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. Author manuscript; available in PMC: 2020 Aug 1.
Published in final edited form as: Autism. 2018 Nov 29;23(6):1419–1430. doi: 10.1177/1362361318815237

Healthcare Access and Services Use among U.S. Children with Autism Spectrum Disorder

Olivia J Lindly 1, Katharine E Zuckerman 2, Karen A Kuhlthau 3
PMCID: PMC6941887  NIHMSID: NIHMS1511522  PMID: 30497274

Abstract

This study aimed to determine associations of healthcare access problems with services use among U.S. children with autism spectrum disorder (ASD). We analyzed 2011–2014 National Health Interview Survey data on 651 children with ASD aged 2–17 years. There were three measures of healthcare access problems: (1) delays accessing healthcare, (2) difficulty affording healthcare, and (3) trouble finding a primary care provider. There were five service use measures: (1) ≥4 office visits, (2) ≥1 well-child visit (WCV), (3) flu vaccine, (4) prescription medication, and (5) ≥1 emergency department (ED) visit. Multivariable regression models estimated associations of ≥1 healthcare access problem with each service use variable, and effect modification by socioeconomic status (SES) and race/ethnicity. Twenty-nine percent of children with ASD had ≥1 healthcare access problem. Having ≥1 healthcare access problem was associated with lower adjusted odds of a WCV or prescription medication use but higher adjusted odds of ≥4 office visits or ≥1 ED visit. No significant association was found for flu vaccine. Associations of healthcare access problems with ED use were most pronounced for higher SES and white, non-Hispanic subgroups. Intervention, such as insurance expansion, is needed to improve healthcare access for children with ASD.

Keywords: autism spectrum disorder, early intervention, emergency medical services, ethnicity, health services accessibility, healthcare disparities, preventive health services, race, socioeconomic status, special education

Autism spectrum disorder (ASD) is a complex, neurodevelopmental condition (American Psychiatric Association, 2013) that affects many children across the lifespan (Baio, Wiggins, & Christensen, 2018). Children with ASD and their families experience persistent and marked health disparities relative to other children as demonstrated by their poor health-related outcomes including reduced life expectancy (Hirvikoski et al., 2016), low quality of life (Kuhlthau et al., 2010), high caregiver stress (Miodrag, Burke, Tanner-Smith, & Hodapp, 2015), and financial burden (Lavelle et al., 2014). In determining targeted strategies to reduce these disparities and ultimately advance health equity, it is critical to generate new knowledge about healthcare access and services use among children with ASD.

Use of certain evidence-based services (e.g., early intensive behavioral and developmental intervention) has the potential to reduce health disparities for children with ASD by improving their health outcomes (Weitlauf et al., 2014; Williamson et al., 2017). Yet, despite often having higher rates of health service utilization compared to other children (Cummings et al., 2016; Schieve et al., 2012), relatively few children with ASD use these specific services (Payakachat, Tilford, & Kuhlthau, 2017; Zablotsky, Pringle, et al., 2015). Children with ASD and their families also commonly experience problems accessing needed services (Lindly, Chavez, & Zuckerman, 2016; Lindly, Sinche, & Zuckerman, 2015; Zablotsky, Pringle, et al., 2015), which may impede their use of certain services. Little research has, however, examined the linkages between healthcare access problems and services use for children with ASD, including disparities that may exist in these associations among children with ASD.

Past research has primarily focused on health services utilization for individuals with ASD, spanning a range of ages, and compared to individuals with other developmental disabilities such as attention deficit/hyperactivity disorder (ADHD) or the general population (Cummings et al., 2016; McIntyre & Zemantic, 2017; Weiss et al., 2018; Zerbo et al., 2018). One study found that older child age, more atypical behavior, and higher household income predicted more total service hours per week among young children with ASD aged 2–7 years (McIntyre & Zemantic, 2017). This study was, however, geographically limited to children in the Northwest who were recruited through early childhood education programs and developmental evaluation clinics, and only educational and therapeutic services were examined. Another study compared a broader array of health services used between children with ASD and other children aged 3–17 years based on registry data from five healthcare systems across 15 states (Cummings et al., 2016). Results suggest that among youth aged 10–17 years, those with ASD may be more likely than those without ASD to use certain specialty and therapy services but less likely to use preventive care services (e.g., flu vaccine, well-child visit). This study did not, however, examine how services use varied between groups based on healthcare access problems. Results from an additional study showed adults with ASD versus adults with ADHD have higher outpatient utilization for primary care, mental health, and laboratory services (Zerbo et al., 2018). These findings are reinforced by another study that found young adults with ASD generally have higher utilization across a range of services relative to young adults with other developmental disabilities or other young adults (Weiss et al., 2018). Neither of these studies on adults with ASD examined relationships of healthcare access problems with services use, pointing to the need for greater research in this area. Moreover, none of these prior studies, explicitly determined how socioeconomic status (SES) and/or race/ethnicity influence such associations. Consequently, there is a need to determine associations of healthcare access problems with services use among U.S. children with ASD, including the role that SES and race/ethnicity play in these relationships.

