Study design and data sources

This cross-sectional analysis utilized publicly available data from the Global Burden of Disease Study 2023 (GBD 2023), coordinated by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington [11]. GBD 2023 provides standardized, comparable estimates of disease burden across 371 diseases and injuries for 204 countries and territories from 1990 to 2023, using Bayesian meta-regression models (DisMod-MR 2.1) to synthesize data from population-based surveys, administrative records, cohort studies, and systematic reviews.

Data extraction: We downloaded GBD 2023 data files directly from the IHME Global Health Data Exchange (GHDx) portal (https://ghdx.healthdata.org/gbd-results-tool). Three Excel files were extracted: (1) containing prevalence and case counts for vision loss, hearing loss, and intellectual disability; (2) containing population denominators by country, sex, and age group; and (3) containing severity-level distributions for each condition. Data were extracted using Python 3.11 (pandas 1.5.3) with automated filtering by location, sex, age group, and cause (REI code). All extractions were verified through double-checking procedures.

Geographic Scope: Our analysis included 25 countries and territories in the LAC region: Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Panama, Paraguay, Peru, Suriname, Trinidad and Tobago, Uruguay, and Venezuela. All aggregated regional estimates for LAC reflect this cohort of 25 countries (S1 Table).

Study population

The study encompassed school-age children and adolescents (5–19 years) residing in the 25 LAC countries. Age stratification followed GBD standard groupings: early childhood (5–9 years) and adolescence (10–19 years). The total population denominator was 149,283,445 individuals based on GBD 2023 population estimates.

Case definitions and diagnostic criteria

Data processing and prevalence calculations

Prevalence Extraction: For each country, condition, sex, and age group, we extracted prevalence (percentage) and case counts (number) directly from GBD 2023 data files. Data were filtered using the following criteria: location (country name), sex (Male, Female, Both), age (5–9 years, 10–19 years), and cause (REI code: sense_vision for vision loss, sense_hearing for hearing loss, neuro_intellectual for intellectual disability). Uncertainty intervals (95% UI) were extracted as lower and upper bounds from GBD draw-level estimates (2.5th and 97.5th percentiles of 1,000 draws).

Regional Aggregation: Regional prevalence estimates for LAC were calculated as population-weighted averages:

where represents the number of children and adolescents with the condition in country i and represents the total population of children and adolescents aged 5–19 years in country i. This approach ensures that countries with larger school-age populations contribute proportionally more to regional estimates. Regional uncertainty intervals were calculated by propagating country-level uncertainty through the aggregation formula.

Severity Distribution Extraction: Severity-level distributions were extracted directly from GBD 2023 severity split files. For each condition and country, we extracted the proportion of cases in each severity category (e.g., for vision: blindness, severe vision loss, moderate vision loss, presbyopia). Absolute numbers per severity level were calculated as: Total children and adolescents with condition Severity proportion. Distributions were validated against published clinical series from Brazil, Mexico, and Chile, with mean absolute deviations of <2 percentage points.

Country-Level Tables: Country-specific prevalence estimates stratified by sex and age group were compiled into supplementary tables (S1 Table: vision, S2 Table: hearing, S3 Table: intellectual disability). Country-specific severity distributions were compiled into supplementary tables (S4 Table: vision, S5 Table: hearing, S6 Table: intellectual disability).

Statistical Approach: This study is descriptive. Prevalence estimates are reported with their 95% uncertainty intervals derived from the GBD 2023 modelling framework, and differences between groups (by sex or age) and between LAC and OECD are expressed as absolute differences in prevalence (percentage points), relative differences (%), and ratios. No inferential hypothesis testing (e.g., p-values) was performed, as the analysis relies on modelled population-level estimates for which uncertainty intervals, rather than significance tests, are the appropriate basis for interpreting the precision of and differences between estimates. This approach is consistent with contemporary guidance to move beyond dichotomous statistical significance testing toward the estimation and transparent interpretation of effects and their uncertainty [20].

Software: All data extraction, processing, and calculations were performed using Python 3.11 (pandas 1.5.3, numpy 1.24.3).

Economic analysis

Perspective and Cost Framework: We adopted a societal perspective following WHO-CHOICE guidelines [21], incorporating: (1) direct medical costs (screening, diagnosis, devices, therapy, medications); (2) direct non-medical costs (transportation, accommodation, special education); (3) indirect costs (productivity losses using human capital approach, family support time valued at minimum wage); and (4) intangible costs (quality of life impacts, not monetized). All monetary values were converted and standardized to 2023 US dollars (US$).

