Pi is 014067361261719 x

8/22/2019 Pi is 014067361261719 x

1/24

Articles

www.thelancet.com Vol 380 December 15/22/29, 2012 2071

Lancet 2012; 380: 207194

See Comment pages 2053, 2054,

2055, 2058, 2060, 2062,

and 2063

See Special Report page 2067

See Articles pages 2095, 2129,

2144, 2163, 2197, and 2224

*Corresponding author

See Online for appendix

Institute for Health Metrics

and Evaluation, University of

Washington, Seattle, WA, USA

(H Wang PhD,

L Dwyer-Lindgren MPH,

K T Lofgren BA,

J K Rajaratnam PhD,

J R Marcus MPH,

A Levin-Rector MPH,

C E Levitz BA,

Prof C J L Murray MD); and

University of Queensland

School of Population Health,

Brisbane, QLD, Australia

(Prof A D Lopez PhD)

Correspondence to:Haidong Wang, Institute for

Health Metrics and Evaluation,

University of Washington,

Seattle, WA 98121, USA

[email protected]

Age-specific and sex-specific mortality in 187 countries,

19702010: a systematic analysis for the Global Burden ofDisease Study 2010

IntroductionAccurate estimation of the number of deaths in each ageand sex group in a country, region, or worldwide is acrucial starting point for assessment of the global burdenof disease. Information about rates of mortality at differentages, especially what might reasonably be regarded aspremature mortality, is an important impetus for publicpolicy action, especially when the causes of prematuremortality can be reliably established. Years of life lost(YLLs) due to premature mortality made up nearly

two-thirds of the global burden of disease in 2010.1 Levelsof mortality have been changing strikingly in the past40 years and substantial progress has been made inreduction of the number of deaths in children youngerthan 5 years, postponing deaths to progressively olderages.2,3 However, the number of young adult deaths hasincreased in the past 20 years, especially in eastern Europebecause of epidemics of mortality related to alcoholoverconsumption and in eastern and southern sub-SaharanAfrica because of HIV/AIDS.1,415 A careful assessment of

Haidong Wang*, Laura Dwyer-Lindgren, Katherine T Lofgren, Julie Knoll Rajaratnam, Jacob R Marcus, Alison Levin-Rector, Carly E Levitz, Alan D Lopez, Christopher J L Murray

SummaryBackground Estimation of the number and rate of deaths by age and sex is a key first stage for calculation of theburden of disease in order to constrain estimates of cause-specific mortality and to measure premature mortality inpopulations. We aimed to estimate life tables and annual numbers of deaths for 187 countries from 1970 to 2010.

Methods We estimated trends in under-5 mortality rate (children aged 04 years) and probability of adult death(1559 years) for each country with all available data. Death registration data were available for more than 100 countriesand we corrected for undercount with improved death distribution methods. We applied refined methods to survey

data on sibling survival that correct for survivor, zero-sibling, and recall bias. We separately estimated mortality fromnatural disasters and wars. We generated final estimates of under-5 mortality and adult mortality from the data withGaussian process regression. We used these results as input parameters in a relational model life table system.Wedeveloped a model to extrapolate mortality to 110 years of age. All death rates and numbers have been estimated with95% uncertainty intervals (95% UIs).

Findings From 1970 to 2010, global male life expectancy at birth increased from 564 years (95% UI 555572) to675 years (669681) and global female life expectancy at birth increased from 612 years (602620) to 733 years(728738). Life expectancy at birth rose by 34 years every decade from 1970, apart from during the 1990s (increasein male life expectancy of 14 years and in female life expectancy of 16 years). Substantial reductions in mortalityoccurred in eastern and southern sub-Saharan Africa since 2004, coinciding with increased coverage of antiretroviraltherapy and preventive measures against malaria. Sex-specific changes in life expectancy from 1970 to 2010 rangedfrom gains of 2329 years in the Maldives and Bhutan to declines of 17 years in Belarus, Lesotho, Ukraine, andZimbabwe. Globally, 528 million (95% UI 516541 million) deaths occurred in 2010, which is about 135% more

than occurred in 1990 (465 million [457474 million]), and 219% more than occurred in 1970 (433 million[422446 million]). Proportionally more deaths in 2010 occurred at age 70 years and older (428% in 2010 vs331%in 1990), and 229% occurred at 80 years or older. Deaths in children younger than 5 years declined by almost 60%since 1970 (164 million [161167 million] in 1970 vs 68 million [6671 million] in 2010), especially atages 159 months (108 million [104111 million] in 1970 vs 40 million [3842 million] in 2010). In all regions,including those most affected by HIV/AIDS, we noted increases in mean ages at death.

Interpretation Despite global and regional health crises, global life expectancy has increased continuously andsubstantially in the past 40 years. Yet substantial heterogeneity exists across age groups, among countries, and overdifferent decades. 179 of 187 countries have had increases in life expectancy after the slowdown in progress in the1990s. Efforts should be directed to reduce mortality in low-income and middle-income countries. Potential under-estimation of achievement of the Millennium Development Goal 4 might result from limitations of demographic dataon child mortality for the most recent time period. Improvement of civil registration system worldwide is crucial forbetter tracking of global mortality.

Funding Bill & Melinda Gates Foundation.

8/22/2019 Pi is 014067361261719 x

2/24

Articles

2072 www.thelancet.com Vol 380 December 15/22/29, 2012

the demographic evidence on the levels of age-specificmortality is an integral component of any Global Burdenof Disease Study: such analyses require the sum of deathsfrom specific causes to equal the independently assessedlevel of mortality from all causes, for every age and sexgroup. Such assessment is not a straightforward additionof reported causes. Because there are likely to be manymore data reported for levels of all-cause mortality thanthere are for individual causes, the independentassessment of age-specific mortality is crucial to constrainthe often less robust estimates of cause-specific mortalitywithin each population group defined by age and sex.

Accurate measurement of age-specific mortality,however, is severely constrained by the fact that mostdeveloping countries have incomplete or no vital regis-tration systems. Estimation of mortality rates requires

application of a suite of demographic estimationmethods that have been developed and refined duringthe past 60 years. For the Global Burden of Disease2000 analysis,16 assessment of age-specific mortality wasimproved in two important ways compared with theGlobal Burden of Disease 1990 analysis. 17 First, estimatesfor 2000 of under-5 mortality, measured as the prob-ability of death between 0 years and 5 years of age ( 5q0),and mortality as a young adult or middle-aged adult,measured as the probability of death between 15 yearsand 60 years of age (45q15), were developed after review ofavailable vital registration, sample registration, andcensus data and the application of the synthetic extinctgenerations and growth balance methods to correct for

under-registration of deaths.1823

When no vital regis-tration, census, or sample registration data were avail-able, adult mortality was predicted on the basis of therate of under-5 mortality. Second, a model life tablesystem was developed by Murray and colleagues togenerate age-specific death rates from estimates ofunder-5 mortality and 45q15.

24 This analysis yielded lifetables for 191 countries in 2000.25 Age-specific deathrates from HIV/AIDS were separately estimated andadded to the model-based estimates from this secondstage, allowing for competing risks. The Global Burdenof Disease 2000 mortality methods have continued to beapplied by the WHO in their annual updates of mortalitypublished in successive World Health Reports of 5q0and

45q15.26,27

An increasing body of evidence suggests asubstantial divergence in the key determinants of trendsin under-5 mortality and adult mortality;2833 therefore,the continued reliance on information about childmortality to predict adult mortality in a large number ofcountries is inadvisable.

The United Nations Population Division (UNPD)has generated demographic estimates since 1951 andproduces biennial assessments of population, mortality,and fertility for every country from 1950 to 2050. 34Compared with WHO, the UNPD uses a broader array ofdata sources such as survey or census data fororphanhood, widowhood, and sibling survival data in

selected countries. Another advantage of the UNPDestimates is that they assess a full time series of mortalityand other demographic parameters for every country ineach 2 year cycle of revision, compared with WHOanalyses that look at serial cross-sectional data formortality. The UNPD estimation strategy places greatemphasis on the demographic balance equation, inwhich population in an age group must equal newentrants to the age group minus deaths plus netmigration (the addition of immigration, minus emi-gration) and exits due to ageing out of a given age group.UNPD mortality estimates are, in effect, a byproduct ofthe primary task of estimating population by age andsex.34 Despite these advantages, the UNPD and WHOapproaches have limitations. For example, neitherapproach produces uncertainty intervals (UIs) for their

estimates of age-specific mortality, despite the verysubstantial uncertainty in the underlying data used toproduce them and uncertainty from model specification.Moreover in countries with large HIV epidemics, bothapproaches assume that the Joint United NationsProgramme on HIV/AIDS (UNAIDS) estimates ofdeaths due to HIV are additive to hypothetical HIV-freelife tables. As a result, demographic sources such as vitalregistration, censuses, or surveys are not used to validateestimates of age-specific death rates in countries withmoderate-to-large HIV epidemics.