SES is often characterized in terms of resources (e.g., income, wealth) and social status (e.g., occupational prestige) (Krieger, Williams, & Moss, 1997) and is related to health (Marmot & Wilkinson, 1999). Race and ethnicity are distinct concepts (Institute of Medicine, 2003) that are related to SES and health (Williams, Priest, & Anderson, 2016), though the mechanisms for these associations in children are complex (Cheng, Goodman, & The Committee on Pediatric Research, 2015). Among children with ASD, some research has shown those who are non-white or Latino are more likely than those who are white or non-Latino to have healthcare access problems (Liptak et al., 2008), less health services use (Broder-Fingert, Shui, Pulcini, Kurowski, & Perrin, 2013), and poorer quality of care (Magaña, Parish, Rose, Timberlake, & Swaine, 2012; Magaña, Parish, & Son, 2015). Children with ASD of low SES, primarily as indicated by public health insurance coverage or low household income, also have greater healthcare access problems (Lin & Yu, 2015) and less health services use compared to those of higher SES (Liptak et al., 2008). Still, little research has examined how SES and race and ethnicity contribute to relationships between healthcare access problems and services use among children with ASD.

For these reasons, we sought to generate new knowledge about relationships of healthcare access with services use including the influence of certain factors fundamental to health, namely SES and race and ethnicity, among a nationally-representative sample of U.S. children with ASD aged 2–17 years. We aimed to (1) determine associations of healthcare access problems with services use, and (2) examine if SES factors or race and ethnicity modify associations of healthcare access problems with services use, among U.S. children with ASD. From past research, we hypothesized that healthcare access problems would be associated with lower utilization, except for emergency department (ED) visits. We further hypothesized that the relationship between having healthcare access problems and lower services use among children with ASD would be especially pronounced for those of lower SES or belonging to non-white race or Hispanic subgroups.

Method

This study was an analysis of secondary, publicly available data on a nationally representative sample of children with ASD aged 2–17 years. Data on children were drawn from four National Health Interview Survey (NHIS) files, spanning 2011 to 2014. The first author’s Institutional Review Board determined this study was exempt.

The Centers for Disease Control and Prevention National Center for Health Statistics conducts the NHIS on an ongoing basis. The NHIS has provided general health information on the resident civilian and noninstitutionalized population since 1957. To help ensure a representative sample, the survey employs a complex sampling design including stratification, clustering, and differential sampling rates (Parsons et al., 2014). The survey is administered in- person by a trained interviewer to one adult per sampled household using computer-assisted personal interviewing technology. The survey is available in English or Spanish. Final response rates for the survey years ranged from 66.6% to 76.8% (National Center for Health Statistics, 2012, 2013, 2014, 2015).

Participants

The sample included 651 U.S. children aged 2–17 years with parent-reported ASD. Because a focus of this study was on SES factors, including health insurance type, we excluded 32 children with ASD who were uninsured or whose health insurance status was unknown. From 2011–13, parents were asked the following question about their child’s ASD status: “Looking at this list, has a doctor or other health professional ever told you that [child’s name] had any of these conditions: autism/autism spectrum disorder?” In 2014, the wording of this item changed to “Did a doctor or health professional ever tell you that [child’s name] has autism, Asperger’s disorder, pervasive developmental disorder, or autism spectrum disorder?”

The study sample represented an estimated 911,294 U.S. children with ASD aged 2–17 years who had health insurance when surveyed. A plurality of children with ASD were aged 2–11 years; male; white and non-Hispanic; had at least one parent with some college or less; had a household income ≥ 200% of the federal poverty level (FPL); had two parents; and had private health insurance (Table 1). Although 60% of children with ASD were reported to be in excellent or very good overall health, 83% were reported to have minor to severe emotional and behavioral difficulties, and 20% had intellectual disability (ID). A higher percentage of children had ASD in 2014 relative to 2011–13, as expected from past research about ASD prevalence differences between 2011–2013 and 2014 due to the NHIS item wording changes (Zablotsky, Black, Maenner, Schieve, & Blumberg, 2015).

Table 1.

Characteristics of U.S. Children with Autism Spectrum Disorder aged 2–17years (n=651)

n %a
Age, years
 2–5 128 20.8
 6–11 268 43.4
 12–17 255 35.8
Sex
 Female 135 22.9
 Male 516 77.1
Race/Ethnicity
 White, non-Hispanic 370 60.7
 Hispanic 141 19.2
 Other race, non-Hispanic 140 20.2
Highest Parent Education Level
 ≤ Some college 392 57.2
 Bachelor's degree or higher 259 42.9
Household Income Level
 0–199% FPL 168 26.1
 ≥ 200% FPL 451 73.9
Family Structure
 Two parents 322 53.7
 Single mother/other family type 329 46.3
Child Health Insurance
 Any private health insurance 345 54.3
 Public health insurance only 301 45.8
Region
 Northeast 115 16.9
 Midwest 137 24.6
 South 216 34.6
 West 183 23.8
Overall Health Status
 Excellent or very good 388 60.1
 Good, fair, or poor 263 39.9
ID Status
 ID 132 20.3
 No ID 519 79.7
Emotional and Behavioral Difficulties
 None 111 17.0
 Minor 210 31.7
 Definite or severe 302 51.3
Survey Year
 2011 137 19.9
 2012 145 21.6
 2013 137 21.0
 2014 232 37.5
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Note. FPL, federal poverty level; ID, intellectual disability; Data source: 2011–2014 National Health Interview Survey.

a

All percentages were computed using survey weighting.

