Summary
Background
Understanding how prevalence, incidence, and mortality of motor neuron diseases change over time and by location is crucial for understanding the causes of these disorders and for health-care planning. Our aim was to produce estimates of incidence, prevalence, and disability-adjusted life-years (DALYs) for motor neuron diseases for 195 countries and territories from 1990 to 2016 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016.
Methods
The motor neuron diseases included in this study were amyotrophic lateral sclerosis, spinal muscular atrophy, hereditary spastic paraplegia, primary lateral sclerosis, progressive muscular atrophy, and pseudobulbar palsy. Incidence, prevalence, and DALYs were estimated using a Bayesian meta-regression model. We analysed 14 165 site-years of vital registration cause of death data using the GBD 2016 cause of death ensemble model. The 84 risk factors quantified in GBD 2016 were tested for an association with incidence or death from motor neuron diseases. We also explored the relationship between Socio-demographic Index (SDI; a compound measure of income per capita, education, and fertility) and age-standardised DALYs of motor neuron diseases.
Findings
In 2016, globally, 330 918 (95% uncertainty interval [UI] 299 522–367 254) individuals had a motor neuron disease. Motor neuron diseases have caused 926 090 (881 566–961 758) DALYs and 34 325 (33 051–35 364) deaths in 2016. The worldwide all-age prevalence was 4·5 (4·1–5·0) per 100 000 people, with an increase in age-standardised prevalence of 4·5% (3·4–5·7) over the study period. The all-age incidence was 0·78 (95% UI 0·71–0·86) per 100 000 person-years. No risk factor analysed in GBD showed an association with motor neuron disease incidence. The largest age-standardised prevalence was in high SDI regions: high-income North America (16·8, 95% UI 15·8–16·9), Australasia (14·7, 13·5–16·1), and western Europe (12·9, 11·7–14·1). However, the prevalence and incidence were lower than expected based on SDI in high-income Asia Pacific.
Interpretation
Motor neuron diseases have low prevalence and incidence, but cause severe disability with a high fatality rate. Incidence of motor neuron diseases has geographical heterogeneity, which is not explained by any risk factors quantified in GBD, suggesting other unmeasured risk factors might have a role. Between 1990 and 2016, the burden of motor neuron diseases has increased substantially. The estimates presented here, as well as future estimates based on data from a greater number of countries, will be important in the planning of services for people with motor neuron diseases worldwide.
Funding
Bill & Melinda Gates Foundation.
Introduction
Motor neuron diseases are a group of neurodegenerative disorders related to upper and lower motor neuron degeneration, including amyotrophic lateral sclerosis, spinal muscular atrophy, hereditary spastic paraplegia, primary lateral sclerosis, progressive muscular atrophy, and pseudobulbar palsy. Amyotrophic lateral sclerosis, the most common motor neuron disease, is clinically characterised by extensive paralysis leading to death generally by respiratory failure, with 50% of patients dying within 15–20 months after diagnosis.1, 2
Although spinal muscular atrophy and hereditary spastic paraplegia are known to have a genetic basis, the causes of other motor neuron diseases remain unknown, but are postulated to combine environmental and genetic factors.1, 3, 4 Genetic variants have been associated with amyotrophic lateral sclerosis,3 whereas the contribution of environmental factors, with the possible exception of smoking,5, 6 is still not clear because of the difficulties in assessment of potential risk factors retrospectively in case-control studies.
Previous reviews and meta-analyses using a worldwide approach to study amyotrophic lateral sclerosis reported a median prevalence of 4·48 per 100 000 (IQR 3·03–6·70)7 and a standardised incidence rate of 1·68 per 100 000 person-years (95% CI 1·50–1·85)8 that varied with geography, sex, and age.8 Amyotrophic lateral sclerosis is rare before age 50 years, with peak incidence at age 70 years followed by a sharp decrease in incidence.8, 9 Previous syntheses of motor neuron disease prevalence,7 incidence,8 and phenotype2 based on population-based studies have had scarce geographical coverage. Although insights into our understanding of the epidemiology of motor neuron diseases, and amyotrophic lateral sclerosis in particular, have been provided by population-based registries in Europe over the past two decades10, 11 and in the USA more recently,12 these epidemiological surveillance systems are not available in other parts of the world. Additionally, worldwide data on other metrics of motor neuron disease burden (eg, mortality, years of life lost [YLLs], years of life lived with disability [YLDs], and disability-adjusted life-years [DALYs]) are still scarce.
Research in context.
Evidence before this study
Motor neuron diseases are a group of rare neurodegenerative disorders (amyotrophic lateral sclerosis, spinal muscular atrophy, hereditary spastic paraplegia, primary lateral sclerosis, progressive muscular atrophy, and pseudobulbar palsy) that are fatal in 50% of affected people within 15 to 20 months after diagnosis. Previous epidemiological studies of motor neuron diseases showed large geographical variations in incidence, but no data on motor neuron disease incidence existed for most countries. We did a systematic review of prevalence, incidence, and mortality risk in PubMed for articles published in English from Jan 1, 1990, to Dec 31, 2015. In the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), systematic reviews for motor neuron diseases are done every 3 years, with new sources suggested by GBD collaborators included in each round of estimates. The search terms were: ((“motor neuron disease”[MeSH Terms] OR (“motor”[All Fields] AND “neuron”[All Fields] AND “disease”[All Fields]) OR “motor neuron disease”[All Fields] OR (“motor”[All Fields] AND “neuron”[All Fields] AND “diseases”[All Fields]) OR “motor neuron diseases”[All Fields]) OR (“amyotrophic lateral sclerosis”[MeSH Terms] OR (“amyotrophic”[All Fields] AND “lateral“[All Fields] AND “sclerosis”[All Fields]) OR “amyotrophic lateral sclerosis”[All Fields]) OR “ALS”[All Fields] OR (“motor neuron disease”[MeSH Terms] OR (“motor“[All Fields] AND “neuron”[All Fields] AND “disease”[All Fields]) OR “motor neuron disease”[All Fields] OR (“primary”[All Fields] AND “lateral”[All Fields] AND “sclerosis”[All Fields]) OR “primary lateral sclerosis”[All Fields]) OR (“PLS”[Journal] OR “pls”[All Fields]) OR (“muscular atrophy, spinal”[MeSH Terms] OR (“muscular”[All Fields] AND “atrophy”[All Fields] AND “spinal”[All Fields]) OR “spinal muscular atrophy”[All Fields] OR (“progressive”[All Fields] AND “muscular”[All Fields] AND “atrophy”[All Fields]) OR “progressive muscular atrophy”[All Fields]) OR “PBP”[All Fields] OR (“pseudobulbar palsy”[MeSH Terms] OR (“pseudobulbar”[All Fields] AND “palsy”[All Fields]) OR “pseudobulbar palsy”[All Fields])) AND ((“epidemiology”[Subheading] OR “epidemiology”[All Fields] OR “epidemiology”[MeSH Terms]) OR “population-based”[All Fields]).
Added value of this study
This is the first concerted effort to make the methods and results of GBD accessible to clinicians and researchers with an interest in motor neuron disease. We show that the burden of motor neuron disease is highest in countries with a high Socio-demographic Index (SDI; a measure of country social development based on education, income, and fertility), especially high-income North America, western Europe, and Australasia, but not in high-income Asia Pacific. We show that age-standardised incidence rates of motor neuron diseases vary within each SDI level, suggesting that causative factors other than sociodemographic development are responsible for the geographical variation in disease occurrence. We also show that the geographical heterogeneity of motor neuron disease burden is not explained by any of the 84 risk factors quantified in GBD, suggesting a role of unmeasured risk factors.
Implication of all the available evidence
The burden of motor neuron diseases is currently greatest in high-income countries and this burden is increasing because of population ageing. Small increases in age-standardised incidence and prevalence rates occurred between 1990 and 2016, but we report larger increases in counts because of population ageing and population growth. Despite the absence of a cure for motor neuron diseases, these findings are important to health service planning as the care of patients with motor neuron disease is intensive and expensive. Additional research into the causes of motor neuron disease is needed.
Within the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016, we quantified the burden of motor neuron diseases in terms of incidence, prevalence, mortality, and DALYs (a sum of YLLs and YLDs) between 1990 and 2016 by age, sex, year, and geographical location. Our study considers motor neuron diseases across the whole age spectrum including spinal muscular atrophy, a rare disorder in the first years of life affecting proximal limbs and respiratory muscles. The relationships between motor neuron disease burden and country development level, as measured by the Socio-demographic Index (SDI), a composite measure of income per capita, years of schooling, and total fertility rate, were also analysed.
Methods
Overview
General methods used in GBD 2016 are reported in the appendix. Specific methods relevant to the estimation of motor neuron disease burden are described here. Motor neuron diseases included in this analysis were amyotrophic lateral sclerosis, spinal muscular atrophy, hereditary spastic paraplegia, primary lateral sclerosis, progressive muscular atrophy, and pseudobulbar palsy. We analysed motor neuron diseases across all ages, and included motor neuron disease with a strong genetic component with high prevalence in the early years of life.
Mortality
The International Classification of Diseases ninth edition (ICD-9) code for motor neuron diseases is 335 and the ICD-10 code is G12. Mortality from motor neuron diseases was modelled by use of CODEm, the cause of death ensemble model used throughout GBD. The model used 14 165 site-years of data—ie, a combination of location and calendar year—as well as predictive covariates on asbestos production, mean serum cholesterol, fruit consumption, average latitude, proportion of the population with access to sanitation, proportion of the population with access to clean water, health-care access and quality,13 education, log-transformed lag-distributed income, and SDI.14 Predictive covariates in our cause of death analytical tool, CODEm, are based on literature review of factors that have been found to be associated with a disease of interest without necessarily having sufficient evidence for a causal relationship. CODEm is designed to choose the set of covariates that best predicts mortality rates given the available data from vital registrations and verbal autopsy studies.
Non-fatal disease modelling
The El Escorial Criteria15 were used as the reference diagnostic criteria for amyotrophic lateral sclerosis. Spinal muscular atrophy cases defined by ICD codes were also included. Data from administrative health records reporting by ICD codes were also included. We identified 55 sources through our systematic literature review, including 47 sources on incidence, covering seven of the 21 world regions, and eight sources on prevalence, covering five of the 21 world regions (appendix). We also added 3 years (2000, 2010, and 2012) of US medical claims data. There were no data for southeast Asia, Oceania, central Asia, eastern Europe, Australasia, the Caribbean, Andean Latin America, central Latin America, south Asia, central sub-Saharan Africa, southern sub-Saharan Africa, and western sub-Saharan Africa. Where data points spanned age ranges greater than 20 years, we split the data into 5-year age bands using the age pattern of the USA, where we had the most detailed motor neuron disease data by age.
We used DisMod-MR 2.1, the Bayesian meta-regression modelling tool developed for GBD, to generate results that were consistent with the available data on prevalence and incidence, as well as with our estimates for mortality due to motor neuron diseases. In the model, we included a setting of no remission (ie, no cure). We used DisMod-MR 2.1 to adjust for systematic bias in the first 2 years of US claims data, because the data for 2000 and 2010 were systematically lower than the data for 2012. Additionally, we included the absolute value of latitude, as a country-level predictor on prevalence, and included national income, with a built-in short lag, as a country-level predictor on excess mortality rate.
Motor neuron disease severity and YLDs
To identify the different health consequences of motor neuron diseases, we analysed data from the largest amyotrophic lateral sclerosis clinical dataset, with a total of 8635 patient records: the Pooled Resource Open-access ALS Clinical Trials (PRO-ACT).16 These data were compiled by the PRO-ACT Consortium, which is a collaboration between Prize4Life and the Northeast ALS Consortium, with funding from the ALS Therapy Alliance. The data available in the PRO-ACT database have been volunteered by PRO-ACT Consortium members.17 We used records with complete information on the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R; n=4838) and selected only the observation at enrolment into a trial to eliminate potential bias from treatment effects.
We used the ALSFRS-R to establish the severity distribution of three symptom domains: motor impairment, respiratory problems, and speech impairment. Motor impairment of the legs was assessed by two questions, and we mapped a combined score of 8 to a state of no impairment, 5–7 to mild impairment, 2–4 to moderate impairment, and 0–1 to severe impairment. Three additional questions assessed fine motor impairment, and a combined score of 12 corresponded to no impairment, 9–11 to mild impairment, 3–8 to moderate impairment, and 0–2 to severe impairment. To summarise total motor impairment, we took the more severe of the two rankings. Respiratory problems were assessed by one question, and a score of 4 indicated no impairment, 3 indicated mild impairment, 2 indicated moderate impairment, and 0–1 indicated severe impairment. Finally, there was one question on speech impairment, and we mapped a score of 4 to no impairment, and any other score to an impaired state.
