Disease burden of Streptococcus pneumoniae and Neisseria meningitidis meningitis in children and adolescents: A population-based study

Abstract

Bacterial meningitis remains a major, life-threatening infection in children and adolescents, with Streptococcus pneumoniae meningitis (SPM) and Neisseria meningitidis meningitis (NMM) accounting for most cases. This study aimed to quantify the global burden and spatiotemporal patterns of SPM and NMM in individuals aged 0 to 19 years from 1990 to 2021, and to provide evidence for integrated, cross-pathogen prevention and improved clinical pathways. Using Global Burden of Disease 2021 estimates for 204 countries and territories, we extracted deaths and disability-adjusted life years (DALYs) for SPM and NMM in individuals aged 0 to 19 years (1990–2021). Trends were assessed using estimated annual percentage change and Joinpoint regression. Spearman correlation and concentration indices quantified inequalities across socio-demographic index (SDI) levels, decomposition analysis identified burden drivers, and Bayesian age–period–cohort modeling projected trends to 2050. In 2021, SPM caused 20,718 deaths (95% UI 14,718–29,192) and 1,823,058 disability-adjusted life years (DALYs; 95% UI 1,301,814–2,561,107), while NMM caused 17,389 deaths (95% UI 12,705–23,603) and 1,552,383 DALYs (95% UI 1,146,164–2,096,751) among those aged 0 to 19. From 1990 to 2021, the overall burden of both pathogens fell substantially worldwide, with a steeper decline for NMM. Decomposition pointed to epidemiological change as the main driver. Burden clustered in children under 5; among those younger than ten, males exceeded females. The magnitude of decline diminished with age. NMM showed a brief global uptick around 1996. Around 2011, Streptococcus pneumoniae supplanted Neisseria meningitidis as the principal pathogen contributing to meningitis burden in this population. Relative inequality between high-SDI and low-SDI countries has continued to widen, with the burden becoming increasingly concentrated in low-SDI regions. From 1990 to 2021, the global burden of SPM and NMM in children and adolescents declined substantially, with NMM declining faster and SPM overtaking NMM around 2011. Burden is highest in children under 5, with males more affected than females under 10. Despite absolute reductions, relative inequality widened, concentrating burden in low-SDI regions such as Western Sub-Saharan Africa. Integrated strategies including expanded vaccination, rapid diagnostics, and strengthened pediatric critical care are essential to reduce burden and narrow inequities.

1. Introduction

Meningitis refers to the inflammation of the meninges caused by infections from pathogens such as bacteria, viruses, fungi, and parasites, or by noninfectious factors such as autoimmune diseases and tumors.^[1] The 2019 Global Burden of Disease (GBD) study revealed that meningitis ranked sixth among the causes of disability-adjusted life years (DALYs) in children under the age of 10, highlighting its significant public health impact in this age group.^[2] Among these, bacterial meningitis is one of the most severe central nervous system infections in children and adolescents, with approximately 12.8% of survivors experiencing debilitating neurological sequelae or moderate to severe neurodevelopmental disorders, and some cases leading to death.^[1,3] The impact does not end at discharge, since long-term loss of function places financial pressure on families and generates substantial costs for society.^[4] Prevention and care continue to rely on vaccination, early intravenous antibiotics, and comprehensive neuroprotective management. Progress has been real, yet notable hurdles remain, including serotype replacement, the rise of non-vaccine serotypes, and increasing antimicrobial resistance.^[1,5] Targeted health policies and practical interventions are therefore needed to lower risk in high-risk groups and to manage spending. These efforts depend on solid epidemiologic data to guide prevention, control, and clinical management.

The most common pathogens responsible for bacterial meningitis in children and adolescents are Streptococcus pneumoniae (SPN) and Neisseria meningitidis (NM).^[5] Both are encapsulated bacteria with high nasopharyngeal carriage rates in children and adolescents, and their polysaccharide capsules facilitate immune evasion, promoting colonization, bloodstream invasion, and entry into the central nervous system by crossing the blood-brain barrier.^[6,7] Both pathogens are associated with higher risks of severe disease and long-term sequelae (approximately 24.7% for SPN and 9.5% for NM).^[3] While the case fatality and disability rates are higher for SPN, NM is more likely to cause outbreaks and spread through close contact.^[5] Public health prevention and control of these 2 pathogens mainly rely on vaccination, which reduces nasopharyngeal carriage to inhibit transmission, decrease disease incidence, and establish herd immunity. However, the overall effectiveness of this strategy is still limited by serotype diversity and the replacement of vaccine types by non-vaccine types.^[5] Despite recent progress, most studies of meningitis burden still pool all ages together, offer limited age-specific detail, and rarely separate the impact of individual pathogens.^[8–10] For SPN and NM in children and adolescents, a group at high risk with serious and lasting harm, the true scale and pattern of burden remain unclear. This uncertainty makes it difficult to design targeted prevention and control measures and to support clinical decisions, which in turn reduces the effectiveness of public health policy.

Using data from GBD 2021, we assess in detail the burden of meningitis caused by SPN and NM in children and adolescents. The aim is to provide reliable and current evidence to shape prevention strategies and guide the allocation of healthcare resources.

2. Methods

2.1. Data sources and extraction

This study used data from the GBD 2021 database (https://vizhub.healthdata.org/gbd-results/) to systematically extract and analyze the burden of Streptococcus pneumoniae meningitis (SPM) and NMM among individuals aged 0 to 19 years for the period 1990 to 2021. Analyses were performed at 4 geographic levels: global, 5 SDI-defined strata (low, low-middle, middle, high-middle, and high SDI), the 21 GBD regions, and all 204 countries and territories.^[2,11] The target population comprised confirmed cases of SPM or NMM aged 0 to 19 years, grouped by the GBD 2021 age categories (0–4, 5–9, 10–14, and 15–19 years). We further examined burden trends by sex (male, female, and both sexes combined) within each age group. The primary outcome measures were Deaths and DALYs, with 95% uncertainty intervals reported. All rates were age-standardized using the GBD global standard population to derive age-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) for improved comparability. In this report ASMRs are expressed per 100,000 population and ASDRs per 100,000 person-years. As this study exclusively used publicly available, anonymized data from the GBD 2021 database, ethical approval was not required.

2.2. Statistical analysis framework

All data processing, statistical analyses, and graphical outputs were performed using Excel 2021 and R 4.4.2 with packages including dplyr, officer, and ggplot2. A two-sided P < .05 was considered statistically significant.