Healthcare Access Problems Assessment

We constructed three composite variables to determine healthcare access problems: trouble finding a general doctor or primary care provider (PCP), difficulty affording needed healthcare, and delays accessing needed healthcare. For trouble finding a general doctor or PCP, parents needed to have answered “Yes” to one or more of three questions about their experiences in the past 12-months including (1) “Did you have any trouble finding a general doctor or provider who would see the child?”; (2) “Were you told by a doctor’s office or clinic that they would not accept the child as a new patient?”; and (3) “Were you told by a doctor’s office or clinic that they would not accept the child’s health care coverage?” Difficulty affording needed healthcare was determined if parents answered “Yes” to one or more of six questions about their experiences being unable to afford specific types of healthcare the child needed in the past 12- months including prescription medication, specialty care, follow-up care, mental health care or counseling, dental care, and eyeglasses. Delays accessing needed healthcare were determined if parents answered “Yes” to one or more of five questions about their experiences having to delay care for their child in the past 12-months because they (1) could not get through on the telephone, (2) could not get an appointment soon enough, (3) child had to wait too long to see the doctor, (4) the doctor’s office or clinic was not open when they could get there, or (5) did not have transportation. We additionally created a variable of ≥1 of the 3 healthcare access problems, if parents experienced trouble finding a general doctor or PCP, difficulty affording needed healthcare, and/or delays accessing needed healthcare for their child.

Health Services Use Measurement

Health services use, ranging from preventive to acute care, was examined with five measures. The following services were examined for the past 12-months: (1) number of office visits (0, 1, 2–3, 4–5, 6–7, 8–9, 10–12, 13–15, ≥ 16), (2) ≥ 1 well-child visit (WCV) that was not when the child was sick or injured, (3) flu vaccination, and (4) ≥ 1 ED visit. Whether the child had regularly taken prescription medication for at least three months was also assessed. Because the median number of office visits among children with ASD was 4–5 and relatively few children with ASD had no office visits (7.6%), we dichotomized this variable at ≥ 4. Children with ≥ 1 ED visit were asked if the most recent ED visit occurred at night or on a weekend and if that ED visit resulted in hospital admission. Parents were additionally asked which of the following applied to the child’s last ED visit: did not have another place to go, doctor’s office or clinic was not open, health provider advised s/he go, the problem was too serious for the doctor’s office or clinic, only hospital could help, the ED is the child’s closest provider, child gets most of his or her care at the ED, and child arrived by ambulance or other emergency vehicle. Parents could select multiple options.

SES, Race and Ethnicity, and Other Covariates

In alignment with common SES definitions (Currie et al., 2012; Krieger et al., 1997; Marmot & Wilkinson, 1999) and relevant past research (Lin & Yu, 2015; Liptak et al., 2008; Magaña et al., 2015), we used the following four variables as SES indicators: health insurance type (any private versus only public health insurance), highest parent education level (some college or less versus college or higher), household income relative to the FPL (0–199% versus 200% or higher), and family structure (two parent household versus single mother or other family type). Similar to prior research and given sample size constraints related to the race category cell sizes, we assessed race and ethnicity using one variable with three categories: White, non-Hispanic; Hispanic; or other race, non-Hispanic including participants identified as Black and non-Hispanic. For the initial bivariate and multivariable analyses, the other race category combined American Indian or Alaska Native, Asian, and multiple races. To examine effect modification in fitting stratified multivariable regression models, we further needed to combine Hispanic and other race, non-Hispanic versus white, non-Hispanic given small cell sizes. To further characterize the study sample and limit bias due to confounding in multivariable analyses, we included the following covariates according to relevant past research (Kogan et al., 2008; Zablotsky, Pringle, et al., 2015): child’s age, sex, census region, overall health status, ID status, and emotional and behavioral difficulties. Because data were combined across four years, we also included a variable for survey year.

Analysis

Descriptive statistics were first computed for all variables. Bivariate statistics were then computed to determine differences in the distributions of each services use type by having ≥ 1 healthcare access problem. We fit multiple logistic regression models estimating the adjusted odds of each services use type, in which the primary independent variable was ≥ 1 healthcare access problem. We further examined multivariable associations of each type of healthcare access problem (e.g., delays) with each type of services use in additional regression models. To determine effect modification by the four SES factors and race/ethnicity in the association of having ≥ 1 healthcare access problem with each service use type, we fit another series of multiple logistic regression models in which cross-product interaction terms between each SES factor or race/ethnicity and the ≥ 1 healthcare access problem variable were included. For models with a statistically significant interaction, we fit a final series of models stratified by each SES factor and race/ethnicity to determine the extent of difference in the adjusted odds ratios. All analyses were weighted per NCHS guidance (Parsons et al., 2014) and performed in Stata 15.0 (StataCorp, 2017).

