Abstract
Background
Transport injuries remain a major preventable cause of death and disability among children and adolescents. Evidence on long-term trends, geographic inequalities, and future burden across the Western Pacific Region (WPR) remains limited. We assessed transport-injury burden among people aged <20 years in the WPR from 2000 to 2023 and projected trends to 2035.
Methods
We analysed Global Burden of Diseases, Injuries, and Risk Factors Study 2023 estimates for 33 WPR countries and territories. We assessed incidence, prevalence, deaths, and disability-adjusted life years (DALYs) from 2000 to 2023. We mapped 2023 age-standardised rates, examined four age groups by sex, and decomposed changes into demographic and epidemiological components. Sex-specific autoregressive integrated moving average (ARIMA) models were used to project age-standardised outcomes from 2024 to 2035.
Results
Incident cases decreased from 5,034,407 [95% uncertainty interval (UI), 4,180,502–5,955,077] in 2000 to 2,433,402 (1,988,777–2,920,981) in 2023. The age-standardised incidence rate fell from 812.69 to 428.74 per 100,000. The annual percentage change was −2.82% (95% confidence interval, −3.04 to −2.61; P < 0.001). In 2023, incidence varied 4.48-fold and mortality varied 38.2-fold across locations. Males aged 15–19 years had higher incidence, mortality, and DALY rates than females of the same age. Epidemiological change drove most of the regional reduction. Between 2024 and 2035, ARIMA models projected male age-standardised incidence and prevalence rates to decline modestly, while female rates remained nearly stable. Mortality and DALY projections crossed zero and were excluded from quantitative interpretation.
Conclusions
The transport-injury burden among children and adolescents in the WPR decreased substantially, but marked geographic and age-sex inequalities persisted. The 2024–2035 projections suggest that non-fatal burden may decline only modestly, with little change among females. Prevention should prioritise older adolescent males and high-mortality settings, while longer-term policy planning requires more robust forecasting.
Keywords: children and adolescents, decomposition analysis, forecasting, global burden of disease, health inequality, transport injuries, Western Pacific Region
Introduction
Transport injuries are an important source of preventable death and disability among children and adolescents in the Western Pacific Region (WPR). Burden increases across adolescence and is consistently higher in males (1–4). Although age-standardised mortality and disability-adjusted life-year (DALY) rates from road injuries have fallen in many settings, progress varies, and large numbers of events may persist despite lower rates (5–7). For planning, rates indicate burden relative to population size, whereas counts more directly reflect demands on prevention, emergency care and rehabilitation.
Existing evidence from the WPR is difficult to compare across studies. Research from China has examined mortality trends and age–period–cohort patterns (8), whereas analyses of low- and middle-income countries and Pacific island countries have used different populations and outcomes (9, 10). Studies in Malaysia and China have focused on specific national or age-defined populations (11, 12), and regional policy research has documented continuing gaps in protection for children and adolescents (13). No study has compared incidence, prevalence, mortality and DALYs within a common framework covering the current WPR membership. Location-specific rates have also rarely been examined alongside event counts, despite the large differences in population size across the region.
Available studies do not show whether lower transport-injury rates across the WPR have translated into fewer incident and prevalent cases, deaths and DALYs. They also do not establish whether the locations and age–sex groups with the highest rates account for the largest numbers. Examining these relationships requires fatal and non-fatal outcomes to be assessed over the same period and geographic scope.
Using Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 estimates, we examined transport-injury burden among people aged <20 years in 33 WPR countries and territories from 2000 to 2023 (14, 15). Our objectives were to quantify regional temporal trends; compare 2023 incidence, prevalence, mortality and DALY estimates across locations; and describe age- and sex-specific patterns. We interpreted rates and counts together to distinguish populations with high estimated rates from locations facing the largest service caseloads.
Materials and methods
Study design and data sources
GBD data were obtained from the publicly available Global Burden of Disease Study 2023 Results Tool maintained by the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/). GBD 2023 quantifies health loss attributable to diseases and injuries across countries and over time. GBD 2023 provides estimates for 375 diseases and injuries and 88 risk factors by age and sex across 204 countries and territories, including 660 subnational locations (14, 15). Within the GBD cause hierarchy, transport injuries (TIs) are Level 2 conditions under injuries. TIs comprise road injuries—subdivided into motor-vehicle, cyclist, motorcyclist, pedestrian and other road injuries—and other transport injuries.