Intervention Costing: Unit costs were derived from multiple sources: (1) WHO-CHOICE database (2021 update) for LAC region [21]; (2) national health insurance reimbursement schedules (Brazil SUS 2023, Mexico IMSS 2023, Chile FONASA 2023); (3) systematic review of 47 published economic evaluations in LAC (2010–2023); and (4) market prices from medical device suppliers. To accurately reflect the regional economic and operational reality, adjustments were applied using the following equation: Base cost 0.7 (public sector efficiency factor) 1.3 (implementation inefficiency factor) Purchasing Power Parity (PPP) conversion factor (country-specific, range 0.3–0.8). The 0.7 multiplier reflects the approximately 30% cost-savings systematically achieved through centralized bulk purchasing and the absence of profit margins in universal health systems, consistent with observed public sector procurement efficiencies in middle-income countries [22]. Conversely, the 1.3 multiplier captures real-world implementation frictions—such as supply chain wastage, administrative overhead, and geographic barriers to reaching remote populations—which typically add 20% to 40% to base commodity costs, aligning with established WHO-CHOICE methodology for programme support costs in resource-limited settings [23]. The specific impact of these bounds on the final economic outcomes was rigorously verified in our deterministic sensitivity analyses. Specific unit costs: Vision screening US$5–15 per child, spectacles US$15–35 per pair; Hearing screening US$3–8 per child, hearing aids US$400–3,000 per pair, cochlear implants US$35,000–50,000 per device; Intellectual disability early intervention US$2,000–25,000 per child over 3 years. It is important to note that the costs reported for cochlear implants represent only the initial surgical and device investments. Lifelong maintenance and hardware upgrades are substantial, yet they were not calculated in this study due to high variability across different device brands, models, and national healthcare coverage policies in the LAC region.

Effectiveness Measurement: Primary outcomes: Disability-Adjusted Life Years (DALYs) averted using GBD 2023 disability weights (vision loss: 0.004–0.195 depending on severity; hearing loss: 0.010–0.215; intellectual disability: 0.012–0.304). Secondary outcomes: Educational attainment gains (years of schooling completed, from cohort studies), employment probability increases (formal sector participation rates), and Quality-Adjusted Life Years (QALYs) for sensitivity analysis using disability weight conversions.

Economic Modeling: We developed Markov cohort models for each condition using TreeAge Pro 2023. Health states: (1) no impairment; (2) mild/moderate impairment with assistive technology; (3) severe/profound impairment without access; (4) post-intervention with assistive technology; (5) death. Annual cycle length (1 year) aligned with academic calendars and cost data availability. Transition probabilities derived from: longitudinal cohort studies in Brazil (n = 3 studies, 2015–2022), Chile (n = 2 studies, 2018–2023), and United States (n = 5 studies, 2010–2023); expert elicitation (n = 12 experts) when empirical data sparse. Models calibrated to reproduce observed severity distributions from GBD 2023 draws; goodness-of-fit evaluated via root mean square error (target <0.05). Time horizon: 20 years (base case), lifetime (sensitivity analysis). Discounting: 3% annually for costs and outcomes (base case); 0% and 5% in sensitivity analysis, following WHO-CHOICE recommendations. Scenario analyses: intervention uptake (50%, 70%, 90%), device replacement cycles (3, 5, 7 years), wage-growth trajectories (0%, 1%, 2% real annual growth).

Return on Investment (ROI) Calculation:

Benefits quantified: (1) Lifetime earnings increases using Mincer wage equations adjusted for LAC labor markets (elasticity: 0.10 per additional year of schooling, from World Bank 2023); (2) Reduced special education costs (average US$5,000–20,000 per child-year avoided); (3) Decreased healthcare utilization (20–40% reduction, from cohort studies); (4) Avoided institutionalization costs (US$20,000–80,000 per child-year), noting that institutionalization is recognized as inconsistent with the rights of persons with disabilities under the CRPD [15]. All benefits discounted at 3% annually. Costs: Intervention costs as described above, including initial costs and recurring costs (device replacement, follow-up visits). The full set of ROI assumptions and input parameters underlying these calculations is documented in S7 Table.

Cost-Effectiveness Thresholds: Following WHO recommendations: Very cost-effective: GDP per capita per DALY averted; Cost-effective: GDP per capita per DALY averted. Regional GDP per capita: US$9,276 (population-weighted average across 25 LAC countries, World Bank 2023).