Results

Healthcare Access Problems: Prevalence and Variation

Overall, 29.0% of U.S. children with ASD experienced ≥ 1 healthcare access problem. Delays accessing needed healthcare were the most prevalent type of healthcare access problem (Figure 1). The most common delays were problems getting an appointment soon enough, having to wait too long in the provider’s office, and the office not being open when the family could take their child. Problems affording care were the next most common type of healthcare access problem experienced, particularly problems affording dental, specialty, or mental health services. Trouble finding a general doctor or PCP (hereinafter PCP) was the least commonly experienced healthcare access problem; however, this was still reported for nearly 1 in 10 children with ASD. The doctor’s office not accepting the child’s insurance was the top reason reported for having had difficulty finding a PCP. As shown in Table 2, significant variation in ≥ 1 healthcare access problem was found by insurance type, region, and emotional and behavioral difficulties among children with ASD. Being female versus male was also associated with significantly greater adjusted odds of difficulty affording needed healthcare. In terms of race/ethnicity and SES, only public versus private health insurance was associated with any trouble finding a general doctor and ≥ 1 healthcare access problem among children with ASD.

Figure 1. Healthcare access problems among US children with autism spectrum disorder aged 2–17 years.

Figure 1.

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This bar chart displays the weighted percentage and 95% confidence interval for each type of healthcare access problem among US children with ASD aged 2–17 years. These statistics are additionally shown for each individual healthcare access problem comprising the three types of healthcare access problems examined.

Table 2.

Multiple Logistic Regression Model Results: Associations of Sociodemographic and Health Characteristics with Healthcare Access Problems and Services Use Among U.S. Children with Autism Spectrum Disorder aged 2–17 Years (n = 651)

Healthcare Access Problems
 Services Use
Trouble Finding PCP 9.4% aOR (95% CI) Delays Getting Needed Healthcare 15.8% aOR (95% CI) Difficulty Affording Healthcare 14.5% aOR (95% CI) ≥1 Healthcare Access Problem 29.3% aOR (95% CI) ≥4 Office Visits 57.2% aOR (95% CI) ≥1 WCV 85.5% aOR (95% CI) Flu Vaccine 44.6% aOR (95% CI) Prescription Medication 45.5% aOR (95% CI) ≥1 ED Visit 22.6% aOR (95% CI)
Age, years
 2–5 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 6–11 1.61 (0.50–5.20) 0.57 (0.23 – 1.44) 0.96 (0.39–2.36) 1.00 (0.46–2.17) 0.89 (0.43–1.83) 0.36 (0.12–1.11) 0.72 (0.39–1.35) 2.47 (1.13–5.37)* 0.69 (0.30–1.61)
 12–17 2.61 (0.80–8.52) 0.36 (0.13–1.03) 0.61 (0.23–1.61) 0.67 (0.32–2.86) 0.93 (0.46–1.88) 0.47 (0.15–1.50) 0.66 (0.36–1.23) 3.01 (1.37–6.60)** 0.75 (0.31–1.81)
Sex
 Male 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Female 0.50 (0.19 –1.30) 1.31 (0.58–2.97) 2.58 (1.20 –5.53)* 1.54 (0.83–2.86) 1.39 (0.73–2.66) 0.59 (0.30–1.19) 0.67 (0.40–1.14) 0.70 (0.39–1.27) 0.82 (0.41–1.64)
Race/ Ethnicity
 White, non-Hispanic 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Hispanic 1.61 (0.60 – 4.30) 1.13 (0.50 – 2.53) 1.51 (0.70–3.26) 0.98 (0.54–1.80) 0.69 (0.37–1.27) 1.66 (0.70–3.95) 1.36 (0.74–2.48) 0.67 (0.35–1.28) 1.96 (0.91–4.18)
 Other race, non-Hispanic 0.75 (0.24–2.31) 1.07 (0.48 – 2.37) 0.99 (0.43–2.28) 0.94 (0.52–1.72) 0.53 (0.29–0.99)* 2.31 (0.98–5.43) 1.53 (0.90–2.60) 0.54 (0.31–0.94)* 0.80 (0.35–1.85)
Highest parent education level
 ≥College 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 ≤Some College 1.00 (0.44–2.27) 0.81 (0.38–1.71) 1.40 (0.62–3.17) 1.12 (0.58–2.16) 0.77 (0.46–1.28) 1.17 (0.58–2.38) 0.73 (0.44–1.19) 1.38 (0.81–2.35) 1.23 (0.66–2.29)
Household income
 ≥200% FPL 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 <200% FPL 0.79 (0.28–2.23) 1.71 (0.72 – 4.04) 1.30 (0.54–3.11) 1.36 (0.67–2.74) 0.62 (0.31–1.25) 1.93 (0.83–4.47) 1.52 (0.75–3.08) 0.61 (0.29–1.27) 0.83 (0.38–1.79)
Family structure
 Two parents 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Single mother/other family type 2.05 (0.93–4.49) 1.72 (0.93–3.18) 1.23 (0.65 – 2.33) 1.46 (0.87–2.47) 1.19 (0.74–1.92) 0.69 (0.40–1.16) 1.38 (0.89–2.14) 1.04 (0.60–1.79) 1.24 (0.73–2.11)
Health insurance
 Any private health insurance 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Public insurance 2.57 (1.07–6.16)* 1.63 (0.77–3.42) 0.98 (0.42 – 2.30) 1.83 (1.01 – 3.30)* 1.68 (0.94–3.00) 0.40 (0.20–0.81)* 0.76 (0.46–1.26) 1.03 (0.59–1.79) 1.76 (0.91–3.39)
Census region
 Northeast 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Midwest 0.75 (0.18–3.08) 2.15 (0.66 –7.00) 2.08 (0.64–6.74) 1.48 (0.59–3.71) 0.73 (0.32–1.65) 0.16 (0.03–0.73)* 1.01 (0.49–2.08) 1.02 (0.51–2.06) 1.96 (0.71–5.43)
 South 0.94 (0.28–3.09) 1.90 (0.61 – 5.90) 2.27 (0.82 – 6.28) 1.33 (0.61 – 2.90) 1.07 (0.51–2.28) 0.14 (0.03–0.62)* 1.07 (0.58–1.98) 1.63 (0.84–3.17) 1.40 (0.58–3.39)
 West 1.46 (0.42 –5.00) 2.32 (0.71 – 7.55) 3.93 (1.36 –11.33)* 2.40 (1.08–5.35)* 1.08 (0.49–2.40) 0.11 (0.02–0.54)* 0.80 (0.39–1.64) 0.62 (0.30–1.28) 1.47 (0.54–3.95)
Overall health status
Excellent or very good 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Good, fair, or poor 0.69 (0.32–1.48) 1.01 (0.48 – 2.10) 1.21 (0.70–2.09) 1.24 (0.73 – 2.12) 1.76 (1.08–2.85)* 1.29 (0.67–2.51) 1.25 (0.79–2.00) 1.58 (0.98–2.52) 1.71 (0.95–3.08)
Intellectual Disability (ID)
 No ID 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 ID 1.45 (0.68–3.10) 1.27 (0.65–2.49) 0.95 (0.42 – 2.16) 1.11 (0.62 – 1.99) 1.32 (0.73–2.38) 0.73 (0.35–1.53) 1.04 (0.62–1.76) 1.71 (0.91–3.22) 0.54 (0.25– 1.16)
Emotional and behavioral difficulties
 None 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 Minor 0.43 (0.11–1.73) 1.47 (0.54 – 4.04) 2.33 (0.81–6.65) 1.43 (0.67–3.05) 1.73 (0.90–3.35) 0.37 (0.14–0.99)* 0.76 (0.42–1.38) 1.82 (0.87–3.78) 0.92 (0.41–2.07)
 Definite or severe 2.28 (0.67– 7.80) 3.07 (1.14–8.27)* 4.07 (1.52 –10.91)** 3.08 (1.41–6.71)** 3.06 (1.57–5.94)** 0.49 (0.20–1.18) 0.71 (0.41–1.25) 3.80 (1.83–7.92)*** 1.52 (0.65–3.54)
Survey year
 2011 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 2012 1.00 (0.33–3.08) 0.34 (0.13–0.86)* 1.25 (0.49 – 3.24) 0.67 (0.31– 1.45) 0.68 (0.36–1.29) 0.63 (0.23–1.74) 0.75 (0.38–1.48) 1.01 (0.47–2.15) 1.28 (0.52–3.16)
 2013 0.95 (0.34 – 2.71) 0.51 (0.20 – 1.31) 1.53 (0.66 – 3.53) 0.88 (0.46 –1.68) 1.51 (0.78–2.94) 0.38 (0.14–1.00) 0.72 (0.38–1.38) 1.25 (0.58–2.69) 1.63 (0.68–3.91)
 2014 0.43 (0.15 – 1.18) 0.41 (0.17 – 0.99)* 0.96 (0.38 – 2.42) 0.50 (0.25 – 1.00)* 0.78 (0.39–1.56) 0.67 (0.26–1.76) 0.90 (0.48–1.66) 1.29 (0.66–2.51) 1.16 (0.56–2.39)
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Note. aOR, adjusted odds ratio; CI, confidence interval; ED, emergency department; FPL, federal poverty level; ID, intellectual disability; PCP, primary care provider; WCV, well-child visit.

