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Science, Public Health Policy, and the Law Article Autism Tsunami: The Impact of Rising Prevalence on the Societal Cost of Autism in the United States

December 2023

Authors:

Mark F Blaxill

Health Choice

Toby Rogers

The Brownstone Institute

As the rates of diagnosed autism spectrum disorders (ASD) reach unprecedented levels, numerous analyses have attempted to model, quantify, and forecast the societal cost of ASD at the country level. These forecast models focus on costs by category and over the lifespan, but place far less emphasis on the effect of rising ASD rates on societal costs over time. Most models make the unsupported assumption that rates have remained constant. As a result, these models obscure understanding and suppress awareness of the most urgent societal issues that surround rising ASD prevalence. Furthermore, they overstate the current costs incurred for the population of adults with ASD, while simultaneously and dramatically underestimating the magnitude of future costs as the ASD population increases. The cost of ASD in the U.S. is estimated here using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Current ASD costs are somewhat lower and projected future costs are much higher than other societal cost of autism models. In this model, total base-case costs of

223 (175-271) billion/year are estimated in 2020;

589 billion/year in 2030,

1.36 trillion/year in 2040, and

5.54 (4.29-6.78) trillion/year by 2060, with substantial potential savings through ASD prevention via identifying and better regulating environmental factors that increase autism risk. This tsunami of rapidly increasing costs raises pressing policy questions. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs demand an urgent focus on prevention strategies.

Total size of the US population by age group from 2016 through 2060, comparing Base Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD ratio of 2.8*

…

Total cost of autism in the U.S. from 2016 through 2060, showing three scenarios for future growth in US autism prevalence*

…

Total cost of autism in the U.S. from 2016 through 2060, broken down by cost category, comparing Base Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD ratio of 2.8 (mean of full 2.1-3.5 range shown in Figure 3)*

…

Total cost of autism in the U.S. from 2016 through 2060, by age group, comparing Base Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD ratio of 2.8*

…

Figures - uploaded by Mark F Blaxill

Content may be subject to copyright.

Content uploaded by Mark F Blaxill

Content may be subject to copyright.

Science, Public Health Policy,

and the Law

Volume 4: 227–256

December, 2023

Clinical and Translational

Research

An Institute for Pure

and Applied Knowledge (IPAK)

Public Health Policy

Initiative (PHPI)

Article

Autism Tsunami: The Impact of Rising Prevalence on

the Societal Cost of Autism in the United States

Mark Blaxill,

Toby Rogers,

Cynthia Nevison

Abstract

As the rates of diagnosed autism spectrum disorders (ASD) reach unprecedented levels, numerous

analyses have attempted to model, quantify, and forecast the societal cost of ASD at the country level.

These forecast models focus on costs by category and over the lifespan, but place far less emphasis on

the effect of rising ASD rates on societal costs over time. Most models make the unsupported assumption

that rates have remained constant. As a result, these models obscure understanding and suppress

awareness of the most urgent societal issues that surround rising ASD prevalence. Furthermore, they

overstate the current costs incurred for the population of adults with ASD, while simultaneously and

dramatically underestimating the magnitude of future costs as the ASD population increases.

The cost of ASD in the U.S. is estimated here using a forecast model that for the first time accounts for

the true historical increase in ASD. Model inputs include ASD prevalence, census population projections,

six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Current

ASD costs are somewhat lower and projected future costs are much higher than other societal cost of

autism models. In this model, total base-case costs of $223 (175–271) billion/year are estimated in 2020;

$589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with

substantial potential savings through ASD prevention via identifying and better regulating environmental

factors that increase autism risk. This tsunami of rapidly increasing costs raises pressing policy questions.

Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto

government, and the soaring total costs demand an urgent focus on prevention strategies.

Share/Alike (see https://creative commons.org/licenses/)

Keywords

Autism spectrum disorder, ASD prevalence, Cost, Future cost projections, Time trends

Holland Center, Minnetonka, MN, USA; mblaxill@hollandcenter.com.

Fellow, Brownstone Institute for Social and Economic Research, Pasadena, CA, USA.

Boulder, CO, USA.

Sci, Pub Health Pol, & Law

Autism Tsunami: Societal Cost of ASD — Dec. 2023

228

Contents

Introduction

228

Methods

230

Results

238

Discussion

240

Conclusion

247

References

250

Appendix

255

Introduction

As reported prevalence rates of autism spectrum

disorders (ASD) have risen during the last three

decades, both in the United States [1–9] and around

the world,[10, 11] increasing attention has been

focused on assessing the future cost of autism on

society. An emerging body of analysis has

addressed the cost of autism with increasing

specificity, especially in the United States and the

United Kingdom. These analyses have followed a

deliberate progression from small pilot surveys of

families to collect data on out-of-pocket expenses,

service utilization, and lost parental income [12] to

larger, more detailed family surveys.[13] More

recently the analyses have extended to full-country

cost estimates based on population prevalence and

with detailed cost models per individual, calculated

variably based on age and severity [14] and to

cross-country cost estimates based on expanded

model inputs with cost segmentation by age,

severity and cost category.[15] Finally, they have

progressed to forecasts of full-country costs

(including in-depth inflation forecasts) using a base

case and multiple scenarios that vary with respect

to prevalence, intellectual disability ratio,

prevalence trends and intervention success [16] and

to state level projections and scenarios of the

lifetime cost of autism.[17]

With one exception (Cakir et al., 2020),[17] the

full-country analyses have primarily assumed

constant autism prevalence over time across the

entire population for their cost estimates. In one of

the publications that assumed constant prevalence,

one of the six scenarios forecasting future costs

incorporated a population estimate with a real

increase in prevalence, but without a birth-cohort-

specific population model (Leigh and Du

2015).[16] In this one scenario, ASD population

prevalence for the entire U.S. was increased in a

stepwise fashion, using Autism and Developmental

Disabilities Monitoring Network rates for 8-year-

olds born in 1992 and 2002–04 of 0.67% (1 in 149,

ADDM report year 2000) [2] and 1.47% (1 in 68,

ADDM report years 2010 and 2012),[6, 7]

respectively, and applying those rates to the entire

population. Although the most recent prevalence

estimates substantially exceed the values used in

that scenario, the future U.S. autism cost, after

including inflation, still rose to a sobering $1.01

trillion per year in 2025, or 3.6% of GDP. In the

single study that relied on rising prevalence

numbers (Cakir et al., 2020), rate increases were

incorporated in a model that calculated the “lifetime

social cost” (not an annual cost) of ASD.[17] In that

analysis, for the modeled population of 2 million

U.S. autism cases born from 1990–2019, total

lifetime costs came to 7 trillion 2019 dollars. The

model was then projected forward through 2029

under two prevalence assumptions: a prevalence

rate for the birth years 2020–29 that would not

change from its recent high level of 2.47% [18] and

a trend-line increase in the decadal average

prevalence for children born from 2020–29 to

4.46%. In the first scenario, the lifetime social cost

of autism for individuals born from 1990–2029

reached $11.5 trillion. In the second, the lifetime

social cost with continuing increases approached

$15 trillion.[17]

In support of the approach undertaken by Cakir

et al. in 2020,[17] the increasingly regular surveys

of ASD prevalence, especially within the U.S.,

suggest a clear increase in prevalence over time.

The earliest surveys of autism rates focused on the

state level [19–21] and reported low rates. Starting

in the late 1990s, researchers utilized administrative

databases in California and the U.S. Department of

Education to provide information on autism time

trends [22–25] and reported increasing prevalence

Sci, Pub Health Pol, & Law

Autism Tsunami: Societal Cost of ASD — Dec. 2023

229

rates. In parallel, the CDC published prevalence

studies that focused on the full range of ASDs,

generally affirming higher rates [26–28] but

reaching no conclusions on time trends.