As a key first step in the Global Burden of Diseases,Injuries, and Risk Factors Study 2010 (hereafter referredto as Global Burden of Disease Study 2010), we

reassessed levels and trends of age-specific mortalityworldwide. Improved methods for estimation ofcompleteness of vital registration,35 analysis of siblinghistory data,36 and synthesis of data with UIs 3,4,37 providethe basis for robust estimation of age-specific deathrates. We used these advances, and a further extensionof the Brass relational model life tables, to develop atime series of annual age-specific mortality rates for187 countries from 1970 to 2010, including uncertainty.In this report and accompanying appendix, we presentthe data, methods, and key findings of the Global Burdenof Disease Study 2010 on levels, trends, and age patternsof mortality worldwide.

MethodsOverviewBecause vital registration systems in most developingcountries are incomplete, measurement of child andadult mortality requires use of multiple sources ofdata and the application of appropriate methods. Ourapproach to mortality estimation can be divided intothree components: estimation of under-5 mortality,estimation of adult mortality, and estimation of age-specific mortality. Figure 1 provides a high-level sum-mary of the mortality estimation process, includingdifferent data sources and analyses that are incorporatedin these three steps and how they interrelate. The first

See Online for appendix

8/22/2019 Pi is 014067361261719 x

3/24

Articles


two steps can be subdivided into the identification of datasources, processing of data to yield estimates of under-5mortality or adult mortality taking into account varioustypes of biases in the data, and data synthesis. The thirdstep uses estimates of 5q0, 45q15, and a model life tablesystem to generate age-specific mortality rates andnumbers of deaths for 187 countries, for each of 24 agegroups, and two sexes, for every year from 1970 to 2010.In each of these three steps, we quantify the uncertainty

due to sampling error, known non-sampling error,missing data, and model parameters.

Estimation of5q0We applied the methods previously described by Lozanoand colleagues2 and Rajaratnam and colleagues.3 Theseefforts incorporated advances in the analysis of summarybirth histories that reduced the error from this datasource.38 We updated the database with recently releasedsurveys, censuses, sample registration data, and vitalregistration data; appendix p 33 summarises the presentdataset and the changes from that used by Lozanoand colleagues2 for their estimation of child mortality

worldwide. Appendix pp 70257 provide plots of the datafor every country and estimates of 5q0 with 95% UIs. Forthe data synthesis step, we have modified how weestimate the prior mean function used in the Gaussianprocess regression (GPR) so that it is exactly analogousto how it is developed for 45q15 (appendix pp 913).Improving on the method used by Lozano andcolleagues,2 we selected the GPR parameters scale andamplitude through out-of-sample predictive validity

testing. We modified this analysis to place moreemphasis on the validity of the 95% UIs. One importantoutput from GPR is an estimate of both the expectedvalue of 5q0 and 95% uncertainty in 5q0. The uncertaintyestimate captures both uncertainty in the prior meanfunction and the data variance from each observation.Data variance is a function of both sampling error andknown non-sampling error. Appendix pp 70257 showhow the 95% UIs around the estimates of 5q0 generatedby GPR widen over long intervals with no observationsand tend to narrow when there are more abundant andmore consistent data available. We have also updated theage and sex model used to estimate sex-specific mortality

Complete birth

historySurveydata

Summary birth

history

Vital registration

Sample registration

Raw5q

0

(by country, year)

Child mortality

Adult mortality

Vitalregistration

Sampleregistration

Survey data

DDM

adjustment

Sibling

history

Raw 45q15

(by country, sex, and year)

Data synthesis

New model life table system

Spatial-temporal regression

Gaussian process regression

Estimates of child and adult mortality

5q0 and 45q15 (non-shock and shock):

by country and sex from 1970 to 2010

Mortality envelope

Deaths by country, year, sex, and age

19702010

Age and sex model

Shock model

Figure 1: Mortality estimation process for the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study

5q0=probability of death from birth to age 5 years. 45q15=probability of death from age 15 years to 60 years. DDM=suite of demographic methods used to assess the

completeness of a vital registration system.

8/22/2019 Pi is 014067361261719 x

4/24

Articles


for early neonatal, late neonatal, postneonatal, and14-year-old mortality by adding new data.2

Estimation of45q15We applied methods used by Rajaratnam and col-leagues4 to assess levels and trends in 45q15 by country.That study took advantage of improved death-dis-tribution methods to assess the completeness of vitalregistration, sample registration, and recall of house-hold deaths in surveys or censuses.35 As a result ofdebate in the demographic literature,36,3945 the siblinghistory corrections to deal with survivor bias (whichrefers to the under-representation of high mortalitysibships in the population), zero-surviving sibship(which comes from the mortality experience of familiesnot represented because none of the siblings were alive

and eligible to respond to the survey), and recall bias(which refers to the downward bias where surveyrespondents fail to report the death of certain siblings)have been improved (appendix pp 45). We refined theoverall assessment of trends in completeness of vitalregistration and sample vital registration by use ofmultiple period estimates of completeness from theoptimised synthetic extinct generations, generalisedgrowth balance, and the combined method, to betteraccount for uncertainty (appendix pp 45). We removedthe formal assumption that child death registration isalways more complete than adult death registration,although this often remains the case.46,47 Importantly,mortality shocks due to wars and natural disasters have

been updated to reflect recent data from the ArmedConflict Database from the International Institute forStrategic Studies (19972011), Uppsala Conflict DataProgram/PRIO Armed Conflict Dataset (19462012),and the International Disaster Database (Centrefor Research on the Epidemiology of Disasters;19502012).4850 In the data synthesis step, the covariatesused in the GPR mean function have been modified tobe lagged distributed income (a lagged version of grossdomestic product, on a logarithmic scale), population-weighted average years of education in ages 1559 yearsby sex, and estimated crude death rates from HIV/AIDSby sex for ages 1559 years.51,52 The predicted crudedeath rate from HIV/AIDS was provided by UNAIDS

and was based on their 2012 assessment. Thesecovariates are used in the prediction model thatgenerates GPR mean functions together with countryrandom effects. The GPR yields a complete distributionof 45q15 for every year. We took 1000 samples from thisdistribution and computed the 95% UI for 45q15 fromthis sampled distribution.

New relational model life table systemEstimates of5q0 and 45q15, each with 95% UIs, can be usedto generate a full set of age-specific death rates with amodel life table system. We have developed such a modellife table system with flexible standards (MLTFS), which

is an extension of the modified logit life table system(MLLT) but designed to deal with two of its keylimitations (appendix pp 1424).24 First, the MLLT did notprovide valid estimates of age-specific mortality insettings with very high 45q15 attributable to HIV. TheMLLT overestimates mortality at older ages and under-estimates it at younger ages compared with reporteddata. Second, the MLLT, when applied to a time series of

5q0 and 45q15 can generate paradoxical trends in age-specific death rates in some age groups. For example,when both 5q0and 45q15are decreasing, the MLLT can yieldimplausible increases in some age-specific death rates.Out-of-sample predictive validity testing shows that theperformance of MLTFS is better than MLLT in predictionof age-specific mortality rates even in populationsaffected by HIV/AIDS.

In all relational model life table systems, the choice ofthe standard age pattern of mortality has an importantinfluence on the results. The MLTFS is designed to usedifferent standards, dependent on how much is knownabout the age pattern of mortality in a specific countryfrom reliable sources. 95% UIs in age-specific deathrates from this analysis capture four sources ofuncertainty: uncertainty in estimates of 5q0, uncertaintyin estimates of 45q15, uncertainty in the choice of thestandard, and uncertainty in model coeffi cients assuggested by King and colleagues (appendix p 22). 37

Another advantage of the MLTFS is that it yieldsimproved estimates of age-specific mortality for theoldest age groups. As survivorship improves with time,

the proportion of deaths that occurs in the oldest agegroups (ie, individuals aged 80 years) increases. Wehave developed a new approach to extrapolate age-specific mortality at ages 7579 years and 8084 years toevery 5 year age group up to age 110 years or older(appendix p 21). This model has notable advantages overthe most commonly used Gompertz model53 and othervariants of it in terms of out-of-sample predictivevalidity.