2.2.1. Trend analysis

We evaluated temporal trends in SPM and NMM from 1990 to 2021 using 2 complementary approaches.^[12,13] First, estimated annual percentage change (EAPC) was calculated by fitting a log-linear regression model ln(Y) = α + βX + ε, where Y denotes the age-standardized rate (ASR), X denotes calendar year, and β is the slope coefficient; EAPC was then computed as 100 × [exp(β) − 1]. Second, Joinpoint regression analysis was conducted using the Joinpoint Regression Program (version 4.9.1.0). The software identified inflection points via Monte Carlo permutation tests and provided the average annual percent change over the whole study period and the annual percent change (APC) for each segment. Trend direction was judged from point estimates and their 95% confidence intervals (CIs): both estimate and CI > 0 indicate an increasing trend; both <0 indicate a decreasing trend; if the 95% CI includes zero, the trend was considered statistically stable.

2.2.2. SDI correlation and inequality analysis

To characterize the relationship between socio-demographic index (SDI) and disease burden, we first examined overall monotonic associations between SDI and ASRs for SPM and NMM using Spearman rank correlation. To capture potential non-linear patterns, we fitted scatter plots with locally weighted scatterplot smoothing. For health-inequality assessment we computed 2 summary measures: the slope index of inequality (SII) and the concentration index (CI). SII quantifies absolute inequality by regressing ASDRs against the midpoint of the cumulative population distribution ranked by SDI; CI quantifies relative inequality as the area between the concentration curve (cumulative proportion of ASDR vs cumulative proportion of population) and the line of equality.^[14,15] In addition, we constructed a health-frontier model with ASDR as the response variable: smoothed efficiency frontiers were generated using local weighting and local polynomial regression under varying bandwidths. To improve robustness, we performed 100 bootstrap replications to estimate mean ASDRs at each SDI level and, by comparing observed ASDRs with the frontier minima, quantified potential improvement gaps in burden reduction across regions.^[15]

2.2.3. APC, BAPC and decomposition analyses

For modeling and forecasting, we employed both the age–period–cohort (APC) model and a Bayesian age–period–cohort (BAPC) model.^[15] The APC model, specified with a Poisson likelihood, decomposes observed counts into age, period, and cohort effects to evaluate their contributions to ASMRs and ASDRs and to comprehensively characterize the complex dynamics of disease burden. Forecasting from 2022 to 2050 was conducted using the BAPC framework: this model likewise adopts a Poisson observation model, decomposes variability into age, period, and cohort components, and uses the integrated nested laplace approximation algorithm to obtain posterior distributions efficiently. Model performance was assessed using rigorous cross-validation to ensure reliable future burden estimates.

In the decomposition analysis, we used demographic attribution to separate the observed changes in burden over time into 3 drivers: shifts in the population age structure, changes in epidemiological risk reflected by age-specific rates, and variation in population size.^[13,14] With this framework, we estimated how much each driver contributed to the overall change in the burden of SPM and NMM.

3. Results

3.1. Global burden

In 2021, the ASMR for SPM in the 0–19 age group was 0.83 (95% UI: 0.59, 1.18), and ASDR was 73.30 (95% UI: 52.12, 103.33). This translates to 20,718 deaths (95% UI: 14,718, 29,192) due to SPM globally in this age group in 2021, resulting in 1823,058 DALYs (95% UI: 1,301,814, 2,561,107). For NMM in the same year, the ASMR was 0.69 (95% UI: 0.50, 0.95), and the ASDR was 62.00 (95% UI: 45.55, 84.07), with 17,389 deaths (95% UI: 12,705, 23,603) and 1,552,383 DALYs (95% UI: 1,146,164, 2,096,751) globally. From 1990 to 2021, both the ASMR and ASDR for SPM and NMM in the 0 to 19 age group showed a significant downward trend (EAPCs and their 95% CIs were both negative). However, the decline for NMM (EAPC for ASMR: −4.62 [95% CI: −4.95, −4.30]; EAPC for ASDR: −4.62 [95% CI: −4.95, −4.29]) was greater than that for SPM (EAPC for ASMR: −2.93 [95% CI: −3.15, −2.72]; EAPC for ASDR: −2.93 [95% CI: −3.15, −2.72]) (Table 1). Pathogen spectrum evolution analysis showed that the proportion of ASMR and ASDR for NMM decreased steadily from 1990 to 2021, while the proportion for SPM gradually increased. Around 2011, SPN overtook NM as the leading pathogen contributing to meningitis burden in this population (Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/R758).

Table 1.

Age-standardized rates and temporal trends of SPM and NMM burden at global, SDI regional, and GBD regional levels, 1990–2021.

