
1independent.471 823··
914 461
209
2 515 260
Murdochin transand390
·· 736
··
64
367 ··
fatty acids
202 725
293 087
164 736
541 757–1 147 258)
(663 158–1 441 054) (144216 363–2 487 874) (209559 603–3 226 994) (117 395–
(1 395–260 843)
(1 155–371 817 734)
(667 481–1 349609)
(890 869–1 284)
(541 75
7 395–211 588)
(160 511–283 740)
(265 936–467 266)
(371 081–649
Institute, Melbourne, VIC, 81not necessarily 163 451)
2 33 312 Diet low in29 431 858
699
76 independent;
100 951
838
sodium
1 19768355
1 732 870
1 047 33 3
642
47 642 Diet high inmilk371 438
1 46
234713
245 150
354 093
104 308
Australia (R Weintraub);(71(20 479–114 435)burden086–85 171) (666 779
9745–57 799) 486)(25(878 780–1 834 541) (776459 900–2 966 107)(1(16016 734–4 105 019) (9745–
(13 085–80 413)
(29 728–171 340)
–40 402)
722–33 273)
(68 attributable
(10 464–58 211)
of the448) 122106–91301 781)
962–1 589
66 779–1 397
(1012–92 859)
(2 107–2 527)
of and
Nottingham,17513 888 ··
12 551University1 636 107 physical
26 439
38 092
Diet high in red meat low
21 330
12 5
··
1 547 833
·
·· Physical inactivity 16 762
3 183 962
2
49(4306–29 007)
317
909 232)
110 940 727)high blo
mediated through718 963) (3425–
3425–22 054)
(7374–45
(10 464–1
(3859–23 763)
activity
(1(6175–37 340) 192)
264749–65835
(1 369 722–1 899 182)
(2 657 204–3
Nottingham, UK
さんびゃくじゅうまん
さんびゃくじゅうはちまん
–96 472)
334 476
(38 818–60 315)
367 296
Diet high in processed
Occupational risk factors meat
(141731 675 095)
870–207
397 198
694 403
(86 848–137 813)
840 857
473 562
334
749 857 数値は死亡数／年 4
116 743

34. 974–3015)
(4534–6279) (506–2563)
(11 750–16 327) (375–1
(460–2456)
(2406–3459)
(6491–9683)
(19
(375–1990)
(331–1758)
(851–4426)
(837–4299)
4 599 environmental risks
82 894
339 965
166 095
Other
28765365
9434051
248
2489 Child and maternalattributable175 366 were not quantified.
6617
16 202
83
16
No data 077) (69 that 757)(2406–3459)
indicates 171–98
deaths
39 926–192
(289 845–402 489) (139 685–193 981)
(7476–12 045)
(1974–3015)
(5322–7938)
(4534–6279) 406) (13 212–19 503) (1974–
undernutrition
(146 049–211
(67 963–99 704)
(13
50 359 ··
21 600
110 261
47
Residential radon965
·· 902
1514 537
··
2114
Suboptimal breastfeeding risk
59
25factor clusters, w
572
5
Table526)
3: Deaths attributable toBoth factors and risk868–67 518)
2
(13 717–31 340)
(69 615–153 539) (273–4660)
(29
n 186–70
sexes (191–3383)
(84–1355)
(36 953–84 059)
(15 540–37 260)
(3
43 601 exposure 18 850
96 330
41 108
Lead Non-exclusive
28765365
7920936
248
2489
6017
132010
0
2010
1990
52 729
22 258
4
6 173–62 072)
(10 926–27
(57 274–135
(23 668–58
(1974–3015) breastfeeding 569)
(4915–7231) (2406–3459) 861)(6491–9683) 327) (1974–