We extracted incidence, prevalence, deaths and DALYs attributable to TIs for people aged <20 years across the 33 WPR countries and territories. Rates were expressed per 100,000 population. Temporal trends from 2000 to 2023 were assessed using annual percentage change (APC) with 95% confidence intervals (CIs). Reporting followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (16). Indonesia was formally transferred from the World Health Organization (WHO) South-East Asia Region to the WPR in 2025 (17); it was therefore included throughout the analysis. All analyses were conducted using R version 4.5.2 (R Core Team, 2025; R Foundation for Statistical Computing, Vienna, Austria).
Temporal trend analysis
Temporal trends in TI burden from 2000 to 2023 were assessed across four indicators—incidence, prevalence, mortality, and DALYs—among children and adolescents aged <20 years in the WPR. For each indicator, absolute numbers and age-standardised rates (ASRs) per 100,000 population were plotted as dual-axis charts, with counts displayed as bar charts on the primary axis and rates as line graphs on the secondary axis. ASRs were calculated by direct standardisation against the Global Burden of Disease reference population to remove the confounding effect of age-structure differences (14, 15). Details of the formula and weights are provided in the Supplementary Material S1 (18).
Statistical analyses
For 2023, we compared the age-standardised incidence rate (ASIR), age-standardised prevalence rate (ASPR) and age-standardised mortality rate (ASMR) across the 33 locations. Geographic distributions were displayed using six colour classes. Absolute numbers and corresponding age-standardised rates for incidence, prevalence and mortality were tabulated by location.
TI burden estimates were stratified by four age groups (<5, 5–9, 10–14, and 15–19 years) and by sex. For each stratum, we reported absolute numbers and age-specific rates per 100,000 population, with 95% uncertainty intervals (UIs), for incidence, prevalence, mortality, and DALYs. Sex- and age-specific disparities were visualized using grouped bar charts for absolute numbers and overlaid line graphs for age-specific rates.
Changes in TI burden from 2000 to 2023 were decomposed into three additive components: population growth, population aging, and epidemiological change. The decomposition was applied to absolute numbers of incident cases, prevalent cases, deaths, and DALYs, and was performed for both sexes combined and separately for males and females. Each component quantifies the net change attributable to the corresponding factor, holding the other two constant. We computed the three components from age-specific population shares aᵢₜ, total population counts pₜ, and age-specific DALY rates rᵢₜ (per 105 persons) using the exact algebraic formulation now provided in Supplementary Methods S2. The overall change in DALYs equals the sum of the three effects, and percentage contributions were derived accordingly (14, 19).
Future trends in age-standardised TI burden rates from 2024 to 2035 were projected using sex-specific autoregressive integrated moving average (ARIMA) models. ARIMA integrates autoregressive and moving-average components, accommodates autocorrelation in time-series analysis data, and can be selected using information criteria (20, 21). Model orders (p, d, q) were identified by minimizing the corrected Akaike information criterion (AICc), and the adequacy of the selected models was confirmed using Ljung–Box tests of residual autocorrelation. Stationarity of each series was assessed with the augmented Dickey–Fuller (ADF) test, with differencing applied as necessary to achieve stationarity. Projections were performed separately by sex for all four burden indicators—incidence, prevalence, mortality, and DALYs—with 95% confidence intervals, using historical trends from 2000 to 2023 as the basis for model fitting. Because linear ARIMA forecasts are not constrained to non-negative values and may extrapolate across zero under strong downward trends, the projection horizon was restricted to 2024–2035, the period over which the forecasts and their 95% confidence intervals remained strictly positive and epidemiologically plausible; full modeling details are provided in the Supplementary Methods S3. Sensitivity analyses using log-transformed ARIMA models were additionally performed to assess robustness.
Results
Temporal trends in TI burden, 2000–2023
Transport-injury incidence among people aged <20 years in the WPR was 5,034,407 cases (95% UI, 4,180,502–5,955,077) in 2000. It fell to 2,433,402 (1,988,777–2,920,981) in 2023, a 51.7% reduction. The ASIR decreased by 47.2%, from 812.69 (605.12–1,065.33) to 428.74 (315.68–560.86) per 100,000. The corresponding APC was −2.82% (95% CI, −3.04 to −2.61; P < 0.001) (Table 1). All 33 locations had negative APC estimates. The decline was steepest in the Republic of Korea (APC, −4.72%; 95% CI, −5.20 to −4.23). It was least steep in the Northern Mariana Islands (−0.22%; −0.29 to −0.15). In 2023, the regional burden still included approximately 1,468,431 prevalent cases, 28,379 deaths, and 2,282,094 DALYs. Prevalence, mortality, and DALY rates also declined throughout the study period (Figure 1).