Following these surveys, the CDC established

the Autism and Developmental Disabilities

Monitoring (ADDM) Network, which began

publishing biennial reports on autism

prevalence.[2–9] The ADDM Network measures a

single birth-year cohort at a time with a focus on

prevalence in 8-year-olds. These “constant-age

tracking” surveys have had the advantage of

reducing ascertainment bias in time-trend

assessment and the disadvantage of possible under-

ascertainment of Asperger’s syndrome, which has a

later average age of diagnosis, in the range of 6.2 to

8.1 years old.[29, 30] The ADDM surveys have

reported an Asperger’s proportion of 9–11% of

surveyed populations (report years 2008, 2010 and

2012) [5–7] while also reporting ASD rate increases

from 1 in 149 in the 2000 report [2] to 1 in 54 in the

2016 report,[9] with rates as high as 1 in 14 in

certain school districts.[31] Most recently, the

National Center on Health Statistics (NCHS) and

the Census Bureau have published a series of

household surveys of the U.S. population asking a

version of the question, “Has a doctor or health

professional ever told you that [sample child] had

Autism, Asperger’s disorder, pervasive

developmental disorder, or autism spectrum

disorder?” These surveys collect more modest

detail on ASD diagnoses in children aged 3–17

years old, [18, 32–34] but the inclusion of older age

cohorts allows for fuller ascertainment of

Asperger’s cases. Both the Census Bureau and the

NCHS surveys report higher prevalence rates than

the ADDM Network and have ranged from 1 in 36

to 1 in 40 in the most recent reports.[18, 34]

Despite this growing body of literature on autism

prevalence, there is still no single authoritative

source of population trends in the U.S. for ASD

rates over time and by severity. The early surveys

focused primarily on the narrower and more severe

definition of autism — infantile autism — as

described in the DSM-III, with some attention to the

broader concept of the Pervasive Developmental

Disorders (PDDs) but offered limited insight on

time trends. Administrative databases from the

California Department of Developmental Services

and IDEA provided more precise birth-year

reporting,[35] which enabled better time-trend

assessment but also allowed for potential

inconsistency in ASD coverage.[1, 24, 36] The

ADDM Network reports have attempted to improve

on the methods of these prior surveys: each ADDM

report estimates prevalence on a specific birth

cohort, while the 2008, 2010 and 2012 reports

detailed the respective proportions of PDD/autistic

disorder (AD), PDD/not otherwise specified (PDD-

NOS) and PDD/Asperger’s Syndrome (AS). By

contrast, the household surveys cover a wide range

of birth years in each snapshot with some, but

variable detail on prevalence by age cohort; these

surveys report substantially higher prevalence rates

than other sources, although the time trends appear

to run parallel to ADDM estimates. The DSM-V

criteria, adopted in 2013, replaced the nomenclature

of “pervasive developmental disorders” (PDDs)

that had been utilized from 1980–2013 in DSM-III,

III-R, and DSM-IV with “autism spectrum

disorder” (ASD) and eliminated altogether the three

primary subcategories of the PDDs — AD, PDD-

NOS, and AS (DSM-IV only).[37] For this reason,

the ability to compare past surveys based on

previous nomenclature and subcategories with

more recent assessments based on the DSM-V

criteria has been impacted.

Meanwhile, most analyses to date of the societal

costs of autism have substantial limitations. Many

have focused on individual family burdens rather

than populations as a whole. When they report on

populations, they often ignore time trends and the

concomitant possibility of rising costs. When they

report on prevalence over time, they typically do

not consider or address the abundant evidence

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

230

described above of increasing rates. These

limitations have important consequences for

calculations of ASD costs. To the extent their

prevalence estimates lag behind the latest evidence,

they tend to underestimate the costs of current

childhood populations. To the extent their past and

projected ASD rates assume unchanging

prevalence, they likely overestimate the cost of

current adult populations and underestimate the

increased cost of future adult populations. To the

extent they lack a detailed assessment of ASD time

trends, they likely mischaracterize the changing

shape of the future ASD costs. Finally, to the extent

that they neglect important shifts in the mix of

future autism costs — e.g. with educational costs

stabilizing while residential and medical costs surge

— they risk leaving important dynamics for the

well-being of individuals with ASD unaddressed.

In this study, we attempt to remedy these

limitations by developing a more comprehensive

and accurate U.S. cost model. We begin with the

best available long-term ASD prevalence rates

using a snapshot that encompasses birth cohorts

from 1931 to 2016. To convert that snapshot to a

current cost for the full U.S. population, we apply

(with substantial amendments and updates) the

most thorough recent estimates of costs per ASD

individual. These cost estimates have been refined

over the years in the cost burden literature and are

partitioned into six different categories (such as

education and individual productivity loss).[15, 17]

We develop models that forecast ASD prevalence

through 2060, using multiple future scenarios to

incorporate the potential impact of rising

prevalence on the future cost burden of ASD.

These forecast models are inspired by previous

work [16, 17] but adopt a different set of prevalence

scenarios that extend further into the future and

consider the possibility of prevention. We develop

annual cost estimates using both constant 2018

dollars as well as current dollars, using three indices

to project the effects of inflation.[16] Finally, we

apply the cost category analysis to historic and

projected models of the U.S. ASD population to

provide better and more finely resolved estimates of

how the annual ASD cost burden in the U.S. will

shift over time.

Accurate economic estimates of the societal cost

of disease are essential for sound law and

policymaking. Autism cost assessments are

especially important because the costs of autism are

larger than for other disorders (e.g. cancer, stroke,

and heart disease) and because autism strikes in

childhood and affects the entire lifespan.[15, 38] As

we show in this paper, the cost patterns with autism

are also unique in that sharply rising prevalence has

created a massive wave of costs that will continue

for decades if policymakers and the public fail to

grasp the possibility and importance of prevention.

Paradoxically, the future costs of autism loom so

large that, rather than responding with a sense of

urgency as one might expect, policymakers thus far

have generally failed to engage with the policy

implications at all.[39] We hope this paper will

serve as a wake-up call for the public health

emergency that the societal cost of autism

represents to the economic future of the U.S.

Methods

We developed annual cost of disease projections for

ASD through 2060 based on a model with four

elements:

1. Historical autism prevalence estimates with

time trend data for both severe and full spectrum

autism rates. We used California time-trend data

(updated from Nevison et al., 2018) [1] for the

severe autism time series and a broader

assessment of the ASD prevalence literature to

estimate a full ASD prevalence including milder

cases.

2. A matrix of costs per individual for multiple

categories applied to multiple age cohorts. We

followed the method of Buescher et al. (2014)

[15] with an expanded approach using more

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

231

refined age cohort and an updated literature

review of individual cost elements all expressed

in 2018 dollars.[17, 40]

3. Projections of the future size of the ASD

population based on three scenarios for future

ASD prevalence. We projected the U.S.

population for the years 2020–2060 using

Census Bureau forecasts by age cohort and

applied future prevalence rates to that

population using three scenarios — Base Case,

Low, and Prevention — for both severe and

broad-spectrum ASD rates.

4. Inflation projections by cost component.

Following Leigh and Du (2015) [16] we applied

three different inflation indexes to our

projections of each future cost per individual

component.

Combining those elements allowed us to estimate a

total ASD cost by year for the United States, both

in total and by cost component.

General approach

Our approach is based on observed autism

prevalence in the California Department of

Developmental Services (CDDS) caseload, for

which data are available for birth years 1931–2016.

Prevalence is then projected forward from birth

year 2014 to 2060 using three different scenarios.

Since, as described below, severe autism accounts

for only about one half to one third of all ASD, we

estimate the total prevalence of ASD by multiplying

severe prevalence from CDDS by a range of

empirical scalars (2.1–3.5). Population projections

through 2060 from the U.S. Census Bureau are used

to translate prevalence into absolute counts of

severe and milder ASD, each resolved annually by

age. The counts are multiplied by six different cost

categories, with costs partitioned by age group and

distinguished between severe and milder ASD.

Finally, an inflation index relative to base year 2018

is applied and compounded to each of the projection

years from 2020–2060. Overall costs are calculated

as a function of birth year, census projection year,

future prevalence scenario, and cost category

indices. Our calculations permit the isolation of any

individual index or set of combined indices by

integrating over the remaining indices. The

Appendix provides more details about the equations

used. The components of the cost calculation are

described in more detail below.