After establishing the severity status, we estimated the relative proportions of each of the 29 combinations of the different levels on the three domains. To derive combined disability weights for each impairment combination, which constitute the different potential health outcomes of motor neuron disease, we used a multiplicative aggregation formula.18
DALYs
The proportions from the ALSFRS-R analysis were applied to the prevalence estimates derived from DisMod-MR 2.1, and the prevalence of each long-term outcome was multiplied by the combined disability weights to get uncorrected YLDs. The initial YLD estimates were adjusted for comorbidity by use of the comorbidity simulation across all causes included in GBD 2016.19 We calculated YLLs by multiplying the number of deaths in each age group by the remaining life expectancy derived from the GBD standard life table (appendix). DALYs were then calculated by summing YLLs and YLDs.
Uncertainty was propagated by sampling 1000 draws at each computational step, which allowed for the combination of uncertainty from different sources, including input data, data processing steps, and residual non-sampling error. Uncertainty intervals (UIs) were defined as the 25th and 975th values of the ordered draws and were used to define significant change over time. This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations (appendix).
Risk factors
In GBD 2016, the contribution of 84 risks and combinations of risks to disease burden was quantified.20 Criteria for inclusion of risks into GBD include: the availability of sufficient evidence for a causal relationship between a risk and one or more disease or injury outcome; evidence to support generalisability of an effect size beyond the populations included in epidemiological studies; availability of sufficient data and methods to enable estimation of exposure levels by country; and the probable importance of a risk factor to disease burden or policy considerations.20
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report. All authors had full access to the data in the study and had final responsibility for the decision to submit for publication.
Results
All results presented in this paper can be found in the GBD 2016 online results tool, with all estimates from 1990 to 2016 for all world regions. The same results can also be explored by use of the GBD 2016 data visualisation tool.
In 2016, 330 918 (95% UI 299 522–367 254) individuals worldwide had a motor neuron disease: 161 901 (48·9%) in high SDI, 50 286 (15·2%) in high-middle SDI, 70 924 (21·4%) in middle SDI, 38 873 (11·8%) in low-middle SDI, and 8545 (2·6%) in low SDI countries (table). The all-age global prevalence was 4·5 (4·1–5·0) per 100 000 population. Globally, we estimated there to be 57 452 (52 522–63 242) incident cases of a motor neuron disease, and all-age global incidence of motor neuron disease to be 0·78 (0·71–0·86) per 100 000 person-years. We estimated there to be 34 325 (33 051–35 364) motor neuron disease-related deaths (table), with an all-age cause-specific mortality rate of 0·46 (0·45–0·48) per 100 000 person-years. Motor neuron diseases caused 926 090 (881 566–961 758) DALYs (table; age-standardised DALYs rate 13·2, 12·5–13·7), of which 70 165 (49 321–93 203) were YLDs (age-standardised rate 1·0, 0·7–1·3) and 855 924 (819 373–883 289) were YLLs (age-standardised rate 12·2, 11·6–12·6).
Table.
Deaths, prevalence, and DALYs for motor neuron disease in 2016 and percentage change of 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 | 34 325 (33 051 to 35 364) | 8·0% (−0·1 to 12·2) | 330 918 (299 522 to 367 254) | 4·5% (3·4 to 5·7) | 926 090 (881 566 to 961 758) | −1·5% (−9·3 to 2·9) |
| High SDI | 21 162 (20 081 to 21 830) | 14·1% (5·2 to 19·1) | 161 901 (150 024 to 174 346) | 19·7% (18·0 to 21·6) | 479 748 (455 714 to 496 340) | 3·8% (−3·7 to 8·2) |
| High-middle SDI | 4712 (4392 to 4972) | 20·7% (2·9 to 31·8) | 50 286 (44 798 to 56 819) | 16·2% (14·2 to 17·9) | 144 696 (134 507 to 153 708) | 0·4% (−19·2 to 17·9) |
| Middle SDI | 5183 (4934 to 5387) | 22·7% (9·7 to 30·6) | 70 924 (61 566 to 82 419) | 12·3% (10·3 to 14·0) | 177 160 (166 987 to 185 970) | 5·7% (−6·0 to 18·0) |
| Low-middle SDI | 2813 (2455 to 3304) | 36·7% (11·7 to 57·3) | 38 873 (33 761 to 45 357) | 10·2% (8·9 to 11·4) | 104 268 (93 547 to 119 177) | 21·6% (4·8 to 37·3) |
| Low SDI | 443 (382 to 648) | 20·2% (−1·8 to 41·9) | 8545 (7405 to 10 007) | 3·8% (2·8 to 5·0) | 19 786 (17 506 to 26 045) | 7·1% (−12·9 to 31·5) |
| High-income North America | 8592 (8226 to 8870) | 27·2% (19·7 to 32·8) | 72 577 (68 184 to 77 282) | 7·4% (4·4 to 10·8) | 204 447 (195 872 to 212 530) | 14·1% (8·0 to 18·9) |
| Canada | 959 (873 to 1045) | 20·2% (6·8 to 33·6) | 10 013 (9208 to 10 952) | 42·1% (35·4 to 49·3) | 22 072 (20 194 to 23 900) | 12·7% (1·0 to 24·3) |
| Greenland | 0 (0 to 1) | 3·3% (−24·6 to 41·1) | 8 (7 to 9) | 29·8% (25·1 to 35·0) | 11 (9 to 14) | −1·4% (−24·6 to 27·5) |
| USA | 7632 (7315 to 7902) | 27·9% (20·3 to 34·0) | 62 531 (58 894 to 66 406) | 3·4% (0·1 to 7·2) | 182 359 (174 927 to 189 911) | 14·3% (8·1 to 19·3) |
| Australasia | 850 (776 to 925) | 20·0% (6·9 to 32·3) | 5065 (4632 to 5506) | 29·4% (25·1 to 33·9) | 18 723 (17 236 to 20 191) | 11·3% (0·0 to 23·1) |
| Australia | 711 (643 to 779) | 21·9% (6·8 to 36·3) | 4247 (3886 to 4621) | 30·3% (25·4 to 35·4) | 15 625 (14 212 to 17 040) | 12·7% (−1·1 to 26·1) |
| New Zealand | 139 (122 to 157) | 10·8% (−5·6 to 26·5) | 818 (747 to 887) | 25·1% (19·8 to 30·8) | 3097 (2730 to 3465) | 3·5% (−10·8 to 18·1) |
| High-income Asia-Pacific | 1919 (1777 to 2055) | −9·2% (−15·6 to −2·9) | 17 574 (15 713 to 19 782) | 18·9% (16·5 to 21·5) | 40 294 (37 320 to 43 001) | −18·1% (−23·4 to −12·8) |
| Brunei | 1 (0 to 1) | 7·3% (−24·0 to 41·3) | 19 (17 to 21) | 16·2% (12·2 to 20·9) | 23 (18 to 28) | 7·3% (−20·7 to 38·8) |
| Japan | 1796 (1660 to 1927) | −2·5% (−10·2 to 4·5) | 13 893 (12 394 to 15 659) | 19·9% (17·6 to 22·5) | 36 101 (33 402 to 38 439) | −11·9% (−18·2 to −6·6) |
| Singapore | 23 (19 to 29) | 9·9% (−14·2 to 39·2) | 243 (219 to 271) | 20·6% (16·0 to 25·1) | 718 (592 to 868) | −1·2% (−21·0 to 24·5) |
| South Korea | 100 (72 to 130) | −33·9% (−55·6 to −8·1) | 3419 (3033 to 3860) | 26·3% (21·5 to 31·3) | 3452 (2658 to 4327) | −34·7% (−52·4 to −12·0) |
| Western Europe | 10 357 (9723 to 10 818) | 15·2% (3·6 to 22·1) | 71 288 (65 330 to 77 548) | 31·2% (29·1 to 33·3) | 227 135 (213 738 to 237 121) | −0·2% (−9·8 to 6·1) |
| Andorra | 2 (2 to 3) | 13·2% (−15·4 to 52·2) | 15 (14 to 17) | 21·0% (16·6 to 25·3) | 50 (36 to 62) | −0·4% (−23·2 to 30·2) |
| Austria | 152 (136 to 168) | 36·8% (20·2 to 54·1) | 1282 (1172 to 1405) | 36·9% (31·6 to 42·3) | 3721 (3367 to 4114) | 17·2% (1·0 to 34·7) |
| Belgium | 263 (232 to 293) | 24·8% (6·5 to 41·6) | 1850 (1699 to 2019) | 37·2% (31·8 to 43·4) | 5983 (5353 to 6648) | 10·9% (−4·9 to 26·9) |
| Cyprus | 13 (11 to 14) | −15·4% (−47·2 to 4·2) | 108 (98 to 118) | 19·7% (14·8 to 24·8) | 303 (273 to 336) | −27·4% (−55·4 to −5·5) |
| Denmark | 134 (114 to 156) | −15·4% (−29·5 to 0·4) | 983 (899 to 1078) | 20·8% (16·5 to 25·9) | 2915 (2487 to 3339) | −19·4% (−31·6 to −5·5) |
| Finland | 183 (159 to 204) | −23·8% (−38·5 to −9·1) | 1207 (1103 to 1319) | 19·5% (14·4 to 25·2) | 3959 (3455 to 4405) | −24·2% (−38·4 to −11·3) |
| France | 1557 (1408 to 1707) | 5·0% (−7·5 to 17·7) | 11 712 (10 762 to 12 638) | 27·3% (22·5 to 32·1) | 34 427 (31 349 to 37 585) | −6·1% (−17·5 to 5·6) |
| Germany | 1911 (1698 to 2129) | 1·3% (−16·3 to 19·9) | 12 502 (11 442 to 13 684) | 27·5% (22·7 to 32·9) | 42 111 (37 875 to 46 516) | −9·9% (−26·6 to 6·6) |
| Greece | 111 (96 to 124) | 156·3% (104·4 to 202·3) | 1141 (1031 to 1256) | 29·4% (24·2 to 34·6) | 2994 (2620 to 3375) | 114·9% (51·4 to 166·4) |
| Iceland | 8 (7 to 9) | 23·7% (5·9 to 41·6) | 61 (56 to 67) | 25·1% (20·2 to 30·2) | 188 (169 to 208) | 12·8% (−2·6 to 28·6) |
| Ireland | 120 (104 to 138) | 24·7% (4·4 to 47·8) | 835 (764 to 911) | 37·6% (31·6 to 44·0) | 2927 (2571 to 3344) | 17·9% (−1·5 to 38·5) |
| Israel | 73 (59 to 89) | −23·2% (−43·1 to −0·9) | 784 (714 to 863) | 17·4% (12·7 to 22·1) | 1991 (1642 to 2373) | −29·6% (−47·2 to −9·8) |
| Italy | 1418 (1 270 to 1 570) | 35·8% (18·6 to 54·5) | 9553 (8745 to 10 416) | 34·5% (28·4 to 40·4) | 30 525 (27 281 to 33 409) | 9·0% (−6·5 to 26·1) |
| Luxembourg | 9 (8 to 10) | 4·4% (−11·4 to 20·3) | 86 (79 to 95) | 27·2% (22·1 to 32·9) | 217 (192 to 245) | −7·5% (−21·3 to 6·4) |
