The Influence of Policy on Early Diagnosis and Other Autism Services: a Narrative Review

Abstract

Little is known about how policy—laws, regulations, procedures, administrative actions, incentives, or voluntary practices of governments or other institutions—shapes access to early autism services including diagnosis before age three. Early diagnosis and treatment are considered critical to improve outcomes. Understanding how policy influences such services could have substantial impact on outcomes for autistic children. We conducted a narrative literature review to determine what is known on how policy impacts early autism diagnosis and treatment. We found 17 articles that describe policy factors promoting early diagnosis and seven articles that identify policy factors affecting variability in autism services. We identified the following themes: (1) state policy factors influence access to diagnosis and other autism services, (2) innovative screening models affect early diagnosis, (3) provider training programs increase autism screening and diagnosis, (4) insurance policy influences autism services variability, and (5) resource availability affects geographic variability in autism services. Although common themes exist, more robust investigation is needed on policy impacting early autism services—beyond insurance and early intervention—and utilizing more rigorous designs.

Appendices

Appendix 1 Aim 1. Policy factors promoting early autism diagnosis evidence table (N = 17)

Article	Purpose	Design and sample	Policy factor examined and relationship to early diagnosis	Major findings
State policy factors influence access to diagnosis and other autism services
1. Barton et al. (2016)	To examine differences in the timing and practices used during autism diagnostic evaluations and interventions with children who are culturally and linguistically diverse (CLD) amongst Colorado-based providers	Cross-sectional survey (n = 133 practitioners)	Provider-specific characteristics and practices influencing the timing of autism diagnosis; advocacy for the implementation of state-level changes in provider training policies to ensure all practices use the same evidence-based standards	The most frequently cited method for determining a child’s eligibility for EI/special education underneath the autism category was the use of standardized tools. Agencies/programs differed in terms of having written guidelines for supporting individuals with autism. In total 57 (42.9%) had screening/ identification procedures. Only 45 (33.8%) reported using autism-specific placement options, and 37 (27.8%) did not. Children with autism from CLD backgrounds are identified at an older age than White children with autism due to factors including cultural differences, difficulty accessing services, language barriers, and lack of knowledgeable professionals. Cultural modifications and adaptations (e.g., the use of an interpreter, translating documents, consulting with the child’s parents) were rarely implemented
2. Martinez et al. (2018)	To examine family experiences with the contextual factors that impact the efficiency of autism diagnosis	Cross-sectional survey (n = 450 families)	Community-based barriers to a timely autism diagnosis	The average delay in diagnosis was 19 months. A total of 54.3% of children experienced shifting diagnoses. A total of 25.8% of families were initially told that their child did not have autism. Families who had problems accessing a diagnostician experienced nearly twice the odds of a delay in diagnosis. Traveling > 60 miles was associated with 2.3 the odds of shifting diagnoses, while delay in diagnosis increased the odds of shifting diagnoses by 70%. Delay in diagnosis was also associated with twice the odds of being told the child did not have autism. If a family completed screening forms, their odds of experiencing a delay in diagnosis were reduced by 37%
3. Rotholz et al. (2017)	The study sought to examine the effectiveness of presumptive eligibility based on a two-tiered screening process on access to early intensive behavioral intervention (EIBI) services in South Carolina	Case study	Policy of “presumptive eligibility”	Implementation of presumptive eligibility in South Carolina has been accompanied by large increases in children ages 18 to 36 months being eligible for and receiving EIBI services
4. Stahmer et al. (2019)	This study described barriers and facilitators to identifying developmental concerns, obtaining an autism diagnosis, and accessing autism-related services from the perspectives of low-resource families from three cultural groups and three primary languages, as well as the providers who serve them	Qualitative focus group study (n = 113; 58 caregivers of children with ASD and 55 ASD service providers)	Barriers and facilitators to a timely ASD diagnosis amongst families from linguistically and culturally diverse backgrounds; Cultural competency of service providers who work with children/families from CLD backgrounds	Barriers to autism diagnosis and service provision included limited provider autism awareness and knowledge delays service access, access to childcare, language barriers, provider perceptions of family challenges, geographic factors (e.g., location, rurality), transportation challenges, long waitlists, stigma towards autism, limited information about autism, and complex system of care for autism. Facilitators to autism diagnosis and service provision included caregiver proactive participation facilitates service access, support from family members, provider advocacy and coordination, provider partnership facilitates caregiver engagement, and support from other parents of children with autism