Critical windows analysis

We synthesized evidence on optimal intervention timing from systematic reviews on early intervention effectiveness (Cochrane, Campbell Collaboration), neurodevelopmental literature on sensitive periods, longitudinal cohort studies tracking intervention outcomes, and expert consensus guidelines (WHO, professional societies). Effect sizes (Cohen’s d) calculated for interventions at different ages, with meta-analysis using random-effects models given expected heterogeneity.

Quality assurance

Data validation included benchmarking GBD prevalence against national household surveys (Brazil PNAD Contínua 2022, Mexico ENSANUT 2021, Chile CASEN 2022), which yielded mean absolute deviations of 0.3 percentage points across conditions. Severity splits were cross-checked with administrative datasets (Brazil SIA/SUS assistive device delivery records, Chile DEIS rehabilitation registries), and country estimates failing GBD’s three-star quality threshold were flagged for sensitivity analysis. Sensitivity analyses varied key parameters and repeated prevalence calculations excluding countries with limited primary data. External validation compared results with published regional studies, while expert review involved disability researchers and policymakers from five LAC countries. All reporting followed Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guidelines for global health estimates.

Ethical considerations

This study did not require ethics committee approval. The analysis used exclusively publicly available, aggregated, population-level data from the Institute for Health Metrics and Evaluation (Global Burden of Disease 2023), WHO/PAHO PLISA, the World Bank, UNESCO, and national statistical agencies. No individual-level data, human participants, human specimens, animals, or field-collected materials were involved. We acknowledge the importance of respectful language, using person-first terminology throughout (“children and adolescents with disabilities” rather than “disabled children and adolescents”). The research was conducted with awareness of potential stigmatization and emphasizes capabilities and intervention opportunities rather than deficits.

Certain interventions such as cataract surgery are often considered necessary to prevent further deterioration. However, others such as cochlear implants can be accused of reinforcing a narrative that one’s body needs correction to be considered normal. The DALYs metric used in the Global Burden of Disease methodology is also subject to ongoing methodological debate, particularly regarding the weighting scheme and the interpretation of severe disability states.

Overall burden and regional distribution

Among the 149,283,445 children and adolescents aged 5–19 years across the 25 LAC countries analyzed, model-based estimates indicate that 9,282,921 individuals (6.22%; 95% UI: 5.89–6.54%) were living with at least one of the three disability conditions examined. This prevalence, while lower than some global estimates that include additional disability types, represents a substantial population requiring targeted interventions and/or support systems (Table 1).

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Table 1. Comprehensive prevalence and burden of sensory and intellectual disabilities among children and adolescents aged 5-19 years in Latin America and the Caribbean compared with OECD countries, Global Burden of Disease 2023.

https://doi.org/10.1371/journal.pone.0348693.t001

The distribution across disability types revealed distinct epidemiological patterns. Hearing loss emerged as the predominant condition, affecting 5,423,844 children and adolescents (3.63%; 95% UI: 3.41–3.86%), representing 58.4% of all children and adolescents estimated cases. This was followed by intellectual developmental disability affecting 2,560,545 children and adolescents (1.71%; 95% UI: 1.58–1.85%), accounting for 27.6%, and vision loss affecting 1,298,632 children and adolescents (0.87%; 95% UI: 0.79–0.96%), comprising 14.0% of the total.

Geographic concentration analysis revealed that Brazil and Mexico, the region’s most populous countries, account for a disproportionate share of total cases, but not of prevalence. Brazil harbors 2,812,480 children and adolescents with at least one disability (30.3% of the regional population experiencing these disabilities) with a combined prevalence of 5.88%, slightly below the regional average. Mexico accounts for 2,211,703 children and adolescents (23.8% of the regional total) with a combined prevalence of 6.12%, approximating the regional mean. Together, these two countries represent 54.1% of all children and adolescents living with the studied disabilities while comprising 59% of the regional child and adolescent population, suggesting marginally lower disability prevalence in these larger countries possibly due to better healthcare infrastructure in urban centers.

Country-level heterogeneity and patterns

Substantial heterogeneity emerged across countries, with overall disability prevalence ranging from 4.82% in Uruguay to 7.93% in Haiti, representing a 1.6-fold variation. This heterogeneity was most pronounced for vision loss (2.7-fold variation from 0.42% in Barbados to 1.15% in Argentina), moderate for hearing loss (1.7-fold from 2.36% in Uruguay to 4.10% in Brazil), and least for intellectual disability (1.4-fold from 1.53% in Peru to 2.08% in Guatemala) (S1 Table, S2 Table, S3 Table).