(4534–6279)288)
(11 750–16 913)
(30 540–75
(12 464–32 936)
(2
36758
10 165 894
5213 965
169
21095
187
3114
931
6429
164 599and maternal
82
339
Child
175 366 7173
83166 3314
202
164 59
Discontinued
Chennai,
the combined effects (139 92
45 792)
(428–19 650) India (1443–30 406) (67 (139 685–193
(866–40426)
96–14 710) Sneha,(69 171–98 757) (2700–93 073) 489) 963–99 704) 981)of m
(296–6915) (146 (289 845–402
062)
(605–14 957)
(139 926–192 077)
undernutrition
049–211819)
breastfeeding
(767–15
(324–7377)
(6
じゅうきゅうまんななせん
ななまんななせん
approximated77the joint effe
7(Prof L
3694045
9350 359Vijayakumar MBBS); Royal 21 172
028
36 965
197 261
316
21
110 741
477775
537
Suboptimal breastfeeding
25 572 270
50 35
Childhood underweight 59 902 713
104
41
9
Children’s Hospital717–31 340) (1517–40 491) 276) (29 868–67 518)
and Critical (169 224–238
(13
8424)
656–112 526)
(64
9(32 186–70766) (29 430–43 394) 953–84 059) 539)(15 540–37 260) 943) (32 186
(242–7511)
(514–15 705) factors
(36 (69 615–153 697) that 497–91 007)
assuming (33 478–50
risk
(87 668–128
(78
43 601 Neurosciences
41 108
30Care Non-exclusive18 850 Theme, 3696 841
390 and
28
51 330
48
1
7192251
050
14 225
52 729 451
22 258 927
43 60
Iron deficiency
21
19 974 913)
independent. However, risk 3
f
(26 173–62 072)Children’s Research (37 477–71 202)
(10
(57 274–135
(23 668–58
Murdoch (20 099)
2889) breastfeeding 195–39 063) (1115–66 871)
473–40 703) (187–14 926–27 569) 540–75 288)861) (12 464–32 936)592) (26 173
(33 769–67
2
(377–29 705)
(30 (14 947–30 321)
(13 595–28 289)
(2
6758
3114
13 931
6429
not necessarily5672
4 598 Institute, Melbourne, VIC, 30 288
5098
10independent;
770
··
··
·· 062)
Discontinued(296–6915)
7173 689
3314 ··
675
Vitamin A deficiency
15
1
(696–14 710)
(1443–30 488)
(605–14 426)
068–25 637)
(2566–8168)
(5625–17 149)
Australia (R Weintraub); (14 884–54 burden attributable
of741
the
breastfeeding
(767–15 819) 165) (324–7377)
(696–1
(7475–29
(2904–9348)
(70
2 93 028
29 431 045
81 699
76316
163
36
779136
Nottingham, 197 375
11 709 University of4256
24
Childhood deficiency
270086–85 171) blo
12
1
–40 402) Zinc underweight
(25 (29 430–43 104 713 666
(78 656–112 766) 722–33 273)394) (71 012–92 859) through 903)
(169 224–238 276) 41(64 497–91 943)
mediated (684880 high 93 02
640–22 049)
(1131–7821)
(5385–45 685)
(2458–16
Nottingham, UK 668–128 697) (33 478–50 007)
(87 (2938–23 883)
(78 656
(1203–9316)
(26
30 390
28 251
51 841
48 225
2
(4915–7231)
結果：幼児期の低体重
49 317
175 909
110 962

35. ··
··
··
Ambient particulate matter
1 549 448
1 373 113
2 910 161
3 223 540
Nopollution
data indicates that attributable deaths were not quantified.