| Malta | 8 (7 to 10) | 14·5% (−5·9 to 40·8) | 62 (56 to 67) | 32·2% (26·7 to 37·9) | 194 (164 to 233) | 15·5% (−4·0 to 38·4) |
| Netherlands | 494 (443 to 554) | 7·8% (−7·1 to 22·8) | 3347 (3060 to 3648) | 28·1% (23·0 to 33·1) | 11 110 (9988 to 12 405) | −1·4% (−14·5 to 11·8) |
| Norway | 124 (107 to 141) | 1·9% (−13·2 to 18·0) | 1046 (961 to 1153) | 28·0% (22·3 to 33·5) | 2764 (2416 to 3125) | −8·4% (−21·2 to 5·3) |
| Portugal | 185 (166 to 205) | 95·7% (70·3 to 124·7) | 1365 (1250 to 1489) | 37·6% (31·7 to 43·8) | 4639 (4173 to 5106) | 48·2% (21·7 to 73·4) |
| Spain | 905 (819 to 991) | 41·1% (25·9 to 56·8) | 6436 (5888 to 7035) | 36·7% (31·0 to 42·3) | 20 680 (18 811 to 22 572) | 12·1% (−1·0 to 26·0) |
| Sweden | 304 (266 to 344) | 35·9% (15·6 to 55·6) | 2153 (1944 to 2416) | 33·5% (28·7 to 39·3) | 6141 (5401 to 897) | 25·3% (7·8 to 43·3) |
| Switzerland | 184 (143 to 233) | −7·4% (−31·0 to 22·2) | 1441 (1314 to 1578) | 22·8% (17·9 to 28·3) | 3971 (3164 to 4972) | −12·9% (−33·9 to 12·7) |
| United Kingdom | 2199 (2075 to 2281) | 23·8% (15·5 to 29·3) | 13 249 (12 000 to 14 556) | 34·5% (31·7 to 37·8) | 45 310 (42 802 to 47 187) | 0·2% (−6·1 to 4·5) |
| Southern Latin America | 477 (423 to 532) | 17·9% (−4·4 to 36·0) | 4527 (4103 to 5021) | 27·8% (24·7 to 31·1) | 12 806 (11 433 to 14 281) | 12·6% (−8·3 to 29·1) |
| Argentina | 279 (247 to 309) | 29·2% (4·0 to 48·4) | 2850 (2576 to 3164) | 27·8% (23·6 to 32·5) | 7570 (6748 to 8342) | 22·0% (−0·3 to 38·8) |
| Chile | 152 (116 to 192) | 4·7% (−26·3 to 41·0) | 1391 (1253 to 1550) | 27·6% (23·3 to 32·1) | 4123 (3177 to 5265) | 0·2% (−28·0 to 33·8) |
| Uruguay | 47 (42 to 51) | −1·5% (−15·5 to 12·7) | 285 (261 to 314) | 23·4% (18·9 to 28·3) | 1112 (998 to 1217) | −4·2% (−17·5 to 8·2) |
| Eastern Europe | 729 (583 to 909) | 36·1% (6·0 to 72·7) | 6497 (5784 to 7341) | 3·2% (0·3 to 6·0) | 21 974 (17 757 to 27 107) | 24·3% (−0·7 to 54·7) |
| Belarus | 33 (26 to 39) | 29·3% (4·2 to 56·1) | 290 (257 to 329) | 5·4% (1·3 to 9·5) | 934 (757 to 1 121) | 17·7% (−3·3 to 42·2) |
| Estonia | 6 (5 to 7) | −1·0% (−16·8 to 14·6) | 45 (41 to 51) | 2·7% (−0·6 to 6·5) | 156 (133 to 177) | −12·5% (−26·0 to 1·6) |
| Latvia | 10 (9 to 12) | 114·3% (75·3 to 151·7) | 66 (59 to 74) | 7·0% (3·3 to 10·7) | 273 (230 to 312) | 85·2% (50·8 to 116·4) |
| Lithuania | 30 (26 to 33) | 133·8% (99·8 to 167·1) | 121 (110 to 135) | 21·3% (15·7 to 26·8) | 743 (658 to 824) | 105·3% (76·2 to 133·9) |
| Moldova | 9 (8 to 11) | 16·9% (−5·7 to 41·9) | 100 (88 to 114) | 1·1% (−2·4 to 5·1) | 297 (254 to 345) | 15·9% (−5·4 to 38·2) |
| Russia | 486 (357 to 655) | 39·9% (−0·3 to 90·8) | 4 587 (4 079 to 5 196) | 2·7% (−1·2 to 6·8) | 15 218 (11 485 to 20 363) | 25·3% (−7·0 to 65·5) |
| Ukraine | 155 (120 to 199) | 18·5% (−10·5 to 57·9) | 1287 (1141 to 1443) | 1·8% (−2·4 to 5·9) | 4352 (3386 to 5553) | 14·0% (−11·8 to 48·9) |
| Central Europe | 691 (636 to 742) | 5·3% (−9·7 to 15·7) | 4449 (3998 to 4974) | 4·2% (2·1 to 6·5) | 19 034 (17 601 to 20 320) | −9·3% (−20·9 to −0·3) |
| Albania | 6 (5 to 7) | −7·2% (−24·2 to 11·7) | 88 (78 to 99) | 6·8% (2·5 to 10·9) | 192 (161 to 221) | −15·0% (−30·7 to 1·4) |
| Bosnia and Herzegovina | 13 (11 to 15) | 22·5% (−18·2 to 57·7) | 128 (114 to 145) | 10·1% (5·6 to 14·8) | 373 (317 to 439) | 16·6% (−19·6 to 46·4) |
| Bulgaria | 17 (14 to 20) | −28·5% (−42·5 to −13·1) | 245 (216 to 277) | −4·5% (−9·1 to 0·1) | 493 (414 to 580) | −40·9% (−51·5 to −28·6) |
| Croatia | 38 (32 to 44) | 12·1% (−5·7 to 32·3) | 179 (161 to 198) | −0·4% (−5·0 to 4·8) | 1054 (907 to 1202) | −6·9% (−21·6 to 10·3) |
| Czech Republic | 77 (69 to 85) | 0·5% (−12·9 to 13·8) | 443 (397 to 496) | −0·8% (−5·1 to 3·8) | 2101 (1893 to 2306) | −16·1% (−28·7 to −2·1) |
| Hungary | 80 (70 to 90) | 0·3% (−14·5 to 16·7) | 393 (353 to 438) | −1·5% (−6·1 to 3·5) | 2289 (2013 to 2578) | −18·6% (−32·2 to −1·4) |
| Macedonia | 4 (4 to 5) | −52·0% (−62·5 to −40·1) | 64 (57 to 72) | −5·8% (−10·4 to −0·8) | 136 (121 to 151) | −70·1% (−78·5 to −58·3) |
| Montenegro | 1 (1 to 1) | 3·0% (−14·6 to 23·0) | 20 (18 to 23) | 4·1% (−0·1 to 8·3) | 39 (33 to 44) | −5·3% (−19·7 to 11·8) |
| Poland | 346 (307 to 389) | 2·9% (−18·9 to 21·2) | 1695 (1528 to 1888) | 6·5% (2·4 to 10·9) | 9135 (8111 to 10 229) | −8·7% (−28·0 to 7·5) |
| Romania | 61 (54 to 68) | 24·4% (1·4 to 47·1) | 639 (566 to 720) | 6·4% (2·2 to 10·7) | 1825 (1623 to 2047) | 8·5% (−12·3 to 29·5) |
| Serbia | 23 (20 to 25) | 22·5% (3·9 to 43·3) | 288 (256 to 327) | 3·9% (−0·5 to 8·3) | 662 (595 to 727) | 8·9% (−7·2 to 26·0) |
| Slovakia | 21 (17 to 24) | 42·5% (13·7 to 69·6) | 192 (171 to 217) | 6·1% (2·1 to 10·2) | 611 (512 to 704) | 25·0% (−0·2 to 50·2) |
| Slovenia | 4 (4 to 5) | −79·2% (−82·8 to −74·5) | 77 (68 to 87) | −20·1% (−25·4 to −14·4) | 124 (106 to 142) | −82·4% (−85·6 to −78·3) |
| Central Asia | 114 (100 to 130) | 5·3% (−5·8 to 20·0) | 1830 (1612 to 2083) | 2·3% (0·6 to 3·9) | 4127 (3637 to 4706) | −0·5% (−10·0 to 10·0) |
| Armenia | 5 (4 to 5) | 5·8% (−12·2 to 24·4) | 73 (65 to 83) | 3·7% (−0·4 to 7·3) | 149 (131 to 168) | −2·8% (−17·5 to 13·1) |
| Azerbaijan | 13 (11 to 16) | 35·7% (0·5 to 101·2) | 215 (190 to 246) | 5·2% (1·6 to 9·2) | 477 (390 to 575) | 24·2% (−5·1 to 78·2) |
| Georgia | 9 (8 to 11) | −6·4% (−26·9 to 21·4) | 102 (90 to 115) | 1·7% (−1·8 to 5·4) | 290 (238 to 353) | −6·3% (−25·9 to 19·9) |
| Kazakhstan | 34 (23 to 47) | −2·2% (−21·6 to 22·5) | 417 (366 to 474) | 4·0% (0·2 to 8·0) | 1180 (836 to 1634) | −6·0% (−23·3 to 15·4) |
| Kyrgyzstan | 6 (6 to 7) | 3·2% (−16·7 to 30·3) | 113 (99 to 129) | −0·1% (−3·7 to 3·5) | 260 (226 to 293) | 1·8% (−17·6 to 27·3) |
| Mongolia | 3 (3 to 4) | 38·4% (0·4 to 76·2) | 60 (53 to 69) | 6·8% (3·1 to 11·0) | 121 (102 to 142) | 21·2% (−3·5 to 47·0) |
| Tajikistan | 5 (4 to 8) | −1·9% (−23·8 to 25·0) | 146 (127 to 169) | −1·1% (−4·5 to 2·2) | 245 (196 to 320) | −13·4% (−34·8 to 10·9) |
| Turkmenistan | 8 (6 to 10) | 23·9% (−3·1 to 46·9) | 107 (94 to 122) | 5·7% (1·8 to 9·3) | 314 (245 to 380) | 11·8% (−12·9 to 33·1) |
| Uzbekistan | 30 (26 to 34) | 37·2% (4·7 to 73·7) | 597 (524 to 682) | 4·0% (0·2 to 8·0) | 1091 (949 to 1249) | 25·9% (−0·4 to 55·0) |
| Central Latin America | 748 (683 to 791) | 57·0% (39·4 to 67·7) | 7609 (6627 to 8760) | 9·7% (8·3 to 11·1) | 23 660 (21 696 to 25 181) | 37·1% (23·0 to 45·8) |
| Colombia | 176 (150 to 201) | 67·9% (44·4 to 93·6) | 1383 (1233 to 1564) | 12·4% (8·2 to 16·6) | 5306 (4562 to 6054) | 41·7% (21·2 to 62·0) |
| Costa Rica | 41 (35 to 46) | 47·6% (23·6 to 73·3) | 209 (189 to 232) | 17·2% (12·5 to 22·2) | 1093 (940 to 1239) | 48·6% (24·4 to 73·2) |
| El Salvador | 9 (8 to 11) | 19·9% (−4·6 to 46·9) | 157 (139 to 180) | 7·7% (4·0 to 11·8) | 296 (250 to 342) | 11·9% (−9·1 to 34·6) |
| Guatemala | 16 (12 to 20) | 26·6% (−14·9 to 113·6) | 367 (319 to 423) | 5·7% (2·0 to 9·5) | 570 (449 to 717) | 19·4% (−16·4 to 88·2) |
| Honduras | 14 (11 to 20) | 43·2% (−0·2 to 99·3) | 192 (168 to 220) | 6·3% (2·4 to 10·5) | 501 (375 to 660) | 25·5% (−11·0 to 70·5) |
| Mexico | 381 (351 to 405) | 64·2% (51·7 to 74·1) | 4187 (3577 to 4949) | 9·8% (8·1 to 11·7) | 12 494 (11 556 to 13 234) | 38·7% (28·5 to 46·7) |
| Nicaragua | 8 (6 to 9) | 33·6% (−1·3 to 73·1) | 148 (130 to 168) | 4·2% (0·6 to 8·0) | 266 (219 to 316) | 20·2% (−6·6 to 51·1) |
| Panama | 12 (10 to 15) | 9·8% (−18·0 to 37·0) | 117 (104 to 131) | 9·4% (5·6 to 13·0) | 384 (312 to 450) | 9·4% (−16·8 to 35·7) |
| Venezuela | 90 (74 to 108) | 28·6% (−4·7 to 67·0) | 850 (751 to 966) | 5·1% (1·6 to 9·0) | 2750 (2235 to 3288) | 27·5% (−5·0 to 65·5) |
| Andean Latin America | 114 (98 to 130) | 8·3% (−9·9 to 26·0) | 1408 (1249 to 1596) | 6·5% (4·3 to 9·0) | 3564 (3085 to 4013) | −1·7% (−18·7 to 14·6) |
| Bolivia | 26 (19 to 34) | 24·1% (−28·0 to 72·0) | 246 (216 to 281) | 7·6% (3·9 to 11·5) | 804 (598 to 1047) | 11·4% (−36·3 to 51·9) |
| Ecuador | 39 (35 to 44) | 60·6% (31·3 to 84·0) | 382 (337 to 435) | 7·6% (3·8 to 11·0) | 1221 (1077 to 1361) | 43·5% (16·9 to 63·4) |
| Peru | 49 (39 to 60) | −17·5% (−36·1 to 7·6) | 779 (691 to 887) | 5·6% (2·3 to 9·3) | 1539 (1243 to 1855) | −24·2% (−41·4 to −0·4) |
| Caribbean | 188 (171 to 204) | 3·6% (−9·5 to 19·9) | 1481 (1326 to 1657) | 2·8% (0·8 to 4·9) | 5721 (5093 to 6327) | −5·0% (−20·5 to 10·3) |
| Antigua and Barbuda | 0 (0 to 0) | −5·7% (−23·1 to 13·6) | 3 (3 to 3) | 4·9% (1·5 to 8·4) | 7 (6 to 8) | −4·7% (−20·7 to 12·7) |