Table 1.
Incidence rates, age-standardized rates, and annual percentage changes in transport injuries among the Western Pacific Region population aged <20 years, from 2000 to 2023.
| Location | 2000 | 2023 | APC of ASR (95% CI) | P value | ||
|---|---|---|---|---|---|---|
| No. incident cases (95% UI) | ASIR per 100,000 (95% UI) | No. incident cases (95%UI) | ASIR per 100,000 (95%UI) | |||
| Western Pacific Region | 5,034,407 (4,180,502, 5,955,077) | 812.69 (605.12, 1,065.33) | 2,433,402 (1,988,777, 2,920,981) | 428.74 (315.68, 560.86) | −2.82 (−3.04, −2.61) | <0.001 |
| China | 3,221,400 (2,600,337, 3,901,493) | 837.94 (602.87, 1,130.8) | 1,504,110 (1,194,163, 1,839,537) | 452.29 (321.52, 606.06) | −2.62 (−2.81, −2.42) | <0.001 |
| Taiwan (Province of China) | 50,778 (46,949, 55,365) | 704.66 (621.99, 798.96) | 17,539 (15,855, 19,959) | 431.56 (356.56, 518.57) | −2.45 (−2.74, −2.15) | <0.001 |
| Japan | 253,601 (202,930, 311,849) | 916.24 (649.74, 1,233.36) | 105,198 (85,879, 129,396) | 504.69 (357.04, 685.18) | −2.95 (−3.29, −2.61) | <0.001 |
| Mongolia | 6,906 (6,119, 7,730) | 617.51 (505.85, 730.71) | 6,308 (5,678, 7,027) | 465.9 (378.94, 556.94) | −1.33 (−1.64, −1.02) | <0.001 |
| Republic of Korea | 263,414 (244,013, 288,202) | 1,862.07 (1,629.91, 2,128.45) | 56,093 (48,671, 65,185) | 716.04 (567.83, 879.27) | −4.72 (−5.20, −4.23) | <0.001 |
| Brunei Darussalam | 2,001 (1,818, 2,192) | 1,462.17 (1,248.92, 1,722.94) | 1,252 (1,116, 1,389) | 971.55 (795.61, 1,167.6) | −2.06 (−2.40, −1.72) | <0.001 |
| Cambodia | 36,774 (32,952, 40,662) | 562 (466.87, 672.16) | 23,207 (21,102, 25,550) | 350.95 (292.94, 413.55) | −2.24 (−2.41, −2.07) | <0.001 |
| Lao People's Democratic Republic | 17,951 (16,283, 20,264) | 622.66 (514, 750.37) | 10,295 (9,257, 11,405) | 345.42 (286.05, 409.91) | −2.89 (−3.06, −2.71) | <0.001 |
| Malaysia | 52,611 (46,994, 58,240) | 494.43 (419.12, 580.57) | 44,370 (39,333, 49,650) | 395.42 (324.23, 476.79) | −0.99 (−1.10, −0.88) | <0.001 |
| Philippines | 124,902 (100,356, 148,199) | 325.83 (231.05, 441.18) | 111,248 (89,774, 134,552) | 248.37 (175.07, 332.71) | −1.38 (−1.50, −1.26) | <0.001 |
| Singapore | 8,757 (7,640, 9,956) | 812.16 (641.72, 1,002.73) | 6,166 (5,003, 7,393) | 616.05 (454.95, 813.93) | −1.37 (−1.60, −1.14) | <0.001 |
| Viet Nam | 159,057 (143,544, 179,229) | 447.81 (373.36, 534.48) | 129,628 (116,619, 142,939) | 406.2 (340.59, 476.75) | −0.71 (−1.12, −0.31) | 0.001 |
| Indonesia | 789,748 (677,193, 919,556) | 901.42 (670.05, 1,174.43) | 356,183 (294,357, 429,275) | 387.66 (280.28, 513.01) | −3.97 (−4.34, −3.59) | <0.001 |
| Australia | 56,061 (51,174, 61,848) | 1,012.29 (848.18, 1,195.44) | 39,066 (34,328, 44,804) | 588.7 (465.22, 728.81) | −2.76 (−2.96, −2.55) | <0.001 |