(1 345 894–1 752 880)
747)
結果：その他の環境リスク
(1 187 639–1 563 793)
1 850
(1 614
(2 546 184–3 286 508) (2 828 854–3 619 148)
Household air pollution from solid
2 309 166
1 900
1 3: Deaths
4 toBoth sexes
715
3 546 399
Table645 956 attributable 579risk factors and risk factor clusters, w
n
fuels 509–2 089 785) (3 717 711–5 365 720 246–2 824 893)516 722)
(1 013) (2 679 627–4 (1 378
411) (1 265
0
2010
1990
Ambient ozone pollution 143 362
66 100
77 087 2010
86
152 434
(25
(30
(47 539–251 885) 256–134 021)
(52 272–267 431) 55
(21 362–115 225)
3
10 165
52 169
21 187
Sneha, 650)
Other environmental risks 209the combined effects of m
109 (866–40 957)
426
923
45 792) 346 751 Chennai, India(2700–93 073) 224 773 030
(428–19
(177 673–243 (91
(640 893–929 935)
Vijayakumar MBBS);
approximated 511)
the
effe
7(Prof L(281 555–413 370) Royal 21 172 565)805–1317775 joint(341 74
3694
Children’s978
28 Hospital and
··
9 424)Residential radon Critical(1517–40 491)
(242–7511)
(514–15 705)
·· 98 992 factors
70
assuming that 133–215 237)
risk
(4098–64 387)
(13
Care and Neurosciences Theme, 36 050
(9140
1
7192
14 927
にじゅうまんきゅうせん
ろくじゅうななまんよんせん
independent. However, risk f
Murdoch(187–14 Research(1115–66 871)
Children’s
317 772
209 923
674 038
2 889)Lead exposure 099)
(377–29 705)
109 224
356 2
(265 722–376 431)
(177 not necessarily independent;
(575 858–779 314)
Institute, Melbourne, VIC, 673–243 565)805–131 511)
(91
(292 5
··
··
··
··
Australia
698 442 (R Weintraub);473of the burden attributable
3
589
1 438 305
2 Child and maternal undernutrition
29 431
81 699
76 163
1 805 224(1 175 257–1 713 103) 8
739
532) University of Nottingham, 906 896–4 175 138)
(569 013–832 012)
(2
–40 402)
(25 722–33 273)
(71 012–92 859) through888) (570 5
mediated 043–2086–85 171) blo
(1 479 (68 219 high
251 368 Nottingham, UK 275 024
1
544 817
数値は死亡数／年
2
49 317
175 909
110 962

36. Intimate partner violence
Diet low in whole grains
Diet low in vegetables
Diet low in seafood omega-3 fatty acids
Occupational low-back pain
結果：2010年の疾病負荷比較
1億7500万生命年
C
High blood pressure
Tobacco smoking, including second-hand smoke
A
Alcohol
Tobacco smoking, including second-hand smoke use
Household air pollution from solid fuels
Childhood underweight
Diet low in
Household air pollution from solid fuels fruits
Alcohol use
High body-mass index
HighHigh blood pressure
fasting plasma glucose
Suboptimal breastfeeding
Childhood underweight
Diet low in fruits
Ambient particulate matter pollution
Ambient particulate matter pollution
Physical inactivity and low physical activity
High fasting plasma glucose
DietDiet high in sodium
high in sodium
High body-mass index
Diet low in nuts and seeds
Diet low in nuts Ironseeds
and deficiency
High total cholesterol
Suboptimal breastfeeding
Iron deficiency
High total cholesterol
Occupational risk factors for injuries
Diet low in whole
Diet low in vegetablesgrains
Diet low in vegetables
Unimproved sanitation
Diet low in whole grains
Diet low in seafood omega-3 fatty acids
Vitamin A deficiency use
Drug
Diet low in seafood omega-3 fatty acids
Occupational risk factors for injuries
B
–0·5
Cancer
Cardiovascular and
circulatory diseases
Chronic respiratory
diseases
Cirrhosis
Digestive diseases
Neurological disorders
Mental and behavioural
disorders
Diabetes, urogenital,
blood, and endocrine
Musculoskeletal disorders
Other non-communicable
diseases
0
2
4
Disability-adjusted life-years (%)
Figure 2: Burden of disease
Childhood underweight
Household air pollution from solid fuels
High blood pressure
Suboptimal breastfeeding
6
HIV/AIDS and tuberculosis
Diarrhoea, lower respiratory
infections, and other common
infectious diseases
Neglected tropical diseases
and malaria
Maternal disorders
Neonatal disorders
Nutritional deficiencies
Other communicable diseases
Transport injuries
Unintentional injuries
Intentional injuries
War and disaster
8

37. 200
Disability-adjusted life-years, 95% uncertainty intervals (millions)
or
or
0.
