Summary
Background
Neurological disorders are now the leading source of disability globally, and ageing is increasing the burden of neurodegenerative disorders, including Parkinson's disease. We aimed to determine the global burden of Parkinson's disease between 1990 and 2016 to identify trends and to enable appropriate public health, medical, and scientific responses.
Methods
Through a systematic analysis of epidemiological studies, we estimated global, regional, and country-specific prevalence and years of life lived with disability for Parkinson's disease from 1990 to 2016. We estimated the proportion of mild, moderate, and severe Parkinson's disease on the basis of studies that used the Hoehn and Yahr scale and assigned disability weights to each level. We jointly modelled prevalence and excess mortality risk in a natural history model to derive estimates of deaths due to Parkinson's disease. Death counts were multiplied by values from the Global Burden of Disease study's standard life expectancy to compute years of life lost. Disability-adjusted life-years (DALYs) were computed as the sum of years lived with disability and years of life lost. We also analysed results based on the Socio-demographic Index, a compound measure of income per capita, education, and fertility.
Findings
In 2016, 6·1 million (95% uncertainty interval [UI] 5·0–7·3) individuals had Parkinson's disease globally, compared with 2·5 million (2·0–3·0) in 1990. This increase was not solely due to increasing numbers of older people, because age-standardised prevalence rates increased by 21·7% (95% UI 18·1–25·3) over the same period (compared with an increase of 74·3%, 95% UI 69·2–79·6, for crude prevalence rates). Parkinson's disease caused 3·2 million (95% UI 2·6–4·0) DALYs and 211 296 deaths (95% UI 167 771–265 160) in 2016. The male-to-female ratios of age-standardised prevalence rates were similar in 2016 (1·40, 95% UI 1·36–1·43) and 1990 (1·37, 1·34–1·40). From 1990 to 2016, age-standardised prevalence, DALY rates, and death rates increased for all global burden of disease regions except for southern Latin America, eastern Europe, and Oceania. In addition, age-standardised DALY rates generally increased across the Socio-demographic Index.
Interpretation
Over the past generation, the global burden of Parkinson's disease has more than doubled as a result of increasing numbers of older people, with potential contributions from longer disease duration and environmental factors. Demographic and potentially other factors are poised to increase the future burden of Parkinson's disease substantially.
Funding
Bill & Melinda Gates Foundation.
Introduction
Neurological disorders are now the leading source of disability globally.1 Among neurological disorders examined in the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2015, Parkinson's disease was the fastest growing in prevalence, disability, and deaths. In that study,1 the overall number of people affected by the disease was estimated to have more than doubled globally from 1990 to 2015. Previous studies have examined the epidemiology of Parkinson's disease for different parts of the world,2 including systematic reviews on the prevalence of Parkinson's disease.3, 4 However, none has examined change in prevalence, disability, and deaths in detail over the past generation for the entire world and across all countries. In GBD 2015, we identified larger variation in Parkinson's disease death rate estimates over time and between countries than we observed in prevalence estimates.1 This pattern suggested that coding practices rather than real changes over time and location were responsible, similar to what was observed for dementia.1
The prevalence of a disease reflects both the incidence and the duration of disease. The incidence of Parkinson's disease is linked to risk and protective factors.2, 5, 6 The most important risk factor is age, but the risk of Parkinson's disease also appears to be associated with industrial chemicals and pollutants, such as pesticides,7 solvents,7 and metals.8, 9 Conversely, smoking is associated with a decreased risk of Parkinson's disease,10 but whether this association is causal is debatable.11 The factors that affect disease duration are less well known, but increasing longevity also translates into longer disease duration.3, 12 Therefore, as ageing and industrialisation increase globally and smoking decreases in some regions, the prevalence of Parkinson's disease seems poised to increase.13, 14 Detailed estimates of the disease burden can help to evaluate the effect of these risk factors and inform efforts to prevent the disease and to care for and treat those affected by the condition.
Research in context.
Evidence before this study
The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2015 examined the epidemiology of Parkinson's disease for different parts of the world and showed that the number of people affected by the condition had more than doubled globally from 1990 to 2015. The increase in deaths from Parkinson's disease was greater than the increase in prevalence, and the large variation in death rates between countries was suggestive of a change in coding practices rather than greater death rates among Parkinson's disease cases. For pragmatic reasons, systematic reviews for Parkinson's disease are scheduled every other iteration of GBD. For GBD 2016, we updated our GBD 2013 PubMed search without language restrictions using the terms (((“Parkinson disease” AND “epidemiology”) AND (“2011/01/01”[PDat]: “2015/12/31”[PDat])) AND (“Parkinson disease” AND “epidemiology”)) to identify articles published between Jan, 1, 2011, and Dec, 31, 2015. Papers were selected if representative of the general population and identification of cases was based on our reference case definition (the presence of at least two of four primary symptoms: rest tremor, bradykinesia, stiffness of limbs and torso, and postural instability) or alternative case definitions (UK Parkinson's Disease Society Brain Bank criteria, and doctor's diagnosis based on International Classification of Diseases codes and prescription of medications specifically for Parkinson's disease).
Added value of this study
We used the results of this search to obtain the data needed to estimate global, regional, and country-specific prevalence and years lived with disability for Parkinson's disease from 1990 to 2016. To address the possible measurement error in Parkinson's disease death rates as reported by vital registration systems, we used a method that was previously applied to dementia in GBD 2015. In a natural history modelling approach, we assume a constant risk of death in Parkinson's disease cases over time and between locations and let the death rates be determined by variations in prevalence. Although the assumption of similar mortality risk in all time periods and countries is problematic, it produces less error than the large variation in death rates estimated previously. We also explored variation in the burden by age, sex, country, region, and Socio-demographic Index. This study showed that counts of prevalence, mortality, and disability-adjusted life-years more than doubled from 1990 to 2016, and that this increase was not solely due to increasing numbers of older people because age-standardised rates also increased in most regions. In addition, the burden of Parkinson's disease increased with increasing Socio-demographic Index.
Implications of all the available evidence
Neurological disorders are now the leading source of disability in the world, and Parkinson's disease is the fastest growing of these disorders. As the population ages and life expectancy increases, the number of individuals with Parkinson's disease will continue to increase as well as the duration of the disease, leading to more patients with advanced Parkinson's disease. To address this burden, primary prevention strategies based on the underlying causes of Parkinson's disease and more effective treatments than are currently available are required. Additional incidence and prevalence studies are needed, especially in areas in which little data are available, to examine time trends and the factors that drive them.
As part of GBD 2016, we aimed to examine the changes from 1990 to 2016 in counts and age-standardised rates of Parkinson's disease for prevalence, disability, and deaths by location and by the Socio-demographic Index (SDI), a composite measure of income per capita, education, and fertility.15
Methods
Overview
The general methods for the studies on the global, regional, and national burden of neurological disorders have been published previously,1 and key aspects are summarised in the appendix. Additional information on derivation of non-fatal and fatal estimates are provided in the appendix as well as on Global Health Data Exchange.
Data sources
The International Classification of Diseases ninth revision (ICD-9) codes used in cause of death analyses for Parkinson's disease are 332 (Parkinson's disease), 332.0 (paralysis agitans), and 332.1 (secondary parkinsonism), and the corresponding ICD-10 codes are G20 (Parkinson's disease), G21 (secondary parkinsonism), and G22 (parkinsonism in diseases classified elsewhere). The reference case definition for Parkinson's disease used in many epidemiological studies is the presence of at least two of the four primary symptoms: rest tremor, bradykinesia, stiffness of the limbs and torso, and postural instability. We also accepted alternative definitions, including the UK Parkinson's Disease Society Brain Bank criteria,16 in addition to ICD-9 and ICD-10 codes, a doctor's diagnosis of Parkinson's disease, and the prescription of Parkinson's disease-specific medications. We also added 3 years of medical claims data (years 2000, 2010, and 2012) from the USA; for a disease such as Parkinson's disease, the data from claims sources would be expected to match true prevalence under the expectation that most patients would be receiving medical attention each year. If datapoints from epidemiological studies spanned ages of more than 20 years, we split the datapoints using the age pattern from the USA, for which the most detailed age data were available.
Disease model
For Parkinson's disease, we have seen large inconsistencies between the cause of death data and the non-fatal data. For example, US vital registration data show a greater than three times increase in the age-standardised rates of death from Parkinson's disease since 1980 without a corresponding increase in the prevalence data over the same time period (appendix). Likewise, we found a greater than 25 times difference across different countries in age-standardised mortality rates for the most recent year of vital registration data available at the time of GBD 2016 (see Causes of Death Visualization), and we did not see such heterogeneity between countries in our non-fatal data. Therefore, these differences are probably the result of changes and inconsistencies in coding practices for certifying deaths from Parkinson's disease. To correct for this bias, we jointly modelled the prevalence and mortality from Parkinson's disease. First, we ran an initial cause of death model using CODEm, the cause of death ensemble model used throughout GBD, and an initial non-fatal model using DisMod-MR 2.1, the Bayesian meta-regression tool developed for GBD. The initial CODEm model used 14 990 site-years of data (ie, a unique combination of calendar year and country) as well as predictive covariates of SDI,15 cumulative cigarette consumption, health-care access and quality,17 education, and mean cholesterol level (a full list of predictive covariates is in the appendix). The initial DisMod-MR 2.1 model included settings of no remission (ie, no cure) and no incidence before the age of 20 years because the disease is exceptional before that age. We excluded all incidence data from the model, since we saw inconsistencies between the available prevalence and incidence data, and we considered measurement error less likely to occur with prevalence data than with incidence data. We let DisMod-MR 2.1 adjust medical claims data to correct for any systematic under-reporting and datapoints with case definitions that differed from the reference. Smoking prevalence and SDI were used as predictive covariates in the model.
We used these initial model results to identify countries with high-quality vital registration systems, age-standardised prevalence of more than five per 10 000, and a population of more than 1 million that also had the highest ratios of cause-specific mortality to prevalence, or highest likelihood to code to Parkinson's disease as a cause of death per prevalent case in the most recent year of estimates. For GBD 2016, these countries were Austria, Finland, and the USA. We then used the log-transformed ratios of cause-specific mortality rate to prevalence in 2016 to run a fixed-effects regression with dummy variables on age and sex. Because the ratio between cause-specific mortality rate and prevalence is equivalent to an excess mortality rate or excess rate of dying among people with Parkinson's disease compared with the general population, we used the results of this regression as input data for a second DisMod-MR 2.1 model. The excess mortality data obtained from the regression model were used as data for the entire 1990–2016 period and for every country except for the three used in the regression model, which retained their own data for 2016, and data for these countries were assumed to be constant over the entire time series. Apart from this addition of excess mortality data, the second DisMod-MR 2.1 model was identical to the initial model and used the same settings and covariates. We used the cause-specific mortality and prevalence results from this model as final outputs because they ensured consistency between the available non-fatal input data and the excess mortality rate in 2016 from the three countries most likely to code to Parkinson's disease as a cause of death.
Severity and years lived with disability
To calculate years lived with disability (YLDs) for Parkinson's disease, we split the overall prevalence from the second DisMod-MR 2.1 model into three severity categories using data reporting on the Hoehn and Yahr stages.18, 19 We used 30 unique sources, covering nine of 21 world regions, and equated a score of 2·0 or less on the Hoehn and Yahr scale to mild Parkinson's disease, a score of 2·5–3·0 to moderate Parkinson's disease, and a score of 4·0–5·0 to severe Parkinson's disease (appendix). These data informed meta-analyses of the proportion of Parkinson's disease that is mild, moderate, and severe. We then used these proportions to split the overall prevalence of Parkinson's disease into the severity categories. Finally, we multiplied the prevalence of each severity category by severity-specific disability weights20 (see appendix for a detailed description) to calculate YLDs. YLDs were then corrected for comorbidity with a simulation that assigned all non-fatal outcomes to hypothetical individuals and adjusted disability in patients who had multiple conditions.
Risk estimation
Of the 84 risks quantified in GBD 2016,21 only smoking was judged to have sufficient evidence for a relationship with Parkinson's disease, with smoking associated with decreased risk.10 The main sources of exposure data were population-based surveys. We used the estimates of exposure, relative risk, and a theoretical minimum level of exposure of zero lifetime cigarettes smoked to calculate population attributable fractions. Further information on risk factor calculations can be found in the GBD 2016 risk factors paper.21
Compilation of results
We calculated years of life lost (YLLs) by multiplying the number of deaths in an age group by the remaining life expectancy in that age group, taken from the GBD standard life table.22 Disability-adjusted life-years (DALYs) were then calculated as the sum of YLLs and YLDs.
Through each computational step, uncertainty was propagated by sampling 1000 draws, which allowed us to combine the uncertainty from input data, corrections to the data, and residual non-sampling error. Uncertainty intervals (UIs) were defined as the 25th and 975th values of the ordered draws.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to the study data and had final responsibility for the decision to submit for publication.