Country-level analysis revealed distinct clusters. High-prevalence countries (>7% combined prevalence) included Haiti (7.93%), Guatemala (7.52%), Honduras (7.31%), and Bolivia (7.18%), characterized by limited health infrastructure, high poverty rates, and substantial rural populations. Medium-prevalence countries (5–7%) encompassed the majority of LAC nations including Brazil, Mexico, Colombia, and Peru. Low-prevalence countries (<5%) included Uruguay (4.82%), Chile (4.95%), and Costa Rica (5.08%), notably countries with more developed health systems and higher Human Development Indices.

Demographic patterns: age and sex distribution

Age-specific prevalence demonstrated distinct patterns by condition. Hearing loss prevalence increased steadily with age: 3.05% at 5−9 years and 3.92% at 10−19 years (28.5% relative increase), likely reflecting cumulative exposure to risk factors including noise, ototoxic medications, and untreated infections. Vision loss showed a similar but less pronounced gradient: 0.62% at 5−9 years and 0.99% at 10−19 years (59.7% relative increase), consistent with progressive myopia and delayed detection of refractive errors. This age gradient was more pronounced in females (69.7% increase from 0.66% to 1.12%) compared to males (50.0% increase from 0.58% to 0.87%). Intellectual disability prevalence remained relatively stable across age groups (1.69–1.76%), reflecting its early-onset nature with most children and adolescents identifiable by school age (S1 Table, S2 Table, S3 Table).

Sex differences revealed distinct patterns across conditions. Hearing loss and intellectual disability showed male predominance: hearing loss 1.17:1 (3.92% males vs 3.34% females), intellectual disability 1.41:1 (2.00% vs 1.42%). In contrast, vision loss demonstrated an inverted pattern with female predominance: female-to-male ratio 1.26:1 (0.97% females vs 0.77% males). This atypical pattern for vision loss likely reflects behavioral differences (greater time dedicated to reading and near-work activities among girls), differential symptom reporting, and earlier puberty in females affecting ocular growth and refraction. The male predominance in hearing loss and intellectual disability likely reflects biological factors (X-linked conditions, hormonal influences on neurodevelopment) and social determinants (differential exposure to occupational noise, care-seeking behaviors) (S1 Table, S2 Table, S3 Table).

Severity distribution: implications for intervention

The distribution of severity within each condition provides critical insights for intervention planning and resource allocation (Table 2). Country-specific severity distributions are detailed in S4 Table (vision), S5 Table (hearing), and S6 Table (intellectual disability).

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Table 2. Detailed severity distribution and intervention requirements for sensory and intellectual disabilities in LAC children and adolescents.

https://doi.org/10.1371/journal.pone.0348693.t002

For vision loss, the high concentration in the moderate category (86.37%) highlights both the magnitude of the need and the opportunity for effective support. Approximately 1,121,609 children and adolescents may experience barriers to learning associated with unaddressed refractive errors, many of whom could benefit from access to simple spectacles costing US$25–35. The smaller proportion requiring more specialized support (blindness 4.72%, severe 5.97%) necessitates targeted services but represents a manageable demand for regional capacity building (S4 Table).

Hearing loss severity distribution similarly favors intervention, with 87.57% of affected children and adolescents (4,749,393 individuals) in mild-to-moderate categories where conventional hearing aids provide substantial benefit. However, the 5.31% (287,870 children and adolescents) with profound or complete hearing loss potentially requiring cochlear implants represents a significant technological and financial challenge, with each implant costing US$35,000–50,000 plus lifetime support (S5 Table).

Intellectual disability presents a more complex distribution. While 61.69% fall into borderline-to-mild categories and may achieve functional independence with appropriate support, the 38.31% with moderate-to-profound disability require intensive, lifelong support systems that remain limited in most LAC countries.

Comparison with OECD countries: identifying gaps

Comparative analysis with OECD countries revealed striking epidemiological differences suggesting both challenges and opportunities (Table 1).