Location	Streptococcus pneumoniae-ASMR			Streptococcus pneumoniae-ASDR			Neisseria meningitidis-ASMR			Neisseria meningitidis-ASDR
	1990 (per 100,000 population, 95% UI)	2021 (per 100,000 population, 95% UI)	EAPCs (95% CI)	1990 (per 100,000 population, 95% UI)	2021 (per 100,000 population, 95% UI)	EAPCs (95% CI)	1990 (per 100,000 population, 95% UI)	2021 (per 100,000 population, 95% UI)	EAPCs (95% CI)	1990 (per 100,000 population, 95% UI)	2021 (per 100,000 population, 95% UI)	EAPCs (95% CI)
Global	2.32 (1.91, 2.80)	0.83 (0.59, 1.18)	−2.93 (−3.15, −2.72)	203.73 (168.58, 245.87)	73.30 (52.12, 103.33)	−2.93 (−3.15, −2.72)	2.89 (2.39, 3.46)	0.69 (0.50, 0.95)	−4.62 (−4.95, −4.30)	256.88 (213.74, 307.20)	62.00 (45.55, 84.07)	−4.62 (−4.95, −4.29)
High SDI	0.15 (0.14, 0.16)	0.02 (0.02, 0.02)	−6.94 (−7.18, −6.71)	13.21 (12.10, 14.39)	1.68 (1.46, 1.91)	−6.80 (−7.03, −6.57)	0.19 (0.18, 0.21)	0.02 (0.01, 0.02)	−8.73 (−9.05, −8.42)	17.20 (15.92, 18.53)	1.49 (1.31, 1.70)	−8.58 (−8.88, −8.27)
High-middle SDI	0.67 (0.55, 0.81)	0.08 (0.07, 0.10)	−6.69 (−6.92, −6.46)	59.22 (48.99, 71.84)	7.22 (5.98, 8.81)	−6.69 (−6.92, −6.46)	0.71 (0.59, 0.85)	0.06 (0.05, 0.08)	−7.82 (−8.07, −7.57)	64.40 (53.37, 77.47)	5.86 (4.88, 7.07)	−7.82 (−8.07, −7.58)
Middle SDI	1.34 (1.14, 1.55)	0.29 (0.22, 0.36)	−4.46 (−4.66, −4.25)	118.31 (100.62, 137.38)	25.35 (19.72, 31.92)	−4.47 (−4.68, −4.26)	1.52 (1.29, 1.74)	0.24 (0.19, 0.30)	−5.71 (−5.98, −5.44)	136.75 (116.30, 156.24)	21.53 (17.32, 26.81)	−5.72 (−5.99, −5.46)
Low-middle SDI	2.80 (2.33, 3.36)	0.80 (0.60, 1.07)	−3.49 (−3.72, −3.26)	245.87 (205.01, 294.73)	70.59 (52.93, 93.84)	−3.48 (−3.71, −3.25)	3.67 (3.06, 4.38)	0.72 (0.54, 0.95)	−4.94 (−5.22, −4.66)	326.33 (273.55, 387.80)	64.71 (49.08, 84.31)	−4.93 (−5.21, −4.65)
Low SDI	6.99 (5.50, 8.90)	2.11 (1.40, 3.18)	−3.54 (−3.74, −3.34)	611.36 (482.31, 778.01)	184.98 (123.31, 278.31)	−3.53 (−3.73, −3.33)	8.89 (7.01, 11.11)	1.71 (1.17, 2.46)	−5.52 (−5.86, −5.18)	784.21 (622.53, 978.50)	151.71 (105.83, 216.66)	−5.51 (−5.85, −5.17)
Andean Latin America	0.76 (0.62, 0.94)	0.08 (0.06, 0.10)	−6.99 (−7.29, −6.69)	66.72 (54.82, 82.41)	6.77 (5.01, 9.16)	−6.96 (−7.25, −6.66)	0.88 (0.72, 1.08)	0.07 (0.05, 0.10)	−8.09 (−8.30, −7.88)	77.43 (64.07, 95.47)	6.34 (4.74, 8.54)	−8.06 (−8.27, −7.85)
Australasia	0.13 (0.12, 0.15)	0.02 (0.02, 0.02)	−7.15 (−7.72, −6.58)	11.60 (10.38, 12.89)	1.71 (1.42, 2.03)	−7.00 (−7.56, −6.44)	0.12 (0.10, 0.13)	0.02 (0.01, 0.02)	−7.84 (−8.89, −6.79)	10.47 (9.37, 11.57)	1.63 (1.36, 1.93)	−7.76 (−8.77, −6.74)
Caribbean	2.28 (1.78, 2.95)	0.98 (0.63, 1.53)	−2.23 (−2.42, −2.04)	202.83 (158.07, 261.59)	88.13 (57.01, 136.07)	−2.20 (−2.39, −2.02)	3.15 (2.54, 3.91)	0.88 (0.57, 1.35)	−3.39 (−3.59, −3.19)	280.07 (226.72, 346.55)	80.12 (52.78, 121.63)	−3.34 (−3.53, −3.15)
Central Asia	1.00 (0.88, 1.13)	0.15 (0.11, 0.21)	−6.39 (−6.71, −6.07)	88.70 (77.57, 99.67)	13.89 (10.02, 19.27)	−6.27 (−6.58, −5.95)	1.30 (1.14, 1.47)	0.12 (0.09, 0.16)	−7.81 (−8.16, −7.46)	116.22 (101.78, 131.46)	11.48 (8.58, 15.34)	−7.61 (−7.95, −7.27)
Central Europe	0.37 (0.34, 0.40)	0.03 (0.02, 0.04)	−7.05 (−7.28, −6.82)	32.87 (30.06,35.59)	2.83 (2.33, 3.49)	−6.99 (−7.22, −6.76)	0.37 (0.34, 0.40)	0.02 (0.02, 0.03)	−8.16 (−8.42, −7.91)	33.76 (30.82, 36.89)	2.17 (1.81, 2.65)	−8.09 (−8.34, −7.85)
Central Latin America	0.64 (0.58, 0.71)	0.10 (0.08, 0.13)	−5.85 (−6.11, −5.59)	56.57 (51.09, 62.06)	8.72 (6.77, 11.58)	−5.83 (−6.09, −5.57)	0.69 (0.62, 0.75)	0.08 (0.07, 0.11)	−6.86 (−7.20, −6.52)	60.93 (54.84, 66.69)	7.54 (5.92, 9.85)	−6.84 (−7.18, −6.51)
Central Sub-Saharan Africa	5.93 (4.17, 7.98)	1.36 (0.85, 2.55)	−4.50 (−4.83, −4.17)	516.45 (365.04, 694.32)	117.28 (74.43, 220.48)	−4.54 (−4.87, −4.21)	8.42 (6.04, 11.15)	1.72 (1.08, 3.08)	−5.16 (−5.69, −4.62)	739.39 (534.42, 973.52)	150.69 (97.04, 268.33)	−5.19 (−5.72, −4.65)
East Asia	1.08 (0.79, 1.39)	0.08 (0.06, 0.11)	−8.31 (−8.62, −7.99)	95.75 (70.24, 123.16)	7.34 (5.69, 9.23)	−8.33 (−8.64, −8.02)	1.13 (0.84, 1.43)	0.06 (0.04, 0.07)	−9.77 (−10.08, −9.46)	102.06 (75.66, 128.43)	5.22 (4.08, 6.48)	−9.80 (−10.09, −9.50)
Eastern Europe	0.38 (0.35, 0.41)	0.07 (0.06, 0.08)	−5.29 (−5.61, −4.96)	34.62 (31.67, 37.87)	6.26 (5.46, 7.14)	−5.27 (−5.59, −4.95)	0.41 (0.38, 0.45)	0.06 (0.05, 0.07)	−6.40 (−6.81, −5.99)	38.48 (35.11, 42.35)	5.68 (4.96, 6.50)	−6.37 (−6.76, −5.98)
Eastern Sub-Saharan Africa	7.74 (5.80, 10.11)	1.67 (1.18, 2.31)	−4.79 (−5.05, −4.52)	675.98 (507.83, 883.03)	145.62 (103.33, 201.44)	−4.79 (−5.06, −4.53)	10.49 (7.80, 13.40)	1.56 (1.12, 2.18)	−6.17 (−6.45, −5.89)	923.83 (691.32, 1177.08)	138.23 (99.70, 191.06)	−6.18 (−6.45, −5.90)
High-income Asia Pacific	0.09 (0.07, 0.11)	0.01 (0.01, 0.01)	−7.72 (−7.96, −7.48)	7.89 (6.52, 9.26)	0.75 (0.64, 0.89)	−7.21 (−7.45, −6.97)	0.09 (0.07, 0.11)	0.01 (0.01, 0.01)	−8.45 (−8.75, −8.15)	8.00 (6.57, 9.38)	0.68 (0.58, 0.81)	−7.91 (−8.20, −7.62)
High-income North America	0.11 (0.10, 0.11)	0.01 (0.01, 0.02)	−7.15 (−7.46, −6.84)	9.45 (8.65, 10.31)	1.30 (1.11, 1.49)	−7.07 (−7.37, −6.77)	0.21 (0.19, 0.23)	0.01 (0.01, 0.02)	−9.92 (−10.34, −9.48)	18.76 (17.09, 20.61)	1.27 (1.10, 1.45)	−9.77 (−10.19, −9.36)
North Africa and Middle East	1.04 (0.83, 1.31)	0.22 (0.15, 0.33)	−4.84 (−5.12, −4.56)	91.68 (73.31, 115.56)	19.50 (13.60, 29.10)	−4.80 (−5.08, −4.52)	1.50 (1.18, 1.92)	0.20 (0.14, 0.30)	−6.21 (−6.69, −5.73)	133.68 (105.07, 170.23)	18.16 (12.56, 26.77)	−6.15 (−6.62, −5.67)
Oceania	1.44 (1.08, 1.95)	0.58 (0.36, 0.92)	−2.86 (−3.15, −2.58)	129.89 (98.10, 174.22)	52.67 (33.41, 83.11)	−2.82 (−3.10, −2.53)	1.64 (1.23, 2.23)	0.56 (0.35, 0.89)	−7.24 (−8.22, −6.24)	152.11 (115.52, 203.70)	53.30 (34.47, 82.44)	−7.05 (−8.01, −6.09)
South Asia	2.43 (1.92, 3.04)	0.70 (0.52, 0.94)	−3.46 (−3.69, −3.23)	213.77 (170.06, 266.45)	62.49 (46.59, 83.45)	−3.42 (−3.64, −3.19)	2.93 (2.35, 3.66)	0.65 (0.48, 0.87)	−4.41 (−4.69, −4.13)	263.10 (213.40, 326.98)	59.27 (44.28, 79.00)	−4.38 (−4.65, −4.11)
Southeast Asia	2.34 (1.80, 2.95)	0.63 (0.48, 0.80)	−3.91 (−4.06, −3.75)	206.43 (159.71, 260.38)	55.34 (42.24, 70.10)	−3.93 (−4.09, −3.77)	2.62 (2.03, 3.29)	0.54 (0.41, 0.68)	−4.96 (−5.17, −4.75)	233.88 (181.80, 293.01)	47.40 (36.64, 59.84)	−5.00 (−5.20, −4.79)
Southern Latin America	0.46 (0.41, 0.51)	0.07 (0.05, 0.08)	−5.37 (−5.78, −4.96)	41.06 (36.98, 45.57)	5.96 (4.89, 7.22)	−5.39 (−5.80, −4.99)	0.48 (0.44, 0.53)	0.06 (0.05, 0.07)	−6.46 (−6.73, −6.19)	43.49 (39.43, 47.88)	5.03 (4.17, 5.98)	−6.51 (−6.78, −6.25)
Southern Sub-Saharan Africa	1.50 (1.18, 1.85)	0.73 (0.54, 0.99)	−1.79 (−2.25, −1.33)	130.27 (103.21, 161.26)	62.78 (46.36, 84.66)	−1.85 (−2.30, −1.40)	1.71 (1.37, 2.13)	0.78 (0.58, 1.05)	−2.74 (−3.14, −2.35)	150.88 (121.01, 187.00)	67.19 (50.46, 90.17)	−2.83 (−3.21, −2.44)
Tropical Latin America	1.29 (1.13, 1.45)	0.11 (0.08, 0.13)	−7.72 (−8.16, −7.27)	114.22 (100.30, 128.55)	9.64 (7.79, 11.69)	−7.68 (−8.11, −7.24)	2.17 (1.91, 2.44)	0.09 (0.08, 0.11)	−10.74 (−11.23, −10.24)	191.34 (168.64, 214.89)	8.64 (7.04, 10.43)	−10.65 (−11.14, −10.16)
Western Europe	0.16 (0.14, 0.17)	0.02 (0.02, 0.03)	−6.33 (−6.69, −5.97)	13.81 (12.71, 14.85)	2.11 (1.82, 2.44)	−6.23 (−6.58, −5.88)	0.17 (0.16, 0.18)	0.02 (0.02, 0.02)	−7.52 (−7.83, −7.22)	15.12 (14.10, 16.19)	1.74 (1.52, 1.99)	−7.44 (−7.74, −7.15)
Western Sub-Saharan Africa	9.34 (6.75, 12.11)	3.06 (1.77, 4.95)	−3.10 (−3.37, −2.83)	816.54 (591.58, 1056.80)	267.21 (155.86, 430.80)	−3.10 (−3.38, −2.83)	11.90 (8.97, 15.43)	2.11 (1.31, 3.31)	−5.91 (−6.40, −5.41)	1046.03 (793.53, 1351.90)	186.55 (116.73, 289.05)	−5.90 (−6.39, −5.41)

