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世界における疾病および死亡リスク要因の定量化(GBD Study 2010 in Lancetの論文紹介)

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世界における疾病および死亡リスク要因の定量化(GBD Study 2010 in Lancetの論文紹介)

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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