so
s,
dks
n,
an
er
co
or
he
en
ex
d)
sk
en
en
as
結果：DALY推定の不確実性
＊つまり推定の不確実性も大きいので
ランキングも絶対的なものではないと
いうこと
150
100
50
0
0
5
10
15
20
25
30
35
40
Rank
Figure 4: 95% uncertainty intervals for risk factors ranked by global attributable disability-adjusted
45

38. DALYs) and 2010 (3·1% [2·6–3·7]; table 3). Although the
fraction of disease burden attributable to iron deficiency
fell relatively little, suboptimal breastfeeding, unimproved
water, unimproved sanitation, vitamin A deficiency, and
five. Unimproved water, unimproved sanitation, vitamin
A deficiency, and zinc deficiency have large uncertainty,
which reflects the substantial uncertainty in the estimates
of etiological effect sizes for these risks.
結果：1990年と2010年の比較
1990
Mean rank
(95% UI)
Risk factor
2010
Risk factor
Mean rank
(95% UI)
% change (95% UI)
Figure 3: Global risk factor ranks with 95% UI for all ages and
PM=particulate matter. UI=uncertainty interval. SHS=second-h
http://healthmetricsandevaluation.org/gbd/visualizations/regi
1·1 (1–2)
1 Childhood underweight
1 High blood pressure
1·1 (1–2)
27% (19 to 34)
2·1 (1–4)
2 Household air pollution
2 Smoking (excluding SHS)
1·9 (1–2)
3% (–5 to 11)
2·9 (2–4)
3 Smoking (excluding SHS)
3 Alcohol use
3·0 (2–4)
28% (17 to 39)
4·0 (3–5)
4 High blood pressure
4 Household air pollution
4·7 (3–7)
–37% (–44 to –29)
5·4 (3–8)
5 Suboptimal breastfeeding
5 Low fruit
5·0 (4–8)
29% (25 to 34)
5·6 (5–6)
6 Alcohol use
6 High body-mass index
6·1 (4–8)
82% (71 to 95)
7·4 (6–8)
7 Ambient PM pollution
7 High fasting plasma glucose
6·6 (5–8)
58% (43 to 73)
7·4 (6–8)
8 Low fruit
8 Childhood underweight
8·5 (6–11)
–61% (–66 to –55)
9·7 (9–12)
9 High fasting plasma glucose
9 Ambient PM pollution
8·9 (7–11)
–7% (–13 to –1)
10·9 (9–14)
10 High body-mass index
10 Physical inactivity
9·9 (8–12)
0% (0 to 0)
11·1 (9–15)
11 Iron deficiency
11 High sodium
11·2 (8–15)
33% (27 to 39)
12·3 (9–17)
12 High sodium
12 Low nuts and seeds
12·9 (11–17)
27% (18 to 32)
13·9 (10–19)
13 Low nuts and seeds
13 Iron deficiency
13·5 (11–17)
–7% (–11 to –4)
14·1 (11–17)
14 High total cholesterol
14 Suboptimal breastfeeding
13·8 (10–18)
–57% (–63 to –51)
16·2 (9–38)
15 Sanitation
15 High total cholesterol
15·2 (12–17)
3% (–13 to 19)
16·7 (13–21)
16 Low vegetables
16 Low whole grains
15·3 (13–17)
39% (32 to 45)
17·1 (10–23)
17 Vitamin A deficiency
17 Low vegetables
15·8 (12–19)
22% (16 to 28)
17·3 (15–20)
18 Low whole grains
18 Low omega-3
18·7 (17–23)
30% (21 to 35)
20·0 (13–29)
19 Zinc deficiency
19 Drug use
20·2 (18–23)
57% (42 to 72)
20·6 (17–25)
20 Low omega-3
20 Occupational injury
20·4 (18–23)
12% (–22 to 58)
20·8 (18–24)
21 Occupational injury