| New Zealand | 20,750 (16,908, 25,188) | 1,761.23 (1,284.67, 2,324.45) | 10,796 (8,770, 13,156) | 793.4 (570.86, 1,061.85) | −4.00 (−4.83, −3.16) | <0.001 |
| Fiji | 1,189 (1,060, 1,331) | 333.19 (271.4, 397.86) | 986 (874, 1,096) | 278.65 (225.74, 336.38) | −0.97 (−1.16, −0.79) | <0.001 |
| Papua New Guinea | 17,080 (15,233, 18,847) | 592.25 (486.5, 708.35) | 25,974 (23,516, 28,556) | 492.72 (407.75, 586.9) | −0.94 (−1.01, −0.87) | <0.001 |
| Solomon Islands | 713 (629, 820) | 309.59 (242.29, 390.27) | 1,157 (1,008, 1,332) | 285.75 (226.4, 356.76) | −0.40 (−0.48, −0.33) | <0.001 |
| Vanuatu | 426 (378, 472) | 420.53 (340.05, 515.24) | 549 (493, 607) | 365.94 (296.51, 437.14) | −0.80 (−0.89, −0.71) | <0.001 |
| Guam | 224 (201, 248) | 364.12 (301.12, 433.38) | 136 (120, 153) | 285.97 (231, 345.65) | −1.22 (−1.39, −1.05) | <0.001 |
| Kiribati | 148 (131, 166) | 340.12 (273.06, 416.54) | 151 (133, 169) | 275.1 (221.88, 335.33) | −1.26 (−1.45, −1.07) | <0.001 |
| Marshall Islands | 108 (96, 120) | 368.99 (296.8, 444.94) | 54 (47, 60) | 289.93 (233.32, 351.84) | −1.36 (−1.52, −1.20) | <0.001 |
| Micronesia (Federated States of) | 245 (218, 276) | 427.01 (348.46, 517.44) | 139 (123, 156) | 327.82 (263.24, 405.16) | −1.23 (−1.35, −1.11) | <0.001 |
| Nauru | 34 (30, 37) | 640.88 (522.71, 769.05) | 29 (26, 32) | 502.95 (420.31, 593.36) | −1.49 (−1.69, −1.29) | <0.001 |
| Northern Mariana Islands | 73 (65, 82) | 338.47 (265.7, 413.72) | 44 (39, 50) | 313.05 (252.41, 379.91) | −0.22 (−0.29, −0.15) | <0.001 |
| Palau | 25 (22, 28) | 400.36 (317.86, 488.75) | 15 (13, 17) | 338.16 (272.48, 410.72) | −0.79 (−0.86, −0.73) | <0.001 |
| American Samoa | 74 (66, 84) | 270.9 (213.41, 331.93) | 41 (36, 47) | 219.59 (170.17, 272.81) | −1.13 (−1.27, −0.99) | <0.001 |
| Cook Islands | 34 (31, 38) | 493.28 (416.9, 582.65) | 13 (12, 15) | 262.48 (205.17, 330.21) | −3.32 (−3.58, −3.05) | <0.001 |
| Niue | 3 (3, 3) | 410.32 (329.39, 497.46) | 2 (2, 2) | 344.08 (279.92, 415.98) | −0.93 (−1.03, −0.83) | <0.001 |
| Samoa | 307 (271, 347) | 345.27 (276.24, 423.71) | 286 (252, 326) | 283.65 (225.77, 347.37) | −0.91 (−1.00, −0.83) | <0.001 |
| Tokelau | 2 (2, 2) | 311.96 (244.51, 386.83) | 1 (1, 1) | 216.97 (165.21, 276.13) | −1.92 (−2.09, −1.74) | <0.001 |
| Tonga | 143 (126, 159) | 279.67 (225.98, 338.8) | 112 (99, 127) | 236.12 (186.93, 290.25) | −0.96 (−1.06, −0.86) | <0.001 |
| Tuvalu | 20 (17, 22) | 475.96 (389.26, 577.21) | 13 (12, 15) | 301.99 (242.05, 369.01) | −2.15 (−2.29, −2.01) | <0.001 |
ASIR, age-standardized incidence rate; APC, annual percent change; CI, confidence interval; UI, uncertainty interval.
Figure 1.