| The Bahamas | 2 (2 to 2) | −5·2% (−28·1 to 31·6) | 14 (13 to 16) | −0·2% (−3·8 to 3·3) | 59 (51 to 69) | −12·5% (−33·6 to 21·4) |
| Barbados | 4 (3 to 4) | 12·7% (−5·7 to 36·5) | 14 (13 to 15) | 4·3% (0·8 to 7·8) | 95 (84 to 107) | 12·0% (−6·5 to 37·2) |
| Belize | 1 (1 to 1) | 136·6% (46·9 to 229·1) | 10 (9 to 11) | 9·8% (5·6 to 13·6) | 33 (25 to 43) | 89·1% (23·8 to 167·1) |
| Bermuda | 0 (0 to 0) | −36·5% (−53·5 to −13·5) | 3 (3 to 4) | −5·4% (−8·9 to −1·9) | 11 (8 to 13) | −41·5% (−55·6 to −18·2) |
| Cuba | 91 (81 to 101) | −2·6% (−17·8 to 18·5) | 502 (449 to 559) | 2·7% (−1·3 to 6·8) | 2500 (2228 to 2769) | −11·1% (−24·1 to 6·7) |
| Dominica | 1 (0 to 1) | 50·4% (4·7 to 88·4) | 3 (2 to 3) | 13·8% (10·1 to 18·4) | 16 (14 to 19) | 51·9% (7·4 to 89·6) |
| Dominican Republic | 16 (13 to 20) | 13·7% (−23·7 to 45·7) | 303 (268 to 344) | 8·5% (4·8 to 12·6) | 529 (428 to 631) | 0·3% (−30·0 to 26·3) |
| Grenada | 1 (0 to 1) | 72·9% (20·0 to 148·8) | 3 (3 to 4) | 13·6% (9·1 to 18·0) | 16 (13 to 19) | 64·5% (16·1 to 133·6) |
| Guyana | 2 (2 to 3) | −1·5% (−22·9 to 20·6) | 18 (16 to 21) | 9·0% (5·1 to 13·3) | 72 (59 to 86) | −8·5% (−27·5 to 12·4) |
| Haiti | 23 (16 to 31) | 16·6% (−31·3 to 62·3) | 225 (196 to 260) | 2·8% (−0·6 to 6·3) | 957 (633 to 1 391) | −1·7% (−45·3 to 45·8) |
| Jamaica | 11 (9 to 14) | 45·5% (4·1 to 92·6) | 91 (81 to 102) | 5·8% (1·8 to 9·8) | 424 (318 to 568) | 31·7% (−12·2 to 87·8) |
| Puerto Rico | 24 (21 to 27) | 16·2% (−6·9 to 39·6) | 156 (141 to 174) | 8·0% (4·1 to 11·5) | 636 (555 to 709) | 5·7% (−14·7 to 25·7) |
| Saint Lucia | 1 (1 to 1) | 61·5% (15·4 to 124·7) | 6 (6 to 7) | 14·8% (10·8 to 19·2) | 33 (30 to 37) | 50·8% (8·1 to 106·5) |
| Saint Vincent and the Grenadines | 1 (1 to 1) | 176·2% (136·0 to 228·0) | 4 (3 to 4) | 13·1% (9·1 to 17·0) | 19 (17 to 21) | 131·0% (86·8 to 178·2) |
| Suriname | 2 (2 to 2) | 55·8% (27·8 to 85·3) | 15 (13 to 17) | 10·0% (6·0 to 14·1) | 65 (56 to 75) | 39·5% (14·7 to 68·3) |
| Trinidad and Tobago | 7 (6 to 7) | 6·5% (−16·4 to 29·3) | 46 (41 to 52) | 7·8% (4·0 to 12·0) | 197 (173 to 221) | 10·0% (−13·5 to 35·0) |
| Virgin Islands | 2 (1 to 2) | 58·9% (21·5 to 95·1) | 6 (6 to 7) | 18·1% (13·4 to 23·5) | 39 (32 to 46) | 45·6% (12·1 to 80·1) |
| Tropical Latin America | 1155 (1073 to 1219) | 79·1% (60·0 to 90·1) | 8125 (7084 to 9365) | 20·0% (17·2 to 23·1) | 33 632 (31 235 to 35 552) | 57·7% (40·5 to 68·1) |
| Brazil | 1132 (1051 to 1197) | 77·3% (58·9 to 88·3) | 7919 (6904 to 9132) | 20·3% (17·4 to 23·5) | 32 909 (30 585 to 34 846) | 56·2% (39·4 to 66·6) |
| Paraguay | 23 (19 to 26) | 215·4% (120·4 to 311·1) | 206 (184 to 233) | 10·8% (6·8 to 14·9) | 723 (610 to 834) | 177·6% (101·4 to 253·5) |
| East Asia | 3097 (2956 to 3234) | −1·3% (−12·4 to 16·3) | 56 465 (48 633 to 65 987) | 18·2% (15·3 to 20·9) | 111 827 (104 988 to 118 511) | −11·6% (−22·7 to 13·2) |
| China | 2988 (2839 to 3122) | −1·1% (−12·4 to 17·4) | 54 405 (46 781 to 63 692) | 18·6% (15·6 to 21·4) | 107 731 (100 956 to 114 351) | −12·1% (−23·8 to 13·8) |
| North Korea | 60 (43 to 96) | 14·6% (−6·1 to 43·8) | 955 (847 to 088) | 2·6% (−0·9 to 6·0) | 2477 (1814 to 3998) | 12·6% (−11·9 to 45·5) |
| Taiwan (province of China) | 49 (40 to 57) | −28·0% (−44·3 to −12·3) | 1104 (981 to 1253) | 13·5% (9·2 to 17·5) | 1619 (1354 to 1881) | −22·9% (−38·2 to −8·1) |
| Southeast Asia | 903 (770 to 997) | 25·2% (0·5 to 37·0) | 16 378 (14 345 to 18 910) | 10·4% (9·2 to 11·6) | 33 240 (28 726 to 36 864) | 10·5% (−5·9 to 20·4) |
| Cambodia | 15 (11 to 19) | 36·7% (−0·1 to 74·7) | 316 (278 to 362) | 10·6% (7·0 to 14·3) | 622 (457 to 783) | 20·6% (−13·0 to 58·5) |
| Indonesia | 335 (232 to 397) | 43·1% (24·6 to 59·8) | 6151 (5193 to 7307) | 10·6% (9·0 to 12·4) | 12 963 (9505 to 15 281) | 17·0% (2·5 to 38·1) |
| Laos | 7 (5 to 10) | 24·9% (−11·7 to 79·0) | 142 (124 to 163) | 11·1% (7·4 to 15·2) | 390 (242 to 652) | 10·1% (−28·4 to 70·3) |
| Malaysia | 43 (33 to 49) | 23·8% (−17·2 to 54·9) | 814 (722 to 925) | 8·6% (4·9 to 12·4) | 1541 (1236 to 1761) | 17·8% (−18·4 to 44·5) |
| Maldives | 0 (0 to 0) | −24·4% (−48·9 to 21·6) | 8 (7 to 10) | 8·7% (5·3 to 12·5) | 13 (11 to 16) | −34·7% (−55·7 to 11·8) |
| Mauritius | 3 (3 to 4) | 2·7% (−14·7 to 21·5) | 41 (37 to 47) | 8·6% (5·4 to 12·9) | 103 (89 to 121) | −3·5% (−18·6 to 13·7) |
| Myanmar | 110 (93 to 131) | 26·7% (−13·7 to 60·1) | 1285 (1139 to 1465) | 13·3% (9·3 to 17·4) | 3879 (3307 to 4615) | 17·6% (−16·1 to 49·6) |
| Philippines | 102 (79 to 124) | 17·0% (−11·3 to 44·4) | 2309 (2034 to 2655) | 6·7% (3·3 to 10·2) | 3903 (3142 to 4678) | 2·5% (−19·9 to 24·7) |
| Sri Lanka | 40 (32 to 51) | 21·6% (−16·3 to 62·9) | 593 (523 to 674) | 9·6% (6·0 to 13·0) | 1360 (1074 to 1677) | −0·2% (−32·6 to 40·2) |
| Seychelles | 0 (0 to 0) | 0·6% (−24·5 to 24·7) | 3 (2 to 3) | 7·5% (4·0 to 11·2) | 7 (5 to 8) | −3·8% (−26·8 to 17·2) |
| Thailand | 123 (106 to 142) | −3·4% (−26·3 to 17·7) | 2166 (1911 to 2458) | 10·0% (6·7 to 13·7) | 4091 (3555 to 4724) | −7·3% (−27·7 to 11·9) |
| Timor–Leste | 1 (1 to 1) | 39·7% (−12·3 to 136·9) | 23 (20 to 26) | 13·6% (10·0 to 17·3) | 39 (27 to 55) | 11·5% (−30·4 to 94·6) |
| Vietnam | 123 (99 to 153) | 28·3% (−6·5 to 69·3) | 2504 (2209 to 2846) | 11·4% (7·8 to 15·6) | 4324 (3536 to 5319) | 14·0% (−13·6 to 46·4) |
| Oceania | 13 (10 to 18) | −5·7% (−21·2 to 12·4) | 212 (187 to 244) | 4·0% (1·6 to 6·5) | 467 (384 to 621) | −3·8% (−19·4 to 15·3) |
| American Samoa | 0 (0 to 0) | −22·1% (−42·3 to −0·3) | 2 (2 to 2) | 0·0% (−3·6 to 3·8) | 5 (4 to 6) | −19·2% (−39·4 to 2·8) |
| Federated States of Micronesia | 0 (0 to 0) | 23·7% (−21·4 to 72·2) | 2 (2 to 2) | 6·4% (2·8 to 10·4) | 8 (6 to 11) | 22·0% (−19·7 to 71·1) |
| Fiji | 2 (1 to 2) | 33·5% (−14·7 to 88·8) | 22 (19 to 25) | 7·0% (3·5 to 10·5) | 52 (40 to 66) | 31·3% (−11·1 to 82·3) |
| Guam | 1 (1 to 1) | −45·2% (−56·6 to −30·4) | 6 (6 to 7) | −16·3% (−20·2 to −12·5) | 30 (24 to 35) | −42·6% (−54·6 to −27·4) |
| Kiribati | 0 (0 to 0) | 46·4% (10·2 to 95·3) | 2 (2 to 2) | 4·7% (1·4 to 8·4) | 9 (7 to 12) | 46·0% (10·7 to 96·7) |
| Marshall Islands | 0 (0 to 0) | 0·9% (−27·8 to 29·8) | 1 (1 to 2) | 4·7% (0·9 to 8·2) | 4 (3 to 6) | 1·1% (−25·3 to 28·8) |
| Northern Mariana Islands | 0 (0 to 0) | 43·9% (2·4 to 97·0) | 3 (3 to 4) | 5·5% (2·1 to 9·2) | 8 (6 to 11) | 30·4% (−6·9 to 78·5) |
| Papua New Guinea | 8 (5 to 12) | −1·3% (−22·4 to 30·4) | 135 (118 to 156) | 6·9% (3·2 to 10·7) | 290 (203 to 424) | −2·6% (−23·6 to 27·6) |
| Samoa | 0 (0 to 1) | −7·6% (−26·6 to 19·1) | 5 (4 to 5) | 5·8% (2·4 to 9·4) | 10 (7 to 17) | −6·6% (−25·4 to 19·0) |
| Solomon Islands | 1 (1 to 1) | 6·6% (−17·0 to 44·9) | 11 (9 to 13) | 5·4% (1·6 to 8·9) | 27 (21 to 40) | 9·5% (−15·2 to 49·2) |
| Tonga | 0 (0 to 0) | −1·4% (−25·5 to 29·2) | 3 (2 to 3) | 5·4% (2·2 to 9·1) | 6 (5 to 8) | 0·1% (−22·7 to 30·8) |
| Vanuatu | 0 (0 to 1) | 14·3% (−15·7 to 52·6) | 6 (5 to 6) | 5·3% (1·7 to 8·9) | 15 (12 to 23) | 17·2% (−13·3 to 56·3) |
| North Africa and Middle East | 1139 (964 to 1276) | 5·7% (−23·6 to 24·5) | 13 291 (11 746 to 15 182) | 3·1% (1·7 to 4·4) | 45 475 (38 290 to 51 031) | −14·3% (−40·1 to 9·3) |
| Afghanistan | 37 (21 to 59) | 31·8% (4·2 to 71·9) | 517 (444 to 603) | 3·2% (−0·8 to 7·0) | 1689 (945 to 2676) | 22·7% (−3·3 to 59·7) |
| Algeria | 60 (51 to 70) | 6·9% (−16·4 to 32·5) | 943 (832 to 1 079) | 2·9% (−1·4 to 7·0) | 2332 (1961 to 2736) | −8·6% (−31·9 to 16·7) |
| Bahrain | 1 (1 to 2) | −7·6% (−31·9 to 21·7) | 36 (31 to 42) | 4·1% (0·4 to 8·3) | 60 (47 to 76) | −10·5% (−32·2 to 17·0) |
| Egypt | 168 (102 to 217) | 29·6% (−1·6 to 62·3) | 1908 (1682 to 2185) | 7·1% (3·0 to 11·6) | 6211 (3881 to 7959) | 7·4% (−16·1 to 33·7) |
| Iran | 106 (86 to 129) | 84·9% (19·8 to 175·5) | 2012 (1781 to 2283) | 7·3% (3·3 to 11·7) | 3839 (3181 to 4608) | 58·6% (9·7 to 120·1) |
| Iraq | 36 (28 to 46) | 25·3% (−13·8 to 72·9) | 782 (676 to 907) | 3·6% (−0·2 to 7·5) | 1684 (1287 to 2256) | 17·1% (−16·8 to 60·1) |
| Jordan | 9 (7 to 11) | 39·2% (−18·7 to 106·4) | 174 (153 to 200) | 4·5% (0·5 to 9·0) | 431 (338 to 530) | 42·3% (−11·0 to 99·7) |
| Kuwait | 5 (4 to 7) | −21·0% (−43·4 to 10·9) | 116 (102 to 134) | −0·3% (−4·2 to 3·9) | 263 (198 to 346) | −29·3% (−47·6 to −1·7) |
| Lebanon | 8 (5 to 11) | −27·7% (−65·6 to 8·0) | 159 (140 to 179) | 3·2% (−1·0 to 7·2) | 273 (182 to 360) | −32·4% (−66·2 to 0·9) |
| Libya | 15 (8 to 20) | 40·9% (−4·5 to 84·2) | 150 (132 to 172) | −3·8% (−7·5 to 0·2) | 560 (317 to 731) | 22·3% (−11·8 to 59·6) |
