The GBD 2016 percentage of well-certified deaths across the time series by location was assigned a 0- to 5-star rating: (5 stars [≥85%], 4 stars [65%-<85%], 3 stars [35%-<65%], 2 stars [10%-<35%], 1 star [>0%-<10%], 0 stars [0%]). Descriptive statistics report the linear fit of a time trend to the data for each location. See the age-standardized mortality rates for aggregate firearm deaths by subcause, year, and location in eTables 8-11 in the Supplement.40
See caption for Figure 1.
Profiles of firearm mortality were defined in relation to the 2016 global median value of the age-standardized rate of firearm homicide (0.99 per 100 000 persons) or firearm suicide (0.72 per 100 000 persons). High firearm homicide and suicide indicate an age-standardized rate (95% uncertainty interval [UI]) greater than the global median; high both, the estimated age-standardized rates for firearm suicide and firearm homicide (and 95% UIs) are greater than the global median; and low both, the estimated age-standardized rates for firearm suicide and firearm homicide (and 95% UIs) are lower than the global median. For 3-letter country codes, see Figures 1 through 8.
The early neonatal period is defined as 0 to 6 days, late neonatal as 7 to 27 days, and postneonatal as 28 to 364 days.
Profiles of firearm mortality were defined in relation to the 2016 global median value of the age-standardized rate of firearm homicide or firearm suicide. High firearm homicide and suicide indicate an age-standardized rate (95% uncertainty interval [UI]) greater than the global median; high both, the estimated age-standardized rates for firearm suicide and firearm homicide (and 95% UIs) are greater than the global median; and low both, the estimated age-standardized rates for firearm suicide and firearm homicide (and 95% UIs) are lower than the global median. For 3-letter country codes, see Figures 1 through 8.
Estimated firearm ownership is represented by an index that combined 2007 Small Arms Survey estimates (derived from firearm registry data), survey data, and expert estimation (see eTable 4 in the Supplement) with the estimated proportion of firearm suicides by location by rescaling each estimate of firearm ownership for a location from 0 to 100 and then averaging these values. The maximum value of this combined metric is a mean score of 100 (United States), while the minimum value is 0.3 (Japan) (eTable 5 in the Supplement). The sociodemographic index is a composite measure of income per capita, fertility, and education level. For 3-letter country codes, see Figures 1 through 8.
eAppendix. Section 1. GBD Overview
1.1 GBD Cause List
eTable 1. Firearm injury categories, legal intervention, & operations of war classification codes (ICD-10) v 2016
1.2 Analysis Locations
1.3 Analysis Time Period
1.4 GATHER Compliance Statement
1.5 GBD Access to Results
1.6 Data Input Sources
2. GBD 2016 Cause of Death Estimates
2.2 Cause of Death Data
2.2.1 Overview of Data Types: Vital Registration & ICD Detail, Verbal Autopsy, Surveys & Censuses on Fraction of Deaths From Selected Injuries, Police Records
2.2.2 Data Disaggregation
2.2.3 Age-Sex Splitting
2.2.4 Assessment & Redistribution of Insufficiently Specific or Implausible Cause of Death Codes
2.2.5 Data Quality Assessment: Causes of Death Star Rating
eTable 2. Effect of Redistribution, CODEm, & CODCorrect on Most Recent Datapoints
2.3 Cause of Death Ensemble Models (CODEm)
2.3.2 Model pool Development
2.3.3 Testing Model Pool on 15% Sample
2.3.4 Ensemble Development & Testing
2.3.5 Final Estimation
eTable 3. Covariates: Cause of Death Ensemble Models by Firearm Subcause
2.4.1 Adjustments Between Cause-Specific & All-Cause Mortality Estimates (CoDCorrect)
2.5.1 Example Data Transformation (eTable 2)
3.1 Firearm Definition
3.2 Small Arms Survey Method for Determining Firearm Access
3.3 Firearm Suicide Proportions (GBD 2016)
eTable 4. Registration Totals & Data Sources for 75 Countries With Comprehensive Independent Estimates of Civilian Firearm Ownership—Small Arms Survey 2007
3.4 Combining Small Arms Survey Data With Estimates of Firearm Suicide Proportions
eTable 5. Estimates of Firearm Ownership for 195 Countries—Proportion of Firearm Suicides From Total Suicides
3.5 Multiple Linear Regression Model
eTable 6A. Model Parameters & Model Fit for Death Rates From Aggregate Firearm Injury, Self-Harm (suicide) by Firearm, & Physical Violence (Homicide) by Firearm
eTable 6B. Small-Arms-Survey-Only Model Parameters & Model Fit for Death Rates From Aggregate Firearm Injury, Self-Harm (Suicide) by Firearm, & Physical Violence (Homicide) by Firearm
eTable 6C. Proportion of Suicide by Firearm (FS/S) Proxy, FS/S-Only Model Parameters & Model Fit for Death Rates From Aggregate Firearm Injury, Self-Harm (Suicide) by Firearm, & Physical Violence (Homicide) by Firearm
3.5.1 Sociodemographic Index
3.5.2 Illicit Drug Producing or Trafficking Countries
eTable 7. Short Form Codes Used by GBD 2016 & the Location Name
5. Supplemental Results—Tables and Figures
eFigure 1A. Global Age-Standardized Deaths per 100,000 Persons From Firearm Injury & Deaths From Conflict & War, 1990-2016
eFigure 1B. Deaths From Firearm Injury and Deaths From Conflict and War, 1990 to 2016
eFigure 2. Rates of Mortality per 100,000 Persons From Unintentional Firearm Injuries for Children (aged <15 y, 2016)
eTable 8. Age-Standardized Aggregate Firearm Injury Deaths per 100,000, Both Sexes Combined—1990, 1995, 2000, 2005, & 2010-2016
eTable 9. Age-Standardized Rate of Firearm Homicide Deaths per 100,000, Both Sexes Combined—1990, 1995, 2000, 2005, & 2010-2016
eTable 10. Age-Standardized Rate of Firearm Suicide Deaths per 100,000, Both Sexes Combined—1990, 1995, 2000, 2005, & 2010-2016
eTable 11. Age-Standardized Rate of Unintentional Firearm Deaths per 100,000, Both Sexes Combined—1990, 1995, 2000, 2005, & 2010-2016
eTable 12A. Number & Rate of Deaths per 100,000 in 1990 & 2016 by Firearm Subcause—1990-2016 for 195 Countries (Males Aged <5 y & 5-9 y)
eTable 12B. Number & Rate of Deaths per 100,000 in 1990 & 2016 by Firearm Subcause—1990-2016 for 195 Countries (Males Aged 10-14 y & 15-19 y)
eTable 13A. Number & Rate of Deaths in 1990 & 2016 by Firearm Subcause—1990-2016 for 195 Countries (Females Aged <5 y & 5-9 y)
eTable 13B. Number & Rate of Deaths in 1990 & 2016 by Firearm Subcause—1990-2016 for 195 Countries (Females Aged 10-14 y & 15-19 y)
eTable 14. Number of Deaths and Age-Standardized Rate of Deaths in 1990 and 2016, and the Annualized Rate of Change (ARC) 1990-2016 in Age-Standardized Rate as a Percent, by Firearm Subcause for 195 Countries and Territories
eTable 15. Ratio of the Rate of Gun Homicides to Gun Suicides, Proportion of Each Firearm Cause of Death by Parent Cause, & Each Firearm Cause of Death as a Proportion of all Firearm Injuries, 2016
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The Global Burden of Disease 2016 Injury Collaborators. Global Mortality From Firearms, 1990-2016. JAMA. 2018;320(8):792–814. doi:https://doi.org/10.1001/jama.2018.10060
What is the burden of firearm mortality at the global, regional, and national level between 1990 and 2016 by sex and age?
Using a combination of deidentified aggregated data from vital registration, verbal autopsy, census and survey data, and police records in models for 195 countries and territories, this study estimated 251 000 (95% uncertainty interval [UI], 195 000-276 000) people died globally from firearm injuries in 2016, compared with 209 000 (95% UI, 172 000-235 000) deaths in 1990. There was an annualized decrease of 0.9% (95% UI, 0.5%-1.3%) in the global rate of age-standardized firearm deaths from 1990 to 2016.
This study provides an estimate of the global burden of firearms deaths in 2016, change in this burden from 1990, and variation in levels and rates among countries.
Understanding global variation in firearm mortality rates could guide prevention policies and interventions.
To estimate mortality due to firearm injury deaths from 1990 to 2016 in 195 countries and territories.
Design, Setting, and Participants
This study used deidentified aggregated data including 13 812 location-years of vital registration data to generate estimates of levels and rates of death by age-sex-year-location. The proportion of suicides in which a firearm was the lethal means was combined with an estimate of per capita gun ownership in a revised proxy measure used to evaluate the relationship between availability or access to firearms and firearm injury deaths.
Firearm ownership and access.
Main Outcomes and Measures
Cause-specific deaths by age, sex, location, and year.