EAPCs quantify temporal trends: negative values indicate decline, with larger absolute values reflecting faster rates.

ASDR = age-standardized DALY rate, ASMR = age-standardized mortality rate, CI = confidence interval, EAPC = estimated annual percentage change, GBD = Global Burden of Disease, NMM = Neisseria meningitidis meningitis, SPM = Streptococcus pneumoniae meningitis, UI = uncertainty interval.

3.2. Regional burden

In 2021, across the 5 SDI regions, the ASMR (0.02 [95% UI: 0.02, 0.02]) and ASDR (1.68 [95% UI: 1.46, 1.91]) for SPM were lowest in high SDI regions. Similarly, the ASMR (0.02 [95% UI: 0.01, 0.02]) and ASDR (1.49 [95% UI: 1.31, 1.70]) for NMM were also lowest in the high SDI regions. In contrast, the highest ASMR (2.11 [95% UI: 1.40, 3.18]) and ASDR (184.98 [95% UI: 123.31, 278.31]) for SPM were observed in low SDI regions, while the highest ASMR (1.71 [95% UI: 1.17, 2.46]) and ASDR (151.71 [95% UI: 105.83, 216.66]) for NMM were also in low SDI regions. In all 5 SDI regions, both SPM and NMM demonstrated significant downward trends in ASMR and ASDR (EAPCs and their 95% CIs were all negative) (Table 1).