5. Stahmer and Mandell (2007)	Study described states’ policies for providing EI services to children with autism under IDEA part C and determined whether state part C policies were associated with the proportion of children diagnosed with autism ages 3–5 years of age served under IDEA Part B (special education)	Mixed methods study that involved semi-structured interviewing and secondary data analysis of the proportion of children ages 3–5 years receiving autism services through IDEA part C (n = 46 IDEA part C agency administrators)	State-level variations in IDEA part C EI policies	In the 46 states, EI referrals could be made by anyone; however, referrals came most commonly from outside the EI system in 74% of states. Requirements regarding the types of professionals who could make an autism diagnosis were quite variable 65% of respondents reported that they provided all 17 EI services mandated for states participating in IDEA part C, but no autism specific services. Of the states represented, 46% indicated that they provided specific autism treatment programs. Within the states that provided no autism-specific services, 40% of respondents emphasized that they offered individualized services to meet the unique needs of each child, regardless of diagnosis. Of the states represented, 20% had guidelines for diagnostic assessment and 26% had guidelines for treatment for children with autism. All states indicated that they followed general part C guidelines for individualizing intervention, providing functional treatment in the natural environment, and including families as an integral part of the intervention process. There was no statistically significant association between any state EI policy and practice variables and the proportion of children with autism ages 3–5 years receiving services through IDEA part B
6. Williams et al. (2021)	This study explored the policies and practices of the lead agencies (regional centers) in California’s EI system, with particular focus on those practices that may support or hinder the provision of early autism identification and intervention services for young children < 3 years	Cross-sectional survey (n = 18 early intervention managers)	California state policies that support or hinder the early identification of children with autism and their access to early intervention services	Of the EI agencies, 85% conducted autism screening with some children. Only 39% conducted autism diagnostic assessments before age 3. Many managers (n = 10; 55.6%) noted that the EI program focuses on the child’s symptoms and not on a disorder or diagnosis. Many managers (n = 8; 44.4%) reported that the same services would be provided with or without a diagnosis. The most common challenges in accessing services included a lack of available providers, traveling distance to providers, and a confusing process for accessing services through health plans
Innovative Screening Models Affect Early Autism Diagnosis
7. Carbone et al. (2020)	This study had the following three aims: (1) describe the proportion of children screened by the Modified Checklist for Autism in Toddlers (M-CHAT), (2) identify characteristics associated with screen completion, and (3) examine associations between autism screening and later autism diagnosis	Mixed-methods study examining EHR data on M-CHAT screening results from children attending 18- and 24- month well-child visits (n = 36,233) and interviews with physicians (n = 12) about M-CHAT usage and autism referral	Universal autism screening using the M-CHAT	A total of 72.8% of children were screened at one well-child visit, and 54.4% of children were screened at both visits. Approximately 1.4% of children were diagnosed with autism. Children who screened positive were more likely to be diagnosed with autism at a younger age than children who screened negative or who were not screened. The M-CHAT’s sensitivity for autism diagnosis was 33.1% and the positive predictive value was 17.8%. Clinicians routinely omitted the follow-up interview and reported uneven referral patterns
8. Choueiri et al. (2021)	This study’s objective was to test a screening model using the Rapid Interactive Screening Test for Autism in Toddlers (RITA-T) to improve autism detection in a diverse, underserved community	Longitudinal study (n = 81 children aged 18–36 months)	The implementation of a two-level autism screening model intended to refer children at a greater risk for developing autism to diagnostic services at an earlier age. This may, in turn, reduce the wait time for receiving a diagnosis and accessing services	Overall, 57 of the 81 participating children received an autism diagnosis. RITA-T mean scores were significantly higher in the autism group than in the non-autism group. Average wait time between referral from EI and autism diagnosis was 6 weeks
9. Dai et al. (2020)	This study examined the utility of rescreening children for autism at 24 months who initially screened negative at 18 months	Longitudinal study (n = 19,685 children aged 16–30 months)	Repeated autism screening in the first 30 months of life	Ten children who screened negative at 18 months and then screened positive at 24 months were diagnosed with autism (catch-24 group). 203 children were diagnosed with autism who screened positive at 18-months. At the time of their evaluation, children in the catch-24 group were approximately 6 months older than children who screened positive at 18-month group when they received their autism diagnosis
10. Eisenhower et al. (2020)	This study examined the screening participation rate, proportion of positive screening outcomes, and influence of health disparities upon participation in and outcomes of a multi-stage autism screening and evaluation model embedded within EI	Longitudinal study (n = 4943 children aged 14–36 months)	The policy factor examined was the two-stage autism screening model used within an EI system. This two-stage screening model is intended to reduce racial/ethnic, linguistic, and socioeconomic disparities in the autism screening process by allowing children from these backgrounds to receive screening services within the bounds of the federally mandated, state-funded EI system	Of the 2465 children who completed stage 1 screening, 43.7% screened positive (n = 1076). Among children screening positive at stage 1 who went on to complete the stage 2 screening, 86.6% screened positive (n = 609). Among children screening positive at stage 2, 84.6% went on to receive a diagnostic evaluation. 83.7% of evaluated children received an autism diagnosis (n = 431). Positive stage 1 screening was predicted by male gender, older age, being of color, and having public insurance. Stage 2 screening completion was only predicted by older age. No demographic factors predicted receipt of an autism diagnosis