Worldwide, it was estimated that 251 000 (95% uncertainty interval [UI], 195 000-276 000) people died from firearm injuries in 2016, with 6 countries (Brazil, United States, Mexico, Colombia, Venezuela, and Guatemala) accounting for 50.5% (95% UI, 42.2%-54.8%) of those deaths. In 1990, there were an estimated 209 000 (95% UI, 172 000 to 235 000) deaths from firearm injuries. Globally, the majority of firearm injury deaths in 2016 were homicides (64.0% [95% UI, 54.2%-68.0%]; absolute value, 161 000 deaths [95% UI, 107 000-182 000]); additionally, 27% were firearm suicide deaths (67 500 [95% UI, 55 400-84 100]) and 9% were unintentional firearm deaths (23 000 [95% UI, 18 200-24 800]). From 1990 to 2016, there was no significant decrease in the estimated global age-standardized firearm homicide rate (−0.2% [95% UI, −0.8% to 0.2%]). Firearm suicide rates decreased globally at an annualized rate of 1.6% (95% UI, 1.1-2.0), but in 124 of 195 countries and territories included in this study, these levels were either constant or significant increases were estimated. There was an annualized decrease of 0.9% (95% UI, 0.5%-1.3%) in the global rate of age-standardized firearm deaths from 1990 to 2016. Aggregate firearm injury deaths in 2016 were highest among persons aged 20 to 24 years (for men, an estimated 34 700 deaths [95% UI, 24 900-39 700] and for women, an estimated 3580 deaths [95% UI, 2810-4210]). Estimates of the number of firearms by country were associated with higher rates of firearm suicide (P < .001; R2 = 0.21) and homicide (P < .001; R2 = 0.35).
Conclusions and Relevance
This study estimated between 195 000 and 276 000 firearm injury deaths globally in 2016, the majority of which were firearm homicides. Despite an overall decrease in rates of firearm injury death since 1990, there was variation among countries and across demographic subgroups.
The Geneva Declaration on Armed Violence and Development (2006) estimated that 90% of violent deaths occurred outside of conflict situations.1 Worldwide, firearms are frequently the lethal means in cases of homicide, suicide, and unintentional injuries, indicating an important public health problem with social and economic costs that extend beyond the immediate loss of life. Rates of firearm-related death vary between locations, and the causal elements in these global disparities are related to complex issues that differ by region and country. These variables include the illegal drug trade,2 substance abuse (including alcohol),3 inadequate support for mental health,4 the social and intergenerational transmission of firearm violence (indicates parents, family members, intimate partners, friends, and peers),5 and socioeconomic inequities6—all of which complicate efforts to generalize across settings. Access to firearms (the availability of firearms to individuals) and level of firearm ownership have been associated with firearm deaths at the population, household, and individual levels, and are associated with the strength and enforcement of laws and regulations controlling firearms.7
Comparative studies of the magnitude of firearm violence are rare but present an important opportunity to examine national, regional, and local patterns that may inform public health strategies. Although national and regional assessments of firearm deaths are available, to our knowledge, no other assessment that evaluates firearm deaths among the 195 countries and territories included in this study has occurred. The primary objective of this study was to undertake a comprehensive assessment of patterns of firearm-related mortality by cause, age, sex, and location using the consistent methods and updated database of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) and to relate these patterns to what is known about national levels of firearms availability.
The 2016 update of the GBD study incorporated additional data sources and refinements to modeling strategies that are substantially improved over previous iterations.8International Classification of Diseases, Tenth Revision (ICD-10) codes providing definitions for causes of death included in this analysis are described in eTable 1 in the Supplement. From the complete cause list developed for GBD 2016, this study presents detailed estimation for levels and rates of death for unintentional firearm deaths, self-harm (suicide) by firearm, and interpersonal violence (homicide) by firearm and summed these to estimate aggregate deaths from these causes. Deaths from conflict and terrorism (conflict hereafter) and deaths attributed to executions and police conflict included deaths from nonfirearm causes and were estimated separately (refer to eTable 1 in the Supplement for ICD-10 definitions of these categories). The level and trend in conflict deaths was provided for comparison and context to firearm homicides, firearm suicides, and unintentional firearm deaths.
The GBD study used deidentified aggregated data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (application number 46665). The cause-of-death database compiled for GBD 2016 contained 13 812 location-years of vital registration data on firearm-related homicide, firearm-related suicide, and firearm-related unintentional injury deaths. The database also included census and survey data, police records for some injuries, and verbal autopsy (an interview with persons familiar with the deceased individual in which health information and a description of events prior to death is obtained to help assign a probable cause of death).9 The GBD 2016 cause-of-death analysis was undertaken across all countries and causes for the complete time series of data available (1980-2016). However, because data were sparse in the GBD database for developing countries prior to 1990, with lower estimated completeness than for later time periods, the cause-specific results in this study were restricted to the time period of 1990-2016. Specific data sources used in the estimation of firearm-related deaths are identifiable through the GBD data tool.10
The GBD study methodology incorporated data of varying completeness and quality using consistent methods for data standardization and adjustments for incomplete data (eAppendix Section 2.2 and eTable 2 in the Supplement with additional details published elsewhere).8 Sources characterized as less than 50% complete in any given location were excluded to minimize the potential for selection bias in incomplete vital registration data. Sources were characterized as nonrepresentative if completeness was estimated to be between 50% and 70%, or they were excluded from the estimation process when greater than 50% of insufficiently specific or implausible cause-of-death codes were found to be at level 1 or level 2 of the GBD cause hierarchy (eAppendix section 1.1 in the Supplement). These completeness estimates were used to inform variance in statistical models, with lower completeness resulting in higher variance. A standardized modeling framework was used for all countries and territories and described in Section 2.3 in the Supplement. For countries with high-quality data, estimates were derived directly from those data. With decreasing data available to the model, for reasons of availability, completeness, or exclusion due to insufficiently specific or implausible cause-of-death codes, model predictions were increasingly derived from covariate data. The list of covariates used for each firearm cause are listed in eTable 3 in the Supplement.
The quality of the vital registration and verbal autopsy database, established for GBD 2016, was assessed based on representativeness of deaths from all causes, including those that were not firearm related. This was quantified using the percent well certified, which is defined as the percentage of total deaths in a country-year for which a detailed cause was known. For vital registration, percent well certified was measured by multiplying the completeness of each country-year of data by the proportion of registered deaths for which a detailed cause was known. A reported cause was considered as being detailed if it contained enough information to be mapped to a GBD level 3 cause of death (eg, ICD-10 code I64, stroke, not specified as hemorrhage or infarction, was considered to be a detailed cause of death [ie, it can be mapped to the GBD level 3 cause—stroke]; whereas, ICD-10 code X59, accidental exposure to other and unspecified factors, was not considered to be a detailed cause [because it can only be mapped to the GBD level 1 cause—injuries]). Completeness was measured by dividing the total number of registered deaths by the all-cause mortality estimates from GBD 2016 for that country-year.
To summarize country performance on this metric, a star rating system was created for GBD 2016 that assigned stars in proportion to the percentage of well-certified deaths across the time series. This system is an overall metric that is based on the completeness of death registration and the fraction of deaths assigned to specified codes, but it does not consider misspecification of deaths. These ratings provide context for assessing overall reliability of estimates for a location and were not used to directly adjust estimates. For each interval, 3 measures were multiplied: (1) completeness of cause-of-death registration; (2) the fraction of deaths that were not assigned to insufficiently specific or implausible cause-of-death codes; and (3) the fraction of deaths that were assigned to detailed GBD causes. Use of these measures produced a percent well certified by location and interval assigned by bins meant to capture a range from highest to lowest: 5 stars (percent well certified ≥85%); 4 stars (65%-<85%); 3 stars (35%-<65%); 2 stars (10%-<35%); 1 star (>0%-<10%); and 0 stars (0% well certified). More information on the calculation of this star rating system for data quality is included in the eAppendix (causes of death data star rating calculation; section 2.2.5 in the Supplement).
The percent well certified incorporates 2 possible sources of bias in GBD study estimates. The first is the completeness of vital registration. Incomplete vital registration data are unlikely to accurately reflect the population of the country it covers and may be overselective across important demographic variables. For example, one study of death registration in rural South Africa found a significant effect of both income and age on the completeness of death registration.11 The second source of bias incorporated in percent well certified was the quantity of insufficiently specific or implausible cause-of-death codes, data in which the cause of death was not directly assignable to a cause analyzed in the GBD study. A greater proportion of insufficiently specific or implausible cause-of-death codes required greater redistribution of these codes to GBD causes, which made the results for those locations more sensitive to the redistribution algorithms. Separate cause-of-death ensemble models, an estimation approach in which a large number of model specifications are systematically tested and models performing best on out-of-sample predictive validity tests are incorporated into a weighted ensemble model, were developed for each of the 3 causes of death by firearm included in GBD 2016. Covariates for these models (eTable 3 in the Supplement) and additional details of model testing and construction are provided in eAppendix section 2.3 in the Supplement. Uncertainty bounds were estimated using 1000 draws from the posterior distribution of cause-specific mortality for each age-sex-year-location and are represented as 95% uncertainty intervals (UIs). These values were considered statistically significant if the UI did not include zero. In general, in countries and territories with high-quality data (ie, vital registration data that were extensive and complete), uncertainty was largely driven by sample sizes; whereas in locations with lower-quality data, the sparsity of data, strength of the covariates used in modeling, or the extent of insufficiently specific or implausible cause-of-death codes contributed to greater estimated uncertainty. Analyses were completed using Python version 2.7.3 and R version 3.2.2. The development and documentation of the GBD 2016 study follows the GATHER statement (Guidelines for Accurate and Transparent Health Estimates Reporting).12
Determining whether rates of firearm injury death are increasing or decreasing for a given location is complicated by the potential for nonlinearity in time trends, particularly as longer time periods are evaluated. To address this challenge, the figures include sparklines (small, graphic representations of trends without coordinates or axes) and summary statistics describing the linear fit to the data for each country and firearm subcause. Estimates of rates of change over the entire data series associated with poor linear fits and visual evidence of nonlinearity in data should be evaluated cautiously.