***

p< .001

**

p< .01

*

p< .05

Data source: 2011–2014 National Health Interview Survey. All analyses were weighted.

Services Use: Prevalence and Variation

More than half (57.2%) of children with ASD had ≥ 4 office visits in the past year, with 13.3% reporting ≥ 16 visits. Of the types of services use examined among children with ASD, the most common was a WCV (85.5%) and 44.6% had a flu vaccine. Nearly one-half (45.5%) of children with ASD took prescription medication in the past three months. Having ≥ 1 ED visit was reported for 22.6% of children with ASD. Among them, 73.4% had their most recent ED visit on a night or weekend and 7.7% of these ED visits resulted in hospital admission. Additional characteristics of these ED visits were as follows: only the hospital could help (83.4%), the doctor’s office or clinic was not open (81.0%), there was nowhere else to go (71.8%), the problem was too serious for the doctor’s office or clinic (25.3%), a healthcare provider advised the child to go (16.8%), the ED was the child’s closest provider (14%), the child gets most care at the ED, and the child arrived by ambulance or another emergency vehicle (1.2%). In terms of SES, public only versus any private insurance was associated with lower adjusted odds of having a WCV. Being identified as other race and non-Hispanic versus White and non-Hispanic was also associated with lower adjusted odds of ≥ 4 office visits and also lower adjusted odds of prescription medication use. Emotional and behavioral difficulties (definite or severe versus none) were, by contrast, associated with higher adjusted odds of ≥ 4 office visits and also had higher adjusted odds of prescription medication use. Table 2 displays additional adjusted associations of sociodemographic and health characteristics with each type of services use examined.