11. Major et al. (2020)	To evaluate the impact of the digital M-CHAT-R screener on the likelihood of physician referral for a developmental evaluation or autism diagnosis, and the average age of patient referral	Case study (n = 1279 children aged 16–30 months)	Comparison of digital and paper-based versions of the M-CHAT-R to determine the timing of autism diagnosis receipt and the age at the time of diagnosis for toddlers who screened positive	A total of 13% of patients in the paper group received developmental referrals, whereas more than twice as many (31%) patients in the digital group were referred. Patients screened with the digital screener had 5.35 times increased odds of receiving a developmental referral by 48 months. The digital group was referred for evaluation at an earlier mean age (23.1 months) compared to the paper group (26.8 months)
12. McNally Keehn et al. (2020)	An innovative tiered system of developmental screening and diagnostic evaluation was designed to improve access to early autism evaluation in children’s local communities and support enrollment in evidence-based interventions	Case study (n = 12 Early Autism Evaluation [EAE] Hub sites)	The development of an innovative tiered system of developmental screening and autism diagnostic evaluation in a state with a large number of “Medically Underserved Areas.”	Over 6 years, a total of 2076 children were evaluated for autism across the EAE Hub system. By 2018, ∼ 72% of expected autism diagnoses in their respective regions and 15% of expected autism diagnoses statewide were made at EAE Hubs. Thirty-three percent of the children evaluated received a diagnosis of autism. Across the EAE Hubs, the mean age at evaluation was 30 months, and the median wait time from referral to EAE Hub evaluation was 62 days
13. Miller et al. (2011)	This study examined the impact of systematic screening in identifying children with autism within a community setting	Longitudinal study (n = 990 children aged 14–30 months)	Systematic autism screening processes designed so that all children would be screened for autism, regardless of whether their developmental trajectory raised any concerns from their parents and/or pediatricians	A total of 1.6% (n = 13) of children screened showed early signs of autism, 10 of whom were newly identified through screening questionnaire scores. A majority of newly identified children were not yet of concern to their caregivers or clinicians. Thirty-two percent of all children, and 78% of uninsured children, would have been missed if screening had been restricted to well-child visits
14. Wieckowski et al. (2021)	This study examined the timing and accuracy of early and repeated screening for autism during well-child visits at 12, 15, or 18 months	Cluster randomized trial (n = 5784 toddlers)	Repeated autism screening in the first 36 months of life beginning at age 12 months, 15 months, or 18 months. This process ensures that children who may have been missed at an earlier age will receive an autism evaluation and diagnosis in a timely manner	The toddlers who began screening at 12 months received an autism diagnosis at a younger age compared to those who began screening at 15 or 18 months. The average age of autism diagnosis was 23.62 months. Repeat screening improved sensitivity for all ages (82.1%), without notably decreasing specificity (all specificity > 93.5%). Screening at 18 months had significantly higher positive predictive value than screening at 12 or 15 months, with a larger number of toddlers from the 18-month group receiving autism diagnoses compared to toddlers from the 12-month group and the 15-month group
Provider Training Programs Increase Autism Screening and Diagnosis
15. Janvier et al. (2016)	This study investigated the feasibility of having early childcare providers screen for autism in underserved community daycare and preschool settings	Longitudinal study (n = 967 children aged 16–36 months)	The policy factor examined was training daycare providers and preschool teachers screen young children for autism using the M-CHAT and SCQ. Because the school system often gatekeeps autism diagnoses, the ability of childcare providers in underserved communities to screen young children at-risk for autism may improve access to early diagnosis and other autism services and reduce inequities therein	Early childcare providers returned screening tools for 90% of the children for whom parental consent had been received. A total of 14% of children screened positive for autism and 3% of the sample met criteria for autism. Among those who screened positive, 34% were lost to follow-up
16. Mazurek et al. (2019)	This pilot study evaluated the feasibility of a newly developed hybrid training model for primary care providers (PCPs) in autism screening and diagnosis for young children (ECHO Autism STAT). This study additionally examined the effects of this training approach on PCP self-efficacy and practice change	Case study (n = 18 PCPs)	The policy factor examined was the ECHO Autism STAT hybrid model for training PCPs in autism screening and diagnostic evaluation for children at the highest risk for developing autism. This training model provides PCPs with both hands-on training in administering standardized screening tools and video-based coaching. The program emphasized both timely diagnosis and appropriate referral for more comprehensive assessment when necessary	The ECHO Autism STAT model is a feasible and scalable solution for training community-based PCPs in the diagnosis of young children at highest risk for autism. PCPs who participated in the ECHO Autism STAT training demonstrated substantial changes in their ability to screen and assess autism in young children. PCPs significantly increased their use of autism-specific screening measures at both 18- and 24-month well child visits (averaging over 90% of all well child visits at these time points) by receiving prompt diagnostic evaluation in primary care, families were able to access services 2 to 6 months earlier than expected
17. Swanson et al. (2014)	The study evaluated the effectiveness of the training program in enhancing autism identification and assessment within community pediatric settings across the state	Case study (n = 27 community pediatric providers)	The policy factor examined was the autism screening and diagnosis training program for community pediatric providers to allow for more efficient and effective diagnosis and treatment	On average, the number of children diagnosed within practice by providers involved in this training increased by 85%. Of the 16 children referred for independent evaluation, 14 participated. Eight children received a forced-choice autism classification by the referring pediatric provider. The confidence of pediatric providers in their autism determination ranged from 86 to 93%