21 Occupational low back pain
21·2 (18–25)
22% (11 to 35)
21·7 (14–34)
22 Unimproved water
22 High processed meat
22·0 (17–31)
22% (2 to 44)
22·6 (19–26)
23 Occupational low back pain
23 Intimate partner violence
23·8 (20–28)
0% (0 to 0)
23·2 (19–29)
24 High processed meat
24 Low fibre
24·4 (19–32)
23% (13 to 33)
24·2 (21–26)
25 Drug use
25 Lead
25·5 (23–29)
160% (143 to 176)
26 Low fibre
26 Sanitation
30 Lead
29 Vitamin A deficiency
31 Zinc deficiency
33 Unimproved water
Ascending order in rank
Descending order in rank

39. Articles
Western
sub-Saharan Africa
Central
sub-Saharan Africa
Eastern
sub-Saharan Africa
Southern
sub-Saharan Africa
結果：地域ごとの要因比較
Oceania
1
2
3
4
1
2
2
1
2
4
1
1
2
1
1
3
6
2
6
5
6
2
2
1
2
1
3
3
3
2
4
5
2
3
5
3
3
2
3
5
7
12
10
4
4
3
5
Alcohol use
3
3
Household air pollution from solid fuels
4
42
Diet low in fruits
5
5
7
7
High body-mass index
6
8
3
1
Caribbean
1
Tobacco smoking, including second-hand smoke
East Asia
High blood pressure
Risk factor
Global
South Asia
North Africa and
Middle East
Andean Latin
America
Central Asia
Southeast Asia
Central
Latin America
Tropical Latin
America
Eastern Europe
Southern Latin
America
Central Europe
High-income
North America
Australasia
Western Europe
11–15
26–30
>40
High-income
Asia Pacific
Figure 5: Risk factors ranked by attributable burden of di
Regions are ordered by mean life expectancy. No data=attri
Ranking legend
1–5
6–10
16–20
21–25
31–35
36–40
2
4
1
6
1
1
6
2
1
11
5
8
5
1
5
6
14
23
20
5
18
11
3
12
7
13
9
1
4
7
2
2
2
7
5
6
5
3
6
7
4
5
10
6
8
5
9
8
8
11
13
2
4
1
4
9
3
2
9
4
3
2
2
17
2
3
14
18
15
11
High fasting plasma glucose
7
7
6
6
5
7
5
10
8
5
3
5
7
6
4
4
7
1
6
10
13
Childhood underweight
8
39
38
37
39
38
38
38
38
32
23
13
25
18
21
14
4
8
9
1
1
1
Ambient particulate matter pollution
9
9
11
26
14
12
24
14
4
27
19
11
10
24
7
19
6
32
25
16
14
7
Physical inactivity and low physical activity
4
5
5
6
6
7
7
10
8
6
8
9
8
5
7
11
7
11
15
15
16
11
6
10
11
11
9
11
9
7
9
13
7
6
13
8
15
14
16
13
21
17
18
Diet low in nuts and seeds
12
11
9
8
8
8
8
8
12
10
8
15
8
12
9
10
13
13
16
22
16
21
Iron deficiency
13
20
32
21
35
22
17
21
4
Suboptimal breastfeeding
2248
10
Diet high in sodium
14
High total cholesterol
15
12
8
9
9
10
9
Diet low in whole grains
16
10
16
16
17
11
Diet low in vegetables
17
14
13
12
13
Diet low in seafood omega-3 fatty acids
18
17
15
13
16
Drug use
19
13
14
10
10
20
Occupational risk factors for injuries
20
24
24
20
25
26
Occupational low back pain
21
15
17
15
23
18
Diet high in processed meat
22
22
12
14
12
15
18
19
14
12
12
17
4
12
6
9
11
10
4
4
24
22
15
14
16
9
15
13
10
10
4
3
3
3
6
13
11
10
16
14
16
10
16
20
14
19
28