Associations of Healthcare Access Problems with Services Use

As shown in Table 3, the adjusted odds of ≥ 4 office visits were greater for children with ASD who had ≥ 1 healthcare access problem compared to those who did not. By contrast, the adjusted odds of ≥ 1 WCV or prescription medication use were each significantly lower for children with ASD who had ≥ 1 healthcare access problem compared to those who did not. In addition, children with ASD who had ≥ 1 healthcare access problem had significantly higher adjusted odds of ≥ 1 ED visit than those who did not have any healthcare access problem. Problems affording needed healthcare was most strongly associated with lower adjusted odds of ≥ 1 WCV; whereas, trouble finding a PCP was most strongly associated with lower adjusted odds of taking prescription medication but higher adjusted odds of ≥ 4 office visits. Delays getting needed healthcare and trouble finding a PCP were each, in separate models, associated with higher adjusted odds of ≥ 1 ED visit. Flu vaccine was not significantly associated with ≥ 1 healthcare access problem among children with ASD.

Table 3.

Bivariate and Multivariable Associations of Healthcare Access Problems with Services Use Among U.S. Children with Autism Spectrum Disorder aged 2–17 (n = 651)

Services Use
≥4 Office Visits ≥1 WCV Flu Vaccine Prescription Medication ≥1 ED Visit
% OR (95% CI) aOR (95% CI) % OR (95% CI) aOR (95% CI) % OR (95% CI) aOR (95% CI) % OR (95% CI) aOR (95% CI) % OR (95% CI) aOR (95% CI)
Healthcare access problems -
≥1 of 3 healthcare access problem
Yes 70.1% 2.18 (1.42–3.33)*** 1.61 (1.00–2.59)* 78.3% 0.47 (0.27–0.80)** 0.51 (0.28–0.92)* 47.9% 1.21 (0.77–1.88) 1.09 (0.67–1.77) 44.3% 0.93 (0.60–1.45) 0.56 (0.32–0.99)* 34.4% 2.42 (1.47–3.97)** 2.29 (1.21–4.34)*
No 51.9% 1.00 1.00 88.5% 1.00 1.00 43.2% 1.00 1.00 46.1% 1.00 1.00 17.8% 1.00 1.00
Delays getting child healthcare
Yes 75.9% 2.71 (1.44–5.07)** 2.08 (0.99–4.34) 85.5% 1.00 (0.52–1.92) 1.31 (0.65–2.64) 48.8% 1.22 (0.70–2.14) 1.03 (0.56–1.92) 43.5% 0.91 (0.52–1.57) 0.76 (0.36–1.60) 41.3% 2.99 (1.71–5.21)*** 2.53 (1.12–5.72)*
No 53.7% 1.00 1.00 85.5% 1.00 1.00 43.8% 1.00 1.00 45.9% 1.00 1.00 19.1% 1.00 1.00
Could not afford any needed care
Yes 64.8% 1.45 (0.80–2.64) 1.04 (0.52–2.09) 70.4% 0.32 (0.16–0.62)** 0.31 (0.16–0.58)*** 43.4% 0.95 (0.54–1.65) 0.83 (0.44–1.56) 51.7% 1.34 (0.77–2.31) 0.96 (0.53–1.75) 22.1% 0.97 (0.50–1.89) 0.57 (0.24–1.33)
No 55.9% 1.00 1.00 88.1% 1.00 1.00 44.8% 1.00 1.00 44.5% 1.00 1.00 22.7% 1.00 1.00
Any trouble finding a general doctor
Yes 81.6% 3.67 (1.76–7.66)** 2.79 (1.36–5.72)** 90.9% 1.77 (0.67–4.63) 1.96 (0.59–6.54) 54.7% 1.57 (0.81–3.06) 1.33 (0.64–2.79) 43.1% 0.90 (0.48–1.68) 0.36 (0.16–0.83)* 44.0% 3.07 (1.57–5.99)** 3.10 (1.37–7.02)**
No 54.7% 1.00 1.00 85.0% 1.00 1.00 43.5% 1.00 1.00 45.8% 1.00 1.00 20.4% 1.00 1.00
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Note. The row percentages used in the computation of weighted odds ratios are shown. Adjusted models included: age, sex, race and ethnicity, parent education, household income, family structure, child health insurance, region, overall health status, ID status, emotional and behavioral difficulties, and survey year. aOR, adjusted odds ratio; CI, confidence interval; ED, emergency department; OR, odds ratio; WCV, well-child visit.

*

p < .05

**

p < .01

***

p < .001

Data source: 2011–2014 National Health Interview Survey.

Effect Modification by SES and Race and Ethnicity

Statistically significant effect modification by each SES factor, except for household income, as well as by race/ethnicity was found in multivariable associations of healthcare access problems and ≥ 1 ED use among children with ASD (Figure 2). The difference in associations of healthcare access problems with ED use between children with ASD who had any private insurance versus public insurance only were the most pronounced (p=.007). That is, children with ASD who had any private health insurance and ≥ 1 healthcare access problem had the highest adjusted odds of ≥ 1 ED visit. Similar patterns were found for children with ASD who had two parents, had at least one parent with a college degree or more, or were White and non- Hispanic.

Figure 2. Adjusted Odds of ≥1 Emergency Department Visit Given ≥1 Healthcare Access Problem Overall and by Socioeconomic Status and Race and Ethnicity among U.S. Children with Autism Spectrum Disorder: Stratified Multiple Logistic Regression Model Results.