Although public attention is frequently focused on firearm homicide, firearm suicides represent the greater fraction of firearm mortality in some locations. These differences in the relative proportion of firearm homicide and firearm suicide may be useful in directing intervention policies or programs. To identify countries with similar profiles of firearm injury deaths, the global median age-standardized mortality rate of firearm homicide and firearm suicide estimated by this study in 2016 was used to establish quadrants defined by the relationship between these 2 rates.
Access to firearms is a necessary precondition for firearm injury to occur; however, the strength of the relationship between access to firearms and variation in levels of firearm violence has not been previously evaluated at the level used in this study. Assessing the relationship between firearm deaths and availability of firearms is challenging, in part because data on the total number and distribution of legal and illegal firearms within civilian populations are limited. Two measures have been extensively used to analyze the relationship between firearm access level of firearm violence. Each measure has distinct advantages and disadvantages.
The first measure uses estimates from the Small Arms Survey, which was last updated in 2007.13 Although estimates from the Small Arms Survey provide the most comprehensive set of firearm registry data (75 countries; eTable 4 in the Supplement), estimates for other locations rely on interpolation from global regressions or independent expert estimation (eAppendix section 3.2 in the Supplement).13
The second approach uses a proxy measure based on the proportion of suicide for which a firearm was the lethal means (the proportion of firearm suicides from total suicides) (estimating firearm access or ownership; eAppendix section 3.3 in the Supplement).14 Although the proportion of suicides due to firearms includes the most recent data available for each location, it does not account for cultural variability in factors connecting firearm access to use as a means of suicide, and it has been validated mostly for Western societies.
To capture the advantages of each measure while addressing some of their separate limitations, a new proxy measure was created by transforming each of the prior options on a scale from 0 to 100 and then averaging both measures (eAppendix Section 3.4 in the Supplement). The maximum value of this combined metric was a mean score of 100 for the United States, while the minimum value was calculated for Japan with a score of 0.3 (eTable 5 in the Supplement). This combined metric was used as a proxy for per capita access to firearms to evaluate the relationship between availability of firearms and deaths from firearm homicide (eTable 6A in the Supplement). To avoid the circularity inherent in using a proxy measure that contains total firearm suicides to evaluate the relationship to total firearm suicide, only the Small Arms Survey data were used to assess the relationship between firearm access and firearm suicide levels (eTable 6B in the Supplement).
Additionally, GBD 2016 developed and refined a sociodemographic index (SDI) as a means for comparing health progress between countries. The SDI score is a composite of the geometric mean of 3 components (lag-dependent income per capita, total fertility rate for the population, and the mean educational attainment in the population older than 15 years of age) and is subsequently rescaled between 0 and 1 (eAppendix section 3.5.1 in the Supplement). An SDI value was calculated for each country-year. For the year 2016, the highest SDI value was estimated for Luxembourg (0.93) and the lowest value was estimated for South Sudan (0.19).8 This study uses SDI values for the year 2016 to evaluate the contribution of the combined role of income, mean fertility, and education to differences in firearm violence between countries.
This study incorporated 2861 sources of data on firearm mortality between 1980 and 2016, with a median of 9 data sources per country. Including sources of data on overall levels of homicide, suicide, or unintentional injuries, all but 20 countries were represented by at least 1 data source between 1980 and 2016, in part because 116 countries had police data on overall levels of homicide. Overall, 21.7% of the data sources on firearm mortality were from 1980-1989, 26.5% were from 1990-1999, 34.5% were from 2000-2009, and 17.3% were from 2010-2016. When considering the quality of the database for all causes of death, a total of 25 countries (12.8%) were given 5-star rating (based on the percent of data well certified; see Methods), 48 (24.6%) were given a 4-star rating, 30 (15.4%) were given a 3-star rating, 21 (10.7%) were given a 2-star rating, 44 (22.5%) were given a 1-star rating, and 27 countries (13.8%) were given a rating of 0 stars (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, and Figure 8).
In 2016, there were an estimated 251 000 (95% UI, 195 000-276 000) firearm injury deaths worldwide; a global age-standardized rate of 3.4 deaths (95% UI, 2.6-3.7) per 100 000 persons (Figure 1). In 1990, there were an estimated 209 000 (95% UI, 172 000 to 235 000) firearm injury deaths. Globally, the number of firearm injury deaths were greater than those from conflict in almost every year between 1990 and 2016 (eFigure 1B in the Supplement), with the maximum difference occurring in 2001 when firearm injury deaths were estimated at 243 000 (95% UI, 188 000-263 000) and conflict deaths were estimated at 38 000 (95% UI, 27 300-49 500). The exception occurred in 1994 when deaths from the genocide in Rwanda contributed to a global conflict death total (551 000 deaths [95% UI, 222 000-874 000]) that exceeded those from firearm injuries (232 000 deaths [95% UI, 186 000-259 000]). Among the countries reporting the most firearm injury deaths in 2016, 50.5% [95% UI, 42.2%-54.8%] of deaths occurred in countries that in combination held less than 10% of the global population15 in that year (data are reported alphabetically by country or territory; Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8): Brazil (43 200 deaths [95% UI, 24 800-50 400]), the United States (37 200 deaths [95% UI, 29 000-41 200]), Mexico (15 400 deaths [95% UI, 8680-18 900]), Colombia (13 300 deaths [95% UI, 9420-16 300]), Venezuela (12 800 deaths [95% UI, 7220-18 300]), and Guatemala (5090 deaths [95% UI, 2650-7250]). A plurality of these deaths occurred in Brazil and the United States (32.0% [95% UI, 27.4%-34.6%]). Nationally, age-standardized rates of firearm injury deaths in 2016 ranged from a low of 0.1 deaths (95% UI, 0.1-0.2) per 100 000 persons in Singapore to 39.2 deaths (95% UI, 27.5-47.4) per 100 000 persons in El Salvador.
Globally, the annualized rate of decrease of 0.9% (95% UI, 0.5%-1.3%) in the rate of aggregate firearm injury deaths reflected variability between locations over time and by firearm subcause (Figure 9). Several countries with high age-standardized rates of firearm injury deaths in 1990 were also among the locations with large annualized rates of decrease between 1990 and 2016. These locations included Greenland, which had the highest age-standardized rate of firearm injury deaths in 1990 and an estimated annualized decrease in those rates of 3.2% (95% UI, 1.5%-4.6%) between 1990 and 2016, and Colombia (ranked second globally in age-standardized rate of firearm injury deaths in 1990), where age-standardized rates decreased by 3.0% (95% UI, 2.3%-3.6%) annually over the same time period. Aggregate firearm injury death rates decreased between 1990 and 2016 in most countries; however, rates increased in 41 countries, of which 3 were significant changes (20 of these increases were in the GBD super region of Latin America and the Caribbean [data are reported alphabetically by country or territory]; Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8). In Latin America and the Caribbean, there was no significant change in some locations with high age-standardized rates of firearm injury deaths in 1990, including Honduras (mean change, −0.2% [95% UI, −2.0% to 1.6%]; Figure 4), El Salvador (mean change, −0.5% [95% UI, −1.6% to 0.4%]; Figure 3), and Guatemala (mean change, 1.7% [95% UI, −1.0% to 4.2%]; Figure 4). Previous shifts in the aggregate firearm mortality rate in these countries were not reflected by the mean rate of change from 1990 to 2016, evident in the poor linear fit of the trend data (R2, 0.00 for Honduras; R2, 0.23 for El Salvador; and R2, 0.70 for Guatemala) and visual inspection of sparklines (Figure 3 and Figure 4).
Globally, aggregate firearm injury deaths were higher for men than for women in each 5-year age bracket in 2016 (Figure 10), with most of these firearm injury deaths occurring for both sexes among 20- to 24-year-olds (estimated deaths among men, 34 700 [95% UI, 24 900-39 700]; indicating an age-specific mortality rate of 11.2 deaths [95% UI, 8.1-12.8] per 100 000 persons) and (estimated deaths among women, 3580 [95% UI, 2810-4210]); indicating an age-specific mortality rate of 1.2 deaths [95% UI, 1.0-1.4] per 100 000 persons). The relative proportions of firearm injury deaths by subcause varied with age and sex (Figure 10). Globally, among children aged 0 to 14 years, there were an estimated 7220 deaths (95% UI, 5690-8200) from a firearm-related injury in 2016, a rate of 0.4 deaths (95% UI, 0.3-0.4) per 100 000 persons, and there were 2.4 times more firearm deaths for boys than girls in this age group (eTable 12 and eTable 13 in the Supplement). As a component of deaths for children aged 0 to 14 years, firearm injury deaths constituted more than 1% of child deaths from all causes in 12 countries. The highest such fractions were in Greenland (2.6% [95% UI, 1.4%-4.1%]) and El Salvador (3.4% [95% UI, 2.2%-4.9%]) (eFigure 2 in the Supplement).