| Morocco | 47 (38 to 55) | 42·2% (13·6 to 72·3) | 783 (690 to 896) | 5·1% (1·3 to 9·1) | 1733 (1418 to 2051) | 21·6% (−2·6 to 50·1) |
| Oman | 5 (3 to 7) | 75·1% (0·5 to 271·7) | 108 (94 to 125) | 7·2% (3·4 to 11·2) | 203 (142 to 279) | 43·3% (−10·6 to 156·2) |
| Palestine | 4 (3 to 5) | 9·5% (−19·2 to 44·7) | 102 (88 to 118) | 3·0% (−0·7 to 7·2) | 179 (153 to 214) | −16·4% (−39·9 to 17·6) |
| Qatar | 2 (1 to 3) | −3·9% (−37·6 to 48·3) | 62 (53 to 71) | 4·4% (0·1 to 8·9) | 92 (67 to 122) | −10·0% (−39·0 to 32·9) |
| Saudi Arabia | 42 (32 to 51) | 88·1% (10·5 to 197·3) | 854 (717 to 1 022) | 7·1% (5·1 to 9·1) | 1683 (1305 to 1997) | 48·2% (−8·7 to 114·8) |
| Sudan | 39 (25 to 51) | 34·0% (8·6 to 64·6) | 618 (540 to 719) | 6·9% (2·8 to 10·8) | 1882 (1092 to 2694) | 21·6% (−8·3 to 64·1) |
| Syria | 19 (15 to 23) | 23·4% (−21·5 to 75·4) | 413 (361 to 477) | 4·0% (−0·1 to 8·3) | 741 (627 to 894) | 5·6% (−34·4 to 44·3) |
| Tunisia | 19 (14 to 23) | 15·6% (−22·2 to 54·7) | 298 (263 to 339) | 5·1% (0·8 to 8·8) | 669 (530 to 820) | −3·1% (−38·2 to 27·2) |
| Turkey | 470 (392 to 566) | −18·9% (−52·2 to 16·3) | 2554 (2274 to 2864) | −1·5% (−5·7 to 2·6) | 18 887 (15 336 to 23 603) | −36·1% (−64·3 to 16·7) |
| United Arab Emirates | 26 (14 to 37) | 67·0% (−4·0 to 170·1) | 266 (231 to 306) | 1·7% (−1·9 to 6·0) | 1082 (614 to 1552) | 49·8% (−12·2 to 139·8) |
| Yemen | 21 (15 to 29) | 40·1% (−0·3 to 100·8) | 420 (365 to 483) | 5·1% (1·0 to 9·1) | 982 (665 to 1356) | 20·8% (−13·6 to 68·4) |
| South Asia | 2711 (2231 to 3277) | 48·9% (18·0 to 76·6) | 32 104 (27 566 to 37 755) | 14·2% (12·8 to 15·7) | 95 154 (80 575 to 109 736) | 35·7% (12·3 to 55·2) |
| Bangladesh | 146 (104 to 240) | −25·6% (−46·2 to 4·4) | 2851 (2501 to 3272) | 8·6% (5·0 to 12·5) | 5726 (4307 to 8582) | −29·6% (−49·4 to 6·8) |
| Bhutan | 1 (1 to 2) | 30·2% (−8·1 to 75·0) | 15 (14 to 18) | 11·8% (7·7 to 15·7) | 40 (31 to 57) | 11·7% (−21·8 to 54·5) |
| India | 2309 (1894 to 2726) | 56·9% (21·4 to 86·5) | 25 276 (21 450 to 30 120) | 16·1% (14·6 to 17·6) | 79 261 (66 554 to 90 980) | 44·0% (15·8 to 65·0) |
| Nepal | 33 (23 to 57) | 45·8% (4·8 to 125·7) | 514 (449 to 592) | 8·1% (4·4 to 12·7) | 1177 (848 to 1929) | 25·4% (−9·3 to 104·3) |
| Pakistan | 223 (175 to 311) | 66·4% (18·0 to 133·3) | 3448 (3020 to 3988) | 7·0% (3·3 to 10·8) | 8950 (7142 to 12 333) | 48·5% (11·2 to 95·9) |
| Southern sub-Saharan Africa | 73 (67 to 79) | 31·4% (6·3 to 55·4) | 1114 (957 to 1317) | 4·7% (3·4 to 6·2) | 2617 (2391 to 2838) | 23·4% (2·6 to 43·5) |
| Botswana | 2 (1 to 3) | 42·5% (−33·1 to 133·9) | 31 (27 to 36) | 6·6% (3·1 to 10·1) | 80 (37 to 119) | 41·8% (−32·3 to 129·7) |
| Lesotho | 2 (1 to 3) | 108·8% (0·0 to 214·2) | 26 (22 to 30) | 6·5% (2·7 to 10·2) | 63 (44 to 89) | 106·2% (2·0 to 206·7) |
| Namibia | 2 (1 to 2) | 10·7% (−30·6 to 58·7) | 32 (28 to 37) | 4·8% (1·2 to 8·1) | 63 (43 to 88) | 9·0% (−29·8 to 51·3) |
| South Africa | 55 (50 to 59) | 29·3% (1·9 to 49·7) | 841 (711 to 1 003) | 5·9% (4·4 to 7·5) | 1932 (1769 to 2108) | 19·8% (−2·6 to 37·4) |
| Swaziland | 1 (0 to 1) | 30·0% (−19·4 to 101·8) | 16 (14 to 19) | 5·0% (1·5 to 8·8) | 31 (20 to 45) | 31·6% (−15·9 to 97·2) |
| Zimbabwe | 12 (9 to 15) | 38·2% (−4·2 to 153·8) | 168 (145 to 197) | −1·4% (−4·7 to 1·7) | 449 (356 to 574) | 33·2% (−5·8 to 126·9) |
| Western sub-Saharan Africa | 273 (220 to 330) | 16·6% (−8·0 to 36·8) | 4075 (3541 to 4754) | 4·5% (2·7 to 6·1) | 13 364 (8976 to 16 996) | 7·8% (−17·8 to 28·8) |
| Benin | 8 (6 to 10) | 22·9% (−8·6 to 63·8) | 114 (99 to 133) | 3·4% (0·0 to 7·3) | 336 (276 to 424) | 8·3% (−22·5 to 43·6) |
| Burkina Faso | 11 (9 to 14) | 23·8% (−7·4 to 73·5) | 175 (152 to 205) | 5·1% (1·8 to 8·5) | 545 (417 to 731) | 17·2% (−11·8 to 61·9) |
| Cameroon | 22 (15 to 28) | 40·0% (−0·5 to 82·3) | 235 (203 to 274) | 1·5% (−1·5 to 4·5) | 1004 (594 to 1407) | 37·8% (−0·7 to 79·4) |
| Cape Verde | 0 (0 to 1) | 71·8% (18·9 to 124·7) | 8 (7 to 9) | 8·2% (4·5 to 11·9) | 18 (15 to 21) | 53·1% (15·3 to 95·7) |
| Chad | 8 (6 to 11) | 11·7% (−14·0 to 39·4) | 140 (121 to 164) | 5·2% (2·0 to 8·4) | 438 (265 to 636) | 10·7% (−14·9 to 39·2) |
| Côte d'Ivoire | 23 (18 to 28) | 24·2% (−7·1 to 58·9) | 230 (201 to 270) | 1·4% (−1·9 to 4·7) | 951 (681 to 1197) | 20·4% (−10·5 to 53·3) |
| The Gambia | 1 (1 to 1) | 5·1% (−16·9 to 30·9) | 21 (18 to 25) | 1·3% (−2·1 to 4·4) | 51 (41 to 65) | −1·0% (−20·9 to 21·5) |
| Ghana | 31 (24 to 40) | 53·6% (17·4 to 147·2) | 309 (269 to 357) | 6·2% (2·8 to 9·8) | 1186 (924 to 1531) | 42·8% (8·0 to 119·1) |
| Guinea | 9 (7 to 12) | 29·8% (−12·3 to 87·6) | 126 (109 to 146) | 0·9% (−2·4 to 4·2) | 380 (296 to 546) | 11·8% (−26·7 to 62·7) |
| Guinea-Bissau | 2 (1 to 2) | 18·2% (−11·2 to 67·0) | 19 (16 to 22) | 2·0% (−1·4 to 5·2) | 71 (54 to 97) | 9·8% (−15·2 to 52·2) |
| Liberia | 3 (2 to 3) | 5·6% (−28·2 to 35·1) | 43 (37 to 51) | 0·4% (−2·6 to 3·8) | 107 (87 to 137) | −10·1% (−42·0 to 17·3) |
| Mali | 8 (6 to 12) | −5·7% (−34·7 to 40·1) | 178 (154 to 209) | 6·2% (3·1 to 9·4) | 430 (327 to 594) | −13·0% (−39·8 to 33·2) |
| Mauritania | 3 (2 to 4) | −6·1% (−32·3 to 24·6) | 51 (44 to 59) | 3·3% (0·0 to 6·7) | 125 (80 to 166) | −3·9% (−29·6 to 27·0) |
| Niger | 9 (7 to 14) | −5·0% (−35·3 to 51·5) | 190 (165 to 223) | 1·8% (−1·1 to 4·7) | 450 (340 to 649) | −19·5% (−47·5 to 37·1) |
| Nigeria | 112 (67 to 152) | 4·3% (−34·5 to 38·5) | 1936 (1687 to 2255) | 6·0% (2·3 to 9·3) | 6316 (3210 to 9266) | −1·8% (−39·0 to 32·5) |
| São Tomé and Príncipe | 0 (0 to 0) | 26·8% (−13·4 to 162·6) | 2 (2 to 2) | 2·8% (−0·4 to 6·2) | 5 (3 to 9) | 9·3% (−26·0 to 110·0) |
| Senegal | 12 (9 to 14) | 16·8% (−6·2 to 46·3) | 164 (141 to 190) | 2·1% (−1·3 to 5·5) | 485 (398 to 592) | 8·4% (−14·4 to 36·4) |
| Sierra Leone | 5 (3 to 6) | 13·6% (−12·8 to 49·5) | 62 (54 to 73) | 2·8% (−0·2 to 6·0) | 223 (133 to 317) | −2·0% (−27·7 to 29·1) |
| Togo | 6 (4 to 8) | 19·7% (−21·1 to 60·8) | 73 (63 to 85) | 1·1% (−2·6 to 4·4) | 245 (154 to 337) | 13·2% (−26·2 to 47·8) |
| Eastern sub-Saharan Africa | 131 (104 to 214) | 6·7% (−18·4 to 36·3) | 3762 (3258 to 4423) | 3·6% (2·5 to 4·8) | 6471 (5350 to 9325) | −6·4% (−29·3 to 29·7) |
| Burundi | 4 (3 to 7) | −20·9% (−41·5 to 13·8) | 98 (84 to 115) | 0·7% (−2·7 to 3·8) | 208 (140 to 342) | −23·5% (−43·3 to 14·4) |
| Comoros | 0 (0 to 1) | −3·8% (−29·9 to 25·9) | 9 (7 to 10) | −0·1% (−3·3 to 3·4) | 15 (11 to 22) | −11·2% (−34·9 to 18·1) |
| Djibouti | 0 (0 to 1) | 23·6% (−15·4 to 87·1) | 11 (10 to 13) | −0·1% (−3·2 to 2·7) | 23 (16 to 33) | 2·5% (−30·9 to 45·0) |
| Eritrea | 2 (1 to 3) | 17·2% (−18·3 to 66·2) | 55 (47 to 64) | 3·8% (0·0 to 7·6) | 93 (71 to 139) | 10·4% (−20·0 to 49·9) |
| Ethiopia | 31 (22 to 61) | −8·5% (−40·0 to 54·1) | 987 (857 to 1153) | 3·9% (0·6 to 7·2) | 1375 (1012 to 2365) | −18·5% (−48·2 to 44·2) |
| Kenya | 15 (12 to 21) | 44·15% (3·2 to 85·8) | 515 (422 to 628) | 3·1% (2·2 to 4·0) | 731 (593 to 925) | 25·0% (−2·5 to 49·1) |
| Madagascar | 8 (5 to 14) | −4·6% (−35·0 to 30·4) | 272 (236 to 316) | 0·4% (−2·7 to 3·7) | 405 (294 to 595) | −12·2% (−40·5 to 16·5) |
| Malawi | 6 (4 to 11) | 15·2% (−23·8 to 73·4) | 164 (142 to 193) | 2·1% (−1·0 to 5·4) | 329 (226 to 515) | −9·8% (−41·6 to 49·5) |
| Mozambique | 10 (6 to 19) | −0·4% (−29·7 to 32·4) | 284 (246 to 333) | 7·1% (3·8 to 10·7) | 501 (348 to 787) | −12·3% (−39·0 to 23·6) |
| Rwanda | 3 (2 to 8) | 6·2% (−40·9 to 67·3) | 110 (95 to 129) | 2·9% (−0·2 to 6·3) | 174 (102 to 388) | −2·2% (−41·2 to 56·6) |
| Somalia | 3 (2 to 6) | −5·6% (−28·9 to 25·6) | 93 (80 to 108) | 0·1% (−3·2 to 3·4) | 157 (108 to 270) | −16·1% (−39·1 to 16·3) |
| South Sudan | 4 (2 to 7) | −1·5% (−26·8 to 31·7) | 131 (114 to 153) | 0·6% (−2·2 to 3·6) | 198 (127 to 332) | −16·5% (−39·5 to 18·7) |
| Tanzania | 23 (16 to 40) | 11·4% (−12·3 to 43·6) | 527 (455 to 615) | 4·0% (0·7 to 7·4) | 1170 (858 to 1765) | −3·4% (−28·3 to 28·0) |
| Uganda | 11 (8 to 21) | 17·3% (−43·2 to 112·1) | 342 (294 to 407) | 5·9% (2·4 to 9·3) | 628 (443 to 1040) | 6·8% (−48·6 to 78·1) |
| Zambia | 10 (7 to 14) | 54·2% (3·8 to 123·4) | 162 (140 to 190) | 3·3% (−0·1 to 7·0) | 465 (318 to 666) | 15·0% (−23·2 to 70·1) |
| Central sub-Saharan Africa | 40 (29 to 65) | −0·5% (−16·0 to 19·5) | 1086 (943 to 1273) | 0·8% (−1·3 to 2·9) | 2007 (1483 to 2934) | −8·8% (−28·2 to 17·1) |
| Angola | 9 (6 to 16) | 18·6% (−22·7 to 81·6) | 263 (228 to 308) | 5·0% (1·8 to 8·4) | 497 (339 to 788) | 9·3% (−24·8 to 69·3) |
| Central African Republic | 2 (2 to 4) | 5·3% (−20·1 to 36·6) | 44 (38 to 52) | −0·6% (−3·9 to 2·7) | 103 (67 to 159) | −0·2% (−25·6 to 32·0) |