Estimation of numbers of deathsTo estimate the number of age-specific deaths by sex,year, and country, we applied two separate processes toage groups 04 years and 5 years and older. For the

04 year age group, we divided each 1 year birth cohortinto 52 birth week cohorts and follow them through toage 5 years. For each birth-week cohort, depending onthe start and end time of membership in each age group,we apply our mortality rate (ie, probability of death) esti-mates from potentially different years. For example, if aperson were born in November of year 1 and survived atleast 2 months, we regarded them as exposed to theunder-1 mortality rate in both year 1 and year 2. Details ofthis method are provided elsewhere.2 For age groups59 years and older, we used estimated age-specificmortality rates and population estimates to generatenumbers of deaths. We took birth and population

8/22/2019 Pi is 014067361261719 x

5/24

Articles


estimates from the most recent World Population Pros-pects (WPP) 2010 revision, Human Mortality Database,and WHO mortality database.34,54,55

We provide estimates for 187 countries that had apopulation of more than 50 000 individuals in 2000. Totake into account smaller populations within a region, wescale regional results by the ratio of the total estimatedregional population divided by the population living incountries for which we provide estimates.

Country-specific values of 5q0 and 45q15 measurementsare available from the authors on request as are themodel life table standards for each country.

Role of the funding sourceThe sponsor of the study had no role in study design,data collection, data analysis, data interpretation, or

writing of the report. The corresponding author had fullaccess to all the data in the study and had finalresponsibility for the decision to submit for publication.

ResultsTable 1 shows how global life expectancy changed in thepast 40 years. From 1970 to 2010, male life expectancy atbirth increased by 111 years and female life expectancyat birth increased by 121 years. The greater increase infemale life expectancy widened the gap between thesexes from 48 years in 1970 to 57 years in 2010. Globallife expectancy increased about 34 years per decade forboth sexes in every decade apart from the 1990s, whensmaller improvements were recorded (14 years for malelife expectancy and 16 years for female life expectancy),largely because of the effect of HIV/AIDS in someregions and deaths related to alcohol in eastern Europeand central Asia,515 coupled with a slowdown in survivalgains in childhood.5658 Appendix pp 5556 provideanother way to visualise the change in the global life

table by plotting global survivorship function by age overtime and comparing this change with the survivorshipfunction for the country with the lowest and highest life

Male life expectancy Female life expectancy

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

0 years 564

(555572)

598

(592602)

628

(623633)

642

(636646)

675

(669681)

612

(602620)

649

(643654)

681

(676686)

698

(693702)

733

(728738)

1 year 629

(619637)

650

(644654)

666

(661671)

672

(666676)

695

(688700)

667

(657676)

693

(688698)

714

(709718)

724

(719728)

749

(744754)

5 years 610(599619)

627(621632)

640(635645)

644(638648)

664(657669)

648(638657)

671(665676)

687(682692)

696(691700)

718(713723)

10 years 566(555575)

582(575587)

594(589599)

597(591602)

616(610622)

604(593613)

625(620631)

641(636646)

649(644653)

670(666675)

15 years 520(509528) 534(528540) 546(541551) 549(544554) 568(562574) 557(547566) 578(573583) 594(589598) 601(596605) 622(617627)

20 years 474

(464483)

489

(483494)

500

(495505)

503

(499508)

522

(515527)

512

(502520)

532

(527537)

547

(543552)

554

(550558)

575

(571580)

25 years 431

(421439)

444

(438449)

455

(451460)

459

(455463)

477

(471482)

468

(459475)

487

(482492)

502

(497506)

509

(505513)

529

(525533)

30 years 387(378394)

400(394404)

411(406415)

416(411419)

433(427438)

424(416431)

441(437446)

456(451460)

465(461468)

484(479488)

35 years 343(335351)

355(350360)

367(362371)

372(368376)

389(383394)

381(373387)

396(392401)

410(406414)

420(416423)

438(434442)

40 years 301

(293308)

312

(307316)

323

(319327)

330

(326333)

345

(340350)

338

(331344)

353

(349357)

366

(362369)

375

(372378)

393

(389396)

45 years 260

(253266)

270

(266275)

280

(276284)

288

(285291)

303

(298307)

296

(289301)

309

(305313)

321

(318325)

331

(328334)

347

(344351)

50 years 221

(215227)

231

(227235)

240

(236243)

248

(245251)

261

(257265)

254

(249259)

267

(264271)

278

(275281)

287

(285290)

303

(300306)

55 years 186(180190)

194(190197)

202(199205)

210(207212)

222(219226)

216(211220)

227(224230)

237(234240)

246(243248)

260(257263)

60 years 153(148156)

160(157163)

167(165170)

174(172176)

186(182188)

178(174182)

188(185191)

198(195200)

206(204208)

219(216221)

65 years 123

(120126)

130

(127132)

136

(134138)

143

(141144)

152

(149154)

144

(141147)

153

(151156)

162

(160164)

169

(167171)

180

(178182)

70 years 98

(96100)

102

(101104)

108

(106110)

114

(113115)

121

(119123)

113

(111116)

121

(119123)

129

(127130)

136

(134137)

145

(143147)

75 years 76

(7578)

79

(7881)

84

(8385)

89

(8890)

95

(9396)

87

(8689)

93

(9295)

100

(99101)

106

(105107)

113

(112115)

80 years 58(5759)

60(5961)

63(6364)

68(6768)

72(7173)

66(6566)

70(6971)

75(7476)

79(7980)

85(8486)

Table 1: Global life expectancy in years (95% uncertainty interval) by sex, year, and age, 19702010

8/22/2019 Pi is 014067361261719 x

6/24

Articles


expectancies for each sex. Substantial improvement insurvival for children younger than 5 years is evident butso also is the shift in the survivorship functions, suchthat an increasing fraction of a birth cohort is pro-gressively surviving to older than 7080 years. Thecontinued survival advantage of female individualscompared with male individuals is also evident. Globally,50% of boys born in 1970 could expect to survive to65 years compared with 70 years for girls, whereas 50%of boys born in 2010 would be expected to survive to73 years compared with 79 yearsfor girls. Table 1 providesadditional details on life expectancies at different ages,and at birth, confirming that all ages have had importantgains in life expectancy in the past 40 years, including atthe oldest ages.

Another way to understand the extent of progress in

reduction of global mortality is to examine the percentagedecline in death rates by age group from 1970 to2010 (figure 2). At the youngest ages (09 years) for bothsexes, rates of mortality have declined by more than 60%since 1970. At 1554 years of age, female death ratesgenerally declined by 4050%. The slightly lower pace ofdecline in mortality at 2534 years was probably due tothe rise of HIV/AIDS in these age groups.13,32,59 Thepercentage decline in mortality for women aged5579 years was also smaller than that at other ages,ranging from 40% to 43%, whereas for age groups olderthan 80 years death rates declined by about 25%. Theslower decline in death rates in the eldest population agegroup (>80 years) was affected by the rising mean age of

the population in this open-ended age group. Declines inmortality in men were uniformly lower than for femalesin all age groups, with differences between the sexesgreatest at 1554 years; indeed, global average mortalityrates in men aged 2039 years declined by 197% in1970 to 2010. Because the effect of HIV/AIDS has beenroughly similar for women and men at the global level,this reduced pace of progress for young men compared

with young women probably reflects slow progress inreduction of mortality from injuries that are predominatein these age-groups. Appendix pp 3435 show, by sex, theglobal age-specific mortality rates by decade. Detailedinformation about the oldest age group (80 years) is alsoincluded in these two appendix tables.

The progress of nations is often measured in termsof reduction in mortality rates, summarised by lifeexpectancy at birth. Table 2 summarises 40 years ofmortality transition in countries, showing life expect-ancy at birth at the beginning of each decade with95% UIs for 187 countries and 21 Global Burden ofDisease regions. In 1970, the highest life expectanciesfor men (7072 years) were noted in Andorra, Greece,and northern Europe populations with either veryfavourable diets or where smoking had only recently

become widespread. In countries where men hadalready been smoking for decades (Australia, NewZealand, UK, and USA), life expectancy was about4 years shorter than it was in the regions with longestestimated life expectancy. Life expectancy values rangeddown to about 3738 years in parts of sub-Saharan Africa(Angola, Mali, and Sierra Leone). In 1970, male lifeexpectancy in Japan ranked 11th worldwide at 696 years(694697). By 1990, male life expectancy in Japan wasthe third-highest in the world after Andorra and Kuwait(table 2), whereas improvements in life expectancy inAustralia, Denmark, New Zealand, UK, and USA weremuch smaller, due in a large part to the effect of tobacco.60More recently, Switzerland and Iceland overtook Japan

in world life expectancy rankings for men, and alongwith Sweden, Australia, and Israel, have life expectanciesin excess of 79 years. In 2010, in women, northernEuropean countries led the global rankings with lifeexpectancy about 5 years higher than men in the sameregion. Life expectancy in Japanese women in 1970 wasjust lower than 75 years (table 2). By 1990, Japanesewomen had the highest life expectancy of any majorpopulation, which continued to 2010 (table 2). Women inAndorra, France, Iceland, Spain, and Switzerland alsohad life expectancies in excess of 84 years, comparedwith 436514 years in Central African Republic, Haiti,Lesotho, and Swaziland.