Results
The results of our analyses can be downloaded from the Global Health Data Exchange and Institute for Health Metrics and Evaluation (Seattle, WA, USA) results tools. Through the systematic analysis we identified 127 data sources on Parkinson's disease, including 91 sources on prevalence covering 16 of the 21 GBD world regions, 34 sources on incidence covering nine world regions, and 11 sources on mortality risk covering two world regions (appendix). The 11 sources on mortality risk are used in the non-fatal modelling process and therefore are marked as belonging to “non-fatal” in the Global Health Data Exchange input data tool. The 11 sources with data on mortality risk are easily identified by the words “mortality” or “survival” in the title. For prevalence, 40 (44·0%) studies were done in western Europe, nine (9·9%) in east Asia, seven (7·7%) each in high-income Asia Pacific, high-income North America, and North Africa and Middle East. 21 (23·0%) studies were from other regions, except for central Asia, central Latin America, tropical Latin America, central sub-Saharan Africa, and southern sub-Saharan Africa, for which no data were available.
In 2016, 6·1 million (95% UI 5·0–7·3) individuals worldwide had Parkinson's disease, of whom 2·9 million (47·5%) were women and 3·2 million (52·5%) were men. 2·1 million (34·4%) of these individuals were from high SDI countries, 3·1 million (50·8%) from high-middle or middle SDI countries, and 0·9 million (14·8%) from low-middle or low SDI countries (table). The number of individuals with Parkinson's disease in 2016 was 2·4 times higher than in 1990 (2·5 million, 95% UI 2·0–3·0). In 1990, 1·1 million (44·0%) cases were in high SDI countries, 1·1 million (44·0%) in high-middle or middle SDI countries, and 0·3 million (12·0%) in low-middle or low SDI countries. The increase in the number of patients with Parkinson's disease worldwide between 1990 and 2016 was not explained exclusively by an increasing number of older people, because global age-standardised prevalence rates increased by 21·7% (95% UI 18·1–25·3) from 1990 to 2016 compared with an increase of 74·3% (69·2–79·6) for crude prevalence rates. The increase in the number of patients with Parkinson's disease between 1990 and 2016 was less pronounced in high SDI countries (9·2%, 95% UI 5·5–13·2) than in other countries, and the largest increase was seen in middle SDI countries (59·8%, 53·2–66·1). The increase in age-standardised prevalence rates between 1990 and 2016 was similar in men (21·4%, 95% UI 17·6–24·9) and women (19·3%, 15·7–22·7). Age-standardised prevalence rates of Parkinson's disease by country varied greater than five times, with the highest rates generally in high-income North America and lowest rates in sub-Saharan Africa (figure 1).
Table.
Deaths, prevalence, and DALYs for Parkinson's disease in 2016 and percentage change between 1990 and 2016 in age-standardised rates by location
|
Deaths |
Prevalence |
DALYs |
||||
|---|---|---|---|---|---|---|
| 2016 counts | Percentage change in age-standardised rates, 1990–2016 | 2016 counts | Percentage change in age-standardised rates, 1990–2016 | 2016 counts | Percentage change in age-standardised rates, 1990–2016 | |
| Global | 211 296 (167 771 to 265 160) | 19·5% (15·6 to 23·3) | 6 062 893 (4 971 461 to 7 324 997) | 21·7% (18·1 to 25·3) | 3 234 514 (2 563 609 to 4 012 766) | 22·1% (18·2 to 25·8) |
| High SDI | 84 911 (69 795 to 103 772) | 11·3% (7·0 to 16·2) | 2 052 069 (1 739 363 to 2 406 677) | 9·2% (5·5 to 13·2) | 1 128 768 (923 886 to 1 359 135) | 9·9% (6·0 to 14·4) |
| High-middle SDI | 44 111 (33 506 to 56 880) | 13·8% (6·3 to 21·6) | 1 328 576 (1 056 629 to 1 639 499) | 20·3% (16·4 to 24·2) | 682 750 (523 447 to 864 241) | 17·1% (10·3 to 24·3) |
| Middle SDI | 54 709 (42 446 to 69 607) | 49·3% (42·7 to 55·8) | 1 778 180 (1 434 399 to 2 166 529) | 59·8% (53·2 to 66·1) | 942 921 (738 016 to 1 174 509) | 57·2% (50·4 to 63·8) |
| Low-middle SDI | 23 409 (17 892 to 30 263) | 45·4% (36·0 to 56·0) | 786 869 (624 622 to 970 524) | 31·6% (28·3 to 34·8) | 409 620 (316 011 to 515 880) | 42·6% (36·0 to 50·5) |
| Low SDI | 4061 (3088 to 5273) | 34·9% (27·0 to 43·4) | 112 859 (88 680 to 141 337) | 20·8% (18·3 to 23·5) | 68 638 (52 894 to 87 401) | 32·4% (26·4 to 39·5) |
| High-income North America | 30 461 (27 651 to 33 532) | 25·7% (14·0 to 38·9) | 811 354 (749 201 to 873 720) | 12·8% (2·8 to 23·8) | 414 699 (366 012 to 460 392) | 18·7% (8·1 to 30·8) |
| Canada | 4343 (3131 to 5477) | 45·4% (15·1 to 71·6) | 103 903 (78 532 to 126 685) | 43·0% (16·5 to 67·0) | 58 911 (43 090 to 73 670) | 44·5% (15·1 to 69·0) |
| Greenland | 1 (1 to 2) | 12·8% (−4·7 to 33·6) | 45 (35 to 57) | 12·7% (6·8 to 19·2) | 24 (17 to 32) | 13·0% (−3·5 to 32·3) |
| USA | 26 117 (24 057 to 28 472) | 22·4% (8·9 to 36·8) | 707 158 (664 026 to 753 627) | 9·5% (−0·8 to 21·8) | 355 735 (320 852 to 392 813) | 15·0% (3·4 to 28·7) |
| Australasia | 1979 (1510 to 2588) | 16·1% (6·9 to 26·4) | 47 265 (37 446 to 58 360) | 9·0% (3·5 to 14·2) | 27 109 (20 483 to 34 716) | 10·9% (3·0 to 19·8) |
| Australia | 1721 (1314 to 2260) | 15·5% (4·9 to 27·3) | 41 016 (32 614 to 50 769) | 8·2% (1·8 to 14·4) | 23 497 (17 742 to 30 279) | 10·1% (1·4 to 19·9) |
| New Zealand | 258 (196 to 338) | 18·5% (5·5 to 33·0) | 6249 (4876 to 7728) | 13·6% (7·5 to 19·8) | 3612 (2736 to 4638) | 14·9% (3·9 to 27·1) |
| High-income Asia Pacific | 13 181 (10 069 to 17 172) | 11·9% (6·2 to 18·3) | 316 347 (248 589 to 395 456) | 21·2% (18·6 to 24·0) | 174 232 (132 665 to 223 694) | 16·5% (11·2 to 22·2) |
| Brunei | 5 (4 to 7) | 17·9% (3·1 to 31·7) | 180 (144 to 221) | 12·5% (6·0 to 18·1) | 95 (73 to 121) | 17·0% (4·0 to 29·7) |
| Japan | 10 936 (8270 to 14 260) | 10·2% (5·7 to 14·8) | 256 455 (201 529 to 321 563) | 21·3% (18·6 to 24·2) | 141 226 (107 717 to 181 551) | 15·7% (11·7 to 19·5) |
| Singapore | 165 (119 to 220) | 11·3% (−8·3 to 35·9) | 4166 (3324 to 5180) | 16·2% (10·6 to 22·3) | 2270 (1709 to 2923) | 12·7% (−4·1 to 34·0) |
| South Korea | 2075 (1459 to 2871) | 24·6% (−3·9 to 59·9) | 55 545 (43 464 to 68 533) | 21·0% (14·9 to 28·1) | 30 642 (22 126 to 41 397) | 21·4% (−3·1 to 50·9) |
| Western Europe | 38 233 (29 203 to 50 366) | 9·0% (0·5 to 17·3) | 828 703 (652 541 to 1 036 222) | 8·4% (0·5 to 15·1) | 487 578 (369 862 to 629 443) | 8·2% (0·0 to 15·7) |
| Andorra | 8 (6 to 10) | 15·9% (−7·2 to 43·8) | 154 (122 to 195) | 12·1% (6·5 to 17·7) | 94 (70 to 125) | 14·1% (−7·2 to 38·0) |
| Austria | 744 (543 to 1011) | 15·1% (5·4 to 26·9) | 15 891 (12 441 to 19 948) | 14·2% (8·2 to 20·0) | 9574 (6988 to 12 832) | 14·6% (5·8 to 24·5) |
| Belgium | 975 (732 to 1276) | 15·5% (3·1 to 28·7) | 20 862 (16 363 to 26 229) | 12·4% (6·7 to 18·4) | 12 338 (9301 to 15 866) | 13·6% (3·2 to 25·3) |
| Cyprus | 58 (43 to 75) | 4·9% (−4·4 to 15·6) | 1245 (982 to 1558) | 16·3% (10·4 to 22·3) | 757 (580 to 978) | 6·0% (−3·0 to 15·4) |
| Denmark | 411 (312 to 541) | 56·7% (30·8 to 88·6) | 9068 (7118 to 11 235) | 45·9% (29·4 to 66·9) | 5463 (4161 to 7061) | 51·3% (28·8 to 79·4) |
| Finland | 525 (384 to 714) | 15·7% (2·6 to 28·8) | 10 258 (8074 to 12 834) | 5·8% (−3·6 to 13·7) | 6935 (5102 to 9185) | 10·3% (−0·8 to 22·0) |
| France | 5798 (4370 to 7590) | −5·1% (−17·3 to 7·5) | 120 455 (94 861 to 151 896) | −2·2% (−13·0 to 8·1) | 70 410 (53 916 to 90 225) | −4·9% (−16·4 to 6·3) |
| Germany | 7306 (5402 to 9675) | 14·5% (1·9 to 28·9) | 162 246 (126 379 to 203 964) | 11·5% (2·8 to 19·7) | 96 664 (73 054 to 127 109) | 12·6% (0·7 to 25·0) |
| Greece | 1066 (801 to 1387) | 11·6% (1·2 to 22·6) | 22 837 (17 855 to 28 778) | 13·2% (7·8 to 18·9) | 13 376 (10 072 to 17 216) | 11·5% (3·0 to 21·3) |
| Iceland | 22 (17 to 29) | 20·4% (8·2 to 33·4) | 474 (375 to 591) | 13·4% (8·2 to 18·5) | 287 (217 to 373) | 17·4% (6·7 to 28·3) |
| Ireland | 251 (187 to 335) | 18·0% (3·8 to 34·1) | 6001 (4712 to 7491) | 17·2% (9·9 to 24·0) | 3451 (2607 to 4524) | 16·4% (3·8 to 30·5) |
| Israel | 411 (304 to 544) | −4·8% (−22·5 to 13·6) | 9395 (7477 to 11 858) | −4·1% (−10·7 to 2·4) | 5338 (4016 to 6964) | −5·6% (−21·1 to 10·5) |
| Italy | 6520 (4878 to 8605) | −5·5% (−20·8 to 12·0) | 144 606 (113 316 to 180 277) | −3·4% (−16·1 to 9·5) | 82 834 (62 455 to 108 059) | −4·6% (−19·5 to 12·2) |
| Luxembourg | 40 (30 to 53) | 18·1% (5·9 to 31·4) | 873 (681 to 1091) | 13·4% (7·9 to 19·9) | 520 (395 to 673) | 16·0% (5·5 to 27·4) |
| Malta | 29 (21 to 39) | 15·3% (−2·2 to 36·3) | 720 (561 to 906) | 15·4% (10·4 to 22·8) | 418 (311 to 550) | 15·4% (−0·2 to 33·6) |