1. Historical prevalence of ASD

Severe ASD from California DDS

Statewide autism counts from CDDS were used as

the basis for the estimation of severe autism

prevalence. The primary datasets were an age-

resolved CDDS snapshot for 2020 tabulating the

number of individuals receiving services for autism,

resolved by individual birth year from 1953 to 2016

(updated by three years from the 2017 snapshot

presented in Nevison et al., 2018).[1] The 2020 data

were supplemented with birth year 1931–1952

autism counts from the 2017 CDDS snapshot to

extend the curve back to birth year 1931. The 2020

snapshot was used as the basis for the Base Case

and Prevention scenarios discussed below. An

additional age-resolved CDDS snapshot for 2014,

resolved by individual birth year from 1931–2010,

was used in the estimation of the Low future

scenario discussed below. The CDDS autism counts

were converted to prevalence in % using California

live birth data as denominators, as per Nevison et

al. (2018).[1]

The 2020 snapshot used the DSM-V category of

autism spectrum disorder (ASD),[37] while the

2014 snapshot was the most recently available

CDDS dataset that was still using the DSM-IV

definitions, in which Autistic Disorder (AD)

diagnoses were distinguished from milder ASD.

[41] Historically, CDDS has focused on “full

syndrome” cases, which were generally diagnosed

with AD (CDDS 1999, 2003),[22, 23] the most

severe expression of autism. Furthermore, to

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

232

qualify for CDDS services, individuals must have a

level of impairment that rises to the level of a

“developmental disability,” where the latter is

defined as a non-physical, substantial disability that

is expected to continue indefinitely. In addition to

an autism diagnosis, CDDS requires that

individuals demonstrate significant functional

disability in three out of seven life challenges,

which include self-care, language, learning,

mobility, self-direction, capacity for independent

living and economic self-sufficiency in order to

qualify for services.[42]

Milder autism prevalence

To estimate the complete prevalence of ASD, we

reviewed the ratio of ASD/AD reported in the

literature (where ASD encompasses and thus

includes AD), including early snapshot surveys;

[19, 20, 27] 8-year-old constant age tracking

data;[2–8, 43] and National Health Interview

Surveys of 3–17-year-olds.[18, 32, 33] The

literature review yielded ASD/AD ratios ranging

from about 2.1 to 3.5. We therefore scaled the

CDDS prevalence data (which as described above

were assumed to reflect AD cases) by multipliers

ranging from 1.1 to 2.5 to estimate the additional

prevalence of individuals with milder ASD. The

1.1–2.5 range in scalars is propagated through the

cost calculations and is represented in the figures as

a window of uncertainty surrounding the mean

value (1.8) of the range.

2. Cost Categories

We applied to our population estimate a cost per

individual per year guided by the approach of

Buescher et al. (2014),[15] but with substantial

revisions and updates. Buescher et al. defined per-

person costs for a number of cost categories for

ASD cases with intellectual disability (ID) and then

generally cut those in half for ASD cases without

ID. In our calculations, we make a conceptually

similar distinction between severe (i.e. CDDS) and

milder ASD, but this is not directly analogous to

Buescher et al.’s “with and without ID” distinction,

since not all CDDS cases are identified as having

ID.

Non-medical Services

We defined a “Non-medical Services” category,

based on recent data compiled for 2017–2018,[45]

which encompasses three of Buescher et al.’s

categories: accommodation, employment support,

and non-medical services. The non-medical

services category also includes community care,

respite care and day care programs.[44] While

Buescher et al. defined only three age groups (0–5,

6–17, 18–64 years),[15] we expanded these into ten

age groups 0–2, 3–6, 7–11, 12–21, 22–31, 32–41,

42–51, 52–61, 62–71, 72–100), both for closer

matching of needs to age and for compatibility with

the age groups defined by CDDS.[47] We

interpolated these ten age groups to define as

continuous annual functions of age, which were

related to birth year via Equation 5 in the Appendix.

We assumed that the same miscellaneous non-

medical costs applied to those severely and more

mildly affected, due to a lack of appropriate data to

distinguish the two.

Individual productivity loss

We used per person annual production values for

2018 in the United States from Davenport et al.

2019 (their Figure A25).[46] These were broken

down by gender, with substantially higher values

for males than females, and divided into five-year

age intervals beginning at age 15 and extending

through age 80+. Since these intervals did not

directly coincide with our Miscellaneous Services

cost category age intervals, we took the appropriate

weighted average of the Davenport et al. data (e.g.

for adults 22–31, we added the production values

for age 20–24, 25–29 and 30–34, weighted by 0.3,

0.5 and 0.2, respectively).[46] We further weighted

the production values by the 80:20 male:female

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

233

ratio of ASD observed in the U.S.[8] We surveyed

the literature on employment patterns in adults with

autism in order to estimate competitive (currently

working, full-time, paid) employment rates rather

than mere participation in work. Much of this

literature focuses on small samples composed

largely of High Functioning Autism (HFA)/

Asperger’s workers [13, 47–53] and reports rates

ranging from 7 to 44%. A few more recent surveys

[54–57] that have larger and more diverse samples,

report competitive employment rates in a similar

range of 7–34%. For our model, we assumed a

100% loss of productivity for severe ASD cases and

a 70% loss of productivity for milder ASD cases.

Parent productivity loss

We assumed 75% loss of maternal productivity for

children with severe ASD and 25% loss of maternal

productivity for children with milder ASD.

For all ages we assumed zero loss of paternal

productivity. These assumptions are based on Cidav

et al. (2012),[58] who found that on average

mothers of children with ASD had a 56% loss of

productivity compared to mothers of neurotypical

children, while fathers showed no statistical

difference in productivity. We estimated the

mother’s age range for each of the ASD age groups

in Table 1 by adding 28 (our assumed average

maternal age at birth) [59] to the children’s age. We

then matched the maternal age to the per-person

annual production values for the United States [46]

and scaled by 0.75 (severe) and 0.25 (mild). We

assumed 0 parental productivity loss for individuals

with ASD age 52 or older.

Education

A comprehensive national survey of special

education costs reported the cost of educating a

large sample of children with disabilities, including

autism, to the cost of regular education (Chambers

et al., 2003).[60] They found an incremental cost

per student with autism of $12,243 ($18,139 in

2018 dollars). Notably autism was the highest cost

disability and this analysis excluded early intensive

behavioral intervention. We applied this estimate

for school-aged children to our model for children

aged 5–21. A more recent Legislative Analyst’s

Office report in California found that incremental

cost for children with disabilities in general was

$17,000 in the 2017–18 fiscal year, which suggests

that autism costs were likely even higher in

California during the report period.[61]

Early Intervention / Behavioral Intervention

Many children with autism receive early

intervention / behavioral intervention (EIBI)

services, usually for Applied Behavioral Analysis.

Some studies (e.g., Ganz 2007) [62] have included

EIBI in their analyses of cost per individual; some

(Buescher et al., 2014) [15] are unclear how they

approach EIBI, and others (Cakir et al., 2020) [17]

exclude EIBI costs. We estimated the average

individual EIBI cost using an average full time (40

hours) EIBI program cost of $63,500, [63–68] with

an average EIBI utilization rate of 12 hours per

week,[69] with a drop-off rate of 86% after early

childhood [62, adjusted for discounting]. We made

no assumption about differential EIBI usage in

severe and mild cases.

Medical Costs

We adopted the analysis of Zuvekas et al. (2020)

[70] for incremental direct medical costs in ASD

children of $5,621. For infants with ASD we

multiplied childhood costs by a factor of 1.4 to

reflect higher medical costs in infancy.[71] For

adults, we assumed a range of incremental costs

starting at $4,000 in young adults [62, adjusted for

discounting] and rising with age to $8,300 in the

elderly.[62, 72, 73] We made no assumption about

differences in medical costs across severe and mild

cases.