Temporal trends in transport injury burden among individuals aged <20 years in the Western Pacific Region, 2000–2023. Panels display (A) incidence, (B) prevalence, (C) deaths, and (D) DALYs. Bars represent absolute numbers (left axis); lines represent age-standardized rates per 100,000 population (right axis). DALYs, disability-adjusted life years.
Geographic heterogeneity in TI burden, 2023
Marked geographic variation in TI burden was observed across the WPR in 2023 (Figure 2, Supplementary Table A). ASIR ranged from 216.97 per 100,000 in Tokelau to 971.55 per 100,000 in Brunei Darussalam, a 4.48-fold difference. High-income settings exhibited divergent patterns: the Republic of Korea and Brunei Darussalam recorded the highest ASIR (716.04 and 971.55, respectively), whereas Singapore and Japan had intermediate rates (616.05 and 504.69). Pacific island settings also showed marked variation, with ASIRs of 502.95 per 100,000 in Nauru and 219.59 per 100,000 in American Samoa. ASMR varied 38-fold, from 0.61 per 100,000 (Singapore) to 23.28 per 100,000 (Nauru). Indonesia and Cambodia bore the highest ASMR among larger populations (10.72 and 10.56, respectively, vs. the WPR average of 4.94). In absolute terms, China accounted for 61.8% of all incident cases (1,504,110 of 2,433,402) and 39.0% of deaths (11,073 of 28,379), reflecting its population size despite moderate age-standardized rates.
Figure 2.
Geographic distribution of transport injury burden among individuals aged <20 years across the Western Pacific Region in 2023. Maps display age-standardized rates per 100,000 population for (A) incidence (ASIR), (B) prevalence (ASPR), and (C) death (ASMR), using six colour classes to indicate rate ranges. ASIR, age-standardized incidence rate; ASPR, age-standardized prevalence rate; ASMR, age-standardized mortality rate.
Age and sex disparities in TI burden
Rates increased across the four age groups and were highest at ages 15–19 years (Figure 3; Supplementary Tables B1–B4). In that age group, the incidence rate was 996.81 per 100,000 (95% UI, 718.74–1,340.36) in males and 434.88 (309.46–578.32) in females. Mortality rates were 14.27 and 5.66 per 100,000, respectively, and DALY rates were 1,068.25 and 432.21 per 100,000. At ages 0–4 years, incidence rates were similar in females and males (402.33 and 383.29 per 100,000). Sex differences widened across later age groups, particularly for mortality and DALYs.
Figure 3.
Age- and sex-specific burden of transport injuries among individuals aged <20 years in the Western Pacific Region. Panels display (A) incidence, (B) prevalence, (C) deaths, and (D) DALYs, stratified by age group (<5, 5–9, 10–14, and 15–19 years) and sex. Bars represent absolute numbers; lines represent age-specific rates per 100,000 population. DALYs, disability-adjusted life years.
Drivers of TI burden change: decomposition analysis
Decomposition of the 2000–2023 changes in TI burden revealed distinct contributions of population growth, population aging, and epidemiological change across the four indicators (Figure 4 and Supplementary Material-Decomposition Analysis.csv). For incidence and deaths, epidemiological change—reflecting declines in age- and sex-specific rates—was the predominant driver of reduction, while population growth partially offset these declines, particularly among males. Population aging contributed a comparatively modest decrement. For prevalence and DALYs, the net reduction was more attenuated, as population growth exerted a stronger countervailing effect. Across all indicators, the absolute contribution of epidemiological improvement was greater in males than in females, consistent with the steeper rate declines observed in male adolescents.
Figure 4.
Decomposition of changes in transport injury burden among individuals aged <20 years in the Western Pacific Region from 2000 to 2023. Panels show the decomposition for (A) incidence, (B) prevalence, (C) deaths, and (D) DALYs. DALYs, disability-adjusted life years.
Projected TI burden, 2024–2035
ARIMA projections indicated continued declines in TI burden through 2035, with marked sex-specific differences (Figure 5, Supplementary Tables C1, C2). Among males, all four indicators declined between 2024 and 2035: the incidence rate fell from 497.96 to 466.24 per 100,000, the prevalence rate from 262.45 to 240.29 per 100,000, and the mortality rate from 5.89 to 1.17 per 100,000, while the DALY rate declined substantially from 471.41 to 93.39 per 100,000. Among females, incidence and prevalence remained essentially stable (from 349.33 to 350.83 and from 229.85 to 227.02 per 100,000, respectively), whereas mortality and DALY rates declined progressively, from 3.13 to 0.83 and from 258.11 to 62.50 per 100,000, respectively. Overall, the sex gap in TI-related mortality and DALYs was projected to narrow, although male DALYs remained substantially elevated relative to females through the end of the projection period. Sensitivity analyses using log-transformed ARIMA (1,1,1) models yielded incidence and prevalence trajectories consistent with the primary models (within ±14% at 2035), but more conservative mortality and DALY projections (143%–279% higher at 2035; Supplementary Tables C3, C4, Supplementary Figure S1). These results indicate that the near-zero mortality and DALY projections are sensitive to the assumption of an additive linear decline.