In 2021, within the 21 GBD regions, the ASMR (0.01 [95% UI: 0.01, 0.01]) and ASDR (0.75 [95% UI: 0.64, 0.89]) for SPM were lowest in the high-income Asia Pacific region. Similarly, NMM had the lowest ASMR (0.01 [95% UI: 0.01, 0.01]) and ASDR (0.68 [95% UI: 0.58, 0.81]) in this region. In contrast, the highest ASMR (3.06 [95% UI: 1.77, 4.95]) and ASDR (267.21 [95% UI: 155.86, 430.80]) for SPM were observed in Western Sub-Saharan Africa, and the highest ASMR (2.11 [95% UI: 1.31, 3.31]) and ASDR (186.55 [95% UI: 116.73, 289.05]) for NMM were similarly highest in this region. From 1990 to 2021, the ASMR and ASDR for both SPM and NMM exhibited significant declines in all GBD regions, with East Asia showing the most pronounced decreases in both SPM and NMM ASMRs and ASDRs (Table 1).

3.3. National burden

In 2021, Andorra reported the lowest disease burden for both SPM and NMM among the 204 countries and territories. Specifically, Andorra’s SPM ASMR was 0.00 (95% UI: 0.00, 0.00) and ASDR was 0.19 (95% UI: 0.13, 0.26), while the corresponding figures for NMM were 0.00 (95% UI: 0.00, 0.00) and 0.14 (95% UI: 0.10, 0.20). The 3 countries with the lowest SPM ASMR and ASDR were Andorra, Armenia, and Australia. For NMM, the countries with the lowest ASMR were Andorra, Republic of Korea, and Singapore, while those with the lowest ASDR were Andorra, Republic of Korea, and Japan. In stark contrast, South Sudan had the highest global disease burden for both SPM and NMM. The ASMR for SPM in South Sudan was 5.64 (95% UI: 3.68, 8.14) and the ASDR was 494.51 (95% UI: 324.75, 711.59); the ASMR and ASDR for NMM were 5.67 (95% UI: 3.63, 8.05) and 504.42 (95% UI: 328.02, 705.86), respectively. The 3 countries with the highest ASMR for both SPM and NMM were South Sudan, Chad, and Niger. The countries with the highest ASDR for SPM were the same, while for NMM, the countries with the highest ASDR were South Sudan, Somalia, and the Central African Republic (Fig. 1 and Table S1, Supplemental Digital Content, https://links.lww.com/MD/R759).

Figure 1.

Disease burden of SPM and NMM in 204 countries and territories in 2021. (A) ASMR of SPM; (B) ASDR of SPM; (C) ASMR of NMM; (D) ASDR of NMM. World maps display the geographic distribution of disease burden among children and adolescents aged 0–19 yr, with darker colors indicating higher burden. ASDR = age-standardized DALY rate, ASMR = age-standardized mortality rate, DALYs = disability-adjusted life years, NMM = Neisseria meningitidis meningitis, SPM = Streptococcus pneumoniae meningitis.

From 1990 to 2021, the ASMR and ASDR for SPM declined in the vast majority of countries. The only exceptions were Dominica, Lesotho, Mauritius, and Seychelles, where the burden of SPM showed an increasing trend. Mauritius saw the most significant increase, with EAPCs for ASMR and ASDR reaching 4.81 (95% CI: 3.77–5.85) and 4.19 (95% CI: 3.26–5.12), respectively. Among the countries with the most significant declines, Türkiye, Puerto Rico, and Republic of Korea experienced the fastest decreases in SPM ASMR, while Türkiye, Saudi Arabia, and Republic of Korea saw the most rapid declines in SPM ASDR. For NMM, Mauritius was the only country where both ASMR and ASDR showed an increasing trend. The countries with the most significant declines in NMM ASMR were Brazil, Republic of Korea, and Türkiye, while the countries with the most pronounced reductions in NMM ASDR were Brazil, Türkiye, and Sudan (Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/R758 and Table S1, Supplemental Digital Content, https://links.lww.com/MD/R759).

3.4. Analysis of age-sex-time associations

Age-sex associations indicated that the disease burden of SPM and NMM in the 0 to 19 age group was primarily concentrated in children under 5 years of age, with a gradual decline as age increased. Within the under-10 age group, the disease burden was higher in males than in females, while no significant sex differences were observed beyond 10 years of age (Fig. 2A–D). Age-time associations showed that globally, as well as across all SDI regions, both the deaths and DALYs due to SPM in children under 5 years of age exhibited a marked decline over time, whereas the decline in deaths and DALYs in the 5 to 19 age group was more gradual. Similarly, global and regional trends for NMM deaths and DALYs also demonstrated an overall decline, with a transient increase around 1996, particularly in low SDI, low-middle SDI, and middle SDI regions. The 5 to 19 age group also showed a slow decline over time. Furthermore, the most severe disease burden was observed in the under-5 age group (Fig. S3, Supplemental Digital Content, https://links.lww.com/MD/R758). Sex-time associations indicated a consistent decline in the ASMR and ASDR for both males and females with regard to SPM globally, and similar trends were observed for NMM, with a brief increase around 1996 globally, and notably in low SDI, low-middle SDI, and middle SDI regions. The trends were consistent across both sexes (Fig. S4, Supplemental Digital Content, https://links.lww.com/MD/R758). Analysis of net and local drifts suggested that as age increased, the rate of decrease in ASMR and ASDR for both SPM and NMM slowed down (Fig. 2E–H). After controlling for period and cohort effects, age-effect analysis showed that the ASMR and ASDR for both SPM and NMM decreased with age, with the most significant decrease occurring in children under 5 years of age (Fig. S5, Supplemental Digital Content, https://links.lww.com/MD/R758). After excluding age and cohort effects, period effects indicated a clear decline in the ASMR and ASDR for both SPM and NMM between 1990 and 2021 (Fig. S6, Supplemental Digital Content, https://links.lww.com/MD/R758). After excluding age and period effects, cohort effects revealed that earlier birth cohorts had higher ASMR and ASDR for both SPM and NMM than later cohorts (Fig. S7, Supplemental Digital Content, https://links.lww.com/MD/R758).

Figure 2.

Analysis of age-sex associations, local drifts, and Joinpoint regression for SPM and NMM. (A) Deaths due to SPM; (B) DALYs due to SPM; (C) Deaths due to NMM; (D) DALYs due to NMM; (E) ASMR of SPM; (F) ASDR of SPM; (G) ASMR of NMM; (H) ASDR of NMM; (I) ASMR of SPM; (J) ASDR of SPM; (K) ASMR of NMM; (L) ASDR of NMM. Panels A–D show age-sex distribution of absolute burden; pink bars indicate females and blue bars indicate males. Panels E–H display local drifts from the APC model; curves show age-specific annual percentage changes and horizontal dashed lines represent net drift. Panels I–L present Joinpoint regression with observed values (dots) and fitted trends (lines); vertical dashed lines indicate inflection points. ASDR = age-standardized DALY rates, ASMR = age-standardized mortality rates, APC = age–period–cohort, DALYs = disability-adjusted life years, NMM = Neisseria meningitidis meningitis, SPM = Streptococcus pneumoniae meningitis.