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

234

Table 1. ASD costs per individual per year in 2018 dollars for six cost categories, distinguishing severe and milder cases

Min

Age

(yrs)

Max

Age

(yrs)

Educa-

tion ($)

EIBI

($)

Parent Productivity

($)

Individual

Productivity ($)

Direct

non-

medical

($)

Medical

($)

Total ($)

Wtd

Avge

($)

Severe

Milder

Severe

Milder

Severe

Milder

36:64

severe:

mild

25,027

8,343

7,869

32,896

16,212

22,219

9,070

18,890

26,348

8,783

4,002

5,621

63,930

46,365

52,689

18,139

2,833

29,769

9,923

4,002

5,621

60,364

40,518

47,663

18,139

2,833

32,394

10,798

7,100

4,970

6,890

4,000

71,356

47,630

56,172

29,927

9,976

44,744

31,321

25,438

4,000

104,109

70,735

82,750

12,377

4,126

65,722

46,006

40,246

4,800

123,145

95,178

105,246

1,864

621

69,409

48,586

51,354

6,800

129,427

107,361

115,305

57,239

40,068

61,951

8,100

127,290

110,119

116,301

22,918

16,042

66,515

8,300

97,733

90,857

93,332

100

66,515

8,300

74,815

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Autism Tsunami: Societal Cost of ASD — Dec. 2023

235

Figure 1. Three scenarios for future growth in US severe autism prevalence to 2060*

* These three scenarios are assumed to follow a logistic growth equation. Black squares show the

California DDS 2020 ASD prevalence snapshot, which is used as the basis for the Base Case and

Prevention scenarios. Gray circles show the California DDS 2014 snapshot, which is used as the basis

for the Low scenario.

3. Scenarios of the Future ASD population size

Census Data and Future U.S. Population

Projections

We used United States Census Bureau population

projection tables, which were based on the 2016

base total U.S. population and provided future

projections every 5 years from 2020–2060 (US

Census Bureau, 2018).[74] The populations were

resolved by 8 age groups (0–4, 5–13, 14–17, 18–24,

25–44, 45–64, 65–84, and 85–100). These age

groups were interpolated to individual yearly ages,

assuming an even distribution among the annual

birth cohorts within each group. While this

assumption is probably not true, particularly for the

85–100 group, this latter group had a relatively

small effect on our calculations. Similarly, we did

not consider uncertainty in future total population

estimates, since these were likely to be

overshadowed by the larger uncertainty in the

future ASD prevalence.

Future Scenarios of Rasd

Future scenarios, resolved annually as a function of

birth year (ibyr), were constructed for ASD

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prevalence, representing Low and Base Case

extensions of the CDDS age-resolved snapshot data

through 2060. These were modeled as an increasing

logistic function using Equation 1 (below), where R

represented future severe ASD prevalence and the

parameters Rinf, α and thalf were derived from a

logistic fit to CDDS snapshot data using the

MATLAB routine fit_logistic.m.[75] Here, Rinf is

the final or asymptotic ASD prevalence at time

infinity, and α and thalf are parameters describing

the rate of growth. We used two different sets of

snapshot data to provide a range of uncertainty in

the future evolution of ASD (Figure 1).

Base Case Scenario

We used parameters derived from a logistic fit to

the 2020 California DDS snapshot of ASD

prevalence (the most recently available) [76]

extending from birth year 1970–2016. Rinf from the

fit is 2.9% (Figure 1).

Low scenario

We used parameters derived from a logistic fit to

the 2014 CDDS snapshot of ASD prevalence

extending from birth year 1970–2010. The Low

scenario already underestimates prevalence

reported by CDDS in the 2020 snapshot in the most

recent years (Figure 1),[76] but this scenario was

included because the 2014 data are the most

recently available snapshot that still use DSM-IV

criteria. The Rinf value from the 2014 snapshot

logistic fit is 1.07%.

Prevention scenario

We created a prevention scenario based on a

variation of the negative logistic curve (Equation 6,

below). The Prevention scenario is included as an

illustrative example of what might be possible if

strategies for reducing ASD risk are identified and

addressed in the near future. While many of the

parameter choices are open for debate, we used the

following assumptions and values: Rprevention was

assumed to follow the Base Case prevalence

scenario until 2025, the assumed birth year of

prevalence decline. Rmax was set equal to

Rbasecase(2021). Thereafter, Rprevention was

assumed to decrease quickly at an accelerated

(relative to the increase over the last 40 years) rate

αx, where x was set at 5 and thalfdec was set at birth

year 2032. In the prevention scenario, autism

prevalence asymptotes to a target prevalence Rmin,

which was set to the CDDS autism prevalence in

birth year 2013 of 0.6% observed among white

children in wealthy counties in California.[77]

All three scenarios are assumed to reflect the

most severe autism cases, and the total ASD

prevalence is calculated by adding in the milder

ASD cases, estimated using the scaling approach

and scaling factors described above.

Equation 1.

Rscenario(ibyr) = 

󰇛󰇛󰇜󰇜

Equation 6.

Rprevention(ibyr) = 

󰇛󰇛󰇜󰇜 + R_min

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4. Inflation projections

Our per-person cost category estimates are based on

2018 dollar values. To account for inflation in

projection years 2020–2060, we followed the

approach of Leigh and Du (2015),[16] who

distinguished between projections for medical, non-

medical and productivity-related inflation and

applied different projection methods to each of

these three cost categories. We applied the non-

medical adjustment factor to education, EIBI, and

direct non-medical services; the productivity

adjustment factor to parent and individual

productivity loss; and the medical adjustment factor

to direct medical costs. Table 2 shows these

adjustment factors compounded annually for 2020–

2060 (at 5-year intervals).

Table 2. Inflation adjustment factors compounded annually for three price indices at 5-year

projection intervals, using from a base year of 2018

Year

Medical

Non-medical

Productivity

2016

0.9282

0.9185

0.9537

2018

1.000

2020

1.087

1.065

2025

1.370

1.259

1.260

2030

1.737

1.477

1.468

2035

2.196

1.729

1.710

2040

2.776

2.024

1.992

2045

3.509

2.369

2.320

2050

4.436

2.773

2.703

2055

5.608

3.246

3.149

2060

7.090

3.799

3.668

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Figure 2. Three scenarios for future growth in US total ASD prevalence*

* These three scenarios use a 2.1–3.5 range of multipliers applied to the severe prevalence curves in

Figure 1. The shaded areas reflect the uncertainty in this scalar approach of converting prevalence of

severe autism from California DDS into total ASD, with the mean value shown as a solid line.

Results

Severe ASD prevalence in the 2020 California DDS

snapshot [76] shows an ongoing upward trajectory,

particularly among Black and Hispanic children,

reaching 1.7% overall among 4-year-olds born in

2016 (Figure 1; Supplementary Figure 1).

Extrapolating the 2020 data forward with Equation

1 leads to a severe ASD prevalence of 2.9% in 2060

in the Base Case scenario, while extrapolation of

the 2014 CDDS snapshot leads to a severe ASD

prevalence of 1.07% in the Low scenario (Figure 1).

This spread (1.07–2.9%) in severe prevalence

corresponds to a total ASD prevalence range of 2.2–

10% by 2060, using the 2.1–3.5 multipliers (Figure

2). The Prevention scenario initially follows the

steeper trajectory of the Base Case scenario but

declines beneath the Low scenario by 2028,

plateauing at 0.6% severe and 1.3–2.1% total ASD

after about 2040 (Figures 1 and 2).

In terms of overall population size, the U.S. ASD

population grows in the base case from 2.9 million

in 2016 to 17.9 million in 2060 (Figure 3). The

childhood population grows substantially, rising

from 2.4 million to 7.8 million over the time period,

a multiple of 3.25 times, but the adult population

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Figure 3. Total size of the US population by age group from 2016 through 2060, comparing Base

Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD ratio of 2.8*

* Top row shows the size of the population. Bottom row shows the age groups as percent of the total.

grows far more rapidly from half a million in 2016

to over 10 million in 2060, a 20-fold increase

overall and a 50-fold increase in elderly individuals

with ASD (Figure 3).

The total cost of ASD in 2020, according to the

Base Case (best guess) scenario, is estimated at

$223 ± 48 billion (Figure 3; Table 3). These costs

increase to $5.5 ± 1.2 trillion by 2060, accounting

for inflation. The Prevention scenario leads to a

substantial reduction in the economic burden ($3.7

± 0.8 trillion by 2060), while the total price tag for

the Low scenario is $170 ± 37 billion in 2020 and

$2.8 ± 0.6 trillion, respectively, by 2060,

accounting for inflation. If inflation is not taken into

account, the total cost of ASD in 2018 dollars under

the Base Case scenario is estimated at $1,393 ± 310

billion in 2060 (Table 3).