Hearing loss prevalence was 33.1% higher in LAC (3.63% vs 2.73%), with the excess concentrated in severe categories (50% higher prevalence of severe-to-complete hearing loss). This differential likely reflects higher exposure to risk factors including untreated otitis media (estimated 10–15% of LAC children and adolescents vs 2–3% in OECD), ototoxic medication use without monitoring, environmental noise in unregulated urban settings. Furthermore, while limited neonatal hearing screening coverage in LAC (30% vs 95% in OECD) leads to missed opportunities for early intervention, which can exacerbate the severity of the condition over time, it also fundamentally contributes to the under-identification of cases. Consequently, similarly to what is observed with vision loss, the reported 33.1% difference is likely an underestimate of the true disparity, suggesting that the actual prevalence gap between LAC and OECD countries may be even greater.

Paradoxically, vision loss prevalence was 26.3% lower in LAC (0.87% vs 1.18%). This unexpected finding likely reflects measurement artifacts rather than genuinely better eye health. Possible explanations include: (1) higher OECD screening rates detecting mild presentations missed in LAC, (2) myopia epidemic in East Asian OECD countries not yet affecting LAC, (3) earlier correction in OECD preventing progression to measurable impairment, or (4) systematic under-diagnosis in LAC rural areas.

Intellectual disability showed 13.6% lower prevalence in LAC (1.71% vs 1.98%), almost certainly reflecting under-diagnosis rather than lower true prevalence. Given higher rates of risk factors in LAC (malnutrition, prematurity, perinatal complications), the expected prevalence would be equal or higher than OECD. The gap suggests 400,000–1,200,000 children and adolescents with intellectual disability remain unidentified in LAC, with profound implications for service planning.

Critical intervention windows: timing matters

Analysis of intervention timing revealed stark differences in outcomes based on developmental windows (Table 3).

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Table 3. Comprehensive economic analysis of childhood and adolescents disabilities in LAC: costs, investments, and returns.

https://doi.org/10.1371/journal.pone.0348693.t003

The current economic costs associated with disability prevalence and its impacts are estimated at US$19.3–29.0 billion annually, equivalent to 0.34–0.51% of regional GDP. The largest component is associated with special education needs (US$6.4–9.6 billion), highlighting costs to education systems that may be reduced through early screening and support. Future productivity losses, calculated using age-earnings profiles and employment probability reductions, amount to US$6.7–9.7 billion annually for each affected cohort.

Investment requirements for comprehensive intervention programs total US$9.45–14.23 billion annually, approximately half the current cost of inaction. This investment would provide: universal screening programs at critical ages, devices and assistive technologies for all who need them, therapy and educational support services, health system strengthening including workforce training, and family support and education programs.

The economic case for intervention is compelling across all three conditions but varies in magnitude. Vision programs demonstrate exceptional returns (10–15:1 benefit-cost ratio) due to low intervention costs and high impact on educational and economic outcomes. A child receiving glasses at age 6 gains an average 2.1 years of education and US$28,000 in lifetime earnings for an investment of US$150–230.

Hearing interventions show strong returns (5–8:1) despite higher device costs. Early identification and hearing aid provision yields 1.8 additional years of schooling and US$45,000 in lifetime productivity gains. While cochlear implants are expensive (US$35,000–50,000), they remain cost-effective for profound hearing loss.

Intellectual disability interventions, while showing the lowest returns (2–3:1), remain economically justified. Early intervention programs costing US$5,000–10,000 annually can enable mild intellectual disability individuals to achieve supported employment, generating US$22,000 in lifetime productivity while reducing family burden and institutionalization costs.

Health system capacity and service gaps

Assessment of current service availability revealed substantial gaps between identified needs and service provision.

For vision loss, while 60–70% of affected children and adolescents are eventually detected (often years late), only 25–35% receive and consistently use corrective lenses. Barriers include cost (even low-cost glasses exceed family means), availability (limited optical services in rural areas), cultural factors (stigma, cosmetic concerns), and quality (inappropriate prescriptions, poor-fitting frames).

Hearing loss shows the weakest cascade, with only 30% detected (usually after language delays manifest), 20% receiving proper audiological assessment, 10–15% obtaining hearing aids, and <5% of children and adolescents with severe presentations accessing cochlear implants. The “lost to follow-up” rate between detection and intervention approaches 50%, reflecting fragmented referral systems.

Intellectual disability services are similarly inadequate, with 40% identified (often only moderate-severe presentations), 25% receiving comprehensive assessment, 20% accessing special education services, and <10% receiving evidence-based early intervention during the critical 0–3 year window.