| Netherlands | 1467 (1099 to 1920) | −6·0% (−22·7 to 11·7) | 33 297 (25 931 to 41 654) | −7·5% (−22·1 to 6·1) | 19 621 (14 640 to 25 313) | −6·7% (−23·9 to 9·2) |
| Norway | 342 (258 to 447) | 93·0% (54·2 to 141·0) | 7517 (5900 to 9463) | 87·1% (54·9 to 122·6) | 4412 (3338 to 5691) | 93·9% (54·9 to 137·2) |
| Portugal | 842 (634 to 1119) | 34·3% (13·1 to 59·4) | 18 496 (14 530 to 23 206) | 31·9% (11·5 to 54·4) | 10 902 (8249 to 14 165) | 32·9% (11·2 to 56·9) |
| Spain | 4363 (3322 to 5775) | 0·6% (−12·6 to 15·7) | 92 971 (73 044 to 116 691) | 8·0% (−4·3 to 20·5) | 54 175 (41 280 to 70 222) | 4·9% (−8·2 to 19·3) |
| Sweden | 921 (679 to 1202) | 18·3% (6·1 to 33·8) | 19 776 (15 538 to 24 631) | 13·6% (9·0 to 18·9) | 11 805 (8933 to 15 111) | 15·1% (4·4 to 27·7) |
| Switzerland | 695 (499 to 942) | 13·9% (−8·9 to 42·4) | 14 979 (11 761 to 18 750) | 10·3% (4·4 to 16·0) | 8857 (6553 to 11 759) | 10·8% (−7·9 to 34·4) |
| UK | 5438 (4194 to 7099) | 32·5% (28·5 to 36·9) | 115 846 (91 722 to 144 139) | 22·3% (20·0 to 24·7) | 69 262 (53 335 to 88 203) | 26·3% (23·0 to 30·1) |
| Southern Latin America | 4149 (3109 to 5407) | 3·1% (−9·1 to 14·7) | 100 190 (77 965 to 125 392) | 5·4% (−4·1 to 14·2) | 57 932 (43 522 to 73 989) | 4·3% (−6·6 to 14·9) |
| Argentina | 2798 (2099 to 3623) | 0·1% (−13·4 to 13·8) | 68 048 (52 574 to 85 157) | 2·5% (−8·5 to 13·5) | 39 297 (29 708 to 49 971) | 1·8% (−10·2 to 14·6) |
| Chile | 1064 (756 to 1437) | 16·5% (−7·1 to 43·0) | 25 845 (20 232 to 32 298) | 19·9% (12·8 to 27·5) | 14 860 (10 643 to 19 579) | 17·1% (−2·7 to 40·4) |
| Uruguay | 287 (216 to 375) | 7·1% (−2·9 to 18·9) | 6289 (4860 to 7952) | 10·9% (2·8 to 18·7) | 3775 (2870 to 4861) | 8·2% (−1·2 to 19·2) |
| Eastern Europe | 12 866 (9222 to 17 122) | 8·5% (−9·1 to 29·9) | 365 078 (282 400 to 459 433) | 6·9% (2·4 to 11·4) | 197 660 (141 621 to 259 341) | 8·6% (−6·4 to 26·8) |
| Belarus | 599 (431 to 820) | 10·8% (−6·2 to 29·9) | 16 588 (12 933 to 20 675) | 9·0% (3·6 to 14·1) | 9005 (6559 to 11 905) | 10·8% (−3·2 to 26·8) |
| Estonia | 121 (88 to 158) | 5·6% (−10·3 to 22·0) | 3078 (2372 to 3877) | 1·5% (−12·1 to 12·6) | 1722 (1259 to 2228) | 3·2% (−12·0 to 18·6) |
| Latvia | 178 (127 to 237) | 7·7% (−4·6 to 21·1) | 4613 (3553 to 5819) | 7·6% (2·3 to 14·2) | 2586 (1905 to 3373) | 8·0% (−3·1 to 20·0) |
| Lithuania | 266 (196 to 349) | 9·2% (−0·4 to 19·6) | 6775 (5234 to 8545) | 8·8% (3·1 to 14·9) | 3795 (2819 to 4927) | 9·6% (0·9 to 18·7) |
| Moldova | 161 (120 to 212) | 5·9% (−6·0 to 20·5) | 4825 (3785 to 6006) | 6·5% (1·6 to 12·3) | 2575 (1932 to 3332) | 6·9% (−3·4 to 19·3) |
| Russia | 8516 (5836 to 12 064) | 9·0% (−16·5 to 41·7) | 244 559 (189 528 to 307 585) | 7·1% (0·9 to 13·6) | 131 691 (91 670 to 180 532) | 9·0% (−12·2 to 36·2) |
| Ukraine | 3025 (2094 to 4191) | 6·9% (−12·3 to 32·3) | 84 640 (65 434 to 106 094) | 5·7% (0·6 to 10·7) | 46 286 (32 447 to 62 092) | 7·1% (−9·3 to 27·4) |
| Central Europe | 9061 (6794 to 11 883) | 8·6% (3·9 to 13·4) | 231 329 (179 712 to 291 637) | 10·2% (7·6 to 12·8) | 131 027 (99 413 to 168 732) | 9·4% (5·1 to 13·7) |
| Albania | 150 (111 to 202) | 10·9% (−4·3 to 27·1) | 4067 (3131 to 5154) | 14·0% (7·6 to 20·5) | 2295 (1709 to 3026) | 12·4% (−0·7 to 26·3) |
| Bosnia and Herzegovina | 253 (186 to 334) | 16·0% (−0·9 to 36·2) | 6631 (5127 to 8317) | 19·5% (13·7 to 25·5) | 3739 (2788 to 4814) | 18·0% (2·9 to 35·5) |
| Bulgaria | 665 (485 to 898) | −5·3% (−21·7 to 11·5) | 16 915 (13 099 to 21 383) | 0·8% (−10·7 to 11·1) | 9672 (7100 to 12 864) | −2·3% (−17·9 to 12·8) |
| Croatia | 402 (300 to 533) | 7·9% (−6·3 to 21·6) | 9662 (7485 to 12 183) | 7·9% (2·5 to 13·7) | 5653 (4220 to 7339) | 8·6% (−3·8 to 20·9) |
| Czech Republic | 866 (647 to 1133) | 7·6% (−1·5 to 17·6) | 22 651 (17 359 to 28 690) | 9·3% (4·0 to 15·4) | 12 719 (9558 to 16 483) | 7·5% (−0·9 to 16·5) |
| Hungary | 822 (608 to 1074) | 8·3% (−3·9 to 21·5) | 20 908 (16 195 to 26 302) | 9·5% (4·3 to 16·1) | 11 898 (8915 to 15 343) | 9·0% (−1·5 to 21·0) |
| Macedonia | 108 (80 to 140) | 5·9% (−3·1 to 15·7) | 3021 (2340 to 3787) | 7·7% (2·3 to 13·4) | 1705 (1280 to 2213) | 6·8% (−2·0 to 15·8) |
| Montenegro | 39 (29 to 51) | 10·9% (−2·5 to 25·1) | 1035 (799 to 1307) | 8·4% (2·7 to 14·3) | 582 (440 to 752) | 10·3% (−1·2 to 22·4) |
| Poland | 2943 (2195 to 3902) | 13·7% (2·5 to 25·5) | 74 905 (58 130 to 93 685) | 14·2% (8·6 to 20·4) | 41 955 (31 278 to 54 100) | 14·0% (4·0 to 23·8) |
| Romania | 1605 (1201 to 2110) | 8·1% (−2·4 to 20·3) | 40 517 (31 427 to 50 995) | 10·2% (4·5 to 15·5) | 23 144 (17 467 to 30 057) | 9·2% (0·2 to 20·0) |
| Serbia | 650 (482 to 832) | 8·7% (−1·5 to 20·8) | 16 702 (12 943 to 20 877) | 7·6% (2·5 to 12·6) | 9540 (7187 to 12 219) | 8·9% (−0·1 to 19·5) |
| Slovakia | 358 (264 to 466) | 8·0% (−4·4 to 21·2) | 9523 (7411 to 11 952) | 9·7% (4·7 to 14·9) | 5368 (3965 to 6965) | 8·6% (−2·2 to 20·0) |
| Slovenia | 200 (150 to 264) | 6·7% (−7·5 to 20·9) | 4792 (3697 to 6028) | 9·7% (4·0 to 15·6) | 2755 (2061 to 3601) | 7·4% (−5·0 to 20·5) |
| Central Asia | 1833 (1353 to 2401) | 10·5% (4·6 to 16·9) | 56 062 (44 137 to 69 550) | 10·4% (7·9 to 12·9) | 29 509 (22 429 to 37 368) | 10·7% (5·4 to 16·4) |
| Armenia | 142 (106 to 188) | 13·5% (1·8 to 27·3) | 3727 (2918 to 4629) | 10·7% (4·1 to 17·1) | 2058 (1565 to 2663) | 13·1% (2·8 to 24·7) |
| Azerbaijan | 235 (165 to 318) | 15·1% (−1·6 to 35·6) | 7307 (5748 to 9051) | 11·9% (7·0 to 17·2) | 3836 (2790 to 4989) | 13·8% (−1·0 to 31·1) |
| Georgia | 236 (171 to 315) | 8·6% (−7·2 to 24·9) | 5900 (4565 to 7397) | 5·2% (0·5 to 11·3) | 3370 (2496 to 4368) | 8·3% (−5·7 to 23·2) |
| Kazakhstan | 397 (288 to 530) | 5·5% (−9·4 to 24·9) | 13 372 (10 530 to 16 557) | 9·0% (3·4 to 14·5) | 6764 (4973 to 8711) | 6·3% (−6·9 to 22·6) |
| Kyrgyzstan | 90 (67 to 117) | 6·7% (−2·8 to 17·7) | 2802 (2209 to 3474) | 5·2% (0·3 to 10·6) | 1465 (1107 to 1854) | 6·9% (−1·5 to 17·1) |
| Mongolia | 40 (29 to 53) | 5·2% (−9·0 to 21·2) | 1353 (1067 to 1694) | 11·2% (5·4 to 17·7) | 704 (523 to 916) | 5·9% (−6·7 to 19·6) |
| Tajikistan | 92 (69 to 121) | 12·0% (−1·9 to 30·7) | 2884 (2269 to 3573) | 8·9% (3·5 to 14·9) | 1533 (1164 to 1961) | 12·0% (−0·8 to 28·4) |
| Turkmenistan | 79 (60 to 103) | 14·2% (4·5 to 24·9) | 2711 (2144 to 3363) | 18·3% (12·3 to 23·9) | 1370 (1044 to 1751) | 16·2% (7·3 to 25·4) |
| Uzbekistan | 522 (387 to 681) | 14·0% (2·4 to 25·7) | 16 006 (12 659 to 19 798) | 15·3% (9·6 to 21·8) | 8409 (6379 to 10 644) | 15·5% (4·7 to 26·4) |
| Central Latin America | 4246 (3249 to 5442) | 13·2% (9·2 to 17·2) | 129 124 (102 593 to 159 008) | 16·6% (14·6 to 18·5) | 67 023 (51 781 to 84 193) | 14·8% (11·1 to 18·5) |
| Colombia | 796 (607 to 1022) | 12·2% (1·4 to 25·2) | 25 930 (20 527 to 32 111) | 15·5% (9·8 to 20·8) | 13 140 (10 059 to 16 860) | 13·4% (3·5 to 25·1) |
| Costa Rica | 110 (83 to 143) | 12·8% (2·4 to 24·4) | 3230 (2532 to 4003) | 15·3% (10·0 to 21·2) | 1700 (1300 to 2192) | 14·0% (4·6 to 24·3) |
| El Salvador | 128 (97 to 168) | 13·8% (0·9 to 28·3) | 3436 (2680 to 4268) | 18·6% (12·2 to 25·1) | 1902 (1440 to 2462) | 13·7% (2·9 to 26·5) |
| Guatemala | 182 (130 to 244) | 15·7% (−8·2 to 41·6) | 5194 (4068 to 6433) | 18·0% (12·3 to 23·8) | 2825 (2078 to 3721) | 15·7% (−5·8 to 37·6) |
| Honduras | 107 (76 to 146) | 19·8% (−3·2 to 47·4) | 2741 (2166 to 3383) | 18·4% (12·8 to 24·4) | 1626 (1168 to 2140) | 18·7% (−2·0 to 43·7) |
| Mexico | 2299 (1752 to 2952) | 14·4% (10·3 to 18·6) | 68 715 (54 711 to 83 874) | 17·7% (15·9 to 19·6) | 35 633 (27 612 to 44 856) | 16·4% (12·8 to 20·0) |
| Nicaragua | 77 (58 to 102) | 13·8% (−1·7 to 32·1) | 2273 (1799 to 2788) | 15·9% (10·6 to 22·0) | 1185 (908 to 1519) | 14·8% (1·6 to 30·4) |
| Panama | 83 (62 to 108) | 11·4% (−3·3 to 29·4) | 2369 (1878 to 2906) | 14·3% (8·5 to 19·4) | 1255 (949 to 1629) | 12·2% (−0·6 to 26·9) |
| Venezuela | 464 (335 to 613) | 9·3% (−6·2 to 28·6) | 15 235 (12 126 to 18 584) | 14·0% (6·9 to 19·9) | 7758 (5734 to 10 079) | 11·0% (−2·9 to 29·0) |
| Andean Latin America | 1093 (832 to 1450) | 15·3% (3·7 to 28·5) | 30 717 (24 372 to 37 972) | 13·0% (9·1 to 16·4) | 16 698 (12 634 to 21 390) | 14·8% (4·7 to 26·3) |
| Bolivia | 202 (147 to 269) | 21·1% (3·1 to 43·7) | 5114 (4011 to 6330) | 14·1% (8·8 to 19·8) | 3003 (2237 to 3871) | 19·2% (3·0 to 38·5) |
| Ecuador | 295 (224 to 383) | 10·8% (0·7 to 22·5) | 8688 (6872 to 10 735) | 13·6% (8·5 to 19·3) | 4589 (3519 to 5833) | 11·2% (2·3 to 21·8) |
| Peru | 596 (436 to 806) | 15·9% (−4·6 to 37·7) | 16 915 (13 398 to 20 887) | 12·3% (6·8 to 18·0) | 9106 (6795 to 11 952) | 15·2% (−2·4 to 34·8) |