Joinpoint regression analysis identified significant downward trends in both the ASMR and ASDR for SPM (−3.349 [95% CI: −3.422, −3.276]) and NMM (−4.747 [95% CI: −5.123, −4.371]) from 1990 to 2021. Specifically, the ASMR for SPM showed inflection points around 1994, 2006, 2013, and 2018, while the ASDR for SPM had inflection points around 1994, 2007, 2013, and 2018, with a slowdown in the decline after 2018. For NMM, inflection points were observed around 1995 and 2008, with a more pronounced decline in ASMR and ASDR after 2008 (Fig. 2I–L).

3.5. Disease burden and SDI correlation

Significant negative correlations were observed between SDI and disease burden metrics for both SPM and NMM in the 0 to 19 age group. At the regional level (21 GBD regions), strong correlations were found for SPM (ASMR: r = −0.902, 95% CI: −0.920–−0.882; ASDR: r = −0.903, 95% CI: −0.920–−0.882; both P < .001) and NMM (ASMR: r = −0.901, 95% CI: −0.916–−0.884; ASDR: r = −0.902, 95% CI: −0.917–−0.885; both P < .001). At the national level (204 countries and territories), similar patterns were observed for SPM (ASMR: r = -0.875, 95% CI: −0.905–−0.841; ASDR: r = −0.880, 95% CI: −0.909–−0.846; both P < .001) and NMM (ASMR: r = −0.878, 95% CI: −0.906–−0.847; ASDR: r = −0.883, 95% CI: −0.909–−0.851; both P < .001). As SDI increased, both ASMR and ASDR steadily decreased (Fig. 3 and Table S2, Supplemental Digital Content, https://links.lww.com/MD/R759).

Figure 3.

Correlation between SDI and disease burden. (A) ASMR of SPM in 21 regions; (B) ASDR of SPM in 21 regions; (C) ASMR of NMM in 21 regions; (D) ASDR of NMM in 21 regions; (E) ASMR of SPM in 204 countries and territories; (F) ASDR of SPM in 204 countries and territories; (G) ASMR of NMM in 204 countries and territories; (H) ASDR of NMM in 204 countries and territories. Panels A–D show regional-level scatter plots (1990–2021) with LOWESS smoothing curves. Panels E–H display country-level scatter plots with individual countries labeled. ASDR = age-standardized DALY rates, ASMR = age-standardized mortality rates, APC = age–period–cohort, DALYs = disability-adjusted life years, LOWESS = locally weighted scatterplot smoothing, NMM = Neisseria meningitidis meningitis, SDI = socio-demographic index, SPM = Streptococcus pneumoniae meningitis.

Cross-country inequality showed significant absolute and relative inequality between the ASDR of SPM and NMM and SDI (Fig. 4A–D). The SII indicated that from 1990 to 2021, the disparity in SPM ASDR between the highest and lowest SDI countries decreased from − 511.80 (95% CI: −549.53, −474.07) to −116.04 (95% CI: −129.46, −102.62). Similarly, the ASDR disparity for NMM decreased from −672.67 (95% CI: −728.56, −616.78) to −112.05 (95% CI: −125.32, −98.78). This suggests a reduction in absolute inequality between the highest and lowest SDI countries, with the disease burden primarily concentrated in low SDI regions. Furthermore, the CI for SPM increased from −0.61 (95% CI: −0.65, −0.56) in 1990 to −0.63 (95% CI: −0.68, −0.57) in 2021, and for NMM, the CI increased from −0.61 (95% CI: −0.66, −0.55) to −0.63 (95% CI: −0.68, −0.57), indicating an exacerbation of relative inequality between high and low SDI countries, with a growing concentration in low SDI regions.

Figure 4.

Cross-country inequality and frontier analysis. (A) SII of SPM; (B) SII of NMM; (C) CI of SPM; (D) CI of NMM; (E) Frontier analysis of SPM; (F) Frontier analysis of NMM. Panels A–B show SII regression comparing 1990 (blue) and 2021 (red); SII measures absolute inequality, with decreasing absolute values indicating narrowing gaps. Panels C–D display concentration curves; curves above the diagonal indicate burden concentration in lower-SDI populations, with CI values shown for 1990 and 2021. Panels E–F present frontier analysis: left sub-panels show temporal trends (1990–2021), right sub-panels show 2021 data with countries labeled by trend direction (blue = decrease, red = increase). ASDR = age-standardized DALY rates, ASMR = age-standardized mortality rates, APC = age–period–cohort, CI = concentration index, DALYs = disability-adjusted life years, NMM = Neisseria meningitidis meningitis, SDI = socio-demographic index, SII = slope index of inequality, SPM = Streptococcus pneumoniae meningitis.

Frontier analysis identified that the 15 countries and regions with the largest gap from the best disease burden for SPM included South Sudan, Chad, Nigeria, Guinea, Burkina Faso, Central African Republic, Comoros, Haiti, Kiribati, Benin, Malawi, Mali, Uganda, Cameroon, and Eritrea. The potential range of improvement in these regions was between 120.50 and 430.84. Among low SDI countries (SDI < 0.50), the countries with the smallest frontier gaps were Solomon Islands, Yemen, Somalia, Nepal, and Bangladesh. In high SDI countries (SDI > 0.85), the countries with the largest frontier gaps were Lithuania, Switzerland, Denmark, Iceland, and the Netherlands. For NMM, the 15 countries with the largest gap from the best disease burden were South Sudan, Central African Republic, Guinea, Nigeria, Kiribati, Haiti, Comoros, Malawi, Chad, Angola, Eritrea, Sierra Leone, Pakistan, Uganda, and Zambia, with the potential range of improvement between 117.82 and 426.18. In low SDI countries, the countries with the smallest frontier gaps were Solomon Islands, Yemen, Somalia, Niger, and Nepal. In high SDI countries, the largest frontier gaps were observed in Lithuania, Denmark, Switzerland, Netherlands, and the United Kingdom (Fig. 4E, F and Table S3, Supplemental Digital Content, https://links.lww.com/MD/R759, Table S4, Supplemental Digital Content, https://links.lww.com/MD/R759).

3.6. Decomposition analysis

At the global level, population growth was the primary factor driving the increase in ASMR and ASDR for both SPM and NMM. In contrast, epidemiological change was the main driver behind the decline in ASMR and ASDR. At the regional level, population growth contributed to the increase in ASMR and ASDR in low SDI and low-middle SDI regions, while epidemiological change led to reductions in both ASMR and ASDR across all SDI strata. Particularly in South Asia, Western Sub-Saharan Africa, and Eastern Sub-Saharan Africa, the correlation between epidemiological change and reductions in ASMR and ASDR was more significant (Fig. S8, Supplemental Digital Content, https://links.lww.com/MD/R758).