Principal findings in context

This comprehensive analysis of 9.3 million children and adolescents estimated with sensory and intellectual disabilities across Latin America and the Caribbean reveals both the magnitude of unmet need and the transformative potential of evidence-based interventions. The overall prevalence of 6.22% aligns with but refines previous global estimates, while uncovering critical regional specificities that challenge assumptions about disability in middle-income settings.

The predominance of hearing loss as the leading disability (3.63% prevalence) diverges from high-income country patterns and reflects LAC’s unique epidemiological profile [2]. The 33% excess compared to OECD countries cannot be dismissed as measurement artifact given the biological plausibility of higher risk exposure. Untreated otitis media, affecting 10–15% of LAC children and adolescents compared to 2–3% in high-income settings, creates a preventable pathway to permanent hearing loss [8]. The widespread use of ototoxic aminoglycosides without audiological monitoring in resource-limited settings, environmental noise in rapidly expanding informal urban settlements, and persistent vaccine-preventable infections (rubella, measles, mumps) compound the burden [7].

The concentration of children and adolescents in mild-to-moderate categories across all three conditions highlights important opportunities to strengthen early identification and support. The finding that 89% of vision loss can be effectively addressed with simple spectacles points to important gaps in service delivery, with over one million children and adolescents experiencing barriers to learning due to unaddressed refractive errors, despite the availability of low-cost solutions (US$25 per pair of glasses). The evidence suggests that these gaps are driven by constraints in service organization, financing, and outreach, rather than by limitations in available technology. [9].

Severity distributions: opportunities hidden in plain sight

The severity distributions identified in this analysis provide a useful basis for prioritizing support. The high concentration of vision loss in moderate categories that can be effectively addressed with glasses (86.37%) highlights a key opportunity for early intervention. The estimated 1.12 million children and adolescents with unaddressed refractive errors represent a substantial population at risk of barriers to learning. Without appropriate support, these children and adolescents may experience difficulties in classroom participation, fall behind academically, and face increased risks of school dropout, with potential long-term implications for educational and socioeconomic outcomes. [24] (S4 Table).

Similarly, the 87.6% of children and adolescents with hearing loss who could benefit from conventional hearing aids, alongside those who learning sign language, challenges narratives of inevitable linguistic isolation. While the global market presents a broad price spectrum—ranging from adult Over-the-Counter (OTC) devices starting around US$200 to premium prescription models exceeding US$8,000—standard pediatric prescription hearing aids are clinically required for this age group. These standard prescription devices, typically costing between US$800 and US$3,000 per pair in the region, can restore functional hearing for the vast majority of affected children and adolescents, just as early exposure to sign language provides a rich and complete foundation for communication. The tragedy is not the technology gap but the coverage and accessibility gap—with current hearing aid coverage in LAC estimated at only 10–20% and limited access to sign language education, millions of children and adolescents remain trapped in preventable communication barriers [2]

The intellectual disability distribution, with 61.7% in borderline-to-mild categories, similarly challenges fatalistic approaches. These 1.58 million children and adolescents are capable of meaningful participation, functional literacy, vocational skills, and semi-independent living with appropriate support. The stark reality that less than 30% access any special education services, and fewer than 10% receive early intervention during the critical 0–3 year window, represents a massive failure to realize human potential [25].

The paradox of LAC versus OECD comparisons

The paradoxical findings of lower vision loss and intellectual disability prevalence in LAC compared to OECD countries merit careful interpretation. For vision loss, the 26% lower prevalence almost certainly reflects measurement and health system artifacts rather than genuinely better eye health. Several mechanisms likely contribute: (1) OECD’s universal vision screening detects mild presentations missed in LAC; (2) the myopia epidemic affecting East Asian OECD countries has not yet reached LAC; (3) earlier correction in OECD prevents progression to measurable impairment; and (4) systematic under-detection in LAC rural and indigenous populations where access to eye care is minimal [26].

A similar phenomenon of systematic under-identification obscures the true burden of hearing loss in the region. The stark contrast in neonatal hearing screening coverage—estimated at approximately 30% in LAC compared to over 95% in OECD countries—inevitably leaves a vast number of cases undetected. Consequently, children and adolescents with mild to moderate hearing loss, or those with progressive auditory conditions, often remain undiagnosed until they present with profound language delays in school settings. Because lower screening coverage directly contributes to this under-identification, the true prevalence difference between LAC and OECD is potentially much greater than current estimates suggest. If screening capacities were equalized, LAC’s prevalence would likely reflect the region’s higher exposure to critical risk factors, such as untreated congenital infections (e.g., cytomegalovirus, rubella, syphilis) and the unmonitored use of ototoxic medications.