Figure 3 shows the change in life expectancy at birth

between 1970 and 2010, by sex. The largest overall gainssince 1970 occurred in the Maldives at 273 years for menand 294 years for women, or an average of about07 years per calendar year. Substantial improvements inlife expectancy at birth, in excess of 20 years, were alsorecorded in Bangladesh, Bhutan, Iran, and Peru for bothsexes, Guatemala and Oman for female life expectancy,and Yemen, Bolivia, Cambodia, and Angola for male lifeexpectancy. Conversely, life expectancy has declined by17 years in populations severely affected by HIV/AIDS(eg, for men and women in Zimbabwe and Lesotho) andthe alcohol crisis in eastern Europe (eg, for men inUkraine and Belarus).9 Even in high-income countries,

Decline(%)

Age group (years)

0

10

20

30

40

50

60

70

80

8/22/2019 Pi is 014067361261719 x

7/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

World 564

(555572)

598

(592602)

628

(623633)

642

(636646)

675

(669681)

612

(602620)

649

(643654)

681

(676686)

698

(693702)

733

(728738)

High-income Asia Pacific 677

(667686)

715

(713717)

743

(741745)

765

(765766)

787

(787788)

732

(724739)

774

(772776)

808

(807810)

833

(832833)

853

(852853)

Brunei 669(659679)

706(695718)

731(724738)

742(735748)

755(743766)

690(680701)

728(717739)

760(753767)

781(773788)

791(780803)

Japan 696

(694697)

736

(735736)

760

(760761)

776

(775776)

793

(793794)

749

(747750)

789

(788790)

820

(819820)

841

(840841)

859

(858859)

Singapore 655

(652657)

686

(684688)

728

(726730)

762

(760763)

788

(786790)

720

(717723)

744

(741746)

779

(777781)

804

(802806)

833

(830835)

South Korea 612

(572653)

641

(633647)

681

(675686)

725

(724727)

765

(763767)

672

(635706)

718

(712724)

762

(757765)

797

(796799)

827

(826829)

Central Asia 560(536584)

599(590607)

621(617625)

619(614624)

646(634658)

639(621658)

683(678690)

703(699707)

704(700708)

733(722742)

Armenia 606

(563652)

653

(636668)

662

(652671)

674

(665683)

689

(672705)

682

(642720)

728

(716741)

742

(734751)

756

(749765)

785

(774796)Azerbaijan 516

(451576)

594

(573616)

623

(614633)

650

(641660)

689

(676702)

602

(546651)

680

(663700)

710

(701719)

727

(718735)

762

(749774)

Georgia 599

(546647)

642

(626657)

654

(644664)

683

(673691)

674

(660687)

689

(645726)

733

(722748)

745

(737753)

773

(765780)

779

(769789)

Kazakhstan 570

(513621)

591

(571609)

611

(601622)

571

(559583)

613

(591633)

663

(623708)

693

(679707)

710

(702718)

688

(677698)

722

(706737)

Kyrgyzstan 539(489587)

580(564599)

613(603622)

615(605626)

622(606639)

623(586660)

670(656683)

695(687703)

705(695713)

719(704732)

Mongolia 531(507556)

565(546583)

576(566584)

590(577602)

603(586622)

563(541583)

612(594631)

636(622645)

647(636659)

693(678708)

Tajikistan 539

(493584)

585

(567602)

609

(598620)

639

(629649)

652

(627675)

592

(543638)

640

(622655)

666

(655676)

688

(678698)

715

(694736)

Turkmenistan 491

(422555)

559

(535582)

591

(578604)

606

(582630)

654

(607695)

566

(508620)

638

(615660)

664

(651676)

687

(666707)

734

(695772)

Uzbekistan 588(538634) 616(599633) 640(631649) 638(628648) 656(619688) 651(610692) 686(672698) 705(696713) 702(693710) 723(695753)

East Asia 604(579628)

648(629666)

673(659687)

684(666696)

729(718740)

635(610662)

679(659697)

716(704728)

741(729750)

789(780799)

China 604(579629)

647(627666)

673(658687)

696(687705)

729(718740)

635(609663)

678(657697)

715(702727)

749(741757)

790(780800)

North Korea 537

(370637)

624

(527684)

664

(605704)

319

(185463)

680

(649707)

578

(427663)

664

(584718)

709

(659743)

446

(291568)

733

(706755)

Taiwan 665

(663667)

691

(690692)

713

(712714)

732

(731733)

759

(758760)

717

(715719)

741

(740743)

766

(765767)

791

(790792)

819

(818820)

South Asia 481(456506)

534(520549)

576(559592)

604(588619)

634(614654)

490(460518)

550(532565)

597(581614)

633(617649)

677(659696)

Afghanistan 402(369434)

433(392467)

522(488555)

519(485555)

582(542628)

416(376455)

451(414488)

517(475557)

508(453553)

573(522617)

Bangladesh 426

(358472)

529

(509548)

581

(562600)

643

(625662)

672

(656688)

475

(426517)

541

(520563)

598

(577621)

677

(658693)

710

(694728)

Bhutan 446(374509)

491(420549)

575(507637)

627(565681)

676(609733)

470(392544)

516(436589)

604(530667)

668(604720)

717(657771)

India 486

(455518)

535

(517553)

573

(552593)

600

(580618)

632

(606657)

491

(457526)

549

(527570)

595

(574615)

629

(610650)

675

(655699)

Nepal 490(460517)

512(486539)

576(556599)

639(620660)

677(655701)

522(488554)

542(512568)

600(579622)

669(650689)

706(686728)

Pakistan 524(489558)

574(550600)

615(596634)

626(603647)

639(607671)

511(466555)

590(561617)

632(613653)

645(618669)

678(648709)

Southeast Asia 553

(533569)

604

(588615)

637

(621647)

656

(641666)

680

(665692)

599

(578616)

649

(632660)

686

(672695)

711

(699719)

739

(728749)

Burma 477

(399551)

523

(443599)

544

(458619)

568

(487650)

607

(514698)

512

(427591)

561

(470638)

587

(509659)

625

(551687)

676

(601736)

(Continues on next page)

8/22/2019 Pi is 014067361261719 x

8/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

(Continued from previous page)

Cambodia 442(371488)

498(472519)

569(558580)

573(563583)

646(633660)

521(467559)

556(534576)

612(601623)

627(617637)

701(688715)

Indonesia 547

(526568)

602

(591612)

635

(627643)

663

(656672)

677

(660692)

576

(555597)

629

(616641)

665

(657672)

695

(687703)

718

(703733)

Laos 453

(356546)

492

(415565)

540

(459608)

585

(497655)

624

(544697)

503

(411585)

539

(461606)

585

(517649)

629

(560691)

671

(602735)

Malaysia 635(630639)

671(663678)

695(694697)

706(704707)

713(710716)

675(670679)

723(716730)

737(736739)

754(753755)

765(762768)

Maldives 502(473531)

575(556594)

653(644660)

718(711725)

775(767783)

510(476545)

565(543588)

648(640656)

738(732745)

804(797812)

Philippines 610

(600619)

608

(601615)

640

(635644)

650

(645654)

666

(655678)

688

(678695)

683

(676690)

720

(715724)

727

(723731)

738

(728748)

Sri Lanka 638

(634641)

680

(677682)

688

(678697)

685

(684686)

716

(703728)

676

(672680)

723

(720726)

763

(755770)

769

(768770)

798

(787807)Thailand 610

(602619)

641

(634646)

692

(681701)

678

(666690)

709

(691725)

664

(656671)

707

(702713)

757

(749765)

754

(745763)

775

(763788)

Timor-Leste 516(488543)

543(520566)

600(585616)

623(611633)

678(663692)

535(504567)

559(533585)

610(592628)

643(631655)

697(681712)

Vietnam 540(481579)

630(605652)

655(641670)

686(671700)

716(693740)

620(573655)

692(671715)

723(711734)

761(751772)

796(780811)

Australasia 679

(678680)

709

(708710)

736

(735736)

768

(767769)

791

(790792)

746

(745748)

778

(777779)

797

(796798)

821

(820822)

836

(835837)

Australia 678

(676679)

710

(709711)

738

(737739)

770

(769771)

792

(791793)

746

(744748)

782

(780783)

800

(799801)

823

(822824)

838

(837839)

New Zealand 685

(683688)

702

(700704)

724

(722726)

758

(757760)

786

(784788)

747

(745749)

763

(760765)

782

(780784)

808

(806810)

827

(825830)

Caribbean 622(613630)

634(627640)

658(654662)

678(674683)

569(437631)

654(640663)

672(662680)

697(692703)

718(713724)

662(578699)

Antigua and Barbuda 658(647670) 672(655687) 707(695718) 735(723748) 741(722759) 676(665686) 713(697729) 757(746767) 776(765788) 790(773805)

Barbados 656

(649662)

687

(681693)

690

(683697)

728

(719736)

