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Although the American Academy of Pediatrics recommends screening for autism spectrum disorder (ASD) for all young children, disparities in ASD diagnosis and intervention in minority children persist. One potential contributor to disparities could be whether physicians take different actions after an initial positive screen based on patient demographics. This study estimated factors associated with physicians completing the follow-up interview for the Modified Checklist for Autism in Toddlers with Follow-up (M-CHAT-F), and referring children to diagnostic services, audiology, and Early Intervention (EI) immediately after a positive screen. Children seen in a large primary care network that has implemented universal ASD screening were included if they screened positive on the M-CHAT parent questionnaire during a 16-30 month well child visit (N = 2882). Demographics, screening results, and referrals were extracted from the electronic health record. Children from lower-income families or on public insurance were more likely to have been administered the follow-up interview. Among children who screened positive, 26% were already in EI, 31% were newly referred to EI, 11% were referred each to audiology and for comprehensive ASD evaluation. 40.2% received at least one recommended referral; 3.7% received all recommended referrals. In adjusted multivariable models, male sex, white versus black race, living in an English-speaking household, and having public insurance were associated with new EI referral. Male sex, black versus white race, and lower household income were associated with referral to audiology. Being from an English-speaking family, white versus Asian race, and lower household income were associated with referral for ASD evaluation. A concurrent positive screen for general developmental concerns was associated with each referral. We found low rates of follow-up interview completion and referral after positive ASD screen, with variations in referral by sex, language, socio-economic status, and race. Understanding pediatrician decision-making about ASD screening is critical to improving care and reducing disparities.

Background Implementation science studies often express interest in “attitudes,” a term borrowed from psychology. In psychology, attitude research has an established methodological and theoretical base, which we briefly summarize here. We then review implementation studies designed to measure attitudes and compare their definitions and methods with those from psychology. Methods A recent review identified 46 studies empirically examining factors associated with implementation. For each of these studies, we evaluated whether authors included attitudes as a construct of interest, and if so, whether and how the construct was defined, measured, and analyzed. Results Most of the articles (29/46 [63%]) mention attitudes as an implementation factor. Six articles include a definition of the construct. Nineteen studies were designed to measure attitudes but lacked clarity in describing how attitudes were measured. Those that explained their measurement approach used methods that differed from one another and from validated methods in social psychology. Few articles described associated analyses or provided results specific to attitudes. Despite the lack of specificity regarding relevant measurement, analysis, and results, the articles often included causal conclusions about the role of attitudes. Conclusions Attitudes may be an important construct to implementation scientists, but studies to date are ambiguous in their definitions of attitudes and inconsistent in the methods used to measure and analyze attitudes. We discuss how implementation studies can apply psychology’s standardized definitions, validated measurement approaches, and causal models that include attitudes. This application of attitude theory and methods could offer implementation research valuable scientific opportunities.

Implementing behavioral health interventions is a complicated process. It has been suggested that implementation strategies should be selected and tailored to address the contextual needs of a given change effort; however, there is limited guidance as to how to do this. This article proposes four methods (concept mapping, group model building, conjoint analysis, and intervention mapping) that could be used to match implementation strategies to identified barriers and facilitators for a particular evidence-based practice or process change being implemented in a given setting. Each method is reviewed, examples of their use are provided, and their strengths and weaknesses are discussed. The discussion includes suggestions for future research pertaining to implementation strategies and highlights these methods’ relevance to behavioral health services and research.

Experts continue to debate how to increase COVID-19 vaccination rates. Some experts advocate for financial incentives. Others argue that financial incentives, especially large ones, will have counterproductive psychological effects, reducing the percent of people who want to vaccinate. Among a racially and ethnically diverse U.S. sample of lower income adults, for whom vaccine uptake has lagged compared with higher income adults, we empirically examine such claims about relatively large and small guaranteed cash payments. In 2021, we conducted a randomized, controlled experiment among U.S. residents with incomes below $80,000 who reported being unvaccinated against COVID-19. Study participants were randomized to one of four study arms. In two arms, respondents first learned about a policy proposal to pay $1,000 or $200 to those who received COVID-19 vaccination and were then asked if, given that policy, they would want to vaccinate. In the two other arms, respondents received either an educational message about this vaccine or received no vaccine information and were then asked if they wanted to vaccinate for COVID-19. The primary analyses estimated and compared the overall percentage in each study arm that reported wanting to vaccinate for COVID-19. In other analyses, we estimated and compared these percentages for subgroups of interest, including gender, race/ethnicity, and education. Among 2,290 unvaccinated adults, 79.7% (95%CI, 76.4-83.0%) of those who learned about the proposed $1,000 payment wanted to get vaccinated, compared with 58.9% (95%CI, 54.8-63.0%) in the control condition without vaccine information, a difference of 20 percentage points. Among those who learned of the proposed $200 payment, 74.8% (95% CI, 71.3-78.4%) wanted to vaccinate. Among those who learned only about the safety and efficacy of COVID-19 vaccines, 68.9% (95% CI, 65.1-72.7%) wanted to vaccinate. Findings were consistent across various subgroups. Despite several study limitations, the results do not support concerns that the financial incentive policies aimed to increase COVID-19 vaccination would have counterproductive effects. Instead, those who learned about a policy with a large or small financial incentive were more likely than those in the control condition to report that they would want to vaccinate. The positive effects extended to subgroups that have been less likely to vaccinate, including younger adults, those with less education, and racial and ethnic minorities. Financial incentives of $1,000 performed similarly to those offering only $200.