| Caribbean | 1169 (883 to 1515) | 10·6% (3·6 to 17·5) | 31 751 (25 123 to 39 315) | 11·4% (7·6 to 14·9) | 17 253 (13 197 to 22 090) | 11·3% (5·5 to 17·4) |
| Antigua and Barbuda | 2 (1 to 3) | 14·5% (0·9 to 30·7) | 57 (45 to 70) | 12·3% (6·8 to 18·0) | 31 (23 to 39) | 13·3% (1·7 to 27·1) |
| The Bahamas | 9 (7 to 12) | 11·5% (−1·8 to 25·6) | 262 (206 to 325) | 9·4% (3·4 to 14·1) | 145 (109 to 184) | 10·5% (−1·1 to 22·2) |
| Barbados | 12 (9 to 17) | 18·6% (5·6 to 32·2) | 314 (247 to 392) | 11·3% (5·5 to 16·7) | 180 (136 to 235) | 16·2% (5·3 to 27·7) |
| Belize | 4 (3 to 5) | 30·4% (12·1 to 48·3) | 105 (82 to 130) | 20·1% (13·8 to 25·9) | 60 (45 to 76) | 28·9% (13·0 to 46·0) |
| Bermuda | 2 (1 to 3) | 12·8% (−3·1 to 30·5) | 52 (41 to 64) | 10·7% (6·0 to 15·9) | 30 (22 to 38) | 11·9% (−2·2 to 26·1) |
| Cuba | 507 (381 to 662) | 7·9% (−4·4 to 20·2) | 12 678 (9985 to 15 908) | 9·0% (2·0 to 16·5) | 7203 (5459 to 9294) | 9·0% (−2·0 to 19·9) |
| Dominica | 2 (1 to 3) | 17·2% (3·5 to 33·8) | 51 (40 to 63) | 15·5% (10·1 to 21·0) | 29 (22 to 37) | 16·5% (4·1 to 31·3) |
| Dominican Republic | 202 (149 to 264) | 13·8% (−3·2 to 30·9) | 5456 (4317 to 6690) | 17·5% (11·5 to 23·6) | 2999 (2282 to 3831) | 14·6% (−0·3 to 30·6) |
| Grenada | 2 (2 to 3) | 29·3% (12·9 to 45·8) | 58 (46 to 72) | 22·7% (17·1 to 28·6) | 34 (26 to 43) | 28·3% (14·1 to 43·2) |
| Guyana | 8 (6 to 10) | 16·8% (3·8 to 30·2) | 297 (235 to 367) | 15·1% (10·0 to 20·3) | 152 (116 to 193) | 16·0% (4·7 to 28·1) |
| Haiti | 103 (74 to 139) | 21·7% (4·3 to 41·3) | 3025 (2368 to 3805) | 14·5% (8·4 to 20·2) | 1738 (1258 to 2333) | 20·7% (4·3 to 39·2) |
| Jamaica | 81 (60 to 107) | 20·8% (3·1 to 42·6) | 1949 (1544 to 2425) | 13·7% (7·8 to 19·2) | 1123 (827 to 1467) | 19·6% (3·4 to 38·1) |
| Puerto Rico | 181 (136 to 235) | 14·1% (2·4 to 26·9) | 4300 (3392 to 5366) | 12·2% (7·1 to 18·6) | 2478 (1872 to 3177) | 13·5% (3·0 to 24·7) |
| Saint Lucia | 5 (4 to 7) | 21·3% (9·6 to 33·1) | 128 (101 to 160) | 18·0% (12·8 to 25·4) | 73 (56 to 93) | 20·8% (10·7 to 31·8) |
| Saint Vincent and the Grenadines | 2 (2 to 3) | 17·1% (4·5 to 31·4) | 62 (49 to 77) | 18·6% (12·6 to 24·6) | 34 (26 to 44) | 17·9% (6·7 to 30·3) |
| Suriname | 10 (8 to 13) | 20·1% (8·4 to 33·9) | 285 (225 to 351) | 14·0% (8·5 to 20·1) | 161 (123 to 206) | 17·8% (6·9 to 29·5) |
| Trinidad and Tobago | 32 (24 to 41) | 10·8% (1·2 to 20·9) | 987 (779 to 1221) | 13·2% (7·2 to 18·8) | 522 (398 to 663) | 11·3% (2·3 to 20·6) |
| Virgin Islands | 5 (4 to 7) | 10·3% (−1·4 to 24·1) | 143 (112 to 181) | 12·8% (7·5 to 18·7) | 83 (62 to 108) | 10·3% (−0·5 to 22·9) |
| Tropical Latin America | 4132 (3149 to 5331) | 15·2% (11·3 to 19·9) | 131 748 (104 807 to 162 882) | 16·5% (14·3 to 18·8) | 67 778 (52 122 to 85 976) | 15·2% (11·8 to 19·2) |
| Brazil | 4033 (3074 to 5199) | 15·0% (11·1 to 19·6) | 128 836 (102 469 to 159 395) | 16·4% (14·2 to 18·7) | 66 204 (50 914 to 84 027) | 15·0% (11·6 to 19·1) |
| Paraguay | 98 (74 to 129) | 23·4% (8·0 to 39·4) | 2912 (2300 to 3602) | 19·4% (13·7 to 25·2) | 1573 (1189 to 2015) | 23·1% (10·7 to 37·0) |
| East Asia | 41 584 (32 292 to 53 090) | 77·4% (64·4 to 89·0) | 1 451 650 (1 162 770 to 1 793 630) | 109·4% (94·2 to 123·8) | 734 156 (567 971 to 920 610) | 93·8% (80·1 to 107·0) |
| China | 40 012 (31 132 to 51 074) | 82·7% (68·3 to 95·3) | 1 407 701 (1 126 570 to 1 738 833) | 115·7% (99·5 to 131·0) | 710 041 (549 539 to 890 276) | 100·4% (85·3 to 114·9) |
| North Korea | 418 (310 to 545) | 15·1% (0·1 to 35·4) | 14 098 (11 067 to 17 482) | 6·0% (1·2 to 10·4) | 7527 (5649 to 9693) | 14·4% (1·5 to 30·5) |
| Taiwan (province of China) | 1154 (868 to 1540) | −20·1% (−32·7 to −6·6) | 29 851 (23 788 to 36 740) | 2·0% (−7·8 to 9·2) | 16 588 (12 664 to 21 336) | −17·2% (−29·5 to −5·5) |
| Southeast Asia | 11 900 (9275 to 14 966) | 34·2% (26·6 to 43·6) | 409 655 (338 902 to 493 631) | 24·7% (21·1 to 29·1) | 213 332 (169 151 to 262 446) | 31·4% (25·2 to 38·5) |
| Cambodia | 172 (130 to 217) | 57·6% (38·1 to 88·1) | 5768 (4623 to 7126) | 26·6% (20·8 to 32·6) | 3302 (2563 to 4119) | 46·9% (30·9 to 68·6) |
| Indonesia | 3490 (2682 to 4487) | 55·1% (39·6 to 76·0) | 146 236 (117 531 to 178 755) | 21·7% (19·1 to 24·2) | 70 145 (54 835 to 88 069) | 43·4% (32·7 to 56·7) |
| Laos | 62 (47 to 81) | 48·0% (32·0 to 71·5) | 2305 (1851 to 2826) | 26·0% (19·9 to 32·3) | 1190 (911 to 1512) | 42·8% (29·5 to 60·6) |
| Malaysia | 514 (386 to 672) | 19·9% (8·5 to 32·7) | 19 586 (15 697 to 23 908) | 26·4% (20·9 to 32·7) | 9694 (7546 to 12 275) | 19·1% (9·4 to 29·0) |
| Maldives | 6 (4 to 8) | 25·8% (2·7 to 53·4) | 187 (152 to 227) | 25·1% (19·7 to 31·1) | 96 (71 to 123) | 25·8% (6·3 to 51·0) |
| Mauritius | 34 (26 to 45) | 19·8% (5·9 to 35·2) | 1224 (971 to 1492) | 21·5% (16·0 to 27·0) | 606 (465 to 765) | 18·1% (6·2 to 31·8) |
| Myanmar | 1129 (859 to 1471) | 45·7% (29·6 to 64·5) | 28 152 (22 550 to 34 703) | 29·4% (23·4 to 35·9) | 19 732 (15 161 to 25 096) | 41·2% (26·7 to 57·4) |
| Philippines | 1132 (851 to 1462) | 17·0% (3·1 to 31·4) | 45 978 (37 005 to 56 836) | 16·9% (11·3 to 22·8) | 22 431 (17 069 to 28 411) | 17·6% (5·5 to 30·5) |
| Sri Lanka | 558 (402 to 758) | 8·2% (−11·3 to 30·0) | 17 814 (14 263 to 21 949) | 20·5% (15·0 to 26·8) | 9475 (6993 to 12 384) | 11·9% (−5·4 to 31·9) |
| Seychelles | 2 (2 to 3) | 16·0% (3·2 to 29·1) | 76 (62 to 92) | 19·0% (13·5 to 25·1) | 39 (30 to 49) | 15·1% (4·0 to 26·6) |
| Thailand | 2400 (1917 to 2980) | 25·2% (8·7 to 46·6) | 76 568 (66 494 to 86 991) | 31·7% (16·3 to 52·6) | 40 432 (32 376 to 48 959) | 27·6% (12·3 to 46·0) |
| Timor-Leste | 13 (9 to 18) | 50·6% (21·4 to 93·9) | 454 (357 to 565) | 26·6% (21·1 to 33·4) | 250 (180 to 334) | 44·8% (19·1 to 77·9) |
| Vietnam | 2389 (1819 to 3083) | 29·9% (11·8 to 51·0) | 64 452 (52 241 to 78 194) | 25·2% (19·6 to 30·9) | 35 840 (28 180 to 44 837) | 27·0% (10·9 to 44·2) |
| Oceania | 62 (48 to 80) | 6·5% (−1·8 to 16·2) | 2687 (2124 to 3321) | 14·8% (11·7 to 18·2) | 1243 (963 to 1560) | 9·1% (1·1 to 18·8) |
| American Samoa | 1 (0 to 1) | −0·8% (−13·9 to 15·6) | 21 (17 to 26) | 11·5% (6·4 to 16·8) | 10 (8 to 13) | 1·3% (−11·4 to 15·4) |
| Federated States of Micronesia | 1 (1 to 2) | 13·4% (−3·6 to 36·1) | 32 (25 to 39) | 19·1% (12·6 to 25·1) | 20 (15 to 26) | 14·5% (−2·5 to 37·3) |
| Fiji | 10 (7 to 14) | 5·4% (−14·9 to 28·7) | 386 (306 to 478) | 14·7% (9·6 to 20·1) | 195 (145 to 256) | 7·5% (−11·3 to 30·6) |
| Guam | 4 (3 to 5) | 5·3% (−7·1 to 18·3) | 116 (91 to 144) | 9·8% (5·2 to 15·8) | 64 (48 to 82) | 5·8% (−5·6 to 17·4) |
| Kiribati | 1 (1 to 1) | 28·3% (11·2 to 47·1) | 26 (20 to 32) | 15·9% (10·5 to 22·2) | 18 (13 to 23) | 25·6% (9·7 to 41·2) |
| Marshall Islands | 0 (0 to 1) | −4·7% (−16·4 to 8·8) | 17 (14 to 22) | 13·3% (6·7 to 20·2) | 8 (6 to 10) | −1·9% (−12·6 to 10·5) |
| Northern Mariana Islands | 0 (0 to 1) | 8·1% (−9·6 to 30·3) | 21 (16 to 26) | 14·0% (8·9 to 20·2) | 9 (7 to 12) | 9·5% (−6·6 to 29·2) |
| Papua New Guinea | 34 (25 to 44) | 18·9% (4·1 to 37·3) | 1414 (1114 to 1760) | 15·7% (10·8 to 20·9) | 692 (526 to 885) | 16·9% (3·3 to 34·0) |
| Samoa | 3 (2 to 4) | 8·5% (−6·4 to 26·0) | 67 (53 to 84) | 13·2% (7·8 to 19·4) | 44 (33 to 56) | 9·1% (−4·9 to 25·1) |
| Solomon Islands | 4 (3 to 5) | 12·7% (−1·1 to 29·3) | 120 (94 to 148) | 12·9% (6·9 to 19·4) | 74 (55 to 95) | 13·2% (−0·2 to 28·8) |
| Tonga | 2 (1 to 2) | 9·8% (−6·4 to 28·9) | 41 (32 to 51) | 13·9% (7·8 to 20·5) | 25 (19 to 32) | 11·0% (−3·8 to 29·3) |
| Vanuatu | 2 (2 to 3) | 14·6% (−0·4 to 31·0) | 69 (54 to 85) | 14·4% (8·8 to 20·9) | 42 (31 to 54) | 14·8% (0·6 to 30·5) |
| North Africa and Middle East | 9460 (7348 to 12 233) | 41·9% (33·2 to 51·7) | 297 861 (239 654 to 367 829) | 41·4% (37·3 to 46·0) | 153 897 (120 636 to 193 371) | 42·8% (35·2 to 51·0) |
| Afghanistan | 183 (139 to 235) | 37·5% (22·1 to 57·9) | 5813 (4574 to 7275) | 27·0% (20·2 to 35·4) | 3436 (2641 to 4334) | 36·2% (22·9 to 52·7) |
| Algeria | 943 (714 to 1243) | 42·4% (27·1 to 59·6) | 24 250 (19 187 to 29 987) | 38·0% (31·1 to 46·1) | 14 128 (10 823 to 18 044) | 42·4% (28·8 to 57·3) |
| Bahrain | 11 (8 to 15) | 26·4% (3·5 to 54·3) | 480 (383 to 603) | 29·0% (23·0 to 35·2) | 226 (170 to 296) | 25·9% (5·4 to 49·7) |
| Egypt | 1436 (1075 to 1860) | 40·2% (22·7 to 61·2) | 48 694 (39 464 to 59 862) | 40·7% (33·0 to 49·8) | 24 460 (18 678 to 31 133) | 41·0% (25·5 to 59·9) |
| Iran | 1811 (1343 to 2381) | 61·2% (35·4 to 96·4) | 59 590 (48 749 to 73 996) | 58·4% (49·1 to 71·0) | 30 138 (23 315 to 38 332) | 62·5% (39·2 to 92·4) |