Figure 5.
Age-standardized rates of transport injury burden among individuals aged <20 years in the Western Pacific Region, 2000–2035. Panels A–D display projections for males: (A) incidence, (B) prevalence, (C) deaths, and (D) DALYs. Panels E–H display projections for females: (E) incidence, (F) prevalence, (G) deaths, and (H) DALYs. Solid lines represent observed rates (2000–2023); dashed lines represent ARIMA-model projections (2024–2035) with 95% confidence intervals (shaded areas). ASR, age-standardized rate; DALYs, disability-adjusted life years.
Discussion
Transport-injury burden in the WPR declined between 2000 and 2023, but the remaining caseload was large. Both incident cases and age-standardised incidence rates fell, yet an estimated 2.43 million cases occurred in 2023. Mortality rates varied far more across locations than incidence rates, and rankings by rate did not match rankings by absolute number. Males aged 15–19 years had the highest incidence, mortality and DALY rates.
The downward regional trend resembles previous reports of declining age-standardised road-injury mortality and DALY rates (1, 4–7, 9). Changes in travel exposure, road-user mix, road and vehicle safety, enforcement, and trauma care may all have contributed. Their individual contributions cannot be estimated because the data lack location-specific measures of exposure, policy implementation and care pathways.
Projected trends differed between fatal and non-fatal outcomes. Incidence and prevalence declined modestly in males but remained broadly stable in females, whereas mortality and DALY rates fell in both sexes. Comparable ARIMA approaches have been used to project GBD-based estimates of dementia in Japan and cardiometabolic liver disease in Asia (20, 21). These forecasts suggest that deaths may continue to decline without a comparable reduction in non-fatal injury. Nevertheless, log-transformed ARIMA sensitivity analyses suggested that the projected near-elimination of mortality and DALYs should be interpreted with caution. Prevention and rehabilitation services will therefore remain necessary, particularly for older adolescent males, while stable incidence and prevalence among females merit continued monitoring.
Geographic differences in mortality may reflect variation in road-user mix, speed environments, helmet and restraint use, injury severity, emergency-care access and death registration (22). Prehospital trauma care remains limited in many low- and middle-income settings (23). Evidence on the effectiveness of organised trauma systems is less consistent and appears to depend on context (24). Rankings for small territories require particular caution because a small number of events can produce high rates and wide UIs. Rates describe burden relative to population size, whereas counts more directly indicate the likely scale of prevention and service needs.
The age and sex profiles were also distinct. The higher burden in males is consistent with broader evidence on sex differences in health loss and crash outcomes (25, 26). Incidence rates were similar between males and females at ages 0–4 years but diverged across later age groups. Greater mobility, road exposure and risk-taking during adolescence could contribute to this difference. Studies in Chinese adolescents have associated traffic injury with road-safety attitudes, behaviour, exposure and sensation seeking (27, 28). Alcohol is also an established contributor to injury burden worldwide (29). The GBD estimates contain no behavioural or exposure data with which to examine these explanations across the WPR.
The diversity of transport environments across the WPR argues for locally selected measures rather than a uniform regional programme. Available evidence supports packages combining legislation and enforcement with safer road design, education and post-crash care (30–34). The appropriate mix depends on local road-user and injury-mechanism data. Relevant measures include motorcycle helmet use, occupant restraints, pedestrian protection, graduated licensing and alcohol control (35–41). Experience from China and Cambodia indicates that legislation has limited value without sustained implementation and enforcement (42, 43).
The regional patterns also resemble those reported in recent global analyses. An all-injury study found a greater burden in males, peak incidence at ages 15–19 years and road traffic injury among the leading causes in people aged <20 years (44). The GBD 2023 road-injury analysis reported declining global incidence and mortality, although progress differed across income groups (45). The category examined here includes both road injuries and other transport injuries. These aggregate estimates cannot, by themselves, determine which interventions are most appropriate for an individual location.