743

(727760)

698

(691704)

733

(727740)

744

(738751)

766

(758774)

770

(756783)

Belize 653

(640666)

674

(663684)

699

(690709)

652

(649656)

689

(673703)

688

(674701)

708

(698718)

743

(734752)

709

(705712)

736

(723750)

Cuba 703(700705)

724(722726)

730(728731)

743(742744)

761(759762)

739(736741)

767(765769)

768(767770)

779(777780)

798(796799)

Dominica 611(597623)

705(694715)

685(674697)

705(695717)

701(686715)

638(624651)

726(715737)

721(710731)

759(748770)

779(764793)

Dominican Republic 635

(626643)

668

(662676)

689

(682696)

705

(696713)

713

(700728)

663

(654671)

699

(692706)

728

(721736)

760

(752768)

763

(751776)

Grenada 628

(604652)

647

(599694)

668

(657678)

676

(667685)

686

(674698)

657

(635677)

687

(639728)

708

(698719)

737

(726747)

735

(722747)

Guyana 603

(594612)

597

(579614)

611

(601622)

619

(608632)

631

(605659)

649

(641658)

657

(641670)

674

(665683)

682

(671694)

691

(669712)

Haiti 473(446498)

490(469510)

533(520545)

577(563591)

325(198431)

493(458520)

509(484531)

550(534567)

588(572604)

436(311517)

Jamaica 658(642673)

703(694712)

728(719737)

721(701743)

733(699773)

694(678709)

727(719735)

743(735751)

749(737762)

773(743803)

Saint Lucia 609

(592628)

643

(635650)

670

(661679)

691

(683700)

709

(686734)

647

(630664)

672

(667677)

709

(701718)

754

(745764)

765

(745788)

Saint Vincent and the Grenadines 603

(589618)

621

(607634)

665

(654677)

676

(666685)

697

(685710)

648

(632663)

670

(658683)

709

(697721)

719

(710729)

745

(731758)

Suriname 627

(608645)

620

(611627)

663

(653673)

687

(675699)

701

(682722)

655

(635678)

677

(666687)

709

(699719)

722

(713732)

752

(737768)

The Bahamas 595(584604)

614(603623)

639(628650)

686(674700)

714(685745)

652(640662)

678(668687)

710(700720)

794(767841)

804(769855)


8/22/2019 Pi is 014067361261719 x

9/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Trinidad and Tobago 633(628638)

657(652662)

660(654664)

648(643653)

662(650674)

681(676685)

707(702712)

724(719729)

721(716727)

753(741764)

Central Europe 658

(654661)

667

(664669)

671

(670672)

694

(693694)

719

(719720)

717

(713720)

736

(734739)

748

(747749)

768

(767768)

792

(791793)

Albania 670

(634705)

684

(648715)

704

(699708)

704

(699710)

720

(692749)

711

(679745)

739

(712764)

760

(755765)

768

(763773)

781

(759802)

Bosnia and Herzegovina 607

(560645)

652

(624674)

689

(687691)

716

(710720)

741

(739744)

664

(626694)

714

(694731)

748

(746750)

767

(763772)

788

(785790)

Bulgaria 687(685689)

686(685688)

682(681684)

684(682686)

701(699703)

730(728732)

740(738741)

748(746750)

752(750754)

770(768772)

Croatia 638(602671)

662(643681)

679(677681)

709(708711)

734(732736)

709(676736)

740(723755)

759(756761)

781(779783)

799(797801)

Czech Republic 662

(660663)

670

(669672)

678

(677679)

717

(716718)

743

(742745)

733

(731735)

742

(741744)

755

(753756)

784

(782785)

807

(805808)Hungary 665

(663667)

656

(655658)

651

(650652)

675

(674676)

704

(703706)

722

(720725)

729

(727731)

739

(737740)

761

(760763)

784

(782785)

Macedonia 641

(592680)

670

(657684)

687

(683690)

708

(705710)

728

(725730)

684

(636721)

712

(699725)

733

(729737)

757

(754759)

772

(770775)

Montenegro 661(604698)

690(656719)

711(694728)

707(703710)

730(722736)

705(659737)

746(721772)

776(762790)

764(760768)

782(775789)

Poland 663(661665)

663(662665)

665(664666)

695(695696)

721(720722)

732(729734)

747(746749)

755(754756)

780(779781)

805(804806)

Romania 655

(652658)

667

(665669)

667

(665668)

676

(674677)

701

(700702)

701

(698704)

722

(720724)

732

(730734)

747

(745748)

776

(774777)

Serbia 661

(623697)

691

(658724)

706

(690722)

719

(718721)

740

(737742)

714

(678744)

751

(725776)

767

(753781)

771

(770773)

795

(792798)

Slovakia 668(666670)

669(667670)

667(665669)

692(690693)

716(714717)

732(730735)

744(742746)

755(753756)

774(773776)

791(789793)

Slovenia 640(604672) 664(654675) 689(687692) 719(717721) 759(756762) 716(690740) 744(735753) 771(768773) 796(793798) 825(822829)

Eastern Europe 634

(631637)

621

(617624)

638

(636641)

600

(598602)

637

(634639)

730

(727733)

729

(726732)

741

(738743)

725

(723727)

749

(747751)

Belarus 672

(665678)

654

(647660)

655

(649660)

623

(618629)

641

(634649)

753

(747758)

751

(746755)

750

(746755)

740

(735744)

760

(755765)

Estonia 652

(648656)

639

(636642)

647

(644651)

647

(644650)

706

(703710)

744

(740748)

741

(738744)

747

(743750)

760

(757763)

806

(802810)

Latvia 657(655659)

637(634639)

645(643647)

645(643647)

689(686692)

745(743748)

741(738743)

747(745750)

757(755759)

785(782787)

Lithuania 670(668673)

656(654658)

662(660664)

663(661665)

687(685689)

752(749754)

755(753757)

761(759763)

772(770774)

793(791796)

Moldova 603

(569634)

620

(611627)

644

(640648)

655

(652658)

655

(650658)

674

(649700)

692

(684699)

716

(712720)

730

(727733)

746

(742749)

Russia 623

(619627)

610

(605614)

632

(628635)

589

(586591)

631

(628633)

727

(723731)

725

(721729)

739

(735742)

720

(717722)

747

(744749)

Ukraine 655(648660)

641(636646)

651(647655)

620(615626)

645(635653)

736(730740)

736(732739)

745(742747)

732(729736)

749(742754)

Western Europe 685(685686)

706(706706)

729(728729)

754(753754)

779(778780)

747(746747)

773(772773)

794(794794)

814(814814)

832(831832)

Andorra 737(697773)

760(724791)

772(754789)

787(774800)

798(788810)

824(800848)

824(798848)

831(816847)

843(831855)

852(842862)

Austria 667

(665669)

689

(687691)

722

(720723)

750

(749751)

777

(775779)

735

(733737)

761

(760763)

789

(787790)

812

(811814)

833

(832835)

Belgium 678

(676680)

699

(698701)

726

(724727)

746

(745747)

767

(764771)

742

(740744)

768

(766769)

792

(790793)

810

(809811)

823

(819826)

Cyprus 702

(682721)

733

(729737)

755

(751760)

763

(759767)

776

(771781)

752

(738766)

774

(770778)

805

(801810)

815

(810819)

829

(824834)


8/22/2019 Pi is 014067361261719 x

10/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Denmark 710

(708712)

713

(711715)

724

(722725)

745

(743746)

768

(766770)

760

(758762)

773

(772775)

779

(778781)

791

(789793)

810

(808813)

Finland 660

(659662)

693

(692695)

710

(708711)

742

(740743)

768

(766770)

743

(741745)

779

(777780)

790

(789792)

812

(810813)

833

(831836)

France 689

(688690)

705

(704706)

730

(729731)

752

(751752)

775

(772778)

763

(761764)

787

(786788)

811

(810812)

827

(827828)

843

(840845)

Germany 677(676678)

697(696698)

719(718720)

748(748749)

775(773777)

737(735738)

763(762764)

784(784785)

809(808809)

828(826831)

Greece 715(713717)

732(731734)

745(743746)

754(753755)

771(768774)

753(750755)

778(776780)

794(792795)

805(804806)

821(819824)

Iceland 714

(709719)

737

(732742)

756

(751760)

781

(777786)

800

(794806)

766

(760771)

792

(786798)

797

(792802)

819

(814824)

844

(837850)

Ireland 690

(688692)

697

(695699)

721

(720723)

739

(737740)

776

(774779)

738

(735741)

756

(754758)

776

(774778)

792

(790794)

822

(819824)Israel 694

(692696)

719

(717721)

746

(744748)

762

(761762)

792

(790794)

730

(727732)

754

(752756)

781

(779783)

804

(803806)

829

(827831)

Italy 687(685689)

709(708710)

736(735737)