| Iraq | 299 (224 to 391) | 23·3% (1·4 to 43·6) | 9777 (7735 to 12 063) | 22·7% (16·2 to 29·7) | 5204 (3940 to 6679) | 23·5% (3·1 to 42·8) |
| Jordan | 83 (59 to 112) | 27·1% (1·4 to 58·2) | 2589 (2084 to 3172) | 28·7% (18·8 to 37·9) | 1353 (999 to 1763) | 26·0% (2·9 to 53·6) |
| Kuwait | 27 (19 to 37) | 42·8% (9·0 to 86·7) | 1280 (1019 to 1622) | 35·7% (28·4 to 44·1) | 572 (417 to 770) | 40·5% (10·8 to 78·1) |
| Lebanon | 168 (125 to 224) | 19·7% (−1·0 to 46·3) | 5114 (4051 to 6360) | 33·9% (26·5 to 41·8) | 2524 (1920 to 3280) | 20·8% (2·1 to 44·6) |
| Libya | 105 (80 to 136) | 38·0% (19·4 to 58·7) | 3109 (2499 to 3866) | 42·2% (31·4 to 54·7) | 1718 (1322 to 2203) | 40·6% (23·3 to 59·6) |
| Morocco | 695 (528 to 908) | 57·3% (37·5 to 102·4) | 20 893 (16 569 to 26 080) | 39·7% (32·4 to 46·9) | 10 968 (8401 to 13 728) | 54·2% (38·0 to 84·4) |
| Oman | 39 (30 to 50) | 63·4% (43·1 to 87·5) | 1496 (1200 to 1859) | 74·3% (65·0 to 84·5) | 724 (567 to 910) | 67·9% (49·3 to 89·6) |
| Palestine | 34 (25 to 43) | 18·2% (2·9 to 35·0) | 1143 (907 to 1419) | 19·8% (13·7 to 26·0) | 595 (461 to 745) | 19·7% (5·9 to 34·6) |
| Qatar | 11 (7 to 16) | 32·5% (−1·3 to 76·5) | 562 (441 to 714) | 34·0% (26·8 to 41·6) | 255 (179 to 354) | 33·7% (3·2 to 75·8) |
| Saudi Arabia | 337 (263 to 432) | 65·7% (43·7 to 94·7) | 12 853 (10 251 to 15 936) | 65·1% (58·8 to 71·4) | 6126 (4814 to 7779) | 67·5% (48·4 to 92·1) |
| Sudan | 349 (261 to 455) | 53·2% (38·6 to 72·5) | 11 758 (9351 to 14 608) | 38·2% (31·8 to 46·6) | 6097 (4668 to 7754) | 50·4% (37·8 to 65·3) |
| Syria | 233 (178 to 303) | 41·8% (27·4 to 56·7) | 7409 (5926 to 9203) | 40·9% (33·7 to 47·8) | 3803 (2945 to 4844) | 42·6% (29·4 to 55·3) |
| Tunisia | 321 (237 to 430) | 40·0% (16·2 to 65·5) | 8450 (6790 to 10 486) | 44·9% (36·3 to 54·3) | 4697 (3568 to 6052) | 43·0% (21·4 to 65·8) |
| Turkey | 2160 (1603 to 2865) | 25·6% (6·8 to 48·0) | 63 708 (50 912 to 79 341) | 33·8% (27·0 to 40·9) | 32 482 (24 631 to 42 095) | 27·8% (11·0 to 46·7) |
| United Arab Emirates | 39 (29 to 51) | 48·6% (18·7 to 86·5) | 2498 (1953 to 3181) | 41·4% (34·5 to 49·4) | 1101 (809 to 1473) | 50·5% (21·1 to 84·5) |
| Yemen | 178 (134 to 230) | 60·9% (41·2 to 86·1) | 6160 (4866 to 7739) | 45·6% (38·0 to 54·0) | 3262 (2521 to 4155) | 58·1% (40·0 to 79·7) |
| South Asia | 21 007 (15 942 to 27 123) | 48·3% (36·8 to 62·4) | 696 108 (552 987 to 860 047) | 29·2% (25·6 to 32·8) | 364 282 (281 631 to 460 552) | 43·8% (35·9 to 53·4) |
| Bangladesh | 1501 (1138 to 1953) | −10·2% (−22·6 to 6·2) | 54 198 (42 488 to 67 532) | 25·0% (18·8 to 31·7) | 25 363 (19 435 to 32 110) | −3·8% (−15·8 to 9·8) |
| Bhutan | 11 (8 to 15) | 43·5% (21·0 to 73·5) | 288 (227 to 357) | 35·8% (28·9 to 43·7) | 171 (129 to 222) | 41·1% (21·1 to 66·1) |
| India | 17 539 (13 317 to 22 637) | 55·8% (42·3 to 71·8) | 575 946 (458 316 to 712 213) | 29·7% (25·9 to 33·5) | 305 274 (235 390 to 385 725) | 49·6% (40·9 to 60·1) |
| Nepal | 319 (236 to 419) | 66·7% (38·1 to 107·3) | 9445 (7390 to 11 806) | 29·5% (23·2 to 36·4) | 5449 (4071 to 7009) | 54·5% (33·8 to 82·1) |
| Pakistan | 1637 (1213 to 2148) | 43·8% (23·6 to 70·0) | 56 231 (43 998 to 70 068) | 27·9% (21·7 to 35·7) | 28 025 (21 124 to 36 119) | 39·5% (22·1 to 59·7) |
| Southern sub-Saharan Africa | 756 (577 to 988) | 25·6% (16·4 to 36·8) | 20 980 (16 480 to 26 027) | 14·3% (12·0 to 16·6) | 11 750 (9090 to 14 869) | 23·5% (16·2 to 33·0) |
| Botswana | 15 (8 to 23) | 26·3% (−23·0 to 69·2) | 460 (361 to 569) | 22·0% (15·3 to 28·8) | 264 (159 to 384) | 25·3% (−18·4 to 63·7) |
| Lesotho | 14 (10 to 19) | 17·2% (−5·3 to 44·2) | 397 (312 to 501) | 18·0% (11·6 to 24·5) | 223 (161 to 295) | 16·8% (−4·2 to 41·0) |
| Namibia | 16 (10 to 23) | 25·7% (−8·4 to 55·2) | 442 (349 to 551) | 20·2% (13·4 to 28·0) | 272 (178 to 374) | 23·6% (−6·4 to 50·5) |
| South Africa | 594 (454 to 787) | 21·3% (12·5 to 31·6) | 17 305 (13 650 to 21 444) | 14·2% (11·7 to 16·7) | 9245 (7128 to 11 715) | 19·9% (12·7 to 28·2) |
| Swaziland | 7 (4 to 10) | 11·2% (−15·0 to 40·7) | 215 (168 to 270) | 15·4% (8·7 to 22·4) | 118 (77 to 164) | 11·5% (−11·3 to 38·8) |
| Zimbabwe | 110 (78 to 146) | 55·9% (25·6 to 143·8) | 2162 (1701 to 2685) | 8·5% (2·7 to 14·3) | 1628 (1189 to 2120) | 47·0% (21·1 to 117·4) |
| Western sub-Saharan Africa | 1578 (1170 to 2059) | 22·6% (13·5 to 32·1) | 44 230 (34 637 to 55 905) | 15·9% (12·6 to 19·3) | 27 359 (20 483 to 35 211) | 21·7% (13·2 to 30·5) |
| Benin | 47 (35 to 62) | 15·6% (2·3 to 31·1) | 1196 (932 to 1541) | 14·1% (7·5 to 20·2) | 811 (610 to 1058) | 16·2% (4·0 to 29·4) |
| Burkina Faso | 59 (44 to 79) | 11·6% (−0·9 to 29·1) | 1537 (1199 to 1939) | 14·4% (8·1 to 21·8) | 1023 (777 to 1308) | 11·3% (0·1 to 26·4) |
| Cameroon | 147 (105 to 195) | 15·5% (−1·4 to 34·6) | 3077 (2420 to 3870) | 11·6% (6·1 to 16·9) | 2268 (1646 to 2958) | 15·3% (−0·7 to 33·6) |
| Cape Verde | 6 (4 to 8) | 31·6% (16·4 to 47·4) | 127 (100 to 157) | 24·5% (18·2 to 31·0) | 78 (59 to 101) | 30·3% (16·0 to 44·6) |
| Chad | 51 (38 to 67) | 11·3% (−0·6 to 24·3) | 1284 (1005 to 1642) | 12·0% (6·4 to 18·3) | 839 (635 to 1087) | 11·3% (−0·4 to 24·6) |
| Côte d'Ivoire | 110 (81 to 144) | 20·8% (8·7 to 35·4) | 2747 (2142 to 3475) | 13·7% (8·5 to 19·8) | 1923 (1440 to 2501) | 20·4% (9·0 to 34·6) |
| The Gambia | 8 (6 to 10) | 18·2% (3·2 to 35·1) | 186 (146 to 237) | 9·6% (3·8 to 15·8) | 126 (96 to 162) | 17·0% (3·2 to 32·0) |
| Ghana | 171 (126 to 225) | 28·4% (12·5 to 50·3) | 4048 (3183 to 5118) | 13·5% (7·8 to 19·4) | 2768 (2085 to 3568) | 26·5% (11·5 to 45·2) |
| Guinea | 58 (43 to 78) | 17·9% (3·1 to 33·5) | 1441 (1121 to 1836) | 11·0% (5·8 to 16·8) | 993 (733 to 1315) | 17·5% (2·8 to 32·3) |
| Guinea-Bissau | 8 (6 to 11) | 16·2% (2·1 to 33·4) | 217 (169 to 274) | 13·1% (8·0 to 18·8) | 148 (111 to 191) | 15·5% (2·7 to 30·5) |
| Liberia | 19 (14 to 25) | 17·6% (4·5 to 32·5) | 508 (400 to 648) | 10·6% (5·2 to 16·6) | 332 (249 to 427) | 17·1% (5·6 to 30·8) |
| Mali | 65 (47 to 86) | 23·1% (6·3 to 44·5) | 1567 (1224 to 2024) | 13·2% (7·4 to 18·9) | 1049 (767 to 1395) | 20·0% (4·4 to 38·7) |
| Mauritania | 23 (17 to 31) | 20·5% (0·3 to 42·0) | 565 (441 to 708) | 15·7% (9·5 to 21·7) | 381 (278 to 501) | 18·9% (0·4 to 38·8) |
| Niger | 62 (45 to 82) | 14·0% (−1·4 to 34·1) | 1732 (1351 to 2224) | 8·0% (2·4 to 14·5) | 1105 (809 to 1450) | 13·1% (−1·6 to 31·5) |
| Nigeria | 613 (429 to 835) | 27·8% (6·4 to 48·8) | 20 851 (16 306 to 26 258) | 19·8% (13·4 to 26·6) | 11 302 (8080 to 15 109) | 26·5% (7·3 to 45·9) |
| São Tomé and Príncipe | 1 (1 to 2) | 22·2% (4·1 to 44·0) | 27 (22 to 34) | 13·8% (8·2 to 20·1) | 17 (13 to 23) | 19·5% (2·9 to 38·4) |
| Senegal | 80 (60 to 105) | 24·8% (13·2 to 39·5) | 1688 (1322 to 2154) | 11·2% (5·5 to 17·1) | 1274 (965 to 1646) | 22·2% (11·4 to 35·5) |
| Sierra Leone | 20 (15 to 26) | 15·4% (2·2 to 31·0) | 637 (498 to 814) | 12·7% (6·4 to 18·7) | 384 (286 to 496) | 15·6% (2·7 to 29·6) |
| Togo | 31 (23 to 40) | 17·5% (4·3 to 33·1) | 794 (619 to 1007) | 15·1% (8·8 to 21·6) | 538 (403 to 695) | 17·9% (5·0 to 32·3) |
| Eastern sub-Saharan Africa | 1975 (1493 to 2591) | 34·6% (25·8 to 44·6) | 46 489 (36 657 to 57 613) | 21·7% (18·6 to 25·0) | 30 752 (23 631 to 39 371) | 31·5% (24·0 to 40·0) |
| Burundi | 42 (30 to 57) | 25·4% (8·5 to 43·7) | 1115 (880 to 1407) | 15·9% (9·4 to 23·1) | 683 (511 to 896) | 23·0% (7·5 to 41·0) |
| Comoros | 4 (3 to 5) | 31·0% (14·4 to 48·5) | 101 (80 to 126) | 23·0% (16·6 to 29·8) | 65 (50 to 84) | 29·1% (14·5 to 45·8) |
| Djibouti | 6 (5 to 9) | 38·2% (14·5 to 63·0) | 153 (119 to 191) | 27·3% (20·3 to 35·4) | 101 (74 to 135) | 35·6% (13·5 to 57·3) |
| Eritrea | 19 (14 to 26) | 43·7% (26·1 to 63·1) | 531 (419 to 668) | 26·9% (19·7 to 34·1) | 344 (256 to 442) | 39·7% (24·6 to 57·2) |
| Ethiopia | 556 (405 to 750) | 40·7% (21·9 to 63·6) | 12 384 (9654 to 15 735) | 24·9% (17·8 to 32·4) | 8580 (6437 to 11 338) | 36·9% (19·5 to 56·8) |
| Kenya | 232 (169 to 309) | 40·9% (23·6 to 67·9) | 6557 (5228 to 8065) | 22·0% (19·7 to 24·7) | 3714 (2782 to 4800) | 36·4% (22·8 to 54·2) |
| Madagascar | 99 (73 to 134) | 17·2% (0·3 to 34·9) | 2940 (2333 to 3665) | 14·1% (7·6 to 20·2) | 1657 (1221 to 2127) | 15·9% (0·2 to 32·0) |
| Malawi | 97 (69 to 130) | 28·3% (3·4 to 60·4) | 2112 (1650 to 2683) | 19·0% (11·6 to 25·6) | 1497 (1091 to 1973) | 26·9% (3·1 to 56·7) |
| Mozambique | 157 (114 to 212) | 21·0% (4·1 to 42·4) | 3376 (2626 to 4246) | 20·7% (13·1 to 27·9) | 2420 (1783 to 3224) | 20·7% (4·6 to 40·7) |