| Congo (Brazzaville) | 3 (2 to 4) | −9·9% (−37·5 to 20·3) | 48 (41 to 55) | 3·2% (−0·1 to 6·9) | 115 (80 to 161) | −11·6% (−38·5 to 16·6) |
| Democratic Republic of the Congo | 24 (16 to 43) | −4·5% (−23·2 to 14·4) | 702 (606 to 823) | −1·1% (−4·2 to 1·7) | 1222 (826 to 1861) | −14·5% (−36·8 to 13·5) |
| Equatorial Guinea | 0 (0 to 1) | 0·3% (−43·4 to 75·5) | 9 (8 to 11) | 24·6% (19·9 to 29·5) | 20 (13 to 31) | −14·9% (−51·6 to 58·0) |
| Gabon | 1 (1 to 2) | 10·1% (−22·4 to 51·3) | 21 (18 to 24) | 2·0% (−1·5 to 5·7) | 50 (37 to 66) | 3·4% (−27·8 to 41·2) |
95% uncertainty intervals are in parentheses. DALYs=disability-adjusted life-years. SDI=Socio-demographic Index.
From 1990 to 2016, the worldwide age-standardised mortality rates of motor neuron disease increased by 8·0% (95% UI −0·1 to 12·2; table). A statistically significant increase in age-standardised mortality was present across all SDI quintiles, except for the low SDI group. Globally, the age-standardised prevalence of motor neuron disease increased by 4·5% (3·4 to 5·7; table) while the all-age prevalence increased by 18·8% (15·28 to 22·21). A map of age-standardised prevalence of motor neuron disease in each country for both sexes is shown in figure 1. Age-standardised prevalence increased in low SDI, low-middle SDI, middle SDI, high-middle SDI, and high SDI quintiles (table). The age-standardised incidence of motor neuron disease was stable in all SDI groups, except for the high SDI quintile where incidence increased by 13·0% (11·9 to 14·1) from 1·60 to 1·81 per 100 000 population (see the GBD 2016 online results tool). No statistically significant change was found in the global age-standardised DALY rate of motor neuron diseases (−1·5%, −9·3 to 2·9; table).
Figure 1.
Age-standardised prevalence of motor neuron 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.
Considering 21 GBD regions, in 2016, the highest all-age prevalence was observed in high-income North America (20·2, 95% UI 19·0–21·5), followed by Australasia (17·7, 16·2–19·2) and western Europe (16·7, 15·3–18·1). The lowest all-age prevalence was in sub-Saharan Africa, especially in central sub-Saharan Africa (0·9, 0·8–1·1), followed by eastern sub-Saharan Africa (1·0, 0·8–1·1) and western sub-Saharan Africa (1·0, 0·9–1·2). Only a small proportion of these geographical differences in the prevalence can be explained by a different age structure of the countries: in 2016, age-standardised prevalence per 100 000 population ranged from a high of 22·6 (20·9–24·7) in Canada to a low of 1·1 (0·9–1·2) in the Central African Republic. Among regions with high SDI values, age-standardised prevalence per 100 000 population was lowest in high-income Asia Pacific, southern Latin America, eastern Europe and central Europe (figure 1). Age-standardised prevalence per 100 000 population was 16·8 (15·8–17·9) in high-income North America, 14·7 (13·5–16·1) in Australasia, 12·9 (11·7–14·1) in western Europe, 1·2 (1·1–1·4) in central sub-Saharan Africa, 1·3 (1·1–1·4) in eastern sub-Saharan Africa, and 1·3 (1·2–1·5) in western sub-Saharan Africa. Half of the world's prevalent cases resided in countries in the highest SDI quintile, most of whom were living in high-income North America and western Europe (table).
The prevalence ranking was generally consistent with that of incidence (see the GBD 2016 online results tool). The highest age-standardised incidence per 100 000 person-years was in Australasia with 2·77 (2·63 to 2·91), followed by high-income North America with 2·30 (2·20 to 2·41) and western Europe with 2·00 (1·89 to 2·11). The lowest age-standardised incidence was in western sub-Saharan Africa (0·36, 0·31 to 0·41), followed by southern sub-Saharan Africa (0·37; 0·32 to 0·43), central Asia (0·39; 0·34 to 0·44), central sub-Saharan Africa (0·39; 0·33 to 0·44), and eastern sub-Saharan Africa (0·39; 0·34 to 0·45). Prevalence was higher in males than females at all ages (figure 2), with the peak of prevalence for males at age 85–89 years, and for females at age 80–84 years. The male to female ratio of age-standardised prevalence was 1·22 (1·19 to 1·24) in 1990 and 1·25 (1·23 to 1·28) in 2016.
Figure 2.
Global prevalence of motor neuron diseases by age and sex, 2016
Prevalence is expressed as the percentage of the population that is affected by the disease. Shaded areas show 95% uncertainty intervals. Values are plotted at the midpoint of 5-year age categories.
The YLL rate curve peaked in the group aged 0–1 year and again in the group aged 70–74 years (figure 3). YLD rates showed a steady increase to a plateau at age 80–84 years (figure 3). The YLD rates were much lower than the YLL rates, reflecting the high case fatality of motor neuron disease.
Figure 3.
Global years lived with disability (YLDs) and years of life lost (YLLs) rates per 100 000 population due to motor neuron diseases by age, 2016
Shaded areas show 95% uncertainty intervals. Values are plotted at the midpoint of 5-year age categories.
The expected age-standardised DALY rates increased with SDI (figure 4), from about five per 100 000 population in countries with a very low SDI to about 30 per 100 000 population in countries with a high SDI. The change in DALYs as SDI increased was greatest at high SDIs. The rates in high-income North America, western Europe, and Australasia were higher than expected based on their SDI levels. By contrast, the age-standardised DALY rates in the high-income Asia Pacific region were lower than expected based on its SDI. None of the 84 risk factors analysed in GBD 2016 were shown to have sufficient evidence of association with motor neuron disease incidence or deaths.
Figure 4.
Age-standardised disability-adjusted-life-year (DALY) rates for motor neuron diseases by 21 Global Burden of Disease regions by Socio-demographic Index (SDI), 1990–2016
The black line represents expected values based on SDI from a regression of all location data over the entire 1990 to 2016 estimation period.
Discussion
More than half of global deaths and close to half of all prevalent cases of motor neuron diseases occurred in three high-income regions: North America, western Europe, and Australasia. Motor neuron disease cases and deaths were far fewer in other parts of the world. Globally, from 1990 to 2016, the all-age prevalence increased more than the age-standardised prevalence, indicating that the largest part of the global prevalence increase was due to ageing.
The geographical differences in age-standardised incidences might be partly due to more accurate case ascertainment and diagnosis of motor neuron disease in high SDI countries compared with countries with low and middle SDIs. For example, a significant geographical difference in motor neuron disease prevalence was observed in the USA and western Europe between regions with different levels of quality of access to health-care systems.10 However, high SDI countries in the Asia Pacific region, where an accurate diagnosis of motor neuron disease is more probable than in low SDI countries, had a lower incidence than high SDI countries in western Europe and North America. This geographical heterogeneity suggests that the differences in the prevalence and incidence of motor neuron disease might be due to ethnicities and ancestries, and that the apparent relationship with SDI might be spurious. In a 2017 study in which subcontinents were considered as surrogates of ancestries, a higher prevalence of amyotrophic lateral sclerosis was reported in Europe, the USA, and New Zealand than in east Asia.8
The increase in age-standardised incidence of motor neuron diseases in the high SDI regions during the study period might, in part, be due to improved diagnosis, whereas the increase in age-standardised prevalence is probably due to improved survival. In particular, non-invasive ventilation can prolong survival21 and has been increasingly included in the usual practice of multidisciplinary motor neuron disease centres. The improvement of quality and reduction of time to certainty of amyotrophic lateral sclerosis diagnosis might also appear to increase the duration of disease.22 Additional contributing factors could include an increase in the public awareness of amyotrophic lateral sclerosis (including that generated by major clinical trials launched since the early 1990s in Europe and the USA), and increased awareness that progressive weakness is not a normal part of ageing.