764(764765)

789(787791)

746(744748)

776(775777)

802(801803)

824(823825)

839(837841)

Luxembourg 665

(661669)

691

(687695)

716

(712720)

746

(742750)

780

(775786)

735

(730740)

760

(755764)

789

(785793)

809

(805813)

822

(817828)

Malta 681

(677686)

699

(695703)

746

(742750)

757

(754761)

771

(766776)

742

(736747)

759

(754764)

797

(792801)

812

(807817)

830

(824836)

Netherlands 708

(707710)

725

(723726)

738

(737739)

754

(753755)

785

(784786)

766

(764767)

791

(789792)

801

(800802)

806

(805807)

826

(824827)

Norway 711(708713)

724(722725)

736(734738)

759(757760)

785(783787)

771(769773)

789(787791)

800(798801)

814(812816)

831(829834)

Portugal 639(635642)

681(680683)

707(705708)

730(729732)

763(762765)

706(702710)

753(750755)

778(777780)

800(799802)

823(822825)

Spain 690(688691) 722(721723) 733(732734) 756(755756) 784(782787) 746(744748) 784(783785) 805(804806) 826(825827) 842(840844)

Sweden 722

(720723)

728

(727729)

748

(747750)

774

(773775)

792

(790794)

771

(770773)

789

(787790)

805

(803806)

820

(819821)

835

(834837)

Switzerland 700

(699702)

724

(722725)

740

(739742)

770

(768771)

797

(795798)

762

(760763)

789

(788791)

810

(808811)

828

(826829)

845

(843847)

UK 687(685688)

704(703705)

729(728729)

754(754755)

778(778779)

750(748751)

765(764766)

783(782784)

801(801802)

819(818820)

Andean Latin America 505(449531)

624(618630)

665(660671)

710(705716)

739(729748)

565(532581)

659(651665)

702(696709)

744(737750)

770(761780)

Bolivia 478

(458502)

552

(537568)

610

(596626)

662

(648675)

697

(673725)

526

(504548)

588

(571605)

635

(620650)

684

(671698)

717

(695741)

Ecuador 609

(602618)

654

(649659)

693

(689698)

712

(707718)

744

(733754)

647

(638655)

704

(698709)

748

(744751)

767

(762771)

798

(789806)

Peru 477

(396517)

635

(626645)

672

(664680)

727

(717736)

752

(738767)

547

(493572)

664

(654672)

706

(698715)

754

(745763)

776

(761790)

Central Latin America 606(602609)

636(633639)

678(676681)

706(703708)

717(714721)

658(654661)

709(706712)

745(742747)

768(766769)

782(779784)

Colombia 642

(635648)

668

(659678)

671

(664676)

683

(676687)

717

(702730)

689

(683696)

722

(714729)

752

(746756)

764

(758768)

783

(773794)

Costa Rica 684

(680688)

735

(731739)

746

(744749)

755

(753757)

771

(769773)

718

(714723)

779

(775782)

788

(785790)

803

(801805)

819

(816821)

El Salvador 565

(555574)

508

(504513)

650

(647654)

684

(681687)

699

(693705)

632

(622640)

689

(683694)

742

(739745)

768

(765771)

782

(777786)

Guatemala 493(484504)

558(552565)

620(617625)

652(649655)

669(663675)

534(524543)

636(628642)

671(666676)

718(715722)

740(735745)

Honduras 547(536557)

642(634649)

670(658682)

680(639724)

705(663746)

586(576598)

685(678693)

709(700718)

712(683739)

732(698765)


8/22/2019 Pi is 014067361261719 x

11/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Mexico 601

(596607)

630

(625634)

683

(678687)

720

(717723)

725

(723728)

657

(650663)

708

(703713)

749

(744753)

774

(771777)

784

(782786)

Nicaragua 548(534562)

626(608642)

677(669686)

701(693709)

715(706722)

614(602626)

684(672695)

729(722736)

757(750764)

775(767782)

Panama 669(665673)

713(706720)

721(713730)

744(734753)

736(723749)

695(691699)

757(751764)

772(763779)

800(792809)

802(789815)

Venezuela 654

(650658)

673

(670675)

698

(696700)

709

(708711)

703

(689715)

701

(696705)

730

(728733)

754

(752756)

778

(776779)

792

(784800)

Southern Latin America 623

(620626)

667

(666669)

691

(690692)

713

(712714)

733

(731734)

695

(692698)

738

(736740)

763

(762764)

786

(785787)

799

(798801)

Argentina 627

(624631)

668

(666670)

690

(689692)

705

(704706)

725

(724726)

700

(696704)

740

(738742)

761

(759763)

781

(780783)

793

(792794)

Chile 606

(602610)

664

(662666)

694

(692695)

740

(739741)

755

(752759)

673

(668678)

730

(727733)

764

(762765)

798

(797799)

815

(812818)Uruguay 651

(648655)670(667672)

693(691695)

707(705709)

726(721731)

724(720728)

741(738744)

769(766771)

785(783788)

804(799809)

Tropical Latin America 580

(567596)

623

(619628)

656

(652659)

684

(682687)

705

(703708)

649

(636662)

692

(686697)

731

(728734)

756

(754759)

777

(774779)

Brazil 578

(565594)

622

(617627)

654

(651658)

683

(681686)

705

(702708)

648

(635661)

691

(686696)

731

(727734)

757

(754759)

777

(775779)

Paraguay 672

(665679)

692

(686699)

722

(715728)

716

(710722)

710

(697724)

693

(686700)

714

(708721)

743

(737749)

749

(743754)

756

(747764)

Nort h Africa and Middle East 52 7(515538)

591(581598)

655(647662)

688(681693)

710(702718)

587(576596)

648(640654)

702(695708)

733(727739)

756(749762)

Algeria 545

(510575)

555

(545563)

693

(669715)

706

(691720)

743

(732754)

583

(552618)

597

(587606)

724

(704743)

742

(726758)

765

(755775)

Bahrain 638

(599676)

696

(677715)

708

(695721)

731

(719743)

764

(748782)

671

(636706)

738

(722754)

723

(711735)

755

(744767)

791

(775807)

Egypt 484(471497) 551(538565) 624(614635) 668(659676) 680(670690) 558(542572) 612(600625) 670(660679) 717(710724) 734(725742)

Iran 506(476537)

580(542600)

646(617667)

686(673700)

716(685746)

562(534591)

652(625668)

710(689728)

746(734757)

778(753802)

Iraq 599(578618)

655(631679)

694(667721)

708(682734)

706(672737)

650(631668)

693(672713)

704(677725)

712(693731)

714(683744)

Jordan 591

(577605)

635

(616652)

703

(677727)

734

(716750)

757

(739775)

641

(627655)

686

(673700)

728

(712743)

736

(719753)

751

(732770)

Kuwait 660

(655666)

706

(702710)

768

(765771)

763

(761766)

761

(758764)

712

(705718)

745

(741750)

792

(789796)

794

(791797)

796

(792799)

Lebanon 672

(649696)

693

(673716)

691

(665711)

744

(728761)

762

(741779)

701

(676725)

725

(705744)

733

(716747)

767

(755779)

789

(775804)

Libya 608(585628)

699(681716)

714(685742)

733(716748)

729(707750)

639(619661)

728(712745)

742(715767)

757(743772)

765(746785)

Morocco 550(525574)

596(576616)

657(637675)

690(670709)

709(683733)

593(573612)

637(624651)

692(682703)

722(709736)

744(722761)

Oman 539(504570)

619(585652)

694(658728)

723(698747)

738(722754)

582(545617)

660(625690)

735(703762)

769(748790)

789(775801)

Palestine 589

(519647)

656

(615693)

687

(655718)

709

(693727)

703

(679727)

622

(559679)

690

(653727)

725

(694756)

756

(741772)

764

(743783)

Qatar 657

(620697)

684

(660706)

759

(749769)

765

(754773)

789

(777800)

706

(673741)

749

(729768)

766

(756777)

778

(768787)

821

(811832)

Saudi Arabia 586(540638)

685(652716)

725(694752)

738(726751)

750(736764)

629(585667)

723(691752)

763(736787)

779(768791)

799(788810)

Syria 631(605655)

675(657694)

695(664722)

727(700750)

751(735766)

689(667708)

728(712746)

735(709760)

764(743784)

802(789814)

Tunisia 558

(541575)

647

(633656)

699

(685713)

722

(700742)

741

(707776)

593

(576610)

688

(675699)

745

(733757)

771

(752788)

789

(757819)


8/22/2019 Pi is 014067361261719 x

12/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Turkey 527

(487565)

590

(573607)

637

(622653)

679

(666693)

712

(695730)

603

(569633)

666

(651681)

709

(697722)

748

(737759)

777

(759793)

United Arab Emirates 661

(627693)

697

(666732)

720

(694745)