| Rwanda | 61 (44 to 81) | 52·5% (32·3 to 74·8) | 1376 (1089 to 1714) | 24·6% (17·9 to 31·6) | 903 (660 to 1176) | 45·8% (27·7 to 68·3) |
| Somalia | 42 (31 to 57) | 19·5% (4·9 to 37·2) | 1107 (865 to 1405) | 13·8% (7·4 to 19·7) | 700 (517 to 919) | 17·8% (4·3 to 34·4) |
| South Sudan | 52 (37 to 71) | 34·2% (13·5 to 58·2) | 1421 (1115 to 1789) | 15·9% (9·2 to 23·1) | 852 (616 to 1125) | 30·5% (11·5 to 51·7) |
| Tanzania | 353 (266 to 460) | 35·2% (17·6 to 56·2) | 7443 (6048 to 9048) | 24·0% (13·4 to 34·4) | 5302 (4081 to 6708) | 31·5% (15·5 to 51·4) |
| Uganda | 167 (123 to 226) | 29·6% (13·2 to 49·0) | 3873 (3046 to 4804) | 23·9% (17·1 to 30·6) | 2553 (1929 to 3310) | 28·6% (13·3 to 46·5) |
| Zambia | 88 (63 to 119) | 33·8% (6·1 to 70·2) | 1963 (1538 to 2453) | 20·4% (14·2 to 27·3) | 1376 (1009 to 1844) | 33·1% (6·5 to 66·0) |
| Central sub-Saharan Africa | 474 (346 to 621) | 22·0% (12·7 to 33·8) | 13 564 (10 608 to 17 192) | 10·1% (5·9 to 14·5) | 8083 (6104 to 10 365) | 19·4% (11·3 to 29·8) |
| Angola | 88 (62 to 123) | 50·2% (21·6 to 89·2) | 2615 (2034 to 3271) | 19·7% (13·2 to 26·4) | 1555 (1125 to 2107) | 41·8% (16·8 to 74·1) |
| Central African Republic | 24 (17 to 32) | 10·9% (−4·9 to 26·3) | 717 (559 to 915) | 8·8% (2·7 to 14·9) | 401 (293 to 525) | 10·5% (−3·8 to 25·4) |
| Congo (Brazzaville) | 30 (21 to 41) | 23·6% (3·5 to 45·1) | 789 (623 to 986) | 16·4% (9·7 to 23·5) | 490 (361 to 648) | 21·5% (2·7 to 42·5) |
| Democratic Republic of the Congo | 306 (224 to 404) | 16·0% (4·8 to 30·1) | 8845 (6920 to 11 285) | 6·7% (1·0 to 12·2) | 5244 (3904 to 6686) | 14·4% (4·8 to 26·2) |
| Equatorial Guinea | 5 (3 to 8) | 58·3% (11·8 to 108·9) | 159 (126 to 197) | 42·3% (33·8 to 50·8) | 91 (59 to 131) | 52·4% (12·3 to 97·9) |
| Gabon | 21 (15 to 28) | 28·3% (9·4 to 51·0) | 439 (346 to 550) | 18·1% (12·4 to 24·8) | 302 (223 to 395) | 25·8% (7·9 to 46·6) |
95% uncertainty intervals are in parentheses. DALYs=disability-adjusted life-years. SDI=Socio-demographic Index. For more details about the rationale for this classification of countries see reference 23.
Figure 1.
Age-standardised prevalence of Parkinson's disease per 100 000 population by location for both sexes, 2016
ATG=Antigua and Barbuda. FSM=Federated States of Micronesia. Isl=Islands. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines.
Globally, Parkinson's disease caused 211 296 deaths (95% UI 167 771–265 160; 93 512, 95% UI 73 702–118 421, in women and 117 784, 93 729–147 607, in men) and 3·2 million DALYs (95% UI 2·6–4·0; 1·4 million, 95% UI 1·1–1·7, in women; 1·8 million, 1·5–2·3, in men) in 2016. Of these, high SDI countries accounted for 84 911 (40·2%) deaths and 1·1 million (34·4%) DALYs, high-middle or middle SDI countries for 98 820 (46·8%) deaths and 1·6 million (50·0%) DALYs, and low-middle or low SDI countries for 27 470 (13·0%) deaths and 0·5 million (15·6%) DALYs. The number of deaths was 2·6 times higher and the number of DALYs was 2·5 times higher in 2016 than in 1990. These increases were not explained exclusively by an increasing number of older people, because age-standardised rates increased from 1990 to 2016 for both deaths and DALYs by about 20% (table). Similar to prevalence, the increases in deaths and DALYs were lowest in high SDI countries and highest in middle SDI countries, and were seen in both men and women.
Parkinson's disease was uncommon before 50 years of age. Prevalence in 2016 increased with age thereafter and peaked between 85 years and 89 years (1·7% for men; 1·2% for women) and decreased after that age (figure 2). Age-standardised prevalence of Parkinson's disease in 2016 was 1·40 times (95% UI 1·36–1·43) higher in men than in women; the male-to-female ratio was similar in 1990 (1·37, 95% UI 1·34–1·40). A similar pattern was seen for the rates of YLLs and YLDs according to age, although the age-related increase was considerably steeper for YLLs than for YLDs, suggesting that Parkinson's disease-related case fatality rises with age (figure 3).
Figure 2.
Global prevalence of Parkinson's disease by age and sex, 2016
Prevalence is expressed as the percentage of the population that is affected by the disease. Shading indicates 95% uncertainty intervals.
Figure 3.
Global YLD and YLL rates per 100 000 population due to Parkinson's disease by age, 2016
Shading indicates 95% uncertainty intervals. YLDs=years lived with disability. YLLs=years of life lost.
The age-standardised rate of DALYs of Parkinson's disease by the 21 GBD world regions generally increased with SDI (figure 4). Sub-Saharan Africa, Latin America and the Caribbean (with the exception of southern Latin America), south Asia, and high-income Asia Pacific had lower age-standardised DALY rates than other regions with similar SDI. Southern Latin America and high-income North America were the regions with highest estimates relative to expected values based on SDI.
Figure 4.
Age-standardised DALY rates for Parkinson's disease by 21 Global Burden of Disease regions by Socio-demographic Index, 1990–2016
Expected values based on Socio-demographic Index and disease rates in all locations are shown as the black line. The black line represents expected values of age-standardised DALY rates for each value of Socio-demographic Index and is based on a Gaussian process regression of results for all Global Burden of Disease locations over the entire 1990–2016 estimation period. DALY=disability-adjusted life-year.
Smoking was found to have a small, protective effect on Parkinson's disease, and would have been expected to prevent 461 194 DALYs (95% UI 324 745–599 845) globally in 2016 if the association was truly causal.
Discussion
Over the past generation, the number of individuals with Parkinson's disease globally has more than doubled to over 6 million. Of all the neurological disorders included in GBD 2015,1 Parkinson's disease was the fastest growing. Ageing populations contributed to much of that growth as crude prevalence rates increased by about 74% from 1990 to 2016 and age-standardised prevalence rates increased by about 22%. However, because age-standardised prevalence, DALYs, and death rates all increased from 1990 to 2016, additional factors are probably important.
First, changes in study methods, availability of higher-quality studies, and greater awareness of diagnosis24 might have led to better estimates of prevalence, DALYs, and deaths since 1990.3 For example, door-to-door studies are less likely to miss individuals who have never been diagnosed and would be missed in health records.25 Our DisMod-MR 2.1 model did not show evidence in favour of a systematic bias between door-to-door surveys and studies based on administrative records; however, establishing such evidence in a model with relatively sparse data is difficult. Although many regions have seen improvements in study methods, these changes alone are probably insufficient to explain the rising burden of Parkinson's disease. For example, the rates of Parkinson's disease have also increased in high-income countries without substantial changes in study methodology.
Second, increasing life expectancy is probably contributing to longer disease duration in individuals with Parkinson's disease and thus to higher prevalence, even if incidence remains constant and individuals with Parkinson's disease show the same time trends in mortality as the general population.12 Indeed, in a meta-analysis26 of ten studies, recent cohorts showed longer disease duration, with an increase of 2·5 years per decade. That study26 showed no clear evidence that the introduction of levodopa and improvements in Parkinson's disease care have led to improvement in survival of individuals with Parkinson's disease compared with similar individuals without Parkinson's disease. A recent study12 estimated the burden of Parkinson's disease in France from 2010 to 2030 under a constant incidence scenario and assuming that the relative risk of death of individuals with Parkinson's disease relative to controls had not changed over time. It showed that the life expectancy of individuals with Parkinson's disease would be expected to increase by approximately 3 years and the age-standardised and sex-standardised prevalence rate by 12% over 20 years. As patients live with Parkinson's disease for more years and the number of individuals with advanced Parkinson's disease increases, studies will be needed to inform the distribution of the severity of the disease in representative samples with simple instruments such as the Hoehn and Yahr scale.