3.7. Predictive analysis

From 2022 to 2050, the overall disease burden of SPM and NMM in the 0 to 19 age group is expected to decrease significantly. By 2050, the ASMR and ASDR for SPM will decrease to 0.13 (95% UI: 0.00, 0.26) and 11.28 (95% UI: −0.10, 22.66), respectively, while for NMM, the ASMR and ASDR will decrease to 0.06 (95% UI: −0.01, 0.14) and 5.34 (95% UI: −0.91, 11.60), respectively (Fig. 5 and Table S5, Supplemental Digital Content, https://links.lww.com/MD/R759, Table S6, Supplemental Digital Content, https://links.lww.com/MD/R759, Table S7, Supplemental Digital Content, https://links.lww.com/MD/R759, Table S8, Supplemental Digital Content, https://links.lww.com/MD/R759).

Figure 5.

Projected global burden of SPM and NMM (2022–2050) using BAPC model. (A) ASMR of SPM; (B) ASDR of SPM; (C) ASMR of NMM; (D) ASDR of NMM. Black dots with lines show observed values (1990–2021); red dots with shaded areas show projections with 95% credible intervals (2022–2050); vertical dashed line indicates forecast starting point. ASDR = age-standardized DALY rates, ASMR = age-standardized mortality rates, ASR = age-standardized rate, BAPC = Bayesian age–period–cohort, DALYs = disability-adjusted life years, NMM = Neisseria meningitidis meningitis, SPM = Streptococcus pneumoniae meningitis.

4. Discussion

This study, based on the GBD database, provides the first systematic assessment of the disease burden of SPM and NMM in the 0 to 19 age group from 1990 to 2021. In 2021, approximately 20,000 children and adolescents globally died from SPM, resulting in over 1.8 million DALYs, while around 17,000 died from NMM, leading to 1.5 million DALYs. Over the past 32 years, the burden of both pathogens declined substantially, with NMM declining more rapidly and SPN consequently overtaking NM as the leading pathogen around 2011. The burden remains highest in children under 5, with males disproportionately affected compared to females under age 10. Decomposition analysis identified epidemiological change as the primary driver of burden reduction. Notably, despite these absolute reductions, relative inequality between high- and low-SDI countries has widened, concentrating disease burden in low-SDI regions.

The high degree of synchrony between the trends for both pathogens suggests a common underlying driving mechanism. Widespread vaccination has driven much of the decline. Since 2000, many countries – including the United States, China, and several in the European Union – have added pneumococcal conjugate vaccines (PCV) for SPN and meningococcal vaccines (Men) for NM to their national programs, with infants and other high-risk groups given priority. As these strategies took hold, full infant coverage rose from roughly 30% to well over 90%, herd immunity emerged, and both incidence and mortality fell markedly.^[16–18] Biology and bedside practice reinforce these gains. SPN and NM both colonize the nasopharynx and spread through respiratory droplets^[19]; either can cross the blood-brain barrier and invade the central nervous system.^[6,7] In children and adolescents, early presentations often look alike, with fever, headache, vomiting, and changes in mental status, and the cerebrospinal fluid profile is similar in both infections; together these features allow prompt recognition and empiric treatment that covers both pathogens.^[5] Evidence from multiple studies indicates that starting a third-generation cephalosporin with vancomycin lowers the risk of severe illness and death.^[1,20,21] Gains in basic health services and access to care linked to global socioeconomic progress have further supported these measures.^[19] As clinics and hospitals become more available, opportunities for environmental exposure and transmission fall, and as primary care expands, the time from first symptoms to diagnosis and treatment in remote areas shortens, which reduces the burden tied to delayed care.^[22–24] Causative patterns have shifted over time. Around 2011, SPN overtook NM as the leading cause of bacterial meningitis in this population. Large MenA conjugate campaigns across the African meningitis belt in 2010 to 2011 likely played a key part,^[25] along with routine MenC programs in Europe and the United States from 2000 to 2010 and adolescent MenACWY vaccination.^[17] At the same time, rising SPN resistance, particularly to penicillin and macrolides, remains a concern; rates are high in many regions and show uneven upward trends.^[26,27] These realities have moved empiric choices toward third generation cephalosporins such as ceftriaxone.^[28] Looking ahead, slowing the spread of resistance will depend on broader PCV use even when program costs are higher, careful antimicrobial stewardship, and sustained genomic surveillance.^[29–31] Rapid diagnostics can help: real-time multiplex PCR and metagenomic next-generation sequencing of cerebrospinal fluid detect SPN quickly and with high sensitivity and specificity, enabling earlier targeted therapy and timely de-escalation of antibiotic coverage.^[30,32]

Our decomposition analysis indicates that shifts in epidemiology were the main force behind the decline in the burden of SPM and NMM, showing the benefits of existing control measures. Projections to 2050 point to a further and marked drop in both conditions, providing empirical backing and practical support for the WHO roadmap, “Defeating meningitis by 2030.”^[33] We also observed a short-lived rise in the burden of NMM around 1996, which coincided with large outbreaks in sub-Saharan Africa and parts of Asia and Europe.^[34–36] At that time, immunization programs relied mainly on polysaccharide vaccines that have weaker immunogenicity, short protection, and do not induce mucosal immunity or durable herd effects.^[34,37,38] Gaps in laboratory diagnosis and early warning in some regions further delayed responses and allowed transmission to continue. Taken together, these lessons argue for sensitive epidemiologic and climate-sensitive surveillance networks as the pentavalent conjugate vaccine is rolled out globally,^[39] along with rapid diagnosis and timely management of close contacts to prevent a repeat of clustered outbreaks.