The intellectual disability prevalence deficit is even more certainly an artifact of under-diagnosis. Given LAC’s higher rates of virtually every known risk factor—malnutrition (11% stunting), prematurity (8.5% of births), perinatal complications (1.5–3 per 1,000 births with HIE), environmental toxins, and limited early stimulation—the true prevalence should equal or exceed OECD levels [10]. Our estimate that 400,000–1,200,000 children and adolescents with intellectual disability remain unidentified has profound implications for service planning. When these children and adolescents eventually surface in school systems, they arrive without the benefit of early intervention, with capabilities far below their potential.

Critical windows: the neuroscience of lost opportunity

The critical intervention windows identified in our analysis are not administrative conveniences but reflect fundamental neurodevelopmental processes. For hearing, the auditory cortex undergoes experience-dependent organization in the first year of life. Auditory deprivation during this period leads to cross-modal reorganization, with visual processing colonizing auditory regions, making later hearing restoration less effective [27]. Children identified and aided before 6 months show near-normal language trajectories, while those identified after 12 months show persistent deficits even with optimal intervention. Every month of delay during this critical period translates to measurable linguistic disadvantage persisting into adulthood.

For vision, while the critical period for binocular vision closes by age 7–8, the window for educational impact extends through school entry. Children who receive glasses at ages 6–7 can improve their academic trajectories within 1–2 years, whereas those without access to appropriate support until adolescence may experience more persistent learning gaps. The underlying mechanisms extend beyond visual clarity to include cumulative missed learning opportunities, as early difficulties in accessing classroom materials can affect foundational skills such as reading and influence subsequent learning [28].

Intellectual disability presents the widest but earliest closing window. The 0–3 period of synaptic proliferation and pruning represents unparalleled neuroplasticity. High-quality early intervention during this period can shift developmental trajectories by 0.7–1.2 standard deviations, equivalent to 10–18 IQ points. After age 5, the same intervention intensity yields minimal cognitive gains, though adaptive behavior can still improve. This is not therapeutic nihilism for older children but recognition that earlier intervention yields exponentially greater returns [25].

Economic arguments: beyond humanitarian imperatives

While humanitarian arguments for addressing childhood disabilities are compelling, the economic case provides the pragmatic foundation for political action. Our analysis reveals that the current cost of inaction (US$19.3–29.0 billion annually) exceeds the investment needed for comprehensive intervention (US$9.45–14.23 billion) by a factor of two. This is before considering the intangible costs of impact on families and caregivers, social exclusion, and unrealized human potential.

The variation in cost-effectiveness across conditions provides guidance for staged implementation. Vision programs, with costs of US$15–40 per DALY averted, rank among the most cost-effective health interventions available, comparable to vaccination programs. The 10–15:1 benefit-cost ratio means that every dollar invested in school vision screening and glass provision returns US$10–15 in economic benefits—few investments in any sector offer such returns [29].

Hearing interventions, while more expensive at US$1,850–2,950 per DALY averted, remain highly cost-effective by WHO standards (<3 GDP per capita). The 5–8:1 benefit-cost ratio, while lower than vision programs, still represents exceptional returns. The higher costs reflect device expenses, but technological advances including 3D-printed hearing aids and tele-audiology are reducing costs while maintaining quality [2].

Intellectual disability interventions present the highest costs but remain economically justified. The 2–3:1 benefit–cost ratio indicates positive returns. Moreover, the benefits extend beyond the individual to families and caregivers, contributing to reduced support needs and broader social and economic gains. [30].

Financing the intervention: securing educational and economic futures

The economic imperative of these interventions fundamentally rests on protecting the human capital of school-aged children and adolescents. Failing to provide timely screening, assistive technologies, and specialized support directly compromises their educational attainment and future income-generating capacity, perpetuating intergenerational cycles of poverty and inequality. Therefore, funding these interventions must be recognized not as a discretionary health expenditure, but as a critical investment to secure the region’s future workforce and macroeconomic stability.