Third, the increase in Parkinson's disease burden might be linked to environmental factors tied to the growing industrialisation of the world. In general, better health is positively associated with socioeconomic level.27, 28, 29 However, with Parkinson's disease, the opposite is true; age-standardised DALY rates due to Parkinson's disease increased with SDI. The reason for this association is not clear. Some environmental exposures tied to industrialisation, including pesticides,7 solvents,7 or metals,8, 9 which are also more common in high SDI countries, might contribute to the increased incidence of Parkinson's disease. For example, in China (a middle SDI country), which has undergone rapid industrial growth since 1990, the age-adjusted prevalence rates of Parkinson's disease more than doubled between 1990 and 2016, the largest increase worldwide. If environmental factors related to industrialisation played a part, an increase in incidence over time would be expected. A few studies have examined time trends in incidence of Parkinson's disease, with inconsistent findings. A study in the USA30 suggested that incidence increased by 24% (95% CI 8–43) per decade between 1976 and 2005 in men but not in women. By contrast, in the Netherlands, the Rotterdam Study31 reported a substantial decrease in Parkinson's disease incidence between 1990 and 2011, without any obvious explanation. One study in Canada32 and another in the USA33 study showed no significant time trends. In Finland, using the Finnish National Prescription Register, the incidence of Parkinson's disease increased between 1997 and 2014 both in rural and urban regions.34 High-quality prospective cohort studies and detailed registries are needed to survey time trends in the worldwide incidence of Parkinson's disease more accurately and understand the factors that might be driving time trends. Alternative explanations for the positive association between the burden of Parkinson's disease and SDI include better ascertainment of Parkinson's disease in higher SDI countries through better study methods or health-care access and disease recognition. However, in lower SDI countries, we included door-to-door studies, when available, that are considered to be less prone to underestimation.25
Fourth, declining smoking rates in some countries,35 although a global health boon, might contribute to higher incidence of Parkinson's disease.14 The risk of Parkinson's disease is decreased by approximately 40% among smokers.10 Whether this association is truly causal or explained by reverse causation or other biases is still debated.11 If the association between smoking and Parkinson's disease were causal, decreasing smoking rates would lead to an increase in the incidence of Parkinson's disease in the future. Assuming a causal inverse association and a 10-year lag to account for the temporal effect of smoking on the incidence of Parkinson's disease, one study in the USA14 estimated that declining smoking rates in the country might increase the projected burden of Parkinson's disease in 2040 by 10%. However, because the lag time between exposure and the actual effect on disease risk is unknown and might actually be longer than 10 years,36 the timing of the potential effect of declining smoking rates on Parkinson's disease incidence is uncertain, and additional studies are needed to examine the effects of changing smoking habits in different parts of the world with different smoking rates and time trends. Regardless of the results of such studies, the adverse health consequences of smoking far outweigh any potential benefit on Parkinson's disease. Finally, changes in the prevalence of other known (eg, head trauma37) or unknown risk or protective factors that were not included in GBD 2016 might contribute to changing incidence rates of Parkinson's disease.
This study confirms that Parkinson's disease is about 1·4 times more frequent in men than women, and this ratio did not change substantially over the study period. Environmental (eg, occupational) exposures to which men are more frequently exposed might contribute to this pattern. The prevalence of Parkinson's disease increased with age. Underascertainment at older ages owing to underdiagnosis, comorbidities, or institutional care might explain the decrease seen in the oldest age groups after the peak between 85 years and 89 years.
The current estimates for the global burden of Parkinson's disease are generated from imperfect data and models that are refined in each iteration of the GBD study. Estimates from GBD studies can vary from year to year as revised estimates are generated on the basis of refined methods and inclusion of more and higher-quality studies that are less likely to underestimate the true burden of the disease. Nonetheless, high-quality epidemiological studies (especially on incidence and disease severity) are still rare for large portions of the world, especially in low-income regions, where such studies are needed to understand trends and guide efforts to reduce the disease burden. Methodological differences for determining prevalence and study shortcomings might result in estimates that vary considerably and underestimate the true burden of Parkinson's disease.38 This under-reporting is well known for Parkinson's disease in studies based on death certificates,39, 40, 41, 42, 43 whereas population-based door-to-door studies are considered a better approach because they are able to capture undiagnosed cases.25 However, disease frequency estimates from population-based studies might be affected by selection bias resulting from non-response, particularly if individuals affected by the disease under investigation are less likely to participate. Non-response is an important issue as participation rates in epidemiological studies have considerably decreased over the past 30 years.44 Another limitation is that because we lack strong predictors for the occurrence of Parkinson's disease, some of the variation between countries is probably due to measurement error that we have been unable to correct. Because we rely on prevalence data to derive our cause of death estimates, any residual measurement error in the prevalence estimates is transposed onto the death estimates for Parkinson's disease. Although the the bias in mortality is an unwanted property, it is less than would have been the case if we had based our estimates on the observed rates of death with Parkinson's disease as the underlying cause from vital registration data. The large variation in death rates over time within the same countries and the even larger variation between countries are implausible and probably explained by changing death coding practices.
Neurological disorders are now the leading source of disability in the world, and Parkinson's disease is the fastest growing of these disorders.1 As the population ages and life expectancy increases, the doubling of the number of individuals with Parkinson's disease between 1990 and 2016 is projected to occur again in the coming generation.12, 13, 14, 45 To address this great health challenge will require action aimed at preventing the disease where feasible and improving the lives of those affected by the condition.46 Among the potential responses available are preventing the disease (eg, by increasing physical activity earlier in adulthood47 and reducing exposure to pesticides7), improving worldwide access to care and effective treatments (eg, levodopa), increasing funding for research (eg, to understand the underlying causes), and development of new therapies.
GBD 2016 Parkinson's Disease Collaborators
E Ray Dorsey*, Alexis Elbaz*, Emma Nichols, Foad Abd-Allah, Ahmed Abdelalim, Jose C Adsuar, Mustafa Geleto Ansha, Carol Brayne, Jee-Young J Choi, Daniel Collado-Mateo, Nabila Dahodwala, Huyen Phuc Do, Dumessa Edessa, Matthias Endres, Seyed-Mohammad Fereshtehnejad, Kyle J Foreman, Fortune Gbetoho Gankpe, Rahul Gupta, Graeme J Hankey, Simon I Hay, Mohamed I Hegazy, Desalegn T Hibstu, Amir Kasaeian, Yousef Khader, Ibrahim Khalil, Young-Ho Khang, Yun Jin Kim, Yoshihiro Kokubo, Giancarlo Logroscino, João Massano, Norlinah Mohamed Ibrahim, Mohammed A Mohammed, Alireza Mohammadi, Maziar Moradi-Lakeh, Mohsen Naghavi, Binh Thanh Nguyen, Yirga Legesse Nirayo, Felix Akpojene Ogbo, Mayowa Ojo Owolabi, David M Pereira, Maarten J Postma, Mostafa Qorbani, Muhammad Aziz Rahman, Kedir T Roba, Hosein Safari, Saeid Safiri, Maheswar Satpathy, Monika Sawhney, Azadeh Shafieesabet, Mekonnen Sisay Shiferaw, Mari Smith, Cassandra E I Szoeke, Rafael Tabarés-Seisdedos, Nu Thi Truong, Kingsley Nnanna Ukwaja, Narayanaswamy Venketasubramanian, Santos Villafaina, Kidu Gidey Weldegwergs, Ronny Westerman, Tissa Wijeratne, Andrea S Winkler, Bach Tran Xuan, Naohiro Yonemoto, Valery L Feigin, Theo Vos, Christopher J L Murray.
*Contributed equally.
Affiliations
Department of Neurology, University of Rochester, USA (E R Dorsey MD); INSERM U1018-CESP, Hôpital Paul Brousse, Villejuif cedex, France (A Elbaz MD); Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA (E Nichols BA, K J Foreman PhD, Prof S I Hay DSc, I Khalil MD, Prof M Naghavi PhD, M Smith MPA, Prof T Vos PhD, Prof C J L Murray DPhil); Department of Neurology, Cairo University, Cairo, Egypt (Prof F Abd-Allah MD, Prof A Abdelalim MD, Prof M I Hegazy PhD); Faculty of Sport Science (J C Adsuar PhD, S Villafaina MSc), and Departamento de Didáctica de la Expresión Musical, Plástica y Corporal (D Collado-Mateo MSc), University of Extremadura, Spain; Public Health, Debre Berhan University, Debre Berhan, Ethiopia (M G Ansha MPH); Public Health and Primary Care, University of Cambridge, UK (Prof C Brayne MD); Biochemistry, Biomedical Science, Seoul National University Hospital, Seoul, South Korea (J-Y J Choi PhD); Facultad de Educación Universidad Autónoma de Chile, Talca, Chile (D Collado-Mateo); Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA (Prof N Dahodwala MD); Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam (H P Do PhD, B T Nguyen MPH, N T Truong BHlthSci); School of Pharmacy (D Edessa MPharm, M S Shiferaw MSc), and School of Nursing and Midwifery (K T Roba PhD), Haramaya University, Harar, Ethiopia; Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany (Prof M Endres MD); Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada (S-M Fereshtehnejad PhD); Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden (S-M Fereshtehnejad); Department of Epidemiology and Biostatistics, Imperial College London, UK (K J Foreman); Neurosurgery Department, Faculty of Medicine and Pharmacy of Fez, Fez, Morocco (F G Gankpe MD); Non-Communicable Disease Department, Laboratoire D'etudes et de Recherche-action en Santé (leras Afrique), Porto Novo, Benin (F G Gankpe); West Virginia Bureau for Public Health, Charleston, WV, USA (Prof R Gupta MD); Health Policy, Management and Leadership, West Virginia University School of Public Health, Morgantown, WV, USA (Prof R Gupta); Medical School, University of Western Australia, Perth, WA, Australia (Prof G J Hankey MD); Department of Reproductive Health, Hawassa University, Hawassa, Ethiopia (D T Hibstu MPH); Hematology-oncology and Stem Cell Transplantation Research Center, Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran (A Kasaeian PhD); Public Health and Community Medicine, Jordan University of Science and Technology, Alramtha, Jordan (Prof Y Khader PhD); Department of Health Policy and Management, College of Medicine, Institute of Health Policy and Management, SNU Medical Research Center, Seoul National University, Seoul, South Korea (Prof Y-H Khang MD); School of Medicine, Xiamen University Malaysia, Sepang, Malaysia (Prof Y J Kim PhD); Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan (Prof Y Kokubo PhD); University of Bari Aldo Moro, Bari, Italy (Prof G Logroscino PhD); Department of Clinical Neurosciences and Mental Health, Faculty of Medicine (J Massano MD), and REQUIMTE/LAQV, Laboratório de Farmacognosia, Departamento de Química, Faculdade de Farmácia (Prof D Pereira), University of Porto, Porto, Portugal (J Massano MD); Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Bandar Tun Razak, Malaysia (Prof N M Ibrahim MD); Department of Public Health, Jigjiga Unviersity, Jigjiga, Ethiopia (M A Mohammed PhD); University of Sydney, Sydney, NSW, Australia (M A Mohammed); Neuroscience Research Center, Baqiyatallah University of Medical Science, Tehran, Iran (Prof A Mohammadi PhD); Community and Family Medicine, Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran (M Moradi-Lakeh MD); Department of Clinical Pharmacy, Mekelle University, Mekelle, Ethiopia (Y L Nirayo MS, K G Weldegwergs MS); Western Sydney University, Penrith, NSW, Australia (F A Ogbo PhD); Department of Medicine, University of Ibadan, Ibadan, Nigeria (Prof M O Owolabi DrM); Department of Pharmacy, University Medical Center Groningen, University of Groningen, Groningen, Netherlands (Prof M J Postma PhD); Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran (M Qorbani PhD); Austin Clinical School of Nursing, La Trobe University, Heidelberg, VIC, Australia (M A Rahman PhD); School of Medicine, Deakin University, Waurn Ponds, VIC, Australia (M A Rahman); Department of Neurosurgery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran (Prof H Safari MD); Department of Public Health, Managerial Epidemiology Research Center, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran (S Safiri PhD); Centre of Advanced Study in Psychology, Utkal University, Bhubaneswar, India (Prof M Satpathy PhD); Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA (M Sawhney PhD); Langone Medical Center, New York University, New York, NY, USA (A Shafieesabet MD); Department of Medicine, Dentistry and Health Science (Prof C E I Szoeke PhD) and Department of Medicine (Prof T Wijeratne MD), University of Melbourne, Melbourne, VIC, Australia (Prof C E I Szoeke PhD); Department of Medicine, University of Valencia, Valencia, Spain (Prof R Tabarés-Seisdedos PhD); Department of Internal Medicine, Federal Teaching Hospital, Abakaliki, Nigeria (K N Ukwaja MD); Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore (Prof N Venketasubramanian FRCP); Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore (Prof N Venketasubramanian); Competence Center of Mortality-follow-up, German National Cohort, Federal Institute for Population Research, Wiesbaden, Germany (R Westerman DSc); Department of Psychology, La Trobe University, Melbourne, Australia (Prof T Wijeratne MD); Department of Neurology, Technical University of Munich, Munich, Germany (Prof A S Winkler PhD); Institute for Health and Society, University of Oslo, Oslo, Norway (Prof A S Winkler PhD); Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam (Prof B T Xuan PhD); Department of Biostatistics, School of Public Health, Kyoto University, Kyoto, Japan (Prof N Yonemoto MPH); National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand (V L Feigin PhD).
Contributors
ERD and AE prepared the first draft. EN, VLF, and TV analysed the data and edited the first draft and final versions of the manuscript. ERD and AE finalised all drafts, and approved the final version of the manuscript. All other authors provided data, developed models, reviewed results, provided guidance on methods, or reviewed the manuscript, and approved the final version of the manuscript.
Declaration of interests
MJP has grants or personal fees from Sigma Tau, Merck Sharp and Dohme, GlaxoSmithKline, Pfizer, Mundipharma, Boehringer Ingelheim, Novavax, Ingress Health, Quintiles, Bayer, Bristol-Myers Squibb, AbbVie, AstraZeneca, Sanofi, Astellas, Mapi, Optumlnsight, Advice, Research & Training in Health Economics Groningen, AscA, Novartis, Swedish Orphan, Innoval, Jansen, Intercept, and Pharmerit. MJP holds stocks in Ingress Health and Pharmacoeconomics Advice Groningen (PAG Ltd). CEIS reports grants from Denmark National Medical Health Research Council, Lundbeck, and Alzheimer's Association, and has a patent PCT/AU2008/001556 issued. All other authors declare no competing interests.