734

(723745)

753

(730776)

679

(642713)

718

(682750)

740

(717763)

754

(743765)

786

(765807)

Yemen 406(339465)

505(432564)

584(514641)

618(549674)

655(592714)

464(393523)

547(474610)

603(526668)

629(551690)

663(593724)

High-income North America 672

(671673)

701

(700702)

720

(719720)

744

(744745)

761

(760762)

747

(746748)

775

(774776)

787

(787788)

797

(796797)

808

(807808)

Canada 694

(692695)

716

(714717)

740

(739741)

765

(764766)

785

(782787)

762

(761764)

786

(785787)

803

(802805)

817

(816818)

827

(825830)

USA 670

(668671)

699

(699700)

717

(717718)

742

(741743)

759

(758759)

746

(744747)

774

(773775)

786

(785787)

795

(794795)

805

(805806)

Oceania 548

(486603)

545

(471600)

546

(464610)

560

(480632)

588

(509657)

590

(528645)

593

(528648)

582

(510644)

596

(518666)

620

(550687)Federated States of Micronesia 543

(451631)555(463637)

590(503667)

623(536696)

634(546717)

592(498673)

611(534676)

636(551707)

666(587736)

683(608747)

Fiji 615

(597632)

606

(594614)

628

(596658)

634

(619647)

656

(639673)

668

(652683)

662

(653671)

683

(653710)

680

(666693)

688

(671704)

Kiribati 504

(442568)

538

(466607)

543

(505582)

557

(528588)

578

(513640)

540

(472603)

579

(515638)

581

(545618)

625

(600649)

650

(598697)

Marshall Islands 551

(481620)

601

(553649)

617

(595638)

615

(582646)

619

(575660)

605

(541667)

667

(624703)

670

(650688)

649

(621676)

660

(619700)

Papua New Guinea 531(454608)

526(437606)

524(432610)

544(446639)

575(481665)

571(493648)

571(490647)

556(468638)

575(482663)

603(519690)

Samoa 622(604641)

621(591649)

641(597684)

665(631696)

684(654709)

683(665702)

702(676724)

711(673746)

719(691746)

734(709758)

Solomon Islands 563

(478649)

572

(479664)

580

(483662)

595

(515669)

605

(522681)

604

(518677)

622

(538703)

614

(522699)

625

(526704)

640

(554714)

Tonga 654(617688) 664(627704) 686(658715) 685(670696) 673(647699) 677(640711) 691(646730) 715(684743) 716(702729) 738(714761)

Vanuatu 560

(474639)

569

(491640)

598

(501679)

610

(523686)

622

(541695)

606

(530676)

622

(542690)

640

(549712)

652

(571719)

669

(598725)

Central sub-Saharan Africa 439(398475)

474(448500)

488(461513)

497(471517)

532(503560)

504(472539)

534(510555)

543(519562)

548(528566)

585(559609)

Angola 377(261485)

408(340480)

439(363519)

492(404582)

579(495665)

476(363582)

501(431565)

517(430590)

555(470640)

639(560720)

Central African Republic 442

(409472)

463

(441485)

451

(430475)

418

(382456)

436

(384493)

526

(497555)

544

(525564)

520

(502538)

475

(440505)

493

(440546)

DR Congo 461

(425495)

496

(467526)

509

(485533)

508

(490530)

528

(496559)

509

(472544)

539

(510566)

550

(530571)

552

(536569)

577

(547605)

Equatorial Guinea 400(291519)

453(363540)

457(359541)

494(412566)

547(435662)

483(368584)

542(461614)

533(435604)

568(495631)

618(529738)

Gabon 501(446555)

550(522576)

562(542583)

535(506564)

550(500600)

577(526623)

627(607647)

643(629657)

618(598640)

633(595674)

Congo 507(467545)

529(495561)

519(490550)

497(473522)

563(523603)

551(515588)

576(545606)

577(555601)

547(524568)

616(579651)

Eastern sub-Saharan Africa 481

(468494)

502

(491511)

509

(500517)

521

(512530)

594

(583603)

516

(501529)

539

(528549)

549

(541556)

553

(544561)

626

(617635)

Burundi 450

(367521)

479

(398543)

467

(378558)

450

(347569)

530

(427630)

463

(368551)

503

(415576)

503

(417585)

480

(369595)

552

(452646)

Comoros 507(474539)

523(487560)

563(526602)

595(545641)

616(565658)

536(499572)

550(512588)

588(545625)

617(569659)

639(591682)

Djibouti 580(505656)

603(532666)

590(522652)

592(509672)

622(546696)

607(531686)

630(558690)

622(551681)

617(528702)

644(551737)

Eritrea 430

(396465)

465

(442489)

505

(485524)

379

(282440)

590

(547629)

484

(452516)

515

(494534)

543

(526561)

472

(381521)

620

(582655)


8/22/2019 Pi is 014067361261719 x

13/24

Articles



1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Ethiopia 422

(390456)

419

(394443)

445

(425462)

514

(498529)

595

(575613)

467

(431506)

465

(438488)

490

(474508)

544

(531558)

623

(605641)

Kenya 561

(534585)

609

(591628)

616

(604629)

560

(537585)

627

(599657)

586

(562610)

631

(614648)

643

(632653)

586

(567604)

669

(646690)

Madagascar 503(483522)

521(496544)

544(528559)

594(576611)

622(589656)

512(489535)

551(528575)

579(565593)

622(609637)

651(618681)

Malawi 400

(380421)

475

(459490)

467

(451481)

437

(417457)

509

(485536)

440

(417462)

514

(497529)

508

(494521)

467

(452484)

549

(527575)

Mauritius 586

(581589)

614

(611617)

651

(649653)

678

(676681)

697

(693700)

647

(642651)

698

(693701)

731

(728734)

748

(744750)

769

(765773)

Mozambique 453

(428481)

493

(475513)

472

(457487)

475

(458493)

500

(469534)

485

(459516)

522

(502542)

528

(512542)

537

(521554)

549

(518583)

Rwanda 447

(415479)

466

(438491)

482

(459503)

475

(457493)

620

(601639)

482

(446516)

499

(471525)

513

(495527)

521

(504536)

671

(655690)Seychelles 606

(594617)641(631650)

620(611628)

626(618634)

613(602624)

667(655679)

717(706728)

724(716733)

734(726743)

718(707729)

Somalia 494

(392592)

508

(431580)

499

(427570)

522

(433606)

546

(454626)

527

(433616)

540

(455621)

530

(442605)

545

(452625)

572

(477656)

Sudan 556

(534577)

576

(561591)

607

(592619)

627

(607645)

669

(642690)

581

(560601)

601

(587615)

636

(623649)

663

(647681)

707

(688729)

Tanzania 506

(482529)

550

(532566)

550

(536564)

542

(520565)

609

(581637)

550

(527573)

592

(574609)

583

(570596)

552

(534569)

626

(602652)

Uganda 529(505555)

498(475519)

481(459506)

488(465510)

583(553614)

558(532583)

542(523560)

529(515545)

531(513550)

625(598651)

Zambia 531(507553)

549(530567)

485(467504)

426(403456)

543(511577)

570(545594)

586(570602)

528(513541)

447(425468)

573(542603)

Sout hern sub-Saharan Africa 544

(510579)

572

(555586)

606

(592619)

535

(522549)

557

(539574)

615

(582640)

647

(633659)

677

(668687)

587

(575600)

606

(588624)

Botswana 565(530601) 632(597666) 639(602679) 525(464574) 681(636736) 611(575645) 679(648709) 693(660730) 555(490605) 740(692806)

Lesotho 487

(457521)

540

(513569)

563

(545583)

470

(446494)

441

(409481)

571

(545597)

627

(608649)

654

(642667)

541

(524562)

507

(472548)

Namibia 559(533585)

570(552587)

591(580604)

523(501544)

584(552616)

611(590633)

629(617640)

658(648669)

575(559593)

649(619676)

South Africa 548(502594)

574(552592)

607(589625)

553(537568)

574(548596)

626(582663)

654(636671)

687(676699)

613(598629)

623(599647)

Swaziland 476

(443504)

547

(521572)

593

(573613)

465

(439491)

474

(435513)

532

(501561)

602

(578626)

652

(637666)

521

(502543)

514

(480551)

Zimbabwe 546

(501587)

571

(546595)

610

(593628)

488

(454540)

511

(466556)

591

(543631)

622

(604642)

649

(636664)

513

(486550)

551

(513593)

Western sub-Saharan Africa 470(455483)

517(506528)

530(521540)

534(526543)

579(567591)

508(492524)

550(536563)

565(554574)

566(557575)

609(596621)

Benin 471(446495)

519(499538)

530(515546)

554(537570)

607(576635)

517(491541)

560(542577)

586(571599)

611(597624)

659(632685)

Burkina Faso 440(415466)

479(459499)

496(480512)

507(490526)

528(466581)

499(471527)

535(513554)

545(531561)

551(533568)

576(527621)

Cameroon 508

(480534)

535

(514553)

565

(553580)

526

(506548)

571

(535609)

530

(502557)

558

(537577)

601

(588614)

567

(551586)

611

(578643)

Cape Verde 606

(569638)

654

(638670)

654

(638669)

681

(638723)

709

(663755)

650

(617683)

699

(685713)

740

(726753)

768

(735802)

791

(753825)

Chad 486(451521)

480(460502)

500(484515)

494(474513)

533(472582)

544(512577)

537(518556)

550(534566)

541(522558)

578(530623)

Cte dIvoire 487(466507)

557(542572)

527(512544)

499(476524)

528(482571)

533(507555)

592(576607)

594(581607)

578(557599)

602(562639)

Ghana 561

(533586)

570

(545593)

593

(574612)

594

(576613)

632

(607657)

587

(557618)

599

(574623)

619

(601637)

622

(607637)

667

(645689)


8/22/2019 Pi is 014067361261719 x

14/24

Articles


gains in life expectancy of 1112 years have been recorded.