When the financial toll of ASD is broken down

into cost categories, child-oriented expenses

(education, EIBI, and parental productivity loss)

account for 54% of all costs in 2020. Toward the

latter half of the future projection, as ASD is

assumed to asymptote to a stable value, the adult-

dominated costs of non-medical services and

individual productivity loss account for an

increasing share of the total burden (63% by 2060).

The Low scenario follows a similar pattern, but

with lower absolute costs. The Prevention scenario

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Table 3. Total ASD costs in selected projection years*

Year

Base Case

Base Case (in

billion 2018 $)

Low scenario

Prevention scenario

2016

147 (116–179)

157 (124–191)

118 (92–143)

147 (116–179)

2020

223 (175–271)

209 (164–254)

170 (133–207)

223 (175–271)

2025

370 (290–450)

291 (228–354)

265 (208–323)

370 (290–450)

2030

589 (461–717)

393 (308–478)

399 (312–486)

580 (454–706)

2040

1,357 (1,059–1,654)

655 (512–798)

830 (646–1,014)

1,165 (908–1,421)

2050

2,853 (2,220–3,486)

995 (776–1,215)

1,598 (1,239–1,956)

2,151 (1,669–2,632)

2060

5,535 (4,291–6,779)

1,393 (1,083–1,703)

2,846 (2,199–3,494)

3,660 (2,822–4,498)

* Unless otherwise noted, all costs are in inflation-adjusted billions of U.S. dollars per year.

offers an interesting contrast in that education and

parental productivity loss diminish, elevating non-

medical services and individual productivity to

consume 78% of total costs by 2060.

Similarly, when broken down into age

categories, 67% of the cost of ASD in the Base Case

scenario in 2020 is due to youth age 21 and under,

with 42% of the total cost due to children age 11

and under alone (Figure 5). This cost breakdown

shifts dramatically moving out toward 2060, when

adults aged 22 and older account for nearly 71% of

all costs. The Low scenario follows a similar

pattern, with lower absolute costs. The Prevention

scenario again offers a contrast with costs among

adults shifting to 91% of total costs by 2060.

Discussion

Previous work on the economic costs of ASD has

provided a strong foundation upon which our study

is built.[12–15, 17, 63, 78] At the same time, most

past studies have focused on different end points

and objectives than our study, such as estimating

the per-capita lifetime cost for an individual with

ASD [15, 63] or estimating the total lifetime

country-wide cost of the generational cohort with

ASD born between 1990 and 2019.[17]

Consequently, direct comparisons of our results to

previous studies are often not straightforward,

especially when “discounting,” i.e. adjusting for the

future depreciation of the value of the dollar, is

invoked in the lifetime calculations.[15, 63]

Another issue is that the previous literature has

focused more attention on the cost per individual

than on the population size component of the

calculation. Only recently have comprehensive

analyses approached the central question of time

trends and the potential for true increases in cost

that will accompany rising population

prevalence.[16, 17]

The analysis by Leigh and Du (2015) [16] is the

most directly comparable to our own, in that it

provides annual U.S. cost estimates both for the

present day (2015) and projected into the future

(2025). Annual cost estimates are probably the most

relevant to policy makers, since they predict actual

dollar amounts for a given budget year. Like us,

Leigh and Du (2015) [16] addressed the implications

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Figure 4. Total cost of autism in the U.S. from 2016 through 2060, showing three scenarios for

future growth in US autism prevalence*

* Ranges of variability around each scenario reflect uncertainty in the scalars applied to severe ASD

prevalence to estimate total (severe + milder) ASD prevalence. The scalars range from 2.1–3.5, with

the mean value shown as a solid line.

of rising ASD prevalence, albeit in a single

scenario. Our analysis is the first to model annual

costs for the entire United States under scenarios

that reflect the strong evidence for rising ASD

prevalence, based on the best available data, and the

consequent future exponential increase in the adult

population of individuals with ASD.

Our total cost estimate in 2016 of $147 billion

dollars is substantially lower than the $268 (range

$162–367 billion) estimated by Leigh and Du

(2015) for 2015 (Table 3).[16] The discrepancy is

due largely to Leigh and Du’s assumption of

historically constant ASD prevalence, which we

would argue leads to an overestimate of the current

adult population with autism. Even by 2025, as the

young adult population with ASD has begun to

expand but the older adult population has not yet

increased, the Leigh and Du estimate of $461

billion (range $276–1,011 billion) still exceeds our

projection of $370 billion (Table 3).[16] The

assumption of constant prevalence is not a trivial

issue for the cost calculation. It leads to a substantial

overestimate of the present-day economic burden,

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Figure 5. Total cost of autism in the U.S. from 2016 through 2060, broken down by cost category,

comparing Base Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD

ratio of 2.8 (mean of full 2.1–3.5 range shown in Figure 3)*

* Top row shows costs in absolute inflation-adjusted dollars. Bottom row shows costs as percent of total.

since in our model ASD costs are higher for adults

than children (also in [17]). Cost analyses that

assume constant prevalence thus place a large

portion of their total cost estimate on an adult

population that does not yet exist. Conversely, as

rates of ASD among children have increased far

above 1%, cost of disease models that assume

constant prevalence (around 1%) tend to understate

the current childhood cost for ASD, even though

this underestimate may be masked by the

overestimate of the adult population when the total

population cost is reported.[16]

If, in fact, ASD rates have risen from 1 in 10,000

for individuals born before 1950 to 1 in 2,500 for

individuals born in the 1980s to nearly 3% for the

current childhood population, then the implications

for the cost burden and its evolving structure over

time are staggering. Our two largest ASD cost

categories are (indirect) individual productivity

loss, which peaks in middle age, followed by direct

non–medical services such as residential housing,

for which costs rise steadily with age (Table 1). In

our calculation, those two categories account for

35% of total costs around 2020 but increase to

nearly 63% of total costs by 2060, as the adult

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Figure 6. Total cost of autism in the U.S. from 2016 through 2060, by age group, comparing Base

Case (left) and Prevention (right) scenarios, assuming a mean total/severe ASD ratio of 2.8*

* Top row shows costs in absolute inflation-adjusted dollars. Bottom row shows costs as percent of total.

population with ASD expands (Figure 4). Similarly,

adults 22 and over account for only 33% of total

ASD costs in 2020, but the adult share increases to

71% by 2060 (Figure 5). At least one federal

funding source, the Social Security Disabilities

Insurance (SSDI) Program provides emerging

evidence of this trend. The Social Security

Administration issues an annual statistical report on

the SSDI Program. This report has long included a

count of adult beneficiaries (ages 18–64) by

diagnostic group. Before 2010, there was no listing

of autistic beneficiaries. In the 2010 report, autistic

disorder was included for the first time and in the

years since its inclusion, the count of adult autistic

SSDI beneficiaries has increased at an annual rate

of 14%: from 72,449 in the 2010 report to 232,003

in the 2019 report.[79, 80]

Year 2016 is the earliest year of our annual cost

calculation. Thus, we cannot compare our results

directly to Cakir et al. (2020),[17] who estimated a

lifetime cost of $7 trillion for the cohorts with ASD

born between 1990 and 2019. However, we can

compare our annual present–day costs based on

Table 1 to a summation of the corresponding costs

assumed by Cakir et al.[17] Here both our and Cakir

et al.’s study assumed the cost category structure

defined by Buescher et al. (2014).[15] Our

estimates are considerably larger than Cakir et al.,

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who assumed (using 2019 dollars) a cost of $49.9

thousand for children 3–17 and $83.4 thousand for

adults age 18+. Our costs, which are broken down

into finer age categories, are similar for children 3–

21 ($48–56 thousand/year) but substantially higher

for adults, especially during the peak earning years

of age 32–61 when our total cost estimates range

from $105–116 thousand/year. The difference is

due mainly to our assumption of substantially

higher individual productivity loss and also higher

direct non-medical costs for adults with ASD.