In 2016, unintentional firearm injuries represented a small fraction of all firearm injuries (an estimated 9.1% [95% UI, 7.7%-11.7%]; global absolute value, 22 900 deaths [95% UI, 18 200-24 800]; Figure 1) but with variability in relative contribution of these deaths at the national level (eTable 14 in the Supplement). In contrast, suicide by firearm resulted in an estimated 67 500 deaths (95% UI, 55 400-84 100) worldwide in 2016, with a global age-standardized rate of suicide by firearm of 0.9 deaths (95% UI, 0.8-1.1) per 100 000 persons (eTable 14 in the Supplement). Age-standardized rates for firearm suicides were highest in Greenland at 22.0 deaths (95% UI, 15.9-32.6) per 100 000 persons (absolute value, 11 deaths [95% UI, 8-16]) in 2016, and in the United States at a rate of 6.4 deaths (95% UI, 5.0-7.5) per 100 000 persons (absolute value, 23 800 deaths [95% UI, 18 500-27 900]) in 2016 (eTable 14 in the Supplement) and lowest in Singapore at a rate of 0.1 deaths (95% UI, 0.0-0.1) per 100 000 persons. In 2016, firearm suicides in the United States represented 35.3% (95% UI, 29.1%-40.3%) of global firearm suicides; in that year, 4.3% of the global population was in the United States.15 Globally, rates of firearm suicide decreased between 1990 and 2016 at an annualized rate of 1.6% (95% UI, 1.1%-2.0%) with the fastest decreases in the Philippines (6.0% [95% UI, 0.6%-8.3%]) and Australia (5.2% [95% UI, 2.2%-6.2%]). However at a national scale, statistically significant decreases were estimated in fewer than half (71 of 195) of countries and territories in this study. The highest statistically significant annualized increase in firearm suicide rate between 1990 and 2016 was estimated for Jamaica (4.5% [95% UI, 0.4%-6.6%]). Uncertainty intervals for a number of other large increases included zero, such as the annualized rate of change estimated for Zimbabwe (2.2% [95% UI, −0.3% to 5.1%]) and Bosnia and Herzegovina (2.0% [95% UI, −3.6% to 4.8%]) (eTable 14 in the Supplement), where nonlinearities in trends over the period 1990 to 2016 were also evident.
Globally, the majority of firearm injury deaths were homicides (an estimated 64.0% [95% UI, 54.2%-68.0%]; absolute value, 161 000 deaths [95% UI, 107 000-182 000]) (eTable 14 in the Supplement), and firearms were the lethal means in more than 50% of all homicides in 49 of 195 countries in 2016 (eTable 15 in the Supplement). In 2016, the highest national age-standardized rate of death from physical violence by firearm occurred in El Salvador (38.9 deaths [95% UI, 27.2-47.1] per 100 000 persons; eTable 14 in the Supplement), and the lowest firearm homicide rate in 2016 was estimated for Singapore (0.0 deaths [95% UI, 0.0-0.1] per 100 000 persons; eTable 14 in the Supplement). Over the period 1990 to 2016, there was no statistically significant annualized change in the global age-standardized firearm homicide rate (−0.2% [95% UI, −0.8% to 0.2%]); this mean change across the full time series encompasses previous increases in the global firearm homicide rate (Figure 1). Underlying the global mean, considerable heterogeneity was estimated at the national level. The largest annualized increase was estimated in Zimbabwe (4.2% [95% UI, 1.7%-6.9%]) (eTable 14 in the Supplement). Uncertainty intervals for a number of other large increases included zero, such as the annualized rate of change estimated in Botswana (5.8% [95% UI, −1.5% to 10.1%]) and Sudan (4.9% [95% UI, −0.9% to 7.4%]), where both nonlinearity in trend data and data completeness were complicating factors. The largest annualized decreases in firearm homicide rates over this time period were estimated in Estonia (6.2% [95% UI, 1.5%-8.6%]) and Taiwan (5.9% [95% UI, 1.8%-8.1%]).
There were 17 countries where rates of both firearm homicide and firearm suicide were estimated to be greater than both median values for these rates (Figure 11) including the United States (firearm homicide rate, 4.0 deaths [95% UI, 2.1-4.8] per 100 000 persons; firearm suicide rate, 6.4 deaths [95% UI, 5.0-7.5] per 100 000 persons), Uruguay (firearm homicide rate, 2.9 deaths [95% UI, 1.2-4.0] per 100 000 persons; firearm suicide rate, 4.2 deaths [95% UI, 3.0-5.5] per 100 000 persons), and Argentina (firearm homicide rate, 3.3 deaths [95% UI, 2.0-4.9] per 100 000 persons; firearm suicide rate, 2.7 deaths [95% UI, 2.1-3.8] per 100 000 persons). Rates of both firearm suicide and firearm homicide were significantly less than the median rate in 29 countries including Singapore (firearm homicide rate, 0.0 deaths [95% UI, 0.0-0.1] per 100 000 persons; firearm suicide rate, 0.1 deaths [95% UI, 0.0-0.1] per 100 000 persons), Japan (firearm homicide rate, 0.0 deaths [95% UI, 0.0-0.1] per 100 000 persons; firearm suicide rate, 0.1 deaths [95% UI, 0.1-0.1] per 100 000 persons), and China (firearm homicide rate, 0.1 deaths [95% UI, 0.0-0.1] per 100 000 persons; firearm suicide rate, 0.1 deaths [95% UI, 0.1-0.1] per 100 000 persons). More broadly, deaths from suicide by firearm were the largest fraction of all firearm injuries in 67 of 195 countries in 2016 (eTable 15 in the Supplement); most of these countries were in the GBD regions of Western Europe, high-income North America, Australasia, and Eastern Europe. Homicides were estimated as the dominant fraction of all firearm injuries in 113 countries in 2016.
Evaluated against a combined proxy measure of firearm access, rates of firearm injury death were estimated to be larger where the firearm access proxy was also large (Figure 12)—a relationship exemplified by locations such as the United States (firearm access index, 100; 10.6 deaths [95% UI, 8.3-11.7] per 100 000 persons) and Venezuela (firearm access index, 40.8; 38.7 deaths [95% UI, 21.9-54.9] per 100 000 persons), where estimated access to firearms was high compared with other countries. The inclusion of countries identified by the US State Department as major illicit drug-producing or drug-transporting countries16 as a factor reduced unexplained variation in a multiple linear regression and was positively associated with firearm homicides (P < .001; R2 = 0.35) (eAppendix section 3.5 and eTable 6A in the Supplement) but not firearm suicides (P = .41; R2 = 0.21) (eTable 6B in the Supplement). Similarly, the inclusion of each location’s SDI level (a composite measure of years of education, per capita income, and fertility rate) improved model fit and was negatively associated with firearm homicides (P < .001; R2 = 0.35) (eTable 6A in the Supplement) but not firearm suicides (P < .18; R2 = 0.21) (eTable 6B in the Supplement).
This modeling study, which used a combination of deidentified aggregated data from vital registration, verbal autopsy, census and survey, and police records estimated the global burden of firearm deaths in 2016 (251 000), the majority of which were firearm homicides (161 000; 64%). Despite an overall decrease in rates of firearm injury deaths since 1990, there was variation among countries and across demographic subgroups.
As with many components of health, illness, and injury, the burden of mortality from firearms is not distributed symmetrically between the sexes or by age. Males are at higher risk of unintentional death while playing with firearms at a young age, of being involved in homicide involving firearms during adolescence and young adulthood, and of the greater use of firearms as a means in suicide throughout adulthood.17 Although men are most often the targets of firearm violence, they are also the most likely perpetrators, often in the context of domestic and relationship violence.18 The gendered nature of firearm violence across causes highlights the need for targeted forms of intervention that address cultural components of firearm use by and against men.
Comparisons of levels and trends in firearm injury deaths are complicated by differences in the factors underlying firearm violence, hindering efforts to find relationships between countries that could suggest effective public health responses. Nonetheless, where firearm mortality dominantly occurs as interpersonal violence, different intervention strategies will likely be necessary in contrast to countries where most firearm mortality occurs as firearm suicide. Identifying countries with similar profiles of firearm violence can provide opportunities to examine how risk factors, histories, cultures, economies, or legal frameworks may have produced similar outcomes.
Although public perception is frequently focused on the use of firearms in homicides, particularly mass shootings,19 suicides involving firearms greatly outnumber firearm homicides in many countries. Among these countries, the presence of firearms in the home has been directly linked to their greater use as a means of suicide,20 as well as to increases in unintentional firearm injury deaths.21 Readily available firearms facilitate unplanned suicide attempts17 and increase the probability of an injury being lethal. Self-directed attempts at harm are more frequently fatal than other firearm-involved violence, resulting in death for as much as 91% of attempts for suicide by firearm, 19% for physical violence by firearm, and 5% for unintentional firearm injuries22—and greater than other methods commonly used in suicide attempts.23 Efforts to reduce the number of firearms in homes and supporting secure storage of existing firearms can reduce unintentional death, particularly for children,24 while limiting immediate access to a means of harm that generally does not allow opportunity for second thoughts.
Access to firearms is not the sole factor determining means of suicide, and some component of the relationship between firearm availability and firearm suicide may be a reflection of regional and local variation in the cultural acceptability of suicide by different methods and for each sex,25 as well as the availability of those means. The low availability of firearms or low access to firearms by civilian populations and strong regulatory frameworks,26 together with differing cultural norms around suicide, are all possible explanations for this pattern. Understanding of the interaction between culture and opportunity can provide critical context for preventive strategies involving means restriction in the case of firearm suicide as well as firearm homicide.