Across all 5 SDI strata and the 21 GBD regions, both the ASMR and ASDR for SPM and NMM fell markedly. Even so, low-SDI settings continue to carry a far heavier burden than high-SDI areas, with Western Sub-Saharan Africa shouldering the greatest load. After group A outbreaks were brought under control with MenAfriVac, serogroups C, W, and parts of X still triggered repeated outbreaks in several West African countries between 2011 and 2017. Laboratory confirmation remained low (median about 20%), and reactive vaccination often started late (median response time about 36 days), which kept the burden high.^[40] In many places PCV is given on a 3 + 0 schedule without a booster, making herd protection for unvaccinated older children hard to sustain in high-transmission settings.^[41] Weak laboratory capacity, difficult specimen transport, and population movement further slow case confirmation and rapid response, reducing both coverage and timeliness of routine and emergency vaccination.^[42] East Asia shows the steepest decline in SPM and NMM burden. Early adoption and sustained coverage of conjugate vaccines – supported by strong surveillance and rapid-response capacity – laid the groundwork for the observed decline, and advances in pediatric critical care plus more prudent antimicrobial use have reinforced it.^[18,43] National trajectories, however, diverge. In 2021, South Sudan carried the highest burden, largely because PCV had not been introduced as a routine, nationwide program; meningococcal vaccination was deployed mainly in outbreak settings, with low uptake, fragile cold chains, and notable wastage – constraints that blunted control.^[44] Mauritius shows a different picture: its burden has risen over 3 decades. Routine PCV began only around 2016 after a long gap, and coverage since then has fluctuated with outbreaks, weakening both direct and herd protection.^[45] As a small island state, even intermittent clusters or imported cases can shift ASRs and the EAPC enough to suggest an upward trend.^[46] By contrast, the Republic of Korea exhibits one of the steepest declines, underpinned by early, high PCV uptake, targeted MenACWY for older children in well-defined high-risk settings, and an integrated surveillance and pediatric care system that has sustained the downward trajectory.^[47,48]

Cross-country patterns are more complicated than a single trend line suggests. Absolute gaps in SPM and NMM among children and adolescents have narrowed, yet relative gaps have widened, and a growing share of the burden now falls on low-SDI settings. The pace of progress clearly differs: high-SDI countries have moved faster and more steadily, while many low-SDI countries have slowed or even stalled, which enlarges the relative divide. Differences in tools and systems help account for this. Beyond wider use of conjugate vaccines, many high-SDI programs employ multiplex PCR in routine diagnostics, shortening the time from sampling to pathogen identification and to antibiotic step-down; this improves detection and strengthens treatment and isolation strategies.^[49] They also maintain organized pathways for early antimicrobial therapy and pediatric critical care, including ventilation, seizure control with sedation when needed, and intracranial pressure management. These capacities reduce both mortality and long-term sequelae.^[50] In most low-SDI countries, many of these pillars remain weak. Frontier analysis adds a note of caution. Several high-SDI countries, such as Lithuania, Switzerland, Denmark, Iceland, the Netherlands, and the United Kingdom, have not achieved the reductions expected for their level of development, indicating that the underlying causes merit closer study.

In children and adolescents aged 0 to 19, most of the burden from SPM and NMM falls on those younger than 5, a result that aligns with prior work on meningitis overall.^[8] Biology offers a clear explanation: immune defenses in early life are still developing, particularly against polysaccharide antigens; maternal antibodies wane after birth and create a window of susceptibility; and young children commonly carry these pathogens in the nasopharynx.^[51–53] The study also reveals, for the first time, that the rate of decline in the disease burden of SPM and NMM decreases with age. This may be attributed to the significant reduction in vaccine-type strains following the completion of primary PCV immunization, while non-vaccine types (such as 8, 12F, and 9N) have increased in relative proportion among older age groups.^[54] Additionally, NM nasopharyngeal carriage peaks during adolescence; although individual disease conversion rates decrease due to acquired immunity, the overall decline in the population level becomes less pronounced.^[55] Moreover, older children and adolescents are more susceptible to post-infection immune imbalances and excessive inflammatory responses, increasing the risk of severe outcomes such as shock and coagulopathy,^[56,57] and their clinical presentations are often atypical, leading to delays in early antimicrobial treatment.^[5] Furthermore, the relatively low absolute case numbers result in unstable estimates of ASR and EAPC/average annual percent change, widening confidence intervals and limiting the absolute space for further decline, which manifests as a “flattening slope” in graphical representations/models. The study also shows that the disease burden of SPM and NMM is greater in males than in females under 10 years of age, consistent with previous research on overall meningitis.^[58] This may be attributed to generally stronger innate and adaptive immune responses in females, who tend to have higher levels of humoral responses^[59] and produce higher vaccine-induced antibody levels,^[60] making boys more susceptible to invasive infections and complications under similar exposure conditions.

5. Limitations

This study has several limitations. GBD estimates rely largely on surveillance and registry data that are pooled through modeling. In settings where monitoring is limited, including many low- and middle-income countries, the burden among children and adolescents may be understated or biased because of underreporting, delayed notification, and diagnostic misclassification. These weaknesses reduce accuracy and undermine comparability across settings. Scale poses another constraint: GBD estimates focus on global, national, and broad regional levels, making it difficult to resolve within-country heterogeneity – provincial variation, urban–rural gaps, and clusters in marginalized groups. Although spatiotemporal smoothing stabilizes estimates, it can also obscure local outbreaks and pockets of vulnerability. Pathogen resolution is limited as well: GBD provides little serotype or genotype detail and sparse data on resistance profiles, restricting assessments of vaccine-type versus non-vaccine-type replacement, resistance trajectories, and their contributions to overall disease burden.

6. Conclusion

From 1990 to 2021, the burdens of SPM and NMM fell markedly, and current trajectories point to further declines. The downturn has not been uniform: an increasing share now occurs in low-SDI settings. Most of the overall reduction reflects epidemiologic change rather than demography, and the heaviest impact remains in children under 5. Among those younger than ten, boys carry more burden than girls, and the pace of decline tapers with age. Although SPM and NMM track in broadly similar directions, they differ in timing, rate, and pathogen mix. NMM showed a short-lived global uptick around 1996 and then declined more steeply than SPM, leading to a shift in the primary cause of bacterial meningitis from NM to SPN around 2011. To drive the burden lower and narrow inequities, programs should maintain full-course vaccination and reinforce primary healthcare. Rapid pathogen testing and optimized antimicrobial therapy deserve priority, and standardized pediatric critical-care pathways can enable timely stabilization and complication prevention – measures that strengthen both prevention and control.

Acknowledgments

The authors sincerely thank the Global Burden of Disease Study 2021 (GBD 2021) and its collaborators for providing the data necessary for this research. Thanks are also due to Professor Lin Qin for her contributions in data statistics.

Author contributions

Conceptualization: Jian Zhang, Qin Jiang.

Data curation: Jian Zhang, Lin Qin.

Formal analysis: Jian Zhang, Lin Qin.

Methodology: Ning Liu.

Supervision: Qin Jiang, Fan Zhang.

Writing – original draft: Jian Zhang.

Writing – review & editing: Jian Zhang, Qin Jiang, Lin Qin, Ning Liu, Fan Zhang.

Supplementary Material

medi-105-e48455-s001.pdf ^{(1MB, pdf)}

medi-105-e48455-s002.pdf ^{(987.2KB, pdf)}