Autistic individuals who are also people of color or from lower socioeconomic strata are historically underrepresented in research. Lack of representation in autism research has contributed to health and healthcare disparities. Reducing these disparities will require culturally competent research that is relevant to under-resourced communities as well as collecting large nationally representative samples, or samples in which traditionally disenfranchised groups are over-represented. To achieve these goals, a diverse group of culturally competent researchers must partner with and gain the trust of communities to identify and eliminate barriers to participating in research. We suggest community-academic partnerships as one promising approach that results in high-quality research built on cultural competency, respect, and shared decision making.

There is growing evidence that efficacious interventions for autism are rarely adopted or successfully implemented in public mental health and education systems. We propose applying diffusion of innovation theory to further our understanding of why this is the case. We pose a practical set of questions that administrators face as they decide about the use of interventions. Using literature from autism intervention and dissemination science, we describe reasons why efficacious interventions for autism are rarely adopted, implemented, and maintained in community settings, all revolving around the perceived fit between the intervention and the needs and capacities of the setting. Finally, we suggest strategies for intervention development that may increase the probability that these interventions will be used in real-world settings.

This study examined predictors of psychiatric hospitalization among children with autism spectrum disorders (ASD). Data were collected from 760 caregivers of children with ASD. Cox regression was used to determine factors associated with hospitalization. Almost 11% were hospitalized. Youth in single parent homes were more likely to be hospitalized (OR = 2.54), as were youth diagnosed at a later age (OR = 1.10). Engaging in self-injurious behavior (OR = 2.14), aggressive behavior (OR = 4.83), and being diagnosed with depression (OR = 2.48) or obsessive compulsive disorder (OR = 2.35) increased the odds of hospitalization. Risk for hospitalization increased with age and over time. The results suggest early diagnosis and community-based interventions for aggressive and self-injurious behaviors may reduce hospitalizations.

Background Implementation strategies increase the adoption of evidence-based practices, but they require resources. Although information about implementation costs is critical for decision-makers with budget constraints, cost information is not typically reported in the literature. This is at least partly due to a need for clearly defined, standardized costing methods that can be integrated into implementation effectiveness evaluation efforts. Methods We present a pragmatic approach to systematically estimating detailed, specific resource use and costs of implementation strategies that combine time-driven activity-based costing (TDABC), a business accounting method based on process mapping and known for its practicality, with a leading implementation science framework developed by Proctor and colleagues, which guides specification and reporting of implementation strategies. We illustrate the application of this method using a case study with synthetic data. Results This step-by-step method produces a clear map of the implementation process by specifying the names, actions, actors, and temporality of each implementation strategy; determining the frequency and duration of each action associated with individual strategies; and assigning a dollar value to the resources that each action consumes. The method provides transparent and granular cost estimation, allowing a cost comparison of different implementation strategies. The resulting data allow researchers and stakeholders to understand how specific components of an implementation strategy influence its overall cost. Conclusion TDABC can serve as a pragmatic method for estimating resource use and costs associated with distinct implementation strategies and their individual components. Our use of the Proctor framework for the process mapping stage of the TDABC provides a way to incorporate cost estimation into implementation evaluation and may reduce the burden associated with economic evaluations in implementation science.

Background Behavioral intention (which captures one’s level of motivation to perform a behavior) is considered a causal and proximal mechanism influencing the use of evidence-based practice (EBP). Implementation studies have measured intention differently, and it is unclear which is most predictive. Some use items referring to “evidence-based practice” in general, whereas others refer to a specific EBP. There are also unresolved debates about whether item stems should be worded “I intend to,” “I will,” or “How likely are you to” and if a single-item measure can suffice. Using each stem to refer to either a specific EBP or to “evidence-based practice,” this study compares the ability of these commonly used measures to predict future EBP implementation. The predictive validity is important for causal model testing and the development of effective implementation strategies. Methods A longitudinal study enrolled 70 teachers to track their use of two EBPs and compare the predictive validity of six different items measuring teachers’ intention. The measures differ by whether an item refers to a specific EBP, or to “evidence-based practices” in general, and whether the stem is worded in one of the three ways: “I intend to,” “I will,” or “How likely are you to.” For each item, linear regressions estimated the variance in future behavior explained. We also compared the predictive validity of a single item versus an aggregate of items by inter-correlating the items using different stems and estimating the explained variance in EBP implementation. Results Depending on the EBP and how intention was measured, the explained variance in implementation ranged from 3.5 to 29.0%. Measures that referred to a specific EBP, rather than “evidence-based practices” in general, accounted for more variance in implementation (e.g., 29.0% vs. 8.6%, and 11.3% vs. 3.5%). The predictive validity varied depending on whether stems were worded “I intend to,” “I will,” or “How likely are you to.” Conclusions The observed strength of the association between intentions and EBP use will depend on how intention is measured. The association was much stronger if an item referred to a specific EBP, rather than EBP in general. To predict implementation, the results support using an aggregate of two or three intention items that refer to the specific EBP. An even more pragmatic measure of intention consisting of a single item can also predict implementation. As discussed, the relationship will also vary depending on the EBP, which has direct implications for causal model testing and the design of implementation strategies.

Racial and ethnic disparities in the use of nine common autism-related services among Medicaid-enrolled children with autism spectrum disorder (ASD) were examined, distinguishing between school and other community-based outpatient settings. Using 2012 Medicaid Analytic Extract data, we identified 117,848 continuously enrolled children with ASD. Several racial and ethnic disparities were found, varying by geography. Black, Asian, and Native American/Pacific Islanders received fewer outpatient services compared with white children, but there was no disparity for Latinx children. Black and Asian children received more school-based services than white children. Disparities in case management/care coordination services were largest and present in each minority group. Geographic variation in receipt of services suggests targets for policy intervention to improve access for minorities with ASD.