1. ABA, applied behavioral analysis; ADOS, The Autism Diagnostic Observation Schedule; ASD, autism spectrum disorder; CLD, culturally or linguistically diverse; EAE, early autism evaluation; EHR, electronic health record; EI, early intervention; EIBI, early intensive behavioral intervention; EIP, early intervention program; HCBS, home and community based services; IDEA, Individuals with Disabilities Education Act; MCHAT (-R), Modified Checklist for Autism in Toddlers (- Revised with Follow-up); PCP, primary care provider; RITA-T, The Rapid Interactive Screening Test for Autism in Toddlers; SCQ, Social Communication Questionnaire; STAT, Screening Tool for Autism in Toddlers

Appendix 2 Aim 2. Policy factors contributing to variability in autism services evidence table (N = 7)

Article	Purpose	Design and sample	Policy factor examined and relationship to variability in autism services	Major findings
Insurance policy influences variability in autism services
1. Baller et al. (2016)	This study examined the experiences of five states that implemented autism insurance mandates to describe—from the perspective of advocates, treatment providers, and insurers in each state—the barriers and facilitators to implementation of these mandates regarding obtaining treatments for children with autism	Qualitative study involving semi-structured interviews (n = 17)	Barriers and facilitators to the implementation of state-level autism insurance mandates	State autism insurance mandates affected the number of available services and the amount of services used. A robust autism delivery system being available prior to the insurance mandate and reduced family cost-sharing associated with accessing autism services were both identified as facilitators to the accessibility of autism services in two or more states. Barriers towards the implementation of autism insurance mandates in multiple states included: lack of autism service capacity, additional credentialing required with individual private insurance companies, low reimbursement rates, delay in the promulgation of regulations, compliance with provider licensure requirements, lack of information for parents, high cost-sharing for autism services, disparities in insurance coverage between public and private plans, lack of compliance by commercial insurance companies, and lack of specificity of the mandate
2. Callaghan and Sylvester (2019)	This investigated autism insurance mandates, passed from 2000 to 2017, across states in terms of six factors explaining variability in their generosity including partisanship, state economic circumstance, legislative professionalism, interest group activity, policy diffusion, and state insurance environment	Policy analysis of autism state insurance mandates	State sociopolitical characteristics (e.g., relative policy need, policy diffusion) were examined in relationship to the presence and generosity of autism state insurance mandates. These factors are proposed to influence the interstate variability in the benefits of their autism insurance mandates	The following factors were significantly associated (p < 0.05) with a state having an autism insurance mandate: democratic control, more liberal citizen ideology, percent of uninsured individuals, percent of employer sponsored insured individuals, number of registered health care interest groups, and legislative professionalism The following factors were significantly associated (p < 0.05) with generosity of the state’s autism insurance mandate after adjusting for relevant policy need (i.e., the percent of children with autism received special education services in the state): democratic government control, citizen ideology, and percent of individuals with employer sponsored insurance
3. Douglas et al. (2017)	This study piloted a structured legal research methodology to code autism insurance mandates that impact access to services such as allied health services for a comprehensive set of states associated with the Autism and Developmental Disability Monitoring Network	Policy analysis of autism insurance laws in 14 states	The policy examined included characteristics of the autism insurance mandates in 14 states, which may contribute to variability in autism services access across states	Autism insurance laws can require private insurance plans to “cover” autism treatment, can require insurance plans to “offer” autism coverage, or require parity for autism services compared to other physical and mental health conditions Effective and compliance dates: 7 states had the same effective and compliance date for insurers, 3 states had compliance