In comparison to Buescher et al. (2014),[15] our

annual costs tend to be smaller for children but

larger for adults. (Note: Leigh and Du (2015) [16]

directly adopted Buescher et al.’s costs, inflated to

2015 dollars.) Using 2011 dollars, Buescher et al.

assumed, for those with severe autism (which they

defined based on co-occurring intellectual

disability [ID]), a total cost of $107.9 thousand/year

for 0–5-year-olds, $86.1 thousand/year for 6–17

year-olds, and $88.0 thousand/year for adults aged

18+. Total costs for milder ASD (i.e. without ID)

were assumed to be about 40% lower. The high

values for children were due to Buescher et al.’s

assumption of weighty values for special education,

including early intervention, and to a lesser extent

to their assumption of direct medical costs of up to

$13 thousand/year. This latter assumption has been

criticized as being too high by a factor of more than

two.[70] EIBI, while a significant cost for children

with ASD, is difficult to estimate with available

sources. Buescher et al.’s large annual cost

estimates for young children with ASD assume near

universal adoption of EIBI from birth. For our

model, we adopt similar costs for a full-time EIBI

program but assume that EIBI begins later, is

utilized less frequently, and is often less than full-

time.

In contrast to their likely overestimate of

children’s ASD costs, Buescher et al. almost

certainly underestimated adult ASD costs,

primarily due to the exceptionally low value of

$10,718/year assumed for individual productivity

loss (both with and without co-occurring ID). Even

when inflated to 2018 dollars, this is far lower than

the mean men’s salary of ~ $76,000/year during

peak earning years in middle age.[46] The reason

for the low value is that Buescher assumed a high

employment rate for all individuals with ASD,

regardless of ID status. In contrast, we assume an

employment rate of 0% and 30% of those with

severe and mild ASD, respectively. Buescher et al.

relied on estimates of ASD workforce participation

that focused on HFA and Asperger’s adults and

equated participation in work with

productivity.[81] By contrast, we define full-time,

unsupported employment, which is generally quite

low in ASD adults, as a more realistic standard for

productivity.

The uncertainty in our calculations is defined by

our range of prevalence scenarios and by the scaling

factors we apply to convert severe autism into total

ASD. We implicitly assume that the uncertainties in

the census projections of overall population and in

the individual cost category prices are subsumed in

those two larger primary uncertainties. Previous

studies have made similar assumptions (e.g. [16]).

With respect to the total:severe ASD scaling

factors, our lower bound (2.1) is based on

comparing ADDM data, which are in some respects

the most authoritative, to the comparable California

DDS snapshot (on which our severe ASD

projections are based), but likely left out many

cases of autism that were previously considered

Asperger’s cases. Our higher bound (3.5) is based

on comparing the midpoint NCHS surveys of

children to their California DDS equivalents: the

NCHS surveys are less rigorous and possibly

subject to overstatement, but likely include more

higher functioning individuals. We made the further

assumption that the growth in prevalence of total

ASD will continue to parallel the growth in severe

ASD cases, with a constant proportionality of 2.1–

3.5. This assumption involves substantial

uncertainty but was made due to a lack of

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information for a better assumption. Other surveys

that report both broad and narrow ASD rates [19,

20, 27, 43] have total:severe ratios that range from

1.1 to 9.2 but are either more variable in their

approach or more restricted in geographic coverage.

Our assumptions about how ASD prevalence

will project into the future are the single largest

uncertainty in our calculation. The Base Case

scenario is our best guess case because it is based

on the most recently available trend data from

California DDS from 2020. The Low scenario was

included as a conservative case with the rationale

that after 2014 CDDS had switched to the DSM-V

criteria and thus may have expanded to include

milder ASD cases in more recent years. Both the

Base Case and Low scenarios project future growth

as an asymptotic logistic function, or S curve, based

on the assumption that the growth in R cannot

continue indefinitely, either because the susceptible

fraction of the population at some point will

saturate or because efforts to identify causal drivers

will accelerate to a new level of urgency as severe

autism prevalence approaches 2.9% (implying total

ASD prevalence of up to 10.0%). These

assumptions might be regarded as conservative or

optimistic given that current total ASD levels of

about 3% have been met with complacency (e.g.

[18]) and that ASD currently appears to be growing

at a steep ongoing rate in the 2020 California DDS

data (Figure 1). In these data, it is notable that ASD

prevalence among 4-year-olds in the 2016 birth

cohort already exceeds that among 5-year-olds born

the previous year (Figure 1). This feature has not

been seen in California DDS data since the 2010

cohort and likely portends a steeper uptick in the

prevalence when the 2016 cohort is fully

diagnosed.[1]

The enormous future costs of ASD projected by

our model (Figure 3) raise the logical question, can

these costs be mitigated or avoided? Leigh and Du

(2015) [16] included an early intervention scenario

in which intensive ABA therapy was assumed to

reduce the future costs of ASD by a factor of two

among affected adults, who were assumed to have

milder symptoms and thus require less care. This

scenario led to a savings of $28 billion by 2025

relative to the Leigh and Du (2015) [16] base case.

We opted for a prevention scenario to explore the

possibility of future mitigation, rather than an

intervention scenario, due to the lack of empirical

evidence that early intervention actually reduces

adult costs by a factor of 2.[82–84] In contrast, the

reduced prevalence of 0.6% severe ASD

(corresponding to 1.3–2.1% total ASD with our

assumed total:severe scaling factors) used in our

Prevention scenario is based on real rates observed

among wealthy white and Asian children in the

California DDS dataset.[77] Severe ASD

prevalence has flattened and even declined among

these children since birth year 2000, suggesting that

wealthy parents have been making changes that

effectively lower their children’s risk of developing

ASD. The Prevention scenario assumes that these

parental strategies and opportunities already used

by wealthy parents to lower their children’s risk of

ASD can be identified and made available rapidly

to lower income children and ethnic minorities,

who are currently experiencing the most rapid

growth in ASD prevalence.[85, 86]

Even under the Prevention scenario, the cost of

ASD soars to $3.7 ± 0.8 trillion annually by 2060,

a 33% reduction from our standard Base Case

scenario price tag of $5.5 ± 1.2 trillion, but still a

steep cost. This is because the Prevention scenario

initially follows the trajectory of the Base Case

scenario and the demographic momentum of the

large ASD population born over the last three

decades still results in large total costs by 2060. The

asymptotic rate of 0.6% severe ASD assumed in the

Prevention scenario is still notably high compared

to historical levels, which were 0.06% in 1980.[1]

If a more dramatic reduction is assumed, e.g. to

0.06% severe ASD by 2040 (following the same

time trajectory of our current Prevention scenario),

the total cost of ASD drops to $3.2 trillion/year by

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2060, again still an enormous cost due to

demographic momentum, but a savings of $2.3

trillion/year over the current business-as-usual Base

Case scenario in 2060.

McDonald and Paul (2010) [10] in a study for

the U.S. Environmental Protection Agency find that

autism rates in the U.S. began to increase sharply in

1987. At that time, the average age of mothers in

the U.S. was 26 years old.[87] The average age of

retirement in the U.S. is 64 [88] and average life

expectancy in the U.S. in 2018 is 78.7 years.[89]

Over the last three decades of rising autism

prevalence, parents incurred a significant

proportion of costs (especially housing but also

increased costs for medical care and supportive

services). However, the first large cohort of autism

parents will begin to retire in 2025. When that

happens, autism costs that were formerly borne by

parents will shift onto local, state, and federal

government. The first large cohort of autism parents

will die on average around 2040. At that point, most

of the costs of autism (hundreds of billions of

dollars annually) will shift permanently onto the

public sector.

Our analysis sheds new light on important,

underexplored policy issues that will inevitably

arise from this shift. Many, and potentially severe,

new constraints on resources will arise should

future demands play out as our scenarios suggest,

including (but not limited to) adult residential

accommodation and caregiver support, not to

mention the broader effect on the economy.

Specifically, as the adult ASD population grows

and ages, where will their residential placements

come from as their parents grow old and die? As

increasing portions of the adult population become

disabled and dependent, where will the caregiving

workforce that must replace parental caregivers

come from and how will their support work be

funded? With a large proportion of the productive-

age workforce unable to contribute to our economy,

how will the America economy suffer as a whole?

As of this writing, governments at all levels in

the U.S. have difficulty even acknowledging the

size and scope of the growth in autism. Even though

the costs of autism are on par with or even exceed

the largest line items in the budget there is currently

no plan to meet this enormous fiscal challenge. In

the absence of a comprehensive plan to either raise

revenue or prevent autism through mitigation of

causal factors, the costs of autism represent a

serious threat to the economic future of the U.S.