27
30
12
11
11
12
14
26
13
17
14
12
15
15
32
24
19
24
13
10
12
15
16
20
10
11
14
18
11
16
12
15
23
23
20
16
14
13
17
17
18
19
15
23
16
17
18
20
23
27
25
25
13
17
18
13
16
18
20
11
19
18
22
19
12
19
24
22
16
25
20
19
22
23
21
21
23
31
12
22
22
20
22
17
20
24
14
15
24
17
24
22
20
26
23
17
24
17
21
19
15
29
7
9
27
19
15
27
24
25
27
28
31
28
28
27
Intimate partner violence
23
18
22
23
22
25
21
22
21
23
26
22
27
19
25
23
21
25
14
18
20
23
Diet low in fibre
24
16
18
18
18
19
15
16
16
25
28
20
18
28
22
22
33
21
33
36
34
36
Unimproved sanitation
25
38
39
39
41
42
40
40
40
40
38
30
37
31
32
28
19
18
18
9
8
9
Lead exposure
26
23
21
19
24
17
19
23
22
20
25
24
23
20
26
21
24
30
20
25
26
26
Diet low in polyunsaturated fatty acids
27
19
19
17
20
21
22
18
26
24
27
21
22
29
24
25
32
23
30
33
30
29
Diet high in trans fatty acids
28
29
23
24
15
23
28
19
28
21
21
33
26
27
17
38
28
34
35
37
36
37
Vitamin A deficiency
29
40
40
38
40
41
41
42
43
41
37
32
34
34
37
33
30
31
17
11
7
8
Occupational particulate matter, gases, and fumes
30
34
33
32
28
32
33
31
23
29
32
28
29
33
31
34
26
33
29
29
29
31
Zinc deficiency
31
37
37
36
37
39
39
39
39
39
29
29
28
25
35
27
31
28
21
13
10
14
Diet high in sugar-sweetened beverages
32
28
31
31
19
33
26
27
37
26
17
25
32
30
28
20
27
26
26
32
32
34
Childhood sexual abuse
33
26
25
22
21
30
25
26
30
28
30
37
30
26
29
30
29
35
31
26
31
27
Unimproved water source
34
41
41
40
38
40
42
41
42
42
40
31
36
35
30
29
34
24
27
12
9
12
Low bone mineral density
35
21
20
25
26
24
30
28
25
30
33
35
35
36
34
32
36
37
38
35
37
33
Occupational noise
36
33
35
34
36
35
35
35
33
33
31
34
31
32
36
35
37
36
34
30
33
32
Occupational carcinogens
37
31
26
29
31
34
32
34
27
38
35
38
33
40
38
40
39
41
37
41
42
42
Diet low in calcium
38
25
28
27
29
27
29
30
31
34
39
39
39
39
40
37
40
39
39
38
39
38
Ambient ozone pollution
39
36
36
41
33
36
43
37
34
43
43
43
43
43
43
43
35
43
43
42
38
41
Residential radon
40
32
27
35
27
28
36
33
32
36
41
41
38
42
41
42
41
42
42
43
43
43
Diet low in milk
41
27
29
30
30
29
34
32
35
37
42
40
41
41
42
39
42
40
41
39
41
39
Occupational asthmagens
42
35
34
33
34
37
37
36
41
35
36
36
42
37
39
36
38
29
36
34
35
35
43
30
30
28
32
31
31
29
36
31
34
42
40
38
33
41
43
38
40
40
40
40
Diet high in red meat

40. Hi
m
e
W Asi
gh
es a
-in
te Pa
rn cifi
co
m
E
e N Au uro c
or str pe
So
ut C th A alas
he en m ia
rn tra er
La l E ica
t u
E in ro
co
-in
gh
Hi
H
Hi ig
ghhi
-innc
coom
me
H
A
W e A si
Hi ig
W es si a
ghhi
-innc estter a PPac
i
coom ernn Eacifific
m e A Euur c
e N Au ro
So Noortusstraoppe
So u C rt h tra la e
utth C en h A mlassia
A
heern en tr m e ia
rn L tra l erric
a
E
Laati l E u icaa