We found that the prevalence of motor neuron disease increases particularly after age 50 years, with a peak at around age 85 years, followed by a rapid decline in males, while in females the curve is flat between age 70 and 85 years, followed by a decline (figure 2). The rapid decline in the oldest age group (ie, ≥85 years) could be due to poor ascertainment because of more complex clinical features or comorbidities, competing mortality from other causes (eg, cardiovascular disease, dementia), or both. Older people are less frequently referred to tertiary neurological care11 and are less likely to get a correct diagnosis than are younger patients. Another factor that might play a part in the observed decline in the prevalence of motor neuron disease in the older population relates to different clinical expression of the disease, with a greater case fatality in this age group (eg, more frequent bulbar motor neuron disease cases).11, 23
Our findings suggest that the prevalence of motor neuron disease is consistently higher in males than females across all age groups, with no changes in the male to female ratio between 1990 and 2016. The ratio is similar to that in studies from European registries, although in those studies incidence of amyotrophic lateral sclerosis in females increased over time.10 The reported change in sex ratio of amyotrophic lateral sclerosis in the earlier study11 has been attributed to changes of exposure for females to possible environmental factors, such as smoking. In GBD, however, the evidence for an association of smoking with all motor neuron disease was considered insufficient.21
Previously reported prevalence per 100 000 population for amyotrophic lateral sclerosis (5·40 in Europe, 3·40 in the USA, and 2·34 in Asia)7 are lower than the GBD 2016 estimates of overall motor neuron disease prevalence per 100 000 population (10·00 in Europe, 19·37 in the USA, and 3·13 in Asia), as expected, because we included additional motor neuron diseases.
This study explored motor neuron diseases in all age groups. About 14% of cases were younger than age 20 years, an approximate estimate of the genetic forms with early clinical onset. The inclusion of spinal muscular atrophy, including all its clinical and genetic phenotypes, has had an effect on DALYs, with the presence of a peak in the first year of life in addition to the second peak in people aged 70 years and older due to amyotrophic lateral sclerosis. However, the inclusion of spinal muscular atrophy and other motor neuron diseases of early life has not produced a substantial change in the age prevalence curve compared with studies including only amyotrophic lateral sclerosis.
Incidence and prevalence of motor neuron disease in early childhood (ie, spinal muscular atrophy and hereditary spastic paraplegia) seems to be lower in GBD 2016 estimates than previous estimates. For example, a prevalence of approximately one to two per 100 000 population and incidence of one in 10 000 livebirths per year have been estimated with spinal muscular atrophy type I (60% of cases).24 A previous report in England, Finland, Norway, and Hungary estimated a birth incidence of spinal muscular atrophy of ten per 100 000 livebirths.25 A larger survey,17 done in 2015, which took data from 27 population-based studies from Europe, Middle East, North America, Asia, South America, Africa, and Australia, reported a similar range, from 5·1 to 16·6 cases per 100 000 livebirths. We found an incidence of 1·9 per 100 000 person-years in the group aged 28 days to 1 year, indicating a relevant risk and burden in the first year of life (see the GBD 2016 online results tool). Age-specific incidence in this age group varied notably among locations: from 0·9 (sub-Saharan Africa) to 13·1 (Australia) per 100 000 person-years. The prevalence of spinal muscular atrophy is established, as for other genetic diseases of early years of life, by several factors, including family size of the proband, the availability of genetic counselling, prenatal diagnosis with genetic testing, and access to and willingness to have pregnancy termination. Additionally, spinal muscular atrophy frequency seems higher in white populations compared with other ethnic groups, similar to amyotrophic lateral sclerosis.26, 27
Several limitations of the study should be considered. First, given the diagnostic challenges of motor neuron diseases, some categories of motor neuron disease are probably still underdiagnosed, especially in older people and ethnic minority groups.28 Although the criteria for amyotrophic lateral sclerosis diagnosis in adults changed several times over the study period,29 we did not detect a systematic bias between data based on El Escorial criteria and data based on alternative case definitions. Second, data are scarce from large parts of the world, including sub-Saharan Africa, most of Latin America, eastern Europe, and south and central Asia. Despite the fact that the Asian population constitutes more than 50% of the world population, few epidemiological studies of motor neuron diseases have been done outside Europe and North America.30 The data indicate a difference in the prevalence and incidence of motor neuron disease, with lower estimates in Asia, Latin America, and Africa, as compared with the rest of the world. This difference might, at least in part, reflect missed diagnosis because of the absence of appropriate diagnostic instruments. Third, mortality rate estimations were based on data classified using ICD-9 and ICD-10. Although both versions of the ICD are highly congruent in the field of motor neuron disease, we cannot exclude an influence of the classification evolution, even if unlikely. As prevalence is related to incidence and disease duration, we should consider that variation in prognosis between geographical area, especially due to variation in care, might explain part of the prevalence variation. For example, tracheostomy is done in about 30% of Japanese patients, whereas in the USA and Europe proportions range between 0% and 10%.31, 32 Conversely, use of non-invasive ventilation appears much higher in the USA (15–35% of cases) as compared with Japan and Europe.33
This report is one of the most accurate so far on motor neuron disease epidemiology, but is still based on inference for large areas. Therefore, further improvements in estimates of the burden of motor neuron disease will require new research in these areas of the world; prevalence and possibly incidence studies in low-middle-income and low-income countries, particularly in sub-Saharan Africa, Latin America, the Caribbean, and Asia, are needed. Epidemiological studies of subpopulations of countries with diverse ethnicities are also important, given the supposed lower motor neuron disease incidence in mixed populations as compared with homogeneous populations.34
Our estimate of the severity of disability is based on the PRO-ACT database and therefore indicative of disability in amyotrophic lateral sclerosis and not in spinal muscular atrophy and other motor neuron diseases. Another limitation is that participants in randomised trials are characterised by a less severe form of amyotrophic lateral sclerosis than are participants from population-based studies.35 Finally, no data exist on the effect of cognitive impairment on disability, an important clinical trait of amyotrophic lateral sclerosis that affects about 50% of individuals at diagnosis,36 and this might have led to underestimation of the overall motor neuron disease disability.
Unfortunately, no effective treatment for motor neuron diseases exists. The only licensed drugs for amyotrophic lateral sclerosis are riluzole and, more recently, edaravone. Even if these drugs change the survivorship, the effect is limited to a few more months of life, and the effect on the disability caused by amyotrophic lateral sclerosis is not clear.37, 38, 39 Conversely, a promising new approach exists for gene-modifying therapy for spinal muscular atrophy.40
These data show that, with ageing of the world population, the burden of motor neuron diseases on health services is likely to increase substantially in coming decades. New epidemiological studies in areas without data are needed. Epidemiological studies in diverse populations might improve estimates of motor neuron disease frequency, understanding of disease phenotypes and risk factors, and the completeness of case ascertainment and referral.41 In the meantime, with limited possibilities for effective medical intervention, these GBD 2016 data are of relevance to health-care planning and resource allocation for the extensive care needs of people with motor neuron diseases.
Acknowledgments
Acknowledgments
Ashish Awasthi received funding from the Department of Science and Technology, Government of India, New Delhi, India, through INSPIRE Faculty Award Scheme. Yun Jin Kim is supported by the Office of Research & Innovation, Xiamen University, Malaysia.
GBD 2016 Motor Neuron Disease Collaborators
Giancarlo Logroscino, Marco Piccininni, Benoît Marin, Emma Nichols, Foad Abd-Allah, Ahmed Abdelalim, Fares Alahdab, Solomon Weldegebreal Asgedom, Ashish Awasthi, Yazan Chaiah, Ahmad Daryani, Huyen Phuc Do, Manisha Dubey, Alexis Elbaz, Sharareh Eskandarieh, Farzaneh Farhadi, Farshad Farzadfar, Seyed-Mohammad Fereshtehnejad, Eduarda Fernandes, Irina Filip, Kyle J Foreman, Abadi Kahsu Gebre, Elena V Gnedovskaya, Samer Hamidi, Simon I Hay, Seyed Sina Naghibi Irvani, John S Ji, Amir Kasaeian, Yun Jin Kim, Lorenzo Giovanni Mantovani, Tivani Phosa Mashamba-Thompson, Man Mohan Mehndiratta, Ali H Mokdad, Gabriele Nagel, Trang Huyen Nguyen, Molly R Nixon, Andrew T Olagunju, Mayowa Ojo Owolabi, Michael A Piradov, Mostafa Qorbani, Amir Radfar, Robert C Reiner, Mohammad Ali Sahraian, Shahabeddin Sarvi, Mehdi Sharif, Omar Temsah, Bach Xuan Tran, Nu Thi Truong, Narayanaswamy Venketasubramanian, Andrea Sylvia Winkler, Ebrahim M Yimer, Valery L Feigin, Theo Vos, Christopher J L Murray.
Affiliations
Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy (Prof G Logroscino PhD, M Piccininni MSc); Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, Pia Fondazione Cardinale G Panico, Tricase, Italy (Prof G Logroscino, M Piccininni); Institute of Neurological Epidemiology and Tropical Neurology, University of Limoges, Limoges, France (Prof B Marin PhD); Institute for Health Metrics and Evaluation (E Nichols BA, K J Foreman PhD, Prof S I Hay FMedSci, Prof A H Mokdad PhD, M R Nixon PhD, R C Reiner PhD, Prof V L Feigin PhD, Prof T Vos PhD, Prof C J L Murray DPhil), and Department of Health Metrics Sciences (Prof C J L Murray), University of Washington, Seattle, WA, USA; Department of Neurology, Cairo University, Cairo, Egypt (Prof F Abd-Allah MD, Prof A Abdelalim MD); Evidence Based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USA (Prof F Alahdab MD); Research Committee, Syrian American Medical Society, Washington, DC, USA (Prof F Alahdab); School of Pharmacy, Mekelle University, Mekelle, Ethiopia (S W Asgedom MSc, A K Gebre MSc, E M Yimer MSc); Indian Institute of Public Health, Gandhinagar, India (A Awasthi PhD); Public Health Foundation of India, Gurugram, India (A Awasthi); College of Medicine, Alfaisal University, Riyadh, Saudi Arabia (Y Chaiah, O Temsah); Toxoplasmosis Research Center, Mazandaran University of Medical Sciences, Sari, Iran (Prof A Daryani PhD, Prof S Sarvi PhD); Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam (H P Do PhD, T H Nguyen BMedSc, N T Truong BHlthSci); UN World Food Programme, New Delhi, India (M Dubey PhD); Centre for Research in Epidemiology and Population Health, National Institute of Health and Medical Research, Paris, France (A Elbaz MD); Multiple Sclerosis Research Center, Neuroscience Institute (S Eskandarieh PhD), Non-communicable Diseases Research Center (F Farzadfar MD, S N Irvani MD), Hematologic Malignancies Research Center (A Kasaeian PhD), MS Research Center (Prof M A Sahraian MD), and Hematology-oncology and Stem Cell Transplantation Research Center (A Kasaeian), Tehran University of Medical Sciences, Tehran, Iran; Iran University of Medical Sciences, Tehran, Iran (F Farhadi MD); Montreal Neurological Institute, McGill University, Montreal, QC, Canada (S Fereshtehnejad PhD); Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (S Fereshtehnejad); REQUIMTE/LAQV, University of Porto, Porto, Portugal (Prof E Fernandes PhD); Psychiatry Department, Kaiser Permanente, Fontana, CA, USA (I Filip MD); Department of Health Sciences (I Filip), A T Still University, Mesa, Arizona, USA (A Radfar MD); Research Center of Neurology, Moscow, Russia (Prof E V Gnedovskaya PhD, Prof M A Piradov DSc); School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates (Prof S Hamidi DrPH); Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (S N Irvani); Environmental Research Center, Duke Kunshan University, Kunshan, China (Prof J S Ji DSc); School of Medicine, Xiamen University Malaysia, Sepang, Malaysia (Prof Y Kim PhD); School of Medicine and Surgery, University of Milan Bicocca, Monza, Italy (Prof L G Mantovani DSc); Department of Public Health Medicine, University of Kwazulu-natal, Durban, South Africa (T P Mashamba-Thompson PhD); Neurology Department, Janakpuri Super Specialty Hospital Society, New Delhi, India (Prof M Mehndiratta MD); Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany (Prof G Nagel PhD); University of Adelaide, Adelaide, SA, Australia (A T Olagunju MD); Department of Psychiatry, University of Lagos, Lagos, Nigeria (A T Olagunju); Institute for Advanced Medical Research and Training, University of Ibadan, Ibadan, Nigeria (Prof M O Owolabi DrM); Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran (M Qorbani PhD); Medichem, Barcelona, Spain (A Radfar); Department of Laboratory Sciences and Department of Basic Sciences, Islamic Azad University, Sari, Iran (Prof M Sharif PhD); Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam (Prof B X Tran PhD); Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore (Prof N Venketasubramanian MBBS); Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore (Prof N Venketasubramanian); Institute of Health and Society, University of Oslo, Oslo, Norway (A S Winkler PhD); Department of Neurology, Technical University of Munich, Munich, Germany (A S Winkler); and National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand (Prof V L Feigin).