High levels of firearm homicide in a belt extending from Mexico to Brazil (and including the Caribbean) have been associated with drug cartels,27 the manufacture and sale of firearms and their illegal trade from the United States,28 and with postconflict movement of firearms into civilian populations in some countries.29 The stock of legal firearms in many of these countries is comparatively small. A recent survey of gun ownership in Mexico identified only 3% of urban households reporting firearm ownership, and the majority (80%) of these reported owning just 1 firearm.30 Difficulties with accounting for illegal firearm ownership31 and the effect of trafficking in firearms on rates of violence in countries with otherwise strong firearm control legislation, may explain some of the variability found in the relationship between firearm availability and associated mortality, particularly for firearm homicide. At the same time, the availability of firearms and the role played by illicit trade are only one dimension of the complex problem of firearm-related violence in the region; multiple structural factors have also been identified as contributors including poverty, social inequalities or rapid social change, alcohol and drug use, and young population age structure.6,29 Violence at the intersection of these cultural factors, together with a high general availability of firearms, combine to produce high rates of mortality through the lethality inherent in the use of firearms.
Both suicide and homicide are defined as intentional behaviors, and thus it should be possible to develop strategies to reduce these forms of violence. A recent review of firearm legislation in the United States by Lee and colleagues32 highlights the association of laws that strengthen background checks or that require a permit to purchase a firearm with reductions in firearm homicide rates, and this general principle is supported by more stringent programs to restrict the use of firearms by civilian populations in other settings.33 The Australian National Firearms Agreement, enacted following the 1996 Port Arthur massacre, has been closely linked with declines in firearm deaths in Australia, particularly firearm suicides and an absence of mass shootings.34 The patterns documented in South Africa and Brazil also support a link between regulatory restrictions on firearm access and subsequent reductions in firearm death rates. In South Africa, rates of violent death decreased following the Firearm Control Act of 200035; it is estimated that this legislation may have prevented more than 4500 deaths across 5 South African cities between 2001 and 2005.36 In Brazil, where much of the stock of civilian firearms are likely unregistered, large decreases in firearm homicides (since the year 2000 in the state of São Paulo) and firearm suicides in the state of Rio de Janeiro have been linked to more effective policing, firearm buy-back programs, and enforcement of firearm control legislation passed in 2003.37 The hypothesis that differences in levels of violence between countries reflect the availability of firearms and the extent of firearm control at a national level is consistent with these findings, although more disaggregated data are needed to transfer the analysis to other settings.
This study has several limitations. Evaluating the burden of firearm deaths on a global scale was constrained by the limitations that apply to the GBD 2016 study generally8 and also by limitations specific to evaluating firearm-related deaths. First, as in prior GBD studies, the accuracy of the estimates depends on the availability of data for each age-sex-year-location. Due to delays in data reporting, estimates for more recent years rely on additional data and trends from prior years. For many countries, data sources are sparse, and the GBD estimates rely heavily on covariate selection in the models and also borrow strength from regional patterns. Second, the GBD study attempts to evaluate and adjust for variation in data quality and completeness using data-standardization techniques, but the resulting estimates are inherently subject to bias from incomplete data sampling in some locations (eAppendix section 2.2 and eTable 2 in the Supplement). Many countries, particularly locations in sub-Saharan Africa, southeast Asia, and the region of North Africa and the Middle East, have limited vital registration data, and the quality of the available data were not evenly distributed. Estimates for countries with low star ratings (as described in Methods) have uncertain validity because using combinations of available sources and estimates for missing data results in a more comprehensive set of estimates but also limits precision and accuracy. The star system is a general assessment of the quality of vital registration and does not necessarily indicate that the quality of the cause of death by firearm is high. Third, due to the complexities involved in the statistical modeling, as well as computational limitations, the incorporation of uncertainty from the redistribution of insufficiently specific or implausible cause-of-death codes has not yet been possible in the GBD 2016. Fourth, method-specific suicide and homicide data were reported by only a subset of countries, and estimates of firearm suicide and homicide were thus based on fewer data than estimates for all suicides and homicides; therefore, underestimation may have occurred in data-poor regions. Fifth, in many locations, the incidence of firearm injury was very low, requiring statistical smoothing to ensure zeroes in vital registration data were incorporated into estimates. Sixth, the study assumes that where a specific cause of death is listed on the death certificate, that death was coded and diagnosed correctly. GBD 2016 does not correct for any systematic bias in coding firearm injury deaths, with the exception of a correction for misdiagnosis of injury deaths in South Africa vital registration data, where a detailed study was available to guide the correction (eAppendix section 2.2.4 in the Supplement). Seventh, unintentional firearm injury deaths, for children in particular, may be misclassified38; in other instances, homicides and suicides may be misclassified as unintentional firearm injury deaths, for cases in which there are strong cultural pressures to mitigate legal implications of homicide or to avoid stigma associated with suicide,39 particularly when intent is not clear. Eighth, the development of a combination proxy measure, which introduced additional uncertainty, was necessary because of a lack of direct measures of firearm ownership in most countries and also because of limitations associated with existing estimates or proxies. Ninth, beyond the scarcity of reliable data on firearm ownership, the role of social or cultural factors that determine willingness to use firearms for suicide, and the extent to which homicide rates may be driven by illegal firearms and not legal ownership, are probable contributors to global variation in firearm-related mortality that were not directly incorporated into these estimates. Tenth, nonlinearities in the trend of data are frequently present, particularly as longer time series of data are analyzed. Annualized rates of change, as reported in the present study, are a simple summary measure that facilitate comparisons across the large number of countries in GBD 2016. Interpreting annual rate of change represents a mean rate of change through time and may provide a misleading presentation of uniform change. This study also reports the annualized rate of change for the most recent decade of data (2006 to 2016) in addition to the rate of change over the entire time series from 1990 to 2016, the linear fit to those data, and annual estimates of levels and rates by location in comprehensive data tables and an online results tool.
This study estimated between 195 000 and 276 000 firearm injury deaths globally in 2016, the majority of which were firearm homicides. Despite an overall decrease in rates of firearm injury death since 1990, there was variation among countries and across demographic subgroups.
Correction: This article was corrected on August 28, 2018, because of incorrect values in the following sections: Levels and Trends in Aggregate Firearm Injury Deaths; and Patterns by Age and Sex.
Accepted for Publication: June 23, 2018.
Corresponding Author: Mohsen Naghavi, MD, PhD, Global Health Department, Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave, Ste 600, Seattle, WA 98121 (email@example.com).