Ea tinn A urro p
Ea st A m op e
steer m e e
TTro
r n e ri
ro p
pi ica n EEurricca
a
CCencal l La EEauroope
en t L t a s p
r
traal ati in stt A e
A
l LLa n A mAssia
attin m e ia
S in A erri
No A SoouthAm eicca
ut m r a
No r A n
i
rtth ndde Cheeasericca
h AA eaan C e astt A a
f
fririca n LLa enntra Assia
ca a attintra l A ia
annd in A l A si
d M Am esiaa
M m
i
i
idddl errcca
dl e i a
So
So u
CCare EEas
utth
a i a
Soribbbestt
r
Eaheenn
So u be a
Ea s r s
utth a n
stter suub
Ce ernn b--S Oh A sn
Ce n su S ah O c A ia
s
W n tr s ah c i
W e traal ubb-Sa aaran eeana
esste l ssu -Sa h ran Aani ia
te rn ubb- haar A fr a
rn s -SSa raan fri ic
suub ahha n A f caa
A
b--S arra frric
Saah ann A ic a
haar A fr a
i
raan fricca
nA a
Af
frrica
ica
th
r as
er
So ibbe t
Ea n
ut an
ste su
h
bCe rn S O As
i
n s a
W tra ub- hara cea a
es l s Sa n nia
te ub ha A
rn -S ra fri
su ah n A ca
b- ar fr
Sa an ic
ha A a
ra fric
nA a
fri
ca
Figure 6: Attributable burden for each risk factor
As percentage of disability-adjusted life-years in 1990 (A), an
disability-adjusted life-years per 1000 people in 1990 (C), and
アルコール＠Eastern Europe
ordered by mean life expectancy. Burden of disease attributab
factors are shown sequentially for ease of presentation. In rea
attributable to different risks overlaps because of multicausal
effects of some risk factors are partly mediated through othe
risks. An interactive version of this figure is available online at
healthmetricsandevaluation.org/gbd/visualizations/regional
Disability-adjusted life-years (per 1000 people)
結果：地域ごとの疾病負荷量比較
0
C
D
800
D
600
400
200
0

41. 主要な結果のまとめ
■ 1990→2010年の間に、リスク要因の影響の主対象は
「小児の感染性疾患→大人の非感染性疾患」へ大きく
シフトした（例：幼児期低体重による疾病負荷↓、高血圧による疾病負荷↑）
■ ただし地域差は大きい（例えば、sub-Saharan Africaでは幼児期低体重
は依然大きい疾病負荷をもたらしている）
■ 煙草、家庭内空気汚染、大気汚染の疾病負荷は依然大
（家庭内排気施設の改善、燃料の代替、自動車・工場や野焼きの制限は優先度高の課題）
■ 豪州、北＆ラテン米を中心に高BMIの疾病負荷が増加

42. 論文の締め
疾病負荷の主要因がわかれば公衆の健康を改善するた
めの公共政策をより効率的に行うことができる
このような異なるリスク要因による疾病負荷の包括的
かつ比較可能な科学的アセスメントを行うことの主要
な利点は、政策についての議論に対してのエビデンス
ベースを提供することである
証拠と共に示された主要なリスク要因は、的を絞った
対策の実施により、少なくともいくつかの地域におい
てその影響が大きく減少した
大きな疾病負荷を持つリスク要因に対しては、迅速な
対応措置を行う必要がある
thx!

43. 参考文献等
DALYについて
池田・田端（1998）『わが国における障害調整生命年（DALY）』医療と社会 v8, n3
http://www.mhlw.go.jp/shingi/2009/07/dl/s0715-12d.pdf
＊他、DALYについてはWikipediaなどの説明も分かりやすいです
Global Burden of Disease Studyについて
WHOのGBDのページ
http://www.who.int/topics/global_burden_of_disease/en/
人口寄与危険度（Population Attributable Risk）について
日本大学の原野悟さんの講義資料『疫学概論 Lesson 15. 関連性の測定』
http://www.juce.jp/senmon/igaku/open/database/013_ekigaku/modules/mod6/epi6_15_D.pdf
NATROMさんの記事『「集団寄与危険割合」って何？疫学指標まとめ。』
http://d.hatena.ne.jp/NATROM/20120324
＊単なる寄与危険度（Attributable Risk）とはちょっと異なるので注意