Contributors
GL, MP, and BM prepared the first draft. EN, VF, and TV analysed the data and edited the first draft and final versions of the manuscript. GL, MP, and BM finalised all drafts, and approved the final version of the manuscript. All other authors provided data, developed models, reviewed results, provided guidance on methodology, or reviewed the manuscript, and approved the final version of the manuscript.
Declaration of interests
We declare no competing interests.
Contributor Information
GBD 2016 Motor Neuron Disease Collaborators:
Giancarlo Logroscino, Marco Piccininni, Benoît Marin, Emma Nichols, Foad Abd-Allah, Ahmed Abdelalim, Fares Alahdab, Solomon Weldegebreal Asgedom, Ashish Awasthi, Yazan Chaiah, Ahmad Daryani, Huyen Phuc Do, Manisha Dubey, Alexis Elbaz, Sharareh Eskandarieh, Farzaneh Farhadi, Farshad Farzadfar, Seyed-Mohammad Fereshtehnejad, Eduarda Fernandes, Irina Filip, Kyle J. Foreman, Abadi Kahsu Gebre, Elena V. Gnedovskaya, Samer Hamidi, Simon I. Hay, Seyed Sina Naghibi Irvani, John S. Ji, Amir Kasaeian, Yun Jin Kim, Lorenzo Giovanni Mantovani, Tivani Phosa Mashamba-Thompson, Man Mohan Mehndiratta, Ali H. Mokdad, Gabriele Nagel, Trang Huyen Nguyen, Molly R Nixon, Andrew T Olagunju, Mayowa Ojo Owolabi, Michael A Piradov, Mostafa Qorbani, Amir Radfar, Robert C Reiner, Mohammad Ali Sahraian, Shahabeddin Sarvi, Mehdi Sharif, Omar Temsah, Bach Xuan Tran, Nu Thi Truong, Narayanaswamy Venketasubramanian, Andrea Sylvia Winkler, Ebrahim M Yimer, Valery L. Feigin, Theo Vos, and Christopher J L Murray
Supplementary Material
References
- 1.Hardiman O, Al-Chalabi A, Chio A. Amyotrophic lateral sclerosis. Nat Rev Dis Prime. 2017;3:17071. doi: 10.1038/nrdp.2017.71. [DOI] [PubMed] [Google Scholar]
- 2.Marin B, Logroscino G, Boumediene F. Clinical and demographic factors and outcome of amyotrophic lateral sclerosis in relation to population ancestral origin. Eur J Epidemiol. 2016;31:229–245. doi: 10.1007/s10654-015-0090-x. [DOI] [PubMed] [Google Scholar]
- 3.Renton AE, Majounie E, Waite A. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron. 2011;72:257–268. doi: 10.1016/j.neuron.2011.09.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.DeJesus-Hernandez M, Mackenzie IR, Boeve BF. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011;72:245–256. doi: 10.1016/j.neuron.2011.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Alonso A, Logroscino G, Hernan MA. Smoking and the risk of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2010;81:1249–1252. doi: 10.1136/jnnp.2009.180232. [DOI] [PubMed] [Google Scholar]
- 6.Kamel F, Umbach DM, Munsat TL, Shefner JM, Sandler DP. Association of cigarette smoking with amyotrophic lateral sclerosis. Neuroepidemiology. 1999;18:194–202. doi: 10.1159/000026211. [DOI] [PubMed] [Google Scholar]
- 7.Chio A, Logroscino G, Traynor BJ. Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature. Neuroepidemiology. 2013;41:118–130. doi: 10.1159/000351153. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Marin B, Boumediene F, Logroscino G. Variation in worldwide incidence of amyotrophic lateral sclerosis: a meta-analysis. Int J Epidemiol. 2017;46:57–74. doi: 10.1093/ije/dyw061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Marin B, Fontana A, Arcuti S. Age-specific ALS incidence: a dose-response meta-analysis. Eur J Epidemiol. 2018;33:621–634. doi: 10.1007/s10654-018-0392-x. [DOI] [PubMed] [Google Scholar]
- 10.Logroscino G, Traynor BJ, Hardiman O. Incidence of amyotrophic lateral sclerosis in Europe. J Neurol Neurosurg Psychiatry. 2010;81:385–390. doi: 10.1136/jnnp.2009.183525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Logroscino G, Traynor BJ, Hardiman O. Descriptive epidemiology of amyotrophic lateral sclerosis: new evidence and unsolved issues. J Neurol Neurosurg Psychiatry. 2008;79:6–11. doi: 10.1136/jnnp.2006.104828. [DOI] [PubMed] [Google Scholar]
- 12.Wagner L, Rechtman L, Jordan H. State and metropolitan area-based amyotrophic lateral sclerosis (ALS) surveillance. Amyotroph Lateral Scler Frontotemporal Degener. 2016;17:128–134. doi: 10.3109/21678421.2015.1074699. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.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]
- 14.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]
- 15.Brooks BR. El Escorial World Federation of Neurology criteria for the diagnosis of amyotrophic lateral sclerosis. Subcommittee on Motor Neuron Diseases/Amyotrophic Lateral Sclerosis of the World Federation of Neurology Research Group on Neuromuscular Diseases and the El Escorial “Clinical limits of amyotrophic lateral sclerosis” workshop contributors. J Neurol Sci. 1994;124(suppl):96–107. doi: 10.1016/0022-510x(94)90191-0. [DOI] [PubMed] [Google Scholar]
- 16.Pooled Resource Open-Access ALS Clinical Trials Database Data Sets. https://nctu.partners.org/ProACT/Data/Index/1 Access data 31/08/2018.
- 17.Atassi N, Berry J, Shui A. The PRO-ACT database: design, initial analyses, and predictive features. Neurology. 2014;83:1719–1725. doi: 10.1212/WNL.0000000000000951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Burstein R, Fleming T, Haagsma J, Salomon JA, Vos T, Murray CJ. Estimating distributions of health state severity for the global burden of disease study. Popul Health Metr. 2015;13:31. doi: 10.1186/s12963-015-0064-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (in press). [DOI] [PMC free article] [PubMed]
- 20.GBD 2016 Risk Factor 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]
- 21.Chio A, Restagno G, Brunetti M. ALS/FTD phenotype in two Sardinian families carrying both C9ORF72 and TARDBP mutations. J Neurol Neurosurg Psychiatry. 2012;83:730–733. doi: 10.1136/jnnp-2012-302219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Traynor BJ, Codd MB, Corr B, Forde C, Frost E, Hardiman O. Amyotrophic lateral sclerosis mimic syndromes: a population-based study. Arch Neurol. 2000;57:109–113. doi: 10.1001/archneur.57.1.109. [DOI] [PubMed] [Google Scholar]
- 23.Dandaba M, Couratier P, Labrunie A. Characteristics and prognosis of oldest old subjects with amyotrophic lateral sclerosis. Neuroepidemiology. 2017;49:64–73. doi: 10.1159/000479969. [DOI] [PubMed] [Google Scholar]
- 24.Verhaart IEC, Robertson A, Wilson IJ. Prevalence, incidence and carrier frequency of 5q-linked spinal muscular atrophy— a literature review. Orphanet J Rare Dis. 2017;12:124. doi: 10.1186/s13023-017-0671-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ogino S, Leonard DG, Rennert H, Ewens WJ, Wilson RB. Genetic risk assessment in carrier testing for spinal muscular atrophy. Am J Med Genet. 2002;110:301–307. doi: 10.1002/ajmg.10425. [DOI] [PubMed] [Google Scholar]
- 26.Hendrickson BC, Donohoe C, Akmaev VR. Differences in SMN1 allele frequencies among ethnic groups within North America. J Med Genet. 2009;46:641–644. doi: 10.1136/jmg.2009.066969. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Zaldivar T, Montejo Y, Acevedo AM. Evidence of reduced frequency of spinal muscular atrophy type I in the Cuban population. Neurology. 2005;65:636–638. doi: 10.1212/01.wnl.0000172860.41953.12. [DOI] [PubMed] [Google Scholar]
- 28.Lee JR, Annegers JF, Appel SH. Prognosis of amyotrophic lateral sclerosis and the effect of referral selection. J Neurol Sci. 1995;132:207–215. doi: 10.1016/0022-510x(95)00154-t. [DOI] [PubMed] [Google Scholar]
- 29.Al-Chalabi A, Hardiman O, Kiernan MC, Chio A, Rix-Brooks B, van den Berg LH. Amyotrophic lateral sclerosis: moving towards a new classification system. Lancet Neurol. 2016;15:1182–1194. doi: 10.1016/S1474-4422(16)30199-5. [DOI] [PubMed] [Google Scholar]
- 30.Shahrizaila N, Sobue G, Kuwabara S. Amyotrophic lateral sclerosis and motor neuron syndromes in Asia. J Neurol Neurosurg Psychiatry. 2016;87:821–830. doi: 10.1136/jnnp-2015-312751. [DOI] [PubMed] [Google Scholar]
- 31.Atsuta N, Watanabe H, Ito M. Age at onset influences on wide-ranged clinical features of sporadic amyotrophic lateral sclerosis. J Neurol Sci. 2009;276:163–169. doi: 10.1016/j.jns.2008.09.024. [DOI] [PubMed] [Google Scholar]
- 32.Rabkin J, Ogino M, Goetz R. Japanese and American ALS patient preferences regarding TIV (tracheostomy with invasive ventilation): a cross-national survey. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15:185–191. doi: 10.3109/21678421.2014.896928. [DOI] [PubMed] [Google Scholar]
- 33.Yamauchi R, Kawamata J, Shimohama S. Comparative study onapplication of invasive and non-invasive ventilation to ALS patients in Japan, the USA and Europe. In: Turnder BJ, Atkin JD, editors. Motor neuron diseases: causes, classification and treatments. Nova Science Publishers; New York: 2012. pp. 203–222. [Google Scholar]
- 34.Zaldivar T, Gutierrez J, Lara G, Carbonara M, Logroscino G, Hardiman O. Reduced frequency of ALS in an ethnically mixed population: a population-based mortality study. Neurology. 2009;72:1640–1645. doi: 10.1212/WNL.0b013e3181a55f7b. [DOI] [PubMed] [Google Scholar]
- 35.Chio A, Canosa A, Gallo S. ALS clinical trials: do enrolled patients accurately represent the ALS population? Neurology. 2011;77:1432–1437. doi: 10.1212/WNL.0b013e318232ab9b. [DOI] [PubMed] [Google Scholar]
- 36.Phukan J, Elamin M, Bede P. The syndrome of cognitive impairment in amyotrophic lateral sclerosis: a population-based study. J Neurol Neurosurg Psychiatry. 2012;83:102–108. doi: 10.1136/jnnp-2011-300188. [DOI] [PubMed] [Google Scholar]
- 37.Fang T, Al Khleifat A, Meurgey JH. Stage at which riluzole treatment prolongs survival in patients with amyotrophic lateral sclerosis: a retrospective analysis of data from a dose-ranging study. Lancet Neurol. 2018;17:416–422. doi: 10.1016/S1474-4422(18)30054-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Traynor BJ, Alexander M, Corr B, Frost E, Hardiman O. An outcome study of riluzole in amyotrophic lateral sclerosis— a population-based study in Ireland, 1996–2000. J Neurol. 2003;250:473–479. doi: 10.1007/s00415-003-1026-z. [DOI] [PubMed] [Google Scholar]
- 39.Zoccolella S, Beghi E, Palagano G. Analysis of survival and prognostic factors in amyotrophic lateral sclerosis: a population based study. J Neurol Neurosurg Psychiatry. 2008;79:33–37. doi: 10.1136/jnnp.2007.118018. [DOI] [PubMed] [Google Scholar]
- 40.Mendell JR, Al-Zaidy S, Shell R. Single-dose gene-replacement therapy for spinal muscular atrophy. New Engl J Med. 2017;377:1713–1722. doi: 10.1056/NEJMoa1706198. [DOI] [PubMed] [Google Scholar]
- 41.Logroscino G, Marin B, Piccininni M. Referral bias in ALS epidemiological studies. PLoS One. 2018;13:e0195821. doi: 10.1371/journal.pone.0195821. [DOI] [PMC free article] [PubMed] [Google Scholar]
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