The Global Burden of Disease 2016 Injury Collaborators: Mohsen Naghavi, PhD; Laurie B. Marczak, PhD; Michael Kutz, BS; Katya Anne Shackelford, BA; Megha Arora, BSA; Molly Miller-Petrie, MSc; Miloud Taki Eddine Aichour, MA; Nadia Akseer, PhD; Rajaa M. Al-Raddadi, PhD; Khurshid Alam, PhD; Suliman A. Alghnam, PhD; Carl Abelardo T. Antonio, MD; Olatunde Aremu, PhD; Amit Arora, PhD; Mohsen Asadi-Lari, PhD; Reza Assadi, PhD; Tesfay Mehari Atey, MS; Leticia Avila-Burgos, PhD; Ashish Awasthi, PhD; Beatriz Paulina Ayala Quintanilla, PhD; Suzanne Lyn Barker-Collo, PhD; Till Winfried Bärnighausen, MD; Shahrzad Bazargan-Hejazi, BEP; Masoud Behzadifar, PhD; Meysam Behzadifar, MS; James R. Bennett, BA; Ashish Bhalla, MD; Zulfiqar A. Bhutta, PhD; Arebu Issa Bilal, MSc; Guilherme Borges, DSc; Rohan Borschmann, PhD; Alexandra Brazinova, MD; Julio Cesar Campuzano Rincon, PhD; Félix Carvalho, PhD; Carlos A. Castañeda-Orjuela, MS; Lalit Dandona, MD; Rakhi Dandona, PhD; Paul I. Dargan, MB; Diego De Leo, DSc; Samath Dhamminda Dharmaratne, MD; Eric L. Ding, DSc; Huyen Phuc Do, PhD; David Teye Doku, PhD; Kerrie E. Doyle, PhD; Tim Robert Driscoll, PhD; Dumessa Edessa, MS; Ziad El-Khatib, PhD; Aman Yesuf Endries, MPH; Alireza Esteghamati, MD; Andre Faro, PhD; Farshad Farzadfar, MD; Valery L. Feigin, PhD; Florian Fischer, PhD; Kyle J. Foreman, PhD; Richard Charles Franklin, PhD; Nancy Fullman, MPH; Neal D. Futran, MD; Tsegaye Tewelde Gebrehiwot, MPH; Reyna Alma Gutiérrez, PhD; Nima Hafezi-Nejad, MD; Hassan Haghparast Bidgoli, PhD; Gessessew Bugssa Hailu, MS; Josep Maria Haro, MD; Hamid Yimam Hassen, MPH; Caitlin Hawley, MSPH; Delia Hendrie, PhD; Martha Híjar, PhD; Guoqing Hu, PhD; Olayinka Stephen Ilesanmi, PhD; Mihajlo Jakovljevic, PhD; Spencer L. James, MD; Sudha Jayaraman, MD; Jost B. Jonas, MD; Amaha Kahsay, MPH; Amir Kasaeian, PhD; Peter Njenga Keiyoro, PhD; Yousef Khader, PhD; Ibrahim A. Khalil, MD; Young-Ho Khang, MD; Jagdish Khubchandani, PhD; Aliasghar Ahmad Kiadaliri, PhD; Christian Kieling, MD; Yun Jin Kim, PhD; Soewarta Kosen, MD; Kristopher J. Krohn, MPH; G. Anil Kumar, PhD; Faris Hasan Lami, PhD; Van C. Lansingh, PhD; Heidi Jane Larson, PhD; Shai Linn, DrPH; Raimundas Lunevicius, PhD; Hassan Magdy Abd El Razek, MD; Muhammed Magdy Abd El Razek, MB; Reza Malekzadeh, MD; Deborah Carvalho Malta, PhD; Amanda J. Mason-Jones, PhD; Richard Matzopoulos, PhD; Peter T. N. Memiah, DrPH; Walter Mendoza, MD; Tuomo J. Meretoja, MD; Haftay Berhane Mezgebe, MS; Ted R. Miller, PhDr; Shafiu Mohammed, PhD; Maziar Moradi-Lakeh, MD; Rintaro Mori, PhD; Devina Nand, MPH; Cuong Tat Nguyen, MPH; Quyen Le Nguyen, MD; Dina Nur Anggraini Ningrum, MPH; Felix Akpojene Ogbo, PhD; Andrew T. Olagunju, MD; George C. Patton, MD; Michael R. Phillips, MD; Suzanne Polinder, MA; Farshad Pourmalek, PhD; Mostafa Qorbani, PhD; Afarin Rahimi-Movaghar, MD; Vafa Rahimi-Movaghar, MD; Mahfuzar Rahman, PhD; Rajesh Kumar Rai, MPH; Chhabi Lal Ranabhat, PhD; David Laith Rawaf, MD; Salman Rawaf, PhD; Ali Rowhani-Rahbar, PhD; Mahdi Safdarian, MD; Saeid Safiri, PhD; Rajesh Sagar, MD; Joseph S. Salama, MS; Juan Sanabria, MD; Milena M. Santric Milicevic, PhD; Rodrigo Sarmiento-Suárez, MPH; Benn Sartorius, PhD; Maheswar Satpathy, PhD; David C. Schwebel, PhD; Soraya Seedat, MD; Sadaf G. Sepanlou, MD; Masood Ali Shaikh, MD; Nigussie Tadesse Sharew, MS; Ivy Shiue, PhD; Jasvinder A. Singh, MD; Mekonnen Sisay, MS; Vegard Skirbekk, PhD; Adauto Martins Soares Filho, DSc; Dan J. Stein, MD; Mark Andrew Stokes, PhD; Mu’awiyyah Babale Sufiyan, MD; Mamta Swaroop, MD; Bryan L. Sykes, PhD; Rafael Tabarés-Seisdedos, PhD; Fentaw Tadese, MPH; Bach Xuan Tran, PhD; Tung Thanh Tran, BMedSc; Kingsley Nnanna Ukwaja, MS; Tommi Juhani Vasankari, MD; Vasily Vlassov, MD; Andrea Werdecker, PhD; Pengpeng Ye, MPH; Paul Yip, PhD; Naohiro Yonemoto, MPH; Mustafa Z. Younis, DrPH; Zoubida Zaidi, DrPH; Maysaa El Sayed Zaki, PhD; Simon I. Hay, DSc; Stephen S. Lim, PhD; Alan D. Lopez, PhD; Ali H. Mokdad, PhD; Theo Vos, PhD; Christopher J. L. Murray, DPhil.
Affiliations of The Global Burden of Disease 2016 Injury Collaborators: Institute for Health Metrics and Evaluation, University of Washington, Seattle (Naghavi, Marczak, Kutz, Shackelford, M. Arora, Miller-Petrie, Bennett, L. Dandona, R. Dandona, Dharmaratne, Feigin, Foreman, Fullman, Hawley, James, Khalil, Krohn, Larson, Salama, Hay, Lim, Lopez, Mokdad, Vos, Murray); Higher National School of Veterinary Medicine, Algiers, Algeria (Aichour); Centre for Global Child Health, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada (Akseer, Bhutta); Department of Family and Community Medicine, King Abdulaziz University, Jeddah, Saudi Arabia (Al-Raddadi); School of Population and Global Health, University of Western Australia, Perth, WA, Australia (Alam); Department of Population Health, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia (Alghnam); Department of Health Policy and Administration, University of the Philippines Manila, Manila, Philippines (Antonio); Department of Public and Community Health, Birmingham City University, Birmingham, England (Aremu); School of Science and Health, Western Sydney University, Penrith, NSW, Australia (A. Arora); Oral Health Services, Sydney Local Health District, Sydney, Australia (A. Arora); Department of Epidemiology and Biostatistics, Iran University of Medical Sciences, Tehran, Iran (Asadi-Lari); International Relations Department, Iranian Ministry of Health and Medical Education, Tehran, Iran (Asadi-Lari); Education Development Center, Mashhad University of Medical Sciences, Mashhad, Iran (Assadi); Department of Pharmacy, Mekelle University, Mekelle, Ethiopia (Atey); Center of Health System Research, The National Institute of Public Health, Cuernavaca, Mexico (Avila-Burgos); Indian Institute of Public Health, Gandhinagar, India (Awasthi); Public Health Foundation of India, Gurugram, India (Awasthi, L. Dandona, R. Dandona, Kumar); The Judith Lumley Centre, La Trobe University, Melbourne, VIC, Australia (Ayala Quintanilla); General Office for Research and Technological Transfer, Peruvian National Institute of Health, Lima, Peru (Ayala Quintanilla); School of Psychology, University of Auckland, Auckland, New Zealand (Barker-Collo); Institute of Public Health, Heidelberg University, Heidelberg, Germany (Bärnighausen, Mohammed); Department of Global Health and Population, Harvard University, Boston, Massachusetts (Bärnighausen); Department of Psychiatry, Charles R. Drew University of Medicine and Science, Los Angeles, California (Bazargan-Hejazi); Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles (Bazargan-Hejazi); Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorranmabad, Iran (M. Behzadifar); Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorranmabad, Iran (M. Behzadifar); Department of Internal Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India (Bhalla); Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan (Bhutta); School of Pharmacy, Addis Ababa University, Addis Ababa, Ethiopia (Bilal); Department of Epidemiology, National Institute of Psychiatry Ramón de la Fuente, Mexico City, Mexico (Borges); Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Melbourne, VIC, Australia (Borschmann); Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia (Borschmann); Institute of Epidemiology, Comenius University, Bratislava, Slovakia (Brazinova); National Institute of Health, Cuernavaca, Mexico (Campuzano Rincon); Escuela de Medicina, Universidad Valle de Cuernavaca, Cuernavaca, Mexico (Campuzano Rincon); Ucibio, Requimte, Faculty of Pharmacy, University of Porto, Porto, Portugal (Carvalho); Colombian National Health Observatory, Instituto Nacional de Salud, Bogota, Colombia (Castañeda-Orjuela); Epidemiology and Public Health Evaluation Group, Universidad Nacional de Colombia, Bogota, Colombia (Castañeda-Orjuela); Department of Clinical Toxicology, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom (Dargan); Department of Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom (Dargan); Australian Institute for Suicide Research and Prevention, Griffith University, Mt Gravatt, QLD, Australia (De Leo); Department of Community Medicine, University of Peradeniya, Peradeniya, Sri Lanka (Dharmaratne); Harvard School of Public Health, Department of Nutrition, Harvard University, Boston, Massachusetts (Ding); Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam (Phuc Do, Tat Nguyen, Le Nguyen, Thanh Tran); Department of Population and Health, University of Cape Coast, Cape Coast, Ghana (Doku); Faculty of Social Sciences, University of Tampere, Tampere, Finland (Doku); School of Health and Biomedical Sciences, Royal Melbourne Institute of Technology University (RMIT), Bundoora, VIC, Australia (Doyle); Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia (Driscoll); School of Pharmacy, Haramaya University, Harar, Ethiopia (Edessa); Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden (El-Khatib); Department of Public Health, Saint Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia (Endries); Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences, Tehran, Iran (Esteghamati); Department of Psychology, Federal University of Sergipe, Sao Cristovao, Brazil (Faro); Non-Communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran (Farzadfar); National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand (Feigin); Department of Public Health Medicine, Bielefeld University, Bielefeld, Germany (Fischer); College of Public Health Medical and Veterinary Science, James Cook University, Douglas, QLD, Australia (Franklin); School of Medicine Department of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle (Futran); Department of Epidemiology and Biostatistics, Jimma University, Jimma, Ethiopia (Gebrehiwot); Epidemiological and Psychosocial Investigations, National Institute of Psychiatry Ramon de la Fuente, Mexico City, Mexico (Gutiérrez); School of Medicine, Tehran University of Medical Sciences, Tehran, Iran (Hafezi-Nejad); Department of Radiology, Johns Hopkins University, Baltimore, Maryland (Hafezi-Nejad); Institute for Global Health, University College London, London, United Kingdom (Haghparast Bidgoli); Biomedical Science Division, School of Medicine, Mekelle University, Mekelle, Ethiopia (Hailu); Research Unit, Parc Sanitari Sant Joan de Déu (CIBERSAM), Sant Boi de Llobregat, Spain (Haro); Department of Medicine, Universitat de Barcelona, Barcelona, Spain (Haro); Department of Public Health, Mizan-Tepi University, Mizan Teferi, Ethiopia (Hassen); Unit of Epidemiology and Social Medicine, University Hospital Antwerp, Wilrijk, Belgium (Hassen); School of Public Health, Curtin University, Bentley, Perth, WA, Australia (Hendrie, Miller); Research Coordination, Fundación Entornos AC, Cuernavaca, Mexico (Híjar); CISS, National Institute of Public Health, Cuernavaca, Mexico (Híjar); Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China (Hu); Department of Public Health and Community Medicine, University of Liberia, Monrovia, Liberia (Ilesanmi); Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia (Jakovljevic); Department of Surgery, Virginia Commonwealth University, Richmond (Jayaraman); Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany (Jonas); Capital Medical University, Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing Ophthalmology & Visual Science Key Laboratory, Beijing, China (Jonas); Department of Nutrition and Dietetics, Mekelle University, Mekelle, Ethiopia (Kahsay); Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran (Kasaeian); Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran (Kasaeian); School of Continuing and Distance Education, University of Nairobi, Nairobi, Kenya (Keiyoro); Public Health and Community Medicine, Jordan University of Science and Technology, Alramtha, Jordan (Khader); Department of Health Policy and Management, College of Medicine, Seoul National University, Seoul, South Korea (Khang); Institute of Health Policy and Management, SNU Medical Research Center, Seoul National University, Seoul, South Korea (Khang); Department of Nutrition and Health Science, Ball State University, Muncie, Indiana (Khubchandani); Clinical Epidemiology Unit, Lund University, Lund, Sweden (Ahmad Kiadaliri); Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil (Kieling); Child & Adolescent Psychiatry Division, Hospital of Clinics of Porto Alegre, Porto Alegre, Brazil (Kieling); School of Medicine, Xiamen University Malaysia, Sepang, Malaysia (Kim); Independent Consultant, Jakarta, Indonesia (Kosen); Department of Community and Family Medicine, Academy of Medical Science, Baghdad, Iraq (Lami); Helpmesee, New York, New York (Lansingh); Department of International Relations, Mexican Institute of Ophthalmology, Queretaro, Mexico (Lansingh); Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom (Larson); School of Public Health, University of Haifa, Haifa, Israel (Linn); General Surgery Department, Aintree University Hospital National Health Service Foundation Trust (NHS), Liverpool, United Kingdom (Lunevicius); School of Medicine, University of Liverpool, Liverpool, United Kingdom (Lunevicius); Department of Cardiology, Damietta University, Damietta, Egypt (H. Magdy Abd El Razek); Ophthalmology Department, Aswan Faculty of Medicine, Aswan, Egypt (M. Magdy Abd El Razek); Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran (Malekzadeh, Sepanlou); Non-Communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran (Malekzadeh, Sepanlou); Departamento Materno Infantil e Saúde Publica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (Carvalho Malta); Department of Health Sciences, University of York, York, United Kingdom (Mason-Jones); Burden of Disease Research Unit, South African Medical Research Council, Cape Town, South Africa (Matzopoulos); School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa (Matzopoulos); Department of Public Health, University of West Florida, Pensacola (Memiah); Peru Country Office, United Nations Population Fund (UNFPA), Lima, Peru (Mendoza); Comprehensive Cancer Center, Breast Surgery Unit, Helsinki University Hospital, Helsinki, Finland (Meretoja); Department of Surgery, University of Helsinki, Helsinki, Finland (Meretoja); School of Pharmacy, Academy of Medical Science, Baghdad, Iraq (Mezgebe); Pacific Institute for Research & Evaluation, Calverton, Maryland (Miller); Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria, Nigeria (Mohammed); Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran (Moradi-Lakeh); Department of Health Policy, National Center for Child Health and Development, Setagaya, Japan (Mori); Health Information Unit, Ministry of Health, Suva, Fiji (Nand); Accamargo Cancer Center, São Paulo, Brazil (Nand); Public Health Department, Semarang State University, Kota Semarang, Indonesia (Ningrum); Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei City, Taiwan (Ningrum); Western Sydney University, Penrith, NSW, Australia (Akpojene Ogbo); School of Medicine, University of Adelaide, Adelaide, SA, Australia (Olagunju); Department of Psychiatry, University of Lagos, Lagos, Nigeria (Olagunju); Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia (Patton); Department of Paediatrics, Murdoch Children’s Research Institute, Melbourne, VIC, Australia (Patton); Shanghai Mental Health Center, Shanghai Jiao Tong University, Shanghai, China (Phillips); Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands (Polinder); School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada (Pourmalek); Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran (Qorbani); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (A. Rahimi-Movaghar); Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran (V. Rahimi-Movaghar); Research and Evaluation Division, BRAC (Bangladesh Rehabilitation Assistance Committee), Dhaka, Bangladesh (Rahman); Society for Health and Demographic Surveillance, Suri, India (Rai); Department of Economics, University of Göttingen, Göttingen, Germany (Rai); Institute for Poverty Alleviation and International Development, Yonsei University, South Korea (Ranabhat); WHO Collaborating Centre for Public Health Education & Training, Imperial College London, London, United Kingdom (D. L. Rawaf); University College London Hospitals, London, United Kingdom (D. L. Rawaf); Department of Primary Care and Public Health, Imperial College London, London, United Kingdom (S. Rawaf); Public Health England, London, United Kingdom (S. Rawaf); Department of Epidemiology, University of Washington, Seattle (Rowhani-Rahbar); Sina Trauma and Surgery Center, Iran University of Medical Sciences, Tehran, Iran (Safdarian); Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran (Safiri); Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), New Delhi, India (Sagar); Department of Surgery, Marshall University, Huntington, West Virginia (Sanabria); Department of Nutrition and Preventive Medicine, Case Western Reserve University, Cleveland, Ohio (Sanabria); Faculty of Medicine, Institute of Social Medicine, Centre School of Public Health and Health Management, University of Belgrade, Belgrade, Serbia (Santric Milicevic); Department of Health and Society, Faculty of Medicine, Universidad Ciencias Aplicadas y Ambientales, Bogotá, Colombia (Sarmiento-Suárez); Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa (Sartorius); UGC Centre of Advanced Study in Psychology, Utkal University, Bhubaneswar, India (Satpathy); Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama (Schwebel); Department of Psychiatry, Stellenbosch University, Cape Town, South Africa (Seedat); Independent Consultant, Karachi, Pakistan (Shaikh); College of Health Science, Debre Berhan University, Debre Berhan, Ethiopia (Sharew); Instituts Für Medizinische Epidemiologie, Biometrie Und Informatik, Martin Luther University Halle-Wittenberg, Haale, Germany (Shiue); School of Medicine, University of Alabama at Birmingham (Singh); Department of Epidemiology, University of Alabama at Birmingham (Singh); School of Pharmacy, Haramaya University, Harar, Ethiopia (Sisay); Norwegian Institute of Public Health, Oslo, Norway (Skirbekk); Department of Diseases and Noncommunicable Diseases and Health Promotion, Federal Ministry of Health, Brasilia, Brazil (Soares Filho); South African Medical Research Council, Cape Town, South Africa (Stein); Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa (Stein); School of Psychology, Deakin University, Burwood, VIC, Australia (Stokes); Department of Community Medicine, Ahmadu Bello University, Zaria, Nigeria (Sufiyan); Department of Surgery, Northwestern University, Chicago, Illinois (Swaroop); Department of Criminology, Law, & Society, University of California Irvine (Sykes); Department of Medicine, University of Valencia, Valencia, Spain (Tabarés-Seisdedos); Department of Public Health, Wollo University, Dessie, Ethiopia (Tadese); Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam (Tran); Department of Internal Medicine, Federal Teaching Hospital, Abakaliki, Nigeria (Ukwaja); UKK Institute for Health Promotion Research, Tampere, Finland (Vasankari); Department of Health Care Management and Economics, National Research University Higher School of Economics, Moscow, Russia (Vlassov); Demographic Change and Ageing, Federal Institute for Population Research, Wiesbaden, Germany (Werdecker); Division of Injury Prevention and Mental Health Improvement, National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Xicheng District, China (Ye); Centre for Suicide Research and Prevention, University of Hong Kong, Hong Kong SAR, China (Yip); Department of Biostatistics, School of Public Health, Kyoto University, Kyoto, Japan (Yonemoto); Healthcare Management-Health Economics & Finance/Global Health, Jackson State University, Jackson, Missouri (Younis); Research Center for Public Health, Tsinghua University, Beijing, China (Younis); Department of Epidemiology, University Hospital of Setif, Setif, Algeria (Zaidi); Faculty of Medical Sciences, A.C.S. Medical College and Hospital, Algiers, Algeria (Zaidi); Faculty of Medicine, Mansoura University, Mansoura, Egypt (El Sayed Zaki); School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia (Lopez).
Author Contributions: Dr Naghavi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Naghavi, Marczak, Kutz, Asadi-Lari, Bazargan-Hejazi, Masoud Behzadifar, Meysam Behzadifar, Bilal, Dargan, Krohn, Lansingh, Mezgebe, Olagunju, Sanabria, Satpathy, Skirbekk, Ukwaja, Zaki, Vos, Mokdad, Murray.