Figure 2.

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Note. This forest plot shows the adjusted odds ratio and 95% confidence interval of any emergency department visit given any healthcare access problem in the past year for each socioeconomic status or racial/ethnic subgroup of U.S. children with ASD aged 2–17 years. Each point estimate and confidence band shown is from a stratified regression model (e.g., white, non-Hispanic children with ASD only). The p-values were computed from a full regression model testing effect modification by each socioeconomic status factor or race/ethnicity among the sample of U.S. children with ASD aged 2–17 years.

DISCUSSION

To our knowledge, this is one of the first studies to examine relationships of healthcare access problems with services use, including effect modification by SES factors and race/ ethnicity among U.S. children with ASD. In line with past research (Chiri & Warfield, 2012; Liptak et al., 2008; Schieve et al., 2011), this study’s results reinforce that healthcare access problems (e.g., problems getting an appointment as soon as needed) remain pervasive for children with ASD nationwide. Certain sociodemographic and health factors (i.e., sex, insurance type, region, emotional and behavioral difficulties) were associated with greater adjusted odds of healthcare access problems highlighting the need for intervention targeting ASD subgroups most prone to experience healthcare access problems such as those who have emotional and behavioral difficulties. Further research is also needed to better understand the finding that among children with ASD, females versus males had higher adjusted odds of experiencing difficulty affording needed healthcare, especially given research showing males with ASD incur greater costs than females with ASD (Wang, Mandell, Lawer, Cidav, & Leslie, 2013).

Healthcare access problems were related to lower odds of ≥ 1 WCV and prescription medication use but higher odds of ≥ 4 office visits and ED use among children with ASD, suggesting healthcare access problems are associated with suboptimal patterns of services utilization in this population. Moreover, having private versus public health insurance, living with two parents versus a single mother or other family type, having at least one parent with a college degree or higher versus some college or less, or being White and non-Hispanic versus Hispanic or other race magnified associations of healthcare access problems with ED use among children with ASD.

In further examining associations of healthcare access problems with WCV, study results revealed that difficulty affording needed care drove the association with reduced odds of WCV among U.S. children with ASD. These results highlight the necessity of adequate health insurance coverage to ensure preventive care is fully utilized among children with ASD. Because families of children with ASD are especially prone to have adverse financial impacts (Zuckerman, Lindly, Bethell, & Kuhlthau, 2014), strategies to increase the affordability of specific and general health services for this population may increase preventive care use. At the policy level, initiatives to expand insurance coverage for ASD treatment like state ASD Insurance Mandates (Mandell et al., 2016) and the 1915(c) Medicaid Home and Community Based Services Waivers (Leslie et al., 2017) may better enable families to afford and use general health services like preventive medical and dental care. More broadly, state expansions of public health insurance programs along with state and local policies limiting family cost-sharing for preventive care services may help all children, including those with ASD, better access needed care. Future examination of associations between state level policies intended to promote adequate insurance coverage for children generally and those with ASD more specifically (e.g., adoption of the Medicaid expansion) is needed to determine what, if any, effects such policies have on reducing ASD-based disparities in preventive care use.

Trouble finding a PCP drove the association of healthcare access problems with higher odds of ≥ 4 office visits and reduced odds of prescription medication use among U.S. children with ASD. Regarding the association with office visits, it may be that children with ASD who are unable to find a PCP are more likely to seek out care elsewhere from different types of healthcare providers. It may also be that families prioritize visits with other types of healthcare providers for children with ASD over visits with a PCP. Not all children with ASD require prescription medication, though, medication may be a useful component of ASD management particularly for older children with comorbidities (Williamson et al., 2017). Insurance plans may require that families have a PCP to obtain certain prescription medication for their child or to be referred to a specialist, like a psychiatrist, who would prescribe certain medications. For this reason, increasing access to PCPs for children with ASD may be especially helpful if prescription medication is indicated. Strategies to increase access to PCPs for children with ASD might include referral to a PCP by the diagnostic team, if the family does not identify the child as having a PCP at the time of diagnosis. Though continuity of care often hinges on factors beyond an individual provider’s control (e.g., families moving), referral of children with ASD to PCPs who may be more knowledgeable about ASD and related services in the family’s community may help foster continuity of care between children with ASD and their PCP.

A similar percentage (22.6%) of children with ASD were found to have ED use in our study relative to prior research showing approximately 20% of younger children with ASD (aged 3–9 years) use the ED (Cummings et al., 2016). In our study, delays getting needed care and trouble finding a PCP were each associated with higher odds of ED use among children with ASD. Furthermore, two of the three most frequently reported reasons for using the ED were that the provider’s office was not open or there was nowhere else to go. Though information about the reasons for most recent ED visit is somewhat limited in the NHIS, the low percentage of children with ASD whose most recent ED visit resulted in hospital admission or who arrived by ambulance or another emergency vehicle to the ED suggest that some ED use may be non-urgent or avoidable for U.S. children with ASD. These findings are, to some extent, corroborated by past research showing the most common reasons for ED visits do not typically differ between children with and without ASD (i.e., acute upper respiratory infection, viral infection, otitis media) (Deavenport-Saman, Lu, Smith, & Yin, 2016) but non-urgent ED use rates are higher for children with ASD relative to other children.