Limitations

Mortality

There is a growing literature on the increased risk

of early mortality in the population with ASD.[90–

99] But the data is complicated by the fact that ASD

prevalence rates started to increase around

1987,[10] so the overwhelming majority of the

current population is comprised of children and

young adults, giving us very little long term

mortality data to work with. The most recent

mortality studies have focused on measuring excess

mortality and causes of death for people on the

spectrum who died during the study period without

much attention to the effect on survival rates of the

overall population. One example is a recent study

that examined deaths from injury; it emphasized the

mean age of death for individuals with autism in

their sample of 36.2 years, compared this to the

mean age of death of 72 years in the general

population without recognizing that the age

distribution in their autism population was not

comparable to that of the general population.[92]

Studies such as these, as well as others that focus

solely on excess mortality and causes of death, are

prone to misinterpretation because they do not

estimate average life expectancy for the ASD

population as a whole. (Said differently,

calculations of average age of death do not take into

account the vast majority of the ASD population

who are alive at the end of the study period.) In two

somewhat dated studies that estimated survival over

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the life span, one (Mouridsen et al, 2008) [95]

estimated a 4–6% lower survival rate over 40 years

following diagnosis in a sample ending in 1984; the

other (Shavelle and Strauss, 1998) [98] estimated

reduced life expectancy of 5–6 years for males and

12 years for female children with autism in a sample

ending in 1996.

The U.S. autism population projections in our

model were based on applying prevalence rates by

birth cohort to census projections and effectively

assumed that the average life expectancy for the

ASD population did not differ substantially from

that of the general population. Given the relative

paucity of and datedness of data on life expectancy,

the relative modesty of the mortality effect, and the

modeling complexity involved in incorporating that

effect, we chose the simpler approach. To the extent

that our ASD prevalence rates for adult autism age

cohorts overstate life expectancy for autistic

individuals, our model may overestimate the

societal cost of ASD. Also, our model was

developed before Covid. In 2020 and 2021, life

expectancy declined for the population as a whole

in the U.S.;[100] it is not clear whether this decline

is temporary or evidence of a new long-term trend,

nor is it clear how life expectancy for the ASD

population may have changed during that time.

Out-of-pocket parental costs

Existing studies of the cost of autism contain

limited information about increased out-of-pocket

expenditures borne by parents. Our model includes

“non-medical services” such as accommodation,

employment support, community care, respite care,

and day-care programs using data from CDDS

(2019) [45] and following the approach used by

Leigh et al. (2016) [44] and Buescher et al.

(2014).[15] One early study, Jarbrink et al.

(2003),[12] used diaries and a questionnaire to

directly measure out-of-pocket costs such as “extra

laundry,” “extra help,” “transport,” and “court

cases/solicitor” that may provide a broader estimate

than our “non-medical services.” Their research

found these out-of-pocket costs represented close to

10% of total costs. To the extent that parents often

incur costs over and above those reflected in the

cost of autism literature, our model may

underestimate the total cost of autism in the U.S.

Conclusion

An increasing volume of research has pointed to the

high and rising economic burden of ASD in the

United States. But the weight of previous cost of

disease assessments have been based on an

assumption of constant prevalence and are therefore

misleading for purposes of policy, provision of

care, and intensity of prevention efforts. Our

analysis combines dynamic birth-year prevalence-

based population forecasts with updated life cycle

cost estimates to compute an alarming set of

projections for the economic impact of what some

have described as the autism “tsunami.” Our model

projects a total population-wide ASD cost in the

U.S. of $5.54 (4.29–6.78) trillion/year by 2060,

accounting for inflation, with potential savings of

$1.9 trillion/year with pursuit of ASD prevention

via identifying and better regulating environmental

factors that increase autism risk. We believe these

projections work against the temptation to

normalize recent trends in ASD prevalence. Rather,

they reinforce the need to address rising autism

prevalence as more than just an urgent public health

concern but also as a policy question with respect

to where resources will come from and how to

mitigate and prevent the worst-case scenarios.

Sci, Pub Health Pol, & Law

Autism Tsunami: Societal Cost of ASD — Dec. 2023

248

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Appendix

Autism cost model, details of calculation

Our base calculation focuses on the cost of the more severe end of the autism spectrum, as defined based on

the California Department of Developmental Services (CDDS) caseload,

cost_severe(ibyr,ipyr,isc,icat) =

Rasd_CDDS(ibyr,isc)*census_pop(iage,ipyr)*costcat_CDDS(iage,icat)*inflate(ipyr,icat) (A1)

where,

cost_severe = the cost of severe autism.

Rasd_CDDS = autism prevalence from California DDS (CDDS) in %.

census_pop = age-resolved census population for each of 10 projection years.

costcat_CDDS = dollar cost of severe autism per individual per year for each of 5 cost categories.

ibyr = birth year, extending from 1931–2060.

ipyr = projection year, indexed in 2016 and at each 5 year milepost from 2020–2060

isc = 3 different future scenarios for U.S. autism prevalence through 2060.

icat = index of 6 cost categories.

iage = index of age of cases in a given projection year, resolved annually from 0–100 years old.

inflate (optional term) = rate of inflation relative to base year 2018, compounded to each of the projection

years from 2020–2060, distinguishing between productivity, medical and nonmedical categories.

Equation 1 permits the isolation of any individual index or set of combined indices by integrating over the

remaining indices. For example, our main results are presented as a function of scenario and projection year,

by integrating over the ibyr and icat indices,

cost_severe(ipyr,isc) = 󰇛 󰇜





*census_pop(iage,ipyr)*{costcat_CDDS(iage,icat)*inflate(ipry,icat)}



 (A2)

In these calculations, iage, ipyr and ibyr are interrelated through Equation 3,

ibyr = ipyr-iage (A3)

In practice, this means that the appropriate matching ibyr index for costcat_CDDS and census_pop, which

are functions of iage, is identified within the ibyr loop and substituted for iage in Equations 1,2 and 4 below.

Since, as described below, severe autism accounts for only about one half to one third of all ASD, we

estimate the additional cost of milder ASD by multiplying Rasd_CDDS by a range of scalars and applying

a variant of Equation 1,

Sci, Pub Health Pol, & Law

Autism Tsunami: Societal Cost of ASD — Dec. 2023

256

cost_milder(ibyr,ipyr,isc,icat) = Rasd_CDDS(ibyr,isc)*scale_factor*

census_pop(iage,ipyr)*costcat_milder(iage,icat)*inflation(ipyr,icat), (A4)

Where,

cost_milder = the cost of milder ASD.

scale_factor = a range of scalars (1.1–2.5) reflecting the ratio of milder ASD to the more severe forms of

ASD served by CDDS.

costcat_milder = dollar cost of milder autism per individual per year for each of 6 cost categories.

Similar to cost_severe, any individual or set of combined indices can be isolated for cost_milder by

integrating over the remaining indices. The total cost of ASD is then,

cost_total = cost_severe + cost_milder (A5).

Systems of Care for Children and Youth with Autism Spectrum Disorders

Chapter

Oct 2022

Andres Pumariega

Vital Statistics Rapid Release Provisional Life Expectancy Estimates for 2021

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Racial/Ethnic Disparities in the Prevalence and Trends of Autism Spectrum Disorder in US Children and Adolescents

Article

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This cross-sectional study uses data from the National Health Interview Survey to assess racial/ethnic disparities in the prevalence and trends of autism spectrum disorder among US children and adolescents.

Healthcare Costs of Pediatric Autism Spectrum Disorder in the United States, 2003–2015

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Aug 2021
J AUTISM DEV DISORD

Published healthcare cost estimates for children with autism spectrum disorder (ASD) vary widely. One possible contributor is different methods of case ascertainment. In this study, ASD case status was determined using two sources of parent reports among 45,944 children ages 3–17 years in the Medical Expenditure Panel Survey (MEPS) linked to the National Health Interview Survey (NHIS) Sample Child Core questionnaire. In a two-part regression model, the incremental annual per-child cost of ASD relative to no ASD diagnosis was

3930 (2018 US dollars) using ASD case status from the NHIS Child Core and

5621 using current-year ASD case status from MEPS. Both estimates are lower than some published estimates but still represent substantial costs to the US healthcare system.