Acquisition, analysis, or interpretation of data: Naghavi, Marczak, Kutz, Shackelford, Ahmad Kiadaliri, Aichour, Akseer, Lami, Alam, Alghnam, Al-Raddadi, Antonio, Aremu, A. Arora, M. Arora, Assadi, Atey, Avila-Burgos, Awasthi, Ayala Quintanilla, Barker-Collo, Bärnighausen, Bennett, Bhalla, Bhutta, Borges, Borschmann, Brazinova, Campuzano Rincon, Carvalho, Castañeda-Orjuela, L. Dandona, R. Dandona, De Leo, Dharmaratne, Ding, Phuc Do, Doku, Doyle, Driscoll, Edessa, El-Khatib, Endries, Esteghamati, Faro, Farzadfar, Feigin, Fischer, Foreman, Franklin, Fullman, Futran, Gebrehiwot, Gutiérrez, Hafezi-Nejad, Haghparast Bidgoli, Hailu, Haro, Hassen, Hawley, Hendrie, Hijar, Hu, Ilesanmi, Jakovljevic, James, Jayaraman, Jonas, Kahsay, Kasaeian, Keiyoro, Khader, Khalil, Khang, Khubchandani, Kieling, Kim, Kosen, Krohn, Kumar, Lansingh, Larson, Linn, Lunevicius, H. Magdy Abd El Razek, M. Magdy Abd El Razek, Malekzadeh, Malta, Mason-Jones, Matzopoulos, Memiah, Mendoza, Meretoja, Mezgebe, Miller, Miller-Petrie, Mohammed, Moradi-Lakeh, Mori, Nand, Tat Nguyen, Le Nguyen, Ningrum, Akpojene Ogbo, Olagunju, Patton, Phillips, Polinder, Pourmalek, Qorbani, A. Rahimi-Movaghar, V. Rahimi-Movaghar, Rahman, Rai, Ranabhat, D. L. Rawaf, S. Rawaf, Rowhani-Rahbar, Safiri, Safdarian, Sagar, Salama, Santric Milicevic, Sarmiento-Suárez, Sartorius, Satpathy, Schwebel, Seedat, Sepanlou, Shaikh, Sharew, Shiue, Singh, Sisay, Soares Filho, Stein, Stokes, Sufiyan, Swaroop, Sykes, Tabares-Seisdedos, Tadese, B. X. Tran, T. Thanh Tran, Ukwaja, Vasankari, Vlassov, Werdecker, Ye, Yip, Yonemoto, Younis, Zaidi, Lopez, Lim, Vos, Mokdad, Hay, Murray.
Drafting of the manuscript: Naghavi, Marczak, Kutz, Shackelford, Aichour, Masoud Behzadifar, Meysam Behzadifar, Campuzano Rincon, Esteghamati, Kim, Lansingh, H. Magdy Abd El Razek, M. Magdy Abd El Razek, Olagunju, D. L. Rawaf, Sanabria, Santric Milicevic, Satpathy, Zaki.
Critical revision of the manuscript for important intellectual content: Naghavi, Marczak, Kutz, Ahmad Kiadaliri, Akseer, Lami, Alam, Alghnam, Al-Raddadi, Antonio, Aremu, A. Arora, M. Arora, Asadi-Lari, Assadi, Atey, Avila-Burgos, Awasthi, Ayala Quintanilla, Barker-Collo, Bärnighausen, Bazargan-Hejazi, Bennett, Bhalla, Bhutta, Bilal, Borges, Borschmann, Brazinova, Carvalho, Castañeda-Orjuela, L. Dandona, R. Dandona, Dargan, De Leo, Dharmaratne, Ding, Phuc Do, Doku, Doyle, Driscoll, Edessa, El-Khatib, Endries, Esteghamati, Faro, Farzadfar, Feigin, Fischer, Foreman, Franklin, Fullman, Futran, Gebrehiwot, Gutiérrez, Hafezi-Nejad, Haghparast Bidgoli, Hailu, Haro, Hassen, Hawley, Hendrie, Hijar, Hu, Ilesanmi, Jakovljevic, James, Jayaraman, Jonas, Kahsay, Kasaeian, Keiyoro, Khader, Khalil, Khang, Khubchandani, Kieling, Kosen, Krohn, Kumar, Lansingh, Larson, Linn, Lunevicius, H. Magdy Abd El Razek, Malekzadeh, Malta, Mason-Jones, Matzopoulos, Memiah, Mendoza, Meretoja, Mezgebe, Miller, Miller-Petrie, Mohammed, Moradi-Lakeh, Mori, Nand, Tat Nguyen, Le Nguyen, Ningrum, Akpojene Ogbo, Olagunju, Patton, Phillips, Polinder, Pourmalek, Qorbani, A. Rahimi-Movaghar, V. Rahimi-Movaghar, Rahman, Rai, Ranabhat, D. L. Rawaf, S. Rawaf, Rowhani-Rahbar, Safiri, Safdarian, Sagar, Salama, Sanabria, Santric Milicevic, Sarmiento-Suárez, Sartorius, Satpathy, Schwebel, Seedat, Sepanlou, Shaikh, Sharew, Shiue, Singh, Sisay, Skirbekk, Soares Filho, Stein, Stokes, Sufiyan, Swaroop, Sykes, Tabares-Seisdedos, Tadese, B. X. Tran, T. Tran, Ukwaja, Vasankari, Vlassov, Werdecker, Ye, Yip, Yonemoto, Younis, Zaidi, Zaki, Lopez, Lim, Vos, Mokdad, Hay, Murray.
Statistical analysis: Naghavi, Marczak, Kutz, Aichour, Alam, Aremu, A. Arora, Bennett, Bilal, Endries, Fischer, Foreman, Hafezi-Nejad, Hailu, Ilesanmi, James, Jonas, Kasaeian, Keiyoro, Khubchandani, Lansingh, Memiah, Mohammed, Olagunju, Qorbani, Safiri, Satpathy, Sharew, Sykes, Tabares-Seisdedos, Tadese, Ukwaja, Yip, Younis, Zaidi, Mokdad, Hay.
Obtained funding: Murray.
Administrative, technical, or material support: Marczak, Shackelford, A. Arora, Awasthi, L. Dandona, R. Dandona, Esteghamati, Farzadfar, Franklin, Hassen, Hawley, Hijar, Hu, Khader, Khalil, Kim, Krohn, Kumar, Lansingh, Meretoja, Miller-Petrie, Mohammed, Moradi-Lakeh, Nand, Akpojene Ogbo, Patton, Polinder, A. Rahimi-Movaghar, Rahman, Ranabhat, S. Rawaf, Rowhani-Rahbar, Salama, Satpathy, Shaikh, Sisay, Sykes, Tabares-Seisdedos, Ukwaja, Vasankari, Ye, Zaki, Lopez, Mokdad, Murray.
Supervision: Naghavi, Marczak, Alghnam, Asadi-Lari, Phuc Do, Faro, Foreman, Hawley, James, Kieling, H. Magdy Abd El Razek, M. Magdy Abd El Razek, V. Rahimi-Movaghar, Ranabhat, Salama, Satpathy, Stokes, Lim, Vos, Mokdad, Murray.
Other (review of the statistical analysis): El-Khatib.
Other (feedback on methods and results): Sarmiento-Suárez.
Other (critical revision, analyis data from Brazil): Malta.
Other (validation of data sources): Doku.
Other (final review): Soares Filho.
Other (whole review of manuscript and modification as necessary): Ranabhat.
Other (as part of the scientific advisory group): Bhutta.
Other (interpretation and discussion of critical data in Mexico): Borges.
Other (policy and practice directions): Shiue.
Other (interpretation and analysis of the inputed data): Hafezi-Nejad.
Other: Al-Raddadi, Atey, Masoud Behzadifar, Meysam Behzadifar, Edessa, Kosen, Matzopoulos, B. X. Tran, Werdecker.
Other (interpretation of findings): Alghnam.
Other (revision of data sources and results): Sepanlou.
Other (obtained data): Feigin.
Other (revision of results and data sources): Malekzadeh.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Ms Ayala Quintanilla reports receipt of support to her institution (the Judith Lumley Centre of La Trobe University) from PRONABEC (the national program of scholarship and educational loan) to support current study of her doctoral course. Dr Kieling reports receipt of authorship royalties from Brazilian publishers Artmed and Manole. Dr Larson reports that her research group has a contract with GlaxoSmithKline for a 15-country study on vaccine acceptance during pregnancy and receipt of funds for convening a research symposium from Merck. Mr Matzopoulos reports serving on the board (unpaid) of Gun Free South Africa. Dr Singh reports receipt of consultancy fees from Savient, Takeda, Regeneron, Merz, Iroko, bioiberica, Crealta/Horizon, allergan, fidia, UBM LLC, WebMD, and the American College of Rheumatology; grants to his institution from Takeda and Savient; serving as the principal investigator for a study funded by Horizon Pharmaceuticals through a grant to DINORA; and serving on the steering committee of OMERACT (Outcome Measures in Rheumatology). Dr Stein reports receipt of personal fees from Lundbeck, Biocodex, Servier, and SUN outside the submitted work. No other disclosures were reported.
Funding/Support: This study was funded by the Bill & Melinda Gates Foundation (OPP1152504).
Role of the Funder/Sponsor: No funding organizations had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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