Contributor Information
GBD 2016 Parkinson's Disease Collaborators:
E. Ray Dorsey, Alexis Elbaz, Emma Nichols, Foad Abd-Allah, Ahmed Abdelalim, Jose C. Adsuar, Mustafa Geleto Ansha, Carol Brayne, Jee-Young J Choi, Daniel Collado-Mateo, Nabila Dahodwala, Huyen Phuc Do, Dumessa Edessa, Matthias Endres, Seyed-Mohammad Fereshtehnejad, Kyle J Foreman, Fortune Gbetoho Gankpe, Rahul Gupta, Graeme J. Hankey, Simon I. Hay, Mohamed I Hegazy, Desalegn T. Hibstu, Amir Kasaeian, Yousef Khader, Ibrahim Khalil, Young-Ho Khang, Yun Jin Kim, Yoshihiro Kokubo, Giancarlo Logroscino, João Massano, Norlinah Mohamed Ibrahim, Mohammed A. Mohammed, Alireza Mohammadi, Maziar Moradi-Lakeh, Mohsen Naghavi, Binh Thanh Nguyen, Yirga Legesse Nirayo, Felix Akpojene Ogbo, Mayowa Ojo Owolabi, David M. Pereira, Maarten J Postma, Mostafa Qorbani, Muhammad Aziz Rahman, Kedir T. Roba, Hosein Safari, Saeid Safiri, Maheswar Satpathy, Monika Sawhney, Azadeh Shafieesabet, Mekonnen Sisay Shiferaw, Mari Smith, Cassandra E I Szoeke, Rafael Tabarés-Seisdedos, Nu Thi Truong, Kingsley Nnanna Ukwaja, Narayanaswamy Venketasubramanian, Santos Villafaina, Kidu gidey weldegwergs, Ronny Westerman, Tissa Wijeratne, Andrea S. Winkler, Bach Tran Xuan, Naohiro Yonemoto, Valery L Feigin, Theo Vos, and Christopher J L Murray
Supplementary Material
References
- 1.GBD 2015 Neurological Disorders Collaborator Group Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 2017;16:877–897. doi: 10.1016/S1474-4422(17)30299-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.de Lau LM, Breteler MM. Epidemiology of Parkinson's disease. Lancet Neurol. 2006;5:525–535. doi: 10.1016/S1474-4422(06)70471-9. [DOI] [PubMed] [Google Scholar]
- 3.Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson's disease: a systematic review and meta-analysis. Mov Disord. 2014;29:1583–1590. doi: 10.1002/mds.25945. [DOI] [PubMed] [Google Scholar]
- 4.Muangpaisan W, Mathews A, Hori H, Seidel D. A systematic review of the worldwide prevalence and incidence of Parkinson's disease. J Med Assoc Thai. 2011;94:749–755. [PubMed] [Google Scholar]
- 5.Bellou V, Belbasis L, Tzoulaki I, Evangelou E, Ioannidis JP. Environmental risk factors and Parkinson's disease: an umbrella review of meta-analyses. Parkinsonism Relat Disord. 2016;23:1–9. doi: 10.1016/j.parkreldis.2015.12.008. [DOI] [PubMed] [Google Scholar]
- 6.Harris R. Epidemiology of chronic diseases. Jones & Bartlett Learning; Burlington, MA: 2012. Epidemiology of Parkinson's disease; pp. 587–602. [Google Scholar]
- 7.Pezzoli G, Cereda E. Exposure to pesticides or solvents and risk of Parkinson disease. Neurology. 2013;80:2035–2041. doi: 10.1212/WNL.0b013e318294b3c8. [DOI] [PubMed] [Google Scholar]
- 8.Weisskopf MG, Weuve J, Nie H. Association of cumulative lead exposure with Parkinson's disease. Environ Health Perspect. 2010;118:1609–1613. doi: 10.1289/ehp.1002339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Vlaar T, Kab S, Schwaab Y, Frery N, Elbaz A, Moisan F. Association of Parkinson's disease with industry sectors: a French nationwide incidence study. Eur J Epidemiol. 2018 doi: 10.1007/s10654-018-0399-3. published online May 5. [DOI] [PubMed] [Google Scholar]
- 10.Li X, Li W, Liu G, Shen X, Tang Y. Association between cigarette smoking and Parkinson's disease: a meta-analysis. Arch Gerontol Geriatr. 2015;61:510–516. doi: 10.1016/j.archger.2015.08.004. [DOI] [PubMed] [Google Scholar]
- 11.Ritz B, Lee PC, Lassen CF, Arah OA. Parkinson disease and smoking revisited: ease of quitting is an early sign of the disease. Neurology. 2014;83:1396–1402. doi: 10.1212/WNL.0000000000000879. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Wanneveich M, Moisan F, Jacqmin-Gadda H, Elbaz A, Joly P. Projections of prevalence, lifetime risk and life expectancy of Parkinson disease (2010–2030) in France. Mov Disord. 2018 doi: 10.1002/mds.27447. published online Aug 25. [DOI] [PubMed] [Google Scholar]
- 13.Dorsey ER, Constantinescu R, Thompson JP. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology. 2007;68:384–386. doi: 10.1212/01.wnl.0000247740.47667.03. [DOI] [PubMed] [Google Scholar]
- 14.Rossi A, Berger K, Chen H, Leslie D, Mailman RB, Huang X. Projection of the prevalence of Parkinson's disease in the coming decades: revisited. Mov Disord. 2018;33:156–159. doi: 10.1002/mds.27063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.GBD 2016 Mortality Collaborators Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1084–1150. doi: 10.1016/S0140-6736(17)31833-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry. 1992;55:181–184. doi: 10.1136/jnnp.55.3.181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.GBD 2015 Healthcare Access and Quality Collaborators Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet. 2017;390:231–266. doi: 10.1016/S0140-6736(17)30818-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology. 1967;17:427–442. doi: 10.1212/wnl.17.5.427. [DOI] [PubMed] [Google Scholar]
- 19.Goetz CG, Poewe W, Rascol O. Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations. Mov Disord. 2004;19:1020–1028. doi: 10.1002/mds.20213. [DOI] [PubMed] [Google Scholar]
- 20.Salomon JA, Haagsma JA, Davis A. Disability weights for the Global Burden of Disease 2013 study. Lancet Glob Health. 2015;3:e712–e723. doi: 10.1016/S2214-109X(15)00069-8. [DOI] [PubMed] [Google Scholar]
- 21.GBD 2016 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1345–1422. doi: 10.1016/S0140-6736(17)32366-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.GBD 2016 Causes of Death Collaborators Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1151–1210. doi: 10.1016/S0140-6736(17)32152-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.GBD 2015 Mortality and Causes of Death Collaborators Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459–1544. doi: 10.1016/S0140-6736(16)31012-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G. Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology. 2016;86:566–576. doi: 10.1212/WNL.0000000000002350. [DOI] [PubMed] [Google Scholar]
- 25.Anderson DW, Rocca WA, de Rijk MC. Case ascertainment uncertainties in prevalence surveys of Parkinson's disease. Mov Disord. 1998;13:626–632. doi: 10.1002/mds.870130403. [DOI] [PubMed] [Google Scholar]
- 26.Macleod AD, Taylor KS, Counsell CE. Mortality in Parkinson's disease: a systematic review and meta-analysis. Mov Disord. 2014;29:1615–1622. doi: 10.1002/mds.25898. [DOI] [PubMed] [Google Scholar]
- 27.Marmot M. The influence of income on health: views of an epidemiologist. Health Aff (Millwood) 2002;21:31–46. doi: 10.1377/hlthaff.21.2.31. [DOI] [PubMed] [Google Scholar]
- 28.Bloom DE, Canning D. Policy forum: public health. The health and wealth of nations. Science. 2000;287:1207–1209. doi: 10.1126/science.287.5456.1207. [DOI] [PubMed] [Google Scholar]
- 29.GBD 2015 SDG Collaborators Measuring the health-related Sustainable Development Goals in 188 countries: a baseline analysis from the Global Burden of Disease Study 2015. Lancet. 2016;388:1813–1850. doi: 10.1016/S0140-6736(16)31467-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Savica R, Grossardt BR, Bower JH, Ahlskog JE, Rocca WA. Time trends in the incidence of Parkinson disease. JAMA Neurol. 2016;73:981–989. doi: 10.1001/jamaneurol.2016.0947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Darweesh SK, Koudstaal PJ, Stricker BH, Hofman A, Ikram MA. Trends in the incidence of Parkinson disease in the general population: the Rotterdam Study. Am J Epidemiol. 2016;183:1018–1026. doi: 10.1093/aje/kwv271. [DOI] [PubMed] [Google Scholar]
- 32.Lix LM, Hobson DE, Azimaee M, Leslie WD, Burchill C, Hobson S. Socioeconomic variations in the prevalence and incidence of Parkinson's disease: a population-based analysis. J Epidemiol Community Health. 2010;64:335–340. doi: 10.1136/jech.2008.084954. [DOI] [PubMed] [Google Scholar]
- 33.Akushevich I, Kravchenko J, Ukraintseva S, Arbeev K, Yashin AI. Time trends of incidence of age-associated diseases in the US elderly population: Medicare-based analysis. Age Ageing. 2013;42:494–500. doi: 10.1093/ageing/aft032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Isotalo J, Vahlberg T, Kaasinen V. Unchanged long-term rural-to-urban incidence ratio of Parkinson's disease. Mov Disord. 2017;32:474–475. doi: 10.1002/mds.26862. [DOI] [PubMed] [Google Scholar]
- 35.Ng M, Freeman MK, Fleming TD. Smoking prevalence and cigarette consumption in 187 countries, 1980–2012. JAMA. 2014;311:183–192. doi: 10.1001/jama.2013.284692. [DOI] [PubMed] [Google Scholar]
- 36.Savica R, Rocca WA, Ahlskog JE. When does Parkinson disease start? Arch Neurol. 2010;67:798–801. doi: 10.1001/archneurol.2010.135. [DOI] [PubMed] [Google Scholar]
- 37.Gardner RC, Byers AL, Barnes DE, Li Y, Boscardin J, Yaffe K. Mild TBI and risk of Parkinson disease: a Chronic Effects of Neurotrauma Consortium study. Neurology. 2018;90:e1771–e1779. doi: 10.1212/WNL.0000000000005522. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.von Campenhausen S, Bornschein B, Wick R. Prevalence and incidence of Parkinson's disease in Europe. Eur Neuropsychopharmacol. 2005;15:473–490. doi: 10.1016/j.euroneuro.2005.04.007. [DOI] [PubMed] [Google Scholar]
- 39.Paulson GW, Gill WM. Are death certificates reliable to estimate the incidence of Parkinson's disease? Mov Disord. 1995;10:678. doi: 10.1002/mds.870100525. [DOI] [PubMed] [Google Scholar]
- 40.Phillips NJ, Reay J, Martyn CN. Validity of mortality data for Parkinson's disease. J Epidemiology Community Health. 1999;53:587–588. doi: 10.1136/jech.53.9.587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Elbaz A, Bower JH, Peterson BJ. Survival study of Parkinson disease in Olmsted County, Minnesota. Arch Neurol. 2003;60:91–96. doi: 10.1001/archneur.60.1.91. [DOI] [PubMed] [Google Scholar]
- 42.Pressley JC, Tang MX, Marder K, Cote LJ, Mayeux R. Disparities in the recording of Parkinson's disease on death certificates. Mov Disord. 2005;20:315–321. doi: 10.1002/mds.20339. [DOI] [PubMed] [Google Scholar]
- 43.Benito-Leon J, Louis ED, Villarejo-Galende A, Romero JP, Bermejo-Pareja F. Under-reporting of Parkinson's disease on death certificates: a population-based study (NEDICES) J Neurol Sci. 2014;347:188–192. doi: 10.1016/j.jns.2014.08.048. [DOI] [PubMed] [Google Scholar]
- 44.Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17:643–653. doi: 10.1016/j.annepidem.2007.03.013. [DOI] [PubMed] [Google Scholar]
- 45.Bach JP, Ziegler U, Deuschl G, Dodel R, Doblhammer-Reiter G. Projected numbers of people with movement disorders in the years 2030 and 2050. Mov Disord. 2011;26:2286–2290. doi: 10.1002/mds.23878. [DOI] [PubMed] [Google Scholar]
- 46.Dorsey ER, Bloem BR. The Parkinson pandemic—a call to action. JAMA Neurol. 2017;75:9–10. doi: 10.1001/jamaneurol.2017.3299. [DOI] [PubMed] [Google Scholar]
- 47.Tanner CM, Comella CL. When brawn benefits brain: physical activity and Parkinson's disease risk. Brain. 2015;138:238–239. doi: 10.1093/brain/awu351. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.