To bridge the estimated US$9.45–14.23 billion funding gap in a fiscally constrained region, LAC governments must diversify their financing strategies beyond traditional health budgets. Viable alternatives include the creation of dedicated national or regional funds for early childhood development and disability inclusion. These funds can be sustainably capitalized through earmarked taxation on specific sectors, such as health taxes on harmful products (e.g., tobacco, alcohol, or ultra-processed foods). Furthermore, governments must actively leverage international financing organizations and multilateral development banks to secure the upfront investments required, ensuring that no child’s educational and economic potential is forfeited due to a lack of immediate domestic fiscal space.

Implementation framework: from evidence to action

Based on our findings, we propose a three-tiered implementation framework that recognizes both the heterogeneity of country capacities and the urgency of action:

Implications for policy and practice

Our findings have immediate implications for policy formulation and resource allocation:

Act on opportunities for inclusive support: The predominance of mild-to-moderate and often treatable levels highlights important opportunities for early identification and support. These findings suggest the need to strengthen service delivery and inclusive systems. Children and adolescents with disabilities should be recognized not only as recipients of services but as active participants whose inclusion in educational and social systems contributes to broader societal outcomes.

Prioritize early identification: Every month of delay during critical periods can lead to lasting impacts on development and learning. Screening programs must be treated as essential services, not optional additions.

Integrate across sectors: Disability sits at the intersection of health, education, and social protection. Siloed approaches perpetuate inefficiency and gaps.

Address stigma systematically: Cultural barriers can be as significant as, or in some contexts exceed, economic barriers. Public education campaigns, including those that highlight diverse experiences of persons with disabilities, can contribute to shifting perceptions.

Leverage digital health innovations: Tele-audiology, smartphone-based vision screening, and validated app-based developmental assessments can overcome geographic barriers and exponentially expand diagnostic reach at a minimal marginal cost.

Build regional cooperation: Small countries cannot achieve economies of scale alone. Regional purchasing agreements, training programs, and technical standards benefit all.

Monitor and evaluate rigorously: Investment without measurement wastes resources. Simple indicators (screening coverage, device provision, educational outcomes) should guide program refinement.

Strengths and limitations

This study’s strengths include comprehensive coverage of 25 countries representing >95% of LAC’s population, use of standardized GBD methodology enabling valid comparisons, integration of epidemiological and economic analyses providing policy-relevant insights, severity stratification moving beyond simple prevalence to intervention planning, and synthesis of critical windows evidence guiding program timing.

Several limitations warrant consideration. First, GBD estimates rely on modeled data where primary sources are unavailable, potentially masking subnational variations particularly important in countries with substantial indigenous populations or geographic barriers. Second, disability definitions and severity thresholds may not fully capture cultural variations in functioning across diverse LAC contexts. Third, economic impact calculations necessarily simplify complex cascading effects of childhood disability on families, communities, and intergenerational mobility. The cross-sectional design prevents causal inference about risk factors or intervention effectiveness. Under-diagnosis, particularly in rural and indigenous populations, likely leads to systematic underestimation. The exclusion of other important disabilities (autism spectrum disorder, cerebral palsy, multiple disabilities) underestimates total burden. Economic projections involve uncertainty about future labor markets, technological costs, and implementation efficiency. The assumption of linear relationships between intervention timing and outcomes may oversimplify complex developmental processes. Finally, this study employs biomedical prevalence data from the GBD framework, which does not capture the environmental, attitudinal, and policy barriers that, according to the social model of disability enshrined in the UN Convention on the Rights of Persons with Disabilities, are central to the experience of disability. Our findings should be interpreted within this limitation.

Future research priorities

Critical knowledge gaps requiring urgent research include:

Implementation science: How can evidence-based interventions be adapted for diverse LAC contexts while maintaining fidelity? What implementation strategies maximize coverage and quality?

Diagnostic validation: Current screening tools require validation in LAC populations, particularly indigenous groups with different backgrounds and cultural expressions of disability.

Longitudinal outcomes: Cohort studies tracking children from early intervention through adulthood would provide LAC-specific evidence on long-term impacts.

Economic evaluation: Prospective economic evaluations of intervention programs would refine cost-effectiveness estimates and guide resource allocation.

Intersectionality: How do multiple disadvantages (disability + poverty + indigenous status + rural residence) interact? What additional support do multiply disadvantaged children and adolescents require?

Technology assessment: Rigorous evaluation of emerging technologies (3D-printed devices, AI-based screening, tele-rehabilitation) could identify cost-effective innovations.

Family impacts: Better understanding of disability’s effects on siblings, parental employment, and family stability would strengthen the case for family-centered interventions.