Since 1990, the largest gains in life expectancy occurredin sub-Saharan African countries, especially Angola,Ethiopia, Niger, and Rwanda, where life expectancy since1990 has increased by 1215 years for men and women,as strategies for HIV control and reduction of childhooddiseases have become widespread and effective. Appen-dix pp 5766 show detailed maps of life expectancy atbirth by sex and decade.

Summary presentation of four decades of changes inmortality can obscure important secular trends that haveoccurred in some regions. Appendix pp 6768 showthe age-standardised mortality rate by region for the1549 year age group in 1970 (and 2004) and the percen-tage change in mortality during 19702010 (and 200410).

Appendix p 68 shows the substantial declines in age-specific mortality that have taken place in eastern andsouthern sub-Saharan Africa since 2004. These declinesoccur in those countries that have scaled up two majorglobal public health programmes: antiretroviral drugsfor advanced HIV disease and malaria interventions,including insecticide-treated bednets and artemisinin-combination therapies.29,6166 The changes we document inadult mortality (for individuals aged 1549 years) weredetermined from an analysis of demographic sources ofdata. Such trends in population health are important todocument and are not widely appreciated, despite theirsignificance for provision of supportive evidence on the

effect of key public health programmes. Appendix p 68

also shows that, since 2004, eastern Europe has had amajor decline in adult mortality of nearly 23%. Thisdecline followed a substantial increase in adult mortalityin the previous two decades and represents a notablechange in mortality trend in that region. The remainingregions span from almost no change in central andsouthern Latin America to about a 30% decline insouthern sub-Saharan Africa. All four sub-SaharanAfrican regions have had at least a 10% decline in adultmortality from 2004 to 2010. Asian regions, includinghigh-income Asia Pacific, have had rapid improvement inthe same period. The decline in east Asia, south Asia, andsoutheast Asia was more than 9% from 2004 to 2010.

Comparative survival assessments focus on levels and

changes in age-specific mortality rates or on summarymeasures of mortality rates such as life expectancyat different ages. Improvements to understanding ofpriorities for health action and the provision of healthservices will require information about populationnumbers by age and sex and age-specific and sex-specificdeath rates to know the distribution of deaths at differentages. For example, metrics such as YLLs or disability-adjusted life years (DALYs) are measures of absolutehealth loss. When these metrics are disaggregated bydisease, injury, or risk factors, they focus attention oncauses that lead to the greatest loss of health to individ-uals. Although global life expectancy at birth has been


1970 1980 1990 2000 2010 1970 1980 1990 2000 2010


Guinea 457(419495)

479(457498)

512(498528)

532(514549)

584(537623)

483(448521)

500(478522)

530(516546)

555(539573)

605(563640)

Guinea-Bissau 387

(287474)

463

(397522)

486

(429546)

506

(438574)

548

(461634)

456

(354540)

512

(440575)

531

(454594)

550

(481615)

586

(508666)

Liberia 494

(468519)

519

(499545)

474

(444497)

527

(510546)

565

(541589)

533

(506562)

554

(530576)

520

(499540)

544

(527561)

579

(552605)

Mali 373

(350398)

428

(411449)

475

(461490)

500

(484517)

569

(529607)

413

(385442)

464

(443484)

499

(484515)

513

(498528)

577

(538612)

Mauritania 525(499555)

552(530574)

592(576610)

602(581622)

633(591672)

555(521581)

578(557600)

613(598626)

626(606645)

657(618688)

Niger 406(382433)

419(398441)

443(424461)

510(493527)

569(517615)

452(426479)

463(442485)

482(464502)

533(516552)

587(541626)

Nigeria 476

(447504)

534

(512557)

538

(521559)

543

(526563)

588

(565614)

509

(480539)

560

(534587)

564

(544584)

562

(545580)

604

(582629)

So Tom and Prncipe 551(535566)

606(589622)

622(598641)

648(630667)

682(644718)

611(594625)

655(639671)

648(629666)

679(662697)

721(690752)

Senegal 442

(419465)

507

(489525)

568

(554581)

576

(560592)

635

(611661)

500

(476523)

554

(537570)

609

(597621)

616

(602630)

671

(652693)

Sierra Leone 378(348412)

412(386440)

458(434479)

478(460495)

565(536593)

453(417486)

485(454511)

525(504547)

534(516551)

609(583631)

The Gambia 439

(374496)

516

(447573)

557

(495616)

583

(515643)

608

(534690)

495

(423556)

555

(476622)

590

(519655)

615

(542681)

640

(559724)

Togo 498

(472524)

544

(524564)

565

(550583)

578

(554601)

583

(547618)

541

(515569)

581

(560602)

601

(585615)

614

(593637)

621

(584656)

Table 2: Life expectancy in years (95% uncertainty intervals) at birth, by sex and decade for 187 countries and 21 Global Burden of Disease regions

8/22/2019 Pi is 014067361261719 x

15/24

Articles


Figure 3: Change in life expectancy at birth, 19702010

(A) Male life expectancy and (B) female life expectancy. ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. Isl=islands. FSM=Federated States of Micronesia. LCA=Saint Lucia.

TTO=Trinidad and Tobago. TLS=Timor-Leste.

A

B

15 to

8/22/2019 Pi is 014067361261719 x

16/24

Articles


increasing, so too has the number of deaths worldwide,from an estimated 433 million (422446 million) in1970 to 528 million (516541 million) in 2010 (table 3).Even at the global level, substantial uncertainty existsabout the number of deaths, which shows the uncertaintyin each step of the estimation process and differentdemographic estimation methods and their interpretation.

For comparison, figure 4 shows estimates of the globalnumber of deaths from various WHO and UNPDestimation exercises.26,27,34,6785 Successive revisions by theUNPD in the WPP have in general led to downward

revisions in their estimates for the same time period asnew data become available. WHO estimates have beenmuch higher than our estimates and those of the UNPD.For 2004, the WHO estimated 5868 million deathsworldwide, 381 million more than the UNPD estimatefrom the WPP 2010 revision and 701 million (136%)higher than our estimates. The rapid rise in our estimatednumber of global deaths in 199495 was due to a series ofmortality shocks. The 1994 genocide in Rwanda killed anestimated 454 200 people (95% UI 339 500695 700) andthe famine in North Korea throughout the 1990s led to

1970 1990 2010

Male deaths

(thousands)

Female deaths

(thousands)

Overall

(thousands)

Male deaths

(thousands)

Female deaths

(thousands)

Overall

(thousands)

Male deaths

(thousands)

Female deaths

(thousands)

Overall

(thousands)

Early neonatal 2157

(20422280)

1519

(13941678)

3676

(34313959)

1841

(17571926)

1286

(11941401)

3127

(29503326)

1264

(12071319)

900

(841972)

2164

(20472284)

Late neonatal 1109

(10801137)

830

(770896)

1939

(18572021)

715

(700729)

548

(512590)

1263

(12201308)

375

(356394)

302

(283325)

677

(645711)

Postneonatal 2859

(27252992)

2350

(21882518)

5209

(49405481)

1935

(18482022)

1636

(15431743)

3570

(34093733)

1091

(10331157)

940

(8791009)

2031

(19242152)

14 years 2949(26483261)

2616(24832723)

5565(51375972)

1927(17732086)

1673(16161725)

3600(33883814)

1075(9761178)

894(843946)

1970(18252119)

59 years 481(466496)

392(378408)

873(860888)

382(372392)

329(318341)

711(703719)

250(240261)

203(194212)

453(437468)

1014 years 276

(259315)

235

(213267)

511

(481568)

233

(225242)

206

(193220)

438

(425455)

200

(188229)

164

(

Pi is 014067361261719 x

Documents