At a practice level, strategies for reducing non-urgent or avoidable ED use among children with ASD could include extended office hours, fast-track appointments for children with ASD and urgent health issues, and the use of after-hours care (e.g., professional after-hours call centers staffed with registered nurses or other clinical staff). On a larger scale, reducing health professional shortage areas remains a national healthcare priority (Health Resources and Services Administration, n.d.); however, this will take time especially in terms of increasing the supply of primary care providers. For these reasons, increased acute care availability and accessibility through urgent care centers, nonemergency hospital based acute care entities, retail- based clinics, and/or telemedicine encounters may also help to reduce ED use among children with ASD. Last, intervention to increase parent health literacy related to general pediatric care through educational materials have shown some promise in terms of reducing ED use in more general pediatric populations (Morrison, Myrvik, Brousseau, Hoffmann, & Stanley, 2013), suggesting such intervention could be adapted for families of children with ASD.

Related to ED use and contrary to our hypotheses, this study also showed the association of healthcare access problems with ED use was especially pronounced for children with ASD of higher SES including those who had any private health insurance, who had 2 parents, whose parent(s) had a college degree or more, or who were White and non-Hispanic. These findings suggest that more advantaged ASD subgroups are most likely to use the ED when healthcare access problems are encountered, possibly because they view the ED to be a more viable healthcare alternative and/or they can afford to pay the ED consultation fees. Conversely, children with ASD from disadvantaged subgroups may be less likely to use the ED in relationship to having healthcare access problems because they perceive the ED negatively, have had bad experiences in the ED getting care for their child with ASD previously, and/or because they cannot incur the out-of-pocket costs. Further research using mixed-methods and longitudinal data is needed to determine how healthcare access problems differentially affect ASD subgroups in terms of ED and other types of services use, which were not assessed in this study, to effectively tailor practice and family-level interventions. In turn, the use of such interventions can be employed to reduce suboptimal services utilization patterns and advance health equity for the ASD population.

In our study, no statistically significant associations were found between healthcare access problems and flu vaccine receipt among U.S. children with ASD. Because flu vaccines are increasingly delivered in non-traditional settings such as schools and pharmacies (Prosser et al., 2008), healthcare access problems may not exert a great influence on whether children including those with ASD receive them. Because children with ASD, on average, have multiple office visits per year and are likely to have at least one WCV, it may also be that access problems are less likely to impede flu vaccine receipt given their relatively high level of interaction with the healthcare system.

Limitations

This study has important limitations. Given the cross-sectional nature of the NHIS data, we cannot draw causal inference regarding the temporal precedence of healthcare access problems and services use for children with ASD. Longitudinal data are needed to further understand the mechanisms by which healthcare access problems affect services use and vice versa for children with ASD, and to test mediation models involving SES factors and race/ethnicity. Our measures of healthcare access problems and services use also limit the inferences we can make. Specifically, the NHIS does not include all possible healthcare access problems (e.g., affordability of acute care), and measures of certain types of services utilization (e.g., WCV) do not include more specific information about the services rendered (e.g., routine immunization). The healthcare access questions were additionally limited by not being about a specific provider or service, and similarly, the items about reasons for ED use were only about the child’s last ED visit. For these reasons, future research that combines different data sources potentially including claims and parent-reported data on experiences accessing care and healthcare quality would be useful. Regarding the effect modification analyses, we had somewhat limited SES and race/ethnicity measures. This was partially due to the data source (e.g., the NHIS does not collect information about a family’s wealth), and was also related to relatively small sample size (e.g., small cell sizes did not permit more granular analysis of racial/ethnic subgroups like American Indians/Alaskan Natives). Due to the small number, we were analytically unable to include uninsured children with ASD in this study. Additional research examining associations between healthcare access problems and services use for this especially vulnerable subgroup of children with ASD is, therefore, warranted. Although the survey years used for this study did have reasonable response rates, there is always the potential for self-selection bias in terms of parents who may be more advantaged and/or engaged being the most likely to participate once randomly selected. Last, we did not include an explicit comparison group in this study given past research, which has established pronounced and persistent healthcare disparities for children with ASD relative to those without ASD and in terms of the study’s primary objectives. Future research could seek to determine if similar associations exist between healthcare access problems and services use and examine the that role SES and race/ethnicity plays in these relationships for children with other types of chronic conditions.

Conclusions

Our study demonstrated healthcare access problems are common among U.S. children with ASD, and healthcare access problems are linked to suboptimal services use in this population. The association between healthcare access problems and greater ED use may be magnified for children with ASD who have higher SES or who are white and non-Hispanic. Tailored strategies to reduce healthcare access problems for children with ASD at both the policy and practice levels may help and promote more optimal services utilization and health across the lifespan.

Acknowledgments

This research was supported by grant T32HS000063 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent official views of the Agency for Healthcare Research and Quality. We thank Amy Shui, M.A. for obtaining the data files and Alison E. Chavez, B.A. for her initial data analysis work. We also thank Huang Lee, Ph.D. for his guidance on the statistical analysis. This work was conducted with support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic healthcare centers. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centers, or the National Institutes of Health.

Contributor Information

Olivia J. Lindly, Harvard Medical School and Massachusetts General Hospital

Katharine E. Zuckerman, Oregon Health & Science University

Karen A. Kuhlthau, Harvard Medical School and Massachusetts General Hospital

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