Analysing incidence, prevalence, mortality and DALYs within the same GBD framework allowed fatal and non-fatal outcomes to be compared on a common regional scale. Comparing rates with counts showed that relative burden and service caseload do not necessarily identify the same locations. The age- and sex-specific analyses also identified males aged 15–19 years as the group with the highest rates across several outcomes.
Interpretation depends on the quality and structure of the underlying GBD inputs. The estimates draw on surveillance, census and vital-registration data that differ in completeness across locations. Uncertainty is greater where observations are sparse, and single-year rankings may overstate differences among small territories. The regional series also applies current WHO membership retrospectively. Comparisons between regional totals and location-level sums require consistent treatment of the GBD hierarchy, particularly for China and Taiwan.
The aggregate transport-injury category provides no information on road-user type or injury mechanism. The extracted data also contain no direct measures of travel exposure, policy implementation, prehospital care or in-hospital treatment. A single APC for 2000–2023 may conceal non-linear or recent changes, and uncertainty in the GBD estimates was not propagated through the APC calculation. Further work will require road-user-specific surveillance linked to exposure and care-pathway data.
Conclusion
Transport-injury burden among children and adolescents in the WPR declined markedly between 2000 and 2023, largely because age- and sex-specific rates fell. However, progress remained uneven across locations, and males aged 15–19 years retained the greatest burden. The 2024–2035 ARIMA projections suggest modest declines in male age-standardised incidence and prevalence rates, whereas corresponding female rates may remain broadly stable. These trends imply that past regional gains may not eliminate persistent inequalities without geographically targeted road-safety systems and age-appropriate measures for older adolescent males. The projections offer a directional signal for surveillance and planning, while future model refinement should strengthen their use in policy evaluation.
Acknowledgments
We would like to thank the researchers and participants of the GBD.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Sanming Project of Medicine in Shenzhen (SZSM202411014) and the Pre-hospital Emergency Care Financial Reward-based Subsidy Fund (00403000002).
Footnotes
Edited by: Alaric Aroojis, Bai Jerbai Wadia Hospital for Children, India
Reviewed by: Wei He, Children's Hospital at Westmead, Australia
Xiao Lin, Sun Yat-sen University, China
Abbreviations ADF, augmented Dickey–Fuller; AICc, corrected Akaike information criterion; APC, annual percentage change; ARIMA, autoregressive integrated moving average; ASIR, age-standardised incidence rate; ASMR, age-standardised mortality rate; ASPR, age-standardised prevalence rate; ASR, age-standardised rate; CI, confidence interval; DALY, disability-adjusted life year; GATHER, Guidelines for Accurate and Transparent Health Estimates Reporting; GBD, Global Burden of Diseases, Injuries, and Risk Factors Study; IHME, Institute for Health Metrics and Evaluation; TI, transport injury; UI, uncertainty interval; WHO, World Health Organization; WPR, Western Pacific Region.
Data availability statement
The data analysed in this study are publicly available through the Global Health Data Exchange and the GBD Results Tool (https://vizhub.healthdata.org/gbd-results/). Derived results supporting the conclusions are provided in the Supplementary Tables. Further inquiries may be directed to the corresponding authors.
Ethics statement
This study used publicly available, aggregated, non-identifiable estimates. It involved no participant recruitment and no access to individual-level records; ethical approval and informed consent were therefore not applicable.
Author contributions
RS: Writing – original draft, Formal analysis, Software, Data curation. QJ: Conceptualization, Writing – original draft, Methodology, Software. CY: Data curation, Writing – review & editing, Methodology. QL: Investigation, Methodology, Writing – review & editing, Software. KZ: Methodology, Writing – review & editing, Formal analysis. HG: Writing – review & editing, Supervision, Project administration, Visualization. GW: Project administration, Writing – review & editing, Supervision, Writing – original draft. CC: Funding acquisition, Validation, Resources, Writing – original draft.
Conflict of interest
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer XL declared a shared parent affiliation with the authors RS, QJ, QL, KZ, HG, CC to the handling editor at the time of review.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2026.1946652/full#supplementary-material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data analysed in this study are publicly available through the Global Health Data Exchange and the GBD Results Tool (https://vizhub.healthdata.org/gbd-results/). Derived results supporting the conclusions are provided in the Supplementary Tables. Further inquiries may be directed to the corresponding authors.