dates after effective dates, and 3 states did not include a compliance date requirement The majority of state laws impacted four types of insurance plans: large group, small group, individual, and state employee health plans Age limits to insurance mandates either capped the age at which a child must have received an autism diagnosis or capped the age of eligibility for receiving autism treatment services Three primary types of service limits were identified (1) limits on services for all autism treatments, (2) limits on behavioral therapy only, and (3) limits based on a combination of age and service costs All state mandates distinguished between behavioral therapy and allied health services Three medical necessity variables emerged including (1) whether a treatment plan was required prior to payment of services, (2) what type of provider was authorized to prescribe the treatment plan, and (3) how often an insurance plan could review the treatment plan for medical necessity Six states distinguished between medical and educational services
4. Mandell et al. (2016)	This study examined if state autism insurance mandates increased treated prevalence of the condition in commercially insured children	Secondary data analysis of inpatient and outpatient facility and professional procedure claims data (n = 1,046,850)	The policy factor examined was implementation of state autism insurance mandates and length of time since implementation	The treated prevalence among eligible children in states with an autism insurance mandate was 1.8 per 1000 children compared with 1.6 per 1000 children in states without a mandate (p = 0.006). The implementation of state-level autism insurance mandates was associated treated with a 10.4% increase in the treated prevalence of eligible children with autism in the first year of implementation, a 17.1% increase in the second year, and an 18.0% increase in the third and later years after implementation. The treated prevalence of autism was lower than community prevalence estimates
5. Velott et al. (2016)	To describe characteristics of 1915(c) Medicaid Home and Community Based Services (HCBS) waivers for children with autism across states and over time	Policy analysis of HCBS waiver applications that targeted children with autism	The policy examined was characteristics of HCBS waivers targeting children with autism. Characteristics of HCBS waivers for children with autism may contribute to variability in autism services access	The authors identified 50 current or former waivers across 29 states that explicitly included children with autism. Within states, waivers differed primarily by the number of individuals served, the targeted population, by the breadth of services offered, and by the cost of services
Resource Availability Affects Geographic Variability in Autism Services
6. DeGuzman et al. (2017)	This study sought to identify geographic gaps in early autism screening for uninsured children younger than 5 years old in Virginia	Retrospective descriptive geospatial analysis	Rurality and uninsurance may contribute to delayed autism diagnosis and other service access for children; these were the policy factors examined in relation to autism evaluation before children turned five years old. Geospatial analysis can help policymakers visualize the availability and dispersion of autism resources	Rural region 1 and urban regions 4 and 5 had a larger number of children under age 5 evaluated for autism in 2015 than urban region 2 and relatively rural region 3. Several counties within regions 2 and 3 had many children under age 5 in 2015, but very few of them received any autism evaluation services. Most developmental pediatricians in Virginia are located in the northeastern and southeastern areas of the state
7. Ning et al. (2019)	This study sought to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to date autism resource epidemiological information	Secondary analysis and simulation study	The policy factors examined included availability of autism resources in states and average distances to these resources	In total, 28,003 autism-related resources were identified across all 3142 US counties. The estimated average distance between an individual with autism and the nearest autism-related resource is ~ 17 km (11 miles). States with the greatest distance to autism resources included AK, NV, WY, MT, and AZ. Diagnostic resources were the least available of all seven resources. States with the highest diagnostic resource load (i.e., more demand than supply) included WV, KY, ME, MI, and NM

1. HCBS, home and community services