California Autism Prevalence by County and Race/Ethnicity: Declining Trends Among Wealthy Whites

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J AUTISM DEV DISORD

County-level ASD prevalence was estimated using an age-resolved snapshot from the California Department of Developmental Services (DDS) for birth years 1993–2013. ASD prevalence increased among all children across birth years 1993–2000 but plateaued or declined thereafter among whites from wealthy counties. In contrast, ASD rates increased continuously across 1993–2013 among whites from lower income counties and Hispanics from all counties. Both white ASD prevalence and rate of change in prevalence were inversely correlated to county income from birth year 2000–2013 but not 1993–2000. These disparate trends within the dataset suggest that wealthy white parents, starting around 2000, may have begun opting out of DDS in favor of private care and/or making changes that effectively lowered their children’s risk of ASD.

Underutilization of Early Intensive Behavioral Intervention Among 3-Year-Old Children with Autism Spectrum Disorder

Article

Full-text available

Jul 2019
J AUTISM DEV DISORD

Funding for early intensive behavioral intervention (EIBI) for children with autism spectrum disorder is rapidly expanding. Yet we know little about children’s utilization, and research on inequities in utilization is lacking. We examined the relationship between utilization during the first year of EIBI and (a) child race-ethnicity and (b) neighborhood characteristics. Using a sample of children eligible for a Medicaid waiver through a novel policy of presumptive eligibility (N = 108), we estimated a series of two-level growth curve models. Children’s average utilization ranged between 24 and 48% of weekly hours, and utilization did not differ by race-ethnicity or neighborhood during the first year. Findings underscore the need to monitor utilization of EIBI and warrant research on the feasibility of EIBI provision in the general population.

The Economic Costs of Autism Spectrum Disorder: A Literature Review

Article

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Jul 2019
J AUTISM DEV DISORD

Autism is associated with a range of costs. This paper reviews the literature on estimating the economic costs of autism spectrum disorder (ASD). More or less 50 papers covering multiple countries (US, UK, Australia, Canada, Sweden, the Netherlands, etc.) were analysed. Six types of costs are discussed in depth: (i) medical and healthcare service costs, (ii) therapeutic costs, (iii) (special) education costs, (iv) costs of production loss for adults with ASD, (v) costs of informal care and lost productivity for family/caregivers, and (vi) costs of accommodation, respite care, and out-of-pocket expenses. A general finding is that individuals with ASD and families with children with ASD have higher costs. Education costs appear to be a major cost component for parents with children with ASD.

The Prevalence of Parent-Reported Autism Spectrum Disorder Among US Children

Article

Full-text available

Nov 2018

: media-1vid110.1542/5839990273001PEDS-VA_2017-4161Video Abstract OBJECTIVES: To estimate the national prevalence of parent-reported autism spectrum disorder (ASD) diagnosis among US children aged 3 to 17 years as well as their treatment and health care experiences using the 2016 National Survey of Children's Health (NSCH). Methods: The 2016 NSCH is a nationally representative survey of 50 212 children focused on the health and well-being of children aged 0 to 17 years. The NSCH collected parent-reported information on whether children ever received an ASD diagnosis by a care provider, current ASD status, health care use, access and challenges, and methods of treatment. We calculated weighted prevalence estimates of ASD, compared health care experiences of children with ASD to other children, and examined factors associated with increased likelihood of medication and behavioral treatment. Results: Parents of an estimated 1.5 million US children aged 3 to 17 years (2.50%) reported that their child had ever received an ASD diagnosis and currently had the condition. Children with parent-reported ASD diagnosis were more likely to have greater health care needs and difficulties accessing health care than children with other emotional or behavioral disorders (attention-deficit/hyperactivity disorder, anxiety, behavioral or conduct problems, depression, developmental delay, Down syndrome, intellectual disability, learning disability, Tourette syndrome) and children without these conditions. Of children with current ASD, 27% were taking medication for ASD-related symptoms, whereas 64% received behavioral treatments in the last 12 months, with variations by sociodemographic characteristics and co-occurring conditions. Conclusions: The estimated prevalence of US children with a parent-reported ASD diagnosis is now 1 in 40, with rates of ASD-specific treatment usage varying by children's sociodemographic and co-occurring conditions.

Births: Final Data for 2019

Article

Apr 2021

Objectives-This report presents 2019 data on U.S. births according to a wide variety of characteristics. Trends in fertility patterns and maternal and infant characteristics are described and interpreted. Methods-Descriptive tabulations of data reported on the birth certificates of the 3.75 million births that occurred in 2019 are presented. Data are presented for maternal age, livebirth order, race and Hispanic origin, marital status, tobacco use, prenatal care, source of payment for the delivery, method of delivery, gestational age, birthweight, and plurality. Selected data by mother's state of residence and birth rates by age are also shown. Trend data for 2010 through 2019 are presented for selected items. Trend data by race and Hispanic origin are shown for 2016-2019. Results-A total of 3,747,540 births were registered in the United States in 2019, down 1% from 2018. The general fertility rate declined from 2018 to 58.3 births per 1,000 women aged 15-44 in 2019. The birth rate for females aged 15-19 fell 4% between 2018 and 2019. Birth rates declined for women aged 20-34 and increased for women aged 35-44 for 2018-2019. The total fertility rate declined to 1,706.0 births per 1,000 women in 2019. Birth rates declined for both married and unmarried women from 2018 to 2019. The percentage of women who began prenatal care in the first trimester of pregnancy rose to 77.6% in 2019; the percentage of all women who smoked during pregnancy declined to 6.0%. The cesarean delivery rate decreased to 31.7% in 2019 (Figure 1). Medicaid was the source of payment for 42.1% of all births in 2019. The preterm birth rate rose for the fifth straight year to 10.23% in 2019; the rate of low birthweight was essentially unchanged from 2018 at 8.31%. Twin and triplet and higher-order multiple birth rates both declined in 2019 compared with 2018.

First Alarm and Time of Diagnosis in Autism Spectrum Disorders

Article

Oct 2020

Early diagnosis of autism spectrum disorder (ASD) is of paramount importance as it opens the road to early intervention, which is associated with better prognosis. However, early diagnosis is often delayed until preschool or school age. The purpose of the current retrospective study was to explore the age of recognition of first alarming symptoms in boys and girls as well as the age at diagnosis of different subtypes of ASD in a small sample. A total of 128 parents’ of children with ASDs were participated in the survey by completing a self-report questionnaire about early signs and symptoms that raised their concern. Parents of children with autism voiced concerns earlier and obtained diagnosis significantly earlier compared to parents of children with Asperger syndrome (p value <0.000). No significant difference (p value<0.05) has been detected between males and females in early manifestation of first signs and symptoms of ASD. The mean age at diagnosis was 3.8 years for autistic disorder, 6.2 years for children with Asperger syndrome and 6.4 years for other, e.g., PDD-NOS. The most commonly reported symptoms were speech and language problems (p value = 0.001) for children who were later diagnosed with autism, while behavior problems (p value = 0.046) as well as difficulties in education at school (p value = 0.013) for children with Asperger syndrome. The gap between early identification and diagnosis pinpoints the urgent need for national systematic early screening, the development of reliable and sensitive diagnostic instruments for infants and toddlers and heightened awareness of early signs of ASD among parents, teachers, and healthcare professionals and providers as well.

The lifetime social cost of autism: 1990–2029

Article

Apr 2020
RES AUTISM SPECT DIS

This cost of illness analysis computes a baseline and future estimate of lifetime social costs associated with autism spectrum disorder (ASD) for the 50 states in the United States (US). The number of cases of ASD are estimated, then multiplied by annual direct and indirect medical and non-medical costs identified in the peer-reviewed literature. This amount is then extrapolated across the number of years each cost type is expected to be incurred to calculate a total lifetime cost for each state in the US from 1990–2019, and to project future cost for 2020–2029. From 1990–2019, there have been an estimated 2 million new cases of (ASD), with social costs of more than

7 trillion. If the future prevalence of ASD remains unchanged over the next decade, there will be an estimated additional 1 million new cases, resulting in an additional

4 trillion to the United States in social costs, however if the rate of increase in prevalence continues, costs could reach nearly $15 trillion by 2029. The financial burden of ASD is significant and identifying the modifiable causes of ASD has the potential to provide tangible benefits.

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